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/NCT06017687</url>
    </required_header>
    <id_info>
      <org_study_id>US Data Collection Study</org_study_id>
      <nct_id>NCT06017687</nct_id>
    </id_info>
    <brief_title>Data Collection and Evaluation of OptiBP Under Investigational Use</brief_title>
    <official_title>Data Collection and Evaluation of a Mobile, Machine-learning-based Solution for Blood Pressure Estimation - the OptiBP Study.</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Biospectal</agency>
        <agency_class>Industry</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Biospectal</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 purpose of this study is to collect data to develop and evaluate the use of&#xD;
        state-of-the-art machine learning approaches within a mobile phone application for the&#xD;
        estimation of blood pressure.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Participants will simultaneously acquire blood pressure measurements through a cuff-based,&#xD;
        automatic, over-the-counter blood pressure monitor on the upper arm, while recording an&#xD;
        optical signal through the camera of a smartphone on the tip of the index of the opposite&#xD;
        arm.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">September 15, 2023</start_date>
    <completion_date type="Anticipated">September 2026</completion_date>
    <primary_completion_date type="Anticipated">September 2024</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>Other</primary_purpose>
      <masking>None (Open Label)</masking>
    </study_design_info>
    <primary_outcome>
      <measure>Collect Blood Pressure (BP) data and assess performance of smartphone based BP estimations models</measure>
      <time_frame>12 months</time_frame>
      <description>To collect data to train a machine-learning based solution for estimating blood pressure. Participants will simultaneously acquire blood pressure measurements through a cuff-based, automatic, over-the-counter blood pressure monitor at the upper arm, while recording an optical signal through the camera of a smartphone on the tip of the index of the opposite arm. The performance of the machine learning model will be assessed by calculating the mean and standard deviation of the error of blood pressure estimations versus cuff-based, automatic, over-the-counter blood pressure monitors.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Safety by assessing inconvenience and adverse events</measure>
      <time_frame>12 months</time_frame>
      <description>To identify potential use errors of an app that use the machine-learning model to estimate blood pressure. And to assess safety of the intervention.</description>
    </secondary_outcome>
    <number_of_arms>1</number_of_arms>
    <enrollment type="Anticipated">500000</enrollment>
    <condition>General Population</condition>
    <arm_group>
      <arm_group_label>Principal Arm</arm_group_label>
      <arm_group_type>Other</arm_group_type>
      <description>Each participant will use the OptiBP Study app on their smartphone</description>
    </arm_group>
    <intervention>
      <intervention_type>Device</intervention_type>
      <intervention_name>OptiBP Study app</intervention_name>
      <description>Each participant will use OptiBP Study app to measure their blood pressure optically by applying their fingertip to their smartphone camera, and subsequently enter the blood pressure values obtained by measuring it with their own blood pressure cuff.</description>
      <arm_group_label>Principal Arm</arm_group_label>
    </intervention>
    <eligibility>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            1. Be at least 18 years old (at least 19 years old in Alabama and Nebraska, at least 21&#xD;
               years old in Puerto Rico);&#xD;
  &#xD;
            2. Live in the United States of America&#xD;
  &#xD;
            3. Have an Android smartphone&#xD;
  &#xD;
            4. Have access to an arm-worn blood pressure monitor (cuff)&#xD;
  &#xD;
            5. Have access to the Google Play store to download the OptiBP study app on their phone&#xD;
  &#xD;
            6. Be comfortable communicating in written and spoken English&#xD;
  &#xD;
            7. Be willing and able to provide informed consent to participate in the study&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            1. Known contact dermatitis to nickel/chromium&#xD;
  &#xD;
            2. Lesion or deficiency on hand, preventing placing a finger on the smartphone camera&#xD;
  &#xD;
            3. Known dysrhythmia like bigeminy, trigeminy, isolated VPB, atrial fibrillation&#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_contact>
      <last_name>Frederic Frappereau</last_name>
      <phone>650-229-8604</phone>
      <email>study@biospectal.com</email>
    </overall_contact>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>August 24, 2023</study_first_submitted>
    <study_first_submitted_qc>August 24, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 30, 2023</study_first_posted>
    <last_update_submitted>September 7, 2023</last_update_submitted>
    <last_update_submitted_qc>September 7, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 11, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <patient_data>
      <sharing_ipd>No</sharing_ipd>
      <ipd_description>The data is anonymized and used for machine learning training</ipd_description>
    </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/NCT06017687
  org study id: US Data Collection Study
  nct id: NCT06017687
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  The purpose of this study is to collect data to develop and evaluate the use of
        state-of-the-art machine learning approaches within a mobile phone application for the
        estimation of blood pressure.
  Participants will simultaneously acquire blood pressure measurements through a cuff-based,
        automatic, over-the-counter blood pressure monitor on the upper arm, while recording an
        optical signal through the camera of a smartphone on the tip of the index of the opposite
        arm.
  allocation: N/A
  intervention model: Single Group Assignment
  primary purpose: Other
  masking: None (Open Label)
  measure: Collect Blood Pressure (BP) data and assess performance of smartphone based BP estimations models
  time frame: 12 months
  description: To collect data to train a machine-learning based solution for estimating blood pressure. Participants will simultaneously acquire blood pressure measurements through a cuff-based, automatic, over-the-counter blood pressure monitor at the upper arm, while recording an optical signal through the camera of a smartphone on the tip of the index of the opposite arm. The performance of the machine learning model will be assessed by calculating the mean and standard deviation of the error of blood pressure estimations versus cuff-based, automatic, over-the-counter blood pressure monitors.
  measure: Safety by assessing inconvenience and adverse events
  time frame: 12 months
  description: To identify potential use errors of an app that use the machine-learning model to estimate blood pressure. And to assess safety of the intervention.
  arm group label: Principal Arm
  arm group type: Other
  description: Each participant will use the OptiBP Study app on their smartphone
  intervention type: Device
  intervention name: OptiBP Study app
  description: Each participant will use OptiBP Study app to measure their blood pressure optically by applying their fingertip to their smartphone camera, and subsequently enter the blood pressure values obtained by measuring it with their own blood pressure cuff.
  arm group label: Principal Arm
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: Accepts Healthy Volunteers
  last name: Frederic Frappereau
  phone: 650-229-8604
  email: study@biospectal.com
  responsible party type: Sponsor
  sharing ipd: No
  ipd description: The data is anonymized and used for machine learning training