Web-based interpretation bias training to reduce anxiety: A sequential, multiple-assignment randomized trial | JoVE Visualize

Area of Science:

Psychology
Clinical Psychology
Digital Health

Background:

Web-based cognitive bias modification for interpretation (CBM-I) is a promising intervention for anxiety.
High dropout rates are a significant challenge for digital mental health interventions like CBM-I.
The effectiveness of supplemental support, such as telecoaching, in mitigating dropout and enhancing CBM-I efficacy requires further investigation.

Purpose of the Study:

To evaluate the effectiveness of web-based CBM-I compared to an active psychoeducation control condition.
To examine the impact of adding low-intensity telecoaching to CBM-I for participants at higher risk of dropout.
To assess the effects of CBM-I and telecoaching on interpretation biases and anxiety symptoms.

Main Methods:

A sequential, multiple-assignment randomized trial involving 1,234 anxious adults was conducted.
Participants were randomly assigned to CBM-I or psychoeducation, with higher-risk CBM-I completers then randomized to receive telecoaching or no coaching.
Outcomes included interpretation biases (Recognition Ratings, Brief Body Sensations Interpretation Questionnaire) and anxiety symptoms (Overall Anxiety Severity and Impairment Scale, Depression Anxiety Stress Scales-Short Form).

Main Results:

CBM-I significantly outperformed psychoeducation in improving interpretation biases and anxiety symptoms, with effects maintained at 2-month follow-up.
Contrary to expectations, CBM-I had significantly higher dropout rates than psychoeducation.
The addition of telecoaching did not significantly improve efficacy or reduce dropout rates, partly due to low participant engagement with coaches.

Conclusions:

Web-based CBM-I is an effective intervention for reducing interpretation biases and anxiety symptoms.
Supplemental low-intensity telecoaching does not appear to effectively mitigate dropout challenges in web-based CBM-I.
Future research should explore alternative strategies to improve engagement and reduce dropout in digital mental health interventions.