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Estimating sleep parameters using an accelerometer without sleep diary.

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 = 60-82 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.

journal_name

Sci Rep

journal_title

Scientific reports

authors

van Hees VT,Sabia S,Jones SE,Wood AR,Anderson KN,Kivimäki M,Frayling TM,Pack AI,Bucan M,Trenell MI,Mazzotti DR,Gehrman PR,Singh-Manoux BA,Weedon MN

doi

10.1038/s41598-018-31266-z

subject

Has Abstract

pub_date

2018-08-28 00:00:00

pages

12975

issue

1

issn

2045-2322

pii

10.1038/s41598-018-31266-z

journal_volume

8

pub_type

临床试验,杂志文章,多中心研究

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