Assessment of Training Load, Sleep, Injuries, and Operational Physical Performance During Basic Military Qualification
Bibliographic record
Abstract
INTRODUCTION: Optimizing training load (TL) and sleep is essential to maximize physical performance and prevent musculoskeletal injuries (MSKIs) for Canadian forces recruits during the 10-week basic military qualification (BMQ) course. The purpose of this study was to assess the TL, sleep duration, the occurrence of MSKIs during the BMQ, and the operation fitness performance during the BMQ. MATERIALS AND METHODS: Forty Canadian recruits, eight females and 32 males, (age 24 ± 5 years; height 176.4 ± 10.4 cm), were monitored with an accelerometer (GENEActiv) on their wrist between weeks 1 and 9 to evaluate the TL and sleep duration. During weeks 2 and 10, the recruits completed an operational fitness evaluation. Injury surveillance was performed over 10 weeks. RESULTS: TL intensity was significantly different (P = 0.0001) from week to week. The weekly average total time of moderate and vigorous physical activity was 189.7 ± 48.1 min and 44.7 ± 15.2 min, respectively. The average sleep duration was 5.4 ± 0.4 h per night and decreased to 4.2 h ± 0.4 during field exercises. A significant difference in sleep duration was observed between recruits with and without a MSKI. The recruits accumulated a total of 95 days under medical restrictions with an average of 3.8 consecutive days. The VO2peak estimated from the Fitness for Operational Requirements of Canadian Armed Forces Employment job-based simulation test significantly improved from weeks 2 to 10 (pre, 47.1 ± 6.3; post: 50.2 ± 5.8; P = 0.001). CONCLUSIONS: TL is of high magnitude and varies from week to week. The reported mean sleep duration per week may perhaps negatively impact the occurrence of MSKI. No significant improvement was detected in operational fitness by the end of the BMQ.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".