The prevalence of mental health challenges and disorders in post-secondary students, demands accessible and efficacious care. Online psychotherapy and psychoeducation programs have shown significant effectiveness in mitigating the risk and clinical symptoms of various mental health disorders. Additionally, artificial intelligence (AI) has become an accessible, effective, and scalable tool supporting the delivery of healthcare. Therefore, the primary objective of this study is to develop an AI-driven online mental health care hub in the Kingston, Frontenac, Lennox \& Addington region for post-secondary students. . This hub will deliver equitable, efficacious, and cost-effective online psychoeducation and psychotherapy in the form of online diagnosis-specific cognitive behavioural therapy (e-CBT). The hub's virtual design aims to adequately address existing gaps in the mental healthcare of these individuals and alleviate the burden placed on mental health services in Canada. Using a rigorous implementation framework, the development of this hub is designed as a multiphase study with three phases. (1) Pre-adoption phase: will assess post-secondary students' current mental health landscape through surveys and focus groups. This information will be used in the development of our online psychoeducation and diagnosis-specific e-CBT programs. (2) Delivery phase: will determine the efficacy and cost-effectiveness of the online psychoeducation and diagnosis-specific e-CBT programs by comparing them to treatment as usual. (3) Post-adoption phase: the collected data from these programs will be analyzed and shared with key stakeholders to guide continuous program scaling and improvement.
The proposed regional mental healthcare hub aims to deliver equitable, efficacious, and cost-effective AI-driven online psychoeducation and diagnosis-specific online cognitive behavioural therapy (e-CBT) programs for post-secondary students in the Kingston, Frontenac, Lennox \& Addington (KFL\&A) region. The initiative will target a broad population of students ranging from those interested in learning more about mental health (online psychoeducation program) to those seeking additional help for their psychiatric symptoms (e-CBT program). Our research team is strongly experienced in designing effective AI-assisted online psychoeducation and diagnosis-specific e-CBT programs for various mental health conditions in diverse populations. Therefore, being as effective as in-person methods of healthcare delivery, it is hypothesized that our online interventions disseminated via a robust implementation science and community collaboration strategy will be a feasible, cost-effective, and pragmatic solution to address accessibility issues with post-secondary student mental healthcare. The proposed mental health hub will offer a comprehensive care model using a stepped-care approach. Its implementation will be guided by a rigorous approach involving seven aims divided into three phases (pre-adoption, delivery, and post-adoption phases). (1) Pre-adoption phase: will assess post-secondary students' current mental health landscape through surveys and focus groups (Aim 1). This information will be used in the development of a 6-week online psychoeducation (Aim 2) and 12-week diagnosis-specific e-CBT (Aim 3) programs. The psychoeducation program will be followed by 6-months of AI-assisted journaling exercises to monitor the student's mental health and determine whether they would benefit from stepping up their care to receive the diagnosis-specific e-CBT program. The diagnosis-specific e-CBT programs will be adapted from prior clinically validated and diagnosis-specific psychotherapy modules developed by the lab. Four diagnosis-specific modules will support the most common mental health disorders in students (GAD, SAD, MDD and ADHD), as indicated by previous studies and the research team's cross-sectional survey of students in Ontario, Canada. Both programs will be delivered through the Online Psychotherapy Tool (OPTT) - a secure cloud-based platform co-developed by the PI that offers online mental health interventions. (2) Delivery phase: will determine the efficacy and cost-effectiveness of the online psychoeducation (Aim 4) and diagnosis-specific e-CBT (Aim 5) programs by comparing them to treatment, as usual, using validated clinical assessment tools. (3) Post-adoption phase: will use the collected data to monitor the success and fidelity of the programs using surveys and focus groups (Aim 6), and to build capacity and scale the project to other Canadian municipalities (Aim 7). As such, in contrast to other initiatives that target student mental health, this will be the first to involve students and end-users in all phases, from conception to implementation. The digital nature of this healthcare program and the implementation of AI technology results in new solutions that enable easier accessibility and broader reach to individuals in significant need of mental healthcare services. Therefore, to support the development of these mental healthcare resources, the research team has assembled a diverse, cross-functional interdisciplinary team of community leaders, including researchers, clinicians, health decision-makers, and end-users. Ultimately, leveraging the extensive expertise of a cross-functional research team, the objective of the study is to develop and implement a successful and effective digital care plan with the potential to improve post-secondary student mental health care substantially.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
NONE
Participants will receive six weekly self-guided psychoeducation sessions. Then, after 6 weeks participants will complete monthly clinical questionnaires for 6 months. Also, participants will begin bi-weekly journaling exercises for 6 months. Based on the journaling entries, if the AI algorithm detects MDD, GAD, SAD or ADHD that requires treatment will make a referral to the diagnosis-specific e-CBT (MDD, GAD, SAD or ADHD). If after 6 months, no referral decision is made by the AI algorithm no extra follow-up will be provided. Once the AI makes a referral recommendation, participants will be placed in a diagnosis-specific e-CBT program specific to the diagnosis determined by the AI algorithm (MDD, GAD, SAD, or ADHD). The asynchronous e-CBT programs will consist of 12 weekly modules and homework. Weekly homework is reviewed by a therapist, who will provide personalized feedback based on the participant's preferred method (i.e., text, video, or audio).
Queen's University, Hotel Dieu Hospital
Kingston, Ontario, Canada
Change in anxiety severity (GAD-7)
Generalized anxiety disorder - 7 item (GAD-7) questionnaire. Increase in score dictates increase in severity. Scale of 0-3
Time frame: weeks 0, 3, 6, follow-up 1, 2, 3, 4, 5, 6 months
Change in depression severity (PHQ-9)
Patient Health Questionnaire - 9 item (PHQ-9). Increase in score dictates increase in severity. Scale of 0-3 on each question.
Time frame: weeks 0, 3, 6, follow-up 1, 2, 3, 4, 5, 6 months
Change in depression severity (QIDS)
Quick Inventory of Depressive Symptomatology (QIDS). Increase in score dictates increase in severity. Scale of 0-3 on each question
Time frame: weeks 0, 3, 6, follow-up 1, 2, 3, 4, 5, 6 months
Change in stress severity (DASS-42)
Depression Anxiety Stress Scale-42 (DASS-42). Increase in score dictates increase in severity. Scale of 0-3 on each question.
Time frame: weeks 0, 3, 6, follow-up 1, 2, 3, 4, 5, 6 months
Change in quality of life (AQoL-8D)
Assessment of Quality of Life-8D (AQoL-8D). Increase in score dictates increase in quality of life (improvement). Scale of 0-5 on each question.
Time frame: weeks 0, 3, 6, follow-up 1, 2, 3, 4, 5, 6 months
Change in resilience (RS-14)
Resilience Scale-14 (RS-14). Increase in score dictates increase in resilience (improvement). Scale of 0-7 on each question
Time frame: weeks 0, 3, 6, follow-up 1, 2, 3, 4, 5, 6 months
Change in social anxiety severity (LSAS)
Liebowitz Social Anxiety Scale (LSAS). Increase in score dictates increase in anxiety severity. Scale of 0-3 on each question.
Time frame: weeks 0, 3, 6, follow-up 1, 2, 3, 4, 5, 6 months
Change in ADHD severity (ASRS)
Adult ADHD Self-Report Scale (ASRS). Increase in score dictates increase in ADHD symptom severity. Scale of 0-5 on each question.
Time frame: weeks 0, 3, 6, follow-up 1, 2, 3, 4, 5, 6 months
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