Does body size impact muscle recruitment during law enforcement physical control simulator use?
Bibliographic record
Abstract
This study examined muscle activation during the ‘push-pull’ component of law enforcement physical abilities testing and assessed activation differences based on sex, height, and body mass index. Fifty participants (40 male) completed the ‘push-pull’ task while surface electromyograms were recorded from ten upper and lower extremity muscles, and six trunk muscles. Muscle activation was amplitude-normalized to maximum voluntary isometric contraction and compared between sexes and tertiles of height and body mass index (BMI). Women had significantly higher activation of anterior deltoid and pectoralis major on the pull, and posterior deltoid and triceps on the push. Significant differences largely remained after controlling for body size in regression analyses. The lowest tertile of height had significantly higher triceps activity on the push. The highest tertile of BMI had significantly higher rectus abdominus and external obliques activity on the pull, and external obliques activation on the push. Practitioner summary: Muscle activation during the ‘push-pull’ component of law enforcement standardised testing was examined, including differences based on sex, height, and BMI. Minimal differences existed between sexes (females had higher deltoid, pectoralis major, triceps activity), height (shorter people had higher triceps activity) and BMI tertiles (larger people had more abdominal activity). Abbreviations: ANOVA: analysis of variance; BMI: body mass index; COPAT: correctional officer's physical abilities test; EMG: electromyogram; IMU: inertial measurement unit; MVIC: maximum voluntary isometric contraction; PARE: physical abilities requirement evaluation; PCS: physical control simulator; POPAT: police officer's physical abilities test; RMS: root mean square
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".