Background: Occupational heat exposure can provoke health problems that increase the risk of certain diseases and affect workers’ ability to maintain healthy and productive lives. This study investigates the effects of occupational heat stress on workers’ physiological strain and labor productivity, as well as examining multiple interventions to mitigate the problem. Methods: We monitored 518 full work-shifts obtained from 238 experienced and acclimatized individuals who work in key industrial sectors located in Cyprus, Greece, Qatar, and Spain. Continuous core body temperature, mean skin temperature, heart rate, and labor productivity were collected from the beginning to the end of all work-shifts. Results: In workplaces where self-pacing is not feasible or very limited, we found that occupational heat stress is associated with the heat strain experienced by workers. Strategies focusing on hydration, work-rest cycles, and ventilated clothing were able to mitigate the physiological heat strain experienced by workers. Increasing mechanization enhanced labor productivity without increasing workers’ physiological strain. Conclusions: Empowering laborers to self-pace is the basis of heat mitigation, while tailored strategies focusing on hydration, work-rest cycles, ventilated garments, and mechanization can further reduce the physiological heat strain experienced by workers under certain conditions.

Background: A set of four case-control (n = 109), randomized-controlled (n = 7), cross-sectional (n = 78), and intervention (n = 47) studies was conducted across three countries to investigate the effects of sun exposure on worker physiology and cognition. Methods: Physiological, subjective, and cognitive performance data were collected from people working in ambient conditions characterized by the same thermal stress but different solar radiation levels. Results: People working under the sun were more likely to experience dizziness, weakness, and other symptoms of heat strain. These clinical impacts of sun exposure were not accompanied by changes in core body temperature but, instead, were linked with changes in skin temperature. Other physiological responses (heart rate, skin blood flow, and sweat rate) were also increased during sun exposure, while attention and vigilance were reduced by 45% and 67%, respectively, compared to exposure to a similar thermal stress without sunlight. Light-colored clothes reduced workers’ skin temperature by 12–13% compared to darker-colored clothes. Conclusions: Working under the sun worsens the physiological heat strain experienced and compromises cognitive function, even when the level of heat stress is thought to be the same as being in the shade. Wearing light-colored clothes can limit the physiological heat strain experienced by the body.

In a series of three companion papers published in this Journal, we identify and validate the available thermal stress indicators (TSIs). In this first paper of the series, we conducted a systematic review (registration: INPLASY202090088) to identify all TSIs and provide reliable information regarding their use (funded by EU Horizon 2020; HEAT-SHIELD). Eight databases (PubMed, Agricultural and Environmental Science Collection, Web of Science, Scopus, Embase, Russian Science Citation Index, MEDLINE, and Google Scholar) were searched from database inception to 15 April 2020. No restrictions on language or study design were applied. Of the 879 publications identified, 232 records were considered for further analysis. This search identified 340 instruments and indicators developed between 200 BC and 2019 AD. Of these, 153 are nomograms, instruments, and/or require detailed non-meteorological information, while 187 can be mathematically calculated utilizing only meteorological data. Of these meteorology-based TSIs, 127 were developed for people who are physically active, and 61 of those are eligible for use in occupational settings. Information regarding the equation, operating range, interpretation categories, required input data, as well as a free software to calculate all 187 meteorology-based TSIs is provided. The information presented in this systematic review should be adopted by those interested in performing on-site monitoring and/or big data analytics for climate services to ensure appropriate use of the meteorology-based TSIs. Studies two and three in this series of companion papers present guidance on the application and validation of these TSIs, to guide end users of these indicators for more effective use.

In a series of three companion papers published in this Journal, we identify and validate the available thermal stress indicators (TSIs). In this third paper, we conducted field experiments across nine countries to evaluate the efficacy of 61 meteorology-based TSIs for assessing the physiological strain experienced by individuals working in the heat. We monitored 372 experi-enced and acclimatized workers during 893 full work shifts. We continuously assessed core body temperature, mean skin temperature, and heart rate data together with pre/post urine specific gravity and color. The TSIs were evaluated against 17 published criteria covering physiological parameters, practicality, cost effectiveness, and health guidance issues. Simple meteorological parameters explained only a fraction of the variance in physiological heat strain (R² = 0.016 to 0.427; p < 0.001), reflecting the importance of adopting more sophisticated TSIs. Nearly all TSIs correlated with mean skin temperature (98%), mean body temperature (97%), and heart rate (92%), while 66% of TSIs correlated with the magnitude of dehydration and 59% correlated with core body temperature (r = 0.031 to 0.602; p < 0.05). When evaluated against the 17 published criteria, the TSIs scored from 4.7 to 55.4% (max score = 100%). The indoor (55.4%) and outdoor (55.1%) Wet-Bulb Globe Temperature and the Universal Thermal Climate Index (51.7%) scored higher compared to other TSIs (4.7 to 42.0%). Therefore, these three TSIs have the highest potential to assess the physiological strain experienced by individuals working in the heat.

Background: The aim of the study was to investigate the effect of a simulated heat-wave on the labour productivity and physiological strain experienced by workers. Methods: Seven males were confined for ten days in controlled ambient conditions. A familiarisation day was followed by three (pre, during, and post-heat-wave) 3-day periods. During each day volunteers participated in a simulated work-shift incorporating two physical activity sessions each followed by a session of assembly line task. Conditions were hot (work: 35.4 °C; rest: 26.3 °C) during, and temperate (work: 25.4 °C; rest: 22.3 °C) pre and post the simulated heat-wave. Physiological, biological, behavioural, and subjective data were collected throughout the study. Results: The simulated heat-wave undermined human capacity for work by increasing the number of mistakes committed, time spent on unplanned breaks, and the physiological strain experienced by the participants. Early adaptations were able to mitigate the observed implications on the second and third days of the heat-wave, as well as impacting positively on the post-heat-wave period. Conclusions: Here, we show for first time that a controlled simulated heat-wave increases workers’ physiological strain and reduces labour productivity on the first day, but it promotes adaptations mitigating the observed implications during the subsequent days.

Objective: We investigated the impact of workplace heat and cold on work time loss. Methods: Field experiments in different industrial sectors were conducted in multiple countries across all seasons between 2016 and 2024. Hundreds of workers were video-recorded and their full shifts (n = 603) were analyzed on a second-by-second basis (n = 16,065,501 sec). Environmental data were recorded using portable weather stations. The Workplace Environmental Labor Loss (WELL) functions were developed to describe work time loss due to workplace temperature. Results: The WELL functions revealed a U-shaped relationship whereby the least work time loss is observed at 18 °C (64 °F), and increases for every degree above or below this optimal temperature. Conclusions: The WELL functions quantify the impact of workplace temperature on work time loss, extending to temperatures previously believed to be unaffected.