<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/NCT03012074</url>
  </required_header>
  <id_info>
    <org_study_id>Pro00071569</org_study_id>
    <nct_id>NCT03012074</nct_id>
  </id_info>
  <brief_title>Episodic to Real-Time Care in Diabetes Self-Management</brief_title>
  <official_title>From Episodic to Real-Time Care in Diabetes Self-Management</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Duke University</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Duke University</source>
  <oversight_info>
    <has_dmc>Yes</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>
      This study will use mobile health technologies to provide everyday data to help patients and&#xD;
      their care providers better understand illness dynamics and develop adaptive approaches to&#xD;
      improve health outcomes in diabetes. Specifically, the study will identify strategies to help&#xD;
      patients adapt using multiple types of self-generated diabetes-related data and help&#xD;
      providers guide patients to better self-manage in real-time, when guidance is needed most.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The investigators will conduct a mixed-methods exploratory designed study and recruit 60&#xD;
      adult patients (age ≥ 18) with type 2 diabetes who will track relevant clinical data over 6&#xD;
      months. Participants will be asked to use a wireless glucose monitor, a cellular body scale,&#xD;
      and a wrist-worn accelerometer, and respond to short bi-weekly text message-based surveys on&#xD;
      medication adherence for 6 months. Data generated from the devices will be plotted as&#xD;
      trajectories that will allow us to conduct trajectory analyses and identify missing data&#xD;
      points and trends leading to attrition, in order to assess the feasibility of having patients&#xD;
      engage in this type of self-monitoring for 6 months. A subset of 20 patients will be&#xD;
      interviewed via telephone at the end of their 6 month self-monitoring period to discuss their&#xD;
      adaptive challenges and successes over the study period. The investigators will present the&#xD;
      trajectories of their data during patient interviews using a visual that we will e-mail or&#xD;
      postal mail, depending on patient preference. This will facilitate discussion of the&#xD;
      challenges participants face in self-management and adaptive practices participants use.&#xD;
      Following the 6 months of self-monitoring, we will conduct interviews with health care&#xD;
      providers in which the investigators will present the data trajectories and explore ways to&#xD;
      achieve continual communication and address real-time challenges with diabetes&#xD;
      self-management.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">March 2, 2017</start_date>
  <completion_date type="Actual">May 30, 2020</completion_date>
  <primary_completion_date type="Actual">May 1, 2018</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>Supportive Care</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Number of participants with type II diabetes who adhere to using mobile technologies to self-monitor multiple types of diabetes-related data</measure>
    <time_frame>6 months</time_frame>
  </primary_outcome>
  <secondary_outcome>
    <measure>Explore the adaptive challenges of patients self-managing diabetes, using monthly interviews over 6 months.</measure>
    <time_frame>6 months</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Explore the successes of patients self-managing diabetes, using monthly interviews over 6 months.</measure>
    <time_frame>6 months</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Change in daily blood glucose levels in patients with diabetes over a 6 month period</measure>
    <time_frame>6 months</time_frame>
    <description>Data generated from the phone-tethered glucose monitor and plotted as a trajectory over a 6 month period</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Change in daily weight in patients with diabetes over a 6 month period</measure>
    <time_frame>6 months</time_frame>
    <description>Data generated from the cellular body scale and plotted as a trajectory over a 6 month period</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Change in daily physical activity in patients with diabetes over a 6 month period</measure>
    <time_frame>6 months</time_frame>
    <description>Data generated from the wrist-worn accelerometer and plotted as a trajectory over a 6 month period</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Medication adherence Survey</measure>
    <time_frame>6 months</time_frame>
    <description>bi-weekly text message-based surveys on medication adherence over a 6 month period.</description>
  </secondary_outcome>
  <other_outcome>
    <measure>Explore health care providers' perspectives on using real-time data, by conducting one-time, individual interviews over a 2 month period.</measure>
    <time_frame>2 months</time_frame>
    <description>Explore ways to achieve continual communication that addresses real-time challenges with diabetes self-management while presenting data trajectories during provider interviews.</description>
  </other_outcome>
  <number_of_arms>1</number_of_arms>
  <enrollment type="Actual">60</enrollment>
  <condition>Diabetes Mellitus, Type II</condition>
  <arm_group>
    <arm_group_label>Patients with Type II Diabetes</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Patients with Type II Diabetes</description>
  </arm_group>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>Type 2 Diabetes Self-Management</intervention_name>
    <description>Patients with Type 2 Diabetes</description>
    <arm_group_label>Patients with Type II Diabetes</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  ≥18 years old&#xD;
&#xD;
          -  able to speak and read English&#xD;
&#xD;
          -  diagnosed with type 2 diabetes mellitus&#xD;
&#xD;
          -  told by their primary care provider to monitor their blood sugar daily&#xD;
&#xD;
          -  owning and using a smart phone&#xD;
&#xD;
          -  capable of giving informed consent&#xD;
&#xD;
          -  with no pre-existing severe medical condition(s) that would interfere with study&#xD;
             participation (e.g., renal failure, severe orthopedic conditions or joint replacement&#xD;
             scheduled within 6 months, paralysis, or cancer)&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  active dementia or psychiatric illness&#xD;
&#xD;
          -  reside in a nursing home&#xD;
&#xD;
          -  participating in another self-management study&#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>Ryan J Shaw, PhD, RN</last_name>
    <role>Principal Investigator</role>
    <affiliation>Duke University School of Nursing</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Duke University School of Nursing</name>
      <address>
        <city>Durham</city>
        <state>North Carolina</state>
        <zip>27710</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <verification_date>January 2020</verification_date>
  <study_first_submitted>December 21, 2016</study_first_submitted>
  <study_first_submitted_qc>January 4, 2017</study_first_submitted_qc>
  <study_first_posted type="Estimate">January 6, 2017</study_first_posted>
  <last_update_submitted>July 1, 2020</last_update_submitted>
  <last_update_submitted_qc>July 1, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">July 2, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>diabetes, mHealth, mobile health technology</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Diabetes Mellitus</mesh_term>
    <mesh_term>Diabetes Mellitus, Type 2</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </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/NCT03012074
org study id: Pro00071569
nct id: NCT03012074
lead sponsor: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
This study will use mobile health technologies to provide everyday data to help patients and
      their care providers better understand illness dynamics and develop adaptive approaches to
      improve health outcomes in diabetes. Specifically, the study will identify strategies to help
      patients adapt using multiple types of self-generated diabetes-related data and help
      providers guide patients to better self-manage in real-time, when guidance is needed most.
The investigators will conduct a mixed-methods exploratory designed study and recruit 60
      adult patients (age ≥ 18) with type 2 diabetes who will track relevant clinical data over 6
      months. Participants will be asked to use a wireless glucose monitor, a cellular body scale,
      and a wrist-worn accelerometer, and respond to short bi-weekly text message-based surveys on
      medication adherence for 6 months. Data generated from the devices will be plotted as
      trajectories that will allow us to conduct trajectory analyses and identify missing data
      points and trends leading to attrition, in order to assess the feasibility of having patients
      engage in this type of self-monitoring for 6 months. A subset of 20 patients will be
      interviewed via telephone at the end of their 6 month self-monitoring period to discuss their
      adaptive challenges and successes over the study period. The investigators will present the
      trajectories of their data during patient interviews using a visual that we will e-mail or
      postal mail, depending on patient preference. This will facilitate discussion of the
      challenges participants face in self-management and adaptive practices participants use.
      Following the 6 months of self-monitoring, we will conduct interviews with health care
      providers in which the investigators will present the data trajectories and explore ways to
      achieve continual communication and address real-time challenges with diabetes
      self-management.
allocation: N/A
intervention model: Single Group Assignment
primary purpose: Supportive Care
masking: None (Open Label)
measure: Number of participants with type II diabetes who adhere to using mobile technologies to self-monitor multiple types of diabetes-related data
time frame: 6 months
measure: Explore the adaptive challenges of patients self-managing diabetes, using monthly interviews over 6 months.
time frame: 6 months
measure: Explore the successes of patients self-managing diabetes, using monthly interviews over 6 months.
time frame: 6 months
measure: Change in daily blood glucose levels in patients with diabetes over a 6 month period
time frame: 6 months
description: Data generated from the phone-tethered glucose monitor and plotted as a trajectory over a 6 month period
measure: Change in daily weight in patients with diabetes over a 6 month period
time frame: 6 months
description: Data generated from the cellular body scale and plotted as a trajectory over a 6 month period
measure: Change in daily physical activity in patients with diabetes over a 6 month period
time frame: 6 months
description: Data generated from the wrist-worn accelerometer and plotted as a trajectory over a 6 month period
measure: Medication adherence Survey
time frame: 6 months
description: bi-weekly text message-based surveys on medication adherence over a 6 month period.
measure: Explore health care providers' perspectives on using real-time data, by conducting one-time, individual interviews over a 2 month period.
time frame: 2 months
description: Explore ways to achieve continual communication that addresses real-time challenges with diabetes self-management while presenting data trajectories during provider interviews.
arm group label: Patients with Type II Diabetes
arm group type: Experimental
description: Patients with Type II Diabetes
intervention type: Behavioral
intervention name: Type 2 Diabetes Self-Management
description: Patients with Type 2 Diabetes
arm group label: Patients with Type II Diabetes
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Ryan J Shaw, PhD, RN
role: Principal Investigator
affiliation: Duke University School of Nursing
facility: 
country: United States
responsible party type: Sponsor
mesh term: Diabetes Mellitus
mesh term: Diabetes Mellitus, Type 2
sharing ipd: No