Cargando…
Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study
BACKGROUND: Digital just-in-time adaptive interventions can reduce binge-drinking events (BDEs; consuming ≥4 drinks for women and ≥5 drinks for men per occasion) in young adults but need to be optimized for timing and content. Delivering just-in-time support messages in the hours prior to BDEs could...
Autores principales: | Bae, Sang Won, Suffoletto, Brian, Zhang, Tongze, Chung, Tammy, Ozolcer, Melik, Islam, Mohammad Rahul, Dey, Anind K |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10196900/ https://www.ncbi.nlm.nih.gov/pubmed/36809294 http://dx.doi.org/10.2196/39862 |
Ejemplares similares
-
Mobile phone text message intervention to reduce binge drinking among young adults: study protocol for a randomized controlled trial
por: Suffoletto, Brian, et al.
Publicado: (2013) -
An Interactive Text Message Intervention to Reduce Binge Drinking in Young Adults: A Randomized Controlled Trial with 9-Month Outcomes
por: Suffoletto, Brian, et al.
Publicado: (2015) -
Mobile phone text-message–based drinking brief interventions for young adults discharged from the emergency department
por: Suffoletto, Brian
Publicado: (2012) -
Mobile Assessment of Acute Effects of Marijuana on Cognitive Functioning in Young Adults: Observational Study
por: Chung, Tammy, et al.
Publicado: (2020) -
Adolescent Binge Drinking: Developmental Context and Opportunities for Prevention
por: Chung, Tammy, et al.
Publicado: (2018)