The goal of this observational study is to learn about remote mental health monitoring technology for adults with treatment-resistant depression. The main question it aims to answer is: are digital mental health monitoring tools (an electronic data capture platform and wearable device (e.g., smartwatch or smart-ring)) feasible to implement alongside clinical treatment for depression? The secondary aim of this study is to inform preliminary clinical parameters for larger, definitive studies.
Participants receiving neuropsychiatric treatment (repetitive transcranial magnetic stimulation, intravenous ketamine, or electroconvulsive therapy) as part of their regular medical care for treatment-resistant depression in the Interventional Psychiatry Program will have their clinical assessment data entered into a digital platform and will wear an accessory-based wearable device for the duration of treatment.
Adults (aged 18+) who are capable of giving informed consent
Patients meeting diagnostic criteria for major depressive disorder (MDD) without psychotic symptoms according to the DSM-5
Currently experiencing a major depressive episode (MDE)
Montgomery-Asperg Depression Rating Scale (MADRS) score greater than 20 at screening
Individuals without Internet access (required to access study platforms)
Medication changes, aside from the treatment received through the IPP, one month (28 days) before screening, or during the entire duration of treatment (variable based on treatment arm)
Participants who do not speak English fluently enough to successfully communicate study information, answer questions accurately, and/or obtain informed consent
Adults suffering from treatment-resistant depression (TRD) who are being treated in the Interventional Psychiatry Program at St. Michael's Hospital, Unity Health Toronto. Patients have a confirmed diagnosis of Major Depressive Disorder (MDD) and are experiencing a Major Depressive Episode (MDE) at the time of enrollment.
Case-only
Retrospective
Not listed
Feasibility
To determine the feasibility of using a web-based digital platform and wearable devices to collect active and passive data, respectively, among adults receiving clinical treatment for treatment-resistant depression. This will be measured by: number of recruitments (minimum threshold of 6-7 participants/month), recruitment rates (relative to invited patient-participants; minimum threshold of 80%), dropout rates (maximum threshold of 30%), reasons for dropout (qualitative), and data completion/adherence rates (minimum threshold of 70%).
Longitudinal Changes in Symptom Presentation
To quantify changes in active (clinical symptoms) and passive (physiological) measures of physical (activity) and mental health (depression, anxiety, sleep) over time. Active data will focus on anxiety and depression symptoms across treatment, as reported on the GAD-7, PHQ-9, and MADRS. Passive data will focus on physiological biometrics (e.g., heart rate, respiration, step count, metabolic equivalency, body temperature, sleep staging, skin conductance response) captured by the wearable devices.
Predicting Treatment Response, Remission, and Relapse
To model and predict the likelihood of a patient achieving anxiety/depression treatment response (reduction greater than or equal to 50% on clinical assessment), remission (assessment score less than clinical threshold), and relapse (assessment score greater than clinical threshold after achieving remission) captured through active self-/clinician-administered assessments, based on longitudinal active and passive data.
Personalized Digital Phenotype Profiling (pDPP)
To construct pDPPs (a.k.a. digital fingerprints) for each participant based on longitudinal active and passive data. This will be achieved using machine learning (ML), artificial intelligence (AI), and advanced data analytic principles.
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Unity Health Toronto
Lead sponsor
University of Toronto
Collaborator
Toronto Metropolitan University
Collaborator
