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Updated: Jul 2, 2025

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
Published on: August 8, 2019
The Annual Rhythms in Sleep, Sedentary Behavior, and Physical Activity of Australian Adults: A Prospective Cohort
Ty Ferguson1, Rachel Curtis1, François Fraysse1
1Alliance for Research in Exercise, Nutrition and Activity (ARENA), University of South Australia, City East Campus, Frome Road, GPO Box 2471, Adelaide, SA, 5001, Australia.
Background:
Sleep, sedentary behavior, and physical activity have fundamental impacts on health and well-being. Little is known about how these behaviors vary across the year.
Purpose:
To investigate how movement-related behaviors change across days of the week and seasons, and describe movement patterns across a full year and around specific temporal events.
Methods:
This cohort study included 368 adults (mean age = 40.2 years [SD = 5.9]) who wore Fitbit activity trackers for 12 months to collect minute-by-minute data on sleep, sedentary behavior, light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Data were analyzed descriptively, as well as through multilevel mixed-effects linear regression to explore associations with specific temporal cycles (day-of-the-week, season) and events.
Results:
Movement patterns varied significantly by day-of-the-week and season, as well as during annual events like Christmas-New Year and daylight saving time (DST) transitions. For example, sleep was longer on weekends (+32 min/day), during autumn and winter relative to summer (+4 and +11 min/day), and over Christmas-New Year (+24 min/day). Sedentary behavior was longer on weekdays, during winter, after Christmas-New Year, and after DST ended (+45, +7, +12, and +8 min/day, respectively). LPA was shorter in autumn, winter, and during and after Christmas-New Year (-6, -15, -17, and -31 min/day, respectively). Finally, there was less MVPA on weekdays and during winter (-5 min/day and -2 min/day, respectively).
Conclusions:
Across the year, there were notable variations in movement behaviors. Identifying high-risk periods for unfavorable behavior changes may inform time-targeted interventions and health messaging.
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