Heatstroke Awareness and Preventive Behaviors Among Automotive Maintenance Workers in Outdoor Environments: A Cross-Sectional Study in Japan
by
, ,
Chieko Yodawara
1,2, 
Yoko Iio
3,
Harumi Ejiri
4,
Saimi Yamamoto
1,5,
Hana Kozai
6,
Mamoru Tanaka
6,
Manato Seguchi
1,7 and
Morihiro Ito
1,3,8,* 1
Graduate School of Life and Health Sciences, Chubu University, 1200 Matsumoto-cho, Kasugai-shi 487-8501, Japan
2
Department of Nursing, Nagoya University of Arts and Sciences, 4-1-1 Sannomaru, Naka-ku, Nagoya-shi 460-0001, Japan
3
Department of Lifelong Sports and Health Sciences, College of Life and Health Sciences, Chubu University, 1200 Matsumoto-cho, Kasugai-shi 487-8501, Japan
4
Department of Nursing, College of Life and Health Sciences, Chubu University, 1200 Matsumoto-cho, Kasugai-shi 487-8501, Japan
5
Department of Early Childhood Education, College of Contemporary Education, Chubu University, 1200 Matsumoto-cho, Kasugai-shi 487-8501, Japan
6
Department of Food and Nutritional Sciences, College of Bioscience and Biotechnology, Chubu University, 1200 Matsumoto-cho, Kasugai-shi 487-8501, Japan
7
Support for Pioneering Research Initiated by the Next Generation (SPRING), Chubu University, 1200 Matsumoto-cho, Kasugai-shi 487-8501, Japan
8
Department of Biomedical Sciences, College of Life and Health Sciences, Chubu University, 1200 Matsumoto-cho, Kasugai-shi 487-8501, Japan
*
Author to whom correspondence should be addressed.
Healthcare 2026, 14(10), 1293; https://doi.org/10.3390/healthcare14101293
Submission received: 7 April 2026
/
Revised: 30 April 2026
/
Accepted: 8 May 2026
/
Published: 10 May 2026
(This article belongs to the Topic New Research in Work-Related Diseases, Safety and Health, 2nd Edition)
Abstract
Background/Objectives: Global climate change has increased occupational heat exposure, posing significant risks to outdoor workers. Automotive maintenance workers face high temperatures, radiant heat from machinery, and physically demanding tasks; however, their awareness and preventive behaviors regarding heat-related illness remain insufficiently understood. This study examined heatstroke awareness and preventive behaviors among automotive maintenance workers in Japan. Methods: A cross-sectional web-based survey was conducted among 371 automotive maintenance workers. Self-reported heat-related illness experience was assessed based on subjective judgment without formal medical diagnosis. Associations between heat-related illness experience and behavioral, physical, and health-related factors were analyzed using chi-square tests with Bonferroni correction and multivariable logistic regression. Results: Approximately 39.6% of participants reported experiencing heat-related illness during summer work. In multivariable analysis, headache (OR: 2.66, 95% CI: 1.25–5.64), dizziness (OR: 2.06, 95% CI: 1.12–3.80), obesity (OR: 1.86, 95% CI: 1.06–3.27), and lower self-perceived health (OR: 2.19, 95% CI: 1.36–3.55) were independently associated with heat-related illness experience. Some preventive behaviors, including wearing cooling garments and frequent hydration, showed associations in the multivariable analysis; however, these findings should be interpreted with caution due to possible reverse causation, small cell sizes, and residual confounding. Conclusions: Behavioral and individual health-related factors, particularly symptom recognition and self-perceived health, are associated with heat-related illness experience among automotive maintenance workers. Interventions focusing on early symptom awareness, risk perception, and self-monitoring may be important components of workplace-based heat illness prevention. Future studies incorporating objective environmental and physiological measurements are needed to clarify causal relationships.
1. Introduction
Global climate change has led to a steady increase in ambient temperatures, intensifying occupational heat exposure worldwide. Outdoor workers are particularly vulnerable to heat-related illnesses, including heatstroke, due to prolonged exposure to high temperatures and physically demanding tasks. Heatstroke is a serious condition characterized by elevated core body temperature and central nervous system dysfunction, potentially leading to severe outcomes or death if not promptly managed. In recent years, increasing attention has been paid to the impact of heat stress on occupational safety, health, and productivity [1,2,3,4].
Among outdoor workers, automotive maintenance workers represent a unique and understudied population. In addition to environmental heat exposure, these workers are exposed to radiant heat from engines and machinery, as well as confined workspaces that can trap heat and humidity. Their work often involves physically demanding tasks, which further increase internal heat production. Despite these risk factors, little is known about how automotive maintenance workers perceive heat-related risks or how they engage in preventive behaviors.
Occupational heat stress has been extensively studied in several high-risk industries. Construction workers are among the most affected groups, second only to agricultural workers, with heat-related illness symptoms reported in approximately 36% of workers across multiple geographic settings [5]. Agricultural workers face similarly elevated risks due to prolonged outdoor exposure and high physical workload [6]. Other sectors, including mining, logistics, manufacturing, and military settings, have also been identified as occupationally vulnerable to heat-related illness [7]. Despite this growing body of evidence across sectors, research on automotive maintenance workers—who face a unique combination of radiant heat from machinery, confined workspaces, and physically demanding tasks—remains scarce.
Previous studies have identified various physiological and environmental risk factors for heatstroke, including high ambient temperature, excessive physical exertion, obesity, inadequate hydration, and insufficient heat acclimatization [8,9,10,11,12,13,14]. However, these studies have primarily focused on environmental or physiological determinants, with limited attention given to behavioral factors. From a behavioral science perspective, individual awareness, risk perception, and self-regulation behaviors play a critical role in preventing heat-related illnesses. The ability to recognize early symptoms, assess personal risk, and take appropriate preventive actions may significantly influence outcomes in high-risk environments [12,13].
Behavioral factors are particularly important in the context of heatstroke, as early symptoms such as headache and dizziness often precede severe conditions. Timely recognition of these symptoms and appropriate behavioral responses, such as resting, cooling, or hydration, are essential to prevent progression to severe heat illness [15]. However, the relationship between workers’ awareness, preventive behaviors, and actual heatstroke experience remains insufficiently understood, especially in specific occupational settings.
Furthermore, preventive measures implemented by workers may not always lead to the intended outcomes. In some cases, individuals who have previously experienced heatstroke may adopt more preventive behaviors, which can complicate the interpretation of associations between behavior and health outcomes [16]. Understanding these behavioral patterns requires careful consideration of both risk perception and prior experience. This study addresses an important gap in the literature by focusing on a high-risk yet understudied occupational group.
Given these gaps, this study aimed to examine heatstroke awareness and preventive behaviors among automotive maintenance workers engaged in outdoor work in Japan, from a behavioral science perspective. Specifically, this study investigated the associations between self-reported heatstroke experience and behavioral, physical, and health-related factors. By focusing on behavioral determinants such as awareness, symptom recognition, and preventive actions, this study seeks to provide insights that can inform the development of effective educational and behavioral interventions for heatstroke prevention in occupational settings.
2. Materials and Methods
2.1. Study Design and Data Collection
A cross-sectional study was conducted in Japan from late March to early April 2025 using an anonymous, self-administered questionnaire survey targeting automotive maintenance workers engaged in outdoor work. The study aimed to assess their awareness of heatstroke and their adaptive behaviors during summer work.
Data collection used a web-based questionnaire administered through Google Forms (anonymous survey), which included 31 items. Workers who consented accessed the survey by scanning the Google Form URL QR code with their mobile phones. By the end of April 2025, researchers collected 494 responses. Of these, 371 valid responses (75.1% response rate) were analyzed. Responses were excluded if participants did not consent to participation, submitted incomplete questionnaires, or did not meet the eligibility criteria of being engaged in outdoor automotive maintenance work. Participants were recruited through their workplaces via QR codes distributed by the research team. The survey targeted workers across multiple regions of Japan, and no financial incentives were provided for participation.
To determine the required sample size for this study, statistical power analysis was conducted using Statistical Package for Social Sciences, version 29, (SPSS; IBM Corp., Armonk, NY, USA). A sample size of 202 was needed to achieve a power of 0.8 and a significance level of 0.05, based on the number of explanatory variables included in the regression model. The sample size used in this study met these criteria.
This study followed the principles of the Declaration of Helsinki and received approval from the Chubu University Ethics Review Board (Approval No. 20240091). Participants received a written explanation outlining the study’s purpose, the voluntary nature of participation, assurance that non-participation would not affect their work, confirmation that results would be used only for the stated purpose, and that the study was anonymous with no identifiable individuals. The explanation also addressed compliance with personal information protection, including data management. Consent was considered obtained when participants completed the questionnaire.
2.2. Survey Items
The survey collected data on basic attributes (sex, age group, and region of workplace), past experiences of heatstroke during work activities, physical symptoms of heatstroke during work and preventive measures, experiences of receiving heatstroke education, lifestyle conditions such as diet and sleep, and health status. For questions about physical symptoms, 13 symptoms were defined according to the Japanese Association for Acute Medicine’s classification of heatstroke and previous research [17]. Table S1 presents a comprehensive overview of all variables used in the analysis, including the corresponding survey items, response options, and coding procedures.
2.3. Statistical Analysis
The collected data were organized using simple tabulation. We hypothesized that self-reported heatstroke experience during work is associated with various living environments and health conditions. To test this, we analyzed the relationship between participants’ heatstroke experience and preventive measures, lifestyle habits, dietary intake, and health status using Pearson’s chi-square test. Variables that reached statistical significance in the chi-square tests were entered into a binary logistic regression analysis to identify factors associated with heatstroke experience. Variable selection was based on both statistical significance and theoretical relevance informed by prior literature on occupational heat stress. Key variables such as headache, dizziness, BMI, and perceived health were included a priori based on prior literature, regardless of their statistical significance in bivariate analysis. Multicollinearity among explanatory variables was assessed using the variance inflation factor (VIF), and no variables exceeded the threshold of VIF > 10. Model fit was evaluated using the Hosmer–Lemeshow test and Nagelkerke R2. Age and sex were included as potential confounders in the model.
The dependent variable was the presence or absence of heatstroke experience. Explanatory variables included physical symptoms during summer work activities, heatstroke prevention measures, living conditions, and health status. Table 1 shows basic attributes. In Table 2, dummy variables were coded as “None” = 0 and “Present” = 1. In Table 3, dummy variables were coded as “Present” = 0 and “None” = 1. For Table 4-A, explanatory variables were coded as dummy variables: “Normal (body mass index [BMI] between 18 and <25)” = 0, “Underweight (<18.5)” = 1, and “Obese (≥25)” = 2. In Table 4-B, explanatory variables were coded as “Normal, Underweight (<18.5)” = 0 and “Obese (≥25)” = 1. Explanatory variables in Table 4-K and L were coded as “Yes” = 0 and “No/Neither” = 1. Odds ratios (OR) and 95% confidence intervals (CI) were calculated for each explanatory variable. The significance level was set at p < 0.05 for all items. To control for the inflated risk of Type I error due to multiple comparisons, the Bonferroni correction was applied to the chi-square tests in Table 2, Table 3 and Table 4. The corrected significance thresholds were set at p < 0.0038 (Table 2, 13 comparisons), p < 0.0020 (Table 3, 25 comparisons), and p < 0.0026 (Table 4, 19 comparisons). The logistic regression analysis was not subject to this correction, as the simultaneous inclusion of variables in the model inherently adjusts for their intercorrelations. SPSS statistical analysis software was used for the analysis.
3. Results
3.1. Participant Attributes
Table 1 presents the relationship between heatstroke experience and participant attributes. Of the 371 workers, 147 (39.6%) reported experiencing heatstroke during summer work, while 224 (60.4%) did not. There were 302 men (81.4%) and 69 women (18.6%). Among these, 112 men (37.1%) and 35 women (50.7%) reported heatstroke experience, indicating that a higher proportion of women reported experiencing heatstroke compared to men.
The largest age group was 20–29 years, with 243 respondents (65.5%). The next largest groups were 30–39 years (77 respondents, 20.8%), 40–49 years (30 respondents, 8.1%), 50–59 years (11 respondents, 2.9%), and 10–19 years (10 respondents, 2.7%). Among respondents who reported experiencing heatstroke, the largest group was 30–39 years (38 respondents, 49.4%). Age was not significantly associated with self-reported heatstroke experience.
Most respondents worked in the “Kanto region” (eastern Japan), with 142 respondents (38.3%), and the “Chubu region” (central Japan), with 138 respondents (37.2%). The “Kinki region” (western Japan) had 45 respondents (12.1%), the Chugoku/Shikoku region had 13 respondents (3.5%), the “Kyushu region” had 12 respondents (3.2%), the “Hokkaido region” in northern Japan had 11 respondents (3.0%), and the “Tohoku region” had 10 respondents (2.7%). There was no correlation between region of employment and self-reported experience of heatstroke.
3.2. Physical Symptoms
Table 2 presents the relationship between heatstroke experience during summer work and physical symptoms. Workers who reported experiencing heatstroke during summer work more frequently reported symptoms such as “headache,” “dry mouth,” “dizziness,” “fatigue/weakness,” “difficulty concentrating/inability to organize thoughts,” “nausea,” “muscle cramps,” “abnormally rapid breathing,” “rapid and weak pulse,” “numbness in the lips,” and “abnormal speech or behavior” compared to those who did not experience heatstroke.
3.3. Preventive Measures
Table 3 presents the relationship between heatstroke experience and preventive measures. Although a higher proportion of workers who reported wearing cooling fabric underwear indicated having experienced heatstroke in the univariate analysis (p = 0.024), this association did not survive Bonferroni correction (threshold p < 0.002) and should be interpreted with caution. This finding may reflect reverse causation, whereby workers who had previously experienced heatstroke were more likely to adopt preventive behaviors thereafter.
Regarding frequent hydration, 352 respondents (94.9%) reported practicing it. The cell size for non-hydrators who reported heatstroke experience was extremely small (n = 2), and this result should therefore be interpreted with extreme caution.
There was no correlation between self-reported heatstroke experience and responses about “use of heat index (WBGT),” “heat acclimatization,” “experience viewing the Ministry of Health, Labour and Welfare’s Heatstroke Prevention Manual,” or “experience receiving education or guidance on heatstroke countermeasures.”
3.4. Living Conditions and Health Status
Table 4 presents the relationship between heatstroke experience and living conditions or health status. For BMI, workers classified as obese (≥25) reported a higher rate of heatstroke experience compared to those classified as normal or underweight (<18.5).
Workers who reported “yes” for “satisfaction with sleep” and “perceived health” had a lower proportion of “experienced heatstroke” compared to those who answered “no” or “neither yes nor no.”
There was no relationship between self-reported heatstroke experience and factors such as food intake, sleep duration, alcohol consumption, smoking status, hypertension, dyslipidemia, illness or treatment, or medication use.
3.5. Factors Contributing to Heatstroke Experience
Table 5 presents the results of the binary logistic regression analysis of factors associated with heatstroke experience. Experiencing headaches during summer work (OR: 2.657, 95% CI: 1.253–5.636, p = 0.011) and experiencing dizziness during summer work (OR: 2.061, CI: 1.118–3.800, p = 0.021) were symptoms linked to self-reported heatstroke. Higher BMI (OR: 1.857, CI: 1.056–3.267, p = 0.032) and self-perceived health status (OR: 2.194, CI: 1.355–3.551, p = 0.001) were significantly associated with heatstroke experience. In contrast, wearing cooling fabric underwear (OR: 0.590, CI: 0.370–0.939, p = 0.026) and frequent hydration (OR: 0.187, CI: 0.041–0.863, p = 0.032) showed associations in the multivariable analysis; however, these findings should be interpreted with caution due to possible reverse causation, small cell sizes, and residual confounding.
4. Discussion
This study examined heatstroke awareness and preventive behaviors among automotive maintenance workers engaged in outdoor work in Japan from a behavioral science perspective. The findings provide novel insights into how behavioral, physical, and health-related factors are associated with self-reported heatstroke experience in a high-risk occupational setting.
Approximately 40% of workers reported experiencing heatstroke during summer work, indicating a substantial burden of heat-related illness in this population. This prevalence appears higher than that reported in some previous occupational studies, suggesting that automotive maintenance workers may be exposed to particularly hazardous thermal environments. In addition to ambient heat, exposure to radiant heat from engines and confined workspaces likely contributes to cumulative heat stress, emphasizing the need for targeted preventive strategies [2,3,4,18].
From a behavioral science perspective, one of the most important findings of this study is the significant association between early symptoms—such as headache and dizziness—and heatstroke experience. These symptoms are generally considered early warning signs of heat-related illness, and their recognition plays a critical role in preventing progression to severe heatstroke. The results suggest that workers who experience these symptoms may either be at higher risk or may become more aware of heat-related physiological changes. Therefore, enhancing symptom awareness and promoting early behavioral responses, such as rest and cooling, are likely to be key components of effective heatstroke prevention strategies [15,17].
In the logistic regression analysis, certain preventive behaviors, including wearing cooling fabric underwear and frequent hydration, showed inverse associations with self-reported heatstroke experience after adjustment for confounders. This suggests that, once the influence of factors such as obesity and poor self-perceived health is statistically controlled, workers who engaged in these behaviors tended to report fewer heatstroke experiences—yet this pattern alone does not confirm a protective effect, given the cross-sectional design. However, neither association survived Bonferroni correction in the univariate chi-square analysis. The discrepancy between the univariate and multivariate findings is consistent with reverse causation—workers who had previously experienced heatstroke may have subsequently increased their use of preventive measures, inflating the apparent association in the univariate analysis. These findings should therefore be interpreted with caution, and longitudinal studies are needed to establish the causal direction of these relationships. In particular, the finding regarding frequent hydration should be considered exploratory only, given the extremely small cell size in the non-hydration group (n = 2 with heatstroke experience), the failure to survive Bonferroni correction, and the wide confidence interval observed in the logistic regression (OR: 0.187, 95% CI: 0.041–0.863). This result should not be interpreted as evidence of a protective effect of hydration.
In addition, preventive behaviors may not always be implemented appropriately or effectively. For example, wearing cooling garments under multiple layers of protective clothing may limit heat dissipation, and hydration practices may vary in timing, volume, and composition. These findings suggest that not only the presence of preventive behaviors, but also their quality and appropriateness, are critical. From a behavioral science standpoint, this underscores the importance of not only promoting preventive actions but also improving knowledge and decision-making related to those actions [12,13].
The present study also identified obesity (BMI ≥ 25) as a factor associated with heatstroke experience, which is consistent with previous studies indicating that higher body fat may impair heat dissipation and increase thermal strain [19,20,21]. In contrast, higher self-perceived health status was associated with a lower likelihood of heatstroke. This finding suggests that subjective health perception may reflect underlying physical condition, lifestyle habits, or self-regulation capacity. Individuals who perceive themselves as healthy may be more likely to engage in adaptive behaviors, monitor their condition, and respond appropriately to heat stress.
Importantly, this study highlights the role of behavioral factors such as risk perception, awareness, and self-monitoring in heatstroke prevention. Workers’ ability to recognize their own vulnerability, interpret physiological signals, and take timely action is central to preventing adverse outcomes. These findings support the need for behavioral interventions that enhance risk perception and promote adaptive decision-making in high-risk environments. Educational programs should focus not only on knowledge dissemination but also on improving practical skills, such as symptom recognition and appropriate response behaviors [12,13].
From an occupational health perspective, these findings have important implications for workplace interventions. For example, incorporating routine health checks, promoting open communication about physical symptoms, and implementing structured education programs may improve early detection and prevention of heatstroke. In addition, environmental and organizational measures—such as monitoring heat indices (e.g., WBGT), adjusting work–rest cycles, and optimizing clothing strategies—should be integrated with behavioral approaches to achieve comprehensive risk management [2,3,4,18].
This study has several limitations. First, the cross-sectional design precludes causal inference, and reverse causation cannot be ruled out. Second, heatstroke experience was self-reported and may be subject to recall bias or misclassification. Third, the sample was predominantly male (81.4%), which is consistent with the occupational distribution of automotive maintenance workers in Japan, where males represent the vast majority of the workforce. The number of female participants (n = 69) may therefore reflect an overrepresentation relative to the general population of this occupation. Accordingly, sex-specific findings should be interpreted in light of this occupational context, and future studies with larger female samples are warranted. Fourth, detailed information on hydration practices, clothing characteristics, and environmental heat exposure was not collected. Objective workplace measurements such as temperature, humidity, wet-bulb globe temperature (WBGT), and working hours were not obtained. Future studies should incorporate meteorological and physiological data to better characterize occupational heat exposure. Fifth, the questionnaire was developed by the research team based on prior literature and established clinical classifications, without formal psychometric validation or pilot testing. This may have affected the reliability and validity of individual items, and future studies should employ formally validated instruments. Finally, the use of a web-based survey may have introduced selection bias. Future studies should employ longitudinal designs, objective physiological measurements, and detailed behavioral assessments to better understand causal relationships and refine intervention strategies.
Despite these limitations, this study provides important contributions by focusing on a high-risk yet understudied occupational group and by examining heatstroke from a behavioral science perspective. The findings suggest that improving awareness, risk perception, and self-regulation behaviors may play a crucial role in preventing heat-related illness. Integrating behavioral interventions with occupational health management strategies may enhance the effectiveness of heatstroke prevention in real-world work environments.
5. Conclusions
This study demonstrated that behavioral factors, including symptom recognition, risk perception, and self-monitoring, are significantly associated with heatstroke experience among automotive maintenance workers. Preventive behaviors such as frequent hydration and the use of cooling garments showed associations with self-reported heatstroke experience; however, these findings should be interpreted cautiously due to potential reverse causation and study design limitations. These findings highlight the importance of behavior-based strategies in occupational heatstroke prevention. Future interventions should focus on improving awareness, decision-making, and appropriate behavioral responses in high-risk work environments. These findings have important implications for occupational health and workplace safety management.
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/healthcare14101293/s1, Table S1: All variables, corresponding survey items, response options, and coding procedures used in the analysis.
Author Contributions
Sample data were obtained by C.Y., Y.I., H.E., S.Y., H.K., M.T., M.S., and M.I. Statistical analysis was performed by C.Y., Y.I. and M.I. The first draft of the manuscript was written by C.Y. and M.I., and all authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
This study followed the principles of the Declaration of Helsinki and received approval from the Chubu University Ethics Review Board (Approval No.: 20240091) (Approval date: 22 March 2025).
Informed Consent Statement
Informed consent was obtained from all participants involved in this study. Written informed consent for publication has been waived as no identifying information was used.
Data Availability Statement
The data presented in this study are available on request from the corresponding author due to privacy and ethical constraints.
Acknowledgments
We greatly appreciate the automotive maintenance workers for their participation in this study.
Conflicts of Interest
The authors declare no conflicts of interest.
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Table 1.
Relationship between heatstroke experience and participant attributes.
| Heatstroke Experience | p-Value | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| No | Yes | ||||||||
| n | (%) | n | (%) | n | (%) | ||||
| Question | 224 | (60.4) | 147 | (39.6) | 371 | (100.0) | |||
| Sex | Male | 190 | (62.9) | 112 | (37.1) | 302 | (81.4) | 0.037 | * |
| Female | 34 | (49.3) | 35 | (50.7) | 69 | (18.6) | |||
| Age | 10–19 | 9 | (90.0) | 1 | (10.0) | 10 | (2.7) | 0.095 | |
| 20–29 | 151 | (62.1) | 92 | (37.9) | 243 | (65.5) | |||
| 30–39 | 39 | (50.6) | 38 | (49.4) | 77 | (20.8) | |||
| 40–49 | 17 | (56.7) | 13 | (43.3) | 30 | (8.1) | |||
| 50–59 | 8 | (72.7) | 3 | (27.3) | 11 | (2.9) | |||
| Region | Hokkaido Region | 7 | (63.6) | 4 | (36.4) | 11 | (3.0) | 0.91 | |
| Tohoku Region | 5 | (50.0) | 5 | (50.0) | 10 | (2.7) | |||
| Kanto Region | 86 | (60.6) | 56 | (39.4) | 142 | (38.3) | |||
| Chubu Region | 86 | (62.3) | 52 | (37.7) | 138 | (37.2) | |||
| Kinki Region | 24 | (53.3) | 21 | (46.7) | 45 | (12.1) | |||
| Chugoku and Shikoku Region | 9 | (69.2) | 4 | (30.8) | 13 | (3.5) | |||
| Kyushu Region | 7 | (58.3) | 5 | (41.7) | 12 | (3.2) | |||
* p < 0.05.
Table 2.
Relationship between heatstroke experience and physical symptoms during work activities.
| Heatstroke Experience | p-Value | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| No | Yes | ||||||||
| n | (%) | n | (%) | n | (%) | ||||
| Question | 224 | (60.4) | 147 | (39.6) | 371 | (100.0) | |||
| Headache | No (not at all) | 71 | (84.5) | 13 | (15.5) | 84 | (22.6) | <0.001 | ** |
| Yes | 153 | (53.3) | 134 | (46.7) | 287 | (77.4) | |||
| Dry mouth | No | 40 | (76.9) | 12 | (23.1) | 52 | (14.0) | 0.009 | ** |
| Yes | 184 | (57.7) | 135 | (42.3) | 319 | (86.0) | |||
| Dizziness | No | 99 | (79.2) | 26 | (20.8) | 125 | (33.7) | <0.001 | ** |
| Yes | 125 | (50.8) | 121 | (49.2) | 246 | (66.3) | |||
| Fatigue/Weakness | No | 64 | (86.5) | 10 | (13.5) | 74 | (19.9) | <0.001 | ** |
| Yes | 160 | (53.9) | 137 | (46.1) | 297 | (80.1) | |||
| Difficulty concentrating/ Inability to organize thoughts | No | 69 | (81.2) | 16 | (18.8) | 85 | (22.9) | <0.001 | ** |
| Yes | 155 | (54.2) | 131 | (45.8) | 286 | (77.1) | |||
| Nausea | No | 138 | (72.3) | 53 | (27.7) | 191 | (51.5) | <0.001 | ** |
| Yes | 86 | (47.8) | 94 | (52.2) | 180 | (48.5) | |||
| Muscle cramps | No | 138 | (70.4) | 58 | (29.6) | 196 | (52.8) | <0.001 | ** |
| Yes | 86 | (49.1) | 89 | (50.9) | 175 | (47.2) | |||
| Abnormally rapid breathing | No | 148 | (69.8) | 64 | (30.2) | 212 | (57.1) | <0.001 | ** |
| Yes | 76 | (47.8) | 83 | (52.2) | 159 | (42.9) | |||
| Rapid and weak pulse | No | 152 | (69.4) | 67 | (30.6) | 219 | (59.0) | <0.001 | ** |
| Yes | 72 | (47.4) | 80 | (52.6) | 152 | (41.0) | |||
| Numbness in the lips | No | 177 | (65.1) | 95 | (34.9) | 272 | (73.3) | 0.002 | ** |
| Yes | 47 | (47.5) | 52 | (52.5) | 99 | (26.7) | |||
| Abnormal speech and behavior | No | 164 | (64.3) | 91 | (35.7) | 255 | (68.7) | 0.022 | * |
| Yes | 60 | (51.7) | 56 | (48.3) | 116 | (31.3) | |||
| Fainting | No | 193 | (60.5) | 126 | (39.5) | 319 | (86.0) | 0.904 | |
| Yes | 31 | (59.6) | 21 | (40.4) | 52 | (14.0) | |||
| Hallucinations | No | 188 | (60.5) | 123 | (39.5) | 311 | (83.8) | 0.948 | |
| Yes | 36 | (60.0) | 24 | (40.0) | 60 | (16.2) | |||
** p < 0.01; * p < 0.05.
Table 3.
Relationship between heatstroke experience and preventive measures.
| Heatstroke Experience | p-Value | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| No | Yes | ||||||||
| N | (%) | n | (%) | n | (%) | ||||
| Question | 224 | (60.4) | 147 | (39.6) | 371 | (100.0) | |||
| Wear moisture-wicking clothing | Yes | 151 | (59.4) | 103 | (40.6) | 254 | (68.5) | 0.590 | |
| No | 73 | (62.4) | 44 | (37.6) | 117 | (31.5) | |||
| Wear cooling fabric underwear | Yes | 115 | (55.3) | 93 | (44.7) | 208 | (56.1) | 0.024 | * |
| No | 109 | (66.9) | 54 | (33.1) | 163 | (43.9) | |||
| Wear clothing with cooling features (air conditioning, cooling packs) | Yes | 61 | (59.2) | 42 | (40.8) | 103 | (27.8) | 0.778 | |
| No | 163 | (60.8) | 105 | (39.2) | 268 | (72.2) | |||
| Change clothes frequently | Yes | 76 | (56.3) | 59 | (43.7) | 135 | (36.4) | 0.224 | |
| No | 148 | (62.7) | 88 | (37.3) | 236 | (63.6) | |||
| Hat modifications | Yes | 19 | (73.1) | 7 | (26.9) | 26 | (7.0) | 0.170 | |
| No | 205 | (59.4) | 140 | (40.6) | 345 | (93.0) | |||
| Use of towels or similar items to shield face and neck from direct sunlight | Yes | 38 | (61.3) | 24 | (38.7) | 62 | (16.7) | 0.872 | |
| No | 186 | (60.2) | 123 | (39.8) | 309 | (83.3) | |||
| Wear long-sleeved clothing | Yes | 33 | (66.0) | 17 | (34.0) | 50 | (13.5) | 0.382 | |
| No | 191 | (59.5) | 130 | (40.5) | 321 | (86.5) | |||
| Wear clothing made from UV-protective materials | Yes | 20 | (58.8) | 14 | (41.2) | 34 | (9.2) | 0.846 | |
| No | 204 | (60.5) | 133 | (39.5) | 337 | (90.8) | |||
| Wear arm sleeves or arm covers | Yes | 24 | (60.0) | 16 | (40.0) | 40 | (10.8) | 0.959 | |
| No | 200 | (60.4) | 131 | (39.6) | 331 | (89.2) | |||
| Frequent hydration | Yes | 207 | (58.8) | 145 | (41.2) | 352 | (94.9) | 0.008 | ** |
| No | 17 | (89.5) | 2 | (10.5) | 19 | (5.1) | |||
| Consume salt directly (e.g., pickled plums, salted candy, tablets) or through sports drinks, etc. | Yes | 164 | (59.4) | 112 | (40.6) | 276 | (74.4) | 0.521 | |
| No | 60 | (63.2) | 35 | (36.8) | 95 | (25.6) | |||
| Apply cold items to the head or neck | Yes | 74 | (54.8) | 61 | (45.2) | 135 | (36.4) | 0.098 | |
| No | 150 | (63.6) | 86 | (36.4) | 236 | (63.6) | |||
| Use of sunscreen | Yes | 41 | (53.9) | 35 | (46.1) | 76 | (20.5) | 0.199 | |
| No | 183 | (62.0) | 112 | (38.0) | 295 | (79.5) | |||
| Reduce alcohol consumption the previous day | Yes | 15 | (71.4) | 6 | (28.6) | 21 | (5.7) | 0.286 | |
| No | 209 | (59.7) | 141 | (40.3) | 350 | (94.3) | |||
| Simultaneous hydration and salt intake | Yes | 209 | (60.2) | 138 | (39.8) | 347 | (93.5) | 0.826 | |
| No | 15 | (62.5) | 9 | (37.5) | 24 | (6.5) | |||
| Health status check | Yes | 106 | (57.0) | 80 | (43.0) | 186 | (50.1) | 0.181 | |
| No | 118 | (63.8) | 67 | (36.2) | 185 | (49.9) | |||
| Use of mist showers and mist fans | Yes | 94 | (64.4) | 52 | (35.6) | 146 | (39.4) | 0.204 | |
| No | 130 | (57.8) | 95 | (42.2) | 225 | (60.6) | |||
| Utilize heat index (WBGT) | Yes | 33 | (58.9) | 23 | (41.1) | 56 | (15.1) | 0.810 | |
| No | 191 | (60.6) | 124 | (39.4) | 315 | (84.9) | |||
| Heat acclimatization | Yes | 91 | (63.6) | 52 | (36.4) | 143 | (38.5) | 0.309 | |
| No | 133 | (58.3) | 95 | (41.7) | 228 | (61.5) | |||
| Lower body temperature before activity (Precooling) | Yes | 35 | (54.7) | 29 | (45.3) | 64 | (17.3) | 0.306 | |
| No | 189 | (61.6) | 118 | (38.4) | 307 | (82.7) | |||
| (Countermeasure) Unknown | Yes | 220 | (60.8) | 142 | (39.2) | 362 | (97.6) | 0.323 | |
| No | 4 | (44.4) | 5 | (55.6) | 9 | (2.4) | |||
| Experience viewing the Ministry of Health, Labour and Welfare’s Heatstroke Prevention Manual | Yes | 20 | (57.1) | 15 | (42.9) | 35 | (9.4) | 0.681 | |
| No | 204 | (60.7) | 132 | (39.3) | 336 | (90.6) | |||
| Experience receiving education or guidance on heatstroke countermeasures | Yes | 72 | (57.6) | 53 | (42.4) | 125 | (33.7) | 0.436 | |
| No | 152 | (61.8) | 94 | (38.2) | 246 | (66.3) | |||
| Warnings or advice from supervisors regarding extreme heat during work | Yes | 118 | (61.1) | 75 | (38.9) | 193 | (52.0) | 0.755 | |
| No | 106 | (59.6) | 72 | (40.4) | 178 | (48.0) | |||
** p < 0.01; * p < 0.05.
Table 4.
Relationship between heatstroke experience and living conditions/health status.
| Heatstroke Experience | p-Value | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| No | Yes | ||||||||
| n | (%) | n | (%) | n | (%) | ||||
| Question | 224 | (60.4) | 147 | (39.6) | 371 | (100.0) | |||
| A. BMI_1 | Normal (between 18 and <25) | 171 | (64.0) | 96 | (36.0) | 267 | (72.0) | 0.026 | * |
| Underweight (<18.5) | 19 | (61.3) | 12 | (38.7) | 31 | (8.4) | |||
| Obese (≥25) | 34 | (46.6) | 39 | (53.4) | 73 | (19.7) | |||
| B. BMI_2 | Normal, Underweight (below 18.5) | 190 | (63.8) | 108 | (36.2) | 298 | (80.3) | 0.007 | ** |
| Obese (≥25) | 34 | (46.6) | 39 | (53.4) | 73 | (19.7) | |||
| C. BMI_3 | Normal, Obese (≥25) | 205 | (60.3) | 135 | (39.7) | 340 | (91.6) | 0.914 | |
| Underweight (<18.5) | 19 | (61.3) | 12 | (38.7) | 31 | (8.4) | |||
| D. Number of meals per day | 3 times/4 times or more | 150 | (61.2) | 95 | (38.8) | 245 | (66.0) | 0.642 | |
| None/1 time/2 times | 74 | (58.7) | 52 | (41.3) | 126 | (34.0) | |||
| E. Number of days eating breakfast | 0: Daily/4–5 days per week/2–3 days per week | 163 | (59.5) | 111 | (40.5) | 274 | (73.9) | 0.557 | |
| Rarely eat | 61 | (62.9) | 36 | (37.1) | 97 | (26.1) | |||
| F. Breakfast portion size | Large | 9 | (45.0) | 11 | (55.0) | 20 | (5.4) | 0.150 | |
| Just right | 88 | (63.8) | 50 | (36.2) | 138 | (37.2) | |||
| Less than average | 93 | (63.3) | 54 | (36.7) | 147 | (39.6) | |||
| Do not eat | 34 | (51.5) | 32 | (48.5) | 66 | (17.8) | |||
| G. Meal size (Lunch) | Large | 37 | (59.7) | 25 | (40.3) | 62 | (16.7) | 0.126 | |
| Just right | 154 | (62.9) | 91 | (37.1) | 245 | (66.0) | |||
| Less than average | 26 | (47.3) | 29 | (52.7) | 55 | (14.8) | |||
| Do not eat | 7 | (77.8) | 2 | (22.2) | 9 | (2.4) | |||
| H. Meal size (Dinner) | Large | 109 | (59.6) | 74 | (40.4) | 183 | (49.3) | 0.721 | |
| Just right | 108 | (62.1) | 66 | (37.9) | 174 | (46.9) | |||
| Less than average | 6 | (54.5) | 5 | (45.5) | 11 | (3.0) | |||
| Do not eat | 1 | (33.3) | 2 | (66.7) | 3 | (0.8) | |||
| I. Frequency of meals with staple food, main dish, and side dish | 1 time/Rarely | 100 | (56.5) | 77 | (43.5) | 177 | (47.7) | 0.258 | |
| 2 times | 80 | (62.0) | 49 | (38.0) | 129 | (34.8) | |||
| 3 times | 44 | (67.7) | 21 | (32.3) | 65 | (17.5) | |||
| J. Sleep duration | 7 h or more | 65 | (65.7) | 34 | (34.3) | 99 | (26.7) | 0.210 | |
| Less than 7 h | 159 | (58.5) | 113 | (41.5) | 272 | (73.3) | |||
| K. Satisfaction with sleep | Yes | 74 | (71.8) | 29 | (28.2) | 103 | (27.8) | 0.005 | ** |
| No/Neither | 150 | (56.0) | 118 | (44.0) | 268 | (72.2) | |||
| L. Perceived health | Yes | 121 | (74.2) | 42 | (25.8) | 163 | (43.9) | <0.001 | ** |
| No/Neither | 103 | (49.5) | 105 | (50.5) | 208 | (56.1) | |||
| M. Alcohol consumption | No | 109 | (58.0) | 79 | (42.0) | 188 | (50.7) | 0.338 | |
| Yes/No response | 115 | (62.8) | 68 | (37.2) | 183 | (49.3) | |||
| N. Smoking | No | 117 | (60.6) | 76 | (39.4) | 193 | (52.0) | 0.920 | |
| Yes/Past smoker | 107 | (60.1) | 71 | (39.9) | 178 | (48.0) | |||
| O. Hypertension | No | 215 | (61.1) | 137 | (38.9) | 352 | (94.9) | 0.234 | |
| Yes | 9 | (47.4) | 10 | (52.6) | 19 | (5.1) | |||
| P. Dyslipidemia | No | 215 | (60.6) | 140 | (39.4) | 355 | (95.7) | 0.730 | |
| Yes | 9 | (56.3) | 7 | (43.8) | 16 | (4.3) | |||
| Q. Anemia | No | 214 | (61.5) | 134 | (38.5) | 348 | (93.8) | 0.087 | |
| Yes | 10 | (43.5) | 13 | (56.5) | 23 | (6.2) | |||
| R. Illness or treatment | No | 188 | (62.5) | 113 | (37.5) | 301 | (81.1) | 0.089 | |
| Yes | 36 | (51.4) | 34 | (48.6) | 70 | (18.9) | |||
| S. Medication use | No | 199 | (62.0) | 122 | (38.0) | 321 | (86.5) | 0.107 | |
| Yes | 25 | (50.0) | 25 | (50.0) | 50 | (13.5) | |||
** p < 0.01; * p < 0.05.
Table 5.
Odds ratios for physical symptoms, preventive measures, health status, etc., related to heatstroke experience.
Table 5.
Odds ratios for physical symptoms, preventive measures, health status, etc., related to heatstroke experience.
| Item | Odds Ratio | Confidence Interval | p-Value | ||
|---|---|---|---|---|---|
| Lower Limit | Upper Limit | ||||
| Physical symptoms of headache a | 2.657 | 1.253 | 5.636 | 0.011 | * |
| Physical symptoms of dizziness a | 2.061 | 1.118 | 3.800 | 0.021 | * |
| Wearing cooling fabric underwear b | 0.590 | 0.370 | 0.939 | 0.026 | * |
| Frequent hydration b | 0.187 | 0.041 | 0.863 | 0.032 | * |
| BMI c | 1.857 | 1.056 | 3.267 | 0.032 | * |
| Self-perceived health d | 2.194 | 1.355 | 3.551 | 0.001 | ** |
** p < 0.01; * p < 0.05; a: None at all = 0, Rarely/Sometimes/Often = 1; b: Yes = 0, No = 1; c: Normal (between 18 and <25)/Underweight (<18.5) = 0, Obese (≥25) = 1; d: Yes = 0, No/Neither = 1.
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Yodawara, C.; Iio, Y.; Ejiri, H.; Yamamoto, S.; Kozai, H.; Tanaka, M.; Seguchi, M.; Ito, M. Heatstroke Awareness and Preventive Behaviors Among Automotive Maintenance Workers in Outdoor Environments: A Cross-Sectional Study in Japan. Healthcare 2026, 14, 1293. https://doi.org/10.3390/healthcare14101293
AMA Style
Yodawara C, Iio Y, Ejiri H, Yamamoto S, Kozai H, Tanaka M, Seguchi M, Ito M. Heatstroke Awareness and Preventive Behaviors Among Automotive Maintenance Workers in Outdoor Environments: A Cross-Sectional Study in Japan. Healthcare. 2026; 14(10):1293. https://doi.org/10.3390/healthcare14101293
Chicago/Turabian StyleYodawara, Chieko, Yoko Iio, Harumi Ejiri, Saimi Yamamoto, Hana Kozai, Mamoru Tanaka, Manato Seguchi, and Morihiro Ito. 2026. "Heatstroke Awareness and Preventive Behaviors Among Automotive Maintenance Workers in Outdoor Environments: A Cross-Sectional Study in Japan" Healthcare 14, no. 10: 1293. https://doi.org/10.3390/healthcare14101293
APA StyleYodawara, C., Iio, Y., Ejiri, H., Yamamoto, S., Kozai, H., Tanaka, M., Seguchi, M., & Ito, M. (2026). Heatstroke Awareness and Preventive Behaviors Among Automotive Maintenance Workers in Outdoor Environments: A Cross-Sectional Study in Japan. Healthcare, 14(10), 1293. https://doi.org/10.3390/healthcare14101293
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