Abstract
The present study examines the temperature variability in physical activity (PA), sedentary behavior (SB), and sleep in a free-living population. A representative sample of 1235 adults (ages 21–70) from Iowa, U.S.A., wore a SenseWear Mini Armband (SWA) for a randomly assigned day. Koppen’s weather climate classification was used to precisely classify the temperature: cold (−13 to 32 °F), cool (32 to 50 °F), mild (50 to 64 °F), warm (64 to 73 °F), and hot (73 to 95 °F). The main effect of three-way ANOVA (age × gender × temperature) had differences for SB and sleep, with older adults having higher levels than younger adults (p < 0.05). However, moderate to vigorous PA (MVPA) did not vary systematically by age or gender, and contrary to expectations, the main effect of the weather was not significant for MVPA (p > 0.05). Participants spent more time participating in PA at cold than at hot temperatures. The results clarify the impact of temperature on shaping PA and SB patterns in adults. The variable impacts and differential patterns by age suggest that weather should be considered when interpreting differences in PA patterns in research or surveillance applications.
Highlights
22.1 to 36.9, and there were no differences in Body mass index (BMI) across the temperature codes (p > 0.05)
The participants’ monthly average income varied, and there were no differences between each temperature category (p > 0.05)
We demonstrated that participants showed differences in physical activity (PA), sedentary behavior (SB), and sleep time (MVPA, step, and EE levels were highest at mild and warm temperatures), and the changes in moderate to vigorous PA (MVPA) patterns were different for men and women at all temperatures
Summary
Temperature differences, precipitation, and sunlight exposure have frequently been reported as barriers to PA [2,3,4] and as factors influencing the amount of PA, SB, and even sleep among populations [5,6]. Weather factors are considered to have a significant influence on participation in PA [7], but a review study documented the inherent complexity of examining the relationship between weather-related factors (i.e., temperature, precipitation, and sunlight) and various activityrelated variables (i.e., PA, SB, and sleep) [8]. Proving the association between season/temperature and PA, SB, and sleep time using objective measurements may affect understanding of the inherent complexity
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