Estimating sleep parameters using an accelerometer without sleep diary

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 sl...

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Bibliographic Details
Published in:Scientific reports 2018-08, Vol.8 (1), p.12975-11, Article 12975
Main Authors: 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
Format: Article
Language:English
Subjects:
Accelerometry
Aged
Aged, 80 and over
Algorithms
Diaries
Female
Humans
Male
Middle Aged
Sleep
Wearable Electronic Devices
Wrist
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-018-31266-z