Comparing Clinical Decision-making of AI Technology to a Multi-professional Care Team in ECBT for Depression

NCT05648175 | Recruiting

• 1 other identifier: 6037580
• Study Type: interventional
• Target: 186
• Locations: 1 country
• Sites: 1

Brief Summary

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 is the most beneficial to the patient. However, this process can add to the costs of these programs. Artificial intelligence (AI) technology has been proposed to offset these costs. Therefore, this study aims to determine a cost-effective method to decrease depressive symptoms and increase treatment adherence to e-CBT. This will be done by comparing AI technology to a multi-professional care team when allocating the correct intensity of care for individuals diagnosed with depression. This study is a double-blinded randomized controlled trial recruiting individuals (n = 186) experiencing depression according to the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5). The degree of care intensity a participant will receive will be randomly decided by either: (1) a machine learning algorithm (n = 93), or (2) an assessment made by a group of healthcare professionals (n = 93). Subsequently, participants will receive depression-specific e-CBT treatment through the secure online platform, OPTT. There will be three available intensities of therapist interaction: (1) e-CBT; (2) e-CBT with a 15-20-minute 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.

Conditions

Depression

Outcome Measures

Primary Outcomes (3)

• Change in Patient Health Questionnaire (PHQ-9) Score

  Scale of 0-3 per question, 0 = not at all, 3 = nearly every day, higher score = worse

  week 0, 4, 7, 10, 13, and 3, 6, and 12-month follow-up.

• Change in Quick Inventory of Depressive Symptoms (QIDS) Score

  Scale of 0-3 per question, 0 = better, 3 = worse

  week 0, 4, 7, 10, 13, and 3, 6, and 12-month follow-up.

• Change in Assessment of Quality of Life (AQoL-8D) Score

  Scale of 0-5 per question, 0 = better, 5 = worse

  week 0, 4, 7, 10, 13, and 3, 6, and 12-month follow-up.

Study Arms (2)

Artificial Intelligence Allocation

EXPERIMENTAL

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.

Behavioral: e-CBT; Behavioral: e-CBT + Phone Call; Behavioral: e-CBT + Phone Call + Pharmacotherapy

Healthcare Team Allocation

ACTIVE COMPARATOR

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).

Behavioral: e-CBT; Behavioral: e-CBT + Phone Call; Behavioral: e-CBT + Phone Call + Pharmacotherapy

Interventions

e-CBT BEHAVIORAL

The participant will submit their weekly homework and receive personalized feedback from their assigned therapist on OPTT. The feedback adds customization by acknowledging the participant's experiences in the past week and ensures the participant has understood the CBT concepts.

Artificial Intelligence Allocation; Healthcare Team Allocation

e-CBT + Phone Call BEHAVIORAL

In addition to the e-CBT program (see 1 above), the participant will receive a weekly phone/video call from their assigned therapist. The goal is to build on the therapeutic relationship and to add personalization with direct verbal encouragement. This phone/video call is limited to a one-time, 15-20 minutes call each intervention week.44 The purpose is to check with the patient on their treatment progress. The secure call will either be a phone or video (via Microsoft Teams) call, depending on the preference of the patient.

Artificial Intelligence Allocation; Healthcare Team Allocation

e-CBT + Phone Call + Pharmacotherapy BEHAVIORAL

In addition to the e-CBT program (see 1 above), the participant will receive standard pharmacotherapy following DSM-5 guidelines. A pharmacotherapy allocation system has been developed (Figure 1; Figure 2) that follows clinical guidelines. All medications will be prescribed by a psychiatrist on the research team. All medications are a part of the clinical standard of care. The medications will be provided to the participant through the normal process of receiving medication (i.e., pharmacy). Participants allocated to the e-CBT + Phone Call + Pharmacotherapy arm will begin the pharmacotherapy optimization process at the same time as they begin the e-CBT program. Oversight of medication in the e-CBT + Pharmacotherapy arm will be conducted by a psychiatrist on the team who will make a judgement regarding whether to alter the medications. This will not require any additional study visits/time commitment for the participants in this arm.

Artificial Intelligence Allocation; Healthcare Team Allocation

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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
• Having consistent and reliable access to the internet

You may not qualify if:

• Active psychosis
• Acute mania
• Severe alcohol, or substance use disorder
• Active suicidal or homicidal ideation
• Currently receiving psychotherapy

Sponsors & Collaborators

• Dr. Nazanin Alavi: lead

Study Sites (1)

Hotel Dieu Hospital

Kingston, Ontario, K7L 5G3, Canada

RECRUITING

MeSH Terms

Conditions

Depression

Interventions

Drug Therapy

Condition Hierarchy (Ancestors)

Behavioral Symptoms; Behavior

Intervention Hierarchy (Ancestors)

Therapeutics

Study Officials

• Nazanin Alavi, MD FRCPC

  nazanin.alavitabari@kingstonhsc.ca

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Nazanin Alavi, MD FRCPC

CONTACT

613-544-3310; nazanin.alavitabari@kingstonhsc.ca

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: CARE PROVIDER, INVESTIGATOR
• Masking Details: To ensure blinding, all participants will complete the intake assessment by the healthcare team (Arm 1) and the Triage Module (Arm 2). Only the relevant data (i.e., Arm 1: intake assessment vs. Arm 2: Triage Module) will be analyzed depending on the treatment arm that the participant is randomly assigned to.
• Purpose: TREATMENT
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Assistant Professor

Model Details: If eligible for this randomized controlled trial, participants (n = 186) will be randomized to receive an e-CBT treatment recommended by a multi-professional healthcare team consisting of a psychiatrist, psychiatric medical resident, and a trained research assistant (Arm 1, control group; n = 93), or the AI machine learning algorithm (Arm 2, experimental group; n = 93).

Study Record Dates

• First Submitted: December 5, 2022
• First Posted: December 13, 2022
• Study Start: December 1, 2022
• Primary Completion: December 1, 2025
• Study Completion: December 1, 2025
• Last Updated: October 18, 2024

Record last verified: 2024-10

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL
• Time Frame: 5 years post-study completion
• Access Criteria: Available upon request

Locations

CA (1)