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/NCT06030323</url>
    </required_header>
    <id_info>
      <org_study_id>A096458</org_study_id>
      <nct_id>NCT06030323</nct_id>
    </id_info>
    <brief_title>Validation of AI Models to Measure Physical Activity After a Stroke</brief_title>
    <acronym>CaRRAT</acronym>
    <official_title>Validation and Testing of Artificial Intelligence Models to Measure Physical Activity in Patients Admitted to Hospital Following a Stroke</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Cambridge University Hospitals NHS Foundation Trust</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Cambridge University Hospitals NHS Foundation Trust</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 research team are developing algorithms using artificial intelligence that use&#xD;
        information collected by accelerometers to detect a person's position, such as whether an&#xD;
        individual is lying, sitting, or standing, and the individual's movements, such as whether&#xD;
        they are taking steps or standing up.&#xD;
  &#xD;
        Sensor location will affect the accuracy of the model and acceptability of the method. The&#xD;
        research team are therefore developing algorithms for four different locations.&#xD;
  &#xD;
        The purpose of the research is for the development of the algorithms and check whether they&#xD;
        accurately recognise different positions and movements in people whose movement is affected&#xD;
        by a stroke, and by being in a hospital environment (e.g. using a profiling bed).&#xD;
  &#xD;
        The research team plan to recruit between 34 and 50 participants who are admitted hospital&#xD;
        due to having a stroke. After providing informed consent, participants will be asked to&#xD;
        complete a one-off assessment with a member of the research team and a ward physiotherapist.&#xD;
        Participants will be asked to wear the four sensors, and move through a series of postures,&#xD;
        walk for up to six minutes, and stand as many times as they feel able in one minute.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">June 24, 2023</start_date>
    <completion_date type="Anticipated">June 24, 2024</completion_date>
    <primary_completion_date type="Anticipated">June 24, 2024</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>Sensitivity and specificity of AI models</measure>
      <time_frame>Collected during hospitalisation (up to 12 weeks)</time_frame>
    </primary_outcome>
    <enrollment type="Anticipated">34</enrollment>
    <condition>Stroke</condition>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>No intervention</intervention_name>
      <description>This is not an interventional study. The study is classified as 'pre-clinical device development or performance testing'. The purpose of the study is to develop and validate the AI models using data collected from people affected by stroke in a hospital environment, to test the accuracy of the optimised models, and to ascertain patient preference for sensor location.</description>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          Acute and sub-acute stroke inpatients&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  admitted to hospital with a diagnosis of an acute stroke.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  unable to provide informed consent;&#xD;
  &#xD;
            -  receiving end-of-life care;&#xD;
  &#xD;
            -  the consultant in charge of their care disagrees with their inclusion&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_contact>
      <last_name>Peter Hartley, PhD</last_name>
      <phone>441223596317</phone>
      <email>peter.hartley@nhs.net</email>
    </overall_contact>
    <location>
      <facility>
        <name>Cambridge University Hospital NHS Foundation Trust</name>
        <address>
          <city>Cambridge</city>
          <state>Cambridgeshire</state>
          <zip>CB2 0QQ</zip>
          <country>United Kingdom</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Peter Hartley, PhD</last_name>
        <phone>01223 596317</phone>
        <email>peter.hartley@nhs.net</email>
      </contact>
    </location>
    <location_countries>
      <country>United Kingdom</country>
    </location_countries>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>August 30, 2023</study_first_submitted>
    <study_first_submitted_qc>September 6, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 11, 2023</study_first_posted>
    <last_update_submitted>September 6, 2023</last_update_submitted>
    <last_update_submitted_qc>September 6, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 11, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Cambridge University Hospitals NHS Foundation Trust</investigator_affiliation>
      <investigator_full_name>Peter Hartley</investigator_full_name>
      <investigator_title>Principal Investigator</investigator_title>
    </responsible_party>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Stroke</mesh_term>
    </condition_browse>
    <!-- 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/NCT06030323
  org study id: A096458
  nct id: NCT06030323
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  The research team are developing algorithms using artificial intelligence that use
        information collected by accelerometers to detect a person's position, such as whether an
        individual is lying, sitting, or standing, and the individual's movements, such as whether
        they are taking steps or standing up.
  
        Sensor location will affect the accuracy of the model and acceptability of the method. The
        research team are therefore developing algorithms for four different locations.
  
        The purpose of the research is for the development of the algorithms and check whether they
        accurately recognise different positions and movements in people whose movement is affected
        by a stroke, and by being in a hospital environment (e.g. using a profiling bed).
  
        The research team plan to recruit between 34 and 50 participants who are admitted hospital
        due to having a stroke. After providing informed consent, participants will be asked to
        complete a one-off assessment with a member of the research team and a ward physiotherapist.
        Participants will be asked to wear the four sensors, and move through a series of postures,
        walk for up to six minutes, and stand as many times as they feel able in one minute.
  observational model: Cohort
  time perspective: Prospective
  measure: Sensitivity and specificity of AI models
  time frame: Collected during hospitalisation (up to 12 weeks)
  intervention type: Other
  intervention name: No intervention
  description: This is not an interventional study. The study is classified as 'pre-clinical device development or performance testing'. The purpose of the study is to develop and validate the AI models using data collected from people affected by stroke in a hospital environment, to test the accuracy of the optimised models, and to ascertain patient preference for sensor location.
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Peter Hartley, PhD
  phone: 441223596317
  email: peter.hartley@nhs.net
  facility: 
  status: Recruiting
  contact: 
  country: United Kingdom
  responsible party type: Principal Investigator
  investigator affiliation: Cambridge University Hospitals NHS Foundation Trust
  investigator full name: Peter Hartley
  investigator title: Principal Investigator
  mesh term: Stroke