Physical activity micropatterns and VILPA for the prevention of cardiovascular disease, cancer and depression

Most adults never exercise, yet nearly everyone moves in short, vigorous bursts during ordinary daily life: climbing stairs, carrying shopping, playing with children, walking quickly to catch a train. We call this VILPA (vigorous intermittent lifestyle physical activity). Our group showed that as little as three to four minutes of VILPA a day is associated with substantially lower cardiovascular disease and cancer risk, and this work has since informed international physical activity guidelines. This project extends the micropattern paradigm to mental health outcomes and to underexamined disease endpoints. The candidate will work with raw accelerometry from cohorts such as All of Us, UK Biobank and ProPASS, using machine-learned activity recognition to characterise the intensity, duration, frequency and clustering of movement bouts, and will test their prospective associations with cardiovascular events, cardiometabolic biomarkers, site-specific cancers, and depressive and anxiety symptoms. There is scope to shape the project around a mental health focus, a cardiometabolic focus, or the methodological question of how micropatterns should be defined and modelled. Training is provided in causal epidemiological design, target trial emulation, competing risk and time-to-event modelling, and reproducible analysis of large sensor datasets.