The novel coronavirus disease 2019 (COVID-19) has emerged as a global pandemic that has affected the lives of billions of people. Clinical studies have reported an association between COVID-19 and cardiac diseases. Remote monitoring powered by wearable sensors impacts medical care by enabling health monitoring outside of the clinic. Wearable devices can provide a noninvasive and continuous multi-parameter assessment of ECG, Heart Rate Variability, arterial blood pressure, oxygen saturation and respiratory rate evaluation. Such monitoring may help predict and prevent cardiovascular events related to COVID-19 addresses the growing demand for a novel 5P (Predictive, Preventive, Participatory, Personalized, and Precision) medicine approach. This article aimed to review current and prospective advances in wearable devices for cardiac monitoring and their progress toward clinical application during the COVID-19 pandemic. We performed bibliometric analysis by Scopus, the largest and well organized bibliographic database and analyzed the top-cited articles in this field. Our analysis includes an overview of the most widespread practical implications of CVD-focused remote patient monitoring techniques based on wearable personalized devices. Assessment for both COVID-related conditions and general cases is included in the analysis. Recent studies have reported that cardiac abnormalities present in 19.7-27.8 % of hospitalized patients with COVID-19. COVID-19 associated myocarditis and heart rate abnormalities frequently occur. Additionally, patients with pre-existing CVD and hypertension are at high risk of worse outcomes. Data from several studies have identified atrial fibrillation as the most common form of arrhythmias in COVID-19 patients. Worsening of existing atrial fibrillation in COVID-19 patients is also a serious clinical concern. Implementation of wearable ECG devices for remote monitoring can improve the management of patients with atrial fibrillation and those at high risk for its development. Telecardiology based on wearable devices and remote monitoring allow out-of-hospital control of COVID-19 patients and patients suffering from chronic diseases at high risk of acute cardiovascular events, ensuring their early detection and tracking.

Introduction. Biomarkers of biological age (BA) are essential for anti-aging research and practice because of their prediction of life expectancy, detection of premature aging, and estimation of anti-ageing programs' effectiveness. The purpose of this study is a clinical validation of the method of biological age estimation based on the analysis of heart rate variability (HRV), artificial intelligence technologies, and biometric monitoring. Methods. In 51 patients who received wellness and rehabilitation services in the medical center "Edem Medical", biological age was determined based on the analysis of HRV and machine learning algorithms. A comparison was made between the proposed method and other known methods of biological age estimation. Biological age estimation by physicians which is based on the Frailty Index was chosen as a reference method. The second method was DNA methylation age (DNAm PhenoAge). This method predicts biological age based on nine parameters of blood (albumin, creatinine, glucose, C-reactive protein, lymphocytes [%], mean corpuscular volume [MCV], red cell distribution width [RDW], alkaline phosphatase, WBC count). Using the «leave one out» technique, an additional algorithm was created for approximating biological age in view of blood test parameters and ECG signals as input data. Morning HRV assessment was performed on empty stomach and after 10-minute rest in horizontal position. ECG was recorded using Mawi Vital multisensor device. The following statistical tests were used to reveal associations between different methods of biological age estimation: 1. bivariate correlation, 2. mean absolute error (MAE), 3. qualitative binary age estimation. Results. All tested methods of BA evaluation were strongly correlated with the reference method (physician-determined age). HRV based approach was superior in comparison with other methods. In 9 out of 10 cases, the qualitative binary age assessment using HRV coincided with the reference method. The HRV method was the most accurate for biological age estimation (3.62 vs 12.62) based on MAE. Conclusion. The method based on HRV is an affordable and convenient approach to biological age estimation. This method offers opportunities for early stratification of individuals at risk of accelerated aging. It combines well with the paradigm of 3 P medicine which is based on Prevention, Prediction, and Personalized approach to each patient

Comprehensive and multidisciplinary rehabilitation is gaining momentum as a useful strategy that aims to improve physical, psychological, and social components of health in subjects affected by violence, trauma, and mental distress. Previous and current wars have prioritized essential diagnostic and rehabilitative services to civil subjects and military servicepersons which can be delivered by skilled physiatrists and allied specialists. Stratifying subjects in need of various rehabilitative procedures and offering them psychological support, balanced nutrition, musculoskeletal care, and socialisation in a safe and relaxing atmosphere may improve their mental and functional capacities and resolve numerous health issues. The choice of comprehensive rehabilitative procedures depends on their availability and understanding of complementary effects of various interventions.