Running Shoe Recommendations Based on Gait Analysis Improve Perceptions of Comfort, Performance and Injury Risk: A Single‐Blind Randomised Crossover Trial
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".