Using the Personalized Advantage Index for Individual Treatment Allocation to Blended Treatment or Treatment as Usual for Depression in Secondary Care

Abstract

A variety of effective psychotherapies for depression are available, but patients who suffer from depression vary in their treatment response. Combining face-to-face therapies with internet-based elements in the sense of blended treatment is a new approach to treatment for depression. The goal of this study was to answer the following research questions: (1) What are the most important predictors determining optimal treatment allocation to treatment as usual or blended treatment? and (2) Would model-determined treatment allocation using this predictive information and the personalized advantage index (PAI)-approach result in better treatment outcomes? Bayesian model averaging (BMA) was applied to the data of a randomized controlled trial (RCT) comparing the efficacy of treatment as usual and blended treatment in depressive outpatients. Pre-treatment symptomatology and treatment expectancy predicted outcomes irrespective of treatment condition, whereas different prescriptive predictors were found. A PAI of 2.33 PHQ-9 points was found, meaning that patients who would have received the treatment that is optimal for them would have had a post-treatment PHQ-9 score that is two points lower than if they had received the treatment that is suboptimal for them. For 29% of the sample, the PAI was five or greater, which means that a substantial difference between the two treatments was predicted. The use of the PAI approach for clinical practice must be further confirmed in prospective research; the current study supports the identification of specific interventions favorable for specific patients.

The Clinical Effectiveness of Blended Cognitive Behavioral Therapy Compared With Face-to-Face Cognitive Behavioral Therapy for Adult Depression: Randomized Controlled Noninferiority Trial

“…Consequently, this is a potential candidate variable for stratification of treatment or a prescriptive variable. In a different approach comparing variables predicting outcomes separately for the 2 groups in a larger sample across 4 countries [70], a lower quality of life and being widowed predicted lower treatment outcome in the blended condition. However, this approach does not include the parameters in a single model comparing the conditions directly in an interaction term.…”

Das Beste aus zwei Welten: Eine systematische Übersicht zu Faktoren der Implementierung einer “Blended Therapy” (Gemischte Therapie) in der Psychotherapeutischen Routineversorgung

“…Weiterhin drängt sich die Frage auf, wie die Therapie am besten angeboten werden sollte, um sowohl Effektivität als auch Kostenvorteile zu gewährleisten [Erbe et al, 2017;Kemmeren et al, 2019]. Des Weiteren ist die Frage interessant, für welche Patient*innengruppen eine blended therapy indiziert sein dürfte oder welche Patient*innen eher von einer f2f-Therapie profitieren [Friedl et al, 2020]. Vor diesem Hintergrund wird der Bedarf an entsprechenden Richtlinien und Behandlungsprotokollen für die blended therapy deutlich [Kenter et al, 2015].…”

Implementing Digital Trainings within Medical Rehabilitations: Improvement of Mental Health and Synergetic Outcomes with Healthcare Service

“…However, even though a few studies have examined the effectiveness of blended psychotherapy in outpatient settings [15], the effectiveness of integrating digital interventions in form of a blended psychotherapy concept into medical, psychosomatic rehabilitation treatment programs has yet to be evaluated.…”