Digital Phenotyping of Anxiety and Anxiety-Related Alcohol Comorbidity and Treatment

焦虑和焦虑相关酒精合并症的数字表型分析和治疗

• 批准号: 10447935
• 负责人: Marilyn Piccirillo
• 金额: $ 15.35万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10447935
• 关键词: Accelerometer; Adult; Alcohol abuse; Alcohol consumption; Alcohol-Related Disorders; Anxiety; Award; Behavior; Behavior Therapy; Behavioral; Bluetooth; Cellular Phone; Cessation of life; Cognitive Therapy; Conduct Clinical Trials; Data; Data Collection; Development; Distress; Ecological momentary assessment; Evaluation; Exhibits; Feedback; Foundations; Future; Health Care Costs; Heterogeneity; Individual; Influentials; Intervention; Mental Depression; Mental Health; Methods; Modeling; Numbness; Outcome; Output; Pattern; Persons; Phase; Phenotype; Post-Traumatic Stress Disorders; Predictive Factor; Process; Psychopathology; Psychotherapy; Randomized; Randomized Clinical Trials; Randomized Controlled Trials; Research; Research Personnel; Risk Factors; Role; Self Medication; Social Anxiety Disorder; Societies; Statistical Data Interpretation; Statistical Models; Substance Use Disorder; Suicide attempt; Symptoms; Technology; Testing; Therapeutic; Therapeutic Intervention; Time; Training; Treatment Efficacy; Work; alcohol abuse therapy; alcohol comorbidity; alcohol effect; alcohol misuse; alcohol relapse; alcohol research; alcohol risk; alcohol use disorder; anxiety-related disorders; base; career; comparison intervention; cost; craving; data streams; design; digital; disability; dual diagnosis; economic cost; efficacy testing; feasibility testing; improved; individualized prevention; innovation; intervention effect; multilevel analysis; negative affect; negative emotional state; novel; personalized intervention; personalized medicine; phenotypic data; premature; psychologic; rate of change; reduce symptoms; reduced alcohol use; skills; success

Project Summary Anxiety and anxiety-related disorders (AARD) are notable risk factors for alcohol use problems (AUP) and their co-occurrence (AARD-AUP) results in tremendous health costs and societal burden. The self-medication model states that those with AARD use alcohol to numb or cope with arousing and distressing negative emotional states; however, longitudinal evidence for self-medication is mixed and psychological treatments integrate cognitive-behavioral therapy (CBT) skills for substance use disorders with CBT interventions for AARD have demonstrated modest success. The heterogeneity in these findings is likely influenced by dynamic and person-specific factors. We hypothesize that results from person-specific analyses can improve our understanding of this heterogeneity and can be harnessed to personalize interventions for AUP. Personalized interventions have the potential to increase symptom reduction and rate of change, as well as therapy retention and alliance. Modeling person-specific patterns of psychopathology has been challenging due to historical limitations with data collection and statistical analyses. However, increasing access to digital phenotyping – an innovative method for collecting single-subject intensive ecological momentary assessment (EMA) integrated with passive sensing smartphone data streams (e.g., GPS data, accelerometer, Bluetooth/call/SMS data) – offers a compelling solution for the development and evaluation of truly personalized psychological interventions. Such data can be used to generate statistical models of a single individual’s pattern of behavior over time. Previous data-driven (i.e., feedback-based) interventions have demonstrated success; however, it is unclear whether using person-specific models to adapt an active intervention reduces symptoms or improves therapeutic efficiency (i.e., rate of change), therapeutic retention, or therapeutic alliance. This K99R00 award will provide training in passive sensing data collection methods and conduct of clinical trials, as well as continued training in alcohol research. In the K99 phase, I will build on previous expertise constructing person-specific models and identifying person-specific factors testing self-medication models of AARD-AUP, developing a method for personalizing a CBT skills-based intervention. In the R00 phase, the feasibility of this intervention will be tested in a pilot three-group parallel randomized clinical trial (RCT), which will test the feasibility of a personalized CBT skills-based interventions compared to two control conditions testing the relative efficacy of personalization and personalization method, respectively. In the personalized condition, the therapist will review identified influential factors from the person-specific model, treat these factors using CBT skills, and track them using the mindLAMP digital phenotyping platforms – testing a data- driven method for personalizing and adapting the content of each session. Findings will inform future R01 applications testing personalized interventions for problematic alcohol use.

项目摘要 焦虑和焦虑相关障碍（AARD）是酒精使用问题（AUP）的显著风险因素， 它们的共同发生（AARD-AUP）导致巨大的健康成本和社会负担。自我药疗 模型指出，AARD患者使用酒精来麻木或科普令人兴奋和痛苦的消极情绪， 情绪状态;然而，自我药疗的纵向证据是混合的，心理治疗 综合认知行为治疗（CBT）技能与CBT干预， AARD取得了一定的成功。这些发现的异质性可能受到动态的影响。 和个人特定因素。我们假设，从个人特定的分析结果可以提高我们的 我们可以理解这种异质性，并可以利用它来个性化AUP干预。个性化 干预措施有可能增加症状减轻和变化率，以及治疗保持率。 和联盟。由于历史原因，对精神病理学的个人特定模式进行建模一直具有挑战性。 数据收集和统计分析的局限性。然而，越来越多的数字表型- 一种创新的单一主题密集生态瞬时评估（EMA）数据采集方法 与无源感测智能电话数据流集成（例如，GPS数据、加速度计、蓝牙/通话/短信 数据）-提供了一个令人信服的解决方案的发展和评估真正个性化的心理 干预措施。这些数据可以用来生成单个个体行为模式的统计模型 随着时间以前的数据驱动（即，反馈）干预措施取得了成功;然而， 不清楚使用个人特定模型来适应积极干预是否会减少症状或改善症状 治疗效率（即，变化率）、治疗保留或治疗联合。 该K99 R 00奖项将提供被动传感数据收集方法和临床 试验，以及酒精研究的持续培训。在K99阶段，我将以以前的专业知识为基础 构建个人特定模型并识别个人特定因素 AARD-AUP，开发个性化CBT技能干预的方法。在R 00阶段， 这种干预的可行性将在一项试点三组平行随机临床试验（RCT）中进行测试， 将测试基于技能的个性化CBT干预措施的可行性，并与两种对照条件进行比较 分别检验个性化和个性化方法的相对功效。在个性化 条件，治疗师将审查从个人特定模型中确定的影响因素，治疗这些 使用CBT技能的因素，并使用mindLAMP数字表型平台跟踪它们-测试数据- 用于个性化和调整每个会话内容的驱动方法。调查结果将告知未来 R 01应用程序测试有问题的酒精使用的个性化干预。