Comparing the efficacy of electronic cognitive behavioral therapy to medication and combination therapy for generalized anxiety disorder: a quasi-experimental clinical trial.

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.

Efficacy of an Electronic Cognitive Behavioral Therapy Program Delivered via the Online Psychotherapy Tool for Depression and Anxiety Related to the COVID-19 Pandemic: Pre-Post Pilot Study.

BACKGROUND: Lockdowns and social distancing resulting from the COVID-19 pandemic have worsened the population's mental health and made it more difficult for individuals to receive care. Electronic cognitive behavioral therapy (e-CBT) is a cost-effective and evidence-based treatment for anxiety and depression and can be accessed remotely. OBJECTIVE: The objective of the study was to investigate the efficacy of online psychotherapy tailored to depression and anxiety symptoms during the pandemic. METHODS: The pilot study used a pre-post design to evaluate the efficacy of a 9-week e-CBT program designed for individuals with depression and anxiety affected by the pandemic. Participants were adults (N=59) diagnosed with major depressive disorder and generalized anxiety disorder, whose mental health symptoms initiated or worsened during the COVID-19 pandemic. The online psychotherapy program focused on teaching coping, mindfulness, and problem-solving skills. Symptoms of anxiety and depression, resilience, and quality of life were assessed. RESULTS: Participants demonstrated significant improvements in symptoms of anxiety (P=.02) and depression (P=.03) after the intervention. Similar trends were observed in the intention-to-treat analysis. No significant differences were observed in resilience and quality-of-life measures. The sample comprised mostly females, making it challenging to discern the benefits of the intervention in males. Although a pre-post design is less rigorous than a controlled trial, this design was selected to observe changes in scores during a critical period. CONCLUSIONS: e-CBT for COVID-19 is an effective and accessible treatment option. Improvements in clinical symptoms of anxiety and depression can be observed in individuals whose mental health is affected by the COVID-19 pandemic. TRIAL REGISTRATION: ClinicalTrials.gov NCT04476667; https://clinicaltrials.gov/study/NCT04476667. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/24913.

Comparison of online and in-person cognitive behavioral therapy in individuals diagnosed with major depressive disorder: a non-randomized controlled trial.

Objective: The increased prevalence of major depressive disorder (MDD) amid the COVID-19 pandemic has resulted in substantial growth in online mental health care delivery. Compared to its in-person counterpart, online cognitive behavioral therapy (e-CBT) is a time-flexible and cost-effective method of improving MDD symptoms. However, how its efficacy compares to in-person CBT is yet to be explored. Therefore, the current study compared the efficacy of a therapist-supported, electronically delivered e-CBT program to in-person therapy in individuals diagnosed with MDD. Methods: Participants (n = 108) diagnosed with MDD selected either a 12 week in-person CBT or an asynchronous therapist-supported e-CBT program. E-CBT participants (n = 55) completed weekly interactive online modules delivered through a secure cloud-based online platform (Online Psychotherapy Tool; OPTT). These modules were followed by homework in which participants received personalized feedback from a trained therapist. Participants in the in-person CBT group (n = 53) discussed sessions and homework with their therapists during one-hour weekly meetings. Program efficacy was evaluated using clinically validated symptomatology and quality of life questionnaires. Results: Both treatments yielded significant improvements in depressive symptoms and quality of life from baseline to post-treatment. Participants who opted for in-person therapy presented significantly higher baseline symptomatology scores than the e-CBT group. However, both treatments demonstrated comparable significant improvements in depressive symptoms and quality of life from baseline to post-treatment. e-CBT seems to afford higher participant compliance as dropouts in the e-CBT group completed more sessions on average than those in the in-person CBT group. Conclusion: The findings support e-CBT with therapist guidance as a suitable option to treat MDD. Future studies should investigate how treatment accessibility is related to program completion rates in the e-CBT vs. in-person group. Clinical Trial Registration: ClinicalTrials.Gov Protocol Registration and Results System (NCT04478058); clinicaltrials.gov/ct2/show/NCT04478058.

Comparing clinical decision-making of AI technology to a multi-professional care team in an electronic cognitive behavioural therapy program for depression: protocol.

Introduction: Depression is a leading cause of disability worldwide, affecting up to 300 million people globally. Despite its high prevalence and debilitating effects, only one-third of patients newly diagnosed with depression initiate treatment. Electronic cognitive behavioural therapy (e-CBT) is an effective treatment for depression and is a feasible solution to make mental health care more accessible. Due to its online format, e-CBT can be combined with variable therapist engagement to address different care needs. Typically, a multi-professional care team determines which combination therapy most benefits the patient. However, this process can add to the costs of these programs. Artificial intelligence (AI) has been proposed to offset these costs. Methods: This study is a double-blinded randomized controlled trial recruiting individuals experiencing depression. The degree of care intensity a participant will receive will be randomly decided by either: (1) a machine learning algorithm, or (2) an assessment made by a group of healthcare professionals. Subsequently, participants will receive depression-specific e-CBT treatment through the secure online platform. There will be three available intensities of therapist interaction: (1) e-CBT; (2) e-CBT with a 15-20-min phone/video call; and (3) e-CBT with pharmacotherapy. This approach aims to accurately allocate care tailored to each patient's needs, allowing for more efficient use of resources. Discussion: Artificial intelligence and providing patients with varying intensities of care can increase the efficiency of mental health care services. This study aims to determine a cost-effective method to decrease depressive symptoms and increase treatment adherence to online psychotherapy by allocating the correct intensity of therapist care for individuals diagnosed with depression. This will be done by comparing a decision-making machine learning algorithm to a multi-professional care team. This approach aims to accurately allocate care tailored to each patient's needs, allowing for more efficient use of resources with the convergence of technologies and healthcare. Ethics: The study received ethics approval and began participant recruitment in December 2022. Participant recruitment has been conducted through targeted advertisements and physician referrals. Complete data collection and analysis are expected to conclude by August 2024. Clinical trial registration: ClinicalTrials.Gov, identifier NCT04747873.