Effectiveness of a 3D bikefitting method in riding pain, fatigue, and comfort: a randomized controlled clinical trial
Bibliographic record
Abstract
To investigate the effects of bike fitting compared to qualitative-based riding posture recommendations on comfort, fatigue, and pain in amateur cyclists. This was a randomised controlled parallel trial of 162 amateur cyclists divided into two groups: bike fitting group (BFG) – participants received a bike fitting session based on 3D kinematic assessments; and a control group (QG) – participants who received a handout containing qualitative-based cycling posture recommendations. Primary outcomes were perceived comfort (FEEL Scale), perceived fatigue (OMNI Scale), and perceived pain (numeric rating pain scale, NRPS). Outcomes were assessed at baseline, when the interventions were delivered, and after 15 days. Intention-to-treat analyses were conducted using student t-tests between pre and post intervention on both groups. All dependent variables from BFG displayed significant statistical difference between both groups post-intervention (p < 0.05). FEEL Scale and OMNI Scale results showed the highest changes of all variables under analysis (mean differences of 3.12 and 3.95 points, respectively); while the body parts with more reduction in riding pain were Groin and Back (mean differences of 1.68 and 1.35, respectively). In conclusion, 3D kinematic bikefit demonstrated superior improvements over riding pain, comfort and fatigue compared to qualitative riding posture recommendations.
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.
How this classification was reachedexpand
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 | 0.060 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".