scholarly journals A Review on Wearable and Contactless Sensing for COVID-19 With Policy Challenges

Author(s):  
Sagar Suresh Kumar ◽  
Kia Dashtipour ◽  
Qammer H. Abbasi ◽  
Muhammad A. Imran ◽  
Wasim Ahmad

The COVID-19 pandemic has affected more than 100 million people worldwide, with around 500,000 cases reported daily. This has led to the overwhelming of healthcare systems even in developed countries such as the US, UK, etc. Remote monitoring of COVID-19 patients with non-serious symptoms can help reduce the burden on healthcare facilities and make them available for high risk groups and the seriously affected. The pandemic has accelerated the demand for the remote patient monitoring (RPM) technologies, and the market is expected to reach 2.14 billion in 2027 from the value of 786.4 million in 2019. In RPM programs, there are two types of sensors that can be used: wearable and contactless. The former, which is currently more widely used, is not only more obtrusive and uncomfortable, but can also lead to cross-infection through patient contact. These two types of technologies are discussed and compared for each vital sign. In the respiratory system, the vital signs are the respiratory rate (RR) and oxygen saturation (SpO2), while for the latter, they are the heart rate/rhythm and the blood pressure (BP). Then, the discussion is broadened to policy level changes needed to expedite the use of such technologies for remote patient monitoring (RPM) in the world. Around 80% of countries' RPM programs are either informal or in a pilot phase, and thus lack policies and an established regulatory framework to implement their programs. The various policies needed to initiate, deliver, and reimburse RPM programs during emergency situations and outbreaks are discussed. Finally, technologies such as contactless systems, robotics, and Internet-of-things (IoT) that will revolutionize healthcare in the future by reducing the interaction between physicians and patients and cross-infection are discussed.

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 776
Author(s):  
Xiaohui Tao ◽  
Thanveer Basha Shaik ◽  
Niall Higgins ◽  
Raj Gururajan ◽  
Xujuan Zhou

Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM can be achieved with noninvasive digital technology without hindering a patient’s daily activities and can enhance the efficiency of healthcare delivery in acute clinical settings. In this study, an RPM system was built using radio frequency identification (RFID) technology for early detection of suicidal behaviour in a hospital-based mental health facility. A range of machine learning models such as Linear Regression, Decision Tree, Random Forest, and XGBoost were investigated to help determine the optimum fixed positions of RFID reader–antennas in a simulated hospital ward. Empirical experiments showed that Decision Tree had the best performance compared to Random Forest and XGBoost models. An Ensemble Learning model was also developed, took advantage of these machine learning models based on their individual performance. The research set a path to analyse dynamic moving RFID tags and builds an RPM system to help retrieve patient vital signs such as heart rate, pulse rate, respiration rate and subtle motions to make this research state-of-the-art in terms of managing acute suicidal and self-harm behaviour in a mental health ward.


2021 ◽  
Vol 10 (18) ◽  
pp. 4218
Author(s):  
Arik Eisenkraft ◽  
Yasmin Maor ◽  
Keren Constantini ◽  
Nir Goldstein ◽  
Dean Nachman ◽  
...  

COVID-19 exerts deleterious cardiopulmonary effects, leading to a worse prognosis in the most affected. This retrospective multi-center observational cohort study aimed to analyze the trajectories of key vitals amongst hospitalized COVID-19 patients using a chest-patch wearable providing continuous remote patient monitoring of numerous vital signs. The study was conducted in five COVID-19 isolation units. A total of 492 COVID-19 patients were included in the final analysis. Physiological parameters were measured every 15 min. More than 3 million measurements were collected including heart rate, systolic and diastolic blood pressure, cardiac output, cardiac index, systemic vascular resistance, respiratory rate, blood oxygen saturation, and body temperature. Cardiovascular deterioration appeared early after admission and in parallel with changes in the respiratory parameters, showing a significant difference in trajectories within sub-populations at high risk. Early detection of cardiovascular deterioration of COVID-19 patients is achievable when using frequent remote patient monitoring.


1996 ◽  
Vol 2 (4) ◽  
pp. 185-191 ◽  
Author(s):  
W G Scanlon ◽  
N E Evans ◽  
G C Crumley ◽  
Z M Mccreesh

Radio-based signalling devices will play an important role in future generations of remote patient monitoring equipment, both at home and in hospital. Ultimately, it will be possible to sample vital signs from patients, whatever their location and without them necessarily being aware that a measurement is being taken. This paper reviews current methods for the transmission by radio of physiological parameters over ranges of 0.3, 3 and 30 m, and describes the radiofrequency hardware required and the carrier frequencies commonly used. Future developments, including full duplex systems and the use of more advanced modulation schemes, are described. The paper concludes with a case study of a human temperature telemeter built to indicate ovulation. Clinical results clearly show the advantage to be had in adopting radio biotelemetry in this instance.


2017 ◽  
pp. 1183-1215
Author(s):  
Lea Skorin-Kapov ◽  
Ognjen Dobrijevic ◽  
Domagoj Piplica

The applicability of advanced mobile technologies in the m-Health domain has led to a number of studies and (limited) commercial products supporting delivery of health services to remote users. A key issue regarding successful delivery and acceptance of such services is meeting their Quality of Service (QoS) and Quality of Experience (QoE) requirements, focusing on technical aspects and end user perceived quality, respectively. In this paper, the authors address the topic of evaluating QoE for non-emergency remote patient monitoring services. They identify relevant QoE influence factors and metrics, and present the results of a QoE evaluation study, whereby they focus on usability aspects. The study involves 26 users testing a prototype version of the Ericsson Mobile Health service, which is based on a smartphone application and measurement of vital signs via medical sensors. The results show a strong correlation between QoE and: perceived effectiveness of the mobile interface (regarding both adequacy of smartphone screen size and smartphone application navigational support), perceived ease of conducting a blood pressure measurement task, and user motivation for service usage.


2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 7554-7554
Author(s):  
Radhika Bansal ◽  
Jonas Paludo ◽  
Adam Holland ◽  
Spychalla Megan ◽  
McClanahan Alli ◽  
...  

7554 Background: Chimeric antigen receptor T-cell therapy (CAR-T) are commonly administered inpatient due to concern for early onset cytokine release syndrome (CRS), especially with axicabtagene ciloleucel (axi-cel). We report Mayo Clinic Rochester experience for hospital-based outpatient (HBO) management of patients (pts) receiving axi-cel and identify opportunities for improvement. HBO is closely integrated with inpatient practice and includes the same specialty trained clinical team. It is the first point of contact 24/7 for pts and triage evaluations. Lymphodepletion chemotherapy and CAR-T infusion is given on HBO followed by daily monitoring till day 8 and thereafter, as clinically needed until admission criteria is met. Methods: We retrospectively analyzed database of pts who received axi-cel between 1/2018 and 1/2021. After 06/2020, remote patient monitoring (RPM) tools were implemented to collect patient-reported neurologic symptoms and vital signs via bluetooth-enabled devices 4 times daily through month 1. Adverse data trends are addressed by the HBO team. Results: Among 72 recipients, 89% received their cells outpatient; 8% remained outpatient for the entire month. CRS and neurotoxicity incidence were comparable to those reported from CIBMTR. Median time to first admission was 2 days (Table). Use of bridging therapy, increased CRP and LDH were associated with early admission (≤3 days). Median time to tocilizumab, steroid, oxygen support, vasopressor was 4 days after admission. Half of HBO visits required intervention such as blood transfusions, IV medications through the first month. Nine pts had enrolled in RPM to date; with 8 having evaluable data. With 4 scheduled entries/day, a median of 1 entry/day was skipped and 2 entries/day were answered incompletely. An average of 57 additional unscheduled entries were generated per pt. Among a median of 373 (range 91-522) readings per pt over the first month, 4% (2%-20%) of the readings generated alerts. An average of 4 alerts were seen within 48 hours prior to admission. Data including additional subjects will be presented at ASCO meeting. Conclusions: We report a feasible outpatient care model for management of axi-cel recipients with safe outcomes. Clinical characteristics associated with more aggressive disease are associated with likelihood of early admission. Early RPM experience suggest use of digital tools could improve monitoring compliance and may predict evolution to symptoms requiring escalation of care.[Table: see text]


Hypertension ◽  
2020 ◽  
Vol 76 (Suppl_1) ◽  
Author(s):  
Nadia A Liyanage-Don ◽  
Joseph E Schwartz ◽  
Nathalie Moise ◽  
Kelsey B Bryant ◽  
Adina Bono ◽  
...  

Introduction: The coronavirus disease 2019 (COVID19) pandemic required strict social distancing to curb transmission. Unfortunately, these measures severely limited healthcare access and chronic disease management. In response, many health organizations rapidly developed or expanded telemedicine to provide care directly to patients at home. Little has been reported about the impact of such interventions on clinical outcomes during COVID19. We examined whether enrollment in a remote patient monitoring (RPM) program for hypertension (HTN) prior to COVID19 was associated with improved blood pressure during the pandemic. Methods: We developed an RPM program that tracked vital signs, medication side effects, and treatment adherence patterns outside of the clinic. Patients were referred by their primary care doctor for uncontrolled HTN or suspected white coat HTN. Patients received a two-way tablet, blood pressure cuff, and virtual nursing support via scheduled video visits. Those referred for uncontrolled HTN who had at least two weeks of data both before and after the onset of COVID19 (defined as the first two weeks of March 2020) were included in the study. A mixed-models analysis that adjusted for serial autocorrelation was used to compare mean systolic blood pressure (SBP) and mean diastolic blood pressure (DBP) in the pre-/post-COVID19 periods. Results: Of 94 patients enrolled in the RPM program to date, 46 had at least two weeks of data both pre-COVID19 and post-COVID19. Mean age was 69.0 ± 10.9 years, 69.6% (32 of 46) were women, 78.3% (36 of 46) were Hispanic, and 63.0% (29 of 46) were Spanish-speaking. Pre-COVID, mean SBP was 132.31 ± 13.99 mmHg, mean DBP was 77.10 ± 9.87 mmHg, and 70% (32 of 46) of patients had uncontrolled BP (>130/80 mmHg per AHA guidelines). Post-COVID, mean SBP was 129.57 ± 13.29 mmHg, mean DBP was 76.00 ± 9.16 mmHg, and 57% (26 of 46) of patients had uncontrolled BP. There was a significant reduction in both mean SBP (β = –2.74, 95% CI –5.21, –0.26, p = 0.03) and mean DBP (β = –1.10, 95% CI –2.22, 0.02, p = 0.05) post-COVID vs. pre-COVID. Discussion: Despite the stress and social isolation associated with COVID19, participation in an RPM program that combines home BP monitoring with virtual nursing support can help maintain and even mildly decrease BP.


2014 ◽  
Vol 6 (4) ◽  
pp. 59-89 ◽  
Author(s):  
Lea Skorin-Kapov ◽  
Ognjen Dobrijevic ◽  
Domagoj Piplica

The applicability of advanced mobile technologies in the m-Health domain has led to a number of studies and (limited) commercial products supporting delivery of health services to remote users. A key issue regarding successful delivery and acceptance of such services is meeting their Quality of Service (QoS) and Quality of Experience (QoE) requirements, focusing on technical aspects and end user perceived quality, respectively. In this paper, the authors address the topic of evaluating QoE for non-emergency remote patient monitoring services. They identify relevant QoE influence factors and metrics, and present the results of a QoE evaluation study, whereby they focus on usability aspects. The study involves 26 users testing a prototype version of the Ericsson Mobile Health service, which is based on a smartphone application and measurement of vital signs via medical sensors. The results show a strong correlation between QoE and: perceived effectiveness of the mobile interface (regarding both adequacy of smartphone screen size and smartphone application navigational support), perceived ease of conducting a blood pressure measurement task, and user motivation for service usage.


2018 ◽  
Vol 11 (2) ◽  
pp. 20-24
Author(s):  
Honoriu Valean ◽  
Cola Cristian ◽  
Andrei Wegroszta ◽  
Cristinel Costea

Abstract This paper discusses mobile notifications in the context of health monitoring system that measure and store vital signs of the patient that are included in this program. The values measured are temperature and cardiac rhythm. This has two Android application, one is used by the patient to monitor his vital signs and the other is used by the physician to be able to see and receive push notifications of each individual patient. The sensors are connected to a Raspberry Pi and these devices send information to the Android smartphone via Bluetooth. The physician can monitor patient data in real time. All the information that is gathered by the smartphone from the sensors are sent to the cloud, can project a history and can detect some anomalies, for example, if the cardiac pulse is not within the limits of an accepted interval.


2020 ◽  
Author(s):  
Alexander Garyfallos

Abstract Forecasting forthcoming "health events" is an extremely challenging task for the Remote Patient Monitoring systems (RPM systems) sector, which relies in real time information and communication technologies. Remote patient monitoring is a medical service which includes following and observing patients that are not in the same location with their health care provider. In general, the patient is equipped with a “smart” monitoring device, and the recorded data (vital signs) are securely transmitted via telecommunication networks to the health care provider. Modern remote patient monitoring devices are small, discrete and easy to wear, allowing "bearers" to move freely and with comfort. In this framework, MOKAAL pc has developed the IFS_RPM service (Integrated Facilitation Services for Remote Patient Monitoring) supplying the necessary ICT infrastructure, which is necessary for the provision of the RPM services. Following the completion of IFS_RPM project, MOKAAL pc launched a research project under the code name "PROPHETTM" .PROPHETTM main objective is to investigate the possibilities of introducing a real time predicting model based on remotely collected vital signs, that would utilize time series of metric data in conjunction with the information stored in the Electronic Health Records (EHR) of the "bearer", attempting to predict in real time, the probability of a "health event" occurring in the near future.To meet this objective, the PROPHETTM project team designed an evolutionary prototype of the "health event" forecasting model, which was developed and tested in a laboratory environment and it will be upgraded to a working prototype to be tested in real conditions, in order to be incorporated into the IFS_RPM system, after reaching its maturity state.


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