Evidence map›Paper›PMID 40546746›Full record

ArticleBMJ open sport & exercise medicine2025

The 'vicious circle' of sports injuries: an analysis of 165 athletics (track and field) athletes over a 39-week follow-up using Markov chains.

Spyridon Iatropoulos, Pascal Edouard

Abstract read
In one paragraph

Article in BMJ open sport & exercise medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

Spyridon IatropoulosLaboratoire Interuniversitaire de Biologie de la Motricité (EA 7424), Université Jean Monnet Saint-Etienne, Lyon 1, Université Savoie Mont-Blanc, Saint-Étienne, Auvergne-Rhône-Alpes, France.ORCID 0000-0003-3381-2403
Pascal EdouardLaboratoire Interuniversitaire de Biologie de la Motricité (EA 7424), Université Jean Monnet Saint-Etienne, Lyon 1, Université Savoie Mont-Blanc, Saint-Étienne, Auvergne-Rhône-Alpes, France.ORCID 0000-0003-1969-3612

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To explore (1) the risk of injury according to the number of previous injuries, and (2) the risk of progression of the athletes' health status according to the impact on athletics participation (ie, no injury/healthy (H), injury with full athletics participation (IF), injury with partial athletics participation (IP) or injury with no athletics participation (IN)). Methods: We performed a secondary analysis of injury data weekly collected using an online self-reported questionnaire from 165 athletics (track-and-field) athletes during 39 weeks. Using Markov chains, we determined the probabilities of (1) sustaining an injury with participation restriction (ICPR=IP+IN) depending on the number of previous ICPR and (2) transitioning from any one injury state to any other (ie, H, IF, IP, IN). Comparisons were made by calculating the ratio and 95% CIs between two probabilities using a bias-corrected accelerated bootstrap method. Results: Compared with the risk of the first ICPR, the risk of a second, third and fourth ICPRs increased on average by 1.9 (1.33 to 2.73), 2.2 (1.43 to 3.56) and 2.42 (1.37 to 4.41) times, respectively. Compared with having no injury (H), experiencing an IF at a given week had a five times higher risk of transitioning to an IP or IN the following week. Conclusions: Subsequent injuries show a higher risk than the first injury. Worse health states at a given week had a higher risk of worsening the following week. Our results highlighted a 'vicious circle' of injuries in athletics.

Indexed as

AthleticsInjuriesPreventionRisk factor

Identifiers

PMID40546746
PMCPMC12182210

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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.