Collagen and Vitamin C Supplementation Increases Lower Limb Rate of Force Development
Bibliographic record
Abstract
BACKGROUND: Exercise and vitamin C-enriched collagen supplementation increase collagen synthesis, potentially increasing matrix density, stiffness, and force transfer. PURPOSE: To determine whether vitamin C-enriched collagen (hydrolyzed collagen [HC] + C) supplementation improves rate of force development (RFD) alongside a strength training program. METHODS: Using a double-blinded parallel design, over 3 weeks, healthy male athletes (n = 50, 18-25 years) were randomly assigned to the intervention (HC + C; 20 g HC + 50 mg vitamin C) or placebo (20 g maltodextrin). Supplements were ingested daily 60 min prior to training. Athletes completed the same targeted maximal muscle power training program. Maximal isometric squats, countermovement jumps, and squat jumps were performed on a force plate at the same time each testing day (baseline, Tests 1, 2, and 3) to measure RFD and maximal force development. Mixed-model analysis of variance compared performance variables across the study timeline, whereas t tests were used to compare the change between baseline and Test 3. RESULTS: Over 3 weeks, maximal RFD in the HC + C group returned to baseline, whereas the placebo group remained depressed (p = .18). While both groups showed a decrease in RFD through Test 2, only the treatment group recovered RFD to baseline by Test 3 (p = .036). In the HC + C group, change in countermovement jumps eccentric deceleration impulse (p = .008) and eccentric deceleration RFD (p = .04) was improved. A strong trend was observed for lower limb stiffness assessed in the countermovement jumps (p = .08). No difference was observed in maximal force or squat jump parameters. CONCLUSION: The HC + C supplementation improved RFD in the squat and countermovement jump alongside training.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".