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 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 | 0.000 |
| 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) | 0.001 | 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".