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.

抽象的 酒精使用障碍 (AUD) 在成年早期达到高峰，因此，美国国家酒精滥用和酒精中毒研究所 (NIAAA) 了解年轻人的特定酒精风险状况以预防 AUD 或进行干预至关重要。神经生物学框架，成瘾神经临床评估 (ANA)，为理解该人群的 AUD 提供了一种创新方法。 ANA 认为，3 个神经功能领域的个体差异可以帮助区分 AUD 的显着临床异质性。 NIMH 研究领域标准 (RDoC) 是一个维度框架，用于根据 6 个核心生物/心理系统的不同程度的功能障碍来调查精神障碍。作为起点，NIAAA 认可了一个初始的 3 领域 ANA 模型，该模型与与大多数 RDoC 系统一样，两个 RDoC 系统（不在最初的 ANA 中）包括睡眠/昼夜节律和社交过程，并且与年轻人酒精风险高度相关。3 域 ANA 模型已得到验证。正在对成年人进行研究并预测治疗结果，包括我们团队的工作尚未在年轻人中进行研究，我们建议将 ANA 模型扩展到年轻人（非大学学生）中。 /学院，年龄 18-25 岁）（N = 350），报告近期中度至大量饮酒的人具体来说，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、神经心理学任务以及参与。这些评估包括推荐/类似的 ANA 措施、RDoC 相关的睡眠/昼夜节律和社交措施，以及用于提高 ANA 可扩展性的新颖智能手机措施。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。成年人广泛使用智能手机，可以通过嵌入式传感器和手机使用日志被动地生成丰富的即时神经行为数据（例如，移动性、社交性），也可以通过调查主动地生成丰富的神经行为数据。这些数字行为指标有望预测精神疾病症状、病程、治疗反应和功能性大脑活动。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，我们将验证年轻人的 ANA 模型。 (ANA-YA) 使用与 3 个 ANA 领域和 RDoC 睡眠/昼夜节律和社会过程相关的基线自我报告和神经心理学测量，然后我们将基线检查 ANA-YA 模型和基线饮酒测量之间的关联。改变在ANA-YA 表型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。 ANA-YA 表型。我们的结果将推进年轻成人 AUD 神经生物学的科学发展，并确定区分该群体酒精风险的有效、有效的评估。