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

RESEARCH ARTICLE A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer Vincent T. van Hees1,2☯*, Séverine Sabia3☯, Kirstie N. Anderson4, Sarah J. Denton1, James Oliver4, Michael Catt1, Jessica G. Abell3, Mika Kivimäki3, Michael I. Trenell1, Archana Singh-Manoux3,5 1 MoveLab – Physical activity and exercise research, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom, 2 Netherlands eScience Center, Amsterdam, The Netherlands, 3 Department of Epidemiology & Public Health, University College London, London, United Kingdom, 4 Regional Sleep Service, Freeman Hospital, Newcastle upon Tyne, United Kingdom, 5 Centre for Research in Epidemiology and Population Health, INSERM, Unit 1018, Villejuif, France ☯ These authors contributed equally to this work. * Abstract OPEN ACCESS Citation: van Hees VT, Sabia S, Anderson KN, Denton SJ, Oliver J, Catt M, et al. (2015) A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer. PLoS ONE 10 (11): e0142533. doi:10.1371/journal.pone.0142533 Editor: Delphine S. Courvoisier, University of Geneva, SWITZERLAND Received: August 23, 2015 Accepted: October 22, 2015 Published: November 16, 2015 Copyright: © 2015 van Hees et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All Whitehall II study data are freely available but not on a public repository. The authors follow MRC guidelines, main funding source for the data collection, and use a gated access policy, details of which are available on the authors website: http://www.ucl.ac.uk/whitehallII/ data-sharing. Data can be requested from severine. . Funding: This work was supported by the US National Institutes of Health (R01AG013196; R01AG034454; R01HL036310), the UK Medical Research Council (K013351), the British Heart Foundation (PG/29605) and the Economic and Social 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. Introduction Large-scale studies have traditionally assessed physical activity and sleep by self-report but objective measurement tools have become increasingly common in the last decade. Studies of physical activity use a hip- or waist-mounted accelerometer that are not suitable for sleep assessment as they are removed prior to bedtime [1]. In sleep studies, parameters are primarily PLOS ONE | DOI:10.1371/journal.pone.0142533 November 16, 2015 1 / 13 A Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer Research Council (ES/J023299). MIT was supported by a Senior Fellowship from the National Institute for Health Research for this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. assessed over several days using a 24 hour wrist-mounted accelerometer, commonly known as actigraphy[2]. In both areas of research, body acceleration is expressed in manufacturer specific ‘count’ values over a specific time window, called epoch. This approach has limitations as estimates from different accelerometers are not comparable and the analyst has limited control over signal processing [3]. Technological advances in accelerometers over recent years now allow collection of high resolution data in universal units of gravitational acceleration. This type of data, also referred to as raw accelerometry, increases analytical freedom and is more amenable to methodological consistency between studies [3]. The wrist-worn version of such accelerometers has become popular, especially in large population studies [4–7], as compliance is equal or better than waist-worn devices [8–10] and it allows assessment of both physical activity and sleep. A number of algorithms have been developed to derive physical activity variables from raw accelerometer data [6,11,12]. In contrast, relatively little has been done on the extraction of sleep parameters from these data [13,14]. The primary signal used to characterise sleep is lack of body movement, but this judgement is complicated by the fact that there are minor body movements during sleep and [15,16] absence of body movement is also possible during periods of wakefulness. Consequently, sleep characterised by accelerometry primarily represents a sustained lack of body movement when the participant reports being in bed or asleep and may not necessarily result in the same sleep classification as the one from polysomnography or self-reported sleep [15]. Polysomnography, the gold standard for assessment of sleep is a multi-parametric test of biophysiological changes that occur during sleep [17] and requires a laboratory setting, making it expensive and infeasible for large scale studies. Previous work on sleep detection from accelerometer data relied on algorithms that use magnitude of acceleration as their input [13,18,19]. We propose a novel method, an easier to interpret description of body kinematics, using accelerometer derived arm angle to detect sleep. We also evaluate the agreement of sleep duration parameter derived from this method with self-reported sleep duration in a large sample of community dwelling older adults. Finally, we investigate (...truncated)