Evidence map›Paper›PMID 42616798›Full record

ArticlePloS one2026

Associations between training behaviors and injuries in performance-oriented parkrunners using traditional statistical methods and machine learning.

Han Wu, Katherine Brooke-Wavell, Clare Stevinson, Hui Fang, Keyi Zhong, Richard C Blagrove

Abstract read
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Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

6 authors.

Han WuNational Centre for Sport and Exercise Medicine, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, United Kingdom.
Katherine Brooke-WavellNational Centre for Sport and Exercise Medicine, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, United Kingdom.
Clare StevinsonNational Centre for Sport and Exercise Medicine, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, United Kingdom.
Hui FangDepartment of Computer Science, Loughborough University, Loughborough, United Kingdom.
Keyi ZhongCenter for Information Management, School of Business and Economics, Loughborough University, Loughborough, United Kingdom.
Richard C BlagroveNational Centre for Sport and Exercise Medicine, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, United Kingdom.ORCID https://orcid.org/0000-0002-7057-8073

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundparkrun is the most popular recreational running event in the UK, yet running-related injuries (RRIs) pose a threat to participation. Training behaviors are likely to influence the likelihood of RRIs with strength and conditioning (S&C) activities widely believed to reduce RRI risk. This study aimed to examine associations between S&C training habits plus broader training behaviors and RRIs using traditional statistical methods and machine learning (ML).

methodsparkrun participants completed an online survey including questions about personal characteristics, training behaviors, and RRIs during the past 12 months. Chi square and Mann-Whitney U tests were used to investigate associations between training behavior and RRIs. ML classifiers were employed to establish multi-factorial prediction of RRIs. Shapley Additive Explanations were used to explain how frequently selected features affect RRI probability.

results1,203 responses were obtained and 570 performance-oriented parkrun participants were used for data analysis. Resistance training (Φ = 0.099, p = 0.018) and stretching/yoga (Φ = 0.095, p = 0.023) were positively associated with RRIs. No other S&C or running training behaviors were associated with RRIs. ML classifiers achieved moderate predictive capacities and found non-linear associations between RRIs and number of interval training sessions per week, a positive association with stretching/yoga, and a negative association with coach-guided training.

conclusionParticipation in S&C activities is not associated with a lower likelihood of RRIs in parkrunners and resistance training and stretching/yoga was linked to greater likelihood of RRIs. ML classifiers suggest there is benefit to performing a high frequency of interval training sessions, excluding stretching/yoga, and having qualified coaches prescribe training.

Indexed as

Athletic InjuriesMachine LearningRunningClassification AlgorithmsFemaleHumansMalePredictive Learning ModelsResistance TrainingSurveys and Questionnaires

Identifiers

PMID42616798
PMCPMC13489425

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.