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. Normally, tests to accurately measure VO2max, which is a key measurement of overall fitness and an important predictor of heart disease and mortality risk. It requires expensive laboratory equipment and is 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. The Cambridge-developed model is robust, transparent and provides accurate predictions based on heart rate and accelerometer data only. Since the model can also detect fitness changes over time, it could also be useful in estimating fitness levels for entire populations and identifying the effects of lifestyle trends. The results are reported in the journal ‘npj Digital Medicine’.