Enhancing Interpretable, Transparent, and Unobtrusive Detection of Acute Marijuana Intoxication in Natural Environments: Harnessing Smart Devices and Explainable AI to Empower Just-In-Time Adaptive Interventions: Longitudinal Observational Study
This study demonstrates the potential of using smartphone sensors and wearable devices for interpretable, transparent, and unobtrusive monitoring of acute marijuana intoxication in daily life. Advanced algorithmic decision-making provides valuable insight into behavioral, physiological, and environmental factors that could support timely interventions to reduce marijuana-related harm. Future real-world applications of these algorithms should be evaluated in collaboration with clinical experts to enhance their practicality and effectiveness.
Authors
- Anind K. Dey (ORCID: https://orcid.org/0000-0002-3004-0770)
- Rahul Islam (ORCID: https://orcid.org/0000-0003-3601-0078)
- Tongze Zhang (ORCID: https://orcid.org/0000-0002-3375-7136)
- Tammy Chung (ORCID: https://orcid.org/0000-0002-1527-2792)
- Sang Won Bae (ORCID: https://orcid.org/0000-0002-2047-1358)
Institutions
- Rutgers, The State University of New Jersey (US)
- University of Washington (US)
- Human Computer Interaction (Switzerland) (CH)
Publication Details
- Journal
- JMIR AI
- Published
- 2024-09-02
- DOI
- https://doi.org/10.2196/52270
- Citations
- 6
- Primary Topic
- Cannabis and Cannabinoid Research
- Type
- article
- Field-Weighted Citation Impact
- 2.69