Quantifying the Predictive Performance of Objectively Measured Physical Activity on Mortality in the UK Biobank.

BACKGROUND Objective measures of physical activity (PA) derived from wrist-worn accelerometers are compared with traditional risk factors in terms of mortality prediction performance in the UK Biobank. METHODS A subset of participants in the UK Biobank study wore a tri-axial wrist-worn accelerometer in a free-living environment for up to 7 days. A total of 82,304 individuals over the age of 50 (439,707 person-years of follow-up, 1,959 deaths) had both accelerometry data that met specified quality criteria and complete data on a set of traditional mortality risk factors. Predictive performance was assessed using cross-validated Concordance (C) for Cox regression models. Forward selection was used to obtain a set of best predictors of mortality. RESULTS In univariate Cox regression, age was the best predictor of all-cause mortality (C=0.681) followed by twelve PA predictors, led by minutes of moderate to vigorous PA (C=0.661) and total acceleration (C=0.661). Overall, 16 of the top 20 predictors were objective PA measures (C from 0.578 to 0.661). Using a threshold of 0.001 improvement in Concordance, the Concordance for the best model that did not include PA measures was 0.735 (9 covariates) compared with 0.748 (12 covariates) for the best model with PA variables (p-value<0.001). CONCLUSIONS Objective measures of PA derived from accelerometry outperform traditional predictors of all-cause mortality in the UK Biobank except age and substantially improve the prediction performance of mortality models based on traditional risk factors. Results confirm and complement previous findings in the National Health and Nutrition Examination Survey (NHANES).