Vladimir Khanassov

Background Primary care is essential in dementia management, offering diagnosis, treatment, and support for people living with dementia (PLWD) and their caregivers. Telemedicine became a key advancement during the COVID-19 pandemic, offering crucial access to care. This study explores the pros and cons of telemedicine for dementia care during the pandemic to guide future improvements. Methods Data collection involved semi-structured interviews with caregivers recruited from a Montreal memory clinic and secondary analysis of two other studies related to dementia and telemedicine, focusing on the educational needs of patients and the impact of the pandemic on health-care services. Data analysis employed the framework method, combining inductive and deductive approaches to code the data and develop categories aligned with Chang’s framework, providing insights into caregivers’ experiences and the challenges and benefits of telemedicine. Results The study involved interviews with four caregivers of people with dementia, complemented by secondary analysis from two Canadian studies. Through framework analysis, four themes were developed: relationship and communication; the advantages and selective suitability of telemedicine (TM) in dementia care; preferences for in-person consultations; and the need to improve awareness and technical confidence in TM. Conclusion This study highlights the potential of telemedicine (TM) as an effective modality for dementia care, particularly during situations like the COVID-19 pandemic, but emphasizes that it cannot fully replace in-person consultations due to the enduring preference for face-to-face interactions.

Background: Depression, characterized by persistent sadness and loss of interest in daily activities, greatly reduces quality of life. Early detection is vital for effective treatment and intervention. While many studies use wearable devices to classify depression based on physical activity, these often rely on intrusive methods. Additionally, most depression classification studies involve large participant groups and use single-stage classifiers without explainability. Objective: This study aims to assess the feasibility of classifying depression using nonintrusive Wi-Fi–based motion sensor data using a novel machine learning model on a limited number of participants. We also conduct an explainability analysis to interpret the model’s predictions and identify key features associated with depression classification. Methods: In this study, we recruited adults aged 65 years and older through web-based and in-person methods, supported by a McGill University health care facility directory. Participants provided consent, and we collected 6 months of activity and sleep data via nonintrusive Wi-Fi–based sensors, along with Edmonton Frailty Scale and Geriatric Depression Scale data. For depression classification, we proposed a HOPE (Home-Based Older Adults’ Depression Prediction) machine learning model with feature selection, dimensionality reduction, and classification stages, evaluating various model combinations using accuracy, sensitivity, precision, and F1-score. Shapely addictive explanations and local interpretable model-agnostic explanations were used to explain the model’s predictions. Results: A total of 6 participants were enrolled in this study; however, 2 participants withdrew later due to internet connectivity issues. Among the 4 remaining participants, 3 participants were classified as not having depression, while 1 participant was identified as having depression. The most accurate classification model, which combined sequential forward selection for feature selection, principal component analysis for dimensionality reduction, and a decision tree for classification, achieved an accuracy of 87.5%, sensitivity of 90%, and precision of 88.3%, effectively distinguishing individuals with and those without depression. The explainability analysis revealed that the most influential features in depression classification, in order of importance, were “average sleep duration,” “total number of sleep interruptions,” “percentage of nights with sleep interruptions,” “average duration of sleep interruptions,” and “Edmonton Frailty Scale.” Conclusions: The findings from this preliminary study demonstrate the feasibility of using Wi-Fi–based motion sensors for depression classification and highlight the effectiveness of our proposed HOPE machine learning model, even with a small sample size. These results suggest the potential for further research with a larger cohort for more comprehensive validation. Additionally, the nonintrusive data collection method and model architecture proposed in this study offer promising applications in remote health monitoring, particularly for older adults who may face challenges in using wearable devices. Furthermore, the importance of sleep patterns identified in our explainability analysis aligns with findings from previous research, emphasizing the need for more in-depth studies on the role of sleep in mental health, as suggested in the explainable machine learning study.

Virtual care (VC) for dementia in primary care settings is an important aspect of healthcare delivery in Canada. However, the evidence informing optimal and sustainable provision of VC for persons living with dementia (PLWD) and their care partners is scarce. The objectives of this study were to (1) describe the frequency of VC use, (2) identify characteristics of PLWD, care partners, and family physicians (FPs) that are associated with the use of VC, and (3) explore FPs’ perceptions of barriers and facilitators to provide VC for PLWD and their care partners. The Alzheimer Society of Canada and College of Family Physicians of Canada conducted three nationwide cross-sectional surveys between October 2020 and April 2021: (1) One with PLWD, (2) one with care partners of PLWD, and (3) one with FPs. Virtual care was defined as two-way synchronous communication by telephone and/or a web camera. The prevalence of VC use among FPs, PLWD, and care partners was described. Logistic regression models were used to determine characteristics of participants (sociodemographic, urbanicity, frequency and availability of support for connecting with FPs, and FPs’ practice characteristics) associated with any VC use (phone and/or video). Inductive thematic analysis of open-ended questions explored FPs’ perceptions. 131 PLWD, 341 care partners, and 125 FPs participated. 61.2% of PLWD, 59.5% of care partners, and 77.4% of FPs reported using VC. The models for PLWD (included age and ethnicity) and care partners (included gender/sex, urbanicity, and receiving support from a family member/friend to connect with FP) were inconclusive. FPs with > 20 years in practice were less likely to provide VC (OR = 0.23, 95%CI: 0.08–0.62, p < 0.01). FPs perceived that preferences regarding virtual vs. in-person care, office/family support, technology and family presence, and remuneration for FPs influenced VC use. Virtual primary dementia care uptake in Canada is substantial and mainly performed via telephone. According to FPs, physician-patient-caregiver partnerships and infrastructure for VC play key roles in using VC. Virtual care could facilitate access to primary care and minimize potential disruptions to in-person care for PLWD. Outcomes of virtual primary care for dementia need further investigation.

BACKGROUND Depression, characterized by persistent sadness and loss of interest in daily activities, greatly reduces quality of life. Early detection is vital for effective treatment and intervention. While many studies use wearable devices to classify depression based on physical activity, these often rely on intrusive methods. Additionally, most depression classification studies involve large participant groups and use single-stage classifiers without explainability. OBJECTIVE This study aims to assess the feasibility of classifying depression using nonintrusive Wi-Fi–based motion sensor data using a novel machine learning model on a limited number of participants. We also conduct an explainability analysis to interpret the model’s predictions and identify key features associated with depression classification. METHODS In this study, we recruited adults aged 65 years and older through web-based and in-person methods, supported by a McGill University health care facility directory. Participants provided consent, and we collected 6 months of activity and sleep data via nonintrusive Wi-Fi–based sensors, along with Edmonton Frailty Scale and Geriatric Depression Scale data. For depression classification, we proposed a HOPE (Home-Based Older Adults’ Depression Prediction) machine learning model with feature selection, dimensionality reduction, and classification stages, evaluating various model combinations using accuracy, sensitivity, precision, and F1-score. Shapely addictive explanations and local interpretable model-agnostic explanations were used to explain the model’s predictions. RESULTS A total of 6 participants were enrolled in this study; however, 2 participants withdrew later due to internet connectivity issues. Among the 4 remaining participants, 3 participants were classified as not having depression, while 1 participant was identified as having depression. The most accurate classification model, which combined sequential forward selection for feature selection, principal component analysis for dimensionality reduction, and a decision tree for classification, achieved an accuracy of 87.5%, sensitivity of 90%, and precision of 88.3%, effectively distinguishing individuals with and those without depression. The explainability analysis revealed that the most influential features in depression classification, in order of importance, were “average sleep duration,” “total number of sleep interruptions,” “percentage of nights with sleep interruptions,” “average duration of sleep interruptions,” and “Edmonton Frailty Scale.” CONCLUSIONS The findings from this preliminary study demonstrate the feasibility of using Wi-Fi–based motion sensors for depression classification and highlight the effectiveness of our proposed HOPE machine learning model, even with a small sample size. These results suggest the potential for further research with a larger cohort for more comprehensive validation. Additionally, the nonintrusive data collection method and model architecture proposed in this study offer promising applications in remote health monitoring, particularly for older adults who may face challenges in using wearable devices. Furthermore, the importance of sleep patterns identified in our explainability analysis aligns with findings from previous research, emphasizing the need for more in-depth studies on the role of sleep in mental health, as suggested in the explainable machine learning study.

Background: The COVID-19 pandemic changed the healthcare system, leading to the rapid evolution and implementation of telemedicine (TM). TM has the potential to improve the quality of primary health care and increase accessibility for the population. However, its use may represent challenges for older people, as they may have distinct needs from the general population due to age-related changes in perceptual, motor, and cognitive capacities. We, thus, aimed to identify potential facilitators and barriers to TM use in primary care for older adults and develop recommendations accordingly. Methods: We conducted a qualitative study to explore the challenges associated with TM use among older adults and healthcare professionals (HCPs) in primary care practice. Interviews were conducted with 29 older adults, and three focus groups involving HCPs from four McGill family medicine sites were organized. Employing a hybrid codebook thematic analysis, guided by the Consolidated Framework for Implementation Research (CFIR), we identified facilitators and barriers affecting the optimal use of TM by older adults and HCPs. We synthesized the results from semi-structured interviews and focus groups. These findings were then presented during a deliberative dialogue with eight participants, including family physicians, nurses, a social worker, and a government-level TM expert, to validate our results. The purpose was to gather feedback, identify and refine actionable recommendations. Subsequently, we utilized a thematic analysis using the same codebook to synthesize findings from the deliberative dialogue. Results: Participants agreed that TM contributed to maintaining the continuity of care and was particularly convenient when there was an existing or established patient-physician relationship or for addressing minor health issues. TM was found to be beneficial for people with limited mobility, reducing their exposure to potentially high-risk environments. However, participants expressed concerns about the lack of visual contact, causing essential details to be overlooked. Additionally, issues related to miscommunication due to language or hearing barriers were identified. HCPs perceived that most older adults did not consider phone consultations a medical act. Participants were open to a hybrid approach, combining in-person consultations and TM, based on their specific health conditions. Building upon these results, we formulated seven key recommendations. Conclusions: Both older adults and HCPs consider TM a good alternative for accessing healthcare services. To improve the effective use of TM, it’s crucial to advocate for a hybrid approach that integrates both in-person and virtual methods. This approach should actively encourage and support individuals in becoming familiar with technological tools.

Background: Family physicians had to deliver care remotely during the COVID-19 pandemic. Their efforts highlighted the importance of developing a primary care telemedicine (TM) model. TM has the potential to provide a high-quality option for primary care delivery. However, it poses unique challenges for older adults. Our aim was therefore to explore the effects of TM and the determinants of its use in primary care for older adults. Methods: In this systematic mixed studies review, MEDLINE, PsycINFO, EMBASE, CINHAL, AgeLine, DARE, Cochrane Library, and clinical trials research registers were searched for articles in English, French or Russian. Two reviewers performed study selection, data extraction and assessment of study quality. TM’s effects were reported through the tabulation of key variables. TM use determinants were interpreted using thematic analysis based on Chang’s framework. All data were integrated using a joint display matrix. Results: From 3,328 references identified, 20 studies were included. They used either phone (n = 8), videoconference (n = 9) or both (n = 3). Among studies reporting positive outcomes in TM experience, ‘user habit or preferences’ was the most cited barrier and ‘location and travel time’ was the most cited facilitator. Only one study reported negative outcomes in TM experience and reported ‘comfort with patient communication’ and ‘user interface, intended use or usability’ as barriers, and ‘technology skills and knowledge’ and ‘location and travel time’ as facilitators. Among studies reporting positive outcomes in service use and usability, no barrier or facilitator was cited more than once. Only one study reported a positive outcome in health-related and behavioural outcomes. Conclusions: TM in older adults’ primary care generally led to positive experiences, high satisfaction and generated an interest towards alternative healthcare delivery model. Future research should explore its efficacy on clinical, health-related and healthcare services use.

Background The COVID-19 pandemic transformed the health care systems, motivating Telemedicine's rapid evolution and implementation. Telemedicine (TM) can potentially improve the quality of primary health care and increase accessibility to the population. Nevertheless, its use may represent a challenge to older people as they may have different needs from the general population due to potential age-related changes in perceptual, motor and cognitive capacities. We thus aimed to identify potential facilitators and barriers to Telemedicine (TM) use in the primary care of older adults and develop recommendations. Methods We conducted a multi-phase study: 1. A systematic mixed-method review to explore determinants in the use of TM for older adults for papers published before July 2021; 2. Qualitative descriptive study, we interviewed 29 older adults and conducted three focus groups and one deliberative dialogue with healthcare professionals from four McGill family medicine sites. The findings were analyzed using deductive thematic analysis based on the Consolidated Framework for Implementation Research (CFIR); 3. We integrated the results from both phases and the deliberative dialogue using thematic analysis. Results The systematic review identified over 3,328 references. We included 21 articles, resulting in positive experiences and high satisfaction and generating interest in TM as a complementary healthcare delivery model. Participants agreed that TM contributed to maintaining the continuity of care and was convenient when there is a previous/established patient-physician relationship and to resolve minor health issues. TM was beneficial for persons with limited mobility; and reduced the exposure of older adults to potential high-risk environments. Nevertheless, participants expressed concerns about the lack of visual contact, causing essential details to be overlooked. Similarly, miscommunication difficulties may emerge due to language or hearing barriers. Family physicians perceived that most patients did not consider phone consultations a medical act. However, participants were amenable to a hybrid approach, combining in-person consultations and Telemedicine, depending on their health conditions. Conclusions Older adults and healthcare professionals consider Telemedicine a good alternative for accessing healthcare services, though it would be necessary to promote a hybrid approach and encourage and support familiarization, adaptability, and accessibility to technological tools.

Background: Despite the importance of virtual primary care, the evidence informing optimal and sustainable provision of virtual care (VC) for people living with dementia (PLWD) and their care partners is scarce. We aimed to describe VC provided by family physicians (FPs) for PLWD in Canada. Methods: Concurrent mixed-methods design. We analyzed questions related to VC in three nationwide cross-sectional surveys conducted with PLWD, care partners, and FPs in the first year of the COVID-19 pandemic. Virtual care was defined as two-way synchronous communication by telephone and/or a web camera. The prevalence of VC use among FPs, PLWD, and care partners was described, and logistic regression models were used to determine factors (sociodemographic, urbanicity, frequency of and availability of support for connecting to FPs, and FPs’ practice characteristics) associated with VC use. Inductive thematic analysis was performed on responses to open-ended questions to explore FPs’ perceptions of barriers and facilitators to using VC. Results: 131 PLWD, 341 care partners, and 125 FPs participated. 61.2% of PLWD, 59.5% of care partners, and 77.4% of FPs had used VC. The models for PLWD (included age and ethnicity) and care partners (included gender, urbanicity, and receiving support from a family member/friend to connect with FP) were inconclusive. Among FPs, having more than 20 years of practice was significantly associated with a decreased likelihood of providing VC (OR=0.23, 95%CI: 0.08-0.62, p<0.01). Care preferences (decision stage), office/family support (preparation stage), technology and family presence (execution stage), and remuneration for FPs (compensation stage) were the most commonly recurring themes affecting VC use. Conclusions: Virtual primary dementia care uptake was substantial and mainly performed via telephone. From the perspective of FPs, optimal VC provision requires patient-care partner-physician shared decision-making, interoperability in healthcare, support for performing VC, and appropriate compensation. Virtual care facilitates access to primary care and minimizes potential disruptions to in-person care for PLWD; however, its outcomes need further investigation.

Introduction Telemedicine (TM) has been adopted by many health authorities to limit unnecessary exposure during COVID-19. Prior to the pandemic, TM was associated with improvement of quality of life of older patients, low hospital admissions and nursing home placement, and high overall patients’ satisfaction due to convenience. However, older adults may face challenges to access TM, such as hearing, visual and cognitive decline, and limited access to Internet and devices. Ongoing vaccination campaigns and sanitary measures are keeping the pandemic under control, but new variants threaten public safety. Specific recommendations on TM use in high-risk populations, such as older adults, are therefore required. Methods and analysis To assess the challenges of TM use in the routine primary healthcare practice of older adults. The research objective is to examine the potential effect of TM; (1) to describe the evidence of TM, (2) to understand the patients, caregivers and clinicians’ experiences with TM use and (3) to develop practice-based and evidence-based recommendations on effective use of TM. Multiphase design: (1) systematic mixed studies review on the evidence of TM use, (2) qualitative descriptive study on the experiences of the patients, caregivers and healthcare professionals. Recommendations will be proposed based on the integration of both studies. In accordance with PRISMA statement, the systematic mixed studies review will be conducted through multiple databases search: MEDLINE, PsycINFO, EMBASE, CINAHL, AgeLine, Cochrane Library. Population studied: Community-dwelling 65 years and older adults using two-way synchronous TM by phone or video in a primary care setting. The qualitative descriptive study will include individual interviews with older adults from four McGill university affiliated primary care practices and focus groups with their healthcare professionals. Ethics and dissemination Ethics approval has been received. Results will inform healthcare professionals and policy-makers on sustainable use of TM in primary care for older adults. PROSPERO registration number The review protocol has been recorded at the PROSPERO, CRD42021237686 (https://www.crd.york.ac.uk/prospero/.

Background: Persons living with dementia have various health and social care needs and expectations, some which are not fully met by health providers, including primary care clinicians. The Quebec Alzheimer plan, implemented in 2014, aimed to cover these needs, but there is no research on the effect this plan had on the needs and expectations of persons living with dementia. The objective of this study is to identify persons living with dementia and caregivers’ met and unmet needs and to describe their experience. Methods: This is a sequential mixed methods explanatory design: Phase 1: cross-sectional study to describe the met and unmet health and social care needs of community-dwelling persons living with dementia using Camberwell Assessment of Need of the Elderly and Carers’ Assessment for Dementia tools. Phase 2: qualitative descriptive study to explore and understand the experiences of persons living with dementia and caregivers with the use of social and healthcare services, using semi-structured interviews. Data from phase 1 was analyzed with descriptive statistics, and from phase 2, with inductive thematic analysis. Results from phases 1 and 2 were compared, contrasted and interpreted together. Results: The mean total number of needs reported by the patients was 5.03 (4.48 and 0.55 met and unmet needs, respectively). Caregivers had 0.52 met needs (3.16 unmet needs). The main needs for both were memory, physical health, eyesight/hearing/communication, medication, looking after home, money/budgeting. Three categories were mentioned by the participants: Persons living with dementia and caregiver’s attitude towards memory decline, their perception of community health services and of the family medicine practice. Conclusions: Our study confirms the findings of other studies on the most common unmet needs of the patients and caregivers that are met partially or not at all. In addition, the participants were satisfied with access to care, and medical services in primary practices, being confident in their family. Our results indicate persons living with dementia and their caregivers need a contact person, a clear explanation of their dementia diagnosis, a care plan, written information on available services, and support for the caregivers.