Monitoring lower limb biomechanical asymmetry and psychological measures in athletic populations—A scoping review
Bibliographic record
Abstract
BACKGROUND: Lower limb biomechanics, including asymmetry, are frequently monitored to determine sport performance level and injury risk. However, contributing factors extend beyond biomechanical and asymmetry measures to include psychological, sociological, and environmental factors. Unfortunately, inadequate research has been conducted using holistic biopsychosocial models to characterize sport performance and injury risk. Therefore, this scoping review summarized the research landscape of studies concurrently assessing measures of lower limb biomechanics, asymmetry, and introspective psychological state (e.g., pain, fatigue, perceived exertion, stress, etc.) in healthy, competitive athletes. METHODS: A systematic search of MEDLINE, Embase, CINAHL, SPORTDiscus, and Web of Science Core Collections was designed and conducted in accordance with PRISMA guidelines. Fifty-one articles were included in this review. RESULTS: Significant relationships between biomechanics (k = 22 studies) or asymmetry (k = 20 studies) and introspective state were found. Increased self-reported pain was associated with decreased range of motion, strength, and increased lower limb asymmetry. Higher ratings of perceived exertion were related to increased lower limb asymmetry, self-reported muscle soreness, and worse jump performance. Few studies (k = 4) monitored athletes longitudinally throughout one or more competitive season(s). CONCLUSION: This review highlights the need for concurrent analysis of introspective, psychological state, and biomechanical asymmetry measures along with longitudinal research to understand the contributing factors to sport performance and injury risk from biopsychosocial modeling. In doing so, this framework of biopsychosocial preventive and prognostic patient-centered practices may provide an actionable means of optimizing health, well-being, and sport performance in competitive athletes.
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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 | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".