100 Long-Distance Triathlons in 100 Days: A Case Study on Ultraendurance, Biomarkers, and Physiological Outcomes.

The physical demands of a single long-distance triathlon (LDT) are sufficient to cause robust physiological perturbations. In this unique case study, an ultraendurance athlete completed 100 LDTs in 100 days (100LDT). PURPOSE: This study aims to describe and analyze this single athlete's performance, physiological biomarkers, and sleep parameters throughout the 100LDT. METHODS: An ultraendurance athlete completed an LDT (2.4-mile swim, 112-mile bike ride, and 26.2-mile marathon) each day for 100 consecutive days. Physical work, physiological biomarkers, and sleep parameters were recorded each night using a wrist-worn photoplethysmographic sensor. Clinical exercise tests were performed before and after the 100LDT. Time-series analysis assessed changes in biomarkers and sleep parameters across the 100LDT, and cross-correlations considered the associations between exercise performance and physiological metrics at varying time lags. RESULTS: The swim and cycling performances varied across the 100LDT, while the run was relatively stable. Resting heart rate, heart-rate variability, oxygen saturation, sleep score, light sleep, sleep efficiency, and sleep duration were all best characterized by cubic models. Additional post hoc subanalyses suggest that the first half of the 100LDT most influenced these dynamics. CONCLUSIONS: The 100LDT resulted in nonlinear alterations to physiological metrics. This world record was a unique event but allows valuable insights into the limits of human endurance performance.

Two cases of COVID-19 monitored by a wearable biosensor-a case report.

Wearable devices have gained popularity in recent years for tracking metrics related to personal health and well-being such as vital signs, motion, and sleep. Wearable devices are considered to have a very high potential value for detecting, monitoring, and controlling the spread of infectious diseases such as COVID-19, based on their ability to collect data in a non-invasive and contactless manner. With the Biostrap wrist-worn device (Biostrap USA LLC, Duarte, CA, USA), a commercially available, clinically validated wearable device that uses photoplethysmography to automatically record physiological data such as resting heart rate, respiratory rate, oxygen saturation (SpO2), and arterial stiffness (AS), we collected biometric data from 933 subjects. We present two cases of patients who have tested positive for the presence of severe acute respiratory syndrome (SARS-CoV-2), a 24-year-old man experiencing major symptoms and another a 49-year-old man with only intermittent fatigue, and show the marked changes in biometric measurements around dates of symptom onset and positive test. We observed a pattern of sustained respiratory rate elevation in both patients, punctuated by a sharp spike in heart rate and decreased AS. The latter contradicted our expectation that during the onset of symptoms of COVID-19, an increase in AS might occur.

Machine learning detection of obstructive hypertrophic cardiomyopathy using a wearable biosensor.

Hypertrophic cardiomyopathy (HCM) is a heritable disease of heart muscle that increases the risk for heart failure, stroke, and sudden death, even in asymptomatic patients. With only 10-20% of affected people currently diagnosed, there is an unmet need for an effective screening tool outside of the clinical setting. Photoplethysmography uses a noninvasive optical sensor incorporated in commercial smart watches to detect blood volume changes at the skin surface. In this study, we obtained photoplethysmography recordings and echocardiograms from 19 HCM patients with left ventricular outflow tract obstruction (oHCM) and a control cohort of 64 healthy volunteers. Automated analysis showed a significant difference in oHCM patients for 38/42 morphometric pulse wave features, including measures of systolic ejection time, rate of rise during systole, and respiratory variation. We developed a machine learning classifier that achieved a C-statistic for oHCM detection of 0.99 (95% CI: 0.99-1.0). With further development, this approach could provide a noninvasive and widely available screening tool for obstructive HCM.

Design Rationale and Performance Evaluation of the Wavelet Health Wristband: Benchtop Validation of a Wrist-Worn Physiological Signal Recorder.

BACKGROUND: Wearable and connected health devices along with the recent advances in mobile and cloud computing provide a continuous, convenient-to-patient, and scalable way to collect personal health data remotely. The Wavelet Health platform and the Wavelet wristband have been developed to capture multiple physiological signals and to derive biometrics from these signals, including resting heart rate (HR), heart rate variability (HRV), and respiration rate (RR). OBJECTIVE: This study aimed to evaluate the accuracy of the biometric estimates and signal quality of the wristband. METHODS: Measurements collected from 35 subjects using the Wavelet wristband were compared with simultaneously recorded electrocardiogram and spirometry measurements. RESULTS: The HR, HRV SD of normal-to-normal intervals, HRV root mean square of successive differences, and RR estimates matched within 0.7 beats per minute (SD 0.9), 7 milliseconds (SD 10), 11 milliseconds (SD 12), and 1 breaths per minute (SD 1) mean absolute deviation of the reference measurements, respectively. The quality of the raw plethysmography signal collected by the wristband, as determined by the harmonic-to-noise ratio, was comparable with that obtained from measurements from a finger-clip plethysmography device. CONCLUSIONS: The accuracy of the biometric estimates and high signal quality indicate that the wristband photoplethysmography device is suitable for performing pulse wave analysis and measuring vital signs.