Using Wear Time for the Analysis of Consumer-Grade Wearables’ Data: A Case Study Using Fitbit Data (Preprint)

BACKGROUND Consumer-grade wearables allow researchers to capture a representative picture of human behavior in the real world over extended periods. However, maintaining users’ engagement remains a challenge and can lead to a decrease in compliance (e.g., wear time in the context of wearable sensors) over time (e.g., “wearables’ abandonment”). OBJECTIVE In this work, we analyzed datasets from diverse populations (e.g., caregivers for various health issues, college students, and pediatric oncology patients) and found evidence that emphasizes the need to account for participants’ wear time in the analysis of consumer-grade wearables data. In Aim 1, we demonstrated the sensitivity of scientific results to different data processing methods with respect to wear time. In Aim 2, we demonstrated the need to adapt wear time requirements based on the research question. METHODS We analyzed 3 Fitbit datasets comprising 6 different clinical and healthy populations. For Aim 1, we analyzed the sensitivity of average daily step count and average daily heart rate at the population and individual levels to different methods of defining “valid” days using wear time. For Aim 2, we evaluated how the wear time requirements can differ between two research questions: (1) the estimation of the average daily step count, and (2) the estimation of the average heart rate while walking. RESULTS Aim 1: We found that the changes in population average daily step count could reach 2,000 steps for different methods of analysis and were dependent on the population. As expected, populations with a low daily wear time (under ~15 hours of wear time per day) showed the most sensitivity to changes in methods of analysis. On the individual level, we observed that around 15% of individuals had a difference in step count higher than 1,000 for 4 of the 6 populations analyzed when using different data processing methods. Those individual differences were higher than 3,000 for close to 5% of individuals across all populations. Average daily heart rate appeared to be robust to changes in wear time. Aim 2: We found that for 5 populations out of 6, around 11% of individuals had enough data for the estimation of average heart rate while walking but not for the estimation of their average daily step count. CONCLUSIONS We leveraged datasets from diverse populations to demonstrate the direct correlation between scientific results from consumer-grade wearable devices and participants’ wear time. Our findings highlighted the importance of a thorough analysis of wear time when processing data from consumer-grade wearables to ensure the relevance and reliability of the associated findings.