Towards Participatory Precision Health With Co-designed Recommendations for Just-in-Time Adaptive Interventions in Adolescents and Young Adults: A Systematic Review
Bibliographic record
Abstract
ABSTRACT Background The transition from adolescence to young adulthood (10-25 years) constitutes a sensitive developmental period marked by rapid biological, psychological, and social change, during which preventive health interventions can shape long-term outcomes. Mobile health (mHealth) tools offer accessible opportunities for tailored support for this population, but often adapt poorly to dynamic contexts, resulting in inconsistent engagement and effects. Just-in-time adaptive interventions (JITAIs), which tailor support in real time by leveraging ongoing data, are increasingly explored as precision health strategies. However, how these mechanisms are designed, implemented, and evaluated for adolescents and young adults (AYAs) has not yet been systematically reviewed. Objectives This review aimed to synthesize the evidence on JITAIs developed for AYAs, examine how their adaptive mechanisms have been designed to support specific health goals and changing AYA contexts, and assess methodological quality of reporting to inform future precision health intervention development. Methods We conducted a systematic review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Synthesis Without Meta-analysis (SWiM) reporting guidelines. Twelve databases were searched for peer-reviewed studies published from 2013 to 2025. Eligible studies were peer-reviewed, focused on participants aged 10 to 25 years, and reported real-time adaptive mobile health interventions consistent with JITAI design principles. Two reviewers independently conducted screening, data extraction, and methodological quality appraisal using Joanna Briggs Institute checklists. AYA co-authors contributed to all phases of the review. Due to substantial heterogeneity in study populations, intervention content, adaptive mechanisms, comparators, and outcome measurements, findings were synthesized narratively and no meta-analysis was conducted. Results 61 unique interventions were included. JITAIs for AYAs addressed substance use (N=24, 39.3%), mental health (N=23, 37.7%), and physical health or chronic conditions (N=14, 23%). JITAI tailoring mechanisms relied predominantly on self-reported behavioral data. Decision rules were typically symptom threshold-based and decision points were commonly daily or event-triggered. Methodological concerns with reporting on intervention administration, participant selection, and outcome measurement reliability were pervasive across all studies, limiting interpretability of observed effects and cross-study comparisons. Ethical considerations, including researcher positioning and reflexivity, alongside the depth of reporting around participatory AYA engagement in design and implementation, were also inconsistent. Conclusion This review contributes a novel perspective to AYA digital health by moving beyond intervention outcomes to systematically examine how core adaptive mechanisms are operationalized for AYAs across multiple health domains, while also directly integrating AYA perspectives into the interpretation of findings and recommendations for future work. In contrast to prior reviews focused primarily on adults or specific conditions, it identifies broader contextual, methodological, and ethical considerations relevant to AYA precision health. Taken together, our findings highlight the critical need for more transparent, contextually responsive, and youth-centered adaptive interventions, alongside more rigorous designs for evaluating adaptive intervention components in daily life contexts. PROSPERO registration: CRD42023473117
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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 | 0.280 | 0.476 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.021 | 0.023 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".