The Association Between Sleep and Musculoskeletal Injuries in Military Personnel: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Musculoskeletal injuries (MSKIs) are a significant health problem in the military. Accordingly, identifying risk factors associated with MSKI to develop targeted strategies that attenuate injury risk remains a top priority within the military. Insufficient sleep has garnered increased attention as a potential risk factor for MSKI in both civilians and military personnel. Yet, there are no systematic evaluations of the potential association between sleep and MSKI in the military. The purpose of this review is to examine the relationship between sleep and injury in military personnel. MATERIALS AND METHODS: Literature searches were performed in multiple electronic databases using keywords relevant to sleep quantity and quality, MSKI, and military populations. Two investigators independently assessed the methodological quality of each study using the Newcastle-Ottawa Scale for cohort studies or an adapted form of this scale for cross-sectional studies. RESULTS: The search yielded 2402 total citations, with 8 studies (3 cohort and 5 cross-sectional) fitting the inclusion criteria. Overall, the systematic review found 5 of the 8 reviewed studies supporting an association between sleep (quality and duration) and MSKI in military personnel. Specifically, poor sleep was associated with increased injury incidence in 2 cohort and 3 cross-sectional studies. CONCLUSION: This is the first systematic review to evaluate the published literature on the association between sleep and MSKI risk in military populations. Although there is currently limited research on this topic, findings suggest that sleep is associated with MSKI and should be considered when designing strategies aimed at reducing MSKI risk in military personnel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".