Affordable measurement of core body temperature (T_c) in a continuous, real-time fashion is now possible. With this advance comes a new data analysis paradigm for occupational epidemiology. We characterize issues arising after obtaining T_c data over 188 workdays for 83 participating farmworkers, a population vulnerable to effects of rising temperatures due to climate change. We describe a novel approach to these data using smoothing and functional data analysis. This approach highlights different data aspects compared with describing T_c at a single time point or summaries of the time course into an indicator function (e.g., did T_c ever exceed 38 °C, the threshold limit value for occupational heat exposure). Participants working in ferneries had significantly higher T_c at some point during the workday compared with those working in nurseries, despite a shorter workday for fernery participants. Our results typify the challenges and opportunities in analyzing big data streams from real-time physiologic monitoring.