Sensor Based Vital Signs Monitoring of Covid 19 Patients During Home Isolation (HSC19)

Sensor Based Vital Signs Monitoring of Patients With Clinical Manifestation of Covid 19 Disease During Home Isolation, a Randomized Feasibility Study

Severe acute respiratory syndrome (SARS) SARS-Cov-2 disease (COVID-19) is an infectious disease caused by a coronavirus. The pandemic first described in Wuhan, China, has since spread across the whole world and caused dramatic strain on health care in many countries. Patients infected with the virus mostly report mild to moderate respiratory symptoms like shortness of breath and coughing, and febrile symptoms. It is of paramount importance to preserve health service capacity by identifying those with serious illness without transferring all infected patients to emergency rooms or Hospitals. In addition, it is important to identify seriously ill patients early enough and before they reach a point of deterioration where they can be extremely challenging to handle in both prehospital and hospital environment.

The present study is designed to sample biosensor data from patients treated and observed at home due to mild and moderate SARS-Cov-2 disease. Such a system would be useful, both for the treatment of individual patients as well as for assessing the efficacy and safety of care given to these patients. Investigators intend to improve quality and safety of home care by continuous monitoring and a set of rules for follow-up.

Investigators hypothesized that patients and local health system may benefit from the feedback of a simple monitoring system, which detects changes in respiration, temperature and circulation variables in combination with the patient's subjective experiences of care. Patients may be referred to hospitalization earlier. In the present study we will use live continuous and non-continuous biosensor data to monitor the development of vital parameters for Covid 19 patients compared with patients who are not monitored electronically (standard of care).

Study Overview

• Status: Completed
• Conditions: COVID 19
• Intervention / Treatment: Device: Biosensors

Detailed Description

Severe acute respiratory syndrome (SARS) SARS-Cov-2 disease (COVID-19) is an infectious disease caused by a coronavirus. The pandemic first described in Wuhan, China, has since spread across the whole world and caused dramatic strain on health care in many countries. The virus spreads primarily through droplets of saliva or discharge from the nose when an infected person coughs or sneezes.1 Patients infected with the virus mostly report mild to moderate respiratory symptoms like shortness of breath and coughing, and febrile symptoms. Most recover without requiring special treatment. However, older people, and those with underlying medical problems (cardiovascular disease, diabetes, chronic respiratory disease, and cancer) are more likely to develop serious illness.1 Younger patients have been reported with serious illness as well. In the present situation, it is of paramount importance to preserve health service capacity by identifying those with serious illness without transferring all infected patients to emergency rooms or Hospitals. In addition, it is important to identify seriously ill patients early enough and before they reach a point of deterioration where they can be extremely challenging to handle in both prehospital and hospital environment.

The number of subjects with positive test of the virus is increasing and so does the number of patients hospitalized.2 In parallel, most patients with positive test result or typical clinical symptoms are at home with information what to do if their clinical symptom status deteriorates.2 The Norwegian Interaction Reform was implemented in 2012.3 Key elements of the reform are guidance of the health care in the future and identify new directions. Prevention and early efforts are important and this will be achieved by creating co-working arenas for different parts of our health system. More health services must be moved closer to where the inhabitants live and simultaneously strengthening the community health system. New tools for monitoring the well-being of the patients must be developed in order to act early enough to avoid severe deterioration of health status and avoid new hospitalization. This goal has become even more important during the Covid 19 pandemic because the healthcare system is not prepared or built to take care of all these patients in hospitals.

In the local community's wearable and wireless biosensors collecting continuous physiological data (CPD) in real time in order to generate information reflecting the patients' current state is established. This is recognized as welfare technology, and it is a generic term for a heterogeneous group of technologies.4 There are few studies documenting their efficacy, effectiveness and efficiency. One key driver for the development of wearable biosensors is the potential to use CPD to generate real-time, clinically actionable insights from predictive analytics that include early warnings of clinical deterioration and prompts for behavioral changes. The advent of machine learning methods that can detect subtle patterns from large sets of CPD may make this achievable.

Using CPD to guide clinical decisions may be a major advance for patients with chronic diseases and at present time when our health system is put on an extreme stretch. This may drive the evolution from episodic to continuous patient care.

The present study is designed to sample biosensor data from patients treated and observed at home due to mild and moderate SARS-Cov-2 disease. Such a system would be useful, both for the treatment of individual patients as well as for assessing the efficacy and safety of care given to these patients. Investigators intend to improve quality and safety of home care by continuous monitoring and a set of rules for follow-up.

Investigators hypothesized that patients and local health system may benefit from the feedback of a simple monitoring system, which detects changes in respiration, temperature and circulation variables in combination with the patient's subjective experiences of care. Patients may be referred to hospitalization earlier. In the present study investigators will use live continuous and non-continuous biosensor data to monitor the development of vital parameters for Covid 19 patients compared with patients who are not monitored electronically (standard of care).

• Study Type: Interventional
• Enrollment (Actual): 138
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Norway
  • Lillestrom, Norway
    • Lillestrom legevakt

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

Description

Inclusion Criteria:

• Valid informed consent.
• All Covid 19 positive patients age ≥18 years who are under care at home for Covid 19 infection.
• Patients with typical Covid 19 clinical symptoms where a test has not been taken may also be included if a test later is positive.
• Able to log into internet.

Exclusion Criteria:

• Age <18 years.
• Covid 19 negative.
• Internals in prison.
• Individuals living in special homes due to need of care.
• Refusal of participation.
• Comorbidity that hinder the patient to run the system.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: SUPPORTIVE_CARE
• Allocation: RANDOMIZED
• Interventional Model: PARALLEL
• Masking: NONE

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
ACTIVE_COMPARATOR: Control; Follow general recommendations fram doctor and health authorities what to do and pay attention to before new contact with health service.	Device: Biosensors; Sensor that detect vital signs; Other Names: Self reporting status
ACTIVE_COMPARATOR: Intervention; Follow general recommendations fram doctor and health authorities what to do and pay attention to before new contact with health service. I addition active reporting of clinical status and continuous vital sign monitoring based on electronic sensors (Welfare technology).	Device: Biosensors; Sensor that detect vital signs; Other Names: Self reporting status

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Stop home isolation	Day during home isolation it was stopped due to hospitalization	1 to 21 days
NEWS score	5 or >3 for one organ system	1 to 21 days

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Clinic at hospitalization	Relevant vital clinical findings	At admittance hospital
Symptoms developed	Symptoms developed during home isolation	Duration of home isolation
Relative/peers evaluation of the patient	Their description of vital sign development	Duration of home isolation
Serious of symptoms at admittance hospital	Referred to ICU, intubated, length of stay	Hospital stay

Collaborators and Investigators

• Sponsor: Lars Wik
• Collaborators: Oslo University Hospital; University of the Basque Country (UPV/EHU); University of Stavanger; Norwegian Telemedicine

Publications and helpful links

General Publications

• Bodapati RK, Kizer JR, Kop WJ, Kamel H, Stein PK. Addition of 24-Hour Heart Rate Variability Parameters to the Cardiovascular Health Study Stroke Risk Score and Prediction of Incident Stroke: The Cardiovascular Health Study. J Am Heart Assoc. 2017 Jul 21;6(7):e004305. doi: 10.1161/JAHA.116.004305.
• Seamless Healthcare Monitoring Advancements in Wearable, Attachable, and Invisible Devices. Chapter 5 Ballistocardiography.
• The Royal College of Physicians. National Early Warning Score (NEWS) 2: Standardising the assessment of acute-illness severity in the NHS. London: RCP; 2017. p. 1-77.
• Williams B, Alberti G, Ball C, et al; Royal College for Physicians: National Early Warning Score (NEWS): Standardising the Assessment of Acute-Illness Severity in the NHS. 2012London, ENG, Royal College of Physicians.
• Meld. St. 16 (2010-2011) Report to the Storting (white paper) Summary - National Health and Care Services Plan. https://www.regjeringen.no/en/dokumenter/meld.-st.-16-2010-2011/id639794/
• Samsudin MI, Liu N, Prabhakar SM, Chong SL, Kit Lye W, Koh ZX, Guo D, Rajesh R, Ho AFW, Ong MEH. A novel heart rate variability based risk prediction model for septic patients presenting to the emergency department. Medicine (Baltimore). 2018 Jun;97(23):e10866. doi: 10.1097/MD.0000000000010866.
• Melillo P, Izzo R, Orrico A, Scala P, Attanasio M, Mirra M, De Luca N, Pecchia L. Automatic prediction of cardiovascular and cerebrovascular events using heart rate variability analysis. PLoS One. 2015 Mar 20;10(3):e0118504. doi: 10.1371/journal.pone.0118504. eCollection 2015.

Helpful Links

• World Health Organization (WHO) recommendation
• Folke Helse Instituttet (FHI) recommendations
• WHO
• Omsyn Welfare Technology
• E Health

Study record dates

Study Major Dates

• Study Start (ACTUAL): April 22, 2020
• Primary Completion (ACTUAL): April 8, 2022
• Study Completion (ACTUAL): September 20, 2022

Study Registration Dates

• First Submitted: April 1, 2020
• First Submitted That Met QC Criteria: April 2, 2020
• First Posted (ACTUAL): April 6, 2020

Study Record Updates

• Last Update Posted (ACTUAL): September 21, 2022
• Last Update Submitted That Met QC Criteria: September 20, 2022
• Last Verified: September 1, 2022

More Information

Terms related to this study

• Keywords: Outcome; symptoms; home isolation; NEWS; biosensors
• Additional Relevant MeSH Terms: Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Virus Diseases; Infections; Respiratory Tract Infections; Respiratory Tract Diseases; Pneumonia, Viral; Pneumonia; Lung Diseases; COVID-19
• Other Study ID Numbers: 127157

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No