Digital health monitoring for adults with treatment-resistant depression: Observational feasibility study protocol

Abstract

Treatment-resistant depression (TRD) affects approximately 30% of adults living with major depressive disorder, resulting in many patients not achieving complete symptom remission and at risk for relapse. Digital health monitoring (DHM) using electronic data capture platforms and wearable devices is emerging as a promising therapeutic adjunct in depression. Research indicates that continuous monitoring of physical and mental health symptoms with smart devices not only helps improve patient health awareness and healthy lifestyle behaviours, but also offers scientists and clinicians insights into patients’ health patterns over time. Critically, there is a paucity of studies informing the feasibility of DHM in TRD and the efficacy of clinically meaningful digital biomarkers informing depression treatment response, remission, and relapse. Before definitive trials evaluating DHM can be designed for patients with TRD, and evidence-based recommendations can be made to inform clinical practice, pilot data is needed. The primary aim is to determine the feasibility of implementing DHM platforms to collect active (clinical assessments) and passive (wearable biometrics) health data among adult outpatients with TRD. The secondary aim is to identify and evaluate digital biomarkers of depression treatment remission and response. This single-site, observational pilot study has received research ethics board approval and recruitment is going. A DHM suite will be offered to 200 outpatients with TRD enrolled for clinical neuropsychiatric treatment. Electronic clinical assessments of anxiety (GAD-7) and depression (PHQ-9 or MADRS) will be administered on treatment days in line with standard care, and wearable devices (smart rings) will be available for participants to wear throughout the course of treatment. Descriptive and inferential group- and individual-level analyses will inform study objectives and data-driven machine learning techniques will develop personalized digital phenotype profiles of the “depressed experience” in patients with TRD. Recruitment has started and will remain open until April 2027. This pilot trial will determine the feasibility of, and provide clinical parameter estimates for, future larger trials implementing DHM platforms in TRD. This area of study might emphasize the need for personalized treatment protocols in TRD that integrate digital tools to augment the standard of care, ultimately leading to the development and advancement of more targeted and effective interventions. Study registration Clinicaltrials.gov NCT06732089. Registered December 9 th , 2024.