Fitbit Research Library

Title	Author	Year	Journal/Proceedings	Category	Device(s)	Population	Data Used	Links
Movement toward a novel activity monitoring device details	Montgomery-Downs, H.E., Insana, S.P. and Bond, J.A.	2012	Sleep and Breathing	Validation	Classic	Adults	Sleep	URL
Accuracy of the Fitbit pedometer for self-paced and prescribed physical activity details	Ramirez, E., Peterson, C., Wu, W. and Norman, G.J.	2012	Medicine & Science in Sports & Exercise	Validation	Classic	Adults	Steps	URL
A comparison of energy expenditure estimation of several physical activity monitors. details	Dannecker, K.L., Sazonova, N.A., Melanson, E.L., Sazonov, E.S. and Browning, R.C.	2013	Medicine and Science in Sports and Exercise	Validation	Classic	Adults	Energy Expenditure	URL
An evaluation of wearable activity monitoring devices details	Guo, F., Li, Y., Kankanhalli, M.S. and Brown, M.S.	2013	Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia - PDM '13	Validation	Ultra One Zip	Adults	Steps Distance	URL
Comparison of steps and energy expenditure assessment in adults of Fitbit Tracker and Ultra to the Actical and indirect calorimetry. details	Noah, A.J., Spierer, D.K., Gu, J. and Bronner, S.	2013	Journal of Medical Engineering & Technology	Validation	Classic Ultra	Adults	Steps Energy Expenditure	URL
Validity of using tri-axial accelerometers to measure human movement - Part II: Step counts at a wide range of gait velocities. details	Fortune, E., Lugade, V., Morrow, M. and Kaufman, K.	2014	Medical Engineering & Physics	Validation	Classic	Adults	Steps	URL
Comparison of FitBit® Ultra to ActiGraph™ GT1M for Assessment of Physical Activity in Young Adults During Treadmill Walking details	Gusmer, R., Bosch, T., Watkins, A., Ostrem, J. and Dengel, D.	2014	The Open Sports Medicine Journal	Validation	Classic	Adults	Steps Energy Expenditure	URL
Validity of consumer-based physical activity monitors. details	Lee, J.-M., Kim, Y. and Welk, G.J.	2014	Medicine and Science in Sports and Exercise	Validation	One Zip	Adults	Energy Expenditure	URL
Effect of walking speed and placement position interactions in determining the accuracy of various newer pedometers details	Park, W., Lee, V.J., Ku, B. and Tanaka, H.	2014	Journal of Exercise Science & Fitness	Validation	Ultra	Adults	Steps	URL
Comparative assessment of sleep quality estimates using home monitoring technology details	Perez-Macias, J.M., Jimison, H., Korhonen, I. and Pavel, M.	2014	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society	Validation	One	Adults	Sleep	URL
An inexpensive device for monitoring patients' weights via automated hovering details	Shaffer, J.A., Diaz, K., Alcántara, C., Edmondson, D., Krupka, D.J., Chaplin, W.F. and Davidson, K.W.	2014	International Journal of Cardiology	Validation	Aria	Adults	Weight	URL
Caloric expenditure assessment among older adults: criterion validity of a novel accelerometry device. details	Stahl, S.T. and Insana, S.P.	2014	Journal of Health Psychology	Validation	Classic	Older Adults	Energy Expenditure	URL
Validation of the Fitbit One activity monitor device during treadmill walking. details	Takacs, J., Pollock, C.L., Guenther, J.R., Bahar, M., Napier, C. and Hunt, M.A.	2014	Journal of Science and Medicine in Sport	Validation	One	Adults	Steps Distance	URL
An Evaluation of Commercial Pedometers for Monitoring Slow Walking Speed Populations. details	Beevi, F.H.A., Miranda, J., Pedersen, C.F. and Wagner, S.	2015	Telemedicine journal and e-health : the official journal of the American Telemedicine Association	Validation	Zip	Adults	Steps	URL
Accuracy of smartphone applications and wearable devices for tracking physical activity data. details	Case, M.A., Burwick, H.A., Volpp, K.G. and Patel, M.S	2015	JAMA	Validation	One Zip Flex	Adults	Steps	URL
Fitbit®: An accurate and reliable device for wireless physical activity tracking details	Diaz, K.M., Krupka, D.J., Chang, M.J., Peacock, J., Ma, Y., Goldsmith, J., Schwartz, J.E. and Davidson, K.W.	2015	International Journal of Cardiology	Validation	One Flex	Adults	Steps Energy Expenditure	URL
Measuring steps with the Fitbit activity tracker: an inter-device reliability study details	Dontje, M.L., de Groot, M., Lengton, R.R., van der Schans, C.P. and Krijnen, W.P.	2015	Journal of Medical Engineering & Technology	Validation	Ultra	Adults	Steps	URL
Are Currently Available Wearable Devices for Activity Tracking and Heart Rate Monitoring Accurate, Precise, and Medically Beneficial? details	El-Amrawy, F. and Nounou, M.I.	2015	Healthcare Informatics Research	Validation	Flex	Adults	Steps	URl
The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: a cross-sectional study. details	Ferguson, T., Rowlands, A.V., Olds, T. and Maher, C.	2015	The International Journal of Behavioral Nutrition and Physical Activity	Validation	One Zip	Adults	Steps Energy Expenditure Intensity Sleep	URL
Using Commercial Activity Monitors to Measure Gait in Patients with Suspected iNPH: Implications for Ambulatory Monitoring. details	Gaglani, S., Moore, J., Haynes, M.R., Hoffberger, J.B. and Rigamonti, D.	2015	Cure¯us	Validation	Ultra	Adults	Steps	URL
Reliability and validity of ten consumer activity trackers. details	Kooiman, T.J.M., Dontje, M.L., Sprenger, S.R., Krijnen, W.P., van der Schans, C.P. and de Groot, M.	2015	BMC Sports Science, Medicine and Rehabilitation	Validation	One Flex	Adults	Steps	URL
Comparison of a Commercial Accelerometer with Polysomnography and Actigraphy in Children and Adolescents details	Meltzer, L.J., Hiruma, L.S., Avis, K., Montgomery-Downs, H. and Valentin, J.	2015	Sleep	Validation	Ultra	Children Adolescents	Sleep	URL
Validity of the Fitbit activity tracker for measuring steps in community-dwelling older adults details	Paul, S.S., Tiedemann, A., Hassett, L.M., Ramsay, E., Kirkham, C., Chagpar, S. and Sherrington, C.	2015	BMJ Open Sport & Exercise Medicine	Validation	One Zip	Older Adults	Steps	URL
Comparison of Three Inexpensive Commercially Available Activity Monitors to a Previously Validated Tri-Axial Accelerometer details	Reid, R.E., Comptour, A.M., Insogna, J.A., Bewski, N.A., Sciortino, C.D. and Andersen, R.E.	2015	Medicine & Science in Sports & Exercise	Validation	One Force	Adults	Steps Intensity	URL
24 Hours of Sleep, Sedentary Behavior, and Physical Activity with Nine Wearable Devices details	Rosenberger, M.E., Buman, M.P., Haskell, W.L., McConnell, M.V. and Carstensen, L.L.	2015	Medicine and Science in Sports and Exercise	Validation	One	Adults	Steps Intensity Sleep	URL
Validation of the Fitbit wireless activity tracker for prediction of energy expenditure. details	Sasaki, J.E., Hickey, A., Mavilia, M., Tedesco, J., John, D., Kozey Keadle, S. and Freedson, P.S.	2015	Journal of Physical Activity & Health	Validation	Classic	Adults	Energy Expenditure	URL
Capturing step counts at slow walking speeds in older adults: comparison of ankle and waist placement of measuring device. details	Simpson, L.A., Eng, J.J., Klassen, T.D., Lim, S.B., Louie, D.R., Parappilly, B., Sakakibara, B.M. and Zbogar, D.	2015	Journal of Rehabilitation Medicine	Validation	One	Older Adults	Steps	URL
Accuracy of the FitBit at walking speeds and cadences relevant to clinical rehabilitation populations details	Singh, A.K., Farmer, C., Van Den Berg, M.L., Killington, M. and Barr, C.J.	2015	Disability and Health Journal	Validation	Zip	Patients Adults	Steps	URL
Step Detection and Activity Recognition Accuracy of Seven Physical Activity Monitors details	Storm, F.A., Heller, B.W. and Mazzà, C.	2015	PLOS ONE	Validation	One	Adults	Steps	URL
Validation of Fitbit-Flex as a measure of free-living physical activity in a community-based phase III cardiac rehabilitation population details	Alharbi, M., Bauman, A., Neubeck, L. and Gallagher, R.	2016	European Journal of Preventive Cardiology	Validation	Flex	Patients Adults	Steps Intensity	URL
An experimental analysis of active living technologies to review device accuracy details	Altamimi, R. and Skinner, G.	2016	2016 IEEE Region 10 Conference (TENCON)	Validation	One Flex	Adults	Steps Distance	URL
Validation of Contemporary Physical Activity Tracking Technologies through Exercise in a Controlled Environment details	Altamimi, R.I. and Skinner, G.D.	2016	International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering	Validation	One Flex	Adults	Distance	URL
How valid are wearable physical activity trackers for measuring steps? details	An, H.-S., Jones, G.C., Kang, S.-K., Welk, G.J. and Lee, J.-M.	2016	European Journal of Sport Science	Validation	Zip Flex	Adults	Steps	URL
Comparison of consumer and research monitors under semistructured settings details	Bai, Y., Welk, G.J., Nam, Y.H., Lee, J.A., Lee, J.M., Kim, Y., Meier, N.F. and Dixon, P.M.	2016	Medicine and Science in Sports and Exercise	Validation	Flex	Adults	Energy Expenditure	URL
Accuracy and precision of smartphone applications and commercially available motion sensors in multiple sclerosis details	Balto, J.M., Kinnett-Hopkins, D.L. and Motl, R.W.	2016	Multiple Sclerosis Journal - Experimental, Translational and Clinical	Validation	One Flex	Patients Adults	Steps	URL
Comparative analysis of actigraphy performance in healthy young subjects details	Bellone, G.J., Plano, S.A., Cardinali, D.P., Chada, D.P., Vigo, D.E. and Golombek, D.A.	2016	Sleep Science	Validation	Flex	Adults	Steps	URL
Comparison of three different physiological wristband sensor systems and their applicability for resilience- and work load monitoring details	Binsch, O., Wabeke, T. and Valk, P.	2016	2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks	Validation	Surge	Adults	Steps Distance Heart Rate	URL
Concurrent Validity of Wearable Activity Trackers 1 in Free-living Conditions. details	Brooke, S., An, H.-S., Kang, S.-K., Noble, J., Berg, K. and Lee, J.-M.	2016	Journal of Strength and Conditioning Research	Validation	Flex Charge HR	Adults	Energy Expenditure Sleep	URL
Accuracy of the Fitbit for Measuring Preschoolers' Physical Activity details	Byun, W., Barry, A. and Lee, J.-M.	2016	Medicine & Science in Sports & Exercise	Validation	Flex	Children	Intensity	URL
Fitbit Provides Valid Measures of Sedentary and Physical Activity in Preschool Children details	Byun, W., Barry, A., Lee, J.-M. and Kim, Y.	2016	Circulation	Validation	Flex	Children	Intensity	URL
Accuracy of Wristband Activity Monitors during Ambulation and Activities. details	Chen, M.-D., Kuo, C.-C., Pellegrini, C.A. and Hsu, M.-J.	2016	Medicine & Science in Sports & Exercise	Validation	Flex	Adults	Steps	URL
Validating The Latest Commercial Physical Activity Monitors details	Chow, J.J., Ward, R.E., Thom, J.M. and Parmenter, B.J.	2016	Medicine & Science in Sports & Exercise	Validation	One Flex Charge Charge HR	Adults	Intensity	URL
Validity and inter-device reliability of dominant and non-dominant wrist worn activity trackers in suburban walking details	De Man, M., Vanderploeg, E., Aimers, N., MacMahon, C., Wise, L. and Parrington, L.	2016	Sensoria: A Journal of Mind, Brain & Culture	Validation	Charge HR	Adults	Steps	URL
Accuracy of Wrist and Hip-worn Commercial Physical Activity Monitors In Free Living Conditions details	Deyarmin, K.N., Snyder, K.M., Mihalik, A.G. and Hargens, T.A.	2016	Medicine & Science in Sports & Exercise	Validation	One Charge	Older Adults Adolescents	Steps	URL
Shaffer, J.A., Ma, Y., Goldsmith, J., Schwartz, J.E. and Davidson, K.W. Validation of the Fitbit One® for physical activity measurement at an upper torso attachment site details	Diaz, K.M., Krupka, D.J., Chang, M.J., Shaffer, J.A., Ma, Y., Goldsmith, J., Schwartz, J.E. and Davidson, K.W.	2016	BMC Research Notes	Validation	One Flex	Adults	Steps Energy Expenditure	URL
A practical validation study of a commercial accelerometer using good and poor sleepers details	Dickinson, D.L., Cazier, J. and Cech, T.	2016	Health Psychology Open	Validation	Charge HR	Adults	Sleep	URL
Physical Activity Assessment Between Consumer- and Research-Grade Accelerometers: A Comparative Study in Free-Living Conditions details	Dominick, M.G., Winfree, N.K., Pohlig, T.R. and Papas, A.M.	2016	JMIR Mhealth Uhealth	Validation	Flex	Adults	Steps Energy Expenditure Intensity	URL
Comparative accuracy of fitness tracking modalities in quantifying energy expenditure details	Dondzila, C. and Garner, D.	2016	Journal of Medical Engineering & Technology	Validation	Charge	Adults	Energy Expenditure	URL
Validation of Consumer-Based Hip and Wrist Activity Monitors in Older Adults With Varied Ambulatory Abilities. details	Floegel, T.A., Florez-Pregonero, A., Hekler, E.B. and Buman, M.P.	2016	The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences	Validation	One Flex	Older Adults	Steps	URL
Reliability and Validity of Ten Consumer Activity Trackers Depend on Walking Speed details	Fokkema, T., Kooiman, T.J., Krijnen, W.P., van der Schans, C.P. and de Groot, M.	2016	Medicine & Science in Sports & Exercise	Validation	Charge HR	Adults	Steps	URL
Estimating Physical Activity and Sedentary Behavior in a Free-Living Context: A Pragmatic Comparison of Consumer-Based Activity Trackers and ActiGraph Accelerometry details	Gomersall, S.R., Ng, N., Burton, N.W., Pavey, T.G., Gilson, N.D. and Brown, W.J.	2016	Journal of Medical Internet Research	Validation	One	Adults	Steps Intensity	URL
Accuracy of Commercial Activity Trackers to Measure Energy Expenditure During a Controlled Exercise Trial details	Hargens, T.A., Deyarmin, K.N., Snyder, K.M., Mihalik, A.G. and Sharpe, L.E.	2016	Medicine & Science in Sports & Exercise	Validation	One Charge	Adults	Energy Expenditure	URL
Validity of FitBit, Jawbone UP, Nike+ and other wearable devices for level and stair walking details	Huang, Y., Xu, J., Yu, B. and Shull, P.B.	2016	Gait & Posture	Validation	One Zip Flex	Adults	Steps Distance	URL
Validation of Biofeedback Wearables for Photoplethysmographic Heart Rate Tracking details	Jo, E., Lewis, K., Directo, D., Kim, M.J. and Dolezal, B.A.	2016	Journal of Sports Science & Medicine	Validation	Charge HR	Adults	Heart Rate	URL
Validity of Wearable Fitness Trackers on Sleep Measure details	Keill, A.K., An, H.-S., Dinkel, D.M. and Lee, J.-M.	2016	Medicine & Science in Sports & Exercise	Validation	Charge HR	Adults	Sleep	URL
"Stepping Up" Activity poststroke: Ankle-Positioned Accelerometer Can Accurately Record Steps During Slow Walking details	Klassen, T.D., Simpson, L.A., Lim, S.B., Louie, D.R., Parappilly, B., Sakakibara, B.M., Zbogar, D. and Eng, J.J.	2016	Physical Therapy	Validation	One	Older Adults Patients	Steps	URl
Examining The Validity Of Fitbit Charge HR For Measuring Heart Rate In Free-living Conditions details	Lee, J.-M.M., An, H., Kang, S.-k., Kim, Y. and Dinkel, D.	2016	Medicine & Science in Sports & Exercise	Validation	Charge HR	Adults	Heart Rate	URL
Evidence of Convergent Validity for Measuring Free-Living Walking Using Wearable Devices details	Lee, M., Choi, J.-Y., Lee, K., Min, J., Kim, J. and Choi, M.C.	2016	Medicine & Science in Sports & Exercise	Validation	One Charge	Adults	Steps	URL
Validation and Accuracy of FITBIT Charge: A Pilot Study in a University Worksite Walking Program details	Leininger, L.J., Cook, B.J. and Adams, K.J.	2016	Journal of Fitness Research	Validation	Charge	Adults	Steps Intensity	URl
Validation And Accuracy Of Fitbit Charge details	Leininger, L.J., Cook, B.J., Jones, V., Bellumori, M. and Adams, K.J.	2016	Medicine & Science in Sports & Exercise	Validation	Charge	Adults	Steps Intensity	URL
Reliability of Sleep Measures from Four Personal Health Monitoring Devices Compared to Research-Based Actigraphy and Polysomnography details	Mantua, J., Gravel, N. and Spencer, R.	2016	Sensors	Validation	Flex	Adults	Sleep	URL
Consumer-based Physical Activity Monitors and Research-grade Accelerometers Underestimate Physical Activity in a Semi-structured Setting details	Montoye, A.H., Tuttle, M.S., Nelson, M.B., Steeves, J.A. and Kaminsky, L.A.	2016	Circulation	Validation	One Zip Flex	Adults	Steps Energy Expenditure	URL
Accuracy of Wearable Devices for Estimating Total Energy Expenditure details	Murakami, H., Kawakami, R., Nakae, S., Nakata, Y., Ishikawa-Takata, K., Tanaka, S. and Miyachi, M.	2016	JAMA Internal Medicine	Validation	Flex	Adults	Energy Expenditure	URL
Validity of Consumer-Based Physical Activity Monitors for Specific Activity Types. details	Nelson, M.B., Kaminsky, L.A., Dickin, D.C. and Montoye, A.H.K.	2016	Medicine and Science in Sports and Exercise	Validation	One Zip Flex	Adults	Steps Energy Expenditure	URL
These Shoes Are Made for Walking: Sensitivity Performance Evaluation of Commercial Activity Monitors under the Expected Conditions and Circumstances Required to Achieve the International Daily Step Goal of 10,000 Steps. details	O'Connell, S., ÓLaighin, G., Kelly, L., Murphy, E., Beirne, S., Burke, N., Kilgannon, O. and Quinlan, L.R.	2016	PLOS ONE	Validation	One	Adults	Steps	URL
A Comparison Study Of The Fitbit Activity Monitor And PSG For Assessing Sleep Patterns And Movement In Children details	Osterbauer, B., Koempel, J.A., Davidson Ward, S.L., Fisher, L.M. and Don, D.M.	2016	Journal of Otolaryngology Advances	Validation	Flex	Children	Sleep	URL
Validation of the Fitbit One, Garmin Vivofit and Jawbone UP activity tracker in estimation of energy expenditure during treadmill walking and running details	Price, K., Bird, S.R., Lythgo, N., Raj, I.S., Wong, J.Y.L. and Lynch, C.	2016	Journal of Medical Engineering & Technology	Validation	One	Adults	Energy Expenditure	URL
Can Commercially Available Pedometers Be Used For Physical Activity Monitoring In Patients With COPD Following Exacerbations? details	Prieto-Centurion, V., Bracken, N., Norwick, L., Zaidi, F., Mutso, A.A., Morken, V., Coultas, D.B., Rand, C.S., Marquez, D.X. and Krishnan, J.A.	2016	Chronic Obstructive Pulmonary Diseases: Journal of the COPD Foundation	Validation	Zip Force	Patients Adults	Steps	URL
Validity and reliability of Fitbit activity monitors compared to ActiGraph GT3X+ with female adults in a free-living environment details	Reid, R.E., Insogna, J.A., Carver, T.E., Comptour, A.M., Bewski, N.A., Sciortino, C. and Andersen, R.E.	2016	Journal of Science and Medicine in Sport	Validation	One Flex	Adults	Steps Intensity	URL
Validation of the Fitbit Zip for monitoring physical activity among free-living adolescents details	Schneider, M. and Chau, L.	2016	BMC Research Notes	Validation	Zip	Adolescents	Steps	URL
Assessment of Distance Measurement with Selected Wearable Devices in Telemonitoring details	Schneider, Z., Shahbazian, J. and Krishnan, S.M.	2016	2016 32nd Southern Biomedical Engineering Conference (SBEC)	Validation	Flex	Adults	Distance	URL
Accuracy Of Wrist-worn Activity Monitors At Three Walking Speeds On The Treadmill details	Smith, J.D.	2016	Medicine & Science in Sports & Exercise	Validation	Flex	Adults	Steps	URL
How accurate are the wrist-based heart rate monitors during walking and running activities? Are they accurate enough? details	Stahl, S.E., An, H.-S., Dinkel, D.M., Noble, J.M. and Lee, J.-M.	2016	BMJ Open Sport & Exercise Medicine	Validation	Charge HR	Adults	Heart Rate	URL
Validity and Reliability of Fitbit Flex for Step Count, Moderate to Vigorous Physical Activity and Activity Energy Expenditure details	Sushames, A., Edwards, A., Thompson, F., McDermott, R. and Gebel, K.	2016	PLOS ONE	Validation	Flex	Adults	Steps Energy Expenditure Intensity	URL
Validity of commercial activity trackers in children with congenital heart disease details	Voss, C., Gardner, R.F., Dean, P.H. and Harris, K.C.	2016	Canadian Journal of Cardiology	Validation	Charge HR	Adolescents	Steps Intensity	URL
Accuracy of Heart Rate Watches: Implications for Weight Management details	Wallen, M.P., Gomersall, S.R., Keating, S.E., Wisløff, U. and Coombes, J.S.	2016	PLOS ONE	Validation	Charge HR	Adults	Steps Energy Expenditure Heart Rate	URL
Accuracy of wrist-worn heart rate monitors details	Wang, R., Blackburn, G., Desai, M. and et al	2016	JAMA Cardiology	Validation	Charge HR	Adults	Heart Rate	URL
Measures of sleep and cardiac functioning during sleep using a multi-sensory commercially-available wristband in adolescents details	de Zambotti, M., Baker, F.C., Willoughby, A.R., Godino, J.G., Wing, D., Patrick, K. and Colrain, I.M	2016	Physiology & Behavior	Validation	Charge HR	Adolescents	Sleep Heart Rate	URL
How Accurate Is Your Activity Tracker? A Comparative Study of Step Counts in Low-Intensity Physical Activities details	Alinia, P., Cain, C., Fallahzadeh, R., Shahrokni, A., Cook, D. and Ghasemzadeh, H.	2017	JMIR Mhealth and Uhealth	Validation	One Zip Flex	Adults	Steps	URL
Step Count Accuracy of the Stepwatch and FitBit One Among Individuals with a Unilateral Transtibial Amputation details	Arch, E.S., Sions, J.M. and Horne, J.R.	2017	American Academy of Orthotists & Prosthetists	Validation	One	Patients Adults	Steps	URL
The Validity of Fitbit Charge in Free Living Conditions details	Bai, Y., Ellingson, L. and Welk, G.	2017	Medicine & Science in Sports & Exercise	Validation	Charge	Adults	Steps Intensity	URL
Comparative evaluation of heart rate-based monitors: Apple Watch vs Fitbit Charge HR details	Bai, Y., Hibbing, P., Mantis, C. and Welk, G.J.	2017	Journal of Sports Sciences	Validation	Charge HR	Adults	Steps Energy Expenditure Heart Rate
The Accuracy of Personal Activity Monitoring Devices details	Battenberg, A.K., Donohoe, S., Robertson, N. and Schmalzried, T.P.	2017	Seminars in Arthroplasty	Validation	One Force	Adults	Steps	URL
Estimation of sleep stages in a healthy adult population from optical plethysmography and accelerometer signals details	Beattie, Z., Oyang, Y., Statan, A., Ghoreyshi, A., Pantelopoulos, A., Russell, A. and Heneghan, C.	2017	Physiological Measurement	Validation	Surge	Adults	Sleep	URL
Estimation of Sleep Stages Using Cardias and Accelerometer Data from a Wrist-Worn Device details	Beattie, Z., Pantelopoulos, A., Ghoreyshi, A., Oyang, Y., Statan, A. and Heneghan, C.	2017	Sleep	Validation	Charge 2	Adults	Sleep	URL
Determining the Reliability of Several Consumer-Based Physical Activity Monitors details	Bock, J., Kaminsky, L., Harber, M. and Montoye, A.	2017	Technologies	Validation	One Zip Flex	Adults	Steps Energy Expenditure Intensity	URL
Accuracy of Wearable Devices for Determining Physiological Measures during Different Physical Activities details	Boudreaux, B.D., Cormier, C.L., Williams, B.M., Hebert, E.P. and Kraemer, R.R.	2017	Medicine & Science in Sports & Exercise	Validation	Charge HR	Adults	Steps Energy Expenditure Heart Rate	URL
Validity of Wearable Activity Monitors during Cycling and Resistance Exercise details	Boudreaux, B.D., Hebert, E.P., Hollander, D.B., Williams, B.M., Cormier, C.L., Naquin, M.R., Gillan, W.W., Gusew, E.E. and Kraemer, R.R.	2017	Medicine & Science in Sports & Exercise	Validation	Charge 2 Blaze	Adults	Energy Expenditure Heart Rate	URL
Validity of Fitbit's active minutes as compared with a research-grade accelerometer and self-reported measures details	Brewer, W., Swanson, B.T. and Ortiz, A.	2017	BMJ Open Sport & Exercise Medicine	Validation		Adults	Steps Intensity	URL
The Accuracy of Heart Rate Monitoring by Some Wrist-Worn Activity Trackers details	Cadmus-Bertram, L., Gangnon, R., Wirkus, E.J., Thraen-Borowski, K.M. and Gorzelitz-Liebhauser, J.	2017	Annals of Internal Medicine	Validation	Charge HR	Adults	Heart Rate	URL
Accuracy of step count measured by physical activity monitors: the effect of gait speed and anatomical placement site. details	Chow, J.J., Thom., J.M., Wewege, M.A., Ward, R.E. and Parmenter, B.J.	2017	Gait & Posture	Validation	One Flex Charge HR	Adults	Steps	URL
Accuracy of Accelerometer (Fitbit® ChargeHR™) Measured Distance and Heart Rate in Patients with Pulmonary Arterial Hypertension details	Chowdhury, A., Sehgal, S., Rabih, F., Gadre, A. and Highland, K.B.	2017	American Journal of Respiratory and Critical Care Medicine	Validation	Charge HR	Patients Adults	Distance Heart Rate	URL
Assessment of laboratory and daily energy expenditure estimates from consumer multi-sensor physical activity monitors details	Chowdhury, E.A., Western, M.J., Nightingale, T.E., Peacock, O.J. and Thompson, D.	2017	PLOS ONE	Validation	Charge HR	Adults	Energy Expenditure	URL
Comparison of wrist-worn Fitbit Flex and waist-worn ActiGraph for measuring steps in free-living adults details	Chu, A.H.Y., Ng, S.H.X., Paknezhad, M., Gauterin, A., Koh, D., Brown, M.S. and Müller-Riemenschneider, F.	2017	PLOS ONE	Validation	Flex	Adults	Steps	URL
Applicability of consumer activity monitor data in marathon events: an exploratory study details	Constantinou, V., Felber, A.E. and Chan, J.L.	2017	Journal of Medical Engineering & Technology	Validation		Adults	Steps Energy Expenditure Distance	URL
Utility of the Fitbit Flex to Evaluate Sleep in Major Depressive Disorder: A comparison against polysomnography and wrist-worn actigraphy details	Cook, J.D., Prairie, M.L., Plante, D.T., Finn, L., Benca, R., Nielsen, G., Straume, S., Nordhus, I., Coleman, J., Lee-Chiong, T., Pancer, J., Swick, T., Committee, S. and Medicine, A.	2017	Journal of Affective Disorders	Validation	Flex	Patients Adults	Sleep	URL
Using Wrist-Worn Actigraphic Devices to Measure Sleep in People with Huntington's Disease details	Dao, P., Maskevich, S., Jumabhoy, R., Drummond, S. and Stout, J.	2017	Sleep	Validation	One	Patients Adults	Sleep	URL
Does Wearable Technology Provide Accurate Heart Rate Measures While Playing Pickleball Doubles? details	Denning, M., Smith, M., Zagrodnik, J. and Ruden, T.	2017	Medicine & Science in Sports & Exercise	Validation	Charge HR	Adults	Heart Rate	URL
An Examination of Body Mass Index Influence on Activity Tracker Accuracy details	Dondzila, C., Johnson, H. and Steckel, M.	2017	Medicine & Science in Sports & Exercise	Validation	One Charge HR	Adults	Steps Intensity	URL
Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices details	Dooley, E.E., Golaszewski, N.M. and Bartholomew, J.B.	2017	JMIR mHealth and uHealth	Validation	Charge HR	Adults	Energy Expenditure Heart Rate	URL
Varying Exercise Intensities: The Accuracy of Three Self-Monitoring Heart Rate and Physical Activity Wearable Devices details	Dooley, E.E., Golaszewski, N.M. and Bartholomew, J.B.	2017	Medicine & Science in Sports & Exercise	Validation	Charge HR	Adults	Energy Expenditure Heart Rate	URL
Quantitative and qualitative measures of poststroke walking activity with inertial technologies details	Duclos, N., Tavares, L.A., Parent, G., Aissaoui, R., Nadeau, S. and Duclos, C.	2017	Annals of Physical and Rehabilitation Medicine	Validation	One	Patients Adults	Steps	URL
The Validity of Consumer-Level Activity Monitors in Healthy Older Adults in Free-Living Conditions details	Farina, N. and Lowry, R.G.	2017	Journal of Aging and Physical Activity	Validation	Charge HR	Older Adults	Steps	URL
Caloric Expenditure Using Indirect Calorimetry, Apple Watch Sport, and Fitbit Zip details	Ferrara CM, Smyth S, Mullan E and Burke C	2017	Journal of Exercise Physiology	Validation	Zip	Adults	Energy Expenditure	URL
Accuracy of Fitbit Activity Trackers During Walking in a Controlled Setting details	Gamez, J.L., Gonzalez, J., Leyva, P., Figueroa, I.A., Lucio, N., Salazar, V.E., Salazar, C., Garcia, M. and Funk, M.D.	2017	Medicine & Science in Sports & Exercise	Validation	One Zip Flex Charge HR Blaze	Adults	Steps	URL
Accuracy of Smartwatches in Assessing College Students’ Energy Expenditure in Exercise with Different Intensities details	Gao, Z., Li, X., Zeng, N., Pope, Z., Yang, H., Liu, W., Xiong, H., Chen, Y., Li, J. and He, W.	2017	Medicine & Science in Sports & Exercise	Validation	Surge	Adults	Energy Expenditure	URL
Variable Accuracy of Wearable Heart Rate Monitors during Aerobic Exercise details	Gillinov, S., Etiwy, M., Wang, R., Blackburn, G., Phelan, D., Gillinov, A.M., Houghtaling, P., Javadikasgari, H. and Desai, M.Y.	2017	Medicine & Science in Sports & Exercise	Validation	Blaze	Adults	Heart Rate	URL
Fitbit Charge HR Wireless Heart Rate Monitor: Validation Study Conducted Under Free-Living Conditions details	Gorny, A.W., Liew, S.J., Tan, C.S. and Müller-Riemenschneider, F.	2017	JMIR mHealth and uHealth	Validation	Charge HR	Adults	Heart Rate	URL
Accuracy of the Fitbit Zip for Measuring Steps for Adolescents With Visual Impairments details	Haegele, J.A., Brian, A.S. and Wolf, D.	2017	Adapted Physical Activity Quarterly	Validation	Zip	Patients Adolescents	Steps	URL
Physical activity among children: objective measurements using Fitbit One® and ActiGraph details	Hamari, L., Kullberg, T., Ruohonen, J., Heinonen, O.J., Díaz-Rodríguez, N., Lilius, J., Pakarinen, A., Myllymäki, A., Leppänen, V. and Salanterä, S.	2017	BMC Research Notes	Validation	One	Children	Steps	URL
Validity Of The Diagnostix 2100 And Fitbit Charge HR In Assessing Heart Rate details	Hardekopf, P. and Fountaine, C.	2017	Medicine & Science in Sports & Exercise	Validation	Charge HR	Adults	Heart Rate	URL
The Accuracy of Wrist-worn Heart Rate Monitors across a Range of Exercise Intensities details	Hough, P., Glaister, M. and Pledger, A.	2017	Journal of Physical Activity Research	Validation	Charge HR	Adults	Heart Rate
Validity of the Fitbit One for Measuring Activity in Community-Dwelling Stroke Survivors details	Hui, J., Heyden, R., Bao, T., Accettone, N., McBay, C., Richardson, J. and Tang, A.	2017	Physiotherapy Canada	Validation	One	Patients Adults	Steps	URL
The Accuracy of Pedometers in Measuring Walking Steps on a Treadmill in College Students details	Husted, H. and Llewellyn, T.	2017	International Journal of Exercise Science	Validation	Charge	Adults	Steps	URL
Comparison of four Fitbit and Jawbone activity monitors with a research-grade ActiGraph accelerometer for estimating physical activity and energy expenditure details	Imboden, M.T., Nelson, M.B., Kaminsky, L.A. and Montoye, A.H.	2017	British Journal of Sports Medicine	Validation	One Zip Flex	Adults	Steps Intensity	URL
Wrist-Worn Activity Monitoring Devices Overestimate Sleep Duration and Efficiency in Healthy Adults details	Jumabhoy, R., Dao, P., Maskevich, S., Stout, J. and Drummond, S.	2017	Sleep	Validation	One	Adults	Sleep	URL
Validity of a commercial wearable sleep tracker in adult insomnia disorder patients and good sleepers details	Kang, S.-G., Kang, J.M., Ko, K.-P., Park, S.-C., Mariani, S. and Weng, J.	2017	Journal of Psychosomatic Research	Validation	Flex	Patients Adults	Sleep	URL
Examination of a Wearable Activity Tracker to Assess Children’s Physical Activity details	Kang, S.-K., An, H.-S., Hong, S. and Lee, J-M.	2017	Medicine & Science in Sports & Exercise	Validation	Charge HR	Children	Intensity	URL
Consumer-Based Physical Activity Monitor as a Practical Way to Measure Walking Intensity During Inpatient Stroke Rehabilitation details	Klassen, T.D., Semrau, J.A., Dukelow, S.P., Bayley, M.T., Hill, M.D. and Eng, J.J.	2017	Stroke	Validation	One	Patients Adults	Steps	URL
Heart Rate Assessment by Smart Watch: Utility or Futility? details	Koshy, A., Sajeev, J., Zureik, M., Rajakariar, K., Street, M., Cosgrave, N., Wong, M., Hamer, A., Cooke, J., Roberts, L. and Teh, A.	2017	Heart, Lung and Circulation	Validation	Blaze	Patients Adults	Heart Rate	URL
Comparison of Wearable Activity Tracker with Actigraphy for Sleep Evaluation and Circadian Rest-Activity Rhythm Measurement in Healthy Young Adults details	Lee, H.-A., Lee, H.-J., Moon, J.-H., Lee, T., Kim, M.-G., In, H., Cho, C.-H. and Kim, L.	2017	Psychiatry Investigation	Validation	Charge HR	Adults	Steps Sleep Heart Rate	URL
Evaluation of Commercial Self-Monitoring Devices for Clinical Purposes: Results from the Future Patient Trial, Phase I. details	Leth, S., Hansen, J., Nielsen, O.W. and Dinesen, B.	2017	Sensors	Validation	One Zip Charge HR	Adults	Steps Heart Rate	URL
Considering interpersonal differences in validating wearable sleep-tracking technologies details	Liang, Z. and Martell, M.A.C.	2017	2017 Tenth International Conference on Mobile Computing and Ubiquitous Network	Validation	Charge 2	Adults	Sleep
Are wearable EEG devices more accurate than fitness wristbands for home sleep Tracking? Comparison of consumer sleep trackers with clinical devices details	Liang, Z. and Nishimura, T.	2017	2017 IEEE 6th Global Conference on Consumer Electronics	Validation	Charge 2	Adults	Sleep
Fitness band accuracy in older community dwelling adults details	Madigan, E.A.	2017	Health Informatics Journal	Validation	Flex	Older Adults	Steps	URL
Step Count Accuracy and Reliability from Activity Monitors Worn by Mobility-Intact Older Adults details	Maganja, S., Donaldson, M. and Mackey, D.	2017	Innovation in Aging	Validation	One Charge	Older Adults	Steps	URL
Validity of the Fitbit? Distance Traveled Feature Among Multiple Speed Trials details	Marton, C.J.	2017	Medicine & Science in Sports & Exercise	Validation	Charge	Adults	Distance	URL
Pilot Validation of Ambulatory Activity Monitors for Sleep Measurement in Huntington's Disease Gene Carriers details	Maskevich, S., Jumabhoy, R., Dao, P.D., Stout, J.C. and Drummond, S.P.	2017	Journal of Huntington's Disease	Validation	One	Patients Adults	Sleep	URL
A Validation Study of the Fitbit One in Daily Life Using Different Time Intervals details	Middelweerd, A., Van der Ploeg, H.P., Van Halteren, A., Twisk, J., Brug, J. and Te Velde, S.J.	2017	Medicine & Science in Sports & Exercise	Validation	One	Adults	Steps Intensity	URL
Assessing the Use of Wrist-Worn Devices in Patients With Heart Failure: Feasibility Study details	Moayedi, Y., Abdulmajeed, R., Duero Posada, J., Foroutan, F., Alba, A.C., Cafazzo, J. and Ross, H.J.	2017	JMIR Cardio	Validation	Charge HR	Patients Adults	Steps Heart Rate
Mobile Device Accuracy for Step Counting Across Age Groups. details	Modave, F., Guo, Y., Bian, J., Gurka, M.J., Parish, A., Smith, M.D., Lee, A.M. and Buford, T.W.	2017	JMIR mHealth and uHealth	Validation	Surge	Older Adults Adults	Steps	URL
Energy Expenditure and Step Count Analysis of the Fitbit Flex Activity Tracker details	Montes, J. and Navalta, J.	2017	Medicine & Science in Sports & Exercise	Validation	Flex	Adults	Steps Energy Expenditure	URL
Comparative Accuracy of a Wrist-Worn Activity Tracker and a Smart Shirt for Physical Activity Assessment details	Montoye, A.H.K., Mitrzyk, J.R. and Molesky, M.J.	2017	Measurement in Physical Education and Exercise	Validation	Charge HR	Adults	Steps Energy Expenditure Heart Rate	URL
Minimal Detectable Change Thresholds and Responsiveness of Zephyr Bioharness & Fitbit Charge Devices. details	Nazari, G. and MacDermid, J.C.	2017	Journal of Strength and Conditioning Research	Validation	Charge HR	Adults	Heart Rate	URL
Accuracy of Consumer Monitors for Measuring Sleep Across Seven Nights details	Nowakowski, S., Grandner, M., Stout-Aguilar, J., Meers, J., Lewis-Powell, Z., Swartz, M., Choi, H. and Lyons, E.	2017	Sleep	Validation	Flex	Adults	Sleep	URL
When a Step Is Not a Step! Specificity Analysis of Five Physical Activity Monitors details	O’Connell, S., ÓLaighin, G. and Quinlan, L.R.	2017	PLOS ONE	Validation	One	Adults	Steps	URL
Sources of Error with Wearable Step Counters details	Park, S., Toth, L.P., Morton, A.L., Pittman, W.L., Sarasaltik, D. and Bassett, D.R.	2017	Medicine & Science in Sports & Exercise	Validation	Zip Charge	Adults	Steps	URL
Accuracy And Reliability Of The Fitbit Charge? Activity Tracker Among Older Adults details	Powers, M., Smith, M., Henderson, O. and Bodman, T.	2017	Medicine & Science in Sports & Exercise	Validation	Charge	Older Adults	Steps	URL
Validation of a self-monitoring tool for use in exercise therapy. details	Powierza, C.S., Clark, M.D., Hughes, J.M., Carneiro, K.A. and Mihalik, J.P.	2017	PM&R	Validation	Charge HR	Adults	Heart Rate	URL
Accuracy of Fitbit Charge 2 Worn at Different Wrist Locations During Exercise details	Salazar, V.E., Lucio, N.D. and Funk, M.D.	2017	Medicine & Science in Sports & Exercise	Validation	Charge 2	Adults	Heart Rate	URL
Step count accuracy and reliability of two activity tracking devices in people after stroke details	Schaffer, S.D., Holzapfel, S.D., Fulk, G. and Bosch, P.R.	2017	Physiotherapy Theory and Practice	Validation	Zip	Patients Adults	Steps	URL
Validity and reliability of the Fitbit Zip as a measure of preschool children's step count details	Sharp, C.A., Mackintosh, K.A., Erjavec, M., Pascoe, D.M. and Horne, P.J.	2017	BMJ Open Sport & Exercise Medicine	Validation	Zip	Children	Steps	URL
Accuracy in Wrist-Worn, Sensor-Based Measurements of Heart Rate and Energy Expenditure in a Diverse Cohort details	Shcherbina, A., Mattsson, C., Waggott, D., Salisbury, H., Christle, J., Hastie, T., Wheeler, M. and Ashley, E.	2017	Journal of Personalized Medicine	Validation	Surge	Adults	Energy Expenditure Heart Rate
Accuracy Of Steps, Energy Expenditure, And Distance In Nine Activity Trackers details	Smith, M.A.	2017	Medicine & Science in Sports & Exercise	Validation	Charge Charge HR Surge	Adults	Steps Energy Expenditure Distance	URL
Fitbit And Actigraph: A Comparison Of Physical Activity And Sedentary Time In Overweight/obese Adults details	Szabo-Reed, A.N., Willis, E., Ptomey, L., Washburn, R. and Donnelly, J.	2017	Medicine & Science in Sports & Exercise	Validation	Flex	Adults	Steps Intensity	URL
Accuracy of a step counter during treadmill and daily life walking by healthy adults and patients with cardiac disease details	Thorup, C.B., Andreasen, J.J., Sørensen, E.E., Grønkjær, M., Dinesen, B.I. and Hansen, J.	2017	BMJ Open	Validation	Zip	Patients Adults	Steps	URL
Validity of Different Activity Monitors to Count Steps in an Inpatient Rehabilitation Setting. details	Treacy, D., Hassett, L., Schurr, K., Chagpar, S., Paul, S.S. and Sherrington, C.	2017	Physical Therapy	Validation	One Charge	Patients Adults	Steps	URL
The Fitbit One Physical Activity Tracker in Men With Prostate Cancer: Validation Study details	Van Blarigan, E.L., Kenfield, S.A., Tantum, L., Cadmus-Bertram, L.A., Carroll, P.R. and Chan, J.M.	2017	JMIR Cancer	Validation	One	Patients Adults	Steps Intensity	URL
Criterion-Validity of Commercially Available Physical Activity Tracker to Estimate Step Count, Covered Distance and Energy Expenditure during Sports Conditions details	Wahl, Y., Düking, P., Droszez, A., Wahl, P. and Mester, J.	2017	Frontiers in Physiology	Validation	Charge Charge HR	Adults	Steps Energy Expenditure Distance	URL
Evaluation on Step Counting Performance of Wristband Activity Monitors in Daily Living Environment details	Wang, L., Liu, T., Wang, Y., Li, Q., Yi, J. and Inoue, Y.	2017	IEEE Access	Validation	Charge	Adults	Steps	URL
Polysomnography vs Consumer Devices: Comparing Common Sleep Indices details	Wedderburn-Bisshop, J., Kharatishvili-Bredt, I., Martin, I. and Tolhurst, S.	2017	Sleep	Validation	Charge HR	Patients Adults	Sleep	URL
Wearable Heart Rate Monitor Technology Accuracy in Research: A Comparative Study Between PPG and ECG Technology details	Weiler, D.T., Villajuan, S.O., Edkins, L., Cleary, S. and Saleem, J.J.	2017	Proceedings of the Human Factors and Ergonomics Society Annual Meeting	Validation	Charge HR	Adults	Heart Rate	URl
Evaluating the Consistency of Current Mainstream Wearable Devices in Health Monitoring: A Comparison Under Free-Living Conditions details	Wen, D., Zhang, X., Liu, X. and Lei, J.	2017	Journal of Medical Internet Research	Validation	Surge	Adults	Steps Energy Expenditure Intensity Distance Sleep	URL
Validation of Three Wearable Fitness Trackers for Measuring Physical Activity in College Students details	Witmer, C., Zhang, P., Godin, S. and Che, D.	2017	Medicine & Science in Sports & Exercise	Validation	Charge HR	Adults	Energy Expenditure	URL
Using Wireless Pedometers to Measure Children's Physical Activity: How Reliable is the Fitbit Zip? details	Xu, T., Byker, E.J. and Gonzales, M.R.	2017	Movement, Health & Exercise	Validation	Zip	Children	Steps	URL
A validation study of Fitbit Charge 2™ compared with polysomnography in adults details	de Zambotti, M., Goldstone, A., Claudatos, S., Colrain, I.M. and Baker, F.C.	2017	Chronobiology International	Validation	Charge 2	Adults	Sleep
Step count accuracy of StepWatch and FitBit One™ among individuals with a unilateral transtibial amputation details	Arch, E.S., Sions, J.M., Horne, J. and Bodt, B.A.	2018	Prosthetics and Orthotics International	Validation	One	Patients Adults	Steps
Assessment of the Fitbit Charge 2 for monitoring heart rate details	Benedetto, S., Caldato, C., Bazzan, E., Greenwood, D.C., Pensabene, V. and Actis, P.	2018	PLOS ONE	Validation	Charge 2	Adults	Heart Rate
Reliability and validity of two fitness tracker devices in the laboratory and home environment for older community-dwelling people details	Burton, E., Hill, K.D., Lautenschlager, N.T., Thøgersen-Ntoumani, C., Lewin, G., Boyle, E. and Howie, E.	2018	BMC Geriatrics	Validation	Flex Charge HR	Older Adults	Steps Intensity Distance Sleep
Classification Accuracy of a Wearable Activity Tracker for Assessing Sedentary Behavior and Physical Activity in 3–5-Year-Old Children details	Byun, W., Lee, J.-M., Kim, Y. and Brusseau, T.	2018	International Journal of Environmental Research and Public Health	Validation	Flex	Children	Intensity
The Use of a Fitbit Device for Assessing Physical Activity and Sedentary Behavior in Preschoolers. details	Byun, W., Kim, Y. and Brusseau, T.A.	2018	The Journal of pediatrics	Validation	Flex	Children	Intensity Sleep	URL
Validation of Fitness Tracker for Sleep Measures in Women with Asthma details	Castner, J., Mammen, M.J., Jungquist, C., Licata, O., Pender, J.J., Wilding, G.E. and Sethi, S.	2018	Journal of Asthma	Validation	Charge	Adults	Sleep
Preliminary concurrent validity of the Fitbit-Zip and ActiGraph activity monitors for measuring steps in people with polymyalgia rheumatica. details	Chandrasekar, A., Hensor, E.M.A., Mackie, S.L., Backhouse, M.R. and Harris, E.	2018	Gait & posture	Validation	Zip	Patients Adults	Steps
Validation of the Fitbit Flex in an Acute Post-Cardiac Surgery Patient Population details	Daligadu, J., Pollock, C.L., Carlaw, K., Chin, M., Haynes, A., Thevaraajah Kopal, T., Tahsinul, A., Walters, K. and Colella, T.J.F.	2018	Physiotherapy Canada	Validation	Flex	Patients	Steps Distance
Validation of PiezoRx Pedometer Derived Sedentary Time details	Donahoe, K., MacDonald, D., Tremblay, M. and Saunders, T.	2018	International Journal of Exercise Science	Validation	Flex	Adults	Intensity
Congruent Accuracy of Wrist-worn Activity Trackers during Controlled and Free-living Conditions details	Dondzilla, C.J., Lewis, C., Lopez, J. and Parker, T.	2018	International Journal of Exercise Science	Validation	Charge HR	Adults	Steps Energy Expenditure Heart Rate
Steps per Day Measured by Consumer Activity Trackers Worn at the Non-Dominant and Dominant Wrist Relative to a Waist-Worn Pedometer details	Edwardson, C.L., Davies, M., Khunti, K., Yates, T. and Rowlands, A.V.	2018	Journal for the Measurement of Physical Behaviour	Validation	Flex	Adults	Steps
Accuracy of Fitbit Surge and Smartphone Apps at Measuring Cycling Distance and Speed details	Gamez, J.L., Figueroa, I.A. and Funk, M.D.	2018	Medicine & Science in Sports & Exercise	Validation	Surge	Adults	Distance
Determining the Validity and Accuracy of Multiple Activity-Tracking Devices in Controlled and Free-Walking Conditions details	Gaz, D.V., Rieck, T.M., Peterson, N.W., Ferguson, J.A., Schroeder, D.R., Dunfee, H.A., Henderzahs-Mason, J.M. and Hagen, P.T.	2018	American Journal of Health Promotion	Validation	One Zip Charge HR	Adults	Steps Distance
Assessment of the accuracy of nutrient calculations of five popular nutrition tracking applications details	Griffiths, C., Harnack, L. and Pereira, M.A.	2018	Public Health Nutrition	Validation		Adults	Food
Predicting Future Glucose Fluctuations Using Machine Learning and Wearable Sensor Data details	HAYERI, A.M.I.R.	2018	Diabetes	Validation		Children	Steps Heart Rate
The Accuracy of Wrist-Worn Heart Rate Monitors Across a Range of Exercise Intensities details	Hough, P.	2018	Medicine & Science in Sports & Exercise	Validation	Charge	Adults	Heart Rate
VALIDITY AND RELIABILITY OF THE FITBIT FLEX™ AND ACTIGRAPH GT3X+ AT JOGGING AND RUNNING SPEEDS. details	Jones, D., Crossley, K., Dascombe, B., Hart, H.F. and Kemp, J.	2018	International journal of sports physical therapy	Validation	Flex	Adults	Steps	URL
Impact Of Placement Of Wrist-worn Activity Monitors During The Lab And Free-living Settings details	Kang, M., Lee, N.H., Jung, H.C., Jeon, S. and Lee, S.	2018	Medicine & Science in Sports & Exercise	Validation		Adults	Steps Energy Expenditure Heart Rate
Use of Consumer Monitors for Estimating Energy Expenditure in Youth details	Kaplan, A.S., Lamunion, S.R., Hibbing, P.R. and Crouter, S.E.	2018	Medicine & Science in Sports & Exercise	Validation	Charge 2	Children	Energy Expenditure Intensity
Validity of wearable activity monitors for tracking steps and estimating energy expenditure during a graded maximal treadmill test details	Kendall, B., Bellovary, B. and Gothe, N.P.	2018	Journal of Sports Sciences	Validation	Flex	Adults	Steps Energy Expenditure	URL
Accuracy and precision of consumer-level activity monitors for stroke detection during wheelchair propulsion and arm ergometry details	Kressler, J., Koeplin-Day, J., Muendle, B., Rosby, B., Santo, E. and Domingo, A.	2018	PLOS ONE	Validation	Flex	Adults	Steps
Accuracy of wearable physical activity trackers in people with Parkinson's disease details	Lamont, R.M., Daniel, H.L., Payne, C.L. and Brauer, S.G.	2018	Gait & Posture	Validation	Charge HR	Patients Adults	Steps Intensity
Comparison of Wearable Trackers’ Ability to Estimate Sleep details	Lee, J.-M., Byun, W., Keill, A., Dinkel, D. and Seo, Y.	2018	International Journal of Environmental Research and Public Health	Validation	Charge HR	Adults	Sleep
Validation of Overall Energy Expenditure Measurements in the Fitbit Charge HR 2 and Apple Watch details	Li, K., Nuss, K., Thomson, E.A., Comstock, A., Blake, S., Reinwald, S., Pimentel, R.E. and Tracy, B.	2018	Medicine & Science in Sports & Exercise	Validation	Charge HR	Adults	Energy Expenditure
Validity of Consumer Activity Wristbands and Wearable EEG for Measuring Overall Sleep Parameters and Sleep Structure in Free-Living Conditions details	Liang, Z. and Chapa Martell, M.A.	2018	Journal of Healthcare Informatics Research	Validation	Charge 2	Adults	Sleep
Validating Fitbit Zip for monitoring physical activity of children in school: a cross-sectional study details	Mooses, K., Oja, M., Reisberg, S., Vilo, J. and Kull, M.	2018	BMC Public Health	Validation	Zip	Children	Steps Intensity
Heart Rate Alters, But Does Not Improve, Calorie Predictions in Fitbit Activity Monitors details	Montoye, A.H., Vusich, J., Mitrzyk, J. and Wiersma, M.	2018	Journal for the Measurement of Physical Behaviour	Validation	Charge HR Flex 2 Charge 2 Blaze	Adults	Energy Expenditure Heart Rate
Reliability of Zephyr Bioharness and Fitbit Charge Measures of Heart Rate and Activity at Rest, During the Modified Canadian Aerobic Fitness Test and Recovery details	Nazari, G., Macdermid, J.C., Kin., K.E.S.R., Richardson, J. and Tang, A.	2018	Journal of Strength and Conditioning Research	Validation	Charge HR	Adults	Heart Rate
Veterans’ Perspectives on Fitbit Use in Treatment for Post-Traumatic Stress Disorder: An Interview Study details	Ng, A., Reddy, M., Zalta, A.K. and Schueller, S.M.	2018	JMIR Mental Health	Validation	Charge HR	Patients	Steps Sleep
Dominant Vs Non-dominant Wrist details	Park, S.	2018	Medicine & Science in Sports & Exercise	Validation	Charge	Adults	Steps
Accuracy of a Wrist-Worn Heart Rate Sensing Device during Elective Pediatric Surgical Procedures details	Pelizzo, G., Guddo, A., Puglisi, A., De Silvestri, A., Comparato, C., Valenza, M., Bordonaro, E. and Calcaterra, V.	2018	Children	Validation	Charge HR	Children Adults	Heart Rate
Comparison Of Daily Energy Expenditure And Weekly Physical Activity Exposure Estimated Using Consumer And Research-grade Physical Activity Monitors During Officer Cadet Initial Military Training details	Powell, S.D., Siddall, A.G., S Thompson, J.E., Edwards, V.C., Jackson, S., Greeves, J.P., Wardle, S., Blacker, S.D. and Myers, S.D.	2018	Medicine & Science in Sports & Exercise	Validation	Surge	Adults	Energy Expenditure
Impact of EPOC adjustment on estimation of energy expenditure using activity monitors details	Pribyslavska, V., Caputo, J.L., Coons, J.M. and Barry, V.W.	2018	Journal of Medical Engineering & Technology	Validation	Surge	Adults	Energy Expenditure Intensity
Physical Activity during Pregnancy Alters Gene Expression in Neonatal Tissue details	Reynolds, L.J., Chavan, N.R., Rice, B.B., Maddox, H.F., Preston, J.D., DeHoff, L.B. and Pearson, K.J.	2018	Medicine & Science in Sports & Exercise	Validation	Flex	Adults	Steps
Consumer Wearable Devices for Activity Monitoring Among Individuals After a Stroke: A Prospective Comparison details	Rozanski, G.M., Aqui, A., Sivakumaran, S. and Mansfield, A.	2018	JMIR Cardio	Validation	Charge HR	Patients Adults	Steps Heart Rate
How well does a commercially available wearable device measure sleep in young athletes? details	Sargent, C., Lastella, M., Romyn, G., Versey, N., Miller, D.J. and Roach, G.D.	2018	Chronobiology International	Validation	Charge HR	Adults	Sleep
Validation of Activity Tracking Procedures in Elderly Patients after Operative Treatment of Proximal Femur Fractures details	Schmal, H., Holsgaard-Larsen, A., Izadpanah, K., Brønd, J.C., Madsen, C.F. and Lauritsen, J.	2018	Rehabilitation Research and Practice	Validation	Flex	Patients	Steps
Comparison of Research- and Consumer-grade Energy Expenditure Estimation Methods during 10 Days of Military Training details	Siddall, A.G., Thompson, J.E.S., Powell, S.D., Edwards, V.C., Kefyalew, S.S., Singh, P.A., Orford, E.R., Venables, M.C., Jackson, S., Greeves, J.P., Blacker, S.D. and Myers, S.D.	2018	Medicine & Science in Sports & Exercise	Validation	Surge	Adults	Energy Expenditure
Heart Rate Equivalency of the Fitbit Charge HR During Continuous Aerobic Exercise details	Stock, J.M., Pohlig, R.T., Botieri, M.J., Edwards, D.G. and Dominick, G.M.	2018	Journal for the Measurement of Physical Behaviour	Validation	Charge HR	Adults	Steps Intensity Heart Rate
Validation of Electronic Activity Monitor Devices During Treadmill Walking details	Tam, K.M. and Cheung, S.Y.	2018	Telemedicine and e-Health	Validation	Charge HR	Adults	Steps
Validity of wrist-worn consumer products to measure heart rate and energy expenditure details	Thiebaud, R.S., Funk, M.D., Patton, J.C., Massey, B.L., Shay, T.E., Schmidt, M.G. and Giovannitti, N.	2018	Digital Health	Validation	Surge	Adults	Energy Expenditure Heart Rate
Video-Recorded Validation of Wearable Step Counters under Free-living Conditions details	Toth, L.P., Park, S., Springer, C.M., Feyerabend, M.D., Steeves, J.A. and Bassett, D.R.	2018	Medicine & Science in Sports & Exercise	Validation	Zip Charge	Adults	Steps
Counting Steps in Activities of Daily Living in People With a Chronic Disease Using Nine Commercially Available Fitness Trackers: Cross-Sectional Validity Study. details	Ummels, D., Beekman, E., Theunissen, K., Braun, S. and Beurskens, A.J.	2018	JMIR mHealth and uHealth	Validation	One Flex	Adults	Steps
The Wearable Technological Device as a Means of Physical Activity Monitoring details	Underhill, A.	2018	Medicine & Science in Sports & Exercise	Validation	Flex	Adults	Steps Intensity
Accuracy of Activity Trackers in Parkinson Disease: Should We Prescribe Them? details	Wendel, N., Macpherson, C.E., Webber, K., Hendron, K., Tamara, D., Cristina, C.-S. and Ellis, T.	2018	Physical Therapy	Validation	Zip Surge	Patients Adults	Steps
The bit doesn't fit: Evaluation of a commercial activity-tracker at slower walking speeds. details	Wong, C.K., Mentis, H.M. and Kuber, R.	2018	Gait & Posture	Validation	Ultra	Older Adults	Steps	URL
Evaluating the Validity of Current Mainstream Wearable Devices in Fitness Tracking Under Various Physical Activities: Comparative Study details	Xie, J., Wen, D., Liang, L., Jia, Y., Gao, L. and Lei, J.	2018	JMIR Mhealth Uhealth	Validation	Surge	Adults	Steps Energy Expenditure Distance Sleep Heart Rate
Personalized effect of health behavior on blood pressure: Machine learning based prediction and recommendation details	Chiang, Po Han; Dey, Sujit	2018	2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)	Validation	Charge HR	Adults	Steps
Accuracy of PurePulse photoplethysmography technology of Fitbit Charge 2 for assessment of heart rate during sleep details	Haghayegh, Shahab; Khoshnevis, Sepideh; Smolensky, Michael H.; Diller, Kenneth R.	2019	Chronobiology International	Validation	Charge 2	Adults	Heart Rate	URL
Heart rate measures from the Apple Watch, Fitbit Charge HR 2, and electrocardiogram across different exercise intensities details	Thomson, Elizabeth A.; Nuss, Kayla; Comstock, Ashley; Reinwald, Steven; Blake, Sophie; Pimentel, Richard E.; Tracy, Brian L.; Li, Kaigang	2019	Journal of Sports Sciences	Validation	Charge 2	Adults	Heart Rate	URL
Validation of Fitbit Charge 2 and Fitbit Alta HR Against Polysomnography for Assessing Sleep in Adults With Obstructive Sleep Apnea details	Moreno-Pino, Fernando; Porras-Segovia, Alejandro; L{\'{o}}pez-Esteban, Pilar; Art{\'{e}}s, Antonio; Baca-Garc{\'{i}}a, Enrique	2019	Journal of Clinical Sleep Medicine	Validation	Charge 2	Adults	Sleep	URL
Validity Evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in Free-Living Environments in an Older Adult Cohort. details	Tedesco, Salvatore; Sica, Marco; Ancillao, Andrea; Timmons, Suzanne; Barton, John; O'Flynn, Brendan	2019	JMIR mHealth and uHealth	Validation	Charge 2	Older Adults	Steps	URL
Accuracy of fitbit wristbands in measuring sleep stage transitions and the effect of user-specific factors details	Liang, Zilu; Chapa-Martell, Mario Alberto	2019	Journal of Medical Internet Research	Validation	Charge 2	Adults	Sleep	URL
Reliability of Zephyr Bioharness and Fitbit Charge Measures of Heart Rate and Activity at Rest, During the Modified Canadian Aerobic Fitness Test, and Recovery details	Nazari, Goris; MacDermid, Joy C.; Sinden, Kathryn E.; Richardson, Julie; Tang, Ada	2019	Journal of Strength and Conditioning Research	Validation	Charge	Adults	Heart Rate	URL
Concurrent validity of the Fitbit for assessing sedentary behavior and moderate-to-vigorous physical activity details	Redenius, Nicklaus; Kim, Youngwon; Byun, Wonwoo	2019	BMC Medical Research Methodology	Validation	Flex	Adults	Intensity	URL
Validity of Cardiorespiratory Fitness Measured with Fitbit Compared to V˙O2max details	Klepin, Katharine; Wing, David; Higgins, Michael; Nichols, Jeanne; Godino, Job G.	2019	Medicine and science in sports and exercise	Validation	Charge 2	Adults	Heart Rate	URL
Preliminary Agreement on Tracking Sleep Between a Wrist-Worn Device Fitbit Alta and Consensus Sleep Diary details	Liu, Jiaxing; Wong, Wing To; Zwetsloot, Inez Maria; Hsu, Yu Cheng; Tsui, Kwok Leung	2019	Telemedicine and e-Health	Validation	Alta	Adults	Sleep	URL
Assessment of Accuracy of Overall Energy Expenditure Measurements for the Fitbit Charge HR 2 and Apple Watch details	Nuss, Kayla J.; Thomson, Elizabeth A.; Courtney, Jimikaye B.; Comstock, Ashley; Reinwald, Steven; Blake, Sophie; Richard, E.; Tracy, Brian L.; Li, Kaigang	2019	American Journal of Health Behavior	Validation	Charge HR	Adults	Energy Expenditure
Validating Fitbit for Evaluation of Physical Activity in Patients with Knee Osteoarthritis: Do Thresholds Matter? details	Silva, Genevieve S.; Yang, Heidi; Collins, Jamie E.; Losina, Elena	2019	ACR Open Rheumatology	Validation	Charge 2	Patients	Intensity	URL
What is the Agreement Between Two Generations of Commercial Accelerometer in a Free-Living Environment for Young to Middle-Aged Adults? details	Jones, Denise M.; Hart, Harvi F.; Crossley, Kay M.; Ackerman, Ilana N.; Kemp, Joanne L.	2019	Journal for the Measurement of Physical Behaviour	Validation	Flex Flex 2	Adults	Steps	URL
Free-Living Comparison of Physical Activity and Sleep Data from Fitbit Activity Trackers Worn on the Dominant and Nondominant Wrists details	Clevenger, Kimberly A.; Molesky, Monroe J.; Vusich, John; Montoye, Alexander H. K.	2019	Measurement in Physical Education and Exercise Science	Validation	Flex Flex 2	Adults	Steps Intensity Distance Sleep	URL
Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages details	Haghayegh, Shahab; Khoshnevis, Sepideh; Smolensky, Michael H.; Diller, Kenneth R.; Castriotta, Richard J.	2020	Chronobiology International	Validation	Charge 2	Adults	Sleep	URL
Performance of a commercial multi-sensor wearable (Fitbit Charge HR) in measuring physical activity and sleep in healthy children details	Godino, Job G.; Wing, David; de Zambotti, Massimiliano; Baker, Fiona C.; Bagot, Kara; Inkelis, Sarah; Pautz, Carina; Higgins, Michael; Nichols, Jeanne; Brumback, Ty; Chevance, Guillaume; Colrain, Ian M.; Patrick, Kevin; Tapert, Susan F.	2020	PLOS ONE	Validation	Charge HR	Children	Sleep	URL
Minimal Detectable Change Thresholds and Responsiveness of Zephyr Bioharness and Fitbit Charge Devices details	Nazari, Goris; MacDermid, Joy C.	2020	Journal of Strength and Conditioning Research	Validation	Charge	Adults	Heart Rate	URL
Impact of placement of fitbit HR under laboratory and free-living conditions details	Jung, Hyun Chul; Kang, Minsoo; Lee, Nan Hee; Jeon, Soeun; Lee, Sukho	2020	Sustainability (Switzerland)	Validation	Charge 2	Adults	Heart Rate	URL
Performance of Fitbit Charge 3 against polysomnography in measuring sleep in adolescent boys and girls details	Menghini, Luca; Yuksel, Dilara; Goldstone, Aimee; Baker, Fiona C.; de Zambotti, Massimiliano	2021	Chronobiology International	Validation	Charge 3	Adolescents	Sleep Heart Rate	URL
A standardized framework for testing the performance of sleep-tracking technology: step-by-step guidelines and open-source code details	Menghini, Luca; Cellini, Nicola; Goldstone, Aimee; Baker, Fiona C.; de Zambotti, Massimiliano	2021	Sleep	Validation	Charge 2	Adults	Sleep	URL
Comparing the accuracy of sleep staging data from a new wearable sleep electroencephalography device versus the Fitbit Charge 3 with polysomnography as reference details	Suzuki, Yoko; Abe, Takashi; Kawana, Fusae; Okabe, Satomi; Kokubo, Toshio; Hoshino, Kazuya; Baba, Misao; Fujiwara, Masaaki; Yanagisawa, Masashi	2021	Clinical Neurophysiology	Validation	Charge 3	Adults	Sleep	URL
Measurement of Heart Rate Using the Polar OH1 and Fitbit Charge 3 Wearable Devices in Healthy Adults During Light, Moderate, Vigorous, and Sprint-Based Exercise: Validation Study details	Muggeridge, D. J.; Hickson, K.; Davies, A. V.; Giggins, O. M.; Megson, I. L.; Gorely, T.; Crabtree, D. R.	2021	JMIR mHealth and uHealth	Validation	Charge 3	Adults	Heart Rate	URL
Comparison of the Wrist-Worn Fitbit Charge 2 and the Waist-Worn Actigraph GTX3 for Measuring Steps Taken in Occupational Settings details	Garza, J. L.; Wu, Z. H.; Singh, M.; Cherniack, M. G.	2021	Annals of work exposures and health	Validation	Charge 2	Adults	Steps	URL
Accuracy of the ActivPAL and Fitbit Charge 2 in measuring step count in Cystic Fibrosis details	Curran, M.; Tierney, A.; Collins, L.; Kennedy, L.; McDonnell, C.; Sheikhi, A.; Walsh, C.; Casserly, B.; Cahalan, R.	2021	Physiotherapy theory and practice	Validation	Charge 2	Patients Adults	Steps	URL
Comparison of free-living physical activity data obtained from a Fitbit Zip, the Apple iPhone Health app and a modified Bouchard Activity Record details	Reynolds, R. C.; Smith, V. M.; Macniven, R.	2021	Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals	Validation	Zip	Adults	Steps Distance	URL
Inter-Device Agreement between Fitbit Flex 1 and 2 for Assessing Sedentary Behavior and Physical Activity details	Kwon, S.; Burns, R. D.; Kim, Y.; Bai, Y.; Byun, W.	2021	International journal of environmental research and public health	Validation	Flex Flex 2	Adults	Steps Intensity	URL
Validity of Fitbit activity monitoring for adults with progressive muscle diseases details	Roberts-Lewis, Sarah F.; White, Claire M.; Ashworth, Mark; Rose, Michael R.	2021	Disability and rehabilitation	Validation	Charge 2	Adults	Steps Energy Expenditure Intensity Heart Rate	URL
Research on physical activity variability and changes of metabolic profile in patients with prediabetes using Fitbit activity trackers data details	Bliudzius, Antanas; Puronaite, Roma; Trinkunas, Justas; Jakaitiene, Audrone; Kasiulevicius, Vytautas	2021	Technology and health care : official journal of the European Society for Engineering and Medicine	Validation		Older Adults		URL
The relationship between sleep and physical activity in an in-patient rehabilitation stroke setting: a cross-sectional study details	Hei Chow, Ching; Fraysse, Francois; Hillier, Susan	2021	Topics in stroke rehabilitation	Validation	Flex	Patients	Steps Sleep	URL
Validity of Tools to Measure Physical Activity in Older Adults Following Total Knee Arthroplasty details	Jasper, Lisa; Beaupre, Lauren A.; Spence, John C.; Jones, C. Allyson	2021	Journal of aging and physical activity	Validation		Patients	Steps Energy Expenditure	URL
Validity of Wrist-Wearable Activity Devices for Estimating Physical Activity in Adolescents: Comparative Study details	Hao, Yingying; Ma, Xiao Kai; Zhu, Zheng; Cao, Zhen Bo	2021	JMIR mHealth and uHealth	Validation	Flex	Adolescents	Steps Energy Expenditure	URL URL URL
Using a consumer-based wearable activity tracker for physical activity goal setting and measuring steps in pregnant women with gestational diabetes mellitus: exploring acceptance and validity details	Ehrlich, Samantha F.; Maples, Jill M.; Barroso, Cristina S.; Brown, Kathleen C.; Bassett, David R.; Zite, Nikki B.; Fortner, Kimberly B.	2021	BMC Pregnancy and Childbirth	Validation	Charge 3	Pregnant Women	Steps
Wrist-Worn Wearables for Monitoring Heart Rate and Energy Expenditure While Sitting or Performing Light-to-Vigorous Physical Activity: Validation Study details	Duking Peter; Giessing Laura; Frenkel Marie Ottilie; Koehler Karsten; Holmberg Hans Christer; Sperlich Billy	2020	JMIR mHealth and uHealth	Validation	Versa	Adults	Energy Expenditure Heart Rate	URL
Wrist-worn devices for the measurement of heart rate and energy expenditure: A validation study for the Apple Watch 6, Polar Vantage V and Fitbit Sense details	Hajj-Boutros, Guy; Landry-Duval, Marie Anne; Comtois, Alain Steve; Gouspillou, Gilles; Karelis, Antony D.	2022	European journal of sport science	Validation	Sense	Adults	Energy Expenditure Heart Rate	URL
Heart Rate Measurement Accuracy of Fitbit Charge 4 and Samsung Galaxy Watch Active2: Device Evaluation Study details	Nissen, Michael; Slim, Syrine; Jager Katharina; Flaucher Madeleine; Huebner Hanna; Danzberger Nina; Fasching Peter A.; Beckmann Matthias W.; Gradl Stefan; Eskofier Bjoern M.	2022	JMIR formative research	Validation	Charge 4	Adults	Heart Rate	URL
HRV Monitoring Using Commercial Wearable Devices as a Health Indicator for Older Persons during the Pandemic details	Rodrigues, Eujessika; Lima, Daniella; Barbosa, Paulo; Gonzaga, Karoline; Guerra, Ricardo Oliveira; Pimentel, Marcela; Barbosa, Humberto; Maciel, {\'{A}}lvaro	2022	Sensors (Basel, Switzerland)	Validation	Inspire HR	Older Adults	Steps Sleep Heart Rate	URL
Commercial Devices Provide Estimates of Energy Balance with Varying Degrees of Validity in Free-Living Adults details	Shook, Robin P.; Yeh, Hung Wen; Welk, Gregory J.; Davis, Ann M.; Ries, Daniel	2022	The Journal of nutrition	Validation	Aria Alta HR	Adults	Heart Rate Weight	URL
On the Security of Bluetooth Low Energy in Two Consumer Wearable Heart Rate Monitors/Sensing Devices details	Kurt Peker, Yeṣem; Bello, Gabriel; Perez, Alfredo J.	2022	Sensors (Basel, Switzerland)	Validation	Charge 4	NA	Heart Rate	URL URL URL
Relationship Between Life-Space Mobility and Health Characteristics in Older Adults Using Global Positioning System Watches details	Chung, Jane; Boyle, Joseph; Wheeler, David C.	2022	Journal of applied gerontology : the official journal of the Southern Gerontological Society	Validation	Surge	Older Adults	Distance	URL
Validity of the Fitbit Ace and Moki Devices for Assessing Steps During Different Walking Conditions in Young Adolescents details	Sun, Xiyao; Adams, Stephanie A.; Li, Chuchu; Booth, Josephine N.; Robertson, Judy; Fawkner, Samantha	2022	Pediatric exercise science	Validation		Adolescents	Steps	URL
Continuous Monitoring of Vital Signs With Wearable Sensors During Daily Life Activities: Validation Study details	Haveman, Marjolein E.; van Rossum, Mathilde C.; Vaseur, Roswita M. E.; van der Riet, Claire; Schuurmann, Richte C. L.; Hermens, Hermie J.; de Vries, Jean Paul P. M.; Tabak, Monique	2022	JMIR formative research	Validation	Charge 3	Adults	Heart Rate	URL
Evaluation of Sleep Parameters and Sleep Staging (Slow Wave Sleep) in Athletes by Fitbit Alta HR, a Consumer Sleep Tracking Device details	Kawasaki, Yu; Kasai, Takatoshi; Sakurama, Yuko; Sekiguchi, Akiko; Kitamura, Eri; Midorikawa, Itsuki; Shiroshita, Nanako; Kawana, Fusae; Ogasawara, Etsuko; Kitade, Mari; Koikawa, Natsue; Matsuda, Takao	2022	Nature and science of sleep	Validation	Alta HR	Adults	Sleep Heart Rate	URL
Agreement of the Apple Watch\textregistered and Fitbit Charge\textregistered for recording step count and heart rate when exercising in water details	Held, Nicholas J.; Perrotta, Andrew S.; Mueller, Tyler; Pfoh-MacDonald, Selina J.	2022	Medical and Biological Engineering and Computing	Validation	Charge 3	Adults	Steps Heart Rate	URL
Comparison of free-living physical activity measurements between ActiGraph GT3X-BT and Fitbit Charge 3 in young people with haemophilia details	Matlary, Ruth Elise D.; Holme, Paal Andre; Glosli, Heidi; Rueegg, Corina Silvia; Grydeland, May	2022	Haemophilia : the official journal of the World Federation of Hemophilia	Validation	Charge 3	Patients	Steps Intensity	URL
Performance of a Multisensor Smart Ring to Evaluate Sleep: In-Lab and Home-Based Evaluation of Generalized and Personalized Algorithms details	Grandner, Michael A.; Bromberg, Zohar; Hadley, Aaron; Morrell, Zoe; Graf, Arnulf; Hutchison, Stephen; Freckleton, Dustin	2022	Sleep	Validation	Charge 4	Adults	Sleep	URL
A catalog of validity indices for step counting wearable technologies during treadmill walking: the CADENCE-adults study details	Mora-Gonzalez, Jose; Gould, Zachary R.; Moore, Christopher C.; Aguiar, Elroy J.; Ducharme, Scott W.; Schuna, John M.; Barreira, Tiago V.; Staudenmayer, John; McAvoy, Cayla R.; Boikova, Mariya; Miller, Taavy A.; Tudor-Locke, Catrine	2022	The international journal of behavioral nutrition and physical activity	Validation	One Zip Ionic	Adults	Steps	URL
Step-count outcomes of 13 different activity trackers: Results from laboratory and free-living experiments details	Nakagata, Takashi; Murakami, Haruka; Kawakami, Ryoko; Tripette, Julien; Nakae, Satoshi; Yamada, Yosuke; Ishikawa-Takata, Kazuko; Tanaka, Shigeho; Miyachi, Motohiko	2022	Gait \& Posture	Validation	Device unspecified	Adults	Steps	URL
The Accuracy of Commercially Available Fitness Trackers in Patients after Stroke details	Holubová, Anna; Malá, Eliška; Hoidekrová, Kristýna; Pětioký, Jakub; Ďuriš, Andrea; Mužík, Jan	2022	Sensors (Basel, Switzerland)	Validation	Alta HR	Adults	Steps	URL
Validity and Acceptability of Wearable Devices for Monitoring Step-Count and Activity Minutes Among People With Multiple Sclerosis details	Lavelle, Grace; Norris, Meriel; Flemming, Julie; Harper, Jamie; Bradley, Joan; Johnston, Helen; Fortune, Jennifer; Stennett, Andrea; Kilbride, Cherry; Ryan, Jennifer M.	2021	Frontiers in Rehabilitation Sciences	Validation	Zip Alta Garmin Vivofit 4	Adults	Steps
Validity of a Global Positioning System-Based Algorithm and Consumer Wearables for Classifying Active Trips in Children and Adults details	Steel, Chelsea; Crist, Katie; Grimes, Amanda; Bejarano, Carolina; Ortega, Adrian; Hibbing, Paul R.; Schipperijn, Jasper; Carlson, Jordan A.	2021	Journal for the Measurement of Physical Behaviour	Validation	Alta	Adults	Distance
Accuracy and Systematic Biases of Heart Rate Measurements by Consumer-Grade Fitness Trackers in Postoperative Patients: Prospective Clinical Trial details	Helmer, Philipp; Hottenrott, Sebastian; Rodemers, Philipp; Leppich, Robert; Helwich, Maja; Pryss, Rüdiger; Kranke, Peter; Meybohm, Patrick; Winkler, Bernd E.; Sammeth, Michael	2022	Journal of Medical Internet Research	Validation	Sense Garmin Fenix 6	Patients	Heart Rate
The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement details	de Angel, Valeria; Adeleye, Fadekemi; Zhang, Yuezhou; Cummins, Nicholas; Munir, Sara; Lewis, Serena; Laporta Puyal, Estela; Matcham, Faith; Sun, Shaoxiong; Folarin, Amos A.; Ranjan, Yatharth; Conde, Pauline; Rashid, Zulqarnain; Dobson, Richard; Hotopf, Matthew	2023	JMIR mental health	Validation	Charge 4	Adults	Steps Sleep Heart Rate
Non-equivalent, but still valid: Establishing the construct validity of a consumer fitness tracker in persons with multiple sclerosis details	Polhemus, Ashley; Sieber, Chloé; Haag, Christina; Sylvester, Ramona; Kool, Jan; Gonzenbach, Roman; von Wyl, Viktor	2023	PLOS digital health	Validation	Inspire HR	Adults	Steps Intensity
Validation of an open-source smartphone step counting algorithm in clinical and non-clinical settings details	Straczkiewicz, Marcin; Keating, Nancy L.; Thompson, Embree; Matulonis, Ursula A.; Campos, Susana M.; Wright, Alexi A.; Onnela, Jukka-Pekka	2023	medRxiv: The Preprint Server for Health Sciences	Validation	Charge 2	Patients	Steps
Clinical validation of an artificial intelligence algorithm offering cross-platform detection of atrial fibrillation using smart device electrocardiograms details	Mannhart, Diego; Lefebvre, Baptiste; Gardella, Christophe; Henry, Christine; Serban, Teodor; Knecht, Sven; Kühne, Michael; Sticherling, Christian; Badertscher, Patrick	2023	Archives of Cardiovascular Diseases	Validation	Sense	Patients	Heart Rate
Performance evaluation of Fitbit Charge 3 and actigraphy vs. polysomnography: Sensitivity, specificity, and reliability across participants and nights details	Eylon, Gal; Tikotzky, Liat; Dinstein, Ilan	2023	Sleep Health	Validation	Charge 3	Adults	Sleep
Accuracy of a Standalone Atrial Fibrillation Detection Algorithm Added to a Popular Wristband and Smartwatch: Prospective Diagnostic Accuracy Study details	Selder, Jasper L.; Te Kolste, Henryk Jan; Twisk, Jos; Schijven, Marlies; Gielen, Willem; Allaart, Cornelis P.	2023	Journal of Medical Internet Research	Validation	Ionic	Patients	NA
Validity of activity wristbands for estimating daily physical activity in primary schoolchildren under free-living conditions: School-Fit study details	Mayorga-Vega, Daniel; Casado-Robles, Carolina; Guijarro-Romero, Santiago; Viciana, Jesús	2023	Frontiers in Public Health	Validation	Garmin Vivofit 2	Children	Steps Intensity
Efficacy of a Commercial Physical Activity Monitor in Longitudinal Tracking of Patients With Pulmonary Hypertension: A Pilot Study details	Rosenzweig, Eliana; Villeda, Gerson Antonio Valencia; Crook, Sarah; Koli, Fatima; Rosenzweig, Erika B.; Krishnan, Usha S.	2023	Texas Heart Institute Journal	Validation	Charge HR	Patients Adults	Steps
Open-Source, Step-Counting Algorithm for Smartphone Data Collected in Clinical and Nonclinical Settings: Algorithm Development and Validation Study details	Straczkiewicz, Marcin; Keating, Nancy L.; Thompson, Embree; Matulonis, Ursula A.; Campos, Susana M.; Wright, Alexi A.; Onnela, Jukka-Pekka	2023	JMIR cancer	Validation	Charge 2	NA	Steps
Comparison of Polysomnography, Single-Channel Electroencephalogram, Fitbit, and Sleep Logs in Patients With Psychiatric Disorders: Cross-Sectional Study details	Kawai, Keita; Iwamoto, Kunihiro; Miyata, Seiko; Okada, Ippei; Fujishiro, Hiroshige; Noda, Akiko; Nakagome, Kazuyuki; Ozaki, Norio; Ikeda, Masashi	2023	Journal of Medical Internet Research	Validation	Sense	Adults	Sleep
Validation of ActiGraph and Fitbit in the assessment of energy expenditure in Huntington's disease details	Simón-Vicente, Lucía; Rodríguez-Fernández, Alejandro; Rivadeneyra-Posadas, Jéssica; Soto-Célix, María; Raya-González, Javier; Castillo-Alvira, Daniel; Calvo, Sara; Mariscal, Natividad; García-Bustillo, Álvaro; Aguado, Laura; Cubo, Esther	2024	Gait \& Posture	Validation	Charge 4	Adults	Energy Expenditure
Comparing self-reported energy intake using an online dietary tool with energy expenditure by an activity tracker details	Vasileiou, Aikaterina; Searle, Dominique; Larsen, Sofus C.; Magkos, Faidon; Horgan, Graham; Stubbs, R. James; Santos, Inês; Palmeira, António L.; Heitmann, Berit L.	2024	Nutrition (Burbank, Los Angeles County, Calif.)	Validation	Charge 2	Adults	Energy Expenditure Heart Rate
Comparison of daily step count between the Fitbit Inspire 3 and the activPAL 3 in adults with transtibial amputation details	Leister, Kyle R.; Burke, Sara E.; Kim, Joon Young; Duenas, Victor H.; Barreira, Tiago V.	2024	Frontiers in Rehabilitation Sciences	Validation	Inspire 3	Adults	Steps
Assessment of Fitbit Charge 4 for sleep stage and heart rate monitoring against polysomnography and during home monitoring in Huntington's disease details	Doheny, Emer P.; Renerts, Klavs; Braun, Andreas; Werth, Esther; Baumann, Christian; Baumgartner, Philipp; Morgan-Jones, Philippa; Busse, Monica; Lowery, Madeleine M.; Jung, Hans H.	2024	Journal of clinical sleep medicine: JCSM: official publication of the American Academy of Sleep Medicine	Validation	Charge 4	Adults	Sleep Heart Rate
Criterion-Related Validity of Consumer-Wearable Activity Trackers for Estimating Steps in Primary Schoolchildren under Controlled Conditions: Fit-Person Study details	Mayorga-Vega, Daniel; Casado-Robles, Carolina; Guijarro-Romero, Santiago; Viciana, Jesús	2024	Journal of Sports Science \& Medicine	Validation	Ace 2	Children	Steps
Performance of wearable sleep trackers during nocturnal sleep and periods of simulated real-world smartphone use details	Willoughby, Adrian R.; Golkashani, Hosein Aghayan; Ghorbani, Shohreh; Wong, Kian F.; Chee, Nicholas I. Y. N.; Ong, Ju Lynn; Chee, Michael W. L.	2024	Sleep Health	Validation	Sense	Adults	Sleep
Testing the Reliability and Validity of the Korean Version of the Pittsburgh Sleep Quality Index Using Fitbit Devices: A Cross-Sectional Analysis details	Lee, Si-Yeon; Choi, Ja-Eun; Lee, Ji-Won; Lee, Yaeji; Park, Jae-Min; Hong, Kyung-Won	2024	Korean Journal of Family Medicine	Validation	Device unspecified	Adults	Sleep
Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table details	White, James W.; Finnegan, Olivia L.; Tindall, Nick; Nelakuditi, Srihari; Brown, David E.; Pate, Russell R.; Welk, Gregory J.; de Zambotti, Massimiliano; Ghosal, Rahul; Wang, Yuan; Burkart, Sarah; Adams, Elizabeth L.; Chandrashekhar, Mvs; Armstrong, Bridget; Beets, Michael W.; Weaver, R. Glenn	2024	PloS One	Validation	Sense	NA	Heart Rate
Accuracy of consumer-based activity trackers to measure and coach patients with lower limb lymphoedema details	Blondeel, Astrid; Devoogdt, Nele; Asnong, Anne; Geraerts, Inge; De Groef, An; Heroes, An-Kathleen; Van Calster, Charlotte; Troosters, Thierry; Demeyer, Heleen; Ginis, Pieter; De Vrieze, Tessa	2024	PloS One	Validation	Zip Alta Inspire	Patients Adults	Steps
Detection of Common Respiratory Infections, Including COVID-19, Using Consumer Wearable Devices in Health Care Workers: Prospective Model Validation Study details	Esmaeilpour, Zeinab; Natarajan, Aravind; Su, Hao-Wei; Faranesh, Anthony; Friel, Ciaran; Zanos, Theodoros P.; D'Angelo, Stefani; Heneghan, Conor	2024	JMIR formative research	Validation	Versa 3 Sense	Adults	Heart Rate
Commercially available activity monitors such as the fitbit charge and apple watch show poor validity in patients with gait aids after total knee arthroplasty details	Kooner, Paul; Baskaran, Sandhya; Gibbs, Vanessa; Wein, Sam; Dimentberg, Ronald; Albers, Anthony	2024	Journal of Orthopaedic Surgery and Research	Validation	Charge	Adults	Steps
Validation of activity trackers to estimate energy expenditure in older adults with cardiovascular risk factors details	Rieckmann, Alina; Jordan, Bas; Burczik, Friederike; Meixner, Jacqueline; Thiel, Christian	2024	PloS One	Validation	Charge 3	Older Adults	Heart Rate
Validity and stability of the international physical activity questionnaire short-form for stroke survivors with preserved walking ability details	de Diego-Alonso, Cristina; Blasco-Abadía, Julia; Doménech-García, Víctor; Bellosta-López, Pablo	2024	Topics in Stroke Rehabilitation	Validation	Inspire 2	Adults	Steps Sleep
Validity of Wrist-Worn Activity Tracker Heart Rate Detection in Fontan Patients During Exercise details	Pierick, Alyson R.; Burke, Kelly J.; Prusi, Megan; Largent, Bethany; Yu, Sunkyung; Lowery, Ray E.; Duimstra, Ashley; Hansen, Jesse E.	2024	Medicine and Science in Sports and Exercise	Validation	Device unspecified	Patients	Heart Rate
Validity of a Consumer-Based Wearable to Measure Clinical Parameters in Patients With Chronic Obstructive Pulmonary Disease and Healthy Controls: Observational Study details	Hermans, Fien; Arents, Eva; Blondeel, Astrid; Janssens, Wim; Cardinaels, Nina; Calders, Patrick; Troosters, Thierry; Derom, Eric; Demeyer, Heleen	2024	JMIR mHealth and uHealth	Validation	Charge 4	Patients	Steps Heart Rate
Accuracy of wrist-worn activity trackers for measuring steps in patients after major abdominal surgery: A validation study details	Li, Zhi; Feng, Weiyan; Zhou, Lili; Gong, Shu	2024	Digital Health	Validation	Inspire HR	Patients	Steps
MHealth devices demonstrate validity and reliability in detecting steps in chronic stroke survivors who rely on assistive devices details	Vieira Costa, Pollyana Helena; Thome Teixeira da Silva, Leonardo Vinicius; Dias de Jesus, Thainá Paula; Dario, Daniella Moura; Torriani-Pasin, Camila; Polese, Janaine Cunha	2024	Journal of Bodywork and Movement Therapies	Validation	Ultra	Adults	Steps
Reliability and Accuracy of the Fitbit Charge 4 Photoplethysmography Heart Rate Sensor in Ecological Conditions: Validation Study details	Ceugniez, Maxime; Devanne, Hervé; Hermand, Eric	2025	JMIR mHealth and uHealth	Validation	Charge 4	Adults	Heart Rate
Wearable technology for mobility measurement in orthopedics and traumatology: a comparison of commercially available systems details	Keppler, A. M.; Zaccaria, R.; Weigert, M.; Keppler, L.; Böcker, W.; Neuerburg, C.; Schniepp, R.; Fürmetz, J.	2025	Archives of Orthopaedic and Trauma Surgery	Validation	Charge 3	Older Adults Children	Steps
Concurrent Validity of a Physical Activity Vital Sign Used in an Adult Preventive Cardiology Clinic details	McCarthy, Margaret; Fletcher, Jason; Melkus, Gail; Vorderstrasse, Allison; Chehade, Mireille; Katz, Stuart	2025	Nursing Research	Validation	Device unspecified	Patients	Steps Distance
Validity of the Fitbit wearable activity monitor to estimate step counts in free-living conditions in ambulatory children and youth living with disability details	Voss, Christine; Bremer, Emily; Sharma, Ritu; Martin Ginis, Kathleen A.; Arbour-Nicitopoulos, Kelly P.	2025	Disability and Rehabilitation	Validation	Charge HR	Children	Steps
Leveraging Accelerometry and Heartrate Data from Consumer Wearables to Predict Physical Activity in Children: A Device Agnostic Approach details	Ghosal, Rahul; White, James W.; Finnegan, Olivia; Nelakuditi, Srihari; Brown, Trey; Pate, Russ; Welk, Greg; de Zambotti, Massimiliano; Wang, Yuan; Burkart, Sarah; Adams, Elizabeth L.; Armstrong, Bridget; Beets, Michael W.; Weaver, R. Glenn	2025	Medicine and Science in Sports and Exercise	Validation	Sense	Children	Steps Heart Rate
Validation of an Open-Source Smartwatch for Continuous Monitoring of Physical Activity and Heart Rate in Adults details	Ravanelli, Nicholas; Lefebvre, KarLee; Brough, Amy; Paquette, Simon; Lin, Wei	2025	Sensors (Basel, Switzerland)	Validation	Charge 5	Adults	Steps
Comparing the Accuracy of Different Wearable Activity Monitors in Patients With Lung Cancer and Providing Initial Recommendations: Protocol for a Pilot Validation Study details	Benzo, Roberto M.; Singh, Rujul; Presley, Carolyn J.; Tetrick, Macy K.; Chaplow, Zachary L.; Hery, Chloe M.; Yu, Jane; Washington, Peter; Penedo, Frank J.; Paskett, Electra D.; Lugade, Vipul; Fortune, Emma	2025	JMIR research protocols	Validation	Charge 6	Adults	Steps Intensity
Continuous heart rate measurements in patients with cardiac disease: Device comparison and development of a novel artefact removal procedure details	Vermunicht, Paulien; Makayed, Katsiaryna; Buyck, Christophe; Knaepen, Lieselotte; Piedrahita Giraldo, Juan Sebastian; Naessens, Sebastiaan; Hens, Wendy; Craenenbroeck, Emeline Van; Laukens, Kris; Desteghe, Lien; Heidbuchel, Hein	2025	Digital Health	Validation	Inspire 2	Patients	Intensity Heart Rate
The Effect of Assistive Devices on the Accuracy of Fitbits in Healthy Individuals: A Brief Report details	Jaworski, John; Makowski, Brian; Weaver, Michael; Seils, Michael; Scheid, Jennifer L.	2025	International Journal of Environmental Research and Public Health	Validation	Inspire 3	Adults	Steps
Validation of the Fitbit Charge 5 for the Detection of Heart Rate and Electrodermal Activity details	Ko, Katherine; McArthur, Genevieve; Johnco, Carly	2025	Psychophysiology	Validation	Charge 5	Adults	Intensity Heart Rate
Capturing Real-World Habitual Sleep Patterns With a Novel User-Centric Algorithm to Preprocess Fitbit Data in the All of Us Research Program: Retrospective Observational Longitudinal Study details	Master, Hiral; Annis, Jeffrey; Ching, Jack H.; Gleichauf, Karla; Han, Lide; Coleman, Peyton; Full, Kelsie M.; Zheng, Neil; Ruderfer, Douglas; Hernandez, John; Schneider, Logan D.; Brittain, Evan L.	2025	Journal of Medical Internet Research	Validation	Device unspecified	Adults	Sleep
Validity of Heart Rate Measurement Using Wearable Devices During Cardiopulmonary Exercise Testing in Patients With Cardiovascular Disease: Prospective Pilot Validation Study details	Kitagaki, Kazufumi; Hongo, Yuji; Futai, Rie; Hasegawa, Takeshi; Morikawa, Hiroshi; Shimoyama, Hisashi	2025	JMIR cardio	Validation	Inspire 3	Patients	Heart Rate

Movement toward a novel activity monitoring device (2012)

Sleep and Breathing

Abstract

Purpose: Although polysomnography is necessary for diagnosis of most sleep disorders, it is also expensive, time-consuming, intrusive, and interferes with sleep. Field-based activity monitoring is increasingly used as an alternative measure that can be used to answer certain clinical and research questions. The purpose of this study was to evaluate the reliability and validity of a novel activity monitoring device (Fitbit) compared to both polysomnography and standard actigraphy (Actiwatch-64). Methods: To test validity, simultaneous Fitbit and actigraph were worn during standard overnight polysomnography by 24 healthy adults at the West Virginia University sleep research laboratory. To test inter-Fitbit reliability, three participants also wore two of the Fitbit devices overnight at home. Results: Fitbit showed high intradevice reliability =96.5–99.1. Fitbit and actigraph differed significantly on recorded total sleep time and sleep efficiency between each other and polysomnography. Bland–Altman plots indicated that both Fitbit and actigraph overestimated sleep efficiency and total sleep time. Sensitivity of both Fitbit and actigraphy for accurately identifying sleep was high within all sleep stages and during arousals; specificity of both Fitbit and actigraph for accurately identifying wake was poor. Specificity of actigraph was higher except for wake before sleep onset; sensitivity of Fitbit was higher in all sleep stages and during arousals. Conclusions: The web-based Fitbit, available at a markedly reduced price and with several convenience factors compared to standard actigraphy, may be an acceptable activity measurement instrument for use with normative populations. However, Fitbit has the same specificity limitations as actigraphy; both devices consistently misidentify wake as sleep and thus overestimate both sleep time and quality. Use of the Fitbit will also require specific validation before it can be used to assess disordered populations and or different age groups.

Accuracy of the Fitbit pedometer for self-paced and prescribed physical activity (2012)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: The purpose of this study was to examine the validity of the Fitbit pedometer (FB; Fitbit Inc.) for measuring steps taken during self-paced and prescribed physical activity. METHODS: A total of 17 adults (8 males, 9 females; age = 41.19, BMI = 28.82 kg/m2) participated in this study. Participants completed four to eight bouts of physical activities in a laboratory condition and in a simulated free-living condition. During the laboratory (treadmill) condition participants completed walking bouts at a slow pace (1.5 mph), a normal pace (3.0 mph), a brisk pace (4.0 mph) and one jogging bout (5.5 mph). Each bout lasted three minutes. Participants then completed 400m self-paced walking bouts at slow, normal, and brisk paces as well as one 400m jog on an outdoor track.. Male participants wore one FB and one Omron HJ-720IT (OM) on the right hip attached to an elastic belt and one FB in the right front pocket. Female participants wore the devices in the same configuration but also wore an additional FB attached to the front/center of the sports bra. Absolute and percent differences were calculated for FB vs. directly observed (DO) steps. Linear mixed models were determined effects of body location, speed, condition (laboratory vs. free-living) on FB accuracy. RESULTS:The magnitude of the difference between DO and the FB devices (M(SD); range) across all activities was small for both the FB-waist (2.0%(4.7%); -6 - 23%) and FB-bra (0.8%(4.3%); -11 - 15%) and moderate for the FB-pocket (8.8%(16.8%); 4 - 74%). Model parameter estimates from the full factorial mixed-model indicated the FB under estimated by 24 (SE = 2.83) steps across all conditions compared to direct observation. Significant parameter estimates for body location, speed and the body location × speed interaction (all p textless .001) indicated that the FB in the pocket underestimated steps more than on the waist, both devices underestimated steps more as speed increased, and the magnitude of the underestimation increased more for the pocket compared to the waist as speed increased. CONCLUSIONS:The FB appears to be a valid device for assessing step counts in adults when worn on the waist. Caution should be taken when assessing activities at higher ambulatory speeds. Further research is needed to better understand the validity of the FB in free-living conditions.

A comparison of energy expenditure estimation of several physical activity monitors. (2013)

Medicine and Science in Sports and Exercise

Abstract

NTRODUCTION: Accurately and precisely estimating free-living energy expenditure (EE) is important for monitoring energy balance and quantifying physical activity. Recently, single and multisensor devices have been developed that can classify physical activities, potentially resulting in improved estimates of EE. PURPOSE: This study aimed to determine the validity of EE estimation of a footwear-based physical activity monitor and to compare this validity against a variety of research and consumer physical activity monitors. METHODS: Nineteen healthy young adults (10 men, 9 women) completed a 4-h stay in a room calorimeter. Participants wore a footwear-based physical activity monitor as well as Actical, ActiGraph, IDEEA, DirectLife, and Fitbit devices. Each individual performed a series of postures/activities. We developed models to estimate EE from the footwear-based device, and we used the manufacturer's software to estimate EE for all other devices. RESULTS: Estimated EE using the shoe-based device was not significantly different than measured EE (mean ± SE; 476 ± 20 vs 478 ± 18 kcal, respectively) and had a root-mean-square error of 29.6 kcal (6.2%). The IDEEA and the DirectLlife estimates of EE were not significantly different than the measured EE, but the ActiGraph and the Fitbit devices significantly underestimated EE. Root-mean-square errors were 93.5 (19%), 62.1 kcal (14%), 88.2 kcal (18%), 136.6 kcal (27%), 130.1 kcal (26%), and 143.2 kcal (28%) for Actical, DirectLife, IDEEA, ActiGraph, and Fitbit, respectively. CONCLUSIONS: The shoe-based physical activity monitor provides a valid estimate of EE, whereas the other physical activity monitors tested have a wide range of validity when estimating EE. Our results also demonstrate that estimating EE based on classification of physical activities can be more accurate and precise than estimating EE based on total physical activity.

Comparison of FitBit® Ultra to ActiGraph™ GT1M for Assessment of Physical Activity in Young Adults During Treadmill Walking (2014)

The Open Sports Medicine Journal

Abstract

Introduction: Accelerometers are used to quantify energy expenditure in field research. The ActiGraph™ GT1M (ActiGraph™) is a commonly used accelerometer for research. The FitBit® Ultra (FitBit®) is a low-cost alternative to the ActiGraph™; however, there is limited research on the validity of this device. Purpose: The pilot study compares the FitBit® against the ActiGraph™ and metabolic cart for measurement of energy expenditure and step counts during treadmill walking. Methods: Thirty-two (25 female) adults, mean age 22±2 years, performed two thirty-minute phases of walking (slow and brisk) on a treadmill while concurrently wearing the FitBit® and the ActiGraph™. Energy expenditure estimates were compared against energy expenditure measured by a metabolic cart. The Pearson’s correlation and t-tests determine the linear association and similarity between the accelerometers. Results: Energy expenditure estimate is moderately correlated between the two accelerometers during slow walking (r=0.584, p=0.011) and strongly correlated during brisk walking (r=0.910, p<0.001). Step count is strongly correlated between the accelerometers during slow (r=0.974, p<0.001) and brisk (r=0.996, p<0.001) walking. The FitBit® significantly underestimated energy expenditure during brisk walking compared to metabolic cart data. There is no difference between the slow and brisk phases’ step counts using either accelerometer. Conclusion: The results of this pilot study suggest that the FitBit® and the ActiGraph™ can be used interchangeably to measure steps, but not to measure kilocalories. Furthermore, the FitBit® underestimates energy expenditure, compared to a metabolic cart, as exercise intensity increases. This limits its ability to accurately measure energy expenditure in active populations.

Validity of consumer-based physical activity monitors. (2014)

Medicine and Science in Sports and Exercise

Abstract

BACKGROUND: Many consumer-based monitors are marketed to provide personal information on the levels of physical activity and daily energy expenditure (EE), but little or no information is available to substantiate their validity. PURPOSE: This study aimed to examine the validity of EE estimates from a variety of consumer-based, physical activity monitors under free-living conditions. METHODS: Sixty (26.4 ± 5.7 yr) healthy males (n = 30) and females (n = 30) wore eight different types of activity monitors simultaneously while completing a 69-min protocol. The monitors included the BodyMedia FIT armband worn on the left arm, the DirectLife monitor around the neck, the Fitbit One, the Fitbit Zip, and the ActiGraph worn on the belt, as well as the Jawbone Up and Basis B1 Band monitor on the wrist. The validity of the EE estimates from each monitor was evaluated relative to criterion values concurrently obtained from a portable metabolic system (i.e., Oxycon Mobile). Differences from criterion measures were expressed as a mean absolute percent error and were evaluated using 95% equivalence testing. RESULTS: For overall group comparisons, the mean absolute percent error values (computed as the average absolute value of the group-level errors) were 9.3%, 10.1%, 10.4%, 12.2%, 12.6%, 12.8%, 13.0%, and 23.5% for the BodyMedia FIT, Fitbit Zip, Fitbit One, Jawbone Up, ActiGraph, DirectLife, NikeFuel Band, and Basis B1 Band, respectively. The results from the equivalence testing showed that the estimates from the BodyMedia FIT, Fitbit Zip, and NikeFuel Band (90% confidence interval = 341.1-359.4) were each within the 10% equivalence zone around the indirect calorimetry estimate. CONCLUSIONS: The indicators of the agreement clearly favored the BodyMedia FIT armband, but promising preliminary findings were also observed with the Fitbit Zip.

An inexpensive device for monitoring patients' weights via automated hovering (2014)

International Journal of Cardiology

Abstract

Daily weight monitoring is integral to the management of heart failure (HF). Indeed, weight gain is a marker of HF decompensation, and daily weight monitoring has been associated with favorable prognosis. Until now, assessment of weight has required patients to attend an in-person visit, which provides only an infrequent snapshot of their weight. Remote hovering practices have gained considerable traction during the past several years, but their use outside of research settings has been limited. We tested the accuracy of remote weight monitoring using the Fitbit® Aria™ weight scale, a commercially available, inexpensive device that communicates data wirelessly, can transmit data remotely to healthcare providers, and yields data that can be banked into medical data repositories. We recruited 33 healthy employees from an academic medical center. Participants were at least 18 years of age, could read or write in English, and were routinely on campus at least 4 days per week. Participants were excluded if they failed to complete their daily weight assessments. Each day participants weighed themselves using both the Fitbit® Aria™ and seca 769 scales, the latter of which served as a gold standard comparator. Participants recorded the date and time of their scale reading and the weights reported by both scales to the nearest 0.1 lb. Participants provided informed consent, and the study protocol was approved by the local institutional review board. 32 participants provided at least 10 weight readings (minimum, 12; maximum, 41; mean, 26; standard deviation [SD], 7.2). Participants' weights ranged from 89.8 lbs to 224.6 lbs, with a mean weight of 150.9 lbs (31.2 lbs) on the Aria™ scale and a mean weight of 150.2 lbs (31.0 lbs) on the seca 769. The Aria™ demonstrated excellent agreement with the seca 769. Multilevel linear regression analysis revealed that the Aria™ systematically provided a weight reading that was 0.6 lbs greater than the seca 769, although the two scales did not differ in their assessment of weight over time. The flood of inexpensive monitoring devices to the consumer market has introduced new possibilities for remote HF management, but little is known about their accuracy. We demonstrated the accuracy of the Fitbit® Aria™ scale compared to the seca 769, a conventional scale designed for use in clinical practice. Daily information about patients' weights can alert clinicians to HF patients who are at high risk for hospitalization. Hovering remotely over patients may also engage patients in their own self-management.

Validation of the Fitbit One activity monitor device during treadmill walking. (2014)

Journal of Science and Medicine in Sport

Abstract

OBJECTIVES: In order to quantify the effects of physical activity such as walking on chronic disease, accurate measurement of physical activity is needed. The objective of this study was to determine the validity and reliability of a new activity monitor, the Fitbit One, in a population of healthy adults. DESIGN: Cross-sectional study. METHODS: Thirty healthy adults ambulated at 5 different speeds (0.90, 1.12, 1.33, 1.54, 1.78 m/s) on a treadmill while wearing three Fitbit One activity monitors (two on the hips and one in the pocket). The order of each speed condition was randomized. Fitbit One step count output was compared to observer counts and distance output was compared to the calibrated treadmill output. Two-way repeated measures ANOVA, concordance correlation coefficients, and Bland and Altman plots were used to assess validity and intra-class correlation coefficients (ICC) were used to assess reliability. RESULTS: No significant differences were noted between Fitbit One step count outputs and observer counts, and concordance was substantial (0.97-1.00). Inter-device reliability of the step count was high for all walking speeds (ICC = 0.95). Percent relative error was less than 1.3%. The distance output of the Fitbit One activity monitors was significantly different from the criterion values for each monitor at all speeds (Ptextless0.001) and exhibited poor concordance (0.0-0.05). Inter-device reliability was excellent for all treadmill speeds (ICC = 0.90). Percent relative error was high (up to 39.6%). CONCLUSIONS: The Fitbit One activity monitors are valid and reliable devices for measuring step counts in healthy young adults. The distance output of the monitors is inaccurate and should be noted with caution.

An Evaluation of Commercial Pedometers for Monitoring Slow Walking Speed Populations. (2015)

Telemedicine journal and e-health : the official journal of the American Telemedicine Association

Abstract

BACKGROUND: Pedometers are considered desirable devices for monitoring physical activity. Two population groups of interest include patients having undergone surgery in the lower extremities or who are otherwise weakened through disease, medical treatment, or surgery procedures, as well as the slow walking senior population. For these population groups, pedometers must be able to perform reliably and accurately at slow walking speeds. The objectives of this study were to evaluate the step count accuracy of three commercially available pedometers, the Yamax (Tokyo, Japan) Digi-Walker(®) SW-200 (YM), the Omron (Kyoto, Japan) HJ-720 (OM), and the Fitbit (San Francisco, CA) Zip (FB), at slow walking speeds, specifically at 1, 2, and 3 km/h, and to raise awareness of the necessity of focusing research on step-counting devices and algorithms for slow walking populations. MATERIALS AND METHODS: Fourteen participants 29.93 ±4.93 years of age were requested to walk on a treadmill at the three specified speeds, in four trials of 100 steps each. The devices were worn by the participants on the waist belt. The pedometer counts were recorded, and the error percentage was calculated. RESULTS: The error rate of all three evaluated pedometers decreased with the increase of speed: at 1 km/h the error rates varied from 87.11% (YM) to 95.98% (FB), at 2 km/h the error rates varied from 17.27% (FB) to 46.46% (YM), and at 3 km/h the error rates varied from 22.46% (YM) to a slight overcount of 0.70% (FB). CONCLUSIONS: It was observed that all the evaluated devices have high error rates at 1 km/h and mixed error rates at 2 km/h, and at 3 km/h the error rates are the smallest of the three assessed speeds, with the OM and the FB having a slight overcount. These results show that research on pedometers' software and hardware should focus more on accurate step detection at slow walking speeds.

Are Currently Available Wearable Devices for Activity Tracking and Heart Rate Monitoring Accurate, Precise, and Medically Beneficial? (2015)

Healthcare Informatics Research

Abstract

Objectives: The new wave of wireless technologies, fitness trackers, and body sensor devices can have great impact on healthcare systems and the quality of life. However, there have not been enough studies to prove the accuracy and precision of these trackers. The objective of this study was to evaluate the accuracy, precision, and overall performance of seventeen wearable devices currently available compared with direct observation of step counts and heart rate monitoring. Methods: Each participant in this study used three accelerometers at a time, running the three corresponding applications of each tracker on an Android or iOS device simultaneously. Each participant was instructed to walk 200, 500, and 1,000 steps. Each set was repeated 40 times. Data was recorded after each trial, and the mean step count, standard deviation, accuracy, and precision were estimated for each tracker. Heart rate was measured by all trackers (if applicable), which support heart rate monitoring, and compared to a positive control, the Onyx Vantage 9590 professional clinical pulse oximeter. Results: The accuracy of the tested products ranged between 79.8% and 99.1%, while the coefficient of variation (precision) ranged between 4% and 17.5%. MisFit Shine showed the highest accuracy and precision (along with Qualcomm Toq), while Samsung Gear 2 showed the lowest accuracy, and Jawbone UP showed the lowest precision. However, Xiaomi Mi band showed the best package compared to its price. Conclusions: The accuracy and precision of the selected fitness trackers are reasonable and can indicate the average level of activity and thus average energy expenditure.

The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: a cross-sectional study. (2015)

The International Journal of Behavioral Nutrition and Physical Activity

Abstract

BACKGROUND: Technological advances have seen a burgeoning industry for accelerometer-based wearable activity monitors targeted at the consumer market. The purpose of this study was to determine the convergent validity of a selection of consumer-level accelerometer-based activity monitors. METHODS: 21 healthy adults wore seven consumer-level activity monitors (Fitbit One, Fitbit Zip, Jawbone UP, Misfit Shine, Nike Fuelband, Striiv Smart Pedometer and Withings Pulse) and two research-grade accelerometers/multi-sensor devices (BodyMedia SenseWear, and ActiGraph GT3X+) for 48-hours. Participants went about their daily life in free-living conditions during data collection. The validity of the consumer-level activity monitors relative to the research devices for step count, moderate to vigorous physical activity (MVPA), sleep and total daily energy expenditure (TDEE) was quantified using Bland-Altman analysis, median absolute difference and Pearson's correlation. RESULTS: All consumer-level activity monitors correlated strongly (r > 0.8) with research-grade devices for step count and sleep time, but only moderately-to-strongly for TDEE (r = 0.74-0.81) and MVPA (r = 0.52-0.91). Median absolute differences were generally modest for sleep and steps (<10% of research device mean values for the majority of devices) moderate for TDEE (<30% of research device mean values), and large for MVPA (26-298%). Across the constructs examined, the Fitbit One, Fitbit Zip and Withings Pulse performed most strongly. CONCLUSIONS: In free-living conditions, the consumer-level activity monitors showed strong validity for the measurement of steps and sleep duration, and moderate valid for measurement of TDEE and MVPA. Validity for each construct ranged widely between devices, with the Fitbit One, Fitbit Zip and Withings Pulse being the strongest performers.

Reliability and validity of ten consumer activity trackers. (2015)

BMC Sports Science, Medicine and Rehabilitation

Abstract

BACKGROUND: Activity trackers can potentially stimulate users to increase their physical activity behavior. The aim of this study was to examine the reliability and validity of ten consumer activity trackers for measuring step count in both laboratory and free-living conditions. METHOD: Healthy adult volunteers (n = 33) walked twice on a treadmill (4.8 km/h) for 30 min while wearing ten different activity trackers (i.e. Lumoback, Fitbit Flex, Jawbone Up, Nike+ Fuelband SE, Misfit Shine, Withings Pulse, Fitbit Zip, Omron HJ-203, Yamax Digiwalker SW-200 and Moves mobile application). In free-living conditions, 56 volunteers wore the same activity trackers for one working day. Test-retest reliability was analyzed with the Intraclass Correlation Coefficient (ICC). Validity was evaluated by comparing each tracker with the gold standard (Optogait system for laboratory and ActivPAL for free-living conditions), using paired samples t-tests, mean absolute percentage errors, correlations and Bland-Altman plots. RESULTS: Test-retest analysis revealed high reliability for most trackers except for the Omron (ICC .14), Moves app (ICC .37) and Nike+ Fuelband (ICC .53). The mean absolute percentage errors of the trackers in laboratory and free-living conditions respectively, were: Lumoback (-0.2, -0.4), Fibit Flex (-5.7, 3.7), Jawbone Up (-1.0, 1.4), Nike+ Fuelband (-18, -24), Misfit Shine (0.2, 1.1), Withings Pulse (-0.5, -7.9), Fitbit Zip (-0.3, 1.2), Omron (2.5, -0.4), Digiwalker (-1.2, -5.9), and Moves app (9.6, -37.6). Bland-Altman plots demonstrated that the limits of agreement varied from 46 steps (Fitbit Zip) to 2422 steps (Nike+ Fuelband) in the laboratory condition, and 866 steps (Fitbit Zip) to 5150 steps (Moves app) in the free-living condition. CONCLUSION: The reliability and validity of most trackers for measuring step count is good. The Fitbit Zip is the most valid whereas the reliability and validity of the Nike+ Fuelband is low.

Comparison of a Commercial Accelerometer with Polysomnography and Actigraphy in Children and Adolescents (2015)

Sleep

Abstract

Study Objectives: To evaluate the reliability and validity of the commercially available Fitbit Ultra (2012) accelerometer compared to polysomnography (PSG) and two different actigraphs in a pediatric sample. Design and Setting: All subjects wore the Fitbit Ultra while undergoing overnight clinical polysomnography in a sleep laboratory; a randomly selected subset of participants also wore either the Ambulatory Monitoring Inc. Motionlogger Sleep Watch (AMI) or Phillips-Respironics Mini-Mitter Spectrum (PRMM). Participants: 63 youth (32 females, 31 males), ages 3–17 years (mean 9.7 years, SD 4.6 years). Measurements: Both “Normal” and “Sensitive” sleep-recording Fitbit Ultra modes were examined. Outcome variables included total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE). Primary analyses examined the differences between Fitbit Ultra and PSG using repeated-measures ANCOVA, with epoch-by-epoch comparisons between Fitbit Ultra and PSG used to determine sensitivity, specificity, and accuracy. Intra-device reliability, differences between Fitbit Ultra and actigraphy, and differences by both developmental age group and sleep disordered breathing (SDB) status were also examined. Results: Compared to PSG, the Normal Fitbit Ultra mode demonstrated good sensitivity (0.86) and accuracy (0.84), but poor specificity (0.52); conversely, the Sensitive Fitbit Ultra mode demonstrated adequate specificity (0.79), but inadequate sensitivity (0.70) and accuracy (0.71). Compared to PSG, the Fitbit Ultra significantly overestimated TST (41 min) and SE (8%) in Normal mode, and underestimated TST (105 min) and SE (21%) in Sensitive mode. Similar differences were found between Fitbit Ultra (both modes) and both brands of actigraphs. Conclusions: Despite its low cost and ease of use for consumers, neither sleep-recording mode of the Fitbit Ultra accelerometer provided clinically comparable results to PSG. Further, pediatric sleep researchers and clinicians should be cautious about substituting these devices for validated actigraphs, with a significant risk of either overestimating or underestimating outcome data including total sleep time and sleep efficiency.

Comparison of Three Inexpensive Commercially Available Activity Monitors to a Previously Validated Tri-Axial Accelerometer (2015)

Medicine & Science in Sports & Exercise

Abstract

Inexpensive personal activity monitors have recently gained popularity with the general public. Given the current wide-spread use of these devices, it is important to ensure that users are attaining accurate information compared to previously validated measures. PURPOSE: To compare the accuracy of the Fitbit Force™ and One™ activity monitors in measuring steps per day and time spent in different intensities of activity with the ActiGraph GT3X™tri-axial accelerometer. METHODS: Twenty-two women, 18-26y.o. with a BMI of 23.24 ± 3.26kg/m2 wore two Fitbit Ones TM (Bra and Waist respectively), a Fitbit Force TM (Wrist), and an ActiGraph GT3X™(Waist) for 7-consecutive days. ActiGraph™ data was processed using Actilife™ V5.1.1 and the Freedson equation activity cut points. Fitbit™ data was processed using their proprietary online software. A multivariate analysis of variance (MANOVA) was used to compare steps per day and time spent in light, moderate and vigorous intensity activities between the four devices. A post-hoc test of the least squared difference was used to determine where any differences occurred. RESULTS: The activity monitors differed in time spent in light F(3,19)= 3.16, ptextgreater.05) and vigorous (F(3,19)= 3.16, ptextgreater.05) intensity activities. Post-hoc comparisons revealed that the GT3X™ differed from all three Fitbit™ devices in the measurement of time spent in light (GT3X= 89.92±36 min, Force=419.97±129.22 min, Bra=379.93±122.2 min, Waist=392.75±114.78 min) and vigorous activities (GT3X=7.05±9.63 min, Force=36.91±27.7 min, Bra=31.29±18.53 min, Waist=30.45±18.3 min). There was no difference found in the number of steps per day or minutes of moderate intensity activity recorded between the four devices. CONCLUSIONS: Fitbit™ activity monitors compare well to other previously validated objective activity monitors in the measurement of steps per day and time spent in moderate intensity activities. However, Fitbit™ devices may not accurately quantify the time spent in light and vigorous intensity activities, and therefore may not be the ideal solution for individuals' personal activity monitoring needs.

Step Detection and Activity Recognition Accuracy of Seven Physical Activity Monitors (2015)

PLOS ONE

Abstract

The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts), Up (Jawbone), One (Fitbit), ActivPAL (PAL Technologies Ltd.), Nike+ Fuelband (Nike Inc.), Tractivity (Kineteks Corp.) and Sensewear Armband Mini (Bodymedia). Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc) were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration. The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications.

Validation of Fitbit-Flex as a measure of free-living physical activity in a community-based phase III cardiac rehabilitation population (2016)

European Journal of Preventive Cardiology

Abstract

Background: Accurate physical activity monitoring is important for cardiac patients. Novel activity monitoring devices may enable precise measurement of physical activity. This study aimed to validate Fitbit-Flex against Actigraph accelerometer for monitoring physical activity. Design: A validation study with a comparative design. Methods: Cardiac patients and family members participating in community-based exercise programs wore Fitbit-Flex and Actigraph simultaneously over four days to monitor daily step counts and minutes of moderate to vigorous physical activity (MVPA). Results: Participants (N?=?48) comprised 52.1% males, with a mean age of 65.6?±?6.9 years and 58.9% had a cardiac diagnosis. Fitbit-Flex and Actigraph were significantly correlated in males, females, total participants and cardiac patients for step counts (r?=?.96; r?=?.95; r?=?.95; r?=?.95), though less so for MVPA (r?=?.81; r?=?.65, r?=?.74; r?=?.71). As step counts increased the differences between Fitbit-Flex and Actigraph also increased. Fitbit-Flex over-estimated step counts in females (556 steps/day), males (1462 steps/day) and total participants (1038 steps/day) as well as for minutes of MVPA in females (4?min/day), males (15?min/day) and total participants (10?min/day). Fitbit-Flex had high sensitivity and specificity in classifying participants who achieved the recommended physical activity guidelines. Conclusion: Fitbit-Flex is accurate in assessing attainment of physical activity guideline recommendations and is useful for monitoring physical activity in cardiac patients. The device does, however, slightly over-estimate step counts and MVPA.

Comparison of consumer and research monitors under semistructured settings (2016)

Medicine and Science in Sports and Exercise

Abstract

PURPOSE: This study evaluated the relative validity of different consumer and research activity monitors during semi-structured periods of sedentary activity, aerobic exercise and resistance exercise. METHODS: A total of 52 participants (28 males) ages 18-65 performed 20-minutes of self-selected sedentary activities, 25-minutes of aerobic exercise, and 25-minutes of resistance exercise, with 5-minutes rest between each activity. Each participant wore five wrist-worn consumer monitors [Fitbit Flex (FBF), Jawbone UP 24 (JU24), Misfit Shine (MS), Nike+Fuelband SE (NFS), Polar Loop (PL)] and two research monitors [Actigraph GT3X+ (GT3X+) on the waist, and BodyMedia Core (BMC) on the arm] while being concurrently monitored with the Oxycon Mobile (OM), a portable metabolic system. The energy expenditure (EE) from different activity sessions were measured by OM and estimated by all monitors. RESULTS: Mean absolute percent error (MAPE) values for the full 80-minute protocol ranged from 15.3% (BMC) to 30.4% (MS). The EE estimates from the GT3X+ was found to be equivalent to those from the OM (±10% equivalence zone: 285.1, 348.5). Correlations between OM and the various monitors were generally high (ranges between 0.71 and 0.90). Three monitors had MAPE values less than 20% for sedentary activity: BMC (15.7%), MS (18.2%), and NFS (20.0%). Two monitors had MAPE values less than 20% for aerobic: BMC (17.2%) and NFS (18.5%). None of the monitors had MAPE values less than 25% for resistance exercise. CONCLUSION: Overall, the research monitors and the FBF, JU24, and NFS provided reasonably accurate total EE estimates at the individual level. However, larger error was evident for individual activities, especially resistance exercise. Further research is needed to examine these monitors across various activities and intensities, and under real-world conditions.

Accuracy of the Fitbit for Measuring Preschoolers' Physical Activity (2016)

Medicine & Science in Sports & Exercise

Abstract

Identifying an accurate and feasible measurement tool for assessing physical activity (PA) is a priority in epidemiologic research. Although wearable activity monitors yields high potentials for being utilized in research, little information is available on the accuracy of those monitors in young children. PURPOSE: To evaluate the accuracy of the Fitbit Flex (FF) activity monitor for assessing preschooler' PA and sedentary behavior (SED) in free-living conditions, using the previously validated accelerometer-based monitor, ActiGraph GT3X+ (AG), as a criterion measure. METHODS: 27 preschool age children (Girl: 41%, Age: 4.9 ± 1.1 yrs, BMI: 16.5 ± 1.6 kgtextperiodcenteredm2) wore AG (on dominant hip) and FF (on the non-dominant wrist) simultaneously for 24 hours. Using age-appropriate cutpoints for AG (Pate's 2006) and manufacturer-specific algorithms for FF, data from AG (15-sec epoch) and FF (60-sec epoch) were reduced in terms of time spent (min/day) in SED, moderate-to-vigorous PA (MVPA), and Total PA (TPA). Pearson correlations was used examine agreement between the estimates from AG and FF. Mean absolute percent errors (MAPEs) were computed as measurement errors. Equivalence test using SAS PROC MIXED procedure was used to compare the 85% confidence intervals (CI) of the estimates from the FF with the respective equivalence zone (EZ; ± 15% of the mean estimates) from the AG. RESULTS: The FF yielded significantly equivalent estimates of SED (FF: Mean (M) = 673 min, 85% CI: 632-714 min vs. AG: M = 631, EZ: 537-726 min) and TPA (FF: M = 337 min, 85% CI: 309-365 min vs. AG: M = 379 min, EZ: 303-435 min) as the AG. However, the estimate of MVPA from the FF was not equivalent to that from the AG. Correlations between FF and AG were consistently high for SED (r = 0.86, P textless.01) and TPA (r = 0.70, P textless.01), but moderate for MVPA (r = 0.59, P textless.01). MAPEs were 9.2, 70.1, and 14.5% for SED, MVPA, and TPA, respectively. CONCLUSIONS: The accuracy of the FF for estimating SED and TPA was supported by the high correlations and significant equivalence to AG. However, relatively large MAPEs and results from equivalence test suggest that the MVPA estimates from the FF were not equivalent to those from AG. Future studies utilizing FF in preschool age children should be aware of these findings. Efforts to replicate our findings with longer monitoring in larger samples are warranted

Fitbit Provides Valid Measures of Sedentary and Physical Activity in Preschool Children (2016)

Circulation

Abstract

Introduction: Both monitoring and promoting PA in children, two important areas in epidemiologic research, require the use of accurate and feasible measurement tools. The Fitbit activity monitor is one of the most widely commercialized, consumer-based activity monitors and its validity and reliability has been determined in adults; however, little research has determined the validity of Fitbit activity monitor in measuring sedentary behavior and PA in preschool-age children. Hypothesis: We hypothesized that, when comparing to the direct observation (DO), the Fitbit Flex (FF) activity monitor would accurately measure the time spent in sedentary, moderate-to-vigorous PA (MVPA), and Total (light-to-vigorous) PA in preschool-age children. Methods: A total of 28 preschool age children (Female: 46%, Age: 4.6 ± 1.0 yrs, BMI: 16.4 ± 1.5 kg·m2) wore the FF (on the wrist) and were directly observed while performing a set of unstructured and structured activities with varying intensity levels such as watching TV, drawing, playing with toys, playing soccer, and shooting basket. Breath-by-breath oxygen consumption were also measured using Oxycon Mobile indirect calorimetry (OM). Data from the FF (PA counts) and the OM (VO2 ml·kg·min-1) were summarized in terms of minute-by-minute basis. The validity of FF for measuring the time spent in sedentary behavior, MVPA, and Total PA against DO was examined using three ways: (1) Pearson correlation examining the relatively agreement between FF and DO; (2) Mean absolute percent error (MAPE) examining the measurement errors of FF; and (3) One-sided equivalence test examining the absolute agreement between FF and DO by comparing the 90% confidence intervals (CI) of the estimates from the FF with the respective equivalence zone (EZ; ± 10% of the mean estimates from the DO). Results: On average, oxygen consumptions for sedentary, light, MVPA were 9.0 ± 1.6, 14.9 ± 3.9, 23.5 ± 5.5, and 33.8 ± 5.6 ml·kg·min-1, respectively. Correlations between FF and DO were consistently high for sedentary (r = 0.81, P <.001), MVPA (r = 0.62, P <.001), and Total PA (r = 0.81, P <.001). MAPEs were 8.2%, 21.1%, and 8.2% for sedentary, MVPA, and Total PA, respectively. The estimates from the FF were not significantly equivalent to those from the DO; sedentary (FF: Mean (M) = 7.8 min, 90% CI: 7.3 - 8.3 min vs. DO: M = 6 min, EZ: 5.4 - 6.6 min), MVPA (FF: M = 7.2 min, 90% CI: 6.2 - 8.2 min vs. DO: M = 12 min, EZ: 10.8 - 13.2 min), and Total PA (FF: M = 14.0 min, 90% CI: 13.6 - 14.4 min vs. DO: M = 16 min, EZ: 14.4 - 17.6 min). Conclusions: The validity of the FF was supported by the consistently high correlations with the criterion measure. However, inconsistent results from equivalence testing warrants subsequent assessment of the validity of the FF as it has a high potential for replacing research-based PA monitors in preschool-age children.

Accuracy of Wristband Activity Monitors during Ambulation and Activities. (2016)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: The main purpose of this study was to examine the accuracy of wristband activity monitors on measuring step counts at prescribed speeds on a treadmill and under short-bouts of common daily activities. METHODS: Thirty healthy young adults wore three wristband activity monitors on both wrists while walking or jogging on a treadmill at different speeds (54, 80, 107, and 134 m·min) and performing six different common daily activities for 5 minutes each. The monitors included the Fitbit Flex, Garmin Vivofit, and Jawbone UP. The common daily activity conditions included 2 sitting activities (playing a tablet computer game, folding laundry), 2 walking activities (pushing a stroller, carrying a bag), and 2 stair climbing activities (down, up). Absolute percentage error (APE) scores were computed to examine the accuracy between actual observed steps and monitor-detected steps. RESULTS: Under the treadmill condition, the APE ranged between 1.5% and 9.6%. Accuracy was improved at faster speeds (134 m·min) for all the monitors (APE < 2.5%). In the common daily activity conditions, substantial step counts were registered when folding laundry. All monitors significantly underestimated actual steps (all APE >33%) when pushing a stroller. Higher APE was observed when wore on the dominant wrist under the common daily activity conditions. CONCLUSIONS: The wristband activity monitors examined were more accurate for measuring step counts between 80 m·min to 134 m·min as compared to a slower speed. Accuracy under each common daily activity condition ranged widely between monitors and activity, with less error when worn on the nondominant wrist. These results will help to inform researchers on the use and accuracy of wristband activity monitors for future studies.

Validating The Latest Commercial Physical Activity Monitors (2016)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: Completing 10,000 steps per day is recommended by the World Health Organisation, the US Centre for Disease Control and the Australian National Heart Foundation. Physical activity monitors (PAM) are popular and have become increasingly available, marketed for their ability to count steps. However, limited data is available on the newer monitors (Fitbits/Jawbone) in comparison to the Actigraph, a validated research-based accelerometer, or a visual step counts, at multiple speeds and placement sites. This study aimed to compare these newer PAMs to the Actigraph and a visual count 1) across different speeds; and 2) at different placement sites in a lab based treadmill test. METHODS: Participants wore an Actigraph wGT3X-BT and Fitbit One around the waist and each wrist, as well as a Fitbit Flex, Fitbit Charge HR and Jawbone UP24 on each wrist while performing 5 x 3 minute bouts on a treadmill at multiple speeds (5.0, 6.5, 8.0, 10.0 and 12.0km/h). A video of their lower limbs was recorded for a visual count (criterion measure) to be completed in duplicate. Paired sample t-tests to test for any significant differences and Spearman's correlations for associations, between the device and visual count were completed. RESULTS: 31 participants (61% male) aged 24.3±5.2yrs ranging in BMI (18.6kg/m2 to 29.9kg/m2) were recruited. Across all speeds the Actigraph (waist) and Fitbit One (waist) were accurate when compared to visual counts (p,0.001), with the Fitbit One (waist) being the most accurate PAM at slower speeds (5.0, 6.5km/h). All PAMs worn around the wrist became more accurate at the faster speeds with the strongest associations at 8.0km/h=Fitbit Charge (r=0.87, p,0.01); 10.0km/h=Fitbit One (r=0.91, p<0.01) and 12.0km/h=Jawbone (r=0.90, p<0.01). Most PAMS, excluding the Fitbit One significantly undercounted at 5.0 and 6.5km/h (p>0.05). At the higher speeds (10.0, 12.0kph) there were no significant differences (p<0.05) between step counts for all devices. CONCLUSION: The step count accuracy of PAMs was affected by speed and placement site. The Fitbit One (waist) was the most accurate PAM across all speeds. Other PAMs worn on the wrist were accurate at the faster speeds only. Future studies should look at the effect of body mass index and outdoor free living environments on the accuracy of these newer PAMs.

Accuracy of Wrist and Hip-worn Commercial Physical Activity Monitors In Free Living Conditions (2016)

Medicine & Science in Sports & Exercise

Abstract

It is recommended that individuals participate in 150 minutes of moderate intensity physical activity or 75 minutes of vigorous physical activity each week. Commercially-available PA monitors are becoming popular for everyday use. These PA monitors provide feedback on steps, energy expenditure, moderate-to-vigorous intensity physical activity (MVPA), and sleep quality, with new models introduced to the public at an increasing rate. Whether these devices provide the consumer with accurate information is not well understood. PURPOSE: To examine the accuracy of a wrist worn commercial device (Fitbit Charge) and a hip worn device (Fitbit One) in regard to steps and MVPA under free living conditions, compared to a research-grade accelerometer (Actigraph GT3X). METHODS: Participants (n=17, Mean SD, Age: 27.8 9.0; BMI: 24.7 4.2) wore 3 devices (Actigraph GT3X, Fitbit Charge and Fitbit One) for 7 consecutive days. Participants were told to go about their regular daily activities while wearing all 3 devices at the same time. The GT3X was worn on the right hip. RESULTS: Paired t-tests showed that mean steps per day between the Actigraph (10510.6 2443.7) and the Fitbit Charge (12537.7 3936.2) differed significantly (P = 0.016). Mean steps per day between the Actigraph and the Fitbit One (11726.7 11726.7) also differed (P = 0.001). Steps between the Fitbit Charge and Fitbit One did not differ (P = 0.195). No significant differences were found in active minutes recorded between any of the devices (53.0 15.2, 52.0 42.9 and 52.8 28.9) for the Actigraph, Charge and One, respectively). Bland-Altman analysis concluded that the Actigraph and Charge (P = 0.02) and the Actigraph and One (P textless 0.01) did not agree for steps, whereas the Charge and the One did agree (P = 0.2). All three devices showed good agreement with regard to MVPA. CONCLUSION: Results showed that the 2 commercial devices did not agree with the Actigraph, a widely used PA monitor for research purposes, in mean step data. However, the devices did show good agreement with regard to MVPA, suggesting that these commercial devices may provide useful feedback for individuals seeking to achieve the current public health PA guidelines for MVPA.

Shaffer, J.A., Ma, Y., Goldsmith, J., Schwartz, J.E. and Davidson, K.W. Validation of the Fitbit One® for physical activity measurement at an upper torso attachment site (2016)

BMC Research Notes

Abstract

Background: The upper torso is recommended as an attachment site for the Fitbit One®, one of the most common wireless physical activity trackers in the consumer market, and could represent a viable alternative to wrist- and hip-attachment sites. The objective of this study was to provide evidence concerning the validity of the Fitbit One® attached to the upper torso for measuring step counts and energy expenditure among female adults. Results: Thirteen female adults completed a four-phase treadmill exercise protocol (1.9, 3.0, 4.0, and 5.2 mph). Participants were fitted with three Fitbit® trackers (two Fitbit One® trackers: one on the upper torso, one on the hip; and a wrist-based Fitbit Flex®). Steps were assessed by manual counting of a video recording. Energy expenditure was measured by gas exchange indirect calorimetry. Concordance correlation coefficients of Fitbit-estimated step counts to observed step counts for the upper torso-attached Fitbit One®, hip-attached Fitbit One® and wrist-attached Fitbit Flex® were 0.98 (95 % CI 0.97–0.99), 0.99 (95 % CI 0.99–0.99), and 0.75 (95 % CI 0.70–0.79), respectively. The percent error for step count estimates from the upper torso attachment site was =3 % for all walking and running speeds. Upper torso step count estimates showed similar accuracy relative to hip attachment of the Fitbit One® and were more accurate than the wrist-based Fitbit Flex®. Similar results were obtained for energy expenditure estimates. Energy expenditure estimates for the upper torso attachment site yielded relative percent errors that ranged from 9 to 19 % and were more accurate than the wrist-based Fitbit Flex®, but less accurate than hip attachment of the Fitbit One®. Conclusions: Our study shows that physical activity measures obtained from the upper torso attachment site of the Fitbit One® are accurate across different walking and running speeds in female adults. The upper torso attachment site of the Fitbit One® outperformed the wrist-based Fitbit Flex® and yielded similar step count estimates to hip-attachment. These data support the upper torso as an alternative attachment site for the Fitbit One®.

Physical Activity Assessment Between Consumer- and Research-Grade Accelerometers: A Comparative Study in Free-Living Conditions (2016)

JMIR Mhealth Uhealth

Abstract

Background: Wearable activity monitors such as Fitbit enable users to track various attributes of their physical activity (PA) over time and have the potential to be used in research to promote and measure PA behavior. However, the measurement accuracy of Fitbit in absolute free-living conditions is largely unknown. Objective: To examine the measurement congruence between Fitbit Flex and ActiGraph GT3X for quantifying steps, metabolic equivalent tasks (METs), and proportion of time in sedentary activity and light-, moderate-, and vigorous-intensity PA in healthy adults in free-living conditions. Methods: A convenience sample of 19 participants (4 men and 15 women), aged 18-37 years, concurrently wore the Fitbit Flex (wrist) and ActiGraph GT3X (waist) for 1- or 2-week observation periods (n=3 and n=16, respectively) that included self-reported bouts of daily exercise. Data were examined for daily activity, averaged over 14 days and for minutes of reported exercise. Average day-level data included steps, METs, and proportion of time in different intensity levels. Minute-level data included steps, METs, and mean intensity score (0 = sedentary, 3 = vigorous) for overall reported exercise bouts (N=120) and by exercise type (walking, n=16; run or sports, n=44; cardio machine, n=20). Results: Measures of steps were similar between devices for average day- and minute-level observations (all P values > .05). Fitbit significantly overestimated METs for average daily activity, for overall minutes of reported exercise bouts, and for walking and run or sports exercises (mean difference 0.70, 1.80, 3.16, and 2.00 METs, respectively; all P values < .001). For average daily activity, Fitbit significantly underestimated the proportion of time in sedentary and light intensity by 20% and 34 respectively, and overestimated time by 3% in both moderate and vigorous intensity (all P values < .001). Mean intensity scores were not different for overall minutes of exercise or for run or sports and cardio-machine exercises (all P values > .05). Conclusions: Fitbit Flex provides accurate measures of steps for daily activity and minutes of reported exercise, regardless of exercise type. Although the proportion of time in different intensity levels varied between devices, examining the mean intensity score for minute-level bouts across different exercise types enabled interdevice comparisons that revealed similar measures of exercise intensity. Fitbit Flex is shown to have measurement limitations that may affect its potential utility and validity for measuring PA attributes in free-living conditions.

Validation of Consumer-Based Hip and Wrist Activity Monitors in Older Adults With Varied Ambulatory Abilities. (2016)

The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences

Abstract

BACKGROUND: The accuracy of step detection in consumer-based wearable activity monitors in older adults with varied ambulatory abilities is not known. METHODS: We assessed the validity of two hip-worn (Fitbit One and Omron HJ-112) and two wrist-worn (Fitbit Flex and Jawbone UP) activity monitors in 99 older adults of varying ambulatory abilities and also included the validity results from the ankle-worn StepWatch as a comparison device. Nonimpaired, impaired (Short Physical Performance Battery Score < 9), cane-using, or walker-using older adults (62 and older) ambulated at a self-selected pace for 100 m wearing all activity monitors simultaneously. The criterion measure was directly observed steps. Intraclass correlation coefficients (ICC), mean percent error and mean absolute percent error, equivalency, and Bland-Altman plots were used to assess accuracy. RESULTS: Nonimpaired adults steps were underestimated by 4.4% for StepWatch (ICC = 0.87), 2.6% for Fitbit One (ICC = 0.80), 4.5% for Omron HJ-112 (ICC = 0.72), 26.9% for Fitbit Flex (ICC = 0.15), and 2.9% for Jawbone UP (ICC = 0.55). Impaired adults steps were underestimated by 3.5% for StepWatch (ICC = 0.91), 1.7% for Fitbit One (ICC = 0.96), 3.2% for Omron HJ-112 (ICC = 0.89), 16.3% for Fitbit Flex (ICC = 0.25), and 8.4% for Jawbone UP (ICC = 0.50). Cane-user and walker-user steps were underestimated by StepWatch by 1.8% (ICC = 0.98) and 1.3% (ICC = 0.99), respectively, where all other monitors underestimated steps by >11.5% (ICCs < 0.05). CONCLUSIONS: StepWatch, Omron HJ-112, Fitbit One, and Jawbone UP appeared accurate at measuring steps in older adults with nonimpaired and impaired ambulation during a self-paced walking test. StepWatch also appeared accurate at measuring steps in cane-users.

Reliability and Validity of Ten Consumer Activity Trackers Depend on Walking Speed (2016)

Medicine & Science in Sports & Exercise

Abstract

Purpose: To examine the test-retest reliability and validity of ten activity trackers for step counting at three different walking speeds. Methods: Thirty-one healthy participants walked twice on a treadmill for 30 minutes while wearing ten activity trackers (Polar Loop, Garmin Vivosmart, Fitbit Charge HR, Apple Watch Sport, Pebble Smartwatch, Samsung Gear S, Misfit Flash, Jawbone Up Move, Flyfit and Moves). Participants walked three walking speeds for ten minutes each; slow (3.2 km[middle dot]h-1), average (4.8 km[middle dot]h-1), and vigorous (6.4 km[middle dot]h-1). To measure test-retest reliability, intraclass correlations (ICCs) were determined between the first and second treadmill test. Validity was determined by comparing the trackers with the gold standard (hand counting), using mean differences, mean absolute percentage errors, and ICCs. Statistical differences were calculated by paired-sample t-tests, Wilcoxon signed-rank tests, and by constructing Bland-Altman plots. Results: Test-retest reliability varied with ICCs ranging from -0.02 to 0.97. Validity varied between trackers and different walking speeds with mean differences between the gold standard and activity trackers ranging from 0.0 to 26.4%. Most trackers showed relatively low ICCs and broad limits of agreement of the Bland-Altman plots at the different speeds. For the slow walking speed, the Garmin Vivosmart and Fitbit Charge HR showed the most accurate results. The Garmin Vivosmart and Apple Watch Sport demonstrated the best accuracy at an average walking speed. For vigorous walking, the Apple Watch Sport, Pebble Smartwatch, and Samsung Gear S exhibited the most accurate results. Conclusion: Test-retest reliability and validity of activity trackers depends on walking speed. In general, consumer activity trackers perform better at an average and vigorous walking speed than at a slower walking speed.

Estimating Physical Activity and Sedentary Behavior in a Free-Living Context: A Pragmatic Comparison of Consumer-Based Activity Trackers and ActiGraph Accelerometry (2016)

Journal of Medical Internet Research

Abstract

Background: Activity trackers are increasingly popular with both consumers and researchers for monitoring activity and for promoting positive behavior change. However, there is a lack of research investigating the performance of these devices in free-living contexts, for which findings are likely to vary from studies conducted in well-controlled laboratory settings. Objective: The aim was to compare Fitbit One and Jawbone UP estimates of steps, moderate-to-vigorous physical activity (MVPA), and sedentary behavior with data from the ActiGraph GT3X+ accelerometer in a free-living context. Methods: Thirty-two participants were recruited using convenience sampling; 29 provided valid data for this study (female: 90%, 26/29; age: mean 39.6, SD 11.0 years). On two occasions for 7 days each, participants wore an ActiGraph GT3X+ accelerometer on their right hip and either a hip-worn Fitbit One (n=14) or wrist-worn Jawbone UP (n=15) activity tracker. Daily estimates of steps and very active minutes were derived from the Fitbit One (n=135 days) and steps, active time, and longest idle time from the Jawbone UP (n=154 days). Daily estimates of steps, MVPA, and longest sedentary bout were derived from the corresponding days of ActiGraph data. Correlation coefficients and Bland-Altman plots with examination of systematic bias were used to assess convergent validity and agreement between the devices and the ActiGraph. Cohen's kappa was used to assess the agreement between each device and the ActiGraph for classification of active versus inactive (=10,000 steps per day and =30 min/day of MVPA) comparable with public health guidelines. Results: Correlations with ActiGraph estimates of steps and MVPA ranged between .72 and .90 for Fitbit One and .56 and .75 for Jawbone UP. Compared with ActiGraph estimates, both devices overestimated daily steps by 8% (Fitbit One) and 14% (Jawbone UP). However, mean differences were larger for daily MVPA (Fitbit One: underestimated by 46%; Jawbone UP: overestimated by 50%). There was systematic bias across all outcomes for both devices. Correlations with ActiGraph data for longest idle time (Jawbone UP) ranged from .08 to .19. Agreement for classifying days as active or inactive using the =10,000 steps/day criterion was substantial (Fitbit One: ?=.68; Jawbone UP: ?=.52) and slight-fair using the criterion of =30 min/day of MVPA (Fitbit One: ?=.40; Jawbone UP: ?=.14). Conclusions: There was moderate-strong agreement between the ActiGraph and both Fitbit One and Jawbone UP for the estimation of daily steps. However, due to modest accuracy and systematic bias, they are better suited for consumer-based self-monitoring (eg, for the public consumer or in behavior change interventions) rather than to evaluate research outcomes. The outcomes that relate to health-enhancing MVPA (eg, “very active minutes” for Fitbit One or “active time” for Jawbone UP) and sedentary behavior (“idle time” for Jawbone UP) should be used with caution by consumers and researchers alike.

Accuracy of Commercial Activity Trackers to Measure Energy Expenditure During a Controlled Exercise Trial (2016)

Medicine & Science in Sports & Exercise

Abstract

Commercially available activity trackers are increasingly popular for the general public in measuring physical activity. Daily energy expenditure (EE) is one of the primary measures provided to the public by these devices. Whether these devices can accurately estimate EE is not clear. PURPOSE: To compare the EE values obtained from two commercially available devices (Fitbit Charge and Fitbit One) to that measured by the Actigraph GT3X and indirect calorimetry (IC). METHODS: Seventeen men (n = 4) and women (n = 13) (Age: Mean ± SD = 27.8 ± 9.0; BMI = 24.7 ± 4.2 kg/m2) completed a submaximal treadmill exercise trial while wearing a Fitbit Charge, a Fitbit One, and an Actigraph GT3X. Oxygen consumption and EE (in kcals) was measured via breath-by-breath IC system. The exercise trial consisted of four, 5-minute stages (2.0 mph/0% grade; 2.0 mph/5%; 3.0 mph/0%; 3.0 mph/5%). To approximate the EE reported by the Fitbit devices, an estimated resting metabolic rate was added to the EE reported by the Actigraph. Repeated measures ANOVA was used to asses the main effects for measurement device and stage. RESULTS: There was a significant main effect for measurement device (P < 0.01) and stage (P < 0.01). Post-hoc analysis determined that the Fitbit Charge (P < 0.01), the Fitbit One (P textless 0.01), and the Actigraph (P < 0.01), all differed from IC in measuring EE. The Fitbit Charge (P < 0.01) overestimated EE across all stages (25.3 ± 4.7, 31.0 ± 3.7, 33.0 ± 5.8 and 34.2 ± 6.1 vs 13.2 ± 2.3, 18.8 ± 3.0, 20.5 ± 2.8 and 26.9 ± 5.0 for Fitbit One and IC, respectively). The Fitbit One (14.4 ± 2.7, 15.3 ± 2.7, 19.9 ± 3.4 and 21.1 ± 4.6; P < 0.01) and the Actigraph (6.7 ± 3.8, 7.4 ± 25.4 ± 8.5 and 26.4 ± 7.9; P textless 0.01) underestimated EE compared to IC across all stages. Significant interactions between IC and the Fitbit One (P < 0.01), and IC and the Actigraph (P < 0.01), however, indicate that they are less sensitive to grade changes than IC. CONCLUSION: Results show that the wrist-worn Fitbit Charge overestimates EE, which may negatively impact the fitness goals of the wearer, particularly as it relates to energy balance and weight loss. The hip worn devices appear to be closer to IC in EE measures, but do not accurately reflect changes in treadmill grade, impacting those who use this exercise mode.

Validity of Wearable Fitness Trackers on Sleep Measure (2016)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: To examine the validity of wearable fitness trackers for estimating total sleep time (TST) with respect to a sleep log as a reference measure. METHODS: Nineteen healthy individuals (mean ± SD; age = 29.5 ± 13.4 years; body mass index = 25.87 ± 5.03 kg textperiodcentered m2) participated in the study. Participants randomly assigned to one of two groups. Group 1 (n = 10) wore the BodyMedia SenseWear Mini Armband (SWA), Basis Peak (BP), and Fitbit Charge HR (FB). Group 2 (n = 9) wore the ActiGraph Sleep (AG), Jawbone UP3 (JU), and Garmin Vivosmart (GV). Trackers were worn on the non-dominant wrist for one night and a sleep log was completed. Two existing sleep algorithms for the AG (Sadeh and Cole-Kripke) and Fitbit sleep sensitive algorithm were also included for comparison. Pearson correlation was used to examine the linearity of mean TST minutes (TSTM) from each tracker compared to the log TSTM. Mean absolute percentage errors (MAPE) of TSTM from each tracker were calculated against the log TSTM. Lastly, mean differences in average TST between the trackers were examined by a general linear model for repeated measures. RESULTS: Pearson correlation coefficients were .32, .69, .24, and -.26 for the SWA, FB, and FBs with regard to log TSTM, respectively. Group 2 correlations between the log TSTM and Sadeh, Cole-Kripke, JU, and GV were .34, .65, .54, and .92, respectively. MAPE were 17.1%, 16.3%, 40.2%, and 32.9% for SWA, FB, FBs, and BP, respectively. MAPE of 17.0%, 11.5%, 14.9%, and 10% were observed for Sadeh, Cole-Kripke, JU, and GV, respectively. Bland-Altman Plots showed no systematic bias between all variables for TST compared with log TSTM. ANOVA and post-hoc analysis revealed a significant difference in the Fitbit sensitive TST (p = .001) in Group 1 (F (5, 51) = 8.06, p = .00) and no significant difference between Group 2 (F (4, 40) = 1.27, p = .296). CONCLUSION: The FB, Cole-Kripke, JU, and GV display the closest estimation of TST compared with log TST minutes. However, further research is needed to validate these monitors with polysomnography.

Examining The Validity Of Fitbit Charge HR For Measuring Heart Rate In Free-living Conditions (2016)

Medicine & Science in Sports & Exercise

Abstract

Optical blood flow sensors (i.e. photoplethysmographic techniques) have recently been utilized in wearable activity trackers. The Fitbit Charge HRTM (FBHR) is one of the widely recognized wearable activity trackers that utilizes Fitbit's proprietary PurePulse optical heart rate (HR) technology to automatically measure wrist-based HR. Despite its increasing popularity, however, no study to date has addressed the validity of FBHR for measuring HR in free-living conditions. PURPOSE: The purpose of this study was to examine the validity of FBHR for measuring HR using a chest strap Polar HR monitor (PHR) as a reference measure in free-living conditions. METHODS: Ten healthy college students (8 males; mean age = 26.5 ± 5.4 years; mean body mass index (BMI) = 24.5 ± 3.23 kgtextperiodcenteredm2) participated in the study. The participants were asked to perform normal daily activities for 8 hours in a day while wearing the PHR (model RS400) on their chest and two FBHRs on their dominant and non-dominant wrists, respectively. HR was recorded every minute and the minute-by-minute HR data from each monitor were synchronized by time of day. Pearson correlation was used to examine the linearity of average beats-per-minute (bpm) estimated from FBHRs with respect to the PHR. Mean differences in average bpm between the monitors were examined by a general linear model for repeated measures. Lastly, mean absolute percentage error (MAPE) of minute-by-minute bpm estimated from the FBHRs were calculated against the PHR. RESULTS: Average HRs (mean ± SD) for PHR, FBHR non-dominant, and FBHR dominant were 75.6 ± 18.5 bpm, 72.8 ± 16.7 bpm, and 73.9 ± 17.06 bpm, respectively. Pearson correlation coefficients (r) between the PHR and FBHR non-dominant and dominant were r=.805 and r=.793, respectively. MAPE were 9.17 ± 10.9% for FBHR non-dominant and 9.71 ± 12.4% for FBHR HR dominant. ANOVA and post-hoc analyses with Bonferroni revealed significant differences in estimating HR from FBHR non-dominant wrist (p=.001) and FBHR dominant wrist (p=.001) compared to PHR monitor. CONCLUSION: The results indicated that the wrist-oriented Fitbit Charge HRTM device does not provide an accurate measurement of HR during free-living condition in this study. However, further research is needed to validate these monitors with a larger sample with different population groups.

Evidence of Convergent Validity for Measuring Free-Living Walking Using Wearable Devices (2016)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: To determine the convergent validity of consumer-based wearable devices for assessing walking during free-living activities. METHODS: A total of 48 healthy adults (10 males, 50.3±10.6 yrs & BMI 24.9±2.5; 38 females, 53.3±7.4 yrs & BMI 24.4±2.9) participated. The consumer-based wearable devices included Actigraph GT3X+(AT), BodyMedia Armband (BM), Fitbit Charge (FC), Fitbit One (FO), Jawbone up (JB), Misfit (MF), and the Nike Fuelband (NF) worn on wrist, waist, or upper arm. The Omron HJ720IT pedometer served as the reference measure. While simultaneously wearing all devices, walking time (6.64±.80min), steps (13.28±1.22), and speed (1.43±.20m/sec) were assessed (10 m walk). Participants also completed selected free-living activities including moving boxes, cleaning desks, mopping, walking up & down stairs, and outdoor walking on hills. On average these activities took 40min to complete. Descriptive statistics, correlation coefficients (r) and paired t-test among the wearable devices and the Omron pedometer were calculated using SPSS 20. RESULTS: It was unable to examine the accuracy of the JB due to constant synchronizing errors. Walking Time, Steps, and Speed showed moderate correlations with the Omron (r = .39, .55, & .43, respectively); but low correlations with wearable devices r = .11 to .23(Time), -.10 to .37(Steps), and -.08 to -.23(Speed). Step counts measured by the devices during free-living were as follows: FC=178±238, NF=1595±156, MF=1628±283, BM=1667±191, FO=1735±174, and AT=1778±180. The Omron (reference) recorded 1749±169 steps. The correlation coefficients between the step counts from the wearable devices and the Omron during free-living were AT=.91, FO=.91, NF =.61, FC=.59, BM=.44, and MF=-.15. All wearable devices showed statistically significant differences when compared to the Omron (t = -2.2 to -14.3, p textless0.05). CONCLUSIONS: : Of the devices examined, AT and FO were the most valid for measuring walking steps during free-living activities. On the other hand, quality control and measurement error associated with JB and MF suggest they should not be used.

Validation and Accuracy of FITBIT Charge: A Pilot Study in a University Worksite Walking Program (2016)

Journal of Fitness Research

Abstract

PURPOSE: Wearable activity monitors (e.g., Fitbit®) have become increasingly popular for both researchers and lay people alike. Recent studies have reported several Fitbit® models correlate with research grade monitors; however none to date have examined the Fitbit® Charge model. Furthermore, none have reported on the accuracy of Fitbit® devices. The primary purpose of this study was to compare step counts assessed by Fitbit® Charge and a research grade accelerometer (i.e., Actigraph®). The secondary purpose of this study was to compare the Fitbit® Charge algorithms for physical activity intensity with an algorithm validated for research purposes. METHODS: Participants (n=8) wore Fitbit® Charge and Actigraph® monitors for 7 days. First, correlations were run to examine the concordance of step counts and physical activity intensity levels derived from Fitbit® Charge and Actigraph® algorithms. Next, accuracy was investigated by paired sample t-tests comparing each device’s assessment of step counts and minutes spent in light, moderate and vigorous intensity activity. RESULTS: Significant correlations were found for all study outcomes (p’s < .05). Significant differences were observed for step counts on all days and weekly total step count (p’s < .05). Analyses of each intensity level revealed significant differences on most days for light and moderate intensity (p’s <.05). For vigorous intensity, only day 6 (p = .007) and accumulated weekly total of minutes were different (p = .014). CONCLUSIONS: Our correlation results were similar to previous research examining validity of other Fitbit® models. However, Fitbit® Charge devices may overestimate step counts. Moreover, the algorithm used by Fitbit® may overestimate light and vigorous physical activity, while underestimating minutes in moderate physical activity. Further research is needed to determine reliability of Fitbit® Charge to assess activity and intensity that reflects current health recommendations. Therefore, researchers should use discretion if considering use of Fitbit® Charge for research purposes.

Validation And Accuracy Of Fitbit Charge (2016)

Medicine & Science in Sports & Exercise

Abstract

URPOSE: Wearable activity monitors (e.g., Fitbit®) have become increasingly popular for both researchers and lay people alike. Recent studies have reported several Fitbit® models correlate with research grade monitors; however none to date have examined the Fitbit® Charge model. Furthermore, none have reported on the accuracy of Fitbit® devices. The primary purpose of this study was to compare step counts assessed by Fitbit® Charge and a research grade accelerometer (i.e., Actigraph®). The secondary purpose of this study was to compare the Fitbit® Charge algorithms for physical activity intensity with an algorithm validated for research purposes. METHODS: Participants (n=8) wore Fitbit® Charge and Actigraph® monitors for 7 days. First, correlations were run to examine the concordance of step counts and physical activity intensity levels derived from Fitbit® Charge and Actigraph® algorithms. Next, accuracy was investigated by paired sample t-tests comparing each device's assessment of step counts and minutes spent in light, moderate and vigorous intensity activity. RESULTS: Significant correlations were found for all study outcomes (p's textless .05). Significant differences were observed for step counts on all days and weekly total step count (p's textless .05). Analyses of each intensity level revealed significant differences on most days for light and moderate intensity (p's textless.05). For vigorous intensity, only day 6 (p = .007) and accumulated weekly total of minutes were different (p = .014). CONCLUSIONS: Our correlation results were similar to previous research examining validity of other Fitbit® models. However, Fitbit® Charge devices may overestimate step counts. Moreover, the algorithm used by Fitbit® may overestimate light and vigorous physical activity, while underestimating minutes in moderate physical activity. Further research is needed to determine reliability of Fitbit® Charge to assess activity and intensity that reflects current health recommendations. Therefore, researchers should use discretion if considering use of Fitbit® Charge for research purposes.

Consumer-based Physical Activity Monitors and Research-grade Accelerometers Underestimate Physical Activity in a Semi-structured Setting (2016)

Circulation

Abstract

Introduction: Consumer-based physical activity (PA) monitors have become popular tools to track PA (steps and energy expenditure [EE]), but little is known about their accuracy and comparability to research-grade PA monitors. Hypothesis: We hypothesized that consumer-based PA monitors would have lower PA measurement accuracy than a research-grade PA monitor. Methods: Healthy adults (n=30 [n=15 male], aged 18-79 years) were fitted with three Fitbit PA monitors (One and Zip, left hip; Flex, non-dominant wrist), one Jawbone PA monitor (UP24, non-dominant wrist), and one research-grade, accelerometer-based PA monitor (ActiGraph Link; right hip). Subjects performed =12 activities from a list of 21 sedentary, household, and ambulatory activities during an 80-minute, semi-structured protocol while wearing all five monitors and a portable metabolic analyzer. Steps and EE were recorded from PA monitors at the beginning and end of the protocol. A previously validated EE prediction equation (Freedson) was used to estimate EE from PA (PAEE) using ActiGraph data. A previous lab visit by subjects was used to estimate resting EE; resting EE was added to PAEE to estimate total EE from the ActiGraph. Criterion measures included a trained research assistant counting total steps (with a hand tally counter) and the metabolic analyzer for measurement of total EE and PAEE (total EE - resting EE). Repeated measures analysis of variance was used to assess step, total EE, and PAEE measures among the consumer-based PA monitors, ActiGraph, and criterion methods. Results: Participants averaged 3,231±104 steps, 294±10 kcals for total EE, and 235±9 kcals for PAEE according to criterion measures. Consumer-based PA monitors and the ActiGraph significantly underestimated steps (mean difference [MD]: 714-1,047 steps [22.1-32.4%], p<0.01), total EE (MD: 37-109 kcals [12.6-37.1%], p<0.05), and PAEE (MD: 35-107 kcals [14.9-45.5%], p<0.05) compared to criterion measures. The Fitbit One, Zip, and Flex monitors estimated significantly higher steps than the ActiGraph (MD: 199-302 steps [9.1-13.8%], p<0.05), but there was no significant difference in steps estimated by the UP24 compared to the ActiGraph (MD: 144 steps [6.6%], p=0.21). All consumer-based monitors estimated significantly higher total EE and PAEE than the ActiGraph (MD: 23-60 kcals [12.6-32.7%] and 22-53 kcals [17.3.0-41.2%], respectively; p<0.05). Conclusions: All PA monitors underestimated steps, total EE, and PAEE compared to the criterion measures. Large variability existed in the step and EE estimates from the PA monitors tested. Contrary to our hypothesis, the research-grade ActiGraph monitor (using Freedson EE prediction equation) had more severe PA underestimates than the consumer-based PA monitors. In conclusion, estimates of PA between studies using consumer-based vs. research-grade PA monitors should be interpreted with caution.

Validity of Consumer-Based Physical Activity Monitors for Specific Activity Types. (2016)

Medicine and Science in Sports and Exercise

Abstract

PURPOSE: Consumer-based physical activity (PA) monitors are popular for individual tracking of PA variables. However, current research has not examined how these monitors track energy expenditure (EE) and steps in distinct activities. This study examined the accuracy of the Fitbits One, Zip, and Flex and Jawbone UP24 for estimating EE and steps for specific activities and activity categories. METHODS: Thirty subjects completed a structured protocol consisting of three sedentary, four household, and four ambulatory/exercise activities. All subjects began by lying on a bed for 10 min; 10 other activities were performed for 5 min each. Indirect calorimetry (COSMED) and researcher-counted steps were criterion measures for EE and step counts, respectively. The Omron HJ-720IT pedometer was used as a comparison of step count accuracy. EE and steps were compared with criterion measures using the Friedman repeated-measures nonparametric test and mean absolute percent error (MAPE). RESULTS: All PA monitors predicted EE within 8% of COSMED for sedentary activity but overestimated EE by 16%-40% during ambulatory activity. All monitors except the Fitbit Flex (within 8% of criterion) underestimated EE by 27%-34% during household activity. EE predictions were accompanied with MAPE textgreater10%. For household activity, the Fitbit Flex estimated steps within 10% of researcher-counted steps; all other monitors underestimated steps by 35%-64%. All monitors estimated steps within 4% of researcher-counted steps and displayed MAPE textless10% during ambulatory activity. The Omron underestimated household steps by 74% but was within 1% for ambulatory steps. All monitors severely underestimated EE and steps during cycling. CONCLUSION: Consumer-based PA monitors should be used cautiously for estimating EE, although they provide accurate measures of steps for structured ambulatory activity, similar to validated pedometers.

These Shoes Are Made for Walking: Sensitivity Performance Evaluation of Commercial Activity Monitors under the Expected Conditions and Circumstances Required to Achieve the International Daily Step Goal of 10,000 Steps. (2016)

PLOS ONE

Abstract

INTRODUCTION: Physical activity is a vitally important part of a healthy lifestyle, and is of major benefit to both physical and mental health. A daily step count of 10,000 steps is recommended globally to achieve an appropriate level of physical activity. Accurate quantification of physical activity during conditions reflecting those needed to achieve the recommended daily step count of 10,000 steps is essential. As such, we aimed to assess four commercial activity monitors for their sensitivity/accuracy in a prescribed walking route that reflects a range of surfaces that would typically be used to achieve the recommended daily step count, in two types of footwear expected to be used throughout the day when aiming to achieve the recommended daily step count, and in a timeframe required to do so. METHODS: Four commercial activity monitors were worn simultaneously by participants (n = 15) during a prescribed walking route reflective of surfaces typically encountered while achieving the daily recommended 10,000 steps. Activity monitors tested were the Garmin Vivofit ™, New Lifestyles' NL-2000 ™ pedometer, Withings Smart Activity Monitor Tracker (Pulse O2) ™, and Fitbit One ™. RESULTS: All activity monitors tested were accurate in their step detection over the variety of different surfaces tested (natural lawn grass, gravel, ceramic tile, tarmacadam/asphalt, linoleum), when wearing both running shoes and hard-soled dress shoes. CONCLUSION: All activity monitors tested were accurate in their step detection sensitivity and are valid monitors for physical activity quantification over the variety of different surfaces tested, when wearing both running shoes and hard-soled dress shoes, and over a timeframe necessary for accumulating the recommended daily step count of 10,000 steps. However, it is important to consider the accuracy of activity monitors, particularly when physical activity in the form of stepping activities is prescribed as an intervention in the treatment or prevention of a disease state.

Validation of the Fitbit One, Garmin Vivofit and Jawbone UP activity tracker in estimation of energy expenditure during treadmill walking and running (2016)

Journal of Medical Engineering & Technology

Abstract

Objectives: To determine the validity of energy expenditure estimation made by the Fitbit One, Garmin Vivofit and Jawbone UP activity trackers during treadmill walking and running. Determining validity of such trackers will inform the interpretation of the data they generate. Design: Cross-sectional study. Method: Fourteen adults walked at 0.70, 1.25, 1.80?ms-1 and ran at 2.22, 2.78, 3.33?ms-1 on a treadmill wearing a Fitbit One, Garmin Vivofit and Jawbone UP. Estimation of energy expenditure from each tracker was compared to measurement from indirect calorimetry (criterion). Paired t-tests, correlation coefficients and Bland–Altman plots assessed agreement and proportional bias. Mean percentage difference assessed magnitude of difference between estimated and criterion energy expenditure for each speed. Results: Energy expenditure estimates from the Fitbit One and Garmin Vivofit correlated significantly (p<?0.01; r=?0.702; 0.854) with criterion across all gait speeds (0.70–3.33?ms-1). Fitbit One, Garmin Vivofit and Jawbone UP correlated significantly (p?<?0.05; r?=?0.729; 0.711; 0.591) with criterion across all walking speeds (0.70–1.80?ms-1). However, only the Garmin Vivofit correlated significantly (p<?0.05; r?=?0.346) with energy expenditure estimations from criterion across running speeds (2.22–3.33?ms-1). Bland–Altman plots showed proportional bias for the Fitbit One and Garmin Vivofit. Energy expenditure estimations of single speeds were overestimated by the Fitbit One and underestimated by the Garmin Vivofit. Conclusions: Energy expenditure reported by the devices distinguished between walking and running, with a general increase as exercise intensity increased. However, the reported energy expenditure from these devices should be interpreted with caution, given their potential bias and error.

Can Commercially Available Pedometers Be Used For Physical Activity Monitoring In Patients With COPD Following Exacerbations? (2016)

Chronic Obstructive Pulmonary Diseases: Journal of the COPD Foundation

Abstract

BACKGROUND Commercially available pedometers have been used as tools to measure endpoints in studies evaluating physical activity promotion programs. However, their accuracy in patients recovering from COPD exacerbations is unknown. The objectives of this study were to 1) assess the relative accuracy of different commercially available pedometers in healthy volunteers and 2) evaluate the accuracy of the top-performing commercially available pedometer in patients recovering from COPD exacerbations following hospital discharge. METHODS: Twelve healthy volunteers wore 2 pedometers, 2 smartphones with pedometer apps and an accelerometer for 15 minutes of indoor activity. The top-performing device in healthy volunteers was evaluated in 4 patients recovering from COPD exacerbations following hospital discharge during 6 minutes of walking performed at home. Bland-Altman plots were employed to evaluate accuracy of each device compared with direct observation (the reference standard). RESULTS: In healthy volunteers, the mean percent error compared to direct observation of the various devices ranged from -49% to +1%. The mean percent error [95% confidence interval (CI)] of the top-performing device in healthy volunteers, the Fitbit Zip(®), was +1% [-33 to +35%], significantly lower than that of the accelerometer (-13% [-56 to +29%], p=0.01). The mean percent error [95% CI] for the Fitbit Zip(®) in patients recovering from COPD exacerbations was -3% [-7 to +12%]. CONCLUSIONS: The accuracy of commercially available pedometers in healthy volunteers is highly variable. The top-performing pedometer in our study, the Fitbit Zip,(®) accurately measures step counts in both healthy volunteers and patients recovering from COPD exacerbations.

Validity and reliability of Fitbit activity monitors compared to ActiGraph GT3X+ with female adults in a free-living environment (2016)

Journal of Science and Medicine in Sport

Abstract

OBJECTIVES Inexpensive activity monitors have recently gained popularity with the general public. Researchers have evaluated these consumer-based monitors in laboratory-conditions. Given the current wide-spread consumer use of these devices, it is important to ensure users are attaining accurate information compared to previously validated measures. This study investigates the accuracy of Fitbit One and Flex activity monitors in measuring steps, sedentary time, and time spent in light, moderate, and vigorous intensity activities with ActiGraph GT3X+ with female adults in free-living conditions. DESIGN Cross-sectional study METHODS Twenty-two women, 21.23±1.63 yrs, BMI: 22.35±2.34kg/m2 wore two Fitbit Ones (bra and waist), one Fitbit Flex on the wrist, and one ActiGraph GT3X+ on the waist for seven-consecutive days. Repeated measures ANOVA was used to explore differences in steps, sedentary time, and time spent in light, moderate and vigorous intensity activities among the four devices. RESULTS No differences were found in number of steps recorded across the four devices. Fitbit One, waist and bra, overestimated time spent in light intensity activities. Fitbit One (waist) and Fitbit Flex overestimated time spent in moderate intensity activities. Fitbit One, waist and bra, and Fitbit Flex overestimated time spent in vigorous intensity activities. All Fitbit activity monitors overestimated MVPA and underestimated sedentary time compared to the ActiGraph. CONCLUSIONS Regardless of wear-location all Fitbit devices provide similar activity monitoring and users can wear the devices wherever best accommodates their lifestyle or needs. Users should not rely solely on these monitors when tracking vigorous and MVPA activities.

Accuracy Of Wrist-worn Activity Monitors At Three Walking Speeds On The Treadmill (2016)

Medicine & Science in Sports & Exercise

Abstract

Wrist-worn activity monitors are becoming increasingly popular with many manufacturers offering such devices. PURPOSE: To assess the accuracy of wrist-worn activity monitors at three walking speeds on a treadmill. METHODS: Seventy-seven participants (age = 24.9±3.5 yrs, ht = 170.0±8.9 cm, wt = 78.6±19.9 kg) were fitted with a Fitbit Flex (FF) on the right wrist, a Movband Model 2 (MV), Lifetrak C200 (LT), Garmin Vivofit (GV), Polar Loop (PL) on the left wrist, and an Omron HJ-113 (HJ) on the right waist. Each participant walked for two minutes at 53.6 m/min (2.0 mph), 80.5 m/min (3.0 mph), and 107.3 m/min (4.0 mph). At the end of each trial monitor counts and actual step counts from a hand tally (AC) were recorded. A repeated measures ANOVA was used to determine significant differences between the counts. Single measure intraclass correlation (ICC) from a two-way random effects ANOVA was used to assess the agreement between AC and monitor counts. Pedometer error was calculated as [(monitor steps-actual steps)/actual steps] * 100. RESULTS: For all speeds, monitor counts were significantly lower than AC (ptextless.05) except for GV and HJ at 53.6 m/min and 80.5 m/min, and for LT and HJ at 107.3 m/min, none of which were significantly different than AC (ptextgreater.05). Agreement according to ICC was highest with HJ for all speeds (0.68, 0.97, and 0.82 during 53.6 m/min, 80.5 m/min, and 107.3 m/min, respectively). Agreement among the wrist worn monitors was highest with GV (0.71) and lowest with FF (0.18) at the slowest speed, highest with GV (0.67) and lowest with LT (0.03) at 80.7 m/min, and highest with PL (0.28) and lowest with LT (0.0) at the fastest speed. Error was greatest with LT at 40.6% while walking at 53.6 m/min, 19.6% with PL at 80.5 m/min, and 19.0% with PL at 107.3 m/min. Error was least with GV at 3.2% while walking at 53.6 m/min. Although error was least with HJ during 53.6 m/min and 107.3 m/min (1.2% and 1.2%, respectively), GV was 3.2% and LT was 7.7% during 80.5 m/min and 107.3 m/min, respectively. CONCLUSION: The hip-worn pedometer (HJ) provides the most accurate step count across all speeds. Among the wrist-worn monitors, GV seems to provide the most accurate measure of step count in this laboratory-controlled study. Weaver, A.L., Stutzman, S.E., Supnet, C. and Olson, D.M.

Validity and Reliability of Fitbit Flex for Step Count, Moderate to Vigorous Physical Activity and Activity Energy Expenditure (2016)

PLOS ONE

Abstract

Objectives: To examine the validity and reliability of the Fitbit Flex against direct observation for measuring steps in the laboratory and against the Actigraph for step counts in free-living conditions and for moderate-to-vigorous physical activity (MVPA) and activity energy expenditure (AEE) overall. Methods: Twenty-five adults (12 females, 13 males) wore a Fitbit Flex and an Actigraph GT3X+ during a laboratory based protocol (including walking, incline walking, running and stepping) and free-living conditions during a single day period to examine measurement of steps, AEE and MVPA. Twenty-four of the participants attended a second session using the same protocol. Results: Intraclass correlations (ICC) for test-retest reliability of the Fitbit Flex were strong for walking (ICC = 0.57), moderate for stair stepping (ICC = 0.34), and weak for incline walking (ICC = 0.22) and jogging (ICC = 0.26). The Fitbit significantly undercounted walking steps in the laboratory (absolute proportional difference: 21.2%, 95%CI 13.0–29.4%), but it was more accurate, despite slightly over counting, for both jogging (6.4%, 95%CI 3.7–9.0%) and stair stepping (15.5%, 95%CI 10.1–20.9%). The Fitbit had higher coefficients of variation (Cv) for step counts compared to direct observation and the Actigraph. In free-living conditions, the average MVPA minutes were lower in the Fitbit (35.4 minutes) compared to the Actigraph (54.6 minutes), but AEE was greater from the Fitbit (808.1 calories) versus the Actigraph (538.9 calories). The coefficients of variation were similar for AEE for the Actigraph (Cv = 36.0) and Fitbit (Cv = 35.0), but lower in the Actigraph (Cv = 25.5) for MVPA against the Fitbit (Cv = 32.7). Conclusion: The Fitbit Flex has moderate validity for measuring physical activity relative to direct observation and the Actigraph. Test-rest reliability of the Fitbit was dependant on activity type and had greater variation between sessions compared to the Actigraph. Physical activity surveillance studies using the Fitbit Flex should consider the potential effect of measurement reactivity and undercounting of steps.

Validity of commercial activity trackers in children with congenital heart disease (2016)

Canadian Journal of Cardiology

Abstract

BACKGROUND Increasing physical activity levels is a high priority to optimize long-term health in children with congenital heart disease (CHD). Commercial activity trackers have been validated in adults and are increasingly used to measure and promote physical activity in pediatric populations, but they have not been validated in children. METHODS In 30 children with CHD aged 10-18yrs, we assessed the validity of physical activity form the wrist-based Fitbit Charge HRTM against hip-based ActiGraph accelerometers under free living conditions for 7 days. We assessed the association between devices by intra-class correlation coefficients and Bland-Altman plots. Receiver-operating curves were used to identify Fitbit step cut-points. RESULTS There was a strong association between the two devices for daily steps across 138 analysed person-days (ICC=0.855, ptextless0.001), but poorer agreement for time spent in physical activity intensities (ICCstextless0.7). Daily Fitbit steps of =12,500 identified meeting physical activity guidelines defined as =60 min MVPA/day. Fitbit devices recorded more steps than accelerometers (-2,242 steps/d, 95%LoA of -7,738 to 3,253). Between-device differences were greater in boys vs. girls. Fitbit devices were worn for longer than accelerometers (-36 min/d, 95%LoA -334 to 261), but overall differences in wear time explained little of the variance in step differences (7%, p=0.048). CONCLUSIONS Commercial activity trackers provide opportunities to remotely monitor physical activity in children with CHD, but absolute values may differ from accelerometers. These findings are important given the increasing emphasis on physical activity promotion and monitoring in children with cardiovascular risk factors.

Accuracy of Heart Rate Watches: Implications for Weight Management (2016)

PLOS ONE

Abstract

Background: Wrist-worn monitors claim to provide accurate measures of heart rate and energy expenditure. People wishing to lose weight use these devices to monitor energy balance, however the accuracy of these devices to measure such parameters has not been established. Aim: To determine the accuracy of four wrist-worn devices (Apple Watch, Fitbit Charge HR, Samsung Gear S and Mio Alpha) to measure heart rate and energy expenditure at rest and during exercise. Methods: Twenty-two healthy volunteers (50% female; aged 24 ± 5.6 years) completed 1-hr protocols involving supine and seated rest, walking and running on a treadmill and cycling on an ergometer. Data from the devices collected during the protocol were compared with reference methods: electrocardiography (heart rate) and indirect calorimetry (energy expenditure). Results: None of the devices performed significantly better overall, however heart rate was consistently more accurate than energy expenditure across all four devices. Correlations between the devices and reference methods were moderate to strong for heart rate (0.67–0.95 [0.35 to 0.98]) and weak to strong for energy expenditure (0.16–0.86 [-0.25 to 0.95]). All devices underestimated both outcomes compared to reference methods. The percentage error for heart rate was small across the devices (range: 1–9%) but greater for energy expenditure (9–43%). Similarly, limits of agreement were considerably narrower for heart rate (ranging from -27.3 to 13.1 bpm) than energy expenditure (ranging from -266.7 to 65.7 kcals) across devices. Conclusion: These devices accurately measure heart rate. However, estimates of energy expenditure are poor and would have implications for people using these devices for weight loss.

How Accurate Is Your Activity Tracker? A Comparative Study of Step Counts in Low-Intensity Physical Activities (2017)

JMIR Mhealth and Uhealth

Abstract

Background: As commercially available activity trackers are being utilized in clinical trials, the research community remains uncertain about reliability of the trackers, particularly in studies that involve walking aids and low-intensity activities. While these trackers have been tested for reliability during walking and running activities, there has been limited research on validating them during low-intensity activities and walking with assistive tools. Objective: The aim of this study was to (1) determine the accuracy of 3 Fitbit devices (ie, Zip, One, and Flex) at different wearing positions (ie, pants pocket, chest, and wrist) during walking at 3 different speeds, 2.5, 5, and 8 km/h, performed by healthy adults on a treadmill; (2) determine the accuracy of the mentioned trackers worn at different sites during activities of daily living; and (3) examine whether intensity of physical activity (PA) impacts the choice of optimal wearing site of the tracker. Methods: We recruited 15 healthy young adults to perform 6 PAs while wearing 3 Fitbit devices (ie, Zip, One, and Flex) on their chest, pants pocket, and wrist. The activities include walking at 2.5, 5, and 8 km/h, pushing a shopping cart, walking with aid of a walker, and eating while sitting. We compared the number of steps counted by each tracker with gold standard numbers. We performed multiple statistical analyses to compute descriptive statistics (ie, ANOVA test), intraclass correlation coefficient (ICC), mean absolute error rate, and correlation by comparing the tracker-recorded data with that of the gold standard. Results: All the 3 trackers demonstrated good-to-excellent (ICC>0.75) correlation with the gold standard step counts during treadmill experiments. The correlation was poor (ICC<0.60), and the error rate was significantly higher in walker experiment compared to other activities. There was no significant difference between the trackers and the gold standard in the shopping cart experiment. The wrist worn tracker, Flex, counted several steps when eating (P<.01). The chest tracker was identified as the most promising site to capture steps in more intense activities, while the wrist was the optimal wearing site in less intense activities. Conclusions: This feasibility study focused on 6 PAs and demonstrated that Fitbit trackers were most accurate when walking on a treadmill and least accurate during walking with a walking aid and for low-intensity activities. This may suggest excluding participants with assistive devices from studies that focus on PA interventions using commercially available trackers. This study also indicates that the wearing site of the tracker is an important factor impacting the accuracy performance. A larger scale study with a more diverse population, various activity tracker vendors, and a larger activity set are warranted to generalize our results.

The Validity of Fitbit Charge in Free Living Conditions (2017)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: Research is accumulating regarding the accuracy of wrist-worn consumer activity monitors in controlled lab settings. However, there is a lack of evidence of validity under free living conditions. The study investigated the accuracy of Fitbit Charge (FBC) for estimating minutes of moderate and vigorous physical activity (MVPA) compared to research grade accelerometers. A secondary purpose was to evaluate the accuracy of steps/day. METHODS: Ninety-four healthy men and women (mean age 41 ± 9 years) wore a FBC as part of a 12-week intervention. The participants were also asked to wear research grade accelerometers concurrently as the criterion physical activity measure during the last week of intervention. A minimal of 10 hours/day wear-time was applied to both methods. Estimated daily MVPA and steps taken from the FBC were compared against the criterion using indicators of Pearson correlation, mean absolute percent errors, mean percent errors, and equivalence testing. The activity classification agreement of MVPA at the minute level was evaluated with Kappa, sensitivity and specificity. RESULTS: Complete data were collected on 61 individuals with an average of 5.5 days of wear-time. The average daily MVPA measured by accelerometer was 76.3 minutes compared to a higher value, 118.7 minutes, estimated by the FBC. The correlation between the two methods was 0.8 (ptextless0.0001). The mean absolute percent errors and mean percent errors were 68.2% and -64.2% indicating a consistent overestimating MVPA by the FBC. The average daily steps were 8,897 and 7,716 measured by FBC and accelerometer, respectively, with a correlation of 0.76 (ptextless0.0001). The mean absolute percent errors and mean percent errors of steps estimated by FBC were 30.0% and -20.1%. Neither of the MVPA and steps measured by FBC fell into the ±10% equivalence zone set up by the accelerometer. The Kappa statistics of the classification agreement between the two methods was 0.32 with a low the sensitivity of 30.1% but a high specificity of 96.7%. CONCLUSIONS: This FBC estimated substantially higher minutes of MVPA in free living conditions among healthy adults and significantly higher steps compared to research grade accelerometer. The researchers who use FBC to track physical activity need to interpret their results with caution.

Estimation of Sleep Stages Using Cardias and Accelerometer Data from a Wrist-Worn Device (2017)

Sleep

Abstract

Introduction: We investigated the ability of a wrist-worn tracker to estimate sleep stages in normal adult sleepers. Such a device could be useful in simplifying sleep research and in increasing public knowledge of sleep. Methods: Movement and cardiac data was collected from 60 adult subjects (36 M: 24 F, ages 34?±?10 yrs) wearing two wrist worn devices (left and right hand) containing a 3D-accelerometer and an optical photoplethysmogram (PPG), while undergoing a sleep stage study using a Type III home sleep testing device. The accelerometer was used to generate various features of movement; the PPG records cardiac peaks generated by each heartbeat, and can be used to determine heart rate and heart rate variability metrics. The sleep study was scored independently by two registered PSG technicians, using consensus AASM scoring rules. Using these labels, an automated classifier and post-processing rule was developed to label 30-second epochs as one of Wake/Light/Deep/REM (note that Stages N1 and N2 were combined into a single “Light” classification). The estimated performance of this automated classifier system was calculated using a leave-one out validation method. The performance metrics were Cohen's kappa (measures the level of agreement greater than chance) and per-epoch accuracy (percent of epochs correctly labeled). Results: The estimated Cohen's kappa was 0.52?±?0.14 for left hand wear, and 0.53?±?0.14 (right hand). The per-epoch accuracy was 69%. Across the population, there was no statistically significant bias in the estimated durations of the wake, light, deep and REM stages versus the gold standard measurements. Conclusion: These results suggest that a wrist worn device with movement and cardiac sensors can be used to determine sleep stages with a reasonable degree of accuracy in normal adult sleepers, but without the cost and artificial sleep environment of a sleep laboratory. The reported performance figures are similar or better than previously reported results from non-EEG based sleep staging using combinations of cardiac, respiratory and movement information.

Accuracy of Wearable Devices for Determining Physiological Measures during Different Physical Activities (2017)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: Wearable technology is ACSM's number one 2016-2017 fitness trend. The accuracy of these devices has yet to be firmly established. The objective of the study was to determine the validity of wearable devices' assessment of step count, heart rate (HR) and caloric expenditure (KCAL) during various physical activities. METHODS: Thirty college students (19 female, 11 male) engaged in 3 activity sessions (sweeping with a broom and dustpan, climbing stairs, and walking 1/4 mile) while wearing an Apple Watch Sport, Fitbit Charge HR, Accusplit Hip Pedometer, and iPhone 6 Plus. Step counts from devices were compared to those objectively recorded using a tally counter. Subjects also completed a treadmill graded exercise test during which HR and KCAL were reported by an Apple Watch Sport, Fitbit Charge HR, and Polar T-31. Values were compared to those from a six-lead ECG and metabolic analyzer. HR was recorded at rest and during each stage. KCAL was determined at the end of the protocol. RESULTS: Correlations between objective step counts and from the devices were: walking (.08 to .84), stair climbing (.12 to .90), and sweeping (.12 to .70). No device was accurate across all activities. The most accurate devices for activities were: walking (Apple Watch, r=.84); stair climbing (iPhone, r=.90); sweeping (Fitbit, r=.70). During the treadmill test, correlations between HR assessed via ECG and devices were: Apple Watch (.76 to .99), Polar T-31 (.72 to .94), and Fitbit (.19 to .98). Heart rate accuracy across the session was highest in the Apple Watch. KCAL from neither the Apple Watch (r=.63) nor Fitbit (r=.48) had a high correlational value to that from the metabolic analyzer. CONCLUSIONS: Fitness-related values provided by wearable devices had varying levels of accuracy when compared to objective step counts, HR and KCAL assessed by calibrated scientific equipment. Accuracy of step counts varied by activity and was higher across activities in the iPhone. HR reported from wearable devices, similarly, had varying levels of accuracy, with the Apple Watch being most accurate across a graded exercise test. Fitness-related information from wearables that are not medical devices and should be considered as estimates and used for motivation. Further validation of these devices should include a variety of physical activity modalities.

Validity of Wearable Activity Monitors during Cycling and Resistance Exercise (2017)

Medicine & Science in Sports & Exercise

Abstract

The use of wearable activity monitors has seen rapid growth; however, the mode and intensity of exercise could affect validity of heart rate (HR) and caloric (energy) expenditure (EE) readings. There is a lack of data regarding the validity of wearable activity monitors during graded cycling regimen and a standard resistance exercise. The present study determined the validity of eight monitors for HR compared to an ECG and seven monitors for EE compared to a metabolic analyzer during graded cycling and resistance exercise. Fifty subjects (28 women, 22 men) completed separate trials of graded cycling and three sets of four resistance exercises at a 10-repetition maximum (RM) load. Monitors included: Apple Watch Series 2 (AWS2), Fitbit Blaze, Fitbit Charge 2, Polar H7 (PH7), Polar A360, Garmin Vivosmart HR, TomTom Touch, and Bose SoundSport Headphones (BSP). HR was recorded after each cycling intensity and following each resistance exercise set. EE was recorded following both protocols. Validity was established as having a mean absolute percent error (MAPE) value of textless10%. The PH7 and BSP were valid during both exercise modes (Cycling: MAPE=6.87%, R=0.79; Resistance Exercise: MAPE=6.31%, R=0.83). During cycling, the AWS2 revealed the greatest HR validity (MAPE=4.14%, R=0.80). The BSP revealed the greatest HR accuracy during resistance exercise (MAPE=6.24%, R=0.86). Across all devices, as exercise intensity increased, there was greater underestimation of HR. No device was valid for EE during cycling or resistance exercise. HR from wearable devices differed at different exercise intensities; EE estimates from wearable devices were inaccurate. Wearable devices are not medical devices and users should employ caution when utilizing these devices for monitoring physiological responses to exercise.

Accuracy of Accelerometer (Fitbit® ChargeHR™) Measured Distance and Heart Rate in Patients with Pulmonary Arterial Hypertension (2017)

American Journal of Respiratory and Critical Care Medicine

Abstract

Journal of Respiratory and Critical Care MedicineRationale: Functional capacity assessment in patients with pulmonary arterial hypertension (PAH) is used to establish treatment goals and prognoses. Higher resting heart rate and poor heart rate recovery after activity has been associated with worse outcomes. Devices that can accurately monitor heart rate and activity can thus be valuable to monitor patients with PAH. Accelerometers measure movement using a piezo-electric crystal and can quantify activity as steps. Several accelerometers can monitor heart rate using optical blood flow sensing by photoplethysmography. Accelerometers have been validated to measure step count in healthy adults and patients with COPD. Studies validating distance and heart rate (HR) measured by activity monitors are discrepant. Validity in patients with PAH has not been studied. The Fitbit® ChargeHR™ is a “watch” type activity monitor that can continuously measure step count and heart rate. It uses height to estimate gait length and calculates distance walked. We examined the accuracy of this device in measuring distance walked and HR at rest and after exercise in patients with PAH. Methods: Thirty patients with WHO Group I PAH were enrolled. Subjects wore a Fitbit® ChargeHR™ during a 6 minute walk test (6MWT). Distance and HR as determined by the Fitbit® and 6MWT were recorded. HR measured using pulse oximetry was used at the reference. The Fitbit® failed to record steps if patients used a walker (n = 6), and these data were not analyzed. The concordance correlation coefficient (CCC) was used to compare the accelerometer with reference values. Results: 30 patients with WHO Group I PAH (23 idiopathic and 7 connective tissue disease), with a mean age 50.49 ± 12.97 were enrolled in the study. Mean six minute walk distance was 368.6 ± 144 m, WHO functional class was I - 0%, II - 56%, III - 34.9 %, and IV - 8.7% The distance walked as determined by the accelerometer did not correlate with distance measured in the 6MWT (CCC 0.52, 95% CI [0.19, 0.74]). Heart rate measured by the Fitbit® did not correlate with HR measured by pulse oximetry at rest or after exertion (CCC and [95% CI] were 0.57 [0.28, 0.76] and 0.24 [-0.03, 0.47]. Conclusions: We demonstrate that the Fitbit® ChargeHR™ did not accurately determine walk distance or resting heart rate in a controlled test environment in patients with WHO Group I PAH. Reported HR and distance walked should be interpreted with caution in patients with PAH.

Assessment of laboratory and daily energy expenditure estimates from consumer multi-sensor physical activity monitors (2017)

PLOS ONE

Abstract

Wearable physical activity monitors are growing in popularity and provide the opportunity for large numbers of the public to self-monitor physical activity behaviours. The latest generation of these devices feature multiple sensors, ostensibly similar or even superior to advanced research instruments. However, little is known about the accuracy of their energy expenditure estimates. Here, we assessed their performance against criterion measurements in both controlled laboratory conditions (simulated activities of daily living and structured exercise) and over a 24 hour period in free-living conditions. Thirty men (n = 15) and women (n = 15) wore three multi-sensor consumer monitors (Microsoft Band, Apple Watch and Fitbit Charge HR), an accelerometry-only device as a comparison (Jawbone UP24) and validated research-grade multi-sensor devices (BodyMedia Core and individually calibrated Actiheart™). During discrete laboratory activities when compared against indirect calorimetry, the Apple Watch performed similarly to criterion measures. The Fitbit Charge HR was less consistent at measurement of discrete activities, but produced similar free-living estimates to the Apple Watch. Both these devices underestimated free-living energy expenditure (-394 kcal/d and -405 kcal/d, respectively; Ptextless0.01). The multi-sensor Microsoft Band and accelerometry-only Jawbone UP24 devices underestimated most laboratory activities and substantially underestimated free-living expenditure (-1128 kcal/d and -998 kcal/d, respectively; Ptextless0.01). None of the consumer devices were deemed equivalent to the reference method for daily energy expenditure. For all devices, there was a tendency for negative bias with greater daily energy expenditure. No consumer monitors performed as well as the research-grade devices although in some (but not all) cases, estimates were close to criterion measurements. Thus, whilst industry-led innovation has improved the accuracy of consumer monitors, these devices are not yet equivalent to the best research-grade devices or indeed equivalent to each other. We propose independent quality standards and/or accuracy ratings for consumer devices are required.

Comparison of wrist-worn Fitbit Flex and waist-worn ActiGraph for measuring steps in free-living adults (2017)

PLOS ONE

Abstract

Introduction: Accelerometers are commonly used to assess physical activity. Consumer activity trackers have become increasingly popular today, such as the Fitbit. This study aimed to compare the average number of steps per day using the wrist-worn Fitbit Flex and waist-worn ActiGraph (wGT3X-BT) in free-living conditions. Methods: 104 adult participants (n = 35 males; n = 69 females) were asked to wear a Fitbit Flex and an ActiGraph concurrently for 7 days. Daily step counts were used to classify inactive (textless10,000 steps) and active (=10,000 steps) days, which is one of the commonly used physical activity guidelines to maintain health. Proportion of agreement between physical activity categorizations from ActiGraph and Fitbit Flex was assessed. Statistical analyses included Spearman's rho, intraclass correlation (ICC), median absolute percentage error (MAPE), Kappa statistics, and Bland-Altman plots. Analyses were performed among all participants, by each step-defined daily physical activity category and gender. Results: The median average steps/day recorded by Fitbit Flex and ActiGraph were 10193 and 8812, respectively. Strong positive correlations and agreement were found for all participants, both genders, as well as daily physical activity categories (Spearman's rho: 0.76–0.91; ICC: 0.73–0.87). The MAPE was: 15.5% (95% confidence interval [CI]: 5.8–28.1%) for overall steps, 16.9% (6.8–30.3%) vs. 15.1% (4.5–27.3%) in males and females, and 20.4% (8.7–35.9%) vs. 9.6% (1.0–18.4%) during inactive days and active days. Bland-Altman plot indicated a median overestimation of 1300 steps/day by the Fitbit Flex in all participants. Fitbit Flex and ActiGraph respectively classified 51.5% and 37.5% of the days as active (Kappa: 0.66). Conclusions: There were high correlations and agreement in steps between Fitbit Flex and ActiGraph. However, findings suggested discrepancies in steps between devices. This imposed a challenge that needs to be considered when using Fitbit Flex in research and health promotion programs.

Utility of the Fitbit Flex to Evaluate Sleep in Major Depressive Disorder: A comparison against polysomnography and wrist-worn actigraphy (2017)

Journal of Affective Disorders

Abstract

Background Sleep disturbance is a common and important component of affective illness. Fitness activity trackers are emerging as alternative means to estimate sleep in psychiatric patients; however, their ability to quantify sleep in mood disorders has not been empirically evaluated. Thus, this study sought to evaluate the utility of the Fitbit Flex (FBF) to estimate sleep in patients with major depressive disorder (MDD) relative to gold standard polysomnography (PSG) and a widely-used actigraph (Actiwatch-2; AW-2). Methods Twenty-one patients with unipolar MDD wore the FBF and AW-2 during in-laboratory PSG. Bland-Altman analysis compared sleep variables among devices. Epoch-by-epoch analysis further evaluated sensitivity, specificity, and accuracy for the FBF and AW-2 relative to PSG. Results The FBF demonstrated significant limitations in quantifying sleep and wake, relative to PSG. In the normal setting, the FBF significantly overestimated sleep time and efficiency, and displayed poor ability to correctly identify wake epochs (i.e. low specificity). In the sensitive setting, the FBF significantly underestimated sleep time and efficiency relative to PSG. Performance characteristics of the FBF were more similar to the AW-2 in the normal compared to sensitive setting. Limitations Participants were young to middle aged and predominantly female, which may limit generalizability of findings. Study design also precluded ability to assess longitudinal performance of FBF. Conclusions The FBF is not an adequate substitute for PSG when quantifying sleep in MDD, and the settings of the device sizably impact its performance relative to PSG and other standard actigraphs. The limitations and capabilities of the FBF should be carefully considered prior to clinical and research implementation.

Using Wrist-Worn Actigraphic Devices to Measure Sleep in People with Huntington's Disease (2017)

Sleep

Abstract

Introduction: Sleep disturbances are an early symptom of Huntington's disease (HD), and may be linked to the cognitive, motor and psychiatric symptoms of HD. Long-term collection of HD ambulatory sleep data can show the impact of sleep disturbances as HD symptoms progress. Actigraphy provides an objective measure of sleep well suited to long-term ambulatory data collection, but is cost prohibitive for large-scale studies. Commercial actigraphy devices may be a good alternative. This study aimed to validate research and commercial actigrapy in people with HD. Methods: Seven people with the gene for Huntington's disease (Age=54.1?±?6.4, 6F) completed an overnight sleep study using polysomnography, research-grade (Actiwatch Spectrum Pro), and consumer-grade (Fitbit One) actigraphy to measure sleep. For each actigraph, sensitivity and specificity compared to polysomnography were calculated. Intraclass correlations (ICCs) and Bland-Altman analyses (BAA) were used to compare the actigraphs to polysomnography on total sleep time (TST), sleep efficiency (SE), sleep latency (SL), and wake after sleep onset (WASO). Results: The sensitivity of the research-grade and consumer-grade actigraphs were 97.13% and 98.90% respectively, and specificities were 31.32% and 27.05% respectively. The only significant ICC was the correspondence between the consumer-grade actigraph and polysomnography for TST (r=0.52, p=.03). Using BAA, both actigraphs overestimated TST (Research-grade - Avg.dif=74.3?±?54.1min; Consumer-grade - Avg.dif=94.3?±?50.2min) and SE (Research-grade - Avg.dif=14.8?±?11.0%; Consumer-grade - Avg.dif=17.5?±?9.8%), and showed good agreement for SL (Research-grade - Avg.dif=-27.9?±?28.0min; Consumer-grade - Avg.dif=-22.0?±?24.7?min). The research-grade actigraph showed good agreement with polysomnography on WASO (Avg.dif=-20.0?±?32.2min), whilst the consumer-grade actigraph underestimated WASO (Avg.dif=-39.0?±?29.1min). Conclusion: In people with HD, neither actigraph showed sufficient agreement with polysomnography to be a suitable replacement for ambulatory sleep measurement, particularly considering the very low specificity values. Our research-grade actigraphy performed notably worse than what is typically seen in patient populations. The development of a HD specific actigraphy algorithm would greatly improve the agreement between actigraphy and polysomnography.

Does Wearable Technology Provide Accurate Heart Rate Measures While Playing Pickleball Doubles? (2017)

Medicine & Science in Sports & Exercise

Abstract

The sport of pickleball is increasing in popularity throughout the United States, especially in middle-aged, and older adults. Research investigating the physiological demands of pickleball is limited. The use of wearable technology is also gaining popularity, however, the accuracy of such devices is under question. PURPOSE: The purpose of this study was to determine if wearable technology provide valid heart rate (HR) measures during pickleball doubles in middle aged adults. METHODS: 8 female, intermediate level pickleball players (IFP = 3.0 ± 0.8; age = 47 ± 11 years; mass = 72.5 ± 12.8 kg; height = 1.70 ± 0.08 m) participated in this study. All subjects played pickleball doubles for 30 minutes. HR was measured using two devices (Fitbit HR, worn on the dominate writs, and Polar HR monitor, worn at the xiphoid process level). Peak and mean HR were determined for each device. A paired sample t-tests was used to determine differences in HR between devices for each dependent variable (HR max and HR mean). A Pearson product-moment correlation coefficient was used to evaluate the concurrent validity between the gold standard (Polar HR system) and the wearable technology (Fitbit HR). RESULTS: Peak HR measured by the Polar system (151.9 ± 15.9 beats/min) was not significantly different from the peak HR measured by the Fitbit HR (149.6 ± 18.0 beats/min; p = 0.69). Similarly, mean HR measured by the Polar system (127.9 ± 17.9 beats/min) was not significantly different from the mean HR measured by the Fitbit HR (121.0 ± 18.2 beats/min; p = 0.23). Concurrent validity between the Polar HR system and the Fitbit HR for both peak (r = 0.6) and mean HR (r = 0.66) was also nonsignificant (p textgreater 0.05). CONCLUSIONS: On average, peak and mean HR was similar between the two devices. These results support the validity of wearable technology; the Fitbit HR was moderately valid in peak and mean HR compared to the Polar HR system. These results are promising for those who own wearable technology and are using it to monitor HR during physical activities such as pickleball doubles. We acknowledge our small sample size and admit that further investigation of the validity of wearable technology on physiological measures during pickleball and other racket sports is warranted.

An Examination of Body Mass Index Influence on Activity Tracker Accuracy (2017)

Medicine & Science in Sports & Exercise

Abstract

A variety of activity trackers with evolving technology are commercially available, yet it remains uncertain the influence body size has on these devices' accuracy (as previous activity trackers have been), which would compromise their generalizability. PURPOSE: To examine the accuracy of consumer-grade activity trackers in quantifying steps and moderate-vigorous physical activity (MVPA) engagement in free-living conditions in a diverse sample of body mass index (BMI) categories. METHODS: Sixty individuals (21.1 ± 1.5 years, 2.3 ± 0.2 ft stride length) across three BMI categories (“Normal” [n=25]: 20-24.9 kg/m2; “Overweight” [n=22]: 25-29.9 kg/m2; “Obese” [n=13]: =30 kg/m2) wore four activity trackers during one 24-hour day (wear time 12.7 ± 1.9 hours). On the non-dominant side of the body, the Fitbit Charge HR (Charge) and Jawbone UP3 (UP3) were worn on the wrist and the Fitbit One (ONE) on the waist, with the NL1000 activity tracker (NL; serving as the criterion device) worn on the dominant side of the waist. Mixed between-within ANOVA analyses were performed to examine differences in steps and MVPA for the activity trackers across three BMI categories. RESULTS: There were no differences in steps or MVPA for the individual devices across BMI categories. In the Normal group, the UP3 (6667 ± 3366 steps) and ONE (7400 ± 8135 steps) underestimated steps (p=.000), compared to the NL (8135 ± 3562 steps). Similarly, in the Overweight group, the UP3 (8799 ± 3986 steps) and ONE (9019 ± 3841 steps) underestimated steps (NL 9312 ± 3986 steps, both < .05). In the Obese group the Charge overestimated steps (8847 ± 4168, < .05) and the ONE underestimated steps (7726 ± 3791, < .05), compared to the NL (8224 ± 4264 steps). The UP3 overestimated MVPA in the Normal group (67.7 ± 44.3 minutes), compared to the NL (27.6 ± 16.3, p=.000). In both the Overweight and Obese groups, the UP3 overestimated MVPA (Overweight: 81.5 ± 44.3 minutes, Obese: 77.5 ± 39.4 minutes, both p=.000), whereas the ONE underestimated MVPA (Overweight: 25.9 ± 26.5 minutes, Obese: 19.5 ± 25.3 minutes, both < .05), compared to the NL (Overweight: 36.6 ± 19.2 minutes, Obese: 31.6 ± 27.3 minutes). CONCLUSION: Within BMI groups, The UP3 underestimated steps and overestimated MVPA, the ONE underestimated steps and MVPA, whereas the Charge performed relatively well for both measures.

Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017)

JMIR mHealth and uHealth

Abstract

Background: Physical activity tracking wearable devices have emerged as an increasingly popular method for consumers to assess their daily activity and calories expended. However, whether these wearable devices are valid at different levels of exercise intensity is unknown. Objective: The objective of this study was to examine heart rate (HR) and energy expenditure (EE) validity of 3 popular wrist-worn activity monitors at different exercise intensities. Methods: A total of 62 participants (females: 58%, 36/62; nonwhite: 47% [13/62 Hispanic, 8/62 Asian, 7/62 black/ African American, 1/62 other]) wore the Apple Watch, Fitbit Charge HR, and Garmin Forerunner 225. Validity was assessed using 2 criterion devices: HR chest strap and a metabolic cart. Participants completed a 10-minute seated baseline assessment; separate 4-minute stages of light-, moderate-, and vigorous-intensity treadmill exercises; and a 10-minute seated recovery period. Data from devices were compared with each criterion via two-way repeated-measures analysis of variance and Bland-Altman analysis. Differences are expressed in mean absolute percentage error (MAPE). Results: For the Apple Watch, HR MAPE was between 1.14% and 6.70%. HR was not significantly different at the start (P=.78), during baseline (P=.76), or vigorous intensity (P=.84); lower HR readings were measured during light intensity (P=.03), moderate intensity (P=.001), and recovery (P=.004). EE MAPE was between 14.07% and 210.84%. The device measured higher EE at all stages (Ptextless.01). For the Fitbit device, the HR MAPE was between 2.38% and 16.99%. HR was not significantly different at the start (P=.67) or during moderate intensity (P=.34); lower HR readings were measured during baseline, vigorous intensity, and recovery (Ptextless.001) and higher HR during light intensity (Ptextless.001). EE MAPE was between 16.85% and 84.98%. The device measured higher EE at baseline (P=.003), light intensity (Ptextless.001), and moderate intensity (P=.001). EE was not significantly different at vigorous (P=.70) or recovery (P=.10). For Garmin Forerunner 225, HR MAPE was between 7.87% and 24.38%. HR was not significantly different at vigorous intensity (P=.35). The device measured higher HR readings at start, baseline, light intensity, moderate intensity (Ptextless.001), and recovery (P=.04). EE MAPE was between 30.77% and 155.05%. The device measured higher EE at all stages (Ptextless.001). Conclusions: This study provides one of the first validation assessments for the Fitbit Charge HR, Apple Watch, and Garmin Forerunner 225. An advantage and novel approach of the study is the examination of HR and EE at specific physical activity intensities. Establishing validity of wearable devices is of particular interest as these devices are being used in weight loss interventions and could impact findings. Future research should investigate why differences between exercise intensities and the devices exist. [JMIR Mhealth Uhealth 2017;5(3):e34]

Varying Exercise Intensities: The Accuracy of Three Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017)

Medicine & Science in Sports & Exercise

Abstract

Physical activity tracking wearables have emerged as a popular method for consumers to assess their daily activity, calories expended and heart rate. However, less is known if these health measures are valid at various levels of exercise intensity. PURPOSE: To examine heart rate (HR) and energy expenditure (EE) validity of three popular wrist-worn activity monitors at different exercise intensities. METHODS: 62 participants (36 females, 46.8% non-white) wore the Apple Watch (AW), Fitbit Charge HR (FCHR) and Garmin Forerunner 225 (GF). Validity was assessed by 2 criterion devices: HR chest strap and a metabolic cart. Participants completed a 10-min seated baseline; separate 4-min stages of light, moderate and vigorous-intensity treadmill exercises; and a 10-min seated recovery. Data from devices were compared to each criterion via two-way RM-ANOVA and Bland-Altman analysis. Differences were expressed in mean absolute percentage error values (MAPE). RESULTS: AW - HR MAPE between 1.14-6.70%. HR not significantly different at the start (p = .13), baseline (p = .76) or vigorous intensity (p = .84). Lower HR readings during light (p textless .05), moderate (p < .01) and recovery (p textless .01). EE MAPE between 14.07-210.84%. Measured higher EE at all stages (p textless .01). FCHR - HR MAPE between 2.38-16.99%. HR not significantly different at start (p = .43) or moderate intensity (p = .34). Lower HR readings during baseline, vigorous and recovery (p textless .001) and higher HR in light (p textless .001). EE MAPE between 16.85-84.98%. Measured higher EE at baseline (p textless .05), light (p textless .001) and moderate (p < .001). GF - HR MAPE between 7.87-24.38%. HR not significantly different at vigorous intensity (p = .35). Measured higher HR readings start, baseline, light, moderate (p textless .001) and recovery (p < .05). EE MAPE between 30.77-155.05%. Measured higher EE at all stages (p < .001). CONCLUSION: This study provides one of the first validation assessments for the Fitbit Charge HR, Apple Watch and Garmin Forerunner 225. An advantage and novel approach of the study is the examination of HR and EE at specific PA intensities. Establishing validity of wearables is of particular interest as these devices are being used in weight loss interventions and could impact findings. Future research should investigate why differences between exercise intensities and the devices exist.

Quantitative and qualitative measures of poststroke walking activity with inertial technologies (2017)

Annals of Physical and Rehabilitation Medicine

Abstract

OBJECTIVE Inertial technologies (activity monitors, inertial sensors) allow the evaluation of the number of steps and spatial-temporal parameters (STP) in daily life environments. However, activity monitors, generally worn at the hip, are not accurate for quantifying steps at slow walking speed (textless0.8m/s). Moreover, current models behind inertial sensors measurements estimate STP supposing symmetry between limbs. The current use of these tools decreases the accuracy of outdoor walking assessment in individuals poststroke since they generally walk slowly and have an asymmetrical gait pattern. The aim of this study was to assess the accuracy of: (1) an activity monitor placed at the ankle to count the number of steps and (2) an algorithm developed to estimate asymmetric STP in individuals poststroke. MATERIAL/PATIENTS AND METHODS Up to now, eight poststroke individuals who were independent walkers (with or without walking aid) were recruited. Study 1: an activity monitor (Fitbit® One) placed at the non-paretic ankle (not at the hip as recommended) was tested during a walk in a commercial center. The number of steps measured was compared to the one measured by video recording. Study 2: four inertial sensors (OPAL, APDM inc.) were placed bilaterally at the ankle and thigh of the participants and used to assess a new algorithm developed to estimate the right and left STP independently. The estimated STP was compared to those measured simultaneously by a GAITRite® walking mat. For each study, the agreement between data provided by the tools was estimated using Altman-Bland charts. RESULTS Among participants, the range of walking speed (0.4–1.2m/s) and STP asymmetry was large. In study 1, the Fitbit® One error for the number of steps was acceptable (0.14–4.60%), for participants walking faster than 0.4m/s. In study 2, the stride times were precisely estimated but the support times were underestimated (−13 [6.2]%) and the oscillation times overestimated (+30.1 [11.3]%). Spatial parameter estimation was inconsistent. DISCUSSION, CONCLUSION Placing the Fitbit® One at the non-paretic ankle is a suitable method for evaluating the amount of poststroke walking, except for very slow walkers. The algorithm for estimating STP from inertial sensors needs further work before being used in the community.

Accuracy of Fitbit Activity Trackers During Walking in a Controlled Setting (2017)

Medicine & Science in Sports & Exercise

Abstract

BACKGROUND: Activity trackers are widely used to measure daily physical activity. Many devices have been shown to measure steps more accurately at higher intensities, however, it is also important to determine accuracy at a walking pace. PURPOSE: To assess 6 popular activity trackers at measuring steps while walking on a treadmill. METHODS: Twenty-six college students (Mean±SD; 22.1±3.7yrs; 25.1±4.0kg/m2; 13 male) walked 500 steps at 3mph on a treadmill while wearing 6 different activity trackers (Pedometer, Fitbit Blaze, Charge HR, Alta, Flex, Zip, One). The Charge HR was placed two fingers above the right wrist while the Flex was next to the wrist bone. The Blaze was placed two fingers above the left wrist while the Alta was next to the wrist bone. The Fitbit Zip and the One were aligned with the hip bone on the left and right waistband respectively. A trained researcher using a hand tally counter counted the steps. Missing values were replaced with the mean value for that device. Step counts were correlated between Fitbit devices and the pedometer and tally counter using Pearson correlations. Significance was set at ptextless0.05. Mean bias scores were calculated between the step counts for each device and the tally counter. Mean Absolute Percent Error (MAPE) values were also calculated for each device relative to the tally counter. RESULTS: Fitbit Zip and One were significantly correlated with the tally counter (r=0.50, ptextless0.05; r=0.68, ptextless0.01, respectively) while the other devices were not significantly correlated. Mean bias and MAPE values were as follows: Fitbit Zip and One were within one half percent of actual steps while wrist-worn Fitbits ranged from 7.0-19.7% from actual step counts. CONCLUSION: Consistent with previous research, activity trackers worn at the waist provide the most accurate step counts compared to wrist-worn models. Differences found in wrist-worn models may result in significant over- or underestimation of activity levels when worn for long periods of time.

Accuracy of Smartwatches in Assessing College Students’ Energy Expenditure in Exercise with Different Intensities (2017)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: Despite the recent prevalence of health wearable devices (e.g., smartwatches), the validity and reliability of smartwatches’ physiological outcomes remain largely unexplored. This study was designed to examine the accuracy of the Microsoft Band (MS), Fitbit Surge HR (FB), TomTom Cardio Watch (TT) and Apple Watch (AW) in assessing energy expenditure (EE) during three exercise sessions with different intensities. METHODS: Participants were 25 college students (12 males; Mage= 23.52, SD = ± 1.04) from Southcenter region in China. They completed three separate 10-minute exercise sessions at light physical activity (LPA, walking at 3.0 km/h), moderate PA (MPA, running at 5.0 km/h), and vigorous PA (VPA, running at 7.0 km/h) conditions on the Hpcosmos treadmill at a highly controlled laboratory. In this study, the AW and TT were placed on the right wrist while the FB and MS were worn on the left wrist. All demographic information (i.e., height, weight, age, gender) was loaded onto each smartwatch prior to testing. EEs of smartwatches were then validated against EE data from a Actigraph wGT3X accelerometer worn on the right side of waist. RESULTS: ANOVAs with repeated measures revealed significant differences between smartwatches for EE, F(9, 16)=45.73, p<0.01, ?2=0.98. When validated against accelerometer, post-hoc comparisons suggested significantly different EE assessments for the FB at LPA and MPA conditions (p<0.01), and for MS and FB at the VPA (p<0.01). Interclass correlations between the accelerometer and smartwatches revealed reliability for EE for the TT at LPA (r=0.65, p<0.01), for the FB, TT and AW at MPA (r=0.53-0.59, p<0.01), and for MS, FB and TT at VPA (r=0.59-0.65, p<0.01). However, only the TT demonstrated excellent agreement with the accelerometer at LPA with coefficient of variation of 2.0%. Additionally, Bland-Altman plots yielded satisfactory precision/no bias for FB and TT measurements against the accelerometers at all conditions (p<0.05), for MS at LPA and VPA (p<0.05), and for AW at VPA (p < 0.05). CONCLUSIONS: Findings suggested EE measurements of smartwatches are far from ideal in terms of validity and reliability. In this study the most valid and reliable measurements were from the TT. Future study may investigate the validity and reliability of smartwatch measurements in everyday life.

Variable Accuracy of Wearable Heart Rate Monitors during Aerobic Exercise (2017)

Medicine & Science in Sports & Exercise

Abstract

Purpose: Athletes and members of the public increasingly rely upon wearable heart rate (HR) monitors to guide physical activity and training. The accuracy of newer, optically based monitors is unconfirmed. We sought to assess the accuracy of 5 optically based HR monitors during various types of aerobic exercise. Methods: Fifty healthy adult volunteers (mean age 38+12 years, 54% female) completed exercise protocols on a treadmill, stationary bicycle and an elliptical trainer (+/- arm movement). Each participant underwent HR monitoring with an electrocardiogaphic, chest strap monitor (Polar H7), forearm monitor (Scosche Rhythm+), and two randomly assigned wrist-worn HR monitors (Apple Watch, Fitbit Blaze, Garmin Forerunner 235, TomTom Spark Cardio), one on each wrist. For each exercise type, HR was recorded at rest, light, moderate and vigorous intensity. Agreement between HR measurements was assessed using Lin's concordance correlation coefficient (rc). Results: Across all exercise conditions, the chest strap monitor (Polar H7) had the best agreement with ECG (rc=.996) followed by the Apple Watch (rc=.92), the TomTom Spark (rc=.83) and Garmin Forerunner (rc=.81). The Scosche Rhythm+ and Fitbit Blaze were less accurate (rc=.75 and rc=.67, respectively). On treadmill, all devices performed well (rc=.88-.93) except the Fitbit Blaze (rc=.76). While bicycling, only the Garmin, Apple Watch and Scosche Rhythm+ had acceptable agreement (rctextgreater.80). On the elliptical trainer without arm levers, only the Apple Watch was accurate (rc=.94). None of the devices was accurate during elliptical trainer use with arm levers (all rc textless .80). Conclusion: The accuracy of wearable, optically based HR monitors varies with exercise type and is greatest on the treadmill and lowest on elliptical trainer. Electrode-containing chest monitors should be used when accurate HR measurement is imperative.

Fitbit Charge HR Wireless Heart Rate Monitor: Validation Study Conducted Under Free-Living Conditions (2017)

JMIR mHealth and uHealth

Abstract

Background: Many modern smart watches and activity trackers feature an optical sensor that estimates the wearer's heart rate. Recent studies have evaluated the performance of these consumer devices in the laboratory. Objective: The objective of our study was to examine the accuracy and sensitivity of a common wrist-worn tracker device in measuring heart rates and detecting 1-min bouts of moderate to vigorous physical activity (MVPA) under free-living conditions. Methods: Ten healthy volunteers were recruited from a large university in Singapore to participate in a limited field test, followed by a month of continuous data collection. During the field test, each participant would wear one Fitbit Charge HR activity tracker and one Polar H6 heart rate monitor. Fitbit measures were accessed at 1-min intervals, while Polar readings were available for 10-s intervals. We derived intraclass correlation coefficients (ICCs) for individual participants comparing heart rate estimates. We applied Centers for Disease Control and Prevention heart rate zone cut-offs to ascertain the sensitivity and specificity of Fitbit in identifying 1-min epochs falling into MVPA heart rate zone. Results: We collected paired heart rate data for 2509 1-min epochs in 10 individuals under free-living conditions of 3 to 6 hours. The overall ICC comparing 1-min Fitbit measures with average 10-s Polar H6 measures for the same epoch was .83 (95% CI .63-.91). On average, the Fitbit tracker underestimated heart rate measures by −5.96 bpm (standard error, SE=0.18). At the low intensity heart rate zone, the underestimate was smaller at −4.22 bpm (SE=0.15). This underestimate grew to −16.2 bpm (SE=0.74) in the MVPA heart rate zone. Fitbit devices detected 52.9% (192/363) of MVPA heart rate zone epochs correctly. Positive and negative predictive values were 86.1% (192/223) and 92.52% (2115/2286), respectively. During subsequent 1 month of continuous data collection (270 person-days), only 3.9% of 1-min epochs could be categorized as MVPA according to heart rate zones. This measure was affected by decreasing wear time and adherence over the period of follow-up. Conclusions: Under free-living conditions, Fitbit trackers are affected by significant systematic errors. Improvements in tracker accuracy and sensitivity when measuring MVPA are required before they can be considered for use in the context of exercise prescription to promote better health. [JMIR Mhealth Uhealth 2017;5(10):e157]

Physical activity among children: objective measurements using Fitbit One® and ActiGraph (2017)

BMC Research Notes

Abstract

BACKGROUND Self-quantification of health parameters is becoming more popular; thus, the validity of the devices requires assessments. The aim of this study was to evaluate the validity of Fitbit One step counts (Fitbit Inc., San Francisco, CA, USA) against Actigraph wActisleep-BT step counts (ActiGraph, LLC, Pensacola, FL, USA) for measuring habitual physical activity among children. DESIGN The study was implemented as a cross-sectional experimental design in which participants carried two waist-worn activity monitors for five consecutive days. METHODS The participants were chosen with a purposive sampling from three fourth grade classes (9-10 year olds) in two comprehensive schools. Altogether, there were 34 participants in the study. From these, eight participants were excluded from the analysis due to erroneous data. Primary outcome measures for step counts were Fitbit One and Actigraph wActisleep-BT. The supporting outcome measures were based on activity diaries and initial information sheets. Classical Bland-Altman plots were used for reporting the results. RESULTS The average per-participant daily difference between the step counts from the two devices was 1937. The range was [116, 5052]. Fitbit One gave higher step counts for all but the least active participant. According to a Bland-Altman plot, the hourly step counts had a relative large mean bias across participants (161 step counts). The differences were partially explained by activity intensity: higher intensity denoted higher differences, and light intensity denoted lower differences. CONCLUSIONS Fitbit One step counts are comparable to Actigraph step counts in a sample of 9-10-year-old children engaged in habitual physical activity in sedentary and light physical activity intensities. However, in moderate-to-vigorous physical activity, Fitbit One gives higher step counts when compared to Actigraph.

Validity Of The Diagnostix 2100 And Fitbit Charge HR In Assessing Heart Rate (2017)

Medicine & Science in Sports & Exercise

Abstract

The importance of exercise intensity is an important factor when maximizing the health benefits that result from exercise. New innovations in consumer based activity trackers and electronic healthcare monitoring devices have been able to quantify the level of exercise intensity based on heart rate (HR) values. However, there is limited research assessing the validity of these devices. PURPOSE: To assess the validity of the Fitbit Charge HR (FB) and ACSC Diagnostix 2100 Fingertip Pulse Oximeter (PO) in assessing heart rate during exercise in comparison to the heart rate from an electrocardiogram (EKG). METHODS: Healthy college students (n=30, 18 females) performed 1 min of standing rest, 10 min of the standard Bruce Protocol test on a treadmill, and a 2 min cool-down walk. Each participant simultaneously wore the FB on the right wrist, the PO on the right index finger, and had 10 electrodes placed on their chest for the 12-lead EKG. HR was recorded from each device every minute. Absolute differences between the 3 HR monitors were compared using repeated measures ANOVA. Pearson r correlation coefficients and Standard Error of Estimate (SEE) were calculated to determine the relationships between each HR monitor vs. EKG. RESULTS: Repeated measures ANOVA indicated a significant difference in HR between the 3 monitors, F(2,58)=16.876, p<0.001. Post hoc tests indicated a significant difference between EKG and FB (132.1 ± 13 vs. 120.5 ± 13.7 bpm, p<0.001), and EKG and PO (132.1 ± 13 vs. 121.9 ± 15.5 bpm, p<0.001). The correlation between the EKG and FB was r=0.64, p<0.001, SEE 10.2 bpm, whereas the correlation between EKG and PO was r=0.63, p<0.001, SEE 10.27 bpm. CONCLUSIONS: Large absolute differences and modest correlation values do not indicate a strong agreement between the FB or PO vs. EKG. Therefore, the extent of each device’s validity to monitor HR is questionable when compared to the gold standard EKG.

Comparison of four Fitbit and Jawbone activity monitors with a research-grade ActiGraph accelerometer for estimating physical activity and energy expenditure (2017)

British Journal of Sports Medicine

Abstract

Background/aim Consumer-based physical activity (PA) monitors have become popular tools to track PA behaviours. Currently, little is known about the validity of the measurements provided by consumer monitors. We aimed to compare measures of steps, energy expenditure (EE) and active minutes of four consumer monitors with one research-grade accelerometer within a semistructured protocol. Methods Thirty men and women (18–80 years old) wore Fitbit One (worn at the waist), Fitbit Zip (waist), Fitbit Flex (wrist), Jawbone UP24 (wrist) and one waist-worn research-grade accelerometer (ActiGraph) while participating in an 80?min protocol. A validated EE prediction equation and active minute cut-points were applied to ActiGraph data. Criterion measures were assessed using direct observation (step count) and portable metabolic analyser (EE, active minutes). A repeated measures analysis of variance (ANOVA) was used to compare differences between consumer monitors, ActiGraph, and criterion measures. Similarly, a repeated measures ANOVA was applied to a subgroup of subjects who didn't cycle. Results Participants took 3321±571 steps, had 28±6?active min and expended 294±56 kcal based on criterion measures. Comparatively, all monitors underestimated steps and EE by 13%–32% (ptextless0.01); additionally the Fitbit Flex, UP24, and ActiGraph underestimated active minutes by 35%–65% (ptextless0.05). Underestimations of PA and EE variables were found to be similar in the subgroup analysis. Conclusion Consumer monitors had similar accuracy for PA assessment as the ActiGraph, which suggests that consumer monitors may serve to track personal PA behaviours and EE. However, due to discrepancies among monitors, individuals should be cautious when comparing relative and absolute differences in PA values obtained using different monitors.

Wrist-Worn Activity Monitoring Devices Overestimate Sleep Duration and Efficiency in Healthy Adults (2017)

Sleep

Abstract

ntroduction: The use of activity monitors for sleep measurement purposes has increased in research and consumer settings. However, validation of such monitors is lacking. This study examined agreement on total sleep time (TST) and sleep efficiency (SE) between polysomnography and three activity monitors - Actiwatch Spectrum Pro (ACT), Fitbit One (FB) and Jawbone UP2 (JB). Differences between polysomnography and each activity monitor, and differences between research-grade ACT and commercial devices FB and JB, were examined. Methods: Twenty-two healthy adults (Mage =29.3, SDage=11.4) had one night of sleep measured by polysomnography and each activity monitor simultaneously in a laboratory. Minute-by-minute data were extracted and compared. Wilcoxon signed-ranks tests assessed statistical differences between measures, and Bland-Altman analyses examined clinically meaningful differences between measures, using cut-offs of ±30 minutes for TST and ±5% for SE. Results: Compared to polysomnography, all activity monitors significantly overestimated TST and SE. Differences between polysomnography and each monitor were also clinically meaningful, as Bland-Altman upper and lower limits of agreement for TST exceeded clinical cut-offs for ACT (-64.7min-+166.1min), FB (-66.1min-+189.2min), and JB (-103.6min-+186.2min). Similarly, upper and lower limits of agreement for SE exceeded clinical cut-offs for ACT (-13.0%-+34.0%), FB (-13.1%-+38.5%), and JB (-20.7%-+37.9%). Compared to ACT, only FB significantly overestimated TST and SE. However, differences between ACT and each of FB and JB were clinically meaningful. For FB, Bland-Altman upper limits of agreement exceeded clinical cut-offs for TST (-18.7min-+40.2min) and SE (-3.8%-+8.1%). For JB, upper and lower limits of agreement exceeded clinical cut-offs for TST (-100.4min-+79.3min) and SE (-20.1%-+16.0%). Agreement between devices decreased as TST and SE decreased. All monitors demonstrated poor specificity (18.8–35.6%), but high sensitivity (94.2–99.2%). Conclusion: Results suggest these models of ACT, FB, and JB cannot be used interchangeably with polysomnography. When activity monitors must be used, such as in field settings, FB and JB cannot replace research-grade ACT. Overall, users should account for each monitor's potential to overestimate or underestimate TST and SE to an unacceptable degree. Future research should examine within-subject variability over time to determine whether monitors can be used to track long-term sleep patterns.

Examination of a Wearable Activity Tracker to Assess Children’s Physical Activity (2017)

Medicine & Science in Sports & Exercise

Abstract

ewer wearable activity monitors have the capability to measure heart rate (HR) from the user’s wrist using an optical blood flow sensor (i.e., photoplethysmography techniques), and none of the previous studies examining children’s free-living activity utilizing a newly developed wearable activity tracker which includes built-in HR sensor. PURPOSE: To examine PA intensity using the wearable HR monitor. METHODS: a total of 43 children (girls = 18, boys = 25), aged 8 - 12 years, participated in the study. Participants were asked to wear the Fitbit Charge HR (FHR) on their left (FHRL), right wrist (FHRR) and Polar HR (PHR) monitor on their chest, while completing 11 different activities lasted 48 mins, monitoring their HR. Oxygen consumption was measured throughout the routine with the Cosmed K4B2 metabolic analyzer. 10 min average values of the resting metabolic rate were used as one metabolic equivalent of task (MET) to categorize children’s PA intensity. Activity intensities using HR from the FHR were classified by calculating children’s target HR between 50 and 75% of their maximum HR as moderate intensity activity and greater than 75% of their maximum HR as a vigorous intensity activity. An estimate of children’s maximum age-related HR was obtained from the 208 - 0.7 (age) equation. Each intensity classified by the MET determined by HR was compared to the intensity classified by the MET determined by resting metabolic rate (i.e., criterion measure) to examine the measurement agreement. McNemar’s test was used to examine the measurement agreement for paired intensity frequency. RESULTS: After setting PA intensity classification with 50% and 75% of HR, the frequency determined by measured MET from the Cosmed K4B2 were 1002, 407, and 120 for light, moderate, and vigorous intensity. The frequency determined by HR monitor and FHR were as follow: 1076, 394, and 58 by PHR, 1393, 127, and 9 by FHRL, 1393, 129, and 7 by FHRR. The values of weighted Kappa statistics from the McNemar’s test were 0.40 (95% CL: 0.36-0.44) for the intensity by PHR, 0.21 (95% CL: 0.18-0.25) for the intensity by FHRL, 0.19 (95% CL: 0.16-0.23) for the intensity by FHRR. CONCLUSIONS: The agreement of PA intensity classified by the wearable activity tracker to the intensity classified the metabolic analyzer showed fair agreement.

Consumer-Based Physical Activity Monitor as a Practical Way to Measure Walking Intensity During Inpatient Stroke Rehabilitation (2017)

Stroke

Abstract

BACKGROUND AND PURPOSE Identifying practical ways to accurately measure exercise intensity and dose in clinical environments is essential to advancing stroke rehabilitation. This is especially relevant in monitoring walking activity during inpatient rehabilitation where recovery is greatest. This study evaluated the accuracy of a readily available consumer-based physical activity monitor during daily inpatient stroke rehabilitation physical therapy sessions. METHODS Twenty-one individuals admitted to inpatient rehabilitation were monitored for a total of 471 one-hour physical therapy sessions which consisted of walking and nonwalking therapeutic activities. Participants wore a consumer-based physical activity monitor (Fitbit One) and the gold standard for assessing step count (StepWatch Activity Monitor) during physical therapy sessions. Linear mixed modeling was used to assess the relationship of the step count of the Fitbit to the StepWatch Activity Monitor. Device accuracy is reported as the percent error of the Fitbit compared with the StepWatch Activity Monitor. RESULTS A strong relationship (slope=0.99; 95% confidence interval, 0.97-1.01) was found between the number of steps captured by the Fitbit One and the StepWatch Activity Monitor. The Fitbit One had a mean error of 10.9% (5.3) for participants with walking velocities textless0.4 m/s, 6.8% (3.0) for walking velocities between 0.4 and 0.8 m/s, and 4.4% (2.8) for walking velocities textgreater0.8 m/s. CONCLUSIONS This study provides preliminary evidence that the Fitbit One, when positioned on the nonparetic ankle, can accurately measure walking steps early after stroke during inpatient rehabilitation physical therapy sessions. CLINICAL TRIAL REGISTRATION URL: https://www.clinicaltrials.gov. Unique identifier: NCT01915368.

Step Count Accuracy and Reliability from Activity Monitors Worn by Mobility-Intact Older Adults (2017)

Innovation in Aging

Abstract

Accurate and reliable measurement of physical activity is necessary to assess adherence to and effects of physical activity interventions within a growing population of physically inactive older adults. The STRIDES Study was designed to determine accuracy (criterion=tally counter) and reliability (test-retest) of step counting in mobility-intact older adults from six wearable activity monitors (Fitbit Charge, Fitbit One, Garmin Vivofit2, Jawbone UP2, Misfit Shine, and New-Lifestyles 1000 pedometer). Participants (n=10, 69–80 years) completed a continuous 400-meter walk (average speed 1.3m/s), a non-continuous 400-meter walk (1.1m/s) designed to mimic daily walking patterns incorporating small disturbances, and two 100-step reliability walks. For the continuous walk, five monitors had small mean errors less than +/-5% (One -0.3%,95%CI=-0.4to-0.2%; Vivofit2 -0.6%,CI=-1.4to0.2%; UP2 -3.2%,CI=-5.7to-0.6%; Shine 0.5%,CI=-6.3to7.4%; New-Lifestyles -0.7%,CI=-1.1to-0.3%) while error for the Charge was larger (-7.7%;CI=-17.1to1.6%). Step counts from the One, Vivofit2 and New-Lifestyles were strongly correlated with criterion (r=0.95). For the non-continuous walk, the One, Vivofit2, and Shine had small errors less than +/-5% (-4.4%,CI=-5.4to-3.5%; -4.2%,CI=-8.1to-0.2%; -1.4%,CI=-5.6to2.8%), while errors for the Charge, UP2 and New-Lifestyles were larger (-8.1%,CI=-15.5to-0.6%; -6.3%,CI=-10.6to-2.1%; -6.2%,CI=-11.4to-0.9%). Only step counts from the One were strongly correlated with criterion (r=0.95). The Charge, One, Vivofit2, and New-Lifestyles were reliable with no significant differences in step counts between trials, while the Shine and UP2 were unreliable (mean differences between trials of 25.6 and 14.6%, p<0.05). In summary, we found that most activity monitors were reliable and accurate for step counting in older adults during continuous walking, while accuracy of some monitors was compromised during non-continuous walking.

Validity of the Fitbit? Distance Traveled Feature Among Multiple Speed Trials (2017)

Medicine & Science in Sports & Exercise

Abstract

A science review by Delago (2014) suggested that the distance-traveled feature on Fitbit® was an accurate measurement. However, as speed increased the accuracy of the Fitbit® decreased, meaning the faster the pace, the greater the error for distance measured. Whereas a systematic review conducted by Evenson et al. (2015) reported only one study that measured the distance-traveled feature. This study concluded that the Fitbit® over-estimated at lower speeds and underestimated at faster speeds. PURPOSE: To examine whether the fitness device, Fitbit® Charge, provides an accurate measurement of the distance-traveled feature at various speeds and to expand on the research regarding the distance-traveled feature. METHODS: Twenty-eight healthy students from a Division III college participated in this study. A repeated measure ANVOA experimental design compared the Fitbit® distance-traveled output under three dependent variable speeds of 2.5mph (76.1m/min), 4.5mph (120.7m/min), and 6mph (160.9m/min) to a Quinton MedTrack ST55 treadmill distance-traveled measurement. All participants completed a six-minute trial for each of three different speeds on a treadmill while wearing the Fitbit® Charge. Following each six-minute trial, the distance-traveled was compared between the treadmill and the Fitbit® output. A repeated measure ANOVA analysis was used to test for significant differences (p < .05) among the three speeds between the Fitbit® and the treadmill. RESULTS: Results did not detect significant differences in distance-traveled between the Fitbit® Charge compared to the treadmill at 2.5mph (F(1,27) = 0.67, p = .42), 4.5mph (F(1,27) = 2.45, p = .13), or 6mph (F(1,24) = .66, p = .43) speeds. CONCULSION: It seems reasonable to conclude that the distance-traveled feature on the Fitbit® Charge is valid when compared to treadmill output at these three speeds. Future research could look at a wider range of speeds to ensure further accuracy.

A Validation Study of the Fitbit One in Daily Life Using Different Time Intervals (2017)

Medicine & Science in Sports & Exercise

Abstract

Purpose: Accelerometer-based wearables can provide the user with real-time feedback through the device's interface and the mobile platforms. Few studies have focused on the minute-by-minute validity of wearables, which is essential for high-quality real-time feedback. This study aims to assess the validity of the Fitbit One compared with the ActiGraph GT3x + for assessing physical activity (i.e., steps, time spent in moderate, vigorous, and moderate–vigorous physical activity) in young adults using traditional time intervals (i.e., days) and smaller time intervals (i.e., minutes and hours). Methods: Healthy young adults (N = 34) wore the ActiGraph GT3x+ and a Fitbit One for 1 wk. Three aggregation levels were used: minute, hour, and day. Mixed models analyses, intraclass correlation coefficients, Bland–Altman analyses, and absolute error percentage for steps per day were conducted to analyze the validity for steps and minutes spent in moderate, vigorous, and moderate–vigorous physical activity. Results: As compared with ActiGraph (mean = 9 steps per minute, 509 steps per hour and 7636 steps per day), the Fitbit One systematically overestimated physical activity for all aggregation levels: on average 0.82 steps per minute, 45 steps per hour, and 677 steps per day. Strong and significant associations were found between ActiGraph and Fitbit results for steps taken (B = 0.72–0.89). Weaker but statistically significant associations were found for minutes spent in moderate, vigorous, and moderate–vigorous physical activity for all time intervals (B = 0.39–0.57). Conclusions: Although the Fitbit One overestimates the step activity compared with the ActiGraph, it can be considered a valid device to assess step activity, including for real-time minute-by-minute self-monitoring. However, agreement and correlation between ActiGraph and Fitbit One regarding time spent in moderate, vigorous, and moderate–vigorous physical activity were lower.

Mobile Device Accuracy for Step Counting Across Age Groups. (2017)

JMIR mHealth and uHealth

Abstract

BACKGROUND Only one in five American meets the physical activity recommendations of the Department of Health and Human Services. The proliferation of wearable devices and smartphones for physical activity tracking has led to an increasing number of interventions designed to facilitate regular physical activity, in particular to address the obesity epidemic, but also for cardiovascular disease patients, cancer survivors, and older adults. However, the inconsistent findings pertaining to the accuracy of wearable devices for step counting needs to be addressed, as well as factors known to affect gait (and thus potentially impact accuracy) such as age, body mass index (BMI), or leading arm. OBJECTIVE We aim to assess the accuracy of recent mobile devices for counting steps, across three different age groups. METHODS We recruited 60 participants in three age groups: 18-39 years, 40-64 years, and 65-84 years, who completed two separate 1000 step walks on a treadmill at a self-selected speed between 2 and 3 miles per hour. We tested two smartphones attached on each side of the waist, and five wrist-based devices worn on both wrists (2 devices on one wrist and 3 devices on the other), as well as the Actigraph wGT3X-BT, and swapped sides between each walk. All devices were swapped dominant-to-nondominant side and vice-versa between the two 1000 step walks. The number of steps was recorded with a tally counter. Age, sex, height, weight, and dominant hand were self-reported by each participant. RESULTS Among the 60 participants, 36 were female (60%) and 54 were right-handed (90%). Median age was 53 years (min=19, max=83), median BMI was 24.1 (min=18.4, max=39.6). There was no significant difference in left- and right-hand step counts by device. Our analyses show that the Fitbit Surge significantly undercounted steps across all age groups. Samsung Gear S2 significantly undercounted steps only for participants among the 40-64 year age group. Finally, the Nexus 6P significantly undercounted steps for the group ranging from 65-84 years. CONCLUSIONS Our analysis shows that apart from the Fitbit Surge, most of the recent mobile devices we tested do not overcount or undercount steps in the 18-39-year-old age group, however some devices undercount steps in older age groups. This finding suggests that accuracy in step counting may be an issue with some popular wearable devices, and that age may be a factor in undercounting. These results are particularly important for clinical interventions using such devices and other activity trackers, in particular to balance energy requirements with energy expenditure in the context of a weight loss intervention program.

Minimal Detectable Change Thresholds and Responsiveness of Zephyr Bioharness & Fitbit Charge Devices. (2017)

Journal of Strength and Conditioning Research

Abstract

The aim of the current study was to define the MDC thresholds for Zephyr Bioharness (ZB) and Fitbit Charge (FC-HR) heart rate variables, and to establish the internal responsiveness of ZB and FC-HR devices in terms of their heart rate, respiratory rate variables and number of steps taken variables, in a healthy sample of males and females at resting, sub-maximal activity and throughout recovery phases. A total of sixty participants (30 females, 48 ± 15) and (30 males, 48 ± 15) were recruited using stratified convenience and snowball sampling approaches from our university student, staff and faculty population. At rest, MDC90 values of ± 4.89 and ± 5.15 were noted for ZB and FC-HR devices respectively. Throughout the recovery, thresholds of ±8.14 for ZB and ± 8.74 for FC-HR were reported. During the sub-maximal phases, scores of ± 8.18 and ± 9.35 were established for ZB and FC-HR respectively. From a resting to a sub-maximal state, the Standardized Response Means for ZB and FC-HR heart rate variables exhibited large responsiveness of SRMZB-HR=4.42; SRMFC-HR=4.04. In addition, the ZB respiratory rate variable showed the largest effect size of SRMZB-RR= 2.55. Similarly, large effect size of SRMFC-ST=1.76 were reported for the FC-HR steps taken variable. From a sub-maximal to a recovery state, he Standardized Response Means for ZB and FC-HR heart rate variables demonstrated effect sizes of SRMZBHR= -3.10; SRMFC-HR= -3.12). Furthermore, the ZB respiratory rate variable displayed similar internal responsiveness measures of SRMZB-RR= -2.45. To conclude, Similar MDC heart rate thresholds can be expected using the ZB and FC-HR devices, and that the two devices demonstrated large internal responsiveness in our healthy male and female cohort when transitioning from resting to submaximal states and vice-versa. Our findings suggest that the ZB and FC-HR devices can be used in monitoring performance improvements or deterioration over time, and would allow qualified personnel in assessing the effectiveness of exercise programs in healthy individuals.

Accuracy of Consumer Monitors for Measuring Sleep Across Seven Nights (2017)

Sleep

Abstract

Introduction: Sleep and fitness trackers available to the consumer market show promise for measuring sleep. However, there are few published studies examining their accuracy compared with well-validated actigraphy devices for multiple nights outside the sleep laboratory. The purpose of this study was to evaluate the reliability and validity of several commercially-available devices compared to a well-validated actigraphy device. Methods: Healthy adults (N=40, mean age= 42?±?14, 50% female) wore a Fitbit Flex, Jawbone Up24, Misfit Shine, and Actiwatch-2 (AW-2) on the same wrist for seven consecutive 24-hour periods. Outcome variables included total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE) averaged over 7 nights. Both “Normal” and “Sensitive” sleep-recording Fitbit modes were examined. Each commercial monitor was compared with AW-2 using paired t-test and Bland-Altman concordance analyses. Clinical acceptance was defined a priori as =60 minutes difference between devices for TST and WASO, and =10% difference for SE. Results: Fitbit (normal), Jawbone, and Misfit were in the clinically acceptable range for TST, with Fitbit (normal) and Misfit overestimating by 2 minutes and 60 minutes, respectively; and Jawbone underestimating by 15 minutes compared with AW-2 (m=406?±?47 minutes). Fitbit (sensitive) overestimated by 160 minutes. All devices met clinically acceptable criteria but underestimated WASO by 13 minutes (Jawbone), 21 minutes (Fitbit sensitive), 23 minutes (Fitbit normal), and 25 minutes (Misfit) compared to AW-2 (m=38?±?15 minutes). Jawbone and Fitbit (normal) met clinically acceptable criteria for SE, with both devices overestimating by 4% and 5%, respectively compared to AW-2 (m=85?±?6%). Misfit overestimated by 12% and Fitbit (sensitive) underestimated by 30%. Conclusion: Fitbit (normal) and Jawbone accelerometers provided clinically comparable result for TST, WASO, and SE when compared to AW-2. Misfit met criteria for TST and WASO but not SE. Fitbit (sensitive) performed poorest. Further research is needed to reevaluate these findings with newer models.

When a Step Is Not a Step! Specificity Analysis of Five Physical Activity Monitors (2017)

PLOS ONE

Abstract

Introduction: Physical activity is an essential aspect of a healthy lifestyle for both physical and mental health states. As step count is one of the most utilized measures for quantifying physical activity it is important that activity-monitoring devices be both sensitive and specific in recording actual steps taken and disregard non-stepping body movements. The objective of this study was to assess the specificity of five activity monitors during a variety of prescribed non-stepping activities. Methods: Participants wore five activity monitors simultaneously for a variety of prescribed activities including deskwork, taking an elevator, taking a bus journey, automobile driving, washing and drying dishes; functional reaching task; indoor cycling; outdoor cycling; and indoor rowing. Each task was carried out for either a specific duration of time or over a specific distance. Activity monitors tested were the ActivPAL micro™, NL-2000™ pedometer, Withings Smart Activity Monitor Tracker (Pulse O2)™, Fitbit One™ and Jawbone UP™. Participants were video-recorded while carrying out the prescribed activities and the false positive step count registered on each activity monitor was obtained and compared to the video. Results: All activity monitors registered a significant number of false positive steps per minute during one or more of the prescribed activities. The Withings™ activity performed best, registering a significant number of false positive steps per minute during the outdoor cycling activity only (P = 0.025). The Jawbone™ registered a significant number of false positive steps during the functional reaching task and while washing and drying dishes, which involved arm and hand movement (P < 0.01 for both). The ActivPAL™ registered a significant number of false positive steps during the cycling exercises (P < 0.001 for both). Conclusion: As a number of false positive steps were registered on the activity monitors during the non-stepping activities, the authors conclude that non-stepping physical activities can result in the false detection of steps. This can negatively affect the quantification of physical activity with regard to step count as an output. The Withings™ activity monitor performed best with regard to specificity during the activities of daily living tested.

Sources of Error with Wearable Step Counters (2017)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: To investigate sources of error with 11 wearable step counting devices, during common types of physical activities. METHODS: 20 participants performed 15 activities for 2 min each, while wearing 11 step counters on the waist, ankle, or non-dominant wrist. Arm activities included: snacking, brushing hair, folding laundry, sweeping, brushing teeth, and meal preparation. Overground activities included: walking holding onto backpack strap, walking with umbrella, walking with hands in pockets, and pushing stroller. Treadmill activities included: walking at 1 mph, walking at 2 mph, walking at 3 mph, walking at 3 mph holding onto bars, and jogging at 6 mph. Wrist-worn devices included: Garmin Vivofit 2, Fitbit Charge, Polar A360, Withings Pulse Ox, and ActiGraph GT3X. Waist-worn devices included: Yamax Digi-Walker SW-200, Fitbit Zip, Omron HJ-322U, and ActiGraph GT3X (without low-frequency extension). Ankle-worn devices included: two StepWatch 3 devices, one with preprogrammed, default settings and one with modified cadence and sensitivity settings. A researcher hand-counted steps during each activity; this served as the criterion. The step counts reported from each device compared to the hand count using a 1-way (1x12) repeated measured ANOVA. If the overall effect for an activity was significant, the outputs from individual devices and the criterion were analyzed using planned contrasts. Devices with significant contrasts (p = 0.05) and observed power greater than 0.8 were considered to be significantly different than the criterion. RESULTS: During arm activities, the wrist-worn devices overcounted steps while hip-worn devices slightly undercounted steps. The ActiGraph GT3X on the wrist greatly overcounted steps during arm activities, while other devices had smaller errors. During treadmill walking at 1 mph, all wrist and hip-worn devices undercounted steps. The ankle-worn device (StepWatch 3) had the smallest error across all activities, especially when programmed with the modified setting. CONCLUSIONS: Individuals using step counting devices should be aware of sources of error in step counts. Contributing factors to error are the wear location, the algorithms used to count steps, and the activities performed.

Accuracy And Reliability Of The Fitbit Charge? Activity Tracker Among Older Adults (2017)

Medicine & Science in Sports & Exercise

Abstract

Wearable technology, including activity trackers, remains a top fitness trend world-wide; however, the ability of individual activity trackers to accurately and consistently record physical activity remains unknown. PURPOSE: To evaluate the accuracy and reliability of step counts from the Fitbit Charge™ among older adults. METHODS: Thirteen participants with a mean age of 70.38 ±4.27 years volunteered to participate and completed all testing. Participants completed a 96 meter walk around a gymnasium while wearing the Fitbit Charge™ activity tracker. Step counts on the activity tracker were recorded before and after the walk to determine the step count. The walk was also video recorded to determine the actual number of steps taken during the walk. Step counts from video analysis were confirmed by two researchers. The same procedures were repeated on a second, non-consecutive day of testing to determine the reliability of the activity tracker. Accuracy of the activity tracker was determined by comparing step counts to the observed step counts from each testing session. The reliability of the activity tracker was determined by correlation analysis and comparison of step counts from the first testing session to the second testing session. RESULTS: The activity tracker significantly underestimated observed steps at both testing sessions by 21.31 steps and 22.62 steps, respectively (p < .05). No difference in step count from the activity tracker was seen from session one to session two (151.85 steps vs. 152.54 steps, p > .05); however, the correlation between the two sessions was only moderate, r = .55, p = .05. CONCLUSION: Among older adults, the Fitbit Charge™ appears to underestimate steps taken even over a short distance. The reliability of the Fitbit Charge™ is questionable given only a moderate correlation between sessions. While preliminary, these results call into question the accuracy and reliability of daily step counts from the Fitbit Charge™. It is suggested that all new activity trackers to hit the market are given careful study to determine their ability to accurately measure daily activity.

Validation of a self-monitoring tool for use in exercise therapy. (2017)

PM&R

Abstract

BACKGROUND: Aerobic exercise at a sub-symptom heart rate has been recommended as therapy for Post-Concussion Syndrome (PCS). Assessing adherence with an accurate heart rate monitoring instrument is difficult limiting the proliferation of large-scale randomized controlled trials. OBJECTIVE: To evaluate the validity of the Fitbit Charge HR against electrocardiogram (EKG) to monitor heart rate during a treadmill-based exercise protocol. DESIGN: A methods comparison study. SETTING: Sports medicine research center within a tertiary care institution. PARTICIPANTS: A convenience sample of 22 healthy participants (12 female) aged 18-26 years (mean age: 22 ± 2 years). METHODS: Fitbit Charge HR heart rate measurements were compared to EKG data concurrently collected while participants completed the Buffalo Concussion Treadmill Test (BCTT). MAIN OUTCOME MEASURES: Agreement between Fitbit Charge HR and EKG was assessed by intraclass correlation coefficients (ICC3,1), Bland-Altman limits of agreement, and percent error. RESULTS: We observed a strong single-measure absolute agreement between Fitbit Charge HR and EKG (ICC3,1 = 0.83; 95% CI: 0.67 - 0.90). Fitbit Charge HR underestimated heart rate compared to EKG (mean difference = -6.04 beats per min (bpm); SD = 10.40 bpm; Bland-Altman 95% limits of agreement = -26.42 to 14.35 bpm). 69.9% of Fitbit heart rate measurements were within 10% error compared to EKG, and 91.5% of all heart rate measurements were within 20% error. CONCLUSIONS: While the mean bias in measuring heart rate was relatively small, the limits of agreement between the Fitbit Charge HR and EKG were broad. Thus, the Fitbit Charge HR would not be a suitable option for monitoring heart rate within a narrow range. For the purposes of post-concussion exercise therapy, the relatively inexpensive cost, easy implementation, and low maintenance make Fitbit Charge HR a viable option for assessing adherence to an exercise program when expensive clinical equipment is unavailable.

Accuracy of Fitbit Charge 2 Worn at Different Wrist Locations During Exercise (2017)

Medicine & Science in Sports & Exercise

Abstract

Many newly released activity monitors use heart rate measured at the wrist to estimate exercise intensity, however, where the device is placed on the wrist may affect accuracy of the measurement. PURPOSE: To determine whether the Pure Pulse technology on the Fitbit Charge 2 will show different heart rate readings when placed on the recommended exercise position compared to the all-day wear position at various exercise intensities. METHODS: Thirty five participants (MEAN ± SD; 22.0 ± 2.9yrs; 23.9 ± 2.6kg/m2; 18 male) consented to participate in a single visit where two Fitbit Charge 2 devices were placed on the non-dominant wrist. Fitbit A was placed 2-3 fingers above the wrist bone. Fitbit B was placed directly above the wrist bone. The treadmill was set at 3 mph with 0% grade. Participants remained at this speed for 4 minutes. Heart rate measurements were taken at the last 10 seconds of each stage from both Fitbits and a polar heart rate monitor (chest strap). The same procedure was followed for 5 and 6 mph. Statistical analyses were performed using IBM SPSS 23.0. A Two-way (speed x location) Repeated Measures ANOVA was used to examine mean differences. Pairwise comparisons with Bonferroni correction were used in post-hoc analysis. Pearson correlations and mean bias between polar heart rate monitor and activity monitors were also calculated for each speed. RESULTS: Repeated Measures ANOVA found significant differences between speeds (ptextless0.01) and location (ptextless0.01), but not for the interaction (p=0.234). Pairwise comparisons indicated significant differences between each speed (ptextless0.01) and between the polar monitor and Fitbit B (ptextless0.05), but not between the polar monitor and Fitbit A (p=0.608). Pearson correlations indicated strong correlations between each Fitbit and the polar monitor (r= .58-.91; all ptextless0.01). Mean bias decreased as speed increased for Fitbit A (mean bias bpm ± SD; -1.1 ± 5.4; -1.9 ± 9.5; -0.4 ± 6.9; -0.3 ± 7.3 for resting, 3mph, 5mph, 6mph respectively) while mean bias for Fitbit B increased as speed increased (-2.8 ± 8.8; -3.1 ± 11.1; -3.9 ± 14.6; -6.7 ± 14.3 for resting, 3mph, 5mph, 6mph respectively). CONCLUSION: Wrist-worn heart rate monitors appear to provide values adequate for recreational use, however, following recommended guidelines on wear-position may impact heart rate readings.

Accuracy Of Steps, Energy Expenditure, And Distance In Nine Activity Trackers (2017)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: To evaluate the accuracy of the step counts, energy expenditure (EE), and distance measured from 9 consumer grade activity trackers. METHODS: Twenty participants completed 1 mile of walking followed by 1 mile of running on a treadmill in the lab. Participants completed 3 sessions of exercise while wearing as many as 4 devices set up for their height, weight, and age in addition to an Actigraph GT3X (GT3X) accelerometer. Devices included in the study were (1) the Fitbit Surge (FBS), Charge (FBC), and Charge HR (FBH); (2) the Garmin Vivoactive (GVA) and Vivosmart HR (GVS); (3) the Jawbone UP2 and UP3; (4) the Polar Loop and; (5) the Microsoft Band 2 (MSB). Data from the devices were compared to the GT3X for steps and American College of Sports Medicine metabolic equations (ACSMME) for estimated EE. Distance recorded by the devices was compared to the 1-mile treadmill completed distance. RESULTS: The GVA and GVS performed best with accurate measures for running steps counted, walking EE, running EE, and walking distance (p < .05). The FBH and UP3 performed worst with only accurate step counts for running (p < .05). The FBH and FBS underestimated walking steps by 370 steps (p < .000) and 318 steps (p = .002) respectively. Only the PL and UP2 were accurate for steps counted at a walking pace; however, both devices underestimated steps at 48 steps (p = .227) and 86 steps (p = .06) respectively. CONCLUSION: Few devices accurately measured steps at speeds lower than 4.5mph when compared to a validated accelerometer; however, most devices may accurately measure steps taken at speeds greater than 4.5mph. Few devices accurately measured EE for a 1-mile walk or run when compared to ACSMME. Few devices accurately measured distance for a walk, and none of the devices accurately measured distance for a run. It is recommended that accuracy should be considered when using activity tracking devices for the measurement of daily physical activity.

Fitbit And Actigraph: A Comparison Of Physical Activity And Sedentary Time In Overweight/obese Adults (2017)

Medicine & Science in Sports & Exercise

Abstract

here is limited information regarding the validity of consumer targeted wearable physical activity (PA) trackers, such as the Fitbit, to assess PA in free-living adults. PURPOSE: To compare daily minutes of sedentary time, and moderate-to-vigorous (MVPA) colleted using two types of activity trackers, in a sample of overweight and obese adults (n = 12, BMI= 37±4 kg/m2, age = 48±12 yrs.) who completed a 6-mo. weight loss intervention (diet + self-directed PA) delivered using Facebook. METHODS: Participants wore a Fitbit FlexTM on their non-dominant wrist continuously over 6 mos. They were also asked to wear an ActiGraph GTX1TM on their non-dominant hip for 7 consecutive days at both baseline and 6 mos. Fitbit and ActiGraph data for a minimum of one 10-hr. day at baseline and 6 mos. was required for inclusion in this analysis. Categorization of sedentary time and MVPA was derived from the Fitbit algorithm or using NHANES Actigraph cut-points (Troiano, 2008). RESULTS: Sedentary time was significantly lower and MVPA was significantly higher when assessed with the Fitbit compared with the ActiGraph at both baseline (sedentary time: Fitbit = 427 ±168, ActiGraph = 639 ±132 min/d, p< 0.001; MPVA: Fitbit= 128 ±49, ActiGraph = 18 ±19 min/d, p <0.0001) or 6 mos. (sedentary time: Fitbit = 459 ±168, ActiGraph = 613 ±103 min/d, p= 0.001; MPVA: Fitbit= 123±53, ActiGraph= 14 ±10 min/d, p<0.0001). Change in both sedentary time and MVPA from baseline to 6 mos. was not statistically significant measured either with the Fitbit (sedentary time = +63 ±46, p =0.16; MVPA = -8±13 min/d, p = 0.72) or the ActiGraph (sedentary time = - 49±52, p =0.39; MVPA = -2±13 min/d, p = 0.48). CONCLUSIONS: The Fitbit significantly underestimated sedentary time and overestimated MVPA when compared with the ActiGraph in a small sample. Changes in sedentary time and MVPA over 6 mos. were non-significant when assessed by either the Fitbit or ActiGraph. These observations suggest that the Fitbit, which is relatively inexpensive when compared with the ActiGraph, may be useful for assessing changes in sedentary time and MVPA in response to an intervention. However, the absolute values for sedentary time and MVPA assessed by the Fitbit are questionable, and worthy of additional investigation in larger samples of free-living adults.Funded by Kansas City Life Sciences Institute

Accuracy of a step counter during treadmill and daily life walking by healthy adults and patients with cardiac disease (2017)

BMJ Open

Abstract

BACKGROUND Step counters have been used to observe activity and support physical activity, but there is limited evidence on their accuracy. OBJECTIVE The purpose was to investigate the step accuracy of the Fitbit Zip (Zip) in healthy adults during treadmill walking and in patients with cardiac disease while hospitalised at home. METHODS Twenty healthy adults aged 39±13.79 (mean ±SD) wore four Zips while walking on a treadmill at different speeds (1.7-6.1 km/hour), and 24 patients with cardiac disease (age 67±10.03) wore a Zip for 24 hours during hospitalisation and for 4 weeks thereafter at home. A Shimmer3 device was used as a criterion standard. RESULTS At a treadmill speed of 3.6 km/hour, the relative error (±SD) for the Zips on the upper body was -0.02±0.67 on the right side and -0.09 (0.67) on the left side. For the Zips on the waist, this was 0.08±0.71 for the right side and -0.08 (0.47) on the left side. At a treadmill speed of 3.6 km/hour and higher, the average per cent of relative error was textless3%. The 24-hour test for the hospitalised patients showed a relative error of -47.15±24.11 (interclass correlation coefficient (ICC): 0.60), and for the 24-hour test at home, the relative error was -27.51±28.78 (ICC: 0.87). Thus, none of the 24-hour tests had less than the expected 20% error. In time periods of evident walking during the 24 h test, the Zip had an average per cent relative error of textless3% at 3.6 km/hour and higher speeds. CONCLUSIONS A speed of 3.6 km/hour or higher is required to expect acceptable accuracy in step measurement using a Zip, on a treadmill and in real life. Inaccuracies are directly related to slow speeds, which might be a problem for patients with cardiac disease who walk at a slow pace.

Validity of Different Activity Monitors to Count Steps in an Inpatient Rehabilitation Setting. (2017)

Physical Therapy

Abstract

Background Commonly used activity monitors have been shown to be accurate in counting steps in active people, however, further validation is needed in slower walking populations. Objectives To determine the validity of activity monitors for measuring step counts in rehabilitation inpatients compared to visually-observed step counts. To explore the influence of gait parameters, activity monitor position and use of walkers on activity monitor accuracy. Methods 166 inpatients admitted to a rehabilitation unit with an average walking speed of 0.4 m/s (SD 0.2) wore 16 activity monitors (seven different devices in different positions) simultaneously during 6-minute and 6-metre walks. The number of steps taken during the tests was also counted by a physiotherapist. Gait parameters were assessed using the GAITRite system. To analyse the influence of different gait parameters, the percentage accuracy for each monitor was graphed against various gait parameters for each activity monitor. Results The StepWatch , Fitbit One worn on the ankle and the ActivPAL showed excellent agreement with observed step count (ICC 2,1 0.98; 0.92; 0.78 respectively). Other devices ( Fitbit Charge , Fitbit One worn on hip, G-Sensor , Garmin Vivofit , Actigraph ) showed poor agreement with the observed step count (ICC 2,1 0.12-0.40). Percentage agreement with observed step count was highest for the StepWatch (mean 98%). The StepWatch and the Fitbit One worn on the ankle maintained accuracy in individuals who walked more slowly and with shorter strides but other devices were less accurate in these individuals. Limitations There were small numbers of participants for some gait parameters. Conclusions The StepWatch showed the highest accuracy and closest agreement with observed step count. This device can be confidently used by researchers for accurate measurement of step counts in inpatient rehabilitation in individuals who walk slowly. If immediate feedback is desired, the Fitbit One when worn on the ankle would be the best choice for this population.

The Fitbit One Physical Activity Tracker in Men With Prostate Cancer: Validation Study (2017)

JMIR Cancer

Abstract

BACKGROUND Physical activity after cancer diagnosis improves quality of life and may lengthen survival. However, objective data in cancer survivors are limited and no physical activity tracker has been validated for use in this population. OBJECTIVE The aim of this study was to validate the Fitbit One's measures of physical activity over 7 days in free-living men with localized prostate cancer. METHODS We validated the Fitbit One against the gold-standard ActiGraph GT3X+ accelerometer in 22 prostate cancer survivors under free-living conditions for 7 days. We also compared these devices with the HJ-322U Tri-axis USB Omron pedometer and a physical activity diary. We used descriptive statistics (eg, mean, standard deviation, median, interquartile range) and boxplots to examine the distribution of average daily light, moderate, and vigorous physical activity and steps measured by each device and the diary. We used Pearson and Spearman rank correlation coefficients to compare measures of physical activity and steps between the devices and the diary. RESULTS On average, the men wore the devices for 5.8 days. The mean (SD) moderate-to-vigorous physical activity (MVPA; minutes/day) measured was 100 (48) via Fitbit, 51 (29) via ActiGraph, and 110 (78) via diary. The mean (SD) steps/day was 8724 (3535) via Fitbit, 8024 (3231) via ActiGraph, and 6399 (3476) via pedometer. Activity measures were well correlated between the Fitbit and ActiGraph: 0.85 for MPVA and 0.94 for steps (all p < 001). The Fitbit's step measurements were well correlated with the pedometer (0.67, P=.001), and the Fitbit's measure of MVPA was well correlated with self-reported activity in the diary (0.84; p < .001). CONCLUSIONS Among prostate cancer survivors, the Fitbit One's activity and step measurements were well correlated with the ActiGraph GT3X+ and Omron pedometer. However, the Fitbit One measured two times more MVPA on average compared with the ActiGraph.

Criterion-Validity of Commercially Available Physical Activity Tracker to Estimate Step Count, Covered Distance and Energy Expenditure during Sports Conditions (2017)

Frontiers in Physiology

Abstract

Background: In the past years, there was an increasing development of physical activity tracker (Wearables). For recreational people, testing of these devices under walking or light jogging conditions might be sufficient. For (elite) athletes, however, scientific trustworthiness needs to be given for a broad spectrum of velocities or even fast changes in velocities reflecting the demands of the sport. Therefore, the aim was to evaluate the validity of eleven Wearables for monitoring step count, covered distance and energy expenditure (EE) under laboratory conditions with different constant and varying velocities. Methods: Twenty healthy sport students (10 men, 10 women) performed a running protocol consisting of four 5 min stages of different constant velocities (4.3; 7.2; 10.1; 13.0 km·h−1), a 5 min period of intermittent velocity, and a 2.4 km outdoor run (10.1 km·h−1) while wearing eleven different Wearables (Bodymedia Sensewear, Beurer AS 80, Polar Loop, Garmin Vivofit, Garmin Vivosmart, Garmin Vivoactive, Garmin Forerunner 920XT, Fitbit Charge, Fitbit Charge HR, Xaomi MiBand, Withings Pulse Ox). Step count, covered distance, and EE were evaluated by comparing each Wearable with a criterion method (Optogait system and manual counting for step count, treadmill for covered distance and indirect calorimetry for EE). Results: All Wearables, except Bodymedia Sensewear, Polar Loop, and Beurer AS80, revealed good validity (small MAPE, good ICC) for all constant and varying velocities for monitoring step count. For covered distance, all Wearables showed a very low ICC (<0.1) and high MAPE (up to 50%), revealing no good validity. The measurement of EE was acceptable for the Garmin, Fitbit and Withings Wearables (small to moderate MAPE), while Bodymedia Sensewear, Polar Loop, and Beurer AS80 showed a high MAPE up to 56% for all test conditions. Conclusion: In our study, most Wearables provide an acceptable level of validity for step counts at different constant and intermittent running velocities reflecting sports conditions. However, the covered distance, as well as the EE could not be assessed validly with the investigated Wearables. Consequently, covered distance and EE should not be monitored with the presented Wearables, in sport specific conditions.

Polysomnography vs Consumer Devices: Comparing Common Sleep Indices (2017)

Sleep

Abstract

Introduction: As public interest in health tracking technology increases, so too does the prevalence of commercially available consumer activity devices and apps utilising actigraphy. With the increase of the population's ability to access this technology, and limited research available on the concordance of these devices with polysomnography (PSG), the accuracy, specificity and sensitivity of the FitbitCharge HR device's common sleep indices were compared and evaluated against those of Level 1 PSG in participants with a diagnosis of respiration within normal limits and those with varying degrees of sleep-disordered breathing (SDB). Methods: Analysis was performed on 41 participants (age, mean ± SD: 55?±?14 years; BMI ± SD: 32?±?6?kg/m2). The different categories of SDB were classified upon AASM PSG guidelines and consequently broken up into normal respiration (AHI = 5); mild OSA (AHI = 5 but = 14.99); moderate OSA (AHI = 15 but = 29.99); severe OSA (AHI = 30.0). Full overnight PSG was performed (in accordance with AASM guidelines) in a laboratory setting and recorded concurrently with the consumer device. Primary outcome measures were limited to sleep-onset latency (SOL), total sleep time (TST), wake after sleep-onset (WASO) and sleep-efficiency (SE). Exclusion criteria were applied. Results: These common sleep indices of Fitbit Charge HR were compared to similar indices of PSG using paired t-tests and Bland-Altman plots. Significant differences were seen in SOL for the participants within the category of respiration within normal limits, moderate OSA and severe OSA; WASO was significantly different in all categories; SE was significantly different in the category of moderate OSA; there were no significant differences in TST at any category. Conclusion: Previous research has shown that actigraphy has high sensitivity in detecting sleep, but poor specificity in detecting wake in persons with respiration within normal limits. This poster shows that there was a significant difference in the participants with SDB in their SOL, SE and WASO but no difference in TST when comparing the FitbitCharge HR to PSG.

Evaluating the Consistency of Current Mainstream Wearable Devices in Health Monitoring: A Comparison Under Free-Living Conditions (2017)

Journal of Medical Internet Research

Abstract

Background: Wearable devices are gaining increasing market attention; however, the monitoring accuracy and consistency of the devices remains unknown. Objective: The purpose of this study was to assess the consistency of the monitoring measurements of the latest wearable devices in the state of normal activities to provide advice to the industry and support to consumers in making purchasing choices. Methods: Ten pieces of representative wearable devices (2 smart watches, 4 smart bracelets of Chinese brands or foreign brands, and 4 mobile phone apps) were selected, and 5 subjects were employed to simultaneously use all the devices and the apps. From these devices, intact health monitoring data were acquired for 5 consecutive days and analyzed on the degree of differences and the relationships of the monitoring measurements ??by the different devices. Results: The daily measurements by the different devices fluctuated greatly, and the coefficient of variation (CV) fluctuated in the range of 2-38% for the number of steps, 5-30% for distance, 19-112% for activity duration, .1-17% for total energy expenditure (EE), 22-100% for activity EE, 2-44% for sleep duration, and 35-117% for deep sleep duration. After integrating the measurement data of 25 days among the devices, the measurements of the number of steps (intraclass correlation coefficient, ICC=.89) and distance (ICC=.84) displayed excellent consistencies, followed by those of activity duration (ICC=.59) and the total EE (ICC=.59) and activity EE (ICC=.57). However, the measurements for sleep duration (ICC=.30) and deep sleep duration (ICC=.27) were poor. For most devices, there was a strong correlation between the number of steps and distance measurements (R2 >.95), and for some devices, there was a strong correlation between activity duration measurements and EE measurements (R2 > .7). A strong correlation was observed in the measurements of steps, distance and EE from smart watches and mobile phones of the same brand, Apple or Samsung (R2 > 88). Conclusions: Although wearable devices are developing rapidly, the current mainstream devices are only reliable in measuring the number of steps and distance, which can be used as health assessment indicators. However, the measurement consistencies of activity duration, EE, sleep quality, and so on, are still inadequate, which require further investigation and improved algorithms. [J Med Internet Res 2017;19(3):e68]

Validation of Three Wearable Fitness Trackers for Measuring Physical Activity in College Students (2017)

Medicine & Science in Sports & Exercise

Abstract

Many wearable fitness trackers have a function measuring physical activity such as energy expenditure but the accuracy of the measures is unclear. PURPOSE: The aim of the study was to validate the energy expenditure estimation of three fitness trackers: LT, FB, and AW for treadmill walking and running. METHODS: The participants of the study were 30 college students (17 males and 13 females) from a public university in Pennsylvania. All participants completed six trials of 10-minute walking and running activities on a treadmill at speeds of 54, 80, 107, 134, 161, & 188 m•min-. Participants were wearing all the fitness trackers and connected to indirect calorimetry during the exercise protocol. All devices provided caloric expenditure while the indirect calorimetry was used as a criterion measure. Resting Metabolic Rate was collected along with a familiarization trial prior to the execution of the exercise protocol. Two-way ANOVA with repeated measures and Pearson Correlation were used to compare the caloric expenditure estimates between the criterion and three fitness trackers. RESULTS: There were no significant differences in energy expenditure estimates between the criterion and AW (mean difference 4.6 kcal, p textgreater 0.05), nor criterion and LT (mean difference 0.8 kcal, p textgreater 0.05). However there was a significant difference between the criterion measure and FB (mean difference 18.2 kcal, p = 0.001). Pearson correlation coefficients (r): the criterion of indirect calorimetry-derived activity energy expenditure yielded the strongest positive correlations with activity energy expenditure estimated from the AW at all speed levels (range from .55 to .85, all ps textless0.01). FB was positively correlated to the criterion measure at five speed levels (range from .46 to .67, ps textless0.05). No positive correlation was found between the criterion and LT measures (range from -.40 to .26). CONCLUSION: AW and FB demonstrated a moderate to high level of validity on measuring physical activity while LT had a low level of validity.

A validation study of Fitbit Charge 2™ compared with polysomnography in adults (2017)

Chronobiology International

Abstract

We evaluated the performance of a consumer multi-sensory wristband (Fitbit Charge 2™), against polysomnography (PSG) in measuring sleep/wake state and sleep stage composition in healthy adults. In-lab PSG and Fitbit Charge 2™ data were obtained from a single overnight recording at the SRI Human Sleep Research Laboratory in 44 adults (19—61 years; 26 women; 25 Caucasian). Participants were screened to be free from mental and medical conditions. Presence of sleep disorders was evaluated with clinical PSG. PSG findings indicated periodic limb movement of sleep (PLMS, > 15/h) in nine participants, who were analyzed separately from the main group (n = 35). PSG and Fitbit Charge 2™ sleep data were compared using paired t-tests, Bland–Altman plots, and epoch-by-epoch (EBE) analysis. In the main group, Fitbit Charge 2™ showed 0.96 sensitivity (accuracy to detect sleep), 0.61 specificity (accuracy to detect wake), 0.81 accuracy in detecting N1+N2 sleep (“light sleep”), 0.49 accuracy in detecting N3 sleep (“deep sleep”), and 0.74 accuracy in detecting rapid-eye-movement (REM) sleep. Fitbit Charge 2™ significantly (p < 0.05) overestimated PSG TST by 9 min, N1+N2 sleep by 34 min, and underestimated PSG SOL by 4 min and N3 sleep by 24 min. PSG and Fitbit Charge 2™ outcomes did not differ for WASO and time spent in REM sleep. No more than two participants fell outside the Bland–Altman agreement limits for all sleep measures. Fitbit Charge 2™ correctly identified 82% of PSG-defined non-REM–REM sleep cycles across the night. Similar outcomes were found for the PLMS group. Fitbit Charge 2™ shows promise in detecting sleep-wake states and sleep stage composition relative to gold standard PSG, particularly in the estimation of REM sleep, but with limitations in N3 detection. Fitbit Charge 2™ accuracy and reliability need to be further investigated in different settings (at-home, multiple nights) and in different populations in which sleep composition is known to vary (adolescents, elderly, patients with sleep disorders).

Preliminary concurrent validity of the Fitbit-Zip and ActiGraph activity monitors for measuring steps in people with polymyalgia rheumatica. (2018)

Gait & posture

Abstract

BACKGROUND Activity monitors provide objective measurements of physical activity, however, the accuracy of these devices in people with polymyalgia rheumatica (PMR) is unknown. Therefore, this study aimed to obtain preliminary evidence of the accuracy of two activity monitors and explore if clinical and gait-related factors altered device accuracy in people with PMR. METHODS The ActiGraph with low frequency extension (+LFE) and standard (-LFE) algorithms, Fitbit-Zip (waist) and Fitbit-Zip (shirt) were concurrently tested using a two-minute walk test (2MWT) and stairs test in 27 people with PMR currently treated with prednisolone. To determine accuracy, activity monitor step-count was compared to a gold-standard step-count (GSSC; calculated from video recording) using Bland-Altman plots. RESULTS The Fitbit-Zip (waist) achieved closest agreement to the GSSC for the 2MWT (mean bias (95%CI): 10 (-3, 23); 95%LOA: -55, 74). The ActiGraph (+LFE) achieved closest agreement to the GSSC for the stairs test (mean bias (95%CI): 0 (-1, 1); 95%LOA: -5, 5). The ActiGraph (-LFE) performed poorly in both tests. All devices demonstrated reduced accuracy in participants with lower gait velocity, reduced stride length, longer double-limb support phase and greater self-reported functional impairment. CONCLUSION Our preliminary results suggest that in controlled conditions, the Fitbit-Zip fairly accurately measures step-count during walking in people with PMR receiving treatment. However, device error was greater than data published in healthy people. The ActiGraph may not be recommended without activation of the LFE. We identified clinical and gait-related factors associated with higher levels of functional impairment that reduced device accuracy. Further work is required to evaluate the validity of the activity monitors in field conditions.

Assessment of the accuracy of nutrient calculations of five popular nutrition tracking applications (2018)

Public Health Nutrition

Abstract

Objective To assess the accuracy of nutrient intake calculations from leading nutrition tracking applications (apps). Design Nutrient intake estimates from thirty 24 h dietary recalls collected using Nutrition Data System for Research (NDSR) were compared with intake calculations from these recalls entered by the researcher into five free nutrition tracking apps. Apps were selected from the Apple App Store based on consumer popularity from the list of free ‘Health and Fitness' apps classified as a nutrition tracking apps. Subjects Dietary recall data collected from thirty lower-income adults. Results Correlations between nutrient intake calculations from NDSR and the nutrition tracking apps ranged from 0textperiodcentered73 to 0textperiodcentered96 for energy and macronutrients. Correlations for the other nutrients examined (Na, total sugars, fibre, cholesterol, saturated fat) ranged from 0textperiodcentered57 to 0textperiodcentered93. For each app, one or more mean nutrient intake calculations were significantly lower than those from NDSR. These differences included total protein (P=0textperiodcentered03), total fat (P=0textperiodcentered005), Na (P=0textperiodcentered02) and cholesterol (P=0textperiodcentered005) for MyFitnessPal; dietary fibre (P=0textperiodcentered04) for Fitbit; total protein (P=0textperiodcentered0004), total fat (P=0textperiodcentered008), Na (P=0textperiodcentered002), sugars (P=0textperiodcentered007), cholesterol (P=0textperiodcentered0006) and saturated fat (P=0textperiodcentered005) for Lose It!; Na (P=0textperiodcentered03) and dietary fibre (P=0textperiodcentered005) for MyPlate; and total fat (P=0textperiodcentered03) for Lifesum. Conclusions Findings suggest that nutrient calculations from leading nutrition tracking apps tend to be lower than those from NDSR, a dietary analysis software developed for research purposes. Further research is needed to evaluate the validity of the apps when foods consumed are entered by consumers.

VALIDITY AND RELIABILITY OF THE FITBIT FLEX™ AND ACTIGRAPH GT3X+ AT JOGGING AND RUNNING SPEEDS. (2018)

International journal of sports physical therapy

Abstract

Background Monitoring levels of physical activity, as an outcome or in guiding rehabilitation, is challenging for clinicians. Personal activity monitors are increasing in popularity and provide potential to enhance rehabilitation protocols. However, research to support the validity and reliability of these devices at jogging and running speeds is limited. Purpose The purpose of this study was to evaluate the validity of the Fitbit Flex™ and ActiGraph GT3X + for measuring step count at jogging and running speeds. A secondary purpose was to examine inter-device reliability of the Fitbit Flex™. Study Design Cross-sectional study. Methods Thirty healthy participants aged between 19 and 50 years, completed a treadmill protocol at jogging and running speeds (8 km/h to 16 km/h). Treadmill speed was progressively increased by intervals of 2 km/h. Each interval was four minutes in duration with a two minute rest period between stages. Participants were encouraged to continue through the graded exercise test until they reached the maximum running speed that they felt they could maintain for four minutes. Step count data was collected for Fitbit Flex™ devices and the ActiGraph GT3X+. Video analysis of step count was used as the criterion measure. Results At speeds of 8 to 14 km/h Mean Absolute Percentage Errors were ≤1% for the Fitbit Flex™ and the ActiGraph GT3X + when compared to step count via video analysis. Standard Error of Measurement between the three Fitbit Flex™ devices was ≤ 7 steps for speeds of 8 to 14 km/h and varied between 9 to 19 steps at 16 km/h. Fitbit Flex™ devices showed good to excellent between device reliability at speeds of 8 to14 km/h (ICC 0.723 to 0.999; p ≤ 0.001). Greater variability was evident with the low participant numbers at 16 km/h (ICC 0.527 to 0.896; p ≥ 0.02). Conclusion Both the Fitbit Flex™ and the ActiGraph GT3X + provide a valid account of steps taken at jogging and running speeds up to 14 km/hr, attainable by non-elite runners on a treadmill. Fitbit Flex™ devices provide equivalent step count output to each other, enabling comparison between devices during treadmill jogging and running. Level of evidence 2b.

Impact Of Placement Of Wrist-worn Activity Monitors During The Lab And Free-living Settings (2018)

Medicine & Science in Sports & Exercise

Abstract

Wrist-worn activity monitors have been widely used to measure heart rates, step counts, and/or calories. While the versatility of altering the wearing locations (i.e., left vs. right; distal vs. proximal) may prove appealing, the influence of monitor placement on the outcome measurements needs to be examined. PURPOSE: This study was to examine the difference in measurements of heart rates, step counts, and calories estimated from the Fitbit monitors worn at different locations. METHODS: Thirty-two healthy male and female, aged 26.03 ± 6.59 years, participated in this study. Participants wore Fitbit monitors at four different locations [Right Proximal (RP), Right Distal (RD), Left Proximal (LP), Left Distal (LD)]. Lab testing consisted of four 5-min phases: slow and brisk walking and jogging at 53.6, 107.3, 160.9 m/min on a treadmill and a recovery. Free living activities involved ten 5-min activities with different intensity levels (e.g., stretching, climbing stairs, jogging). Heart rates, step counts, and calories were recorded during various activities. Repeated measures ANOVAs were performed with a monitor placement as a within-subjects factor on 14 lab and free-living activities for each outcome measurement. Bonferroni technique was used to adjust the alpha level of .004 (.05/14). The Greenhouse-Geisser (G-G) adjusted F and degrees of freedom were reported. RESULTS: Overall, there were no significant differences in measurements of heart rates, step counts, and calories estimated from the four Fitbit monitors during the lab activities. In free-living activities, step counts were significantly different during climbing stairs, F(2.86, 88.76) = 5.16, G-G p = .003 and sports, F(1.24, 38.40) = 27.93, G-G p < .001. Step counts estimated from LD (446 ± 49) were significantly higher than Fitbit monitors worn at RP (413 ± 61) and RD (417 ± 49) when climbing stairs. Fitbit monitors worn at LP (649 ± 64) and LD (642 ± 70) estimated significantly higher step counts than Fitbit monitors worn at RP (587 ± 75) and RD (565 ± 73) as participated in sports. CONCLUSION: This study revealed that monitor placement does not make a significant influence on the measurements of heart rates and calories during the lab and free- living settings. Further studies on the impact of monitor placement against criterion measures are warranted.

Use of Consumer Monitors for Estimating Energy Expenditure in Youth (2018)

Medicine & Science in Sports & Exercise

Abstract

Consumer-grade physical activity monitors (PAMs) have been extensively examined for estimating energy expenditure (EE) in adults; however, few studies have examined their ability to estimate EE in youth. PURPOSE: The purpose of this study was to examine equivalence between predicted EE (consumer-grade PAMs) and measured EE (indirect calorimetry) in youth. METHODS: Ninety-five youth (mean(SD); age, 12.2(3.5) yr; 49% male) performed 30 min of supine rest and 16 structured activities ranging from sedentary behaviors to vigorous intensities. Each structured activity was performed twice: once for 60-90 s and once for 4-5 min. During all testing, participants wore an Apple Watch 2 (AW, left wrist) and Mymo Activity Tracker (MT, right hip). Subsamples wore two Misfit Shine 2 devices (MSH, right hip; MSS, right shoe, n = 27), a Samsung Gear Fit 2 (SG, right wrist, n = 44), and/or a Fitbit Charge 2 (FC, right wrist, n = 53). A Cosmed K4b2 was used as the criterion measure of EE. Oxygen consumption was converted to EE in kilocalories (kcal), then summed over the whole trial to obtain gross EE. Net EE was calculated by subtracting the estimated basal EE (Schofield’s equation) from the measured gross EE. For all PAMS, EE was recorded at the start and end of the trial, and the difference was used for analysis. 95% equivalence testing with ±10% equivalence zone was used to assess equivalence between the estimated (PAM) and measured (K4b2) EE. All PAMs estimated gross EE, except for AW which estimated net EE, thus separate equivalence test was performed for the AW. Mean absolute percent error (MAPE) was used to assess individual-level error. RESULTS: Equivalence testing results and MAPE are summarized in Table 1. CONCLUSION: Only the MT was equivalent to the K4b2, however it also had the largest individual error. Caution is warranted when using consumer-grade PAMs in youth for tracking EE as there is a high degree of variability in device output.

Validating Fitbit Zip for monitoring physical activity of children in school: a cross-sectional study (2018)

BMC Public Health

Abstract

BACKGROUND Modern activity trackers, including the Fitbit Zip, enable the measurement of both the step count as well as physical activity (PA) intensities. However, there is a need for field-based validation studies in a variety of populations before using trackers for research. Therefore, the purpose of the current study was to investigate the validity of Fitbit Zip step count, moderate to vigorous physical activity (MVPA) and sedentary minutes, in different school segments in 3rd grade students. METHODS Third grade students (N = 147, aged 9-10 years) wore a Fitbit Zip and an ActiGraph GT3x-BT accelerometer simultaneously on a belt for five days during school hours. The number of steps, minutes of MVPA and sedentary time during class time, physical education lessons and recess were extracted from both devices using time filters, based on the information from school time tables obtained from class teachers. The validity of the Fitbit Zip in different school segments was assessed using Bland-Altman analysis and Spearman's correlation. RESULTS There was a strong correlation in the number of steps in all in-school segments between the two devices (r = 0.85-0.96, P < 0.001). The Fitbit Zip overestimated the number of steps in all segments, with the greatest overestimation being present in physical education lessons (345 steps). As for PA intensities, the agreement between the two devices in physical education and recess was moderate for MVPA minutes (r = 0.56 and r = 0.72, P < 0.001, respectively) and strong for sedentary time (r = 0.85 and r = 0.87, P < 0.001, respectively). During class time, the correlation was weak for MVPA minutes (r = 0.24, P < 0.001) and moderate for sedentary time (r = 0.57, P < 0.001). For total in-school time, the correlation between the two devices was strong for steps (r = 0.98, P < 0.001), MVPA (r = 0.80, P < 0.001) and sedentary time (r = 0.94, P < 0.001). CONCLUSION In general, the Fitbit Zip can be considered a relatively accurate device for measuring the number of steps, MVPA and sedentary time in students in a school-setting. However, in segments where sedentary time dominates (e.g. academic classes), a research-grade accelerometer should be preferred.

Reliability of Zephyr Bioharness and Fitbit Charge Measures of Heart Rate and Activity at Rest, During the Modified Canadian Aerobic Fitness Test and Recovery (2018)

Journal of Strength and Conditioning Research

Abstract

The purpose of current study was to determine the intra-session and inter-session reliability of Zephyr Bioharness and Fitbit Charge variables in both healthy men and women at rest, during the Modified Canadian Aerobic Fitness Test (mCAFT) and throughout recovery. Stratified convenience and snowball sampling were used to recruit sixty participants (30 females, 48 ± 15) and (30 males, 48 ± 15) from McMaster University student, staff and faculty population. At rest, intra-session average heart rate (beats/min.) Intra-class correlation coefficients (ICCs) and Standard Error of Measurement [SEM] for Zephyr ranged from (0.94 to 0.97) [1.17 - 1.70] and (0.92 - 0.97) [1.45 - 2.10] for Fitbit Charge. During the mCAFT, the Zephyr ICCs and (SEM) ranged from 0.31 - 0.99 (1.28 - 8.10) and 0.45 - 0.99 (1.45 - 8.71) for the Fitbit Charge. Throughout the recovery, the ICCs and (SEM) ranged from 0.44 - 0.98 (1.26 - 10.47) and 0.45 - 0.98 (1.15 - 11.90) for Zephyr and Fitbit devices respectively. At rest, intersession ICCs (SEM) for Zephyr and Fitbit ranged from 0.90 - 0.94 (1.73 - 2.37) and 0.88 - 0.94 (1.83 - 2.67) respectively. At mCAFT, the Zephyr ICCs (SEM) ranged from 0.91 - 0.97 (3.12 - 4.64) and 0.85 - 0.98 (3.28 - 4.88) for the Fitbit. Throughout the recovery, the ICCs (SEM) ranged from 0.93 - 0.97 (2.65 - 4.66) and 0.76 -0.91 (3.17 - 4.67) for Zephyr and Fitbit devices respectively. To conclude both the Zephyr Bioharness and Fitbit Charge devices demonstrated excellent reliability measures throughout the three phases. The findings from our study adds to the existing pool of literature regarding the reliability parameters of wearable devices and suggests that stable and consistent measures of heart rate and physical activity can be obtained using Zephyr Bioharness and Fitbit Charge devices among healthy male and female participants at rest, during a standardized sub-maximal fitness test (mCAFT) and throughout recovery.

Veterans’ Perspectives on Fitbit Use in Treatment for Post-Traumatic Stress Disorder: An Interview Study (2018)

JMIR Mental Health

Abstract

Background: The increase in availability of patient data through consumer health wearable devices and mobile phone sensors provides opportunities for mental health treatment beyond traditional self-report measurements. Previous studies have suggested that wearables can be effectively used to benefit the physical health of people with mental health issues, but little research has explored the integration of wearable devices into mental health care. As such, early research is still necessary to address factors that might impact integration including patients' motivations to use wearables and their subsequent data. Objective: The aim of this study was to gain an understanding of patients’ motivations to use or not to use wearables devices during an intensive treatment program for post-traumatic stress disorder (PTSD). During this treatment, they received a complementary Fitbit. We investigated the following research questions: How did the veterans in the intensive treatment program use their Fitbit? What are contributing motivators for the use and nonuse of the Fitbit? Methods: We conducted semistructured interviews with 13 veterans who completed an intensive treatment program for PTSD. We transcribed and analyzed interviews using thematic analysis. Results: We identified three major motivations for veterans to use the Fitbit during their time in the program: increase self-awareness, support social interactions, and give back to other veterans. We also identified three major reasons certain features of the Fitbit were not used: lack of clarity around the purpose of the Fitbit, lack of meaning in the Fitbit data, and challenges in the veteran-provider relationship. Conclusions: To integrate wearable data into mental health treatment programs, it is important to understand the patient’s perspectives and motivations in using wearables. We also discuss how the military culture and PTSD may have contributed to our participants' behaviors and attitudes toward Fitbit usage. We conclude with possible approaches for integrating patient-generated data into mental health treatment settings that may address the challenges we identified. [JMIR Ment Health 2018;5(2):e10415]

Comparison Of Daily Energy Expenditure And Weekly Physical Activity Exposure Estimated Using Consumer And Research-grade Physical Activity Monitors During Officer Cadet Initial Military Training (2018)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: Wearable physical activity monitors provide the capability to estimate the physical demands of military training and to potentially inform training practices. This study aimed to compare both the daily energy expenditure (EE) and weekly physical activity (PA) measured from two wrist-worn activity monitoring devices in Officer Cadets (OC) during initial military training. METHODS: Forty (26 male, 14 female) OC’s (mean ± SD: age 24 ± 2, height 1.76 ± 0.08 m, body mass 79.21 ± 9.97 kg) wore a consumer (“CN”; Fitbit Surge, San Francisco, USA) and a research-grade (“RG”; GENEActiv Original, Activinsights, Cambridge, UK) activity monitor during the second 14-week term of British Army OC training. A filtering process was implemented to exclude training days that did not fulfil wear-time criteria. Bivariate Pearsons correlations and limits of agreement (LoA) were used to compare EE measurement and duration of sedentary, light, moderate and vigorous PA between devices. RESULTS: Mean daily estimated EE from the CN and RG were 13.9 ± 2.5 and 15.7 ± 1.8 MJ.day-1, respectively. Estimated daily EE ranged from 8.6 ± 2.8 (day 35) to 22.4 ± 6.7 MJ.day-1 (day 49) in CN and from 10.7 ± 4.5 (day 55) to 21.3 ± 8.1 MJ.day-1 (day 49) in RG. There was a strong correlation between EE in CN and RG over 14 weeks (r = 0.761, p<0.001). However, the LoA indicated that CN underestimated EE (mean bias [95% CI] by -1.4 [-16 - 3.2] MJ.day-1, p<0.001) compared to the RG. Mean daily estimates from CN were 1069 ± 86 sedentary minutes, 221 ± 44 light minutes, 40 ± 14 moderate minutes and 56 ± 16 vigorous minutes. RG estimated 543 ± 53 sedentary minutes, 90 ± 16 light minutes, 249 ± 52 moderate minutes and 33 ± 14 vigorous minutes. All intensities were significantly different between CN and RG (p<0.05). The CN overestimated on sedentary minutes (526 mins.day-1 [319 - 734] mins.day-1), light minutes (131 [39 - 224] mins.day-1) and vigorous minutes (24 [-15 - 62] mins.day-1), but underestimated moderate minutes -208 [-295 - -122] mins.day-1). CONCLUSION: Consumer grade physical activity monitors provide an easily accessible tool for monitoring military training but more research is required to improve their accuracy before they can be used to inform training practices.

Physical Activity during Pregnancy Alters Gene Expression in Neonatal Tissue (2018)

Medicine & Science in Sports & Exercise

Abstract

Offspring born to mothers who exercise during pregnancy have been shown to have reduced birth weight and body weight during adolescence; however, mechanisms to explain this phenomenon are lacking. PURPOSE: This study examined whether male infants born to mothers with low levels of naturally occurring physical activity during pregnancy (<6,000 steps/day; <6K) had altered expression of genes related to glucose metabolism and adipogenesis compared to infants born to mothers with greater than 6,000 steps/day (>6K). METHODS: Physical activity levels were assessed via Fitbit Flex during the second and third trimester of pregnancy. The dartos layer of the foreskin was collected following circumcision in full term, singleton, male neonates (n = 6 per group). Tissue was homogenized, RNA isolated, and a NanoString code set run to quantify a panel of genes related to glucose metabolism and adipogenesis, as well as housekeeping genes. RESULTS: Peroxisome proliferator-activated receptor alpha (<6K: 86.4±9.2 vs. >6K: 59.4±5.0) and cAMP responsive element binding protein 1 (<6K: 285.7±36.8 vs. >6K: 197.4±12.1), genes encoding proteins that are involved in partially regulating adipogenesis, and DNA methyltransferase 1 (DNMT1) (<6K: 132.2±6.4 vs. >6K: 112.5±3.7), a gene coding an enzyme involved in the regulation of DNA methylation patterns, were significantly decreased in the infants born to mothers with higher levels of physical activity during pregnancy (p<0.05). Adiponectin receptor 1 (<6K: 490.1±38.1 vs. >6K: 743.3±99.7), a receptor that is downregulated in obese, insulin resistant populations, and glucose transporter 1 (<6K: 88.0±15.7 vs. >6K: 153.3±13.0), a glucose transporter responsible for basal levels of glucose uptake in most cells, were significantly increased in the tissue of neonates born to mothers with higher levels of physical activity (p<0.05). CONCLUSION: These preliminary data suggest that low levels of physical activity during pregnancy are associated with increased gene expression of markers of adipogenesis and decreased markers involved in insulin sensitivity and glucose uptake. Further, these adaptations or other gene expression changes may be epigenetically regulated as DNMT1 levels were significantly lower in the lower physical activity group.

Consumer Wearable Devices for Activity Monitoring Among Individuals After a Stroke: A Prospective Comparison (2018)

JMIR Cardio

Abstract

Background: Activity monitoring is necessary to investigate sedentary behavior after a stroke. Consumer wearable devices are an attractive alternative to research-grade technology, but measurement properties have not been established. Objective: The purpose of this study was to determine the accuracy of 2 wrist-worn fitness trackers: Fitbit Charge HR (FBT) and Garmin Vivosmart (GAR). Methods: Adults attending in- or outpatient therapy for stroke (n=37) wore FBT and GAR each on 2 separate days, in addition to an X6 accelerometer and Actigraph chest strap monitor. Step counts and heart rate data were extracted, and the agreement between devices was determined using Pearson or Spearman correlation and paired t or Wilcoxon signed rank tests (one- and two-sided). Subgroup analyses were conducted. Results: Step counts from FBT and GAR positively correlated with the X6 accelerometer (ρ=.78 and ρ=.65, Ptextless.001, respectively) but were significantly lower (Ptextless.01). For individuals using a rollator, there was no significant correlation between step counts from the X6 accelerometer and either FBT (ρ=.42, P=.12) or GAR (ρ=.30, P=.27). Heart rate from Actigraph, FBT, and GAR demonstrated responsiveness to changes in activity. Both FBT and GAR positively correlated with Actigraph for average heart rate (r=.53 and .75, Ptextless.01, respectively) and time in target zone (ρ=.49 and .74, Ptextless.01, respectively); these measures were not significantly different, but nonequivalence was found. Conclusions: FBT and GAR had moderate to strong correlation with best available reference measures of walking activity in individuals with subacute stroke. Accuracy appears to be lower among rollator users and varies according to heart rhythm. Consumer wearables may be a viable option for large-scale studies of physical activity. [JMIR Cardio 2018;2(1):e1]

Validation of Activity Tracking Procedures in Elderly Patients after Operative Treatment of Proximal Femur Fractures (2018)

Rehabilitation Research and Practice

Abstract

Background. Early postoperative physical activity in elderly patients suffering from proximal femoral fractures may reduce mortality. We hypothesized that activity trackers can reliably and objectively monitor the in-hospital mobilization, correlating with functional independence and quality of life. Methods. Three different tracker types (Fitbit™ flex, Misfit™ Shine, and Axivity AX3) at three locations (wrist, ankle, and femur) recorded steps and signal vector magnitudes (SVM) in 22 patients. They were years old, were equally distributed between the sexes, and had an ASA score of . Single protocoled activity events () were clinically categorized into 4 levels and correlated with the monitored signals. Additionally, and days after the operation, the EuroQol-5D and the Barthel-20 index supplemented this data. Results. All measurements at the wrist (Fitbit, Misfit) resulted in unacceptable accuracy; however, sensitivity and specificity reached around 90% using the Misfit at the ankle. Applying this combination, the correlation between real and measured steps ( = 0.99) and the category discrimination were statistically significant (). A discriminant analysis featured the calculation of four activity levels based on SVM measurements using the Axivity tracker at the femur. A cluster analysis showed a 100% agreement between the clinically observed and the calculated activity levels. The amount of active minutes or periods and both the EuroQol-5D and the Barthel-20 indices significantly increased between the analyzed time points after the operation. However, only the Barthel-20 was associated with the measured activity levels (). Conclusion. The Misfit and the Axivity trackers can reliably monitor activity in elderly patients after operative treatment of proximal femur fractures. However, the wear location is decisive. Objectively measured activity correlated with functional independence and quality of life.

Comparison of Research- and Consumer-grade Energy Expenditure Estimation Methods during 10 Days of Military Training (2018)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: Wearable physical activity monitoring devices have improved the ability to estimate free-living total energy expenditure (TEE) but their application during arduous military training alongside more well-established techniques has not been widely documented. This study aimed to assess the validity of two wrist-worn activity monitors to estimate TEE by evaluating performance against doubly labelled water (DLW) during British Army Officer Cadet (OC) training. METHODS: Twenty (10 male and 10 female) OCs (mean ± SD: age 22 ± 1 years, height 1.73 ± 0.08 m, body mass 77.0 ± 9.3 kg) wore one research-grade accelerometer (GENEActiv, Cambridge, UK; “RES”) on the dominant wrist and one commercially available (FITBIT SURGE, USA; “COM”) monitor on the non-dominant wrist for 10 days of training. Immediately prior to this 10-day period, participants consumed a bolus of DLW and provided daily urine samples, which were analysed by mass spectrometry to determine TEE. Bivariate correlations and limits of agreement were calculated to compare the 10-day mean TEE from DLW with both activity monitors to evaluate device performance. RESULTS: TEE (mean ± SD) from DLW, RES and COM were 17.2 ± 2.7 MJ.day-1 (4112 ± 652 kcal.day-1), 17.3 ± 2.8 MJ.day-1 (4129 ± 677 kcal.day-1) and 15.1 ± 3.7 MJ.day-1 (3607 ± 888 kcal.day-1), respectively. TEE from DLW was linearly correlated with both RES (r=0.786, p<0.001) and COM (r=0.888, p<0.001). Despite a stronger association with DLW however, COM tended to underestimate TEE (mean bias [95% CI]) by -2.1 [-5.6-1.4] MJ.day-1 (-505 [-1348-339] kcal.day-1; p<0.05). In contrast, mean TEE from RES was similar to DLW (-0.05 [-3.6-3.5] MJ.day-1; -11 [-867-845] kcal.day-1; p>0.05). CONCLUSION: Wearable physical activity monitors provides a cheaper and more practical method for estimating free-living TEE than DLW, and could be useful for military populations. However, this study suggests a consumer monitor may underperform, by underestimating TEE, during physically demanding training in comparison to a research- grade device.

Counting Steps in Activities of Daily Living in People With a Chronic Disease Using Nine Commercially Available Fitness Trackers: Cross-Sectional Validity Study. (2018)

JMIR mHealth and uHealth

Abstract

BACKGROUND Measuring physical activity with commercially available activity trackers is gaining popularity. People with a chronic disease can especially benefit from knowledge about their physical activity pattern in everyday life since sufficient physical activity can contribute to wellbeing and quality of life. However, no validity data are available for this population during activities of daily living. OBJECTIVE The aim of this study was to investigate the validity of 9 commercially available activity trackers for measuring step count during activities of daily living in people with a chronic disease receiving physiotherapy. METHODS The selected activity trackers were Accupedo (Corusen LLC), Activ8 (Remedy Distribution Ltd), Digi-Walker CW-700 (Yamax), Fitbit Flex (Fitbit inc), Lumoback (Lumo Bodytech), Moves (ProtoGeo Oy), Fitbit One (Fitbit inc), UP24 (Jawbone), and Walking Style X (Omron Healthcare Europe BV). In total, 130 persons with chronic diseases performed standardized activity protocols based on activities of daily living that were recorded on video camera and analyzed for step count (gold standard). The validity of the trackers' step count was assessed by correlation coefficients, t tests, scatterplots, and Bland-Altman plots. RESULTS The correlations between the number of steps counted by the activity trackers and the gold standard were low (range: -.02 to .33). For all activity trackers except for Fitbit One, a significant systematic difference with the gold standard was found for step count. Plots showed a wide range in scores for all activity trackers; Activ8 showed an average overestimation and the other 8 trackers showed underestimations. CONCLUSIONS This study showed that the validity of 9 commercially available activity trackers is low measuring steps while individuals with chronic diseases receiving physiotherapy engage in activities of daily living.

The Wearable Technological Device as a Means of Physical Activity Monitoring (2018)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: To assess the use of a wearable technological device for the increase in attainment of physical activity (PA) with the goal of preventing Type II Diabetes Mellitus (T2DM) through weight loss. The Fitbit offers an advantage to other activity trackers when used for study purposes, in that it can provide continuous measurement of PA across the entire study period. METHODS: Men and women over the age of 40, and at risk for prediabetes, were recruited. The study period was four months in length, with an initial four weeks of baseline PA testing, followed by 12 weeks of lifestyle intervention. Individualized PA goals were set. PA monitoring was very successful. Participants averaged 72 days of PA tracking with 46% of participants reaching the maximal number of days (77). The Fitbit Flex measures steps, minutes sedentary, minutes lightly active, minutes fairly active, minutes very active, and total active minutes. RESULTS: Participants (N = 13) were aged 65.03 (SD = 8.3) years. At baseline, participants were performing 220.8 (SD = 249.0) minutes per week of moderate intensity PA. Participants increased moderate intensity PA to 243.3 (SD = 198.8) minutes per week. At baseline, participants were accumulating 7511.6 steps/day (SD = 3271.2) increasing to 8177.6 steps/day (SD = 3078.9) taken during the three month intervention. No statistical significance was found. A Pearson CC (0.598) showed there was a positive trend with minutes of moderate-to-vigorous activity and weight loss. 36% of the variants of weight loss was influenced by minutes of PA. Compliance to wearing the Fitbit was very good with 93.1% of the weeks having data tracked for at least ≥ 6 days/week. Wear time was corroborated by Fitbit data, which showed of the 1415 tracked days, only 9.3% days recorded were of less than 2000 steps. Barriers to technology were low and 100% of participants strongly agreed that continuous monitoring of weight and PA encouraged them to make healthy lifestyle changes. CONCLUSIONS: Of particular importance was that 46% of participants initially self-reported that they were physically active for more than 150 minutes per week, which shows many people underestimate their actual PA. Continuous monitoring of PA through wearable technology can be a useful modality aiding in weight loss.

Accuracy of Activity Trackers in Parkinson Disease: Should We Prescribe Them? (2018)

Physical Therapy

Abstract

Background Wearable, consumer-grade activity trackers have become widely available as a means of monitoring physical activity in the form of step counts. However, step counts may not be accurate in persons with Parkinson disease (PD) due to atypical gait characteristics. Objective This study aimed to investigate the accuracy of 4 consumer-grade activity trackers in individuals with PD while ambulating during continuous and discontinuous walking tasks. Design This study used a cross-sectional design. Methods Thirty-three persons with PD (Hoehn & Yahr stages 1–3) donned 4 models of activity trackers on the less affected side of their bodies. Participants performed 2 continuous walking tasks (2-minute walk tests at comfortable and fast speeds) and 2 discontinuous walking tasks (a simulated household course and an obstacle negotiation course) in an outpatient setting. Bland-Altman plots and intraclass correlation coefficients [ICC(2,1)] were computed as a measure of agreement between actual steps taken (reference standard: video recording) and steps recorded by each tracker. Results The accuracy of the activity trackers varied widely, with ICCs ranging from − 0.03 to 0.98. Overall, the most accurate device across all tasks was the Fitbit Zip, and the least accurate was the Jawbone Up Move during the simulated household course. All activity trackers were more accurate for continuous compared walking tasks compared with discontinuous walking tasks. Waist-mounted devices were more accurate then wrist-mounted devices with continuous tasks. Bland-Altman plots revealed that all activity trackers underestimated step counts. Limitations All walking tasks were measured over relatively short distances. Conclusion In persons with mild-to-moderate PD, waist-worn activity trackers may be prescribed to monitor bouts of continuous walking with reasonable accuracy; however, activity trackers have little utility in monitoring discontinuous walking common in household settings.

Evaluating the Validity of Current Mainstream Wearable Devices in Fitness Tracking Under Various Physical Activities: Comparative Study (2018)

JMIR Mhealth Uhealth

Abstract

Background: Wearable devices have attracted much attention from the market in recent years for their fitness monitoring and other health-related metrics; however, the accuracy of fitness tracking results still plays a major role in health promotion. Objective: The aim of this study was to evaluate the accuracy of a host of latest wearable devices in measuring fitness-related indicators under various seminatural activities. Methods: A total of 44 healthy subjects were recruited, and each subject was asked to simultaneously wear 6 devices (Apple Watch 2, Samsung Gear S3, Jawbone Up3, Fitbit Surge, Huawei Talk Band B3, and Xiaomi Mi Band 2) and 2 smartphone apps (Dongdong and Ledongli) to measure five major health indicators (heart rate, number of steps, distance, energy consumption, and sleep duration) under various activity states (resting, walking, running, cycling, and sleeping), which were then compared with the gold standard (manual measurements of the heart rate, number of steps, distance, and sleep, and energy consumption through oxygen consumption) and calculated to determine their respective mean absolute percentage errors (MAPEs). Results: Wearable devices had a rather high measurement accuracy with respect to heart rate, number of steps, distance, and sleep duration, with a MAPE of approximately 0.10, whereas poor measurement accuracy was observed for energy consumption (calories), indicated by a MAPE of up to 0.44. The measurements varied for the same indicator measured by different fitness trackers. The variation in measurement of the number of steps was the highest (Apple Watch 2: 0.42; Dongdong: 0.01), whereas it was the lowest for heart rate (Samsung Gear S3: 0.34; Xiaomi Mi Band 2: 0.12). Measurements differed insignificantly for the same indicator measured under different states of activity; the MAPE of distance and energy measurements were in the range of 0.08 to 0.17 and 0.41 to 0.48, respectively. Overall, the Samsung Gear S3 performed the best for the measurement of heart rate under the resting state (MAPE of 0.04), whereas Dongdong performed the best for the measurement of the number of steps under the walking state (MAPE of 0.01). Fitbit Surge performed the best for distance measurement under the cycling state (MAPE of 0.04), and Huawei Talk Band B3 performed the best for energy consumption measurement under the walking state (MAPE of 0.17). Conclusions: At present, mainstream devices are able to reliably measure heart rate, number of steps, distance, and sleep duration, which can be used as effective health evaluation indicators, but the measurement accuracy of energy consumption is still inadequate. Fitness trackers of different brands vary with regard to measurement of indicators and are all affected by the activity state, which indicates that manufacturers of fitness trackers need to improve their algorithms for different activity states. [JMIR Mhealth Uhealth 2018;6(4):e94]

Accuracy of PurePulse photoplethysmography technology of Fitbit Charge 2 for assessment of heart rate during sleep (2019)

Chronobiology International

Abstract

Elevated asleep heart rate (HR) is a risk factor for cardiovascular disease and other-cause morbidity and mortality. We assessed the accuracy of Fitbit Inc. PurePulse{\textregistered} photoplethysmography with reference to three-lead electrocardiography (ECG) in determining HR during sleep. HR of 35 (17 female) healthy adults 25.1 ± 10.6 years of age (mean ± SD) was continuously recorded throughout a single night of sleep. There was no significant difference in asleep HR mean (0.09 beats per minute [bpm], P = 0.426) between Fitbit photoplethysmography and ECG; plus, there was excellent intraclass correlation (0.998) and narrow Bland–Altman agreement range (2.67 bpm). The regression analysis of Bland–Altman plot of mean asleep HR indicates Fitbit tends to slightly overestimate reference values in the lower range of HR (HR {\textless} 50 bpm) by 0.51 bpm and slightly underestimate reference values in the higher range of HR (HR {\textgreater} 80 bpm) by 0.63 bpm. Mixed model analysis of epoch-by-epoch (5-min epochs) asleep HR showed significant “U” shape trend (P {\textless} 0.001) in amount of Fitbit error (absolute amount of difference between ECG and Fitbit values regardless of overestimation or underestimation) in regard to HR, i.e. smaller error in the medium range of HR (60–80 bpm) and slightly larger error for lower ({\textless}60 bpm) and higher ({\textgreater}80 bpm) ranges of HR. However, effect of age, body mass index, gender, and subjective sleep quality measured by Pittsburgh sleep quality index (good/poor sleepers) on error in estimating HR by the Fitbit method was not significant. It is concluded that Fitbit photoplethysmography suitably tracks HR during sleep in healthy young adults.

Validation of Fitbit Charge 2 and Fitbit Alta HR Against Polysomnography for Assessing Sleep in Adults With Obstructive Sleep Apnea (2019)

Journal of Clinical Sleep Medicine

Abstract

Study Objectives: Consumer wearable devices may be a helpful method of assessing sleep, but validation is required for their use in clinical practice. Our aim was to validate two models of Fitbit sleep trackers that rely on both accelerometer and heart rate sensors against polysomnography in participants with obstructive sleep apnea (OSA). Methods: Participants were adults presenting with symptoms of OSA and attending our outpatient sleep clinic. A polysomnography (PSG) was applied to all participants at the same time they were wearing a Fitbit sleep tracker. Using paired t tests and Bland-Altman plots, we compared the sleep measures provided by the wearable devices with those obtained by PSG. Since Fitbit devices' automatic detection of sleep start time can cause bias, we performed a correction using Huber loss function-based linear regression and a leave-one-out strategy. Results: Our sample consisted of 65 patients. Diagnosis of OSA was confirmed on 55 (84.6{\%}). There were statistically significant differences between PSG and Fitbit measures for all sleep outcomes but rapid eye movement sleep. Fitbit devices overestimated total sleep time, and underestimated wake after sleep onset and sleep onset latency. After correction of bias, Fitbit-delivered measures of sleep onset latency did not significantly differ of those provided by PSG. Conclusions: Fitbit wearable devices showed an acceptable sensitivity but poor specificity. Consumer sleep trackers still have insufficient accuracy for clinical settings, especially in clinical populations. Solving technical issues and optimizing clinically-oriented features couldmake themapt for their use in clinical practice in a nondistant future.

Validity Evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in Free-Living Environments in an Older Adult Cohort. (2019)

JMIR mHealth and uHealth

Abstract

BACKGROUND Few studies have investigated the validity of mainstream wrist-based activity trackers in healthy older adults in real life, as opposed to laboratory settings. OBJECTIVE This study explored the performance of two wrist-worn trackers (Fitbit Charge 2 and Garmin vivosmart HR+) in estimating steps, energy expenditure, moderate-to-vigorous physical activity (MVPA) levels, and sleep parameters (total sleep time [TST] and wake after sleep onset [WASO]) against gold-standard technologies in a cohort of healthy older adults in a free-living environment. METHODS Overall, 20 participants ({\textgreater}65 years) took part in the study. The devices were worn by the participants for 24 hours, and the results were compared against validated technology (ActiGraph and New-Lifestyles NL-2000i). Mean error, mean percentage error (MPE), mean absolute percentage error (MAPE), intraclass correlation (ICC), and Bland-Altman plots were computed for all the parameters considered. RESULTS For step counting, all trackers were highly correlated with one another (ICCs{\textgreater}0.89). Although the Fitbit tended to overcount steps (MPE=12.36{\%}), the Garmin and ActiGraph undercounted (MPE 9.36{\%} and 11.53{\%}, respectively). The Garmin had poor ICC values when energy expenditure was compared against the criterion. The Fitbit had moderate-to-good ICCs in comparison to the other activity trackers, and showed the best results (MAPE=12.25{\%}), although it underestimated calories burned. For MVPA levels estimation, the wristband trackers were highly correlated (ICC=0.96); however, they were moderately correlated against the criterion and they overestimated MVPA activity minutes. For the sleep parameters, the ICCs were poor for all cases, except when comparing the Fitbit with the criterion, which showed moderate agreement. The TST was slightly overestimated with the Fitbit, although it provided good results with an average MAPE equal to 10.13{\%}. Conversely, WASO estimation was poorer and was overestimated by the Fitbit but underestimated by the Garmin. Again, the Fitbit was the most accurate, with an average MAPE of 49.7{\%}. CONCLUSIONS The tested well-known devices could be adopted to estimate steps, energy expenditure, and sleep duration with an acceptable level of accuracy in the population of interest, although clinicians should be cautious in considering other parameters for clinical and research purposes.

Accuracy of fitbit wristbands in measuring sleep stage transitions and the effect of user-specific factors (2019)

Journal of Medical Internet Research

Abstract

Background: It has become possible for the new generation of consumer wristbands to classify sleep stages based on multisensory data. Several studies have validated the accuracy of one of the latest models, that is, Fitbit Charge 2, in measuring polysomnographic parameters, including total sleep time, wake time, sleep efficiency (SE), and the ratio of each sleep stage. Nevertheless, its accuracy in measuring sleep stage transitions remains unknown. Objective: This study aimed to examine the accuracy of Fitbit Charge 2 in measuring transition probabilities among wake, light sleep, deep sleep, and rapid eye movement (REM) sleep under free-living conditions. The secondary goal was to investigate the effect of user-specific factors, including demographic information and sleep pattern on measurement accuracy. Methods: A Fitbit Charge 2 and a medical device were used concurrently to measure a whole night's sleep in participants' homes. Sleep stage transition probabilities were derived from sleep hypnograms. Measurement errors were obtained by comparing the data obtained by Fitbit with those obtained by the medical device. Paired 2-tailed t test and Bland-Altman plots were used to examine the agreement of Fitbit to the medical device. Wilcoxon signed-rank test was performed to investigate the effect of user-specific factors. Results: Sleep data were collected from 23 participants. Sleep stage transition probabilities measured by Fitbit Charge 2 significantly deviated from those measured by the medical device, except for the transition probability from deep sleep to wake, from light sleep to REM sleep, and the probability of staying in REM sleep. Bland-Altman plots demonstrated that systematic bias ranged from 0{\%} to 60{\%}. Fitbit had the tendency of overestimating the probability of staying in a sleep stage while underestimating the probability of transiting to another stage. SE{\textgreater}90{\%} (P=.047) was associated with significant increase in measurement error. Pittsburgh sleep quality index (PSQI){\textless}5 and wake after sleep onset (WASO){\textless}30 min could be associated to significantly decreased or increased errors, depending on the outcome sleep metrics. Conclusions: Our analysis shows that Fitbit Charge 2 underestimated sleep stage transition dynamics compared with the medical device. Device accuracy may be significantly affected by perceived sleep quality (PSQI), WASO, and SE.

Reliability of Zephyr Bioharness and Fitbit Charge Measures of Heart Rate and Activity at Rest, During the Modified Canadian Aerobic Fitness Test, and Recovery (2019)

Journal of Strength and Conditioning Research

Abstract

The purpose of this study was to determine the intrasession and intersession reliability of Zephyr Bioharness (ZB) and Fitbit Charge variables in both healthy men and women at rest, during the Modified Canadian Aerobic Fitness Test (mCAFT), and throughout recovery. Stratified convenience and snowball sampling were used to recruit 60 participants (30 women, 48 ± 15 years) and (30 men, 48 ± 15 years) from McMaster University student, staff, and faculty population. At rest, intrasession average heart rate (b{\textperiodcentered}min -1 ). Intraclass correlation coefficients (ICCs) and Standard Error of Measurement [SEM] for Zephyr ranged from (0.94-0.97) [1.17-1.70] to (0.92-0.97) [1.45-2.10] for Fitbit Charge. During the mCAFT, the Zephyr ICCs and (SEM) ranged from 0.31-0.99 (1.28-8.10) to 0.45-0.99 (1.45-8.71) for the Fitbit Charge. Throughout the recovery, the ICCs and (SEM) ranged from 0.44-0.98 (1.26-10.47) to 0.45-0.98 (1.15-11.90) for Zephyr and Fitbit devices, respectively. At rest, intersession ICCs (SEM) for Zephyr and Fitbit ranged from 0.90-0.94 (1.73-2.37) to 0.88-0.94 (1.83-2.67), respectively. At mCAFT, the Zephyr ICCs (SEM) ranged from 0.91-0.97 (3.12-4.64) to 0.85-0.98 (3.28-4.88) for the Fitbit. Throughout the recovery, the ICCs (SEM) ranged from 0.93-0.97 (2.65-4.66) to 0.76-0.91 (3.17-4.67) for Zephyr and Fitbit devices, respectively. To conclude, both the ZB and Fitbit Charge devices demonstrated excellent reliability measures throughout the 3 phases. The findings from our study add to the existing pool of literature regarding the reliability parameters of wearable devices and suggest that stable and consistent measures of heart rate and physical activity can be obtained using ZB and Fitbit Charge devices among healthy male and female participants at rest, during a standardized submaximal fitness test (mCAFT), and throughout recovery.

Concurrent validity of the Fitbit for assessing sedentary behavior and moderate-to-vigorous physical activity (2019)

BMC Medical Research Methodology

Abstract

Background: Recent advances in sensor technologies have promoted the use of consumer-based accelerometers such as Fitbit Flex in epidemiological and clinical research; however, the validity of the Fitbit Flex in measuring sedentary behavior (SED) and physical activity (PA) has not been fully determined against previously validated research-grade accelerometers such as ActiGraph GT3X+. Therefore, the purpose of this study was to examine the concurrent validity of the Fitbit Flex against ActiGraph GT3X+ in a free-living condition. Methods: A total of 65 participants (age: M = 42, SD = 14 years, female: 72{\%}) each wore a Fitbit Flex and GT3X+ for seven consecutive days. After excluding sleep and non-wear time, time spent (min/day) in SED and moderate-to-vigorous PA (MVPA) were estimated using various cut-points for GT3X+ and brand-specific algorithms for Fitbit, respectively. Repeated measures one-way ANOVA and mean absolute percent errors (MAPE) served to examine differences and measurement errors in SED and MVPA estimates between Fitbit Flex and GT3X+, respectively. Pearson and Spearman correlations and Bland-Altman (BA) plots were used to evaluate the association and potential systematic bias between Fitbit Flex and GT3X+. PROC MIXED procedure in SAS was used to examine the equivalence (i.e., the 90{\%} confidence interval with ±10{\%} equivalence zone) between the devices. Results: Fitbit Flex produced similar SED and low MAPE (mean difference [MD] = 37 min/day, P =.21, MAPE = 6.8{\%}), but significantly higher MVPA and relatively large MAPE (MD = 59-77 min/day, P {\textless}.0001, MAPE = 56.6-74.3{\%}) compared with the estimates from GT3X+ using three different cut-points. The correlations between Fitbit Flex and GT3X+ were consistently higher for SED (r = 0.90, $\rho$ = 0.86, P {\textless}.01), but weaker for MVPA (r = 0.65-0.76, $\rho$ = 0.69-0.79, P {\textless}.01). BA plots revealed that there is no apparent bias in estimating SED. Conclusion: In comparison with the GT3X+ accelerometer, the Fitbit Flex provided comparatively accurate estimates of SED, but the Fitbit Flex overestimated MVPA under free-living conditions. Future investigations using the Fitbit Flex should be aware of present findings.

Validity of Cardiorespiratory Fitness Measured with Fitbit Compared to V˙O2max (2019)

Medicine and science in sports and exercise

Abstract

PURPOSE: Cardiorespiratory fitness (CRF), broadly defined as the body's ability to utilize oxygen, is a well-established prognostic marker of health, but it is not routinely measured. This may be due to the difficulty of acquiring high-quality CRF measures. The purpose of this study was to independently determine the validity of the Fitbit Charge 2's measure of CRF (Fitbit CRF). METHODS: Sixty-five healthy adults between the ages of 18 and 45 yr (55{\%} female, 45{\%} male) were recruited to undergo gold standard V˙O2max testing and wear a Fitbit Charge 2 continuously for 1 wk during which they were instructed to complete a qualifying outdoor run to derive the Fitbit CRF (units: mL{\textperiodcentered}kg{\textperiodcentered}min). This measure was compared with V˙O2max measures (units: mL{\textperiodcentered}kg{\textperiodcentered}min) epoched at 15 and 60 s. RESULTS: Bland-Altman analyses revealed that Fitbit CRF had a positive bias of 1.59 mL{\textperiodcentered}kg{\textperiodcentered}min compared with laboratory data epoched at 15 s and 0.30 mL{\textperiodcentered}kg{\textperiodcentered}min compared with data epoched at 60 s (n = 60). F statistics (2.09; 0.08) and P values (0.133; 0.926) from Bradley-Blackwood tests for the concordance of Fitbit CRF with 15- and 60-s laboratory data, respectively, support the null hypothesis of equal means and variances, indicating there is concordance between the two measures. Mean absolute percentage error was less than 10{\%} for each comparison. CONCLUSIONS: The Fitbit Charge 2 provides an acceptable level of validity when measuring CRF in young, healthy, and fit adults who are able to run. Further research is required to determine if it is a potentially useful tool in clinical practice and epidemiological research to quantify, categorize, and longitudinally track risk for adverse outcomes.

Validating Fitbit for Evaluation of Physical Activity in Patients with Knee Osteoarthritis: Do Thresholds Matter? (2019)

ACR Open Rheumatology

Abstract

Objective: We sought to evaluate the performance of Fitbit in estimating ActiGraph-derived moderate-to-vigorous physical activity (MVPA) and sedentary time in the knee osteoarthritis (OA) population. Methods: We used data from two weeks of Fitbit and ActiGraph wear among knee OA subjects. In primary analyses, we used literature-based ActiGraph thresholds of 200 and 1924 counts/min (triaxial vector magnitude) for sedentary and MVPA time as the gold standard to which we compared three sets of Fitbit thresholds informed by literature and data (Youden index). We also considered personalized, stride length-based Fitbit thresholds. In sensitivity analyses, we used uniaxial, vertical axis-based as well as personalized, BMI-based ActiGraph thresholds. We calculated agreement, sensitivity, and specificity of Fitbit in classifying sedentary and MVPA time. Results: In the primary analysis (vector magnitude thresholds), maximum agreement for sedentary and MVPA time was 67.0{\%} from the Youden index-based and 91.1{\%} from the stride length-based Fitbit thresholds. For sedentary time, the 20 strides/min threshold had the highest sensitivity (97.6{\%}), and Youden-derived 1 stride/min had the highest specificity (51.6{\%}). For MVPA, Youden-derived 14 strides/min yielded 72.8{\%} sensitivity, and using stride length yielded 98.6{\%} specificity. MVPA time ranged from 49-323 min/d, depending on threshold used, with literature-based and personalized thresholds leading to more conservative estimates of MVPA than Youden-derived thresholds. Conclusion: Using Fitbit for MVPA and sedentary time assessment may lead to inaccurate estimates of both. Fitbit MVPA estimates were generally more conservative than ActiGraph estimates. Incorporating individuals' characteristics did not meaningfully improve Fitbit performance. Caution should be exercised when measuring activity using Fitbit.

Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages (2020)

Chronobiology International

Abstract

We compared performance in deriving sleep variables by both Fitbit Charge 2™, which couples body movement (accelerometry) and heart rate variability (HRV) in combination with its proprietary interpretative algorithm (IA), and standard actigraphy (Motionlogger{\textregistered} Micro Watch Actigraph: MMWA), which relies solely on accelerometry in combination with its best performing ‘Sadeh' IA, to electroencephalography (EEG: Zmachine{\textregistered} Insight+ and its proprietary IA) used as reference. We conducted home sleep studies on 35 healthy adults, 33 of whom provided complete datasets of the three simultaneously assessed technologies. Relative to the Zmachine EEG method, Fitbit showed an overall Kappa agreement of 54{\%} in distinguishing wake/sleep epochs and sensitivity of 95{\%} and specificity of 57{\%} in detecting sleep epochs. Fitbit, relative to EEG, underestimated sleep onset latency (SOL) by {\~{}}11 min and overestimated sleep efficiency (SE) by {\~{}}4{\%}. There was no statistically significant difference between Fitbit and EEG methods in measuring wake after sleep onset (WASO) and total sleep time (TST). Fitbit showed substantial agreement with EEG in detecting rapid eye movement and deep sleep, but only moderate agreement in detecting light sleep. The MMWA method showed 51{\%} overall Kappa agreement with the EEG one in detecting wake/sleep epochs, with sensitivity of 94{\%} and specificity of 53{\%} in detecting sleep epochs. MMWA, relative to EEG, underestimated SOL by {\~{}}10 min. There was no significant difference between Fitbit and MMWA methods in amount of bias in estimating SOL, WASO, TST, and SE; however, the minimum detectable change (MDC) per sleep variable with Fitbit was better (smaller) than with MMWA, respectively, by {\~{}}10 min, {\~{}}16 min, {\~{}}22 min, and {\~{}}8{\%}. Overall, performance of Fitbit accelerometry and HRV technology in conjunction with its proprietary IA to detect sleep vs. wake episodes is slightly better than wrist actigraphy that relies solely on accelerometry and best performing Sadeh IA. Moreover, the smaller MDC of Fitbit technology in deriving sleep parameters in comparison to wrist actigraphy makes it a suitable option for assessing changes in sleep quality over time, longitudinally, and/or in response to interventions.

Minimal Detectable Change Thresholds and Responsiveness of Zephyr Bioharness and Fitbit Charge Devices (2020)

Journal of Strength and Conditioning Research

Abstract

Nazari, G and MacDermid, JC. Minimal detectable change thresholds and responsiveness of zephyr bioharness and Fitbit Charge devices. J Strength Cond Res 34(1): 257-263, 2020-The aim of the current study was to define the minimal detectable change (MDC) thresholds for Zephyr Bioharness (ZB) and Fitbit Charge Heart Rate (FC-HR) variables and to establish the internal responsiveness of ZB and FC-HR devices in terms of their HR, respiratory rate variables, and number of steps taken variables, in a healthy sample of men and women at resting, submaximal activity and throughout recovery phases. A total of sixty participants (30 women, 48 ± 15) and (30 men, 48 ± 15) were recruited using stratified convenience and snowball sampling approaches from our university student, staff, and faculty population. At rest, MDC90 values of ±4.89 and ±5.15 were noted for ZB and FC-HR devices, respectively. Throughout the recovery, thresholds of ±8.14 for ZB and 8.74 for FC-HR were reported. During the submaximal phases, scores of ±8.18 and ±9.35 were established for ZB and FC-HR, respectively. From a resting to a submaximal state, the standardized response means (SRMs) for ZB and FC-HR variables exhibited large responsiveness of SRMZB-HR = 4.42; SRMFC-HR = 4.04. In addition, the ZB respiratory rate variable showed the largest effect size of SRMZB-RR = 2.55. Similarly, the large effect size of SRMFC-ST = 1.76 was reported for the FC-HR steps taken variable. From a submaximal to a recovery state, the SRMs for ZB and FC-HR variables demonstrated effect sizes of SRMZB-HR = -3.10; SRMFC-HR = -3.12. Furthermore, the ZB respiratory rate variable displayed similar internal responsiveness measures of SRMZB-RR = -2.45. To conclude, Similar MDC HR thresholds can be expected using the ZB and FC-HR devices and that the two devices demonstrated large internal responsiveness in our healthy male and female cohort when transitioning from resting to submaximal states and vice-versa. Our findings suggest that the ZB and FC-HR devices can be used in monitoring performance improvements or deterioration over time and would allow qualified personnel in assessing the effectiveness of exercise programs in healthy individuals.

Performance of Fitbit Charge 3 against polysomnography in measuring sleep in adolescent boys and girls (2021)

Chronobiology International

Abstract

We evaluated the performance of Fitbit Charge 3™ (FC3), a multi-sensor commercial sleep-tracker, for measuring sleep in adolescents against gold-standard laboratory polysomnography (PSG). Single-night PSG and FC3 sleep outcomes were compared in thirty-nine adolescents (22 girls; 16–19 years), 12 of whom presented with clinical/subclinical DSM-5 insomnia symptoms (7 girls). Discrepancy analysis, Bland–Altman plots, and epoch-by-epoch analyses were used to evaluate FC3 performance. The influence of several factors potentially affecting FC3 performance (e.g., sex, age, body mass index, firmware version, and magnitude of heart rate changes between consecutive PSG epochs) was also tested. In the sample of healthy adolescents, FC3 systematically underestimated PSG total sleep time by about 11 min and sleep efficiency by 2.5%, and overestimated wake after sleep onset by 9 min. Proportional biases were detected for “light” and “deep” sleep duration, resulting in significant underestimation of these parameters for those participants having longer PSG N1+ N2 and N3 durations, respectively. No significant systematic bias was detected for sleep efficiency and sleep onset latency. Epoch-by-epoch analysis showed sleep-stage sensitivity (average proportion of PSG epochs correctly classified by the device for a given sleep stage) of 68% for wake, 78% for “light” sleep, 59% for “deep” sleep, and 69% for rapid eye movement (REM) sleep in healthy sleepers. Similar results were found in the sample of adolescents with insomnia symptoms. Body mass index was positively associated with FC3-PSG discrepancies in wake after sleep onset (R2 = .16, p = .048). The magnitude of the heart rate acceleration/deceleration between consecutive PSG epochs was an important factor affecting FC3 classifications of sleep stages. Our results are in line with a general trend in the literature, suggesting better performance for the recently introduced multi-sensor devices compared to motion-only devices, although further developments are needed to improve accuracy in sleep stage classification and wake detection. Further insight is needed to determine factors potentially affecting device performance, such as accuracy and reliability (consistency of performance over time), in different samples and conditions.

A standardized framework for testing the performance of sleep-tracking technology: step-by-step guidelines and open-source code (2021)

Sleep

Abstract

Sleep-tracking devices, particularly within the consumer sleep technology (CST) space, are increasingly used in both research and clinical settings, providing new opportunities for large-scale data collection in highly ecological conditions. Due to the fast pace of the CST industry combined with the lack of a standardized framework to evaluate the performance of sleep trackers, their accuracy and reliability in measuring sleep remains largely unknown. Here, we provide a step-by-step analytical framework for evaluating the performance of sleep trackers (including standard actigraphy), as compared with gold-standard polysomnography (PSG) or other reference methods. The analytical guidelines are based on recent recommendations for evaluating and using CST from our group and others (de Zambotti and colleagues; Depner and colleagues), and include raw data organization as well as critical analytical procedures, including discrepancy analysis, Bland-Altman plots, and epoch-by-epoch analysis. Analytical steps are accompanied by open-source R functions (depicted at https://sri-human-sleep.github.io/sleep-trackers-performance/AnalyticalPipeline_v1.0.0.html). In addition, an empirical sample dataset is used to describe and discuss the main outcomes of the proposed pipeline. The guidelines and the accompanying functions are aimed at standardizing the testing of CSTs performance, to not only increase the replicability of validation studies, but also to provide ready-to-use tools to researchers and clinicians. All in all, this work can help to increase the efficiency, interpretation, and quality of validation studies, and to improve the informed adoption of CST in research and clinical settings.

Measurement of Heart Rate Using the Polar OH1 and Fitbit Charge 3 Wearable Devices in Healthy Adults During Light, Moderate, Vigorous, and Sprint-Based Exercise: Validation Study (2021)

JMIR mHealth and uHealth

Abstract

Background: Accurate, continuous heart rate measurements are important for health assessment, physical activity, and sporting performance, and the integration of heart rate measurements into wearable devices has extended its accessibility. Although the use of photoplethysmography technology is not new, the available data relating to the validity of measurement are limited, and the range of activities being performed is often restricted to one exercise domain and/or limited intensities. Objective: The primary objective of this study was to assess the validity of the Polar OH1 and Fitbit Charge 3 devices for measuring heart rate during rest, light, moderate, vigorous, and sprint-type exercise. Methods: A total of 20 healthy adults (9 female; height: mean 1.73 [SD 0.1] m; body mass: mean 71.6 [SD 11.0] kg; and age: mean 40 [SD 10] years) volunteered and provided written informed consent to participate in the study consisting of 2 trials. Trial 1 was split into 3 components: 15-minute sedentary activities, 10-minute cycling on a bicycle ergometer, and incremental exercise test to exhaustion on a motorized treadmill (18-42 minutes). Trial 2 was split into 2 components: 4 × 15-second maximal sprints on a cycle ergometer and 4 × 30- to 50-m sprints on a nonmotorized resistance treadmill. Data from the 3 devices were time-aligned, and the validity of Polar OH1 and Fitbit Charge 3 was assessed against Polar H10 (criterion device). Validity was evaluated using the Bland and Altman analysis, Pearson moment correlation coefficient, and mean absolute percentage error. Results: Overall, there was a very good correlation between the Polar OH1 and Polar H10 devices (r=0.95), with a mean bias of −1 beats{\textperiodcentered}min-1 and limits of agreement of −20 to 19 beats{\textperiodcentered}min-1. The Fitbit Charge 3 device underestimated heart rate by 7 beats{\textperiodcentered}min-1 compared with Polar H10, with a limit of agreement of −46 to 33 beats{\textperiodcentered}min-1 and poor correlation (r=0.8). The mean absolute percentage error for both devices was deemed acceptable (<5%). Polar OH1 performed well across each phase of trial 1; however, validity was worse for trial 2 activities. Fitbit Charge 3 performed well only during rest and nonsprint-based treadmill activities. Conclusions: Compared with our criterion device, Polar OH1 was accurate at assessing heart rate, but the accuracy of Fitbit Charge 3 was generally poor. Polar OH1 performed worse during trial 2 compared with the activities in trial 1, and the validity of the Fitbit Charge 3 device was particularly poor during our cycling exercises.

Comparison of free-living physical activity data obtained from a Fitbit Zip, the Apple iPhone Health app and a modified Bouchard Activity Record (2021)

Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals

Abstract

Issue addressed: Physical activity tracking devices have potential to improve public health, but their data needs to be reliable. No study has compared movement data between the Fitbit Zip, Apple iPhone Health app and physical activity records in a community setting over 10 days. Methods: University students aged 18+ years wore both a Fitbit Zip and an iPhone at/near their right waist and completed a modified Bouchard Activity Record (BAR) for 10 days in a free-living setting. Comparisons were made between the Fitbit Zip and iPhone for the number of steps and the distance travelled and between the Fitbit Zip and BAR for the minutes of activity in three different intensities. Results: Eighteen students provided sufficient data for inclusion. There were strong correlations between steps per day (r =.87) and distance travelled (r =.88) between the Fitbit Zips and iPhones. However, the Fitbit Zip measured significantly more steps per day (mean 8437 vs 7303; P ≤.001) and greater distances (mean 5.9 vs 4.9; P ≤.001) than the iPhone. Correlations between the Fitbit Zips and the BARs were moderate for minutes of total (r =.51) and light (r =.40) activity and weak for moderate/fairly active (r =.20) and vigorous/very active (r =.25). Conclusions: There were strong correlations between the physical activity data measured by Fitbit Zips and iPhones, but the iPhone Health app significantly underestimated the number of steps per day taken and the distance travelled when compared to the Fitbit Zip. So what?: Understanding the comparability of accelerometer devices provides useful information for future pragmatic physical activity measurement.

The relationship between sleep and physical activity in an in-patient rehabilitation stroke setting: a cross-sectional study (2021)

Topics in stroke rehabilitation

Abstract

Background: Appropriate sleep and physical activity are known to be important for positive neuroplastic changes in the brain and therefore may affect stroke recovery. Objective: To investigate the relationship between sleep and participation in different intensity levels of physical activity; to investigate the convergent validity of a commercially available device (Fitbit Flex) in measuring sleep and physical activity in people with stroke. Methods: A cross-sectional observational study in people with stroke undergoing rehabilitation. Participants wore two accelerometers on their unaffected wrist for seven consecutive days and recorded a sleep log, the Pittsburgh Sleep Quality Index and Fatigue Assessment Scale. Any relationships between sleep and activity were assessed with linear regression. Pearson and intra-class correlation coefficients were used to assess the validity of the two accelerometers (Fitbit Flex against the validated GENEActiv). Results: Twenty-three patients with stroke were recruited. Twenty complete data sets were analyzed. Participants had approximately 9 hours of sleep and 13 hours of sedentary behavior per day, with 99 minutes spent on physical activity (16 min spent on moderate to vigorous activity). Time spent on sleep was significantly related to sedentary, light and moderate physical activity time (r = −.67,.22,.20). The ICC of the Fitbit Flex in measuring light physical activity was.884 p < .001 but was not correlated for other measures. Conclusions: More sleep was related to less sedentary behavior and more time spent on physical activity. Fitbit Flex was only valid for measuring light physical activity.

Validity of Wrist-Wearable Activity Devices for Estimating Physical Activity in Adolescents: Comparative Study (2021)

JMIR mHealth and uHealth

Abstract

Background: The rapid advancements in science and technology of wrist-wearable activity devices offer considerable potential for clinical applications. Self-monitoring of physical activity (PA) with activity devices is helpful to improve the PA levels of adolescents. However, knowing the accuracy of activity devices in adolescents is necessary to identify current levels of PA and assess the effectiveness of intervention programs designed to increase PA. Objective: The study aimed to determine the validity of the 11 commercially available wrist-wearable activity devices for monitoring total steps and total 24-hour total energy expenditure (TEE) in healthy adolescents under simulated free-living conditions. Methods: Nineteen (10 male and 9 female) participants aged 14 to 18 years performed a 24-hour activity cycle in a metabolic chamber. Each participant simultaneously wore 11 commercial wrist-wearable activity devices (Mi Band 2 [XiaoMi], B2 [Huawei], Bong 2s [Meizu], Amazfit [Huamei], Flex [Fitbit], UP3 [Jawbone], Shine 2 [Misfit], GOLiFE Care-X [GoYourLife], Pulse O2 [Withings], Vivofit [Garmin], and Loop [Polar Electro]) and one research-based triaxial accelerometer (GT3X+ [ActiGraph]). Criterion measures were total EE from the metabolic chamber (mcTEE) and total steps from the GT3X+ (AGsteps). Results: Pearson correlation coefficients r for 24-hour TEE ranged from.78 (Shine 2, Amazfit) to.96 (Loop) and for steps ranged from 0.20 (GOLiFE) to 0.57 (Vivofit). Mean absolute percent error (MAPE) for TEE ranged from 5.7% (Mi Band 2) to 26.4% (Amazfit) and for steps ranged from 14.2% (Bong 2s) to 27.6% (Loop). TEE estimates from the Mi Band 2, UP3, Vivofit, and Bong 2s were equivalent to mcTEE. Total steps from the Bong 2s were equivalent to AGsteps. Conclusions: Overall, the Bong 2s had the best accuracy for estimating TEE and total steps under simulated free-living conditions. Further research is needed to examine the validity of these devices in different types of physical activities under real-world conditions.

Using a consumer-based wearable activity tracker for physical activity goal setting and measuring steps in pregnant women with gestational diabetes mellitus: exploring acceptance and validity (2021)

BMC Pregnancy and Childbirth

Abstract

Background: Activity monitoring devices may be used to facilitate goal-setting, self-monitoring, and feedback towards a step-based physical activity (PA) goal. This study examined the performance of the wrist-worn Fitbit Charge 3™ (FC3) and sought opinions on walking and stepping-in-place from women with gestational diabetes (GDM). Methods: Participants completed six 2-min metronome-assisted over ground bouts that varied by cadence (67, 84, or 100 steps per minute) and mode (walking or stepping-in-place; N = 15), with the sequence randomized. Steps were estimated by FC3 and measured, in duplicate, by direct observation (hand-tally device, criterion). Equivalence testing by the two one-sided tests (TOST) method assessed agreement within ± 15%. Mean absolute percent error (MAPE) of steps were compared to 10%, the accuracy standard of the Consumer Technology Association (CTA)™. A subset (n = 10) completed a timed, 200-m self-paced walk to assess natural walking pace and cadence. All participants completed semi-structured interviews, which were transcribed and analyzed using descriptive and interpretive coding. Results: Mean age was 27.0 years (SD 4.2), prepregnancy BMI 29.4 kg/m2 (8.3), and gestational age 32.8 weeks (SD 2.6). The FC3 was equivalent to hand-tally for bouts of metronome-assisted walking and stepping-in-place at 84 and 100 steps per minute (i.e., P <.05), although walking at 100 steps per minute (P =.01) was no longer equivalent upon adjustment for multiple comparisons (i.e., at P <.007). The FC3 was equivalent to hand-tally during the 200-m walk (i.e., P <.001), in which mean pace was 68.2 m per minute (SD 10.7), or 2.5 miles per hour, and mean cadence 108.5 steps per minute (SD 6.5). For walking at 84 and 100 steps per minute, stepping-in-place at 100 steps per minute, and the 200-m walk, MAPE was within 10%, the accuracy standard of the CTA™. Interviews revealed motivation for PA, that stepping-in-place was an acceptable alternative to walking, and competing responsibilities made it difficult to find time for PA. Conclusions: The FC3 appears to be a valid step counter during the third trimester, particularly when walking or stepping-in-place at or close to women's preferred cadence.

Wrist-Worn Wearables for Monitoring Heart Rate and Energy Expenditure While Sitting or Performing Light-to-Vigorous Physical Activity: Validation Study (2020)

JMIR mHealth and uHealth

Abstract

Background: Physical activity reduces the incidences of noncommunicable diseases, obesity, and mortality, but an inactive lifestyle is becoming increasingly common. Innovative approaches to monitor and promote physical activity are warranted. While individual monitoring of physical activity aids in the design of effective interventions to enhance physical activity, a basic prerequisite is that the monitoring devices exhibit high validity. Objective: Our goal was to assess the validity of monitoring heart rate (HR) and energy expenditure (EE) while sitting or performing light-to-vigorous physical activity with 4 popular wrist-worn wearables (Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa). Methods: While wearing the 4 different wearables, 25 individuals performed 5 minutes each of sitting, walking, and running at different velocities (ie, 1.1 m/s, 1.9 m/s, 2.7 m/s, 3.6 m/s, and 4.1 m/s), as well as intermittent sprints. HR and EE were compared to common criterion measures: Polar-H7 chest belt for HR and indirect calorimetry for EE. Results: While monitoring HR at different exercise intensities, the standardized typical errors of the estimates were 0.09-0.62, 0.13-0.88, 0.62-1.24, and 0.47-1.94 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 0.9%-4.3%, 2.2%-6.7%, 2.9%-9.2%, and 4.1%-19.1%, respectively, for the 4 wearables. While monitoring EE at different exercise intensities, the standardized typical errors of the estimates were 0.34-1.84, 0.32-1.33, 0.46-4.86, and 0.41-1.65 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 13.5%-27.1%, 16.3%-28.0%, 15.9%-34.5%, and 8.0%-32.3%, respectively. Conclusions: The Apple Watch Series 4 provides the highest validity (ie, smallest error rates) when measuring HR while sitting or performing light-to-vigorous physical activity, followed by the Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, in that order. The Apple Watch Series 4 and Polar Vantage V are suitable for valid HR measurements at the intensities tested, but HR data provided by the Garmin Fenix 5 and Fitbit Versa should be interpreted with caution due to higher error rates at certain intensities. None of the 4 wrist-worn wearables should be employed to monitor EE at the intensities and durations tested.

Wrist-worn devices for the measurement of heart rate and energy expenditure: A validation study for the Apple Watch 6, Polar Vantage V and Fitbit Sense (2022)

European journal of sport science

Abstract

The purpose of this study was to investigate the accuracy of 3 recently released wrist-worn devices (Apple Watch 6, Polar Vantage V and Fitbit Sense) for heart rate and energy expenditure during various activities. The study population consisted of 60 young healthy individuals (30 men and 30 women; age: 24.9 ± 3.0 years, BMI: 23.1 ± 2.7 kg/m2). Heart rate and energy expenditure were measured using the Polar H10 and Metamax 3B, respectively (reference measures) as well as with the 3 wrist-worn devices during 5 different activities (sitting, walking, running, resistance exercises and cycling). The Apple Watch 6 displayed the highest level of accuracy for heart rate measurement with a coefficient of variation (CV) (%) of less than 5% for all 5 activities, whereas the Polar Vantage V and the Fitbit Sense presented various degrees of accuracy (from high to poor accuracy) dependent on the activity (CVs between 2.44-8.80% and 4.14-10.76%, respectively). As for energy expenditure, all 3 devices displayed poor accuracy for all 5 physical activities (CVs between 14.68-24.85% for Apple Watch 6, 16.54-25.78% for Polar Vantage V and 13.44-29.66% for Fitbit Sense). Results of the present study indicate that the Apple Watch 6 was the most accurate for measuring heart rate across all 5 activities, whereas variable levels of accuracy for heart rate measurement for the Polar Vantage V and the Fitbit Sense were observed depending on the activity. As for energy expenditure, all 3 devices showed poor accuracy during all activities. Highlights The Apple Watch 6 was the most accurate for measuring heart rate, whereas the Polar Vantage V and Fitbit Sense showed variable results dependent on the activity The Apple Watch 6, Polar Vantage V and Fitbit Sense showed poor accuracy for energy expenditure during 5 different physical activities Healthcare care professionals, athletes/coaches and the general population may want to proceed with caution on the clinical utility of energy expenditure of these devices during the implementation of an exercise training or nutritional programme.

Heart Rate Measurement Accuracy of Fitbit Charge 4 and Samsung Galaxy Watch Active2: Device Evaluation Study (2022)

JMIR formative research

Abstract

Background: Fitness trackers and smart watches are frequently used to collect data in longitudinal medical studies. They allow continuous recording in real-life settings, potentially revealing previously uncaptured variabilities of biophysiological parameters and diseases. Adequate device accuracy is a prerequisite for meaningful research. Objective: This study aims to assess the heart rate recording accuracy in two previously unvalidated devices: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Methods: Participants performed a study protocol comprising 5 resting and sedentary, 2 low-intensity, and 3 high-intensity exercise phases, lasting an average of 19 minutes 27 seconds. Participants wore two wearables simultaneously during all activities: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Reference heart rate data were recorded using a medically certified Holter electrocardiogram. The data of the reference and evaluated devices were synchronized and compared at 1-second intervals. The mean, mean absolute error, mean absolute percentage error, Lin concordance correlation coefficient, Pearson correlation coefficient, and Bland-Altman plots were analyzed. Results: A total of 23 healthy adults (mean age 24.2, SD 4.6 years) participated in our study. Overall, and across all activities, the Fitbit Charge 4 slightly underestimated the heart rate, whereas the Samsung Galaxy Watch Active2 overestimated it (-1.66 beats per minute [bpm]/3.84 bpm). The Fitbit Charge 4 achieved a lower mean absolute error during resting and sedentary activities (seated rest: 7.8 vs 9.4; typing: 8.1 vs 11.6; laying down [left]: 7.2 vs 9.4; laying down [back]: 6.0 vs 8.6; and walking slowly: 6.8 vs 7.7 bpm), whereas the Samsung Galaxy Watch Active2 performed better during and after low- and high-intensity activities (standing up: 12.3 vs 9.0; walking fast: 6.1 vs 5.8; stairs: 8.8 vs 6.9; squats: 15.7 vs 6.1; resting: 9.6 vs 5.6 bpm). Conclusions: Device accuracy varied with activity. Overall, both devices achieved a mean absolute percentage error of just <10%. Thus, they were considered to produce valid results based on the limits established by previous work in the field. Neither device reached sufficient accuracy during seated rest or keyboard typing. Thus, both devices may be eligible for use in respective studies; however, researchers should consider their individual study requirements.

HRV Monitoring Using Commercial Wearable Devices as a Health Indicator for Older Persons during the Pandemic (2022)

Sensors (Basel, Switzerland)

Abstract

Remote monitoring platforms based on advanced health sensors have the potential to become important tools during the COVID-19 pandemic, supporting the reduction in risks for affected populations such as the elderly. Current commercially available wearable devices still have limitations to deal with heart rate variability (HRV), an important health indicator of human aging. This study analyzes the role of a remote monitoring system designed to support health services to older people during the complete course of the COVID-19 pandemic in Brazil, since its beginning in Brazil in March 2020 until November 2021, based on HRV. Using different levels of analysis and data, we validated HRV parameters by comparing them with reference sensors and tools in HRV measurements. We compared the results obtained for the cardiac modulation data in time domain using samples of 10 elderly people's HRV data from Fitbit Inspire HR with the results provided by Kubios for the same population using a cardiac belt, with the data divided into train and test, where 75% of the data were used for training the models, with the remaining 25% as a test set for evaluating the final performance of the models. The results show that there is very little difference between the results obtained by the remote monitoring system compared with Kubios, indicating that the data obtained from these devices might provide accurate results in evaluating HRV in comparison with gold standard devices. We conclude that the application of the methods and techniques used and reported in this study are useful for the creation and validation of HRV indicators in time series obtained by means of wearable devices based on photoplethysmography sensors; therefore, they can be incorporated into remote monitoring processes as seen during the pandemic.

Commercial Devices Provide Estimates of Energy Balance with Varying Degrees of Validity in Free-Living Adults (2022)

The Journal of nutrition

Abstract

Background: The challenges of accurate estimation of energy intake (EI) are well-documented, with self-reported values 12%-20% below expected values. New approaches rely on gold-standard assessments of the other components of energy balance, energy expenditure (EE) and energy storage (ES), to estimate EI. Objectives: The purpose of this study was to evaluate the validity, repeatability, and measurement error of consumer devices when estimating energy balance in a free-living population. Methods: Twenty-four healthy adults (14 women, 10 men; mean ± SD age: 30.7 ± 8.2 y) completed two 14-d assessment periods, including assessments of EE and ES using gold-standard [doubly labeled water (DLW) and DXA] and commercial devices [Fitbit Alta HR activity monitor (Alta) and Fitbit Aria wireless body composition scale (Aria)], and of EI by dietician-administered recalls. Accuracy and validity were assessed using Spearman correlation, interclass correlation, mean absolute percentage error, and equivalency testing. We also applied linear measurement error modeling including error in gold-standard devices and within-subject repeated-measures design to calibrate consumer devices and quantify error. Results: There was moderate to strong agreement for EE between the Fitbit Alta and DLW at each time point (rs = 0.82 and 0.66 for Times 1 and 2, respectively). There was weak agreement for ES between the Fitbit Aria and DXA (rs = 0.15 and 0.49 for Times 1 and 2, respectively). Correlations between methods to assess EI ranged from weak to strong, with agreement between the DXA/DLW-calculated EI and dietary recalls being the highest (rs = 0.63 for Time 1 and 0.73 for Time 2). Only EE from the Fitbit Alta at Time 1 was equivalent to the DLW value using equivalency testing. Conclusions: Commercial devices provide estimates of energy balance in free-living adults with varying degrees of validity compared to gold-standard techniques. EE estimates were the most robust overall, whereas ES estimates were generally poor.

Continuous Monitoring of Vital Signs With Wearable Sensors During Daily Life Activities: Validation Study (2022)

JMIR formative research

Abstract

Background: Continuous telemonitoring of vital signs in a clinical or home setting may lead to improved knowledge of patients' baseline vital signs and earlier detection of patient deterioration, and it may also facilitate the migration of care toward home. Little is known about the performance of available wearable sensors, especially during daily life activities, although accurate technology is critical for clinical decision-making. Objective: The aim of this study is to assess the data availability, accuracy, and concurrent validity of vital sign data measured with wearable sensors in volunteers during various daily life activities in a simulated free-living environment. Methods: Volunteers were equipped with 4 wearable sensors (Everion placed on the left and right arms, VitalPatch, and Fitbit Charge 3) and 2 reference devices (Oxycon Mobile and iButton) to obtain continuous measurements of heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2), and temperature. Participants performed standardized activities, including resting, walking, metronome breathing, chores, stationary cycling, and recovery afterward. Data availability was measured as the percentage of missing data. Accuracy was evaluated by the median absolute percentage error (MAPE) and concurrent validity using the Bland-Altman plot with mean difference and 95% limits of agreement (LoA). Results: A total of 20 volunteers (median age 64 years, range 20-74 years) were included. Data availability was high for all vital signs measured by VitalPatch and for HR and temperature measured by Everion. Data availability for HR was the lowest for Fitbit (4807/13,680, 35.14% missing data points). For SpO2 measured by Everion, median percentages of missing data of up to 100% were noted. The overall accuracy of HR was high for all wearable sensors, except during walking. For RR, an overall MAPE of 8.6% was noted for VitalPatch and that of 18.9% for Everion, with a higher MAPE noted during physical activity (up to 27.1%) for both sensors. The accuracy of temperature was high for VitalPatch (MAPE up to 1.7%), and it decreased for Everion (MAPE from 6.3% to 9%). Bland-Altman analyses showed small mean differences of VitalPatch for HR (0.1 beats/min [bpm]), RR (−0.1 breaths/min), and temperature (0.5 °C). Everion and Fitbit underestimated HR up to 5.3 (LoA of −39.0 to 28.3) bpm and 11.4 (LoA of −53.8 to 30.9) bpm, respectively. Everion had a small mean difference with large LoA (−10.8 to 10.4 breaths/min) for RR, underestimated SpO2 (>1%), and overestimated temperature up to 2.9 °C. Conclusions: Data availability, accuracy, and concurrent validity of the studied wearable sensors varied and differed according to activity. In this study, the accuracy of all sensors decreased with physical activity. Of the tested sensors, VitalPatch was found to be the most accurate and valid for vital signs monitoring.

Comparison of free-living physical activity measurements between ActiGraph GT3X-BT and Fitbit Charge 3 in young people with haemophilia (2022)

Haemophilia : the official journal of the World Federation of Hemophilia

Abstract

Introduction: Measurement of physical activity (PA) using commercial activity trackers such as Fitbit devices has become increasingly popular, also for people with haemophilia (PWH). The accuracy of the Fitbit model Charge 3 has not yet been examined. Aims: To compare the Fitbit Charge 3 against the research-grade accelerometer ActiGraph GT3X-BT in measuring average daily steps and minutes spent in different PA intensities. Methods: Twenty-four young PWH wore a wrist-worn Fitbit Charge 3 and hip-worn ActiGraph GT3X-BT simultaneously for seven consecutive days in free-living conditions. Correlation of and differences between the devices for daily averages of PA parameters were assessed using Pearson's correlation coefficient and paired t-test, respectively. Agreement between devices was assessed using Bland-Altman plots. Results: Twenty participants (mean age 21.8) were included in the analyses. We found moderate to high correlations between Fitbit and ActiGraph measured daily averages for all PA variables, but statistically significant differences between devices for all variables except daily minutes of moderate PA. Fitbit overestimated average daily steps, minutes of light, vigorous and moderate-to-vigorous PA. Bland-Altman plots showed a measurement bias between devices for all parameters with increasing overestimation by the Fitbit for higher volumes of PA. Conclusion: The Fitbit Charge 3 overestimated steps and minutes of light, moderate and moderate-to-vigorous PA as compared to the ActiGraph GT3X-BT, and this bias increased with PA volume. The Fitbit should therefore be used with caution in research, and we advise users of the device to be cognizant of this overestimation.

Performance of a Multisensor Smart Ring to Evaluate Sleep: In-Lab and Home-Based Evaluation of Generalized and Personalized Algorithms (2022)

Sleep

Abstract

Study objectives: Wearable sleep technology has rapidly expanded across the consumer market due to advances in technology and increased interest in personalized sleep assessment to improve health and mental performance. We tested the performance of a novel device, the Happy Ring, alongside other commercial wearables (Actiwatch 2, Fitbit Charge 4, Whoop 3.0, Oura Ring V2), against in-lab polysomnography (PSG) and an at-home EEG-derived sleep monitoring device, the Dreem 2 Headband. Methods: 36 healthy adults with no diagnosed sleep disorders and no recent use of medications or substances known to affect sleep pattern were assessed across 77 nights. Subjects participated in a single night of in-lab PSG and 2 nights of at-home data collection. The Happy Ring includes sensors for skin conductance, movement, heart rate, and skin temperature. The Happy Ring utilized two machine-learning derived scoring algorithms: a "generalized" algorithm that applied broadly to all users, and a "personalized" algorithm that adapted to individual subjects' data. Epoch-by-epoch analyses compared the wearable devices to in-lab PSG and to at-home EEG Headband. Results: Compared to in-lab PSG, the "generalized" and "personalized" algorithms demonstrated good sensitivity (94% and 93%, respectively) and specificity (70% and 83%, respectively). The Happy Personalized model demonstrated a lower bias and more narrow limits of agreement across Bland-Altman measures. Conclusion: The Happy Ring performed well at home and in the lab, especially regarding sleep/wake detection. The personalized algorithm demonstrated improved detection accuracy over the generalized approach and other devices, suggesting that adaptable, dynamic algorithms can enhance sleep detection accuracy.

A catalog of validity indices for step counting wearable technologies during treadmill walking: the CADENCE-adults study (2022)

The international journal of behavioral nutrition and physical activity

Abstract

Background: Standardized validation indices (i.e., accuracy, bias, and precision) provide a comprehensive comparison of step counting wearable technologies. Purpose: To expand a previously published child/youth catalog of validity indices to include adults (21-40, 41-60 and 61-85 years of age) assessed across a range of treadmill speeds (slow [0.8-3.2 km/h], normal [4.0-6.4 km/h], fast [7.2-8.0 km/h]) and device wear locations (ankle, thigh, waist, and wrist). Methods: Two hundred fifty-eight adults (52.5 ± 18.7 years, 49.6% female) participated in this laboratory-based study and performed a series of 5-min treadmill bouts while wearing multiple devices; 21 devices in total were evaluated over the course of this multi-year cross-sectional study (2015-2019). The criterion measure was directly observed steps. Computed validity indices included accuracy (mean absolute percentage error, MAPE), bias (mean percentage error, MPE), and precision (correlation coefficient, r; standard deviation, SD; coefficient of variation, CoV). Results: Over the range of normal speeds, 15 devices (Actical, waist-worn ActiGraph GT9X, activPAL, Apple Watch Series 1, Fitbit Ionic, Fitbit One, Fitbit Zip, Garmin vivoactive 3, Garmin vivofit 3, waist-worn GENEActiv, NL-1000, PiezoRx, Samsung Gear Fit2, Samsung Gear Fit2 Pro, and StepWatch) performed at < 5% MAPE. The wrist-worn ActiGraph GT9X displayed the worst accuracy across normal speeds (MAPE = 52%). On average, accuracy was compromised across slow walking speeds for all wearable technologies (MAPE = 40%) while all performed best across normal speeds (MAPE = 7%). When analyzing the data by wear locations, the ankle and thigh demonstrated the best accuracy (both MAPE = 1%), followed by the waist (3%) and the wrist (15%) across normal speeds. There were significant effects of speed, wear location, and age group on accuracy and bias (both p < 0.001) and precision (p ≤ 0.045). Conclusions: Standardized validation indices cataloged by speed, wear location, and age group across the adult lifespan facilitate selecting, evaluating, or comparing performance of step counting wearable technologies. Speed, wear location, and age displayed a significant effect on accuracy, bias, and precision. Overall, reduced performance was associated with very slow walking speeds (0.8 to 3.2 km/h). Ankle- and thigh-located devices logged the highest accuracy, while those located at the wrist reported the worst accuracy.

Step-count outcomes of 13 different activity trackers: Results from laboratory and free-living experiments (2022)

Gait \& Posture

Abstract

BACKGROUND: Many activity trackers have been developed, but steps can still be inconsistent from one monitor to another. RESEARCH QUESTION: What are the differences and associations between the steps of 13 selected consumer-based and research-grade wearable devices during 1 standardized day in a metabolic chamber and 15-day free-living trials? METHODS: In total, 19 healthy adults between 21 and 50 years-old participated in this study. Participants were equipped with 12 accelerometer-based active trackers and one pedometer (Yamasa) in order to monitor the number of steps per day. The devices were worn on the waist (ActiGraph, Omron, Actimarker, Lifedorder, Withings, and Yamasa) or non-dominant wrist (Fitbit, Garmin, Misfit, EPSON, and Jawbone), or placed in a pocket (Omron CaloriScan, and TANITA). Participants performed structured activities over a 24 h period in a chamber (Standardized day), and steps were monitored in the same participants in free-living trials for 15 successive days using the same monitors (free-living days). RESULTS: When the 13 monitors were ranked by their steps, waist-worn ActiGraph was located at the center (7th) of the monitors both in the Standardized (12,252 ± 598 steps/day, mean ± SD) and free-living days (9295 ± 4027 steps/day). The correlation between the accelerometer-based devices was very high (r = 0.87-0.99). However, the steps of Yamasa was significantly lower in both trials than ActiGraph. The wrist-worn accelerometers had significantly higher steps than other devices both trials (P {\textless} 0.05). The differences between ActiGraph and Actimarker or Lifecorder was less than 100 steps/day in the Standardized day, and the differences between ActiGraph and Active Style Pro was less than 100 steps/day in the free-living days. Regression equation was also performed for inter-device compatibility. SIGNIFICANCE: Step obtained from the wrist-worn, waist-worn, and pocket-type activity trackers were significantly different from each other but still highly correlated in free-living conditions.

Validity and Acceptability of Wearable Devices for Monitoring Step-Count and Activity Minutes Among People With Multiple Sclerosis (2021)

Frontiers in Rehabilitation Sciences

Abstract

Multiple wearable devices that purport to measure physical activity are widely available to consumers. While they may support increases in physical activity among people with multiple sclerosis (MS) by providing feedback on their performance, there is little information about the validity and acceptability of these devices. Providing devices that are perceived as inaccurate and difficult to use may have negative consequences for people with MS, rather than supporting participation in physical activity. The aim of this study was, therefore, to assess the validity and acceptability of commercially available devices for monitoring step-count and activity time among people with MS. Nineteen ambulatory adults with MS [mean (SD) age 52.1 (11.9) years] participated in the study. Step-count was assessed using five commercially available devices (Fitbit Alta, Fitbit Zip, Garmin Vivofit 4, Yamax Digi Walker SW200, and Letscom monitor) and an activPAL3μ while completing nine everyday activities. Step-count was also manually counted. Time in light activity, moderate-to-vigorous activity, and total activity were measured during activities using an Actigraph GT3X accelerometer. Of the 19 participants who completed the validity study, fifteen of these people also wore the five commercially available devices for three consecutive days each, and participated in a semi-structured interview regarding their perception of the acceptability of the monitors. Mean percentage error for step-count ranged from 12.1\% for the Yamax SW200 to -112.3\% for the Letscom. Mean step-count as manually determined differed to mean step-count measured by the Fitbit Alta (p = 0.002), Garmin vivofit 4 (p {\textless} 0.001), Letscom (p {\textless} 0.001) and the research standard device, the activPAL3μ (p {\textless} 0.001). However, 95\% limits of agreement were smallest for the activPAL3μ and largest for the Fitbit Alta. Median percentage error for activity minutes was 52.9\% for the Letscom and 100\% for the Garmin Vivofit 4 and Fitbit Alta compared to minutes in total activity. Three inductive themes were generated from participant accounts: Interaction with device; The way the device looks and feels; Functionality. In conclusion, commercially available devices demonstrated poor criterion validity when measuring step-count and activity time in people with MS. This negatively affected the acceptability of devices, with perceived inaccuracies causing distrust and frustration. Additional considerations when designing devices for people with MS include an appropriately sized and lit display and ease of attaching and charging devices.

The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement (2023)

JMIR mental health

Abstract

BACKGROUND: Remote measurement technologies (RMTs) such as smartphones and wearables can help improve treatment for depression by providing objective, continuous, and ecologically valid insights into mood and behavior. Engagement with RMTs is varied and highly context dependent; however, few studies have investigated their feasibility in the context of treatment. OBJECTIVE: A mixed methods design was used to evaluate engagement with active and passive data collection via RMT in people with depression undergoing psychotherapy. We evaluated the effects of treatment on 2 different types of engagement: study attrition (engagement with study protocol) and patterns of missing data (engagement with digital devices), which we termed data availability. Qualitative interviews were conducted to help interpret the differences in engagement. METHODS: A total of 66 people undergoing psychological therapy for depression were followed up for 7 months. Active data were gathered from weekly questionnaires and speech and cognitive tasks, and passive data were gathered from smartphone sensors and a Fitbit (Fitbit Inc) wearable device. RESULTS: The overall retention rate was 60\%. Higher-intensity treatment (χ21=4.6; P=.03) and higher baseline anxiety (t56.28=-2.80, 2-tailed; P=.007) were associated with attrition, but depression severity was not (t50.4=-0.18; P=.86). A trend toward significance was found for the association between longer treatments and increased attrition (U=339.5; P=.05). Data availability was higher for active data than for passive data initially but declined at a sharper rate (90\%-30\% drop in 7 months). As for passive data, wearable data availability fell from a maximum of 80\% to 45\% at 7 months but showed higher overall data availability than smartphone-based data, which remained stable at the range of 20\%-40\% throughout. Missing data were more prevalent among GPS location data, followed by among Bluetooth data, then among accelerometry data. As for active data, speech and cognitive tasks had lower completion rates than clinical questionnaires. The participants in treatment provided less Fitbit data but more active data than those on the waiting list. CONCLUSIONS: Different data streams showed varied patterns of missing data, despite being gathered from the same device. Longer and more complex treatments and clinical characteristics such as higher baseline anxiety may reduce long-term engagement with RMTs, and different devices may show opposite patterns of missingness during treatment. This has implications for the scalability and uptake of RMTs in health care settings, the generalizability and accuracy of the data collected by these methods, feature construction, and the appropriateness of RMT use in the long term.

Non-equivalent, but still valid: Establishing the construct validity of a consumer fitness tracker in persons with multiple sclerosis (2023)

PLOS digital health

Abstract

Tools for monitoring daily physical activity (PA) are desired by persons with multiple sclerosis (MS). However, current research-grade options are not suitable for longitudinal, independent use due to their cost and user experience. Our objective was to assess the validity of step counts and PA intensity metrics derived from the Fitbit Inspire HR, a consumer-grade PA tracker, in 45 persons with MS (Median age: 46, IQR: 40-51) undergoing inpatient rehabilitation. The population had moderate mobility impairment (Median EDSS 4.0, Range 2.0-6.5). We assessed the validity of Fitbit-derived PA metrics (Step count, total time in PA, time in moderate to vigorous PA (MVPA)) during scripted tasks and free-living activity at three levels of data aggregation (minute, daily, and average PA). Criterion validity was assessed though agreement with manual counts and multiple methods for deriving PA metrics via the Actigraph GT3X. Convergent and known-groups validity were assessed via relationships with reference standards and related clinical measures. Fitbit-derived step count and time in PA, but not time in MVPA, exhibited excellent agreement with reference measures during scripted tasks. During free-living activity, step count and time in PA correlated moderately to strongly with reference measures, but agreement varied across metrics, data aggregation levels, and disease severity strata. Time in MVPA weakly agreed with reference measures. However, Fitbit-derived metrics were often as different from reference measures as reference measures were from each other. Fitbit-derived metrics consistently exhibited similar or stronger evidence of construct validity than reference standards. Fitbit-derived PA metrics are not equivalent to existing reference standards. However, they exhibit evidence of construct validity. Consumer-grade fitness trackers such as the Fitbit Inspire HR may therefore be suitable as a PA tracking tool for persons with mild or moderate MS.

Validation of an open-source smartphone step counting algorithm in clinical and non-clinical settings (2023)

medRxiv: The Preprint Server for Health Sciences

Abstract

Background: Step counts are increasingly used in public health and clinical research to assess wellbeing, lifestyle, and health status. However, estimating step counts using commercial activity trackers has several limitations, including a lack of reproducibility, generalizability, and scalability. Smartphones are a potentially promising alternative, but their step-counting algorithms require robust validation that accounts for temporal sensor body location, individual gait characteristics, and heterogeneous health states. Objective: Our goal was to evaluate an open-source step-counting method for smartphones under various measurement conditions against step counts estimated from data collected simultaneously from different body locations ("internal" validation), manually ascertained ground truth ("manual" validation), and step counts from a commercial activity tracker (Fitbit Charge 2) in patients with advanced cancer ("wearable" validation). Methods: We used eight independent datasets collected in controlled, semi-controlled, and free-living environments with different devices (primarily Android smartphones and wearable accelerometers) carried at typical body locations. Five datasets (N=103) were used for internal validation, two datasets (N=107) for manual validation, and one dataset (N=45) used for wearable validation. In each scenario, step counts were estimated using a previously published step-counting method for smartphones that uses raw sub-second level accelerometer data. We calculated mean bias and limits of agreement (LoA) between step count estimates and validation criteria using Bland-Altman analysis. Results: In the internal validation datasets, participants performed 751.7±581.2 (mean±SD) steps, and the mean bias was -7.2 steps (LoA -47.6, 33.3) or -0.5%. In the manual validation datasets, the ground truth step count was 367.4±359.4 steps while the mean bias was -0.4 steps (LoA -75.2, 74.3) or 0.1 %. In the wearable validation dataset, Fitbit devices indicated mean step counts of 1931.2±2338.4, while the calculated bias was equal to -67.1 steps (LoA -603.8, 469.7) or a difference of 0.3 %. Conclusions: This study demonstrates that our open-source step counting method for smartphone data provides reliable step counts across sensor locations, measurement scenarios, and populations, including healthy adults and patients with cancer.

Performance evaluation of Fitbit Charge 3 and actigraphy vs. polysomnography: Sensitivity, specificity, and reliability across participants and nights (2023)

Sleep Health

Abstract

GOAL AND AIMS: Compare the accuracy and reliability of sleep/wake classification between the Fitbit Charge 3 and the Micro Motionlogger actigraph when applying either the Cole-Kripke or Sadeh scoring algorithms. Accuracy was established relative to simultaneous Polysomnography recording. Focus technology: Fitbit Charge 3 and actigraphy. Reference technology: Polysomnography. SAMPLE: Twenty-one university students (10 females). DESIGN: Simultaneous Fitbit Charge 3, actigraphy, and polysomnography were recorded over 3 nights at the participants' homes. CORE ANALYTICS: Total sleep time, wake after sleep onset, sensitivity, specificity, positive predictive value, and negative predictive value. ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES: Variability of specificity and negative predictive value across subjects and across nights. CORE OUTCOMES: Fitbit Charge 3 and actigraphy using the Cole-Kripke or Sadeh algorithms exhibited similar sensitivity in classifying sleep segments relative to polysomnography (sensitivity of 0.95, 0.96, and 0.95, respectively). Fitbit Charge 3 was significantly more accurate in classifying wake segments (specificity of 0.69, 0.33, and 0.29, respectively). Fitbit Charge 3 also exhibited significantly higher positive predictive value than actigraphy (0.99 vs. 0.97 and 0.97, respectively) and a negative predictive value that was significantly higher only relative to the Sadeh algorithm (0.41 vs. 0.25, respectively). IMPORTANT ADDITIONAL OUTCOMES: Fitbit Charge 3 exhibited significantly lower standard deviation in specificity values across subjects and negative predictive value across nights. CORE CONCLUSION: This study demonstrates that Fitbit Charge 3 is more accurate and reliable in identifying wake segments than the examined FDA-approved Micro Motionlogger actigraphy device. The results also highlight the need to create devices that record and save raw multi-sensor data, which are necessary for developing open-source sleep or wake classification algorithms.

Accuracy of a Standalone Atrial Fibrillation Detection Algorithm Added to a Popular Wristband and Smartwatch: Prospective Diagnostic Accuracy Study (2023)

Journal of Medical Internet Research

Abstract

BACKGROUND: Silent paroxysmal atrial fibrillation (AF) may be difficult to diagnose, and AF burden is hard to establish. In contrast to conventional diagnostic devices, photoplethysmography (PPG)-driven smartwatches or wristbands allow for long-term continuous heart rhythm assessment. However, most smartwatches lack an integrated PPG-AF algorithm. Adding a standalone PPG-AF algorithm to these wrist devices might open new possibilities for AF screening and burden assessment. OBJECTIVE: The aim of this study was to assess the accuracy of a well-known standalone PPG-AF detection algorithm added to a popular wristband and smartwatch, with regard to discriminating AF and sinus rhythm, in a group of patients with AF before and after cardioversion (CV). METHODS: Consecutive consenting patients with AF admitted for CV in a large academic hospital in Amsterdam, the Netherlands, were asked to wear a Biostrap wristband or Fitbit Ionic smartwatch with Fibricheck algorithm add-on surrounding the procedure. A set of 1-min PPG measurements and 12-lead reference electrocardiograms was obtained before and after CV. Rhythm assessment by the PPG device-software combination was compared with the 12-lead electrocardiogram. RESULTS: A total of 78 patients were included in the Biostrap-Fibricheck cohort (156 measurement sets) and 73 patients in the Fitbit-Fibricheck cohort (143 measurement sets). Of the measurement sets, 19/156 (12\%) and 7/143 (5\%), respectively, were not classifiable by the PPG algorithm due to bad quality. The diagnostic performance in terms of sensitivity, specificity, positive predictive value, negative predictive value, and accuracy was 98\%, 96\%, 96\%, 99\%, 97\%, and 97\%, 100\%, 100\%, 97\%, and 99\%, respectively, at an AF prevalence of {\textasciitilde}50\%. CONCLUSIONS: This study demonstrates that the addition of a well-known standalone PPG-AF detection algorithm to a popular PPG smartwatch and wristband without integrated algorithm yields a high accuracy for the detection of AF, with an acceptable unclassifiable rate, in a semicontrolled environment.

Validity of activity wristbands for estimating daily physical activity in primary schoolchildren under free-living conditions: School-Fit study (2023)

Frontiers in Public Health

Abstract

INTRODUCTION: The use of activity wristbands to monitor and promote schoolchildren's physical activity (PA) is increasingly widespread. However, their validity has not been sufficiently studied, especially among primary schoolchildren. Consequently, the main purpose was to examine the validity of the daily steps and moderate-to-vigorous PA (MVPA) scores estimated by the activity wristbands Fitbit Ace 2, Garmin Vivofit Jr 2, and the Xiaomi Mi Band 5 in primary schoolchildren under free-living conditions. MATERIALS AND METHODS: An initial sample of 67 schoolchildren (final sample = 62; 50\% females), aged 9-12 years old (mean = 10.4 ± 1.0 years), participated in the present study. Each participant wore three activity wristbands (Fitbit Ace 2, Garmin Vivofit Jr 2, and Xiaomi Mi Band 5) on his/her non-dominant wrist and a research-grade accelerometer (ActiGraph wGT3X-BT) on his/her hip as the reference standard (number of steps and time in MVPA) during the waking time of one day. RESULTS: Results showed that the validity of the daily step scores estimated by the Garmin Vivofit Jr 2 and Xiaomi Mi Band 5 were good and acceptable (e.g., MAPE = 9.6/11.3\%, and lower 95\% IC of ICC = 0.87/0.73), respectively, as well as correctly classified schoolchildren as meeting or not meeting the daily 10,000/12,000-step-based recommendations, obtaining excellent/good and good/acceptable results (e.g., Garmin Vivofit Jr 2, k = 0.75/0.62; Xiaomi Mi Band 5, k = 0.73/0.53), respectively. However, the Fitbit Ace 2 did not show an acceptable validity (e.g., daily steps: MAPE = 21.1\%, and lower 95\% IC of ICC = 0.00; step-based recommendations: k = 0.48/0.36). None of the three activity wristbands showed an adequate validity for estimating daily MVPA (e.g., MAPE = 36.6-90.3\%, and lower 95\% IC of ICC = 0.00-0.41) and the validity for the MVPA-based recommendation tended to be considerably lower (e.g., k = -0.03-0.54). CONCLUSIONS: The activity wristband Garmin Vivofit Jr 2 obtained the best validity for monitoring primary schoolchildren's daily steps, offering a feasible alternative to the research-grade accelerometers. Furthermore, this activity wristband could be used during PA promotion programs to provide accurate feedback to primary schoolchildren to ensure their accomplishment with the PA recommendations.

Efficacy of a Commercial Physical Activity Monitor in Longitudinal Tracking of Patients With Pulmonary Hypertension: A Pilot Study (2023)

Texas Heart Institute Journal

Abstract

Background: Patients with pulmonary arterial hypertension have quality-of-life limitations, decreased exercise capacity, and poor prognosis if the condition is left untreated. Standard exercise testing is routinely performed to evaluate patients with pulmonary arterial hypertension but may be limited in its ability to monitor activity levels in daily living. Objective: To evaluate the validity of the commercial Fitbit Charge HR as a tool to assess real-time exercise capacity as compared with standard exercise testing. Methods: Ambulatory pediatric and adult patients were enrolled and given a Fitbit with instructions to continuously wear the device during waking hours. Patients underwent a 6-minute walk test, cardiopulmonary exercise test, and a 36-Item Short Form Health Survey on the day of enrollment and follow-up. Twenty-seven ambulatory patients with pulmonary arterial hypertension were enrolled, and 21 had sufficient data for analyses (median age, 25 years [range, 13-59 years]; 14 female participants). Results: Daily steps measured by the Fitbit had a positive correlation with 6-minute walk distance (r = 0.72, P = .03) and an inverse trend with World Health Organization functional class. On the 36-Item Short Form Health Survey, 77% of patients reported improvement in vitality (P = .055). At follow-up, there was a strong correlation between number of steps recorded by Fitbit and role limitations because of physical problems (r = 0.88, P = .02) and weaker correlations with other quality-of-life markers. Conclusion: The findings of this pilot study suggest activity monitors may have potential as a simple and novel method of assessing longitudinal exercise capacity and activity levels in patients with pulmonary hypertension. Further study in larger cohorts of patients is warranted to determine which accelerometer measures correlate best with outcomes.

Open-Source, Step-Counting Algorithm for Smartphone Data Collected in Clinical and Nonclinical Settings: Algorithm Development and Validation Study (2023)

JMIR cancer

Abstract

Background: Step counts are increasingly used in public health and clinical research to assess well-being, lifestyle, and health status. However, estimating step counts using commercial activity trackers has several limitations, including a lack of reproducibility, generalizability, and scalability. Smartphones are a potentially promising alternative, but their step-counting algorithms require robust validation that accounts for temporal sensor body location, individual gait characteristics, and heterogeneous health states. Objective: Our goal was to evaluate an open-source, step-counting method for smartphones under various measurement conditions against step counts estimated from data collected simultaneously from different body locations ("cross-body" validation), manually ascertained ground truth ("visually assessed" validation), and step counts from a commercial activity tracker (Fitbit Charge 2) in patients with advanced cancer ("commercial wearable" validation). Methods: We used 8 independent data sets collected in controlled, semicontrolled, and free-living environments with different devices (primarily Android smartphones and wearable accelerometers) carried at typical body locations. A total of 5 data sets (n=103) were used for cross-body validation, 2 data sets (n=107) for visually assessed validation, and 1 data set (n=45) was used for commercial wearable validation. In each scenario, step counts were estimated using a previously published step-counting method for smartphones that uses raw subsecond-level accelerometer data. We calculated the mean bias and limits of agreement (LoA) between step count estimates and validation criteria using Bland-Altman analysis. Results: In the cross-body validation data sets, participants performed 751.7 (SD 581.2) steps, and the mean bias was -7.2 (LoA -47.6, 33.3) steps, or -0.5%. In the visually assessed validation data sets, the ground truth step count was 367.4 (SD 359.4) steps, while the mean bias was -0.4 (LoA -75.2, 74.3) steps, or 0.1%. In the commercial wearable validation data set, Fitbit devices indicated mean step counts of 1931.2 (SD 2338.4), while the calculated bias was equal to -67.1 (LoA -603.8, 469.7) steps, or a difference of 3.4%. Conclusions: This study demonstrates that our open-source, step-counting method for smartphone data provides reliable step counts across sensor locations, measurement scenarios, and populations, including healthy adults and patients with cancer. Keywords: accelerometer; cancer; open-source; smartphone; step count; validation; wearable.

Comparison of Polysomnography, Single-Channel Electroencephalogram, Fitbit, and Sleep Logs in Patients With Psychiatric Disorders: Cross-Sectional Study (2023)

Journal of Medical Internet Research

Abstract

BACKGROUND: Sleep disturbances are core symptoms of psychiatric disorders. Although various sleep measures have been developed to assess sleep patterns and quality of sleep, the concordance of these measures in patients with psychiatric disorders remains relatively elusive. OBJECTIVE: This study aims to examine the degree of agreement among 3 sleep recording methods and the consistency between subjective and objective sleep measures, with a specific focus on recently developed devices in a population of individuals with psychiatric disorders. METHODS: We analyzed 62 participants for this cross-sectional study, all having data for polysomnography (PSG), Zmachine, Fitbit, and sleep logs. Participants completed questionnaires on their symptoms and estimated sleep duration the morning after the overnight sleep assessment. The interclass correlation coefficients (ICCs) were calculated to evaluate the consistency between sleep parameters obtained from each instrument. Additionally, Bland-Altman plots were used to visually show differences and limits of agreement for sleep parameters measured by PSG, Zmachine, Fitbit, and sleep logs. RESULTS: The findings indicated a moderate agreement between PSG and Zmachine data for total sleep time (ICC=0.46; P{\textless}.001), wake after sleep onset (ICC=0.39; P=.002), and sleep efficiency (ICC=0.40; P=.006). In contrast, Fitbit demonstrated notable disagreement with PSG (total sleep time: ICC=0.08; wake after sleep onset: ICC=0.18; sleep efficiency: ICC=0.10) and exhibited particularly large discrepancies from the sleep logs (total sleep time: ICC=-0.01; wake after sleep onset: ICC=0.05; sleep efficiency: ICC=-0.02). Furthermore, subjective and objective concordance among PSG, Zmachine, and sleep logs appeared to be influenced by the severity of the depressive symptoms and obstructive sleep apnea, while these associations were not observed between the Fitbit and other sleep instruments. CONCLUSIONS: Our study results suggest that Fitbit accuracy is reduced in the presence of comorbid clinical symptoms. Although user-friendly, Fitbit has limitations that should be considered when assessing sleep in patients with psychiatric disorders.

Comparison of daily step count between the Fitbit Inspire 3 and the activPAL 3 in adults with transtibial amputation (2024)

Frontiers in Rehabilitation Sciences

Abstract

INTRODUCTION: Physical activity has significant positive effects on health. Accelerometers can be used to track daily physical activity. The Fitbit Inspire 3 is a commercially available health and fitness tracker, but its validity for tracking steps among individuals with transtibial amputation has not been examined. Therefore, the purpose of this study was to evaluate the concurrent validity of the Fitbit Inspire 3 for assessing free-living daily steps in adults with transtibial amputation. METHODS: Participants (n = 79) completed a general health survey and were provided with a Fitbit Inspire 3 and activPAL 3 accelerometer to wear concurrently for seven days in their home environment. Relationships between the activPAL and Fitbit Inspire 3 were examined using Pearson's Correlation. Paired samples t-tests, mean difference, mean absolute difference, and equivalence testing were used to compared daily step counts between Fitbit Inspire 3 and activPAL 3. RESULTS: Average step counts were 5,768 ± 3,750 (mean ± SD) and 4,674 ± 3,081 by the Fitbit Inspire 3 and activPAL, respectively. A high correlation (r = 0.93) but significant mean difference was found between the activPAL 3 and Fitbit Inspire 3 (p {\textless} 0.001). The mean absolute difference between the devices was 1,347 ± 1,184 steps. On average, the Fitbit Inspire 3 counted 1,094 ± 1,423 more daily steps than the activPAL 3. Equivalency could not be claimed between the devices. DISCUSSION: The Fitbit Inspire 3 counted more steps compared to the activPAL. Because of the significant mean differences and the large mean absolute difference between the devices, the activPAL 3 and Fitbit Inspire 3 are not interchangeable for estimating physical activity in individuals with transtibial amputation. However, due to the high correlation, the devices will produce similar classification rankings based on step counts.

Assessment of Fitbit Charge 4 for sleep stage and heart rate monitoring against polysomnography and during home monitoring in Huntington's disease (2024)

Journal of clinical sleep medicine: JCSM: official publication of the American Academy of Sleep Medicine

Abstract

Study objectives: Wearable devices, monitoring sleep stages and heart rate (HR), bring the potential for longitudinal sleep monitoring in patients with neurodegenerative diseases. Sleep quality reduces with disease progression in Huntington's disease (HD). However, the involuntary movements characteristic of HD may affect the accuracy of wrist-worn devices. This study compares sleep stage and heart rate data from the Fitbit Charge 4 (FB) against polysomnography (PSG) in participants with HD. Methods: Ten participants with manifest HD wore a FB during overnight hospital-based PSG, and for nine of these participants continued to wear the FB for seven nights at home. Sleep stages (30s epochs) and minute-by-minute HR were extracted and compared against PSG data. Results: FB-estimated total sleep and wake times, and sleep stage times were in good agreement with PSG, with intra-class correlations 0.79-0.96. However, poor agreement was observed for Wake After Sleep Onset, and the number of awakenings. FB detected wake with 68.6±15.5% sensitivity and 93.7±2.5% specificity, rapid eye movement (REM) sleep with high sensitivity and specificity (78.7±31.9%, 95.6±2.3%), and deep sleep with lower sensitivity but high specificity (56.4±28.8%, 95.0±4.8%). FB HR was strongly correlated with PSG, and the mean absolute error between FB and PSG HR data was 1.16 ± 0.42 bpm. At home, longer sleep and shorter wake times were observed compared to hospital data, while percentage sleep stage times were consistent with hospital data. Conclusions: Results suggest the potential for long-term monitoring of sleep patterns using wrist-worn wearable devices as part of symptom management in HD. Keywords: PPG; PSG; home monitoring; neurodegeneration; wearable sensor.

Criterion-Related Validity of Consumer-Wearable Activity Trackers for Estimating Steps in Primary Schoolchildren under Controlled Conditions: Fit-Person Study (2024)

Journal of Sports Science \& Medicine

Abstract

The purposes were to examine the criterion-related validity of the steps estimated by consumer-wearable activity trackers (wrist-worn activity trackers: Fitbit Ace 2, Garmin Vivofit Jr, and Xiomi Mi Band 5; smartphone applications: Pedometer, Pedometer Pacer Health, and Google Fit/Apple Health) and their comparability in primary schoolchildren under controlled conditions. An initial sample of 66 primary schoolchildren (final sample = 56; 46.4% females), aged 9-12 years old (mean = 10.4 ± 1.0 years), wore three wrist-worn activity trackers (Fitbit Ace 2, Garmin Vivofit Jr 2, and Xiaomi Mi Band 5) on their non-dominant wrist and had three applications in two smartphones (Pedometer, Pedometer Pacer Health, and Google Fit/Apple Health for Android/iOS installed in Samsung Galaxy S20+/iPhone 11 Pro Max) in simulated front trouser pockets. Primary schoolchildren's steps estimated by the consumer-wearable activity trackers and the video-based counting independently by two researchers (gold standard) were recorded while they performed a 200-meter course in slow, normal and brisk pace walking, and running conditions. Results showed that the criterion-related validity of the step scores estimated by the three Samsung applications and the Garmin Vivofit Jr 2 were good-excellent in the four walking/running conditions (e.g., MAPE = 0.6-2.3%; lower 95% CI of the ICC = 0.81-0.99), as well as being comparable. However, the Apple applications, Fitbit Ace 2, and Xiaomi Mi Band 5 showed poor criterion-related validity and comparability on some walking/running conditions (e.g., lower 95% CI of the ICC < 0.70). Although, as in real life primary schoolchildren also place their smartphones in other parts (e.g., schoolbags, hands or even somewhere away from the body), the criterion-related validity of the Garmin Vivofit Jr 2 potentially would be considerably higher than that of the Samsung applications. The findings of the present study highlight the potential of the Garmin Vivofit Jr 2 for monitoring primary schoolchildren's steps under controlled conditions.

Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table (2024)

PloS One

Abstract

The purpose of this study was to evaluate the reliability and validity of the raw accelerometry output from research-grade and consumer wearable devices compared to accelerations produced by a mechanical shaker table. Raw accelerometry data from a total of 40 devices (i.e., n = 10 ActiGraph wGT3X-BT, n = 10 Apple Watch Series 7, n = 10 Garmin Vivoactive 4S, and n = 10 Fitbit Sense) were compared to reference accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). Two-way random effects absolute intraclass correlation coefficients (ICC) tested inter-device reliability. Pearson product moment, Lin's concordance correlation coefficient (CCC), absolute error, mean bias, and equivalence testing were calculated to assess the validity between the raw estimates from the devices and the reference metric. Estimates from Apple, ActiGraph, Garmin, and Fitbit were reliable, with ICCs = 0.99, 0.97, 0.88, and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the reference CCCs = 0.88, 0.83, and 0.85, respectively, while estimates from Garmin exhibited moderate concordance CCC = 0.59 based on the mean aggregation method. ActiGraph, Apple, and Fitbit produced similar absolute errors = 16.9mg, 21.6mg, and 22.0mg, respectively, while Garmin produced higher absolute error = 32.5mg compared to the reference. ActiGraph produced the lowest mean bias 0.0mg (95\%CI = -40.0, 41.0). Equivalence testing revealed raw accelerometry data from all devices were not statistically significantly within the equivalence bounds of the shaker speed. Findings from this study provide evidence that raw accelerometry data from Apple, Garmin, and Fitbit devices can be used to reliably estimate movement; however, no estimates were statistically significantly equivalent to the reference. Future studies could explore device-agnostic and harmonization methods for estimating physical activity using the raw accelerometry signals from the consumer wearables studied herein.

Detection of Common Respiratory Infections, Including COVID-19, Using Consumer Wearable Devices in Health Care Workers: Prospective Model Validation Study (2024)

JMIR formative research

Abstract

BACKGROUND: The early detection of respiratory infections could improve responses against outbreaks. Wearable devices can provide insights into health and well-being using longitudinal physiological signals. OBJECTIVE: The purpose of this study was to prospectively evaluate the performance of a consumer wearable physiology-based respiratory infection detection algorithm in health care workers. METHODS: In this study, we evaluated the performance of a previously developed system to predict the presence of COVID-19 or other upper respiratory infections. The system generates real-time alerts using physiological signals recorded from a smartwatch. Resting heart rate, respiratory rate, and heart rate variability measured during the sleeping period were used for prediction. After baseline recordings, when participants received a notification from the system, they were required to undergo testing at a Northwell Health System site. Participants were asked to self-report any positive tests during the study. The accuracy of model prediction was evaluated using respiratory infection results (laboratory results or self-reports), and postnotification surveys were used to evaluate potential confounding factors. RESULTS: A total of 577 participants from Northwell Health in New York were enrolled in the study between January 6, 2022, and July 20, 2022. Of these, 470 successfully completed the study, 89 did not provide sufficient physiological data to receive any prediction from the model, and 18 dropped out. Out of the 470 participants who completed the study and wore the smartwatch as required for the 16-week study duration, the algorithm generated 665 positive alerts, of which 153 (23.0\%) were not acted upon to undergo testing for respiratory viruses. Across the 512 instances of positive alerts that involved a respiratory viral panel test, 63 had confirmed respiratory infection results (ie, COVID-19 or other respiratory infections detected using a polymerase chain reaction or home test) and the remaining 449 had negative upper respiratory infection test results. Across all cases, the estimated false-positive rate based on predictions per day was 2\%, and the positive-predictive value ranged from 4\% to 10\% in this specific population, with an observed incidence rate of 198 cases per week per 100,000. Detailed examination of questionnaires filled out after receiving a positive alert revealed that physical or emotional stress events, such as intense exercise, poor sleep, stress, and excessive alcohol consumption, could cause a false-positive result. CONCLUSIONS: The real-time alerting system provides advance warning on respiratory viral infections as well as other physical or emotional stress events that could lead to physiological signal changes. This study showed the potential of wearables with embedded alerting systems to provide information on wellness measures.

Validation of activity trackers to estimate energy expenditure in older adults with cardiovascular risk factors (2024)

PloS One

Abstract

Objectives: To compare different types of activity trackers recording physical activity energy expenditure (PAEE) and examine their criterion validity against indirect calorimetry (IC) as the gold standard in adults over 60 years of age with a special focus on women with cardiovascular risk. Design: Synchronous registrations of PAEE were performed with up to four different devices to determine criterion validity against IC while participants performed a protocol of simulated activities in a laboratory setting. Method: Thirty-four participants (25 women, 9 men) with at least a light cardiac risk performed a protocol of simulated activities in a laboratory setting (daily living activities, cycle ergometer test). PAEE was simultaneously assessed by IC, two research-grade activity trackers (ActiGraph-wGT3X-BT and Actiheart-4) and two consumer-level activity trackers (OMRON pedometer and Fitbit Charge-3). Tracker-derived PAEE was compared with PAEE calculated from IC descriptively and by Bland-Altman plots. Results: The ActiGraph (0.7 ± 0.4 kcal/min), the Actiheart (1.1 ± 0.6 kcal/min) and the OMRON (0.8 ± 0.6 kcal/min) underestimated, while the Fitbit (3.4 ± 1.2 kcal/min) overestimated PAEE compared to IC-PAEE (2.0 ± 0.5 kcal/min). The Bland-Altman limits of agreement (LoA) against IC were +0.5/+2.2 kcal/min for the ActiGraph, -0.3/+2.1 kcal/min for the Actiheart, -3.7/+1.0 kcal/min for the Fitbit, and -0.5/+2.9 kcal/min for the OMRON. The magnitude of the deviation varied considerably depending on the activity (e.g. walking, cleaning, cycle ergometer test). Conclusions: The research-grade activity trackers estimated PAEE with higher validity than the commercially available activity trackers. The partly very wide LoA have to be critically considered when assessing PAEE in the context of health service research, as individual Physical Activity behaviour may be under- or overestimated.

Validity and stability of the international physical activity questionnaire short-form for stroke survivors with preserved walking ability (2024)

Topics in Stroke Rehabilitation

Abstract

Background: Stroke survivors usually present sedentary lifestyles and fail to comply with the World Health Organization physical activity recommendations. Reliable, low-cost, and fast tools are needed to monitor physical activity levels in this population. Objectives: This study aimed to evaluate the content and face validity, construct validity, and test-retest stability of the International Physical Activity Questionnaire Short-Form (IPAQ-SF) in stroke survivors. Methods: One hundred and twenty stroke survivors able to walk independently and preserved comprehension and communication abilities (61 ± 12 years, 35% female) were involved in this psychometric study. Participants completed the interview form of the IPAQ-SF via standardized videoconference twice, one week apart, under identical conditions, to evaluate test-retest stability. Correlations between IPAQ-SF and the caloric expenditure during the minutes of activity registered with the Fitbit Inspire 2 activity tracker wristband and 6-Metre Timed Walk (6MTW) were explored to assess construct validity. Results: The IPAQ-SF showed good content and face validity. "Moderate" to "strong" correlations were found with the Fitbit Inspire 2 (rho: 0.40 to 0.63), while "weak" to "moderate" correlations were found with the 6MTW (rho: 0.35 to 0.50). Test-retest stability was "moderate" to "excellent" (κ: 0.844 to 0.881; ICC: 0.533 to 0.917). Conclusions: The IPAQ-SF demonstrated satisfactory content and construct validity, and stability in stroke survivors, supporting its clinical and research utility when the data collection is conducted by trained evaluators using a standardized interview protocol in large samples. Keywords: Stroke; physical activity; psychometric properties; questionnaire; reliability; sedentary behavior; validity.

Validity of a Consumer-Based Wearable to Measure Clinical Parameters in Patients With Chronic Obstructive Pulmonary Disease and Healthy Controls: Observational Study (2024)

JMIR mHealth and uHealth

Abstract

BACKGROUND: Consumer-based wearables are becoming more popular and provide opportunities to track individual's clinical parameters remotely. However, literature about their criterion and known-groups validity is scarce. OBJECTIVE: This study aimed to assess the validity of the Fitbit Charge 4, a wrist-worn consumer-based wearable, to measure clinical parameters (ie, daily step count, resting heart rate [RHR], heart rate variability [HRV], respiratory rate [RR], and oxygen saturation) in patients with chronic obstructive pulmonary disease (COPD) and healthy controls in free-living conditions in Belgium by comparing it with medical-grade devices. METHODS: Participants wore the Fitbit Charge 4 along with three medical-grade devices: (1) Dynaport MoveMonitor for 7 days, retrieving daily step count; (2) Polar H10 for 5 days, retrieving RHR, HRV, and RR; and (3) Nonin WristOX2 3150 for 4 nights, retrieving oxygen saturation. Criterion validity was assessed by investigating the agreement between day-by-day measures of the Fitbit Charge 4 and the corresponding reference devices. Known-groups validity was assessed by comparing patients with COPD and healthy controls. RESULTS: Data of 30 patients with COPD and 25 age- and gender-matched healthy controls resulted in good agreement between the Fitbit Charge 4 and the corresponding reference device for measuring daily step count (intraclass correlation coefficient [ICC2,1]=0.79 and ICC2,1=0.85, respectively), RHR (ICC2,1=0.80 and ICC2,1=0.79, respectively), and RR (ICC2,1=0.84 and ICC2,1=0.77, respectively). The agreement for HRV was moderate (healthy controls: ICC2,1=0.69) to strong (COPD: ICC2,1=0.87). The agreement in measuring oxygen saturation in patients with COPD was poor (ICC2,1=0.32). The Fitbit device overestimated the daily step count and underestimated HRV in both groups. While RHR and RR were overestimated in healthy controls, no difference was observed in patients with COPD. Oxygen saturation was overestimated in patients with COPD. The Fitbit Charge 4 detected significant differences in daily step count, RHR, and RR between patients with COPD and healthy controls, similar to those identified by the reference devices, supporting known-groups validity. CONCLUSIONS: Although the Fitbit Charge 4 shows mainly moderate to good agreement, measures of clinical parameters deviated from the reference devices, indicating that monitoring patients remotely and interpreting parameters requires caution. Differences in clinical parameters between patients with COPD and healthy controls that were measured by the reference devices were all detected by the Fitbit Charge 4.

Accuracy of wrist-worn activity trackers for measuring steps in patients after major abdominal surgery: A validation study (2024)

Digital Health

Abstract

BACKGROUND: Wearable activity trackers provide a simple and objective measurement of postoperative mobilization. However, few have validated the accuracy of trackers in patients after major abdominal surgery. OBJECTIVE: To examine the accuracy of wrist-worn activity trackers to measure steps of patients in early mobilization after major abdominal surgery, and to explore the influence of clinical variables and gait parameters on the accuracy of trackers. METHODS: Forty-five patients after major abdominal surgery were recruited to participate in modified six-minute walk tests wearing three trackers simultaneously, the Fitbit Inspire HR, Xiaomi MI 4, and HONOR 5. The differences in displayed steps before and after the walking test were considered as the step counts measured by the trackers; the actual steps taken were determined as the average of the values manually counted by two researchers. The intraclass correlation coefficient, Bland-Altman method, mean percentage error, and mean absolute percentage error were used to assess the accuracy of trackers with reference to manual step counts. RESULTS: The three trackers undercounted postoperative steps by -65.5\% to -23.5\%. Analysis showed low-to-good agreement between step counts recorded by trackers and actual steps (ICC = 0.35-0.75); the mean absolute percentage errors ranged from 24.5\% to 65.7\%. For all trackers, mean absolute percentage errors correlated negatively with postoperative days (r = -0.626 to -0.744), walking speed (r = -0.714 to -0.854), step length (r = -0.466 to -0.615), and cadence (r = -0.681 to -0.790), while there were positive correlations between mean absolute percentage errors and the number of abdominal drains (r = 0.450-0.514). CONCLUSIONS: The specific activity trackers used in this study might not be reliable tools for measuring steps counts during the walking test in the early postoperative period for patients undergoing major abdominal surgery.

Reliability and Accuracy of the Fitbit Charge 4 Photoplethysmography Heart Rate Sensor in Ecological Conditions: Validation Study (2025)

JMIR mHealth and uHealth

Abstract

Background: Wrist-worn photoplethysmography (PPG) sensors allow for continuous heart rate (HR) measurement without the inconveniences of wearing a chest belt. Although green light PPG technology reduces HR measurement motion artifacts, only a limited number of studies have investigated the reliability and accuracy of wearables in non-laboratory-controlled conditions with actual specific and various physical activity movements. Objective: The purpose of this study was to (1) assess the reliability and accuracy of the PPG-based HR sensor of the Fitbit Charge 4 (FC4) in ecological conditions and (2) quantify the potential variability caused by the nature of activities. Methods: We collected HR data from participants who performed badminton, tennis, orienteering running, running, cycling, and soccer while simultaneously wearing the FC4 and the Polar H10 chest belt (criterion sensor). Skin tone was assessed with the Fitzpatrick Skin Scale. Once data from the FC4 and criterion data were synchronized, accuracy and reliability analyses were performed, using intraclass correlation coefficients (ICCs), Lin concordance correlation coefficients (CCCs), mean absolute percentage errors (MAPEs), and Bland-Altman tests. A linear univariate model was also used to evaluate the effect of skin tone on bias. All analyses were stratified by activity and pooled activity types (racket sports and running sports). Results: A total of 77.5 hours of HR recordings from 26 participants (age: mean 21.1, SD 5.8 years) were analyzed. The highest reliability was found for running sports, with ICCs and CCCs of 0.90 and 0.99 for running and 0.80 and 0.93 for orienteering running, respectively, whereas the ICCs and CCCs were 0.37 and 0.78, 0.42 and 0.88, 0.65 and 0.97, and 0.49 and 0.81 for badminton, tennis, cycling, and soccer, respectively. We found the highest accuracy for running (bias: 0.1 beats per minute [bpm]; MAPE 1.2%, SD 4.6%) and the lowest for badminton (bias: -16.5 bpm; MAPE 16.2%, SD 14.4%) and soccer (bias: -16.5 bpm; MAPE 17.5%, SD 20.8%). Limit of agreement (LOA) width and artifact rate followed the same trend. No effect of skin tone was observed on bias. Conclusions: LOA width, bias, and MAPE results found for racket sports and soccer suggest a high sensitivity to motion artifacts for activities that involve "sharp" and random arm movements. In this study, we did not measure arm motion, which limits our results. However, whereas individuals might benefit from using the FC4 for casual training in aerobic sports, we cannot recommend the use of the FC4 for specific purposes requiring high reliability and accuracy, such as research purposes. Keywords: Fitbit; Fitbit Charge 4; accuracy; device; ecological; ecological conditions; exercise; heart rate; heart rate sensor; photoplethysmography; physical activity; reliability; sensitivity; sensor; usefulness; variability; wearables; wrist-worn device.

Wearable technology for mobility measurement in orthopedics and traumatology: a comparison of commercially available systems (2025)

Archives of Orthopaedic and Trauma Surgery

Abstract

BACKGROUND: Wearable activity sensors offer valuable insights into physical activity and are increasingly used in clinical and rehabilitation settings. However, most are designed for healthy individuals, necessitating a thorough evaluation of their applicability for pathological gait patterns. This study aims to assess the accuracy of commercially available wearables in measuring gait patterns among patients with lower limb injuries compared to healthy individuals. METHODS: A prospective cohort study enrolled 40 participants divided into four groups: Group A (younger patients with lower limb injuries with age {\textless} 75y), Group B (younger healthy individuals with age 75y), Group C (elderly patients with lower limb injuries and age 75y), and Group D (elderly healthy individuals with age {\textgreater} 75y). Mobility was assessed in real-world scenarios using four wearable devices (Apple Watch Series 4, Fitbit Charge 3, ActivPal 4, and StappOne Insoles V1.0) across three gait speeds in a gait laboratory, with GAITrite mats and video as gold standards. RESULTS: Accuracy varied significantly between devices. The accelerometer-based wearables (Apple Watch Series 4, Fitbit Charge 3 and ActivPal 4™) underestimated cumulative step count compared to pressure-based Stappone v1. 0, especially for slow and restricted gait patterns (Groups C and D). Relative Difference of Wearables Measurements to the true numbers of steps (Group C: AW -21.83\%, FB -28.99\%, AP -20.00\% versus SO 0.00\% - Group D: AW -8.51\%, FB -14.29\%, AP -20.00\% versus SO 4.55\%). Zero measurements occurred frequently with wrist-worn devices, highlighting their limitations in detecting slow or restricted movements. In contrast, pressure-based StappOne Insoles demonstrated superior accuracy, with minimal deviations across all groups and gait speeds. The inaccuracy was exacerbated by factors such as the use of mobility aids, partial weight-bearing, and postoperative restrictions, which altered arm and leg movements. CONCLUSIONS: Accelerometer-based wearables require algorithmic improvements to address the challenges of slow and pathological gait patterns. The frequent occurrence of zero measurements with wrist-worn devices underscores their limited utility in clinical populations. Practical challenges, such as altered movement patterns due to mobility aids and partial weight-bearing, further limit their accuracy. Pressure-based systems, while accurate, face practicality issues for daily use. These findings emphasize the need for tailored wearable technologies for orthopedic and trauma patients. LEVEL OF EVIDENCE: Prospective cohort study, Level of Evidence 2.

Concurrent Validity of a Physical Activity Vital Sign Used in an Adult Preventive Cardiology Clinic (2025)

Nursing Research

Abstract

BACKGROUND: In clinical settings, counseling patients on physical activity starts by assessing patients' current physical activity levels. Self-report measures of PA are generally easy to administer; however, they may be too long to be convenient and are known to correlate poorly with objective measures of physical activity. OBJECTIVE: To assess the concurrent validity of a self-report three-question physical activity vital sign with objective Fitbit step counts and the distance walked during a 6-min walk test. METHODS: This pilot study tested a best practice advisory embedded in the Epic electronic health record, which was designed to prompt providers in a preventive cardiology clinic to counsel patients reporting low levels of physical activity . Patients were invited to participate in the remote patient monitoring phase to assess the change in their physical activity by wearing a Fitbit for 12 weeks and completing a 6-min walk test at baseline and 12 weeks. This analysis used the cross-sectional data collected in this phase. Pearson correlations were conducted between self-reported physical activity, Fitbit step counts, and the distance walked during the 6-min walk-a measure associated with current physical activity levels. Kappa coefficients were calculated to assess agreement between the self-reported physical activity and step counts. RESULTS: Participants who enrolled in the Fitbit monitoring were approximately 50\% female, with the majority identified as White non-Hispanic adults. Their most common cardiovascular risk factor was hypertension. The self-reported physical activity vital signs were significantly associated with step counts at baseline and 12 weeks but were not associated with the distance during the 6-min walk test. However, the distance walked was significantly associated with step counts at baseline and 12 weeks. The Kappa results demonstrate a poor level of agreement between two categories (meeting or not meeting current physical activity guidelines) of self-report physical activity vitals and the objective Fitbit step counts. DISCUSSION: There were moderate correlations between the self-reported physical activity vital signs and the Fitbit step counts, but there was lack of agreement when they were categorized. Further validation of this physical activity vital sign is warranted.

Leveraging Accelerometry and Heartrate Data from Consumer Wearables to Predict Physical Activity in Children: A Device Agnostic Approach (2025)

Medicine and Science in Sports and Exercise

Abstract

INTRODUCTION: This study examined the potential of a device agnostic approach for predicting physical activity energy expenditure (PAEE) from research grade and consumer wearable accelerometry and heartrate (HR) raw data compared to indirect calorimetry in children. METHODS: Two-hundred and thirty-one 5-12-year-olds (52.4\% male) of diverse skin tone and body weights participated in a 60-minute protocol with multiple activities at varying intensities. Children wore two of three consumer wearables (Apple Watch Series 7, Garmin Vivoactive 4S, Fitbit Sense) and a research grade accelerometer (ActiGraph GT9X) on their non-dominant wrist, and a chest-placed, research grade HR monitor (Actiheart 5, ECG), concurrently. Children also wore a K5 criterion measure of PAEE (i.e., COSMED K5). Cross-sectional time series (CSTS), generalized additive mixed effects model (GAMM) and random forest (RF) were used to estimate minute-by-minute PAEE from features extracted from raw accelerometry and HR data. Variance explained (R2), in addition to other metrics, evaluated agreement between estimated and criterion measurements. RESULTS: For the research grade devices (i.e., ActiGraph accelerometry and Actiheart HR) R2 was 0.74, 0.74, and 0.76 for CSTS, GAMM, and RF, respectively. For Apple R2 was 0.77, 0.76, and 0.78, Garmin's was 0.73, 0.73, and 0.75, and Fitbit's was 0.63, 0.65, and 0.67 for CSTS, GAMM, and RF, respectively. Across all other evaluation metrics, a similar pattern was observed with Fitbit performing the worst but with little variability between the modeling approaches or the other devices. CONCLUSIONS: Except for Fitbit, accelerometry and HR data from consumer wearables predicted PAEE comparably to research grade devices and there was little variability across modeling approach. These outcomes support deploying a consumer wearable device-agnostic approach for PAEE estimation in children.

Comparing the Accuracy of Different Wearable Activity Monitors in Patients With Lung Cancer and Providing Initial Recommendations: Protocol for a Pilot Validation Study (2025)

JMIR research protocols

Abstract

BACKGROUND: Wearable activity monitors (WAMs) provide insights into physical activity (PA) and are widely used in behavioral interventions and cancer survivorship research. However, validation studies of wearable devices in populations with cancer are scarce, and existing studies using activity monitors in patients with cancer lack standardization. This gap is particularly significant in patients with lung cancer (LC), who often experience unique mobility challenges and gait impairments that may affect device accuracy. This study addresses this gap by validating the Fitbit Charge 6, ActiGraph LEAP, and activPAL3 micro in patients with LC in both laboratory and free-living conditions and developing a standardized framework for assessing wearable devices in populations with cancer and impaired mobility. OBJECTIVE: This study aims to validate and compare the accuracy of consumer-grade (Fitbit Charge 6) and research-grade (activPAL3 micro and ActiGraph LEAP) WAMs in patients with LC under both laboratory and free-living conditions. Moreover, this protocol aims to establish standardized procedures that can be adapted for validating current and future generations of wearable devices while accounting for disease-specific factors that may impact measurement accuracy. METHODS: In total, 15 adults diagnosed with LC (stages 1-4) will participate in laboratory and free-living protocols, wearing Fitbit Charge 6, activPAL3 micro, and ActiGraph LEAP devices simultaneously. The laboratory protocol will consist of a series of structured activities, including variable-time walking trials, sitting and standing tests, posture changes, and gait speed assessments. Activities will be video recorded for validation. In the free-living protocol, participants will wear the devices continuously for 7 days except during water-based activities. WAM-based outcome measures will include step count; time spent at light, moderate, and vigorous PA intensity levels; posture; and posture changes (only the activPAL3 micro measured posture). Validated survey instruments will be administered both before and after WAM data collection to control for potential confounding factors that may influence movement patterns and device accuracy. Laboratory-based validity measures will compare WAM data to video-recorded observations. Sensitivity, specificity, positive predictive value, and agreement will also be determined. Free-living agreement between devices will be assessed using Bland-Altman plots, intraclass correlation analysis, and 95\% limits of agreement. RESULTS: Data collection is ongoing, with 11 participants enrolled and 7 (64\%) having completed both in-laboratory and free-living protocols. On average, enrolled participants are aged 63.0 (SD 7.8; range 50.0-73.0) years, with 8 (73\%) participants being women. Participant enrollment is expected to conclude in mid-2025, and initial findings are expected to be disseminated by the end of 2025. CONCLUSIONS: This is the first study that validates WAM accuracy for populations with LC while providing comprehensive recommendations for future validation studies. This study will provide critical insights into the accuracy and reliability of WAMs for assessing PA in LC survivors, which are essential for interpreting clinical research and informing future interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/70472.

Continuous heart rate measurements in patients with cardiac disease: Device comparison and development of a novel artefact removal procedure (2025)

Digital Health

Abstract

INTRODUCTION: Heart rate (HR) monitors could objectively measure physical activity intensity in patients with cardiac disease. However, thorough validation of HR monitors in cardiac populations during daily life, compared to gold-standard Holter monitoring, remains limited. Photoplethysmography (PPG)-based HR data provides near-continuous data, spanning longer periods, but improved algorithms to filter unreliable data are needed. METHODS: This observational, prospective pilot study compared the accuracy of two wearables for HR monitoring (electrocardiogram [ECG]-based Polar H10 chest strap and PPG-based Fitbit Inspire 2 wrist tracker) against Holter monitoring in 15 patients with atrial fibrillation (AF), heart failure (HF) and coronary artery disease referred for cardiac rehabilitation (CR). All devices were worn simultaneously for 24 h. We developed and assessed an artefact removal procedure (ARP) using logistic regression machine learning models to detect unreliable PPG data. RESULTS: The ECG-based chest strap showed a strong correlation (r = 0.94) and clinically acceptable errors (mean absolute error, MAE = 3.4 bpm; mean absolute percentage error, MAPE = 4.9\%). Photoplethysmography data exhibited weaker correlation (r = 0.69) and higher errors (MAE = 8.3 bpm, MAPE = 14.3\%), with highest accuracies in CR and lowest in HF and especially AF. After implementing the ARP, PPG-based HR data improved to a correlation of 0.75, with MAE of 7.2 bpm and MAPE of 12.4\%. The procedure removed nearly one-third of unreliable data, achieving an 81\% accuracy. CONCLUSIONS: While ECG-based monitors provide HR data with clinical acceptable accuracy, PPG-based monitors present accuracy challenges. Our machine learning procedure showed potential to filter unreliable PPG-based HR data, which could help measure physical activity intensity in cardiac disease continuously.

Capturing Real-World Habitual Sleep Patterns With a Novel User-Centric Algorithm to Preprocess Fitbit Data in the All of Us Research Program: Retrospective Observational Longitudinal Study (2025)

Journal of Medical Internet Research

Abstract

Background: Commercial wearables such as Fitbit quantify sleep metrics using fixed calendar times as default measurement periods, which may not adequately account for individual variations in sleep patterns. To address this limitation, experts in sleep medicine and wearable technology developed a user-centric algorithm designed to more accurately reflect actual sleep behaviors and improve the validity of wearable-derived sleep metrics. Objective: This study aims to describe the development of a new user-centric algorithm, compare its performance with the default calendar-relative algorithm, and provide a practical guide for analyzing All of Us Fitbit sleep data on a cloud-based platform. Methods: The default and user-centric algorithms were implemented to preprocess and compute sleep metrics related to schedule, duration, and disturbances using high-resolution Fitbit sleep data from 8563 participants (median age 58.1 years, 6002/8341, 71.96%, female) in the All of Us Research Program (version 7 Controlled Tier). Variations in typical sleep patterns were calculated by examining the differences in the mean number of primary sleep logs classified by each algorithm. Linear mixed-effects models were used to compare differences in sleep metrics across quartiles of variation in typical sleep patterns. Results: Out of 8,452,630 total sleep logs collected over a median of 4.2 years of Fitbit monitoring, 401,777 (4.75%) nonprimary sleep logs identified by the default algorithm were reclassified as primary sleep by the user-centric algorithm. Variation in typical sleep patterns ranged from -0.08 to 1. Among participants with the greatest variation in typical sleep patterns, the user-centric algorithm identified significantly more total sleep time (by 17.6 minutes; P<.001), more wake after sleep onset (by 13.9 minutes; P<.001), and lower sleep efficiency (by 2.0%; P<.001), on average. Differences in sleep stage metrics between the 2 algorithms were modest. Conclusions: The user-centric algorithm captures the natural variability in sleep schedules, providing an alternative approach to preprocess and evaluate sleep metrics related to schedule, duration, and disturbances. A publicly available R package facilitates the implementation of this algorithm for clinical and translational research. Keywords: All of Us Research Program; Fitbit; R package; algorithms; metrics; sleep.

Validity of Heart Rate Measurement Using Wearable Devices During Cardiopulmonary Exercise Testing in Patients With Cardiovascular Disease: Prospective Pilot Validation Study (2025)

JMIR cardio

Abstract

BACKGROUND: Wearable devices offer a promising solution for remotely monitoring heart rate (HR) during home-based cardiac rehabilitation. However, evidence regarding their accuracy across varying exercise intensities and patient profiles remains limited, particularly in populations with cardiovascular disease (CVD) such as those with heart failure (HF). OBJECTIVE: The objective of this study was to evaluate the accuracy of HR measurements obtained using the Fitbit Inspire 3 during cardiopulmonary exercise testing (CPX) in patients with CVD, including those with HF. METHODS: In this single-center, prospective pilot study, we enrolled 30 patients with CVD undergoing CPX. HR was simultaneously recorded using electrocardiography and the Fitbit Inspire 3 at 1-minute intervals across various CPX phases: rest, exercise below and above the anaerobic threshold (AT), and recovery. The correlation between the two methods was assessed using the Pearson correlation coefficient. Measurement error was quantified by mean absolute error and mean absolute percentage error (MAPE), with a MAPE of ≤10\% defined as the threshold for acceptable agreement. RESULTS: All data points were 630 points per minute. The Fitbit Inspire 3 device demonstrated a strong overall correlation with electrocardiography-derived HR (r=0.90; IQR 0.88-0.91) and an acceptable MAPE of 5.40\% (SD 8.33\%). The total error was 14.9\% (94/630), with overestimation and underestimation of 37 (5.8\%) points and 57 (9\%) points, respectively. The rate of HR underestimation reached 19 (16\%) points during exercise above the AT, compared to 1 (3\%) point at rest. When stratified by HF stage (B vs C), underestimation was more pronounced in patients with HF (14/275, 5\% points vs 40/355, 11.2\% points). CONCLUSIONS: The Fitbit Inspire 3 provides acceptable validity for HR monitoring during CPX in patients with CVD. However, clinicians should interpret HR data with caution during high-intensity exercise, especially in patients with HF.

Fitbit Research Library

Movement toward a novel activity monitoring device (2012)

Sleep and Breathing

Abstract

Accuracy of the Fitbit pedometer for self-paced and prescribed physical activity (2012)

Medicine & Science in Sports & Exercise

Abstract

A comparison of energy expenditure estimation of several physical activity monitors. (2013)

Medicine and Science in Sports and Exercise

Abstract

An evaluation of wearable activity monitoring devices (2013)

Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia - PDM '13

Abstract

Comparison of steps and energy expenditure assessment in adults of Fitbit Tracker and Ultra to the Actical and indirect calorimetry. (2013)

Journal of Medical Engineering & Technology

Abstract

Validity of using tri-axial accelerometers to measure human movement - Part II: Step counts at a wide range of gait velocities. (2014)

Medical Engineering & Physics

Abstract

Comparison of FitBit® Ultra to ActiGraph™ GT1M for Assessment of Physical Activity in Young Adults During Treadmill Walking (2014)

The Open Sports Medicine Journal

Abstract

Validity of consumer-based physical activity monitors. (2014)

Medicine and Science in Sports and Exercise

Abstract

Effect of walking speed and placement position interactions in determining the accuracy of various newer pedometers (2014)

Journal of Exercise Science & Fitness

Abstract

Comparative assessment of sleep quality estimates using home monitoring technology (2014)

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Abstract

An inexpensive device for monitoring patients' weights via automated hovering (2014)

International Journal of Cardiology

Abstract

Caloric expenditure assessment among older adults: criterion validity of a novel accelerometry device. (2014)

Journal of Health Psychology

Abstract

Validation of the Fitbit One activity monitor device during treadmill walking. (2014)

Journal of Science and Medicine in Sport

Abstract

An Evaluation of Commercial Pedometers for Monitoring Slow Walking Speed Populations. (2015)

Telemedicine journal and e-health : the official journal of the American Telemedicine Association

Abstract

Accuracy of smartphone applications and wearable devices for tracking physical activity data. (2015)

JAMA

Abstract

Fitbit®: An accurate and reliable device for wireless physical activity tracking (2015)

International Journal of Cardiology

Abstract

Measuring steps with the Fitbit activity tracker: an inter-device reliability study (2015)

Journal of Medical Engineering & Technology

Abstract

Are Currently Available Wearable Devices for Activity Tracking and Heart Rate Monitoring Accurate, Precise, and Medically Beneficial? (2015)

Healthcare Informatics Research

Abstract

The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: a cross-sectional study. (2015)

The International Journal of Behavioral Nutrition and Physical Activity

Abstract

Using Commercial Activity Monitors to Measure Gait in Patients with Suspected iNPH: Implications for Ambulatory Monitoring. (2015)

Cure¯us

Abstract

Reliability and validity of ten consumer activity trackers. (2015)

BMC Sports Science, Medicine and Rehabilitation

Abstract

Comparison of a Commercial Accelerometer with Polysomnography and Actigraphy in Children and Adolescents (2015)

Sleep

Abstract

Validity of the Fitbit activity tracker for measuring steps in community-dwelling older adults (2015)

BMJ Open Sport & Exercise Medicine

Abstract

Comparison of Three Inexpensive Commercially Available Activity Monitors to a Previously Validated Tri-Axial Accelerometer (2015)

Medicine & Science in Sports & Exercise

Abstract

24 Hours of Sleep, Sedentary Behavior, and Physical Activity with Nine Wearable Devices (2015)

Medicine and Science in Sports and Exercise

Abstract

Validation of the Fitbit wireless activity tracker for prediction of energy expenditure. (2015)

Journal of Physical Activity & Health

Abstract

Capturing step counts at slow walking speeds in older adults: comparison of ankle and waist placement of measuring device. (2015)