Long-Term Effects of a Kinematic Bikefitting Method on Pain, Comfort, and Fatigue: A Prospective Cohort Study
Bibliographic record
Abstract
The purpose of this study is to analyze the long-term riders’ subjective responses to a standardized bikefitting method on their bicycles. Eighty-six amateur mountain bikers had their riding posture and bicycle components ergonomically adjusted through a 3D kinematic bikefitting method. Validated subjective scales (Feeling, OMNI, and Numerical Rating Pain Scale) were used to assess their overall riding comfort and fatigue along with localized pain for six body parts. Data were collected just before intervention (baseline or pre), immediately after (or post), and 30, 60, 90, and 120 days after the bikefit session. A Student’s t-test comparing before bikefit and after 120 days showed significant (p < 0.05) reduction in localized pain for all six body parts and riding comfort along with a large effect size effect (d = 1.18) for riding comfort. Although initially reduced, fatigue scores gradually increased over the months, showing a high correlation (r = 0.946) with increased monthly training volume. In conclusion, overall riding discomfort and pain were significantly decreased after a standardized kinematic bikefit session even after 120 days post intervention. However, fatigue scores began to rise after 30 days, showing a high correlation with increasing monthly training volume.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".