Fitness levels can be accurately predicted using wearabledevices—no exercise required Cambridge_Uni NaturePortfolio
and an important predictor of heart disease and mortality risk—require expensive laboratory equipment and are mostly limited to elite athletes. The new method uses machine learning to predict VO2max—the capacity of the body to carry out aerobic work—during everyday activity, without the need for contextual information such as GPS measurements.
"VO2max isn't the only measurement of fitness, but it's an important one for endurance, and is a strong predictor of diabetes, heart disease, and other mortality risks," said co-author Dr. Soren Brage from Cambridge's MRC Epidemiology Unit."However, since most VO2max tests are done on people who are reasonably fit, it's hard to get measurements from those who are not as fit and might be at risk of cardiovascular disease.
The baseline data from 11,059 participants in the Fenland Study was compared with follow-up data from seven years later, taken from a subset of 2,675 of the original participants. A third group of 181 participants from the UK Biobank Validation Study underwent lab-based VO2max testing to validate the accuracy of the algorithm. The machine learning model had strong agreement with the measured VO2max scores at both baseline and follow-up testing .
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