<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04453774</url>
  </required_header>
  <id_info>
    <org_study_id>3185</org_study_id>
    <nct_id>NCT04453774</nct_id>
  </id_info>
  <brief_title>At Home Monitoring for Patients With Covid19</brief_title>
  <official_title>Covidfree@Home: At Home Monitoring Using Mobile Devices for Patients With Covid19</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>University Health Network, Toronto</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Sunnybrook Health Sciences Centre</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>University of Toronto</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>University Health Network, Toronto</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      The vast majority of individuals with Covid19 have mild illness that can be managed in the&#xD;
      outpatient setting. A small but significant number of these people will deteriorate and&#xD;
      require hospitalization. Symptoms are a poor - and possibly late - indicator for&#xD;
      deterioration. While people who have died, and/or been cared for in the ICU or hospital have&#xD;
      been well characterized, there remains a dearth of information about the clinical course of&#xD;
      people in the outpatient setting. Most notably, it is not known when to escalate to hospital&#xD;
      care. The consequence of non-escalation when needed is significant patient morbidity and&#xD;
      mortality, of escalation when not needed is unnecessarily overwhelmed hospitals. Technologies&#xD;
      for clinical management and early diagnostics for severe Covid19 infection will address this&#xD;
      challenge.&#xD;
&#xD;
      The research goal of this study is to use real-time remote patient monitoring to detect which&#xD;
      patients with Covid19 are at risk of deterioration to bring to hospital, while at the same&#xD;
      ensuring the worried will receive reassurance so they stay at home. The clinical goal is to&#xD;
      help clinicians provide excellent care using ubiquitous mobile phones.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      People with COVID infection recovering at home or in long-term care are at high risk of&#xD;
      hospitalization and death, a reservoir of the disease, and the source of any second wave.&#xD;
      Three important gaps still besiege their well-being and, consequently, the well-being of all&#xD;
      of us. First, we cannot yet accurately predict the approximately 10% who deteriorate and need&#xD;
      hospitalization. Deterioration happens quick and without warning. Delayed detection of&#xD;
      deterioration worsens patient outcomes. Second, COVID patients feel terrified and alone. This&#xD;
      leads them to come to EDs when not indicated, to have poor mental health and to risk&#xD;
      violating physical distancing rules. Third, the health of people with COVID cannot be&#xD;
      improved without having a means of studying and understanding what they are going through.&#xD;
      None of these gaps are being filled by public health.&#xD;
&#xD;
      It is imperative that Ontario have an effective and safe outpatient care and research&#xD;
      strategy for people with COVID isolated at home and in long term care to survive this COVID&#xD;
      pandemic.&#xD;
&#xD;
      The investigators are building a mobile smartwatch/smartphone application to create a&#xD;
      scalable safe virtual system that meets the care needs of COVID patients at home and in long&#xD;
      term care (including reassurance when they are doing well), that uses continuous symptom,&#xD;
      heart rate, respiratory rate, cough and other monitoring to predict who needs to go to&#xD;
      hospital in real time and that provides a research platform to learn how to further improve&#xD;
      and preserve their health.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Active, not recruiting</overall_status>
  <start_date type="Actual">November 1, 2020</start_date>
  <completion_date type="Anticipated">October 1, 2023</completion_date>
  <primary_completion_date type="Actual">July 1, 2022</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>N/A</allocation>
    <intervention_model>Single Group Assignment</intervention_model>
    <primary_purpose>Health Services Research</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Number of participants with an unplanned hospital admissions</measure>
    <time_frame>30 days</time_frame>
    <description>Number of participants with an unplanned emergency department visits leading to hospital admission</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Number of participants with planned hospital admissions that are found to be necessary</measure>
    <time_frame>30 days</time_frame>
    <description>Number of participants with planned hospital admissions that are found to be necessary</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Number of participants with an emergency department visits not resulting in hospital admission</measure>
    <time_frame>30 days</time_frame>
    <description>Number of participants with an emergency department visit not resulting in a hospital admission</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Number of planned hospital admissions which are found to be unnecessary</measure>
    <time_frame>30 days</time_frame>
    <description>Number of planned hospital admissions which are found to be unnecessary</description>
  </secondary_outcome>
  <number_of_arms>1</number_of_arms>
  <enrollment type="Actual">432</enrollment>
  <condition>Coronavirus</condition>
  <arm_group>
    <arm_group_label>Covi19 patients receiving intervention</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>We will collect self-reported symptoms via a questionnaire, temperature and oxygen saturation will be entered by the patient and passive near continuous sensing of heart rate, audio for cough detection, respiratory rate, cough and physical activity from a smart watch. The smart watch then transmits this sensor data to the paired smartphone.</description>
  </arm_group>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>Covidfree@home</intervention_name>
    <description>Mobile phone, Covidfree@home app, thermometer, pulse oximeter, smart watch</description>
    <arm_group_label>Covi19 patients receiving intervention</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria&#xD;
&#xD;
          -  Patients who tested positive for Covid19&#xD;
&#xD;
          -  Patients experiencing symptoms of Covid19 and have been deemed to likely have Covid19&#xD;
&#xD;
        Exclusion Criteria&#xD;
&#xD;
          -  Does not speak English&#xD;
&#xD;
          -  Unable to use a mobile smartphone and smart watch&#xD;
&#xD;
          -  Unable to complete questionnaires on own&#xD;
&#xD;
          -  Significant comorbid condition that would confound symptoms and sensor readings&#xD;
&#xD;
          -  Deemed palliative with goals of care being comfort measures only&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Robert Wu</last_name>
    <role>Principal Investigator</role>
    <affiliation>University Health Network, Toronto</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Sunnybrook Health Sciences Centre</name>
      <address>
        <city>Toronto</city>
        <state>Ontario</state>
        <zip>M4N3M5</zip>
        <country>Canada</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>University Health Network</name>
      <address>
        <city>Toronto</city>
        <state>Ontario</state>
        <zip>M5G 2Cr</zip>
        <country>Canada</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Canada</country>
  </location_countries>
  <verification_date>May 2023</verification_date>
  <study_first_submitted>May 6, 2020</study_first_submitted>
  <study_first_submitted_qc>June 29, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 1, 2020</study_first_posted>
  <last_update_submitted>May 12, 2023</last_update_submitted>
  <last_update_submitted_qc>May 12, 2023</last_update_submitted_qc>
  <last_update_posted type="Actual">May 15, 2023</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>University Health Network, Toronto</investigator_affiliation>
    <investigator_full_name>Robert Wu</investigator_full_name>
    <investigator_title>General Internal Medicine Site Director</investigator_title>
  </responsible_party>
  <keyword>telemedicine</keyword>
  <keyword>clinical care</keyword>
  <keyword>predictive modeling</keyword>
  <keyword>epidemiology</keyword>
  <keyword>hospitalization</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Coronavirus Infections</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04453774
org study id: 3185
nct id: NCT04453774
lead sponsor: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
The vast majority of individuals with Covid19 have mild illness that can be managed in the
      outpatient setting. A small but significant number of these people will deteriorate and
      require hospitalization. Symptoms are a poor - and possibly late - indicator for
      deterioration. While people who have died, and/or been cared for in the ICU or hospital have
      been well characterized, there remains a dearth of information about the clinical course of
      people in the outpatient setting. Most notably, it is not known when to escalate to hospital
      care. The consequence of non-escalation when needed is significant patient morbidity and
      mortality, of escalation when not needed is unnecessarily overwhelmed hospitals. Technologies
      for clinical management and early diagnostics for severe Covid19 infection will address this
      challenge.

      The research goal of this study is to use real-time remote patient monitoring to detect which
      patients with Covid19 are at risk of deterioration to bring to hospital, while at the same
      ensuring the worried will receive reassurance so they stay at home. The clinical goal is to
      help clinicians provide excellent care using ubiquitous mobile phones.
People with COVID infection recovering at home or in long-term care are at high risk of
      hospitalization and death, a reservoir of the disease, and the source of any second wave.
      Three important gaps still besiege their well-being and, consequently, the well-being of all
      of us. First, we cannot yet accurately predict the approximately 10% who deteriorate and need
      hospitalization. Deterioration happens quick and without warning. Delayed detection of
      deterioration worsens patient outcomes. Second, COVID patients feel terrified and alone. This
      leads them to come to EDs when not indicated, to have poor mental health and to risk
      violating physical distancing rules. Third, the health of people with COVID cannot be
      improved without having a means of studying and understanding what they are going through.
      None of these gaps are being filled by public health.

      It is imperative that Ontario have an effective and safe outpatient care and research
      strategy for people with COVID isolated at home and in long term care to survive this COVID
      pandemic.

      The investigators are building a mobile smartwatch/smartphone application to create a
      scalable safe virtual system that meets the care needs of COVID patients at home and in long
      term care (including reassurance when they are doing well), that uses continuous symptom,
      heart rate, respiratory rate, cough and other monitoring to predict who needs to go to
      hospital in real time and that provides a research platform to learn how to further improve
      and preserve their health.
allocation: N/A
intervention model: Single Group Assignment
primary purpose: Health Services Research
masking: None (Open Label)
measure: Number of participants with an unplanned hospital admissions
time frame: 30 days
description: Number of participants with an unplanned emergency department visits leading to hospital admission
measure: Number of participants with planned hospital admissions that are found to be necessary
time frame: 30 days
description: Number of participants with planned hospital admissions that are found to be necessary
measure: Number of participants with an emergency department visits not resulting in hospital admission
time frame: 30 days
description: Number of participants with an emergency department visit not resulting in a hospital admission
measure: Number of planned hospital admissions which are found to be unnecessary
time frame: 30 days
description: Number of planned hospital admissions which are found to be unnecessary
arm group label: Covi19 patients receiving intervention
arm group type: Experimental
description: We will collect self-reported symptoms via a questionnaire, temperature and oxygen saturation will be entered by the patient and passive near continuous sensing of heart rate, audio for cough detection, respiratory rate, cough and physical activity from a smart watch. The smart watch then transmits this sensor data to the paired smartphone.
intervention type: Device
intervention name: Covidfree@home
description: Mobile phone, Covidfree@home app, thermometer, pulse oximeter, smart watch
arm group label: Covi19 patients receiving intervention
criteria: 
gender: All
minimum age: N/A
maximum age: N/A
healthy volunteers: No
last name: Robert Wu
role: Principal Investigator
affiliation: University Health Network, Toronto
facility: 
facility: 
country: Canada
responsible party type: Principal Investigator
investigator affiliation: University Health Network, Toronto
investigator full name: Robert Wu
investigator title: General Internal Medicine Site Director
mesh term: Coronavirus Infections
sharing ipd: No