(PS8-94) Applications of Digital Phenotyping in Digital Evidence-based Treatment (UP) for Improving Anxiety and Depression Outcomes

As the demand for mental health care continues to rise, the limited availability of clinicians poses a significant challenge to delivering timely and effective treatment. Expanding workforce capacity and integrating digital solutions are critical steps toward addressing this gap. However, innovation in mental health care must ensure that increased accessibility does not come at the expense of treatment quality. In response to this need, we developed the Digital Clinic, a hybrid model that combines synchronous telehealth therapy with asynchronous digital interventions to enhance engagement and clinical outcomes. This model integrates digital phenotyping through the mindLAMP app, allowing for real-time symptom tracking, passive data collection (e.g., sleep patterns, home time, screen duration), and personalized interventions guided by both clinicians and Digital Navigators, a new care team role designed to optimize app engagement and support digital equity.

In this project, we present the operational framework of the Digital Clinic, including its eight-week, manualized telehealth therapy approach utilizing the Unified Protocol (UP) and the integration of digital phenotyping into clinical care. We illustrate patient engagement patterns with the mindLAMP app, capturing both active (survey completion, therapeutic exercises) and passive (behavioral data tracking) interactions. Additionally, we share clinical outcomes from a pilot study evaluating the effectiveness of this hybrid model in treating anxiety and depression.

Preliminary findings suggest that patients using the Digital Clinic demonstrate significant reductions in PHQ-9 and GAD-7 scores by visit 4 and visit 8, comparable to or exceeding traditional longer-term treatment outcomes (Chang et al., 2023). This work underscores the potential for digital phenotyping-informed interventions to bridge the gap between accessibility and quality in mental health care. By incorporating real-time patient data into treatment planning, the Digital Clinic model offers a scalable approach to improving intervention outcomes for individuals with anxiety and depression. Future research will explore how patient-clinician-app interactions shape engagement and treatment response, providing insights into optimizing digital tools for broader clinical applications.