Determining the Efficacy of Electronic Cognitive Behavioral Therapy for Generalized Anxiety Disorder Compared to Pharmaceutical Interventions: Protocol for a Quasi-Experimental Study
Bibliographic record
Abstract
BACKGROUND: Generalized anxiety disorder (GAD) is an extremely prevalent and debilitating mental health disorder. Currently, the gold standard treatment for GAD is cognitive behavioral therapy (CBT) and/or pharmacotherapy. The most common medications used to treat GAD are selective serotonin reuptake inhibitors and selective norepinephrine reuptake inhibitors. While CBT is the gold standard treatment for GAD, it is costly, time-consuming, and often inaccessible. Fortunately, the electronic delivery of CBT (e-CBT) has emerged as a promising solution to address these barriers. e-CBT has shown to offer comparable results to in-person CBT while improving accessibility for patients and time efficiency for clinicians. OBJECTIVE: This study aims to investigate the treatment efficacy of e-CBT compared to and in conjunction with pharmacotherapy for GAD. METHODS: This study will use a quasi-experimental design to allow patients the freedom to choose which treatment modality they would like to receive. Participants with a diagnosis of GAD will be enrolled in 1 of 3 possible treatment arms: (1) e-CBT, (2) medication, or (3) a combination of e-CBT and medication. The e-CBT program will include a 12-week psychotherapy program delivered through the Online Psychotherapy Tool-a secure, cloud-based, digital mental health platform. The treatment efficacy of e-CBT will be compared with that of medication alone and medication in combination with e-CBT. RESULTS: The study received ethics approval in April 2019 and participant recruitment began in June 2019. Participant recruitment has been conducted through social media advertisements, physical advertisements, and physician referrals. To date, 146 participants (e-CBT: n=53; medication: n=49; combination: n=44) have been recruited. Data collection is expected to conclude by June 2021, and data analysis is expected to be completed by October 2021. Linear regression (for continuous outcomes) and binomial regression (for categorical outcomes) analysis will be conducted using interpretive qualitative methods. CONCLUSIONS: If either the efficacy of e-CBT is shown to be comparable to that of medication or the effects of both treatments are augmented when used in tandem, these findings could have major implications on the mental health care system. e-CBT is a more accessible and affordable treatment that could increase mental health care capacity 4-fold if proven viable. TRIAL REGISTRATION: ClinicalTrials.gov NCT04478526; https://clinicaltrials.gov/ct2/show/NCT04478526. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/27772.
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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 | 0.042 | 0.039 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.060 | 0.010 |
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".