MyHealthyGut: development of a theory-based self-regulatory app to effectively manage celiac disease
Bibliographic record
Abstract
BACKGROUND: Celiac disease affects approximately 1% of the North American population and the only treatment is to follow a strict gluten-free (GF) diet. Unfortunately, the GF diet can be challenging, and poor adherence can lead to detrimental physical and psychological health outcomes for people with celiac disease. The goal of this study was to design, develop and pilot test a smartphone app (MyHealthyGut), to promote effective self-management of celiac disease and improve gut health. In Part 1, feedback from end-users (adults with celiac disease) regarding the desired functions and content of an app to manage celiac disease was gathered. Part 2 was a pilot test of the MyHealthyGut app with end-users and healthcare professionals. METHODS: Part 1: 118 adults diagnosed with celiac disease participated in the initial survey. Based on findings from this study, version 1.0 of the app was created. Part 2: 12 adults with celiac disease engaged in focus groups to provide feedback after testing the app for a 1-week period; and seven healthcare professionals (dietitians and physicians) provided online feedback about the app after using it for a 2-week period. RESULTS: Part 1: over 90% of participants indicated a need for an app for celiac disease. Ease of use, available functions, nutritious GF recipes and cost were the top four most important perceived factors to 40-60% of participants for an app to manage celiac disease. Over 25% of participants also indicated it was important to have a list of the top 100 GF foods and evidence-based supplements, the ability to track symptoms and cooking tips. Part 2: focus group participants suggested revisions to the app pertaining to functionality and ease of use (e.g., clearly marked way-finding buttons, enhance onboarding), improving the symptom journaling feature, and app content (e.g., add information on irritable bowel syndrome). The majority of healthcare professionals reported positive perceptions of the app and reported similar revisions to content, functionality and ease of use. CONCLUSIONS: Health-related mobile applications make smartphones useful tools in providing point of care to the user. Participants reported a need for the MyHealthyGut app, listed desired content, features and functions and provided feedback to revise the content, features and functions of version 1.0 of the MyHealthyGut app. MyHealthyGut is the first evidence-based app that may be helpful in empowering users to effectively self-manage celiac disease and promote general gut health.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".