Weather associations with physical activity, sedentary behaviour and sleep patterns of Australian adults: a longitudinal study with implications for climate change

Abstract

Background Weather is a potentially important influence on how time is allocated to sleep, sedentary behaviour and physical activity across the 24-h day. Extremes of weather (very hot, cold, windy or wet) can create undesirable, unsafe outdoor environments for exercise or active transport, impact the comfort of sleeping environments, and increase time indoors. This 13-month prospective cohort study explored associations between weather and 24-h movement behaviour patterns. Methods Three hundred sixty-eight adults (mean age 40.2 years, SD 5.9, 56.8% female) from Adelaide, Australia, wore Fitbit Charge 3 activity trackers 24 h a day for 13 months with minute-by-minute data on sleep, sedentary behaviour, light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) collected remotely. Daily weather data included temperature, rainfall, wind, cloud and sunshine. Multi-level mixed-effects linear regression analyses (one model per outcome) were used. Results Ninety thousand eight hundred one days of data were analysed. Sleep was negatively associated with minimum temperature (-12 min/day change across minimum temperature range of 31.2 °C, p = 0.001). Sedentary behaviour was positively associated with minimum temperature (+ 12 min/day, range = 31.2 oC, p = 0.006) and wind speed (+ 10 min/day, range = 36.7 km/h, p< 0.001), and negatively associated with sunshine (-17 min/day, range = 13.9 h, p < 0.001). LPA was positively associated with minimum temperature (+ 11 min/day, range = 31.2 °C, p = 0.002), cloud cover (+ 4 min/day, range = 8 eighths, p = 0.008) and sunshine (+ 17 min/day, range = 13.9 h, p < 0.001), and negatively associated with wind speed (-8 min/day, range = 36.7 km/h, p < 0.001). MVPA was positively associated with sunshine (+ 3 min/day, range = 13.9 h, p < 0.001) and negatively associated with minimum temperature (-13 min/day, range = 31.2 oC, p < 0.001), rainfall (-3 min/day, range = 33.2 mm, p = 0.006) and wind speed (-4 min/day, range = 36.7 km/h, p < 0.001). For maximum temperature, a significant (p < 0.05) curvilinear association was observed with sleep (half-U) and physical activity (inverted-U), where the decrease in sleep duration appeared to slow around 23 °C, LPA peaked at 31 oC and MVPA at 27 °C. Conclusions Generally, adults tended to be less active and more sedentary during extremes of weather and sleep less as temperatures rise. These findings have the potential to inform the timing and content of positive movement behaviour messaging and interventions. Trial registration The study was prospectively registered on the Australian New Zealand Clinical Trial Registry (Trial ID: ACTRN12619001430123).

Association between Weather and Self-Monitored Steps in Individuals with Prediabetes and Type 2 Diabetes in Sweden over Two Years

“…A previous study that analyzed the relationship between physical activity behavior and weather conditions using a wrist-worn Fitbit activity tracker over 13 months discovered that physical activity intensity played a crucial role in engaging in activity in adverse weather conditions. For instance, precipitation affected adults who partook in moderate to vigorous physical activity significantly more than those who partook in light physical activity [43]. This may be because weather conditions such as ice, snow, or severe cold would be more of an impediment for higher-intensity activities like running than for lower-intensity activities like walking.…”

“…The improved accessibility and accuracy of weather forecasting through digital platforms and public broadcasting services and warnings help individuals make informed decisions about when to be active [24,45], which may have contributed to the lower impact of weather on their physical activity. In addition, Ferguson et al (2023) suggested that light physical activity is not significantly affected by extreme weather if proper clothing and equipment, such as an umbrella, are used to address the challenge [43]. The factors above highlight some of the significance of having effective adaptation strategies to cope with different weather conditions at the individual level [46].…”

Temporal Interactions between Maintenance of Cerebral Cortex Thickness and Physical Activity from an Individual Person Micro-Longitudinal Perspective and Implications for Precision Medicine

“…Similarly, group-level work indicates that physical activity has associations with multiple factors, including, e.g., diet [78], gut microbiome [79], emotion regulation [80], fitness [81], weather [82], and metabolic [83] factors. Adding to group-level findings, studies using micro-longitudinal tracking and within-individual analyses have shown physical activity's associations with, e.g., sleep [84][85][86][87] and stress [88].…”

Day-to-day variability in accelerometer-measured physical activity in mid-aged Australian adults

“…A second limitation is that exclusion of those who did not wear the accelerometer for the required number of hours and days, resulted in under inclusion of data from older participants, so our estimates of MVPA may be higher than in the general population of mid-age adults. A third limitation is each participant provided a single week of data, and variability estimates may be different at different times of the year/in different seasons [ 29 , 30 ]. We also acknowledge that, because the accelerometers were not waterproof, we did not collect data during water-based activities, which may mean that estimates of acceleration, MVPA and/or variability were underestimated.…”