Introduction

A common approach to personalizing psychological interventions is the allocation of treatment modules to individual patients based on cut-off scores on questionnaires, which are mostly based on group studies. However, this way, intraindividual variation and temporal dynamics are not taken into account. Automated individual time series analyses are a possible solution, since these can identify the factors influencing the targeted symptom in a specific individual, and associated modules can be allocated accordingly. The aim of this study was to illustrate how automated individual time series analyses can be applied to personalize cognitive behavioral therapy for cancer-related fatigue in cancer survivors and how this procedure differs from allocating modules based on questionnaires.

Methods

This study was a case report series (n = 3). Patients completed ecological momentary assessments at the start of therapy, and after three treatment modules (approximately 14 weeks). Assessments were analyzed with AutoVAR, an R package that automates the process of finding optimal vector autoregressive models. The results informed the treatment plan.

Results

Three cases were described. From the ecological momentary assessments and automated time series analyses three individual treatment plans were constructed, in which the most important predictor for cancer-related fatigue was treated first. For two patients, this led to the treatment ending after the follow-up ecological momentary assessments. One patient continued treatment until six months, the standard treatment time in regular treatment. All three treatment plans differed from the treatment plans informed by questionnaire scores.

Discussion

This study is one of the first to apply time series analyses in systematically personalizing psychological treatment. An important strength of this approach is that it can be used for every modular cognitive behavioral intervention where each treatment module addresses specific maintaining factors. Whether or not personalized CBT is more efficacious than standard, non-personalized CBT remains to be determined in controlled studies comparing it to usual care.

中文翻译：

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使用生态瞬时评估和自动个体时间序列分析对癌症相关疲劳进行个性化认知行为疗法：病例报告系列

介绍

个性化心理干预的一种常见方法是根据问卷的截止分数为个体患者分配治疗模块，问卷主要基于小组研究。然而，这种方式，没有考虑到个体内的变化和时间动态。自动化的个体时间序列分析是一种可能的解决方案，因为它们可以识别影响特定个体目标症状的因素，并且可以相应地分配相关模块。本研究的目的是说明如何应用自动化的个人时间序列分析来个性化针对癌症幸存者的癌症相关疲劳的认知行为疗法，以及该程序与基于问卷分配模块的不同之处。

方法

本研究是一个病例报告系列（n  = 3）。患者在治疗开始时和三个治疗模块后（大约 14 周）完成了生态瞬时评估。使用 AutoVAR 分析评估，这是一个 R 包，可自动查找最佳向量自回归模型的过程。结果为治疗计划提供了依据。

结果

描述了三个案例。通过生态瞬时评估和自动时间序列分析，构建了三个单独的治疗计划，其中最重要的癌症相关疲劳预测因子被首先处理。对于两名患者，这导致治疗在后续生态瞬时评估后结束。一名患者继续治疗至六个月，标准治疗时间为常规治疗。所有三个治疗计划都不同于问卷分数告知的治疗计划。

讨论

这项研究是最早将时间序列分析应用于系统个性化心理治疗的研究之一。这种方法的一个重要优势是它可以用于每个模块化认知行为干预，其中每个治疗模块都针对特定的维持因素。个性化 CBT 是否比标准的 CBT 更有效，非个性化 CBT 仍有待通过对照研究将其与常规护理进行比较来确定。