Ambulatory assessment (AA) techniques (e.g., ecological momentary assessment, daily diaries, experiencing sampling) have provided critical tests of theories about the development of alcohol use disorder (AUD) by identifying within-person processes (such as negative or reinforcement, stress exposure, or social context) that can raise the risk for problem drinking and in turn AUD. AA methods are the leading methodological approach in the push towards personalized medicine because it provides a compelling platform for assessment, diagnosis, real-time monitoring, and just-in-time interventions. However, the current utility of AA for personalized models of AUD risk is limited because risky drinking and the risk factors for it (such as changes in moods, stress, or social contexts) change at different scales of time. In other words, even heavy drinkers may only drink a few times a week, but their emotions, stressors and social contexts change multiple times a day. Current AA methods that rely on self-report data have to sample frequently enough to be sensitive to change, long enough to observe sufficient drinking episodes, and to do so while avoiding participant burnout. Passive mobile sensing, which uses sensors (such as GPS, accelerometer, light meter, etc.) available on most smartphones, has been shown in preliminary studies to predict the probability of drinking episodes, but those studies have used relatively small samples. The present career development award aims to develop the candidate?s expertise in passive mobile sensing and the machine learning methods used to analyze passive mobile sensing data. The research proposal will analyze passive mobile sensing data collected in a large sample of regular drinking and marijuana using young adults (age 18 ? 22, n = 500; 95.2% who drink), who will be followed using AA over 8 successive weekends as part of a parent R01 (DA 047247). The research goal is to identify passive mobile sensing models of risk factors for drinking (stress, social contexts, sleep, mood, and impulsive states), as well as the drinking episodes themselves. The candidate will develop expertise in these methods and models that will further the development of a research program aimed at developing person specific models of risk for AUD.
The career development plan will develop expertise of the investigator in the collection and analysis of passive mobile sensing data, with an emphasis on the application of machine learning methods. The proposed research study will use passive mobile sensing data in a large ambulatory assessment sample of regular drinkers, to develop models linking passive mobile sensing data with predictors of drinking episodes (such as stress, social context, and impulsiveness) and the drinking episodes themselves.