eCBT Plus vs Multi-professional Care Team for Depression

Overseen By Nazanin Alavi, MD FRCPC

Age: 18+

Sex: Any

Travel: May Be Covered

Time Reimbursement: Varies

Trial Phase: Academic

Recruiting

Sponsor: Dr. Nazanin Alavi

Disqualifiers: Psychosis, Mania, Substance use, others

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. ^{1 2 3 4 5}

Is 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. ^{1 4 5 6 7}

How 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. ^{5 8 9 10 11}

Research Team

Nazanin Alavi, MD FRCPC

Principal Investigator

[e-mail]

Eligibility 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.

Inclusion Criteria

Diagnosed with MDD by a trained research assistant according to the criteria outlined in the DSM-5

Ability to provide informed consent

Ability to speak and read English

Exclusion Criteria

You are currently undergoing therapy for mental health.

You are currently experiencing severe mental health issues that affect your thoughts and behavior.

You have thoughts or plans about hurting yourself or someone else.

Trial Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

1 visit (virtual)

Treatment

Participants receive depression-specific e-CBT treatment through the secure online platform, OPTT, with varying intensities of therapist interaction

13 weeks

Weekly sessions (virtual)

Follow-up

Participants are monitored for changes in depressive symptoms and quality of life

12 months

Follow-up assessments at 3, 6, and 12 months

Treatment Details

Interventions

• AI Technology (Artificial Intelligence)
• e-CBT (Behavioural Intervention)
• e-CBT + Phone Call (Behavioural Intervention)
• e-CBT + Phone Call + Pharmacotherapy (Behavioural Intervention)

Trial Overview 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.

Participant Groups

Group I: Artificial Intelligence Allocation Experimental Treatment 3 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 Allocation Active Control 3 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).

Who Is Running the Clinical Trial?

Dr. Nazanin Alavi

Lead Sponsor

Trials

14

Recruited

1,100+

Dr. Nazanin Alavi

Lead Sponsor

Trials

14

Recruited

1,100+

Queen's University

Lead Sponsor

Trials

382

Recruited

122,000+

Findings from Research

Computerised cognitive behaviour therapy (cCBT) has been shown to effectively reduce depressive symptoms, comparable to face-to-face therapy, and is more effective than waiting lists or standard treatment.

This study will explore how different types of human support (brief vs. extended, expert vs. assistant) impact the effectiveness and acceptability of cCBT, using a sample of 200 adults with non-suicidal depression to ensure robust results.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computerised therapy for depression with clinician vs. assistant and brief vs. extended phone support: study protocol for a randomised controlled trial. Gega, L., Swift, L., Barton, G., et al.[2021]

The study involving 17 patients with major depressive disorder found that an artificial intelligence-powered clinical decision support system (CDSS) did not significantly increase appointment length, indicating its feasibility for use in clinical settings.

Most patients (92%) and a majority of physicians (71%) reported that the CDSS was easy to use, suggesting it could enhance treatment personalization and potentially improve the patient-physician relationship.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

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. Popescu, C., Golden, G., Benrimoh, D., et al.[2021]

A systematic review of 10 studies found that using artificial intelligence (AI), particularly conversational AI agents like chatbots, can significantly enhance psychotherapy outcomes and reduce symptoms in patients with emotional disorders.

Patients reported high satisfaction and engagement when AI was integrated into therapy, suggesting that AI can make psychological interventions more personalized and effective, although further robust research is needed.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review. Gual-Montolio, P., Jaén, I., Martínez-Borba, V., et al.[2022]

This study will evaluate the effectiveness of an innovative internet-based cognitive behavioral therapy (iCBT) program enhanced by artificial intelligence (AI) technologies to improve depression among 1400 healthy workers during the COVID-19 pandemic.

The program, called SMART-CBT, consists of a 6-week course with machine-guided exercises and aims to provide a cost-effective mental health solution that can be widely disseminated, marking the first randomized controlled trial of its kind.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

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. Kawakami, N., Imamura, K., Watanabe, K., et al.[2021]

A systematic review of 29 studies on computerized cognitive behavioral therapy (cCBT) for depression found that user acceptance is generally high, with 8 studies reporting very high acceptance and 17 reporting high acceptance levels.

Despite the positive reception of cCBT, the review highlighted significant methodological issues in how user acceptance was measured and defined, indicating a need for clearer operationalization in future research.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

User Acceptance of Computerized Cognitive Behavioral Therapy for Depression: Systematic Review. Rost, T., Stein, J., Löbner, M., et al.[2018]

In a pilot study involving 82 adults, blended cognitive behavioral therapy (bCBT) showed larger improvements in depressive symptoms compared to standard cognitive behavioral therapy (CBT), although these differences were not statistically significant.

bCBT demonstrated superior outcomes in secondary measures of psychological health and anxiety symptoms, suggesting it may offer additional benefits over traditional CBT, warranting further investigation in larger randomized controlled trials.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Enhancing the effectiveness of CBT for patients with unipolar depression by integrating digital interventions into treatment: A pilot randomized controlled trial. Kalde, J., Atik, E., Stricker, J., et al.[2023]

Adding scheduled telephone support (STS) to internet-delivered cognitive behavioral therapy (iCBT) significantly improved treatment adherence, with 58% of patients reaching midtreatment compared to 36% in the usual support group.

Patients receiving STS also showed greater improvement in depressive symptoms, with an average decrease of 3.63 points on the Beck Depression Inventory, compared to just 1.1 points in the control group.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Scheduled Telephone Support for Internet Cognitive Behavioral Therapy for Depression in Patients at Risk for Dropout: Pragmatic Randomized Controlled Trial. Pihlaja, S., Lahti, J., Lipsanen, JO., et al.[2021]

In a study involving 369 adults with depression, telephone-facilitated computerized cognitive behavior therapy (cCBT) showed significantly better outcomes at 4 months compared to minimally supported cCBT, with participants being twice as likely to no longer be depressed (odds ratio 2.05).

While the benefits of telephone facilitation diminished by 12 months, the overall results indicated that this approach could lead to lower depression scores and improved anxiety, suggesting that telephone support may enhance the effectiveness of cCBT for treating depression.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

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. Brabyn, S., Araya, R., Barkham, M., et al.[2022]

References

1. England pubmed.ncbi.nlm.nih.gov

Computerised therapy for depression with clinician vs. assistant and brief vs. extended phone support: study protocol for a randomised controlled trial. [2021]

2. Canada pubmed.ncbi.nlm.nih.gov

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]

3. Switzerland pubmed.ncbi.nlm.nih.gov

Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review. [2022]

4. Canada pubmed.ncbi.nlm.nih.gov

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]

5. Canada pubmed.ncbi.nlm.nih.gov

User Acceptance of Computerized Cognitive Behavioral Therapy for Depression: Systematic Review. [2018]

6. England pubmed.ncbi.nlm.nih.gov

Enhancing the effectiveness of CBT for patients with unipolar depression by integrating digital interventions into treatment: A pilot randomized controlled trial. [2023]

7. New Zealand pubmed.ncbi.nlm.nih.gov

An Internet-based program for depression using activity and physiological sensors: efficacy, expectations, satisfaction, and ease of use. [2022]

8. Canada pubmed.ncbi.nlm.nih.gov

Computerized Cognitive Behavioral Therapy Intervention for Depression Among Veterans: Acceptability and Feasibility Study. [2022]

9. Canada pubmed.ncbi.nlm.nih.gov

Scheduled Telephone Support for Internet Cognitive Behavioral Therapy for Depression in Patients at Risk for Dropout: Pragmatic Randomized Controlled Trial. [2021]

10. England pubmed.ncbi.nlm.nih.gov

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]

11. United States pubmed.ncbi.nlm.nih.gov

Effect of Computer-Assisted Cognitive Behavior Therapy vs Usual Care on Depression Among Adults in Primary Care: A Randomized Clinical Trial. [2022]