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.

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Institutions

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
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article

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

Anind K. Dey, Rahul Islam, Tongze Zhang, Tammy Chung et al.
6 citations
JMIR AI
Cannabis and Cannabinoid Research
2.69
article

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

Anind K. Dey, Rahul Islam, Tongze Zhang, Tammy Chung, Sang Won Bae
article en
6 citations

Abstract

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.

JMIR AIVol. 4
Rutgers, The State University of New Jersey (US), University of Washington (US), Human Computer Interaction (Switzerland) (CH)
Zero hunger
Openalex Percentile: Top 10%
Cannabis and Cannabinoid Research
2.69
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