Collagen peptides supplementation improves function, pain, and physical and mental outcomes in active adults
Bibliographic record
Abstract
Introduction: Chronic pain affects 19% of adults in the United States, with increasing prevalence in active and aging populations.Pain can limit physical activity and activities of daily living (ADLs), resulting in declined mental and social health.Nutritional interventions for pain currently target inflammation or joint health, but few influence both.Collagen, the most abundant protein in the human body and constituent of the extra cellular matrix, is such a nutraceutical.While there have been reports of reductions in pain with short-term collagen peptide (CP) supplementation, there are no long-term studies specifically in healthy middle-aged active adults.Purpose: To determine the effects of daily CP consumption over 3, 6, and 9 months on survey measures of pain, function, and physical and mental health using The Knee Injury & Osteoarthritis Outcomes Score (KOOS) and Veterans Rand 12 (VR-12) in middle-aged active adults.Methods: This study was a double-blind randomized control trial with three treatment groups (Placebo, 10 g/d CP, and 20 g/d CP).Results: Improvements in ADLs (p = .031,η p 2 = .096)and pain (p = .037,η p 2 = .164)were observed with 10 g/d CP over 6 months, although pain only improved in high frequency exercisers (>180 min/week).Additionally, VR-12 mental component scores (MCS) improved with 10 g/d of CP over 3-9 months (p = .017,η p 2 = .309),while physical component scores (PCS) improved with 20 g/d of CP over 3-9 months, but only in females (p = .013,η p 2 = .582).Conclusion: These findings suggest 10 to 20 g/d of CP supplementation over 6 to 9 months may improve ADLs, pain, MCS, and PCS in middle-aged active adults.
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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) | 0.001 | 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) | 0.003 | 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".