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Background: A pressing challenge during the COVID-19 pandemic and beyond is to provide accessible and scalable mental health support to isolated older adults in the community. The Telehealth Intervention Program for Older Adults (TIP-OA) is a large-scale, volunteer-based, friendly telephone support program designed to address this unmet need., Methods: A prospective cohort study of 112 TIP-OA participants aged ≥60 years old was conducted in Quebec, Canada (October 2020-June 2021). The intervention consisted of weekly friendly phone calls from trained volunteers. The primary outcome measures included changes in scores of stress, depression, anxiety, and fear surrounding COVID-19, assessed at baseline, 4 and 8-weeks. Additional subgroup analyses were performed with participants with higher baseline scores., Results: The subgroup of participants with higher baseline depression scores (PHQ9 ≥10) had significant improvements in depression scores over the 8-week period measured [mean change score = -2.27 (±4.76), 95%CI (-3.719, -0.827), p = 0.003]. Similarly, participants with higher baseline anxiety scores (GAD7 ≥10) had an improvement over the same period, which, approached significance ( p = 0.06). Moreover, despite peaks in the pandemic and related stressors, our study found no significant ( p ≥ 0.09) increase in stress, depression, anxiety or fear of COVID-19 scores., Discussion: This scalable, volunteer-based, friendly telephone intervention program was associated with decreased scores of depression and anxiety in older adults who reported higher scores at baseline (PHQ 9 ≥10 and GAD7 ≥10)., Competing Interests: Author SR receives a salary award from the Fonds de Recherche de Québec-Santé (FRQS), is a steering committee member for AbbVie, and is a shareholder of Aifred Health. HS has a CIHR fellowship award, MITACS fellowship award, and AGE-WELL award. Author EM receives salary support from the Fond de recherche Santé Québec. Author SK has received research support from Brain and Behavior Foundation, National institute on Aging, BrightFocus Foundation, Brain Canada, Canadian Institute of Health Research, Canadian Consortium on Neurodegeneration in Aging, Centre for Ageing and Brain Health Innovation, Centre for Addiction and Mental Health, an Academic Scholars Award from the Department of Psychiatry, University of Toronto, and Equipment support from Soterix Medical. Author SBo receives funds from the Canada research Chairs program and various provincial and federal granting agencies. He is president of, and owns equity in, Cliniques et Development In Virtuo; a company that distributes virtual reality environments. Conflicts of interest are managed under UQO's conflicts of interest policy. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Sekhon, Lavin, Vacaflor, Rigas, Cinalioglu, Su, Bodenstein, Dikaios, Goodman, Raymond, Ibrahim, Bein, Gruber, Se, Sasi, Walsh, Nazar, Hanganu, Berkani, Royal, Schiavetto, Looper, Launay, McDonald, Seitz, Kumar, Beauchet, Khoury, Bouchard, Battistini, Fallavollita, Miresco, Bruneau, Vahia, Bukhari and Rej.)

Background: Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction., Aims: Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician-patient interaction., Method: Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback., Results: All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician-patient interaction., Conclusions: The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician-patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.

Introduction: Social-distancing due to COVID-19 has led to social isolation, stress, and mental health issues in older adults, while overwhelming healthcare systems worldwide. Telehealth involving phone calls by trained volunteers is understudied and may be a low-cost, scalable, and valuable preventive tool for mental health. In this context, from patient participatory volunteer initiatives, we have adapted and developed an innovative volunteer-based telehealth intervention program for older adults (TIP-OA). Methods and analysis: To evaluate TIP-OA, we are conducting a mixed-methods longitudinal observational study. Participants: TIP-OA clients are older adults (age ≥ 60) recruited in Montreal, Quebec. Intervention: TIP-OA volunteers make weekly friendly phone calls to seniors to check in, form connections, provide information about COVID-19, and connect clients to community resources as needed. Measurements: Perceived stress, fear surrounding COVID-19, depression, and anxiety will be assessed at baseline, and at 4- and 8-weeks. Semi-structured interviews and focus groups will be conducted to assess the experiences of clients, volunteers, and stakeholders. Results: As of October 15th, 2020, 150 volunteers have been trained to provide TIP-OA to 305 older clients. We will consecutively select 200 clients receiving TIP-OA for quantitative data collection, plus 16 volunteers and 8 clinicians for focus groups, and 15 volunteers, 10 stakeholders, and 25 clients for semi-structured interviews. Discussion: During COVID-19, healthcare professionals' decreased availability and increased needs related to geriatric mental health are expected. If successful and scalable, volunteer-based TIP-OA may help prevent and improve mental health concerns, improve community participation, and decrease healthcare utilization. Clinical Trial Registration : ClinicalTrials.gov NCT04523610; https://clinicaltrials.gov/ct2/show/NCT04523610?term=NCT04523610&draw=2&rank=1., Competing Interests: DS is a site investigator on a clinical trial sponsored by Hoffmann La Roche. VN receives research funding from Brain Canada, Eli Lilly, as well as Merck & Biogen Pharmaceutical companies (project completed). He declares no influence or impact on the project under consideration. SK receives research support from Brain and Behavior Foundation, National Institute on Aging, BrightFocus Foundation, Brain Canada, Canadian Institute of Health Research, Center for Aging and Brain Health Innovation, Center for Addiction and Mental Health, University of Toronto. He also receives equipment support from Soterix Medical. GA served on the Advisory Board of Eisai and of Janssen Pharmaceuticals. He also served on the Speakers Bureaus of Allergan, Otsuka, and Takeda-Lundbeck and his work was supported by P50 MH113838. SR receives a junior investigator salary award from the Fonds de Recherche Quebec–Santé and an investigator-initiated research grant from Satellite Healthcare (Dialysis Company) for an unrelated project. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Dikaios, Sekhon, Allard, Vacaflor, Goodman, Dwyer, Lavin-Gonzalez, Mahdanian, Park, Walsh, Sasi, Nazar, Gruber, Su, Hanganu, Royal, Schiavetto, Cinalioglu, Rigas, Launay, Beauchet, McDonald, Seitz, Kumar, Nair, Miresco, Bruneau, Alexopoulos, Looper, Vahia, Rej and Bukhari.)

Background: Deep learning has utility in predicting differential antidepressant treatment response among patients with major depressive disorder, yet there remains a paucity of research describing how to interpret deep learning models in a clinically or etiologically meaningful way. In this paper, we describe methods for analyzing deep learning models of clinical and demographic psychiatric data, using our recent work on a deep learning model of STAR*D and CO-MED remission prediction. Methods: Our deep learning analysis with STAR*D and CO-MED yielded four models that predicted response to the four treatments used across the two datasets. Here, we use classical statistics and simple data representations to improve interpretability of the features output by our deep learning model and provide finer grained understanding of their clinical and etiological significance. Specifically, we use representations derived from our model to yield features predicting both treatment non-response and differential treatment response to four standard antidepressants, and use linear regression and t -tests to address questions about the contribution of trauma, education, and somatic symptoms to our models. Results: Traditional statistics were able to probe the input features of our deep learning models, reproducing results from previous research, while providing novel insights into depression causes and treatments. We found that specific features were predictive of treatment response, and were able to break these down by treatment and non-response categories; that specific trauma indices were differentially predictive of baseline depression severity; that somatic symptoms were significantly different between males and females, and that education and low income proved important psycho-social stressors associated with depression. Conclusion: Traditional statistics can augment interpretation of deep learning models. Such interpretation can lend us new hypotheses about depression and contribute to building causal models of etiology and prognosis. We discuss dataset-specific effects and ideal clinical samples for machine learning analysis aimed at improving tools to assist in optimizing treatment., (Copyright © 2020 Mehltretter, Rollins, Benrimoh, Fratila, Perlman, Israel, Miresco, Wakid and Turecki.)

Introduction: The heterogeneity of symptoms and complex etiology of depression pose a significant challenge to the personalization of treatment. Meanwhile, the current application of generic treatment approaches to patients with vastly differing biological and clinical profiles is far from optimal. Here, we conduct a meta-review to identify predictors of response to antidepressant therapy in order to select robust input features for machine learning models of treatment response. These machine learning models will allow us to learn associations between patient features and treatment response which have predictive value at the individual patient level; this learning can be optimized by selecting high-quality input features for the model. While current research is difficult to directly apply to the clinic, machine learning models built using knowledge gleaned from current research may become useful clinical tools., Methods: The EMBASE and MEDLINE/PubMed online databases were searched from January 1996 to August 2017, using a combination of MeSH terms and keywords to identify relevant literature reviews. We identified a total of 1909 articles, wherein 199 articles met our inclusion criteria., Results: An array of genetic, immune, endocrine, neuroimaging, sociodemographic, and symptom-based predictors of treatment response were extracted, varying widely in clinical utility., Limitations: Due to heterogeneous sample sizes, effect sizes, publication biases, and methodological disparities across reviews, we could not accurately assess the strength and directionality of every predictor., Conclusion: Notwithstanding our cautious interpretation of the results, we have identified a multitude of predictors that can be used to formulate a priori hypotheses regarding the input features for a computational model. We highlight the importance of large-scale research initiatives and clinically accessible biomarkers, as well as the need for replication studies of current findings. In addition, we provide recommendations for future improvement and standardization of research efforts in this field., (Copyright © 2018 Elsevier B.V. All rights reserved.)