Computer-delivered behavioural activation and approach-avoidance training in major depression: Proof of concept and initial outcomes

Towards implementation of cognitive bias modification in mental health care: State of the science, best practices, and ways forward

Cognitive bias modification (CBM) has evolved from an experimental method testing cognitive mechanisms of psychopathology to a promising tool for accessible digital mental health care. While we are still discovering the conditions under which clinically relevant effects occur, the dire need for accessible, effective, and low-cost mental health tools underscores the need for implementation where such tools are available. Providing our expert opinion as Association for Cognitive Bias Modification members, we first discuss the readiness of different CBM approaches for clinical implementation, then discuss key considerations with regard to implementation. Evidence is robust for approach bias modification as an adjunctive intervention for alcohol use disorders and interpretation bias modification as a stand-alone intervention for anxiety disorders. Theoretical predictions regarding the mechanisms by which bias and symptom change occur await further testing. We propose that CBM interventions with demonstrated efficacy should be provided to the targeted populations. To facilitate this, we set a research agenda based on implementation frameworks, which includes feasibility and acceptability testing, co-creation with end-users, and collaboration with industry partners.

Comparison of two reaction-time-based and one foraging-based behavioral approach-avoidance tasks in relation to interindividual differences and their reliability

Approaching rewards and avoiding punishments is a fundamental aspect of behavior, yet individuals differ in the extent of these behavioral tendencies. One popular method to assess differences in approach-avoidance tendencies and even modify them, is using behavioral tasks in which spontaneous responses to differently valenced stimuli are assessed (e.g., the visual joystick and the manikin task). Understanding whether these reaction-time-based tasks map onto the same underlying constructs, how they predict interindividual differences in theoretically related constructs and how reliable they are, seems vital to make informed judgements about current findings and future studies. In this preregistered study, 168 participants (81 self-identified men, 87 women) completed emotional face versions of these tasks as well as an alternative, foraging-based paradigm, the approach-avoidance-conflict task, and answered self-report questionnaires regarding anxiety, aggression, depressive symptoms, behavioral inhibition and activation. Importantly, approach-avoidance outcome measures of the two reaction-time-based tasks were unrelated with each other, showed little relation to self-reported interindividual differences and had subpar internal consistencies. In contrast, the approach-avoidance-conflict task was related to behavioral inhibition and aggression, and had good internal consistencies. Our study highlights the need for more research into optimizing behavioral approach-avoidance measures when using task-based approach-avoidance measures to assess interindividual differences.

Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms

Introduction

Personalization is a much-discussed approach to improve adherence and outcomes for Digital Mental Health interventions (DMHIs). Yet, major questions remain open, such as (1) what personalization is, (2) how prevalent it is in practice, and (3) what benefits it truly has.

Methods

We address this gap by performing a systematic literature review identifying all empirical studies on DMHIs targeting depressive symptoms in adults from 2015 to September 2022. The search in Pubmed, SCOPUS and Psycinfo led to the inclusion of 138 articles, describing 94 distinct DMHIs provided to an overall sample of approximately 24,300 individuals.

Results

Our investigation results in the conceptualization of personalization as purposefully designed variation between individuals in an intervention's therapeutic elements or its structure. We propose to further differentiate personalization by what is personalized (i.e., intervention content, content order, level of guidance or communication) and the underlying mechanism [i.e., user choice, provider choice, decision rules, and machine-learning (ML) based approaches]. Applying this concept, we identified personalization in 66% of the interventions for depressive symptoms, with personalized intervention content (32% of interventions) and communication with the user (30%) being particularly popular. Personalization via decision rules (48%) and user choice (36%) were the most used mechanisms, while the utilization of ML was rare (3%). Two-thirds of personalized interventions only tailored one dimension of the intervention.

Discussion

We conclude that future interventions could provide even more personalized experiences and especially benefit from using ML models. Finally, empirical evidence for personalization was scarce and inconclusive, making further evidence for the benefits of personalization highly needed.

Systematic Review Registration

Identifier: CRD42022357408.