Estimating sleep parameters using an accelerometer without sleep diary

van Hees, V. T.; Sabia, S.; Jones, S. E.; Wood, A. R.; Anderson, K. N.; Kivimaki, M.; Frayling, T. M.; Pack, A. I.; Bucan, M.; Mazzotti, D. R.; Gehrman, P. R.; Singh-Manoux, A.; Weedon, M. N.

doi:10.1101/257972

bioRxiv · 10.1101/257972

Estimating sleep parameters using an accelerometer without sleep diary

van Hees, V. T.·Sabia, S.·Jones, S. E.·Wood, A. R.·Anderson, K. N.·Kivimaki, M.·Frayling, T. M.·Pack, A. I.·Bucan, M.·Mazzotti, D. R.·Gehrman, P. R.·Singh-Manoux, A.·Weedon, M. N.

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Abstract

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=60-82years) 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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van Hees, V. T., Sabia, S., Jones, S. E., Wood, A. R., Anderson, K. N., Kivimaki, M., Frayling, T. M., Pack, A. I., Bucan, M., Mazzotti, D. R., Gehrman, P. R., Singh-Manoux, A., Weedon, M. N.. 2018-02-01. Estimating sleep parameters using an accelerometer without sleep diary. https://doi.org/10.1101/257972

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