An App-Based Intervention for Pediatric Weight Management: Pre-Post Acceptability and Feasibility Trial
Bibliographic record
Abstract
BACKGROUND: A multidisciplinary approach to weight management is offered at tier 3 pediatric weight management services in the United Kingdom. Encouraging dietary change is a major aim, with patients meeting with dieticians, endocrinologists, psychologists, nurse specialists, and social workers on average every other month. OBJECTIVE: This research sought to trial an inhibitory control training smartphone app-FoodT-with the clinic population of a pediatric weight management service. FoodT has shown positive impacts on food choice in adult users, with resulting weight loss. It was hoped that when delivered as an adjunctive treatment alongside the extensive social, medical, psychological, and dietetic interventions already offered at the clinic, the introduction of inhibitory control training may offer patients another tool that supports eating choice. In this feasibility trial, recruitment, retention, and app use were the primary outcomes. An extensive battery of measures was included to test the feasibility and acceptability of these measures for future powered trials. METHODS: FoodT was offered to pediatric patients and their parents during a routine clinic appointment, and patients were asked to use the app at home every day for the first week and once per week for the rest of the month. Feasibility and acceptability were measured in terms of recruitment, engagement with the app, and retention to the trial. A battery of psychometric tests was given before and after app use to assess the acceptability of collecting data on changes to food choices and experiences that would inform future trial work. RESULTS: A total of 12 children and 10 parents consented (22/62, 35% of those approached). Further, 1 child and no parents achieved the recommended training schedule. No participants completed the posttrial measures. The reasons for not wanting to be recruited to the trial included participants not considering their weight to be connected to eating choices and not feeling that the app suited their needs. No reasons are known for noncompletion. CONCLUSIONS: It is unclear whether the intervention itself or the research processes, including the battery of measures, prevented completion. It is therefore difficult to make any decisions as to the value that the app has within this setting. Important lessons have been learned from this research that have potential broad relevance, including the importance of co-designing interventions with service users and avoiding deterring people from early-stage participation in extensive data collection.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".