Engagement With a Smartphone-Delivered Dietary Education Intervention and Its Relation to Dietary Intake and Cardiometabolic Risk Markers in People With Type 2 Diabetes: Secondary Analysis of a Randomized Controlled Trial
Bibliographic record
Abstract
Background: Mobile health (mHealth) interventions offer a promising way to support healthy lifestyle habits, but effectiveness depends on user engagement. Maintaining high user engagement in app-based interventions is important, yet challenging. Objective: We aimed to examine the association between user engagement with an app-based dietary education for people with type 2 diabetes and changes in diet quality, dietary intake, and clinical measures. Methods: In this randomized clinical trial, people with type 2 diabetes were recruited within primary care and randomized 1:1 to a 12-week smartphone-delivered app-based dietary education or control group. Participants were followed up after 3, 6, and 12 months. Dietary intake was assessed using a food frequency questionnaire. The control group received the app at the 3-month follow-up. User engagement was analyzed among all participants. Categories of high (100%), moderate (50%-99.9%), and low (<50%) user engagement were created based on the percentage of activities completed in the app. We used paired t tests to compare mean changes in diet quality, dietary intake, and clinical markers within user engagement groups, and fitted linear regression models to analyze differences in change between groups. Results: Data from 119 participants (60.5%, 72/119 men) were analyzed. The mean age at baseline was 63.2 (SD 10.3) years and mean BMI was 30.1 (SD 5.1) kg/m2. User engagement was high with an average of 77.1% of app activities completed. More than half (53.8%, 64/119) of the users showed high user engagement, 21.8% (26/119) moderate, and 24.4% (29/119) low. Directly following the app-based education, a significant difference in change was seen for whole grains (β=20.4, 95%CI 0.57-40.3) in participants with high user engagement compared to the low user engagement group who decreased their intake (P=.03). At follow-up after 6 to 9 months after completed education, significant differences in change were seen for fiber, wholegrains, carbohydrates, saturated fat, sodium, and total energy in the moderate compared with the low engagement group, and a significant difference in change was seen for carbohydrates in the high, compared with the low, user engagement group. Conclusions: User engagement was generally high for the smartphone-based dietary education, suggesting that an app targeting dietary habits is feasible to use. Those with higher user engagement seem to maintain healthier dietary behaviours over time, compared to those with low user engagement. Future mHealth interventions should focus on ways to engage those with low interest.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".