<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/NCT02983812</url>
  </required_header>
  <id_info>
    <org_study_id>825189</org_study_id>
    <nct_id>NCT02983812</nct_id>
  </id_info>
  <brief_title>Developing Enhanced Prediction Models</brief_title>
  <acronym>PREDICT</acronym>
  <official_title>Developing Enhanced Prediction Models to Identify Patients at Risk for Hospital Readmission by Collecting Patient-Generated Health Data</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>University of Pennsylvania</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>University of Pennsylvania</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
  </oversight_info>
  <brief_summary>
    <textblock>
      In this study, patients will be prospectively enrolled for data collection to design&#xD;
      prediction models that integrate claims data (inpatient, outpatient, and pharmacy),&#xD;
      electronic health record data (on clinical, social, and behavioral indicators), and&#xD;
      patient-generated activity data. Patients will be randomized to use either a smartphone or a&#xD;
      wearable activity tracking device to capture patient-generated health data.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Many hospital readmissions could be prevented if higher risk patients were identified and&#xD;
      effective interventions then targeted towards these individuals. However, most existing&#xD;
      claims-based predictive models perform poorly and do not provide timely and actionable&#xD;
      information. In this study, researchers will prospectively enroll patients for data&#xD;
      collection to design prediction models that integrate claims data (inpatient, outpatient, and&#xD;
      pharmacy), electronic health record data (on clinical, social, and behavioral indicators),&#xD;
      and use wearable devices or smartphones to collect patient-generated data (physical activity&#xD;
      and sleep patterns). Patients will be randomized to use either a smartphone or a wearable&#xD;
      activity tracking device to capture patient-generated health data.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">January 23, 2017</start_date>
  <completion_date type="Actual">December 7, 2019</completion_date>
  <primary_completion_date type="Actual">June 7, 2019</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>Hospital readmission within 30 days of discharge</measure>
    <time_frame>30 days</time_frame>
    <description>Patient readmission within 30 days of initial discharge</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>The secondary outcome measures include re-hospitalization within 90 days of discharge.</measure>
    <time_frame>90 days</time_frame>
    <description>Patient readmission within 90 days of initial discharge</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Re-hospitalization within 6 months of discharge</measure>
    <time_frame>6 months</time_frame>
    <description>Patient readmission within 6 months of initial discharge</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Emergency department visits within 6 months of discharge</measure>
    <time_frame>6 months</time_frame>
    <description>Patient visit to the emergency department within 6 months of initial discharge</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Total health care cost utilization in 6 months after discharge</measure>
    <time_frame>6 months</time_frame>
    <description>The cost of health care services for patient within 6 months of initial discharge</description>
  </secondary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Actual">500</enrollment>
  <condition>Acute Myocardial Infraction or Chest Pain</condition>
  <condition>Chronic Obstructive Pulmonary Disease (COPD)</condition>
  <condition>Congestive Heart Failure (CHF)</condition>
  <condition>Diabetes</condition>
  <condition>Pneumonia</condition>
  <arm_group>
    <arm_group_label>Activity Monitoring - Smartphone</arm_group_label>
    <description>Patients in the activity monitoring - smartphone group will be randomly assigned to track their data using a smartphone app (which collects step counts) for 6 months. There will be no intervention for either group, both are being passively monitored.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Activity Monitoring - Wearable</arm_group_label>
    <description>Patients in the activity monitoring - wearable device group will be randomly assigned to track their data using a wearable activity tracker (which collects step counts and sleep patterns/duration). There will be no intervention for either group, both are being passively monitored.</description>
  </arm_group>
  <eligibility>
    <study_pop>
      <textblock>
        The study population will be drawn from adults admitted to one of the University of&#xD;
        Pennsylvania Health System hospitals. Patients will be invited to participate by a member&#xD;
        of the research team prior to hospital discharge.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          1. Be 18 years or older&#xD;
&#xD;
          2. Be able to provide informed consent&#xD;
&#xD;
          3. Be admitted to the Hospital of the University of Pennsylvania or Penn Presbyterian&#xD;
             Medical Center&#xD;
&#xD;
          4. Have a smartphone or tablet compatible with activity tracking devices&#xD;
&#xD;
          5. Plan to be discharged to home&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
        Have no medical condition which prohibits them from ambulating or plan for any medical&#xD;
        procedure over the next 6 months that would prohibit them from ambulating.&#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_official>
    <last_name>Mitesh Patel</last_name>
    <role>Principal Investigator</role>
    <affiliation>University of Pennsylvania</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Penn Medicine</name>
      <address>
        <city>Philadelphia</city>
        <state>Pennsylvania</state>
        <zip>19103</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <verification_date>February 2020</verification_date>
  <study_first_submitted>November 16, 2016</study_first_submitted>
  <study_first_submitted_qc>December 1, 2016</study_first_submitted_qc>
  <study_first_posted type="Estimate">December 6, 2016</study_first_posted>
  <last_update_submitted>February 5, 2020</last_update_submitted>
  <last_update_submitted_qc>February 5, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">February 6, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Lung Diseases, Obstructive</mesh_term>
    <mesh_term>Pulmonary Disease, Chronic Obstructive</mesh_term>
    <mesh_term>Heart Failure</mesh_term>
    <mesh_term>Chest Pain</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
    <ipd_description>Individual participant data will not be made available.</ipd_description>
  </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/NCT02983812
org study id: 825189
nct id: NCT02983812
lead sponsor: 
has dmc: No
In this study, patients will be prospectively enrolled for data collection to design
      prediction models that integrate claims data (inpatient, outpatient, and pharmacy),
      electronic health record data (on clinical, social, and behavioral indicators), and
      patient-generated activity data. Patients will be randomized to use either a smartphone or a
      wearable activity tracking device to capture patient-generated health data.
Many hospital readmissions could be prevented if higher risk patients were identified and
      effective interventions then targeted towards these individuals. However, most existing
      claims-based predictive models perform poorly and do not provide timely and actionable
      information. In this study, researchers will prospectively enroll patients for data
      collection to design prediction models that integrate claims data (inpatient, outpatient, and
      pharmacy), electronic health record data (on clinical, social, and behavioral indicators),
      and use wearable devices or smartphones to collect patient-generated data (physical activity
      and sleep patterns). Patients will be randomized to use either a smartphone or a wearable
      activity tracking device to capture patient-generated health data.
observational model: Cohort
time perspective: Prospective
measure: Hospital readmission within 30 days of discharge
time frame: 30 days
description: Patient readmission within 30 days of initial discharge
measure: The secondary outcome measures include re-hospitalization within 90 days of discharge.
time frame: 90 days
description: Patient readmission within 90 days of initial discharge
measure: Re-hospitalization within 6 months of discharge
time frame: 6 months
description: Patient readmission within 6 months of initial discharge
measure: Emergency department visits within 6 months of discharge
time frame: 6 months
description: Patient visit to the emergency department within 6 months of initial discharge
measure: Total health care cost utilization in 6 months after discharge
time frame: 6 months
description: The cost of health care services for patient within 6 months of initial discharge
arm group label: Activity Monitoring - Smartphone
description: Patients in the activity monitoring - smartphone group will be randomly assigned to track their data using a smartphone app (which collects step counts) for 6 months. There will be no intervention for either group, both are being passively monitored.
arm group label: Activity Monitoring - Wearable
description: Patients in the activity monitoring - wearable device group will be randomly assigned to track their data using a wearable activity tracker (which collects step counts and sleep patterns/duration). There will be no intervention for either group, both are being passively monitored.
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Mitesh Patel
role: Principal Investigator
affiliation: University of Pennsylvania
facility: 
country: United States
responsible party type: Sponsor
mesh term: Lung Diseases, Obstructive
mesh term: Pulmonary Disease, Chronic Obstructive
mesh term: Heart Failure
mesh term: Chest Pain
sharing ipd: No
ipd description: Individual participant data will not be made available.