Counterweight mass influences single-leg cycling biomechanics
Bibliographic record
Abstract
INTRODUCTION: Single-leg cycling is a commonly used intervention in exercise physiology that has applications in exercise training and rehabilitation. The addition of a counterweight to the contralateral pedal helps single-leg cycling mimic cycling patterns of double-leg cycling. To date, no research has tested (a) the influence of a wide range of counterweight masses on a person's cycling biomechanics and (b) the optimal counterweight mass to emulate double-leg cycling. OBJECTIVES: The purpose of this study was to determine the effects of varying counterweights on the kinematics (joint angles) and kinetics (joint moments, work) of cycling using a 3D analysis. METHODS: Twelve participants cycled at 50W or 100W with different counterweight masses (0 to 30 lbs, 2.5 lbs increments), while we analyzed the pedal force data, joint angles, joint moments, and joint power of the lower limb using 3D motion capture and 3D instrumented pedals to create participant-specific musculoskeletal models. RESULTS: The results showed that no single-leg cycling condition truly emulated double-leg cycling with respect to all measured variables, namely pedal forces (p ≤ 0.05), joint angles (p ≤ 0.05), joint moments(p ≤ 0.05), and joint powers (p ≤ 0.05), but higher counterweights resulted in single-leg cycling that was statistically similar (p > 0.05), but descriptively, asymptotically approached the biomechanics of double-leg cycling. CONCLUSION: We suggest that a 20-lb counterweight is a conservative estimate of the counterweight required for using single-leg cycling in exercise physiology studies, but further modifications are needed to the cycle ergometer for the biomechanics of single-leg cycling to match those of double-leg cycling.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".