Version 1
: Received: 22 March 2024 / Approved: 27 March 2024 / Online: 27 March 2024 (06:02:14 CET)
How to cite:
Eckart, A.; Ghimire, P.S.; Stavitz, J. Exploring Predictive Validity of Multifactorial Injury Risk Models and Associated Clinical Measures in the U.S. Population. Preprints2024, 2024031658. https://doi.org/10.20944/preprints202403.1658.v1
Eckart, A.; Ghimire, P.S.; Stavitz, J. Exploring Predictive Validity of Multifactorial Injury Risk Models and Associated Clinical Measures in the U.S. Population. Preprints 2024, 2024031658. https://doi.org/10.20944/preprints202403.1658.v1
Eckart, A.; Ghimire, P.S.; Stavitz, J. Exploring Predictive Validity of Multifactorial Injury Risk Models and Associated Clinical Measures in the U.S. Population. Preprints2024, 2024031658. https://doi.org/10.20944/preprints202403.1658.v1
APA Style
Eckart, A., Ghimire, P.S., & Stavitz, J. (2024). Exploring Predictive Validity of Multifactorial Injury Risk Models and Associated Clinical Measures in the U.S. Population. Preprints. https://doi.org/10.20944/preprints202403.1658.v1
Chicago/Turabian Style
Eckart, A., Pragya Sharma Ghimire and James Stavitz. 2024 "Exploring Predictive Validity of Multifactorial Injury Risk Models and Associated Clinical Measures in the U.S. Population" Preprints. https://doi.org/10.20944/preprints202403.1658.v1
Abstract
Background Popular movement-based injury risk screens have been shown to lack predictive precision, leading to interest in multifactorial models. Furthermore, there is a lack of research regarding injury risk assessment for those currently or planning to be recreationally active. This study aims to provide injury risk insights by analyzing multifactorial injury risk models and as-sociated clinical measures in the U.S. population. Methods Data related to injury, inflammatory markers, physical functioning, body composition, physical activity, and other variables from 21,033 respondents were extracted from NHANES. Odds ratios for self-reported injury were calculated for single predictors and risk models. Case-control and principal component analyses (PCA) were conducted to elucidate confounders and identify risk factor clusters, respectively. Receiver operating characteristic analysis was used to test the precision of a risk factor cluster to identify pain points and functional difficulties. Results Sociodemographic, individual, and lifestyle factors were strongly associated with higher odds of injury. Increases in fibrinogen and C-reactive protein were significantly associated with all risk groups. Membership to the high-risk group (age over 40, obesity, no muscle-strengthening activities, sedentary lifestyle, and low back pain) predicted at least one functional difficulty with 67.4% sensitivity and 87.2% specificity. In the injury group, bone turnover markers were higher, yet confounded by age and there was a significantly higher prevalence of self-reported osteoporosis compared to the control. In males, low testosterone was associated with injury, and higher estradiol was associated with pain and functional difficulties. In females, higher levels of follicle-stimulating hormone were associated with functional difficulties. PCA revealed four high-risk profiles, with markers and activities showing distinct loadings. Conclusion A comprehensive approach to injury risk assessment should consider the nexus of aging, lifestyle, and chronic disease to enhance tailored injury prevention strategies, fostering safe and effective physical activity participation and reducing the burden of musculoskeletal disorders.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
