Self-supervised machine learning to characterise step counts from wrist-worn accelerometers in the UK Biobank by Small, SR, Chan, S, Walmsley, R, von Fritsch, L, Acquah, A, Mertes, G, Feakins, BG, Creagh, A, Strange, A, Matthews, CE, Clifton, DA, Price, AJ, Khalid, S, Bennett, D, Doherty, A

“…Our data indicate an inverse dose-response association, where taking 6,430-8,277 daily steps was associated with 37% [25-48%] and 28% [20-35%] lower risk of fatal CVD and all-cause mortality up to seven years later, compared to those taking fewer steps each day.</p> <br> <p><strong>Conclusions:</strong> We have developed an open and transparent method that markedly improves the measurement of steps in large-scale wrist-worn accelerometer datasets. …”