Year:
Applicant:
Institution:
Email:
zargoush@mcmaster.ca
Keywords:
aging
early mobility limitation
machine learning
mobility
mobility decline
physical function
technology
Project ID:
190240
Approved Project Status:
Project Summary
Problems with everyday mobility, such as walking or driving, are common in older adulthood and can negatively impact health and social functioning. It is important to understand early changes in older peoples’ mobility and to identify those who will benefit from further healthcare follow-up and early preventative treatment. The proposed project will use Machine Learning (ML) techniques applied to data from the Canadian Longitudinal Study on Aging (CLSA) to find the most relevant predictors of early mobility problems. The project will inform other sub-projects of a larger program of research focused on advancing the assessment of early mobility limitation in older Canadians. Through this project, we hope to identify the biological, medical, psychosocial, lifestyle, and economic factors that predict early mobility limitation. Eventually, we hope to help older people, healthcare professionals, and policymakers to prevent or delay late-life mobility problems through early detection.