xml: <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/NCT06011356</url>
    </required_header>
    <id_info>
      <org_study_id>22HH7499</org_study_id>
      <nct_id>NCT06011356</nct_id>
    </id_info>
    <brief_title>Smart Watch Insights for Prevention of Exacerbations and Enhance Rehabilitation - Movement Study</brief_title>
    <acronym>SWIPER-MOVES</acronym>
    <official_title>Smart Watch Insights for Prevention of Exacerbations and Enhance Rehabilitation - Movement Study</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Imperial College London</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Imperial College London</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>
        Aims of the study:&#xD;
  &#xD;
          1. To deliver a scalable wellbeing programme to the local population of Imperial College&#xD;
             Healthcare NHS Trust, focusing on movement.&#xD;
  &#xD;
          2. To describe the natural history of long-term conditions using digital data from a&#xD;
             smartwatch.&#xD;
  &#xD;
          3. To identify digital information that is routinely collected by a smart watch that can be&#xD;
             used to predict outcomes in patients with long term conditions.&#xD;
  &#xD;
          4. To identify factors that determine whether participants engage with and improve in a&#xD;
             movement programme.&#xD;
  &#xD;
        Adult patients who are registered to the Imperial NHS Care Information Exchange (CIE), an NHS&#xD;
        patient-facing electronic health record, are eligible to participate in the study.&#xD;
        Participants will receive a smart watch for self-monitoring of their movement and wellbeing&#xD;
        and be asked to wear the device as much as possible. They will be asked to download a&#xD;
        smartphone application called Connected Life, which displays movement and information on&#xD;
        heart rate, breathing and oxygen levels to both the participant and the research team&#xD;
        (digital data). Participants will receive secure login details for the Connected Life&#xD;
        application from the research team, to ensure data privacy. The research team will look at&#xD;
        participants' health records, and attempt to identify associations between the digital data&#xD;
        and clinical information. This will allow the research team to identify digital data that&#xD;
        predicts the onset and natural history of long term conditions, which may potentially allow&#xD;
        for earlier diagnosis for future patients. The primary outcome of the study is the&#xD;
        identification of trends in movement based on step-count data recorded by the smartwatch.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Enrolling by invitation</overall_status>
    <start_date type="Anticipated">August 30, 2023</start_date>
    <completion_date type="Anticipated">August 30, 2026</completion_date>
    <primary_completion_date type="Anticipated">August 30, 2026</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Prospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Step count</measure>
      <time_frame>24 months</time_frame>
      <description>Change and trends in step count</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Quality of life (EQ-5D-5L)</measure>
      <time_frame>24 months</time_frame>
      <description>Change in quality of life as measured by the EQ-5D-5L questionnaire</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Patient Activation Measure (PAM)</measure>
      <time_frame>24 months</time_frame>
      <description>Change in patient engagement and confidence over own health, as measured by the Patient Activation Measure</description>
    </secondary_outcome>
    <enrollment type="Anticipated">1000</enrollment>
    <condition>Cardiovascular Diseases</condition>
    <condition>Non-Alcoholic Fatty Liver Disease</condition>
    <condition>Respiratory Disease</condition>
    <condition>Diabetes</condition>
    <intervention>
      <intervention_type>Device</intervention_type>
      <intervention_name>Smartwatch</intervention_name>
      <description>Activity tracker (step count, calories burned), measures heart rate, heart variability and other physiological variables.</description>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          We will recruit study participants from patients registered to the Imperial College&#xD;
          Healthcare NHS Trust Care Information Exchange (CIE), a patient-facing electronic health&#xD;
          record. Patients on CIE have already provided consent to be approached for NHS research.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Aged over 18 years&#xD;
  &#xD;
            -  Registered with the patient-facing electronic health record (Care Information&#xD;
               Exchange)&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Unwilling or unable to provide informed consent&#xD;
  &#xD;
            -  Arm or wrist injury or condition prohibiting the safe use of a smartwatch&#xD;
  &#xD;
            -  Any visual impairment preventing the use of the smartwatch or smartphone application.&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Nicholas S Peters, MD</last_name>
      <role>Principal Investigator</role>
      <affiliation>Imperial College Healthcare NHS Trust</affiliation>
    </overall_official>
    <location>
      <facility>
        <name>Imperial College Healthcare NHS Trust</name>
        <address>
          <city>London</city>
          <state>United Kingdom (+44)</state>
          <zip>NW25QS</zip>
          <country>United Kingdom</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>United Kingdom</country>
    </location_countries>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 21, 2023</study_first_submitted>
    <study_first_submitted_qc>August 21, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 25, 2023</study_first_posted>
    <last_update_submitted>August 21, 2023</last_update_submitted>
    <last_update_submitted_qc>August 21, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 25, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>Digital health</keyword>
    <keyword>Smart wearable</keyword>
    <keyword>Population health</keyword>
    <keyword>Digital biomarkers</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Liver Diseases</mesh_term>
      <mesh_term>Fatty Liver</mesh_term>
      <mesh_term>Non-alcoholic Fatty Liver Disease</mesh_term>
      <mesh_term>Respiratory Tract Diseases</mesh_term>
      <mesh_term>Respiration Disorders</mesh_term>
      <mesh_term>Cardiovascular Diseases</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>Undecided</sharing_ipd>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: 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/NCT06011356
  org study id: 22HH7499
  nct id: NCT06011356
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  Aims of the study:
  
          1. To deliver a scalable wellbeing programme to the local population of Imperial College
             Healthcare NHS Trust, focusing on movement.
  
          2. To describe the natural history of long-term conditions using digital data from a
             smartwatch.
  
          3. To identify digital information that is routinely collected by a smart watch that can be
             used to predict outcomes in patients with long term conditions.
  
          4. To identify factors that determine whether participants engage with and improve in a
             movement programme.
  
        Adult patients who are registered to the Imperial NHS Care Information Exchange (CIE), an NHS
        patient-facing electronic health record, are eligible to participate in the study.
        Participants will receive a smart watch for self-monitoring of their movement and wellbeing
        and be asked to wear the device as much as possible. They will be asked to download a
        smartphone application called Connected Life, which displays movement and information on
        heart rate, breathing and oxygen levels to both the participant and the research team
        (digital data). Participants will receive secure login details for the Connected Life
        application from the research team, to ensure data privacy. The research team will look at
        participants' health records, and attempt to identify associations between the digital data
        and clinical information. This will allow the research team to identify digital data that
        predicts the onset and natural history of long term conditions, which may potentially allow
        for earlier diagnosis for future patients. The primary outcome of the study is the
        identification of trends in movement based on step-count data recorded by the smartwatch.
  observational model: Cohort
  time perspective: Prospective
  measure: Step count
  time frame: 24 months
  description: Change and trends in step count
  measure: Quality of life (EQ-5D-5L)
  time frame: 24 months
  description: Change in quality of life as measured by the EQ-5D-5L questionnaire
  measure: Patient Activation Measure (PAM)
  time frame: 24 months
  description: Change in patient engagement and confidence over own health, as measured by the Patient Activation Measure
  intervention type: Device
  intervention name: Smartwatch
  description: Activity tracker (step count, calories burned), measures heart rate, heart variability and other physiological variables.
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: Accepts Healthy Volunteers
  last name: Nicholas S Peters, MD
  role: Principal Investigator
  affiliation: Imperial College Healthcare NHS Trust
  facility: 
  country: United Kingdom
  responsible party type: Sponsor
  mesh term: Liver Diseases
  mesh term: Fatty Liver
  mesh term: Non-alcoholic Fatty Liver Disease
  mesh term: Respiratory Tract Diseases
  mesh term: Respiration Disorders
  mesh term: Cardiovascular Diseases
  sharing ipd: Undecided