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Record W3107047808 · doi:10.1093/milmed/usaa524

Most Military Runners Report Recent Changes in Running Parameters Before Lower Limb Injury Onset

2020· article· en· W3107047808 on OpenAlexaffabout
Major Anny Fredette, Jean‐Sébastien Roy, Jean-Franç ois Esculier, Kadija Perreault

Bibliographic record

VenueMilitary Medicine · 2020
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsRunning Injury ClinicUniversity of British ColumbiaUniversité LavalCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre for Interdisciplinary Research in RehabilitationCanadian Armed Forces
Fundersnot available
KeywordsMedicinePhysical therapyPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

INTRODUCTION: While running is a popular activity because of the health and fitness benefits it provides, the yearly incidence of running-related injuries (RRI) is high across all populations of runners, including military members. The etiology of RRI is multifactorial, and despite the numerous studies on risk factors for RRI, there is no clear consensus in the literature on the relative contribution of several intrinsic or extrinsic risk factors to the development of RRI. Furthermore, little is known on RRI profile and running parameters among Canadian military members. The objectives of this study were to (1) describe the clinical presentation of lower limb RRI and running profile among military members and (2) explore any association between recent changes in running parameters (volume or intensity) and specific RRI diagnoses. MATERIALS AND METHODS: This cross-sectional study was conducted in 107 military members from the Canadian Armed Forces (Valcartier Military Base) who presented with a restriction or interruption of running because of lower limb running-related pain. The following variables were collected during a physiotherapy evaluation: injury location and diagnosis, running kinematics (foot strike pattern and step rate), degree of minimalism of running shoes, running parameters in the last 3 months before consultation (volume, duration, frequency, and intensity), and recent changes in training before pain onset. Descriptive statistical analyses were conducted to describe the clinical presentation and running profile, while chi-square tests and multiple correspondence analysis were used to explore the association between recent changes in running parameters and diagnosis. This study was approved by the institutional ethics committee, and participants signed a detailed consent form. RESULTS: Among the 107 participants included in the study (mean age: 30.7 ± 8.9 years; 13 females), the most common diagnoses were patellofemoral pain (26.2%), medial tibial stress syndrome (11.2%), plantar fasciopathy (9.3%), and sciatica (9.3%). The average Minimalist Index of running shoes was 27.6 ± 18.5%. Step rate was 161.7 ± 10.3 steps per minute, and 73.3% of the participants used a rearfoot strike pattern. The majority of military runners reported previous RRI, gradual onset of symptoms, and recent changes in their training parameters (75.7%) before injury onset. No association was found between recent changes in running volume ((χ2(4) = 2.849; P = .606)) or intensity ((χ2(4) = 1.381; P = .855)) and diagnosis. CONCLUSION: This is the first study to specifically investigate RRI and running profile among Canadian military members. The most common injuries were located at the knee, and the most frequent diagnosis was patellofemoral pain. The majority of military runners reported previous RRI as well as recent changes in their running parameters before injury onset, but unique types of recent changes were not associated with specific diagnoses. This study illustrates the need to further investigate the impact of training loads on the development of RRI.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.243
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2020
Admission routes2
Has abstractyes

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