Using Smartphone Assessments for Personalized Prediction of Problematic Alcohol Use

使用智能手机评估对有问题的饮酒进行个性化预测

• 批准号: 9973396
• 负责人: Aidan Gregory Craver Wright
• 金额: $ 60.64万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9973396
• 关键词: Acute; Address; Affect; Alcohol abuse; Alcohol consumption; Alcohols; Behavior; Behavior Control; Behavioral; Behavioral Model; Bluetooth; Cellular Phone; Cessation of life; Clinical; Communities; Complex; Cross-Sectional Studies; Dangerousness; Data; Data Analytics; Data Collection; Detection; Development; Diagnosis; Ecological momentary assessment; Economics; Emotional; Enrollment; Environment; Female; Foundations; Future; Gender; Group Meetings; Health; Heavy Drinking; Hour; Individual; Individuality; Intervention; Knowledge; Life; Location; Machine Learning; Measurement; Measures; Methodology; Methods; Modeling; Morbidity - disease rate; Motivation; National Institute on Alcohol Abuse and Alcoholism; Outcome; Participant; Patient Self-Report; Pattern; Persons; Predictive Factor; Prevalence; Process; Protocols documentation; Psychological Models; Public Health; Research; Research Design; Research Personnel; Risk; Risk Factors; Sampling; Science; Series; Social Environment; Statistical Methods; Strategic Planning; Surveys; Symptoms; System; Techniques; Technology; Telephone; Testing; Time; Translating; United States; Vehicle crash; Violence; adaptive intervention; alcohol behavior; alcohol related consequences; alcohol research; alcohol use disorder; base; behavioral health; bench to bedside; binge drinking; craving; deep learning; deep neural network; drinking; drinking behavior; economic cost; emotion regulation; falls; follow-up; high risk drinking; improved; in vivo; innovation; longitudinal analysis; machine learning algorithm; male; mortality; multitask; novel; novel strategies; personalized medicine; personalized predictions; positive emotional state; predictive modeling; prevent; prospective; psychologic; psychosocial; real time monitoring; recruit; sensor; social; social group; working group; young adult

PROJECT SUMMARY/ABSTRACT Risky drinking, such as binge (5+/4+ drinks per 2-hour occasion for males/females) and high-intensity (2-3x the rates of binge) drinking, is highly prevalent among young adults and associated with severe acute and longer-term negative behavioral and health outcomes. However, given its prevalence, individuals who engage in such activities comprise a heterogeneous group. Researchers have had a hard time identifying the varied behavioral processes that are predictive of negative alcohol-related consequences and problematic trajectories across time. Predicting who will go on to develop lasting problems and whose risky alcohol use behavior is developmentally-limited is especially challenging. Part of the hindrance comes from the methods that are currently used to study this diverse behavior. In particular, researchers often use cross-sectional studies to look across individuals. While this has reaped invaluable knowledge regarding differences among individuals in their drinking patterns, it does not reveal the dynamic processes that contribute to maintaining such behaviors or make one more likely to have negative consequences. However, increasingly hypotheses pertain to these dynamic processes. This requires arriving at quantitative descriptions of individuals' emotional and behavioral processes. The science can move towards a more nuanced understanding of the varied mechanisms contributing to problematic alcohol use by arriving at valid descriptions of individual-level (i.e., personalized) processes. We propose to make advances towards personalized quantitative models in four ways: 1) develop an informed intensive longitudinal research design that enables acquisition of relevant variables across time on a daily basis and across the span of one year; 2) use innovative measurement technologies that enable objective assessment of contextual features related to drinking; 3) collect data using state-of-the-art phone applications that enable self-report and passive data collection where the user does not need to interface; and 4) implement cutting- edge machine learning algorithms that can reliably arrive at individual-level detection and predictive models that can be used as the foundation for future just-in-time adaptive interventions. We will accomplish this by enrolling N=300 young adult risky drinkers who will complete a 120-day ambulatory assessment protocol completing surveys on smartphones that are also equipped with passive sensors and applications, and then provide 4 waves of data on alcohol use and associated variables (e.g., consequences) over one year. In the end, our endeavors will create novel approaches to measuring and modeling behavioral processes related to high-risk drinking that capture the individuality of each participant. These endeavors will provide the framework for accurate detection and prediction of daily drinking and long-term problematic alcohol use trajectories that support future scientific and clinical efforts.

项目总结/摘要 危险饮酒，如狂饮（男性/女性每2小时5+/4+杯）和高强度（2- 3倍） 酗酒率在年轻人中非常普遍，并与严重的急性和 长期的负面行为和健康结果。然而，鉴于其普遍性， 在这样的活动中包含了一个不同的群体。研究人员很难识别出 预测酒精相关的负面后果和问题轨迹的行为过程 穿越时空预测谁将继续发展持久的问题，谁的危险饮酒行为是 发育受限的人尤其具有挑战性。部分障碍来自于 目前用于研究这种不同的行为。特别是，研究人员经常使用横断面研究， 看看个人。虽然这已经获得了关于个体差异的宝贵知识， 他们的饮酒模式，它并没有揭示动态过程，有助于保持这种行为 或使其更有可能产生负面后果。然而，越来越多的假设涉及这些 动态过程这就需要对个体的情绪和行为进行定量描述 流程.科学可以朝着更细致的理解不同的 通过对酒精使用问题的有效描述， 个体水平（即，个性化）过程。 我们建议在四个方面向个性化定量模型迈进：1）开发一个知情的 深入的纵向研究设计，可以每天获取不同时间的相关变量 2）使用创新的测量技术，使客观的评估 与饮酒相关的背景特征; 3）使用最先进的手机应用程序收集数据， 自我报告和被动数据收集，用户不需要接口;以及4）实施切割- 边缘机器学习算法，可以可靠地达到个人水平的检测和预测模型 这可用作未来及时适应性干预措施的基础。我们将通过以下方式实现这一目标： 招募N=300名年轻成人危险饮酒者，他们将完成120天的动态评估方案 完成对智能手机的调查，这些智能手机也配备了被动传感器和应用程序，然后 提供关于酒精使用和相关变量的4波数据（例如，后果）一年以上。 最后，我们的努力将创造新的方法来测量和建模相关的行为过程， 到高风险饮酒，抓住每个参与者的个性。这些努力将提供 准确检测和预测日常饮酒和长期问题酒精使用的框架 支持未来科学和临床工作的轨迹。