Evidence map›Paper›PMID 42730390›Full record

SynthesisFrontiers in public health2026

Age-moderating effects and training characteristics of neuromuscular training for preventing knee injuries: a systematic review and meta-analysis.

Youwei Yao, Ming Pan, Xuesong Niu

Abstract readSystematic ReviewMeta-Analysis
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.

Youwei YaoSchool of Strength and Conditioning, Shenyang Sport University, Shenyang, Liaoning, China.
Ming PanDepartment of Pharmacy, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China.
Xuesong NiuSchool of Strength and Conditioning, Shenyang Sport University, Shenyang, Liaoning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Sports-related knee injuries are common and may cause pain, joint damage, reduced sport participation, and long-term physical activity limitations. Although neuromuscular training (NMT) is widely used for injury prevention, whether its effectiveness is moderated by age or intervention characteristics remains unclear. This systematic review and meta-analysis evaluated the effectiveness of NMT for preventing sports-related knee injuries and examined the influence of age and training characteristics. Methods: PubMed, Embase, Web of Science, The Cochrane Library, and EBSCO (SPORTDiscus) were searched from inception to September 30, 2025. Thirty studies were included following PRISMA guidelines. Random-effects meta-analysis, subgroup analyses, meta-regression, heterogeneity assessment, and risk-of-bias assessment using RoB 2 and ROBINS-I were performed. Results: Thirty studies were included. NMT significantly reduced knee injury risk in athletes (effect estimate = 0.670, 95% CI: 0.547-0.820, Conclusion: NMT significantly reduces knee injury risk in athletes, with no clear evidence of age-related moderation. Structured, multicomponent, and standardized programs may have practical advantages, but findings on program structure, components, standardization, and dose should be interpreted cautiously because these analyses were exploratory. Systematic review registration: PROSPERO, under registration number CRD420261327352.

Indexed as

Athletic InjuriesKnee InjuriesAge FactorsAthletesFemaleHumansageknee injurymeta-analysisneuromuscular trainingsports injury prevention

Identifiers

PMID42730390
PMCPMC13566741

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.