Design and Development of a Digital Weight Management Intervention (ToDAy): Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The Tailored Diet and Activity (ToDAy) study aims to build on the campaign by adding a digital intervention with the potential to provide wide-reaching, cost-effective weight management support. OBJECTIVE: The ToDAy study aims to build a tailored intervention using mobile technology to improve diet and physical activity behaviours in adults with overweight and obesity. The main objectives were to identify behavior change techniques for diet and physical activity (PA) change for weight loss and explore preferences for digital intervention features that would be effective in changing diet and PA behaviors. METHODS: This qualitative study uses the principles of a person-based approach to intervention development; the behavioral intervention technology framework; and the capability, opportunity, motivation, and behavior (COM-B) framework. Focus groups and telephone interviews were conducted with 56 adults in Western Australia. Open-ended questions and example intervention features were used to explore the usability and acceptability of the self-monitoring tools, knowledge about effective weight-loss strategies, and acceptability of tailored feedback. Findings from the focus groups and interviews were analyzed using thematic analysis. RESULTS: Qualitative findings revealed an awareness of key public health messages but a lack of confidence in how to perform these behaviors to help manage their weight. A total of 4 major themes were identified and mapped to the domains of the COM-B framework: (1) misinformation, (2) environmental support, (3) social norms, and (4) confidence. CONCLUSIONS: This study explores users' capability, opportunity, and motivation to perform the target behaviors for weight loss. The findings suggested that a digital weight management intervention using a mobile food record and activity trackers to inform tailored feedback may be acceptable and feasible. Participants expressed a preference for simple expert advice, digital self-monitoring tools, and visual feedback. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/12782.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".