使用密集的纵向数据进行时间变化的效应建模:在成恢复期间检查渴望,影响,自我效能和物质使用之间的动态联系
Samuel W Stull1, Ashley N Linden-Carmichael2, Christy K Scott3
1Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA.
Addiction (Abingdon, England)
|July 7, 2023
概括
时间变化效应建模 (TVEM) 与强度纵向数据 (ILD) 揭示了情绪和渴望如何动态影响成恢复. 影响可以是时间变化的风险或保护因素,影响恢复结果.
科学领域:
- 生物行为健康 生物行为健康
- 药物成科学 药物成科学
- 统计建模 统计建模
背景情况:
- 时间变化效应建模 (TVEM) 提供了先进的方法来分析生物行为健康中的动态模式.
- 密集的纵向数据 (ILD) 对于捕捉成等过程的复杂性至关重要.
- 与ILD相结合的TVEM提供了一个灵活的框架来理解时间依赖的关联和调节效应.
研究的目的:
- 提供TVEM与ILD用于成研究的应用概述.
- 为了证明TVEM在分析成恢复动态方面的实用性.
- 使成科学家能够对时间变化的过程进行新的分析.
主要方法:
- 在密集的纵向数据 (ILD) 上利用时间变化效应建模 (TVEM).
- 从事生态瞬间评估 (EMA) 数据来自参与者在上恢复的前90天.
- 渴望,影响和恢复结果之间的估计关联,包括随时间变化的适度效应.
主要成果:
- 确定了早晨渴望和当天恢复结果之间的动态关联.
- 揭示了正面和负面影响对当天恢复结果的时间变化的影响.
- 已经证明,影响力动态地调节了渴望和恢复结果之间的关系,作为风险或保护因素.
结论:
- TVEM是了解成和康复的时间动态的强大工具.
- 影响在成恢复中起着重要的,时间变化的作用,特别是在与渴望结合时.
- 未来的研究可以利用TVEM来探索新的研究问题,通过以多种方式运行"时间".
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