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Comparison of sleep parameters from wrist-worn ActiGraph and Actiwatch devices.

Fangyu LiuJennifer A SchrackSarah K WanigatungaJill A RabinowitzLinchen HeAmal Asiri WanigatungaVadim ZipunnikovEleanor M SimonsickLuigi FerruciAdam P Spira
Published in: Sleep (2023)
Sleep and physical activity, two important health behaviors, are often studied independently using different accelerometer types and body locations. Understanding whether accelerometers designed for monitoring each behavior can provide similar sleep parameter estimates may help determine whether one device can be used to measure both behaviors. 331 adults (70.7±13.7 years) from the Baltimore Longitudinal Study of Aging (BLSA) wore the ActiGraph GT9X Link and the Actiwatch 2 simultaneously on the non-dominant wrist for 7.0±1.6 nights. Total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, number of wake bouts, mean wake bout length, and sleep fragmentation index (SFI) were extracted from ActiGraph using the Cole-Kripke algorithm and from Actiwatch using the software default algorithm. These parameters were compared using paired t-tests, Bland-Altman plots, and Deming regression models. Stratified analyses were performed by age, sex, and BMI. Compared to the Actiwatch, the ActiGraph estimated comparable TST and sleep efficiency, but fewer wake bouts, longer WASO, longer wake bout length, and higher SFI (all p<0.001). Both devices estimated similar 1-minute and 1% differences between participants for TST and SFI (β=0.99, 95% CI: 0.95, 1.03, and 0.91, 1.13, respectively), but not for other parameters. These differences varied by age, sex, and/or BMI. The ActiGraph and the Actiwatch provide comparable absolute and relative estimates of TST, but not other parameters. The discrepancies could result from device differences in movement collection and/or sleep scoring algorithms. Further comparison and calibration is required before these devices can be used interchangeably.
Keyphrases
  • physical activity
  • sleep quality
  • body mass index
  • machine learning
  • healthcare
  • functional connectivity
  • public health
  • weight loss
  • human health
  • resting state