Context-aware Sleep Analysis with Intraday Steps and Heart Rate Time Series Data from Consumer Activity Trackers

Abstract

: Wearable consumer activity trackers have become a popular tool for longitudinal monitoring of sleep quality. However, sleep data were routinely visualized in isolation from other contextual information. In this paper, we proposed a sleep analytics method to identify the associations between sleep quality and the contextual data that are readily measurable with a single Fitbit device. Different from prior studies that only focused on the daily aggregation of the contextual factors (e.g., total step counts), our method considers the intraday temporal patterns of these factors. Time-domain, frequency-domain, and nonlinear features were derived using the minute-by-minute intraday step and heart rate time series. The results showed that some of the identified contextual features such as the zero-crossing of steps and the absolute energy of heart rate could lead to actionable insights. While the nonlinear features—such as the average and longest diagonal line length derived through the recurrent quantitative analysis of the step time series—may not lead to insights that can be immediately acted on, they generated new hypotheses for further scientific studies. The results also showed that when dealing with data of consumer wearables, the individual-level analysis could generate more personally relevant insight than the cohort-level analysis.