Altman Allison Diamond, Shapiro Lauren A, Fisher Aaron J
Idiographic Dynamic Lab, Psychology Department, University of California, Berkeley, Berkeley, CA, United States.
Psychology Department, The Wright Institute, Berkeley, CA, United States.
Front Psychol. 2020 Apr 24;11:782. doi: 10.3389/fpsyg.2020.00782. eCollection 2020.
While psychotherapy treatments are largely effective, the processes and mechanisms underlying such positive changes remain somewhat unknown. Focusing on a single participant from a treatment outcome study that used a modular-based cognitive behavior therapy protocol, this article aims to answer this question by identifying changes in specific symptomatology over the course of the treatment. Using quantitative data derived from digital health methodology, we analyzed whether a given therapeutic intervention was related to downstream effects in predicted symptom domains, to assess the accuracy of our interventions.
This case study employed an observational N-of-1 study design. The participant ( = 1) was a female in the age range of 25-35 years. Using digital health data from ambulatory assessment surveys completed prior to and during therapy, separate linear regression analyses were conducted to assess if hypothesized treatment targets reduced after a given module, or intervention.
Support was found for some of the hypothesized quantitative changes (e.g., decreases in avoidance after exposures module), yet not for others (e.g., decreases in rumination following the mindfulness module).
We present data and results from our analyses to offer an example of a novel design that may allow for a greater understanding of the nature of symptom changes with increased granularity throughout the course of a psychological treatment from the use of digital health tools.
虽然心理治疗方法大多有效,但这些积极变化背后的过程和机制仍不太明确。本文聚焦于一项治疗结果研究中的一名参与者,该研究采用了基于模块的认知行为治疗方案,旨在通过确定治疗过程中特定症状学的变化来回答这个问题。利用从数字健康方法中获得的定量数据,我们分析了给定的治疗干预是否与预测症状领域的下游效应相关,以评估我们干预措施的准确性。
本案例研究采用观察性单病例研究设计。参与者(n = 1)为一名年龄在25至35岁之间的女性。利用治疗前和治疗期间完成的动态评估调查中的数字健康数据,进行了单独的线性回归分析,以评估在给定模块或干预后,假设的治疗目标是否降低。
部分假设的定量变化得到了支持(例如,暴露模块后回避行为减少),但其他一些变化未得到支持(例如,正念模块后沉思减少)。
我们展示了分析的数据和结果,以提供一种新颖设计的示例。这种设计可能有助于更深入地理解症状变化的本质,通过使用数字健康工具,在心理治疗过程中以更高的粒度进行观察。