Heart rate variability with photoplethysmography in 8 million individuals: a cross-sectional study

Autonomic dysfunction in neurodegenerative disease

In addition to their more studied cognitive and motor effects, neurodegenerative diseases are also associated with impairments in autonomic function - the regulation of involuntary physiological processes. These autonomic impairments manifest in different ways and at different stages depending on the specific disease. The neural networks responsible for autonomic regulation in the brain and body have characteristics that render them particularly susceptible to the prion-like spread of protein aggregation involved in neurodegenerative diseases. Specifically, the axons of these neurons - in both peripheral and central networks - are long and poorly myelinated axons, which make them preferential targets for pathological protein aggregation. Moreover, cortical regions integrating information about the internal state of the body are highly connected with other brain regions, which increases the likelihood of intersection with pathological pathways and prion-like spread of abnormal proteins. This leads to an autonomic 'signature' of dysfunction, characteristic of each neurodegenerative disease, that is linked to the affected networks and regions undergoing pathological aggregation.

More variable circadian rhythms in epilepsy captured by long-term heart rate recordings from wearable sensors

OBJECTIVE

The circadian rhythm synchronizes physiological and behavioral patterns with the 24-h light-dark cycle. Disruption to the circadian rhythm is linked to various health conditions, although optimal methods to describe these disruptions remain unclear. An emerging approach is to examine the intraindividual variability in measurable properties of the circadian rhythm over extended periods. Epileptic seizures are modulated by circadian rhythms, but the relevance of circadian rhythm disruption in epilepsy remains unexplored. Our study investigates intraindividual circadian variability in epilepsy and its relationship with seizures.

METHODS

We retrospectively analyzed >70 000 h of wearable smartwatch data (Fitbit) from 143 people with epilepsy (PWE) and 31 healthy controls. Circadian oscillations in heart rate time series were extracted, daily estimates of circadian period, acrophase, and amplitude properties were produced, and estimates of the intraindividual variability of these properties over an entire recording were calculated.

RESULTS

PWE exhibited greater intraindividual variability in period (76 vs. 57 min, d = .66, p < .001) and acrophase (64 vs. 48 min, d = .49, p = .004) compared to controls, but not in amplitude (2 beats per minute, d = -.15, p = .49). Variability in circadian properties showed no correlation with seizure frequency nor any differences between weeks with and without seizures.

SIGNIFICANCE

For the first time, we show that heart rate circadian rhythms are more variable in PWE, detectable via consumer wearable devices. However, no association with seizure frequency or occurrence was found, suggesting that this variability might be underpinned by the epilepsy etiology rather than being a seizure-driven effect.

Age-related variations in heart rate variability profiles among patients with schizophrenia and major depressive disorder

Patients with psychiatric disorders exhibit general autonomic dysregulation and elevated cardiovascular risks, which could be indexed by heart rate variability (HRV). However, HRV is susceptible to age and other patient-specific factors. This study aimed to investigate the HRV profile and age-related variations, as well as the potential influence of sex, BMI, and HR on HRV in psychiatric populations. There were 571 consecutive patients diagnosed with schizophrenia (SZ) (N = 282) or major depressive disorder (MDD) (N = 289) recruited and classified as adolescent (11-21 years) and adult (> 21 years) groups. HRV indices were measured with 3-minute resting ECG recordings. Compared to adolescent subjects, all time-domain and nonlinear HRV indices were notably reduced in adults, while frequency-domain HRV was comparable. Between SZ and MDD groups, only HTI differed significantly. Age and psychiatric disorders exhibited complex interaction effects on HRV. Stratified by age stage, MDD patients exhibited slightly higher HRV in adolescence but slightly lower HRV in adulthood. In logistic regression analysis, HTI and SD2 were significantly distinctive between adolescents and adults in MDD group, while pNN50 was distinctive in SZ group. Moreover, female subjects demonstrated lower time-domain HRV, LF/HF and SD2 than males. HR exhibited inverse relationship with three domain HRV. No significant effect of BMI was observed. In psychiatric populations, compared to adolescents, adults decreased in time-domain and nonlinear HRV, but not in frequency-domain HRV. Age and psychotic disorders exhibited complex interaction effects on HRV. Sex and HR also emerged as important influencing factors of HRV.

Specificity of cranial cutaneous manipulations in modulating autonomic nervous system responses and physiological oscillations: A controlled study

Significant autonomic nervous system responses to a specific osteopathic intervention, the cranial vault hold (CVH), have recently been demonstrated in forehead skin blood volume changes, heart rate, and respiration frequencies. The specificity of the CVH-intervention-related autonomic responses yet requires differentiation. Thus, we compared autonomic responses to CVH with responses to compression of the fourth ventricle (CV4) and to two corresponding SHAM conditions. Analysis of frequencies and amplitudes for changes in skin blood volume and respiration in low (LF; 0.05-0.12 Hz), intermediate (IM; 0.12-0.18 Hz), and high (HF; 0.18-0.4 Hz) frequency bands, and metrics of heartrate variability revealed significant decreases in LF range (from 0.12 to 0.10 Hz), increased LF and decreased IM durations, and increased skin blood volume amplitudes in response to CVH, but no significant skin blood volume responses to any of the control interventions. Ratio changes for respiration and skin blood volume frequencies approximately at 3:1 during CVH, remained unchanged in all other interventions. Heart rate decreased across conditions, indicating an increase in parasympathetic tone. This was also indicated by a significant increase in root mean of squared successive difference following CV4. We incurred that rhythmic response patterns in the LF and IM bands only appeared in CVH. This suggests specific physiological responses to CVH warranting further investigation by studying e.g., responses to CVH in physical or mental health disorders with autonomic involvement.

Assessing the clinical reliability of short-term heart rate variability: insights from controlled dual-environment and dual-position measurements

Heart rate variability (HRV) is a widely recognized biomarker for autonomic nervous system regulation, applicable in clinical and athletic settings to monitor health and recovery. Despite its extensive use, HRV measurement reliability is influenced by numerous factors, necessitating controlled conditions for accurate assessments. This study investigates the reliability of short-term HRV measurements in various settings and positions, aiming to establish consistent protocols for HRV monitoring and interpretation. We assessed morning HRV in 34 healthy, physically active adults across supine and standing positions, at home and in the laboratory, over a 24-hour period. Environment significantly impacted standing HRV. Home measurements exhibited slightly lower variance compared to lab settings, underscoring the importance of environment control. Our findings confirm the high reliability of HRV measurements, indicating their robustness in capturing autonomic changes, provided a rigorous methodology is employed. Here we show that effective and reliable HRV assessment is possible across various conditions, contingent upon strict management of confounding factors. This research supports the utility of HRV as a non-invasive diagnostic tool, emphasizing its importance in health management and potential in broadening applications to diverse populations. Future studies are encouraged to expand these assessments to include varied demographic and clinical profiles, enhancing HRV integration into routine health evaluations.

Circadian rhythm of heart rate and activity: A cross-sectional study

Circadian rhythms are governed by a biological clock, and are known to occur in a variety of physiological processes. We report results on the circadian rhythm of heart rate observed using a wrist-worn wearable device (Fitbit), consisting of over 17,000 individuals over the course of 30 days. We obtain an underlying heart rate circadian rhythm from the time series heart rate by modeling the circadian rhythm as a sum over the first two Fourier harmonics. The first Fourier harmonic accounts for the approximate 24-hour rhythmicity of the body clock, while the second harmonic accounts for non-sinusoidal perturbations. From the diurnal modulation of heart rate, we obtain the following circadian parameters: (i) amplitude of modulation, (ii) bathyphase, and (iii) acrophase. We also consider the circadian rhythm of activity and show that in most individuals, the circadian rhythm of heart rate lags the circadian rhythm of activity. The widespread availability of smartwatches and trackers may enable individuals who are interested in observing their circadian rhythms of numerous physiological parameters, and to measure longitudinal changes in circadian parameters in response to various changes in health-related variables such as diet, sleep, exercise, or illness.

Evaluating a 12-week aerobic exercise intervention in adults with persisting post-concussive symptoms

Background

Although guidelines support aerobic exercise in sub-acute mild traumatic brain injury (mTBI), evidence for adults with persisting post-concussive symptoms (PPCS) after mTBI is lacking. The objective was to evaluate the impact of a sub-symptom threshold aerobic exercise intervention on overall symptom burden and quality of life in adults with PPCS.

Methods

This prospective cohort study was nested within the ACTBI Trial (Aerobic Exercise for treatment of Chronic symptoms following mild Traumatic Brain Injury). A total of 50 adults with a diagnosis of mTBI, PPCS and exercise intolerance completed a 12-week sub-symptom threshold aerobic exercise intervention either immediately after enrollment (i-AEP group; n = 27) or following 6-weeks of stretching (d-AEP group; n = 23). Data from all participants (n = 50) were included in the combined AEP (c-AEP) group. The primary outcome was symptom burden on the Rivermead Post Concussion Symptoms Questionnaire (RPQ). Secondary outcomes included measures of quality of life and specific post-concussive symptoms (depressive and anxiety symptoms, functional impact of headache, fatigue, sleep, dizziness and exercise tolerance). Heart rate, blood pressure and heart rate variability were also assessed to understand autonomic function response to intervention.

Results

Participants were a mean (SD) of 42.6 (10.9) years old (74% female) and 25.1 (14.1) months post-mTBI. Following 12-weeks of intervention participants had a significant improvement in symptom burden on the RPQ (i-AEP: mean change = -9.415, p < 0.001; d-AEP: mean change = -3.478, p = 0.034; c-AEP: mean change = -6.446, p < 0.001). Participants also had significant improvement in quality of life (i-AEP: mean change = 9.879, p < 0.001; d-AEP: mean change = 7.994, p < 0.001, c-AEP: mean change = 8.937, p < 0.001), dizziness (i-AEP: mean change = -11.159, p = 0.001; d-AEP: mean change = -6.516, p = 0.019; c-AEP: -8.837, p < 0.001) and exercise tolerance (i-AEP: mean change = 5.987, p < 0.001; d-AEP: mean change = 3.421, p < 0.001; c-AEP: mean change = 4.703, p < 0.001). Headache (mean change = -5.522, p < 0.001) and depressive symptoms (mean change = -3.032, p = 0.001) improved in the i-AEP group. There was no change in measures of autonomic function.

Conclusion

A 12-week aerobic exercise intervention improves overall symptom burden, quality of life and specific symptom domains in adults with PPCS. Clinicians should consider prescription of progressive, individualized, sub-symptom threshold aerobic exercise for adults with PPCS even if presenting with exercise intolerance and months-to-years of symptoms.

Impact of macrohemodynamic manipulations during cardiopulmonary bypass on finger microcirculation assessed by photoplethysmography signal components

Objective.Continuous monitoring of the hemodynamic coherence between macro and microcirculation is difficult at the bedside. We tested the role of photoplethysmography (PPG) to real-time assessment of microcirculation during extreme manipulation of macrohemodynamics induced by the cardiopulmonary bypass (CPB).Approach.We analyzed the alternating (AC) and direct (DC) components of the finger PPG in 12 patients undergoing cardiac surgery with CPB at five moments: (1) before-CPB; (2) CPB-start, at the transition from pulsatile to non-pulsatile blood flow; (3) CPB-aortic clamping, at a sudden decrease in pump blood flow and volemia.; (4) CPB-weaning, during step-wise 20% decreases in pump blood flow and opposite proportional increases in native pulsatile blood flow; and (5) after-CPB.Main results.Nine Caucasian men and three women were included for analysis. Macrohemodynamic changes during CPB had an immediate impact on the PPG at all studied moments. Before-CPB the AC signal amplitude showed a median and IQR values of 0.0023(0.0013). The AC signal completely disappeared at CPB-start and at CPB-aortic clamping. During CPB weaning its amplitude progressively increased but remained lower than before CPB, at 80% [0.0008 (0.0005);p< 0.001], 60% [0.0010(0.0006);p< 0.001], and 40% [0.0013(0.0009);p= 0.011] of CPB flow. The AC amplitude returned close to Before-CPB values at 20% of CPB flow [0.0015(0.0008);p= 0.081], when CPB was completely stopped [0.0019 (0.0009);p= 0.348], and at after-CPB [0.0021(0.0009);p= 0.687]. The DC signal Before-CPB [0.95(0.02)] did not differ statistically from CPB-start, CPB-weaning and After-CPB. However, at CPB-aortic clamping, at no flow and a sudden drop in volemia, the DC signal decreased from [0.96(0.01)] to [0.94(0.02);p= 0.002].Significance.The macrohemodynamic alterations brought on by CPB were consistent with changes in the finger's microcirculation. PPG described local pulsatile blood flow (AC) as well as non-pulsatile blood flow and volemia (DC) in the finger. These findings provide plausibility to the use of PPG in ongoing hemodynamic coherence monitoring.

Integrating genome-wide information and wearable device data to explore the link of anxiety and antidepressants with pulse rate variability

This study explores the genetic and epidemiologic correlates of long-term photoplethysmography-derived pulse rate variability (PRV) measurements with anxiety disorders. Individuals with whole-genome sequencing, Fitbit, and electronic health record data (N = 920; 61,333 data points) were selected from the All of Us Research Program. Anxiety polygenic risk scores (PRS) were derived with PRS-CS after meta-analyzing anxiety genome-wide association studies from three major cohorts- UK Biobank, FinnGen, and the Million Veterans Program (NTotal =364,550). PRV was estimated as the standard deviation of average five-minute pulse wave intervals over full 24-hour pulse rate measurements (SDANN). Antidepressant exposure was defined as an active antidepressant prescription at the time of the PRV measurement in the EHR. Anxiety PRS and antidepressant use were tested for association with daily SDANN. The potential causal effect of anxiety on PRV was assessed with one-sample Mendelian randomization (MR). Anxiety PRS was independently associated with reduced SDANN (beta = -0.08; p = 0.003). Of the eight antidepressant medications and four classes tested, venlafaxine (beta = -0.12, p = 0.002) and bupropion (beta = -0.071, p = 0.01), tricyclic antidepressants (beta = -0.177, p = 0.0008), selective serotonin reuptake inhibitors (beta = -0.069; p = 0.0008) and serotonin and norepinephrine reuptake inhibitors (beta = -0.16; p = 2×10-6) were associated with decreased SDANN. One-sample MR indicated an inverse effect of anxiety on SDANN (beta = -2.22, p = 0.03). Anxiety and antidepressants are independently associated with decreased PRV, and anxiety appears to exert a causal effect on reduced PRV. Those observational findings provide insights into the impact of anxiety on PRV.

Accuracy of smartwatches for the remote assessment of exercise capacity

Exercise capacity is a strong independent predictor of cardiovascular and all-cause mortality. The utilization of well-established submaximal tests of exercise capacity such as the 6-min walk test (6MWT), 3-min step test (3MST) and 10-chair rise test (10CRT) in the community would improve patient care but requires remote monitoring technology. Consumer grade smartwatches provide such an opportunity, however, their accuracy in measuring physiological responses to these tests is unclear. The aim of this study was to determine the accuracy of consumer grade smartwatches in assessing exercise capacity to develop a framework for remote, unsupervised testing. 16 healthy adults (7 male (44%), age median 27 [interquartile range (IQR) 26,29] years) performed 6MWTs using two protocols: (1) standard-straight 30 m laps (6MWT-standard) and 2) continuous lap-circular 240 m laps around a park (6MWT-continuous lap), 3MSTs and 10CRTs. Each one of these four tests was performed three times across two clinic visits. Each participant was fitted with a Garmin Vivoactive4 and Fitbit Sense smartwatch to measure three parameters: distance, step counts and heart rate (HR) response. Reference measures were a meter-wheel, hand tally counter and ECG, respectively. Mean HR was measured at rest, peak exercise and recovery. Agreement was measured using Bland-Altman analysis for repeated measures and summarized as median absolute percentage errors (MAPE). Distance during 6MWT-continuous lap had better agreement than during 6MWT-standard for both Garmin (MAPE: 6.4% [3.0, 10.4%] versus 20.1% [13.9, 28.4%], p < 0.001) and Fitbit (8.0% [2.9, 10.1% versus 18.8% [15.2, 28.1%], p < 0.001). Garmin measured step count more accurately than Fitbit (MAPE: 1.8% [0.9, 2.9%] versus 8.0% [2.6, 12.3%], p < 0.001). Irrespective of test, both devices showed excellent accuracy in measuring HR at rest and recovery (≤ 3%), while accuracy decreased during peak exercise (Fitbit: ~ 12% and Garmin: ~ 7%). In young adults without mobility difficulties, exercise capacity can be measured remotely using standardized tests and consumer grade smartwatches.

Frequency-Domain Features and Low-Frequency Synchronization of Photoplethysmographic Waveform Variability and Heart Rate Variability with Increasing Severity of Cardiovascular Diseases

Objective-Heart rate variability (HRV) and photoplethysmographic waveform variability (PPGV) are available approaches for assessing the state of cardiovascular autonomic regulation. The goal of our study was to compare the frequency-domain features and low-frequency (LF) synchronization of the PPGV and HRV with increasing severity of cardiovascular diseases. Methods-Our study included 998 electrocardiogram (ECG) and finger photoplethysmogram (PPG) recordings from subjects, classified into five categories: 53 recordings from healthy subjects, aged 28.1 ± 6.2 years, 536 recordings from patients with hypertension (HTN), 49.0 ± 8.8 years old, 185 recordings from individuals with stable coronary artery disease (CAD) (63.9 ± 9.3 years old), 104 recordings from patients with myocardial infarction (MI) that occurred three months prior to the recordings (PMI) (65.1 ± 11.0 years old), and 120 recordings from study subjects with acute myocardial infarction (AMI) (64.7 ± 11.5 years old). Spectral analyses of the HRV and PPGV were carried out, along with an assessment of the synchronization strength between LF oscillations of the HRV and of PPGV (synchronization index). Results-Changes in all frequency-domain indices and the synchronization index were observed along the following gradient: healthy subjects → patients with HTN → patients with CAD → patients with PMI → patients with AMI. Similar frequency-domain indices of the PPGV and HRV show little relationship with each other. Conclusions-The frequency-domain indices of the PPGV are highly sensitive to the development of any cardiovascular disease and, therefore, are superior to the HRV indices in this regard. The S index is an independent parameter from the frequency-domain indices.

Simultaneous Determination of Tobacco Smoke Exposure and Stress Biomarkers in Saliva Using In-Tube SPME and LC-MS/MS for the Analysis of the Association between Passive Smoking and Stress

Passive smoking from environmental tobacco smoke not only increases the risk of lung cancer and cardiovascular disease but may also be a stressor triggering neuropsychiatric and other disorders. To prevent these diseases, understanding the relationship between passive smoking and stress is vital. In this study, we developed a simple and sensitive method to simultaneously measure nicotine (Nic) and cotinine (Cot) as tobacco smoke exposure biomarkers, and cortisol (CRT), serotonin (5-HT), melatonin (MEL), dopamine (DA), and oxytocin (OXT) as stress-related biomarkers. These were extracted and concentrated from saliva by in-tube solid-phase microextraction (IT-SPME) using a Supel-Q PLOT capillary as the extraction device, then separated and detected within 6 min by liquid chromatography-tandem mass spectrometry (LC-MS/MS) using a Kinetex Biphenyl column (Phenomenex Inc., Torrance, CA, USA). Limits of detection (S/N = 3) for Nic, Cot, CRT, 5-HT, MEL, DA, and OXT were 0.22, 0.12, 0.78, 0.39, 0.45, 1.4, and 3.7 pg mL-1, respectively, with linearity of calibration curves in the range of 0.01-25 ng mL-1 using stable isotope-labeled internal standards. Intra- and inter-day reproducibilities were under 7.9% and 14.6% (n = 5) relative standard deviations, and compound recoveries in spiked saliva samples ranged from 82.1 to 106.6%. In thirty nonsmokers, Nic contents positively correlated with CRT contents (R2 = 0.5264, n = 30), while no significant correlation was found with other biomarkers. The standard deviation of intervals between normal beats as the standard measure of heart rate variability analysis negatively correlated with CRT contents (R2 = 0.5041, n = 30). After passive smoke exposure, Nic levels transiently increased, Cot and CRT levels rose over time, and 5-HT, DA, and OXT levels decreased. These results indicate tobacco smoke exposure acts as a stressor in nonsmokers.

Signal completion using generative adversarial networks for enhanced photoplethysmography measurement accuracy

Despite the growing adoption of wearable photoplethysmography (PPG) devices in personal health management, their measurement accuracy remains limited due to susceptibility to noise. This paper proposes a novel signal completion technique using generative adversarial networks that ensures both global and local consistency. Our approach innovatively addresses both short- and long-term PPG variations to restore waveforms while maintaining waveform consistency within and between pulses. We evaluated our model by removing up to 50 % of segments from segmented PPG waveforms and comparing the original and reconstructed waveforms, including systolic peak information. The results demonstrate that our method accurately reconstructs waveforms with high fidelity, producing natural and seamless transitions without discontinuities at reconstructed boundaries. Additionally, the reconstructed waveforms preserve typical PPG shapes with minimal distortion, underscoring the effectiveness and novelty of our technique.

Examining the Light Heart Mobile Device App for Assessing Human Pulse Interval and Heart Rate Variability: Validation Study

BACKGROUND

Pulse interval is a biomarker of psychological and physiological health. Pulse interval can now be assessed using mobile phone apps, which expands researchers' ability to assess pulse interval in the real world. Prior to implementation, measurement accuracy should be established.

OBJECTIVE

This investigation evaluated the validity of the Light Heart mobile app to measure pulse interval and pulse rate variability in healthy young adults.

METHODS

Validity was assessed by comparing the pulse interval and SD of normal pulse intervals obtained by Light Heart to the gold standard, electrocardiogram (ECG), in 14 young healthy individuals (mean age 24, SD 5 years; n=9, 64% female) in a seated posture.

RESULTS

Mean pulse interval (Light Heart: 859, SD 113 ms; ECG: 857, SD 112 ms) demonstrated a strong positive linear correlation (r=0.99; P<.001) and strong agreement (intraclass correlation coefficient=1.00, 95% CI 0.99-1.00) between techniques. The Bland-Altman plot demonstrated good agreement for the mean pulse interval measured with Light Heart and ECG with evidence of fixed bias (-1.56, SD 1.86; 95% CI -5.2 to 2.1 ms), suggesting that Light Heart overestimates pulse interval by a small margin. When Bland-Altman plots were constructed for each participant's beat-by-beat pulse interval data, all participants demonstrated strong agreement between Light Heart and ECG with no evidence of fixed bias between measures. Heart rate variability, assessed by SD of normal pulse intervals, demonstrated strong agreement between techniques (Light Heart: mean 73, SD 23 ms; ECG: mean 73, SD 22 ms; r=0.99; P<.001; intraclass correlation coefficient=0.99, 95% CI 0.97-1.00).

CONCLUSIONS

This study provides evidence to suggest that the Light Heart mobile app provides valid measures of pulse interval and heart rate variability in healthy young adults.

Advanced cardiovascular physiology in an individual with type 1 diabetes after 10-year ketogenic diet

Adults with type 1 diabetes (T1D) have an elevated risk for cardiovascular disease (CVD) compared with the general population. HbA1c is the primary modifiable risk factor for CVD in T1D. Fewer than 1% of patients achieve euglycemia (<5.7% HbA1c). Ketogenic diets (KD; ≤50 g carbohydrate/day) may improve glycemia and downstream vascular dysfunction in T1D by reducing HbA1c and insulin load. However, there are concerns regarding the long-term CVD risk from a KD. Therefore, we compared data collected in a 60-day window in an adult with T1D on exogenous insulin who consumed a KD for 10 years versus normative values in those with T1D (T1D norms). The participant achieved euglycemia with an HbA1c of 5.5%, mean glucose of 98 [5] mg/dL (median [interquartile range]), 90 [11]% time-in-range 70-180 mg/dL (T1D norms: 1st percentile for all), and low insulin requirements of 0.38 ± 0.03 IU/kg/day (T1D norms: 8th percentile). Seated systolic blood pressure (SBP) was 113 mmHg (T1D norms: 18th percentile), while ambulatory awake SBP was 132 ± 15 mmHg (T1D target: <130 mmHg), blood triglycerides were 69 mg/dL (T1D norms: 34th percentile), low-density lipoprotein was 129 mg/dL (T1D norms: 60th percentile), heart rate was 56 beats/min (T1D norms: >1SD below the mean), carotid-femoral pulse wave velocity was 7.17 m/s (T1D norms: lowest quartile of risk), flow-mediated dilation was 12.8% (T1D norms: >1SD above mean), and cardiac vagal baroreflex gain was 23.5 ms/mmHg (T1D norms: >1SD above mean). Finally, there was no indication of left ventricular diastolic dysfunction from echocardiography. Overall, these data demonstrate below-average CVD risk relative to T1D norms despite concerns regarding the long-term impact of a KD on CVD risk.NEW & NOTEWORTHY Adults with type 1 diabetes (T1D) have a 10-fold higher risk for cardiovascular disease (CVD) compared with the general population. We assessed cardiovascular health metrics in an adult with T1D who presented with a euglycemic HbA1c after following a ketogenic diet for the past 10 years. Despite concerns about the ketogenic diet increasing CVD risk, the participant exhibited below-average CVD risk relative to others with T1D when considering all outcomes together.

Measures of nutrition status and health for weight-inclusive patient care: A narrative review

In healthcare, weight is often equated to and used as a marker for health. In examining nutrition and health status, there are many more effective markers independent of weight. In this article, we review practical and emerging clinical applications of technologies and tools used to collect non-weight-related data in nutrition assessment, monitoring, and evaluation in the outpatient setting. The aim is to provide clinicians with new ideas about various types of data to evaluate and track in nutrition care.

Typical everyday movements cause specific patterns in heart rate

Physical inactivity and sedentary behaviour are important risk factors for cardiovascular disease. Knowledge about the impact of everyday movements on cardiac autonomic regulation is sparse. This study aims to provide evidence that typical everyday movements show a clear impact on heart rate regulation. 40 healthy participants performed two everyday movements: (1) calmly kneeling down ("tie one's shoes") and standing up again and (2) raising the arms to the horizontal ("expressive yawning"). Both movements elicited reproducible pattern in the sequence of heart periods. Local minima and local maxima appeared in the transient period of approx. 30 s. The regulatory response for ergometer cycling, which was used as control, did not show a pattern formation. Calmly performed everyday movements are able to elicit rich cardiac regulatory responses including specific patterns in heart rate. These newly described patterns have multiple implications for clinical and rehabilitative medicine, basic research, digital health data processing, and public health. If carried out regularly these regulatory responses may help to mitigate the burden of physical inactivity and enrich cardiovascular regulation.

Personalized interpretable prediction of perceived sleep quality: Models with meaningful cardiovascular and behavioral features

Understanding a person's perceived quality of sleep is an important problem, but hard due to its poor definition and high intra- as well as inter-individual variation. In the short term, sleep quality has an established impact on cognitive function during the following day as well as on fatigue. In the long term, good quality sleep is essential for mental and physical health and contributes to quality of life. Despite the need to better understand sleep quality as an early indicator for sleep disorders, perceived sleep quality has been rarely modeled for multiple consecutive days using biosignals. In this paper, we present novel insights on the association of cardiac activity and perceived sleep quality using an interpretable modeling approach utilizing the publicly available intensive-longitudinal study M2Sleep. Our method takes as input signals from commodity wearable devices, including motion and blood volume pulses. Despite processing only simple and clearly interpretable features, we achieve an accuracy of up to 70% with an AUC of 0.76 and reduce the error by up to 36% compared to related work. We further argue that collected biosignals and sleep quality labels should be normalized per-participant to enable a medically insightful analysis. Coupled with explainable models, this allows for the interpretations of effects on perceived sleep quality. Analysis revealed that besides higher skin temperature and sufficient sleep duration, especially higher average heart rate while awake and lower minimal activity of the parasympathetic and sympathetic nervous system while asleep increased the chances of higher sleep quality.

Assessment of Heat Exposure and Health Outcomes in Rural Populations of Western Kenya by Using Wearable Devices: Observational Case Study

BACKGROUND

Climate change increasingly impacts health, particularly of rural populations in sub-Saharan Africa due to their limited resources for adaptation. Understanding these impacts remains a challenge, as continuous monitoring of vital signs in such populations is limited. Wearable devices (wearables) present a viable approach to studying these impacts on human health in real time.

OBJECTIVE

The aim of this study was to assess the feasibility and effectiveness of consumer-grade wearables in measuring the health impacts of weather exposure on physiological responses (including activity, heart rate, body shell temperature, and sleep) of rural populations in western Kenya and to identify the health impacts associated with the weather exposures.

METHODS

We conducted an observational case study in western Kenya by utilizing wearables over a 3-week period to continuously monitor various health metrics such as step count, sleep patterns, heart rate, and body shell temperature. Additionally, a local weather station provided detailed data on environmental conditions such as rainfall and heat, with measurements taken every 15 minutes.

RESULTS

Our cohort comprised 83 participants (42 women and 41 men), with an average age of 33 years. We observed a positive correlation between step count and maximum wet bulb globe temperature (estimate 0.06, SE 0.02; P=.008). Although there was a negative correlation between minimum nighttime temperatures and heat index with sleep duration, these were not statistically significant. No significant correlations were found in other applied models. A cautionary heat index level was recorded on 194 (95.1%) of 204 days. Heavy rainfall (>20 mm/day) occurred on 16 (7.8%) out of 204 days. Despite 10 (21%) out of 47 devices failing, data completeness was high for sleep and step count (mean 82.6%, SD 21.3% and mean 86.1%, SD 18.9%, respectively), but low for heart rate (mean 7%, SD 14%), with adult women showing significantly higher data completeness for heart rate than men (2-sided t test: P=.003; Mann-Whitney U test: P=.001). Body shell temperature data achieved 36.2% (SD 24.5%) completeness.

CONCLUSIONS

Our study provides a nuanced understanding of the health impacts of weather exposures in rural Kenya. Our study's application of wearables reveals a significant correlation between physical activity levels and high temperature stress, contrasting with other studies suggesting decreased activity in hotter conditions. This discrepancy invites further investigation into the unique socioenvironmental dynamics at play, particularly in sub-Saharan African contexts. Moreover, the nonsignificant trends observed in sleep disruption due to heat expose the need for localized climate change mitigation strategies, considering the vital role of sleep in health. These findings emphasize the need for context-specific research to inform policy and practice in regions susceptible to the adverse health effects of climate change.

The effect of exercise training on heart rate variability in patients with hypertension: A systematic review and meta-analysis

We aimed to assess the effect of exercise training on heart rate variability (HRV) in hypertensive patients and to provide practical recommendations. We systematically searched seven databases for randomized controlled trials (RCTs) comparing the efficacy of exercise interventions vs. non-exercise control for HRV in adults with hypertension. HRV parameters, blood pressure (BP), and heart rate (HR) from the experimental and control groups were extracted to carry out meta-analysis. To explore the heterogeneity, we performed sensitivity analysis, sub-analysis, and meta-regression. Twelve RCTs were included, and the main results demonstrated exercise produced improvement in root mean square of successive RR-intervals differences (RMSSD) and high frequency (HF), and reductions in LF/HF, resting systolic blood pressure (SBP), and HR. The sub-analysis and meta-regression showed that AE improved more HRV indices and was effective in reducing BP compared with RE. Follow-up duration was also an important factor. Data suggests exercise training has ameliorating effects on HRV parameters, resting SBP, and HR in hypertensive patients, showing enhanced autonomic nervous system function and vagal activity. This effect may be better realized with exercise interventions of 4 weeks or more. Considering our results and the hypertension practice guidelines, we tend to recommend patients choose supervised AE.

Digital Phenotyping of Geriatric Depression Using a Community-Based Digital Mental Health Monitoring Platform for Socially Vulnerable Older Adults and Their Community Caregivers: 6-Week Living Lab Single-Arm Pilot Study

BACKGROUND

Despite the increasing need for digital services to support geriatric mental health, the development and implementation of digital mental health care systems for older adults have been hindered by a lack of studies involving socially vulnerable older adult users and their caregivers in natural living environments.

OBJECTIVE

This study aims to determine whether digital sensing data on heart rate variability, sleep quality, and physical activity can predict same-day or next-day depressive symptoms among socially vulnerable older adults in their everyday living environments. In addition, this study tested the feasibility of a digital mental health monitoring platform designed to inform older adult users and their community caregivers about day-to-day changes in the health status of older adults.

METHODS

A single-arm, nonrandomized living lab pilot study was conducted with socially vulnerable older adults (n=25), their community caregivers (n=16), and a managerial social worker over a 6-week period during and after the COVID-19 pandemic. Depressive symptoms were assessed daily using the 9-item Patient Health Questionnaire via scripted verbal conversations with a mobile chatbot. Digital biomarkers for depression, including heart rate variability, sleep, and physical activity, were measured using a wearable sensor (Fitbit Sense) that was worn continuously, except during charging times. Daily individualized feedback, using traffic signal signs, on the health status of older adult users regarding stress, sleep, physical activity, and health emergency status was displayed on a mobile app for the users and on a web application for their community caregivers. Multilevel modeling was used to examine whether the digital biomarkers predicted same-day or next-day depressive symptoms. Study staff conducted pre- and postsurveys in person at the homes of older adult users to monitor changes in depressive symptoms, sleep quality, and system usability.

RESULTS

Among the 31 older adult participants, 25 provided data for the living lab and 24 provided data for the pre-post test analysis. The multilevel modeling results showed that increases in daily sleep fragmentation (P=.003) and sleep efficiency (P=.001) compared with one's average were associated with an increased risk of daily depressive symptoms in older adults. The pre-post test results indicated improvements in depressive symptoms (P=.048) and sleep quality (P=.02), but not in the system usability (P=.18).

CONCLUSIONS

The findings suggest that wearable sensors assessing sleep quality may be utilized to predict daily fluctuations in depressive symptoms among socially vulnerable older adults. The results also imply that receiving individualized health feedback and sharing it with community caregivers may help improve the mental health of older adults. However, additional in-person training may be necessary to enhance usability.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06270121; https://clinicaltrials.gov/study/NCT06270121.

Predicting cardiovascular disease risk using photoplethysmography and deep learning

Cardiovascular diseases (CVDs) are responsible for a large proportion of premature deaths in low- and middle-income countries. Early CVD detection and intervention is critical in these populations, yet many existing CVD risk scores require a physical examination or lab measurements, which can be challenging in such health systems due to limited accessibility. We investigated the potential to use photoplethysmography (PPG), a sensing technology available on most smartphones that can potentially enable large-scale screening at low cost, for CVD risk prediction. We developed a deep learning PPG-based CVD risk score (DLS) to predict the probability of having major adverse cardiovascular events (MACE: non-fatal myocardial infarction, stroke, and cardiovascular death) within ten years, given only age, sex, smoking status and PPG as predictors. We compare the DLS with the office-based refit-WHO score, which adopts the shared predictors from WHO and Globorisk scores (age, sex, smoking status, height, weight and systolic blood pressure) but refitted on the UK Biobank (UKB) cohort. All models were trained on a development dataset (141,509 participants) and evaluated on a geographically separate test (54,856 participants) dataset, both from UKB. DLS's C-statistic (71.1%, 95% CI 69.9-72.4) is non-inferior to office-based refit-WHO score (70.9%, 95% CI 69.7-72.2; non-inferiority margin of 2.5%, p<0.01) in the test dataset. The calibration of the DLS is satisfactory, with a 1.8% mean absolute calibration error. Adding DLS features to the office-based score increases the C-statistic by 1.0% (95% CI 0.6-1.4). DLS predicts ten-year MACE risk comparable with the office-based refit-WHO score. Interpretability analyses suggest that the DLS-extracted features are related to PPG waveform morphology and are independent of heart rate. Our study provides a proof-of-concept and suggests the potential of a PPG-based approach strategies for community-based primary prevention in resource-limited regions.

Respiratory modulation of the heart rate: A potential biomarker of cardiorespiratory function in human

In recent decade, wearable digital devices have shown potentials for the discovery of novel biomarkers of humans' physiology and behavior. Heart rate (HR) and respiration rate (RR) are most crucial bio-signals in humans' digital phenotyping research. HR is a continuous and non-invasive proxy to autonomic nervous system and ample evidence pinpoints the critical role of respiratory modulation of cardiac function. In the present study, we recorded longitudinal (7 days, 4.63 ± 1.52) HR and RR of 89 freely behaving human subjects (Female: 39, age 57.28 ± 5.67, Male: 50, age 58.48 ± 6.32) and analyzed their dynamics using linear models and information theoretic measures. While HR's linear and nonlinear characteristics were expressed within the plane of the HR-RR directed flow of information (HR→RR - RR→HR), their dynamics were determined by its RR→HR axis. More importantly, RR→HR quantified the effect of alcohol consumption on individuals' cardiorespiratory function independent of their consumed amount of alcohol, thereby signifying the presence of this habit in their daily life activities. The present findings provided evidence for the critical role of the respiratory modulation of HR, which was previously only studied in non-human animals. These results can contribute to humans' phenotyping research by presenting RR→HR as a digital diagnosis/prognosis marker of humans' cardiorespiratory pathology.

A dataset for fatigue estimation during shoulder internal and external rotation movements using wearables

Wearable sensors have recently been extensively used in sports science, physical rehabilitation, and industry providing feedback on physical fatigue. Information obtained from wearable sensors can be analyzed by predictive analytics methods, such as machine learning algorithms, to determine fatigue during shoulder joint movements, which have complex biomechanics. The presented dataset aims to provide data collected via wearable sensors during a fatigue protocol involving dynamic shoulder internal rotation (IR) and external rotation (ER) movements. Thirty-four healthy subjects performed shoulder IR and ER movements with different percentages of maximal voluntary isometric contraction (MVIC) force until they reached the maximal exertion. The dataset includes demographic information, anthropometric measurements, MVIC force measurements, and digital data captured via surface electromyography, inertial measurement unit, and photoplethysmography, as well as self-reported assessments using the Borg rating scale of perceived exertion and the Karolinska sleepiness scale. This comprehensive dataset provides valuable insights into physical fatigue assessment, allowing the development of fatigue detection/prediction algorithms and the study of human biomechanical characteristics during shoulder movements within a fatigue protocol.

Changes in Maternal Heart Rate Variability and Photoplethysmography Morphology after Corticosteroid Administration: A Prospective, Observational Study

Background: Owing to the association between dysfunctional maternal autonomic regulation and pregnancy complications, assessing non-invasive features reflecting autonomic activity-e.g., heart rate variability (HRV) and the morphology of the photoplethysmography (PPG) pulse wave-may aid in tracking maternal health. However, women with early pregnancy complications typically receive medication, such as corticosteroids, and the effect of corticosteroids on maternal HRV and PPG pulse wave morphology is not well-researched. Methods: We performed a prospective, observational study assessing the effect of betamethasone (a commonly used corticosteroid) on non-invasively assessed features of autonomic regulation. Sixty-one women with an indication for betamethasone were enrolled and wore a wrist-worn PPG device for at least four days, from which five-minute measurements were selected for analysis. A baseline measurement was selected either before betamethasone administration or sufficiently thereafter (i.e., three days after the last injection). Furthermore, measurements were selected 24, 48, and 72 h after betamethasone administration. HRV features in the time domain and frequency domain and describing heart rate (HR) complexity were calculated, along with PPG morphology features. These features were compared between the different days. Results: Maternal HR was significantly higher and HRV features linked to parasympathetic activity were significantly lower 24 h after betamethasone administration. Features linked to sympathetic activity remained stable. Furthermore, based on the PPG morphology features, betamethasone appears to have a vasoconstrictive effect. Conclusions: Our results suggest that administering betamethasone affects maternal autonomic regulation and cardiovasculature. Researchers assessing maternal HRV in complicated pregnancies should schedule measurements before or sufficiently after corticosteroid administration.

Development of Prediction Model for Intensive Care Unit Admission Based on Heart Rate Variability: A Case-Control Matched Analysis

This study aimed to develop a predictive model for intensive care unit (ICU) admission by using heart rate variability (HRV) data. This retrospective case-control study used two datasets (emergency department [ED] patients admitted to the ICU, and patients in the operating room without ICU admission) from a single academic tertiary hospital. HRV metrics were measured every 5 min using R-peak-to-R-peak (R-R) intervals. We developed a generalized linear mixed model to predict ICU admission and assessed the area under the receiver operating characteristic curve (AUC). Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated from the coefficients. We analyzed 610 (ICU: 122; non-ICU: 488) patients, and the factors influencing the odds of ICU admission included a history of diabetes mellitus (OR [95% CI]: 3.33 [1.71-6.48]); a higher heart rate (OR [95% CI]: 3.40 [2.97-3.90] per 10-unit increase); a higher root mean square of successive R-R interval differences (RMSSD; OR [95% CI]: 1.36 [1.22-1.51] per 10-unit increase); and a lower standard deviation of R-R intervals (SDRR; OR [95% CI], 0.68 [0.60-0.78] per 10-unit increase). The final model achieved an AUC of 0.947 (95% CI: 0.906-0.987). The developed model effectively predicted ICU admission among a mixed population from the ED and operating room.

pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis

Objective.Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and being increasingly used for a variety of research and clinical applications to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and limited open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers.Approach.This work describes the creation of a standard Python toolbox, denotedpyPPG, for long-term continuous PPG time-series analysis and demonstrates the detection and computation of a high number of fiducial points and digital biomarkers using a standard fingerbased transmission pulse oximeter.Main results.The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2054 adult polysomnography recordings totaling over 91 million reference beats. The algorithm outperformed the open-source original Matlab implementation by ∼5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points.Significance.Based on these fiducial points,pyPPGengineered a set of 74 PPG biomarkers. Studying PPG time-series variability usingpyPPGcan enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models.pyPPGis available onhttps://physiozoo.com/.

Cardiorespiratory fitness is linked with heart rate variability during stress in "at-risk" adults

BACKGROUND

Physiological mechanisms explaining why cardiorespiratory fitness (CRF) predicts cardiovascular morbidity and mortality are incompletely understood. We examined if CRF modifies vagally mediated heart rate variability (HRV) during acute physical or psychosocial stress or night-time sleep in adults with cardiovascular risk factors.

METHODS

Seventy-eight adults (age 56 years [IQR 50-60], 74% female, body mass index 28 kg/m2 [IQR 25-31]) with frequent cardiovascular risk factors participated in this cross-sectional study. They went through physical (treadmill cardiopulmonary exercise test [CPET]) and psychosocial (Trier Social Stress Test for Groups [TSST-G]) stress tests and night-time sleep monitoring (polysomnography). Heart rate (HR) and vagally mediated HRV (root mean square of successive differences between normal R-R intervals [RMSSD]) were recorded during the experiments and analyzed by taking account of potential confounders.

RESULTS

CRF (peak O2 uptake) averaged 99% (range 78-126) in relation to reference data. From pre-rest to moderate intensities during CPET and throughout TSST-G, HR did not differ between participants with CRF below median (CRFlower) and CRF equal to or above median (CRFhigher), whereas CRFhigher had higher HRV than CRFlower, and CRF correlated positively with HRV in all participants. Meanwhile, CRF had no independent associations with HR or HRV levels during slow-wave sleep, the presence of metabolic syndrome was not associated with recorded HR or HRV levels, and single factors predicted HRV responsiveness independently only to limited extents.

CONCLUSIONS

CRF is positively associated with prevailing vagally mediated HRV at everyday levels of physical and psychosocial stress in adults with cardiovascular risk factors.

Heart rate and breathing effects on attention and memory (HeartBEAM): study protocol for a randomized controlled trial in older adults

BACKGROUND

In healthy people, the "fight-or-flight" sympathetic system is counterbalanced by the "rest-and-digest" parasympathetic system. As we grow older, the parasympathetic system declines as the sympathetic system becomes hyperactive. In our prior heart rate variability biofeedback and emotion regulation (HRV-ER) clinical trial, we found that increasing parasympathetic activity through daily practice of slow-paced breathing significantly decreased plasma amyloid-β (Aβ) in healthy younger and older adults. In healthy adults, higher plasma Aβ is associated with greater risk of Alzheimer's disease (AD). Our primary goal of this trial is to reproduce and extend our initial findings regarding effects of slow-paced breathing on Aβ. Our secondary objectives are to examine the effects of daily slow-paced breathing on brain structure and the rate of learning.

METHODS

Adults aged 50-70 have been randomized to practice one of two breathing protocols twice daily for 9 weeks: (1) "slow-paced breathing condition" involving daily cognitive training followed by slow-paced breathing designed to maximize heart rate oscillations or (2) "random-paced breathing condition" involving daily cognitive training followed by random-paced breathing to avoid increasing heart rate oscillations. The primary outcomes are plasma Aβ40 and Aβ42 levels and plasma Aβ42/40 ratio. The secondary outcomes are brain perivascular space volume, hippocampal volume, and learning rates measured by cognitive training performance. Other pre-registered outcomes include plasma pTau-181/tTau ratio and urine Aβ42. Recruitment began in January 2023. Interventions are ongoing and will be completed by the end of 2023.

DISCUSSION

Our HRV-ER trial was groundbreaking in demonstrating that a behavioral intervention can reduce plasma Aβ levels relative to a randomized control group. We aim to reproduce these findings while testing effects on brain clearance pathways and cognition.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05602220. Registered on January 12, 2023.

Prediction of the efficacy of group cognitive behavioral therapy using heart rate variability based smart wearable devices: a randomized controlled study

BACKGROUND

Depression and anxiety are common and disabling mental health problems in children and young adults. Group cognitive behavioral therapy (GCBT) is considered that an efficient and effective treatment for these significant public health concerns, but not all participants respond equally well. The aim of this study was to examine the predictive ability of heart rate variability (HRV), based on sensor data from consumer-grade wearable devices to detect GCBT effectiveness in early intervention.

METHODS

In a study of 33 college students with depression and anxiety, participants were randomly assigned to either GCBT group or a wait-list control (WLC) group. They wore smart wearable devices to measure their physiological activities and signals in daily life. The HRV parameters were calculated and compared between the groups. The study also assessed correlations between participants' symptoms, HRV, and GCBT outcomes.

RESULTS

The study showed that participants in GCBT had significant improvement in depression and anxiety symptoms after four weeks. Higher HRV was associated with greater improvement in depressive and anxious symptoms following GCBT. Additionally, HRV played a noteworthy role in determining how effective GCBT was in improve anxiety(P = 0.002) and depression(P = 0.020), and its predictive power remained significant even when considering other factors.

CONCLUSION

HRV may be a useful predictor of GCBT treatment efficacy. Identifying predictors of treatment response can help personalize treatment and improve outcomes for individuals with depression and anxiety.

TRIAL REGISTRATION

The trial has been retrospectively registered on [22/06/2023] with the registration number [NCT05913349] in the ClinicalTrials.gov. Variations in heart rate variability (HRV) have been associated with depression and anxiety, but the relationship of baseline HRV to treatment outcome in depression and anxiety is unclear. This study predicted GCBT effectiveness using HRV measured by wearable devices. 33 students with depression and anxiety participated in a trial comparing GCBT and wait-list control. HRV parameters from wearables correlated with symptoms (PHQ, PSS) and GCBT effectiveness. Baseline HRV levels are strongly associated with GCBT treatment outcomes. HRV may serve as a useful predictor of efficacy of GCBT treatment,facilitating personalized treatment approaches for individuals with depression and anxiety.

Deep learning-based dynamic ventilatory threshold estimation from electrocardiograms

BACKGROUND AND OBJECTIVE

The ventilatory threshold (VT) marks the transition from aerobic to anaerobic metabolism and is used to assess cardiorespiratory endurance. A conventional way to assess VT is cardiopulmonary exercise testing, which requires a gas analyzer. Another method for measuring VT involves calculating the heart rate variability (HRV) from an electrocardiogram (ECG) by computing the variability of heartbeats. However, the HRV method has some limitations. ECGs should be recorded for at least 5 minutes to calculate the HRV, and the result may depend on the utilized ECG preprocessing algorithms.

METHODS

To overcome these problems, we developed a deep learning-based model consisting of long short-term memory (LSTM) and convolutional neural network (CNN) for a lead II ECG. Variables reflecting subjects' physical characteristics, as well as ECG signals, were input into the model to estimate VT. We applied joint optimization to the CNN layers to generate an informative latent space, which was fed to the LSTM layers. The model was trained and evaluated on two datasets, one from the Bruce protocol and the other from a protocol including multiple tasks (MT).

RESULTS

Acceptable performances (mean and 95% CI) were obtained on the datasets from the Bruce protocol (-0.28[-1.91,1.34] ml/min/kg) and the MT protocol (0.07[-3.14,3.28] ml/min/kg) regarding the differences between the predictions and labels. The coefficient of determination, Pearson correlation coefficient, and root mean square error were 0.84, 0.93, and 0.868 for the Bruce protocol and 0.73, 0.97, and 3.373 for the MT protocol, respectively.

CONCLUSIONS

The results indicated that it is possible for the proposed model to simultaneously assess VT with the inputs of successive ECGs. In addition, from ablation studies concerning the physical variables and the joint optimization process, it was demonstrated that their use could boost the VT assessment performance of the model. The proposed model enables dynamic VT estimation with ECGs, which could help with managing cardiorespiratory fitness in daily life and cardiovascular rehabilitation in patients.

Time-domain heart rate variability features for automatic congestive heart failure prediction

AIMS

Heart failure is a serious condition that often goes undiagnosed in primary care due to the lack of reliable diagnostic tools and the similarity of its symptoms with other diseases. Non-invasive monitoring of heart rate variability (HRV), which reflects the activity of the autonomic nervous system, could offer a novel and accurate way to detect and manage heart failure patients. This study aimed to assess the feasibility of using machine learning techniques on HRV data as a non-invasive biomarker to classify healthy adults and those with heart failure.

METHODS AND RESULTS

We used digitized electrocardiogram recordings from 54 adults with normal sinus rhythm and 44 adults categorized into New York Heart Association classes 1, 2, and 3, suffering from congestive heart failure. All recordings were sourced from the PhysioNet database. Following data pre-processing, we performed time-domain HRV analysis on all individual recordings, including root mean square of the successive difference in adjacent RR interval (RRi) (RMSSD), the standard deviation of RRi (SDNN, the NN stands for natural or sinus intervals), the standard deviation of the successive differences between successive RRi (SDSD), the number or percentage of RRi longer than 50 ms (NN50 and pNN50), and the average value of RRi [mean RR interval (mRRi)]. In our experimental classification performance evaluation, on the computed HRV parameters, we optimized hyperparameters and performed five-fold cross-validation using four machine learning classification algorithms: support vector machine, k-nearest neighbour (KNN), naïve Bayes, and decision tree (DT). We evaluated the prediction accuracy of these models using performance criteria, namely, precision, recall, specificity, F1 score, and overall accuracy. For added insight, we also presented receiver operating characteristic (ROC) plots and area under the ROC curve (AUC) values. The overall best performance accuracy of 77% was achieved when KNN and DT were trained on computed HRV parameters with a 5 min time window. KNN obtained an AUC of 0.77, while DT attained 0.78. Additionally, in the classification of severe congestive heart failure, KNN and DT had the best accuracy of 91%, with KNN achieving an AUC of 0.88 and DT obtaining 0.92.

CONCLUSIONS

The results show that HRV can accurately predict severe congestive heart failure. The findings of this study could inform the use of machine learning approaches on non-invasive HRV, to screen congestive heart failure individuals in primary care.

Wearable Devices: Implications for Precision Medicine and the Future of Health Care

Wearable devices are integrated analytical units equipped with sensitive physical, chemical, and biological sensors capable of noninvasive and continuous monitoring of vital physiological parameters. Recent advances in disciplines including electronics, computation, and material science have resulted in affordable and highly sensitive wearable devices that are routinely used for tracking and managing health and well-being. Combined with longitudinal monitoring of physiological parameters, wearables are poised to transform the early detection, diagnosis, and treatment/management of a range of clinical conditions. Smartwatches are the most commonly used wearable devices and have already demonstrated valuable biomedical potential in detecting clinical conditions such as arrhythmias, Lyme disease, inflammation, and, more recently, COVID-19 infection. Despite significant clinical promise shown in research settings, there remain major hurdles in translating the medical uses of wearables to the clinic. There is a clear need for more effective collaboration among stakeholders, including users, data scientists, clinicians, payers, and governments, to improve device security, user privacy, data standardization, regulatory approval, and clinical validity. This review examines the potential of wearables to offer affordable and reliable measures of physiological status that are on par with FDA-approved specialized medical devices. We briefly examine studies where wearables proved critical for the early detection of acute and chronic clinical conditions with a particular focus on cardiovascular disease, viral infections, and mental health. Finally, we discuss current obstacles to the clinical implementation of wearables and provide perspectives on their potential to deliver increasingly personalized proactive health care across a wide variety of conditions.

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review

Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.

Unconstrained Piezoelectric Vascular Electronics for Wireless Monitoring of Hemodynamics and Cardiovascular Health

The patient-centered healthcare requires timely disease diagnosis and prognostic assessment, calling for individualized physiological monitoring. To assess the postoperative hemodynamic status of patients, implantable blood flow monitoring devices are highly expected to deliver real time, long-term, sensitive, and reliable hemodynamic signals, which can accurately reflect multiple physiological conditions. Herein, an implantable and unconstrained vascular electronic system based on a piezoelectric sensor immobilized is presented by a "growable" sheath around continuously growing arterial vessels for real-timely and wirelessly monitoring of hemodynamics. The piezoelectric sensor made of circumferentially aligned polyvinylidene fluoride nanofibers around pulsating artery can sensitively perceive mechanical signals, and the growable sheath bioinspired by the structure and function of leaf sheath has elasticity and conformal shape adaptive to the dynamically growing arterial vessels to avoid growth constriction. With this integrated and smart design, long-term, wireless, and sensitive monitoring of hemodynamics are achieved and demonstrated in rats and rabbits. It provides a simple and versatile strategy for designing implantable sensors in a less invasive way.

Age Differences in Upper Gastrointestinal Symptoms and Vagal Modulation in Women With Irritable Bowel Syndrome

BACKGROUND/AIMS

Patients with irritable bowel syndrome (IBS) often report upper gastrointestinal (GI) (e.g., nausea and heartburn), somatic, and emotional symptoms. This study seeks to examine the relationships among younger and older women with IBS and indicators of autonomic nervous system (ANS) function and daily nausea and heartburn symptoms.

METHODS

Women were recruited through clinics and the community. Nocturnal heart rate variability (HRV) was obtained using ambulatory electrocardiogram Holter monitors. Individual symptom severity and frequency were collected using 28-day diaries. All variables were stratified by younger (<46 years) and older (≥46 years) age groups.

RESULTS

Eighty-nine women with IBS were included in this descriptive correlation study (n = 57 younger; n = 32 older). Older women had reduced indices of vagal activity when compared to younger women. In older women, there was an inverse correlation between nausea and vagal measures (Ln RMSSD, r = -.41, p = .026; Ln pNN50, r = -.39, p = .034). Heartburn in older women was associated with sleepiness (r = .59, p < .001) and anger (r = .48, p = .006). Nausea was significantly correlated with anger in the younger group (r = .41, p = .001). There were no significant relationships between HRV indicators and nausea and heartburn in younger women.

CONCLUSIONS

Age-related differences in ANS function that are associated with nausea may portend unique opportunities to better understand the vagal dysregulation in women with IBS.

Considerations on How to Prevent Parkinson's Disease Through Exercise

The increasing prevalence of people with Parkinson's disease (PD) necessitates a high priority for finding interventions to delay or even prevent the onset of PD. There is converging evidence that exercise may exert disease-modifying effects in people with clinically manifest PD, but whether exercise also has a preventive effect or is able to modify the progression of the pathology in the prodromal phase of PD is unclear. Here we provide some considerations on the design of trials that aim to prevent PD through exercise. First, we discuss the who could benefit from exercise, and potential exercise-related risks. Second, we discuss what specific components of exercise mediate the putative disease-modifying effects. Third, we address how methodological challenges such as blinding, adherence and remote monitoring could be handled and how we can measure the efficacy of exercise as modifier of the course of prodromal PD. We hope that these considerations help in designing exercise prevention trials for persons at risk of developing PD.

Diffuse transmittance visible spectroscopy using smartphone flashlight for photoplethysmography and vital signs measurements

Photoplethysmography (PPG), with its wide range of applications, has become one of the most promising modalities for healthcare monitoring technology. In this work, we present a new PPG measurement technique based on diffuse transmittance spectroscopy (DTS) with the help of a smartphone built-in flashlight as an alternative broadband light source. The blood Volume Pulse (BVP) signal was extracted from recorded transmittance spectra at 620 nm. The results were compared with the ground truth and conventional contact finger PPG sensors. A very high correlation was found between the diffuse transmittance signal and the reference PPG signals (r = 0.997, p < 0.0001). The accuracy and root mean square error (RMSE) were 99.23% and 0.8 bpm, respectively. In addition, a Bland-Altman analysis showed a good agreement between both techniques, with a very small bias between mean paired differences of heart rate observations. A simple forward model for diffuse transmittance spectra for different levels of blood oxygen saturation is developed and supported by experimental measurements. It was also found that blood oxygen saturation (SpO2) can be estimated with the aid of DTS based smartphone flash by tracking the wavelength corresponding to the oxygenation level in the visible range between orange and red regions of the visible spectrum particularly in the range between 610 and 635 nm for 26 healthy subjects. 624 nm on average seems to be the wavelength that corresponds with the normal blood oxygenation level. These findings show the potential of DTS PPG to reliably extract cardiac frequency and estimate SpO2 with adequate accuracy. The results also demonstrate the capability of smartphone flash as a miniature visible light source for recording multispectral PPG signals and quantifying vital signs in the transmission mode at the fingertip with acceptable signal quality over a wide range of wavelengths from 550 nm to 650 nm.

Using wearable activity trackers for research in the global south: Lessons learned from adolescent psychotherapy research in Kenya

Wearable activity trackers have emerged as valuable tools for health research, providing high-resolution data on measures such as physical activity. While most research on these devices has been conducted in high-income countries, there is growing interest in their use in the global south. This perspective discusses the challenges faced and strategies employed when using wearable activity trackers to test the effects of a school-based intervention for depression and anxiety among Kenyan youth. Lessons learned include the importance of validating data output, establishing an internal procedure for international procurement, providing on-site support for participants, designating a full-time team member for wearable activity tracker operation, and issuing a paper-based information sheet to participants. The insights shared in this perspective serve as guidance for researchers undertaking studies with wearables in similar settings, contributing to the evidence base for mental health interventions targeting youth in the global south. Despite the challenges to set up, deploy and extract data from wearable activity trackers, we believe that wearables are a relatively economical approach to provide insight into the daily lives of research participants, and recommend their use to other researchers.

Smartwatch-derived sleep and heart rate measures complement step counts in explaining established metrics of MS severity

BACKGROUND

Passive remote monitoring of patients with MS (PwMS) with sensor-based wearable technologies promises near-continuous evaluation with high ecological validity. Step counts correlate strongly with traditional measures of MS severity. We hypothesized that remote monitoring of sleep and heart rate will yield complementary information.

METHODS

We recruited 31 PwMS and 31 age- and sex-matched healthy volunteers (HV) as part of the dreaMS feasibility study (NCT04413032). Fitbit Versa 2 smartwatches were worn for 6 weeks and provided a total of 25 features for activity, heart rate, and sleep. Features were selected based on their pairwise intercorrelation (Pearson |r| < 0.6), test-retest reliability (intraclass correlation coefficient ≥ 0.6 or median coefficient of variation < 0.2) and group comparisons between HV and PwMS with moderate disability (expanded disability status scale (EDSS) ≥ 3.5) (rank-biserial |r| ≥ 0.5). These selected features were correlated with clinical reference tests (EDSS, timed 25-foot walk (T25FW), MS-walking scale (MSWS-12)) in PwMS, and multivariate models adjusted for age, sex, and disease duration were compared.

RESULTS

We analyzed 28 PwMS (68% female, mean age 44 years, median EDSS 3.0) and 26 HV in our primary analysis. The objectively selected features discriminated well between HV and PwMS with moderate disability with rank-biserial r = 0.83 for Total number of steps, 0.51 for Deep sleep proportion, -0.51 for Median heart rate, 0.85 for Proportion very active, and 0.65 for Total number of floors. In PwMS they correlated strongly with the three clinical reference tests EDSS (strongest Spearman ρ = -0.75 for Proportion very active), T25FW (-0.75 for Total number of floors), and MSWS-12 (-0.72 for Total number of floors). Deep sleep proportion and Median heart rate complemented Total number of steps in explaining the variance of reference tests.

CONCLUSIONS

Activity, deep sleep and heart rate measures can be derived reliably from smartwatches and contain independent clinically meaningful information about MS severity, highlighting their potential for continuous passive monitoring in both clinical trials and clinical care of PwMS.

Validity and Efficacy of the Elite HRV Smartphone Application during Slow-Paced Breathing

Slow-paced breathing is a clinical intervention used to increase heart rate variability (HRV). The practice is made more accessible via cost-free smartphone applications like Elite HRV. We investigated whether Elite HRV can accurately measure and augment HRV via its slow-paced breathing feature. Twenty young adults completed one counterbalanced cross-over protocol involving 10 min each of supine spontaneous (SPONT) and paced (PACED; 6 breaths·min-1) breathing while RR intervals were simultaneously recorded via a Polar H10 paired with Elite HRV and reference electrocardiography (ECG). Individual differences in HRV between devices were predominately skewed, reflecting a tendency for Elite HRV to underestimate ECG-derived values. Skewness was typically driven by a limited number of outliers as median bias values were ≤1.3 ms and relative agreement was ≥very large for time-domain parameters. Despite no significant bias and ≥large relative agreement for frequency-domain parameters, limits of agreement (LOAs) were excessively wide and tended to be wider during PACED for all HRV parameters. PACED significantly increased low-frequency power (LF) for Elite HRV and ECG, and between-condition differences showed very large relative agreement. Elite HRV-guided slow-paced breathing effectively increased LF values, but it demonstrated greater precision during SPONT and in computing time-domain HRV.

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU

Predicting in-hospital cardiac arrest in patients admitted to an intensive care unit (ICU) allows prompt interventions to improve patient outcomes. We developed and validated a machine learning-based real-time model for in-hospital cardiac arrest predictions using electrocardiogram (ECG)-based heart rate variability (HRV) measures. The HRV measures, including time/frequency domains and nonlinear measures, were calculated from 5 min epochs of ECG signals from ICU patients. A light gradient boosting machine (LGBM) algorithm was used to develop the proposed model for predicting in-hospital cardiac arrest within 0.5-24 h. The LGBM model using 33 HRV measures achieved an area under the receiver operating characteristic curve of 0.881 (95% CI: 0.875-0.887) and an area under the precision-recall curve of 0.104 (95% CI: 0.093-0.116). The most important feature was the baseline width of the triangular interpolation of the RR interval histogram. As our model uses only ECG data, it can be easily applied in clinical practice.

Long-term association of ultra-short heart rate variability with cardiovascular events

Heart rate variability (HRV) is a cardiac autonomic marker with predictive value in cardiac patients. Ultra-short HRV (usHRV) can be measured at scale using standard and wearable ECGs, but its association with cardiovascular events in the general population is undetermined. We aimed to validate usHRV measured using ≤ 15-s ECGs (using RMSSD, SDSD and PHF indices) and investigate its association with atrial fibrillation, major adverse cardiac events, stroke and mortality in individuals without cardiovascular disease. In the National Survey for Health and Development (n = 1337 participants), agreement between 15-s and 6-min HRV, assessed with correlation analysis and Bland-Altman plots, was very good for RMSSD and SDSD and good for PHF. In the UK Biobank (n = 51,628 participants, 64% male, median age 58), after a median follow-up of 11.5 (11.4-11.7) years, incidence of outcomes ranged between 1.7% and 4.3%. Non-linear Cox regression analysis showed that reduced usHRV from 15-, 10- and 5-s ECGs was associated with all outcomes. Individuals with low usHRV (< 20th percentile) had hazard ratios for outcomes between 1.16 and 1.29, p < 0.05, with respect to the reference group. In conclusion, usHRV from ≤ 15-s ECGs correlates with standard short-term HRV and predicts increased risk of cardiovascular events in a large population-representative cohort.

A fast permutation entropy for pulse rate variability online analysis with one-sample recursion

Pulse rate variability (PRV) signals are extracted from pulsation signal can be effectively used for cardiovascular disease monitoring in wearable devices. Permutation entropy (PE) algorithm is an effective index for the analysis of PRV signals. However, PE is computationally intensive and impractical for online PRV processing on wearable devices. Therefore, to overcome this challenge, a fast permutation entropy (FPE) algorithm is proposed based on the microprocessor data updating process in this paper, which can analyze PRV signals with single-sample recursive. The simulation data and PRV signals extracted from pulse signals in "Fantasia database" were utilized to verify the performance and accuracy of the improved methods. The results show that the speed of FPE is 211 times faster than PE and maintain the accuracy of algorithm (Root Mean Squared Error = 0) for simulation data with a length of 10,000 samples and embedded dimension m = 5, time delay τ = 5, buffer length Lw = 512. For the RRV signals with 3000∼5000 samples, the result show that the consumption of FPE is less than 0.2 s, which is 175 times faster than PE. This indicates that FPE has better application performance than PE. Furthermore, a low-cost wearable signal detection system is developed to verify the proposed method, the result show that the proposed method can calculate the FPE of PRV signal online with single-sample recursive calculation. Subsequently, entropy-based features are used to explore the performance of decision trees in identifying life-threatening arrhythmias, and the method resulted in a classification accuracy of 85.43%. It can therefore be inferred that the proposed method has great potential in cardiovascular disease.

ECG-Free Heartbeat Detection in Seismocardiography and Gyrocardiography Signals Provides Acceptable Heart Rate Variability Indices in Healthy and Pathological Subjects

Cardio-mechanical monitoring techniques, such as Seismocardiography (SCG) and Gyrocardiography (GCG), have received an ever-growing interest in recent years as potential alternatives to Electrocardiography (ECG) for heart rate monitoring. Wearable SCG and GCG devices based on lightweight accelerometers and gyroscopes are particularly appealing for continuous, long-term monitoring of heart rate and its variability (HRV). Heartbeat detection in cardio-mechanical signals is usually performed with the support of a concurrent ECG lead, which, however, limits their applicability in standalone cardio-mechanical monitoring applications. The complex and variable morphology of SCG and GCG signals makes the ECG-free heartbeat detection task quite challenging; therefore, only a few methods have been proposed. Very recently, a template matching method based on normalized cross-correlation (NCC) has been demonstrated to provide very accurate detection of heartbeats and estimation of inter-beat intervals in SCG and GCG signals of pathological subjects. In this study, the accuracy of HRV indices obtained with this template matching method is evaluated by comparison with ECG. Tests were performed on two public datasets of SCG and GCG signals from healthy and pathological subjects. Linear regression, correlation, and Bland-Altman analyses were carried out to evaluate the agreement of 24 HRV indices obtained from SCG and GCG signals with those obtained from ECG signals, simultaneously acquired from the same subjects. The results of this study show that the NCC-based template matching method allowed estimating HRV indices from SCG and GCG signals of healthy subjects with acceptable accuracy. On healthy subjects, the relative errors on time-domain indices ranged from 0.25% to 15%, on frequency-domain indices ranged from 10% to 20%, and on non-linear indices were within 8%. The estimates obtained on signals from pathological subjects were affected by larger errors. Overall, GCG provided slightly better performances as compared to SCG, both on healthy and pathological subjects. These findings provide, for the first time, clear evidence that monitoring HRV via SCG and GCG sensors without concurrent ECG is feasible with the NCC-based template matching method for heartbeat detection.

Integrating Genome-wide information and Wearable Device Data to Explore the Link of Anxiety and Antidepressants with Heart Rate Variability

Background

Anxiety disorders are associated with decreased heart rate variability (HRV), but the underlying mechanisms remain elusive.

Methods

We selected individuals with whole-genome sequencing, Fitbit, and electronic health record data (N=920; 61,333 data points) from the All of Us Research Program. Anxiety PRS were derived with PRS-CS after meta-analyzing anxiety genome-wide association studies from three major cohorts-UK Biobank, FinnGen, and the Million Veterans Program (N Total =364,550). The standard deviation of average RR intervals (SDANN) was calculated using five-minute average RR intervals over full 24-hour heart rate measurements. Antidepressant exposure was defined as an active antidepressant prescription at the time of the HRV measurement in the EHR. The associations of daily SDANN measurements with the anxiety PRS, antidepressant classes, and antidepressant substances were tested. Participants with lifetime diagnoses of cardiovascular disorders, diabetes mellitus, and major depression were excluded in sensitivity analyses. One-sample Mendelian randomization (MR) was employed to assess potential causal effect of anxiety on SDANN.

Results

Anxiety PRS was independently associated with reduced SDANN (beta=-0.08; p=0.003). Of the eight antidepressant medications and four classes tested, venlafaxine (beta=-0.12, p=0.002) and bupropion (beta=-0.071, p=0.01), tricyclic antidepressants (beta=-0.177, p=0.0008), selective serotonin reuptake inhibitors (beta=-0.069; p=0.0008) and serotonin and norepinephrine reuptake inhibitors (beta=-0.16; p=2×10 -6 ) were associated with decreased SDANN. One-sample MR indicated an inverse effect of anxiety on SDANN (beta=-2.22, p=0.03).

Conclusions

Anxiety and antidepressants are independently associated with decreased HRV, and anxiety appears to exert a causal effect on HRV. Our observational findings provide novel insights into the impact of anxiety on HRV.

Consumer Wearable Health and Fitness Technology in Cardiovascular Medicine: JACC State-of-the-Art Review

The use of consumer wearable devices (CWDs) to track health and fitness has rapidly expanded over recent years because of advances in technology. The general population now has the capability to continuously track vital signs, exercise output, and advanced health metrics. Although understanding of basic health metrics may be intuitive (eg, peak heart rate), more complex metrics are derived from proprietary algorithms, differ among device manufacturers, and may not historically be common in clinical practice (eg, peak V˙O2, exercise recovery scores). With the massive expansion of data collected at an individual patient level, careful interpretation is imperative. In this review, we critically analyze common health metrics provided by CWDs, describe common pitfalls in CWD interpretation, provide recommendations for the interpretation of abnormal results, present the utility of CWDs in exercise prescription, examine health disparities and inequities in CWD use and development, and present future directions for research and development.

On the Reliability of Wearable Technology: A Tutorial on Measuring Heart Rate and Heart Rate Variability in the Wild

Wearable sensors are quickly making their way into psychophysiological research, as they allow collecting data outside of a laboratory and for an extended period of time. The present tutorial considers fidelity of physiological measurement with wearable sensors, focusing on reliability. We elaborate on why ensuring reliability for wearables is important and offer statistical tools for assessing wearable reliability for between participants and within-participant designs. The framework offered here is illustrated using several brands of commercially available heart rate sensors. Measurement reliability varied across sensors and, more importantly, across the situations tested, and was highest during sleep. Our hope is that by systematically quantifying measurement reliability, researchers will be able to make informed choices about specific wearable devices and measurement procedures that meet their research goals.

Wearable derived cardiovascular responses to stressors in free-living conditions

Stress contributes to the progression of many diseases. Despite stress' contribution towards disease, few methods for continuously measuring stress exist. We investigated if continuously measured cardiovascular signals from a wearable device can be used as markers of stress. Using wearable technology (WHOOP Inc, Boston, MA) that continuously measures and calculates heart rate (HR) and heart rate variability (root-mean-square of successive differences; HRV), we assessed duration and magnitude of deviations in HR and HRV around the time of a run (from 23665 runs) or high-stress work (from 8928 high-stress work events) in free-living conditions. HR and HRV were assessed only when participants were motionless (HRmotionless). Runs were grouped into light, moderate, and vigorous runs to determine dose response relationships. When examining HRmotionless and HRV throughout the day, we found that these metrics display circadian rhythms; therefore, we normalized HRmotionless and HRV measures for each participant relative to the time of day. Relative to the period within 30 minutes leading up to a run, HRmotionless is elevated for up to 180-210 minutes following a moderate or vigorous run (P<0.05) and is unchanged or reduced following a light run. HRV is reduced for at least 300 minutes following a moderate or vigorous run (P<0.05) and is unchanged during a light run. Relative to the period within 30 minutes leading up to high-stress work, HRmotionless is elevated during and for up to 30 minutes following high-stress work. HRV tends to be lower during high-stress work (P = 0.06) and is significantly lower 90-300 minutes after the end of the activity (P<0.05). These results demonstrate that wearables can quantify stressful events, which may be used to provide feedback to help individuals manage stress.