The work was written on behalf of the Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine (icm.charite.de), which is the innovation driver in the field of digital transformation in cardiac medicine in Berlin, Germany. The institute at Charité-Universitätsmedizin Berlin combines modern imaging methods with data science (AI, visual analysis, etc.) and biophysical modelling (hemodynamics, metabolism, etc.) to develop new approaches for diagnostics, therapy planning and decision support systems. The work is therefore always in a direct clinical context (bench-to-bedside) and unites interdisciplinary teams of physicians, mathematicians, computer scientists, physicists and engineers.
Wrist-worn devices with heart rate monitoring have become increasingly popular. Although current guidelines advise to consider clinical symptoms and exercise tolerance during decision making in heart disease, it remains unknown to which extent wearables can help to determine functional capacity measures.
Wrist-worn devices with heart rate monitoring have become increasingly popular. Although current guidelines advise to consider clinical symptoms and exercise tolerance during decision making in heart disease, it remains unknown to which extent wearables can help to determine such functional capacity measures. In clinical settings, the 6-minute walk test is a widely used measure of exercise tolerance and a predictor of patient-centred outcomes. In patients with cardiovascular disease, including valve disease, current guidelines advise considering exercise capacity for diagnostics and treatment planning. Wrist-worn devices are constantly improving and have become available to large parts of the population. Today’s sensors typically include mechanical and optical methods to measure activity and heart rate that provide information on individual exercise intensities and gross energy expenditure.
Previous studies have identified wrist-worn devices, accelerometers and pedometers as effective tools to increase patients´ daily activity and have explored the associations between physical activity, cardiovascular events and risk factors. Whereas many devices are designed to record activity, it has not been studied if wrist-worn devices can predict six-minute walk tests to accurately assess exercise capacity and enable comparisons between patients.
The 6-minute walk test has been clinically validated and has been used to determine the effects of therapeutic interventions and prognosis. Although standardized medical exercise tests such as 6-minute walk tests are easy to perform, they still require visiting healthcare services. Wrist-worn devices could offer the advantage of broad availability and may allow performing measurements at home and during everyday activity. Additionally, wearable devices can provide continuous monitoring which enables trends to be identified, making it easier to distinguish the deteriorating patient from the patient that is doing well.
We, therefore, aimed to analyse if 6-minute walk test results can be predicted by heart rate-based activity profiles obtained from wrist-worn devices in combination with literature data in patients with valvular heart disease.
In this study, we used heart rate monitoring from wearables in combination with literature-based reference data from a recent meta-analysis to determine the daily amount of time spent in different levels of activity.
The study was part of the ‘EurValve’ research initiative, focusing on decision support in patients with valvular heart disease. The project’s aim was to implement and test, in a relevant clinical target cohort, a Decision Support System (DSS) for aortic and mitral valve replacement and repair. The main component of the DSS was a combined 0D model of the cardiovascular system that includes modification options for valve repair and replacement, aiming to predict the hemodynamic effects of different types of treatment. The multi-centre study was conducted at three European sites in the Netherlands, the United Kingdom and Germany.
A total of n = 107 sensor datasets with 1,019,748 min of recordings were analysed. The time spent in moderate activity was able to predict outcomes of a 6-minute walk test in patients with valvular heart disease. In combination with information on a patient´s gender, age, BMI and disease type, absolute 6-minute walk test distances as well as the probability of achieving target 6-minute walk distances can be predicted (Figure 1).
Furthermore, the uncertainty of these model-based predictions is demonstrated and overlapped with the minimal detectable changes and the minimal clinically important differences of the 6-minute walk test. The findings open up the possibility for monitoring exercise capacity during everyday life and may therefore help to identify clinical deterioration early. Further studies in larger cohorts and a variety of disease groups are required to improve the method’s accuracy and to investigate if continuous recordings can provide helpful additional information for patient-specific diagnostic processes and therapy planning.