User-Centered Design of a Mobile App for Weight and Health Management in Adolescents With Complex Health Needs: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Growing research has been conducted into the deployment and evaluation of mobile technology interventions for weight management in adolescents. However, no work has yet been conducted toward the development of these technologies for adolescents with complex health needs receiving specialized tertiary-level health care. OBJECTIVE: The aim of this study was to conduct a user-centered needs assessment of adolescents interested in weight management with complex health needs requiring specialized health care services, their parents, and health care providers (HCPs) to inform the design and development of a mobile app for weight and health management. METHODS: A qualitative study design was employed. Participants were recruited from two tertiary health care centers. Separate audiotaped focus group interviews were conducted with adolescents aged 12 to 18 years, parents, and HCPs. Interviews were transcribed, and field notes were collected by research staff. Iterative simple content analysis was performed independently by 4 research team members using computer software NVivo (QSR International) 10.0. RESULTS: A total of 19 adolescents, 16 parents, and 21 HCPs were interviewed. Qualitative analysis revealed seven major themes related to app functionality: healthy eating, social support, self-monitoring, communicating with HCPs, supporting mental health, gamification and incentives, and user interface (UI) design. Adolescents provided several ideas related to each feature, whereas parents' views focused on assistance with meal planning and greater access to HCPs. HCPs viewed the app as a novel and more acceptable platform to connect remotely with adolescents than conventional methods. They also strongly endorsed the value of social support capabilities and the ability to connect with an HCP. CONCLUSIONS: This is the first study to conduct a qualitative needs assessment in adolescents receiving specialized health care services toward the design of a mobile app for weight and health management. Our results indicate that core components of the app should include tailored meal recommendations and assistance with meal planning, social networking for peer support, customized and convenient tracking, remote access to HCPs, features to support mental health, and an attractive and engaging UI. These findings will be used to develop and evaluate a mobile app targeting adolescents with complex health needs.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".