Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study (Preprint)

Abstract

BACKGROUND Digital behavioral interventions can reduce binge drinking events (BDEs: consuming 4+/5+ drinks per occasion for women/men) in young adults, however, they may not be optimized for timing or content. Delivering support in the hours prior to a predicted drinking event could improve the impact of that support. OBJECTIVE In this paper, our goal is to explore the feasibility of predicting future, that is, same-day, BDEs using smartphone sensor data passively collected prior to the onset of drinking occasions. We also aim to identify the sensor features and associated behavior patterns related to drinking event planning that contribute most to predicting BDEs on weekend and weekdays, respectively. METHODS We collected usable phone sensor data from 75 young adults (ages 21-25; mean =22.4 (SD=1.9) drinkers who self-reported drinking behavior for up to 14 weeks. We developed a machine learning model to predict BDEs (versus non-drinking events and low-risk drinking events (1-3 or 1-4 drinks per occasion for females and males, respectively) using smartphone sensor data (e.g., accelerometer, location). We tested various "prediction distance" time windows (more proximal: 1-hour; to distant: 6-hour) from the onset of drinking. We also tested various analysis time windows (i.e., amount of data to be analyzed), ranging from 1 to 12 hours prior to the onset of drinking, because this determines the amount of data that needs to be stored on the phone to compute the model. RESULTS The best performing machine learning model using 3-, and 6-hours of phone sensor data at a 1-hour distance prior to the event predicted same-day BDEs with an accuracy of 91.4% on weekends and 91.3% for weekdays. Some of the most important phone sensor-based behavioral markers contributing to model accuracy were latitude and longitude of GPS locations, power of movement (i.e., the absolute value of the change in velocity of body movement), radius of gyration (an indicator of travel), battery charge level, and smartphone interaction. CONCLUSIONS We demonstrated the potential use of smartphone sensor data and machine learning to accurately predict future binge drinking events, identifying “windows of opportunity” to trigger Just-In Time interventions prior to the onset of BDEs, with the promise of reducing the likelihood of BDEs in young adults.