Recruiting

AI Technology vs. Multi-professional Care Team

Sponsor:

Dr. Nazanin Alavi

Code:

NCT05648175

Conditions

Depression

Eligibility Criteria

Sex: All

Age: 18+

Healthy Volunteers: Not accepted

Interventions

e-CBT

e-CBT + Phone Call

e-CBT + Phone Call + Pharmacotherapy

Study Details

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

Study ID

NCT05648175

Start date

Dec 1, 2022

Status verified date

Oct, 2024

Completion date

Dec 1, 2025

Anticipated

Primary completion date

Dec 1, 2025

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 18+

Healthy Volunteers: Not accepted

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
  • Having consistent and reliable access to the internet

Exclusion Criteria:

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

Study Design

Enrollment

186 participants

Anticipated

Allocation

Randomized

Intervention Model

Parallel Assignment

Primary purpose

Treatment

Interventions and Outcome Measures

Arms

experimental: Artificial Intelligence Allocation

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.

active comparator: Healthcare Team Allocation

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).

Interventions

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.

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.

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.

Primary outcome measure

  • Change in Patient Health Questionnaire (PHQ-9) Score [ Time Frame: week 0, 4, 7, 10, 13, and 3, 6, and 12-month follow-up. ]
  • Change in Quick Inventory of Depressive Symptoms (QIDS) Score [ Time Frame: week 0, 4, 7, 10, 13, and 3, 6, and 12-month follow-up. ]
  • Change in Assessment of Quality of Life (AQoL-8D) Score [ Time Frame: week 0, 4, 7, 10, 13, and 3, 6, and 12-month follow-up. ]

Central Contacts and Locations

Central contacts

Nazanin Alavi, MD FRCPC

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

Locations

Hotel Dieu Hospital

Recruiting

Kingston, Ontario, Canada, K7L 5G3

Contacts

Nazanin Alavi

6479672079nazanin.alavitabari@kingstonhsc.ca

More Information

Sponsor

Dr. Nazanin Alavi

Last update posted

Oct 18, 2024

Last verified

Oct, 2024

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-10. This information was provided to ClinicalTrials.gov by Dr. Nazanin Alavi on 2024-10-18.