Anderson KN

Bucan M

Frayling TM

Gehrman PR

Jones SE

Kivimaki M

Mazzotti DR

Pack AI

Sabia S

Singh-Manoux BA

Trenell MI

van Hees VT

Weedon MN

Wood AR

\ua9 2018, The Author(s). 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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Estimating sleep parameters using an accelerometer without sleep diary

https://eprint.ncl.ac.uk/fulltext.aspx?url=251341/9C48BEF3-C61E-4777-9EBB-A451F556F4F0.pdf&pub_id=251341

Estimating sleep parameters using an accelerometer without sleep diary

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