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使用经验采样法支持临床实践:以问题性大麻使用为例。

Using the experience sampling method to support clinical practice: An illustration with problematic cannabis use.

机构信息

Department of Psychology, University of Washington, Seattle, WA, USA.

Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA.

出版信息

Psychother Res. 2024 Feb;34(2):241-260. doi: 10.1080/10503307.2023.2184284. Epub 2023 Mar 28.

Abstract

The experience sampling method (ESM) has been frequently used in clinical research; however, there is low translational uptake in clinical practice. This may be due to challenges with interpreting individual-level data at granular intervals. We provide an illustrative example of how ESM can be leveraged to generate personalized cognitive-behavioral strategies for problematic cannabis use. We conducted a descriptive case series analysis using ESM data from 30 individuals reporting on problematic cannabis use, craving, affect, and coping four times daily for 16-days (t = 64, T = 1,920). Analyzing ESM data using descriptive statistics and visualizations from individuals with similar clinical and demographic profiles supported a diverse array of personalized clinical insights and recommendations for each case. These recommendations included psychoeducation regarding affect- and boredom-regulation strategies, functional analyses of occasions during which cannabis was not used, and discussions on how cannabis use intersects with one's personal values. While many clinicians utilize measurement-based care, barriers have limited the incorporation of ESM towards personalized, data-informed approaches to treatment. We provide an illustrative example of how ESM data can be used to generate actionable treatment strategies for problematic cannabis use and highlight continued challenges with interpreting time-series data.

摘要

经验采样法(ESM)在临床研究中被频繁使用;然而,在临床实践中,它的转化应用率很低。这可能是由于在细粒度间隔上解释个体水平数据的挑战所致。我们提供了一个说明性的例子,说明如何利用 ESM 生成针对有问题的大麻使用的个性化认知行为策略。我们使用报告有问题的大麻使用、渴望、情绪和应对策略的 30 名个体的 ESM 数据进行描述性病例系列分析,每天四次共进行 16 天(t=64,T=1920)。使用描述性统计和来自具有相似临床和人口统计学特征的个体的可视化对 ESM 数据进行分析,支持了针对每个案例的多样化个性化临床见解和建议。这些建议包括关于情绪和无聊调节策略的心理教育、对未使用大麻时的场合的功能分析,以及关于大麻使用如何与个人价值观相交的讨论。虽然许多临床医生利用基于测量的护理,但障碍限制了 ESM 向个性化、数据驱动的治疗方法的整合。我们提供了一个说明性的例子,说明如何利用 ESM 数据生成针对有问题的大麻使用的可操作治疗策略,并强调了对时间序列数据进行解释的持续挑战。

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