Isolating together during COVID-19: Results from the Telehealth Intervention Program for older adults.

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).

Using a simulation centre to evaluate preliminary acceptability and impact of an artificial intelligence-powered clinical decision support system for depression treatment on the physician-patient interaction.

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.

Connecting During COVID-19: A Protocol of a Volunteer-Based Telehealth Program for Supporting Older Adults' Health.

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.

Analysis of Features Selected by a Deep Learning Model for Differential Treatment Selection in Depression.

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.

A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder.

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.

The persistence of mind-brain dualism in psychiatric reasoning about clinical scenarios.

OBJECTIVE: Despite attempts in psychiatry to adopt an integrative biopsychosocial model, social scientists have observed that psychiatrists continue to operate according to a mind-brain dichotomy in ways that are often covert and unacknowledged and suggest that the same intuitive cognitive schemas that people use to make judgments of responsibility lead to dualistic reasoning among clinicians. The goal of this study was to confirm these observations. METHOD: Self-report questionnaires were sent to the 270 psychiatrists and psychologists in the Department of Psychiatry at McGill University. In response to clinical vignettes, the participants rated the level of intentionality, controllability, responsibility, and blame attributable to the patients, as well as the importance of neurobiological, psychological, and social factors in explaining the patients' symptoms. RESULTS: A total of 136 faculty members (50.4%) responded, and 127 were included in the analysis. Factor analysis revealed a single dimension of responsibility regarding the patients' illnesses that correlated positively with ratings of psychological etiology and negatively with ratings of neurobiological etiology. Psychological and neurobiological ratings were inversely correlated. Multivariate analyses of variance supported these results. CONCLUSIONS: Mental health professionals continue to employ a mind-brain dichotomy when reasoning about clinical cases. The more a behavioral problem is seen as originating in "psychological" processes, the more a patient tends to be viewed as responsible and blameworthy for his or her symptoms; conversely, the more behaviors are attributed to neurobiological causes, the less likely patients are to be viewed as responsible and blameworthy.