Understanding the Relationship between Engagement Markers and Psychosocial Outcomes in a Digital Mental Health Intervention for Anxiety

Author: ORCID icon orcid.org/0000-0002-1039-3157
Vela de la Garza Evia, Angel, Systems Engineering - School of Engineering and Applied Science, University of Virginia
Barnes, Laura, EN-Engr Sys & Environment, University of Virginia

As the prevalence of anxiety and depression continues to grow worldwide, digital mental health interventions (DMHIs) have played a key role in scaling and expanding the reach of mental health services in a cost-effective manner. Although studies have shown that DMHIs reduce symptom severity, low user engagement and high attrition rates limit the significance of these conclusions. Consequently, it is important to develop a better understanding of how engagement patterns relate to intervention outcomes. This research aims to understand the relationship between participant engagement and the psychosocial outcomes of anxiety and interpretation bias in MindTrails, a free web-based DMHI. In our work, we defined engagement markers based on completion rate and time spent on training and assessment components. We then extracted engagement features related to these markers from 697 participants who enrolled in the MindTrails Calm Thinking study. These features were used in a clustering analysis to identify two engagement pattern groups characterized by the amount of time spent in the intervention. After defining engagement groups, we developed multilevel models to investigate between-group differences in outcomes throughout the intervention. Our results demonstrate that while there were no significant differences in anxiety outcomes, both engagement groups significantly differed in their improvement of certain interpretation bias outcomes. Overall, the findings highlight the complexity of using time-related engagement markers while furthering the understanding of participant interaction with DMHIs.

MS (Master of Science)
engagement, anxiety, cognitive bias modification, multilevel modeling, clustering
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