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. 2022 Mar 4;22(5):2001.
doi: 10.3390/s22052001.

HRV Monitoring Using Commercial Wearable Devices as a Health Indicator for Older Persons during the Pandemic

Eujessika Rodrigues  1 , Daniella Lima  2 , Paulo Barbosa  2 , Karoline Gonzaga  2 , Ricardo Oliveira Guerra  1 , Marcela Pimentel  1 , Humberto Barbosa  3 , Álvaro Maciel  1
Affiliations

HRV Monitoring Using Commercial Wearable Devices as a Health Indicator for Older Persons during the Pandemic

Eujessika Rodrigues et al. Sensors (Basel). .

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.

Keywords: heart rate variability; remote monitoring; wearables.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Overview of the SMH mobile app. (a) is a walk screen and (b) is a sleep screen.
Figure 2
Figure 2
Overview of the SMH web dashboard.
Figure 3
Figure 3
History of heart rate variability screen.
Figure 4
Figure 4
Excerpt of the swagger documentation of the HRV metrics.
Figure 5
Figure 5
Metrics data model for HRV.
Figure 6
Figure 6
Model overview.
Figure 7
Figure 7
Development process workflow.
Figure 8
Figure 8
ADF Test.
Figure 9
Figure 9
Comparison of the interpolation methods.
Figure 10
Figure 10
Data splitting.
Figure 11
Figure 11
Hyperparameter tuning.
Figure 12
Figure 12
Neural Networks’ models.
Figure 13
Figure 13
Loss versus number of epoch.
Figure 14
Figure 14
Comparison of the correction methods.
Figure 15
Figure 15
Information flow of the experiment to validate HRV parameters in the SMH platform.
Figure 16
Figure 16
Standard deviation of the of all normal RR intervals (SDNN) of the same group of patients for Kubios and SMH.
Figure 17
Figure 17
The root mean square of successive differences between normal heartbeats (RMSSD) for Kubios and SMH.
Figure 18
Figure 18
Results for the percentage of successive RR intervals that differ by more than 50 ms (pNN50) using Kubios and SMH.
See this image and copyright information in PMC

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