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

Journals

  1. Robbins R, Seixas A, Walton Masters L, Chanko N, Diaby F, Vieira D, Jean-Louis G. Sleep Tracking: a Systematic Review of the Research Using Commercially Available Technology. Current Sleep Medicine Reports 2019;5(3):156 View
  2. de Zambotti M, Cellini N, Menghini L, Sarlo M, Baker F. Sensors Capabilities, Performance, and Use of Consumer Sleep Technology. Sleep Medicine Clinics 2020;15(1):1 View
  3. Kamei T, Kanamori T, Yamamoto Y, Edirippulige S. The use of wearable devices in chronic disease management to enhance adherence and improve telehealth outcomes: A systematic review and meta-analysis. Journal of Telemedicine and Telecare 2020:1357633X2093757 View
  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
  6. Claudel S, Tamura K, Troendle J, Andrews M, Ceasar J, Mitchell V, Vijayakumar N, Powell-Wiley T. Comparing Methods to Identify Wear-Time Intervals for Physical Activity With the Fitbit Charge 2. Journal of Aging and Physical Activity 2021;29(3):529 View
  7. 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