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There are many approaches to maintaining wellness, including taking a simple vacation to attending highly structured wellness retreats, which typically regulate the attendee's personal time and activities. In a healthy English-speaking cohort of 112 women and men (aged 30–80 years), this study examined the effects of participating in either a 6-days intensive wellness retreat based on Ayurvedic medicine principles or unstructured 6-days vacation at the same wellness center setting. Heart rate variability (HRV) was monitored continuously using a wearable ECG sensor patch for up to 7 days prior to, during, and 1-month following participation in the interventions. Additionally, salivary cortisol levels were assessed for all participants at multiple times during the day. Continual HRV monitoring data in the real-world setting was seen to be associated with demographic [HRVALF: βAge = 0.98 (95% CI = 0.96–0.98), false discovery rate (FDR) < 0.001] and physiological characteristics [HRVPLF: β = 0.98 (95% CI = 0.98–1), FDR =0.005] of participants. HRV features were also able to quantify known diurnal variations [HRVLF/HF: βACT:night vs. early−morning = 2.69 (SE = 1.26), FDR < 0.001] along with notable inter- and intraperson heterogeneity in response to intervention. A statistically significant increase in HRVALF [β = 1.48 (SE = 1.1), FDR < 0.001] was observed for all participants during the resort visit. Personalized HRV analysis at an individual level showed a distinct individualized response to intervention, further supporting the utility of using continuous real-world tracking of HRV at an individual level to objectively measure responses to potentially stressful or relaxing settings.

Introduction:Low and middle-income countries (LMICs) bear a disproportionately high burden of sepsis, contributing to an estimated 90% of global sepsis-related deaths. Critical care capabilities needed for septic patients, such as continuous vital sign monitoring, are often unavailable in LMICs.Aim:This study aimed to assess the feasibility and accuracy of using a small wireless, wearable biosensor device linked to a smartphone, and a cloud analytics platform for continuous vital sign monitoring in emergency department (ED) patients with suspected sepsis in Rwanda.Methods:This was a prospective observational study of adult and pediatric patients (≥ 2 months) with suspected sepsis presenting to Kigali University Teaching Hospital ED. Biosensor devices were applied to patients’ chest walls and continuously recorded vital signs (including heart rate and respiratory rate) for the duration of their ED course. These vital signs were compared to intermittent, manually-collected vital signs performed by a research nurse every 6-8 hours. Pearson’s correlation coefficients were calculated over the study population to determine the correlation between the vital signs obtained from the biosensor device and those collected manually.Results:42 patients (20 adults, 22 children) were enrolled. Mean duration of monitoring with the biosensor device was 34.4 hours. Biosensor and manual vital signs were strongly correlated for heart rate (r=0.87, pDiscussion:Wearable biosensor devices can be feasibly implemented and provide accurate continuous vital sign measurements in critically ill pediatric and adult patients with suspected sepsis in a resource-limited setting. Further prospective studies evaluating the impact of biosensor devices on improving clinical outcomes for septic patients are needed.

Continuous, remote monitoring of multiple vital signs in patients with Ebola, coupled with personalized data analytics, can warn health care workers of critical changes in patients’ status much earlier than with conventional intermittent monitoring—which typically occurs just every 8 hours—and without risking the safety of health care workers. This sophisticated technology, in turn, could lead to earlier initiation of lifesaving interventions, better health outcomes, and reduced risk of spreading the virus. A consortium of academic and industry partners, STAMP2 (which stands for Sensor Technology & Analytics to Monitor, Predict and Protect Ebola patients), has developed such a novel, wearable patient sensor—resembling a band-aid—that tracks and wirelessly transmits multiple vital signs to remote health care workers in non-red zone observation areas. The technology, which includes the MultiSense sensor coupled with state-of-the-art, real-time data analytical capabilities, called Personalized Physiology Analytics (PPA), sends patient-specific automated alerts of any important changes in the patient’s condition without requiring the health care worker to constantly monitor display screens. The technology was developed in response to the Grand Ebola Challenge, sponsored by the United States Agency for International Development (USAID), to develop innovative solutions to improve the safety of health care workers and care of patients with, or at high risk for, Ebola virus infection.

Implantable devices have shown promise in reducing rehospitalization for heart failure (HF), but less expensive non-invasive approaches are needed. The LINK-HF study examined the performance of a personalized analytics platform using continuous multivariate data streams to predict rehospitalization

Background The recent Ebola epidemic in West Africa strained existing healthcare systems well beyond their capacities due to the extreme volume and severity of illness of the patients. The implementation of innovative digital technologies within available care centres could potentially improve patient care as well as healthcare worker safety and effectiveness. Methods We developed a Modular Wireless Patient Monitoring System (MWPMS) and conducted a proof of concept study in an Ebola treatment centre (ETC) in Makeni, Sierra Leone. The system was built around a wireless, multiparametric ‘band-aid’ patch sensor for continuous vital sign monitoring and transmission, plus sophisticated data analytics. Results were used to develop personalised analytics to support automated alerting of early changes in patient status. Results During the 3-week study period, all eligible patients (n=26) admitted to the ETC were enrolled in the study, generating a total of 1838 hours of continuous vital sign data (mean of 67.8 hours/patient), including heart rate, heart rate variability, activity, respiratory rate, pulse transit time (inversely related to blood pressure), uncalibrated skin temperature and posture. All patients tolerated the patch sensor without problems. Manually determined and automated vital signs were well correlated. Algorithm-generated Multivariate Change Index, pulse transit time and arrhythmia burden demonstrated encouraging preliminary findings of important physiological changes, as did ECG waveform changes. Conclusions In this proof of concept study, we were able to demonstrate that a portable, deployable system for continuous vital sign monitoring via a wireless, wearable sensor supported by a sophisticated, personalised analytics platform can provide high-acuity monitoring with a continuous, objective measure of physiological status of all patients that is achievable in virtually any healthcare setting, anywhere in the world.

A similarity-based modeling (SBM) technique is demonstrated that provides very early annunciation of the onset of gas path faults in aircraft engines. This powerful approach is shown to provide high fidelity estimates for real-time condition monitoring of aircraft engine signals. These estimates are used to detect the onset of changes in the inter-relationship between the various signals using a sophisticated set of built-in algorithms and tools. The ability of the SBM software to reliably detect subtle changes in signal behavior that are characteristic of a developing anomaly is coupled with a diagnostic rules engine to enable a rapid and robust fault recognition capability. The SBM software operates using a set of algorithms that construct a multivariate nonparametric model of the traditional monitoring sensors (pressure transducers, thermocouples, flow meters, etc.) present in the system. This model is used to generate real-time estimates of sensor values that represent normal system operation. A series of sophisticated tools compares these very high fidelity estimates to the actual sensor readings to detect discrepancies. Finally, a series of logic rules derived from a combination of engineering analysis and experience is applied to the output from the modeling engine in real-time to alert the user of developing serious conditions that need either immediate or planned maintenance attention. The software system provides a complete approach to asset monitoring that minimizes down time, maximizes availability, encodes (preserves) operator knowledge and lowers the overall costs associated with maintaining the assets. In this paper, we demonstrate the use of the similarity-based modeling approach for detecting faults in the gas path of aircraft engines. Some results from the monitoring of over 1,100 engines at a major commercial airline over a two-year period are described. Operationally, the early detection of developing engine faults has prevented delays and cancellations, and has contributed to a reduction in the airline’s in-flight shutdown rate. Financially, this approach has led to significant cost savings by the prevention of major secondary damage.Copyright © 2005 by ASME