There is a substantial burden of occupational health effects from heat exposure. We sought to assess the accuracy of estimated core body temperature (CBT est ) derived from an algorithm that uses sequential heart rate and initializing CBT, 1 compared with gastrointestinal temperature measured using more invasive ingestible sensors (CBT gi ), among outdoor agricultural workers. We analyzed CBT est and CBT gi data from Washington State, USA, pear and apple harvesters collected across one work shift in 2015 (13,413 observations, 35 participants) using Bland Altman methods. The mean (standard deviation, range) CBT gi was 37.7 (0.4, 36.5–39.4)°C. Overall CBT bias (limits of agreement) was −0.14 (±0.76) °C. Biases ranged from −0.006 to −0.75 °C. The algorithm, which does not require the use of ingestible sensors, may be a practical tool in research among groups of workers for evaluating the effectiveness of interventions to prevent adverse occupational heat health effects.

Background Farmworkers are at risk of heat-related illness (HRI). We sought to: 1) evaluate the effectiveness of farmworker Spanish/English participatory heat education and a supervisor decision-support mobile application (HEAT intervention) on physiological heat strain; and 2) describe factors associated with HRI symptoms reporting. Methods We conducted a parallel, comparison group intervention study from May–September of 2019 in Central/Eastern Washington State, USA. We used convenience sampling to recruit adult outdoor farmworkers and allocated participating crews to intervention (n = 37 participants) and alternative-training comparison (n = 38 participants) groups. We measured heat strain monthly using heart rate and estimated core body temperature to compute the maximum work-shift physiological strain index (PSImax) and assessed self-reported HRI symptoms using a weekly survey. Multivariable linear mixed effects models were used to assess associations of the HEAT intervention with PSImax, and bivariate mixed models were used to describe factors associated with HRI symptoms reported (0, 1, 2+ symptoms), with random effects for workers. Results We observed larger decreases in PSImax in the intervention versus comparison group for higher work exertion levels (categorized as low, low/medium-low, and high effort), after adjustment for maximum work-shift ambient Heat Index (HImax), but this was not statistically significant (interaction − 0.91 for high versus low/medium-low effort, t = − 1.60, p = 0.11). We observed a higher PSImax with high versus low/medium-low effort (main effect 1.96, t = 3.81, p < 0.001) and a lower PSImax with older age (− 0.03, t = − 2.95, p = 0.004), after covariate adjustment. There was no clear relationship between PSImax and the number of HRI symptoms reported. Reporting more symptoms was associated with older age, higher HImax, 10+ years agricultural work, not being an H-2A guest worker, and walking > 3 min to get to the toilet at work. Conclusions Effort level should be addressed in heat management plans, for example through work/rest cycles, rotation, and pacing, in addition to education and other factors that influence heat stress. Both symptoms and indicators of physiological heat strain should be monitored, if possible, during periods of high heat stress to increase the sensitivity of early HRI detection and prevention. Structural barriers to HRI prevention must also be addressed. Trial registration ClinicalTrials.gov Registration Number: NCT04234802, date first posted 21/01/2020.