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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.

The present comprehensive review (i) summarizes the current knowledge on the impacts of occupational heat stress on outdoor workers, (ii) provides a historical background on this issue, (iii) presents a meta-analysis of published data, (iv) explores inter-individual and intra-individual factors, (v) discusses the available heat mitigation strategies, (vi) estimates physical work capacity, labour productivity, and metabolic rate for the year 2030, and (vii) provides an overview of existing policy and legal frameworks on occupational heat exposure. Meta-analytic findings from 38 field studies that involved monitoring 2,409 outdoor workers across 41 jobs in 21 countries suggest that occupational heat stress increases the core (r = 0.44) and skin (r = 0.44) temperatures, as well as the heart rate (r = 0.38) and urine specific gravity (r = 0.13) of outdoor workers (all p < 0.05). Moreover, it diminishes the capacity of outdoor workers for manual labour (r = -0.82; p < 0.001) and is responsible for more than two thirds of the reduction in their metabolic rate. Importantly, our analysis shows that physical work capacity is projected to be highly affected by the ongoing anthropogenic global warming. Nevertheless, the metabolic rate and, therefore, labour productivity are projected to remain at levels higher than the workers' physical work capacity, indicating that people will continue to work more intensely than they should to meet their financial obligations for food and shelter. In this respect, complementary measures targeting self-pacing, hydration, work-rest regimes, ventilated garments, and mechanization can be adopted to protect outdoor workers.

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 experienced 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.

Objectives: To derive an empirical model for the impact of aerobic fitness (maximal oxygen consumption; V̇O2max in mL∙kg-1∙min-1) on physical work capacity (PWC) in the heat.Design: Prospective, repeated measures.Methods: Total work completed during 1 h of treadmill walking at a fixed heart rate of 130 b∙min-1 was assessed in 19 young adult males across a variety of warm and hot climate types, characterised by wet-bulb globe temperatures (WBGT) ranging from 12 to 40 °C. For data presentation and obtaining initial parameter estimates for modelling, participants were grouped into low (n = 6, 74 trials), moderate (n = 8, 76 trials), and high (n = 5, 29 trials) fitness, with group mean V̇O2max 42, 52, and 64 mL∙kg-1∙min-1, respectively. For the heated conditions (WBGT 18 to 40 °C), we calculated PWC% by expressing total energy expenditure (kJ above resting) in each trial relative to that achieved in a cool reference condition (WBGT = 12 °C = 100% PWC).Results: The relative reduction in energy expenditure (PWC%) caused by heat was significantly smaller by up to 16% for the fit participants compared to those with lower aerobic capacity. V̇O2max also modulated the relationship between sweat rate and body temperature changes to increasing WBGT. Including individual V̇O2max data in the PWC prediction model increased the predicting power by 4%.Conclusions: Incorporating individual V̇O2max improved the predictive power of the heat stress index WBGT for Physical Work Capacity in the heat. The largest impact of V̇O2max on PWC was observed at a WBGT between 25 and 35 °C.