Eccentric and Concentric Resistance Exercise Comparison for Knee Osteoarthritis
Bibliographic record
Abstract
INTRODUCTION: This study aimed to compare the efficacy of eccentrically focused resistance exercise (ECC RT) to concentrically focused resistance exercise (CNC RT) on knee osteoarthritis (OA) symptoms and strength. METHODS: Ninety participants consented. Participants were randomized to CNC RT, ECC RT, or a wait-list, no-exercise control group. Four months of supervised exercise training was completed using traditional weight machines (CNC RT) or modified-matched machines that overloaded the eccentric action (ECC RT). Main outcomes included one-repetition maximal strength (knee extension, leg flexion, and leg press), weekly rate of strength gain, Western Ontario and McMaster University Osteoarthritis Index (WOMAC) total score and subscores. RESULTS: Fifty-four participants (60-85 yr, 61% women) completed the study. Both CNC RT and ECC RT groups showed 16%-28% improvement relative to the wait-list, no-exercise control group (P = 0.003-0.005) for all leg strength measures. The rate of weekly strength gain was greater for CNC RT than for ECC RT for leg press and knee flexion (by 2.9%-4.8%; both, P < 0.05) but not knee extension (0.7%; P = 0.38). There were no significant differences in WOMAC total and subscores across groups over time. Leg press strength change was the greatest contributor to change in WOMAC total scores (R = 0.223). The change in knee flexion strength from baseline to month 4 was a significant predictor of the change in WOMAC pain subscore (F ratio = 4.84, df = 45, P = 0.032). Both modes of strength training were well tolerated. CONCLUSIONS: Both resistance training types effectively increased leg strength. Knee flexion and knee extension muscle strength can modify function and pain symptoms irrespective of muscle contraction type. Which mode to pick could be determined by preference, goals, tolerance to the contraction type, and equipment availability.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".