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

Assessment of symptom severity in psychotic disorder patients based on heart rate variability and accelerometer mobility data

BACKGROUND

Advancement in mental health care requires easily accessible, efficient diagnostic and treatment assessment tools. Viable biomarkers could enable objectification and automation of the diagnostic and treatment process, currently dependent on a psychiatric interview. Available wearable technology and computational methods make it possible to incorporate heart rate variability (HRV), an indicator of autonomic nervous system (ANS) activity, into potential diagnostic and treatment assessment frameworks as a biomarker of disease severity in mental disorders, including schizophrenia and bipolar disorder (BD).

METHOD

We used a commercially available electrocardiography (ECG) chest strap with a built-in accelerometer, i.e. Polar H10, to record R-R intervals and physical activity of 30 hospitalized schizophrenia or BD patients and 30 control participants through ca. 1.5-2 h time periods. We validated a novel approach to data acquisition based on a flexible, patient-friendly and cost-effective setting. We analyzed the relationship between HRV and the Positive and Negative Syndrome Scale (PANSS) test scores, as well as the HRV and mobility coefficient. We also proposed a method of rest period selection based on R-R intervals and mobility data. The source code for reproducing all experiments is available on GitHub, while the dataset is published on Zenodo.

RESULTS

Mean HRV values were lower in the patient compared to the control group and negatively correlated with the results of the PANSS general subcategory. For the control group, we also discovered the inversely proportional dependency between the mobility coefficient, based on accelerometer data, and HRV. This relationship was less pronounced for the treatment group.

CONCLUSIONS

HRV value itself, as well as the relationship between HRV and mobility, may be promising biomarkers in disease diagnostics. These findings can be used to develop a flexible monitoring system for symptom severity assessment.

Highlighting Unseen Activity Through 48-Hour Continuous Measurement in Subacute Stroke Rehabilitation: Preliminary Cohort Study

BACKGROUND

Motor impairments not only lead to a significant reduction in patient activity levels but also trigger a further deterioration in motor function due to deconditioning, which is an issue that is particularly pronounced during hospitalization. This deconditioning can be countered by sustaining appropriate activity levels. Activities that occur outside of scheduled programs, often overlooked, are critical in this context. Wearable technology, such as smart clothing, provides a means to monitor these activities.

OBJECTIVE

This study aimed to observe activity levels in patients who had strokes during the subacute phase, focusing on both scheduled training sessions and other nontraining times in an inpatient rehabilitation environment. A smart clothing system is used to simultaneously measure heart rate and acceleration, offering insights into both the amount and intensity of the physical activity.

METHODS

In this preliminary cohort study, 11 individuals undergoing subacute stroke rehabilitation were enrolled. The 48-hour continuous measurement system, deployed at admission and reassessed 4 weeks later, monitored accelerometry data for physical activity (quantified with a moving SD of acceleration [MSDA]) and heart rate for intensity (quantified with percent heart rate reserve). The measurements were performed using a wearable activity monitoring system, the hitoe (NTT Corporation and Toray Industries, Inc) system comprising a measuring garment (wear or strap) with integrated electrodes, a data transmitter, and a smartphone. The Functional Independence Measure was used to assess the patients' daily activity levels. This study explored factors such as differences in activity during training and nontraining periods, correlations with activities of daily living (ADLs) and age, and changes observed after 4 weeks.

RESULTS

A significant increase was found in the daily total MSDA after the 4-week program, with the average percent heart rate reserve remaining consistent. Physical activity during training positively correlated with ADL levels both at admission (ρ=0.86, P<.001) and 4 weeks post admission (ρ=0.96, P<.001), whereas the correlation between age and MSDA was not significant during training periods at admission (ρ=-0.41, P=.21) or 4 weeks post admission (ρ=-0.25, P=.45). Conversely, nontraining activity showed a negative correlation with age, with significant negative correlations with age at admission (ρ=-0.82, P=.002) and 4 weeks post admission (ρ=-0.73, P=.01).

CONCLUSIONS

Inpatient rehabilitation activity levels were positively correlated with ADL levels. Further analysis revealed a strong positive correlation between scheduled training activities and ADL levels, whereas nontraining activities showed no such correlation. Instead, a negative correlation between nontraining activities and age was observed. These observations suggest the importance of providing activity opportunities for older patients, while it may also suggest the need for adjusting the activity amount to accommodate the potentially limited fitness levels of this demographic. Future studies with larger patient groups are warranted to validate and further elucidate these findings.

Differences in Heart Rate Variability in the Frequency Domain between Different Groups of Patients

Background and Objectives: Heart rate variability (HRV) is defined as a physiological variation in duration between sinus beats. The aim of this study was to research and analyze the HRV between various groups of patients. Materials and Methods: A retrospective study was conducted in an outpatient setting. Patients who had undergone a tilt-table test were selected for this study and were divided into three groups based on their self-reported health anamnesis: group 1 (n = 84, mean age 45.8 ± 17.8) consisted of patients with no known orthostatic intolerance or neurodegenerative disease, group 2 consisted of patients with a known or suspected orthostatic intolerance (n = 50, mean age 46.5 ± 18.6), and group 3 consisted of patients with a known or suspected neurodegenerative disorder (n = 29, mean age 55.6 ± 20.4). During the tilt-table test, HRV frequency-domain parameters-normalized low frequency (LFnu) and high frequency (HFnu), absolute powers-absolute low frequency (LF-RRI), absolute high frequency (HF-RRI), and LF/HF ratio-were recorded during 5 min rest in the supine position. Results: Group 1 had a reduced LFnu at 52.93% (SD: 18.00) compared to group 2 at 58.57% (18.06) and group 3 at 61.80% (SD: 17.74), and group 1 had increased HFnu: group 1-47.08% (SD: 17.97), group 2-41.41% (SD: 18.03), and group 3-38.16% (SD: 14.7). LFnu and HFnu differences were statistically significant (p < 0.05). LF-RRI was reported as follows: group 1-531.32 ms2 (SD: 578.57), group 2-346.2 ms2 (SD: 447.96), and group 3-143.21 ms2 (SD: 166.96). HF-RRI was reported as follows: group 1-835.87 ms2 (SD: 1625.42), group 2-297.46 ms2 (SD: 507.15), and group 3-70.83 ms2 (SD: 75.67). LF-RRI and HF-RRI comparisons between groups were statistically significant (p < 0.001). LF/HF ratios were reported as follows: group 1-1.91 (SD: 2.29), group 2-2.43 (SD: 2.33), and group 3-2.54 (SD: 2.17). LF/HF ratio comparisons between groups were statistically significant at p < 0.05. Conclusions: This study shows that patients with known or suspected orthostatic intolerance and neurodegenerative disorders have reduced HRV, possibly caused by reduced parasympathetic modulation. HRV in patients with known or suspected neurodegenerative disorders is reduced more severely than in patients with orthostatic disorders. Other studies in HRV have indicated a possible increase of risk in cardiovascular disorders in patients with reduced HRV, and therefore, HRV analysis could be a potential clinical diagnostic tool. However, the lack of universally agreed upon methodology, reference values, and possible external and internal factor influence hinders the introduction of HRV examinations into wider clinical practice.

Wearable EMG Measurement Device Using Polyurethane Foam for Motion Artifact Suppression

We propose the use of a specially designed polyurethane foam with a plateau region in its mechanical characteristics-where stress remains nearly constant during deformation-between the electromyography (EMG) electrode and clothing to suppress motion artifacts in EMG measurement. Wearable EMG devices are receiving attention for monitoring muscle weakening due to aging. However, daily EMG measurement has been challenging due to motion artifacts caused by changes in the contact pressure between the bioelectrode and the skin. Therefore, this study aims to measure EMG signals in daily movement environments by controlling the contact pressure using polyurethane foam between the bioelectrode on the clothing and the skin. Through mechanical calculations and finite element method simulations of the polyurethane foam's effect, we clarified that the characteristics of the polyurethane foam significantly influence contact pressure control and that the contact pressure is adjustable through the polyurethane foam thickness. The optimization of the design successfully controlled the contact pressure between the bioelectrode and skin from 1.0 kPa to 2.0 kPa, effectively suppressing the motion artifact in EMG measurement.

Classification of exercise fatigue levels by multi-class SVM from ECG and HRV

Among the various physiological signals, electrocardiogram (ECG) is a valid criterion for the classification of various exercise fatigue. In this study, we combine features extracted by deep neural networks with linear features from ECG and heart rate variability (HRV) for exercise fatigue classification. First, the ECG signals are converted into 2-D images by using the short-term Fourier transform (STFT), and image features are extracted by the visual geometry group (VGG) . The extracted image and linear features of ECG and HRV are sent to the different types of classifiers to distinguish distinct exercise fatigue level. To validate performance, the proposed methods are tested on (i) an open-source EPHNOGRAM dataset and (ii) a self-collected dataset (n = 51). The results reveal that the classification based on the concatenated features has the highest accuracy, and the calculation time of the system is also significantly reduced. This demonstrates that the proposed novel hybrid approach can be used to assist in improving the accuracy and timeliness of exercise fatigue classification in a real-time exercise environment. The experimental results show that the proposed method outperforms other recent state-of-the-art methods in terms of accuracy 96.90%, sensitivity 96.90%, F1-score of 0.9687 in EPHNOGRAM and accuracy 92.17%, sensitivity 92.63%, F1-score of 0.9213 in self-collected dataset.

Night time heart rate predicts next-day pain in fibromyalgia and primary back pain

Introduction

Primary chronic pain is pain that persists for over 3 months without associated measurable tissue damage. One of the most consistent findings in primary chronic pain is its association with autonomic hyperactivation. Yet whether the autonomic hyperactivation causes the pain or results from it is still unclear. It is also unclear to what extent autonomic hyperactivation is related to experienced pain intensity in different subtypes or primary chronic pain.

Objectives

Our first aim was to test lagged relationships between the markers of autonomic activation (heart rate) and pain intensity to determine its directionality. The main question here was whether autonomic biomarkers predict pain intensity or whether pain intensity predicts autonomic biomarkers. The second aim was to test whether this relationship is different between people with primary back pain and people with fibromyalgia.

Methods

Sixty-six patients with chronic pain were observed over an average of 81 days. Sleep heart rate and heart rate variability were measured with a wearable sensor, and pain intensity was assessed from daily subjective reports.

Results

The results showed a predictive relationship between sleep heart rate and next-day pain intensity (P < 0.05), but not between daily pain intensity and next night heart rate. There was no interaction with the type of chronic pain.

Conclusions

These findings suggest that autonomic hyperactivation, whether stress-driven or arising from other causes, precedes increases in primary chronic pain. Moreover, the present results suggest that autonomic hyperactivation is a common mechanism underlying the pain experience in fibromyalgia and chronic back pain.

Heart Rate Variability (HRV) serves as an objective correlate of distress and symptom burden in multiple sclerosis

Background

Autonomic nervous system (ANS) dysfunction is frequently seen in people living with multiple sclerosis (MS). Heart rate variability (HRV) is an easy and objective index for evaluating ANS functioning, and it has been previously used to explore the association between ANS and the experience of symptom burden in other chronic diseases. Given ANS functioning can be influenced by physical and psychological factors, this study investigated whether emotional distress and/or the presence of ANS dysfunction is associated with symptom severity in people living with MS.

Methods

Participants with MS and healthy controls (HC) with no history of cardiac conditions were recruited to self-collect HR data sampled from a chest strap HR monitor (PolarH10). Short-term HR signal was collected for five minutes, and time and frequency HRV analyses were performed and compared between groups. HRV values were then compared to self-reported distress (Kessler Psychological Distress Scale) and MS participants' self-reported measures of symptom burden (SymptoMScreen).

Results

A total of n = 23 adults with MS (51 ± 12 years, 65 % female, median Patient Determined Disease Steps [PDDS]: 3.0) and n = 23 HCs (43 ± 18 years, 40 % female) completed the study procedures. All participants were able to complete the chest strap placement and HR data capture independently. Participants with MS, compared to the HC participants, had a significantly lower parasympathetic activation as shown by lower values of the root mean square of successive differences between normal heartbeats (RMSSD: 21.86 ± 9.84 vs. 43.13 ± 20.98 ms, p = 0.002) and of high-frequency (HF) power band (HF-HRV: 32.69 ± 12.01 vs. 42.39 ± 7.96 nu, p = 0.016), indicating an overall lower HRV in the MS group. Among individuals with MS, HF-HRV was significantly correlated with the severity of self-reported MS symptoms (r = -0.548, p = 0.010). Participants with MS also reported higher levels of distress compared to HC participants (18.32 ± 6.05 vs. 15.00 ± 4.61, p = 0.050), and HRV correlated with the severity of distress in MS participants (r = -0.569, p = 0.007). A significant mediation effect was also observed, with emotional distress fully mediating the association between HRV and symptom burden.

Conclusions

These findings suggest the potential for ANS dysfunction, as measured by HRV (i.e., lower value of HF power), to be utilized as an objective marker of symptom burden in people living with MS. Moreover, it is apparent that the relationship between HRV and symptom burden is mediated by emotional distress.

Measuring Heart Rate Accurately in Patients With Parkinson Disease During Intense Exercise: Usability Study of Fitbit Charge 4

BACKGROUND

Parkinson disease (PD) is the second most common neurodegenerative disease, affecting approximately 1% of the world's population. Increasing evidence suggests that aerobic physical exercise can be beneficial in mitigating both motor and nonmotor symptoms of the disease. In a recent pilot study of the role of exercise on PD, we sought to confirm exercise intensity by monitoring heart rate (HR). For this purpose, we asked participants to wear a chest strap HR monitor (Polar Electro Oy) and the Fitbit Charge 4 (Fitbit Inc) wrist-worn HR monitor as a potential proxy due to its convenience. Polar H10 has been shown to provide highly accurate R-R interval measurements. Therefore, we treated it as the gold standard in this study. It has been shown that Fitbit Charge 4 has comparable accuracy to Polar H10 in healthy participants. It has yet to be determined if the Fitbit is as accurate as Polar H10 in patients with PD during rest and exercise.

OBJECTIVE

This study aimed to compare Fitbit Charge 4 to Polar H10 for monitoring HR in patients with PD at rest and during an intensive exercise program.

METHODS

A total of 596 exercise sessions from 11 (6 male and 5 female) participants were collected simultaneously with both devices. Patients with early-stage PD (Hoehn and Yahr ≤2) were enrolled in a 6-month exercise program designed for patients with PD. They participated in 3 one-hour exercise sessions per week. They wore both Fitbit and Polar H10 during each session. Sessions included rest, warm-up, intense exercise, and cool-down periods. We calculated the bias in the HR of the Fitbit Charge 4 at rest (5 min) and during intense exercise (20 min) by comparing the mean HR during each of the periods to the respective means measured by Polar H10 (HRFitbit - HRPolar). We also measured the sensitivity and specificity of Fitbit Charge 4 to detect average HRs that exceed the threshold for intensive exercise, defined as 70% of an individual's theoretical maximum HR. Different types of correlations between the 2 devices were investigated.

RESULTS

The mean bias was 1.68 beats per minute (bpm) at rest and 6.29 bpm during high-intensity exercise, with an overestimation by Fitbit Charge 4 in both conditions. The mean bias of the Fitbit across both rest and intensive exercise periods was 3.98 bpm. The device's sensitivity in identifying high-intensity exercise sessions was 97.14%. The correlation between the 2 devices was nonlinear, suggesting Fitbit's tendency to saturate at high values of HR.

CONCLUSIONS

The performance of Fitbit Charge 4 is comparable to Polar H10 for assessing exercise intensity in a cohort of patients with PD (mean bias 3.98 bpm). The device could be considered a reasonable surrogate for more cumbersome chest-worn devices in future studies of clinical cohorts.

Heart Rate Variability Measurement through a Smart Wearable Device: Another Breakthrough for Personal Health Monitoring?

Heart rate variability (HRV) is a measurement of the fluctuation of time between each heartbeat and reflects the function of the autonomic nervous system. HRV is an important indicator for both physical and mental status and for broad-scope diseases. In this review, we discuss how wearable devices can be used to monitor HRV, and we compare the HRV monitoring function among different devices. In addition, we have reviewed the recent progress in HRV tracking with wearable devices and its value in health monitoring and disease diagnosis. Although many challenges remain, we believe HRV tracking with wearable devices is a promising tool that can be used to improve personal health.

Feasibility, Acceptability, and Usability of Physiology and Emotion Monitoring in Adults and Children Using the Novel Time2Feel Smartphone Application

The present study tests the feasibility, acceptability, and utility of the novel smartphone application-Time2Feel-to monitor family members' emotional experiences, at the experiential and physiological level, and their context. To our knowledge, Time2Feel is the first of its kind, having the capability to monitor multiple members' emotional experiences simultaneously and survey users' emotional experiences when experiencing an increase in physiological arousal. In this study, a total of 44 parents and children used Time2Feel along with the Empatica E4 wrist-wearable device for 10 days. Engagement rates were within the acceptable range and consistent with previous work using experience sampling methods. Perceived ease of use and satisfaction fell mostly in the moderate range, with users reporting challenges with connectivity. We further discuss how addressing connectivity would increase acceptability. Finally, Time2Feel was successful at identifying physiological deviations in electrodermal activity for parents and children alike, and even though responses to those deviation-generated surveys were largely consistent with random survey responses, some differences were noted for mothers and fathers. We discuss the implications of using Time2Feel for understanding families' emotional and stressful experiences day-to-day.

Accuracy of heart rate measured by military-grade wearable ECG monitor compared with reference and commercial monitors

INTRODUCTION

Physiological monitoring of soldiers can indicate combat readiness and performance. Despite demonstrated use of wearable devices for HR monitoring, commercial options lack desired military features. A newly developed OMNI monitor includes desired features such as long-range secure data transmission. This study investigated the accuracy of the OMNI to measure HR via accuracy of R-R interval duration relative to research-grade ECG and commercial products.

METHODS

54 healthy individuals (male/female=37/17, age=22.2±3.6 years, height=173.0±9.1 cm, weight=70.1±11.2 kg) completed a submaximal exercise test while wearing a reference ECG (Biopac) and a randomly assigned chest-based monitor (OMNI, Polar H10, Equivital EQ-02, Zephyr Bioharness 3). All participants also wore two wrist-based photoplethysmography (PPG) devices, Garmin fēnix 6 and Empatica E4. Bland-Altman analyses of agreement, concordance correlation coefficient (CCC) and root-mean-squared error (RMSE) were used to determine accuracy of the OMNI and commercial devices relative to Biopac. Additionally, a linear mixed-effects model evaluated the effects of device and exercise intensity on agreement.

RESULTS

Chest-based devices showed superior agreement with Biopac for measuring R-R interval compared with wrist-based ones in terms of mean bias, CCC and RMSE, with OMNI demonstrating the best scores on all metrics. Linear mixed-effects model showed no significant main or interaction effects for the chest-based devices. However, significant effects were found for Garmin and Empatica devices (p<0.001) as well as the interaction effects between both Garmin and Empatica and exercise intensity (p<0.001).

CONCLUSIONS

Chest-based ECG devices are preferred to wrist-based PPG devices due to superior HR accuracy over a range of exercise intensities, with the OMNI device demonstrating equal, if not superior, performance to other commercial ECG monitors. Additionally, wrist-based PPG devices are significantly affected by exercise intensity as they underestimate HR at low intensities and overestimate HR at high intensities.

Use of Consumer Wearables in Health Research: Issues and Considerations

As wearable devices, which allow individuals to track and self-manage their health, become more ubiquitous, the opportunities are growing for researchers to use these sensors within interventions and for data collection. They offer access to data that are captured continuously, passively, and pragmatically with minimal user burden, providing huge advantages for health research. However, the growth in their use must be coupled with consideration of their potential limitations, in particular, digital inclusion, data availability, privacy, ethics of third-party involvement, data quality, and potential for adverse consequences. In this paper, we discuss these issues and strategies used to prevent or mitigate them and recommendations for researchers using wearables as part of interventions or for data collection.

Wearable Devices to Diagnose and Monitor the Progression of COVID-19 Through Heart Rate Variability Measurement: Systematic Review and Meta-Analysis

BACKGROUND

Recent studies have linked low heart rate variability (HRV) with COVID-19, indicating that this parameter can be a marker of the onset of the disease and its severity and a predictor of mortality in infected people. Given the large number of wearable devices that capture physiological signals of the human body easily and noninvasively, several studies have used this equipment to measure the HRV of individuals and related these measures to COVID-19.

OBJECTIVE

The objective of this study was to assess the utility of HRV measurements obtained from wearable devices as predictive indicators of COVID-19, as well as the onset and worsening of symptoms in affected individuals.

METHODS

A systematic review was conducted searching the following databases up to the end of January 2023: Embase, PubMed, Web of Science, Scopus, and IEEE Xplore. Studies had to include (1) measures of HRV in patients with COVID-19 and (2) measurements involving the use of wearable devices. We also conducted a meta-analysis of these measures to reduce possible biases and increase the statistical power of the primary research.

RESULTS

The main finding was the association between low HRV and the onset and worsening of COVID-19 symptoms. In some cases, it was possible to predict the onset of COVID-19 before a positive clinical test. The meta-analysis of studies reported that a reduction in HRV parameters is associated with COVID-19. Individuals with COVID-19 presented a reduction in the SD of the normal-to-normal interbeat intervals and root mean square of the successive differences compared with healthy individuals. The decrease in the SD of the normal-to-normal interbeat intervals was 3.25 ms (95% CI -5.34 to -1.16 ms), and the decrease in the root mean square of the successive differences was 1.24 ms (95% CI -3.71 to 1.23 ms).

CONCLUSIONS

Wearable devices that measure changes in HRV, such as smartwatches, rings, and bracelets, provide information that allows for the identification of COVID-19 during the presymptomatic period as well as its worsening through an indirect and noninvasive self-diagnosis.

The use of heart rate variability in esports: A systematic review

Heart rate variability (HRV) is a psychophysiological measure of particular interest in esports due to its potential to monitor player self-regulation. This study aimed to systematically review the utilisation of HRV in esports. Consideration was given to the methodological and theoretical underpinnings of previous works to provide recommendations for future research. The protocol was made available on the Open Science Framework. Inclusion criteria were empirical studies, examining HRV in esports, using esports players, published in English. Exclusion criteria were non-peer-reviewed studies, populations with pre-existing clinical illness other than Internet Gaming Disorder (IGD), opinion pieces or review papers. In November 2022 a search of Web of Science, PubMed, and EBSCOHost identified seven studies using HRV in esports. Risk of bias was assessed using the Mixed Methods Appraisal Tool. Narrative review identified two primary uses of HRV in esports; stress response and IGD. A lack of theoretical and methodological underpinning was identified as a major limitation of current literature. Further investigation is necessary before making recommendations regarding the use of HRV in esports. Future research should employ sound theoretical underpinning such as the use of vagally mediated HRV and the robust application of supporting methodological guidelines when investigating HRV in esports.

The use of commercial wrist-worn technology to track physiological outcomes in behavioral interventions

PURPOSE OF REVIEW

The aim of this review is to provide an overview of the use of commercial wrist-worn mobile health devices to track and monitor physiological outcomes in behavioral interventions as well as discuss considerations for selecting the optimal device.

RECENT FINDINGS

Wearable technology can enhance intervention design and implementation. The use of wrist-worn wearables provides the opportunity for tracking physiological outcomes, thus providing a unique approach for assessment and delivery of remote interventions. Recent findings support the utility, acceptability, and benefits of commercial wrist-worn wearables in interventions, and they can be used to continuously monitor outcomes, remotely administer assessments, track adherence, and personalize interventions. Wrist-worn devices show acceptable accuracy when measuring heart rate, blood pressure, step counts, and physical activity; however, accuracy is dependent on activity type, intensity, and device brand. These factors should be considered when designing behavioral interventions that utilize wearable technology.

SUMMARY

With the continuous advancement in technology and frequent product upgrades, the capabilities of commercial wrist-worn devices will continue to expand, thus increasing their potential use in intervention research. Continued research is needed to examine and validate the most recent devices on the market to better inform intervention design and implementation.

Application of wearable devices for monitoring cardiometabolic dysfunction under the exposome paradigm

Environmental factors, including chemical/physical pollutants, as well as lifestyle and psychological factors, contribute greatly to the pathways leading to cardiometabolic diseases with a heavy disease burden and economic loss. The concept of exposomes provides a novel paradigm for combining all exposure characteristics to evaluate disease risk. A solution-like exposome requires technological support to provide continuous data to monitor vital signs and detect abnormal fluctuations. Wearable devices allow people to conveniently monitor signals during their daily routines. These new technologies empower users to more actively prevent and manage cardiometabolic disease by reviewing risk factors of the disease, especially lifestyle factors, such as sleeping time, screen time, and mental health condition. Devices with multiple sensors can monitor electrocardiography data, oxygen saturation, intraocular pressure, respiratory rate, and heart rate to enhance the exposome study and provide precise suggestions for disease prevention and management.

The Future of Stress Management: Integration of Smartwatches and HRV Technology

In the modern world, stress has become a pervasive concern that affects individuals' physical and mental well-being. To address this issue, many wearable devices have emerged as potential tools for stress detection and management by measuring heart rate, heart rate variability (HRV), and various metrics related to it. This literature review aims to provide a comprehensive analysis of existing research on HRV tracking and biofeedback using smartwatches pairing with reliable 3rd party mobile apps like Elite HRV, Welltory, and HRV4Training specifically designed for stress detection and management. We apply various algorithms and methodologies employed for HRV analysis and stress detection including time-domain, frequency-domain, and non-linear analysis techniques. Prominent smartwatches, such as Apple Watch, Garmin, Fitbit, Polar, and Samsung Galaxy Watch, are evaluated based on their HRV measurement accuracy, data quality, sensor technology, and integration with stress management features. We describe the efficacy of smartwatches in providing real-time stress feedback, personalized stress management interventions, and promoting overall well-being. To assist researchers, doctors, and developers with using smartwatch technology to address stress and promote holistic well-being, we discuss the data's advantages and limitations, future developments, and the significance of user-centered design and personalized interventions.

A meta-analysis investigating the outcomes and correlation between heart rate variability biofeedback training on depressive symptoms and heart rate variability outcomes versus standard treatment in comorbid adult populations

BACKGROUND AND AIM

Heart rate variability biofeedback (HRVB) has previously been used to ameliorate depressive symptoms but its uses for tackling depressive symptoms in an array of comorbid adult patients is less established. This meta-analysis aims to evaluate whether HRVB is a useful tool to reduce depressive symptoms and improve HRV relative to standard treatment in adult comorbid populations, while also attempting to establish the association between the two outcomes.

METHODS

An extensive literature review was conducted using several databases including PubMed, Cinahl, Medline, Web of science and clinical.gov/UK register. A total of 149 studies were identified with 9 studies, totalling 428 participants were analysed using a random effects model.

RESULTS

Depressive outcomes yielded a mean effect size g=0.478 (CI 95% 0.212, 0.743) with HRV outcomes, yielding a mean effect size of g=0.223 (95% CI 0.036 to 0.411). Total heterogeneity was non-significant for depressive outcomes (Q= 13.77, p=0.088 I^=42.86%) and HRV (Q= 1.598, p=0.991, I^=0.000%) which indicates that little variance existed for the included studies.

CONCLUSIONS

In summary, the outcomes demonstrate that HRVB can improve both clinically relevant depressive symptoms and physiological HRV outcomes in various comorbid conditions in adult populations, while the correlation between the two was moderately negative, but non-significant.

Validation of In-Shoe Force Sensors during Loaded Walking in Military Personnel

The loadsol^® wireless in-shoe force sensors can be useful for in-field measurements. However, its accuracy is unknown in the military context, whereby soldiers have to carry heavy loads and walk in military boots. The purpose of this study was to establish the validity of the loadsol^® sensors in military personnel during loaded walking on flat, inclined and declined surfaces. Full-time Singapore Armed Forces (SAF) personnel (n = 8) walked on an instrumented treadmill on flat, 10° inclined, and 10° declined gradients while carrying heavy loads (25 kg and 35 kg). Normal ground reaction forces (GRF), perpendicular to the contact surface, were simultaneously measured using both the loadsol^® sensors inserted in the military boots and the Bertec instrumented treadmill as the gold standard. A total of eight variables of interest were compared between loadsol^® and treadmill, including four kinetic (impact peak force, active peak force, impulse, loading rate) and four spatiotemporal (stance time, stride time, cadence, step length) variables. Validity was assessed using Bland-Altman plots and 95% Limits of Agreement (LoA). Bias was calculated as the mean difference between the values obtained from loadsol^® and the instrumented treadmill. Results showed similar force-time profiles between loadsol^® sensors and the instrumented treadmill. The bias of most variables was generally low, with a narrow range of LoA. The high accuracy and good agreement with standard laboratory equipment suggest that the loadsol^® system is a valid tool for measuring normal GRF during walking in military boots under heavy load carriage.

The Impact of Heart Rate Variability Monitoring on Preventing Severe Cardiovascular Events

The increase in the incidence of cardiovascular diseases worldwide raises concerns about the urgent need to increase definite measures for the self-determination of different parameters, especially those defining cardiac function. Heart rate variability (HRV) is a non-invasive method used to evaluate autonomic nervous system modulation on the cardiac sinus node, thus describing the oscillations between consecutive electrocardiogram R-R intervals. These fluctuations are undetectable except when using specialized devices, with ECG Holter monitoring considered the gold standard. HRV is considered an independent biomarker for measuring cardiovascular risk and for screening the occurrence of both acute and chronic heart diseases. Also, it can be an important predictive factor of frailty or neurocognitive disorders, like anxiety and depression. An increased HRV is correlated with rest, exercise, and good recovery, while a decreased HRV is an effect of stress or illness. Until now, ECG Holter monitoring has been considered the gold standard for determining HRV, but the recent decade has led to an accelerated development of technology using numerous devices that were created specifically for the pre-hospital self-monitoring of health statuses. The new generation of devices is based on the use of photoplethysmography, which involves the determination of blood changes at the level of blood vessels. These devices provide additional information about heart rate (HR), blood pressure (BP), peripheral oxygen saturation (SpO²), step counting, physical activity, and sleep monitoring. The most common devices that have this technique are smartwatches (used on a large scale) and chest strap monitors. Therefore, the use of technology and the self-monitoring of heart rate and heart rate variability can be an important first step in screening cardiovascular pathology and reducing the pressure on medical services in a hospital. The use of telemedicine can be an alternative, especially among elderly patients who are associated with walking disorders, frailty, or neurocognitive disorders.

The effect of the COVID-19 pandemic on substance use patterns and physiological dysregulation in pregnant and postpartum women

BACKGROUND

The SARS-CoV-2/COVID-19 pandemic has been associated with increased stress levels and higher alcohol use, including in pregnant and postpartum women. In the general population, alcohol use is associated with dysregulation in the autonomic nervous system (ANS), which is indexed by heart rate variability (HRV). The objectives of this study were to: (1) characterize changes in substance use during the SARS-CoV-2/COVID-19 pandemic via a baseline self-report survey followed by mobile ecological momentary assessment (mEMA) of substance use; and (2) examine the associations between momentary substance use and ambulatory HRV measures in pregnant and postpartum women.

METHODS

Pregnant and postpartum women were identified from the ENRICH-2 prospective cohort study. Participants were administered a baseline structured phone interview that included the Coronavirus Perinatal Experiences (COPE) survey and ascertained the prevalence of substance use. Over a 14-day period, momentary substance use was assessed three times daily, and HRV measurements were captured via wearable electronics. Associations between momentary substance use and HRV measures (root mean square of successive differences [RMSSD] and low frequency/high frequency [LF/HF] ratio) were examined using a mixed effects model that included within-subject (WS) and between-subject (BS) effects and adjusted for pregnancy status and participant age.

RESULTS

The sample included 49 pregnant and 22 postpartum women. From a combination of a baseline and 14-day mEMA surveys, 21.2% reported alcohol use, 16.9% reported marijuana use, and 8.5% reported nicotine use. WS effects for momentary alcohol use were associated with the RMSSD (β = -0.14; p = 0.005) and LF/HF ratio (β = 0.14; p = 0.01) when controlling for pregnancy status and maternal age. No significant associations were observed between HRV measures and instances of marijuana or nicotine use.

CONCLUSIONS

These findings highlight the negative effect of the SARS-CoV-2/COVID-19 pandemic on the psychological health of pregnant and postpartum women associated with substance use, and in turn, ANS dysregulation, which potentially puts some women at risk of developing a substance use disorder.

Methods for analyzing infant heart rate variability: A preliminary study

Heart rate (HR) and heart rate variability (HRV) reflect autonomic development in infants. To better understand the autonomic response in infants, reliable HRV recordings are vital, yet no protocol exists. The purpose of this paper is to present reliability of a common procedure for analysis from two different file types. In the procedure, continuous electrocardiograph recordings of 5-10 min are obtained at rest in infants at 1 month of age by using a Hexoskin Shirt-Junior's (Carre Technologies Inc., Montreal, QC, Canada). Electrocardiograph (ECG; .wav) and R-R interval (RRi; .csv) files are extracted. The RRi of the ECG signal is generated by VivoSense (Great Lakes NeuroTechnologies, Independence, OH). Two MATLAB (The MathWorks, Inc., Natick, MA) scripts converted files for analysis with Kubios HRV Premium (Kubios Oy, Kuopio, Finland). A comparison was made between RRi and ECG files for HR and HRV parameters, and then tested with t tests and correlations via SPSS. There are significant differences in root mean squared successive differences between recording types, with only HR and low-frequency measures significantly correlated together. Recording with Hexoskin and analysis with MATLAB and Kubios enable infant HRV analysis. Differences in outcomes exist between procedures, and standard methodology for infant HR analysis is needed.

Energy Balance, Hormonal Status, and Military Performance in Strenuous Winter Training

Severe energy deficit may impair hormonal regulation and physical performance in military trainings. The aim of this study was to examine the associations between energy intake, expenditure, and balance, hormones and military performance during a winter survival training. Two groups were studied: the FEX group (n = 46) had 8-day garrison and field training, whereas the RECO group (n = 26) had a 36-h recovery period after the 6-day garrison and field training phase. Energy intake was assessed by food diaries, expenditure via heart rate variability, body composition by bioimpedance, and hormones by blood samples. Strength, endurance and shooting tests were done for evaluating military performance. PRE 0 d, MID 6 d, POST 8 d measurements were carried out. Energy balance was negative in PRE and MID (FEX -1070 ± 866, -4323 ± 1515; RECO -1427 ± 1200, -4635 ± 1742 kcal·d^-1). In POST, energy balance differed between the groups (FEX -4222 ± 1815; RECO -608 ± 1107 kcal·d^-1 (p < 0.001)), as well as leptin, testosterone/cortisol ratio, and endurance performance (p = 0.003, p < 0.001, p = 0.003, respectively). Changes in energy intake and expenditure were partially associated with changes in leptin and the testosterone/cortisol ratio, but not with physical performance variables. The 36-h recovery restored energy balance and hormonal status after strenuous military training, but these outcomes were not associated with strength or shooting performance.

Are Activity Wrist-Worn Devices Accurate for Determining Heart Rate during Intense Exercise?

The market for wrist-worn devices is growing at previously unheard-of speeds. A consequence of their fast commercialization is a lack of adequate studies testing their accuracy on varied populations and pursuits. To provide an understanding of wearable sensors for sports medicine, the present study examined heart rate (HR) measurements of four popular wrist-worn devices, the (Fitbit Charge (FB), Apple Watch (AW), Tomtom runner Cardio (TT), and Samsung G2 (G2)), and compared them with gold standard measurements derived by continuous electrocardiogram examination (ECG). Eight athletes participated in a comparative study undergoing maximal stress testing on a cycle ergometer or a treadmill. We analyzed 1,286 simultaneous HR data pairs between the tested devices and the ECG. The four devices were reasonably accurate at the lowest activity level. However, at higher levels of exercise intensity the FB and G2 tended to underestimate HR values during intense physical effort, while the TT and AW devices were fairly reliable. Our results suggest that HR estimations should be considered cautiously at specific intensities. Indeed, an effective intervention is required to register accurate HR readings at high-intensity levels (above 150 bpm). It is important to consider that even though none of these devices are certified or sold as medical or safety devices, researchers must nonetheless evaluate wrist-worn wearable technology in order to fully understand how HR affects psychological and physical health, especially under conditions of more intense exercise.

"We've Got This": Middle-Aged and Older Couples' Satisfying Relationships and We-Talk Promote Better Physiological, Relational, and Emotional Responses to Conflict

OBJECTIVE

Conflict poses multiple relational and health risks. Dyadic stress theories suggest satisfaction and communication alter cardiovascular and autonomic function, key pathways from troubled relationships to poor health. However, "we-talk," a positive communication pattern, can strengthen relationships and promote health. We examined how each spouse's satisfaction and we-talk were related to conflict's physiological, relational, and emotional toll.

METHODS

Married couples ( n = 107 couples, 214 individuals, ages 40-87 years) who were mostly White, highly educated, and higher-income Americans in different-gender relationships engaged in 20-minute conflict discussions while wearing monitors to assess heart rate variability (HRV). Spouses rated their closeness immediately after conflict and their conflict rumination 2 hours later. Conflict transcriptions measured we-talk, or the proportion of first-person plural pronouns (we, us, our).

RESULTS

Satisfied spouses or those in mutually satisfying relationships had higher HRV during conflict ( b = 0.0001, p = .049), felt closer immediately after conflict ( b = 0.07, p < .001), and ruminated less about the conflict 2 hours later ( b = -0.26, p = .026). Spouses' HRV was highest ( b = 0.0002, p = .002) and rumination was lowest ( b = -0.49, p = .019) when they or their partners were satisfied and used we-talk more often. Women's HRV ( b = 0.0001, p = .035) and rumination ( b = -0.01, p = .02) benefited when both spouses were satisfied, as did closeness when women were satisfied ( b = 0.10, p < .001). Men's closeness benefited when they ( b = 0.04, p = .003) or their wives ( b = 0.04, p = .002) were satisfied.

CONCLUSIONS

The combination of mutually satisfying relationships and we-talk was associated with better relational and health outcomes after conflict. These findings are important for middle-aged and older couples whose relationships are central to their health.

How aging couples' emotional and physiological associations change across positive, supportive, and conflictual discussions: Roles of capitalization and responsive behaviors

BACKGROUND

Couples' emotions and physiology change across interactions and based on behaviors. Aging couples' emotions and physiology may be closely related as they spend more time together and rely on each other for support. We examined aging couples' emotional and physiological associations across multiple indices and marital interactions; we also assessed how couples' capitalization and responsive behaviors during the first discussion were protective in subsequent emotional conversations.

METHODS

Married couples (n = 107 couples, 214 individuals) engaged in positive event, social support, and conflict discussions. Emotional and physiological assessments across discussions included: positive and negative emotion, electrodermal activity, systolic and diastolic blood pressure, heart rate, and heart rate variability. We coded partners' capitalization and responsive behaviors during the first discussion.

RESULTS

There were ties in spouses' positive emotion, negative emotion, electrodermal activity, systolic blood pressure, heart rate, and heart rate variability during positive, supportive, and conflictual discussions. Emotional and physiological associations reflecting shared stress (negative emotion, electrodermal activity, systolic blood pressure) were stronger in couples who were less capitalizing or responsive earlier that day; associations reflecting physiological adaptation (heart rate variability) were stronger for more capitalizing and responsive couples.

CONCLUSION

Aging couples' emotions and physiology tracked together during discussions central to maintaining relationships, and their past behaviors carried over into future interactions and across contexts. Enthusiastic, caring, and understanding behaviors may protect partners from shared emotional and physiological stress; lacking such behaviors may increase emotional and physiological vulnerability. This research identifies behavioral, emotional, and physiological pathways connecting relationships to health in adulthood.

A Survey on Wearable Sensors for Mental Health Monitoring

Mental illness, whether it is medically diagnosed or undiagnosed, affects a large proportion of the population. It is one of the causes of extensive disability, and f not properly treated, it can lead to severe emotional, behavioral, and physical health problems. In most mental health research studies, the focus is on treatment, but fewer resources are focused on technical solutions to mental health issues. The present paper carried out a systematic review of available literature using PRISMA guidelines to address various monitoring solutions in mental health through the use of wearable sensors. Wearable sensors can offer several advantages over traditional methods of mental health assessment, including convenience, cost-effectiveness, and the ability to capture data in real-world settings. Their ability to collect data related to anxiety and stress levels, as well as panic attacks, is discussed. The available sensors on the market are described, as well as their success in providing data that can be correlated with the aforementioned health issues. The current wearable landscape is quite dynamic, and the current offerings have enough quality to deliver meaningful data targeted for machine learning algorithms. The results indicate that mental health monitoring is feasible.

Wearable Electrocardiography for Physical Activity Monitoring: Definition of Validation Protocol and Automatic Classification

Wearable devices are rapidly spreading thanks to multiple advantages. Their use is expanding in several fields, from medicine to personal assessment and sport applications. At present, more and more wearable devices acquire an electrocardiographic (ECG) signal (in correspondence to the wrist), providing potentially useful information from a diagnostic point of view, particularly in sport medicine and in rehabilitation fields. They are remarkably relevant, being perceived as a common watch and, hence, considered neither intrusive nor a cause of the so-called "white coat effect". Their validation and metrological characterization are fundamental; hence, this work aims at defining a validation protocol tested on a commercial smartwatch (Samsung Galaxy Watch3, Samsung Electronics Italia S.p.A., Milan, Italy) with respect to a gold standard device (Zephyr BioHarness 3.0, Zephyr Technology Corporation, Annapolis, MD, USA, accuracy of ±1 bpm), reporting results on 30 subjects. The metrological performance is provided, supporting final users to properly interpret the results. Moreover, machine learning and deep learning models are used to discriminate between resting and activity-related ECG signals. The results confirm the possibility of using heart rate data from wearable sensors for activity identification (best results obtained by Random Forest, with accuracy of 0.81, recall of 0.80, and precision of 0.81, even using ECG signals of limited duration, i.e., 30 s). Moreover, the effectiveness of the proposed validation protocol to evaluate measurement accuracy and precision in a wide measurement range is verified. A bias of -1 bpm and an experimental standard deviation of 11 bpm (corresponding to an experimental standard deviation of the mean of ≈0 bpm) were found for the Samsung Galaxy Watch3, indicating a good performance from a metrological point of view.

Ergonomic human-robot collaboration in industry: A review

In the current industrial context, the importance of assessing and improving workers' health conditions is widely recognised. Both physical and psycho-social factors contribute to jeopardising the underlying comfort and well-being, boosting the occurrence of diseases and injuries, and affecting their quality of life. Human-robot interaction and collaboration frameworks stand out among the possible solutions to prevent and mitigate workplace risk factors. The increasingly advanced control strategies and planning schemes featured by collaborative robots have the potential to foster fruitful and efficient coordination during the execution of hybrid tasks, by meeting their human counterparts' needs and limits. To this end, a thorough and comprehensive evaluation of an individual's ergonomics, i.e. direct effect of workload on the human psycho-physical state, must be taken into account. In this review article, we provide an overview of the existing ergonomics assessment tools as well as the available monitoring technologies to drive and adapt a collaborative robot's behaviour. Preliminary attempts of ergonomic human-robot collaboration frameworks are presented next, discussing state-of-the-art limitations and challenges. Future trends and promising themes are finally highlighted, aiming to promote safety, health, and equality in worldwide workplaces.

Feasibility of Using an Armband Optical Heart Rate Sensor in Naturalistic Environment

Consumer-grade heart rate (HR) sensors including chest straps, wrist-worn watches and rings have become very popular in recent years for tracking individual physiological state, training for sports and even measuring stress levels and emotional changes. While the majority of these consumer sensors are not medical devices, they can still offer insights for consumers and researchers if used correctly taking into account their limitations. Multiple previous studies have been done using a large variety of consumer sensors including Polar® devices, Apple® watches, and Fitbit® wrist bands. The vast majority of prior studies have been done in laboratory settings where collecting data is relatively straightforward. However, using consumer sensors in naturalistic settings that present significant challenges, including noise artefacts and missing data, has not been as extensively investigated. Additionally, the majority of prior studies focused on wrist-worn optical HR sensors. Arm-worn sensors have not been extensively investigated either. In the present study, we validate HR measurements obtained with an arm-worn optical sensor (Polar OH1) against those obtained with a chest-strap electrical sensor (Polar H10) from 16 participants over a 2-week study period in naturalistic settings. We also investigated the impact of physical activity measured with 3-D accelerometers embedded in the H10 chest strap and OH1 armband sensors on the agreement between the two sensors. Overall, we find that the arm-worn optical Polar OH1 sensor provides a good estimate of HR (Pearson r = 0.90, p <0.01). Filtering the signal that corresponds to physical activity further improves the HR estimates but only slightly (Pearson r = 0.91, p <0.01). Based on these preliminary findings, we conclude that the arm-worn Polar OH1 sensor provides usable HR measurements in daily living conditions, with some caveats discussed in the paper.

Cellular Integrative Immune Markers in Elite Athletes

The integrative immune markers neutrophil-lymphocyte-ratio (NLR), platelet-lymphocyte-ratio (PLR) and systemic immune inflammation index (SII) are established markers in clinical patient care. Adoption of these markers in elite athletics might prove beneficial for monitoring training and health. Blood samples of 195 healthy national Olympic squad athletes were collected before a graded bicycle-ergometric exercise test until complete exhaustion. Measurements included white blood cells, lymphocytes and platelets, allowing for the calculation of the integrative immune markers. Correlations between athlete characteristics (sex, age, sporting discipline, training experience, training volume) and integrative immune marker-values were assessed. In a subgroup analysis a second blood sample was collected from 25 athletes at 1 minute after exercise test to assess its effect on the immune marker levels.An inverse correlation between peak power output and SII-level (Pearson correlation coefficient=-.270, p<.001) and NLR-level (Pearson correlation coefficient=-.249, p<.001) was found. Athletes with higher aerobic fitness had significantly lower values of SII and PLR compared to athletes with lower aerobic fitness. An elevated SII (p=.003) and a reduced PLR (p=.001) was documented as acute response to the exercise test. The integrative immune markers might be a promising tool for monitoring training and health in elite athletes.

On the Feasibility of Real-Time HRV Estimation Using Overly Noisy PPG Signals

Heart Rate Variability (HRV) is a biomarker that can be obtained non-invasively from the electrocardiogram (ECG) or the photoplethysmogram (PPG) fiducial points. However, the accuracy of HRV can be compromised by the presence of artifacts. In the herein presented work, a Simulink® model with a deep learning component was studied for overly noisy PPG signals. A subset with these noisy signals was selected for this study, with the purpose of testing a real-time machine learning based HRV estimation system in substandard artifact-ridden signals. Home-based and wearable HRV systems are prone to dealing with higher contaminated signals, given the less controlled environment where the acquisitions take place, namely daily activity movements. This was the motivation behind this work. The results for overly noisy signals show that the real-time PPG-based HRV estimation system produced RMSE and Pearson correlation coefficient mean and standard deviation of 0.178 ± 0.138 s and 0.401 ± 0.255, respectively. This RMSE value is roughly one order of magnitude above the closest comparative results for which the real-time system was also used.

Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability

Cardiac monitoring based on wearable photoplethysmography (PPG) is widespread because of its usability and low cost. Unfortunately, PPG is negatively affected by various types of disruptions, which could introduce errors to the algorithm that extracts pulse rate variability (PRV). This study aims to identify the nature of such artifacts caused by various types of factors under the conditions of precisely planned experiments. We also propose methods for their reduction based solely on the PPG signal while preserving the frequency content of PRV. The accuracy of PRV derived from PPG was compared to heart rate variability (HRV) derived from the accompanying ECG. The results indicate that filtering PPG signals using the discrete wavelet transform and its inverse (DWT/IDWT) is suitable for removing slow components and high-frequency noise. Moreover, the main benefit of amplitude demodulation is better preparation of the PPG to determine the duration of pulse cycles and reduce the impact of some other artifacts. Post-processing applied to HRV and PRV indicates that the correction of outliers based on local statistical measures of signals and the autoregressive (AR) model is only important when the PPG is of low quality and has no effect under good signal quality. The main conclusion is that the DWT/IDWT, followed by amplitude demodulation, enables the proper preparation of the PPG signal for the subsequent use of PRV extraction algorithms, particularly at rest. However, post-processing in the proposed form should be applied more in the situations of observed strong artifacts than in motionless laboratory experiments.

Overnight sleeping heart rate variability of Army recruits during a 12-week basic military training course

Purpose

This study aimed to quantify sleeping heart rate (HR) and HR variability (HRV) alongside circulating tumor necrosis factor alpha (TNFα) concentrations during 12-week Basic Military Training (BMT). We hypothesised that, despite a high allostatic load, BMT would increase cardiorespiratory fitness and HRV, while lowering both sleeping HR and TNFα in young healthy recruits.

Methods

Sixty-three recruits (18–43 years) undertook ≥ 2 overnight cardiac frequency recordings in weeks 1, 8 and 12 of BMT with 4 h of beat-to-beat HR collected between 00:00 and 06:00 h on each night. Beat-to-beat data were used to derive HR and HRV metrics which were analysed as weekly averages (totalling 8 h). A fasted morning blood sample was collected in the equivalent weeks for the measurement of circulating TNFα concentrations and predicted VO₂max was assessed in weeks 2 and 8.

Results

Predicted VO₂max was significantly increased at week 8 (+ 3.3 ± 2.6 mL kg⁻¹ min⁻¹; p < 0.001). Sleeping HR (wk1, 63 ± 7 b min⁻¹) was progressively reduced throughout BMT (wk8, 58 ± 6; wk12, 55 ± 6 b min⁻¹; p < 0.01). Sleeping HRV reflected by the root mean square of successive differences (RMSSD; wk1, 86 ± 50 ms) was progressively increased (wk8, 98 ± 50; wk12, 106 ± 52 ms; p < 0.01). Fasted circulating TNFα (wk1, 9.1 ± 2.8 pg/mL) remained unchanged at wk8 (8.9 ± 2.5 pg/mL; p = 0.79) but were significantly reduced at wk12 (8.0 ± 2.4 pg/mL; p < 0.01).

Conclusion

Increased predicted VO₂max, HRV and reduced HR during overnight sleep are reflective of typical cardiorespiratory endurance training responses. These results indicate that recruits are achieving cardiovascular health benefits despite the high allostatic load associated with the 12-week BMT.

A Heartbeat Classifier for Continuous Prediction Using a Wearable Device

Heartbeat monitoring may play an essential role in the early detection of cardiovascular disease. When using a traditional monitoring system, an abnormal heartbeat may not appear during a recording in a healthcare facility due to the limited time. Thus, continuous and long-term monitoring is needed. Moreover, the conventional equipment may not be portable and cannot be used at arbitrary times and locations. A wearable sensor device such as Polar H10 offers the same capability as an alternative. It has gold-standard heartbeat recording and communication ability but still lacks analytical processing of the recorded data. An automatic heartbeat classification system can play as an analyzer and is still an open problem in the development stage. This paper proposes a heartbeat classifier based on RR interval data for real-time and continuous heartbeat monitoring using the Polar H10 wearable device. Several machine learning and deep learning methods were used to train the classifier. In the training process, we also compare intra-patient and inter-patient paradigms on the original and oversampling datasets to achieve higher classification accuracy and the fastest computation speed. As a result, with a constrain in RR interval data as the feature, the random forest-based classifier implemented in the system achieved up to 99.67% for accuracy, precision, recall, and F1-score. We are also conducting experiments involving healthy people to evaluate the classifier in a real-time monitoring system.

Accuracy and Precision of Wearable Devices for Real-Time Monitoring of Swimming Athletes

Nowadays, the use of wearable devices is spreading in different fields of application, such as healthcare, digital health, and sports monitoring. In sport applications, the present trend is to continuously monitor the athletes’ physiological parameters during training or competitions to maximize performance and support coaches. This paper aims to evaluate the performances in heart rate assessment, in terms of accuracy and precision, of both wrist-worn and chest-strap commercial devices used during swimming activity, considering a test population of 10 expert swimmers. Three devices were employed: Polar H10 cardiac belt, Polar Vantage V2, and Garmin Venu Sq smartwatches. The former was used as a reference device to validate the data measured by the two smartwatches. Tests were performed both in dry and wet conditions, considering walking/running on a treadmill and different swimming styles in water, respectively. The measurement accuracy and precision were evaluated through standard methods, i.e., Bland–Altman plot, analysis of deviations, and Pearson’s correlation coefficient. Results show that both precision and accuracy worsen during swimming activity (with an absolute increase of the measurement deviation in the range of 13–56 bpm for mean value and 49–52 bpm for standard deviation), proving how water and arms movement act as relevant interference inputs. Moreover, it was found that wearable performance decreases when activity intensity increases, highlighting the need for specific research for wearable applications in water, with a particular focus on swimming-related sports activities.

Peak Detection and HRV Feature Evaluation on ECG and PPG Signals

Heart Rate Variability (HRV) evaluates the autonomic nervous system regulation and can be used as a monitoring tool in conditions such as cardiovascular diseases, neuropathies and sleep staging. It can be extracted from the electrocardiogram (ECG) and the photoplethysmogram (PPG) signals. Typically, the HRV is obtained from the ECG processing. Being the PPG sensor widely used in clinical setups for physiological parameters monitoring such as blood oxygenation and ventilatory rate, the question arises regarding the PPG adequacy for HRV extraction. There is not a consensus regarding the PPG being able to replace the ECG in the HRV estimation. This work aims to be a contribution to this research area by comparing the HRV estimation obtained from simultaneously acquired ECG and PPG signals from forty subjects. A peak detection method is herein introduced based on the Hilbert transform: Hilbert Double Envelope Method (HDEM). Two other peak detector methods were also evaluated: Pan-Tompkins and Wavelet-based. HRV parameters for time, frequency and the non-linear domain were calculated for each algorithm and the Pearson correlation, T-test and RMSE were evaluated. The HDEM algorithm showed the best overall results with a sensitivity of 99.07% and 99.45% for the ECG and the PPG signals, respectively. For this algorithm, a high correlation and no significant differences were found between HRV features and the gold standard, for the ECG and PPG signals. The results show that the PPG is a suitable alternative to the ECG for HRV feature extraction.

Cognitive, Sleep, and Autonomic Responses to Induction of a Ketogenic Diet in Military Personnel: A Pilot Study

BACKGROUND: This pilot study examined the effect of a 2-wk ketogenic diet (KD) compared with a carbohydrate (CHO) diet in military personnel on cognitive performance, mood, sleep, and heart rate variability (HRV).METHODS: A randomized-controlled, cross-over trial was conducted with eight male military personnel (age, 36 ± 7 yr; body mass, 83.7 ± 9.2 kg; BMI, 26.0 ± 2.3 kg · m−2). Subjects ingested their habitual diet for 7 d (baseline), then an iso-energetic KD (∼25 g CHO/d) or CHO diet (∼285 g CHO/d) for 14 d (adaptation), separated by a 12-d washout. HRV, fasting capillary blood D-βHB, and glucose concentration, mood, and sleep were measured daily. Cognitive performance was measured on the 7th day of baseline and the 7th and 14th days of adaptation. Data were analyzed using a series of linear mixed models.RESULTS: Mean weekly D-βHB was higher (95% CI, +0.34 to +2.38 mmol · L−1) and glucose was lower (−0.45 to −0.21 mmol · L−1) in the KD compared with the CHO diet. Cognitive performance (Psychomotor Vigilance Task, 2-choice reaction time, and running memory continuous performance test) and mean weekly fatigue, vigor, and sleep (sleep duration, sleep efficiency, and sleep onset latency) were similar between diets. A diet × week interaction for HRV approached significance, with exploratory analyses suggesting HRV was lower compared with baseline during week-2 adapt (−27 to +4 ms) in the KD.DISCUSSION: A 2-wk induction to a KD in male military personnel does not appear to affect cognitive performance, mood, or sleep, but may lower HRV, indicating increased physiological stress.Shaw DM, Henderson L, van den Berg M. Cognitive, sleep, and autonomic responses to induction of a ketogenic diet in military personnel: a pilot study. Aerosp Med Hum Perform. 2022; 93(6):507–516.

[Application and research of smart wearable devices for heart and brain diseases related to high altitude]

Smart wearable devices play an increasingly important role in physiological monitoring and disease prevention because they are portable, real-time, dynamic and continuous.The popularization of smart wearable devices among people under high-altitude environment would be beneficial for the prevention for heart and brain diseases related to high altitude. The current review comprehensively elucidates the effects of high-altitude environment on the heart and brain of different population and experimental subjects, the characteristics and applications of different types of wearable devices, and the limitations and challenges for their application. By emphasizing their application values, this review provides practical reference information for the prevention of high-altitude disease and the protection of life and health.

Wearable activity trackers-advanced technology or advanced marketing?

Wearable devices represent one of the most popular trends in health and fitness. Rapid advances in wearable technology present a dizzying display of possible functions: from thermometers and barometers, magnetometers and accelerometers, to oximeters and calorimeters. Consumers and practitioners utilize wearable devices to track outcomes, such as energy expenditure, training load, step count, and heart rate. While some rely on these devices in tandem with more established tools, others lean on wearable technology for health-related outcomes, such as heart rhythm analysis, peripheral oxygen saturation, sleep quality, and caloric expenditure. Given the increasing popularity of wearable devices for both recreation and health initiatives, understanding the strengths and limitations of these technologies is increasingly relevant. Need exists for continued evaluation of the efficacy of wearable devices to accurately and reliably measure purported outcomes. The purposes of this review are (1) to assess the current state of wearable devices using recent research on validity and reliability, (2) to describe existing gaps between physiology and technology, and (3) to offer expert interpretation for the lay and professional audience on how best to approach wearable technology and employ it in the pursuit of health and fitness. Current literature demonstrates inconsistent validity and reliability for various metrics, with algorithms not publicly available or lacking high-quality validation studies. Advancements in wearable technology should consider standardizing validation metrics, providing transparency in used algorithms, and improving how technology can be tailored to individuals. Until then, it is prudent to exercise caution when interpreting metrics reported from consumer-wearable devices.

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

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.

A Reasonable Officer: Examining the Relationships Among Stress, Training, and Performance in a Highly Realistic Lethal Force Scenario

Under conditions of physiological stress, officers are sometimes required to make split-second life-or-death decisions, where deficits in performance can have tragic outcomes, including serious injury or death and strained police-community relations. The current study assessed the performance of 122 active-duty police officers during a realistic lethal force scenario to examine whether performance was affected by the officer's level of operational skills training, years of police service, and stress reactivity. Results demonstrated that the scenario produced elevated heart rates (i.e., 150 beats per minute), as well as perceptual and cognitive distortions, such as tunnel vision, commensurate with those observed in naturalistic use of force encounters. The average performance rating from the scenario was 59%, with 27% of participants making at least one lethal force error. Elevated stress reactivity was a predictor of poorer performance and increased lethal force errors. Level of training and years of police service had differential and complex effects on both performance and lethal force errors. Our results illustrate the need to critically reflect on police training practices and continue to make evidence-based improvements to training. The findings also highlight that while training may significantly improve outcomes, flawless performance is likely not probable, given the limits of human performance under stress. Implications for the objective reasonableness standard, which is used to assess the appropriateness of force in courts of law, are discussed.

Is there an association between socioeconomic status and the degree of diurnal variation in heart rate?

BACKGROUND

Disruption in circadian rhythms is associated with cardiovascular disease and may play a role in socioeconomic differences in cardiovascular disease prevalence. However, it is unclear whether low SES is associated with a lower diurnal rhythm in autonomic activity markers. We investigated the association between SES and the amplitude of the daily fluctuation of heart rate.

METHODS

We included data of 450 participants of a HELIUS sub-study in Amsterdam, the Netherlands. Participants wore an Actiheart monitor (CamNtech), a chest-worn monitor which measures heart rate every 15 s for several days. Cosinor analysis was performed on the time series of heart rate within each participant. We analyzed the association between the cosinor parameters (amplitude, midline and peak time of the diurnal HR rhythm) and SES indicators (education, occupational class and a proxy of income) in multivariate linear regression models, adjusting for age, sex and ethnicity.

RESULTS

There was a clear diurnal rhythm in the average heart rates, with a peak between noon and 18:00 and a trough between 04:00 and 06:00. This rhythm was present for all categories of education, occupation and income proxy. The estimates for the cosinor parameters did not differ consistently and significantly between categories of education, occupation or income proxy.

CONCLUSIONS

We did not find any consistent evidence to support our hypothesis of a diminished amplitude in the diurnal variation of heart rate in individuals with lower SES. Future studies should explore SES differences in the diurnal variation in markers of autonomic activity other than heart rate.

Immersive Virtual Reality Influences Physiologic Responses to Submaximal Exercise: A Randomized, Crossover Trial

Objectives: This cross-sectional, randomly assigned study aimed to assess the influence of immersive virtual reality (VR) on exercise tolerance expressed as the duration of a submaximal exercise test (ET) on a cycle ergometer. Methods: The study enrolled 70 healthy volunteers aged 22-25years. Each participant performed an ET with and without VR. Time- and frequency-domain heart rate variability (HRV) parameters were analyzed for the first 3min (T1), the last 3min (T2), and the time at which the shorter of the two tests terminated (Tiso). In the time domain, a SD of R-R intervals (SDNN) and a root mean square of successive R-R interval differences (RMSSD) in milliseconds were computed. The following spectral components were considered: low frequency (LF), high frequency (HF), total power (TP), and LF/HF ratio. The study was registered in ClinicalTrials.gov (NCT04197024). Results: Compared to standard ET, tests in immersive VR lasted significantly longer (694 vs. 591s, p<0.00001) and were associated with lower HR response across the range of corresponding exercise levels, averaging 5-8 beats/min. In the multiple regression analysis, the ET duration was positively determined by male sex, immersion in VR, and negatively determined by HRT1 and RMSSDT1. Conclusion: Exercising in VR is associated with lower HR which allowed subjects to exercise for a longer time before reaching target heart rate (HR). In addition, the increase in exercise duration was found to be related to an adjustment in autonomic nervous activity at a given work rate favoring parasympathetic predominance.

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

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.

Applying Heart Rate Variability to Monitor Health and Performance in Tactical Personnel: A Narrative Review

Human performance optimization of tactical personnel requires accurate, meticulous, and effective monitoring of biological adaptations and systemic recovery. Due to an increased understanding of its importance and the commercial availability of assessment tools, the use of heart rate variability (HRV) to address this need is becoming more common in the tactical community. Measuring HRV is a non-invasive, practical method for objectively assessing a performer's readiness, workload, and recovery status; when combined with additional data sources and practitioner input, it provides an affordable and scalable solution for gaining actionable information to support the facilitation and maintenance of operational performance. This narrative review discusses the non-clinical use of HRV for assessing, monitoring, and interpreting autonomic nervous system resource availability, modulation, effectiveness, and efficiency in tactical populations. Broadly, HRV metrics represent a complex series of interactions resulting from internal and external stimuli; therefore, a general overview of HRV applications in tactical personnel is discussed, including the influence of occupational specific demands, interactions between cognitive and physical domains, and recommendations on implementing HRV for training and recovery insights into critical health and performance outcomes.