Predicting extreme weather impacts on physical activity and sleep patterns using real-world data from wrist-worn accelerometers

The increasing frequency of extreme weather events, such as heat waves, is among the most pressing consequences of climate change, with profound implications for human health and well-being. Despite increasing incidence of extreme weather events globally, there is a lack of understanding on the impact of hot weather on health outcomes. In this study, we utilized machine learning techniques to explore how variations in outdoor temperature influence physical activity and sleep patterns, two critical determinants of physical and mental health. Using data from 90,434 participants in the UK Biobank, recorded via wrist-worn accelerometers, linked with meteorological data from the UK Met Office, we analysed therelationship between outdoor temperature (5°C to 30°C) and daily magnitudes and durations of a) physical activity and b) sleep, whilst adjusting for sociodemographic, clinical, lifestyle, seasonality, precipitation, and regional variables. Our results reveal that moderate-to-vigorous physical activity (MVPA) increases with temperature, reaching its peak at 25°C,but plateaus thereafter. Conversely, sedentary behaviour and sleep disturbances significantly intensify as temperatures reach 30. Here tested in UK settings, our approach is generalisable to other climatic regions and determinants of health and should be further investigated in regions with high climate-vulnerability. These findings emphasize the role of machine learning in identifying health risks associated with climate change andunderscore the necessity of climate-adaptive public health strategies to mitigate these effects.