@article{BAE2021108972,
title = {Mobile phone sensor-based detection of subjective cannabis intoxication in young adults: A feasibility study in real-world settings},
author = {Sang Won Bae and Tammy Chung and Rahul Islam and Brian Suffoletto and Jiameng Du and Serim Jang and Yuuki Nishiyama and Raghu Mulukutla and Anind Dey},
url = {https://www.sciencedirect.com/science/article/pii/S0376871621004671
https://doi.org/10.1016/j.drugalcdep.2021.108972},
doi = {https://doi.org/10.1016/j.drugalcdep.2021.108972},
issn = {0376-8716},
year = {2021},
date = {2021-01-01},
journal = {Drug and Alcohol Dependence},
pages = {108972},
abstract = {Background
Given possible impairment in psychomotor functioning related to acute cannabis intoxication, we explored whether smartphone-based sensors (e.g., accelerometer) can detect self-reported episodes of acute cannabis intoxication (subjective “high” state) in the natural environment.
Methods Young adults (ages 18–25) in Pittsburgh, PA, who reported cannabis use at least twice per week, completed up to 30 days of daily data collection: phone surveys (3 times/day), self-initiated reports of cannabis use (start/stop time, subjective cannabis intoxication rating: 0–10, 10 = very high), and continuous phone sensor data. We tested multiple models with Light Gradient Boosting Machine (LGBM) in distinguishing “not intoxicated” (rating = 0) vs subjective cannabis “low-intoxication” (rating = 1–3) vs “moderate-intensive intoxication” (rating = 4–10). We tested the importance of time features (i.e., day of the week, time of day) relative to smartphone sensor data only on model performance, since time features alone might predict “routines” in cannabis intoxication.
Results Young adults (N = 57; 58 % female) reported 451 cannabis use episodes, mean subjective intoxication rating = 3.77 (SD = 2.64). LGBM, the best performing classifier, had 60 % accuracy using time features to detect subjective “high” (Area Under the Curve [AUC] = 0.82). Combining smartphone sensor data with time features improved model performance: 90 % accuracy (AUC = 0.98). Important smartphone features to detect subjective cannabis intoxication included travel (GPS) and movement (accelerometer).
Conclusions
This proof-of-concept study indicates the feasibility of using phone sensors to detect subjective cannabis intoxication in the natural environment, with potential implications for triggering just-in-time interventions.},
keywords = {Acute intoxication, Cannabis smoking, Light gradient boosting machine model, Mobile phone sensors},
pubstate = {published},
tppubtype = {article}
}