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13 pages, 421 KiB

Open AccessArticle

Use of a Wearable Biosensor to Study Heart Rate Variability in Chronic Obstructive Pulmonary Disease and Its Relationship to Disease Severity

by Seon-Cheol Park, Narongkorn Saiphoklang, Donghyun Jung, David Gomez, Jonathan E. Phillips, Brett A. Dolezal, Donald P. Tashkin, Igor Barjaktarevic and Christopher B. Cooper

Sensors 2022, 22(6), 2264; https://doi.org/10.3390/s22062264 - 15 Mar 2022

Cited by 9 | Viewed by 3597

Abstract

The purpose of this study was to explore the relationships between heart rate variability (HRV) and various phenotypic measures that relate to health and functional status in chronic obstructive pulmonary disease (COPD), and secondly, to demonstrate the feasibility of ascertaining HRV via a [...] Read more.

The purpose of this study was to explore the relationships between heart rate variability (HRV) and various phenotypic measures that relate to health and functional status in chronic obstructive pulmonary disease (COPD), and secondly, to demonstrate the feasibility of ascertaining HRV via a chest-worn wearable biosensor in COPD patients. HRV analysis was performed using SDNN (standard deviation of the mean of all normal R-R intervals), low frequency (LF), high frequency (HF), and LF/HF ratio. We evaluated the associations between HRV and COPD severity, class of bronchodilator therapy prescribed, and patient reported outcomes. Seventy-nine participants with COPD were enrolled. There were no differences in SDNN, HF, and LF/HF ratio according to COPD severity. The SDNN in participants treated with concurrent beta-agonists and muscarinic antagonists was lower than that in other participants after adjusting heart rate (beta coefficient −3.980, p = 0.019). The SDNN was positively correlated with Veterans Specific Activity Questionnaire (VSAQ) score (r = 0.308, p = 0.006) and handgrip strength (r = 0.285, p = 0.011), and negatively correlated with dyspnea by modified Medical Research Council (mMRC) questionnaire (r = −0.234, p = 0.039), health status by Saint George’s Respiratory Questionnaire (SGRQ) (r = −0.298, p = 0.008), symptoms by COPD Assessment Test (CAT) (r = −0.280, p = 0.012), and BODE index (r = −0.269, p = 0.020). When measured by a chest-worn wearable device, reduced HRV was observed in COPD participants receiving inhaled beta-sympathomimetic agonist and muscarinic antagonists. HRV was also correlated with various health status and performance measures. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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21 pages, 6119 KiB

Open AccessArticle

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

by Eujessika Rodrigues, Daniella Lima, Paulo Barbosa, Karoline Gonzaga, Ricardo Oliveira Guerra, Marcela Pimentel, Humberto Barbosa and Álvaro Maciel

Sensors 2022, 22(5), 2001; https://doi.org/10.3390/s22052001 - 4 Mar 2022

Cited by 20 | Viewed by 6144

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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27 pages, 2217 KiB

Open AccessArticle

Load Position Estimation Method for Wearable Devices Based on Difference in Pulse Wave Arrival Time

by Kazuki Yoshida and Kazuya Murao

Sensors 2022, 22(3), 1090; https://doi.org/10.3390/s22031090 - 31 Jan 2022

Cited by 2 | Viewed by 2965

Abstract

With the increasing use of wearable devices equipped with various sensors, information on human activities, biometrics, and surrounding environments can be obtained via sensor data at any time and place. When such devices are attached to arbitrary body parts and multiple devices are [...] Read more.

With the increasing use of wearable devices equipped with various sensors, information on human activities, biometrics, and surrounding environments can be obtained via sensor data at any time and place. When such devices are attached to arbitrary body parts and multiple devices are used to capture body-wide movements, it is important to estimate where the devices are attached. In this study, we propose a method that estimates the load positions of wearable devices without requiring the user to perform specific actions. The proposed method estimates the time difference between a heartbeat obtained by an ECG sensor and a pulse wave obtained by a pulse sensor, and it classifies the pulse sensor position from the estimated time difference. Data were collected at 12 body parts from four male subjects and one female subject, and the proposed method was evaluated in both user-dependent and user-independent environments. The average F-value was 1.0 when the number of target body parts was from two to five. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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14 pages, 1593 KiB

Open AccessArticle

On the Security of Bluetooth Low Energy in Two Consumer Wearable Heart Rate Monitors/Sensing Devices

by Yeṣem Kurt Peker, Gabriel Bello and Alfredo J. Perez

Sensors 2022, 22(3), 988; https://doi.org/10.3390/s22030988 - 27 Jan 2022

Cited by 9 | Viewed by 6297

Abstract

Since its inception in 2013, Bluetooth Low Energy (BLE) has become the standard for short-distance wireless communication in many consumer devices, as well as special-purpose devices. In this study, we analyze the security features available in Bluetooth LE standards and evaluate the features [...] Read more.

Since its inception in 2013, Bluetooth Low Energy (BLE) has become the standard for short-distance wireless communication in many consumer devices, as well as special-purpose devices. In this study, we analyze the security features available in Bluetooth LE standards and evaluate the features implemented in two BLE wearable devices (a Fitbit heart rate wristband and a Polar heart rate chest wearable) and a BLE keyboard to explore which security features in the BLE standards are implemented in the devices. In this study, we used the ComProbe Bluetooth Protocol Analyzer, along with the ComProbe software to capture the BLE traffic of these three devices. We found that even though the standards provide security mechanisms, because the Bluetooth Special Interest Group does not require that manufacturers fully comply with the standards, some manufacturers fail to implement proper security mechanisms. The circumvention of security in Bluetooth devices could leak private data that could be exploited by rogue actors/hackers, thus creating security, privacy, and, possibly, safety issues for consumers and the public. We propose the design of a Bluetooth Security Facts Label (BSFL) to be included on a Bluetooth/BLE enabled device’s commercial packaging and conclude that there should be better mechanisms for informing users about the security and privacy provisions of the devices they acquire and use and to educate the public on protection of their privacy when buying a connected device. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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13 pages, 3768 KiB

Open AccessArticle

Heart Rate and Distance Measurement of Two Multisport Activity Trackers and a Cellphone App in Different Sports: A Cross-Sectional Validation and Comparison Field Study

by Mario Budig, Michael Keiner, Riccardo Stoohs, Meike Hoffmeister and Volker Höltke

Sensors 2022, 22(1), 180; https://doi.org/10.3390/s22010180 - 28 Dec 2021

Cited by 14 | Viewed by 3183

Abstract

Options for monitoring sports have been continuously developed by using activity trackers to determine almost all vital and movement parameters. The aim of this study was to validate heart rate and distance measurements of two activity trackers (Polar Ignite; Garmin Forerunner 945) and [...] Read more.

Options for monitoring sports have been continuously developed by using activity trackers to determine almost all vital and movement parameters. The aim of this study was to validate heart rate and distance measurements of two activity trackers (Polar Ignite; Garmin Forerunner 945) and a cellphone app (Polar Beat app using iPhone 7 as a hardware platform) in a cross-sectional field study. Thirty-six moderate endurance-trained adults (20 males/16 females) completed a test battery consisting of walking and running 3 km, a 1.6 km interval run (standard 400 m outdoor stadium), 3 km forest run (outdoor), 500/1000 m swim and 4.3/31.5 km cycling tests. Heart rate was recorded via a Polar H10 chest strap and distance was controlled via a map, 400 m stadium or 50 m pool. For all tests except swimming, strong correlation values of r > 0.90 were calculated with moderate exercise intensity and a mean absolute percentage error of 2.85%. During the interval run, several significant deviations (p < 0.049) were observed. The swim disciplines showed significant differences (p < 0.001), with the 500 m test having a mean absolute percentage error of 8.61%, and the 1000 m test of 55.32%. In most tests, significant deviations (p < 0.001) were calculated for distance measurement. However, a maximum mean absolute percentage error of 4.74% and small mean absolute error based on the total route lengths were calculated. This study showed that the accuracy of heart rate measurements could be rated as good, except for rapid changing heart rate during interval training and swimming. Distance measurement differences were rated as non-relevant in practice for use in sports. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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Bland–Altman diagrams of heart rate measurements (n = 36). (a) Garmin 945 vs. criterion, (b) Garmin 945 vs. criterion without swim, (c) Polar Ignite vs. criterion, (d) Polar Ignite vs. criterion without swim, (e) Garmin 945 vs. Polar Ignite, (f) Garmin 945 vs. Polar Ignite without swim, (g,h) heart rate lines Garmin 945, Polar Ignite and chest trap Polar H10 (g) swim 500 m, (h) swim 1000 m; min = minute, mean = mean value, SD = standard deviation of the difference. Full article ">Figure 2
HR interval, 1.6 km interval run, p-value expressed in bars and corresponding HR graph at start/middle/end of each interval phase (n = 36); min = minute, incr. = increasing run, subm. = submaximal run, trot = trotting run; * = p < 0.05. Full article ">Figure 3
Resting heart rate (n = 36). (a) p-values expressed in bars, tracker compared to criterion measurement chest strap and as a line between both activity trackers, (b) corresponding HR graph, both trackers and the chest strap, min = minutes. Full article ">Figure 4
Boxplot of distance deviations from the criterion measurement in the various disciplines (n = 36). (a) p-values compared to criterion and between activity trackers, (b) corresponding heart rate graphs, min = minutes. Full article ">

11 pages, 1946 KiB

Open AccessArticle

Validity of the Wrist-Worn Polar Vantage V2 to Measure Heart Rate and Heart Rate Variability at Rest

by Olli-Pekka Nuuttila, Elisa Korhonen, Jari Laukkanen and Heikki Kyröläinen

Sensors 2022, 22(1), 137; https://doi.org/10.3390/s22010137 - 26 Dec 2021

Cited by 24 | Viewed by 6893

Abstract

Heart rate (HR) and heart rate variability (HRV) can be monitored with wearable devices throughout the day. Resting HRV in particular, reflecting cardiac parasympathetic activity, has been proposed to be a useful marker in the monitoring of health and recovery from training. This [...] Read more.

Heart rate (HR) and heart rate variability (HRV) can be monitored with wearable devices throughout the day. Resting HRV in particular, reflecting cardiac parasympathetic activity, has been proposed to be a useful marker in the monitoring of health and recovery from training. This study examined the validity of the wrist-based photoplethysmography (PPG) method to measure HR and HRV at rest. Recreationally endurance-trained participants recorded pulse-to-pulse (PP) and RR intervals simultaneously with a PPG-based watch and reference heart rate sensor (HRS) at a laboratory in a supine position (n = 39; 5-min recording) and at home during sleep (n = 29; 4-h recording). In addition, analyses were performed from pooled laboratory data (n = 11344 PP and RR intervals). Differences and correlations were analyzed between the HRS- and PPG-derived HR and LnRMSSD (the natural logarithm of the root mean square of successive differences). A very good agreement was found between pooled PP and RR intervals with a mean bias of 0.17 ms and a correlation coefficient of 0.993 (p < 0.001). In the laboratory, HR did not differ between the devices (mean bias 0.0 bpm), but PPG slightly underestimated the nocturnal recordings (mean bias −0.7 bpm, p < 0.001). PPG overestimated LnRMSSD both in the laboratory (mean bias 0.20 ms, p < 0.001) and nocturnal recordings (mean bias 0.17 ms, p < 0.001). However, very strong intraclass correlations in the nocturnal recordings were found between the devices (HR: 0.998, p < 0.001; LnRMSSD: 0.931, p < 0.001). In conclusion, PPG was able to measure HR and HRV with adequate accuracy in recreational athletes. However, when strict absolute values are of importance, systematic overestimation, which seemed to especially concern participants with low LnRMSSD, should be acknowledged. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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13 pages, 4019 KiB

Open AccessArticle

Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning

by Alessio Staffini, Thomas Svensson, Ung-il Chung and Akiko Kishi Svensson

Sensors 2022, 22(1), 34; https://doi.org/10.3390/s22010034 - 22 Dec 2021

Cited by 20 | Viewed by 5791

Abstract

Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlying cardiovascular and respiratory diseases as well as [...] Read more.

Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlying cardiovascular and respiratory diseases as well as mood disorders. Given the importance of accurate modeling and reliable predictions of heart rate fluctuations for the prevention and control of certain diseases, it is paramount to identify models with the best performance in such tasks. The objectives of this study were to compare the results of three different forecasting models (Autoregressive Model, Long Short-Term Memory Network, and Convolutional Long Short-Term Memory Network) trained and tested on heart rate beats per minute data obtained from twelve heterogeneous participants and to identify the architecture with the best performance in terms of modeling and forecasting heart rate behavior. Heart rate beats per minute data were collected using a wearable device over a period of 10 days from twelve different participants who were heterogeneous in age, sex, medical history, and lifestyle behaviors. The goodness of the results produced by the models was measured using both the mean absolute error and the root mean square error as error metrics. Despite the three models showing similar performance, the Autoregressive Model gave the best results in all settings examined. For example, considering one of the participants, the Autoregressive Model gave a mean absolute error of 2.069 (compared to 2.173 of the Long Short-Term Memory Network and 2.138 of the Convolutional Long Short-Term Memory Network), achieving an improvement of 5.027% and 3.335%, respectively. Similar results can be observed for the other participants. The findings of the study suggest that regardless of an individual’s age, sex, and lifestyle behaviors, their heart rate largely depends on the pattern observed in the previous few minutes, suggesting that heart rate can be reasonably regarded as an autoregressive process. The findings also suggest that minute-by-minute heart rate prediction can be accurately performed using a linear model, at least in individuals without pathologies that cause heartbeat irregularities. The findings also suggest many possible applications for the Autoregressive Model, in principle in any context where minute-by-minute heart rate prediction is required (arrhythmia detection and analysis of the response to training, among others). Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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11 pages, 9160 KiB

Open AccessArticle

Comparison of Heart Rate Monitoring Accuracy between Chest Strap and Vest during Physical Training and Implications on Training Decisions

by Jakub Parak, Mikko Salonen, Tero Myllymäki and Ilkka Korhonen

Sensors 2021, 21(24), 8411; https://doi.org/10.3390/s21248411 - 16 Dec 2021

Cited by 15 | Viewed by 6256

Abstract

Heart rate (HR) and heart rate variability (HRV) based physiological metrics such as Excess Post-exercise Oxygen Consumption (EPOC), Energy Expenditure (EE), and Training Impulse (TRIMP) are widely utilized in coaching to monitor and optimize an athlete’s training load. Chest straps, and recently also [...] Read more.

Heart rate (HR) and heart rate variability (HRV) based physiological metrics such as Excess Post-exercise Oxygen Consumption (EPOC), Energy Expenditure (EE), and Training Impulse (TRIMP) are widely utilized in coaching to monitor and optimize an athlete’s training load. Chest straps, and recently also dry electrodes integrated to special sports vests, are used to monitor HR during sports. Mechanical design, placement of electrodes, and ergonomics of the sensor affect the measured signal quality and artefacts. To evaluate the impact of the sensor mechanical design on the accuracy of the HR/HRV and further on to estimation of EPOC, EE, and TRIMP, we recorded HR and HRV from a chest strap and a vest with the same ECG sensor during supervised exercise protocol. A 3-lead clinical Holter ECG was used as a reference. Twenty-five healthy subjects (six females) participated. Mean absolute percentage error (MAPE) for HR was 0.76% with chest strap and 3.32% with vest. MAPE was 1.70% vs. 6.73% for EE, 0.38% vs. 8.99% for TRIMP and 3.90% vs. 54.15% for EPOC with chest strap and vest, respectively. Results suggest superior accuracy of chest strap over vest for HR and physiological metrics monitoring during sports. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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Sensor placement on body: (a) ECG sensor attached to a chest strap and the reference device; (b) Vest and the reference device. ECG sensor was attached to the vest on the back side. Full article ">Figure 2
Comparison of HR and RRI from sports sensor and ECG Holter reference during the whole protocol: (a) Strap HR poor and average case; (b) strap RRI poor and average case; (c) vest HR poor and average case; (d) vest RRI poor and average case. Full article ">Figure 3
Bland−Altman plot comparing the sensor device and ECG holter reference HR and RRI during entire protocol including all measurements (20 for strap and 22 for vest) (solid horizontal line: bias, dashed lines: 95% confidence limits of agreement): (a) RRI strap vs. reference; (b) RRI vest vs. reference; (c) HR in 5 s windows strap vs. reference; (d) HR in 5 s windows vest vs. reference. Full article ">Figure 4
Bland−Altman plot comparing training parameters estimation with using input from sensor device and reference ECG holter during entire protocol (solid horizontal line: bias, dashed lines: 95% confidence limits of agreement): (a) Strap—Total Energy Expenditure; (b) Vest—Total Energy Expenditure; (c) Strap—EPOC; (d) Vest—EPOC; (e) Strap—TRIMP (f) Vest—TRIMP. Full article ">

18 pages, 4606 KiB

Open AccessArticle

What Is behind Changes in Resting Heart Rate and Heart Rate Variability? A Large-Scale Analysis of Longitudinal Measurements Acquired in Free-Living

by Marco Altini and Daniel Plews

Sensors 2021, 21(23), 7932; https://doi.org/10.3390/s21237932 - 27 Nov 2021

Cited by 45 | Viewed by 20095

Abstract

The aim of this study was to investigate the relationship between heart rate and heart rate variability (HRV) with respect to individual characteristics and acute stressors. In particular, the relationship between heart rate, HRV, age, sex, body mass index (BMI), and physical activity [...] Read more.

The aim of this study was to investigate the relationship between heart rate and heart rate variability (HRV) with respect to individual characteristics and acute stressors. In particular, the relationship between heart rate, HRV, age, sex, body mass index (BMI), and physical activity level was analyzed cross-sectionally in a large sample of 28,175 individuals. Additionally, the change in heart rate and HRV in response to common acute stressors such as training of different intensities, alcohol intake, the menstrual cycle, and sickness was analyzed longitudinally. Acute stressors were analyzed over a period of 5 years for a total of 9 million measurements (

320 \pm 374

measurements per person). HRV at the population level reduced with age (p < 0.05, r = −0.35, effect size = moderate) and was weakly associated with physical activity level (p < 0.05, r = 0.21, effect size = small) and not associated with sex (p = 0.35, d = 0.02, effect size = negligible). Heart rate was moderately associated with physical activity level (p < 0.05, r = 0.30, effect size = moderate) and sex (p < 0.05, d = 0.63, effect size = moderate) but not with age (p = 0.35, r = −0.01). Similar relationships between BMI, resting heart rate (p < 0.05, r = 0.19, effect size = small), and HRV (p < 0.05, r = −0.10, effect size = small) are shown. In response to acute stressors, we report a 4.6% change in HRV (p < 0.05, d = 0.36, effect size = small) and a 1.3% change in heart rate (p < 0.05, d = 0.38, effect size = small) in response to training, a 6% increase in heart rate (p < 0.05, d = 0.97, effect size = large) and a 12% reduction in HRV (p < 0.05, d = 0.55, effect size = moderate) after high alcohol intake, a 1.6% change in heart rate (p < 0.05, d = 1.41, effect size = large) and a 3.2% change in HRV (p < 0.05, d = 0.80, effect size = large) between the follicular and luteal phases of the menstrual cycle, and a 6% increase in heart rate (p < 0.05, d = 0.97, effect size = large) and 10% reduction in HRV (p < 0.05, d = 0.47, effect size = moderate) during sickness. Acute stressors analysis revealed how HRV is a more sensitive but not specific marker of stress. In conclusion, a short resting heart rate and HRV measurement upon waking using a smartphone app can effectively be used in free-living to quantify individual stress responses across a large range of individuals and stressors. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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21 pages, 639 KiB

Open AccessArticle

Wavelet Analysis and Self-Similarity of Photoplethysmography Signals for HRV Estimation and Quality Assessment

by Alexander Neshitov, Konstantin Tyapochkin, Evgeniya Smorodnikova and Pavel Pravdin

Sensors 2021, 21(20), 6798; https://doi.org/10.3390/s21206798 - 13 Oct 2021

Cited by 16 | Viewed by 7469

Abstract

Peak-to-peak intervals in Photoplethysmography (PPG) can be used for heart rate variability (HRV) estimation if the PPG is collected from a healthy person at rest. Many factors, such as a person’s movements or hardware issues, can affect the signal quality and make some [...] Read more.

Peak-to-peak intervals in Photoplethysmography (PPG) can be used for heart rate variability (HRV) estimation if the PPG is collected from a healthy person at rest. Many factors, such as a person’s movements or hardware issues, can affect the signal quality and make some parts of the PPG signal unsuitable for reliable peak detection. Therefore, a robust HRV estimation algorithm should not only detect peaks, but also identify corrupted signal parts. We introduce such an algorithm in this paper. It uses continuous wavelet transform (CWT) for peak detection and a combination of features derived from CWT and metrics based on PPG signals’ self-similarity to identify corrupted parts. We tested the algorithm on three different datasets: a newly introduced Welltory-PPG-dataset containing PPG signals collected with smartphones using the Welltory app, and two publicly available PPG datasets: TROIKAand PPG-DaLiA. The algorithm demonstrated good accuracy in peak-to-peak intervals detection and HRV metric estimation. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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Examples of PPG signals: (a) a clean signal with clearly visible peaks; (b) a noisy signal where peaks associated to cardiac cycles still can be recognized; (c) a corrupted signal where no accurate peak detection is possible. Full article ">Figure 2
Mexican hat wavelet function <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>ψ</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>. Full article ">Figure 3
Algorithm workflow. Full article ">Figure 4
The PPG signal (top) and its corresponding STFT spectrogram (bottom), part of the PPG signal of subject 01 in the TROIKA dataset. Heart rate frequency is growing from <math display="inline"><semantics> <mrow> <mn>1.2</mn> </mrow> </semantics></math> Hz to <math display="inline"><semantics> <mrow> <mn>1.9</mn> </mrow> </semantics></math> Hz. Full article ">Figure 5
Continuity filter highlighting curves with bounded rates of change; the horizontal length is adjusted to correspond to 10 s of time and the vertical length corresponds to a change in frequency of <math display="inline"><semantics> <mrow> <mn>0.3</mn> </mrow> </semantics></math> Hz. Full article ">Figure 6
Filtered spectrogram. Black lines show the curves consisting of local maxima in the spectrogram columns. Full article ">Figure 7
Scaled Mexican hat wavelet and its frequency spectrum for the smallest scale <math display="inline"><semantics> <msub> <mi>a</mi> <mn>1</mn> </msub> </semantics></math> and the largest scale <math display="inline"><semantics> <msub> <mi>a</mi> <mn>50</mn> </msub> </semantics></math> in the chosen scale range. Full article ">Figure 8
PPG signal, the corresponding scalogram computed with the Mexican hat wavelet for the scale range <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mn>1</mn> </msub> <mo>,</mo> <mo>…</mo> <msub> <mi>a</mi> <mn>50</mn> </msub> </mrow> </semantics></math>, and the ridge lines in the scalogram. This signal is a part of the red channel PPG from subject 01 in the Welltory-PPG-dataset. The R peaks in the signal are detected as top points of the ridge lines chosen by the filtration Algorithm 3. Full article ">Figure 9
Part of the S3 signal of the PPG-DaLiA dataset. Full article ">Figure 10
Filtration of the RR intervals of <a href="#sensors-21-06798-f009" class="html-fig">Figure 9</a>. Green intervals are kept by the algorithm and red intervals are discarded. Full article ">Figure 11
PPG-DaLiA, Subject S3, interval 0 to 100 s. Full article ">

11 pages, 512 KiB

Open AccessArticle

The Use of a Smartphone Application in Monitoring HRV during an Altitude Training Camp in Professional Female Cyclists: A Preliminary Study

by Alejandro Javaloyes, Manuel Mateo-March, Agustín Manresa-Rocamora, Santiago Sanz-Quinto and Manuel Moya-Ramón

Sensors 2021, 21(16), 5497; https://doi.org/10.3390/s21165497 - 15 Aug 2021

Cited by 1 | Viewed by 3151

Abstract

Altitude training is a common strategy to improve performance in endurance athletes. In this context, the monitoring of training and the athletes’ response is essential to ensure positive adaptations. Heart rate variability (HRV) has been proposed as a tool to evaluate stress and [...] Read more.

Altitude training is a common strategy to improve performance in endurance athletes. In this context, the monitoring of training and the athletes’ response is essential to ensure positive adaptations. Heart rate variability (HRV) has been proposed as a tool to evaluate stress and the response to training. In this regard, many smartphone applications have emerged allowing a wide access to recording HRV easily. The purpose of this study was to describe the changes of HRV using a validated smartphone application before (Pre-TC), during (TC), and after (Post-TC) an altitude training camp in female professional cyclists. Training load (TL) and vagal markers of heart rate variability (LnRMSSD, LnRMSSDcv) of seven professional female cyclists before, during, and after and altitude training camp were monitored. Training volume (SMD = 0.80), LnRMSSD (SMD = 1.06), and LnRMSSDcv (SMD = −0.98) showed moderate changes from Pre-TC to TC. Training volume (SMD = 0.74), TL (SMD = 0.75), LnRMSSD (SMD = −1.11) and LnRMSSDcv (SMD = 0.83) showed moderate changes from TC to Post-TC. Individual analysis showed that heart rate variability responded differently among subjects. The use of a smartphone application to measure HRV is a useful tool to evaluate the individual response to training in female cyclists. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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10 pages, 415 KiB

Open AccessArticle

Return-to-Work Screening by Linear Discriminant Analysis of Heart Rate Variability Indices in Depressed Subjects

by Toshikazu Shinba, Keizo Murotsu, Yosuke Usui, Yoshinori Andow, Hiroshi Terada, Nobutoshi Kariya, Yoshitaka Tatebayashi, Yoshiki Matsuda, Go Mugishima, Yujiro Shinba, Guanghao Sun and Takemi Matsui

Sensors 2021, 21(15), 5177; https://doi.org/10.3390/s21155177 - 30 Jul 2021

Cited by 6 | Viewed by 3217

Abstract

Using a linear discriminant analysis of heart rate variability (HRV) indices, the present study sought to verify the usefulness of autonomic measurement in major depressive disorder (MDD) patients by assessing the feasibility of their return to work after sick leave. When reinstatement was [...] Read more.

Using a linear discriminant analysis of heart rate variability (HRV) indices, the present study sought to verify the usefulness of autonomic measurement in major depressive disorder (MDD) patients by assessing the feasibility of their return to work after sick leave. When reinstatement was scheduled, patients’ HRV was measured using a wearable electrocardiogram device. The outcome of the reinstatement was evaluated at one month after returning to work. HRV indices including high- and low-frequency components were calculated in three conditions within a session: initial rest, mental task, and rest after task. A linear discriminant function was made using the HRV indices of 30 MDD patients from our previous study to effectively discriminate the successful reinstatement from the unsuccessful reinstatement; this was then tested on 52 patients who participated in the present study. The discriminant function showed that the sensitivity and specificity in discriminating successful from unsuccessful returns were 95.8% and 35.7%, respectively. Sensitivity is high, indicating that normal HRV is required for a successful return, and that the discriminant analysis of HRV indices is useful for return-to-work screening in MDD patients. On the other hand, specificity is low, suggesting that other factors may also affect the outcome of reinstatement. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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17 pages, 1874 KiB

Open AccessArticle

Demonstrating the Applicability of Smartwatches in PM_2.5 Health Impact Assessment

by Ming-Chien Mark Tsou, Shih-Chun Candice Lung and Chih-Hui Cheng

Sensors 2021, 21(13), 4585; https://doi.org/10.3390/s21134585 - 4 Jul 2021

Cited by 7 | Viewed by 3305

Abstract

Smartwatches are being increasingly used in research to monitor heart rate (HR). However, it is debatable whether the data from smartwatches are of high enough quality to be applied in assessing the health impacts of air pollutants. The objective of this study was [...] Read more.

Smartwatches are being increasingly used in research to monitor heart rate (HR). However, it is debatable whether the data from smartwatches are of high enough quality to be applied in assessing the health impacts of air pollutants. The objective of this study was to assess whether smartwatches are useful complements to certified medical devices for assessing PM_2.5 health impacts. Smartwatches and medical devices were used to measure HR for 7 and 2 days consecutively, respectively, for 49 subjects in 2020 in Taiwan. Their associations with PM_2.5 from low-cost sensing devices were assessed. Good correlations in HR were found between smartwatches and certified medical devices (r_s > 0.6, except for exercise, commuting, and worshipping). The health damage coefficients obtained from smartwatches (0.282% increase per 10 μg/m³ increase in PM_2.5) showed the same direction, with a difference of only 8.74% in magnitude compared to those obtained from certified medical devices. Additionally, with large sample sizes, the health impacts during high-intensity activities were assessed. Our work demonstrates that smartwatches are useful complements to certified medical devices in PM_2.5 health assessment, which can be replicated in developing countries. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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21 pages, 5227 KiB

Open AccessEditor’s ChoiceArticle

The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring

by Marco Altini and Hannu Kinnunen

Sensors 2021, 21(13), 4302; https://doi.org/10.3390/s21134302 - 23 Jun 2021

Cited by 86 | Viewed by 48499

Abstract

Consumer-grade sleep trackers represent a promising tool for large scale studies and health management. However, the potential and limitations of these devices remain less well quantified. Addressing this issue, we aim at providing a comprehensive analysis of the impact of accelerometer, autonomic nervous [...] Read more.

Consumer-grade sleep trackers represent a promising tool for large scale studies and health management. However, the potential and limitations of these devices remain less well quantified. Addressing this issue, we aim at providing a comprehensive analysis of the impact of accelerometer, autonomic nervous system (ANS)-mediated peripheral signals, and circadian features for sleep stage detection on a large dataset. Four hundred and forty nights from 106 individuals, for a total of 3444 h of combined polysomnography (PSG) and physiological data from a wearable ring, were acquired. Features were extracted to investigate the relative impact of different data streams on 2-stage (sleep and wake) and 4-stage classification accuracy (light NREM sleep, deep NREM sleep, REM sleep, and wake). Machine learning models were evaluated using a 5-fold cross-validation and a standardized framework for sleep stage classification assessment. Accuracy for 2-stage detection (sleep, wake) was 94% for a simple accelerometer-based model and 96% for a full model that included ANS-derived and circadian features. Accuracy for 4-stage detection was 57% for the accelerometer-based model and 79% when including ANS-derived and circadian features. Combining the compact form factor of a finger ring, multidimensional biometric sensory streams, and machine learning, high accuracy wake-sleep detection and sleep staging can be accomplished. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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19 pages, 1450 KiB

Open AccessArticle

Analysis of Gender Differences in HRV of Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Mobile-Health Technology

by Lluis Capdevila, Jesús Castro-Marrero, José Alegre, Juan Ramos-Castro and Rosa M Escorihuela

Sensors 2021, 21(11), 3746; https://doi.org/10.3390/s21113746 - 28 May 2021

Cited by 7 | Viewed by 7978

Abstract

In a previous study using mobile-health technology (mHealth), we reported a robust association between chronic fatigue symptoms and heart rate variability (HRV) in female patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). This study explores HRV analysis as an objective, non-invasive and easy-to-apply marker [...] Read more.

In a previous study using mobile-health technology (mHealth), we reported a robust association between chronic fatigue symptoms and heart rate variability (HRV) in female patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). This study explores HRV analysis as an objective, non-invasive and easy-to-apply marker of ME/CFS using mHealth technology, and evaluates differential gender effects on HRV and ME/CFS core symptoms. In our methodology, participants included 77 ME/CFS patients (32 men and 45 women) and 44 age-matched healthy controls (19 men and 25 women), all self-reporting subjective scores for fatigue, sleep quality, anxiety, and depression, and neurovegetative symptoms of autonomic dysfunction. The inter-beat cardiac intervals are continuously monitored/recorded over three 5-min periods, and HRV is analyzed using a custom-made application (iOS) on a mobile device connected via Bluetooth to a wearable cardiac chest band. Male ME/CFS patients show increased scores compared with control men in all symptoms and scores of fatigue, and autonomic dysfunction, as with women in the first study. No differences in any HRV parameter appear between male ME/CFS patients and controls, in contrast to our findings in women. However, we have found negative correlations of ME/CFS symptomatology with cardiac variability (SDNN, RMSSD, pNN50, LF) in men. We have also found a significant relationship between fatigue symptomatology and HRV parameters in ME/CFS patients, but not in healthy control men. Gender effects appear in HF, LF/HF, and HFnu HRV parameters. A MANOVA analysis shows differential gender effects depending on the experimental condition in autonomic dysfunction symptoms and HF and HFnu HRV parameters. A decreased HRV pattern in ME/CFS women compared to ME/CFS men may reflect a sex-related cardiac autonomic dysfunction in ME/CFS illness that could be used as a predictive marker of disease progression. In conclusion, we show that HRV analysis using mHealth technology is an objective, non-invasive tool that can be useful for clinical prediction of fatigue severity, especially in women with ME/CFS. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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Figure 1
Screenshots sequence corresponding to the custom-made application (FitLab® App). The first screen (left) shows all the stored recordings in the app pending to be synchronized with the server and the possibility to start a new one (“+” symbol). The next three screens ask for the situation where the recording takes place. The last screen (on the right) shows the instantaneous heart rate (in BPM) and the RR series. Full article ">Figure 2
Simple regression analysis between physical fatigue perception (FIS-40) and HRV parameters differentiating ME/CFS patients (n = 32) and Control men (n = 19). Physical FIS-40 score is significantly explained from (A) SDNN (p = 0.005), (B) RMSSD (p = 0.026), (C) LF (p = 0.002), and (D) HF (p = 0.016), for ME/CFS patients (black squares, upper regression lines), but not for those healthy controls (white circles, bottom regression lines). Full article ">Figure 3
Comparison of the HRV time-domain indices in the whole sample. Mean ± SEM of (A) mean of RR intervals (meanRR), (B) standard deviation of all RR intervals (SDNN), (C) root mean square of differences of successive RR intervals (RMSSD), and (D) the proportion derived by dividing the number of interval differences of successive RR intervals greater than 50 ms by the total number of RR intervals (pNN50). a,b Mean values with unlike letters were significantly different between groups (two-way ANOVA and Duncan’s post hoc comparison, p < 0.05). W-C: Healthy control women (n = 25); W-F: ME/CFS women (n = 44); M-C: Healthy control men (n = 18); M-F: ME/CFS men (n = 32). Full article ">Figure 4
Comparison of the HRV frequency-domain indices in the sample. Mean ± SEM of (A) power of the low frequency band (LF), (B) power of the high frequency band (HF), (C) LF/HF ratio, and (D) normalized HF value (HFnu). a,b Mean values with unlike letters were significantly different between groups (two-way ANOVA and Duncan’s post hoc comparison, p < 0.05). W-C: Healthy control women (n = 25); W-F: ME/CFS women (n = 44); M-C: Healthy control men (n = 18); M-F: ME/CFS men (n = 32). Full article ">

12 pages, 967 KiB

Open AccessArticle

Wrist-Based Photoplethysmography Assessment of Heart Rate and Heart Rate Variability: Validation of WHOOP

by Clint R. Bellenger, Dean J. Miller, Shona L. Halson, Gregory D. Roach and Charli Sargent

Sensors 2021, 21(10), 3571; https://doi.org/10.3390/s21103571 - 20 May 2021

Cited by 39 | Viewed by 10518 | Correction

Abstract

Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV quantification via photoplethysmography (PPG). This study evaluated the validity of WHOOP’s PPG-derived HR and HRV against electrocardiogram-derived (ECG) measures. HR and [...] Read more.

Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV quantification via photoplethysmography (PPG). This study evaluated the validity of WHOOP’s PPG-derived HR and HRV against electrocardiogram-derived (ECG) measures. HR and HRV were assessed via WHOOP 2.0 and ECG over 15 opportunities during October–December 2018. WHOOP-derived pulse-to-pulse (PP) intervals were edited with WHOOP’s proprietary filter, in addition to various filter strengths via Kubios HRV software. HR and HRV (Ln RMSSD) were quantified for each filter strength. Agreement was assessed via bias and limits of agreement (LOA), and contextualised using smallest worthwhile change (SWC) and coefficient of variation (CV). Regardless of filter strength, bias (≤0.39 ± 0.38%) and LOA (≤1.56%) in HR were lower than the CV (10–11%) and SWC (5–5.5%) for this parameter. For Ln RMSSD, bias (1.66 ± 1.80%) and LOA (±5.93%) were lowest for a 200 ms filter and WHOOP’s proprietary filter, which approached or exceeded the CV (3–13%) and SWC (1.5–6.5%) for this parameter. Acceptable agreement was found between WHOOP- and ECG-derived HR. Bias and LOA in Ln RMSSD approached or exceeded the SWC/CV for this variable and should be interpreted against its own level of bias precision. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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14 pages, 993 KiB

Open AccessEditor’s ChoiceArticle

Validity of the Polar H7 Heart Rate Sensor for Heart Rate Variability Analysis during Exercise in Different Age, Body Composition and Fitness Level Groups

by Adrián Hernández-Vicente, David Hernando, Jorge Marín-Puyalto, Germán Vicente-Rodríguez, Nuria Garatachea, Esther Pueyo and Raquel Bailón

Sensors 2021, 21(3), 902; https://doi.org/10.3390/s21030902 - 29 Jan 2021

Cited by 33 | Viewed by 6838

Abstract

This work aims to validate the Polar H7 heart rate (HR) sensor for heart rate variability (HRV) analysis at rest and during various exercise intensities in a cohort of male volunteers with different age, body composition and fitness level. Cluster analysis was carried [...] Read more.

This work aims to validate the Polar H7 heart rate (HR) sensor for heart rate variability (HRV) analysis at rest and during various exercise intensities in a cohort of male volunteers with different age, body composition and fitness level. Cluster analysis was carried out to evaluate how these phenotypic characteristics influenced HR and HRV measurements. For this purpose, sixty-seven volunteers performed a test consisting of the following consecutive segments: sitting rest, three submaximal exercise intensities in cycle-ergometer and sitting recovery. The agreement between HRV indices derived from Polar H7 and a simultaneous electrocardiogram (ECG) was assessed using concordance correlation coefficient (CCC). The percentage of subjects not reaching excellent agreement (CCC > 0.90) was higher for high-frequency power (P_HF) than for low-frequency power (P_LF) of HRV and increased with exercise intensity. A cluster of unfit and not young volunteers with high trunk fat percentage showed the highest error in HRV indices. This study indicates that Polar H7 and ECG were interchangeable at rest. During exercise, HR and P_LF showed excellent agreement between devices. However, during the highest exercise intensity, CCC for P_HF was lower than 0.90 in as many as 60% of the volunteers. During recovery, HR but not HRV measurements were accurate. As a conclusion, phenotypic differences between subjects can represent one of the causes for disagreement between HR sensors and ECG devices, which should be considered specifically when using Polar H7 and, generally, in the validation of any HR sensor for HRV analysis. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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Figure 1
Example of the RR intervals for one subject throughout the entire test. Dotted lines separate the different test segments: resting (SREST), cycling (SCY) and recovery (SREC). SCY was divided into three stages corresponding to 60, 70 and 80% of HRmax, denoted as SCY60, SCY70 and SCY80, respectively. Full article ">Figure 2
Example of <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mrow> <mi>H</mi> <mi>R</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, PLF (n) and PHF (n) obtained from RRE for one subject: Resting segment (left) and cycling segment (right). Note that the axes have different scales. <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mrow> <mi>H</mi> <mi>R</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> = instantaneous HR signal; PLF (n) = Instantaneous low-frequency power; PHF (n) = Instantaneous high-frequency power; RRE = RR intervals series from the ECG. Full article ">Figure 3
Example of RR intervals recorded by both devices during the recovery segment for one subject. (a) RRE = RR intervals series recorded by ECG; (b) RRP = RR intervals series recorded by PolarH7. Full article ">

15 pages, 2683 KiB

Open AccessArticle

Influence of Artefact Correction and Recording Device Type on the Practical Application of a Non-Linear Heart Rate Variability Biomarker for Aerobic Threshold Determination

by Bruce Rogers, David Giles, Nick Draper, Laurent Mourot and Thomas Gronwald

Sensors 2021, 21(3), 821; https://doi.org/10.3390/s21030821 - 26 Jan 2021

Cited by 33 | Viewed by 5782

Abstract

Recent study points to the value of a non-linear heart rate variability (HRV) biomarker using detrended fluctuation analysis (DFA a1) for aerobic threshold determination (HRVT). Significance of recording artefact, correction methods and device bias on DFA a1 during exercise and HRVT is unclear. [...] Read more.

Recent study points to the value of a non-linear heart rate variability (HRV) biomarker using detrended fluctuation analysis (DFA a1) for aerobic threshold determination (HRVT). Significance of recording artefact, correction methods and device bias on DFA a1 during exercise and HRVT is unclear. Gas exchange and HRV data were obtained from 17 participants during an incremental treadmill run using both ECG and Polar H7 as recording devices. First, artefacts were randomly placed in the ECG time series to equal 1, 3 and 6% missed beats with correction by Kubios software’s automatic and medium threshold method. Based on linear regression, Bland Altman analysis and Wilcoxon paired testing, there was bias present with increasing artefact quantity. Regardless of artefact correction method, 1 to 3% missed beat artefact introduced small but discernible bias in raw DFA a1 measurements. At 6% artefact using medium correction, proportional bias was found (maximum 19%). Despite this bias, the mean HRVT determination was within 1 bpm across all artefact levels and correction modalities. Second, the HRVT ascertained from synchronous ECG vs. Polar H7 recordings did show an average bias of minus 4 bpm. Polar H7 results suggest that device related bias is possible but in the reverse direction as artefact related bias. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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Figure 1
Inclusion criteria, numbers of included participants and data intervals for the threefold purpose of this study. Full article ">Figure 2
Regression plots for all ECG derived DFA a1 NA vs. DFA a1 for each artefact condition and correction method. (A) vs. DFA a1 1% AC; (B) vs. DFA a1 1% MC; (C) vs. DFA a1 3% AC; (D) vs. DFA a1 3% MC; (E) vs. DFA a1 6% AC; (F) vs. DFA a1 6% MC. Bisection lines in light gray. Slope and Pearson’s r shown in bottom right of each plot. Full article ">Figure 3
Bland Altman analysis of ECG derived DFA a1 NA vs. DFA a1 for each artefact condition and correction method using regression based mean and standard deviations. (A) vs. DFA a1 1% AC; (B) vs. DFA a1 1% MC; (C) vs. DFA a1 3% AC; (D) vs. DFA a1 3% MC; (E) vs. DFA a1 6% AC; (F) vs. DFA a1 6% MC. Center solid line in each plot represents the mean bias (difference) between each paired value as relative percent (difference/mean × 100). The top and bottom dashed lines are 1.96 standard deviations from the mean difference. Pearson’s r for the regression line of bias with p value shown on top right of each plot. Full article ">Figure 4
Bland Altman analysis of ECG derived HRVT NA vs. HRVT for each artefact condition and correction method. (A) vs. 1% AC; (B) vs. 1% MC; (C) vs. 3% AC; (D) vs. 3% MC; (E) vs. 6% AC; (F) vs. 6% MC. Center solid line in each plot represents the mean bias (difference) between each paired value. The top and bottom lines are 1.96 standard deviations from the mean difference. Net bias with standard deviation (SD) in top right portion of each plot with standard deviation (SD) in top right portion of each plot. Full article ">Figure 5
Comparison of heart rate at HRVT between ECG vs. Polar H7 data using Bland Altman analysis. The one outlier (labeled as X) represents the only participant with no artefact in both ECG and Polar H7 time series. Center line represents the mean bias (difference) between each paired value. The top and bottom lines are 1.96 standard deviation from the mean difference. Bias and standard deviation (SD) listed in upper right corner. Full article ">Figure 6
Time-varying analysis (window width: 120 s, grid interval: 5 s), DFA a1 for matched time series containing no artefact in one representative participant, ECG (solid triangle), Polar H7 (open circle), ECG 6% MC (open triangle). Full article ">

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19 pages, 1639 KiB

Open AccessEditor’s ChoiceReview

Wearable Devices Suitable for Monitoring Twenty Four Hour Heart Rate Variability in Military Populations

by Katrina Hinde, Graham White and Nicola Armstrong

Sensors 2021, 21(4), 1061; https://doi.org/10.3390/s21041061 - 4 Feb 2021

Cited by 89 | Viewed by 18343

Abstract

Heart rate variability (HRV) measurements provide information on the autonomic nervous system and the balance between parasympathetic and sympathetic activity. A high HRV can be advantageous, reflecting the ability of the autonomic nervous system to adapt, whereas a low HRV can be indicative [...] Read more.

Heart rate variability (HRV) measurements provide information on the autonomic nervous system and the balance between parasympathetic and sympathetic activity. A high HRV can be advantageous, reflecting the ability of the autonomic nervous system to adapt, whereas a low HRV can be indicative of fatigue, overtraining or health issues. There has been a surge in wearable devices that claim to measure HRV. Some of these include spot measurements, whilst others only record during periods of rest and/or sleep. Few are capable of continuously measuring HRV (≥24 h). We undertook a narrative review of the literature with the aim to determine which currently available wearable devices are capable of measuring continuous, precise HRV measures. The review also aims to evaluate which devices would be suitable in a field setting specific to military populations. The Polar H10 appears to be the most accurate wearable device when compared to criterion measures and even appears to supersede traditional methods during exercise. However, currently, the H10 must be paired with a watch to enable the raw data to be extracted for HRV analysis if users need to avoid using an app (for security or data ownership reasons) which incurs additional cost. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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