A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer
By V. V. van Hees, S. Sabia, K. Anderson, Sarah J. Denton, James R Oliver, M. Catt, J. Abell, M. Kivimaki, M. Trenell, A. Singh‐Manoux. PLoS ONE, 2015
664 citations recorded by the source corpus.
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.
Subfields: Experimental and Cognitive Psychology, Physiology, Public Health, Environmental and Occupational Health
View Paper: https://www.semanticscholar.org/paper/615ea99332b5fd1fdcc0b2472b1712697dc8c25b
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Citations in the corpus
- GGIR: A Research Community–Driven Open Source R Package for Generating Physical Activity and Sleep Outcomes From Multi-Day Raw Accelerometer Data (2019, methodology): “…mistakes in sleep diary data g.part4 provides the option to visualize the differences between accelerometer-based estimates and sleep diary; this was for example used in van Hees et al. (van Hees et al., 2015) to identify a dozen of problematic nights out of 27000 nights (Figure 2, panel a).”