Fitness levels can be accurately predicted using wearable devices - no exercise required

Cambridge researchers have developed a method for measuring overall fitness accurately on wearable devices - and more robustly than current consumer smartwatches and fitness monitors - without the wearer needing to exercise. You don't need an expensive test in a lab to get a real measurement of fitness - the wearables we use every day can be just as powerful, if they have the right algorithm behind them Cecilia Mascolo Normally, tests to accurately measure VO2max - a key measurement of overall fitness 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. In what is by far the largest study of its kind, the researchers gathered activity data from more than 11,000 participants in the Fenland Study using wearable sensors, with a subset of participants tested again seven years later. The researchers used the data to develop a model to predict VO2max, which was then validated against a third group that carried out a standard lab-based exercise test. The model showed a high degree of accuracy compared to lab-based tests, and outperforms other approaches. Some smartwatches and fitness monitors currently on the market claim to provide an estimate of VO2max, but since the algorithms powering these predictions aren-t published and are subject to change at any time, it's unclear whether the predictions are accurate, or whether an exercise regime is having any effect on an individual's VO2max over time.