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

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

Interventions

BEHAVIORAL

e-CBT

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.

BEHAVIORAL

e-CBT + Phone Call

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.

BEHAVIORAL

e-CBT + Phone Call + Pharmacotherapy

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.

Sponsors & Collaborators

• Dr. Nazanin Alavi

  lead OTHER

Principal Investigators

• Nazanin Alavi, MD FRCPC · [email protected]

Study Design

Allocation

RANDOMIZED

Purpose

TREATMENT

Masking

DOUBLE

Model

PARALLEL

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2022-12-01

Primary Completion

2025-12-01

Completion

2025-12-01

Countries

• Canada

Study Locations