Deep Phenotyping of Heavy Drinking in Young Adults with Behavioral Scales, Neuropsychological Tasks, and Smartphone Sensing Technology

通过行为量表、神经心理学任务和智能手机传感技术对年轻人酗酒进行深度表型分析

• 批准号: 10585512
• 负责人: Kelly S DeMartini
• 金额: $ 68.3万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-05 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10585512
• 关键词: Address; Adolescence; Adult; Age; Alcohol Phenotype; Alcohol consumption; Alcohols; Behavior; Behavioral; Biological; Brain; Cellular Phone; Characteristics; Chronic; Circadian Rhythms; Classification; Clinical; Communication; Computers; Cost Analysis; Dairying; Data; Data Collection; Development; Diagnosis; Digital biomarker; Dimensions; Disease; Distal; Environment; Equation; Ethnic Origin; Factor Analysis; Functional disorder; Goals; Heavy Drinking; Heterogeneity; Hour; Incidence; Individual Differences; Intervention; Longitudinal Studies; Machine Learning; Maps; Measurement; Measures; Mental disorders; Methods; Modeling; National Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; Neurobiology; Neuropsychology; Online Systems; Outcome; Participant; Patient Self-Report; Persons; Phenotype; Population; Precision Medicine Initiative; Prediction of Response to Therapy; Psychiatry; Public Health; Questionnaires; Race; Recommendation; Reporting; Research; Research Domain Criteria; Risk; Science; Sleep; Social Processes; Statistical Models; Surveys; Symptoms; System; Targeted Research; Telephone; Testing; Translating; Treatment outcome; Work; World Health Organization; addiction; alcohol misuse; alcohol related consequences; alcohol research; alcohol risk; alcohol use disorder; circadian; clinical heterogeneity; college; cost; deter alcohol use; diagnostic strategy; diaries; digital; disorder risk; drinking; emotional functioning; executive function; follow-up; improved; incentive salience; innovation; neurobehavioral; novel; precision medicine; psychologic; sensor; sensor technology; sex; social; time use; treatment response; validation studies; young adult; young adult alcohol use

ABSTRACT Alcohol use disorder (AUD) reaches peak levels during young adulthood, making it critical that we understand person-specific alcohol risk profiles in young adults to prevent AUD or intervene before this disorder becomes chronic. The National Institute on Alcohol Abuse & Alcoholism’s (NIAAA) neurobiological framework, the Addictions Neuroclinical Assessment (ANA), offers an innovative approach for understanding AUD in this population. The ANA posits that individual differences in 3 neurofunctional domains can help differentiate the substantial clinical heterogeneity in AUD. The ANA builds upon the NIMH Research Domain Criteria (RDoC), a dimensional framework for investigating mental disorders in terms of varying degrees of dysfunction in 6 core biological/psychological systems. As a starting point, NIAAA endorsed an initial 3-domain ANA model, which aligns with most RDoC systems. Two RDoC systems, not in the initial ANA, include sleep/circadian and social processes and are highly relevant to young adult alcohol risk. The 3-domain ANA model has been validated in research with adults and predicts treatment outcomes, including work by our team. It has yet to be investigated in young adults. We propose to study the ANA model, expanded to include sleep/circadian and social processes, in young adults (non-college/college, ages 18-25) (N=350), who report recent moderate to heavy drinking. Specifically, young adults will participate in a 12-month longitudinal study, which involves completing self-report questionnaires, neuropsychological tasks, and engaging in passive and active smartphone data collection. These assessments include recommended/similar ANA measures, RDoC-relevant sleep/circadian and social measures, and novel smartphone measures to improve ANA scalability. Smartphone data collection is rigorous, unobtrusive, scalable, and highly relevant for young adults given their extensive smartphone use. Smartphones can generate rich moment-by-moment neurobehavioral data (e.g., mobility, sociality) passively through embedded sensors and phone usage logs and actively through survey prompts. These digital behavioral indicators show promise for predicting psychiatric disorder symptoms, course, treatment response, and functional brain activity. We will use data from study participants to achieve the following aims: For Aim 1, we will validate an ANA model for young adults (ANA-YA) using baseline self-report and neuropsychological measures related to the 3 ANA domains and RDoC sleep/circadian and social processes. We will then examine baseline associations between the ANA-YA model and baseline drinking measures. We will also explore longitudinal change in ANA-YA phenotypes and test whether these changes predict 12-month alcohol outcomes. For Aim 2, we will examine the baseline associations of smartphone data to ANA-YA domains and then examine longitudinal change in smartphone data and whether these changes predict 12-month ANA-YA phenotypes. Our results will advance the science of young adult AUD neurobiology and identify efficient, valid assessments for distinguishing alcohol risk in this group.

摘要 酒精使用障碍（AUD）在年轻时达到峰值水平，这使得我们了解年轻人的特定酒精风险状况以预防AUD或在这种疾病成为慢性之前进行干预至关重要。国家酒精滥用和酒精中毒研究所（NIAAA）的神经生物学框架，成瘾神经临床评估（ANA），为了解这一人群的AUD提供了一种创新的方法。ANA认为3个神经功能领域的个体差异有助于区分AUD的临床异质性。ANA建立在NIMH研究领域标准（RDoC）的基础上，这是一个维度框架，用于调查6个核心生物/心理系统中不同程度的功能障碍。作为一个起点，NIAAA认可了一个初始的3域ANA模型，该模型与大多数RDoC系统保持一致。两个RDoC系统，而不是在最初的ANA，包括睡眠/昼夜节律和社会过程，是高度相关的年轻成人酒精风险。3域ANA模型已在成人研究中得到验证，并预测了治疗结果，包括我们团队的工作。它还有待于在年轻人中进行研究。我们建议研究ANA模型，扩展到包括睡眠/昼夜节律和社会过程，在年轻人（非大学/大学，年龄18-25）（N=350），谁报告最近中度到重度饮酒。具体来说，年轻人将参加一项为期12个月的纵向研究，其中包括完成自我报告问卷，神经心理学任务，并参与被动和主动的智能手机数据收集。这些评估包括推荐的/类似的ANA测量，RDoC相关的睡眠/昼夜节律和社交测量，以及改善ANA可扩展性的新型智能手机测量。智能手机数据收集是严格的，不引人注目的，可扩展的，并且与年轻人高度相关，因为他们广泛使用智能手机。智能手机可以生成丰富的每时每刻的神经行为数据（例如，移动性、社交性），通过嵌入式传感器和电话使用日志被动地，以及通过调查提示主动地。这些数字行为指标显示出预测精神疾病症状、病程、治疗反应和功能性大脑活动的前景。我们将使用研究参与者的数据来实现以下目标：对于目标1，我们将使用基线自我报告和与3个ANA领域和RDoC睡眠/昼夜节律和社会过程相关的神经心理学测量来验证年轻人的ANA模型（ANA-YA）。然后，我们将检查ANA-YA模型和基线饮酒测量之间的基线关联。我们还将探索ANA-YA表型的纵向变化，并测试这些变化是否预测12个月的酒精结果。对于目标2，我们将检查智能手机数据与ANA-YA域的基线关联，然后检查智能手机数据的纵向变化以及这些变化是否预测12个月的ANA-YA表型。我们的研究结果将推进年轻成年人AUD神经生物学的科学，并确定有效的评估，以区分该组的酒精风险。