Comparing the efficacy of electronic cognitive behavioral therapy to medication and combination therapy for generalized anxiety disorder: a quasi-experimental clinical trial
Bibliographic record
Abstract
Background: Generalized anxiety disorder (GAD) is a debilitating mental health disorder with first-line treatments include cognitive behavioral therapy (CBT) and pharmacotherapy. CBT is costly, time-consuming, and inaccessible. Electronic delivery (e-CBT) is a promising solution to address these barriers. However, due to the novelty of this intervention, more research testing the e-CBT efficacy independently and in conjunction with other treatments is needed. Objective: This study investigated the efficacy of e-CBT compared to and in conjunction with pharmacotherapy for GAD. Methods: This study employed a quasi-experimental design where patients selected their preferred treatment modality. Patients with GAD were enrolled in either e-CBT, medication, or combination arms. The 12-week e-CBT program was delivered through a digital platform. The medications followed clinical guidelines. The efficacy of each arm was evaluated using questionnaires measuring depression, anxiety, and stress severity, as well as quality of life. Results: There were no significant differences between arms (N e-CBT = 41; N Medication = 41; N Combination = 33) in the number of weeks completed or baseline scores. All arms showed improvements in anxiety scores after treatment. The medication and combination arms improved depression scores. The e-CBT and Combination arms improved quality of life, and the combination arm improved stress scores. There were no differences between the groups in depression, anxiety, or stress scores post-treatment. However, the combination arm had a significantly larger improvement in quality of life. Gender and treatment arm were not predictors of dropout, whereas younger age was. Conclusion: Incorporating e-CBT on its own or in combination with pharmaceutical interventions is a viable option for treating GAD. Treating GAD with e-CBT or medication appears to offer significant improvements in symptoms, with no meaningful difference between the two. Combining the treatments also offer significant improvements, while not necessarily superior to either independently. The findings suggest that all options are viable. Taking the patient's preferred treatment route based on their lifestyle, personality, and beliefs into account when deciding on treatment should be a priority for care providers.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".