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

Primary Purpose

COVID 19

Status
Completed
Phase
Not Applicable
Locations
Norway
Study Type
Interventional
Intervention
Biosensors
Sponsored by
Lars Wik
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional supportive care trial for COVID 19 focused on measuring symptoms, biosensors, home isolation, NEWS, Outcome

Eligibility Criteria

18 Years - undefined (Adult, Older Adult)All SexesAccepts Healthy Volunteers

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.

Sites / Locations

  • Lillestrom legevakt

Arms of the Study

Arm 1

Arm 2

Arm Type

Active Comparator

Active Comparator

Arm Label

Control

Intervention

Arm Description

Follow general recommendations fram doctor and health authorities what to do and pay attention to before new contact with health service.

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).

Outcomes

Primary Outcome Measures

Stop home isolation
Day during home isolation it was stopped due to hospitalization
NEWS score
5 or >3 for one organ system

Secondary Outcome Measures

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

Full Information

First Posted
April 1, 2020
Last Updated
September 20, 2022
Sponsor
Lars Wik
Collaborators
University of Stavanger, Oslo University Hospital, Norwegian Telemedicine, University of the Basque Country (UPV/EHU)
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1. Study Identification

Unique Protocol Identification Number
NCT04335097
Brief Title
Sensor Based Vital Signs Monitoring of Covid 19 Patients During Home Isolation
Acronym
HSC19
Official Title
Sensor Based Vital Signs Monitoring of Patients With Clinical Manifestation of Covid 19 Disease During Home Isolation, a Randomized Feasibility Study
Study Type
Interventional

2. Study Status

Record Verification Date
September 2022
Overall Recruitment Status
Completed
Study Start Date
April 22, 2020 (Actual)
Primary Completion Date
April 8, 2022 (Actual)
Study Completion Date
September 20, 2022 (Actual)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Sponsor-Investigator
Name of the Sponsor
Lars Wik
Collaborators
University of Stavanger, Oslo University Hospital, Norwegian Telemedicine, University of the Basque Country (UPV/EHU)

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
No

5. Study Description

Brief Summary
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).
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).

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
COVID 19
Keywords
symptoms, biosensors, home isolation, NEWS, Outcome

7. Study Design

Primary Purpose
Supportive Care
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Model Description
Two groups, control and intervention.
Masking
None (Open Label)
Masking Description
Sensor monitoring can not be masked since it is the prerequisite for the measures.
Allocation
Randomized
Enrollment
138 (Actual)

8. Arms, Groups, and Interventions

Arm Title
Control
Arm Type
Active Comparator
Arm Description
Follow general recommendations fram doctor and health authorities what to do and pay attention to before new contact with health service.
Arm Title
Intervention
Arm Type
Active Comparator
Arm Description
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).
Intervention Type
Device
Intervention Name(s)
Biosensors
Other Intervention Name(s)
Self reporting status
Intervention Description
Sensor that detect vital signs
Primary Outcome Measure Information:
Title
Stop home isolation
Description
Day during home isolation it was stopped due to hospitalization
Time Frame
1 to 21 days
Title
NEWS score
Description
5 or >3 for one organ system
Time Frame
1 to 21 days
Secondary Outcome Measure Information:
Title
Clinic at hospitalization
Description
Relevant vital clinical findings
Time Frame
At admittance hospital
Title
Symptoms developed
Description
Symptoms developed during home isolation
Time Frame
Duration of home isolation
Title
Relative/peers evaluation of the patient
Description
Their description of vital sign development
Time Frame
Duration of home isolation
Title
Serious of symptoms at admittance hospital
Description
Referred to ICU, intubated, length of stay
Time Frame
Hospital stay

10. Eligibility

Sex
All
Gender Based
Yes
Gender Eligibility Description
Ti legally give consent
Minimum Age & Unit of Time
18 Years
Accepts Healthy Volunteers
Accepts Healthy Volunteers
Eligibility Criteria
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.
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Lars Wik, MD
Organizational Affiliation
Oslo University Hospital
Official's Role
Principal Investigator
Facility Information:
Facility Name
Lillestrom legevakt
City
Lillestrom
Country
Norway

12. IPD Sharing Statement

Plan to Share IPD
No
Citations:
PubMed Identifier
28733431
Citation
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.
Results Reference
background
Citation
Seamless Healthcare Monitoring Advancements in Wearable, Attachable, and Invisible Devices. Chapter 5 Ballistocardiography.
Results Reference
background
Citation
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.
Results Reference
background
Citation
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.
Results Reference
background
Citation
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/
Results Reference
background
PubMed Identifier
29879021
Citation
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.
Results Reference
result
PubMed Identifier
25793605
Citation
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.
Results Reference
result
Links:
URL
http://www.who.int/emergencies/diseases/novel-coronavirus-2019
Description
World Health Organization (WHO) recommendation
URL
http://www.fhi.no/sv/smittsomme-sykdommer/corona/dags--og-ukerapporter/dags--og-ukerapporter-om-koronavirus/
Description
Folke Helse Instituttet (FHI) recommendations
URL
http://www.who.int/
Description
WHO
URL
http://www.omsyn.no/
Description
Omsyn Welfare Technology
URL
http://ehealthresearch.no/en/about-us
Description
E Health

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

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