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Personal traits as the core determinant of human heat tolerance: a multi-factorial interaction analysis based on the health ecological model
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Yan ZHOU1, Cui MA1, Tiaoxia WEI1, Yongjun LUO2, Wanchun LUO1
Journal of Army Medical University | 2026, 48(9) : 1250 - 1261
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Journal of Army Medical University | 2026, 48(9): 1250-1261
• Intelligent Medicine and Prediction Model •
Personal traits as the core determinant of human heat tolerance: a multi-factorial interaction analysis based on the health ecological model
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Yan ZHOU1, Cui MA1, Tiaoxia WEI1, Yongjun LUO2, Wanchun LUO1
Affiliations
  • 1Department of Mathematics, College of Basic Medical Sciences, Key Laboratory of Ministry of Education for Extreme Environmental Medicine, Army Medical Service Base, College of Basic Medical Sciences, Army Medical University (Third Military Medical University), Chongqing, China
  • 2Department of Military Medical Geography, Key Laboratory of Ministry of Education for Extreme Environmental Medicine, Army Medical Service Base, College of Basic Medical Sciences, Army Medical University (Third Military Medical University), Chongqing, China
Published: 2026-05-15 doi: 10.16016/j.2097-0927.202512043
Outline
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Objective

With the increasing frequency of human heat exposure events driven by global climate warming, this study employed the health ecological model to systematically investigate the influencing factors of heat tolerance and their interaction pathways and mechanisms across multiple dimensions including personal traits, behavioral characteristics, environmental adaptation behaviors, and interpersonal networks, aiming to address the risk of heat injury in high-temperature environments.

Methods

A cross-sectional survey was conducted on 2 596 university faculty and students in Chongqing from August 21 to September 13, 2024, via the Wenjuanxing and WeChat platforms. Based on the survey data, heat tolerance was classified into 3 levels (low, moderate, strong). Chi-square test, multinomial logistic regression, and structural equation modeling were applied to analyze group differences, independent effects, and path relationships of demographic characteristics and factors at each level of the health ecological model on heat tolerance.

Results

Heat tolerance exhibited group differences with sociodemographic characteristics (sex, age, heatwave experience), personal traits, behavioral characteristics, environmental adaptation behaviors (shower frequency, wearing hats outdoors), and interpersonal networks (weather monitoring, timely seeking help for heatstroke)(P<0.05). Specifically, females demonstrated significantly lower probabilities of achieving moderate heat tolerance (OR=0.496, 95%CI: 0.390 to 0.631) and strong heat tolerance (OR=0.250, 95%CI: 0.178 to 0.349) compared to males (P<0.001). The participants aged 16 to 18 years (OR=3.778, 95%CI: 2.028 to 7.039) and 19 to 21 years (OR=1.913, 95%CI: 1.021 to 3.586) were more likely to attain strong heat tolerance than those aged ≥26 years(P<0.05), and those without heatwave experience showed significantly reduced probability of achieving strong heat tolerance (OR=0.475, 95%CI: 0.257 to 0.878,P<0.05). Structural equation modeling further revealed the multipath interactions among these factors. The male advantage in heat tolerance was manifested not only through direct physiological effects (standardized path coefficient=0.261) but also indirectly via enhancement of personal traits (standardized path coefficient=0.185). Although increasing age exerted a direct negative effect on heat tolerance (standardized path coefficient=-0.067), its promotion of personal traits (standardized path coefficient=0.038) indirectly offset this adverse effect. Personal traits emerged as the core determinant of heat tolerance, with a standardized path coefficient as high as 0.696, substantially exceeding all other factors, and serving as a critical mediating variable regulating the overall effects of sex, age, and behavioral characteristics on heat tolerance. Furthermore, although behavioral characteristics showed no significant direct effect on heat tolerance (P=0.871), they generated significant indirect effects through strengthening personal traits (standardized path coefficient=0.304), whereas environmental adaptation behaviors demonstrated a negative impact (standardized path coefficient=-0.143), suggesting that over-reliance on environmental adaptation may suppress the expression of heat tolerance.

Conclusion

Heat tolerance is influenced by multiple integrated factors, among which personal traits constitute the core determinant exerting significant direct and indirect effects on heat tolerance. Enhancing personal traits represents the key strategy for improving heat tolerance and reducing the risk of heat injury.

heat tolerance  /  health ecological model  /  behavioral characteristics  /  personal traits
周彦, 马翠, 魏调霞, 罗勇军, 罗万春. 个人特质是影响人体热耐力的核心因素:基于健康生态学模型的多因素交互作用分析. 陆军军医大学学报, 2026 , 48 (9) : 1250 -1261 . DOI: 10.16016/j.2097-0927.202512043
Yan ZHOU, Cui MA, Tiaoxia WEI, Yongjun LUO, Wanchun LUO. Personal traits as the core determinant of human heat tolerance: a multi-factorial interaction analysis based on the health ecological model[J]. Journal of Army Medical University, 2026 , 48 (9) : 1250 -1261 . DOI: 10.16016/j.2097-0927.202512043
Year 2026 volume 48 Issue 9
65
26
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Article Info
doi: 10.16016/j.2097-0927.202512043
  • Receive Date:2025-12-09
  • Online Date:2026-10-23
  • Published:2026-05-15
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History
  • Received:2025-12-09
  • Revised:2026-03-17
Affiliations
    1Department of Mathematics, College of Basic Medical Sciences, Key Laboratory of Ministry of Education for Extreme Environmental Medicine, Army Medical Service Base, College of Basic Medical Sciences, Army Medical University (Third Military Medical University), Chongqing, China
    2Department of Military Medical Geography, Key Laboratory of Ministry of Education for Extreme Environmental Medicine, Army Medical Service Base, College of Basic Medical Sciences, Army Medical University (Third Military Medical University), Chongqing, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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