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

mHealth for Data Collection and Research (917) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1317) mHealth for Wellness, Behavior Change and Prevention (2754) Wearable Devices and Sensors (825) Exercise and Diet Tracking for Diabetes Patients (78) Apps, Mobile, Wearables for Diabetes (288) Fitness Trackers and Smart Pedometers/Accelerometers (543)

Published on in Vol 6, No 6 (2018): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/8122, first published .
Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes

Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes

Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes

Authors of this article:

James Weatherall1 Author Orcid Image ;   Yurek Paprocki1 Author Orcid Image ;   Theresa M Meyer2 Author Orcid Image ;   Ian Kudel3 Author Orcid Image ;   Edward A Witt3 Author Orcid Image
Article Authors Cited by (23) Tweetations (9) Metrics

Journals

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  4. Liang Z, Chapa-Martell M. Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors. JMIR mHealth and uHealth 2019;7(6):e13384 View
  5. 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
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  8. Nagpal M, Barbaric A, Sherifali D, Morita P, Cafazzo J. Patient-Generated Data Analytics of Health Behaviors of People Living With Type 2 Diabetes: Scoping Review. JMIR Diabetes 2021;6(4):e29027 View
  9. Skovlund S, Nicolucci A, Balk-Møller N, Berthelsen D, Glümer C, Perrild H, Kjær P, Nørgaard L, Troelsen L, Pietraszek A, Hessler D, Kaplan S, Ejskjær N. Perceived Benefits, Barriers, and Facilitators of a Digital Patient-Reported Outcomes Tool for Routine Diabetes Care: Protocol for a National, Multicenter, Mixed Methods Implementation Study. JMIR Research Protocols 2021;10(9):e28391 View
  10. Martínez-Rodríguez A, Martínez-Olcina M, Mora J, Navarro P, Caturla N, Jones J. Anxiolytic Effect and Improved Sleep Quality in Individuals Taking Lippia citriodora Extract. Nutrients 2022;14(1):218 View
  11. Huhn S, Axt M, Gunga H, Maggioni M, Munga S, Obor D, Sié A, Boudo V, Bunker A, Sauerborn R, Bärnighausen T, Barteit S. The Impact of Wearable Technologies in Health Research: Scoping Review. JMIR mHealth and uHealth 2022;10(1):e34384 View
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  14. Kyytsönen M, Vehko T, Anttila H, Ikonen J, Lai Y. Factors associated with use of wearable technology to support activity, well-being, or a healthy lifestyle in the adult population and among older adults. PLOS Digital Health 2023;2(5):e0000245 View
  15. Kytö M, Koivusalo S, Tuomonen H, Strömberg L, Ruonala A, Marttinen P, Heinonen S, Jacucci G. Supporting the Management of Gestational Diabetes Mellitus With Comprehensive Self-Tracking: Mixed Methods Study of Wearable Sensors. JMIR Diabetes 2023;8:e43979 View
  16. 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
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Books/Policy Documents

  1. Liang Z. IoT Technologies for Health Care. View