Anthropometric and fitness predictors of operational preparedness among Malaysian firefighters: a clustering and multivariate logistic regression approach
Introduction: The evolving scope of responsibilities within the Malaysian Fire and Rescue Department necessitates a high level of physical and anthropometric readiness among firefighter recruits. Modern firefighting now encompasses complex land, sea, and air rescue operations alongside conventional...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
FEADEF
2025-07-01
|
| Series: | Retos: Nuevas Tendencias en Educación Física, Deportes y Recreación |
| Subjects: | |
| Online Access: | https://revistaretos.org/index.php/retos/article/view/116579 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849717881004097536 |
|---|---|
| author | Borhanudin Mohd Yusof Mohamed Rabiu Muazu Musa Mohamad Nizam Nazarudin Anwar P. P. Abdul Majeed Naresh Bhaskar Raj Vijayamurugan Eswaramoorth |
| author_facet | Borhanudin Mohd Yusof Mohamed Rabiu Muazu Musa Mohamad Nizam Nazarudin Anwar P. P. Abdul Majeed Naresh Bhaskar Raj Vijayamurugan Eswaramoorth |
| author_sort | Borhanudin Mohd Yusof Mohamed |
| collection | DOAJ |
| description |
Introduction: The evolving scope of responsibilities within the Malaysian Fire and Rescue Department necessitates a high level of physical and anthropometric readiness among firefighter recruits. Modern firefighting now encompasses complex land, sea, and air rescue operations alongside conventional fire suppression, requiring rigorous preparedness.
Objective: This study aimed to evaluate the fitness and anthropometric profiles of Bomba recruits, identify key variables distinguishing excellent from average fitness performers, and develop a predictive model to classify future high-performing firefighters.
Methodology: A total of 746 recruits underwent a final assessment of anthropometric and fitness parameters. K-means clustering was utilised to categorise recruits into Excellent Fitness Readiness (EFR) and Average Fitness Readiness (AFR) groups. Mann-Whitney U tests were then employed to determine significant differences in the measured variables between the groups. Subsequently, a logistic regression model was developed to predict the likelihood of recruits achieving EFR status.
Results: Results indicated that five out of nine variables, the 2.4 km run, shuttle run, inclined pull-ups, standing broad jump, and sit-ups, significantly differentiated the two groups (p < 0.05). The regression model demonstrated strong predictive power (AUC = 0.94, sensitivity = 0.94, specificity = 0.77, accuracy = 89%). Notably, improved performance in pull-ups and standing broad jumps increased the likelihood of being in the EFR group by 66.8% and 4.3%, respectively. Conversely, slower shuttle and 2.4 km run times markedly reduced the odds by 85.8% and 33%. Discussion: These findings emphasise the operational relevance of targeted fitness parameters and provide a data-driven framework for optimising firefighter recruitment and training.
Conclusions: These findings emphasise the operational relevance of targeted fitness parameters and provide a data-driven framework for optimising firefighter recruitment and training.
|
| format | Article |
| id | doaj-art-8d986cbdb4e64373a2e3f8acfe95462e |
| institution | DOAJ |
| issn | 1579-1726 1988-2041 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | FEADEF |
| record_format | Article |
| series | Retos: Nuevas Tendencias en Educación Física, Deportes y Recreación |
| spelling | doaj-art-8d986cbdb4e64373a2e3f8acfe95462e2025-08-20T03:12:32ZengFEADEFRetos: Nuevas Tendencias en Educación Física, Deportes y Recreación1579-17261988-20412025-07-016910.47197/retos.v69.116579Anthropometric and fitness predictors of operational preparedness among Malaysian firefighters: a clustering and multivariate logistic regression approachBorhanudin Mohd Yusof MohamedRabiu Muazu Musahttps://orcid.org/0000-0001-5332-1770Mohamad Nizam NazarudinAnwar P. P. Abdul MajeedNaresh Bhaskar Rajhttps://orcid.org/0000-0003-3367-2914Vijayamurugan Eswaramoorth Introduction: The evolving scope of responsibilities within the Malaysian Fire and Rescue Department necessitates a high level of physical and anthropometric readiness among firefighter recruits. Modern firefighting now encompasses complex land, sea, and air rescue operations alongside conventional fire suppression, requiring rigorous preparedness. Objective: This study aimed to evaluate the fitness and anthropometric profiles of Bomba recruits, identify key variables distinguishing excellent from average fitness performers, and develop a predictive model to classify future high-performing firefighters. Methodology: A total of 746 recruits underwent a final assessment of anthropometric and fitness parameters. K-means clustering was utilised to categorise recruits into Excellent Fitness Readiness (EFR) and Average Fitness Readiness (AFR) groups. Mann-Whitney U tests were then employed to determine significant differences in the measured variables between the groups. Subsequently, a logistic regression model was developed to predict the likelihood of recruits achieving EFR status. Results: Results indicated that five out of nine variables, the 2.4 km run, shuttle run, inclined pull-ups, standing broad jump, and sit-ups, significantly differentiated the two groups (p < 0.05). The regression model demonstrated strong predictive power (AUC = 0.94, sensitivity = 0.94, specificity = 0.77, accuracy = 89%). Notably, improved performance in pull-ups and standing broad jumps increased the likelihood of being in the EFR group by 66.8% and 4.3%, respectively. Conversely, slower shuttle and 2.4 km run times markedly reduced the odds by 85.8% and 33%. Discussion: These findings emphasise the operational relevance of targeted fitness parameters and provide a data-driven framework for optimising firefighter recruitment and training. Conclusions: These findings emphasise the operational relevance of targeted fitness parameters and provide a data-driven framework for optimising firefighter recruitment and training. https://revistaretos.org/index.php/retos/article/view/116579Firefighter Fitness ReadinessAnthropometric ProfilingOccupational PerformanceTraining OptimizationLogistic Regression Model |
| spellingShingle | Borhanudin Mohd Yusof Mohamed Rabiu Muazu Musa Mohamad Nizam Nazarudin Anwar P. P. Abdul Majeed Naresh Bhaskar Raj Vijayamurugan Eswaramoorth Anthropometric and fitness predictors of operational preparedness among Malaysian firefighters: a clustering and multivariate logistic regression approach Retos: Nuevas Tendencias en Educación Física, Deportes y Recreación Firefighter Fitness Readiness Anthropometric Profiling Occupational Performance Training Optimization Logistic Regression Model |
| title | Anthropometric and fitness predictors of operational preparedness among Malaysian firefighters: a clustering and multivariate logistic regression approach |
| title_full | Anthropometric and fitness predictors of operational preparedness among Malaysian firefighters: a clustering and multivariate logistic regression approach |
| title_fullStr | Anthropometric and fitness predictors of operational preparedness among Malaysian firefighters: a clustering and multivariate logistic regression approach |
| title_full_unstemmed | Anthropometric and fitness predictors of operational preparedness among Malaysian firefighters: a clustering and multivariate logistic regression approach |
| title_short | Anthropometric and fitness predictors of operational preparedness among Malaysian firefighters: a clustering and multivariate logistic regression approach |
| title_sort | anthropometric and fitness predictors of operational preparedness among malaysian firefighters a clustering and multivariate logistic regression approach |
| topic | Firefighter Fitness Readiness Anthropometric Profiling Occupational Performance Training Optimization Logistic Regression Model |
| url | https://revistaretos.org/index.php/retos/article/view/116579 |
| work_keys_str_mv | AT borhanudinmohdyusofmohamed anthropometricandfitnesspredictorsofoperationalpreparednessamongmalaysianfirefightersaclusteringandmultivariatelogisticregressionapproach AT rabiumuazumusa anthropometricandfitnesspredictorsofoperationalpreparednessamongmalaysianfirefightersaclusteringandmultivariatelogisticregressionapproach AT mohamadnizamnazarudin anthropometricandfitnesspredictorsofoperationalpreparednessamongmalaysianfirefightersaclusteringandmultivariatelogisticregressionapproach AT anwarppabdulmajeed anthropometricandfitnesspredictorsofoperationalpreparednessamongmalaysianfirefightersaclusteringandmultivariatelogisticregressionapproach AT nareshbhaskarraj anthropometricandfitnesspredictorsofoperationalpreparednessamongmalaysianfirefightersaclusteringandmultivariatelogisticregressionapproach AT vijayamuruganeswaramoorth anthropometricandfitnesspredictorsofoperationalpreparednessamongmalaysianfirefightersaclusteringandmultivariatelogisticregressionapproach |