PREVALENCE, PATTERN AND PREDICTORS OF ACADEMIC RELATED MUSCULOSKELETAL DISORDERS AMONG UNDERGRADUATES OF THE UNIVERSITY OF BENIN-A MIXED STUDY DESIGN
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Abstract
Academic-related musculoskeletal disorders (ARMSDs) are increasingly common among university students due to prolonged sitting, poor posture, and
extended study hours. This study investigated the prevalence and determinants of ARMSDs among undergraduates, with a focus on ergonomic behavior, academic workload, and demographic characteristics. A descriptive cross- sectional research design was adopted, and data were collected through a structured questionnaire administered to undergraduates. Descriptive statistics were used to summarize the prevalence and patterns of ARMSDs, while Chi- square tests and Binary Logistic Regression were employed to identify significant associations and predictors of ARMSDs. Results: Findings revealed a high prevalence of musculoskeletal symptoms, especially in the neck, lower back, and shoulders. The Chi-square analysis showed that academic workload and academic level were significantly associated with ARMSDs (p < 0.001), while gender and ergonomic behavior were not statistically significant (p > 0.05). The Binary Logistic Regression model further identified academic level as the only significant predictor of ARMSDs (B = 0.006, p = 0.001, Exp(B) = 1.006), The model explained approximately 6.9% of the variance in ARMSDs (Nagelkerke R² = 0.069). Conclusion: The study concludes that academic workload and progression are major contributors to the development of ARMSDs among undergraduates. These findings underscore the cumulative effects of academic stress, prolonged study duration, and suboptimal posture on students’ musculoskeletal health. iii
Keywords: Academic-related musculoskeletal disorders, undergraduates, ergonomic behavior, academic workload, Chi-square analysis.
extended study hours. This study investigated the prevalence and determinants of ARMSDs among undergraduates, with a focus on ergonomic behavior, academic workload, and demographic characteristics. A descriptive cross- sectional research design was adopted, and data were collected through a structured questionnaire administered to undergraduates. Descriptive statistics were used to summarize the prevalence and patterns of ARMSDs, while Chi- square tests and Binary Logistic Regression were employed to identify significant associations and predictors of ARMSDs. Results: Findings revealed a high prevalence of musculoskeletal symptoms, especially in the neck, lower back, and shoulders. The Chi-square analysis showed that academic workload and academic level were significantly associated with ARMSDs (p < 0.001), while gender and ergonomic behavior were not statistically significant (p > 0.05). The Binary Logistic Regression model further identified academic level as the only significant predictor of ARMSDs (B = 0.006, p = 0.001, Exp(B) = 1.006), The model explained approximately 6.9% of the variance in ARMSDs (Nagelkerke R² = 0.069). Conclusion: The study concludes that academic workload and progression are major contributors to the development of ARMSDs among undergraduates. These findings underscore the cumulative effects of academic stress, prolonged study duration, and suboptimal posture on students’ musculoskeletal health. iii
Keywords: Academic-related musculoskeletal disorders, undergraduates, ergonomic behavior, academic workload, Chi-square analysis.
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