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
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
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
Gender Differences in Regional Brain Activity in Patients with Chronic Primary Insomnia: Evidence from a Resting-State fMRI Study details	Dai, X.-J., Nie, X., Liu, X., Pei, L., Jiang, J., Peng, D.-c., Gong, H.-H., Zeng, X.-J., Wáng, Y.-X.J. and Zhan, Y.	2016	Journal of Clinical Sleep Medicine	Measurement	Flex	Patients Adults	Sleep	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
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
SleepSight: a wearables-based relapse prevention system for schizophrenia details	Kerz, M., Folarin, A., Meyer, N., Begale, M., MacCabe, J. and Dobson, R.J.	2016	Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing	Usability	Charge HR	Patients Adults	Sleep	UR
Consumer sleep tracking devices: a review of mechanisms, validity and utility details	Kolla, B.P., Mansukhani, S. and Mansukhani, M.P.	2016	Expert Review of Medical Devices	Review	Classic Ultra	NA	Sleep	URL
A Personalized Approach for Detecting Unusual Sleep from Time Series Sleep-Tracking Data details	Liang, Z.,Martell, M.A.C., and Nishimura T.	2016	2016 IEEE International Conference on Healthcare Informatics (ICHI)	Methods	Charge HR	Adults	Sleep	URL
Sleep Tracking in the Real World: A Qualitative Study into Barriers for Improving Sleep details	Liang, Z. and Ploderer, B.	2016	Proceedings of the 28th Australian Conference on Computer-Human Interaction	Usability		Adults	Sleep	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
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
Sleep Tracking, Wearable Technology, and Opportunities for Research and Clinical Care details	Shelgikar, A.V., Anderson, P.F. and Stephens, M.R.	2016	Chest	Review	One Flex Charge Surge	NA	Sleep	URL
Health Outcomes of Information System Use Lifestyles among Adolescents: Videogame Addiction, Sleep Curtailment and Cardio-Metabolic Deficiencies. details	Turel, O., Romashkin, A. and Morrison, K.M.	2016	PLOS ONE	Measurement	Ultra One	Adolescents	Sleep	URL
Sleep quality, but not quantity, is associated with self-perceived minor error rates among emergency department nurses details	Weaver, A.L., Stutzman, S.E., Supnet, C. and Olson, D.M.	2016	International Emergency Nursing	Measurement		Adults	Sleep	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
Pulmonary exposure to metal fume particulate matter cause sleep disturbances in shipyard welders details	Chuang, H.-C., Su, T.-Y., Chuang, K.-J., Hsiao, T.-C., Lin, H.-L., Hsu, Y.-T., Pan, C.-H., Lee, K.-Y., Ho, S.-C. and Lai, C.-H.	2017	Environmental Pollution	Measurement	Charge HR	Adults	Sleep
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
Restoring effective sleep tranquility (REST): A feasibility and pilot study details	Eakman, A.M., Schmid, A.A., Henry, K.L., Rolle, N.R., Schelly, C., Pott, C.E. and Burns, J.E.	2017	British Journal of Occupational Therapy	Intervention	Flex	Patients Adults	Sleep	URL
Home-Based Trials in Adolescent Migraine details	Gelfand, A.A., Qubty, W., Patniyot, I., Grimes, B., Pletcher, M.J., Goadsby, P.J. and Cummings, S.R.	2017	JAMA Neurology	Intervention		Adolescents	Sleep	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
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
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
Determining Resident Sleep during and after Call with Commercial Sleep Monitoring Devices. details	Morhardt, D.R., Luckenbaugh, A., Goldstein, C. and Faerber, G.J.	2017	Urology	Measurement	Flex	Adults	Sleep	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
Effect of sleep curtailment on dietary behavior and physical activity: A randomized crossover trial details	Tajiri, E., Yoshimura, E., Hatamoto, Y., Tanaka, H. and Shimoda, S.	2017	Physiology & Behavior	Measurement	Charge HR	Adults	Sleep	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
Habitual sleep duration and sleep duration variation are independently associated with body mass index details	Xu, X., Conomos, M.P., Manor, O., Rohwer, J.E., Magis, A.T. and Lovejoy, J.C.	2017	International Journal of Obesity	Measurement	Charge HR	Adults	Sleep	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
Feeling validated yet? A scoping review of the use of consumer-targeted wearable and mobile technology to measure and improve sleep details	Baron, K.G., Duffecy, J., Berendsen, M.A., Cheung Mason, I., Lattie, E.G. and Manalo, N.C.	2018	Sleep Medicine Reviews	Review	One Flex Charge Charge HR	NA	Sleep
Technology-Assisted Behavioral Intervention to Extend Sleep Duration: Development and Design of the Sleep Bunny Mobile App. details	Baron, K.G., Duffecy, J., Reid, K., Begale, M. and Caccamo, L.	2018	JMIR Mental Health	Intervention	Flex	Adults	Sleep
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
The role of sleep in adolescents' daily stress recovery: Negative affect spillover and positive affect bounce-back effects details	Chue, A.E., Gunthert, K.C., Kim, R.W., Alfano, C.A. and Ruggiero, A.R.	2018	Journal of Adolescence	Measurement	Flex	Adolescents	Sleep	URL
Buying time: a proof-of-concept randomized controlled trial to improve sleep quality and cognitive function among older adults with mild cognitive impairment details	Falck, R.S., Davis, J.C., Best, J.R., Li, L.C., Chan, P.C.Y., Wyrough, A.B., Landry, G.J. and Liu-Ambrose, T.	2018	Trials	Intervention	Flex	Older Adults	Sleep
Effects of Sleep, Physical Activity, and Shift Work on Daily Mood: a Prospective Mobile Monitoring Study of Medical Interns details	Kalmbach, D.A., Fang, Y., Arnedt, J.T., Cochran, A.L., Deldin, P.J., Kaplin, A.I. and Sen, S.	2018	Journal of General Internal Medicine	Measurement	One Charge	Adults	Sleep	URL
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
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
Altered connection properties of important network hubs may be neural risk factors for individuals with primary insomnia details	Liu, X., Zheng, J., Liu, B.-X. and Dai, X.-J.	2018	Scientific Reports	Measurement	Flex	Patients Adults	Sleep
Development of a Just-in-Time Adaptive mHealth Intervention for Insomnia: Usability Study details	Pulantara, I.W., Parmanto, B. and Germain, A.	2018	JMIR Human Factors	Methods	Charge	Adults	Sleep	URL
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
Feasibility Of Recording Sleep Quality And Sleep Duration Using Fitbit In Children With Asthma details	Sheth, A., Yip, H., Jaimini, U., Sridharan, V., Venkataramanan, R., Thirunarayam, K., Banerjee, T. and Kalra, M.	2018	Sleep	Usability	Flex	Children Adolescents	Sleep
Sleeping Well Trial: Increasing the effectiveness of treatment with continuous positive airway pressure using a weight management program in overweight adults with obstructive sleep apnoea-A stepped wedge randomised trial protocol details	Truby, H., Edwards, B.A., O'Driscoll, D.M., Young, A., Ghazi, L., Bristow, C., Roem, K., Bonham, M.P., Murgia, C., Day, K., Haines, T.P. and Hamilton, G.S.	2018	Nutrition & Dietetics	Methods	Flex	Patients Adults	Sleep	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
Ability of the Fitbit Alta HR to quantify and classify sleep in patients with suspected central disorders of hypersomnolence: A comparison against polysomnography details	Cook, Jesse D.; Eftekari, Sahand C.; Dallmann, Erika; Sippy, Megan; Plante, David T.	2019	Journal of Sleep Research	Usability	Alta HR	Adults	Sleep	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
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
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
Investigating the within-person relationships between activity levels and sleep duration using Fitbit data details	Liao, Yue; Robertson, Michael C.; Winne, Andrea; Wu, Ivan H. C.; Le, Thuan A.; Balachandran, Diwakar D.; Basen-Engquist, Karen M.	2020	Translational Behavioral Medicine	Intervention	Flex 2	Adults	Sleep	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
Comparison of the Fitbit\textregistered charge and polysomnography for measuring sleep quality in children with sleep disordered breathing details	Hakim, Mohammed; Miller, Rebecca; Hakim, Mumin; Tumin, Dmitry; Tobias, Joseph D.; Jatana, Kris R.; Raman, Vidya T.	2022	Minerva pediatrics	Usability	Charge	Children	Sleep	URL
Obstructive Sleep Apnea Home-Monitoring Using a Commercial Wearable Device details	Mokhtaran, Mehrshad; Sacchi, Lucia; Tibollo, Valentina; Risi, Irene; Ramella, Vittoria; Quaglini, Silvana; Fanfulla, Francesco	2022	Studies in health technology and informatics	Intervention	Charge HR	Patients	Sleep	URL
Effects of Sleep-Extend on glucose metabolism in women with a history of gestational diabetes: a pilot randomized trial details	Reutrakul, Sirimon; Martyn-Nemeth, Pamela; Quinn, Lauretta; Rydzon, Brett; Priyadarshini, Medha; Danielson, Kirstie K.; Baron, Kelly G.; Duffecy, Jennifer	2022	Pilot and feasibility studies	Measurement	Device unspecified	Adults	Sleep	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
Pre S cription Digita L Th E rap E utic for P atients with I nsomnia ( SLEEP-I): a protocol for a pragmatic randomised controlled trial details	Dreyer, Rachel P.; Berkowitz, Alyssa; Yaggi, Henry Klar; Schneeberg, Lynelle; Shah, Nilay D.; Emanuel, Lindsay; Kolla, Bhanuprakash; Jeffery, Molly Moore; Deeg, Mark; Ervin, Keondae; Thorndike, Frances; Ross, Joseph S.	2022	BMJ open	Intervention	Inspire 2	Patients	Sleep	URL
Feasibility, acceptability, and effectiveness of school-based dance movement psychotherapy for children with emotional and behavioral difficulties details	Moula, Zoe; Powell, Joanne; Brocklehurst, Shirley; Karkou, Vicky	2022	Frontiers in psychology	Intervention	Device unspecified	Children	Sleep	URL
Validation of Fitbit Charge 4 for assessing sleep in Chinese patients with chronic insomnia: A comparison against polysomnography and actigraphy details	Dong, Xiaofang; Yang, Sen; Guo, Yuanli; Lv, Peihua; Wang, Min; Li, Yusheng	2022	PloS One	Usability	Charge 4	Adults	Sleep	URL
Intra-individual impact of the COVID-19 pandemic on mental health and sleep in young adults details	Knickerbocker, Kelly J.; Cox, Evelyn A.; Dhawka, Luvna; Woods, Kerri; Ingram, Krista K.	2022	PloS One	Case Study Measurement	Versa 2	Adults	Sleep	URL
Evaluation of sleep quality and duration using wearable sensors in shift laborers of construction industry: A public health perspective details	Sathvik, S.; Krishnaraj, L.; Irfan, Muhammad	2022	Frontiers in Public Health	Case Study	Device unspecified	Adults	Sleep	URL
Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults details	Liang, Zilu	2022	Frontiers in Medical Technology	Measurement	Charge 3	Adults	Sleep
A randomized, double-blind, placebo-controlled trial investigating the effects of an Ocimum tenuiflorum (Holy Basil) extract (HolixerTM) on stress, mood, and sleep in adults experiencing stress details	Lopresti, Adrian L.; Smith, Stephen J.; Metse, Alexandra P.; Drummond, Peter D.	2022	Frontiers in Nutrition	Medical Case Study	Charge 4	Adults	Sleep
Medicinal cannabis improves sleep in adults with insomnia: a randomised double-blind placebo-controlled crossover study details	Ried, Karin; Tamanna, Tasnuva; Matthews, Sonja; Sali, Avni	2022	Journal of Sleep Research	Intervention	Inspire HR	Adults	Sleep
Sleep and seizure risk in epilepsy: bed and wake times are more important than sleep duration details	Stirling, Rachel E.; Hidajat, Cindy M.; Grayden, David B.; D'Souza, Wendyl J.; Naim-Feil, Jodie; Dell, Katrina L.; Schneider, Logan D.; Nurse, Ewan; Freestone, Dean; Cook, Mark J.; Karoly, Philippa J.	2022	Brain: A Journal of Neurology	Case Study	Device unspecified	Adults	Sleep
Performance of the Fitbit Charge 2 and Galaxy Watch 2 compared with polysomnography in assessing patients with obstructive sleep apnoea details	Byun, Jung-Ick; Noh, Kyung Chul; Shin, Won Chul	2023	Chronobiology International	Usability	Charge 2	Patients	Sleep
A useful tool or a new challenge? Hand-wrist-worn sleep trackers in patients with insomnia details	Ojalvo, Dávid; Pacheco, André Pekkola; Benedict, Christian	2023	Journal of Sleep Research	Usability	Inspire 2	Patients	Sleep
Daytime Sleep-Tracking Performance of Four Commercial Wearable Devices During Unrestricted Home Sleep details	Chinoy, Evan D.; Cuellar, Joseph A.; Jameson, Jason T.; Markwald, Rachel R.	2023	Nature and Science of Sleep	Usability	Inspire HR	Adults	Sleep	URL
Performance of seven consumer sleep-tracking devices compared with polysomnography details	Chinoy, Evan D.; Cuellar, Joseph A.; Huwa, Kirbie E.; Jameson, Jason T.; Watson, Catherine H.; Bessman, Sara C.; Hirsch, Dale A.; Cooper, Adam D.; Drummond, Sean P. A.; Markwald, Rachel R.	2021	Sleep	Usability Validation	Alta HR	Adults	Sleep
Performance of Four Commercial Wearable Sleep-Tracking Devices Tested Under Unrestricted Conditions at Home in Healthy Young Adults details	Chinoy, Evan D.; Cuellar, Joseph A.; Jameson, Jason T.; Markwald, Rachel R.	2022	Nature and Science of Sleep	Usability Validation	Inspire HR	Adults	Sleep
Influence of sleep duration and sex on age-related differences in heart rate variability: Findings from program 4 of the HAIE study details	Gonzales, Joaquin U.; Elavsky, Steriani; Cipryan, Lukáš; Jandačková, Vera; Burda, Michal; Jandačka, Daniel	2023	Sleep Medicine	Measurement	Charge 3 Charge 4	Adults	Sleep	URL
Exploratory Validation of Sleep-Tracking Devices in Patients with Psychiatric Disorders details	Ogasawara, Masaya; Takeshima, Masahiro; Kosaka, Shumpei; Imanishi, Aya; Itoh, Yu; Fujiwara, Dai; Yoshizawa, Kazuhisa; Ozaki, Norio; Nakagome, Kazuyuki; Mishima, Kazuo	2023	Nature and Science of Sleep	Usability	Sense	Patients	Sleep
Gut microbiota composition and functionality are associated with REM sleep duration and continuous glucose levels details	Arnoriaga-Rodríguez, María; Leal, Yenny; Mayneris-Perxachs, Jordi; Pérez-Brocal, Vicente; Moya, Andrés; Ricart, Wifredo; Fernández-Balsells, Mercè; Fernández-Real, José Manuel	2023	The Journal of Clinical Endocrinology and Metabolism	Case Study	Charge 3	Adults	Sleep
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
Sleep Duration on Workdays Is Correlated with Subjective Workload and Subjective Impact of High Workload on Sleep in Young Healthy Adults details	von Gall, Charlotte; Muth, Thomas; Angerer, Peter	2023	Brain Sciences	Case Study	Inspire	Adults	Sleep
Linking Sleep and Aggression: Examining the Role of Response Inhibition and Emotional Processing details	Bozzay, Melanie L.; Verona, Edelyn	2023	Clinical Psychological Science: A Journal of the Association for Psychological Science	Case Study	Flex	Adults	Sleep
Validation of the relationship between rapid eye movement sleep and sleep-related erections in healthy adults by a feasible instrument Fitbit Charge2 details	Liu, Guodong; Zhang, Yuyang; Zhang, Wei; Wu, Xu; Jiang, Hui; Zhang, Xiansheng	2023	Andrology	Case Study	Charge 2	Adults	Sleep	URL
A Longitudinal Pilot Study of Stress and Sleep in First-Year Osteopathic Medical Students details	Pattanaik, Sambit; Fastring, Danielle; Bateman, Robert C.	2023	Journal of Medical Education and Curricular Development	Case Study	Charge 3	Adults	Sleep
The Effects of Auricular Acupressure on Menopausal Symptoms, Stress, and Sleep in Postmenopausal Middle-Aged Women: A Randomized Single-Blind Sham-Controlled Trial details	Kim, Bomi; Park, Hyojung	2023	Journal of Midwifery \& Women's Health	Case Study	Charge 4	Adults	Sleep
Start-of-day oculomotor screening demonstrates the effects of fatigue and rest during a total immersion training program details	Roberts, Scott; Kufahl, Peter R.; Ryznar, Rebecca J.; Norris, Taylor; Patel, Sagar; Gubler, K. Dean; Paz, Dean; Schwimer, Greg; Besserman, Richard; LaPorta, Anthony J.	2023	Surgery	Case Study	Device unspecified	Adults	Sleep
Evaluation of the effectiveness of sleep hygiene education and FITBIT devices on quality of sleep and psychological worry: a pilot quasi-experimental study among first-year college students details	Bani Issa, Wegdan; Hijazi, Heba; Radwan, Hadia; Saqan, Roba; Al-Sharman, Alham; Samsudin, A. B. Rani; Fakhry, Randa; Al-Yateem, Nabeel; Rossiter, Rachel C.; Ibrahim, Ali; Moustafa, Ibrahim; Naja, Farah; Alameddine, Mohamad; Abbas, Nada; Abdelrahim, Dana N.; Al-Shujairi, Arwa; Awad, Manal	2023	Frontiers in Public Health	Case Study	Flex	Adults	Sleep
The impact of mindfulness training on posttraumatic stress disorder symptoms, subjective sleep quality, and objective sleep outcomes in police officers details	Imhoff-Smith, Theodore P.; Grupe, Daniel W.	2023	Psychological Trauma: Theory, Research, Practice and Policy	Case Study	Charge 2	Adults	Sleep
Resident Sleep During Traditional Home Call Compared to Night Float details	Chowdhary, Apoorva; Davis, John A.; Ding, Leona; Taravati, Parisa; Feng, Shu	2023	Journal of Academic Ophthalmology (2017)	Case Study	Alta HR	Adults	Sleep
Effects of sleep-corrected social jetlag on measures of mental health, cognitive ability, and brain functional connectivity in early adolescence details	Yang, Fan Nils; Picchioni, Dante; Duyn, Jeff H.	2023	Sleep	Case Study	Charge HR	Adolescents	Sleep
Latent Profiles of Sleep Patterns in Early Adolescence: Associations With Behavioral Health Risk details	Zhang, Linhao; Sasser, Jeri; Doane, Leah D.; Peltz, Jack; Oshri, Assaf	2023	The Journal of Adolescent Health: Official Publication of the Society for Adolescent Medicine	Case Study	Charge HR	Adolescents	Sleep
Effects of Sleep on Adolescents' Appetite, Dietary Intake, and Weight details	Juelich, Janell; Owens, Rhoda; Denny, Dawn; Raatz, Susan; Lindseth, Glenda	2023	SAGE open nursing	Case Study	Charge 3	Adolescents	Sleep
Postponing sleep after a stressful day: Patterns of stress, bedtime procrastination, and sleep outcomes in a daily diary approach details	Schmidt, Laura I.; Baetzner, Anke S.; Dreisbusch, Marina I.; Mertens, Alica; Sieverding, Monika	2023	Stress and Health: Journal of the International Society for the Investigation of Stress	Case Study	Charge HR	Adults	Sleep
Chronotype-Dependent Sleep Loss Is Associated with a Lower Amplitude in Circadian Rhythm and a Higher Fragmentation of REM Sleep in Young Healthy Adults details	von Gall, Charlotte; Holub, Leon; Pfeffer, Martina; Eickhoff, Simon	2023	Brain Sciences	Case Study	Inspire	Adults	Sleep
The effect of sleep-corrected social jetlag on crystalized intelligence, school performance, and functional connectome in early adolescence details	Yang, Fan Nils; Picchioni, Dante; Duyn, Jeff H.	2023	medRxiv: The Preprint Server for Health Sciences	Case Study	Charge HR	Adolescents	Sleep
Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study details	Lee, Taeyoung; Cho, Younghoon; Cha, Kwang Su; Jung, Jinhwan; Cho, Jungim; Kim, Hyunggug; Kim, Daewoo; Hong, Joonki; Lee, Dongheon; Keum, Moonsik; Kushida, Clete A.; Yoon, In-Young; Kim, Jeong-Whun	2023	JMIR mHealth and uHealth	Validation Systematic Review	Sense 2	Adults	Sleep
Functional brain connectivity predicts sleep duration in youth and adults details	Mummaneni, Anurima; Kardan, Omid; Stier, Andrew J.; Chamberlain, Taylor A.; Chao, Alfred F.; Berman, Marc G.; Rosenberg, Monica D.	2023	Human Brain Mapping	Case Study	Device unspecified	Children	Sleep
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
Selecting a sleep tracker from EEG-based, iteratively improved, low-cost multisensor, and actigraphy-only devices details	Ong, Ju Lynn; Golkashani, Hosein Aghayan; Ghorbani, Shohreh; Wong, Kian F.; Chee, Nicholas I. Y. N.; Willoughby, Adrian R.; Chee, Michael W. L.	2023	Sleep Health	Validation Systematic Review	Sense	Adults	Sleep
Evaluating Accuracy in Five Commercial Sleep-Tracking Devices Compared to Research-Grade Actigraphy and Polysomnography details	Kainec, Kyle A.; Caccavaro, Jamie; Barnes, Morgan; Hoff, Chloe; Berlin, Annika; Spencer, Rebecca M. C.	2024	Sensors (Basel, Switzerland)	Validation Systematic Review	Versa Inspire Garmin Vivosmart	Adults	Sleep
Links Between Objectively-Measured Hourly Smartphone Use and Adolescent Wake Events Across Two Weeks details	Garrett, Shedrick L.; Burnell, Kaitlyn; Armstrong-Carter, Emma L.; Nelson, Benjamin W.; Prinstein, Mitchell J.; Telzer, Eva H.	2023	Journal of Clinical Child and Adolescent Psychology: The Official Journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53	Case Study	Inspire 2	Adolescents	Sleep
Exploring Variations in Sleep Perception: Comparative Study of Chatbot Sleep Logs and Fitbit Sleep Data details	Jang, Hyunchul; Lee, Siwoo; Son, Yunhee; Seo, Sumin; Baek, Younghwa; Mun, Sujeong; Kim, Hoseok; Kim, Icktae; Kim, Junho	2023	JMIR mHealth and uHealth	Case Study	Inspire 2	Adults	Sleep
Patient Engagement and Provider Effectiveness of a Novel Sleep Telehealth Platform and Remote Monitoring Assessment in the US Military: Pilot Study Providing Evidence-Based Sleep Treatment Recommendations details	Wickwire, Emerson M.; Collen, Jacob; Capaldi, Vincent F.; Williams, Scott G.; Assefa, Samson Z.; Adornetti, Julianna P.; Huang, Kathleen; Venezia, Janet M.; Jones, Rachell L.; Johnston, Christine W.; Thomas, Connie; Thomas, Mary Ann; Mounts, Charles; Drake, Christopher L.; Businelle, Michael S.; Grandner, Michael A.; Manber, Rachel; Albrecht, Jennifer S.	2023	JMIR formative research	Medical	Inspire 2	Patients	Sleep
Association between sleep quality and nocturnal erection monitor by RigiScan in erectile dysfunction patients: a prospective study using fitbit charge 2 details	Zhang, Yuyang; Zhang, Wei; Feng, Xingliang; Liu, Guodong; Wu, Xu; Jiang, Hui; Zhang, Xiansheng	2023	Basic and Clinical Andrology	Case Study	Charge 2	Patients	Sleep
A comparison of the rates of clock-based nocturnal hypoglycemia and hypoglycemia whilst asleep among people living with diabetes: findings from the Hypo-METRICS study details	Martine-Edith, Gilberte; Divilly, Patrick; Zaremba, Natalie; Søholm, Uffe; Broadley, Melanie; Baumann, Petra Martina; Mahmoudi, Zeinab; Gomes, Mikel; Ali, Namam; Abbink, Evertine J.; de Galan, Bastiaan E.; Brøsen, Julie Maria Bøggild; Pedersen-Bjergaard, Ulrik; Vaag, Allan A.; McCrimmon, Rory; Renard, Eric; Heller, Simon; Evans, Mark; Cigler, Monika; Mader, Julia K.; Speight, Jane; Pouwer, Frans; Amiel, Stephanie; Choudhary, Pratik	2024	Diabetes Technology \& Therapeutics	Case Study	Charge 4	Adults	Sleep
Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review details	Schyvens, An-Marie; Van Oost, Nina Catharina; Aerts, Jean-Marie; Masci, Federica; Peters, Brent; Neven, An; Dirix, Hélène; Wets, Geert; Ross, Veerle; Verbraecken, Johan	2024	JMIR mHealth and uHealth	Systematic Review	Charge 4 Garmin Vivosmart 4	NA	Sleep
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
A pilot randomised controlled trial exploring the feasibility and efficacy of a human-AI sleep coaching model for improving sleep among university students details	Liu, Jintana; Ito, Sakura; Ngo, Tra My; Lawate, Ashwini; Ong, Qi Chwen; Fox, Tatiana Erlikh; Chang, Si Yuan; Phung, Duy; Nair, Elizabeth; Palaiyan, Malar; Joty, Shafiq; Abisheganaden, John; Lee, Chuen Peng; Lwin, May Oo; Theng, Yin Leng; Ho, Moon-Ho Ringo; Chia, Michael; Bojic, Iva; Car, Josip	2024	Digital Health	Intervention	Inspire 2	Adults	Sleep
Associations between sleep health, negative reinforcement learning, and alcohol use among South Florida college students with elevated internalizing symptoms details	Sollenberger, Nathan A.; Cummings, Logan R.; Freitag, Josefina; Trucco, Elisa M.; Gomez, Sthefany; Giraldo, Melanie; Muse, Gabriela; Mattfeld, Aaron T.; McMakin, Dana L.	2024	Alcohol (Fayetteville, N.Y.)	Case Study	Charge 4	Adults	Sleep
Pilot study comparing sleep logs to a commercial wearable device in describing the sleep patterns of physicians-in-training details	Hassinger, Amanda B.; Kwon, Misol; Wang, Jia; Mishra, Archana; Wilding, Gregory E.	2024	PloS One	Usability	Inspire	Adults	Sleep
The relationship between wearable-derived sleep features and relapse in Major Depressive Disorder details	Matcham, F.; Carr, E.; Meyer, N.; White, K. M.; Oetzmann, C.; Leightley, D.; Lamers, F.; Siddi, S.; Cummins, N.; Annas, P.; de Girolamo, G.; Haro, J. M.; Lavelle, G.; Li, Q.; Lombardini, F.; Mohr, D. C.; Narayan, V. A.; Penninx, B. W. H. J.; Coromina, M.; Riquelme Alacid, G.; Simblett, S. K.; Nica, R.; Wykes, T.; Brasen, J. C.; Myin-Germeys, I.; Dobson, R. J. B.; Folarin, A. A.; Ranjan, Y.; Rashid, Z.; Dineley, J.; Vairavan, S.; Hotopf, M.; RADAR-CNS consortium	2024	Journal of Affective Disorders	Case Study	Charge	Adults	Sleep
Effect of desflurane maintenance on postoperative sleep quality in patients undergoing elective breast surgery: A non-inferiority randomized controlled trail details	Wang, Xiaohua; Xiong, Bingrui; Wu, Tangjing; Liu, Xin; Li, Ke; Wang, Shan; Deng, Ming-Gang; Peng, Mian	2024	Sleep Medicine	Case Study	Charge 2	Patients	Sleep
Individual-Level Experiences of Structural Inequity and Their Association with Subjective and Objective Sleep Outcomes in the Adolescent Brain Cognitive Development Study details	Harriman, Nigel Walsh; Chen, Jarvis T.; Lee, Sunmin; Slopen, Natalie	2024	The Journal of Adolescent Health: Official Publication of the Society for Adolescent Medicine	Case Study	Charge HR	Adolescents	Sleep
An adapted transdiagnostic sleep and circadian intervention for adults with excess weight and suboptimal sleep health: pilot study results details	Imes, Christopher C.; Kline, Christopher E.; Patel, Sanjay R.; Sereika, Susan M.; Buysse, Daniel J.; Harvey, Allison G.; Burke, Lora E.	2024	Sleep Advances: A Journal of the Sleep Research Society	Intervention	Charge 4	Adults	Sleep
Accuracy of parent-reported sleep duration among adolescents assessed using accelerometry details	Turan, Ozerk; Garner, Jonathan; Chang, Linda; Isaiah, Amal	2024	Pediatric Research	Case Study	Charge 2 Charge 4	Children	Sleep
Sleep Behavior in Royal Australian Navy Shift Workers by Shift and Exposure to the SleepTank App details	Devine, Jaime K.; Cooper, Nadine; Choynowski, Jake; Hursh, Steven R.	2024	Military Medicine	Measurement	Versa 2	Adults	Sleep
Variations in objectively measured sleep parameters in patients with different premature ejaculation syndromes details	Wu, Xu; Zhang, Yuyang; Jiang, Hui; Zhang, Xiansheng	2024	The Journal of Sexual Medicine	Case Study	Charge 2	Patients	Sleep
Scrolling Your Sleep Away: The Effects of Bedtime Device Use on Sleep Among Young Adults with Poor Sleep details	Chkhaidze, Ana; Millar, Brett M.; Revenson, Tracey A.; Mindlis, Irina	2024	International Journal of Behavioral Medicine	Intervention	Charge 3	Adults	Sleep
Accuracy of Three Commercial Wearable Devices for Sleep Tracking in Healthy Adults details	Robbins, Rebecca; Weaver, Matthew D.; Sullivan, Jason P.; Quan, Stuart F.; Gilmore, Katherine; Shaw, Samantha; Benz, Abigail; Qadri, Salim; Barger, Laura K.; Czeisler, Charles A.; Duffy, Jeanne F.	2024	Sensors (Basel, Switzerland)	Case Study	Sense 2	Adults	Sleep
Associations of seasonally available global positioning systems-derived walkability and objectively measured sleep in the Nurses' Health Study 3 Mobile Health Substudy details	Hu, Cindy R.; Wilt, Grete E.; Roscoe, Charlotte; Iyer, Hari S.; Kessler, William H.; Laden, Francine; Chavarro, Jorge E.; Coull, Brent; Redline, Susan; James, Peter; Hart, Jaime E.	2024	Environmental Epidemiology (Philadelphia, Pa.)	Intervention	Charge HR Charge 2 Charge 3	Adults	Sleep
Assessment of sleep characteristics using Fitbit Charge 4 in head and neck cancer patients undergoing palliative chemotherapy and radiotherapy: a prospective observational study details	Jain, Anuj; Suryavanshi, Jha; Waindeskar, Vaishali; Gupta, Manish; Kaushal, Ashutosh; Kumar, Harish	2024	Palliative Care and Social Practice	Case Study	Charge 4	Patients	Sleep
Biometrically measured sleep in medical students as a predictor of psychological health and academic experiences in the preclinical years details	Oberleitner, Lindsay M.; Baxa, Dwayne M.; Pickett, Scott M.; Sawarynski, Kara E.	2024	Medical Education Online	Survey Research	Charge HR	Adults	Sleep
Prospective associations between Sleep, Sensation-Seeking and Mature Screen Usage in Early Adolescents: Findings from the Adolescent Brain Cognitive Development (ABCD) Study details	Zhang, Linhao; Oshri, Assaf; Carvalho, Cory; Uddin, Lucina Q.; Geier, Charles; Nagata, Jason M.; Cummins, Kevin; Hoffman, Elizabeth A.; Tomko, Rachel L.; Chaarani, Bader; Squeglia, Lindsay M.; Wing, David; Mason, Michael J.; Fuemmeler, Bernard; Lisdahl, Krista; Tapert, Susan F.; Baker, Fiona C.; Kiss, Orsolya	2024	Sleep	Case Study	Charge HR	Adolescents	Sleep
Impact of Environmental Noise and Sleep Health on Pediatric Hypertension Incidence: ABCD Study details	De Moraes, Augusto César F.; Ma, Martin Y.; Nascimento-Ferreira, Marcus V.; Hunt, Ethan H.; Hoelscher, Deanna M.	2024	Journal of the American Heart Association	Case Study	Charge HR 2	Adolescents	Sleep
Increased Steps in Japanese Older Adults Associated with Improved Winter Sleep Quality details	Harai, Nozomi; Okuma, Hideyuki; Tsuchiya, Kyoichiro; Matsuoka, Satoshi; Kashiwagi, Kenji	2024	Gerontology \& Geriatric Medicine	Case Study	Charge 5	Older Adults	Sleep
Adherence to the Mediterranean diet and sleep quality are inter-correlated with flash glucose monitoring (FGM)-measured glycemia among children with type 1 diabetes details	Muayyad, Mariam; Abusnana, Salah; Mussa, Bashair M.; Helal, Radwa; Abdelrahim, Dana N.; Abdelreheim, Naguib Hassan; Al Amiri, Elham; Daboul, Mays; Al-Abadla, Zainab; Lessan, Nader; Faris, MoezAlIslam E.	2024	Journal of Education and Health Promotion	Case Study	Ace 3 Ace 2	Patients	Sleep
Efficacy of Auricular Acupressure Combined With Positive Psychological Techniques on Sleep Quality in Cancer Patients Undergoing Radiotherapy: A Randomized Controlled Trial details	Bi, Liuna; Gao, Wenjuan; Zhang, Qian; Li, Na; Zhang, Xian; Han, Jing	2024	Cancer Nursing	Intervention	Charge 3	Patients	Sleep
Feasibility of at-Home Sleep Monitoring in Adolescents with and without Concussion details	Donahue, Catherine C.; Smulligan, Katherine L.; Wingerson, Mathew J.; Kniss, Joshua R.; Simon, Stacey L.; Wilson, Julie C.; Howell, David R.	2024	Nature and Science of Sleep	Usability	Charge 2	Adolescents	Sleep
Commonly Used Dose of Montmorency Tart Cherry Powder Does Not Improve Sleep or Inflammation Outcomes in Individuals with Overweight or Obesity details	Tucker, Robin M.; Kim, Nahyun; Gurzell, Eric; Mathi, Sruti; Chavva, Shreya; Senthilkumar, Dharshini; Bartunek, Olivia; Fenton, Kayla C.; Herndon-Fenton, Sidney J.; Cardino, Vanessa N.; Cooney, Gabrielle M.; Young, Sam; Fenton, Jenifer I.	2024	Nutrients	Case Study	Inspire 3	Adults	Sleep
Dialysis-Imposed Patterns of Nocturnal Sleep Duration: A Multi-Center Prospective Study in Patients Using a Wearable Activity Tracker details	Han, Maggie; van der Sande, Frank M.; Kooman, Jeroen P.; Tao, Xia; Preciado, Priscila; Tisdale, Lela; Thwin, Ohnmar; Kotanko, Peter	2025	Kidney360	Case Study	Charge 2	Patients	Sleep
Validation of the Caen Chronotype Questionnaire: Exploring the added value of amplitude and correlations with actigraphy details	Hickman, Robert; Lai Jie, Daniel; Shergill, Sukhi; Laborde, Sylvain; D'Oliveira, Teresa C.	2025	Chronobiology International	Case Study	Charge 4	Adults	Sleep
Assessment of Sleep Measures and their agreement: Youth-Reported, Caregiver-Reported, and Fitbit-Derived Data in a Large Early Adolescent Cohort details	Kiss, Orsolya; Shaska, Adrianna; Müller-Oehring, Eva M.; Hasler, Brant P.; Franzen, Peter L.; Fitzgerald, Douglas H.; Clark, Duncan B.; Baker, Fiona C.	2025	Sleep	Case Study	Charge 2	Adolescents	Sleep
Tension and Trauma Releasing Exercises for People with Multiple Sclerosis: A Randomized Controlled Trial details	Skovgaard, Lasse; Trénel, Philipp; Hanehøj, Kirsten; Lynning, Marie	2025	Advances in Mind-Body Medicine	Intervention Measurement	Charge 3	Patients	Sleep
Sleep duration and efficiency moderate the effects of prenatal and childhood ambient pollutant exposure on global white matter microstructural integrity in adolescence details	Cotter, Devyn L.; Kiss, Orsolya; Ahmadi, Hedyeh; de Jesus, Alethea; Schwartz, Joel; Baker, Fiona C.; Hackman, Daniel A.; Herting, Megan M.	2025	bioRxiv: The Preprint Server for Biology	Case Study	Charge 2	Adolescents	Sleep

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.

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.

Gender Differences in Regional Brain Activity in Patients with Chronic Primary Insomnia: Evidence from a Resting-State fMRI Study (2016)

Journal of Clinical Sleep Medicine

Abstract

Study Objectives: To explore the regional brain activities in patients with chronic primary insomnia (PCPIs) and their sex differences. Methods: Forty-two PCPIs (27 females, 15 males) and 42 good sleepers (GSs; 24 females, 18 males) were recruited. Six PCPIs (3 males, 3 females) were scanned twice by MRI to examine the test-retest reliability. Amplitude of low frequency fluctuation (ALFF) method was used to assess the local brain features. The mean signal values of the different ALFF areas were analyzed with a receiver operating characteristic (ROC) curve. Simple linear regression analysis was performed to investigate the relationships between clinical features and different brain areas. Results: Both female and male PCPIs showed higher ALFF in the temporal lobe and occipital lobe, especially in female PCPIs. Female PCPIs had lower ALFF in the bilateral cerebellum posterior lobe, left dorsolateral prefrontal cortex, and bilateral limbic lobe; however, male PCPIs showed lower ALFF in the left occipital gyrus. The mean signal value of the cerebellum in female PCPIs showed negative correlations with negative emotions. Compared with male PCPIs, female PCPIs showed higher ALFF in the bilateral middle temporal gyrus and lower ALFF in the left limbic lobe. The different areas showed high test-retest stability (Clusters of contiguous volumes = 1080 mm3 with an intraclass correlation coefficient = 0.80) and high degree of sensitivity and specificity. Conclusions: Female PCPIs showed more regional brain differences with higher and lower ALFF responses than male PCPIs. However, they shared analogous excessive hyperarousal mechanism and wide variations in aberrant brain areas.

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.

Health Outcomes of Information System Use Lifestyles among Adolescents: Videogame Addiction, Sleep Curtailment and Cardio-Metabolic Deficiencies. (2016)

PLOS ONE

Abstract

BACKGROUND AND OBJECTIVE: Obesity is a rising problem among adolescents in modern societies; it results in long-term cardio-metabolic problems. Possible overlooked drivers of obesity and its consequent cardio-metabolic deficits include videogame addiction and the resulting curtailed sleep; both are growing problems among adolescents. The objective of this study is to examine possible associations among these concepts in adolescents, as a means to point to plausible interventions. METHODS: Data were collected from 94 adolescents who play videogames and are enrolled in outpatient clinics, using surveys, wearable sleep monitors (FitBit), physical exams, and blood tests at three points in time. These data were subjected to structural equation modeling (SEM) analyses and bootstrapping-based mediation testing procedures. RESULTS: Videogame addiction among adolescents was negatively associated with sleep duration (ß = -0.24). Sleep duration was negatively associated with obesity (ß = -0.30), which in turn was associated with elevated blood pressure (ß = 0.26), low high-density lipoprotein cholesterol (ß = -0.18), high triglycerides (ß = 0.61), and high insulin resistance (ß = 0.39). The model explained 36.2% of the variation in sleep duration, 32.7% of the variation in obesity, and between 12.8% and 28.1% of the variation in cardio-metabolic indicators. Post-hoc analyses indicated that curtailed sleep is a possible full mediator of the association between videogame addiction, abdominal obesity and the associated cardio-metabolic deficits. CONCLUSION: The findings point to possible information systems use lifestyle-health links, which behooves researchers and practitioners to pay closer attention to possible adverse health outcomes of technology-related addictions. Interventions that target problematic video-gaming and sleep should be devised as a possible means for improving adolescents' long-term cardio-metabolic health.

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.

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.

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.

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.

Effect of sleep curtailment on dietary behavior and physical activity: A randomized crossover trial (2017)

Physiology & Behavior

Abstract

Our objective was to clarify the effect of sleep curtailment on energy intake (EI) and physical activity under free-living conditions. Participants were 16 healthy women aged 21–22 years. A randomized crossover trial design was used to compare a short sleep condition (SS): 4 h/night (2:00–6:00) and a control sleep condition (CS): 7 h/night (23:00–6:00). Each condition comprised 3 consecutive nights. Sleep duration was assessed using a wristwatch-type accelerometer at home. All living activities except sleeping were free-living. Physical activity was assessed using a tri-axial accelerometer, and was categorized by intensity level (sedentary; sedentary to light; moderate to vigorous). Participants were asked to purchase and consume meals with visible nutrient information. EI was evaluated by adding values from these food labels. Mean sleep duration in the two conditions was significantly different (4.3 ± 0.3 vs. 7.1 ± 0.4 h, p < 0.01). For the shared wakefulness period in the two conditions (6:00–23:00), step counts and physical activity were not significantly different. Sedentary time (878 ± 61 vs. 727 ± 40 min, p < 0.01), and sedentary to light-intensity activity time (1122 ± 18 vs. 932 ± 63 min, p textless 0.01) were significantly increased in SS (waking time, 06:00–02:00) compared with CS (waking time, 06:00–23:00). However, these significant effects were clearly attenuated after adjustment for awake time (p ≥ 0.05). Total EI was not significantly different between conditions (8.64 ± 0.82 vs. 8.46 ± 1.28 MJ, p = 0.05), nor were leptin levels (p > 0.05), but insulin and cortisol levels after SS were significantly higher than after CS (p < 0.05). In this study, physical activity was increased in the SS condition and attributed to differences in awake time between conditions. However, there were no differences in EI. Further studies to investigate the effect of sleep curtailment on weight gain through stress and insulin resistance are necessary.

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.

Habitual sleep duration and sleep duration variation are independently associated with body mass index (2017)

International Journal of Obesity

Abstract

BACKGROUND: Sleep plays a vital role in maintaining homeostasis and promoting health. Previous studies show that shorter sleep duration is associated with elevated body mass index (BMI) and other cardiovascular risk factors. The goal of this study was to investigate the effects of habitual sleep duration and nightly sleep duration variation based on daily device-recorded data on BMI and obesity-related biomarkers. METHOD: 748 individuals (50.6% females, 85.4% European-Americans, average age: 49.7 years old) participated in a commercial lifestyle coaching program beginning in July 2015. Daily sleep data were recorded by Fitbit Charge HRTM wristbands. Clinical laboratory blood tests were measured up to 3 times over a 12-month period. Linear regression models were used for cross-sectional analyses, and generalized estimating equations for longitudinal analyses. All models were adjusted for age, sex, geographic location, season, genetic ancestry inferred from whole genome sequencing data, and BMI (if applicable). Multiple testing issues were corrected by False Discovery Rate (FDR). RESULTS: We calculated habitual sleep duration and nightly sleep duration variation. In general, females slept 15-minutes longer on average than males. A negative correlation was found between habitual sleep duration and BMI (ß=-1.12, standard error=0.25, Ptextless0.001). Moreover, we identified a positive correlation between sleep duration variation and BMI (ß=2.97, standard error=0.79, Ptextless0.001) while controlling for sleep duration, indicating that larger sleep duration variation is significantly and independently associated with increased BMI. CONCLUSION: We explored the impact of habitual sleep duration and sleep duration variation, and identified that shorter habitual sleep duration and larger duration variation were independently associated with increased BMI.

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).

Technology-Assisted Behavioral Intervention to Extend Sleep Duration: Development and Design of the Sleep Bunny Mobile App. (2018)

JMIR Mental Health

Abstract

BACKGROUND Despite the high prevalence of short sleep duration (29.2% of adults sleep textless6 hours on weekdays), there are no existing theory-based behavioral interventions to extend sleep duration. The popularity of wearable sleep trackers provides an opportunity to engage users in interventions. OBJECTIVE The objective of this study was to outline the theoretical foundation and iterative process of designing the "Sleep Bunny," a technology-assisted sleep extension intervention including a mobile phone app, wearable sleep tracker, and brief telephone coaching. We conducted a two-step process in the development of this intervention, which was as follows: (1) user testing of the app and (2) a field trial that was completed by 2 participants with short sleep duration and a cardiovascular disease risk factor linked to short sleep duration (body mass index [BMI] textgreater25). METHODS All participants had habitual sleep duration textless6.5 hours verified by 7 days of actigraphy. A total of 6 individuals completed initial user testing in the development phase, and 2 participants completed field testing. Participants in the user testing and field testing responded to open-ended surveys about the design and utility of the app. Participants in the field testing completed the Epworth Sleepiness Scale and also wore an actigraph for a 1-week baseline period and during the 4-week intervention period. RESULTS The feedback suggests that users enjoyed the wearable sleep tracker and found the app visually pleasing, but they suggested improvements to the notification and reminder features of the app. The 2 participants who completed the field test demonstrated significant improvements in sleep duration and daytime sleepiness. CONCLUSIONS Further testing is needed to determine effects of this intervention in populations at risk for the mental and physical consequences of sleep loss.

Buying time: a proof-of-concept randomized controlled trial to improve sleep quality and cognitive function among older adults with mild cognitive impairment (2018)

Trials

Abstract

Current evidence suggests that good quality sleep is associated with preserved cognitive function and reduced dementia risk in older adults. Sleep complaints are especially common among older adults with mild cognitive impairment (MCI), and this may contribute to their increased risk for progression to dementia. Thus, improving their sleep may be important for maintaining their cognitive health. Chronotherapy is a set of intervention strategies that can improve sleep quality through strengthening the entrainment of the biological clock to the solar light-dark cycle, and includes strategies such as (1) bright light therapy (BLT); (2) physical activity (PA); and (3) good sleep hygiene. Of these strategies, BLT is the most potent and is based on providing individualized timing to entrain circadian rhythms. Thus, a personalized chronotherapy intervention of individually timed BLT and individually tailored PA promotion, in conjunction with general sleep hygiene education may promote older adult sleep quality. We therefore aim to carry out a proof-of-concept randomized controlled trial (RCT) to examine the efficacy of such a personalized chronotherapy intervention to improve sleep quality among older adults with MCI. This was a 24-week RCT of a personalized chronotherapy intervention aimed to primarily improve sleep quality as measured by the MotionWatch8©. Participants in the personalized chronotherapy group (INT) will receive four once-weekly, general sleep hygiene education classes, followed by 20 weeks of (1) individually timed BLT and (2) bi-weekly, individually tailored PA counseling phone calls in conjunction with receiving a consumer-available PA tracker--the Fitbit® Flex™. Ninety-six adults (aged 65-85 years) classified as having MCI (i.e., Mini-Mental State Exam (MMSE) ≥ 24; Montreal Cognitive Assessment (MoCA) ≤ 26; without dementia or significant functional impairment) will be randomized to either INT or a waitlist control group (CON). The results of this trial will help determine if a personalized chronotherapy intervention that includes individually timed BLT and individually tailored PA promotion, along with general sleep hygiene education can promote sleep quality among older adults at increased risk for dementia. Our results will help inform best practices for promoting sleep quality among older adults with MCI. ClinicalTrials.gov , NCT02926157 . Registered on 6 October 2016.

Development of a Just-in-Time Adaptive mHealth Intervention for Insomnia: Usability Study (2018)

JMIR Human Factors

Abstract

Background: Healthy sleep is a fundamental component of physical and brain health. Insomnia, however, is a prevalent sleep disorder that compromises functioning, productivity, and health. Therefore, developing efficient treatment delivery methods for insomnia can have significant societal and personal health impacts. Cognitive behavioral therapy for insomnia (CBTI) is the recommended first-line treatment of insomnia but access is currently limited for patients, since treatment must occur in specialty sleep clinics, which suffer from an insufficient number of trained clinicians. Smartphone-based interventions offer a promising means for improving the delivery of CBTI. Furthermore, novel features such as real-time monitoring and assessment, personalization, dynamic adaptations of the intervention, and context awareness can enhance treatment personalization and effectiveness, and reduce associated costs. Ultimately, this “Just in Time Adaptive Intervention” for insomnia--an intervention approach that is acceptable to patients and clinicians, and is based on mobile health (mHealth) platform and tools--can significantly improve patient access and clinician delivery of evidence-based insomnia treatments. Objective: This study aims to develop and assess the usability of a Just in Time Adaptive Intervention application platform called iREST (“interactive Resilience Enhancing Sleep Tactics”) for use in behavioral insomnia interventions. iREST can be used by both patients and clinicians. Methods: The development of iREST was based on the Iterative and Incremental Development software development model. Requirement analysis was based on the case study’s description, workflow and needs, clinician inputs, and a previously conducted BBTI military study/implementation of the Just in Time Adaptive Intervention architecture. To evaluate the usability of the iREST mHealth tool, a pilot usability study was conducted. Additionally, this study explores the feasibility of using an off-the-shelf wearable device to supplement the subjective assessment of patient sleep patterns. Results: The iREST app was developed from the mobile logical architecture of Just in Time Adaptive Intervention. It consists of a cross-platform smartphone app, a clinician portal, and secure 2-way communications platform between the app and the portal. The usability study comprised 19 Active Duty Service Members and Veterans between the ages of 18 and 60. Descriptive statistics based on in-app questionnaires indicate that on average, 12 (mean 12.23, SD 8.96) unique devices accessed the clinician portal per day for more than two years, while the app was rated as “highly usable”, achieving a mean System Usability Score score of 85.74 (SD 12.37), which translates to an adjective rating of “Excellent”. The participants also gave high scores on “ease of use and learnability” with an average score of 4.33 (SD 0.65) on a scale of 1 to 5. Conclusions: iREST provides a feasible platform for the implementation of Just in Time Adaptive Intervention in mHealth-based and remote intervention settings. The system was rated highly usable and its cross-platformness made it readily implemented within the heavily segregated smartphone market. The use of wearables to track sleep is promising; yet the accuracy of this technology needs further improvement. Ultimately, iREST demonstrates that mHealth-based Just in Time Adaptive Intervention is not only feasible, but also works effectively. [JMIR Hum Factors 2018;5(2):e21]

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.

Ability of the Fitbit Alta HR to quantify and classify sleep in patients with suspected central disorders of hypersomnolence: A comparison against polysomnography (2019)

Journal of Sleep Research

Abstract

Measuring sleep duration and early onset rapid eye movement sleep (REMS) is critical in the assessment of suspected central disorders of hypersomnolence (CDH). Current multi-sensor activity trackers that integrate accelerometry and heart rate are purported to accurately quantify sleep time and REMS; however, their utility in suspected CDH has not been established. This investigation aimed to determine the ability of a current, multi-sensor tracker, Fitbit Alta HR (FBA-HR), to quantify and classify sleep in patients with suspected CDH relative to polysomnography (PSG). Forty-nine patients (46 female; mean age, 30.3 ± 9.84 years) underwent ad libitum PSG with concurrent use of the FBA-HR. FBA-HR sleep variable quantification was assessed using Bland-Altman analysis. FBA-HR all sleep (AS), light sleep (LS; PSG N1 + N2), deep sleep (DS; PSG N3) and REMS classification was evaluated using epoch-by-epoch comparisons. FBA-HR-detected sleep-onset rapid eye movement periods (SOREMPs) were compared against PSG SOMREMPs. FBA-HR displayed significant overestimation of total sleep time (11.6 min), sleep efficiency (1.98{\%}) and duration of deep sleep (18.2 min). FBA-HR sensitivity and specificity were as follows: AS, 0.96, 0.58; LS, 0.73, 0.72;DS, 0.67, 0.92; REMS, 0.74, 0.93. The device failed to detect any nocturnal SOREMPs. Device performance did not differ appreciably among diagnostic subgroups. These results suggest FBA-HR cannot replace EEG-based measurements of sleep and wake in the diagnostic assessment of suspected CDH, and that improvements in device performance are required prior to adoption in clinical or research settings.

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.

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.

Investigating the within-person relationships between activity levels and sleep duration using Fitbit data (2020)

Translational Behavioral Medicine

Abstract

The advancement of wearable technologies provides opportunities to continuously track individuals' daily activity levels and sleep patterns over extended periods of time. These data are useful in examining the reciprocal relationships between physical activity and sleep at the intrapersonal level. The purpose of this study is to test the bidirectional relationships between daily activity levels and sleep duration. The current study analyzed activity and sleep data collected from a Fitbit device as part of a 6 month employer-sponsored weight loss program. A total of 105 overweight/obese adults were included (92{\%} female, 70{\%} obese, and 44{\%} Hispanic). Multilevel models were used to examine (a) whether daily active and sedentary minutes predicted that night's sleep duration and (b) whether sleep duration predicted active and sedentary minutes the following day. Potential extended effects were explored by using a 2 day average of the activity minutes/sleep duration as the predictor. No significant relationships between active minutes and sleep duration were found on a daily basis. However, having less sleep over two nights than one's usual level was associated with an increased likelihood of engaging in some physical activity the following day. There was a significant bidirectional negative association between sedentary minutes and sleep duration for both the daily and 2 day models. Data from wearable trackers, such as Fitbit, can be used to investigate the daily within-person relationship between activity levels and sleep duration. Future studies should investigate other sleep metrics that may be obtained from wearable trackers, as well as potential moderators and mediators of daily activity levels and sleep.

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.

Effects of Sleep-Extend on glucose metabolism in women with a history of gestational diabetes: a pilot randomized trial (2022)

Pilot and feasibility studies

Abstract

Women with a history of gestational diabetes (GDM) are at 7-fold increase in the risk of developing diabetes. Insufficient sleep has also been shown to increase diabetes risk. This study aimed to explore the feasibility of a sleep extension in women with a history of GDM and short sleep, and effects on glucose metabolism. Women age 18–45 years with a history of GDM and actigraphy confirmed short sleep duration (<7 h/night) on weekdays were randomized at a ratio of 1 control (heathy living information) to 2 cases (6 weeks of “Sleep-Extend” intervention: use of a Fitbit, weekly digital content, and weekly coaching to increase sleep duration). An oral glucose tolerance test (OGTT), 7-day actigraphy recording, and questionnaires were obtained at baseline and 6 weeks. Mean differences between baseline and end-of-intervention parameters were compared using independent samples t-tests. Mean (SD) sleep duration increased within the Sleep-Extend group (n=9, +26.9 (42.5) min) but decreased within the controls (n=5, − 9.1 (20.4) min), a mean difference (MD) of 35.9 min (95% confidence interval (CI) − 8.6, 80.5). Fasting glucose increased, but less in Sleep-Extend vs. control groups (1.6 (9.4) vs 10.4 (8.2) mg/dL, MD − 8.8 mg/dL (95% CI − 19.8, 2.1), while 2-h glucose levels after an OGTT did not differ. Compared to controls, Sleep-Extend had decreased fatigue score (MD − 10.6, 95%CI − 20.7, − 0.6), and increased self-report physical activity (MD 5036 MET- minutes/week, 95%CI 343, 9729. Fitbit compliance and satisfaction in Sleep-Extend group was high. Sleep extension is feasible in women with a history of GDM, with benefits in fatigue and physical activity, and possibly glucose metabolism. These data support a larger study exploring benefits of sleep extension on glucose metabolism in these high-risk women. ClinicalTrials.gov , NCT03638102 (8/20/2018)

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.

Pre S cription Digita L Th E rap E utic for P atients with I nsomnia ( SLEEP-I): a protocol for a pragmatic randomised controlled trial (2022)

BMJ open

Abstract

Introduction Cognitive behavioural therapy for insomnia (CBT-I) is effective at treating chronic insomnia, yet in-person CBT-I can often be challenging to access. Prior studies have used technology to bridge barriers but have been unable to extensively assess the impact of the digital therapeutic on real-world patient experience and multidimensional outcomes. Among patients with insomnia, our aim is to determine the impact of a prescription digital therapeutic (PDT) (PEAR-003b, FDA-authorised as Somryst; herein called PDT) that provides mobile-delivered CBT-I on patient-reported outcomes (PROs) and healthcare utilisation. Methods and analysis We are conducting a pragmatically designed, prospective, multicentre randomised controlled trial that leverages Hugo, a unique patient-centred health data-aggregating platform for data collection and patient follow-up from Hugo Health. A total of 100 participants with insomnia from two health centres will be enrolled onto the Hugo Health platform, provided with a linked Fitbit (Inspire 2) to track activity and then randomised 1:1 to receive (or not) the PDT for mobile-delivered CBT-I (Somryst). The primary outcome is a change in the insomnia severity index score from baseline to 9-week postrandomisation. Secondary outcomes include healthcare utilisation, health utility scores and clinical outcomes; change in sleep outcomes as measured with sleep diaries and a change in individual PROs including depressive symptoms, daytime sleepiness, health status, stress and anxiety. For those allocated to the PDT, we will also assess engagement with the PDT. Ethics and dissemination The Institutional Review Boards at Yale University and the Mayo Clinic have approved the trial protocol. This trial will provide important data to patients, clinicians and policymakers about the impact of the PDT device delivering CBT-I on PROs, clinical outcomes and healthcare utilisation. Findings will be disseminated to participants, presented at professional meetings and published in peer-reviewed journals. Trial registration number NCT04909229.

Feasibility, acceptability, and effectiveness of school-based dance movement psychotherapy for children with emotional and behavioral difficulties (2022)

Frontiers in psychology

Abstract

Background: Schools have been increasingly employing dance movement psychotherapists to support children cope with daily worries and stress, express and understand their emotions, develop self-awareness and self-esteem. However, evidence on the impact of dance movement psychotherapy as a tool for prevention of mental health difficulties in childhood remains limited. Methods: Sixteen children (aged 7-9) with mild emotional and behavioral difficulties from two primary schools were randomly assigned to a Dance Movement Psychotherapy (DMP) intervention or to a waiting list, within a larger pilot cross-over randomized controlled study which aimed to (a) test whether all elements of study design can work together and run smoothly in a full-scale RCT; and (b) investigate the effectiveness of arts therapies in improving children's health related quality of life (HRQOL; EQ-5D-Y), wellbeing and life functioning (Child Outcome Rating Scale; CORS), emotional and behavioral difficulties (Strengths and Difficulties Questionnaire; SDQ), and duration of sleep (Fitbits). The therapeutic process was also evaluated through interviews with children, participant observations, the Children's Session Rating Scale (CSRS), and ratings of adherence to the therapeutic protocol. Results: The findings indicated that DMP led to improvements in children's life functioning, wellbeing, duration of sleep, emotional and behavioral difficulties, but not in quality of life. The improvements were maintained at the follow-up stages, up to 6 months post-intervention. Interviews with children also suggested positive outcomes, such as self-expression; emotional regulation; mastery and acceptance of emotions; improved self-confidence and self-esteem; reduced stress; and development of positive relationships. However, children would have preferred smaller groups and longer sessions. Conclusion: This study indicated that all outcome measures would be suitable for inclusion in a larger randomized controlled trial, though the EQ-5D-Y is not recommended as a stand-alone measure due to its lack of sensitivity and specificity for young participants. The adherence to the therapeutic protocol ratings differed between children and adults, highlighting the need to include children's voice in future research. Strategies are also proposed of how to conduct randomization of participants in ways that do not hinder the therapeutic process.

Validation of Fitbit Charge 4 for assessing sleep in Chinese patients with chronic insomnia: A comparison against polysomnography and actigraphy (2022)

PloS One

Abstract

Our research aims to assess the performance of a new generation of consumer activity trackers (Fitbit Charge 4TM: FBC) to measure sleep variables and sleep stage classifications in patients with chronic insomnia, compared to polysomnography (PSG) and a widely used actigraph (Actiwatch Spectrum Pro: AWS). We recruited 37 participants, all diagnosed with chronic insomnia disorder, for one night of sleep monitoring in a sleep laboratory using PSG, AWS, and FBC. Epoch-by-epoch analysis along with Bland-Altman plots was used to evaluate FBC and AWS against PSG for sleep-wake detection and sleep variables: total sleep time (TST), sleep efficiency (SE), waking after sleep onset (WASO), and sleep onset latency (SOL). FBC sleep stage classification of light sleep (LS), deep sleep (DS), and rapid eye movement (REM) was also compared to that of PSG. When compared with PSG, FBC notably underestimated DS (-41.4, p {\textless} 0.0001) and SE (-4.9\%, p = 0.0016), while remarkably overestimating LS (37.7, p = 0.0012). However, the TST, WASO, and SOL assessed by FBC presented no significant difference from that assessed by PSG. Compared with PSG, AWS and FBC showed great accuracy (86.9\% vs. 86.5\%) and sensitivity (detecting sleep; 92.6\% vs. 89.9\%), but comparatively poor specificity (detecting wake; 35.7\% vs. 62.2\%). Both devices showed better accuracy in assessing sleep than wakefulness, with the same sensitivity but statistically different specificity. FBC supplied equivalent parameters estimation as AWS in detecting sleep variables except for SE. This research shows that FBC cannot replace PSG thoroughly in the quantification of sleep variables and classification of sleep stages in Chinese patients with chronic insomnia; however, the user-friendly and low-cost wearables do show some comparable functions. Whether FBC can serve as a substitute for actigraphy and PSG in patients with chronic insomnia needs further investigation.

Intra-individual impact of the COVID-19 pandemic on mental health and sleep in young adults (2022)

PloS One

Abstract

The COVID-19 pandemic has posed unique academic, social, financial, and health-related challenges for young adults. While numerous studies have documented average increases in reported mental health issues in the general population, few have measured the magnitude of changes in mental health symptoms and sleep difficulties within individuals. Here, we measure the impact of the COVID-19 pandemic on mental health and sleep of university students pre- and mid-pandemic. Prior to the pandemic (Fall 2019), individuals (n = 23) were recruited to participate in an eight-day, comprehensive sleep study using Fitbit® actigraphy. Participants also completed detailed mental health and sleep surveys, including depression (BDI-II), anxiety (STAI), and sleep disturbance (PROMIS) surveys. One year later, these individuals repeated the study during the pandemic (Fall 2020); participants completed the original surveys and sleep study, in addition to a targeted survey on mental and sleep health due to the pandemic. Self-reported levels of anxiety, depression, and sleep disturbance, and sleep parameters, measured by actigraphy, were compared within the same individuals pre- and mid-pandemic. Self-report survey data revealed that three-quarters of participants experienced an increase in stress and anxiety due to the pandemic. In addition, intra-individual depression and anxiety symptoms increased to clinically significant levels within individuals from pre- to mid-pandemic. Over two-thirds of participants reported sleeping less, and more than half reported that their sleep health had worsened during the pandemic. Changes in sleep disturbance were positively associated with changes in depression and anxiety, reinforcing the robust relationship between poor sleep quality and mental health. Furthermore, individuals who reported greater sleep disturbance during the pandemic experienced lower relative proportions of both REM and deep sleep. The impact of the COVID-19 pandemic on university students is multi-faceted-mental health, sleep quality, and the amount of restorative sleep are negatively affected by the pandemic environment. These compounded effects exacerbate the health consequences of the pandemic and highlight a need for increased attention to the prevention and treatment of mental health disorders, particularly in vulnerable populations of young adults.

Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults (2022)

Frontiers in Medical Technology

Abstract

It is often assumed that healthy people have the genuine ability to maintain tight blood glucose regulation. However, a few recent studies revealed that glucose dysregulation such as hyperglycemia may occur even in people who are considered normoglycemic by standard measures and were more prevalent than initially thought, suggesting that more investigations are needed to fully understand the within-day glucose dynamics of healthy people. In this paper, we conducted an analysis on a multi-modal dataset to examine the relationships between glycemic variability when people were awake and that when they were sleeping. The interstitial glucose levels were measured with a wearable continuous glucose monitoring (CGM) technology FreeStyle Libre 2 at every 15 min interval. In contrast to the traditional single-time-point measurements, the CGM data allow the investigation into the temporal patterns of glucose dynamics at high granularity. Sleep onset and offset timestamps were recorded daily with a Fitbit Charge 3 wristband. Our analysis leveraged the sleep data to split the glucose readings into segments of awake-time and in-sleep, instead of using fixed cut-off time points as has been done in existing literature. We combined repeated measure correlation analysis and quantitative association rules mining, together with an original post-filtering method, to identify significant and most relevant associations. Our results showed that low overall glucose in awake time was strongly correlated to low glucose in subsequent sleep, which in turn correlated to overall low glucose in the next day. Moreover, both analysis techniques identified significant associations between the minimal glucose reading in sleep and the low blood glucose index the next day. In addition, the association rules discovered in this study achieved high confidence (0.75-0.88) and lift (4.1-11.5), which implies that the proposed post-filtering method was effective in selecting quality rules.

A randomized, double-blind, placebo-controlled trial investigating the effects of an Ocimum tenuiflorum (Holy Basil) extract (HolixerTM) on stress, mood, and sleep in adults experiencing stress (2022)

Frontiers in Nutrition

Abstract

Background: In Ayurveda, Ocimum tenuiflorum (Holy Basil) is referred to as "the elixir of life" and is believed to promote longevity and general wellbeing. Although limited, there are clinical trials to suggest Ocimum tenuiflorum has anti-stress effects. Purpose: Examine the effects of a standardized Ocimum tenuiflorum extract (HolixerTM) on subjective and objective measures of stress and sleep quality in adults experiencing stress. Study design: Two-arm, parallel-group, 8-week, randomized, double-blind, placebo-controlled trial. Australian and New Zealand Clinical Trials Registry trial registration number ACTRN12621000609853. Methods: One hundred volunteers aged 18-65 years received either 125 mg of Ocimum tenuiflorum twice daily or a placebo. Outcome measures included the Perceived Stress Scale (PSS) (primary outcome measure), Profile of Mood States, Athens Insomnia Scale (AIS), Restorative Sleep Questionnaire, and the Patient-Reported Outcomes Measurement Information System-29. Sleep quality was also assessed using a wrist-worn sleep tracker (Fitbit), and stress changes were examined by measuring between-group differences in hair cortisol and stress responses after exposure to an experiment stress procedure known as the Maastricht Acute Stress Test (MAST). Results: Compared to the placebo, Ocimum tenuiflorum supplementation was associated with greater improvements in PSS (p = 0.003) and AIS (p = 0.025) scores; and at week 8, concentrations in hair cortisol were also lower (p = 0.025). Moreover, Ocimum tenuiflorum supplementation was associated with a buffered stress responses after exposure to the MAST as demonstrated by significantly lower concentrations in salivary cortisol (p = 0.001), salivary amylase (p = 0.001), systolic (p = 0.010) and diastolic (p = 0.025) blood pressure, and subjective stress ratings (p < 0.001). Ocimum tenuiflorum supplementation was well-tolerated with no reports of major adverse effects. Conclusion: The results from this trial suggest that 8 weeks of supplementation with an Ocimum tenuiflorum extract (HolixerTM) may reduce objective and subjective measures of stress, and improve subjective measures of sleep quality. However, further research using gold-standard objective sleep measures will be required to substantiate the sleep-related findings.

Sleep and seizure risk in epilepsy: bed and wake times are more important than sleep duration (2022)

Brain: A Journal of Neurology

Abstract

Sleep duration, sleep deprivation and the sleep-wake cycle are thought to play an important role in the generation of epileptic activity and may also influence seizure risk. Hence, people diagnosed with epilepsy are commonly asked to maintain consistent sleep routines. However, emerging evidence paints a more nuanced picture of the relationship between seizures and sleep, with bidirectional effects between changes in sleep and seizure risk in addition to modulation by sleep stages and transitions between stages. We conducted a longitudinal study investigating sleep parameters and self-reported seizure occurrence in an ambulatory at-home setting using mobile and wearable monitoring. Sixty subjects wore a Fitbit smartwatch for at least 28 days while reporting their seizure activity in a mobile app. Multiple sleep features were investigated, including duration, oversleep and undersleep, and sleep onset and offset times. Sleep features in participants with epilepsy were compared to a large (n = 37921) representative population of Fitbit users, each with 28 days of data. For participants with at least 10 seizure days (n = 34), sleep features were analysed for significant changes prior to seizure days. A total of 4956 reported seizures (M = 83, SD = 130) and 30485 recorded sleep nights (M = 508, SD = 445) were included in the study. There was a trend for participants with epilepsy to sleep longer than the general population, although this difference was not significant. Just 5 of 34 participants showed a significant difference in sleep duration the night before seizure days compared to seizure-free days. However, 14 of 34 subjects showed significant differences between their sleep onset (bed) and/or offset (wake) times prior to seizure occurrence. In contrast to previous studies, the current study found undersleeping was associated with a marginal 2\% decrease in seizure risk in the following 48 h (p {\textless} 0.01). Nocturnal seizures were associated with both significantly longer sleep durations and increased risk of a seizure occurring in the following 48 h. Overall, the presented results demonstrated that day-to-day changes in sleep duration had a minimal effect on reported seizures, while patient-specific changes in bed- and wake-times were more important for identifying seizure risk the following day. Nocturnal seizures were the only factor that significantly increased the risk of seizures in the following 48 h on a group level. Wearables can be utilised to identify these sleep-seizure relationships and guide clinical recommendations or improve seizure forecasting algorithms.

Daytime Sleep-Tracking Performance of Four Commercial Wearable Devices During Unrestricted Home Sleep (2023)

Nature and Science of Sleep

Abstract

Purpose: Previous studies have found that many commercial wearable devices can accurately track sleep-wake patterns in laboratory or home settings. However, nearly all previous studies tested devices under conditions with fixed time in bed (TIB) and during nighttime sleep episodes only. Despite its relevance to shift workers and others with irregular sleep schedules, it is largely unknown how devices track daytime sleep. Therefore, we tested the sleep-tracking performance of four commercial wearable devices during unrestricted home daytime sleep. Participants and Methods: Participants were 16 healthy young adults (6 men, 10 women; 26.6 ± 4.6 years, mean ± SD) with habitual daytime sleep schedules. Participants slept at home for 1 week under unrestricted conditions (ie, self-selecting TIB) using a set of four commercial wearable devices and completed reference sleep logs. Wearables included the Fatigue Science ReadiBand, Fitbit Inspire HR, Oura Ring, and Polar Vantage V Titan. Daytime sleep episode TIB biases and frequencies of missed and false-positive daytime sleep episodes were examined. Results: TIB bias was low in general for all devices on most daytime sleep episodes, but some exhibited large biases (eg, > 1 h). Total missed daytime sleep episodes were as follows: Fatigue Science: 3.6%; Fitbit: 4.8%; Oura: 6.0%; Polar: 37.3%. Missed episodes occurred most often when TIB was short (eg, naps < 4 h). Conclusion: When daytime sleep episodes were recorded, the devices generally exhibited similar performance for tracking TIB (ie, most episodes had low bias). However, the devices failed to detect some daytime episodes, which occurred most often when TIB was short, but varied across devices (especially Polar, which missed over one-third of episodes). Findings suggest that accurate daytime sleep tracking is largely achievable with commercial wearable devices. However, performance differences for missed recordings suggest that some devices vary in reliability (especially for naps), but improvements could likely be made with changes to algorithm sensitivities.

Performance of seven consumer sleep-tracking devices compared with polysomnography (2021)

Sleep

Abstract

STUDY OBJECTIVES: Consumer sleep-tracking devices are widely used and becoming more technologically advanced, creating strong interest from researchers and clinicians for their possible use as alternatives to standard actigraphy. We, therefore, tested the performance of many of the latest consumer sleep-tracking devices, alongside actigraphy, versus the gold-standard sleep assessment technique, polysomnography (PSG). METHODS: In total, 34 healthy young adults (22 women; 28.1 ± 3.9 years, mean ± SD) were tested on three consecutive nights (including a disrupted sleep condition) in a sleep laboratory with PSG, along with actigraphy (Philips Respironics Actiwatch 2) and a subset of consumer sleep-tracking devices. Altogether, four wearable (Fatigue Science Readiband, Fitbit Alta HR, Garmin Fenix 5S, Garmin Vivosmart 3) and three nonwearable (EarlySense Live, ResMed S+, SleepScore Max) devices were tested. Sleep/wake summary and epoch-by-epoch agreement measures were compared with PSG. RESULTS: Most devices (Fatigue Science Readiband, Fitbit Alta HR, EarlySense Live, ResMed S+, SleepScore Max) performed as well as or better than actigraphy on sleep/wake performance measures, while the Garmin devices performed worse. Overall, epoch-by-epoch sensitivity was high (all ≥0.93), specificity was low-to-medium (0.18-0.54), sleep stage comparisons were mixed, and devices tended to perform worse on nights with poorer/disrupted sleep. CONCLUSIONS: Consumer sleep-tracking devices exhibited high performance in detecting sleep, and most performed equivalent to (or better than) actigraphy in detecting wake. Device sleep stage assessments were inconsistent. Findings indicate that many newer sleep-tracking devices demonstrate promising performance for tracking sleep and wake. Devices should be tested in different populations and settings to further examine their wider validity and utility.

Performance of Four Commercial Wearable Sleep-Tracking Devices Tested Under Unrestricted Conditions at Home in Healthy Young Adults (2022)

Nature and Science of Sleep

Abstract

PURPOSE: Commercial wearable sleep-tracking devices are growing in popularity and in recent studies have performed well against gold standard sleep measurement techniques. However, most studies were conducted in controlled laboratory conditions. We therefore aimed to test the performance of devices under naturalistic unrestricted home sleep conditions. PARTICIPANTS AND METHODS: Healthy young adults (n = 21; 12 women, 9 men; 29.0 ± 5.0 years, mean ± SD) slept at home under unrestricted conditions for 1 week using a set of commercial wearable sleep-tracking devices and completed daily sleep diaries. Devices included the Fatigue Science Readiband, Fitbit Inspire HR, Oura ring, and Polar Vantage V Titan. Participants also wore a research-grade actigraphy watch (Philips Respironics Actiwatch 2) for comparison. To assess performance, all devices were compared with a high performing mobile sleep electroencephalography headband device (Dreem 2). Analyses included epoch-by-epoch and sleep summary agreement comparisons. RESULTS: Devices accurately tracked sleep-wake summary metrics (ie, time in bed, total sleep time, sleep efficiency, sleep latency, wake after sleep onset) on most nights but performed best on nights with higher sleep efficiency. Epoch-by-epoch sensitivity (for sleep) and specificity (for wake), respectively, were as follows: Actiwatch (0.95, 0.35), Fatigue Science (0.94, 0.40), Fitbit (0.93, 0.45), Oura (0.94, 0.41), and Polar (0.96, 0.35). Sleep stage-tracking performance was mixed, with high variability. CONCLUSION: As in previous studies, all devices were better at detecting sleep than wake, and most devices compared favorably to actigraphy in wake detection. Devices performed best on nights with more consolidated sleep patterns. Unrestricted sleep TIB differences were accurately tracked on most nights. High variability in sleep stage-tracking performance suggests that these devices, in their current form, are still best utilized for tracking sleep-wake outcomes and not sleep stages. Most commercial wearables exhibited promising performance for tracking sleep-wake in real-world conditions, further supporting their consideration as an alternative to actigraphy.

Influence of sleep duration and sex on age-related differences in heart rate variability: Findings from program 4 of the HAIE study (2023)

Sleep Medicine

Abstract

Heart rate variability (HRV) is an important marker of cardiac autonomic regulation and health. We examined the influence of sleep duration and sex on HRV in younger and middle-aged adults. Cross-sectional data (888 participants, 44% women) were analyzed from Program 4 of the Healthy Aging in Industrial Environment study (HAIE). Sleep duration was measured across 14 days using Fitbit Charge monitors. Short-term EKG recordings were used to evaluate HRV in the time (RMSSD) and frequency domains (low frequency (LF) and high frequency (HF) power). Regression analysis showed age was associated with lower HRV across all HRV variables (all P < 0.001). Sex was a significant predictor for LF (β = 0.52) and HF (β = 0.54; both P < 0.001) in normalized units. Similarly, sleep duration was only associated with HF in normalized units (β = 0.06, P = 0.04). To explore this finding further, participants within each sex were separated into groups based on age (<40 and ≥ 40y) and adequate sleep duration (<7 and ≥7 h). Middle-aged women with sleep durations <7 h, but not ≥7 h, had lower HRV than younger women after adjusting for medications, respiratory frequency, and cardiorespiratory fitness (peak VO2). Middle-aged women with sleep durations <7 h also had lower RMSSD (33 ± 2 vs. 41±4 ms, P = 0.04), HF power (5.6 ± 0.1 vs. 6.0 ± 0.1 log ms2, P = 0.04), and HF in normalized units (39 ± 1 vs. 48 ± 2, P = 0.01) than middle-aged women with sleep durations ≥7 h. In contrast, middle-aged men irrespective of sleep duration had lower HRV than younger men. These results suggest that adequate sleep duration may positively influence HRV in middle-aged women but not men.

Gut microbiota composition and functionality are associated with REM sleep duration and continuous glucose levels (2023)

The Journal of Clinical Endocrinology and Metabolism

Abstract

CONTEXT: Sleep disruption is associated with poorer glucose metabolic control and altered gut microbiota in animal models. OBJECTIVE: We aimed to evaluate the possible links among REM sleep duration, continuous glucose levels and gut microbiota composition. DESIGN: This is an observational, prospective, real-life, cross-sectional case-control study. SETTING: Tertiary Hospital recruiting healthy volunteers. PATIENTS OR OTHER PARTICIPANTS: One-hundred and eighteen (60 with obesity), middle-aged (39.1-54.8) subjects. INTERVENTION(S): Glucose variability and REM sleep duration were assessed by 10-day continuous glucose monitoring (CGM) (Dexcom G6®) and wrist-actigraphy (Fitbit Charge 3®), respectively. MAIN OUTCOME MEASURE(S): . Glucose variability was evaluated through standard deviation (SD), coefficient of variation (CV) and interquartile range (IQR). The percentage of time in range (\% TIR), 126-139 mg/dL (TIR2) and 140-199 mg/dL (TIR3) were calculated. Shotgun metagenomics sequencing was applied to study gut microbiota taxonomy and functionality. RESULTS: Increased glycemic variability (SD, CV and IQR) was observed among subjects with obesity in parallel to increased \% TIR2 and \% TIR3. REM sleep duration was independently associated with the \% TIR3 (β=-0.339; p {\textless} 0.001) and glucose variability (SD, β=-0.350; p {\textless} 0.001). Microbial taxa from Christensenellaceae family (Firmicutes phylum) were positively associated with REM sleep and negatively with glucose continuous monitoring levels, while bacteria from Enterobacteriacea family and bacterial functions involved in iron metabolism showed opposite associations. CONCLUSIONS: Decreased REM sleep duration was independently associated with a worse glucose profile. The associations of species from Christensenellaceae and Enterobacteriaceae families with REM sleep duration and continuous glucose values suggest an integrated picture of metabolic health.

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.

Validation of the relationship between rapid eye movement sleep and sleep-related erections in healthy adults by a feasible instrument Fitbit Charge2 (2023)

Andrology

Abstract

Background: Sleep, particularly rapid eye movement sleep, has been found to be associated with sleep-related erections. While RigiScan is currently a more accurate method for monitoring nocturnal erectile events, the Fitbit, a smart wearable device, shows great potential for sleep monitoring. Objectives: To analyze the relationship between sleep-related erections and sleep by recruiting sexually active, healthy men for simultaneous monitoring of sleep and nocturnal penile tumescence and rigidity. Patients and methods: Using Fitbit Charge2 and RigiScan, we simultaneously monitored nocturnal sleep and erections in 43 healthy male volunteers, and analyzed the relationship between sleep periods and erectile events with the Statistical Package for Social Sciences. Results: Among all erectile events, 89.8% were related to rapid eye movement, and 79.2% of all rapid eye movement periods were associated with erectile events. Moreover, a statistical correlation was shown between the duration of rapid eye movement and the time of total erectile events (first night: 𝜌 = 0.316, p = 0.039; second night: 𝜌 = 0.370, p = 0.015). Discussion and conclusion: Our study shows a potential link between sleep-related erections and rapid eye movement sleep, which has implications for the current examination of sleep-related erections and further research into the mechanisms of erectile function. Meanwhile, the wearable device Fitbit has shown a potential promise for sleep monitoring in patients with erectile dysfunction. The results provide an alternative approach for further research on the relationship between erectile function and sleep with large sample sizes in the future.

The Effects of Auricular Acupressure on Menopausal Symptoms, Stress, and Sleep in Postmenopausal Middle-Aged Women: A Randomized Single-Blind Sham-Controlled Trial (2023)

Journal of Midwifery \& Women's Health

Abstract

INTRODUCTION: Middle-aged women in the postmenopausal period experience menopause symptoms, stress, and poor sleep quality due to hormonal changes. Nonetheless, most of them recognize such symptoms as an aging process without receiving proper treatment, and there are few nonpharmacologic interventions available. METHODS: This was a randomized single-masked, sham-controlled trial. For the intervention group, a vaccaria seed was applied to the auricular points of Shenmen, anterior lobe, adrenal glands, central rim, and endocrine that are related to the menopause symptoms, stress, and sleep while applying a seed to the auricular points not related to the forementioned symptoms to the control group. The Menopause Rating Scale (MRS), Perceived Stress Scale (PSS), heart rate variability, and electroencephalogram (EEG) were measured before the intervention, 4 weeks after the intervention, and 8 weeks after the intervention. Actigraphy was measured with a Fitbit, and the Pittsburgh Sleep Quality Index (PSQI) was measured before and after the intervention. The study was registered with the World Health Organization International Clinical Trials Registry Platform (KCT0007364). RESULTS: The MRS showed significant differences over time in the intervention group (F, 22.057; P {\textless} .001). There was a significant difference over time in the PSS (F, 22.576; P {\textless} .001), stress index measured by heart rate variability (F, 14.027; P = .001), and antistress quotient of the right brain measured by EEG (F, 4.865; P = .033). Sleep quality, measured by the PSQI (t = -4.050, P {\textless} .001), and sleep efficiency measured by actigraphy (t = 5.996, P {\textless} .001) were found to be significantly different over time in the intervention group. DISCUSSION: This study demonstrated that auricular acupressure is effective in improving menopause symptoms, stress, and sleep in postmenopausal middle-aged women. Therefore, auricular acupressure may be a useful nonpharmacologic intervention for alleviating these symptoms in this population.

Evaluation of the effectiveness of sleep hygiene education and FITBIT devices on quality of sleep and psychological worry: a pilot quasi-experimental study among first-year college students (2023)

Frontiers in Public Health

Abstract

BACKGROUND: College students report disturbed sleep patterns that can negatively impact their wellbeing and academic performance. OBJECTIVES: This study examined the effect of a 4-week sleep hygiene program that included sleep education and actigraph sleep trackers (FITBITs) on improving sleep quality and reducing psychological worry without control group. DESIGN SETTINGS AND PARTICIPANTS: A pilot quasi-experimental design, participants were randomly selected medical and health sciences from a university students in the United-Arab-Emirates. METHODS: Students were asked to wear FITBITs and log their daily sleep data and completed the Pittsburgh Sleep Quality Index (PSQI) and Penn State Worry Questionnaire (PSWQ). Extensive sleep hygiene education was delivered via lectures, a WhatsApp group, and the Blackboard platform. In total, 50 students completed pre-and post-assessments and returned FITBIT data. RESULTS: There was a significant difference in the prevalence of good sleep postintervention compared with pre-intervention (46\% vs. 28\%; p = 0.0126). The mean PSQI score was significantly lower post-intervention compared with pre-intervention (6.17 ± 3.16 vs. 7.12.87; p = 0.04, Cohen's d 0.33). After the intervention, subjective sleep quality, sleep latency, and daytime dysfunction were significantly improved compared with pre-intervention (p {\textless} 0.05). In addition, FITBIT data showed total sleep time and the number of restless episodes per night were significantly improved postintervention compared with pre-intervention (p = 0.013). The mean PSWQ score significantly decreased from pre-intervention to p = 0.049, Cohen' d = 0.25. The correlation between PSQI and PSWQ scores was significant post-intervention (β = 0.40, p = 0.02). CONCLUSION: Our results may inform university educational policy and curricular reform to incorporate sleep hygiene awareness programs to empower students and improve their sleep habits.

The impact of mindfulness training on posttraumatic stress disorder symptoms, subjective sleep quality, and objective sleep outcomes in police officers (2023)

Psychological Trauma: Theory, Research, Practice and Policy

Abstract

OBJECTIVE: Sleep disturbances cooccur with posttraumatic stress disorder (PTSD) and are often correlated with PTSD severity. Previous research has shown that sleep problems mediate the relationship between PTSD and negative physical and mental health outcomes but has relied on self-reported sleep quality. We tested the effects of mindfulness training-previously shown to improve sleep quality and reduce PTSD symptoms-on subjective and objective sleep metrics and relationships with reduced PTSD symptoms. METHOD: Following baseline data collection in 114 law enforcement officers, we randomly assigned participants to either an 8-week mindfulness training group or a waitlist control group. We repeated assessments immediately posttraining and at 3-month follow-up. Self-reported PTSD symptoms and subjective sleep quality were measured at each visit with the PTSD checklist and Pittsburgh Sleep Quality Index (PSQI), respectively. Participants also wore a Fitbit Charge 2 continuously over the course of a 4- to 6-day work week following each visit, from which we extracted two distinct objective sleep metrics: total minutes of sleep and sleep efficiency. RESULTS: At baseline, PTSD symptoms were correlated with PSQI scores but not objective Fitbit metrics. Relative to waitlist, mindfulness training led to improved subjective sleep quality and reduced PTSD symptoms. Reduced PTSD symptoms mediated the improvement in subjective sleep quality following mindfulness training. Neither objective sleep metric demonstrated improvements following mindfulness training, nor did these metrics mediate reduced PTSD symptoms. CONCLUSIONS: This study provides evidence linking improved subjective sleep quality, but not objective sleep markers, to reductions in PTSD symptoms following mindfulness training. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Latent Profiles of Sleep Patterns in Early Adolescence: Associations With Behavioral Health Risk (2023)

The Journal of Adolescent Health: Official Publication of the Society for Adolescent Medicine

Abstract

Purpose: The present study characterized sleep profiles in a national longitudinal sample of early adolescents and examined whether profiles predicted later behavioral problems. Methods: Three waves of data (2016-2021) were obtained from the Adolescent Behavior and Cognitive Development study, including 3,326 participants with both weekday and weekend sleep data measured by Fitbit wearables (age range 10.58-13.67 years; 49.3% female). Latent profile analysis was utilized to identify sleep profiles using multiple sleep indicators (duration, latency, efficiency, wake minutes, wake counts, and midpoint). We then explored whether demographic predictors predicted profile membership and tested the latent sleep profiles' predictive utility of internalizing and externalizing symptoms. Results: Four profiles were identified: average sleep (40.39%), high duration & high wakefulness (28.58%), high efficiency, low duration &low wakefulness (16.86%), and low duration & low efficiency (14.17%). Participants with older age, males, higher body mass index, and advanced pubertal status were more likely to be classified in the low duration & low efficiency profile than the average group. Participants with lower income, minority identification, older age, and higher body mass index were more likely to be classified in the high efficiency, low duration &low wakefulness than the average group. Participants with lower parental education and males were more likely to be in the high sleep duration & high wakefulness than the average group. The low duration & low efficiency group had the highest attention problems, social problems, and rule-breaking behaviors. Discussion: Our findings highlight unique sleep patterns in early adolescence and their prospective links with internalizing and externalizing problems. Keywords: Externalizing problems; Internalizing problems; Latent profile; Risk; Sleep.

Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study (2023)

JMIR mHealth and uHealth

Abstract

Background: Consumer sleep trackers (CSTs) have gained significant popularity because they enable individuals to conveniently monitor and analyze their sleep. However, limited studies have comprehensively validated the performance of widely used CSTs. Our study therefore investigated popular CSTs based on various biosignals and algorithms by assessing the agreement with polysomnography. Objective: This study aimed to validate the accuracy of various types of CSTs through a comparison with in-lab polysomnography. Additionally, by including widely used CSTs and conducting a multicenter study with a large sample size, this study seeks to provide comprehensive insights into the performance and applicability of these CSTs for sleep monitoring in a hospital environment. Methods: The study analyzed 11 commercially available CSTs, including 5 wearables (Google Pixel Watch, Galaxy Watch 5, Fitbit Sense 2, Apple Watch 8, and Oura Ring 3), 3 nearables (Withings Sleep Tracking Mat, Google Nest Hub 2, and Amazon Halo Rise), and 3 airables (SleepRoutine, SleepScore, and Pillow). The 11 CSTs were divided into 2 groups, ensuring maximum inclusion while avoiding interference between the CSTs within each group. Each group (comprising 8 CSTs) was also compared via polysomnography. Results: The study enrolled 75 participants from a tertiary hospital and a primary sleep-specialized clinic in Korea. Across the 2 centers, we collected a total of 3890 hours of sleep sessions based on 11 CSTs, along with 543 hours of polysomnography recordings. Each CST sleep recording covered an average of 353 hours. We analyzed a total of 349,114 epochs from the 11 CSTs compared with polysomnography, where epoch-by-epoch agreement in sleep stage classification showed substantial performance variation. More specifically, the highest macro F1 score was 0.69, while the lowest macro F1 score was 0.26. Various sleep trackers exhibited diverse performances across sleep stages, with SleepRoutine excelling in the wake and rapid eye movement stages, and wearables like Google Pixel Watch and Fitbit Sense 2 showing superiority in the deep stage. There was a distinct trend in sleep measure estimation according to the type of device. Wearables showed high proportional bias in sleep efficiency, while nearables exhibited high proportional bias in sleep latency. Subgroup analyses of sleep trackers revealed variations in macro F1 scores based on factors, such as BMI, sleep efficiency, and apnea-hypopnea index, while the differences between male and female subgroups were minimal. Conclusions: Our study showed that among the 11 CSTs examined, specific CSTs showed substantial agreement with polysomnography, indicating their potential application in sleep monitoring, while other CSTs were partially consistent with polysomnography. This study offers insights into the strengths of CSTs within the 3 different classes for individuals interested in wellness who wish to understand and proactively manage their own sleep. Keywords: Fitbit Sense 2, Amazon Halo Rise, SleepRoutine; airables; artificial intelligence; comparative study; consumer sleep trackers; deep learning; multicenter study; nearables; polysomnography; sleep monitoring; sleep stage; wearables.

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.

Selecting a sleep tracker from EEG-based, iteratively improved, low-cost multisensor, and actigraphy-only devices (2023)

Sleep Health

Abstract

AIMS: Evaluate the performance of 6 wearable sleep trackers across 4 classes (EEG-based headband, research-grade actigraphy, iteratively improved consumer tracker, low-cost consumer tracker). FOCUS TECHNOLOGY: Dreem 3 headband, Actigraph GT9X, Oura Ring Gen3, Fitbit Sense, Xiaomi Mi Band 7, Axtro Fit3. REFERENCE TECHNOLOGY: In-lab polysomnography with 3-reader, consensus sleep scoring. SAMPLE: Sixty participants (26 males) across 3 age groups (18-30, 31-50, and 51-70years). DESIGN: Overnight in a sleep laboratory from habitual sleep time to wake time. CORE ANALYTICS: Discrepancy and epoch-by-epoch analyses for sleep/wake (2-stage) and sleep-stage (4-stage; wake/light/deep/rapid eye movement) classification (devices vs. polysomnography). CORE OUTCOMES: EEG-based Dreem performed the best (2-stage kappa=0.76, 4-stage kappa=0.76-0.86) with the lowest total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset discrepancies vs. polysomnography. This was followed by the iteratively improved consumer trackers: Oura (2-stage kappa=0.64, 4-stage kappa=0.55-0.70) and Fitbit (2-stage kappa=0.58, 4-stage kappa=0.45-0.60) which had comparable total sleep time and sleep efficiency discrepancies that outperformed accelerometry-only Actigraph (2-stage kappa=0.47). The low-cost consumer trackers had poorest overall performance (2-stage kappa{\textless}0.31, 4-stage kappa{\textless}0.33). IMPORTANT ADDITIONAL OUTCOMES: Proportional biases were driven by nights with poorer sleep (longer sleep onset latencies and/or wake after sleep onset). CORE CONCLUSION: EEG-based Dreem is recommended when evaluating poor quality sleep or when highest accuracy sleep-staging is required. Iteratively improved non-EEG sleep trackers (Oura, Fitbit) balance classification accuracy with well-tolerated, and economic deployment at-scale, and are recommended for studies involving mostly healthy sleepers. The low-cost trackers, can log time in bed but are not recommended for research use.

Exploring Variations in Sleep Perception: Comparative Study of Chatbot Sleep Logs and Fitbit Sleep Data (2023)

JMIR mHealth and uHealth

Abstract

Background: Patient-generated health data are important in the management of several diseases. Although there are limitations, information can be obtained using a wearable device and time-related information such as exercise time or sleep time can also be obtained. Fitbits can be used to acquire sleep onset, sleep offset, total sleep time (TST), and wakefulness after sleep onset (WASO) data, although there are limitations regarding the depth of sleep and satisfaction; therefore, the patient's subjective response is still important information that cannot be replaced by wearable devices. Objective: To effectively use patient-generated health data related to time such as sleep, it is first necessary to understand the characteristics of the time response recorded by the user. Therefore, the aim of this study was to analyze the characteristics of individuals' time perception in comparison with wearable data. Methods: Sleep data were acquired for 2 weeks using a Fitbit. Participants' sleep records were collected daily through chatbot conversations while wearing the Fitbit, and the two sets of data were statistically compared. Results: In total, 736 people aged 30-59 years were recruited for this study, and the sleep data of 543 people who wore a Fitbit and responded to the chatbot for more than 7 days on the same day were analyzed. Research participants tended to respond to sleep-related times on the hour or in 30-minute increments, and each participant responded within the range of 60-90 minutes from the value measured by the Fitbit. On average for all participants, the chat responses and the Fitbit data were similar within a difference of approximately 15 minutes. Regarding sleep onset, the participant response was 8 minutes and 39 seconds (SD 58 minutes) later than that of the Fitbit data, whereas with respect to sleep offset, the response was 5 minutes and 38 seconds (SD 57 minutes) earlier. The participants' actual sleep time (AST) indicated in the chat was similar to that obtained by subtracting the WASO from the TST measured by the Fitbit. The AST was 13 minutes and 39 seconds (SD 87 minutes) longer than the time WASO was subtracted from the Fitbit TST. On days when the participants reported good sleep, they responded 19 (SD 90) minutes longer on the AST than the Fitbit data. However, for each sleep event, the probability that the participant's AST was within ±30 and ±60 minutes of the Fitbit TST-WASO was 50.7% and 74.3%, respectively. Conclusions: The chatbot sleep response and Fitbit measured time were similar on average and the study participants had a slight tendency to perceive a relatively long sleep time if the quality of sleep was self-reported as good. However, on a participant-by-participant basis, it was difficult to predict participants' sleep duration responses with Fitbit data. Individual variations in sleep time perception significantly affect patient responses related to sleep, revealing the limitations of objective measures obtained through wearable devices. Keywords: Fitbit; PGHD; chat; patient-generated health data; self-report; sleep; sleep diary; sleep log; sleep time; wearables.

Patient Engagement and Provider Effectiveness of a Novel Sleep Telehealth Platform and Remote Monitoring Assessment in the US Military: Pilot Study Providing Evidence-Based Sleep Treatment Recommendations (2023)

JMIR formative research

Abstract

Background: Sleep problems are common and costly in the US military. Yet, within the military health system, there is a gross shortage of trained specialist providers to address sleep problems. As a result, demand for sleep medicine care far exceeds the available supply. Telehealth including telemedicine, mobile health, and wearables represents promising approaches to increase access to high-quality and cost-effective care. Objective: The purpose of this study was to evaluate patient engagement and provider perceived effectiveness of a novel sleep telehealth platform and remote monitoring assessment in the US military. The platform includes a desktop web portal, native mobile app, and integrated wearable sensors (ie, a commercial off-the-shelf sleep tracker [Fitbit]). The goal of the remote monitoring assessment was to provide evidence-based sleep treatment recommendations to patients and providers. Methods: Patients with sleep problems were recruited from the Internal Medicine clinic at Walter Reed National Military Medical Center. Patients completed intensive remote monitoring assessments over 10 days (including a baseline intake questionnaire, daily sleep diaries, and 2 daily symptom surveys), and wore a Fitbit sleep tracker. Following the remote monitoring period, patients received assessment results and personalized sleep education in the mobile app. In parallel, providers received a provisional patient assessment report in an editable electronic document format. Patient engagement was assessed via behavioral adherence metrics that were determined a priori. Patients also completed a brief survey regarding ease of completion. Provider effectiveness was assessed via an anonymous survey. Results: In total, 35 patients with sleep problems participated in the study. There were no dropouts. Results indicated a high level of engagement with the sleep telehealth platform, with all participants having completed the baseline remote assessment, reviewed their personalized sleep assessment report, and completed the satisfaction survey. Patients completed 95.1% of sleep diaries and 95.3% of symptom surveys over 10 days. Patients reported high levels of satisfaction with most aspects of the remote monitoring assessment. In total, 24 primary care providers also participated and completed the anonymous survey. The results indicate high levels of perceived effectiveness and identified important potential benefits from adopting a sleep telehealth approach throughout the US military health care system. Conclusions: Military patients with sleep problems and military primary care providers demonstrated high levels of engagement and satisfaction with a novel sleep telehealth platform and remote monitoring assessment. Sleep telehealth approaches represent a potential pathway to increase access to evidence-based sleep medicine care in the US military. Further evaluation is warranted. Keywords: application; behavioral; care; effective care; effectiveness; insomnia; monitoring; obstructive sleep apnea; patient engagement; remote monitoring; sleep; sleep disorders; telehealth; wearables.

Association between sleep quality and nocturnal erection monitor by RigiScan in erectile dysfunction patients: a prospective study using fitbit charge 2 (2023)

Basic and Clinical Andrology

Abstract

Background: Few studies were conducted to explore the association between sleep quality and nocturnal erection. Here, we intended to explore the association between sleep quality and nocturnal erection monitor when conducting nocturnal erection monitor. All erectile dysfunction (ED) patients underwent sleep monitors using Fitbit Charge 2™ (Fitbit Inc.) and nocturnal penile tumescence and rigidity (NPTR) monitors using RigiScan® (GOTOP medical, Inc., USA) for two nights. Subsequently, the patients were divided into two groups: Group A included patients who experienced effective erections only on the second night, while Group B included patients who had effective erections on both nights. To explore the associations between NPTR parameters and sleep parameters, a comparative analysis was performed between Group A and Group B for both nights. Results: Finally, our study included 103 participants, with 47 patients in Group A and 56 patients in Group B. Notably, the Group A patients showed significant improvements in NPTR parameters on the second night compared to the first night. Conversely, the NPTR parameters on Group B of the second night did not demonstrate a superior outcome when compared to the second night of Group A. Interestingly, it was found that only the disparities in sleep parameters accounted for the variation in NPTR parameters between the two groups on the first night. After correlation and ROC analysis, we identified the rapid eye movement (REM) sleep time and wake after sleep onset (WASO) time monitoring by the Fitbit Charge 2 as the primary parameters for predicting abnormal NPTR results in the first night. Conclusions: Therefore, our study strongly suggests a close association between sleep parameters and NPTR parameters. It emphasizes the importance of incorporating sleep monitoring alongside nocturnal erection monitoring to enhance the reliability of the NPTR results. Keywords: Erectile dysfunction; Fitbit Charge 2™; Nocturnal erection monitor; RigiScan; Sleep quality.

Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review (2024)

JMIR mHealth and uHealth

Abstract

BACKGROUND: Despite being the gold-standard method for objectively assessing sleep, polysomnography (PSG) faces several limitations as it is expensive, time-consuming, and labor-intensive; requires various equipment and technical expertise; and is impractical for long-term or in-home use. Consumer wrist-worn wearables are able to monitor sleep parameters and thus could be used as an alternative for PSG. Consequently, wearables gained immense popularity over the past few years, but their accuracy has been a major concern. OBJECTIVE: A systematic review of the literature was conducted to appraise the performance of 3 recent-generation wearable devices (Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP) in determining sleep parameters and sleep stages. METHODS: Per the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, a comprehensive search was conducted using the PubMed, Web of Science, Google Scholar, Scopus, and Embase databases. Eligible publications were those that (1) involved the validity of sleep data of any marketed model of the candidate wearables and (2) used PSG or an ambulatory electroencephalogram monitor as a reference sleep monitoring device. Exclusion criteria were as follows: (1) incorporated a sleep diary or survey method as a reference, (2) review paper, (3) children as participants, and (4) duplicate publication of the same data and findings. RESULTS: The search yielded 504 candidate articles. After eliminating duplicates and applying the eligibility criteria, 8 articles were included. WHOOP showed the least disagreement relative to PSG and Sleep Profiler for total sleep time (-1.4 min), light sleep (-9.6 min), and deep sleep (-9.3 min) but showed the largest disagreement for rapid eye movement (REM) sleep (21.0 min). Fitbit Charge 4 and Garmin Vivosmart 4 both showed moderate accuracy in assessing sleep stages and total sleep time compared to PSG. Fitbit Charge 4 showed the least disagreement for REM sleep (4.0 min) relative to PSG. Additionally, Fitbit Charge 4 showed higher sensitivities to deep sleep (75\%) and REM sleep (86.5\%) compared to Garmin Vivosmart 4 and WHOOP. CONCLUSIONS: The findings of this systematic literature review indicate that the devices with higher relative agreement and sensitivities to multistate sleep (ie, Fitbit Charge 4 and WHOOP) seem appropriate for deriving suitable estimates of sleep parameters. However, analyses regarding the multistate categorization of sleep indicate that all devices can benefit from further improvement in the assessment of specific sleep stages. Although providers are continuously developing new versions and variants of wearables, the scientific research on these wearables remains considerably limited. This scarcity in literature not only reduces our ability to draw definitive conclusions but also highlights the need for more targeted research in this domain. Additionally, future research endeavors should strive for standardized protocols including larger sample sizes to enhance the comparability and power of the results across studies.

A pilot randomised controlled trial exploring the feasibility and efficacy of a human-AI sleep coaching model for improving sleep among university students (2024)

Digital Health

Abstract

OBJECTIVE: Sleep quality is a crucial concern, particularly among youth. The integration of health coaching with question-answering (QA) systems presents the potential to foster behavioural changes and enhance health outcomes. This study proposes a novel human-AI sleep coaching model, combining health coaching by peers and a QA system, and assesses its feasibility and efficacy in improving university students' sleep quality. METHODS: In a four-week unblinded pilot randomised controlled trial, 59 university students (mean age: 21.9; 64\% males) were randomly assigned to the intervention (health coaching and QA system; n = 30) or the control conditions (QA system; n = 29). Outcomes included efficacy of the intervention on sleep quality (Pittsburgh Sleep Quality Index; PSQI), objective and self-reported sleep measures (obtained from Fitbit and sleep diaries) and feasibility of the study procedures and the intervention. RESULTS: Analysis revealed no significant differences in sleep quality (PSQI) between intervention and control groups (adjusted mean difference = -0.51, 95\% CI: [-1.55-0.77], p = 0.40). The intervention group demonstrated significant improvements in Fitbit measures of total sleep time (adjusted mean difference = 32.5, 95\% CI: [5.9-59.1], p = 0.02) and time in bed (adjusted mean difference = 32.3, 95\% CI: [2.7-61.9], p = 0.03) compared to the control group, although other sleep measures were insignificant. Adherence was high, with the majority of the intervention group attending all health coaching sessions. Most participants completed baseline and post-intervention self-report measures, all diary entries, and consistently wore Fitbits during sleep. CONCLUSIONS: The proposed model showed improvements in specific sleep measures for university students and the feasibility of the study procedures and intervention. Future research may extend the intervention period to see substantive sleep quality improvements.

Associations between sleep health, negative reinforcement learning, and alcohol use among South Florida college students with elevated internalizing symptoms (2024)

Alcohol (Fayetteville, N.Y.)

Abstract

Negative reinforcement is proposed to mediate associations between sleep and alcohol use, especially among people with depression and/or anxiety symptoms. Worse sleep (e.g., shorter duration, less efficiency, more irregular timing) exacerbates negative emotions, which alcohol may temporarily relieve. Not yet examined, we propose sleep indirectly impacts early stages of alcohol use via differences in negative reinforcement learning (NRL), since sleep impacts emotion, reward response, and learning. The current study aimed to replicate associations between sleep and alcohol use, test associations with NRL, and examine indirect associations between sleep health and alcohol use via NRL among 60 underage college students (ages 18-20 years, 77\% female) varying in depression and anxiety symptoms. Participants wore Fitbit smartwatches and completed daily diaries measuring sleep and substance use for ∼14 days before completing two computer tasks assessing social (SNRL) and monetary (MNRL) negative reinforcement learning. Robust generalized linear models tested direct associations within the proposed model. SNRL performance was positively associated with alcohol use, but no other associations were observed. Statistical mediation models failed to indicate indirect effects of sleep on alcohol use via SNRL or MNRL performance. Post-hoc exploratory models examining depression and anxiety symptoms as moderators of direct associations indicated several interactions. Positive associations between sleep timing variability and alcohol use were weakened at higher anxiety symptom severity and stronger at higher depression symptom severity. The positive association between SNRL performance and alcohol use was also stronger at higher depression symptom severity. Among students with elevated depression symptoms, variable sleep timing and stronger SNRL performance were independently associated with more alcohol use, but indirect effects were not supported. Future research should replicate findings, confirm causality of interactions, and examine sleep timing and behavioral responses to negative social stimuli as targets for improving alcohol-related outcomes among underage college students with elevated depressive symptoms.

Pilot study comparing sleep logs to a commercial wearable device in describing the sleep patterns of physicians-in-training (2024)

PloS One

Abstract

With the increasing burden of professional burnout in physicians, attention is being paid to optimizing sleep health, starting in training. The multiple dimensions of physicians' sleep are not well described due to obstacles to easily and reliably measuring sleep. This pilot study tested the feasibility of using commercial wearable devices and completing manual sleep logs to describe sleep patterns of medical students and residents. Prospective pilot study of 50 resident physicians and medical students during a single year of training. Participants completed a manual sleep log while concurrently wearing the Fitbit Inspire device for 14-consecutive days over three clinical rotations of varying work schedules: light, medium, and heavy clinical rotations. Study completion was achieved in 24/50 (48\%) participants. Overall correlation coefficients between the sleep log and Fitbit were statistically low; however, the discrepancies were acceptable, i.e., Fitbit underestimated time in bed and total sleep time by 4.3 and 2.7 minutes, respectively. Sleep onset time and waketime were within 8 minutes, with good agreement. Treatment of sleep episodes during the day led to variance in the data. Average missingness of collected data did not vary between medical students or residents or by rotation type. When comparing the light to heavy rotations, hours slept went from 7.7 (±0.64) to 6.7 (±0.88), quality-of-life and sleep health decreased and stress, burnout, and medical errors increased. Burnout was significantly associated with worse sleep health, hours worked, and quality-of-life. Prospective data collection of sleep patterns using both sleep logs and commercial wearable devices is burdensome for physicians-in-training. Using commercial wearable devices may increase study success as long as attention is paid to daytime sleep. In future studies investigating the sleep of physicians, the timing of data collection should account for rotation type.

Effect of desflurane maintenance on postoperative sleep quality in patients undergoing elective breast surgery: A non-inferiority randomized controlled trail (2024)

Sleep Medicine

Abstract

BACKGROUND: Postoperative sleep disturbance (PSD) is prevalent in perioperative patients，and has significant impact on postoperative recovery and prognosis. The aim of this study was to investigate the effect of desflurane maintenance on postoperative sleep quality, in order to optimize patients' perioperative sleep management. METHOD: A total of 118 patients undergoing elective breast surgery were randomized to receive either desflurane-based volatile anesthesia (desflurane group) or propofol-based total intravenous anesthesia (propofol group) for anesthesia maintenance. The primary outcome was the quality of sleep, which was assessed by the Pittsburgh Sleep Quality Index (PSQI) on 3 days after operation (POD3). Secondary outcomes were PSQI on postoperative day 7 (POD7) and 30 (POD30), and postoperative anxiety, depression, and pain score, as well as objective sleep parameters including total sleep time (TST), WASO (Wakefulness after sleep onset), REM (Rapid eye movement) and NREM (Non-rapid Eye Movement) measured by Fitbit Charge 2TM during the initial 3 postoperative days. RESULTS: The global PSQI scores on POD3 in the desflurane group was non-inferior to that in the propofol group [mean (SD) 8.47 (3.46) vs. 7.65 (3.16); mean difference (95 \% CI) 0.82 (-0.43, 2.07); p {\textless} 0.001 for non-inferiority]. There were no significant differences in PSQI scores on POD3 and POD7. In addition, the score of anxiety, depression, and pain on the 3rd, 7th, and 30th day after surgery have no significant differences between the propofol and the desflurane group, respectively. The postoperative NREM was higher in the desflurane group than that in the propofol group. CONCLUSION: The effects of desflurane-based volatile anesthesia maintenance on postoperative sleep quality is not inferior to that of propofol-based total intravenous anesthesia, and these two drugs may have different effects on the sleep structure. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04805775.

Individual-Level Experiences of Structural Inequity and Their Association with Subjective and Objective Sleep Outcomes in the Adolescent Brain Cognitive Development Study (2024)

The Journal of Adolescent Health: Official Publication of the Society for Adolescent Medicine

Abstract

PURPOSE: Research has documented that adolescent sleep is impacted by various stressors, including interpersonal experiences and structural disadvantage. This study extends existing knowledge by empirically examining interconnected individual experiences of structural inequity and assessing its association with subjective and objective sleep outcomes. METHODS: We utilized data from the Adolescent Brain and Cognitive Development Study to identify seven conceptual domains of structural inequity: perceived discrimination, low school inclusivity, neighborhood safety, unmet medical needs, legal problems, material hardship, and housing insecurity. We operationalized experiences of structural inequity as latent classes, a cumulative exposure, and each domain separately. Sleep disturbances were measured using the Sleep Disturbance Scale, and sleep duration was assessed using Fitbits. Mixed effects linear regression estimated the association between our measures of structural inequity, longitudinal sleep disturbances, and cross-sectional sleep duration. RESULTS: Latent class analysis revealed common exposure profiles (low risk, interpersonal, and systemic) of experiences of structural inequity across our sample. In longitudinal models, structural inequity was associated with higher Sleep Disturbance Scale scores, whether measured as latent classes, a cumulative exposure, or individual domains. Individuals with interpersonal exposures, those with at least one exposure, and those with legal problems, material hardship, and housing insecurity had lower mean sleep duration. DISCUSSION: Results are consistent with literature that frames structural inequity as a lifelong determinant of sleep disturbance and duration. Adolescence represents a crucial time for interventions aimed at improving sleep and redressing inequities throughout the life course; our work can inform the development of policies and interventions toward this end.

An adapted transdiagnostic sleep and circadian intervention for adults with excess weight and suboptimal sleep health: pilot study results (2024)

Sleep Advances: A Journal of the Sleep Research Society

Abstract

STUDY OBJECTIVES: This single-arm, mixed-methods, pilot study examined the feasibility and preliminary efficacy of an adapted version of the transdiagnostic intervention for sleep and circadian dysfunction (TranS-C) on multidimensional sleep health (MDSH) in a sample of adults with excess weight and suboptimal sleep health. METHODS: Participants received up to eight, weekly, remotely delivered, tailored TranS-C sessions. At pre- and post-intervention, the Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale, and 7 days of Fitbit data were used to evaluate changes in sleep dimensions (regularity, alertness, timing, satisfaction, duration, and efficiency) and the composite MDSH score. Study feasibility examined recruitment, data collection, and intervention engagement (completion of core TranS-C sessions). Acceptability of the intervention was assessed with semi-structured interviews, which were analyzed using thematic analysis. RESULTS: From 85 referrals, 11 individuals were eligible, and 10 completed the study. All intervention participants completed the measures needed to calculate their composite MDSH score and completed the core intervention sessions. Themes from interviews support the intervention's remote delivery approach, applicability of the information provided, and impact on self-reported health. The intervention resulted in a large improvement in the mean composite MDSH score (Cohen's d = 1.17). Small-to-large effects were also observed for individual sleep health dimensions except for timing. CONCLUSIONS: Adapted TranS-C is acceptable for adults with excess weight and suboptimal sleep health and may be effective at improving short-term MDSH. With changes to recruitment methods, a larger study is feasible. Limitations include the small sample size and the lack of a control condition.

Sleep Behavior in Royal Australian Navy Shift Workers by Shift and Exposure to the SleepTank App (2024)

Military Medicine

Abstract

Introduction: Rotating shiftwork schedules are known to disrupt sleep in a manner that can negatively impact safety. Consumer sleep technologies (CSTs) may be a useful tool for sleep tracking, but the standard feedback provided by CSTs may not be salient to shift-working populations. SleepTank is an app that uses the total sleep time data scored by a CST to compute a percentage that equates hours of sleep to the fuel in a car and warns the user to sleep when the "tank" is low. Royal Australian Navy aircraft maintenance workers operating on a novel rotational shift schedule were given Fitbit Versa 2s to assess sleep timing, duration, and efficiency across a 10-week period. Half of the participants had access to just the Fitbit app while the other half had access to Fitbit and the SleepTank app. The goal of this study was to evaluate differences in sleep behavior between shifts using an off-the-shelf CST and to investigate the potential of the SleepTank app to increase sleep duration during the 10-week rotational shift work schedule. Materials and methods: Royal Australian Navy volunteers agreed to wear a Fitbit Versa 2 with the SleepTank app (SleepTank condition), or without the SleepTank app (Controls), for up to 10 weeks from May to July 2023 during the trial of a novel shift rotation schedule. Participants from across 6 units worked a combination of early (6:00 AM to 2:00 PM), day (7:30 AM to 4:30 PM), late (4:00 PM to 12:00 AM), and night shifts (12:00 AM to 6:00 AM) or stable day shifts (6:00 AM to 4:00 PM). Differences in sleep behavior (time in bed, total sleep time, bedtime, wake time, sleep efficiency [SE]) between conditions and shift types were tested using Analysis of Variance. This study was approved by the Australian Departments of Defence and Veterans' Affairs Human Research Ethics Committee. Results: Thirty-four participants completed the full study (n = 17 Controls; n = 17 SleepTank). There was a significant effect of shift type on 24-hour time in bed (TIB24; F(4,9) = 8.15, P < .001, η2 = 0.15) and total sleep time (TST24; F(4,9) = 8.54, P < .001, η2 = 0.18); both were shorter in early shifts and night shifts compared to other shift types. TIB24 and TST24 were not significantly different between conditions, but there was a trend for greater SE in the SleepTank condition relative to Controls (F(1,9) = 2.99, P = .08, η2 = 0.11). Conclusions: Sleep data collected by Fitbit Versa 2s indicated shorter sleep duration (TIB24, TST24) for Royal Australian Navy workers during early and late shifts relative to stable day shifts. Access to the SleepTank app did not greatly influence measures of sleep duration but may be protective against fatigue by affecting SE. Further research is needed to evaluate the utility of the SleepTank app as a means of improving sleep hygiene in real-world, shift-working environments.

Variations in objectively measured sleep parameters in patients with different premature ejaculation syndromes (2024)

The Journal of Sexual Medicine

Abstract

BACKGROUND: Poor sleep quality is now a cause of sexual dysfunction. AIM: To investigate variations in sleep quality among patients with different types of premature ejaculation (PE) and a control group. METHODS: Patients with PE were categorized into groups according to 4 types: lifelong (LPE), acquired (APE), variable (VPE), and subjective (SPE). Basic demographic information about the participants was first collected, and then clinical data were obtained. OUTCOMES: Outcomes included the 5-item International Index of Erectile Function, Premature Ejaculation Diagnostic Tool, 7-item Generalized Anxiety Disorder, 9-item Patient Health Questionnaire, Pittsburgh Sleep Quality Index, self-estimated intravaginal ejaculation latency time (minutes), and sleep monitoring parameters obtained from a wearable device (Fitbit Charge 2). RESULTS: A total of 215 participants were enrolled in the study, of which 136 patients with PE were distributed as follows: LPE (31.62\%), APE (42.65\%), VPE (10.29\%), and SPE (15.44\%). Subjective scales showed that patients with APE were accompanied by a higher prevalence of erectile dysfunction, anxiety, and depression, as well as poorer sleep quality (assessed by the Pittsburgh Sleep Quality Index). The results of objective sleep parameters revealed that average durations of sleep onset latency (minutes) and wake after sleep onset (minutes) in patients with APE (mean ± SD; 20.03 ± 9.14, 55 ± 23.15) were significantly higher than those with LPE (15.07 ± 5.19, 45.09 ± 20.14), VPE (13.64 ± 3.73, 38.14 ± 11.53), and SPE (14.81 ± 4.33, 42.86 ± 13.14) and the control group (12.48 ± 3.45, 37.14 ± 15.01; P {\textless} .05). The average duration of rapid eye movement (REM; minutes) in patients with APE (71.34 ± 23.18) was significantly lower than that in patients with LPE (79.67 ± 21.53), VPE (85.93 ± 6.93), and SPE (80.86 ± 13.04) and the control group (86.56 ± 11.93; P {\textless} .05). Similarly, when compared with the control group, patients with LPE had significantly longer durations of sleep onset latency and wake after sleep onset and a significantly shorter duration of REM sleep. CLINICAL IMPLICATIONS: Our study suggests that clinicians should pay attention not only to male physical assessment but also to mental health and sleep quality. STRENGTHS AND LIMITATIONS: This study suggests that changes in sleep structure occur in patients with PE, which may provide some direction for future research. However, the cross-sectional study design does not allow us to conclude that sleep is a risk factor for PE. CONCLUSION: After controlling for traditional parameters such as age, erectile dysfunction, anxiety, and depression, sleep parameters are independently associated with PE. Patients with APE and LPE show significant alterations in sleep parameters, with patients with APE having notably poorer sleep quality, whereas patients with VPE and SPE have sleep parameters similar to controls.

Associations of seasonally available global positioning systems-derived walkability and objectively measured sleep in the Nurses' Health Study 3 Mobile Health Substudy (2024)

Environmental Epidemiology (Philadelphia, Pa.)

Abstract

BACKGROUND: Sleep is influenced by the environments that we experience while awake and while asleep. Neighborhood walkability has been linked with chronic disease and lifestyle factors, such as physical activity; however, evidence for the association between walkability and sleep is mixed. Extant studies assign walkability based on residential addresses, which does not account for mobility. We examined the association between walkability and sleep in the Nurses' Health Study 3 (NHS3) Mobile Health Substudy (MHS). METHODS: From 2018 to 2020, individuals in the United States-based NHS3 prospective cohort participated in the MHS, in which minute-level global positioning systems (GPS) data and objective sleep duration and efficiency measures were collected via a custom smartphone application and Fitbit, respectively, for four 7-day periods across a year to capture seasonal variability. Census tract walkability was calculated by summing z-scores of population density (2015-2019 American Community Survey), business density (2018 Infogroup), and intersection density (2018 TIGER/Line road shapefiles). We ran generalized additive mixed models with penalized splines to estimate the association between walkability and sleep, adjusting for individual-level covariates as well as GPS-based exposure to environmental and contextual factors. RESULTS: The average main sleep period duration was 7.9 hours and the mean sleep efficiency was 93\%. For both sleep duration and sleep efficiency, we did not observe an association with daily average walkability exposure. CONCLUSION: In this study of women across the United States, we found that daily GPS-based neighborhood walkability exposure during wake time was not associated with objective wearable-derived sleep duration or sleep efficiency.

Assessment of sleep characteristics using Fitbit Charge 4 in head and neck cancer patients undergoing palliative chemotherapy and radiotherapy: a prospective observational study (2024)

Palliative Care and Social Practice

Abstract

BACKGROUND: Sleep disturbance is prevalent among cancer patients. The quantification of this sleep disturbance is missing, especially in palliative care settings. AIM: The aim of this study was to study the sleep patterns of the patients undergoing palliative chemotherapy and radiotherapy for head and neck cancer (HNC) using a Fitbit Charge 4 sleep-tracking device. DESIGN: Prospective observational study. SETTING: A total of 110 HNC patients undergoing palliative chemotherapy and radiotherapy at a tertiary care teaching hospital in Central India. RESULTS: Forty-four percent of patients had a poor sleep score (less than 60). Average sleep duration was 218.66 ± 139.05 min; non-rapid eye movement (NREM) sleep duration 197.7 ± 115.91 (light NREM 171.36 ± 104 and deep NREM 23.36 ± 16.73); REM sleep duration was 30.44 ± 34.14 min. The Pittsburgh Sleep Quality Index was 10.23 ± 3.45, which indicated sleep deprivation over the past 1 month. Moderate levels of anxiety, depression, confusion, and distress existed in the cohort. Statistically significant but weak correlation existed between sleep score, anxiety, and depression. Strong correlation existed between distress score and sleep score. Confusion score did not have a significant correlation with sleep score. CONCLUSION: HNC patients in palliative care settings were chronically sleep deprived. Sleep architecture was also disturbed. Moderate levels of anxiety, depression, confusion, and distress existed in the studied cohort; these psychosocial disturbances had a weak correlation with the sleep score and are likely to be multifactorial. TRIAL REGISTRATION: Institutional Ethics Committee number: IHEC-LOP/2020/IM0349. The study has been registered with clinical trial registry of India with registration number CTRI/2021/03/032400 (http://www.ctri.in).

Feasibility of at-Home Sleep Monitoring in Adolescents with and without Concussion (2024)

Nature and Science of Sleep

Abstract

Background: Poor sleep is associated with longer recovery following adolescent concussion, making the longitudinal assessment of sleep important for monitoring recovery and identifying sleep disruptions. An important consideration for successful monitoring of sleep following concussion is the feasibility and adherence of a given sleep monitoring tool when used in an at-home environment. Understanding the usability of different sleep monitoring tools is essential for determining their applicability for longitudinal assessment in an ecologically valid environment. Purpose: The purpose of this study was to: (1) report on the adherence and feasibility of at-home sleep monitoring in adolescents following concussion, and (2) compare outcomes of subjective and wearable measures of sleep between adolescents with and without a concussion. Patients and methods: Participants included adolescents within 21 days of a concussion and uninjured controls that participated in four separate, prospective and longitudinal investigations of sleep following concussion. Sleep data was measured with: (1) Dreem Headband; (2) Philips Actiwatch; (3) Fitbit; and (4) subjective sleep diary. Sleep data was collected nightly, and adherence was defined as percentage of nights the participant used the sleep-monitoring tool over the study duration. Independent t-tests and effect sizes were calculated for the following sleep data outcomes as measured by each of the monitoring tools: duration, efficiency, latency, wake after sleep onset. Results: Sleep data for a total of 183 adolescents (104 with concussion, 79 uninjured controls) was assessed. Adherence rates across all devices ranged from 53% to 98%, with the subjective sleep diary showing the highest adherence rate for both groups (concussion: 91%, control: 94%). Across the four different monitoring tools, adolescents with a concussion demonstrated longer duration, latency, wake after sleep onset, and lower (worse) efficiency, with medium to large effect sizes. Conclusion: The results indicate that at-home sleep monitoring is a feasible approach for tracking sleep in adolescents following concussion.

Dialysis-Imposed Patterns of Nocturnal Sleep Duration: A Multi-Center Prospective Study in Patients Using a Wearable Activity Tracker (2025)

Kidney360

Abstract

BACKGROUND: In patients on hemodialysis, the effects of determinants of sleep duration are not widely studied. Using wearable activity trackers, we aimed to characterize natural and hemodialysis-imposed temporal patterns of nocturnal sleep. METHODS: In this yearlong prospective observational study, patients on in-center hemodialysis were equipped with activity trackers (Fitbit® Charge 2™). Nocturnal sleep duration was assessed according to dialysis start time (early starters: before 8 a.m., late starters: others), dialysis versus interdialytic days (post-dialysis day and 2nd interdialytic day), weekdays, and seasons. Clinical, laboratory, and hemodialysis treatment data were extracted from electronic medical records. Linear mixed-effects models were constructed to determine the effect of various time patterns and predictors of nocturnal sleep duration. RESULTS: 109 patients contributed data (age 54±12 years, 73\% males, 23\% diabetic). Sleep duration was 276±91 minutes; 102 (94\%) patients slept on average less than the recommended 420 minutes per night. On dialysis days, participants slept 55 (95\% CI [51,59]) and 48 (95\% CI [43,54]) minutes less compared to post-dialysis and 2nd interdialytic days, respectively. Early starters slept on average 40 (95\% CI [6,74]) minutes less compared to late starters. On dialysis days, early starters slept 86 (95\% CI [55,118]) minutes less compared to late starters. We observed greater sleep-wake disturbance in early starters. Irrespective of dialysis schedule, patients slept on average 26 (95\% CI [19,33]) to 32 (95\% CI [24,40]) minutes longer on Sunday. In winter, sleep was 7 (95\% CI [1,13]) to 10 (95\% CI [5,16]) minutes shorter. In multivariate analysis, higher blood pressure and higher serum creatinine were significantly associated with shorter sleep duration. CONCLUSIONS: On average, patients on hemodialysis slept less than the recommended amount of time. The timing of hemodialysis treatment has pronounced effects on sleep duration and could be considered in patient care.

Validation of the Caen Chronotype Questionnaire: Exploring the added value of amplitude and correlations with actigraphy (2025)

Chronobiology International

Abstract

Chronotype self-report instruments are time and cost-efficient measures to profile diurnal or time-of-day preferences. The Caen Chronotype Questionnaire (CCQ) captures morningness and eveningness (CCQ-ME) and a circadian amplitude dimension for diurnal variation (distinctiveness; CCQ-DI). This study extends prior multilanguage validations for the English version of the CCQ. In total, 628 participants enrolled from a UK working population (mean age 30.34 ± 8.36 years, 61.3\% female) including a subset of shift workers (n = 179; mean age 27.62 ± 5.95 years, 49.2\% female). A subsample of participants also wore a consumer-grade actigraph device (Fitbit Charge 4) for seven days to compare chronotype estimates with objective sleep-wake parameters (n = 22; mean age 27.05 ± 3.99 years, 81.8\% female, 90.9\% worked standard daytime schedules, and 9.1\% worked rotating shifts). All participants completed online chronotype measures, including the CCQ and Morningness-Eveningness Questionnaire (MEQ), depressive symptoms (Patient Health Questionnaire; PHQ-9), sleep quality (Pittsburgh Sleep Quality Index; PSQI), and other outcome measures. Results from the Confirmatory Factor Analysis (CFA) offer support for a two-factor structure of the CCQ in an English-speaking sample, highlighting how individual preferences for the timing of activities is associated with chronotype (morningness-eveningness; ME) and a second subjective amplitude dimension (DI). However, in contrast with the original CCQ structure, a more parsimonious solution and best overall fit involved the reduction of the original 16-item questionnaire (8 items per factor) to 4 ME items and 5 DI items. Convergent validity with the reduced CCQ scale (rME) and the MEQ was also established. The CCQ was sensitive in discriminating differences in actigraphic sleep-wake timings between morning-and evening-oriented individuals. Regression models demonstrated that amplitude (CCQ-DI) was a significant predictor explaining most of the variance in depressive symptoms (PHQ-9) compared to other variables. Overall, the English version of the CCQ was shown to be a robust tool in estimating chronotype in a sample of adults based in the UK.

Assessment of Sleep Measures and their agreement: Youth-Reported, Caregiver-Reported, and Fitbit-Derived Data in a Large Early Adolescent Cohort (2025)

Sleep

Abstract

STUDY OBJECTIVES: Adequate sleep is essential for adolescents' physical, emotional, and cognitive well-being. However, accurately capturing the complex components of sleep in this demographic is challenging, especially with retrospective self-report measures. This study aims to compare sleep data obtained from youth reports, caregiver reports, and Fitbit devices among early adolescents. METHODS: Data from 11,879 adolescents (11-14 years, 47.83\% female), in Year 2 of the Adolescent Brain Cognitive Development (ABCD) Study, were analyzed. Adolescents self-reported their sleep characteristics using the Munich Chronotype Questionnaire, and caregivers provided data through the Children's Sleep Disturbance Scale. Additionally, a subset of participants (N = 3,803) wore Fitbit Charge 2 devices for 21 days. We assessed the questionnaires' internal consistency and utilized Bland-Altman and interclass correlation analyses for comparing self-reported sleep characteristics with Fitbit measures. RESULTS: Youth-reported and caregiver-reported sleep questionnaires demonstrated acceptable internal consistency. Discrepancies between caregiver and adolescent reports were more pronounced when adolescents reported sleep periods of less than 7 hours. Compared to Fitbit measurements, adolescents' self-reports showed a reasonably high agreement on sleep period and bedtime, while agreement on duration of wakefulness after sleep onset and sleep onset latency was poor. CONCLUSIONS: Results show reasonable agreement between adolescent self-reports and Fitbit measurements of sleep period and indicate their usefulness in assessing sleep behavior in adolescents. Caregivers provided valuable perspectives of the youth's sleep disturbances, however, they tended to overestimate sleep duration. These findings offer important methodological insights and highlight the necessity of adopting multi-dimensional approaches to assess sleep in adolescents.

Tension and Trauma Releasing Exercises for People with Multiple Sclerosis: A Randomized Controlled Trial (2025)

Advances in Mind-Body Medicine

Abstract

CONTEXT: Some studies suggests that mind-body therapies may be helpful for people with multiple sclerosis (PwMS) for symptom management. PwMS have reported benefits from Tension and Trauma Releasing Exercises (TRE); however, only limited research exists on the potential benefits of TRE. OBJECTIVE: The study intended to determine whether an eight-week intervention of Tension and Trauma Releasing Exercises (TRE), a mind-body therapy, could primarily reduce fatigue and secondarily decrease other MS-related symptoms and to explore new, experimental outcome measures-sleep, hand motor function, gait function, cognitive function, or mood-for use in future research on TRE for PwMS. DESIGN: The research team performed a randomized controlled trial (RCT). SETTING: The study took place at the Danish MS Society in Valby, Denmark. PARTICIPANTS: Participants were 28 members of the society who had an MS diagnosis and lived in the Copenhagen area. INTERVENTION: The research team performed simple randomization to allocate participants to the intervention and control groups. The intervention group received eight sessions of TRE and performed TRE daily at home, while the control group continued their usual care only. OUTCOME MEASURES: The primary outcome measure was self-reported fatigue measured via the Modified Fatigue Impact Scale (MFIS) at baseline and postintervention. Daily self-reported levels of nine MS-related symptoms as well as stress levels were obtained as secondary endpoints, using a digital tool, the MS PRO. For explorative purposes, daily measures of sleep duration and quality were obtained, via the Fitbit Charge 3 activity tracker. Finally, smartphone-based measurements of hand motor function, gait, cognition, and mood were obtained via the Floodlight Open (FLO) app. RESULTS: On primary outcomes, no significant differences between the TRE intervention group and the control group were detected at follow-up in the unadjusted analyses. However, when adjusted for imbalance in covariates, significant group differences were obtained in the MFIS total score (P = .03) as well as marginal significance in the MFISphys score (P = .06). The TRE intervention significantly reduced reported levels of spasticity and pain across the whole intervention period, and it showed a significant effect of gradual improvement in the 5 U-turn test over the study period. CONCLUSIONS: Positive effects on both primary and secondary outcomes from the TRE intervention were seen when adjusting for covariate imbalance. Therefore, it cannot be ruled out that TRE significantly improves fatigue in people with MS. Furthermore, the current study underlines the potential effect of bias-adjustment, when covariate imbalance is present. However, further research is needed to confirm the positive effects.

Sleep duration and efficiency moderate the effects of prenatal and childhood ambient pollutant exposure on global white matter microstructural integrity in adolescence (2025)

bioRxiv: The Preprint Server for Biology

Abstract

Background: Air pollution is a ubiquitous neurotoxicant associated with alterations in structural connectivity. Good habitual sleep may be an important protective lifestyle factor due to its involvement in the brain waste clearance and its bidirectional relationship with immune function. Wearable multisensory devices may provide more objective measures of sleep quantity and quality. We investigated whether sleep duration and efficiency moderated the relationship between prenatal and childhood pollutant exposure and whole-brain white matter microstructural integrity at ages 10-13 years. Methods: We used multi-shell diffusion-weighted imaging data collected on 3T MRI scanners and objective sleep data collected with Fitbit Charge 2 from the 2-year follow-up visit for 2178 subjects in the Adolescent Brain Cognitive Development Study®. White matter tracts were identified using a probabilistic atlas. Restriction spectrum imaging was performed to extract restricted normalized isotropic (RNI) and directional (RND) signal fraction parameters for all white matter tracts, then averaged to calculate global measures. Sleep duration was calculated by summing the time spent in each sleep stage; sleep efficiency was calculated by dividing sleep duration by time spent in bed. Using an ensemble-based modeling approach, air pollution concentrations of PM2.5, NO2, and O3 were assigned to each child's residential addresses during the prenatal period (9-month average before birthdate) as well as at ages 9-10 years. Multi-pollutant linear mixed effects models assessed the associations between global RNI and RND and sleep-by-pollutant interactions, adjusting for appropriate covariates. Results: Sleep duration interacted with childhood NO2 exposure and sleep efficiency interacted with prenatal O3 exposure to affect RND at ages 10-13 years. Longer sleep duration and higher sleep efficiency in the context of higher pollutant exposure was associated with lower RND compared to those with similar pollutant exposure but shorter sleep duration and lower sleep efficiency. Conclusions: Low-level air pollution poses a risk to brain health in youth, and healthy sleep duration and efficiency may increase resilience to its harmful effects on white matter microstructural integrity. Future studies should evaluate the generalizability of these results in more diverse cohorts as well as utilize longitudinal data to understand how sleep may impact brain health trajectories in the context of pollution over time. Keywords: adolescence; air pollution; ground-level ozone; objective sleep measures; prenatal exposure; white matter microstructural integrity.

Fitbit Research Library

Movement toward a novel activity monitoring device (2012)

Sleep and Breathing

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

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

Sleep

Abstract

Gender Differences in Regional Brain Activity in Patients with Chronic Primary Insomnia: Evidence from a Resting-State fMRI Study (2016)

Journal of Clinical Sleep Medicine

Abstract

A practical validation study of a commercial accelerometer using good and poor sleepers (2016)

Health Psychology Open

Abstract

Validity of Wearable Fitness Trackers on Sleep Measure (2016)

Medicine & Science in Sports & Exercise

Abstract

SleepSight: a wearables-based relapse prevention system for schizophrenia (2016)

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Abstract

Consumer sleep tracking devices: a review of mechanisms, validity and utility (2016)

Expert Review of Medical Devices

Abstract

A Personalized Approach for Detecting Unusual Sleep from Time Series Sleep-Tracking Data (2016)

2016 IEEE International Conference on Healthcare Informatics (ICHI)

Abstract

Sleep Tracking in the Real World: A Qualitative Study into Barriers for Improving Sleep (2016)

Proceedings of the 28th Australian Conference on Computer-Human Interaction

Abstract

Reliability of Sleep Measures from Four Personal Health Monitoring Devices Compared to Research-Based Actigraphy and Polysomnography (2016)

Sensors

Abstract

A Comparison Study Of The Fitbit Activity Monitor And PSG For Assessing Sleep Patterns And Movement In Children (2016)

Journal of Otolaryngology Advances

Abstract

Sleep Tracking, Wearable Technology, and Opportunities for Research and Clinical Care (2016)

Chest

Abstract

Health Outcomes of Information System Use Lifestyles among Adolescents: Videogame Addiction, Sleep Curtailment and Cardio-Metabolic Deficiencies. (2016)

PLOS ONE

Abstract

Sleep quality, but not quantity, is associated with self-perceived minor error rates among emergency department nurses (2016)

International Emergency Nursing

Abstract

Estimation of sleep stages in a healthy adult population from optical plethysmography and accelerometer signals (2017)

Physiological Measurement

Abstract

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

Sleep

Abstract

Pulmonary exposure to metal fume particulate matter cause sleep disturbances in shipyard welders (2017)

Environmental Pollution

Abstract

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

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

Sleep

Abstract

Restoring effective sleep tranquility (REST): A feasibility and pilot study (2017)

British Journal of Occupational Therapy

Abstract

Home-Based Trials in Adolescent Migraine (2017)

JAMA Neurology

Abstract

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

Sleep

Abstract

Validity of a commercial wearable sleep tracker in adult insomnia disorder patients and good sleepers (2017)

Journal of Psychosomatic Research

Abstract

Considering interpersonal differences in validating wearable sleep-tracking technologies (2017)

2017 Tenth International Conference on Mobile Computing and Ubiquitous Network

Abstract

Are wearable EEG devices more accurate than fitness wristbands for home sleep Tracking? Comparison of consumer sleep trackers with clinical devices (2017)

2017 IEEE 6th Global Conference on Consumer Electronics

Abstract

Pilot Validation of Ambulatory Activity Monitors for Sleep Measurement in Huntington's Disease Gene Carriers (2017)