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van Hees, Vincent Theodoor; Sabia, S; Jones, S E; Wood, A R; Anderson, K N; Kivimäki, M; Frayling, T M; Pack, A I; Bucan, M; Trenell, M I; Mazzotti, Diego R; Gehrman, P R; Singh-Manoux, B A; Weedon, M N
Scientific reports, 08/2018, Volume: 8, Issue: 1Journal Article
Wrist worn raw-data accelerometers are used increasingly in large-scale population research. We examined whether sleep parameters can be estimated from these data in the absence of sleep diaries. Our heuristic algorithm uses the variance in estimated z-axis angle and makes basic assumptions about sleep interruptions. Detected sleep period time window (SPT-window) was compared against sleep diary in 3752 participants (range = 60-82 years) and polysomnography in sleep clinic patients (N = 28) and in healthy good sleepers (N = 22). The SPT-window derived from the algorithm was 10.9 and 2.9 minutes longer compared with sleep diary in men and women, respectively. Mean C-statistic to detect the SPT-window compared to polysomnography was 0.86 and 0.83 in clinic-based and healthy sleepers, respectively. We demonstrated the accuracy of our algorithm to detect the SPT-window. The value of this algorithm lies in studies such as UK Biobank where a sleep diary was not used.
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