Cognition and Motor Function: A Novel Outcome Measure for Studies on Pre-Dementia Syndromes

Advances in dementia research have shifted attention towards earlier stages in the natural history, such as Mild Cognitive Impairment. The current gold standard outcome measure, the Alzheimer’s Disease Assessment Scale-Cognitive Subscale, is not optimally responsive to changes in pre-dementia populations. Modifications to scoring methodology and content have improved the measurement performance of the ADAS-Cog. However, no published modifications have addressed a second key shift in the field towards understanding motor function as an important component of dementia and pre-dementia syndromes. This thesis used a Pooled Index approach to combine an ADAS-Cog-Proxy measure with assessments of gait velocity and dual-task cost. The responsiveness of the PI to baseline discrimination between older adults with normal cognition, Subjective Cognitive Impairment, and MCI was similar to the ADAS-Cog-Proxy. The PI demonstrated greater responsiveness than the ADAS-Cog-Proxy to change over 6mo. and 48mo., but not 36mo. of follow-up. Overall, motor function assessments improve ADAS-Cog responsiveness

Developing artificial intelligence and machine learning to support primary care research and practice

This thesis was motivated by the potential to use everyday data , especially that collected in electronic health records (EHRs) as part of healthcare delivery, to improve primary care for clients facing complex clinical and/or social situations. Artificial intelligence (AI) techniques can identify patterns or make predictions with these data, producing information to learn about and inform care delivery. Our first objective was to understand and critique the body of literature on AI and primary care. This was achieved through a scoping review wherein we found the field was at an early stage of maturity, primarily focused on clinical decision support for chronic conditions in high-income countries, with low levels of primary care involvement and model evaluation in real-world settings. Our second objective was to demonstrate how AI methods can be applied to problems in descriptive epidemiology. To achieve this, we collaborated with the Alliance for Healthier Communities, which provides team-based primary health care through Community Health Centres (CHCs) across Ontario to clients who experience barriers to regular care. We described sociodemographic, clinical, and healthcare use characteristics of their adult primary care population using EHR data from 2009-2019. We used both simple statistical and unsupervised learning techniques, applied with an epidemiological lens. In addition to substantive findings, we identified potential avenues for future learning initiatives, including the development of decision support tools, and methodological considerations therein. Our third objective was to advance interpretable AI methodology that is well-suited for heterogeneous data, and is applicable in clinical epidemiology as well as other settings. To achieve this, we developed a new hybrid feature- and similarity-based model for supervised learning. There are two versions, fit by convex optimization with a sparsity-inducing penalty on the kernel (similarity) portion of the model. We compared our hybrid models with solely feature- and similarity-based approaches using synthetic data and using CHC data to predict future loneliness or social isolation. We also proposed a new strategy for kernel construction with indicator-coded data. Altogether, this thesis progressed AI for primary care in general and for a particular health care organization, while making research contributions to epidemiology and to computer science

A mobile app to identify lifestyle indicators related to undergraduate mental health (smart healthy campus): Observational app-based ecological momentary assessment

Background: Undergraduate studies are challenging, and mental health issues can frequently occur in undergraduate students,straining campus resources that are already in demand for somatic problems. Cost-effective measures with ubiquitous devices,such as smartphones, offer the potential to deliver targeted interventions to monitor and affect lifestyle, which may result inimprovements to student mental health. However, the avenues by which this can be done are not particularly well understood,especially in the Canadian context.Objective: The aim of this study is to deploy an initial version of the Smart Healthy Campus app at Western University, Canada,and to analyze corresponding data for associations between psychosocial factors (measured by a questionnaire) and behaviorsassociated with lifestyle (measured by smartphone sensors).Methods: This preliminary study was conducted as an observational app-based ecological momentary assessment. Undergraduatestudents were recruited over email, and sampling using a custom 7-item questionnaire occurred on a weekly basis.Results: First, the 7-item Smart Healthy Campus questionnaire, derived from fully validated questionnaires-such as the BriefResilience Scale; General Anxiety Disorder-7; and Depression, Anxiety, and Stress Scale-21-was shown to significantly correlatewith the mental health domains of these validated questionnaires, illustrating that it is a viable tool for a momentary assessmentof an overview of undergraduate mental health. Second, data collected through the app were analyzed. There were 312 weeklyresponses and 813 sensor samples from 139 participants from March 2019 to March 2020; data collection concluded whenCOVID-19 was declared a pandemic. Demographic information was not collected in this preliminary study because of technicallimitations. Approximately 69.8% (97/139) of participants only completed one survey, possibly because of the absence of anyincentive. Given the limited amount of data, analysis was not conducted with respect to time, so all data were analyzed as a singlecollection. On the basis of mean rank, students showing more positive mental health through higher questionnaire scores tendedto spend more time completing questionnaires, showed more signs of physical activity based on pedometers, and had their devicesrunning less and plugged in charging less when sampled. In addition, based on mean rank, students on campus tended to reportmore positive mental health through higher questionnaire scores compared with those who were sampled off campus. Some datafrom students found in or near residences were also briefly examined.Conclusions: Given these limited data, participants tended to report a more positive overview of mental health when on campusand when showing signs of higher levels of physical activity. These early findings suggest that device sensors related to physical activity and location are useful for monitoring undergraduate students and designing interventions. However, much more sensordata are needed going forward, especially given the sweeping changes in undergraduate studies due to COVID-19

Data-Driven Decision Support Tool Co-Development with a Primary Health Care Practice Based Learning Network

Background: The Alliance for Healthier Communities is a learning health system that supports Community Health Centres (CHCs) across Ontario, Canada to provide team-based primary health care to people who otherwise experience barriers to care. This case study describes the ongoing process and lessons learned from the first Alliance for Healthier Communities’ Practice Based Learning Network (PBLN) data-driven decision support tool co-development project. Methods: We employ an iterative approach to problem identification and methods development for the decision support tool, moving between discussion sessions and case studies with CHC electronic health record (EHR) data. We summarize our work to date in terms of six stages: population-level descriptive-exploratory study, PBLN team engagement, decision support tool problem selection, sandbox case study 1: individual-level risk predictions, sandbox case study 2: population-level planning predictions, project recap and next steps decision. Results: The population-level study provided an initial point of engagement to consider how clients are (not) represented in EHR data and to inform problem selection and methodological decisions thereafter. We identified three meaningful types of decision support, with initial target application areas: risk prediction/screening, triaging specialized program referrals, and identifying care access needs. Based on feasibility and expected impact, we started with the goal to support earlier identification of mental health decline after diabetes diagnosis. As discussions deepened around clinical use cases associated with example prediction task set ups, the target problem evolved towards supporting the upstream task of organizational planning and advocacy for adequate mental health care service capacity to meet incoming needs. Conclusions: This case study contributes towards a tool to support diabetes and mental health care, as well as lays groundwork for future CHC decision support tool initiatives. We share lessons learned and reflections from our process that other primary health care organizations may use to inform their own co-development initiatives

Inaugural Artificial Intelligence for Public Health Practice (AI4PHP) Retreat: Ontario, Canada

The Artificial Intelligence (AI) for Public Health Practice Retreat was a hybrid event held in October 2022 in London, Ontario to achieve three main goals: 1) Identify both the goals of public health practitioners and the tasks that they undertake as part of their practice to achieve those goals that could be supported by AI, 2) Learn from existing examples and the experience of others about facilitators and barriers to AI for public health, and 3) Support new and strengthen existing connections between public health practitioners and AI researchers. The retreat included a keynote presentation, group brainstorming exercises, breakout group activities, case studies, and interspersed breaks for networking and reflection. There were 38 attendees from across Ontario, and a guest speaker from New York. Major themes that emerged from discussions included the need for greater attention to AI applications in public health given the potential benefits and enthusiasm; rigorous data collection, data quality, and data accessibility as a foundational factor that needs urgent attention; and the need for an equitable systems-thinking approach to AI amidst the breadth of public health functions, interventions, and population-based applications. Attendees expressed a desire for continued engagement and collaboration between public health practice and AI researchers

Primary Care Informatics Response to Covid-19 Pandemic: Adaptation, Progress, and Lessons from Four Countries with High ICT Development

OBJECTIVE: Internationally, primary care practice had to transform in response to the COVID pandemic. Informatics issues included access, privacy, and security, as well as patient concerns of equity, safety, quality, and trust. This paper describes progress and lessons learned. METHODS: IMIA Primary Care Informatics Working Group members from Australia, Canada, United Kingdom and United States developed a standardised template for collection of information. The template guided a rapid literature review. We also included experiential learning from primary care and public health perspectives. RESULTS: All countries responded rapidly. Common themes included rapid reductions then transformation to virtual visits, pausing of non-COVID related informatics projects, all against a background of non-standardized digital development and disparate territory or state regulations and guidance. Common barriers in these four and in less-resourced countries included disparities in internet access and availability including bandwidth limitations when internet access was available, initial lack of coding standards, and fears of primary care clinicians that patients were delaying care despite the availability of televisits. CONCLUSIONS: Primary care clinicians were able to respond to the COVID crisis through telehealth and electronic record enabled change. However, the lack of coordinated national strategies and regulation, assurance of financial viability, and working in silos remained limitations. The potential for primary care informatics to transform current practice was highlighted. More research is needed to confirm preliminary observations and trends noted

Cognition and motor function: The gait and cognition pooled index.

BackgroundThere is a need for outcome measures with improved responsiveness to changes in pre-dementia populations. Both cognitive and motor function play important roles in neurodegeneration; motor function decline is detectable at early stages of cognitive decline. This proof of principle study used a Pooled Index approach to evaluate improved responsiveness of the predominant outcome measure (ADAS-Cog: Alzheimer's Disease Assessment Scale-Cognitive Subscale) when assessment of motor function is added.MethodsCandidate Pooled Index variables were selected based on theoretical importance and pairwise correlation coefficients. Kruskal-Wallis and Mann-Whitney U tests assessed baseline discrimination. Standardized response means assessed responsiveness to longitudinal change.ResultsFinal selected variables for the Pooled Index include gait velocity, dual-task cost of gait velocity, and an ADAS-Cog-Proxy (statistical approximation of the ADAS-Cog using similar cognitive tests). The Pooled Index and ADAS-Cog-Proxy scores had similar ability to discriminate between pre-dementia syndromes. The Pooled Index demonstrated trends of similar or greater responsiveness to longitudinal decline than ADAS-Cog-Proxy scores.ConclusionAdding motor function assessments to the ADAS-Cog may improve responsiveness in pre-dementia populations