Implementation of a Digital Health Tool for Patients Awaiting Input From a Specialist Weight Management Team: Observational Study
Bibliographic record
Abstract
BACKGROUND: Digital tools are increasingly used on a population level as a weight loss strategy for people living with overweight and obesity. Evidence supports the feasibility of digital tools for the management of obesity in a community setting, but there is only emerging evidence for the feasibility of such tools in specialist weight management services. No study has assessed the uptake of digital tools among patients awaiting their first appointment with a specialist weight management service. OBJECTIVE: The objective of this study was to understand interest, acceptance, and engagement with a digital behavioral change platform to support specialist weight management. METHODS: This was an observational study registered as a service innovation. All patients on the waiting list for a first appointment in the tier 3 weight management service at University Hospitals Coventry and Warwickshire National Health Service (NHS) Trust were eligible to access the NHS-approved digital tool. Data on interest and engagement with the digital tool were collected. Routine clinical data were used to describe patient demographics. Focus groups were held to explore patients' views on the use of digital tools as part of a specialist weight management service. RESULTS: A total of 199 patients on the waiting list were informed about the available digital tool. Just over a half (n=102, 51.3%) of patients were interested in using the app, with over one-third (n=68, 34%) of all patients engaging with the app. Overall, a third of patients on the waiting list (n=63, 32%) did not respond to the invite and 34 (17%) of patients expressed no interest in the app. Emotional eating and higher BMI was associated with interest in the Gro Health app. Male gender was associated with reduced engagement with the app. There were no differences in interest in the Gro Health app according to age, ethnicity, metabolic measures of glycemia, and lipid profile. CONCLUSIONS: It is feasible to offer digital tools such as Gro Health to patients awaiting their first appointment with specialist weight management services. Future research should explore barriers and facilitators of engagement with digital tools. Additionally, there is a need to further evaluate the effectiveness of such tools in specialist weight management services.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".