eCBT Plus vs Multi-professional Care Team for Depression

Overseen By Nazanin Alavi, MD FRCPC

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Dr. Nazanin Alavi

Disqualifiers: Psychosis, Mania, Substance use, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 4

Duration: 13 weeks

186 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests two methods to determine the best support level for individuals using online therapy for depression. One method employs artificial intelligence (AI) to analyze personal information and recommend a treatment, while the other relies on a team of healthcare professionals to make the decision. The goal is to find a cost-effective way to help people adhere to their therapy and reduce depression symptoms. Individuals may be a good fit if they have been diagnosed with major depressive disorder (MDD), are not currently in therapy, and have steady internet access. As an unphased trial, this study provides a unique opportunity to contribute to innovative research that could enhance online therapy for depression.

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.

Is there any evidence suggesting that this trial's treatments are likely to be safe?

Research has shown that AI technology holds promise in managing depression. Studies have found that AI can accurately diagnose and predict depression symptoms. For instance, one study demonstrated that a specific AI model predicted depression with over 99% accuracy. This suggests that AI tools might be reliable and safe for managing depression.

Electronic cognitive behavioral therapy (e-CBT) is already a well-known treatment for depression. It is considered safe and effective, and many people tolerate it well. Combining e-CBT with other methods like phone calls or medication usually aims to enhance treatment, not due to safety concerns.

Overall, both AI technology and e-CBT treatments appear to be safe options based on current research. However, consulting healthcare professionals is always important to ensure any treatment is appropriate. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about this trial because it explores how AI technology can personalize depression treatment by using machine learning and natural language processing (NLP) to analyze patients' mental health data. Unlike traditional treatments which rely heavily on medication or therapist assessments, this approach uses an AI-driven Triage Module to determine the treatment intensity based on a participant's symptom profile and PHQ-9 score. Additionally, the trial compares this innovative AI method to a traditional multi-professional healthcare team approach, potentially offering insights into more tailored and efficient care strategies for depression. This could lead to more personalized and adaptive treatment plans, enhancing overall patient outcomes.

What evidence suggests that this trial's treatments could be effective for depression?

Research has shown that online cognitive behavioral therapy (e-CBT) effectively treats depression. Studies have found that it reduces symptoms by providing structured therapy accessible at one's own pace online. In this trial, one group of participants will receive treatment allocation through AI technology, which matches individuals with the right treatment by analyzing large amounts of data to predict and track depression, thereby personalizing care. Another group will receive treatment allocation through a multi-professional healthcare team, which assesses various factors to determine the appropriate level of care. Overall, both e-CBT and AI offer promising methods for managing depression more effectively. ^{1 2 3 4 6}

Who Is on the Research Team?

Nazanin Alavi, MD FRCPC

Principal Investigator

[e-mail]

Are You a Good Fit for This Trial?

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.

Timeline for a Trial Participant

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

What Are the Treatments Tested in This Trial?

Interventions

• AI Technology
• e-CBT
• e-CBT + Phone Call
• e-CBT + Phone Call + Pharmacotherapy

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.

How Is the Trial Designed?

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+

Published Research Related to This Trial

The study tested the acceptability and feasibility of the unguided computerized cognitive behavioral therapy (cCBT) 'Beating the Blues' (BtB) among 49 US military veterans with mild to moderate depression, showing that the intervention was acceptable to participants.

While the study did not fully meet all feasibility criteria, it suggests that cCBT can be effectively implemented without professional guidance, indicating potential for stand-alone digital mental health interventions in veteran populations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computerized Cognitive Behavioral Therapy Intervention for Depression Among Veterans: Acceptability and Feasibility Study. Stearns-Yoder, KA., Ryan, AT., Smith, AA., et al.[2022]

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]

An Internet-based computerized cognitive behavioral therapy (CCBT) program significantly improved depression and anxiety symptoms in unemployed men at risk of depression, with greater improvements observed in participants using physiological sensors compared to those who did not.

Participants reported high levels of satisfaction and usability with the CCBT program, scoring an average of 88 out of 100, indicating that the program is not only effective but also well-received and easy to use.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

An Internet-based program for depression using activity and physiological sensors: efficacy, expectations, satisfaction, and ease of use. Botella, C., Mira, A., Moragrega, I., et al.[2022]

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]

In a randomized clinical trial involving 175 primary care patients with depression, clinician-supported computer-assisted cognitive behavior therapy (CCBT) significantly improved depressive symptoms compared to treatment as usual (TAU) at posttreatment and follow-up points (3 and 6 months).

CCBT was particularly effective in a diverse population, including many low-income individuals with limited internet access, suggesting it can be a valuable treatment option in primary care settings where traditional therapy may be less accessible.

NCBI (Pubmed)

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. Wright, JH., Owen, J., Eells, TD., et al.[2022]

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]

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]

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]

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]

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]

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]

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