Machelle Wilchesky

Introduction: Increasing numbers of older adults live with dementia especially in low- and middle-income countries. Most prevention relies on controlling conditions that increase the risk of dementia, such as hypertension, diabetes and sight and hearing loss. Recent pharmacological treatments have limited efficacy and are prohibitively expensive. Social isolation increases the risk of dementia and evidence suggests interventions to increase social engagement can reduce the risk possibly by supporting executive function. Three key features of authentic participatory research – shared governance, use of evidence and innovation – align with elements of executive function. Our pilot tests the feasibility and acceptability of participatory dementia prevention in Montreal, Canada and rural Botswana. Methods and analysis: This pilot randomised controlled trial will recruit women and men aged 65 years and older who are socially isolated (not in paid or voluntary employment, not married) in urban Montreal and two rural communities in Botswana. In Botswana, we will randomly allocate participants in one community to receive the intervention immediately while the control community will receive the intervention after 18 months. In Montreal, individual participants will be randomly allocated to immediate or delayed intervention groups before or after their grouping by shared concern. We aim to recruit at least 100 participants in Botswana and 100 in Montreal. The intervention has six steps: individuals identify priority concerns; participants join groups with shared concerns; groups discuss the concern and potential solutions; groups share the concern and possible solutions with other stakeholders; implementation of solutions; and self-evaluation of the process. We will measure executive function using the Frontal Assessment Battery, and apply a questionnaire to measure integral brain health, physical health and social isolation at baseline, at 18 months and at 33 months. We will analyse narratives of change at the end of the intervention periods to understand participants’ experience of the intervention. Ethics and dissemination: The McGill University Faculty of Medicine and Health Sciences Institutional Review Board approved the entire project on 18 September 2024, Study number: A06-M27-24A. The Psychosocial Research Committee (REC) of CIUSSS West-Central Montreal Research Ethics Board (REB) approved recruitment of clinic-related participants in Montreal on 3 March 2025, Project 2025-4112. The Health Research and Development Division of the Ministry of Health, Botswana, approved the study in Botswana on 24 June 2024, Reference number: HPRD 6/14/1. In Botswana, we will disseminate the findings in community meetings in the two pilot communities and with government and non-government stakeholders convened by the Ministry of Health and the Social Development Department of the Ministry of Local Government and Traditional Affairs. In Montreal, we will disseminate the findings in research meetings and in other relevant fora. We will give oral and poster presentations about the study in relevant local, national and international academic conferences and meetings and publish papers about the findings in peer-reviewed, indexed academic journals. Trial registration number: ISRCTN14273485 .

The COVID-19 pandemic negatively affected residents of long-term care facilities (LTCFs). This situation highlighted the necessity to translate evidence-based policies and practices into this setting. However, implementing innovations represents a challenge for LTCFs. This study aims to identify the facilitators and barriers to the implementation of innovations in Canadian LTCFs. We conducted a longitudinal correlational study involving different LTCFs participants and the use of the Organizational Readiness for Knowledge Translation (OR4KT) measurement tool before and after the implementation of an innovation. Only data collected at T1 were considered in the analysis due to the low response rate obtained at T2. A mixed-model effect with a random effect for LTCFs was used to account for within organization dependency on responses. In total, 256 respondents from 55 (60.4%) LTCFs participated in the study of which 107 participants were high and middle-level managers, and 149 were non-managerial staff. Variations in OR4KT scores were explained by some organizational factors. The province of location of the LTCF emerged as a significant factor for five subscales of the OR4KT as well as for the total score. The presence of a medical director and belonging to a chain (an organization with multiple LTCFs) seemed to be associated with higher OR4KT scores. Successful LTCF innovation implementation may rely on the geographical administration of healthcare (e.g., the province), the presence of a medical director and the organizational contextual factors between the different actors of LTCFs and the health system.

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: Long‐term care (LTC) residents are a previously untested and highly vulnerable population at risk of elder abuse (EA) and its many negative health outcomes. The detection of elder abuse within the LTC context is urgent and time‐sensitive.Objective: The overarching aim of this study is to evaluate the feasibility of implementing the Elder Abuse Suspicion Index – long‐term care (EASI‐ltc^©): the first comprehensive detection tool of its kind designed specifically to identify the abuse of cognitively‐apt persons living in LTC. Preliminary tool validity will also be evaluated.Methods: This observational pilot study is taking place within seven LTC institutions in Montreal Canada. It begins with the administration of the EASI‐ltc on a random sample of eligible LTC residents. Residents subsequently undergo a follow‐up assessment led by a trained and experienced social worker, which is used as the ‘silver standard’ reference for validation. Residents are asked to reflect on their own experiences as participants in one on‐one interviews, and key stakeholders (e.g., administrators, staff members, family, companions, and friends) are asked to provide retroactive feedback via an online survey. Survey respondents are also invited to participate in interviews to clarify their responses. Potential harmful consequences arising from EASI‐ltc administration is being evaluated.Results: Results pertaining to tool validity, feasibility, and acceptability obtained from data collected during the first 8 months will be presented.Conclusion: The EASI‐ltc is the first published comprehensive tool to detect elder abuse in this vulnerable population. This ongoing study responds to an urgent need to provide tools to identify abuse and give a voice to LTC residents locally, nationally, and beyond.

Background: Screening for elder abuse can cause victims to experience feelings of unpleasantness and/or relive painful memories which can be an ethical concern. Ensuring the safety of all participants/users, in our case long‐term care (LTC) residents, is of the utmost importance.Method: Drawing from approaches used in the intimate partner violence and clinical trials literature, we developed a novel typology of harm and a series of procedures to evaluate any negative consequences that might be incurred as a result of participating in the Piloting the Elder Abuse Suspicion Index‐long term care^©: A Mixed Methods Feasibility Study. Our typology includes subjective and objective reporting from a multitude of sources, including: 1) Interviews with LTC residents using a modified Consequences of Screening Tool, adapted from MacMillan et al, 2009; Opinions of the 2) Study social worker conducting validation assessments and 3) LTC residents and institutional stakeholders; 4) Feedback from study research assistants who administer the tool using a specialized Adverse Events Form; and 5) Chart reviews. Procedures include the creation of an independent interdisciplinary pilot study safety committee, chaired by a geriatric psychiatrist and convened to conduct interim evaluations after the first 5, 10, and 20 residents have participated, the liberation of social work teams, who are on standby and available to intervene as necessary, and the creation of participant resource cards displaying pertinent phone numbers for immediate assistance.Result: Both quantitative and qualitative analyses of harms detected among the first 50 participating LTC residents of this ongoing study will be presented.Conclusion: To our knowledge, this is the first elder abuse study to conduct a thorough analysis of assessment‐induced harm.

Background: Wearable sensor technology shows promise for neuropsychiatric symptoms (NPS) identification/monitoring in persons living with dementia (PLWD). Accelerometry measures acceleration of body segments and physical activity. This study explores the diagnostic test accuracy (DTA) of accelerometry to identify the following 6 NPS associated with motor activity: aberrant motor behavior (AMB), aggression, agitation, anxiety, apathy, and wandering.Methods: As part of a larger systematic review, we conducted an extensive literature search in nine health science and engineering databases. Meta‐analysis involved converting correlations to Fisher's Z scores, calculating 95% confidence intervals using the R Studio Metacor package, and assessing fixed effects, DerSimonian and Laird's random effects models, and heterogeneity.Results: Out of 12,853 identified records, 84 reports were retained for analysis. Eight reports that provided 12 datasets assessed the DTA of accelerometry data for identifying/monitoring apathy (n=4), AMB (n=1), anxiety (n=2) agitation (n=5), aggression (n=2), and wandering (n=2). Studies included one single‐blinded trial, one non‐randomized trial, and ten observational studies. A random‐effect meta‐analysis yielded a pooled correlation across studies of r=0.61 (0.50; 0.70), heterogeneity I²=68%. When the wandering NPS subgroup was eliminated from analysis, heterogeneity fell considerably to I²=36%, with a pooled correlation of r=0.55 (0.47; 0.62).Conclusions: These findings suggest that accelerometry consistently provides good to moderate DTA for identifying/ monitoring NPS associated with motor behavior in PLWD. Future research should prioritize standardizing the reporting of measurement procedures, signal thresholds, outcome measures, devices, and reference standards.

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 Potentially avoidable emergency department transfers (PAEDTs) and hospitalizations (PAHs) from long-term care (LTC) homes are two key quality improvement metrics. We aimed to: 1) Measure proportions of PAEDTs and PAHs in a Quebec sample; and 2) Compare them with those reported for the rest of Canada. Methods We conducted a repeated cross-sectional study of residents who were received at one tertiary hospital between April 2017 and March 2019 from seven LTC homes in Quebec, Canada. The MedUrge emergency department database was used to extract transfers and resident characteristics. Using published definitions, PAEDTs and PAHs were identified from principal emergency department and hospitalization diagnoses, respectively. PAEDT and PAH proportions were compared to those reported by the Canadian Institute for Health Information. Results A total of 1,233 transfers by 692 residents were recorded, among which 36.3% were classified as being potentially avoidable: 22.8% ‘PAEDT only’, 11.6% ‘both PAEDT & PAH’, and 1.9% ‘PAH only’. Shortness of breath was the most common reason for transfer. Pneumonia was the most common diagnosis from the ‘both PAEDT & PAH’ category. PAEDTs and PAHs accounted for 95% and 37% of potentially avoidable transfers, respectively. Among 533 hospitalizations, 31.3% were PAHs. These proportions were comparable to the rest of Canada, with some differences in proportions of transfers due to congestive heart failure, urinary tract infection, and implanted device management. Conclusions PAEDTs far outweigh PAHs in terms of frequency, and their monitoring is important for quality assurance as they may inform LTC-level interventions aimed at their reduction.