W31 – Combining Virtual Psychotherapy with Innovative Monitoring Techniques: Using an Interdisciplinary Approach to a Multi-Level Problem

W31 – Combining Virtual Psychotherapy with Innovative Monitoring Techniques: Using an Interdisciplinary Approach to a Multi-Level Problem

Saturday, Oct. 21
14:30 – 15:30 (1 hr)
Meeting Room: Port Alberni (4th floor – North Tower)
Nazanin Alavi*, MD, FRCPC; Jasleen Jagayat, BSc; Jazmin Eadie, BA; Callum Stephenson, BScH MSc; Sarah Zhu, BScH Student; Mohsen Omrani, MD PhD; Georgina Layzell, BBP; Nazanin Alavi, MD FRCPC

CanMEDS Roles:

  1. Health Advocate
  2. Medical Expert
  3. Scholar

At the end of this session, participants will be able to: 1) Understand the accessibility and scalability benefits associated with virtual psychotherapy; 2) Learn to incorporate techniques into their virtual psychotherapy delivery to assist with patient monitoring and improve outcomes; and 3) Understand how to tailor virtual psychotherapy programs to specific population subsets to make content more relatable and digestible.

With an increasing demand for mental health treatments, we are reaching a tipping point in the health care system. The gold standard treatment for various mental health disorders is psychotherapy; however, it is often inaccessible, ineffective, and time-consuming. Many have turned to virtually delivered psychotherapy (e-psychotherapy). Although great promise has been shown, additional steps are needed to help e-psychotherapy reach its full potential. The Queen’s University Online Psychotherapy Lab has developed online treatments for many mental health disorders through the Online Psychotherapy Tool (OPTT), a secure web-based psychotherapy platform. Through our programs, we can provide patients with geographically and temporally accessible personalized treatments. Through this workshop, Dr. Nazanin Alavi will moderate instruction on several ways we have successfully implemented additional techniques to push the capabilities of e-psychotherapy even further. Jasleen Jagayat will discuss the use of artificial intelligence in patient monitoring and prediction of treatment adherence, as well as using a stepped-care intensity, depending on patient needs. Jazmin Eadie and Georgina Layzell will discuss how to tailor e-psychotherapy programs to a specific population subset, specifically oncology and palliative care patients. Callum Stephenson will discuss how neuroimaging can provide further insight into treatment outcomes in e-psychotherapy for patients with obsessive–compulsive disorder. Finally, Sarah Zhu will discuss how fitness trackers can be implemented into e-psychotherapy programs for monitoring treatment outcomes, specifically in patients with insomnia. Using these techniques, the capabilities of e-psychotherapy can be augmented, helping to further relieve the overwhelming burden placed on the health care system in Canada.

W31a – Using Machine Learning to Determine Treatment Interventions in Electronic Cognitive-Behavioural Therapy for Depression
Jasleen Jagayat, BSc

At the end of this session, participants will be able to: 1) Compare online cognitive-behavioural therapy to traditional face-to-face therapy; 2) Describe the role of a stratified care model and benefits of incorporating such a model into mental health care; and 3) Consider using additional interventions to assist psychotherapy outcomes and explore relevant guiding factors.

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 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 most beneficial to the patient. However, this process can add to the costs of these programs. Artificial intelligence (AI) has been proposed to offset these costs. This study aims to determine a cost-effective method to decrease depressive symptoms and increase treatment adherence to e-CBT. This was done by comparing AI technology to a multi-professional care team when allocating the correct intensity of care for people diagnosed with depression. This study is a double-blind randomized controlled trial recruiting individuals with depression. The degree of care intensity a participant received was randomly decided by either an AI algorithm or assessment made by a group of healtcare professionals. Subsequently, participants received depression-specific e-CBT treatment through a secure online platform. There were three available intensities of therapist interaction: e-CBT, e-CBT with a weekly 15- to 20-minute phone or video call, and e-CBT with a phone or video call and pharmacotherapy. This approach aims to accurately allocate care tailored to each patient’s needs, allowing for more efficient use of resources with the convergence of technologies and health care.


  1. Nicholas J, Ringland KE, Graham AK, et al. Stepping up: predictors of ‘stepping’ within an iCBT stepped-care intervention for depression. Int J Environ Res Public Health 2019;16(23):4689.
  2. Karyotaki E, Ebert DD, Donkin L, et al. Do guided Internet-based interventions result in clinically relevant changes for patients with depression? An individual participant data meta-analysis. Clin Psychol Rev 2018;63:80–92.

W31b – Efficacious Web-Based Psychotherapy to Address Depression and Anxiety Among Patients Receiving Oncological and Palliative Care: An Open-Label Randomized Controlled Trial
Jazmin Eadie, BA

At the end of this session, participants will be able to: 1) Learn about unique psychological stressors that impact oncological and palliative populations; 2) Learn the barriers this population might experience when attending face-to-face therapy; and 3) Understand how web-based psychotherapy can be tailored to and benefit oncological and palliative populations.

Oncological and palliative care patients face an increased risk of depression and anxiety. Cognitive-behavioural therapy (CBT) and mindfulness have successfully improved this population’s mental health. In-person psychotherapy is often costly and lacking accessibility, with long wait lists and strict scheduling, which can be prohibitive for these patients. Electronic CBT (e-CBT) is a promising alternative that has shown efficacy in this and other patient populations. This study aims to quantify the effectiveness of e-CBT and mindfulness therapy in oncological and palliative patients with depression and anxiety symptoms. Participants with depression or anxiety related to their diagnosis were recruited from care settings and randomly assigned to eight weekly e-CBT/mindfulness modules or treatment as usual. Modules included CBT concepts, problem-solving, mindfulness, homework, and personalized feedback from their therapist through a secure platform. Participants completed the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) in weeks one, four, and eight. Significant decreases in PHQ-9 and GAD-7 scores in individuals support the efficacy hypothesis. Currently, 10 patients in the e-CBT/mindfulness arm and 12 in the treatment-as-usual arm have completed the study. Decreases in PHQ-9 and GAD-7 scores within the e-CBT group support the efficacy hypothesis. Specifically, PHQ-9 scores decreased over the three repeated measures (analysis of variance [ANOVA], two groups, three repeated measures, and the decrease in GAD-7 scores were similarly significant). As hypothesized, the results suggest that e-CBT/mindfulness therapy is an affordable, accessible, and efficacious mental health treatment for this population.


  1. Barrera I, Spiegel D. Review of psychotherapeutic interventions on depression in cancer patients and their impact on disease progression. Int Rev Psychiatry 2014;26:31–43.
  2. Walker J, Hansen CH, Martin P, et al. Prevalence, associations, and adequacy of treatment of major depression in patients with cancer: a cross-sectional analysis of routinely collected clinical data. Lancet Psychiatry 2014;1:343–350.

W31c – Using Electronically Delivered Therapy and Brain Imaging to Understand Obsessive–Compulsive Disorder Pathology: A Pilot Feasibility Study
Callum Stephenson, BScH MSc

At the end of this session, participants will be able to: 1) Understand the insight neuroimaging can offer into psychotherapeutic treatment outcomes and effectiveness; 2) Understand how to implement and design online psychotherapeutic interventions for obsessive–compulsive disorder patients effectively; and 3) Understand how changes in neural activation can indicate changes in behavioural traits.

Current psychotherapeutic treatments for obsessive–compulsive disorder (OCD), while somewhat effective, yield low accessibility and scalability. A lack of knowledge regarding the neural pathology of OCD may hinder the development of innovative treatments. Previous research has observed baseline brain activation patterns in OCD patients, elucidating some understanding of the implications; however, by using neuroimaging to observe the effects of treatment on brain activation, a complete picture of OCD can be drawn. This pilot study implemented an electronic cognitive-behavioural therapy (e-CBT) program for OCD and observed its effects on cortical activation levels during a symptom provocation task. It was hypothesized that abnormal activations could be attenuated following treatment. OCD patients completed a 16-week e-CBT program administered through an online platform, mirroring in-person content. Treatment efficacy was evaluated with behavioural questionnaires and neuroimaging. Activation levels were assessed at the resting state and during the symptom provocation task. Seven participants completed the program, with significant improvements observed between baseline and posttreatment for symptom severity and levels of functioning. No significant improvement was observed in the quality of life. No significant changes in cortical activation were observed between baseline and posttreatment. This project sheds light on the application of e-CBT as a tool to evaluate the effects of treatment on cortical activation, setting the stage for a larger-scale study. The program showed promise in feasibility and effectiveness. Although there were no significant findings regarding changes in cortical activation, the trends agreed with previous literature, suggesting future work could provide insight into whether e-CBT offers cortical effects comparable to in-person psychotherapy.


  1. Thorsen AL, van den Heuvel OA, Hansen B, et al. Neuroimaging of psychotherapy for obsessive–compulsive disorder: a systematic review. Psychiatry Res Neuroimaging 2015;233(3):306–313.
  2. Shephard E, Stern ER, van den Heuvel OA, et al. Toward a neurocircuit-based taxonomy to guide treatment of obsessive-compulsive disorder. Mol Psychiatry 2021;26(9):4583–4604.

W31d – Investigating the Effectiveness of Electronically Delivered Cognitive-Behavioural Therapy Compared to Pharmaceutical Interventions in Treating Insomnia
Sarah Zhu, BScH Student

At the end of this session, participants will be able to: 1) Learn about the characteristics of sleep medication, psychotherapy, and online psychotherapy in treating insomnia; 2) Understand the effectiveness of therapist-guided electronically delivered cognitive-behavioural therapy (eCBTi) compared to trazodone; and 3) Learn about the clinical feasibility and benefits of e-CBTi to improve health care capacity and accessibility.

Insomnia is one of the most prevalent sleep disorders characterized by an inability to fall or stay asleep. Available treatments include pharmacotherapy and cognitive-behavioural therapy for insomnia (CBTi). Although CBTi is the first-line treatment, it has limited availability. Therapist-guided electronic delivery of CBT for insomnia (e-CBTi) offers scalable solutions to enhance access to CBTi. While e-CBTi produces comparable outcomes to in-person CBTi, there is a lack of comparison to active pharmacotherapies. Therefore, direct comparisons between e-CBTi and trazodone, one of the most frequently prescribed medications for insomnia, is essential in establishing the effectiveness of this novel digital therapy. This study aims to compare the effectiveness of therapist-guided e-CBTi to trazodone in patients with insomnia. Participants diagnosed with insomnia were randomly assigned to two groups: treatment as usual (TAU) + trazodone and TAU + e-CBTi for seven weeks. Each weekly sleep module was delivered through the online psychotherapytool (OPTT), a secure online mental health care delivery platform. Changes in insomnia symptoms were evaluated with clinically validated symptomatology questionnaires, Fitbits, and other behavioural variables. From pre- to posttreatment, preliminary results suggested that e-CBTi patients reported more significant improvements in insomnia severity, sleep duration, and sleep efficiency, compared to pharmacotherapy patients. The results suggested the clinical feasibility and effectiveness of the e-CBTi program in insomnia treatment. These findings can be used to develop more accessible and affordable treatment options and influence clinical practices for insomnia to further expand mental health care capacity in this population.


  1. Erten Uyumaz B, Feijs L, Hu J. A review of digital cognitive behavioral therapy for insomnia (CBT-I apps): are they designed for engagement? Int J Environ Res Public Health 2021;18(6):2929.
  2. Jaffer KY, Chang T, Vanle B, et al. Trazodone for insomnia: a systematic review. Innov Clin Neurosci 2017;14(7–8):24–34.