TY - JOUR
T1 - Mobile phone sensor-based detection of subjective cannabis intoxication in young adults
T2 - A feasibility study in real-world settings
AU - Bae, Sang Won
AU - Chung, Tammy
AU - Islam, Rahul
AU - Suffoletto, Brian
AU - Du, Jiameng
AU - Jang, Serim
AU - Nishiyama, Yuuki
AU - Mulukutla, Raghu
AU - Dey, Anind
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/11/1
Y1 - 2021/11/1
N2 - 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.
AB - 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.
KW - Acute intoxication
KW - Cannabis smoking
KW - Light gradient boosting machine model
KW - Mobile phone sensors
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U2 - 10.1016/j.drugalcdep.2021.108972
DO - 10.1016/j.drugalcdep.2021.108972
M3 - Article
C2 - 34530315
AN - SCOPUS:85114823647
SN - 0376-8716
VL - 228
JO - Drug and Alcohol Dependence
JF - Drug and Alcohol Dependence
M1 - 108972
ER -