Passive mobile sensing and machine learning for the detection of drinking episodes

项目摘要 动态评估（AA）技术（例如，生态瞬时评估，每日日记， 经验取样）提供了关于酒精使用障碍发展的理论的关键测试 (AUD)通过识别人的内部过程（如消极或强化，压力暴露或社会 这可能会增加问题饮酒的风险，进而增加澳元。AA方法是领先的 方法论的方法在推动个性化医疗，因为它提供了一个引人注目的平台 用于评估、诊断、实时监测和及时干预。然而，目前的效用 个性化澳元风险模型的AA是有限的，因为风险饮酒及其风险因素（如 情绪、压力或社会环境的变化）在不同的时间尺度上变化。换句话说，即使是重 饮酒者可能一周只喝几次酒，但他们的情绪、压力源和社会环境会发生多重变化。 一天三次。目前依靠自我报告数据的AA方法必须足够频繁地采样以保持灵敏度 改变，足够长的时间来观察足够的饮酒事件，并且在这样做的同时避免参与 倦怠被动移动的传感，它使用传感器（如GPS、加速度计、测光表等）可用 在大多数智能手机上，初步研究表明，它可以预测饮酒事件的概率， 但这些研究使用的样本相对较小。职业发展奖旨在发展 候选人在被动移动的传感和用于分析的机器学习方法方面的专业知识 被动的移动的感测数据。该研究计划将分析被动移动的传感数据收集在一个 大量经常饮酒和吸食大麻的年轻人（18 - 22岁，n = 500; 95.2%饮酒）， 作为父母R 01（DA 047247）的一部分，将使用AA在连续8个周末进行随访。研究 目标是识别饮酒风险因素（压力，社会环境，睡眠， 情绪和冲动状态），以及饮酒事件本身。候选人将发展 这些方法和模型的专业知识，将进一步发展一个研究计划，旨在 开发针对个人的AUD风险模型。

PROJECT SUMMARY 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.