Personalizing cognitive behavioral therapy for cancer-related fatigue using ecological momentary assessments followed by automated individual time series analyses: A case report series

Abstract

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.

Highlights

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  Individual time series analyses can be used to systematically personalize CBT.

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  Individual temporal dynamics between symptom and treatable factors can be studied.

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  CBT based on individual time series analyses differs from usual modular approaches.

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  Assessment during treatment enables the personalization of treatment duration.