The association of bike fitting with injury, comfort, and pain during cycling: An international retrospective survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it