Application of a Continuous Temperature Monitoring System among Healthcare Workers During the COVID-19 Pandemic: Prospective Cohort Study (Preprint)

2020 ◽  
Author(s):  
Chun-Yin Yeh ◽  
Yen-Chin Chen ◽  
Yi-Ting Chung ◽  
Chang-Chun Chen ◽  
Bo-Heng Chen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Cohort Study ◽  
Body Temperature ◽  
Monitoring System ◽  
Body Surface ◽  
Prospective Cohort ◽  
Healthcare Workers ◽  
Temperature Monitoring ◽  
Body Surface Temperature ◽  
Continuous Temperature

BACKGROUND Healthcare workers (HCWs) are recommended to measure their body temperature every 8 hours to reduce the risk of cross infections during the COVID-19 pandemic in Taiwan. However, temperature reporting accuracy among HCWs is difficult to attain due to busy working schedules and high chances of human errors. OBJECTIVE This study describes the application of a continuous temperature monitoring system (HEARThermo Care AI.) based on the Internet of Things (IoT) among HCWs in hospitals during the COVID-19 outbreak. METHODS A prospective cohort study was conducted among HCWs in a major tertiary hospital in southern Taiwan. HCWs participated in this study wore HEARThermo, an innovative wearable device used to measure body surface temperature and heart rate every 10s, to continue monitoring their body surface temperature and heart rate during working hours. The HEARThermo Care AI. system combined with the routine body temperature measurement flow were used to automatedly notify the manager about the HCWs with fever risks. The completion rate of body temperature measurements was calculated as the number of HCWs using the continuous temperature monitoring system divided by the number of HCWs on duty. RESULTS A total of 52 HCWs (medical doctors, nurses, and interns) working in the medical ward between April 22 and June 30, 2020, voluntarily participated. The completion rate of body temperature measurements increased from 77.7% to 85% among HCWs in hospitals using HEARThermo Care AI. system. All the HCWs who received warning messages were reconfirmed by their managers and found they had discomforts at that time. CONCLUSIONS The application of the continuous temperature monitoring system serves as a solution to early identify HCWs suspected of having discomforts during the COVID-19 pandemic.

scholarly journals Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7540
Author(s):  
Lei Zhang ◽  
Yanjin Zhu ◽  
Mingliang Jiang ◽  
Yuchen Wu ◽  
Kailian Deng ◽  
...  
Keyword(s):  
Body Temperature ◽  
Surface Temperature ◽  
Body Surface ◽  
Daily Activity ◽  
Original Data ◽  
Temperature Monitoring ◽  
Normal Distribution Curve ◽  
Temperature Detection ◽  
Body Surface Temperature ◽  
Human Behavior Recognition

Existing wearable systems that use G-sensors to identify daily activities have been widely applied for medical, sports and military applications, while body temperature as an obvious physical characteristic that has rarely been considered in the system design and relative applications of HAR. In the context of the normalization of COVID-19, the prevention and control of the epidemic has become a top priority. Temperature monitoring plays an important role in the preliminary screening of the population for fever. Therefore, this paper proposes a wearable device embedded with inertial and temperature sensors that is used to apply human behavior recognition (HAR) to body surface temperature detection for body temperature monitoring and adjustment by evaluating recognition algorithms. The sensing system consists of an STM 32-based microcontroller, a 6-axis (accelerometer and gyroscope) sensor, and a temperature sensor to capture the original data from 10 individual participants under 4 different daily activity scenarios. Then, the collected raw data are pre-processed by signal standardization, data stacking and resampling. For HAR, several machine learning (ML) and deep learning (DL) algorithms are implemented to classify the activities. To compare the performance of different classifiers on the seven-dimensional dataset with temperature sensing signals, evaluation metrics and the algorithm running time are considered, and random forest (RF) is found to be the best-performing classifier with 88.78% recognition accuracy, which is higher than the case of the absence of temperature data (<78%). In addition, the experimental results show that participants’ body surface temperature in dynamic activities was lower compared to sitting, which can be associated with the possible missing fever population due to temperature deviations in COVID-19 prevention. According to different individual activities, epidemic prevention workers are supposed to infer the corresponding standard normal body temperature of a patient by referring to the specific values of the mean expectation and variance in the normal distribution curve provided in this paper.

scholarly journals COVID-19 vaccine impact on rates of SARS-CoV-2 cases and post vaccination strain sequences among healthcare workers at an urban academic medical center: a prospective cohort study

2021 ◽  
Author(s):  
Tara C. Bouton ◽  
Sara Lodi ◽  
Jacquelyn Turcinovic ◽  
Sarah E. Weber ◽  
Emily Quinn ◽  
...  
Keyword(s):  
Cohort Study ◽  
Prospective Cohort Study ◽  
Prospective Cohort ◽  
Healthcare Workers ◽  
Positive Impact ◽  
Medical Center ◽  
Academic Medical Center ◽  
Post Vaccination ◽  
Unvaccinated Control ◽  
Rate Ratios

AbstractBackgroundCOVID-19 vaccine trials and post-implementation data suggest vaccination decreases SARS-CoV-2 infections. We examine COVID-19 vaccination’s impact on SARS-CoV-2 case rates and viral diversity among healthcare workers (HCW) during a high community prevalence period.MethodsA prospective cohort study from Boston Medical Center (BMC)’s HCW vaccination program, where staff received two doses of BNT162b2 or mRNA-1273. We included PCR-confirmed SARS-CoV-2 cases among HCWs from December 09, 2020 to February 23, 2021. Weekly SARS-CoV-2 rates per 100,000 person-day overall and by time from first injection (1-14 and >14 days) were compared with surrounding community rates. Viral genomes were sequenced from SARS CoV-2 positive samples.ResultsSARS-CoV-2 cases occurred in 1.4% (96/7109) of HCWs given at least a first dose and 0.3% (17/5913) of HCWs given both vaccine doses. Adjusted SARS-CoV-2 infection rate ratios were 0.73 (95% CI 0.53-1.00) 1-14 days and 0.18 (0.10-0.32) >14 days from first dose. HCW SARS-CoV-2 cases >14 days from initial dose compared to within 14 days were more often older (46 versus 38 years, p=0.007), Latinx (10% versus 8%, p=0.03), and asymptomatic (48% versus 11%, p=0.0002). SARS-CoV-2 rates among HCWs fell below those of the surrounding community, with a 18% versus 11% weekly decrease respectively (p=0.14). Comparison of 48 SARS-CoV-2 genomes sequenced from post-first dose cases did not indicate selection pressure towards known spike-antibody escape mutations.ConclusionsOur results indicate a positive impact of COVID-19 vaccines on SARS-CoV-2 case rates. Post-vaccination isolates did not show unusual genetic diversity or selection for mutations of concern.Main PointCases of SARS-CoV-2 among health care workers dropped rapidly with COVID-19 vaccination. Sequencing 48 breakthrough infections (overwhelmingly in 14 days after 1st dose) showed no clear sign of any differences in spike protein compared with time-matched, unvaccinated control sequences.

scholarly journals Wearable Heart Rate and Body Temperature Monitoring Device for Healthcare

2020 ◽  
Vol 1535 ◽  
pp. 012002
Author(s):  
Aslina Abu Bakar ◽  
Sarminadira Shaikh A. Rahim ◽  
Ahmad Rashidy Razali ◽  
Emilia Noorsal ◽  
Rosfariza Radzali ◽  
...  
Keyword(s):  
Heart Rate ◽  
Body Temperature ◽  
Temperature Monitoring ◽  
Monitoring Device

GSM Based Heart Rate and Temperature Monitoring System

2015 ◽  
Vol V4 (04) ◽  
Author(s):  
Mr. Aniket V. Kale ◽  
Miss. Supriya D. Gawade ◽  
Miss. Sayali Y. Jadhav ◽  
Mr. Samrat A. Patil ◽  
Keyword(s):  
Heart Rate ◽  
Monitoring System ◽  
Temperature Monitoring
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