A Mobile Phone Intervention to Improve Obesity-Related Health Behaviors of Adolescents Across Europe: Iterative Co-Design and Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Promotion of physical activity, healthy eating, adequate sleep, and reduced sedentary behavior in adolescents is a major priority globally given the current increase in population health challenges of noncommunicable diseases and risk factors such as obesity. Adolescents are highly engaged with mobile technology, but the challenge is to engage them with mobile health (mHealth) technology. Recent innovations in mobile technology provide opportunities to promote a healthy lifestyle in adolescents. An increasingly utilized approach to facilitate increased engagement with mHealth technology is to involve potential users in the creation of the technology. OBJECTIVE: This study aimed to describe the process of and findings from co-designing and prototyping components of the PEGASO Fit for Future (F4F) mHealth intervention for adolescents from different cultural backgrounds. METHODS: A total of 74 adolescents aged 13 to 16 years from Spain, Italy, and the United Kingdom participated in the co-design of the PEGASO F4F technology. In 3 iterative cycles over 12 months, participants were involved in the co-design, refinement, and feasibility testing of a system consisting of diverse mobile apps with a variety of functions and facilities to encourage healthy weight-promoting behaviors. In the first iteration, participants attended a single workshop session and were presented with mock-ups or early-version prototypes of different apps for user requirements assessment and review. During the second iteration, prototypes of all apps were tested by participants for 1 week at home or school. In the third iteration, further developed prototypes were tested for 2 weeks. Participants' user experience feedback and development ideas were collected through focus groups and completion of questionnaires. RESULTS: For the PEGASO F4F technology to be motivating and engaging, participants suggested that it should (1) allow personalization of the interface, (2) have age-appropriate and easy-to-understand language (of icons, labels, instructions, and notifications), (3) provide easily accessible tutorials on how to use the app or navigate through a game, (4) present a clear purpose and end goal, (5) have an appealing and self-explanatory reward system, (6) offer variation in gamified activities within apps and the serious game, and (7) allow to seek peer support and connect with peers for competitive activities within the technology. CONCLUSIONS: Incorporating adolescents' preferences, the PEGASO F4F technology combines the functions of a self-monitoring, entertainment, advisory, and social support tool. This was the first study demonstrating that it is possible to develop a complex mobile phone-based technological system applying the principles of co-design to mHealth technology with adolescents across 3 countries. The findings from this study informed the development of an mHealth system for healthy weight promotion to be tested in a controlled multinational pilot trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".