Towards Automated, Interpretable and Unobtrusive Detection of Acute Marijuana Intoxication in the Natural Environment: Harnessing Smartphones, Wearables, Machine Learning and Explainable AI to Empower Clinical Decision Support for Just-In-Time Adaptive Interventions (Preprint)

<sec> <title>BACKGROUND</title> Acute marijuana intoxication can impair motor skills and cognitive functions (e.g., attention, information processing). However, existing tools (e.g., blood, urine, saliva tests) do not accurately reflect ‘real-time’ acute marijuana intoxication. </sec> <sec> <title>OBJECTIVE</title> Considering the absence of screening tools to detect acute marijuana intoxication and impairment-related harms, our objective is to examine whether integration of smartphone-based sensors with a wearable activity tracker (Fitbit), as more accessible devices using passive sensing, can enhance detection of episodes of acute marijuana intoxication in real-world settings. No prior work has determined the potential of utilizing data from both phone sensors and a wearable device to improve the accuracy of algorithms in detecting acute marijuana intoxication in real-life scenarios (‘outside of lab settings’), nor focused on developing explainable AI (XAI) to provide insights into the algorithmic decision-making process, specifically in detecting episodes of moderate-intensive marijuana intoxication, leveraging passive sensing technologies captured in real-world contexts. </sec> <sec> <title>METHODS</title> To address these aims, we collected daily data using the Experience Sampling Method (ESM) for up to 30 days from 33 young adults using personal smartphone sensors and a Fitbit, and self-reported marijuana use. Participants provided subjective ratings of marijuana intoxication within 15 min of starting to use marijuana and during semi-random prompts 3 times per day: “low-intoxication” (rating = 1–3) vs “moderate-intensive intoxication” (rating = 4–10) vs. “not-intoxicated” (rating = 0). </sec> <sec> <title>RESULTS</title> Using the EXtreme Gradient Boosting Machine classifier (XGBoost) to model this data, our results indicated that the best model (MobiFit-model), which combined data from off-the-shelf mobile phone and wearable technologies, achieved accuracy of 99% (AUC=0.99, F1-score =0.85) in detecting acute marijuana intoxication (i.e., subjective sense of intoxication) in the natural environment. F1-score, which balances sensitivity and specificity, showed a significant improvement of 13% and 11% for the combined model (MobiFit) compared to using Mobile and Fitbit individually, respectively. Explainable AI (XAI) presented algorithmic decisions which revealed that self-reported moderate-intensive marijuana intoxication was associated with smartphone sensors and Fitbit features, specifically: elevated minimum heart rate, increased micro-movements, but reduced macro-movement (i.e., a smaller radius of gyration via GPS), and increased noise energy level around the participants. </sec> <sec> <title>CONCLUSIONS</title> This study demonstrates the promise that mobile phone sensors and off-the-shelf wearable devices hold for automated and continuous detection of acute marijuana intoxication in daily life. Advanced algorithmic decision-making processes could provide insight into behavioral, physiological and environmental features’ contributions that may be most useful, for example, in triggering the delivery of just-in-time interventions to prevent marijuana-related harm; however, in order to make the algorithm applicable in real-world settings, the usefulness and effectiveness of such algorithms-driven decisions need to undergo robust evaluation in collaboration with clinical experts. </sec>