A Novel Wearable Device for Continuous Temperature Monitoring & Fever Detection

Compact Vital-Sensing Band with Uninterrupted Power Supply for Core Body Temperature and Pulse Rate Monitoring

Although wearable devices for continuous monitoring of vital signs have undergone significant advancements, their need for frequent recharging precludes continuous operation, potentially leading to adverse outcomes being overlooked. Additionally, the scattered locations of the sensors hamper wearability. Herein, we present a compact vital-sensing band with uninterrupted power supply designed for continuous monitoring of core body temperature (CBT) and pulse rate. The band─which comprises two sensors, a power source (i.e., a flexible thermoelectric generator (TEG) and a battery), and a flexible circuit─is worn on the forearm. The CBT is calculated by measuring the skin temperature and heat flux, while a triboelectric nanogenerator-based self-powered pressure sensor is utilized for pulse rate monitoring. The TEG is a flexible unit that converts body heat into electricity, accumulating a total energy of 314 mJ (100%). Out of this total energy, only 43.2 mJ (7.2%) is utilized for CBT measurements, while the remaining 270.80 mJ (92.8%) is stored in the battery. This enables reliable and continuous operation of the vital-sensing band, highlighting its potential for use in healthcare applications.

Continuous Temperature Telemonitoring of Patients with COVID-19 and Other Infectious Diseases Treated in Hospital-at-Home: Viture® System Validation

Body temperature must be monitored in patients receiving Hospital-at-Home (HaH) care for COVID-19 and other infectious diseases. Continuous temperature telemonitoring (CTT) detects fever and patient deterioration early, facilitating decision-making. We performed a validation clinical study assessing the safety, comfort, and impact on healthcare practice of Viture®, a CTT system, compared with a standard digital axillary thermometer in 208 patients with COVID-19 and other infectious diseases treated in HaH at the Navarra University Hospital (HUN). Overall, 3258 pairs of measurements showed a clinical bias of -0.02 °C with limits of agreement of -0.96/+0.92 °C, a 95% acceptance rate, and a mean absolute deviation of 0.36 (SD 0.30) °C. Viture® detected 3 times more febrile episodes and revealed fever in 50% more patients compared with spot measurements. Febrile episodes were detected 7.23 h (mean) earlier and modified the diagnostic and/or therapeutic approach in 43.2% of patients. Viture® was validated for use in a clinical setting and was more effective in detecting febrile episodes than conventional methods.

Improving management of febrile neutropenia in oncology patients: the role of artificial intelligence and machine learning

INTRODUCTION

Artificial intelligence (AI) and machine learning (ML) have the potential to revolutionize the management of febrile neutropenia (FN) and drive progress toward personalized medicine.

AREAS COVERED

In this review, we detail how the collection of a large number of high-quality data can be used to conduct precise mathematical studies with ML and AI. We explain the foundations of these techniques, covering the fundamentals of supervised and unsupervised learning, as well as the most important challenges, e.g. data quality, 'black box' model interpretation and overfitting. To conclude, we provide detailed examples of how AI and ML have been used to enhance predictions of chemotherapy-induced FN, detection of bloodstream infections (BSIs) and multidrug-resistant (MDR) bacteria, and anticipation of severe complications and mortality.

EXPERT OPINION

There is promising potential of implementing accurate AI and ML models whilst managing FN. However, their integration as viable clinical tools poses challenges, including technical and implementation barriers. Improving global accessibility, fostering interdisciplinary collaboration, and addressing ethical and security considerations are essential. By overcoming these challenges, we could transform personalized care for patients with FN.

Advances in Printed Electronic Textiles

Electronic textiles (e-textiles) have emerged as a revolutionary solution for personalized healthcare, enabling the continuous collection and communication of diverse physiological parameters when seamlessly integrated with the human body. Among various methods employed to create wearable e-textiles, printing offers unparalleled flexibility and comfort, seamlessly integrating wearables into garments. This has spurred growing research interest in printed e-textiles, due to their vast design versatility, material options, fabrication techniques, and wide-ranging applications. Here, a comprehensive overview of the crucial considerations in fabricating printed e-textiles is provided, encompassing the selection of conductive materials and substrates, as well as the essential pre- and post-treatments involved. Furthermore, the diverse printing techniques and the specific requirements are discussed, highlighting the advantages and limitations of each method. Additionally, the multitude of wearable applications made possible by printed e-textiles is explored, such as their integration as various sensors, supercapacitors, and heated garments. Finally, a forward-looking perspective is provided, discussing future prospects and emerging trends in the realm of printed wearable e-textiles. As advancements in materials science, printing technologies, and design innovation continue to unfold, the transformative potential of printed e-textiles in healthcare and beyond is poised to revolutionize the way wearable technology interacts and benefits.

Model for Predicting Complications of Hemodialysis Patients Using Data From the Internet of Medical Things and Electronic Medical Records

Intelligent models for predicting hemodialysis-related complications, i.e., hypotension and the deterioration of the quality or obstruction of the AV fistula, based on machine learning (ML) methods were established to offer early warnings to medical staff and give them enough time to provide pre-emptive treatment. A novel integration platform collected data from the Internet of Medical Things (IoMT) at a dialysis center and inspection results from electronic medical records (EMR) to train ML algorithms and build models. The selection of the feature parameters was implemented using Pearson's correlation method. Then, the eXtreme Gradient Boost (XGBoost) algorithm was chosen to create the predictive models and optimize the feature choice. 75% of collected data are used as a training dataset and the other 25% are used as a testing dataset. We adopted the prediction precision and recall rate of hypotension and AV fistula obstruction to measure the effectiveness of the predictive models. These rates were sufficiently high at approximately 71%-90%. In the context of hemodialysis, hypotension and the deterioration of the quality or obstruction of the arteriovenous (AV) fistula affect treatment quality and patient safety and may lead to a poor prognosis. Our prediction models with high accuracies can provide excellent references and signals for clinical healthcare service providers. Clinical and Translational Impact Statement-With the integrated dataset collected from IoMT and EMR, the superior predictive results of our models for complications of hemodialysis patients are demonstrated. We believe, after enough clinical tests are implemented as planned, these models can assist the healthcare team in making appropriate preparations in advance or adjusting the medical procedures to avoid these adverseevents.

A Novel Non-Invasive Thermometer for Continuous Core Body Temperature: Comparison with Tympanic Temperature in an Acute Stroke Clinical Setting

There is a growing research interest in wireless non-invasive solutions for core temperature estimation and their application in clinical settings. This study aimed to investigate the use of a novel wireless non-invasive heat flux-based thermometer in acute stroke patients admitted to a stroke unit and compare the measurements with the currently used infrared (IR) tympanic temperature readings. The study encompassed 30 acute ischemic stroke patients who underwent continuous measurement (Tcore) with the novel wearable non-invasive CORE device. Paired measurements of Tcore and tympanic temperature (Ttym) by using a standard IR-device were performed 3−5 times/day, yielding a total of 305 measurements. The predicted core temperatures (Tcore) were significantly correlated with Ttym (r = 0.89, p < 0.001). The comparison of the Tcore and Ttym measurements by Bland−Altman analysis showed a good agreement between them, with a low mean difference of 0.11 ± 0.34 °C, and no proportional bias was observed (B = −0.003, p = 0.923). The Tcore measurements correctly predicted the presence or absence of Ttym hyperthermia or fever in 94.1% and 97.4% of cases, respectively. Temperature monitoring with a novel wireless non-invasive heat flux-based thermometer could be a reliable alternative to the Ttym method for assessing core temperature in acute ischemic stroke patients.

Feasibility, Acceptability, and Performance of a Continuous Temperature Monitor in Older Adults and Staff in Congregate-Living Facilities

OBJECTIVES

Residents of congregate-living facilities are susceptible to disability and mortality from infection given the presence of advanced age, multimorbidity, and frailty-as demonstrated in the recent COVID pandemic. This study assessed the feasibility, acceptability, and applicability of a continuous temperature monitoring device in a congregate-living facility with residents of independent living, assisted living, and their care-providing staff. We hypothesized that a wearable device compared with daily manual temperature assessment would be well tolerated and more effective at detecting temperature variances than current standard of care body temperature assessment.

DESIGN

Feasibility study.

SETTING AND PARTICIPANTS

Residents of assisted and independent living and staff of a retirement community.

METHODS

Thirty-five participants, including residents in assisted- and independent-living facilities (25) and staff (10) were enrolled in a 90-day feasibility study and wore a continuous temperature sensor from March to July 2021. Primary outcomes included study completion, ability to reapply the sensor, temperature data acquisition, and data availability from the sensors. A secondary analysis of the temperature data involved comparing the method of obtaining temperature using the continuous monitoring device against standard of care using traditional manual thermometers.

RESULTS

Overall, 91.3% of residents, who were in the study during the first reapplication, were able to apply the device without assistance (21 of 23), and 80% of resident participants completed the study (20 of 25). For staff participants, completion rates and reapplication rates were 100%. Data acquisition rates from the continuous temperature devices were much higher than manual temperatures. Four episodes of fever were detected by the devices; manual temperature checks did not identify these events.

CONCLUSIONS AND IMPLICATIONS

Continuous temperature monitoring in an older adult population and the staff in congregate-living facilities is feasible and acceptable. This approach identified fever undetected by current standard of care indicating the capability of this device for earlier detection of fevers.