Identifying Personality-Related Behavioral Phenotypes for Binge Drinking Using Smartphone Sensors and Machine Learning

项目总结/摘要 年轻人酗酒是一个严重的公共卫生问题。增加的主要障碍是 酗酒干预措施的有效性是人与人之间酒精预测因子的异质性 使用/误用。治疗可以通过将人们与基于人格特征的干预措施相匹配来改善， 增加酗酒的风险，但更好地了解将特征与饮酒联系起来的日常行为 这些干预措施要想有效，就需要有一个小插曲。酒精使用/滥用的理论规定了多种 人格特质影响问题饮酒的行为途径，包括 广泛参与高风险行为，自我选择进入高风险社交饮酒环境，并调节情绪 用酒精这种情境化的行为模式是关键的风险因素， 个性化治疗。这项拟议中的研究将使用机器学习方法来识别自然主义， 从智能手机传感器数据预测狂饮的个性相关行为表型（例如，GPS， 文本/呼叫活动）。该项目的数据将来自一项正在进行的NIAAA资助的年轻人研究 经常暴饮暴食（预计N = 300）。每日饮酒和持续、不引人注目的跟踪 智能手机传感器数据收集自母研究的120天动态评估的参与者 议定书为了实现制定更有针对性的干预措施的长期目标，这项研究有3个 具体目标：（1）澄清谁有酗酒的风险，并解决记忆偏差的问题， 影响先前依赖于酒精使用回顾性报告的研究， 在日常水平上评估人格特质和饮酒，（2）揭示被动感知的行为/情境 与机器学习方法预测酗酒的人格特征相关的风险因素，（3） 量化人格特质和酗酒之间的关系有多少是被动解释的 感知行为表型。拟议的研究和培训活动将在 匹兹堡大学。该奖学金将为申请人提供必要的专门培训， 成为有影响力的独立临床科学家。培训将围绕三个目标：（1）增进知识 酒精使用病因学/维持机制与定期导师会议，指导阅读，研讨会， 和杂志俱乐部，（2）获得专业知识，应用动态评估跟踪酒精使用， 与父研究数据收集，参加实验室会议，并指导应用实践，和（3）学习 机器学习技术，用于分析被动传感数据，并指导方法的应用， 相关课程、讲习班和研讨会。拟议研究的结果将提高精度 通过识别可以为干预措施的发展提供信息的行为标志物， 根据一个人的独特特征（NIAAA战略计划目标4A）。

PROJECT SUMMARY/ABSTRACT Binge drinking in young adults is a significant public health problem. A major barrier to increasing the efficacy of binge drinking interventions is the heterogeneity between people in predictors of alcohol use/misuse. Treatment can be improved by matching people to interventions based on personality traits that increase risk for binge drinking, but a better understanding of the everyday behaviors linking traits to drinking episodes is needed for such interventions to be effective. Theories of alcohol use/misuse specify multiple behavioral pathways through which personality traits influence problematic drinking, including tendencies to engage broadly in high-risk behavior, self-select into high-risk social drinking contexts, and regulate emotions with alcohol. Such contextualized behavior patterns are key risk factors that can be modified with more personalized treatment. The proposed study will use machine learning methods to identify naturalistic, personality-related behavioral phenotypes that predict binge drinking from smartphone sensor data (e.g., GPS, text/call activity). Data for this project will be drawn from an ongoing NIAAA-funded study of young adults that regularly binge drink (anticipated N = 300). Daily alcohol use and continuous, unobtrusive tracking of smartphone sensor data are collected from participants in the parent study’s 120-day ambulatory assessment protocol. Towards the long-term objective of developing more targeted interventions, this study has 3 specific aims: (1) clarify who is at risk for binge drinking and addressing the problem of recall bias that affects prior research reliant on retrospective reports of alcohol use by establishing associations between personality traits and drinking assessed at the daily level, (2) uncover passively sensed behavioral/contextual risk factors related to personality traits that predict binge drinking with machine learning methods, (3) quantify how much of the relationship between personality traits and binge drinking is explained by passively sensed behavioral phenotypes. The proposed research and training activities will be conducted at the University of Pittsburgh. This fellowship will provide specialized training necessary for the applicant to become an impactful independent clinical scientist. Training will focus on three goals: (1) enhance knowledge of alcohol use etiology/maintenance mechanisms with regular mentor meetings, guided readings, seminars, and journal clubs, (2) gain expertise in applying ambulatory assessment for tracking alcohol use by assisting with the parent study data collection, attending lab meetings, and guided applied practice, and (3) learn machine learning techniques for analyzing passive sensing data with mentored application of methods, relevant courses, workshops, and seminars. Results of the proposed study will advance precision medicine by identifying behavioral markers that can inform development of interventions based on a person’s unique characteristics (NIAAA Strategic Plan Objective 4A).