A Network Analytic Model of Adherence and Abstinence

依从性和禁欲性的网络分析模型

• 批准号: 8693224
• 负责人: Steven L Bernstein
• 金额: $ 24.75万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-05-15 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8693224
• 关键词: AIDS/HIV problem; Abstinence; Accounting; Address; Adherence; Alcohol abuse; Alcohol or Other Drugs use; Alcohols; Antiviral Agents; Antiviral Therapy; Back; Behavior; Behavioral; Biochemical Markers; Biological; Biological Markers; Buprenorphine; Chronic; Chronic Disease; Clinical; Clinical Treatment; Clinical Trials; Coin; Cotinine; Data; Data Set; Diabetes Mellitus; Diet; Dose; Drug abuse; Drug usage; Electronics; Epidemiology; Graph; HIV; HIV Infections; Health; Health Personnel; Health behavior; Highly Active Antiretroviral Therapy; Human; Hypertension; Individual; Intervention; Interview; Life; Measurement; Measures; Medical; Medicine; Methods; Modeling; Naloxone; Opiate Addiction; Opiates; Outcome; Pathway Analysis; Patient Self-Report; Pattern; Persons; Pharmaceutical Preparations; Play; Prevention; Property; Provider; Records; Regimen; Research; Research Personnel; Role; Series; Sex Behavior; Side; Sleep; Smoke; Smoking; Substance Addiction; Substance Use Disorder; Substance abuse problem; Techniques; Time; TimeLine; Tobacco; Variant; Viral Load result; alcohol use disorder; arm; base; clinically relevant; drinking; improved; indexing; innovation; insight; medication compliance; novel; nutrition; operation; outcome forecast; pill; premature; public health relevance; response; smoking cessation; theories; tobacco abstinence; treatment adherence

DESCRIPTION (provided by applicant): Health behavior is the single greatest determinant of health. We want people to take their medication as prescribed, follow healthful diets, get adequate sleep, and avoid harmful substances and addictions. These actions may be thought of as binary behaviors-adherence and abstinence-that individuals may engage in-or not-on a daily basis. Yet, clinical trials of substance use treatment or abstinence use widely disparate measures of effect. There is no obvious biological or behavioral explanation for these disparate measurements. Further, measures of adherence and abstinence often fail to account for the rich variation in the pattern of the health behavior. These patterns of adherence or abstinence may further contribute to the individual's prognosis. This proposal offers a novel method to measure adherence and abstinence, using concepts adapted from network analysis. The gamma index, which measures the connectedness of doses taken (or substances not taken), accounts for the pattern of an individual's adherence, or abstinence, in an innovative algebraic model. This index can be used to model adherence and abstinence to examine their effect on clinically relevant biologic outcomes. Gamma is bounded at a value of 0 for perfect non-adherence (or non-abstinence), and 1 for perfect adherence (or perfect abstinence). We propose to analyze a series of datasets from completed clinical trials in four domains (1) adherence to Highly Active Antiretroviral Therapy (HAART) in subjects with HIV/AIDS; (2) adherence to buprenorphine treatment in subjects with opioid dependence; (3) abstinence from tobacco in trials of smoking cessation; and (4) abstinence or reduction in drinking in trials of subjects with alcohol use disorders. In trials of antiviral adherence, we will examine associations between the index and changes in viral load. In trials of tobacco, alcohol and drug treatment, we will examine associations between the index and long-term abstinence rates. The Specific Aims of this project are to (1) Assess the ability of the gamma index to improve the explanatory power of adherence on changes in viral load in trials of antiviral therapy in individuals infected with HIV, and (2) Assess the ability of the gamma index to explain differences in cessation endpoints in trials of subjects with substance use disorders, such as alcohol abuse, drug use, and smoking. Daily records of adherence (or substance use) are needed for this modeling, which makes these studies, which employ ecological momentary assessment (EMA) methods, timeline follow-back techniques, or electronic pill cap data, ideal. The outstanding interdisciplinary team for this project includes renowned experts in adherence, graph theory and network analysis, the treatment of HIV, and use of licit and illicit substances. The gamma index offers to provide new insights into underlying patterns of health behavior, their relationship to clinical outcomes, a means of quantifying these patterns, and the possibility of novel real-time interventions to promote healthy behaviors.

描述（由申请人提供）：健康行为是健康的最大决定因素。我们希望人们按照处方服药，遵循健康饮食，获得充足的睡眠，并避免有害物质和成瘾。 这些行为可能被认为是二元行为——坚持和节制——个人可能每天都会参与或不参与。然而，物质使用治疗或戒断的临床试验使用了广泛不同的效果衡量标准。对于这些不同的测量结果没有明显的生物学或行为学解释。此外，坚持和节制的衡量标准常常无法解释健康行为模式的丰富变化。这些坚持或禁欲的模式可能进一步有助于个人的预后。该提案提供了一种使用网络分析改编的概念来衡量依从性和禁欲性的新颖方法。伽马指数衡量服用剂量（或未服用物质）的关联性，在创新的代数模型中解释了个人的依从性或禁欲模式。该指数可用于模拟依从性和禁欲性，以检查它们对临床相关生物学结果的影响。伽马值的界限为 0 表示完全不遵守（或不禁欲），1 表示完全遵守（或完全禁欲）。 我们建议分析四个领域已完成的临床试验的一系列数据集：（1）艾滋病毒/艾滋病患者坚持高效抗逆转录病毒治疗（HAART）的情况； (2)阿片类药物依赖受试者坚持丁丙诺啡治疗； (3) 在戒烟试验中戒烟； (4) 在患有酒精使用障碍的受试者的试验中戒酒或减少饮酒。在抗病毒治疗依从性试验中，我们将检查关联性 指数和病毒载量变化之间的关系。在烟草、酒精和药物治疗的试验中，我们将检查该指数与长期戒烟率之间的关联。该项目的具体目标是 (1) 评估 γ 指数提高 HIV 感染者抗病毒治疗试验中病毒载量变化依从性的解释能力的能力，以及 (2) 评估 γ 指数的能力伽马指数来解释对患有物质使用障碍（例如酗酒、吸毒和吸烟）受试者的试验中戒烟终点的差异。该模型需要每日记录依从性（或药物使用），这使得这些采用生态瞬时评估（EMA）方法、时间线跟踪技术或电子药帽数据的研究成为理想选择。该项目的杰出跨学科团队包括依从性、图论和网络分析、艾滋病毒治疗以及合法和非法物质使用方面的知名专家。伽玛指数提供了对健康行为的潜在模式、它们与临床结果的关系、量化这些模式的方法以及促进健康行为的新型实时干预措施的可能性的新见解。