06 / 2022 – 12 / 2022
Cardiovascular disease was identified as the leading cause of death worldwide in 2019. In the United States, heart failure (HF) accounts for 9.9 % of deaths associated with cardiovascular disease . While the estimated prevalence of HF from 1998 and onwards seemed relatively stable , it is projected to rise by 46 % in adults from 2012 to 2030, estimating an increase from 2.4 % to 3.0 % in the total population . With prevalence rising, the estimated overall costs of HF are also projected to grow, suggesting a total increase of 127 % from 30.7 billion to 69.8 billion US dollars in the US in the same period .
Based on the 2021 published guidelines by the European Society of Cardiology , decompensated HF is the rapid or gradual onset of symptoms severe enough for the patients to require immediate medical intervention. It is associated with high hospitalization, rehospitalization and mortality rates. Early detection of decompensated HF is therefore essential to reduce mortality rates successfully. Typical symptoms and clinical signs are congestion, hypoperfusion, abnormalities in heart sounds (especially third and fourth heart sound) as well as abnormal heart rhythm (tachycardia, arrhythmia) .
With the emerging trend of wearables, especially smartwatches, access to biomedical parameters, such as heart rate and oxygen saturation, is becoming more available. Smartwatches can provide meaningful patient-reported parameters, and are therefore useful tools. However, they are not yet standardly integrated into telemonitoring systems for HF patients . Studies suggest that telemonitoring systems show beneficial outcomes when included in the care of HF patients regarding overall mortality and hospitalization rates [6,7]. The reduced hospitalization rate also leads to the reduction of costs for overall HF treatment . Parameters such as heart rate, heart rate variability, oxygen saturation, and ECGs are being investigated in different telemonitoring concepts . Additionally, they can already be captured by modern smartwatches . The potential of self-recorded ECGs has previously been shown with the identification of atrial fibrillation . Besides detecting abnormal heart rhythm, other valuable parameters, e.g., QRS duration, can be extracted from smartwatch-derived data. The importance of a prolonged QRS complex for the prognosis of HF patients has already been discussed extensively . It has been established that 1-lead ECGs accurately measure most baseline intervals, including the QRS complex .
Therefore, the goal of this master thesis is to extend the knowledge of how different parameters, extracted and calculated from commercial smartwatch data, can be used to monitor HF and detect decompensation early. For this purpose, relevant parameters that evaluate the health status of HF patients and that can be captured by smartwatch data will be identified based on literature research. A database will be created by conducting a study to validate the previously defined parameters. The study will consist of two groups: a patient group diagnosed with HF and a healthy group. In the final step, differences between the two groups will be investigated.
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