Emerging Technologies for Identifying Atrial Fibrillation.

Atrial fibrillation (AF) is a major cause of morbidity and mortality globally, and much of this is driven by challenges in its timely diagnosis and treatment. Existing and emerging mobile technologies have been used to successfully identify AF in a variety of clinical and community settings, and while these technologies offer great promise for revolutionizing AF detection and screening, several major barriers may impede their effectiveness. The unclear clinical significance of device-detected AF, potential challenges in integrating patient-generated data into existing healthcare systems and clinical workflows, harm resulting from potential false positives, and identifying the appropriate scope of population-based screening efforts are all potential concerns that warrant further investigation. It is crucial for stakeholders such as healthcare providers, researchers, funding agencies, insurers, and engineers to actively work together in fulfilling the tremendous potential of mobile technologies to improve AF identification and management on a population level.

Detection of Tachyarrhythmias in a Large Cohort of Infants Using Direct-to-Consumer Heart Rate Monitoring.

OBJECTIVE: Current estimates of the incidence of tachyarrhythmias in infants rely on clinical documentation and may not reflect the true rate in the general population. Our aim was to describe the epidemiology of tachyarrhythmia detected in a large cohort of infants using direct-to-consumer heart rate (HR) monitoring. STUDY DESIGN: Data were collected from Owlet Smart Sock devices used in infants in the US with birthdates between February 2017 and February 2019. We queried the HR data for episodes of tachyarrhythmia (HR of ≥240 bpm for >60 seconds). RESULTS: The study included 100 949 infants (50.8% male) monitored for more than 200 million total hours. We identified 5070 episodes of tachyarrhythmia in 2508 infants. The cumulative incidence of tachyarrhythmia in our cohort was 2.5% over the first year of life. The median age at the time of the first episode of tachyarrhythmia was 36 days (range, 1-358 days). Tachyarrhythmia was more common in infants with congenital heart disease (4.0% vs 2.4%; P = .015) and in females (2.7% vs 2.0%; P < .001). The median length of an episode was 7.3 minutes (range, 60 seconds to 5.4 hours) and the probability of an episode lasting longer than 45 minutes was 16.8% (95% CI, 15.4%-18.3%). CONCLUSIONS: We found the cumulative incidence of tachyarrhythmia among infants using direct-to-consumer HR monitors to be higher than previously reported in studies relying on clinical diagnosis. This finding may represent previously undetected subclinical disease in young infants, the significance of which remains uncertain. Clinicians should be prepared to discuss these events with parents.

AVNRT captured by Apple Watch Series 4: Can the Apple watch be used as an event monitor?

Wrist-worn devices are popular for heart rate monitoring, including use of photoplethysmography. The Apple Watch series 4 can identify atrial fibrillation. We describe a case of identification re-entrant supraventricular tachycardia not identified by outpatient rhythm monitoring, however, was identified by the Apple Watch series 4, which lead to electrophysiology study and successful ablation of atrioventricular nodal re-entrant tachycardia.

The use of internal heart rate loggers in determining cardiac breakpoints of fish.

As marine environments are influenced by global warming there is a need to thoroughly understand the relationship between physiological limits and temperature in fish. One quick screening method of a physiological thermal tipping point is the temperature at which maximum heart rate (ƒHmax) can no longer scale predictably with warming and is referred to as the Arrhenius break temperature (TAB). The use of this method has been successful for freshwater fish by using external electrodes to detect an electrocardiogram (ECG), however, the properties of this equipment pose challenges in salt water when evaluating marine fish. To overcome these challenges, this study aimed to explore the potential use of implantable heart rate loggers to quantify the TAB of Chrysoblephus laticeps, a marine Sparid, following the ECG method protocols where ƒHmax is monitored over an acute warming event and the TAB is subsequently identified using a piece-wise linear regression model. Of the nine experimental fish, only five (56%) returned accurate ƒHmax data. The TAB of successful trials was identified each time and ranged from 18.09 to 20.10 °C. This study therefore provides evidence that implantable heart rate loggers can estimate TAB of fish which can be applied to many marine species.

Early prognostication of neurological outcome by heart rate variability in adult patients with out-of-hospital sudden cardiac arrest.

BACKGROUND: Most deaths of comatose survivors of out-of-hospital sudden cardiac arrest result from withdrawal of life-sustaining treatment (WLST) decisions based on poor neurological prognostication and the family's intention. Thus, accurate prognostication is crucial to avoid premature WLST decisions. However, targeted temperature management (TTM) with sedation or neuromuscular blockade against shivering significantly affects early prognostication. In this study, we investigated whether heart rate variability (HRV) analysis could prognosticate poor neurological outcome in comatose patients undergoing hypothermic TTM. METHODS: Between January 2015 and December 2017, adult patients with out-of-hospital sudden cardiac arrest, successfully resuscitated in the emergency department and admitted to the intensive care unit of the Niigata University in Japan, were prospectively included. All patients had an initial Glasgow Coma Scale motor score of 1 and received hypothermic TTM (at 34 °C). Twenty HRV-related variables (deceleration capacity; 4 time-, 3 geometric-, and 7 frequency-domain; and 5 complexity variables) were computed based on RR intervals between 0:00 and 8:00 am within 24 h after return of spontaneous circulation (ROSC). Based on Glasgow Outcome Scale (GOS) at 2 weeks after ROSC, patients were divided into good outcome (GOS 1-2) and poor outcome (GOS 3-5) groups. RESULTS: Seventy-six patients were recruited and allocated to the good (n = 22) or poor (n = 54) outcome groups. Of the 20 HRV-related variables, ln very-low frequency (ln VLF) power, detrended fluctuation analysis (DFA) (α1), and multiscale entropy (MSE) index significantly differed between the groups (p = 0.001), with a statistically significant odds ratio (OR) by univariate logistic regression analysis (p = 0.001). Multivariate logistic regression analysis of the 3 variables identified ln VLF power and DFA (α1) as significant predictors for poor outcome (OR = 0.436, p = 0.006 and OR = 0.709, p = 0.024, respectively). The area under the receiver operating characteristic curve for ln VLF power and DFA (α1) in predicting poor outcome was 0.84 and 0.82, respectively. In addition, the minimum value of ln VLF power or DFA (α1) for the good outcome group predicted poor outcome with sensitivity = 61% and specificity = 100%. CONCLUSIONS: The present data indicate that HRV analysis could be useful for prognostication for comatose patients during hypothermic TTM.

Continuous heart rate monitoring for automatic detection of atrial fibrillation with novel bio-sensing technology.

BACKGROUND: Asymptomatic atrial fibrillation [AF] is an important cause of fatal or disabling stroke. A continuous heart-rate monitoring device, comfortable enough to be worn continuously and reliable enough to detect AF, would allow for prompt initiation of anticoagulation therapy to prevent stroke. METHODS: We studied a new custom-made wearable photo-plethysmograph [PPG] wrist-watch sensor, specifically designed for continuous heart rate monitoring and incorporating contact and motion noise-filters. We tested its ability to automatically detect AF in patients undergoing elective cardioversion of AF, using simultaneously recorded electrocardiogram [ECG] as gold standard. RESULTS: A total of 18,608 consecutive R-R-interval measurements were recorded simultaneously with PPG and ECG in 20 patients, including 12,521 [67.3%] R-R-intervals during AF and 6087 [32.7%] R-R intervals during sinus rhythm. Scatter plots and Bland-Altman plots demonstrated that the PPG signals were highly correlated to the simultaneously recorded ECG [R = 0.980, p < 0.001], both during AF and during sinus rhythm. The automatic algorithm distinguished AF from sinus rhythm with a sensitivity of 100% and specificity of 93.1%. CONCLUSIONS: This PPG-based wrist-watch sensor reliably detected AF in non-ambulatory patients.

An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work.

Several unobtrusive sensors have been tested in studies to capture physiological reactions to stress in workplace settings. Lab studies tend to focus on assessing sensors during a specific computer task, while in situ studies tend to offer a generalized view of sensors' efficacy for workplace stress monitoring, without discriminating different tasks. Given the variation in workplace computer activities, this study investigates the efficacy of unobtrusive sensors for stress measurement across a variety of tasks. We present a comparison of five physiological measurements obtained in a lab experiment, where participants completed six different computer tasks, while we measured their stress levels using a chest-band (ECG, respiration), a wristband (PPG and EDA), and an emerging thermal imaging method (perinasal perspiration). We found that thermal imaging can detect increased stress for most participants across all tasks, while wrist and chest sensors were less generalizable across tasks and participants. We summarize the costs and benefits of each sensor stream, and show how some computer use scenarios present usability and reliability challenges for stress monitoring with certain physiological sensors. We provide recommendations for researchers and system builders for measuring stress with physiological sensors during workplace computer use.

Continuous Distant Measurement of the User's Heart Rate in Human-Computer Interaction Applications.

In real world scenarios, the task of estimating heart rate (HR) using video plethysmography (VPG) methods is difficult because many factors could contaminate the pulse signal (i.e., a subjects' movement, illumination changes). This article presents the evaluation of a VPG system designed for continuous monitoring of the user's heart rate during typical human-computer interaction scenarios. The impact of human activities while working at the computer (i.e., reading and writing text, playing a game) on the accuracy of HR VPG measurements was examined. Three commonly used signal extraction methods were evaluated: green (G), green-red difference (GRD), blind source separation (ICA). A new method based on an excess green (ExG) image representation was proposed. Three algorithms for estimating pulse rate were used: power spectral density (PSD), autoregressive modeling (AR) and time domain analysis (TIME). In summary, depending on the scenario being studied, different combinations of signal extraction methods and the pulse estimation algorithm ensure optimal heart rate detection results. The best results were obtained for the ICA method: average RMSE = 6.1 bpm (beats per minute). The proposed ExG signal representation outperforms other methods except ICA (RMSE = 11.2 bpm compared to 14.4 bpm for G and 13.0 bmp for GRD). ExG also is the best method in terms of proposed success rate metric (sRate).

Perrogator: A Portable Energy-Efficient Interrogator for Dynamic Monitoring of Wavelength-Based Sensors in Wearable Applications.

In this paper, we report the development of a portable energy-efficient interrogator (Perrogator) for wavelength-based optical sensors. The interrogator is based on a compact solution encompassing a white light source and the spectral convolution between the sensor and a tunable filter, which is acquired by a photodetector, where a microcontroller has two functions: (i) To control the filter tuning and to (ii) acquire the photodetector signal. Then, the data is sent to a single-board computer for further signal processing. Furthermore, the employed single-board computer has a Wi-Fi module, which can be used to send the sensors data to the cloud. The proposed approach resulted in an interrogator with a resolution as high as 3.82 pm (for 15.64 nm sweeping range) and maximum acquisition frequency of about 210 Hz (with lower resolution ~15.30 pm). Perrogator was compared with a commercial fiber Bragg grating (FBG) interrogator for strain measurements and good agreement between both devices was found (1.226 pm/µÎµ for the commercial interrogator and 1.201 pm/µÎµ for the proposed approach with root mean square error of 0.0144 and 0.0153, respectively), where the Perrogator has the additional advantages of lower cost, higher portability and lower energy consumption. In order to demonstrate such advantages in conjunction with the high acquisition frequency allowed us to demonstrate two wearable applications using the proposed interrogation device over FBG and Fabry-Perot interferometer (FPI) sensors. In the first application, an FBG-embedded smart textile for knee angle assessment was used to analyze the gait of a healthy person. Due to the capability of reconstructing the FBG spectra, it was possible to employ a technique based on the FBG wavelength shift and reflectivity to decouple the effects of the bending angle and axial strain on the FBG response. The measurement of the knee angle as well as the estimation of the angular and axial displacements on the grating that can be correlated to the variations of the knee center of rotation were performed. In the second application, a FPI was embedded in a chest band for simultaneous measurement of breath and heart rates, where good agreement (error below 5%) was found with the reference sensors in all analyzed cases.

Can heart rate variability parameters derived by a heart rate monitor differentiate between atrial fibrillation and sinus rhythm?

BACKGROUND: Heart rate variability (HRV) parameters, and especially RMSSD (root mean squared successive differences in RR interval), could distinguish atrial fibrillation (AF) from sinus rhythm(SR) in horses, as was demonstrated in a previous study. If heart rate monitors (HRM) automatically calculating RMSSD could also distinguish AF from SR, they would be useful for the monitoring of AF recurrence. The objective of the study was to assess whether RMSSD values obtained from a HRM can differentiate AF from SR in horses. Furthermore, the impact of artifact correction algorithms, integrated in the analyses software for HRV analyses was evaluated. Fourteen horses presented for AF treatment were simultaneously equipped with a HRM and an electrocardiogram (ECG). A two-minute recording at rest, walk and trot, before and after cardioversion, was obtained. RR intervals used were those determined automatically by the HRM and by the equine ECG analysis software, and those obtained after manual correction of QRS detection within the ECG software. RMSSD was calculated by the HRM software and by dedicated HRV software, using six different artifact filters. Statistical analysis was performed using the Wilcoxon signed-rank test and receiver operating curves. RESULTS: The HRM, which applies a low level filter, produced high area under the curve (AUC) (> 0.9) and cut off values with high sensitivity and specificity. Similar results were obtained for the ECG, when low level artifact filtering was applied. When no artifact correction was used during trotting, an important decrease in AUC (0.75) occurred. CONCLUSION: In horses treated for AF, HRMs with automatic RMSSD calculations distinguish between AF and SR. Such devices might be a useful aid to monitor for AF recurrence in horses.

Fetal cardiac time intervals in healthy pregnancies - an observational study by fetal ECG (Monica Healthcare System).

BACKGROUND: Fetal electrocardiogram (fECG) can detect QRS signals in fetuses from as early as 17 weeks' gestation; however, the technique is limited by the minute size of the fetal signal relative to noise ratio. The aim of this study was to evaluate precise fetal cardiac time intervals (fCTIs) with the help of a newly developed fetal ECG device (Monica Healthcare System). METHODS: In a prospective manner we included 15-18 healthy fetuses per gestational week from 32 weeks onwards. The small and wearable Monica AN24 monitoring system uses standard ECG electrodes placed on the maternal abdomen to monitor fECG, maternal ECG and uterine electromyogram (EMG). Fetal CTIs were estimated on 1000 averaged fetal heart beats. Detection was deemed successful if there was a global signal loss of less than 30% and an analysis loss of the Monica AN24 signal separation analysis of less than 50%. Fetal CTIs were determined visually by three independent measurements. RESULTS: A total of 149 fECGs were performed. After applying the requirements 117 fECGs remained for CTI analysis. While the onset and termination of P-wave and QRS-complex could be easily identified in most ECG patterns (97% for P-wave, PQ and PR interval and 100% for QRS-complex), the T-wave was detectable in only 41% of the datasets. The CTI results were comparable to other available methods such as fetal magnetocardiography (fMCG). CONCLUSIONS: Although limited and preclinical in its use, fECG (Monica Healthcare System) could be an additional useful tool to detect precise fCTIs from 32 weeks' gestational age onwards.

Influence of Movement Quality on Heart Rate While Performing the Dance-Specific Aerobic Fitness Test (DAFT) in Preprofessional Contemporary Dancers.

OBJECTIVES: To explore whether movement quality has influence on heart rate (HR) frequency during the dance-specific aerobic fitness test (DAFT). METHODS: Thirteen contemporary university dance students (age 19 ± 1.46 yrs) underwent two trials performing the DAFT while wearing a Polar HR monitor (Kempele, Finland). During the first trial, dancers were asked to perform the movements as if they were performing on stage, whereas during the second trial, standardized verbal instructions were given to reduce the quality of movement (e.g., no need to perform technically correct pliés). The variables measured at each trial were HR for all five stages of the DAFT and HR recovery (1 and 2 min after finishing the DAFT), movement quality (MQ) score, and rate of perceived exertion score (RPE). RESULTS: There were significant differences in HR between Trial 1 and Trial 2. For all stages and the resting period, HR was lower during Trial 2 (p<0.001). Also, the RPE score was significantly lower and the MQ score was significantly higher, indicating a poorer performance, during Trial 2 (both p<0.001). CONCLUSION: The results suggest that DAFT performance with lower movement quality elicits lower HR frequency and RPE during the DAFT. We recommend that specific instructions be given to participants about executing the movement sequence during the DAFT before testing commences. Also, movement quality must be taken into account when interpreting HR results from the DAFT in order to distinguish if a dancer's low HR results from good aerobic fitness or from poor performance of the movement sequence.

The Contactless Vital Sensing System Precisely Reflects R-R Interval in Electrocardiograms of Healthy Subjects.

BACKGROUND: The aim of the present study was to investigate the validity of the contactless vital sensing system that we previously developed. METHODS: A total of 111 healthy Japanese subjects were recruited from the University of Occupational and Environmental Health and the Panasonic Corporation AVC Networks Company. All subjects underwent an evaluation using the contactless vital sensing system and electrocardiography (ECG). We compared the R-R interval obtained using the new contactless sensing system to that obtained using ECG. RESULTS: A very strong correlation was observed between the instruments. CONCLUSIONS: This result confirms the validity of the new contactless sensing system in evaluating healthy subjects.

A Device Histogram-Based Simple Predictor of Mortality Risk in ICD and CRT-D Patients: The Heart Rate Score.

BACKGROUND: We hypothesized that survival in implantable cardioverter defibrillator (ICD) and cardiac resynchronization therapy defibrillator (CRT-D) patients is predicted by baseline Heart Rate Score. METHODS: Heart Rate Score is determined from the atrial paced and sensed histogram of a DDD ICD or CRT-D, and defined as percent of beats in the histogram in the tallest 10 beats/min range bin. It was calculated at initial remote monitoring for patients enrolled in LATITUDE® without persistent atrial fibrillation, and with pulse generators implanted in 2006-2011. Univariate, multivariate, and Kaplan-Meier analyses determined the impact of Heart Rate Score on survival. RESULTS: Of 57,893 ICDs and 67,929 CRT-Ds followed for 2.4 ± 1.5 years, each 10% increase in Heart Rate Score was associated with decreased survival (CRT-D hazard ratio [HR] 1.07 95%, confidence interval 1.06-1.07, P < 0.0001; ICD HR 1.05, 95% confidence interval 1.04-1.06, P < 0.0001). Multivariate analysis showed survival decreased with increasing age, atrial fibrillation, presence of a shock in first-year follow-up, and increasing programmed lower pacing rate in ICD and CRT-D patients. Increased percent right ventricular pacing predicted mortality in ICD patients, while male gender and lower percent left ventricular pacing predicted mortality in CRT patients. Heart Rate Score predicted survival independent of those variables. Heart Rate Score correlates with heart rate variability (standard deviation of average R-R intervals [SDANN]) when both are obtainable, but SDANN was only present in 6% of patients with Heart Rate Score >70%. CONCLUSION: A simple device histogram measure, Heart Rate Score, predicts survival in ICD and CRT-D patients independent of the available variables, and even when SDANN is unavailable.

Agreement Between a Smartphone Pulse Sensor Application and Electrocardiography for Determining lnRMSSD.

Esco, MR, Flatt, AA, and Nakamura, FY. Agreement between a smartphone pulse sensor application and electrocardiography for determining lnRMSSD. J Strength Cond Res 31(2): 380-385, 2017-The purpose of this study was to determine the agreement between a smartphone pulse finger sensor (SPFS) and electrocardiography (ECG) for determining ultra-short-term heart rate variability in 3 different positions. Thirty college-aged men (n = 15) and women (n = 15) volunteered to participate in this study. Sixty-second heart rate measures were simultaneously taken with the SPFS and ECG in supine, seated, and standing positions. The log transformed root mean square of successive R-R interval differences (lnRMSSD) was calculated from the SPFS and ECG. The lnRMSSD values were 81.5 ± 11.7 using ECG and 81.6 ± 11.3 using SPFS (p = 0.63, Cohen's d = 0.01) in the supine position, 76.5 ± 8.2 using ECG and 77.5 ± 8.2 using SPFS (p = 0.007, Cohen's d = 0.11) in the seated position, and 66.5 ± 9.2 using ECG and 67.8 ± 9.1 using SPFS (p < 0.001, Cohen's d = 0.15) in the standing position. The SPFS showed a possibly strong correlation to the ECG in all 3 positions (r values from 0.98 to 0.99). In addition, the limits of agreement (constant error ± 1.98 SD) were -0.13 ± 2.83 for the supine values, -0.94 ± 3.47 for the seated values, and -1.37 ± 3.56 for the standing values. The results of the study suggest good agreement between the SPFS and ECG for measuring lnRMSSD in supine, seated, and standing positions. Although significant differences were noted between the 2 methods in the seated and standing positions, the effect sizes were trivial.

MXene-Sponge Based High-Performance Piezoresistive Sensor for Wearable Biomonitoring and Real-Time Tactile Sensing.

Electrode microfabrication technologies such as lithography and deposition have been widely applied in wearable electronics to boost interfacial coupling efficiency and device performance. However, a majority of these approaches are restricted by expensive and complicated processing techniques, as well as waste discharge. Here, helium plasma irradiation is employed to yield a molybdenum microstructured electrode, which is constructed into a flexible piezoresistive pressure sensor based on a Ti3 C2 Tx nanosheet-immersed polyurethane sponge. This electrode engineering strategy enables the smooth transition between sponge deformation and MXene interlamellar displacement, giving rise to high sensitivity (1.52 kPa-1 ) and good linearity (r2  = 0.9985) in a wide sensing range (0-100 kPa) with a response time of 226 ms for pressure detection. In addition, both the experimental characterization and finite element simulation confirm that the hierarchical structures modulated by pore size, plasma bias, and MXene concentration play a crucial role in improving the sensing performance. Furthermore, the as-developed flexible pressure sensor is demonstrated to measure human radial pulse, detect finger tapping, foot stomping, and perform object identification, revealing great feasibility in wearable biomonitoring and health assessment.

A multi-point heart rate monitoring using a soft wearable system based on fiber optic technology.

Early diagnosis can be crucial to limit both the mortality and economic burden of cardiovascular diseases. Recent developments have focused on the continuous monitoring of cardiac activity for a prompt diagnosis. Nowadays, wearable devices are gaining broad interest for a continuous monitoring of the heart rate (HR). One of the most promising methods to estimate HR is the seismocardiography (SCG) which allows to record the thoracic vibrations with high non-invasiveness in out-of-laboratory settings. Despite significant progress on SCG, the current state-of-the-art lacks both information on standardized sensor positioning and optimization of wearables design. Here, we introduce a soft wearable system (SWS), whose novel design, based on a soft polymer matrix embedding an array of fiber Bragg gratings, provides a good adhesion to the body and enables the simultaneous recording of SCG signals from multiple measuring sites. The feasibility assessment on healthy volunteers revealed that the SWS is a suitable wearable solution for HR monitoring and its performance in HR estimation is strongly influenced by sensor positioning and improved by a multi-sensor configuration. These promising characteristics open the possibility of using the SWS in monitoring patients with cardiac pathologies in clinical (e.g., during cardiac magnetic resonance procedures) and everyday life settings.

Remote Monitoring of a Work-From-Home Employee to Identify Stress: A Case Report.

How do you assess the mental wellness of your work-from-home employees? This case study reports on how an occupational health nurse used work-from-home employee's own phone and Fitbit™ smartwatch to obtain heart rate data to screen for high periods of stress. Telemedicine and telemetry allowed the occupational health nurses to screen an employee when the nurse could not assess the employee face-to-face. When the occupational health nurses identified an at-risk employee, the occupational health nurses referred the employee to the Employee Assistance Program (EAP) for counseling. Leveraging heart rate data on a smartwatch is a free intervention that is scalable and has a demonstrated outcome measure with a positive return on investment.

The accuracy of fitness watches for the measurement of heart rate and energy expenditure during moderate intensity exercise.

BACKGROUND: As new technology emerges and updated fitness watches are released to the market, it is important to examine their accuracy. The aim of the current study was to examine the accuracy of three commercially available activity trackers in assessing heart rate (HR) and energy expenditure (EE) during moderate intensity exercise. METHODS: Twenty healthy participants (Age: 20.5±0.7 yrs., Ht: 173.4±10.8 cm, BM: 72.8±13.9 kg, BMI: 24.0±2.5 kg/m2) wore two fitness watches (FB: Fitbit VersaTM, San Francisco, CA, USA; and PI: Polar IgniteTM, Polar Electro Oy, Kempele, Finland) and a chest-worn HR monitor (PTP: Polar TeamPro SensorTM, Polar Electro) during a 12-minute exercise protocol at incremental speeds. An electrocardiogram (ECG) and indirect calorimetry were used as criterion measures for HR and EE. Mean absolute percentage error (MAPE) was calculated to determine measurement error. RESULTS: The MAPE values for HR were 11.6±8.7% for the FB, 11.0±10.0% for the PI, and 6.3±5.2% for the PTP. For EE, MAPE values were 9.6±7.2% for the FB, 16.7±19.6% for the PI and 13.8±13.0% for the PTP. CONCLUSIONS: Fitness watches relying on optical measures of HR underestimate HR compared to criterion measures during moderate intensity exercise. Despite providing a more accurate measure of HR, a chest-worn monitor does not provide a more accurate estimate of EE compared to fitness watches. The Fitbit provided the most accurate measure of EE when compared to the Polar Ignite watch and chest-worn device.

A simple method for detecting chaos in nature.

Chaos, or exponential sensitivity to small perturbations, appears everywhere in nature. Moreover, chaos is predicted to play diverse functional roles in living systems. A method for detecting chaos from empirical measurements should therefore be a key component of the biologist's toolkit. But, classic chaos-detection tools are highly sensitive to measurement noise and break down for common edge cases, making it difficult to detect chaos in domains, like biology, where measurements are noisy. However, newer tools promise to overcome these limitations. Here, we combine several such tools into an automated processing pipeline, and show that our pipeline can detect the presence (or absence) of chaos in noisy recordings, even for difficult edge cases. As a first-pass application of our pipeline, we show that heart rate variability is not chaotic as some have proposed, and instead reflects a stochastic process in both health and disease. Our tool is easy-to-use and freely available.