What to Consider When Developing Multidomain Mobile Health Interventions for Lifestyle Management
Bibliographic record
Abstract
Unlabelled: Mobile health (mHealth) interventions can transform health care delivery and improve public health. At the same time, the evidence on lifestyle interventions continues to grow. They show promising results in preventing and treating noncommunicable diseases and enhancing health-related quality of life. These factors highlight the potential of multidomain mHealth interventions for lifestyle management. This viewpoint paper focuses on drawing valuable lessons from past experiences and providing guidance to developers of mHealth interventions for lifestyle management. We underscore the critical role of sharing practical insights to advance innovation in the field of mHealth interventions. We used an iterative consensus process to derive lessons learned, identify challenges, and reflect on possible actions. Our insights are based on our experience in developing 2 smartphone-based lifestyle interventions. Challenges and corresponding options in the following areas are presented: target population (preferences, personalization, and delivery), user involvement and testing, human support, and multidomain interventions (interdisciplinarity, flexibility, and core team). The development of multidomain mHealth interventions for lifestyle management requires a participatory and iterative approach involving relevant stakeholders (including end users) so that the right people get the right content at the right time. Additionally, it is crucial to consider established frameworks, guidelines, and regulations; allocate appropriate resources; and form a core team committed to the project's aims and open to working in an interdisciplinary team.
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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 | 0.063 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".