Protocol for a randomized trial to predict the efficacy of cognitive and behavioral interventions for symptoms of depression.
Cognitive behavioral therapy (CBT) is one of the most common interventions for depression and has two key components: Cognitive Restructuring (CR) and Behavioral Activation (BA). However, no evidence-based guidelines exist to help clients and clinicians decide whether CBT would be a good first-line treatment for a given individual based on their personal characteristics, and which CBT intervention would benefit them more. We propose that specific capacities to learn from new information and experiences are prerequisites for response to CBT and that BA and CR require different learning capacities. In this study, we aim to develop predictive models of symptom change based on computationally-derived variables from behavioral tasks, in addition to clinical and demographic self-report data, to identify parameters and variables that can determine which individuals with depressive symptoms would benefit from CBT-based interventions and, ideally, which specific interventions they would benefit from more.
We plan to recruit at least 1,500 adult participants who report having symptoms of depression and reside in U.S. After completing a series of questionnaires and behavioral tasks to assess their learning propensities, participants will be randomly assigned to a BA or a CR group. Using an online self-help tool, participants will then engage with designated modules according to their assigned group for five weeks. We will assess symptoms 1 week post-intervention (main end point of study) and follow up at 6, 18, and 42 weeks post-intervention. Upon enrolling and consenting into the main study, participants will be randomly assigned to either the training dataset or the held-out test dataset at a ratio of 2:1. This enables a clean separation of training and test datasets and prevent data leakage. We plan to build cross-validated predictive algorithms on the training dataset, and preregister our analysis plan before we validate our models and hypotheses in the held-out, unseen, test dataset. Enrollment of the study started 23rd January, 2024.
ClinicalTrials.gov, identifier (NCT06631183). The protocol follows the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guidelines. Numbers in brackets follow subsection numbers in the guidelines.
We plan to recruit at least 1,500 adult participants who report having symptoms of depression and reside in U.S. After completing a series of questionnaires and behavioral tasks to assess their learning propensities, participants will be randomly assigned to a BA or a CR group. Using an online self-help tool, participants will then engage with designated modules according to their assigned group for five weeks. We will assess symptoms 1 week post-intervention (main end point of study) and follow up at 6, 18, and 42 weeks post-intervention. Upon enrolling and consenting into the main study, participants will be randomly assigned to either the training dataset or the held-out test dataset at a ratio of 2:1. This enables a clean separation of training and test datasets and prevent data leakage. We plan to build cross-validated predictive algorithms on the training dataset, and preregister our analysis plan before we validate our models and hypotheses in the held-out, unseen, test dataset. Enrollment of the study started 23rd January, 2024.
ClinicalTrials.gov, identifier (NCT06631183). The protocol follows the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guidelines. Numbers in brackets follow subsection numbers in the guidelines.
Authors
Ding Ding, Chiu Chiu, Moon Moon, Ren Ren, Turner Turner, Shoval Shoval, Niv Niv, Berwian Berwian
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