Is it on? An algorithm for discerning wrist-accelerometer non-wear times from sleep/wake activity.
The accuracy of sleep/wake estimates derived with actigraphy is often dependent on researchers being able to discern non-wear times from sleep or quiescent wakefulness when confronted by discrepancies in a sleep log. Without knowing when an accelerometer is being worn, non-wear could be inferred from periods of inactivity unlikely to occur while in bed. Data collected in our laboratory suggest that more than 50% of inactive periods during time in bed are <8 min in duration. This duration may be an appropriate minimum threshold for routine non-wear classification during self-reported wake. Higher thresholds could be chosen to derive non-wear definitions for self-reported bedtimes depending on the desired level of certainty. To determine non-wear at thresholds of 75%, 95% and 99%, for example, would require periods of inactivity lasting ≥18 min, ≥53 min and ≥85 min, respectively.
a Appleton Institute for Behavioural Science , Central Queensland University , Adelaide , Australia.|b Bushfire Cooperative Research Centre , East Melbourne , Victoria , Australia.
Anastasi
A
Kosmadopoulos
a Appleton Institute for Behavioural Science , Central Queensland University , Adelaide , Australia.
David
D
Darwent
a Appleton Institute for Behavioural Science , Central Queensland University , Adelaide , Australia.
Gregory D
GD
Roach
D056044:Actigraphy / Q000379:methods; D000328:Adult; D000465:Algorithms; D002940:Circadian Rhythm / Q000502:physiology*; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory; D009043:Motor Activity / Q000502:physiology; D057566:Self Report; D012890:Sleep / Q000502:physiology*; D013997:Time Factors; D014851:Wakefulness / Q000502:physiology*; D014953:Wrist; D055815:Young Adult
Actigraphy; activity cycles; biostatistics; nonwear; physical activity; sedentary behavior; sleep