Development of a Mobile App to Monitor the Effectiveness of a Hydrolyzed Cartilage Matrix Supplement on Joint Discomfort: Real-World Study
Bibliographic record
Abstract
BACKGROUND: Joint discomfort is a widespread and growing problem in active adults. The rising interest in preventative nutrition has increased the demand for supplements reducing joint discomfort. Protocols assessing the effect of a nutritional intervention on health commonly involve a series of face-to-face meetings between participants and study staff that can weigh on resources, participant availabilities, and even increase dropout rates. Digital tools are increasingly added to protocols to facilitate study conduct, but fully digitally run studies are still scarce. With the increasing interest in real-world studies, the development of health apps for mobile devices to monitor study outcomes is of great importance. OBJECTIVE: The purpose of this real-world study was to develop a specific mobile app, Ingredients for Life, to conduct a 100% digital study testing the effectiveness of a hydrolyzed cartilage matrix (HCM) supplement on joint discomfort in a heterogeneous group of healthy, active consumers. METHODS: The Ingredients for Life mobile app using a visual analog scale was specifically developed to monitor the variation in joint pain after exercise by the study participants. A total of 201 healthy and physically active women and men (18-72 years old) with joint pain completed the study over a period of 16 weeks. Participants were randomly allocated to the study groups and did not receive any dietary or lifestyle advice. Each participant indicated one area of joint pain and logged the type and duration of their weekly activities. They received blinded study supplements and took a daily regimen of 1 g of HCM (HCM group) or 1 g of maltodextrin (placebo group) for 12 weeks while weekly logging joint pain scores in the app. This was followed by a 4-week washout period during which participants continued reporting their joint pain scores (until the end of week 16). RESULTS: Joint pain was reduced within 3 weeks of taking a low dosage of HCM (1 g/day), regardless of gender, age group, and activity intensity when compared with the placebo group. After stopping supplementation, joint pain scores gradually increased but still remained significantly lower than those of the placebo group after 4 weeks of washout. The low dropout rate (<6% of participants, mainly in the placebo group) demonstrates that the digital study was well received by the study population. CONCLUSIONS: The digital tool allowed us to measure a heterogeneous group of active adults in a real-world setting (without any lifestyle intervention), thus promoting inclusivity and diversity. With low dropout rates, it demonstrates that mobile apps can generate qualitative, quantifiable, real-world data showcasing supplement effectiveness. The study confirmed that the oral intake of a low dose (1 g/day) of HCM led to a significant reduction of joint pain from 3 weeks after starting supplementation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".