ArticleJMIR mHealth and uHealth2018
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.
Article in JMIR mHealth and uHealth, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 35 citations in OpenAlex.
- Trial
- Interpretable Framework for Sleep Monitoring: Applying Statistical Control Charts to Physiological Data Streams.Sensors (Basel, Switzerland) · 2026Article
- Exploring Variations in Sleep Perception: Comparative Study of Chatbot Sleep Logs and Fitbit Sleep Data.JMIR mHealth and uHealth · 2023Article
- Supporting the Management of Gestational Diabetes Mellitus With Comprehensive Self-Tracking: Mixed Methods Study of Wearable Sensors.JMIR diabetes · 2023Article
- 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 · 2023Article
- Smart Consumer Wearables as Digital Diagnostic Tools: A Review.Diagnostics (Basel, Switzerland) · 2022Review
- The Impact of Wearable Technologies in Health Research: Scoping Review.JMIR mHealth and uHealth · 2022Article
- Patient-Generated Data Analytics of Health Behaviors of People Living With Type 2 Diabetes: Scoping Review.JMIR diabetes · 2021Article
- 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 · 2021Article
- Comparing Methods to Identify Wear-Time Intervals for Physical Activity With the Fitbit Charge 2.Journal of aging and physical activity · 2021Article
- Article
- A Multi-Level Classification Approach for Sleep Stage Prediction With Processed Data Derived From Consumer Wearable Activity Trackers.Frontiers in digital health · 2021Article
- 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 · 2020Article
- Sensors Capabilities, Performance, and Use of Consumer Sleep Technology.Sleep medicine clinics · 2020Review
- Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors.JMIR mHealth and uHealth · 2019Article
- Sleep tracking: A systematic review of the research using commercially available technology.Current sleep medicine reports · 2019Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFew studies assessing the correlation between patient-reported outcomes and patient-generated health data from wearable devices exist.
objectiveThe aim of this study was to determine the direction and magnitude of associations between patient-generated health data (from the Fitbit Charge HR) and patient-reported outcomes for sleep patterns and physical activity in patients with type 2 diabetes mellitus (T2DM).
methodsThis was a pilot study conducted with adults diagnosed with T2DM (n=86). All participants wore a Fitbit Charge HR for 14 consecutive days and completed internet-based surveys at 3 time points: day 1, day 7, and day 14. Patient-generated health data included minutes asleep and number of steps taken. Questionnaires assessed the number of days of exercise and nights of sleep problems per week. Means and SDs were calculated for all data, and Pearson correlations were used to examine associations between patient-reported outcomes and patient-generated health data. All respondents provided informed consent before participating.
resultsThe participants were predominantly middle-aged (mean 54.3, SD 13.3 years), white (80/86, 93%), and female (50/86, 58%). Use of oral T2DM medication correlated with the number of mean steps taken (r=.35, P=.001), whereas being unaware of the glycated hemoglobin level correlated with the number of minutes asleep (r=-.24, P=.04). On the basis of the Fitbit data, participants walked an average of 4955 steps and slept 6.7 hours per day. They self-reported an average of 2.0 days of exercise and 2.3 nights of sleep problems per week. The association between the number of days exercised and steps walked was strong (r=.60, P<.001), whereas the association between the number of troubled sleep nights and minutes asleep was weaker (r=.28, P=.02).
conclusionsFitbit and patient-reported data were positively associated for physical activity as well as sleep, with the former more strongly correlated than the latter. As extensive patient monitoring can guide clinical decisions regarding T2DM therapy, passive, objective data collection through wearables could potentially enhance patient care, resulting in better patient-reported outcomes.
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