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Optimizing Outcomes in Psychotherapy for Anxiety Disorders Using Smartphone-Based and Passive Sensing Features:

Miriam Müller-Bardorff¹, Ava Schulz¹, Christina Paersch¹

¹Experimental Psychopathology and Psychotherapy, Department of Psychiatry and Psychology, University of Zurich, Zurich, Switzerland.

JMIR Research Protocols

|May 14, 2024

Summary

Predictive algorithms can identify patients likely to benefit from cognitive behavioral therapy (CBT) for anxiety disorders. This research developed models to forecast CBT treatment response and understand underlying mechanisms.

Keywords:

CBT EMA anxiety cognitive behavioral therapy digital ecological momentary assessment passive sensing transdiagnostic

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Area of Science:

Psychiatry and Mental Health
Digital Health
Machine Learning in Healthcare

Background:

Cognitive behavioral therapy (CBT) is effective for anxiety disorders, but only half of patients benefit.
Predictive algorithms using digital and passive sensing data could improve patient selection for CBT.

Purpose of the Study:

To identify predictive features for transdiagnostic CBT response in anxiety disorders.
To investigate mechanisms underlying treatment response to CBT.

Main Methods:

A randomized controlled trial comparing CBT to a waitlist (3:1 ratio, N=150).
Multimodal data collection including self-reports, experimental tasks, biological samples, ecological momentary assessments, and passive sensing.
Machine learning models (e.g., XGBoost, PRN, KNN) used for predictive modeling.

Main Results:

The trial is completed, and results are pending dissemination.
Data analysis involved feature selection and machine learning for response prediction.
Mechanistic hypotheses regarding self-efficacy and daily symptoms were investigated.

Conclusions:

The study aims to enhance CBT by accurately predicting treatment response.
Understanding and augmenting treatment mechanisms will enable personalized anxiety disorder treatment.