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Supporting Adolescent Engagement with Artificial Intelligence-Driven Digital Health Behavior Change Interventions.
Giovanelli, Alison; Rowe, Jonathan; Taylor, Madelynn; Berna, Mark; Tebb, Kathleen P; Penilla, Carlos; Pugatch, Marianne; Lester, James; Ozer, Elizabeth M.
  • Giovanelli A; Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States.
  • Rowe J; Department of Computer Science, North Carolina State University, Raleigh, CA, United States.
  • Taylor M; Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States.
  • Berna M; Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States.
  • Tebb KP; Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States.
  • Penilla C; Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States.
  • Pugatch M; Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States.
  • Lester J; Department of Computer Science, North Carolina State University, Raleigh, CA, United States.
  • Ozer EM; Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States.
J Med Internet Res ; 25: e40306, 2023 05 24.
Article en En | MEDLINE | ID: mdl-37223987
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.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Conducta del Adolescente Tipo de estudio: Etiology_studies Límite: Adolescent / Humans Idioma: En Año: 2023 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Conducta del Adolescente Tipo de estudio: Etiology_studies Límite: Adolescent / Humans Idioma: En Año: 2023 Tipo del documento: Article