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

使用智能手机传感器和机器学习识别酗酒的人格相关行为表型

• 批准号: 10537056
• 负责人: Whitney Ringwald
• 金额: $ 4.68万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10537056
• 关键词: Accelerometer; Address; Affect; African American; Age; Alcohol consumption; Alcohols; Behavior; Behavioral; Black race; Bluetooth; Cellular Phone; Characteristics; Clinical; Consumption; Dangerousness; Data; Data Collection; Detection; Devices; Educational workshop; Emotions; Etiology; Event; Fellowship; Female; Frequencies; Funding; Goals; Heterogeneity; Impulsivity; Individual; Individual Differences; Intervention; Intervention Studies; Journals; Knowledge; Length; Link; Machine Learning; Maintenance; Maps; Mediator of activation protein; Mentors; Methods; Modeling; Movement; National Institute on Alcohol Abuse and Alcoholism; Neurotic Disorders; Outcome; Parents; Participant; Pathway interactions; Patient Self-Report; Pattern; Personality; Personality Traits; Persons; Predictive Factor; Process; Protocols documentation; Public Health; Reading; Reporting; Research; Research Activity; Resolution; Risk; Risk Factors; Safety; Sampling; Scientist; Sexually Transmitted Diseases; Specific qualifier value; Strategic Planning; Stream; Techniques; Text; Time; Training; Training Activity; Universities; Violence; Woman; addiction; aged; alcohol misuse; alcohol risk; base; behavioral phenotyping; binge drinking; contextual factors; drinking; experience; frontier; high risk; high risk behavior; high risk drinking; improved; machine learning method; meetings; men; personalized medicine; precision medicine; sensor; social; theories; therapy development; trait; vehicular accident; young adult

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).

项目概要/摘要 年轻人酗酒是一个严重的公共卫生问题，是增加酗酒的一个主要障碍。 酗酒干预措施的有效性是酒精预测因素中人与人之间的异质性 使用/误用可以通过根据人格特征匹配人们的干预措施来改善。 增加酗酒的风险，但更好地了解与饮酒相关的日常行为 此类干预措施需要多次发作才能有效。 人格特质影响饮酒问题的行为途径，包括倾向 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 这种情境化的行为模式是关键的风险因素，可以通过更多措施来改变。 拟议的研究将使用机器学习方法来识别自然、 通过智能手机传感器数据（例如 GPS、 该项目的数据将来自 NIAAA 资助的一项针对年轻人的持续研究。 经常酗酒（预计 N = 300）。 智能手机传感器数据是从家长研究 120 天动态评估的参与者那里收集的 为了制定更有针对性的干预措施的长期目标，本研究有 3 个方案。 具体目标：（1）明确谁有酗酒的风险，并解决记忆偏差问题 通过在酒精使用之间建立关联，影响依赖于酒精使用回顾性报告的先前研究 每天评估人格特质和饮酒情况，(2) 揭示被动感知的行为/情境 与通过机器学习方法预测酗酒的人格特征相关的风险因素，(3) 量化人格特质与酗酒之间的关系在多大程度上是由被动解释的 所提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。 成为一名有影响力的独立临床科学家 培训将侧重于三个目标：（1）增强知识。 通过定期导师会议、指导阅读、研讨会等方式了解酒精使用病因/维持机制， 和期刊俱乐部，(2) 通过协助获得应用动态评估来跟踪酒精使用情况的专业知识 与家长一起研究数据收集、参加实验室会议和指导应用实践，以及 (3) 学习 通过指导应用方法来分析被动传感数据的机器学习技术， 相关课程、讲习班和研讨会的拟议研究结果将提高精确度。 通过识别可以为干预措施的制定提供信息的行为标记来进行医学 基于个人的独特特征（NIAAA 战略计划目标 4A）。