Effects of Multicomponent Injury Prevention Programs on Children and Adolescents’ Fundamental Movement Skills: A Systematic Review With Meta-Analyses
Bibliographic record
Abstract
OBJECTIVE: Fundamental movement skills (FMS) are essential to participate in physical activity. Understanding the effects of multicomponent injury prevention programs (MIPP) on FMS may help promote safe physical activity. Our objective was to synthesize the evidence on the effects of MIPP on biomechanical outcomes and neuromuscular performance measured on children and adolescents while performing FMS. DATA SOURCE: We searched PubMed, SPORTDiscus, Web of Science, and SCOPUS. STUDY INCLUSION AND EXCLUSION CRITERIA: We included peer-reviewed randomized controlled trials, published in English, that analyzed the effects of MIPP on biomechanics and neuromuscular performance of FMS in participants under 18 years of age. DATA EXTRACTION: Two reviewers screened the articles, assessed the quality of the evidence using the Physiotherapy Evidence Database (PEDro) scale, and synthesized the data. DATA SYNTHESIS: We conducted meta-analyses and reported the characteristics, outcomes, and risk of bias of studies. RESULTS: We included 27 articles that reported data from 1,427 participants. Positive effects on FMS were reported in 23 of the 27 included articles. Vertical Jump, running speed, acceleration, and dynamic balance presented positive-significant pooled effect sizes. Dribbling and horizontal jump presented non-significant pooled effect sizes. CONCLUSION: MIPP can positively affect FMS in children and adolescents in sports-related settings. Lack of participant compliance and implementation fidelity may affect MIPP effectiveness. Including MIPP in physical literacy interventions, physical education classes, and organized physical activity may lead to functional adaptations that help promote safe physical activity.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".