This paper is in the following e-collection/theme issue:

Wearable Devices and Sensors (825) Fitness Trackers and Smart Pedometers/Accelerometers (543) Sleep Monitoring, Sleep Quality, Sleep Disorders (170)

Published on in Vol 7, No 6 (2019): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13384, first published .
Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors

Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors

Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors

Authors of this article:

Zilu Liang1, 2 Author Orcid Image ;   Mario Alberto Chapa-Martell3 Author Orcid Image
Article Authors Cited by (49) Tweetations (16) Metrics

Journals

  1. Liang Z, Ploderer B. How Does Fitbit Measure Brainwaves. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(1):1 View
  2. Louzon P, Andrews J, Torres X, Pyles E, Ali M, Du Y, Devlin J. Characterisation of ICU sleep by a commercially available activity tracker and its agreement with patient-perceived sleep quality. BMJ Open Respiratory Research 2020;7(1):e000572 View
  3. Guillodo E, Lemey C, Simonnet M, Walter M, Baca-García E, Masetti V, Moga S, Larsen M, Ropars J, Berrouiguet S. Clinical Applications of Mobile Health Wearable–Based Sleep Monitoring: Systematic Review. JMIR mHealth and uHealth 2020;8(4):e10733 View
  4. Thota D. Evaluating the Relationship Between Fitbit Sleep Data and Self-Reported Mood, Sleep, and Environmental Contextual Factors in Healthy Adults: Pilot Observational Cohort Study. JMIR Formative Research 2020;4(9):e18086 View
  5. Lunsford-Avery J, Keller C, Kollins S, Krystal A, Jackson L, Engelhard M. Feasibility and Acceptability of Wearable Sleep Electroencephalogram Device Use in Adolescents: Observational Study. JMIR mHealth and uHealth 2020;8(10):e20590 View
  6. Devine J, Chinoy E, Markwald R, Schwartz L, Hursh S. Validation of Zulu Watch against Polysomnography and Actigraphy for On-Wrist Sleep-Wake Determination and Sleep-Depth Estimation. Sensors 2020;21(1):76 View
  7. Mishra T, Wang M, Metwally A, Bogu G, Brooks A, Bahmani A, Alavi A, Celli A, Higgs E, Dagan-Rosenfeld O, Fay B, Kirkpatrick S, Kellogg R, Gibson M, Wang T, Hunting E, Mamic P, Ganz A, Rolnik B, Li X, Snyder M. Pre-symptomatic detection of COVID-19 from smartwatch data. Nature Biomedical Engineering 2020;4(12):1208 View
  8. Zhang Y, Folarin A, Sun S, Cummins N, Bendayan R, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Matcham F, White K, Lamers F, Siddi S, Simblett S, Myin-Germeys I, Rintala A, Wykes T, Haro J, Penninx B, Narayan V, Hotopf M, Dobson R. Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study. JMIR mHealth and uHealth 2021;9(4):e24604 View
  9. Rhee S, Kim C, Shin D, Steinhubl S. Present and Future of Digital Health in Diabetes and Metabolic Disease. Diabetes & Metabolism Journal 2020;44(6):819 View
  10. Liang Z, Chapa-Martell M. A Multi-Level Classification Approach for Sleep Stage Prediction With Processed Data Derived From Consumer Wearable Activity Trackers. Frontiers in Digital Health 2021;3 View
  11. Elavsky S, Klocek A, Knapova L, Smahelova M, Smahel D, Cimler R, Kuhnova J. Feasibility of Real-time Behavior Monitoring Via Mobile Technology in Czech Adults Aged 50 Years and Above: 12-Week Study With Ecological Momentary Assessment. JMIR Aging 2021;4(4):e15220 View
  12. Rykov Y, Thach T, Bojic I, Christopoulos G, Car J. Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling. JMIR mHealth and uHealth 2021;9(10):e24872 View
  13. de Vries H, Kamphuis W, Oldenhuis H, van der Schans C, Sanderman R. Moderation of the Stressor-Strain Process in Interns by Heart Rate Variability Measured With a Wearable and Smartphone App: Within-Subject Design Using Continuous Monitoring. JMIR Cardio 2021;5(2):e28731 View
  14. Devine J, Schwartz L, Choynowski J, Hursh S. Expert Demand for Consumer Sleep Technology Features and Wearable Devices: A Case Study. IoT 2022;3(2):315 View
  15. Hong J, Tran H, Jung J, Jang H, Lee D, Yoon I, Hong J, Kim J. End-to-End Sleep Staging Using Nocturnal Sounds from Microphone Chips for Mobile Devices. Nature and Science of Sleep 2022;Volume 14:1187 View
  16. Ni C, Hou G, Tang Y, Wang J, Chen W, Yang Y, Wang Z, Zhou W. Quantitative study of the effects of early standardized ambulation on sleep quality in patients after hepatectomy. Frontiers in Surgery 2022;9 View
  17. Stucky B, Clark I, Azza Y, Karlen W, Achermann P, Kleim B, Landolt H. Validation of Fitbit Charge 2 Sleep and Heart Rate Estimates Against Polysomnographic Measures in Shift Workers: Naturalistic Study. Journal of Medical Internet Research 2021;23(10):e26476 View
  18. Liang Z. Context-Aware Sleep Health Recommender Systems (CASHRS): A Narrative Review. Electronics 2022;11(20):3384 View
  19. Budig M, Stoohs R, Keiner M. Validity of Two Consumer Multisport Activity Tracker and One Accelerometer against Polysomnography for Measuring Sleep Parameters and Vital Data in a Laboratory Setting in Sleep Patients. Sensors 2022;22(23):9540 View
  20. Devine J, Choynowski J, Garcia C, Simoes A, Guelere M, Godoy B, Silva D, Pacheco P, Hursh S. Pilot Sleep Behavior across Time during Ultra-Long-Range Flights. Clocks & Sleep 2021;3(4):515 View
  21. Liang Z. What Does Sleeping Brain Tell About Stress? A Pilot Functional Near-Infrared Spectroscopy Study Into Stress-Related Cortical Hemodynamic Features During Sleep. Frontiers in Computer Science 2021;3 View
  22. Kuosmanen E, Visuri A, Risto R, Hosio S. Comparing consumer grade sleep trackers for research purposes: A field study. Frontiers in Computer Science 2022;4 View
  23. Fritz H, Kinney K, Wu C, Schnyer D, Nagy Z. Data fusion of mobile and environmental sensing devices to understand the effect of the indoor environment on measured and self-reported sleep quality. Building and Environment 2022;214:108835 View
  24. Karas M, Marinsek N, Goldhahn J, Foschini L, Ramirez E, Clay I. Predicting Subjective Recovery from Lower Limb Surgery Using Consumer Wearables. Digital Biomarkers 2020;4(Suppl. 1):73 View
  25. Ungaro C, De Chavez P. Sleep habits of high school student-athletes and nonathletes during a semester. Journal of Clinical Sleep Medicine 2022;18(9):2189 View
  26. Babaei N, Hannani N, Dabanloo N, Bahadori S. A Systematic Review of the Use of Commercial Wearable Activity Trackers for Monitoring Recovery in Individuals Undergoing Total Hip Replacement Surgery. Cyborg and Bionic Systems 2022;2022 View
  27. Leightley D, Lavelle G, White K, Sun S, Matcham F, Ivan A, Oetzmann C, Penninx B, Lamers F, Siddi S, Haro J, Myin-Germeys I, Bruce S, Nica R, Wickersham A, Annas P, Mohr D, Simblett S, Wykes T, Cummins N, Folarin A, Conde P, Ranjan Y, Dobson R, Narayan V, Hotopf M. Investigating the impact of COVID-19 lockdown on adults with a recent history of recurrent major depressive disorder: a multi-Centre study using remote measurement technology. BMC Psychiatry 2021;21(1) View
  28. Windrix C, Vandyck K, Tanaka K, Butt A. Night Float Rotations: Continued Questions With Few Answers. Anesthesia & Analgesia 2023;136(6):e41 View
  29. Zhu Y, Stephenson C, Moghimi E, Jagayat J, Nikjoo N, Kumar A, Shirazi A, Patel C, Omrani M, Alavi N, Köpke S. Investigating the effectiveness of electronically delivered cognitive behavioural therapy (e-CBTi) compared to pharmaceutical interventions in treating insomnia: Protocol for a randomized controlled trial. PLOS ONE 2023;18(5):e0285757 View
  30. Carlson E, Wilckens K, Wheeler M, Lipsitz L. The Interactive Role of Sleep and Circadian Rhythms in Episodic Memory in Older Adults. The Journals of Gerontology: Series A 2023;78(10):1844 View
  31. Hoang N, Liang Z. Knowledge Discovery in Ubiquitous and Personal Sleep Tracking: Scoping Review. JMIR mHealth and uHealth 2023;11:e42750 View
  32. Jang H, Lee S, Son Y, Seo S, Baek Y, Mun S, Kim H, Kim I, Kim J. Exploring Variations in Sleep Perception: Comparative Study of Chatbot Sleep Logs and Fitbit Sleep Data. JMIR mHealth and uHealth 2023;11:e49144 View
  33. Stirling R, Hidajat C, Grayden D, D’Souza W, Naim-Feil J, Dell K, Schneider L, Nurse E, Freestone D, Cook M, Karoly P. Sleep and seizure risk in epilepsy: bed and wake times are more important than sleep duration. Brain 2023;146(7):2803 View
  34. Kelly K, Kolbeinsson H, Blanck L, Khan M, Kyriakakis R, Assifi M, Wright G, Chung M. Can we let our patients sleep in the hospital? A randomized controlled trial of a pragmatic sleep protocol in surgical oncology patients. Journal of Surgical Oncology 2024;129(4):827 View
  35. Jaiswal S, Pawelek J, Warshawsky S, Quer G, Trieu M, Pandit J, Owens R. Using New Technologies and Wearables for Characterizing Sleep in Population-based Studies. Current Sleep Medicine Reports 2024;10(1):82 View
  36. Hassinger A, Velez C, Wang J, Mador M, Wilding G, Mishra A. Association between sleep health and rates of self-reported medical errors in intern physicians: an ancillary analysis of the Intern Health Study. Journal of Clinical Sleep Medicine 2024;20(2):221 View
  37. Waqar S, Ghani Khan M. Sleep stage prediction using multimodal body network and circadian rhythm. PeerJ Computer Science 2024;10:e1988 View
  38. Daniore P, Nittas V, Haag C, Bernard J, Gonzenbach R, von Wyl V. From wearable sensor data to digital biomarker development: ten lessons learned and a framework proposal. npj Digital Medicine 2024;7(1) View

Books/Policy Documents

  1. Liang Z. IoT Technologies for Health Care. View
  2. BaHammam A, Pandi-Perumal S, Hunasikatti M. Sleep Apnea Frontiers. View