<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/NCT03288207</url>
  </required_header>
  <id_info>
    <org_study_id>170162</org_study_id>
    <secondary_id>17-H-0162</secondary_id>
    <nct_id>NCT03288207</nct_id>
  </id_info>
  <brief_title>Tailoring Mobile Health Technology to Reduce Obesity and Improve Cardiovascular Health in Resource-Limited Neighborhood Environments</brief_title>
  <official_title>Tailoring Mobile Health Technology to Reduce Obesity and Improve Cardiovascular Health in Resource-Limited Neighborhood Environments: A Multi-Level, Community-Based Physical Activity Intervention</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>National Heart, Lung, and Blood Institute (NHLBI)</agency>
      <agency_class>NIH</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>George Washington University</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>National Institutes of Health Clinical Center (CC)</source>
  <oversight_info>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      Background:&#xD;
&#xD;
      Heart disease is a leading cause of death. People can reduce their heart disease risk by&#xD;
      exercising more. Mobile health technology may make people more successful at increasing their&#xD;
      exercise. This includes things like physical activity monitors and smartphone apps.&#xD;
&#xD;
      Objective:&#xD;
&#xD;
      To find out if mobile health technology can increase physical activity.&#xD;
&#xD;
      Eligibility:&#xD;
&#xD;
      African American women ages 21-75 who:&#xD;
&#xD;
        -  Are overweight or obese&#xD;
&#xD;
        -  Live in certain areas near Washington, DC&#xD;
&#xD;
        -  Have a smartphone that can use the study app&#xD;
&#xD;
      Design:&#xD;
&#xD;
      At visit 1, participants will&#xD;
&#xD;
        -  Answer survey questions. These may be about medical history, physical activity, and&#xD;
           weight. They may also cover body image, health perception, and spirituality.&#xD;
&#xD;
        -  Have body size measured and get blood tests&#xD;
&#xD;
        -  Get a device to wear on the wrist. It will record physical activity and hours of sleep.&#xD;
&#xD;
        -  Learn how to download and use the study mobile app&#xD;
&#xD;
      For 2 weeks, researchers will collect data about participants physical activity.&#xD;
&#xD;
      Then participants will have a study visit with additional blood tests.&#xD;
&#xD;
      All participants will get messages from the app that encourage exercise.&#xD;
&#xD;
      Some participants will get data from the app about exercise near their home or work.&#xD;
&#xD;
      Some participants may get face-to-face coaching.&#xD;
&#xD;
      Participants may get wireless devices. These measure body weight, blood pressure, and blood&#xD;
      glucose. Participants can measure these at home and upload the data to the app for the study.&#xD;
&#xD;
      Participants will have visits after 3 and 6 months. They will repeat the visit 1 tests.&#xD;
&#xD;
      ...&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Targeted, effective behavioral interventions are critically needed to ameliorate the&#xD;
      disproportionate prevalence of poor cardiometabolic health for African-American women. We&#xD;
      propose a sequential, multiple-assignment, randomized trial targeting physical activity (PA)&#xD;
      among at-risk African-American women in resource-limited, Washington, D.C. communities using&#xD;
      mobile health (mHealth) technology. We hypothesize that by beginning a community-based,&#xD;
      adaptive PA intervention with remote coaching tailored to neighborhood environment PA&#xD;
      resources, we will see greater increases in PA levels as compared to standard remote&#xD;
      coaching. In Aim 1, we will determine if beginning an adaptive intervention with remote&#xD;
      coaching tailored to neighborhood environment resources and delivered using mHealth&#xD;
      technology (wearables and mobile applications) will lead to a greater PA increase (as&#xD;
      measured by steps per day) as compared to standard remote coaching. In Aim 2, we will examine&#xD;
      which of four embedded adaptive interventions produce the largest PA increase over the&#xD;
      six-month study period. In Aim 3, we will evaluate the feasibility of remote capture of&#xD;
      cardiometabolic measures, including blood pressure, weight, and glucose, using mHealth&#xD;
      technology. We will also examine intervention effects on cardiometabolic health (adiposity,&#xD;
      blood pressure, fasting lipids/glucose, self-reported PA, dietary intake, cigarette smoking).&#xD;
      In Aim 4, we will characterize effects of increasing PA on integrated serologic&#xD;
      cytokine/chemokine and lipid inflammatory intermediates to identify potential novel&#xD;
      inflammatory pathways linked to cardiometabolic risk phenotypes most responsive to the&#xD;
      multi-level, community-based PA intervention. We will also determine the feasibility of&#xD;
      measuring behavioral and psychosocial mediating factors of the relationship between PA change&#xD;
      and cardiometabolic health in this intervention, including chronic&#xD;
      psychological/environmental stress and sedentary behavior/sleep. This project provides&#xD;
      fundamental knowledge towards the development of tailored, effective behavioral interventions&#xD;
      incorporating mHealth technology to promote health among populations most impacted by health&#xD;
      disparities.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Recruiting</overall_status>
  <start_date type="Actual">June 21, 2018</start_date>
  <completion_date type="Anticipated">March 28, 2026</completion_date>
  <primary_completion_date type="Anticipated">February 9, 2025</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>Randomized</allocation>
    <intervention_model>Sequential Assignment</intervention_model>
    <primary_purpose>Basic Science</primary_purpose>
    <masking>Single (Participant)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>The difference in physical activity (PA) change between an adaptive intervention with remote coaching tailored to neighborhood resources (referred to as tailored-to-place coaching) versus beginning w/ standard remote coaching</measure>
    <time_frame>baseline, and up to 6 months</time_frame>
    <description>The difference in physical activity (PA) change (as measured by steps/day) by beginning an adaptive intervention with remote coaching tailored to neighborhood resources (referred to as tailored-to-place coaching) versus beginning with standard remote coaching.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Determine which embedded adaptive interventions produce the largest PA increase</measure>
    <time_frame>Up to 6 months</time_frame>
    <description>Determine which of four embedded adaptive interventions produce the largest PA increase over six months</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Examine the feasibility of incorporating methods for remote capture of CV health measures</measure>
    <time_frame>Up to 6 months</time_frame>
    <description>Examine the feasibility of incorporating methods for remote capture of CV health measures (weight, blood pressure, blood glucose) in a target community-based population</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Examine the effect of an adaptive community-based intervention targeting Physical Activity on Cardiovascular health measures</measure>
    <time_frame>Up to 6 months</time_frame>
    <description>Examine the effect of an adaptive community-based intervention targeting Physical Activity on Cardiovascular health measures (BMI, blood pressure, fasting lipids, fasting plasma glucose, dietary intake, [self-reported minutes of moderate/vigorous PA, cigarette smoking)</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Identify potential novel inflammatory pathways linked to cardiometabolic risk phenotypes</measure>
    <time_frame>Up to 6 months</time_frame>
    <description>Characterize effects of increasing PA on integrated serologic cytokine/chemokine and lipid inflammatory intermediates to identify potential novel inflammatory pathways linked to cardiometabolic risk phenotypes most responsive to the multi-level, community-based PA intervention</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Examine the feasibility of measuring potential psychosocial and behavioral mediators of the relationship between PA change and CV health</measure>
    <time_frame>Up to 6 months</time_frame>
    <description>Examine the feasibility of measuring potential psychosocial and behavioral mediators of the relationship between PA change and CV health, such as chronic stress and sedentary behavior/sleep</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Measure exposure to COVID-19 and psychosocial stress caused by the pandemic</measure>
    <time_frame>Up to 6 months</time_frame>
    <description>Measure exposure to COVID-19 and psychosocial stress caused by the pandemic as potential confounders of immunologic outcomes and psychosocial stressors</description>
  </secondary_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Anticipated">265</enrollment>
  <condition>Obesity</condition>
  <arm_group>
    <arm_group_label>Group 2 Label: PA monitor with standard remote coaching (SRC)</arm_group_label>
    <arm_group_type>Other</arm_group_type>
    <description>African American women who are at risk for cardiovascular outcomes in resource-limited communities in the Washington D.C. area.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Group 1 Label: PA monitor with remote coaching tailored to place</arm_group_label>
    <arm_group_type>Other</arm_group_type>
    <description>African American women who are at risk for cardiovascular outcomes in resource-limited communities in the Washington D.C. area.</description>
  </arm_group>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>Bluetooth-enabled glucometer</intervention_name>
    <description>Bluetooth-enabled glucometer</description>
    <arm_group_label>Group 1 Label: PA monitor with remote coaching tailored to place</arm_group_label>
    <arm_group_label>Group 2 Label: PA monitor with standard remote coaching (SRC)</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>Bluetooth-enabled scale</intervention_name>
    <description>Bluetooth-enabled scale</description>
    <arm_group_label>Group 1 Label: PA monitor with remote coaching tailored to place</arm_group_label>
    <arm_group_label>Group 2 Label: PA monitor with standard remote coaching (SRC)</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>Global Positioning System (GPS) Device</intervention_name>
    <description>Global Positioning System (GPS) Device</description>
    <arm_group_label>Group 1 Label: PA monitor with remote coaching tailored to place</arm_group_label>
    <arm_group_label>Group 2 Label: PA monitor with standard remote coaching (SRC)</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>Step it Up mobile app</intervention_name>
    <description>Step it Up mobile app</description>
    <arm_group_label>Group 1 Label: PA monitor with remote coaching tailored to place</arm_group_label>
    <arm_group_label>Group 2 Label: PA monitor with standard remote coaching (SRC)</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        -  INCLUSION CRITERIA:&#xD;
&#xD;
        Individuals eligible for this protocol are overweight or obese (BMI &gt;= 25 kg/m2) African&#xD;
        American women aged 21-75 years who live in Washington, DC Wards 5,7, or 8 and neighboring&#xD;
        areas of Prince George s County, MD. Eligible participants should also have access to a&#xD;
        smartphone compatible with the mobile app for the protocol that they can use for the study.&#xD;
        Eligible participants must be able to provide informed consent independently and also speak&#xD;
        and read English at the 8th grade level.&#xD;
&#xD;
        EXCLUSION CRITERIA:&#xD;
&#xD;
          -  Medical condition, including recent unintentional weight loss, that might prohibit&#xD;
             safe participation in the intervention&#xD;
&#xD;
          -  Heart disease as indicated by history of myocardial infarction, documented obstructive&#xD;
             coronary artery disease on coronary angiography, coronary artery stent placement,&#xD;
             congestive heart failure, significant structural heart disease (e.g. hypertrophic or&#xD;
             dilated cardiomyopathy, valvular heart disease)&#xD;
&#xD;
          -  Physically unable to perform the physical activity for any reason&#xD;
&#xD;
          -  Pregnant women due to large hormonal changes during pregnancy that affect study&#xD;
             variables and potential pregnancy-related restrictions on exercise. Self-reported&#xD;
             pregnancy status will be accepted.&#xD;
&#xD;
        Pilot Study INCLUSION CRITERIA:&#xD;
&#xD;
          -  Must be an African-American female&#xD;
&#xD;
          -  Must be within the age of 21-75 years old&#xD;
&#xD;
          -  Must be overweight or obese (Body Mass Index (BMI) greater than or equal to 25 kg/m&#xD;
             &lt;=)&#xD;
&#xD;
          -  Must live in Washington DC Wards (5, 7, or 8) or live in Prince George s County,&#xD;
             Maryland.&#xD;
&#xD;
          -  Must have a smartphone that is compatible with the study software (mobile app)&#xD;
&#xD;
          -  Must be willing to use the software on personal smartphone for the study&#xD;
&#xD;
          -  Must be able to provide consent&#xD;
&#xD;
          -  Must be willing to wear the wrist-worn physical activity device for the study&#xD;
&#xD;
          -  Must not be pregnant&#xD;
      </textblock>
    </criteria>
    <gender>Female</gender>
    <minimum_age>21 Years</minimum_age>
    <maximum_age>75 Years</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Tiffany M Powell-Wiley, M.D.</last_name>
    <role>Principal Investigator</role>
    <affiliation>National Heart, Lung, and Blood Institute (NHLBI)</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Valerie Morales-Mitchelll</last_name>
    <phone>(301) 827-4981</phone>
    <email>valerie.mitchell@nih.gov</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Tiffany M Powell-Wiley, M.D.</last_name>
    <phone>(301) 594-3735</phone>
    <email>powelltm2@mail.nih.gov</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>National Institutes of Health Clinical Center</name>
      <address>
        <city>Bethesda</city>
        <state>Maryland</state>
        <zip>20892</zip>
        <country>United States</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>For more information at the NIH Clinical Center contact Office of Patient Recruitment (OPR)</last_name>
      <phone>800-411-1222</phone>
      <phone_ext>TTY8664111010</phone_ext>
      <email>prpl@cc.nih.gov</email>
    </contact>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <link>
    <url>https://clinicalstudies.info.nih.gov/cgi/detail.cgi?A_2017-H-0162.html</url>
    <description>NIH Clinical Center Detailed Web Page</description>
  </link>
  <reference>
    <citation>Writing Group Members; Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Das SR, de Ferranti S, Despres JP, Fullerton HJ, Howard VJ, Huffman MD, Isasi CR, Jimenez MC, Judd SE, Kissela BM, Lichtman JH, Lisabeth LD, Liu S, Mackey RH, Magid DJ, McGuire DK, Mohler ER 3rd, Moy CS, Muntner P, Mussolino ME, Nasir K, Neumar RW, Nichol G, Palaniappan L, Pandey DK, Reeves MJ, Rodriguez CJ, Rosamond W, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Woo D, Yeh RW, Turner MB; American Heart Association Statistics Committee; Stroke Statistics Subcommittee. Executive Summary: Heart Disease and Stroke Statistics--2016 Update: A Report From the American Heart Association. Circulation. 2016 Jan 26;133(4):447-54. doi: 10.1161/CIR.0000000000000366. No abstract available.</citation>
    <PMID>26811276</PMID>
  </reference>
  <reference>
    <citation>Boggs DA, Rosenberg L, Cozier YC, Wise LA, Coogan PF, Ruiz-Narvaez EA, Palmer JR. General and abdominal obesity and risk of death among black women. N Engl J Med. 2011 Sep 8;365(10):901-8. doi: 10.1056/NEJMoa1104119.</citation>
    <PMID>21899451</PMID>
  </reference>
  <reference>
    <citation>Lightwood J, Bibbins-Domingo K, Coxson P, Wang YC, Williams L, Goldman L. Forecasting the future economic burden of current adolescent overweight: an estimate of the coronary heart disease policy model. Am J Public Health. 2009 Dec;99(12):2230-7. doi: 10.2105/AJPH.2008.152595. Epub 2009 Oct 15.</citation>
    <PMID>19833999</PMID>
  </reference>
  <verification_date>August 16, 2023</verification_date>
  <study_first_submitted>September 19, 2017</study_first_submitted>
  <study_first_submitted_qc>September 19, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">September 20, 2017</study_first_posted>
  <last_update_submitted>September 8, 2023</last_update_submitted>
  <last_update_submitted_qc>September 8, 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>
  <keyword>Community-Based Participatory Research</keyword>
  <keyword>Obesity</keyword>
  <keyword>Cardiovascular Disease Risk</keyword>
  <keyword>Social Determinants of Health</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Obesity</mesh_term>
  </condition_browse>
  <!-- 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/NCT03288207
org study id: 170162
secondary id: 17-H-0162
nct id: NCT03288207
lead sponsor: 
collaborator: 
is fda regulated drug: No
is fda regulated device: No
Background:

      Heart disease is a leading cause of death. People can reduce their heart disease risk by
      exercising more. Mobile health technology may make people more successful at increasing their
      exercise. This includes things like physical activity monitors and smartphone apps.

      Objective:

      To find out if mobile health technology can increase physical activity.

      Eligibility:

      African American women ages 21-75 who:

        -  Are overweight or obese

        -  Live in certain areas near Washington, DC

        -  Have a smartphone that can use the study app

      Design:

      At visit 1, participants will

        -  Answer survey questions. These may be about medical history, physical activity, and
           weight. They may also cover body image, health perception, and spirituality.

        -  Have body size measured and get blood tests

        -  Get a device to wear on the wrist. It will record physical activity and hours of sleep.

        -  Learn how to download and use the study mobile app

      For 2 weeks, researchers will collect data about participants physical activity.

      Then participants will have a study visit with additional blood tests.

      All participants will get messages from the app that encourage exercise.

      Some participants will get data from the app about exercise near their home or work.

      Some participants may get face-to-face coaching.

      Participants may get wireless devices. These measure body weight, blood pressure, and blood
      glucose. Participants can measure these at home and upload the data to the app for the study.

      Participants will have visits after 3 and 6 months. They will repeat the visit 1 tests.

      ...
Targeted, effective behavioral interventions are critically needed to ameliorate the
      disproportionate prevalence of poor cardiometabolic health for African-American women. We
      propose a sequential, multiple-assignment, randomized trial targeting physical activity (PA)
      among at-risk African-American women in resource-limited, Washington, D.C. communities using
      mobile health (mHealth) technology. We hypothesize that by beginning a community-based,
      adaptive PA intervention with remote coaching tailored to neighborhood environment PA
      resources, we will see greater increases in PA levels as compared to standard remote
      coaching. In Aim 1, we will determine if beginning an adaptive intervention with remote
      coaching tailored to neighborhood environment resources and delivered using mHealth
      technology (wearables and mobile applications) will lead to a greater PA increase (as
      measured by steps per day) as compared to standard remote coaching. In Aim 2, we will examine
      which of four embedded adaptive interventions produce the largest PA increase over the
      six-month study period. In Aim 3, we will evaluate the feasibility of remote capture of
      cardiometabolic measures, including blood pressure, weight, and glucose, using mHealth
      technology. We will also examine intervention effects on cardiometabolic health (adiposity,
      blood pressure, fasting lipids/glucose, self-reported PA, dietary intake, cigarette smoking).
      In Aim 4, we will characterize effects of increasing PA on integrated serologic
      cytokine/chemokine and lipid inflammatory intermediates to identify potential novel
      inflammatory pathways linked to cardiometabolic risk phenotypes most responsive to the
      multi-level, community-based PA intervention. We will also determine the feasibility of
      measuring behavioral and psychosocial mediating factors of the relationship between PA change
      and cardiometabolic health in this intervention, including chronic
      psychological/environmental stress and sedentary behavior/sleep. This project provides
      fundamental knowledge towards the development of tailored, effective behavioral interventions
      incorporating mHealth technology to promote health among populations most impacted by health
      disparities.
allocation: Randomized
intervention model: Sequential Assignment
primary purpose: Basic Science
masking: Single (Participant)
measure: The difference in physical activity (PA) change between an adaptive intervention with remote coaching tailored to neighborhood resources (referred to as tailored-to-place coaching) versus beginning w/ standard remote coaching
time frame: baseline, and up to 6 months
description: The difference in physical activity (PA) change (as measured by steps/day) by beginning an adaptive intervention with remote coaching tailored to neighborhood resources (referred to as tailored-to-place coaching) versus beginning with standard remote coaching.
measure: Determine which embedded adaptive interventions produce the largest PA increase
time frame: Up to 6 months
description: Determine which of four embedded adaptive interventions produce the largest PA increase over six months
measure: Examine the feasibility of incorporating methods for remote capture of CV health measures
time frame: Up to 6 months
description: Examine the feasibility of incorporating methods for remote capture of CV health measures (weight, blood pressure, blood glucose) in a target community-based population
measure: Examine the effect of an adaptive community-based intervention targeting Physical Activity on Cardiovascular health measures
time frame: Up to 6 months
description: Examine the effect of an adaptive community-based intervention targeting Physical Activity on Cardiovascular health measures (BMI, blood pressure, fasting lipids, fasting plasma glucose, dietary intake, [self-reported minutes of moderate/vigorous PA, cigarette smoking)
measure: Identify potential novel inflammatory pathways linked to cardiometabolic risk phenotypes
time frame: Up to 6 months
description: Characterize effects of increasing PA on integrated serologic cytokine/chemokine and lipid inflammatory intermediates to identify potential novel inflammatory pathways linked to cardiometabolic risk phenotypes most responsive to the multi-level, community-based PA intervention
measure: Examine the feasibility of measuring potential psychosocial and behavioral mediators of the relationship between PA change and CV health
time frame: Up to 6 months
description: Examine the feasibility of measuring potential psychosocial and behavioral mediators of the relationship between PA change and CV health, such as chronic stress and sedentary behavior/sleep
measure: Measure exposure to COVID-19 and psychosocial stress caused by the pandemic
time frame: Up to 6 months
description: Measure exposure to COVID-19 and psychosocial stress caused by the pandemic as potential confounders of immunologic outcomes and psychosocial stressors
arm group label: Group 2 Label: PA monitor with standard remote coaching (SRC)
arm group type: Other
description: African American women who are at risk for cardiovascular outcomes in resource-limited communities in the Washington D.C. area.
arm group label: Group 1 Label: PA monitor with remote coaching tailored to place
arm group type: Other
description: African American women who are at risk for cardiovascular outcomes in resource-limited communities in the Washington D.C. area.
intervention type: Device
intervention name: Bluetooth-enabled glucometer
description: Bluetooth-enabled glucometer
arm group label: Group 1 Label: PA monitor with remote coaching tailored to place
arm group label: Group 2 Label: PA monitor with standard remote coaching (SRC)
intervention type: Device
intervention name: Bluetooth-enabled scale
description: Bluetooth-enabled scale
arm group label: Group 1 Label: PA monitor with remote coaching tailored to place
arm group label: Group 2 Label: PA monitor with standard remote coaching (SRC)
intervention type: Device
intervention name: Global Positioning System (GPS) Device
description: Global Positioning System (GPS) Device
arm group label: Group 1 Label: PA monitor with remote coaching tailored to place
arm group label: Group 2 Label: PA monitor with standard remote coaching (SRC)
intervention type: Device
intervention name: Step it Up mobile app
description: Step it Up mobile app
arm group label: Group 1 Label: PA monitor with remote coaching tailored to place
arm group label: Group 2 Label: PA monitor with standard remote coaching (SRC)
criteria: 
gender: Female
minimum age: 21 Years
maximum age: 75 Years
healthy volunteers: Accepts Healthy Volunteers
last name: Tiffany M Powell-Wiley, M.D.
role: Principal Investigator
affiliation: National Heart, Lung, and Blood Institute (NHLBI)
last name: Valerie Morales-Mitchelll
phone: (301) 827-4981
email: valerie.mitchell@nih.gov
last name: Tiffany M Powell-Wiley, M.D.
phone: (301) 594-3735
email: powelltm2@mail.nih.gov
facility: 
status: Recruiting
contact: 
country: United States
url: https://clinicalstudies.info.nih.gov/cgi/detail.cgi?A_2017-H-0162.html
description: NIH Clinical Center Detailed Web Page
citation: Writing Group Members; Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Das SR, de Ferranti S, Despres JP, Fullerton HJ, Howard VJ, Huffman MD, Isasi CR, Jimenez MC, Judd SE, Kissela BM, Lichtman JH, Lisabeth LD, Liu S, Mackey RH, Magid DJ, McGuire DK, Mohler ER 3rd, Moy CS, Muntner P, Mussolino ME, Nasir K, Neumar RW, Nichol G, Palaniappan L, Pandey DK, Reeves MJ, Rodriguez CJ, Rosamond W, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Woo D, Yeh RW, Turner MB; American Heart Association Statistics Committee; Stroke Statistics Subcommittee. Executive Summary: Heart Disease and Stroke Statistics--2016 Update: A Report From the American Heart Association. Circulation. 2016 Jan 26;133(4):447-54. doi: 10.1161/CIR.0000000000000366. No abstract available.
PMID: 26811276
citation: Boggs DA, Rosenberg L, Cozier YC, Wise LA, Coogan PF, Ruiz-Narvaez EA, Palmer JR. General and abdominal obesity and risk of death among black women. N Engl J Med. 2011 Sep 8;365(10):901-8. doi: 10.1056/NEJMoa1104119.
PMID: 21899451
citation: Lightwood J, Bibbins-Domingo K, Coxson P, Wang YC, Williams L, Goldman L. Forecasting the future economic burden of current adolescent overweight: an estimate of the coronary heart disease policy model. Am J Public Health. 2009 Dec;99(12):2230-7. doi: 10.2105/AJPH.2008.152595. Epub 2009 Oct 15.
PMID: 19833999
responsible party type: Sponsor
mesh term: Obesity