Comparing the Efficacy of an Electronically Delivered Cognitive Behavioral Therapy Program to a Mental Health Check-In Program for Generalized Anxiety Disorder: Protocol for a Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Generalized anxiety disorder (GAD) is a prevalent anxiety disorder, with cognitive behavioral therapy (CBT) being the gold standard treatment. However, it is inaccessible and costly to many, as the mental health industry is overwhelmed by the demand for treatment. This means effective, accessible, and time-saving strategies must be developed to combat these problems. Web-based interventions for mental health disorders are an innovative and promising way to address these barriers. While electronically delivered CBT (e-CBT) has already proved productive and scalable for treating anxiety, other less resource-intensive interventions can be innovated. Checking up on mental health face-to-face has been shown to provide similar benefits to patients with anxiety disorders previously, but more research is needed to evaluate the efficacy of web-based delivery of this intervention. OBJECTIVE: This study will compare the efficacy of e-CBT and a web-based mental health check-in program to treat GAD. These programs will both be delivered through a secure, web-based care delivery platform. METHODS: We will randomly allocate participants (N=100) who are 18 years or older with a confirmed diagnosis of GAD to either an e-CBT program or a mental health check-in program over 12 weeks to address their anxiety symptoms. Participants in the e-CBT arm will complete predesigned modules and homework assignments while receiving personalized feedback and asynchronous interaction with a therapist through the platform. Participants in the mental health check-in arm will be contacted weekly through the web-based platform's written chat feature (messaging system). Therapists will ask the participants a series of predesigned questions that revolve around a different theme each week to prompt conversation. Using clinically validated questionnaires, the efficacy of the e-CBT arm will be compared to the mental health check-in arm. These questionnaires will be completed at baseline, week 6, and week 12. RESULTS: The study received ethics approval in April 2021, and participant recruitment began in May 2021. Participant recruitment has been conducted through targeted advertisements and physician referrals. Complete data collection and analysis are expected to conclude by August 2023. Linear and binomial regression (continuous and categorical outcomes, respectively) will be conducted. CONCLUSIONS: To the research team's knowledge, this will be the first study to date comparing the efficacy of e-CBT with a web-based mental health check-in program to treat GAD. The findings from this study can help progress the development of more scalable, accessible, and efficacious mental health treatments. TRIAL REGISTRATION: ClinicalTrials.gov NCT04754438; https://classic.clinicaltrials.gov/ct2/show/NCT04754438. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48899.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.028 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.071 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".