Lower Limb Strength Asymmetries Predicts Perceptions Of Limb Function In University Athletes
Bibliographic record
Abstract
Previous musculoskeletal injury is a primary risk factor for future injury in part due to persistent neuromuscular deficits and reduced psychological readiness. Central to the aim of reducing sport injuries and increasing athletic performance, a bio-psycho-social approach to athlete monitoring has been adopted that incorporates both objective measures of neuromuscular function and subjective assessments of psychological readiness such as the Sport Fitness Index (SFI) which quantifies athletes’ perceptions of lower-limb function (perceived-function). However, the relationship between perceived-function and objective functional measures such as the between-limb asymmetry index (AI) remains unknown. PURPOSE: To evaluate the relationship between perceived-function and limb AI across a lower body muscle strength testing battery. METHODS: University athletes (n = 196, females: n = 96) from six high risk sports for lower body injury completed the SFI, maximal countermovement jump (CMJ) testing with extra loads corresponding to 0%, 30% and 60% body mass (n = 5), unilateral CMJ (n = 3) testing, and unilateral repeated hop testing (n = 20) on a dual force plate system. Athletes also performed isokinetic multi-joint eccentric and concentric strength testing in a robotic leg press dynamometer. Between-limb AIs were calculated for each testing condition. Perceived-function in the SFI was rated on a 0-100 scale, to obtain GOOD (SFI >86), FAIR (61 < SFI < 85), and POOR (SFI < 60) perceived-function groups. A cumulative link mixed effects model was used to examine the effects of between-limb AI on perceived-function groups. RESULTS: For every absolute unit increase in isokinetic concentric strength AI and CMJ with 60% body mass concentric impulse AI, the odds of belonging to a lower perceived-function group increased by 1.036 (95% CI: 1.001-1.070) and 1.161 (CI: 1.010-1.336) times; respectively. CONCLUSIONS: Increased concentric muscle strength asymmetries predicted decreased perceived-function in university athletes. By combining introspective measures of limb function such as the SFI along with mechanical muscle function testing, it is possible to achieve an integrated bio-psycho-social approach to preseason testing that may inform targeted intervention protocols to mitigate injury risk on a group level.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".