Application of mobility training methods in sporting populations: A systematic review of performance adaptations
Bibliographic record
Abstract
This systematic review investigates influences of mobility training in sporting populations on performance outcomes. The search strategy involved Embase, MEDLINE Complete, Sports Discus and manual search from inception to March 2022. Mobility training studies with a minimum three-week, or 10-session duration in healthy sporting populations of any age were included. Twenty-two studies comprising predominantly young adult or junior athletes were analysed from 319 retrieved articles. Performance outcomes were strength, speed, change of direction, jumping, balance, and sport-specific skills. Fifteen studies randomized participants with only four indicating systematic allocation concealment and blinding of outcomes assessors in only one study. In 20 of 22 studies mobility training was of some benefit or helped to maintain sports performance to a larger degree than control conditions. Control conditions, which were generally no activity conditions, were primarily non-significant. The majority of evidence suggests that a range of mobility training methods may improve key sports performance variables or are unlikely to impair performance over time. Therefore, coaches can consider the potential benefits of including comprehensive mobility programmes with minimal risk of impairing performance. Higher-quality studies in homogenous populations are necessary to confirm performance changes.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".