PACO Physical Activity and Cognition in Middle and Late Adulthood: Insights from National Cohort Studies and Mobile Sensing Using Machine Learning
The benefits of physical activity for cognitive and physical health across the adult lifespan and in old age are undisputed and of immense societal relevance. Still significant gaps exist in our understanding of the daily neurobiological and behavioral processes, the most effective intervention designs, and the role of sample diversity and middle adulthood. These gaps can only be addressed through transdisciplinary approaches that integrate insights from behavioral sciences, biomedicine, and computer science. The PACO project is a collaboration between the Network Aging Research (NAR) at Heidelberg University and the Karlsruhe Institute of Technology (KIT), with the objective of integrating their expertise in two complementary fields: physical activity and cognition, as studied by NAR, and mobile computing and machine learning, as studied by KIT. Strategically the project strengthens and integrates both universities’ foci on health technology and lifespan development, thus addressing the societal challenges of creating technology for the benefit of an aging society.
The objective is to conduct pilot projects based on existing unique cohort data (i.e., NAKO) and novel mobile and stationary sensing approaches. This will address the daily variation and individual diversity in both cognition and physical activity, and facilitate a deeper understanding of the bidirectional neurobiological and behavioral processes. Based on the gained insights, an application for a DFG-GRK will be prepared and the topics of doctoral theses will be refined. In addition, the curriculum, supervision guide, and selection materials will be developed. Upon completion of their studies, graduates will have acquired transdisciplinary knowledge on the bidirectional links between physical activity and cognition, including individual differences to leverage personalized prevention strategies. They will also have developed expertise in sophisticated mobile technology and state-of-the-art machine learning methods. This will result in the creation of a group of uniquely qualified interdisciplinary doctoral students, who will be excellently equipped to address current societal needs.