Clinical trials

The goal of this clinical trial is to design an online Cognitive Behavioural Therapy (CBT) and Acceptance Commitment Therapy (ACT) program and to learn if it can treat anxiety in women transitioning into menopause (perimenopause). The main questions it aims to answer are: * Is our online psychotherapy program a practical and acceptable means of managing anxiety during perimenopause? * Does our online psychotherapy program work in improving anxiety levels during perimenopause? Participants will participate in weekly e-CBT module sessions tailored to perimenopausal anxiety and will be given weekly feedback on assignments from trained care providers through a secure online platform. Participants will complete questionnaires at the beginning, middle, and at end of the study, as well as at the three and six-month follow-up.

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.

Insomnia is defined as the inability to fall asleep or stay asleep at night and it is one of the most prevalent sleep disorders that can have deleterious impacts on health and this population's quality of life. Currently, both pharmaceutical interventions (trazodone) and cognitive behavioral therapy (CBTi) are widely used to treat patients with insomnia. Although CBTi has been efficacious in many patients, multitude of barriers for receiving treatment such as its limited availability of therapists, high costs and long wait times challenge its ability in sufficiently meeting the population's health needs and demands. To improve the delivery of CBT, electronically delivered CBTi (e-CBTi) has been developed as an accessible and effective alternative intervention for improving sleep outcomes in patients with insomnia. While evidence suggest that e-CBTi is effective when compared to placebos/waitlist control, evidence comparing guided e-CBTi to pharmaceutical interventions is still insufficient and needs further exploration.