Predictive Models for Musculoskeletal injuries for swimmers in Jordan | ||||
| Journal of Applied Sports Science | ||||
| Article 6, Volume 13, Issue 2, December 2023, Page 30-38 PDF (847.36 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/jass.2023.246310.1097 | ||||
 View on SCiNiTO | ||||
| Author | ||||
Mahmoud momani 
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| Faculty of Health Sciences and Well-Being ,School of Nursing and Health Studies University of Sunderland | ||||
| Abstract | ||||
| This study is the first of its kind to investigate musculoskeletal injuries in Jordanian swimmers. It aimed to identify common sports injuries in male and female swimmers aged 18 to 25 in Jordan. The research involved 173 participants affiliated with the Jordanian Swimming Federation, using correlation and regression analysis. The study explored the intricate relationship between the age of starting swimming, training routines, and competitive involvement. It revealed negative correlations between the age of initiation and various performance measures, indicating that starting swimming at a younger age is associated with increased competition participation, more training time, and longer training distances. Furthermore, a strong negative correlation was observed between the number of competitions and weekly training time, suggesting that more competitions require more training. Conversely, a positive correlation was found between weekly training distance and training time, indicating that longer training sessions cover greater distances. It's important to note that these correlations do not imply causation, and other factors may contribute. Regression analysis revealed that the selected independent variables significantly influenced the prediction of swimming injuries, explaining around 37.3% of the variability. The number of competitions emerged as the most influential factor, negatively impacting injury prediction, meaning that more competitions were associated with fewer injuries. Additionally, variables like weekly training time, weekly training distance, and the age of starting swimming positively affected injury prediction. The study presented a predictive equation for estimating swimmers' injuries, providing valuable insights for injury prevention and athlete well-being. | ||||
| Keywords | ||||
| Injury; Correlation analysis; Regression Analysis; Swimmers; Jordan | ||||
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