A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer

TLDR

Wrist‑worn accelerometers are widely used for physical activity monitoring, yet their effectiveness for sleep assessment remains unclear. The study aimed to develop a generic, accelerometer‑based algorithm for estimating sleep duration that can be compared across studies. The algorithm defines sleep as at least five minutes of no arm‑angle change exceeding 5° during periods marked as sleep in the log, and was validated against polysomnography, showing that a longer window and lower angle threshold improved wakefulness sensitivity while the opposite improved sleep sensitivity. The algorithm’s sleep‑duration estimates showed moderate agreement with questionnaire measures (kappa 0.32–0.39) and were lower for time in bed among women, depressed individuals, those with insomnia symptoms, and on weekends, but total sleep duration did not differ by group.

Abstract

Wrist-worn accelerometers are increasingly being used for the assessment of physical activity in population studies, but little is known about their value for sleep assessment. We developed a novel method of assessing sleep duration using data from 4,094 Whitehall II Study (United Kingdom, 2012–2013) participants aged 60–83 who wore the accelerometer for 9 consecutive days, filled in a sleep log and reported sleep duration via questionnaire. Our sleep detection algorithm defined (nocturnal) sleep as a period of sustained inactivity, itself detected as the absence of change in arm angle greater than 5 degrees for 5 minutes or more, during a period recorded as sleep by the participant in their sleep log. The resulting estimate of sleep duration had a moderate (but similar to previous findings) agreement with questionnaire based measures for time in bed, defined as the difference between sleep onset and waking time (kappa = 0.32, 95%CI:0.29,0.34) and total sleep duration (kappa = 0.39, 0.36,0.42). This estimate was lower for time in bed for women, depressed participants, those reporting more insomnia symptoms, and on weekend days. No such group differences were found for total sleep duration. Our algorithm was validated against data from a polysomnography study on 28 persons which found a longer time window and lower angle threshold to have better sensitivity to wakefulness, while the reverse was true for sensitivity to sleep. The novelty of our method is the use of a generic algorithm that will allow comparison between studies rather than a "count" based, device specific method.