Effect of a Daily Collagen Peptide Supplement on Digestive Symptoms in Healthy Women: 2-Phase Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: The effect of dietary collagen on managing digestive symptoms is currently lacking in the literature. OBJECTIVE: To gain a better understanding of this issue, we conducted a 2-phase mixed methods study. METHODS: ). Our aim was, first, to determine the feasibility of conducting a fully digital mixed methods study; second, the study explored the effect of an 8-week daily supplementation of 20 g dietary collagen peptide (Peptan) on digestive symptoms. Phase 2 was a prospective, open-label, longitudinal, single-arm study. Participation involved 2 weeks of baseline tracking (digestive symptoms, mood, stool, and lifestyle) using an app, followed by 8 weeks of tracking and taking 20 g collagen peptide supplement split into 2 dosages per day. Participants were required to complete a web-based symptom questionnaire at baseline, week 2, and week 8, as well as participate in 2 scheduled video interviews. RESULTS: Phase 1 revealed that consumer awareness of collagen for digestive health is low (64/204, 31.4%). Among the dietitians prescribing collagen for their patients, the most common dosage was 20 g a day with notable effects after 6 weeks of intake. Within the phase 2 study, of the 40 recruited participants, 14 (35%) completed the full course of supplementation. The findings indicate that 93% (13/14) of those who completed the study experienced a reduction in digestive symptoms, which included bloating. CONCLUSIONS: A mixed methods digital study design is feasible and acceptable for collecting relevant data in a real-life setting. The use of a 20 g daily collagen peptide supplement may reduce bloating and improve mild digestive symptoms in otherwise healthy female adults in the absence of any other dietary or lifestyle interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT04245254; https://clinicaltrials.gov/ct2/show/NCT04245254.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".