Discrepancies between Personal and Ambient Temperatures at Hourly Scale: Insights from Wearable Sensors and Environmental Modeling

Accurate assessment of personal exposure to ambient temperature is essential for understanding temperature-related health impacts. However, the bias from using ambient temperature estimates as proxies for personal exposure remains underexplored, particularly on the hourly scale. This study integrated wearable temperature sensors with a high-resolution (1 km, hourly) ambient temperature model to evaluate individual-level ambient exposure among 94 individuals in Connecticut, USA, monitored across seasons between October 2023 and January 2025. Personal temperature was consistently higher than ambient temperature, with a larger difference during cooler months. The hourly difference exhibited a distinct unimodal diurnal pattern, smallest in the early afternoon. Linear mixed-effects models identified ambient temperature, hour of day, month, solar radiation, and the nightlight index as the key predictors of personal temperature and its deviation from ambient conditions. The temperature difference was well characterized by the model (marginal R² of 0.854). Extreme gradient boosting with SHapley Additive Explanations confirmed the ambient temperature and hour of day as the most influential features, with albedo and other environmental factors showing smaller effects. Findings highlight systematic seasonal and diurnal biases in ambient-based metrics, underscoring the need to account for these patterns in the assessment of thermal exposures.