RT Journal Article
SR Electronic
T1 Estimating sleep parameters using an accelerometer without sleep diary
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 257972
DO 10.1101/257972
A1 V.T. van Hees
A1 S. Sabia
A1 S.E. Jones
A1 A.R. Wood
A1 K.N. Anderson
A1 M. Kivimäki
A1 T.M. Frayling
A1 A. I. Pack
A1 M Bucan
A1 M.I. Trenell
A1 Diego R. Mazzotti
A1 P. R. Gehrman
A1 B. A. Singh-Manoux
A1 M. N. Weedon
YR 2018
UL http://biorxiv.org/content/early/2018/06/25/257972.abstract
AB 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.