The research, led by Seyed Mahdi Mousavi with Habibollah Dehghan and Saeid Yazdanirad, combined two established multicriteria decision-making techniques: the Fuzzy Delphi Method and the Fuzzy Analytic Hierarchy Process. The team first identified candidate heat stress indices and screening criteria through a literature review and consultation with a panel of 25 experts. The Fuzzy Delphi Method then filtered this pool, retaining six evaluation criteria and seven heat stress indices for detailed analysis. The Fuzzy Analytic Hierarchy Process, using Chang’s extent analysis, was subsequently applied to derive weights for each criterion and an overall priority score for every index.

Three criteria dominated the weighting results. Predictive capability for cognitive performance carried the largest weight at 0.265, followed by physiological relevance at 0.229 and reliability or reproducibility at 0.177. In other words, the experts and the model alike placed the greatest value on whether an index can actually forecast cognitive impairment, whether it reflects real physiological strain, and whether its measurements can be reproduced consistently across settings.

Among the seven indices evaluated, the Universal Thermal Climate Index emerged as the top performer, with a priority score of 0.214. The Predicted Heat Strain index followed closely at 0.201, and the widely used Wet-Bulb Globe Temperature ranked third at 0.168. Traditional empirical indices, which rely on simpler environmental or experiential inputs, consistently demonstrated lower suitability for cognitive-related heat stress assessment. The pattern suggests that indices capturing the full physiological burden of heat, rather than a narrow slice of environmental conditions, are better positioned to signal when mental performance may be compromised.

To test whether these rankings were an artifact of the model’s assumptions, the researchers performed a one-at-a-time sensitivity analysis in which individual inputs were increased by 10 percent. The rankings proved exceptionally stable: Spearman’s rank correlation coefficients ranged from 0.964 to 1.000 across the perturbed scenarios, indicating that the ordering of indices would hold up even if expert judgments or criterion weights shifted modestly.

The authors argue that the framework offers occupational safety and health practitioners a transparent and robust decision-support tool. Rather than defaulting to the most familiar metric, organizations assessing heat-related cognitive risk in workplaces can use the prioritization to select indices whose strengths align with the demands of cognitive performance evaluation, such as physiological grounding and predictive validity.

The study’s scope is defined by its method rather than by field measurements: it prioritizes indices through expert consensus and structured modeling, and it does not itself validate the indices against measured cognitive outcomes in workers. Still, by formally weighting predictive capability, physiological relevance and reproducibility, and by demonstrating that the resulting hierarchy is stable under sensitivity testing, the analysis provides a clear evidence-based starting point for choosing which heat stress index to deploy when the stakes include not just physical comfort but the cognitive reliability of the workforce.

Subject of Research: Occupational heat stress indices and cognitive performance assessment

Article Title: Prioritizing heat stress indices for cognitive performance assessment using a Fuzzy Delphi–FAHP framework

Article References: Mousavi, S. M., Dehghan, H., & Yazdanirad, S. (2026). Prioritizing heat stress indices for cognitive performance assessment using a Fuzzy Delphi–FAHP framework. Scientific Reports. https://doi.org/10.1038/s41598-026-77210-4

Image Credits: AI Generated

DOI: 10.1038/s41598-026-77210-4

Keywords: heat stress, cognitive performance, occupational health, Fuzzy Delphi, FAHP, UTCI, WBGT, Scientific Reports, Prioritizing, heat, stress, indices