Supporting Adolescent Engagement with Artificial Intelligence-Driven Digital Health Behavior Change Interventions

Alison Giovanelli*, Jonathan Rowe, Madelynn Taylor, Mark Berna, Kathleen P. Tebb, Carlos Penilla, Marianne Pugatch, James Lester, Elizabeth M. Ozer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Understanding and optimizing adolescent-specific engagement with behavior change interventions will open doors for providers to promote healthy changes in an age group that is simultaneously difficult to engage and especially important to affect. For digital interventions, there is untapped potential in combining the vastness of process-level data with the analytical power of artificial intelligence (AI) to understand not only how adolescents engage but also how to improve upon interventions with the goal of increasing engagement and, ultimately, efficacy. Rooted in the example of the INSPIRE narrative-centered digital health behavior change intervention (DHBCI) for adolescent risky behaviors around alcohol use, we propose a framework for harnessing AI to accomplish 4 goals that are pertinent to health care providers and software developers alike: measurement of adolescent engagement, modeling of adolescent engagement, optimization of current interventions, and generation of novel interventions. Operationalization of this framework with youths must be situated in the ethical use of this technology, and we have outlined the potential pitfalls of AI with particular attention to privacy concerns for adolescents. Given how recently AI advances have opened up these possibilities in this field, the opportunities for further investigation are plenty.

Original languageEnglish (US)
Article numbere40306
JournalJournal of medical Internet research
Volume25
DOIs
StatePublished - 2023

Keywords

  • adolescence
  • adolescent
  • AI ethics
  • artificial intelligence
  • BCT
  • behavior change
  • behavioral intervention
  • digital health behavior change
  • engagement
  • ethical
  • ethics
  • machine learning
  • model
  • operationalization
  • optimization
  • privacy
  • risky behavior
  • security
  • trace log data
  • youth

ASJC Scopus subject areas

  • Health Informatics

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