Evidence map›Paper›PMID 41815969›Full record

SynthesisFrontiers in public health2026

Implementation effectiveness, barriers, and real-world outcomes of neuromuscular training programs for ACL injury prevention in female athletes: systematic review with narrative synthesis using SWiM framework.

Yongzhe Gao, Lu Qi, Tongwu Yu

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yongzhe GaoWoosuk University, Wanju-gun, Republic of Korea.
Lu QiGuilin University of Electronic Technology, Guilin, China.
Tongwu YuAnhui Communications Vocational and Technical College, Hefei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: ACL injuries disproportionately affect female athletes. Neuromuscular training (NMT) is effective in controlled studies, but real-world adoption and adherence remain poor. Objective: To synthesize evidence on implementation effectiveness, barriers/facilitators, and real-world outcomes of NMT for ACL prevention in female athletes using narrative synthesis. Methods: Following SWiM, we searched PubMed, SPORTDiscus, Scopus, and Web of Science (2014-2025) for controlled studies reporting implementation outcomes. NMT was defined as a multi-component intervention including ≥2 of: plyometrics, strength, balance/proprioception, agility, or sport-specific movement training. Barriers/facilitators were thematically analyzed using CFIR, and findings were organized within the RE-AIM framework. Study quality was assessed with MMAT 2018 and used for sensitivity and certainty appraisal rather than exclusion. Results: Thirteen studies ( Conclusion: Implementation quality appears to be a major determinant of real-world effectiveness for ACL prevention, potentially as important as program selection. Comprehensive support strategies outperform passive dissemination, underscoring the need to prioritize implementation science and systematic professional development to sustain injury-prevention programs. Systematic review registration: https://inplasy.com/inplasy-2025-6-0057/, identifier INPLASY202560057.

Indexed as

Anterior Cruciate Ligament InjuriesAthletesAthletic InjuriesSwimmingFemaleHumansProgram EvaluationACL injury preventionfemale athletesimplementation scienceneuromuscular trainingreal-world effectivenesssynthesis without meta-analysissystematic review

Identifiers

PMID41815969
PMCPMC12971891

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.