Accepted for/Published in: JMIR Research Protocols
Date Submitted: Sep 8, 2022
Date Accepted: Oct 20, 2022
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Optimizing Outcomes in Psychotherapy for Anxiety Disorders (OPTIMAX): Study Protocol for a Randomized Controlled Trial on Efficacy and Response Prediction using smartphone-based and passive sensing features
ABSTRACT
Background:
Psychotherapy, such as cognitive behavioral therapy (CBT), currently have the strongest evidence of durable symptom changes for most psychological disorders, such as anxiety disorders. Nevertheless, only about half of individuals treated with CBT benefit from it. Predictive algorithms, including digital assessments and passive sensing features, could better identify patients who would benefit from CBT and thus improve treatment choices.
Objective:
This study aims to establish predictive features that forecast respons to transdiagnostic CBT in anxiety disorders and to investigate key mechanisms underlying treatment responses.
Methods:
This study is a two-armed randomized controlled clinical trial. We include patients with anxiety disorders who are randomized to either transdiagnostic CBT (CBT) [1] or a waitlist (WAIT). We index key features to predict responses prior to starting treatment using subjective self-report questionnaires, experimental tasks, biological samples, ecological momentary assessments, activity tracking, and smartphone-based passive sensing to derive a multimodal feature set for predictive modeling. Additional assessments take place weekly at mid- and post-treatment and at 6- and 12-month follow-ups to index anxiety and depression symptom severity. We aim to include 150 patients, randomized to CBT versus WAIT at a 3:1 ratio. The dataset will be subject to full feature and important features selected by minimal redundancy and maximal relevance (mRMR) feature selection and then fed into machine leaning models, including eXtreme gradient boosting (XGBoost), pattern recognition network, k-nearest neighborhood (KNN) to forecast treatment response.The performance of the developed models will be evaluated. In addition to predictive modeling, we will test specific mechanistic hypotheses (eg, association between self-efficacy, daily symptoms obtained using ecological momentary assessments, and treatment response) to elucidate mechanisms underlying treatment response.
Results:
This study was approved by the Cantonal Ethics Committee, Zurich. The results will be disseminated through publications in scientific peer-reviewed journals and conference presentations.
Conclusions:
The aim of the current trial is to improve current CBT treatment by precise forecasting of treatment response and by understanding and potentially augmenting underpinning mechanisms and personalizing treatment. Clinical Trial: NCT03945617
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