Physical fitness is important in view of reducing risks for a number of non-communicable diseases, both for individuals and policy-makers. In this paper, we present a prototype tool that combines forecasting of individual fitness parameters of schoolchildren to early adulthood with estimation of relative risk for all-cause early mortality in adulthood based on the forecasted fitness. This tool is a first step in the development of a platform that will show age, gender, and geographical distributions of risk and suggest potential interventions.
Keywords: CrowdHEALTH; SLOfit; exercise; machine learning.