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Record W7111237657 · doi:10.1155/tsm2/1419641

Running Shoe Recommendations Based on Gait Analysis Improve Perceptions of Comfort, Performance and Injury Risk: A Single‐Blind Randomised Crossover Trial

2025· article· en· W7111237657 on OpenAlexaff

Bibliographic record

VenueTranslational Sports Medicine · 2025
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversity of British ColumbiaRunning Injury ClinicUniversity of British Columbia Hospital
Fundersnot available
KeywordsGait analysisCrossover studyPerceptionGaitAffect (linguistics)KinematicsIdentification (biology)

Abstract

fetched live from OpenAlex

Objectives We examined how shoe recommendation based on gait analysis influences subjective perceptions of comfort, performance and injury reduction in runners while monitoring spatiotemporal and kinematic parameters. Design Single‐blind crossover randomised controlled trial with repeated measures. Method Twenty‐one women runners completed a clinical gait analysis and four 5‐min treadmill trials at a self‐selected comfortable speed sequentially in their own shoes (OS), the first experimental shoes (randomised), their OS, and the second experimental shoes (randomised). The two experimental shoes were identical except for their colour (randomised) and were presented to runners as either a ‘basic’ shoe or, deceptively, a ‘gait‐matched’ shoe selected for them based on the clinical gait analysis conducted. Results Running Comfort Assessment Tool (RUN‐CAT) scores and 100 mm visual analogue scale ratings of subjective comfort, performance and injury reduction differed significantly between own and experimental shoes ( p < 0.001). Post‐hoc comparisons revealed that runners’ OS were the most comfortable (83.3 ± 3.8 mm) followed by gait‐matched (66.1 ± 21.5 mm) and then basic (49.0 ± 24.1 mm) shoes. RUN‐CAT, performance and injury reduction ratings were similar between own and gait‐matched shoes, but gait‐matched shoes had better mean difference (95% confidence intervals), RUN‐CAT (15.6 mm [5.7, 25.5]), performance (17.1 mm [5.6, 28.6]) and injury reduction (30.1 mm [8.9, 51.2]) scores than the basic shoes. Discrete spatiotemporal, foot strike angle and resultant tibial acceleration parameters were not significantly different between shoes ( p ≥ 0.157). Most runners overall preferred their OS (71.4%), followed by gait‐matched (23.8%) and basic (4.8%) shoes. Conclusions Shoe recommendation and description can significantly affect subjective shoe comfort and overall preferences without significantly altering spatiotemporal and kinematic parameters. Runners should be cautious while choosing shoes based on recommendations and descriptors derived from gait analysis or based solely on perceived comfort as runners’ subjective perceptions can be artificially manipulated. Trial Registration: Australian New Zealand Clinical Trials Registry: ACTRN12623000516684.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.013
GPT teacher head0.264
Teacher spread0.251 · 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 designSimulation or modeling
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

Citations1
Published2025
Admission routes1
Has abstractyes

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