Promoting Rapid Uptake of Multilevel Latent Class Modeling via Best Practices: Investigating Heterogeneity in Daily Substance Use Patterns

通过最佳实践促进多级潜在类建模的快速采用：调查日常物质使用模式的异质性

• 批准号: 10739994
• 负责人: STEPHANIE T LANZA
• 金额: $ 73.37万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10739994
• 关键词: Acceleration; Acute; Adderall; Address; Adoption; Affect; Age; Alcohol consumption; Alcohols; Anxiety; Award; Behavior; Cannabis; Cellular Phone; Characteristics; Chronic; Code; Collection; Complex; Complex Mixtures; Computer software; Data; Data Analyses; Data Set; Demographic Factors; Development; Devices; Disease; Disparity; Drug abuse; Educational workshop; Ensure; Environment; Estimation Techniques; Ethnic Origin; Etiology; Exhibits; Financial Hardship; Guide prevention; Heterogeneity; Individual; Institution; Intervention; Investigation; Knowledge; Life; Manuscripts; Measurement; Methods; Modeling; Outcome; Pattern; Performance; Persons; Policies; Prevention; Psychosocial Factor; Public Health; Race; Research; Research Personnel; Resources; Risk; Risk Factors; Scientist; Sexual and Gender Minorities; Site; Social Characteristics; Specific qualifier value; Statistical Data Interpretation; Stress; Subgroup; Techniques; Time; Translating; United States Substance Abuse and Mental Health Services Administration; Urbanicity; advanced analytics; analytical method; clinically relevant; cost estimate; diaries; high risk; innovation; learning materials; next generation; novel; polysubstance use; psychologic; psychosocial; repository; response; sex; substance misuse; substance use; symposium; therapy design; trait; university student; uptake; vaping; vaping nicotine; webinar; young adult

PROJECT SUMMARY Substance use among young adult college students remains a major public health concern, with recent escalations in nonmedical Adderall use, vaping nicotine, and co-use of alcohol and cannabis. The landscape of substance use continues to evolve, with new modes of use (e.g., vaping) and rapid legislative changes. Daily use behaviors can take many forms, defined by number and type of substances used, mode of use, and quantity used. Further, daily use patterns may be related to both day-level psychosocial factors (e.g., motives, affect, stress) and person-level characteristics (e.g., sex, race/ethnicity, financial strain). Use of multiple substances within a day (“co-use”) is increasingly prevalent and confers high risk for acute and chronic consequences, but is insufficiently studied. The unprecedented amount of high-quality, intensive longitudinal data (ILD) on substance use and associated risk factors holds critical information that can help explain substance use etiology and inform the development of the next generation of interventions that are tailored to those who need it, when they need it. However, advanced methods for analyzing ILD—including multilevel latent class analysis (MLCA) and multilevel latent transition analysis (MLTA)—are needed to characterize the heterogeneity of patterns of use that unfold in daily life and to identify novel day-level intervention targets. These methods enable researchers to discover clinically relevant substance use behavior patterns and day-to- day transitions, dynamic risk factors embedded in daily life that are associated with problematic use patterns, and person-level characteristics that indicate for whom these risk factors are most salient. Yet MLCA and MLTA are underused for ILD analysis, in part due to the dearth of guidance for applied researchers. Guided by a socio-ecological framework, we will evaluate, apply, and disseminate MLCA and MLTA to analyze ILD, considering four ways to specify models: marginal modeling, a fully parametric random effects model, a common factor model, and a non-parametric approach for random effects. We will translate best practices for applying MLCA (Aim 1) and MLTA (Aim 2) to ILD on substance use and co-use. We will conduct coordinated MLCA and MLTA of daily data from two large studies of college students (3CAM: n=343, 15,798 person-days; Project SELF: n=2068, 33,722 person-days). Empirical analyses will disentangle person-level risk factors (e.g., trait anxiety) and dynamic risk factors (e.g., morning affect) to isolate potential novel, dynamic intervention targets. We will examine individual characteristics (e.g., race/ethnicity) as moderators to investigate disparities and identify group-specific mechanisms. To maximize our overall impact (Aim 3) and ensure broad, rigorous adoption of MLCA and MLTA, we propose a broad range of dissemination activities to include online instructional material, a code repository, workshops, and a conference. Findings from studies using these techniques will guide prevention scientists and clinicians in designing interventions targeting the most salient risk factors for specific individuals at the best time for intervention.

项目摘要 年轻的成年大学生使用药物仍然是一个主要的公共卫生问题， 非医疗Adderall使用的升级，vaping尼古丁，以及酒精和大麻的共同使用。的景观 物质的使用继续发展，新的使用模式（例如，#21453;，以及快速的立法变革。每日 使用行为可以采取多种形式，由所使用的物质的数量和类型、使用模式和 使用的数量。此外，日常使用模式可能与日常水平的心理社会因素（例如，动机， 影响，压力）和个人水平特征（例如，性别、种族/民族、经济压力）。使用多个 一天之内摄入过量物质（“共同使用”）的现象日益普遍，并带来急性和慢性疾病的高风险。 后果，但研究不够。数量空前的高质量、密集的纵向 关于物质使用和相关风险因素的数据（ILD）包含关键信息，可以帮助解释 物质使用病因学，并为下一代干预措施的制定提供信息， 然而，分析ILD的先进方法-包括多水平 潜在类别分析（MLCA）和多级潜在转换分析（MLTA）-需要表征 研究日常生活中使用模式的异质性，并确定新的日常干预目标。 这些方法使研究人员能够发现临床相关的物质使用行为模式和日常生活， 日间转换，与有问题的使用模式相关的嵌入日常生活中的动态风险因素， 以及个人水平的特征，表明这些风险因素对谁来说最突出。然而，MLCA和 MLTA未充分用于ILD分析，部分原因是缺乏对应用研究人员的指导。指导 一个社会生态框架，我们将评估，应用和传播MLCA和MLTA来分析ILD， 考虑四种指定模型的方法：边际模型，完全参数随机效应模型， 共同因素模型和随机效应的非参数方法。我们将最佳实践转化为 将《MLCA》（目标1）和《MLTA》（目标2）适用于药物使用和共同使用的ILD。我们将进行协调 来自两项大型大学生研究的每日数据的MLCA和MLTA（3CAM：n=343，15，798人-天; 项目SELF：n=2068，33 722人日）。实证分析将理清个人层面的风险因素（例如， 特质焦虑）和动态风险因素（例如，早晨影响），以隔离潜在的新颖、动态干预 目标的我们将研究个体特征（例如，种族/民族）作为调节器，以调查差异 并确定特定群体的机制。为了最大限度地发挥我们的整体影响（目标3），并确保广泛，严格 通过《MLCA》和《MLTA》，我们建议开展广泛的传播活动，包括在线 教学材料、代码库、研讨会和会议。使用这些方法的研究结果 技术将指导预防科学家和临床医生设计针对最突出的 在最佳干预时间对特定个体的风险因素进行评估。