How do road runners select their shoes? A systematic review
Bibliographic record
Abstract
Running shoes are often considered essential to participate in running. Runners may look for recommendations from friends, specialty running stores, and healthcare professionals when selecting shoes. Despite the existence of shoe prescription guidelines, these recommendations are often not evidence-based or designed with runners’ preferences in mind. This review aims to synthesize original research that identifies how road runners select running shoes. Following PROSPERO registration (CRD42021242523), the PubMed®, Scopus®, Web of Science®, and SPORTDiscus™ electronic databases were systematically searched in March 2021, and monitored until 1 February 2022. Original research that identified factors influencing running shoe selection in road runners published in English were included. Data were qualitatively synthesized. Seven studies representing 1947 road runners were included, and conducted either online, in laboratories, or via interview. Forty influencing factors were identified and thematically sub-grouped into five categories: subjective, shoe-specific characteristics, market features, peer evaluation, and runner characteristics. Comfort, cushioning, fit, and price were cited most frequently as influential factors in road runners’ footwear selection. Most of the studies reviewed were not specifically designed to address the research question of this review. Lack of consistent definitions and varying research methods are found across studies. There is limited research targeting the factors that influence running shoe selection. Comfort and cushioning appear to be the most important factors in shoe selection, although the relationship between both variables may confound their individual importance. Runners also consider fit, price, and several other factors when selecting shoes. Shoe choice remains relatively unexplored, with no running shoe selection study conducted in store.
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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 | 0.016 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".