Walk Smarter, Not Harder: Effects of Cadence Manipulation on Gait Biomechanics in Patients with Patellofemoral Osteoarthritis
Bibliographic record
Abstract
PURPOSE: This study aimed to investigate the effect of walking cadence on knee flexion angular impulse and peak external flexion moment in patients with patellofemoral osteoarthritis (OA). METHODS: Forty-eight patients with patellofemoral OA underwent repeated quantitative gait analyses on an instrumented treadmill using a randomized crossover design. Walking trials were conducted at a fixed gait speed, under three cadence conditions: (i) preferred cadence, (ii) +10% increased cadence, and (iii) -10% decreased cadence, completed in random order. Using a linear mixed model, we tested the association of cadence conditions with surrogate measures of patellofemoral load (primary outcome measure: knee flexion angular impulse) while controlling for body mass. We then repeated the analyses while sequentially replacing the dependent variable with secondary outcome measures. RESULTS: Walking with increased cadence decreased (adjusted mean difference [95% confidence interval]) the knee flexion angular impulse (-0.85 N·m·s -1 [-1.52 to -0.18], d = 0.20) and peak knee flexion moment (-4.11 N·m [-7.35 to -0.86], d = 0.24), whereas walking with decreased cadence increased the knee flexion angular impulse (1.83 N·m·s -1 [1.15 to 2.49], d = 0.42) and peak knee flexion moment (3.55 N·m [0.30 to 6.78], d = 0.21). Similar decreases and increases were observed for secondary outcome measures. CONCLUSIONS: Walking with increased cadence, while maintaining a fixed gait speed, reduces knee flexion angular impulse as well as other surrogate measures of knee loading in patients with patellofemoral OA.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".