Use of wearable technology to enhance response to the Coronavirus (COVID-19) pandemic

Redox-Concatenated Aptamer Integrated Skin Mimicking Electrochemical Patch for Noninvasive Detection of Cortisol

This research focuses on developing and validating a wearable electrochemical biosensor called the concatenated aptamer integrated skin patch, also known as the Captain Patch. The main objective is to detect cortisol levels in sweat, which can provide valuable insights into an individual's health. The biosensor utilizes a corrugated surface that mimics the skin, allowing for better attachment and an improved electrochemical performance. The study demonstrates the successful application of Captain Patch on the human body by using artificially spiked sweat samples. The results indicate good measurement accuracy and conformity when the patch is worn on the body. However, for long-term usage, the patch needs to be changed every 3-4 h or worn three times a day to enable monitoring of cortisol levels. Despite the need for frequent patch changes, the cost-effectiveness and ease of operation make these skin patches suitable for longitudinal cortisol monitoring and other sweat analytes. By customization of the biorecognition probe, the developed biowearable can be used to monitor a variety of vital biomarkers. Overall, Captain Patch, with its capability of detecting specific health markers such as cortisol, hints at the future potential of wearables to offer valuable data on various other biomarkers. Our approach presents the first step in integrating a cost-effective wearable electrochemical patch integrated with a redox-concatenated aptamer for noninvasive biomarker detection. This personalized approach to monitoring can lead to improved patient outcomes and increased patient engagement in managing their health.

Assessing Adherence to Multi-Modal Oura Ring Wearables From COVID-19 Detection Among Healthcare Workers

Background Identifying early signs of a SARS-CoV-2 infection in healthcare workers could be a critical tool in reducing disease transmission. To provide this information, both daily symptom surveys and wearable device monitoring could have utility, assuming there is a sufficiently high level of participant adherence. Purpose The aim of this study is to evaluate adherence to a daily symptom survey and a wearable device (Oura Ring) among healthcare professionals (attending physicians and other clinical staff) and trainees (residents and medical students) in a hospital setting during the early stages of the COVID-19 pandemic. Methods In this mixed-methods observational study, the data were a subset (N=91) of those collected as part of the larger TemPredict Study. Demographic data analyses were conducted with descriptive statistics. Participant adherence to the wearable device protocol was reported as the percentage of days that sleep was recorded, and adherence to the daily survey was reported as the percentage of days with submitted surveys. Comparisons for the primary (wearable and survey adherence of groups) and secondary (adherence patterns among subgroups) outcomes were conducted using descriptive statistics, two-tailed independent t-tests, and Welch's ANOVA with post hoc analysis using Games-Howell. Results Wearable device adherence was significantly higher than the daily symptom survey adherence for most participants. Overall, participants were highly adherent to the wearable device, wearing the device an average of 87.8 ± 11.6% of study nights compared to survey submission, showing an average of 63.8 ± 27.4% of study days. In subgroup analysis, we found that healthcare professionals (HCPs) and medical students had the highest adherence to wearing the wearable device, while medical residents had lower adherence in both wearable adherence and daily symptom survey adherence. Conclusions These results indicated high participant adherence to wearable devices to monitor for impending infection in the course of a research study conducted as part of clinical practice. Subgroup analysis indicated HCPs and medical students maintained high adherence, but residents' adherence was lower, which is likely multifactorial, with differences in work demands and stress contributing to the findings. These results can guide the development of adherence strategies for a wearable device to increase the quality of data collection and assist in disease detection in this and future pandemics.

Medición de parámetros de signos vitales para emisión de alertas móviles

Una preocupación en las personas, consiste en cómo conocer su inmediato estado de salud. Un indicador clave para ello, lo reportan los parámetros de signos vitales: temperatura, frecuencia cardíaca y oxigenación; Los dispositivos wearables, permiten ese monitoreo mediante conexión con un móvil; sin embargo, muchos usuarios desconocen o no logran la interpretación adecuada de tales reportes. Por esta razón, además de monitorear automáticamente los signos vitales (en periodos determinados de tiempo), el objetivo del sistema propuesto, consiste en emitir alertas que se envían automáticamente, no solo al usuario del dispositivo, sino también a un acompañante mediante mensaje de texto, cada vez que alguno de estos parámetros se encuentra fuera del rango normal permitido.  Esto se logra con un modelo de categorización de usuarios soportado por un sistema de reglas que describe los rangos normales, estableciendo criterios para emitir las alertas. El beneficio principal del sistema propuesto, es tener información específica y en tiempo real, de un parámetro corporal permitiendo advertir un estado alterado de salud, insumo importante, para tomar decisiones médicas. Abstract, one of the main concerns of people, is how to know their immediate state of health. A key indicator for this is reported by vital signs parameters such as temperature, heart rate and oxygenation; The wearable devices, allow such monitoring by connecting to a cell phone; however, many users are unaware or fail to achieve the proper interpretation of such reports. For this reason, in addition to monitoring vital signs, the objective of this proposed system is to issue alerts that are sent automatically, not only to the user of the device, but also to a companion via text message whenever any of these parameters is outside the normal allowed range.  This is achieved with a user categorization model supported by a system of rules that describes the normal allowed ranges, establishing criteria for issuing alerts. The main benefit of the proposed system is to have specific information in real time, of a body parameter that allows warning of an altered state of health, which is useful as information for making medical decisions. 

Recent Advances, Opportunities, and Challenges in Developing Nucleic Acid Integrated Wearable Biosensors for Expanding the Capabilities of Wearable Technologies in Health Monitoring

Wearable biosensors are becoming increasingly popular due to the rise in demand for non-invasive, real-time monitoring of health and personalized medicine. Traditionally, wearable biosensors have explored protein-based enzymatic and affinity-based detection strategies. However, in the past decade, with the success of nucleic acid-based point-of-care diagnostics, a paradigm shift has been observed in integrating nucleic acid-based assays into wearable sensors, offering better stability, enhanced analytical performance, and better clinical applicability. This narrative review builds upon the current state and advances in utilizing nucleic acid-based assays, including oligonucleotides, nucleic acid, aptamers, and CRISPR-Cas, in wearable biosensing. The review also discusses the three fundamental blocks, i.e., fabrication requirements, biomolecule integration, and transduction mechanism, for creating nucleic acid integrated wearable biosensors.

Development of a low-cost wearable device for Covid-19 self-quarantine monitoring system

Objectives

The objective of this study is to develop a Bluetooth-based low-cost wearable device for a self-quarantine monitoring system.

Study design

The designed wearable device focuses on data transmission via Bluetooth, integration of tracking, tracing, and fencing into a single system, and low energy usage from its battery.

Methods

We design a wearable device using smartphone equipped with GPS, a communication module, Bluetooth low energy (BLE) and a high-capacity battery as a solution for low-cost device with excellent efficiency. We divide the designed system into two parts, the client and the server parts. The client parts are wearable device attached to the individual being monitored and the mobile phone as GPS and telecommunications module. Whereas the server parts are user interface, digital map, notification system, and backend database. Then, the whole system was tested in laboratory and field scale.

Results

We tested functions of integrated device such as wearable device, mobile applications, and server for laboratory scale test. Then, performing field test with geofencing, communication module, battery, web interface, and resource computing usage. The field test was conducted on a small scale with a limited number of trial patients. We found that the designed wearable device was successfully implemented for both self-quarantine and centralized quarantine requirements. The majority of the components used met the specifications and functioned properly as well.

Conclusions

A BLE-enabled wearable device can be used for tracking self-quarantine patients. The laboratory and field scale tests demonstrate that the designed wearable device functions properly and meets the requirements. We anticipate that this low-cost wearable device is effective in limiting Covid-19 virus spread and preventing the formation of a new Covid-19 virus-infected cluster.

A physiological signal compression approach using optimized Spindle Convolutional Auto-encoder in mHealth applications

Background and Objectives

The COVID-19 pandemic manifested the need of developing robust digital platforms for facilitating healthcare services such as consultancy, clinical therapies, real time remote monitoring, early diagnosis and future predictions. Innovations made using technologies such as Internet of Things (IoT), edge computing, cloud computing and artificial intelligence are helping address this crisis. The urge for remote monitoring, symptom analysis and early detection of diseases lead to tremendous increase in the deployment of wearable sensor devices. They facilitate seamless gathering of physiological data such as electrocardiogram (ECG) signals, respiration traces (RESP), galvanic skin response (GSR), pulse rate, body temperature, photoplethysmograms (PPG), oxygen saturation (SpO2) etc. For diagnosis and analysis purpose, the gathered data needs to be stored. Wearable devices operate on batteries and have a memory constraint. In mHealth application architectures, this gathered data is hence stored on cloud based servers. While transmitting data from wearable devices to cloud servers via edge devices, a lot of energy is consumed. This paper proposes a deep learning based compression model SCAElite that reduces the data volume, enabling energy efficient transmission.

Results

Stress Recognition in Automobile Drivers dataset and MIT-BIH dataset from PhysioNet are used for validation of algorithm performance. The model achieves a compression ratio of up to 300 fold with reconstruction errors within 8% over the stress recognition dataset and 106.34-fold with reconstruction errors within 8% over the MIT-BIH dataset. The computational complexity of SCAElite is 51.65% less compared to state-of-the-art deep compressive model.

Conclusion

It is experimentally validated that SCAElite guarantees a high compression ratio with good quality restoration capabilities for physiological signal compression in mHealth applications. It has a compact architecture and is computationally more efficient compared to state-of-the-art deep compressive model.

Recent State of Wearable IMU Sensors Use in People Living with Spasticity: A Systematic Review

Spasticity is a disabling characteristic of neurological disorders, described by a velocity-dependent increase in muscle tone during passive stretch. During the last few years, many studies have been carried out to assess spasticity using wearable IMU (inertial measurements unit) sensors. This review aims to provide an updated framework of the current research on IMUs wearable sensors in people living with spasticity in recent studies published between 2017 and 2021. A total of 322 articles were screened, then finally 10 articles were selected. Results show the lack of homogenization of study procedures and missing apparatus information in some studies. Still, most studies performed adequately on measures of reporting and found that IMUs wearable data was successful in their respective purposes and goals. As IMUs estimate translational and rotational body motions, we believe there is a strong potential for these applications to estimate velocity-dependent exaggeration of stretch reflexes and spasticity-related characteristics in spasticity. This review also proposes new directions of research that should be challenged by larger study groups and could be of interest to both researchers as well as clinicians. The use of IMUs to evaluate spasticity is a promising avenue to provide an objective measurement as compared to non-instrumented traditional assessments.

"Wearables on vogue": a scoping review on wearables on physical activity and sedentary behavior during COVID-19 pandemic

Background

Wearables are intriguing way to promote physical activity and reduce sedentary behavior in populations with and without chronic diseases. However, the contemporary evidence demonstrating the effectiveness of wearables on physical health during the COVID-19 pandemic has yet to be explored.

Aim

The present review aims to provide the readers with a broader knowledge of the impact of wearables on physical health during the pandemic.

Methods

Five electronic databases (Web of Science, Scopus, Ovid Medline, Cumulative Index to Nursing and Allied Health Literature and Embase) were searched. The eligibility criteria of the studies to be included were based on PICOT criteria: population (adults, children and elderly), intervention (wearable, smartphones), comparison (any behavioral intervention), outcome (physical activity or sedentary behavior levels) and time frame (between December 1st, 2019 and November 19th, 2021). The present scoping review was framed as per the guidelines of the Arksey and O’Malley framework.

Results

Of 469 citations initially screened, 17 articles were deemed eligible for inclusion and potential scoping was done. Smartphone-based applications with inbuilt accelerometers were commonly used, while a few studies employed smart bands, smartwatches for physical health monitoring. Most of the studies observed the increased use of wearables in healthy adults followed by elderly, children and pregnant women. Considerable reduction (almost—50%) in physical activity during the pandemic: daily step count (− 2812 steps/min), standing (− 32.7%) and walking (− 52.2%) time was found.

Conclusion

Wearables appears to be impending means of improving physical activity and reducing sedentary behavior remotely during the COVID-19 pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11332-021-00885-x.

Gait Assessment Using Wearable Sensor-Based Devices in People Living with Dementia: A Systematic Review

The ability of people living with dementia to walk independently is a key contributor to their overall well-being and autonomy. For this reason, understanding the relationship between dementia and gait is significant. With rapidly emerging developments in technology, wearable devices offer a portable and affordable alternative for healthcare experts to objectively estimate kinematic parameters with great accuracy. This systematic review aims to provide an updated overview and explore the opportunities in the current research on wearable sensors for gait analysis in adults over 60 living with dementia. A systematic search was conducted in the following scientific databases: PubMed, Cochrane Library, and IEEE Xplore. The targeted search identified 1992 articles that were potentially eligible for inclusion, but, following title, abstract, and full-text review, only 6 articles were deemed to meet the inclusion criteria. Most studies performed adequately on measures of reporting, in and out of a laboratory environment, and found that sensor-derived data are successful in their respective objectives and goals. Nevertheless, we believe that additional studies utilizing standardized protocols should be conducted in the future to explore the impact and usefulness of wearable devices in gait-related characteristics such as fall prognosis and early diagnosis in people living with dementia.

Modelling and Evaluation of the Absorption of the 866 MHz Electromagnetic Field in Humans Exposed near to Fixed I-RFID Readers Used in Medical RTLS or to Monitor PPE

The aim of this study was to model and evaluate the Specific Energy Absorption Rate (SAR) values in humans in proximity to fixed multi-antenna I-RFID readers of passive tags under various scenarios mimicking exposure when they are incorporated in Real-Time Location Systems (RTLS), or used to monitor Personal Protective Equipment (PPE). The sources of the electromagnetic field (EMF) in the modelled readers were rectangular microstrip antennas at a resonance frequency in free space of 866 MHz from the ultra-high frequency (UHF) RFID frequency range of 865-868 MHz. The obtained results of numerical modelling showed that the SAR values in the body 5 cm away from the UHF RFID readers need consideration with respect to exposure limits set by international guidelines to prevent adverse thermal effects of exposure to EMF: when the effective radiated power exceeds 5.5 W with respect to the general public/unrestricted environments exposure limits, and with respect to occupational/restricted environments exposure limits, when the effective radiated power exceeds 27.5 W.

Review: How Can Intelligent Robots and Smart Mechatronic Modules Facilitate Remote Assessment, Assistance, and Rehabilitation for Isolated Adults With Neuro-Musculoskeletal Conditions?

Worldwide, at the time this article was written, there are over 127 million cases of patients with a confirmed link to COVID-19 and about 2.78 million deaths reported. With limited access to vaccine or strong antiviral treatment for the novel coronavirus, actions in terms of prevention and containment of the virus transmission rely mostly on social distancing among susceptible and high-risk populations. Aside from the direct challenges posed by the novel coronavirus pandemic, there are serious and growing secondary consequences caused by the physical distancing and isolation guidelines, among vulnerable populations. Moreover, the healthcare system's resources and capacity have been focused on addressing the COVID-19 pandemic, causing less urgent care, such as physical neurorehabilitation and assessment, to be paused, canceled, or delayed. Overall, this has left elderly adults, in particular those with neuromusculoskeletal (NMSK) conditions, without the required service support. However, in many cases, such as stroke, the available time window of recovery through rehabilitation is limited since neural plasticity decays quickly with time. Given that future waves of the outbreak are expected in the coming months worldwide, it is important to discuss the possibility of using available technologies to address this issue, as societies have a duty to protect the most vulnerable populations. In this perspective review article, we argue that intelligent robotics and wearable technologies can help with remote delivery of assessment, assistance, and rehabilitation services while physical distancing and isolation measures are in place to curtail the spread of the virus. By supporting patients and medical professionals during this pandemic, robots, and smart digital mechatronic systems can reduce the non-COVID-19 burden on healthcare systems. Digital health and cloud telehealth solutions that can complement remote delivery of assessment and physical rehabilitation services will be the subject of discussion in this article due to their potential in enabling more effective and safer NMSDK rehabilitation, assistance, and assessment service delivery. This article will hopefully lead to an interdisciplinary dialogue between the medical and engineering sectors, stake holders, and policy makers for a better delivery of care for those with NMSK conditions during a global health crisis including future pandemics.

Deployment of artificial intelligence for radiographic diagnosis of COVID-19 pneumonia in the emergency department

OBJECTIVE

The coronavirus disease 2019 pandemic has inspired new innovations in diagnosing, treating, and dispositioning patients during high census conditions with constrained resources. Our objective is to describe first experiences of physician interaction with a novel artificial intelligence (AI) algorithm designed to enhance physician abilities to identify ground-glass opacities and consolidation on chest radiographs.

METHODS

During the first wave of the pandemic, we deployed a previously developed and validated deep-learning AI algorithm for assisted interpretation of chest radiographs for use by physicians at an academic health system in Southern California. The algorithm overlays radiographs with "heat" maps that indicate pneumonia probability alongside standard chest radiographs at the point of care. Physicians were surveyed in real time regarding ease of use and impact on clinical decisionmaking.

RESULTS

Of the 5125 total visits and 1960 chest radiographs obtained in the emergency department (ED) during the study period, 1855 were analyzed by the algorithm. Among these, emergency physicians were surveyed for their experiences on 202 radiographs. Overall, 86% either strongly agreed or somewhat agreed that the intervention was easy to use in their workflow. Of the respondents, 20% reported that the algorithm impacted clinical decisionmaking.

CONCLUSIONS

To our knowledge, this is the first published literature evaluating the impact of medical imaging AI on clinical decisionmaking in the emergency department setting. Urgent deployment of a previously validated AI algorithm clinically was easy to use and was found to have an impact on clinical decision making during the predicted surge period of a global pandemic.