Estimating sleep parameters using an accelerometer without sleep diary

Sci Rep. 2018 Aug 28;8(1):12975. doi: 10.1038/s41598-018-31266-z.

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.

Publication types

  • Clinical Trial
  • Multicenter Study
  • Research Support, N.I.H., Extramural

MeSH terms

  • Accelerometry*
  • Aged
  • Aged, 80 and over
  • Algorithms*
  • Female
  • Humans
  • Male
  • Middle Aged
  • Sleep*
  • Wearable Electronic Devices*