Parenting Journal · Volume 2 · Issue 5 · Licensed source edition
Supporting Adolescent Engagement with Artificial Intelligence–Driven Digital Health Behavior Change Interventions
Originally published 2023-05-24 · Journal of Medical Internet Research
JMIR Publications Inc. · DOI 10.2196/40306 ↗
AI / system type
Mixed proposed predictive AI and content-generation roles; conceptual design
Child-facing evidenceExamines children or child-focused systems. Transfer to everyday family life must be assessed separately.
The AI connection
The paper proposes AI and machine-learning analysis and generation to support adolescent digital health interventions.
Why this reading matters
Offers a specific way to discuss respectful data use beyond simply accepting a health app’s engagement claims.
Editorial reading guide · Respect
What should happen to the traces my teenager leaves while using a health app?
Using behavioral traces to improve engagement creates questions about adolescent privacy and autonomy as well as technical possibilities. The framework foregrounds those risks while acknowledging that transfer to health behavior requires empirical validation.
Study context
The authors use the INSPIRE narrative-centered intervention and related literature to propose an AI measurement, modeling, optimization, and generation framework.
Age / stage: Adolescents, illustrated through alcohol-risk behavior intervention design
Read with three questions
- Which data might be used to infer engagement?
- Could optimizing engagement conflict with a young person’s own goals?
- Which proposed AI roles still lack direct adolescent health evidence?
Keep the limits in view
This is a framework, not a trial of the full AI system. Many proposed roles are extrapolated from other domains, and effective, fair transfer to adolescent health behavior or outcomes remains unproven.
The text below is the original article’s licensed repository edition. Its wording is not rewritten. Use the Paper Agent beside the text to locate evidence; open the source for figures, tables and complete context.
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Original authorship, publication date, journal and DOI are preserved. This issue’s TRICK placement and reading questions are editorial additions, not claims made by the original authors.
Alison Giovanelli; Jonathan Rowe; Madelynn Taylor; Mark Berna; Kathleen P Tebb; Carlos Penilla; Marianne Pugatch; James Lester; Elizabeth M Ozer; Tiffany Leung; Nicole Levitz; Yaltafit Jeem; Nicos Maglaveras (2023). Supporting Adolescent Engagement with Artificial Intelligence–Driven Digital Health Behavior Change Interventions Journal of Medical Internet Research. https://doi.org/10.2196/40306
Permissions checked against the saved article notice; repository publication notices screened 2026-11-04. This is not a new peer-review decision. The source authors and publishers are not represented as endorsing this issue.