Musculoskeletal injuries in military personnel – descriptive epidemiology, risk factor identification, and prevention

Lovalekar, Mita, Hauret, Keith, Roy, Tanja, Taylor, Kathryn, Blacker, Sam D., Newman, Phillip, Yanovich, Ran, Fleischmann, Chen, Nindl, Bradley C., Jones, Bruce and Michelle, Canham-Chervak (2021) Musculoskeletal injuries in military personnel – descriptive epidemiology, risk factor identification, and prevention. Journal of Science and Medicine in Sport. ISSN 1440-2440

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Abstract

Objectives: To provide an overall perspective on musculoskeletal injury (MSI) epidemiology, risk factors, and preventive strategies in military personnel.

Design: Narrative review.

Methods: The thematic session on MSIs in military personnel at the 5th International Congress on Soldiers’ Physical Performance (ICSPP) included eight presentations on the descriptive epidemiology, risk factor identification, and prevention of MSIs in military personnel. Additional topics presented were bone anabolism, machine learning analysis, and the effects of non-steroidal anti-inflammatory drugs (NSAIDs) on MSIs. This narrative review focuses on the thematic session topics and includes identification of gaps in existing literature, as well as areas for future study.
Results: MSIs cause significant morbidity among military personnel. Physical training and occupational tasks are leading causes of MSI limited duty days (LDDs) for the U.S. Army. Recent studies have shown that MSIs are associated with the use of NSAIDs. Bone MSIs are very common in training; new imaging technology such as high resolution peripheral quantitative computed tomography allows visualization of bone microarchitecture and has been used to assess new bone formation during military training. Physical activity monitoring and machine learning have important applications in monitoring and informing evidence-based solutions to prevent MSIs.

Conclusions: Despite many years of research, MSIs continue to have a high incidence among military personnel. Areas for future research include quantifying exposure when determining MSI risk; understanding associations between health-related components of physical fitness and MSI occurrence; and application of innovative imaging, physical activity monitoring and data analysis techniques for MSI prevention and return to duty.

Item Type: Article
Uncontrolled Keywords: Military personnel, Machine learning, Public health, Fractures, Stress
Subjects: Q Science > Q Science (General)
Q Science > QP Physiology
R Medicine > R Medicine (General)
Divisions: Departments > Sport and Exercise Sciences
Depositing User: Sam Blacker
Date Deposited: 20 May 2021 09:29
Last Modified: 20 May 2021 09:29
URI: http://eprints.chi.ac.uk/id/eprint/5752

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