Microfluidics in structured multimaterial fibers.

Traditional fabrication techniques for microfluidic devices utilize a planar chip format that possesses limited control over the geometry of and materials placement around microchannel cross-sections. This imposes restrictions on the design of flow fields and external forces (electric, magnetic, piezoelectric, etc.) that can be imposed onto fluids and particles. Here we report a method of fabricating microfluidic channels with complex cross-sections. A scaled-up version of a microchannel is dimensionally reduced through a thermal drawing process, enabling the fabrication of meters-long microfluidic fibers with nonrectangular cross-sectional shapes, such as crosses, five-pointed stars, and crescents. In addition, by codrawing compatible materials, conductive domains can be integrated at arbitrary locations along channel walls. We validate this technology by studying unexplored regimes in hydrodynamic flow and by designing a high-throughput cell separation device. By enabling these degrees of freedom in microfluidic device design, fiber microfluidics provides a method to create microchannel designs that are inaccessible using planar techniques.

Detection of Common Respiratory Infections, Including COVID-19, Using Consumer Wearable Devices in Health Care Workers: Prospective Model Validation Study.

BACKGROUND: The early detection of respiratory infections could improve responses against outbreaks. Wearable devices can provide insights into health and well-being using longitudinal physiological signals. OBJECTIVE: The purpose of this study was to prospectively evaluate the performance of a consumer wearable physiology-based respiratory infection detection algorithm in health care workers. METHODS: In this study, we evaluated the performance of a previously developed system to predict the presence of COVID-19 or other upper respiratory infections. The system generates real-time alerts using physiological signals recorded from a smartwatch. Resting heart rate, respiratory rate, and heart rate variability measured during the sleeping period were used for prediction. After baseline recordings, when participants received a notification from the system, they were required to undergo testing at a Northwell Health System site. Participants were asked to self-report any positive tests during the study. The accuracy of model prediction was evaluated using respiratory infection results (laboratory results or self-reports), and postnotification surveys were used to evaluate potential confounding factors. RESULTS: A total of 577 participants from Northwell Health in New York were enrolled in the study between January 6, 2022, and July 20, 2022. Of these, 470 successfully completed the study, 89 did not provide sufficient physiological data to receive any prediction from the model, and 18 dropped out. Out of the 470 participants who completed the study and wore the smartwatch as required for the 16-week study duration, the algorithm generated 665 positive alerts, of which 153 (23.0%) were not acted upon to undergo testing for respiratory viruses. Across the 512 instances of positive alerts that involved a respiratory viral panel test, 63 had confirmed respiratory infection results (ie, COVID-19 or other respiratory infections detected using a polymerase chain reaction or home test) and the remaining 449 had negative upper respiratory infection test results. Across all cases, the estimated false-positive rate based on predictions per day was 2%, and the positive-predictive value ranged from 4% to 10% in this specific population, with an observed incidence rate of 198 cases per week per 100,000. Detailed examination of questionnaires filled out after receiving a positive alert revealed that physical or emotional stress events, such as intense exercise, poor sleep, stress, and excessive alcohol consumption, could cause a false-positive result. CONCLUSIONS: The real-time alerting system provides advance warning on respiratory viral infections as well as other physical or emotional stress events that could lead to physiological signal changes. This study showed the potential of wearables with embedded alerting systems to provide information on wellness measures.

Mindful breathing as an effective technique in the management of hypertension.

Introduction: Hypertension is one of the most important, modifiable risk factors for cardiovascular disease. The popularity of wearable devices provides an opportunity to test whether device guided slow mindful breathing may serve as a non-pharmacological treatment in the management of hypertension. Methods: Fitbit Versa-3 and Sense devices were used for this study. In addition, participants were required to own an FDA or Health Canada approved blood pressure measuring device. Advertisements were shown to 655,910 Fitbit users, of which 7,365 individuals expressed interest and filled out the initial survey. A total of 1,918 participants entered their blood pressure readings on at least 1 day and were considered enrolled in the study. Participants were instructed to download a guided mindful breathing app on their smartwatch device, and to engage with the app once a day prior to sleep. Participants measured their systolic and diastolic blood pressure prior to starting each mindful breathing session, and again after completion. All measurements were self reported. Participants were located in the United States or Canada. Results: Values of systolic and diastolic blood pressure were reduced following mindful breathing. There was also a decrease in resting systolic and diastolic measurements when measured over several days. For participants with a systolic pressure ≥ 130 mmHg, there was a decrease of 9.7 mmHg following 15 min of mindful breathing at 6 breaths per minute. When measured over several days, the resting systolic pressure decreased by an average of 4.3 mmHg. Discussion: Mindful breathing for 15 min a day, at a rate of 6 breaths per minute is effective in lowering blood pressure, and has both an immediate, and a short term effect (over several days). This large scale study demonstrates that device guided mindful breathing with a consumer wearable for 15 min a day is effective in lowering blood pressure, and a helpful complement to the standard of care.

Occurrence of Relative Bradycardia and Relative Tachycardia in Individuals Diagnosed With COVID-19.

The COVID-19 disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has become one of the worst global pandemics of the century. Wearable devices are well suited for continuously measuring heart rate. Here we show that the Resting Heart Rate is modified for several weeks following a COVID-19 infection. The Resting Heart Rate shows 3 phases: 1) elevated during symptom onset, with average peak increases relative to the baseline of 1.8% (3.4%) for females (males), 2) decrease thereafter, reaching a minimum on average ≈13 days after symptom onset, and 3) subsequent increase, reaching a second peak on average ≈28 days from symptom onset, before falling back to the baseline ≈112 days from symptom onset. All estimates vary with disease severity.

Measurement of respiratory rate using wearable devices and applications to COVID-19 detection.

We show that heart rate enabled wearable devices can be used to measure respiratory rate. Respiration modulates the heart rate creating excess power in the heart rate variability at a frequency equal to the respiratory rate, a phenomenon known as respiratory sinus arrhythmia. We isolate this component from the power spectral density of the heart beat interval time series, and show that the respiratory rate thus estimated is in good agreement with a validation dataset acquired from sleep studies (root mean squared error = 0.648 min-1, mean absolute error = 0.46 min-1, mean absolute percentage error = 3%). We use this respiratory rate algorithm to illuminate two potential applications (a) understanding the distribution of nocturnal respiratory rate as a function of age and sex, and (b) examining changes in longitudinal nocturnal respiratory rate due to a respiratory infection such as COVID-19. 90% of respiratory rate values for healthy adults fall within the range 11.8-19.2 min-1 with a mean value of 15.4 min-1. Respiratory rate is shown to increase with nocturnal heart rate. It also varies with BMI, reaching a minimum at 25 kg/m2, and increasing for lower and higher BMI. The respiratory rate decreases slightly with age and is higher in females compared to males for age <50 years, with no difference between females and males thereafter. The 90% range for the coefficient of variation in a 14 day period for females (males) varies from 2.3-9.2% (2.3-9.5%) for ages 20-24 yr, to 2.5-16.8% (2.7-21.7%) for ages 65-69 yr. We show that respiratory rate is often elevated in subjects diagnosed with COVID-19. In a 7 day window from D-1 to D+5 (where D0 is the date when symptoms first present, for symptomatic individuals, and the test date for asymptomatic cases), we find that 36.4% (23.7%) of symptomatic (asymptomatic) individuals had at least one measurement of respiratory rate 3 min-1 higher than the regular rate.

Assessment of physiological signs associated with COVID-19 measured using wearable devices.

Respiration rate, heart rate, and heart rate variability (HRV) are some health metrics that are easily measured by consumer devices, which can potentially provide early signs of illness. Furthermore, mobile applications that accompany wearable devices can be used to collect relevant self-reported symptoms and demographic data. This makes consumer devices a valuable tool in the fight against the COVID-19 pandemic. Data on 2745 subjects diagnosed with COVID-19 (active infection, PCR test) were collected from May 21 to September 11, 2020, consisting of PCR positive tests conducted between February 16 and September 9. Considering male (female) participants, 11.9% (11.2%) of the participants were asymptomatic, 48.3% (47.8%) recovered at home by themselves, 29.7% (33.7%) recovered at home with the help of someone else, 9.3% (6.6%) required hospitalization without ventilation, and 0.5% (0.4%) required ventilation. There were a total of 21 symptoms reported, and the prevalence of symptoms varies by sex. Fever was present in 59.4% of male subjects and in 52% of female subjects. Based on self-reported symptoms alone, we obtained an AUC of 0.82 ± 0.017 for the prediction of the need for hospitalization. Based on physiological signs, we obtained an AUC of 0.77 ± 0.018 for the prediction of illness on a specific day. Respiration rate and heart rate are typically elevated by illness, while HRV is decreased. Measuring these metrics, taken in conjunction with molecular-based diagnostics, may lead to better early detection and monitoring of COVID-19.

Iso-acoustic focusing of cells for size-insensitive acousto-mechanical phenotyping.

Mechanical phenotyping of single cells is an emerging tool for cell classification, enabling assessment of effective parameters relating to cells' interior molecular content and structure. Here, we present iso-acoustic focusing, an equilibrium method to analyze the effective acoustic impedance of single cells in continuous flow. While flowing through a microchannel, cells migrate sideways, influenced by an acoustic field, into streams of increasing acoustic impedance, until reaching their cell-type specific point of zero acoustic contrast. We establish an experimental procedure and provide theoretical justifications and models for iso-acoustic focusing. We describe a method for providing a suitable acoustic contrast gradient in a cell-friendly medium, and use acoustic forces to maintain that gradient in the presence of destabilizing forces. Applying this method we demonstrate iso-acoustic focusing of cell lines and leukocytes, showing that acoustic properties provide phenotypic information independent of size.

Monitoring sepsis using electrical cell profiling.

Sepsis is a potentially lethal condition that may be ameliorated through early monitoring of circulating activated leukocytes for faster stratification of severity of illness and improved administration of targeted treatment. Characterization of the intrinsic electrical properties of leukocytes is label-free and can provide a quick way to quantify the number of activated cells as sepsis progresses. Iso-dielectric separation (IDS) uses dielectrophoresis (DEP) to characterize the electrical signatures of cells. Here, we use IDS to show that activated and non-activated leukocytes have different electrical properties. We then present a double-sided version of the IDS platform to increase throughput to characterize thousands of cells. This new platform is less prone to cell fouling and allows faster characterization. Using peripheral blood samples from a cecal ligation and puncture (CLP) model of polymicrobial sepsis in mice, we estimate the number of activated leukocytes by looking into differences in the electrical properties of cells. We show for the first time using animal models that electrical cell profiling correlates with flow cytometry (FC) results and that IDS is therefore a good candidate for providing rapid monitoring of sepsis by quantifying the number of circulating activated leukocytes.

Rapid dielectrophoretic characterization of single cells using the dielectrophoretic spring.

Dielectrophoresis-based cell separation has significant promise for separation of cells from heterogeneous mixtures based on their electrical properties and is used in diverse areas ranging from hematopoietic stem cell purification to cancer cell isolation. The electrical properties of cells in heterogeneous populations determine if and how well cell subpopulations are separable, and therefore the utility of dielectrophoretic separation is fundamentally determined by our ability to measure electrical properties of cell populations on a cell-by-cell basis. We developed an automated system for electrical characterization of cells that can characterize 1000's of individual cells across a range of conditions (>30 conditions/h). The system uses a continuous-flow microfluidic device and a method termed the dielectrophoretic spring that uses the force balance between dielectrophoresis and fluid drag to measure electrical properties of cells independent of size. We present characterization of the method with beads and cells as well as its application to rapidly find conditions that can discriminate neutrophils with different activation states.