eCBT Plus vs Multi-professional Care Team for Depression
Recruiting in Palo Alto (17 mi)
Overseen ByNazanin Alavi, MD FRCPC
Age: 18+
Sex: Any
Travel: May be covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: Dr. Nazanin Alavi
No Placebo Group
Trial Summary
What is the purpose of this trial?This trial tests an online therapy called e-CBT for people with depression. It compares the effectiveness of AI versus a healthcare team in deciding the level of care needed. The goal is to find a cost-effective way to reduce depression symptoms and improve treatment adherence. Internet-based cognitive behavioral therapy (ICBT) has been shown to be effective in treating depression in several studies.
Will I have to stop taking my current medications?
The trial protocol does not specify whether you need to stop taking your current medications. However, since one of the treatment options includes pharmacotherapy (medication treatment), you may be able to continue your current medications.
What data supports the effectiveness of the eCBT Plus vs Multi-professional Care Team treatment for depression?
Research shows that computerized cognitive behavioral therapy (cCBT) is effective in reducing depressive symptoms, comparable to face-to-face therapy. Additionally, AI-powered tools can enhance psychotherapy by providing real-time recommendations, improving patient engagement and satisfaction.
12345Is eCBT Plus safe for humans?
Research indicates that computerized cognitive behavioral therapy (cCBT), which includes digital and online versions of therapy, is generally safe for humans. Studies have shown it to be effective and well-accepted, with safety comparable to traditional face-to-face therapy.
14567How is the eCBT Plus treatment different from other depression treatments?
The eCBT Plus treatment is unique because it combines electronic cognitive behavioral therapy (eCBT) with additional support options like phone calls and medication, making it more accessible and potentially more effective by increasing user engagement and adherence compared to traditional therapy methods.
5891011Eligibility Criteria
This trial is for individuals diagnosed with Major Depressive Disorder (MDD) as per DSM-5, who can consent, speak and read English, and have reliable internet access. It excludes those currently in psychotherapy, experiencing psychosis or acute mania, having thoughts of suicide or homicide, or severe substance abuse issues.Participant Groups
The study compares AI decision-making to a multi-professional team's approach in assigning care levels for e-CBT treatment of depression. Participants will be randomly placed into groups receiving different intensities of e-CBT: alone; with calls; or with medication.
2Treatment groups
Experimental Treatment
Active Control
Group I: Artificial Intelligence AllocationExperimental Treatment3 Interventions
Allocation of treatment intensity by the proposed AI algorithm will be based on the machine learning and natural language processing (NLP) of textual data provided by participants and their PHQ-9 score collected through a pre-treatment screening module called the Triage Module. This module, developed by the research team, (1) provides psychoeducation on the effects of psychotherapy, (2) collects PHQ-9 scores, and (3) asks participants six open-ended questions regarding their mental health history, their experiences with mental health disorders, and what mental health difficulties they are currently facing. Based on the participant's answers to the open-ended questions, a variable called "Symptomatic Score" will be calculated using the NLP algorithm.
Group II: Healthcare Team AllocationActive Control3 Interventions
Allocation of treatment intensity by the multi-professional healthcare team will be based on the following criteria:
1. The severity of MDD symptoms (using DSM-5 criteria).
2. Mental health factors (prior treatments and responses, current and past psychotic/manic episodes, current and past suicidal/homicidal ideation/attempts, family mental health history, past psychiatric history, and hospital admissions).
3. Medical factors (current medical conditions and medications, personal and family medical history).
4. Social factors (support system and living situation, and occupational, social, and personal functional impairment).
Find A Clinic Near You
Research locations nearbySelect from list below to view details:
Hotel Dieu HospitalKingston, Canada
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Who is running the clinical trial?
Dr. Nazanin AlaviLead Sponsor
Queen's UniversityLead Sponsor
References
Computerised therapy for depression with clinician vs. assistant and brief vs. extended phone support: study protocol for a randomised controlled trial. [2021]Computerised cognitive behaviour therapy (cCBT) involves standardised, automated, interactive self-help programmes delivered via a computer. Randomised controlled trials (RCTs) and observational studies have shown than cCBT reduces depressive symptoms as much as face-to-face therapy and more than waiting lists or treatment as usual. cCBT's efficacy and acceptability may be influenced by the "human" support offered as an adjunct to it, which can vary in duration and can be offered by people with different levels of training and expertise.
Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study. [2021]Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence-powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows.
Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review. [2022]Emotional disorders are the most common mental disorders globally. Psychological treatments have been found to be useful for a significant number of cases, but up to 40% of patients do not respond to psychotherapy as expected. Artificial intelligence (AI) methods might enhance psychotherapy by providing therapists and patients with real- or close to real-time recommendations according to the patient's response to treatment. The goal of this investigation is to systematically review the evidence on the use of AI-based methods to enhance outcomes in psychological interventions in real-time or close to real-time. The search included studies indexed in the electronic databases Scopus, Pubmed, Web of Science, and Cochrane Library. The terms used for the electronic search included variations of the words "psychotherapy", "artificial intelligence", and "emotional disorders". From the 85 full texts assessed, only 10 studies met our eligibility criteria. In these, the most frequently used AI technique was conversational AI agents, which are chatbots based on software that can be accessed online with a computer or a smartphone. Overall, the reviewed investigations indicated significant positive consequences of using AI to enhance psychotherapy and reduce clinical symptomatology. Additionally, most studies reported high satisfaction, engagement, and retention rates when implementing AI to enhance psychotherapy in real- or close to real-time. Despite the potential of AI to make interventions more flexible and tailored to patients' needs, more methodologically robust studies are needed.
Effectiveness of an Internet-Based Machine-Guided Stress Management Program Based on Cognitive Behavioral Therapy for Improving Depression Among Workers: Protocol for a Randomized Controlled Trial. [2021]The effect of an unguided internet-based cognitive behavioral therapy (iCBT) stress management program on depression may be enhanced by applying artificial intelligence (AI) technologies to guide participants adopting the program.
User Acceptance of Computerized Cognitive Behavioral Therapy for Depression: Systematic Review. [2018]Computerized cognitive behavioral therapy (cCBT) has been proven to be effective in depression care. Moreover, cCBT packages are becoming increasingly popular. A central aspect concerning the take-up and success of any treatment is its user acceptance.
Enhancing the effectiveness of CBT for patients with unipolar depression by integrating digital interventions into treatment: A pilot randomized controlled trial. [2023]Blended cognitive behavioral therapy (bCBT) combines face-to-face therapy with digital elements, such as digital health apps. This pilot study aimed to explore the effectiveness and safety of a novel bCBT application for treating unipolar depression in adults combined with cognitive behavioral therapy (CBT) compared to CBT alone in routine care.
An Internet-based program for depression using activity and physiological sensors: efficacy, expectations, satisfaction, and ease of use. [2022]Computerized cognitive behavioral therapy (CCBT) has been shown to be efficacious. Moreover, CCBT can be enhanced by using physiological and activity sensors, but there is no evidence about the acceptability of all these tools. The objective of this study is to examine the efficacy, expectations, satisfaction, and ease of use of an Internet-based CCBT program for preventing depression, with and without sensors (electroencephalography, electrocardiograhpy ECG, and actigraphy), in a high-risk population (unemployed men).
Computerized Cognitive Behavioral Therapy Intervention for Depression Among Veterans: Acceptability and Feasibility Study. [2022]Computerized cognitive behavioral therapies (cCBTs) have been developed to deliver efficient, evidence-based treatment for depression and other mental health conditions. Beating the Blues (BtB) is one of the most empirically supported cCBTs for depression. The previous trial of BtB with veterans included regular guidance by health care personnel, which increased the complexity and cost of the intervention.
Scheduled Telephone Support for Internet Cognitive Behavioral Therapy for Depression in Patients at Risk for Dropout: Pragmatic Randomized Controlled Trial. [2021]Therapist-supported, internet-delivered cognitive behavioral therapy (iCBT) is efficient in the treatment of depression. However, the optimal mode and intensity of therapist support remain to be identified. Scheduled telephone support (STS) may improve adherence and outcomes but, as it is time- and resource-consuming, should be reserved for patients for whom the usual support may be insufficient.
The second Randomised Evaluation of the Effectiveness, cost-effectiveness and Acceptability of Computerised Therapy (REEACT-2) trial: does the provision of telephone support enhance the effectiveness of computer-delivered cognitive behaviour therapy? A randomised controlled trial. [2022]Computerised cognitive behaviour therapy (cCBT) is an efficient form of therapy potentially improving access to psychological care. Indirect evidence suggests that the uptake and effectiveness of cCBT can be increased if facilitated by telephone, but this is not routinely offered in the NHS.
Effect of Computer-Assisted Cognitive Behavior Therapy vs Usual Care on Depression Among Adults in Primary Care: A Randomized Clinical Trial. [2022]Depression is a common disorder that may go untreated or receive suboptimal care in primary care settings. Computer-assisted cognitive behavior therapy (CCBT) has been proposed as a method for improving access to effective psychotherapy, reducing cost, and increasing the convenience and efficiency of treatment for depression.