Brain connectivity patterns and environmental factors predict which older adults will successfully increase physical activity after receiving a cardiovascular diagnosis. Nagashree Thovinakere and colleagues studied 295 cognitively healthy but physically inactive older adults from the UK Biobank who received cardiovascular diagnoses during a roughly four-year period. The authors tracked which people increased their activity level to the moderate-to-vigorous physical activity levels recommended by the World Health Organization, using both self-reports and accelerometer data. The authors used machine learning to identify key predictive factors for physical activity behavior change, finding that a combined model incorporating brain imaging, behavioral, and contextual features achieved the strongest predictive performance. Important predictors included access to green spaces, social support from friends and family, executive function abilities, and specific patterns of brain connectivity between networks involved in self-control and planning. Participants who increased physical activity showed cognitive benefits, particularly in working memory and executive function. According to the authors, a multimodal brain-behavior fingerprint can help predict physical activity adherence, which could help clinicians. In addition, the importance of factors such as green space and social support can help guide policy to increase heart health at the population level. Individual motivation helps but structural and contextual factors also determine whether a person sticks with a heart-healthy exercise routine.
PNAS Nexus
Social determinants of health and brain connectivity predict physical activity behavior change after new cardiovascular diagnosis
21-Oct-2025