The association of bike fitting with injury, comfort, and pain during cycling: An international retrospective survey
Bibliographic record
Abstract
Although bike fitting is recommended to help reduce injury risk, little empirical evidence exists to indicate an association between bike fitting and injury incidence. The aim of the study was to determine the effect of bike fitting on self-reported injury, comfort, and pain while cycling from a worldwide survey of cyclists. A total of 849 cyclists completed an online questionnaire between February and October 2016. Questionnaire collected data on respondent demographics, cycling profile, bike fitting, comfort and pain while cycling, and injury history. The main predictor variable was bike fitting (yes, by the respondent, i.e. user bike fitting; yes, by a professional service; or no). Covariates included demographic and cycling profile characteristics. Logistic regression models estimated the odds of injury within the last 12 months, reporting a comfortable body posture while cycling, and not reporting pain while cycling. Odds ratios (OR) with 95% confidence intervals (CI) were reported. User bike fitting was associated with increased odds of reporting a comfortable posture (OR = 2.28, 95%CI: 1.06, 4.68). User (OR = 2.35; 95%CI: 1.48, 3.84) and professional bike fitting (OR = 2.35; 95%CI: 1.42, 3.98) were both associated with increased odds of not reporting pain while cycling. No associations were found between bike fitting and injury within the last 12 months. In conclusion, we found an association between bike fitting and reported comfort and pain while cycling. We recommend integrating bike fitting into cycling maintenance. However, further studies with longer follow-up are necessary to determine the presence of an association between bike fitting and injury.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".