Digital Support for Healthier Eating Habits Among Patients With Type 2 Diabetes: Protocol for a Randomized Clinical Trial Within Primary Care (HAPPY Trial)
Bibliographic record
Abstract
BACKGROUND: Despite the large impact that dietary habits have in the management of diabetes, few tools for supporting healthy eating habits are available for persons with diabetes. OBJECTIVE: The aim of this randomized clinical trial is to evaluate the effect of a 12-week, mobile health (mHealth), app-based intervention promoting healthy eating habits among patients with type 2 diabetes. METHODS: The HAPPY (Healthy eating using APP technologY) trial is a randomized clinical trial with two arms aiming to include 200 patients, 18 years of age or older, with type 2 diabetes. Both women and men are eligible for inclusion. Study participants are randomized 1:1 to an intervention group, where they are instructed to use a smartphone app promoting healthy eating, or to a control group, where they receive standard primary care only, for a period of 12 weeks. Each week a new topic (eg, vegetable intake) is introduced via the app. After an introduction text, the user is given a topic-related activity to perform (eg, eat one additional serving of vegetables per day during that week). The app records daily progress and sends automatic reminders or feedback to the user. Dietary intake, body composition, clinical variables, and biomarkers are measured at baseline and at 3- and 6-month follow-ups. An extensive web-based questionnaire comprising several validated questionnaires assessing a number of lifestyle factors is distributed via email at baseline and at 3-, 6-, and 12-month follow-ups; lifestyle factors include, for example, sleep, physical activity, eating behavior, and health-related quality of life. The effect of the intervention on dietary intake (primary outcome) and on glycated hemoglobin and blood lipid levels, body composition, blood pressure, other lifestyle factors, and overall health (secondary outcomes) will be assessed. RESULTS: Data collection is ongoing. Recruitment of participants started in January 2019. Findings from the study are expected to be published by the end of 2021. CONCLUSIONS: Technology development provides new ways to promote and support long-term adherence to healthier eating habits. mHealth-based approaches allow for real-time interaction and the delivery of an intervention at any time. Further, focusing on overall diet allows the user to apply new knowledge to current eating patterns, creating an individualized approach. In this study, we evaluate the effect of using a new smartphone app promoting healthy eating habits on dietary intake, clinical markers, and lifestyle factors among patients with type 2 diabetes. TRIAL REGISTRATION: ClinicalTrials.gov NCT03784612; https://clinicaltrials.gov/ct2/show/NCT03784612. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24422.
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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 | 0.025 | 0.020 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.081 | 0.014 |
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".