Improving Sleep Bout Detection from a Thigh-Worn Accelerometer in Young Adults

abstract: Introduction: There is currently a lack of industry-wide gold standardization in accelerometer study protocols, including within sleep-focused studies. This study seeks to address accuracy of accelerometer data in detection of the beginnings and ends of sleep bouts in young adults with polysomnography (PSG) corroboration. An existing algorithm used to differentiate valid/invalid wear time and detect bouts of sleep has been modified with the goal of maximizing accuracy of sleep bout detection. Methods: Three key decisions and thresholds of the algorithm have been modified with three experimental values each being tested. The main experimental variable Sleepwindow controls the amount of time before and after a determined bout of sleep that is searched for additional sedentary time to incorporate and consider part of the same sleep bout. Results were compared to PSG and sleep diary data for absolute agreement of sleep bout start time (START), end time (END) and time in bed (TIB). Adjustments were made for outliers as well as sleep latency, snooze time, and the sum of both. Results: Only adjustments made to a sleep window variable yielded altered results. Between a 5-, 15-, and 30-minute window, a 15-minute window incurred the least error and most agreement to comparisons for START, while a 5-minute window was best for END and TIB. Discussion: Contrary to expectation, corrections for snooze, latency, and both did not substantially improve agreement to PSG. Algorithm-derived estimates of START and END always fell after sleep diary and PSG both, suggesting either participants’ sedentary behavior beginning and ends were at a delay from sleep and wake times, or the algorithm estimates consistently later times than appropriate. The inclusion of a sleep window variable yields substantial variety in results. A 15-minute window appears best at determining START while a 5-minute window appears best for END and TIB. Further investigation on the optimal window length per demographic and condition is required