Optimizing Outcomes in Psychotherapy for Anxiety Disorders Using Smartphone-Based and Passive Sensing Features: Protocol for a Randomized Controlled Trial
Background
Psychotherapies like cognitive behavioral therapy (CBT) are effective for anxiety disorders, but not everyone benefits from them. This study aims to use digital assessments and passive sensing features to better identify patients who would benefit from CBT.
Objective
This study aims to establish predictive features that forecast responses to transdiagnostic CBT in anxiety disorders and to investigate key mechanisms underlying treatment responses.
Methods
The study is a 2-armed randomized controlled clinical trial including patients with anxiety disorders. Key features are indexed using various assessments and smartphone-based passive sensing to predict treatment responses. Machine learning models will be used to forecast treatment response, and specific mechanistic hypotheses will be tested to understand treatment response mechanisms.
Results
The trial is now completed and the results will be disseminated through publications in scientific peer-reviewed journals and conference presentations.
Conclusions
The aim of this trial is to improve current CBT treatment by precise forecasting of treatment response and by understanding and potentially augmenting underpinning mechanisms and personalizing treatment.
TRIAL REGISTRATION: ClinicalTrials.gov NCT03945617; ClinicalTrials.gov
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42547
PMID: 38743473 | DOI: 10.2196/42547
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