Abstract

Remote monitoring platforms based on advanced health sensors have the potential to become important tools during the COVID-19 pandemic, supporting the reduction in risks for affected populations such as the elderly. Current commercially available wearable devices still have limitations to deal with heart rate variability (HRV), an important health indicator of human aging. This study analyzes the role of a remote monitoring system designed to support health services to older people during the complete course of the COVID-19 pandemic in Brazil, since its beginning in Brazil in March 2020 until November 2021, based on HRV. Using different levels of analysis and data, we validated HRV parameters by comparing them with reference sensors and tools in HRV measurements. We compared the results obtained for the cardiac modulation data in time domain using samples of 10 elderly people’s HRV data from Fitbit Inspire HR with the results provided by Kubios for the same population using a cardiac belt, with the data divided into train and test, where 75% of the data were used for training the models, with the remaining 25% as a test set for evaluating the final performance of the models. The results show that there is very little difference between the results obtained by the remote monitoring system compared with Kubios, indicating that the data obtained from these devices might provide accurate results in evaluating HRV in comparison with gold standard devices. We conclude that the application of the methods and techniques used and reported in this study are useful for the creation and validation of HRV indicators in time series obtained by means of wearable devices based on photoplethysmography sensors; therefore, they can be incorporated into remote monitoring processes as seen during the pandemic.

Highlights

  • Heart rate variability (HRV) consists of the periodic oscillation between heartbeats, and these oscillations are modulated by the Autonomic Nervous System [1,2]

  • Since this study was motivated by the COVID-19 pandemic scenario that imposed isolation constraints to elderlies, these results show that commercial wearable devices combined with the SMH platform can be used as an effective tool for heart rate variability (HRV) monitoring

  • In this study we reported the challenges and strategies in the process of building intelligent models to fill in missing data from heart rate time series and the methods of implementing heart rate variability classifiers, acquired using a commercial wearable device, namely Fitbit

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Summary

Introduction

Heart rate variability (HRV) consists of the periodic oscillation between heartbeats, and these oscillations are modulated by the Autonomic Nervous System [1,2]. One of the results of the aging process is the decline in the ability of the autonomic nervous system to adjust blood flow in response to external situations. This autonomic control can be assessed and monitored in a non-invasive way through the heart rate variability being considered as a parameter to estimate the interaction between the brain and the cardiovascular system in elderly individuals [2]. Low HRV reflects an abnormal and insufficient adaptation of the autonomic nervous system, which may indicate the presence of physiological changes relevant to the individual’s health

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