Exploring the Use of Digital Technology to Support Health Behavior Change in Young People Under the Care of Complications of Excess Weight (CCEW) Clinics: Qualitative Patient-Centered Design Study
Bibliographic record
Abstract
BACKGROUND: Specialist multidisciplinary clinics have been established to provide care for the burgeoning number of young people presenting with comorbidities related to severe obesity in childhood. Digital technology, an integral component of most young people's lives, may enable clinics to offer accessible, ongoing support between appointments to the patients, thereby increasing the likelihood of successful health behavior change. However, while short-term engagement with technology-based behavior change interventions is good, engagement tends to decrease over time, limiting their overall impact. Little is known about the views of young people living with obesity on the role of digital technology as an adjunct to current traditional care pathways. OBJECTIVE: This study aims to explore the views of adolescent patients and their families on whether digital technology should be used by obesity services to support health behavior change. METHODS: Participants included patients aged between 10 and 16 years from an obesity clinic, along with their adult family members. Four focus groups and co-design workshops, facilitated by a cross-disciplinary team of clinicians, academics, and technology innovators, explored young people's health priorities, identified the barriers to and facilitators of health behavior change, and co-designed ways in which technology could be used to support them in overcoming these barriers to achieving their health goals. Data were analyzed using inductive content analysis, with findings integrated with key co-design workshop outputs. RESULTS: . The mean socioeconomic decile was 4.3 (SD 2; range 1-8). Participants did not mention weight as an important aspect of their health. Instead, mental health, sleep, and peer support were identified as the domains where patients felt they would most benefit from additional support. Addressing these aspects of health was viewed as foundational to all other aspects of health, with poor mental health, sleep, and social support reducing young people's ability to engage in the process of health behavior change. Participants reported that technology could help provide this support as an adjunct to in-person support. Participants expressed a preference for technologies able to individually tailor content to the young person's needs, including relatable peer-produced content. The need for support for both the young people and their family members was highlighted, along with the need to integrate in-person strategies to maintain engagement with any technological offering. CONCLUSIONS: There is clear potential for digital technology to support the holistic health priorities of young people receiving specialist care for the comorbidities of excess weight. This study's findings will serve as a foundation for developing innovative approaches to the use of technology to support this high-need population.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".