<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/NCT04473326</url>
  </required_header>
  <id_info>
    <org_study_id>2020P000846</org_study_id>
    <secondary_id>P30AG064199-01</secondary_id>
    <nct_id>NCT04473326</nct_id>
  </id_info>
  <brief_title>Reinforcement Learning in Diabetes Mellitus Trial</brief_title>
  <acronym>REINFORCE</acronym>
  <official_title>Optimizing Message Framing for Healthy Habits for Patients With Type 2 Diabetes - Phase II (Pragmatic Trial)</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Brigham and Women's Hospital</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>National Institute on Aging (NIA)</agency>
      <agency_class>NIH</agency_class>
    </collaborator>
  </sponsors>
  <source>Brigham and Women's Hospital</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>
      Reinforcement learning is an advanced analytic method that discovers each individual's&#xD;
      pattern of responsiveness by observing their actions and then implements a personalized&#xD;
      strategy to optimize individuals' behaviors using trial and error. The goal of this pilot&#xD;
      study is to develop and test a novel reinforcement learning-enhanced text messaging program&#xD;
      to support medication adherence in patients with type 2 diabetes. Type 2 diabetes is an&#xD;
      optimal condition in which to test this program, as it is one of the most prevalent chronic&#xD;
      conditions in the US adult population and requires most patients to be on daily or twice&#xD;
      daily doses of medications. This pilot study will be a parallel randomized pragmatic trial&#xD;
      comparing medication adherence and clinical outcomes for adults aged 18-84 with type 2&#xD;
      diabetes who are prescribed 1-3 daily oral medications for this disease. Participants will be&#xD;
      randomized to one of two arms for the duration of the study period: (1) a reinforcement&#xD;
      learning intervention arm with up to daily, tailored text messages based on time-varying&#xD;
      treatment-response patterns; or (2) a control arm with up to daily, un-tailored text&#xD;
      messages. Our outcomes of interest will be medication adherence, as measured by electronic&#xD;
      pill bottles, and HbA1c levels.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The goal of this pilot study is to develop and test a novel reinforcement learning-enhanced&#xD;
      text messaging program to support medication adherence in patients with type 2 diabetes. This&#xD;
      pilot study will be a parallel randomized pragmatic trial comparing medication adherence and&#xD;
      clinical outcomes for adults aged 18-84 with type 2 diabetes who are prescribed 1-3 daily&#xD;
      oral medications for this disease. Participants will be randomized to one of two arms for the&#xD;
      duration of the study period: 1) a reinforcement learning intervention arm with up to daily,&#xD;
      tailored text messages based on time-varying treatment response patterns, or 2) a control arm&#xD;
      with up to daily, untailored text messages. Our outcomes of interest will be medication&#xD;
      adherence, as measured by electronic pill bottles, and HbA1c levels.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">February 4, 2021</start_date>
  <completion_date type="Actual">January 28, 2022</completion_date>
  <primary_completion_date type="Actual">January 4, 2022</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>Parallel Assignment</intervention_model>
    <primary_purpose>Health Services Research</primary_purpose>
    <masking>Double (Investigator, Outcomes Assessor)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Medication Adherence</measure>
    <time_frame>6 months</time_frame>
    <description>Medication adherence to type 2 diabetes oral medications (averaged) as measured by the number of dates and times of pillbottle openings in the electronic pill bottles</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Glycemic Control</measure>
    <time_frame>6 months</time_frame>
    <description>Change in glycated hemoglobin A1c from baseline to end of the 6-month follow-up</description>
  </secondary_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Actual">60</enrollment>
  <condition>Diabetes Mellitus, Type 2</condition>
  <condition>Medication Adherence</condition>
  <arm_group>
    <arm_group_label>Reinforcement Learning Intervention Arm</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Up to daily, tailored text messages.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Control Arm</arm_group_label>
    <arm_group_type>No Intervention</arm_group_type>
    <description>Up to daily, untailored text messages.</description>
  </arm_group>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>Reinforcement Learning</intervention_name>
    <description>Participants in the intervention arm will receive up to daily, tailored text messages based on their electronic pill bottle-measured adherence. Given the participants' baseline characteristics and time-varying responses to the messages, a reinforcement learning algorithm will deliver different text messages and adapt over time to determine which type of messaging works best for each individual participant.</description>
    <arm_group_label>Reinforcement Learning Intervention Arm</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion criteria:&#xD;
&#xD;
          -  Age between 18-84 years&#xD;
&#xD;
          -  Diagnosed with type 2 diabetes mellitus (T2DM) and are prescribed between 1-3 daily&#xD;
             oral medications for this disease&#xD;
&#xD;
          -  Currently have a smartphone with a data plan or WiFi at home&#xD;
&#xD;
          -  HbA1c level ≥7.5%&#xD;
&#xD;
          -  Basic working knowledge of English&#xD;
&#xD;
          -  Willing and able to set up the platform and adhere to study procedures&#xD;
&#xD;
          -  Either not currently using a pillbox or willing to use electronic pill bottles (EDMs)&#xD;
             for diabetes medications for the duration of the study&#xD;
&#xD;
        Exclusion criteria:&#xD;
&#xD;
          -  Patients with active enrollment in another diabetes trial within Mass General Brigham&#xD;
&#xD;
          -  Patients who receive daily assistance with taking their medications at home&#xD;
&#xD;
          -  Patients who are unable to receive text messages for more than 3 days in a row during&#xD;
             the study period&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>84 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <location>
    <facility>
      <name>Brigham and Women's Hospital</name>
      <address>
        <city>Boston</city>
        <state>Massachusetts</state>
        <zip>02120</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <verification_date>February 2023</verification_date>
  <study_first_submitted>July 13, 2020</study_first_submitted>
  <study_first_submitted_qc>July 13, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 16, 2020</study_first_posted>
  <results_first_submitted>December 16, 2022</results_first_submitted>
  <results_first_submitted_qc>February 6, 2023</results_first_submitted_qc>
  <results_first_posted type="Actual">February 8, 2023</results_first_posted>
  <last_update_submitted>February 6, 2023</last_update_submitted>
  <last_update_submitted_qc>February 6, 2023</last_update_submitted_qc>
  <last_update_posted type="Actual">February 8, 2023</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Brigham and Women's Hospital</investigator_affiliation>
    <investigator_full_name>Julie Lauffenburger</investigator_full_name>
    <investigator_title>Assistant Professor</investigator_title>
  </responsible_party>
  <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>
  <provided_document_section>
    <provided_document>
      <document_type>Study Protocol and Statistical Analysis Plan</document_type>
      <document_has_protocol>Yes</document_has_protocol>
      <document_has_icf>No</document_has_icf>
      <document_has_sap>Yes</document_has_sap>
      <document_date>April 1, 2021</document_date>
      <document_url>https://ClinicalTrials.gov/ProvidedDocs/26/NCT04473326/Prot_SAP_001.pdf</document_url>
    </provided_document>
    <provided_document>
      <document_type>Informed Consent Form</document_type>
      <document_has_protocol>No</document_has_protocol>
      <document_has_icf>Yes</document_has_icf>
      <document_has_sap>No</document_has_sap>
      <document_date>June 29, 2020</document_date>
      <document_url>https://ClinicalTrials.gov/ProvidedDocs/26/NCT04473326/ICF_000.pdf</document_url>
    </provided_document>
  </provided_document_section>

  <clinical_results>

    <participant_flow>
      <group_list>
        <group group_id="P1">
          <title>Reinforcement Learning Intervention Arm</title>
          <description>Up to daily, tailored text messages.&#xD;
Reinforcement Learning: Participants in the intervention arm will receive up to daily, tailored text messages based on their electronic pill bottle-measured adherence. Given the participants' baseline characteristics and time-varying responses to the messages, a reinforcement learning algorithm will deliver different text messages and adapt over time to determine which type of messaging works best for each individual participant.</description>
        </group>
        <group group_id="P2">
          <title>Control Arm</title>
          <description>Up to daily, untailored text messages.</description>
        </group>
      </group_list>
      <period_list>
        <period>
          <title>Overall Study</title>
          <milestone_list>
            <milestone>
              <title>STARTED</title>
              <participants_list>
                <participants group_id="P1" count="29"/>
                <participants group_id="P2" count="31"/>
              </participants_list>
            </milestone>
            <milestone>
              <title>COMPLETED</title>
              <participants_list>
                <participants group_id="P1" count="28"/>
                <participants group_id="P2" count="28"/>
              </participants_list>
            </milestone>
            <milestone>
              <title>NOT COMPLETED</title>
              <participants_list>
                <participants group_id="P1" count="1"/>
                <participants group_id="P2" count="3"/>
              </participants_list>
            </milestone>
          </milestone_list>
          <drop_withdraw_reason_list>
            <drop_withdraw_reason>
              <title>Death</title>
              <participants_list>
                <participants group_id="P1" count="0"/>
                <participants group_id="P2" count="1"/>
              </participants_list>
            </drop_withdraw_reason>
            <drop_withdraw_reason>
              <title>Withdrawal by Subject</title>
              <participants_list>
                <participants group_id="P1" count="1"/>
                <participants group_id="P2" count="2"/>
              </participants_list>
            </drop_withdraw_reason>
          </drop_withdraw_reason_list>
        </period>
      </period_list>
    </participant_flow>

    <baseline>
      <group_list>
        <group group_id="B1">
          <title>Reinforcement Learning Intervention Arm</title>
          <description>Up to daily, tailored text messages.&#xD;
Reinforcement Learning: Participants in the intervention arm will receive up to daily, tailored text messages based on their electronic pill bottle-measured adherence. Given the participants' baseline characteristics and time-varying responses to the messages, a reinforcement learning algorithm will deliver different text messages and adapt over time to determine which type of messaging works best for each individual participant.</description>
        </group>
        <group group_id="B2">
          <title>Control Arm</title>
          <description>Up to daily, untailored text messages.</description>
        </group>
        <group group_id="B3">
          <title>Total</title>
          <description>Total of all reporting groups</description>
        </group>
      </group_list>
      <analyzed_list>
        <analyzed>
          <units>Participants</units>
          <scope>Overall</scope>
          <count_list>
            <count group_id="B1" value="29"/>
            <count group_id="B2" value="31"/>
            <count group_id="B3" value="60"/>
          </count_list>
        </analyzed>
      </analyzed_list>
      <measure_list>
        <measure>
          <title>Age</title>
          <units>years</units>
          <param>Mean</param>
          <dispersion>Standard Deviation</dispersion>
          <class_list>
            <class>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="60.31" spread="11.35"/>
                    <measurement group_id="B2" value="56.68" spread="12.89"/>
                    <measurement group_id="B3" value="58.43" spread="12.20"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Sex: Female, Male</title>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <category_list>
                <category>
                  <title>Female</title>
                  <measurement_list>
                    <measurement group_id="B1" value="12"/>
                    <measurement group_id="B2" value="14"/>
                    <measurement group_id="B3" value="26"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Male</title>
                  <measurement_list>
                    <measurement group_id="B1" value="17"/>
                    <measurement group_id="B2" value="17"/>
                    <measurement group_id="B3" value="34"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Race (NIH/OMB)</title>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <category_list>
                <category>
                  <title>American Indian or Alaska Native</title>
                  <measurement_list>
                    <measurement group_id="B1" value="0"/>
                    <measurement group_id="B2" value="0"/>
                    <measurement group_id="B3" value="0"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Asian</title>
                  <measurement_list>
                    <measurement group_id="B1" value="2"/>
                    <measurement group_id="B2" value="1"/>
                    <measurement group_id="B3" value="3"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Native Hawaiian or Other Pacific Islander</title>
                  <measurement_list>
                    <measurement group_id="B1" value="0"/>
                    <measurement group_id="B2" value="0"/>
                    <measurement group_id="B3" value="0"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Black or African American</title>
                  <measurement_list>
                    <measurement group_id="B1" value="6"/>
                    <measurement group_id="B2" value="7"/>
                    <measurement group_id="B3" value="13"/>
                  </measurement_list>
                </category>
                <category>
                  <title>White</title>
                  <measurement_list>
                    <measurement group_id="B1" value="17"/>
                    <measurement group_id="B2" value="17"/>
                    <measurement group_id="B3" value="34"/>
                  </measurement_list>
                </category>
                <category>
                  <title>More than one race</title>
                  <measurement_list>
                    <measurement group_id="B1" value="1"/>
                    <measurement group_id="B2" value="2"/>
                    <measurement group_id="B3" value="3"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Unknown or Not Reported</title>
                  <measurement_list>
                    <measurement group_id="B1" value="3"/>
                    <measurement group_id="B2" value="4"/>
                    <measurement group_id="B3" value="7"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Region of Enrollment</title>
          <units>participants</units>
          <param>Number</param>
          <class_list>
            <class>
              <title>United States</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="29"/>
                    <measurement group_id="B2" value="31"/>
                    <measurement group_id="B3" value="60"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Self-reported automaticity of medication-taking, n(%)</title>
          <description>Higher scores are better (i.e., higher automaticity indicates that respondents think that medication-taking is more automatic) based on a self-reported measure of automaticity</description>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <category_list>
                <category>
                  <title>0-1 (no or low automaticity)</title>
                  <measurement_list>
                    <measurement group_id="B1" value="7"/>
                    <measurement group_id="B2" value="12"/>
                    <measurement group_id="B3" value="19"/>
                  </measurement_list>
                </category>
                <category>
                  <title>2+ (high automaticity)</title>
                  <measurement_list>
                    <measurement group_id="B1" value="22"/>
                    <measurement group_id="B2" value="19"/>
                    <measurement group_id="B3" value="41"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Baseline HbA1c, mean (sd)</title>
          <units>percentage of glycated hemoglobin</units>
          <param>Mean</param>
          <dispersion>Standard Deviation</dispersion>
          <class_list>
            <class>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="8.99" spread="1.39"/>
                    <measurement group_id="B2" value="9.10" spread="1.47"/>
                    <measurement group_id="B3" value="9.05" spread="1.42"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Education level, n(%)</title>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <category_list>
                <category>
                  <title>High school graduate or below</title>
                  <measurement_list>
                    <measurement group_id="B1" value="7"/>
                    <measurement group_id="B2" value="5"/>
                    <measurement group_id="B3" value="12"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Some college/college graduate</title>
                  <measurement_list>
                    <measurement group_id="B1" value="15"/>
                    <measurement group_id="B2" value="19"/>
                    <measurement group_id="B3" value="34"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Post-graduate</title>
                  <measurement_list>
                    <measurement group_id="B1" value="7"/>
                    <measurement group_id="B2" value="7"/>
                    <measurement group_id="B3" value="14"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Number of self-reported physicians, n(%)</title>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <category_list>
                <category>
                  <title>1-3</title>
                  <measurement_list>
                    <measurement group_id="B1" value="10"/>
                    <measurement group_id="B2" value="15"/>
                    <measurement group_id="B3" value="25"/>
                  </measurement_list>
                </category>
                <category>
                  <title>4+</title>
                  <measurement_list>
                    <measurement group_id="B1" value="19"/>
                    <measurement group_id="B2" value="16"/>
                    <measurement group_id="B3" value="35"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Number of self-reported medications, n(%)</title>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <category_list>
                <category>
                  <title>1</title>
                  <measurement_list>
                    <measurement group_id="B1" value="2"/>
                    <measurement group_id="B2" value="2"/>
                    <measurement group_id="B3" value="4"/>
                  </measurement_list>
                </category>
                <category>
                  <title>2-4</title>
                  <measurement_list>
                    <measurement group_id="B1" value="6"/>
                    <measurement group_id="B2" value="4"/>
                    <measurement group_id="B3" value="10"/>
                  </measurement_list>
                </category>
                <category>
                  <title>5+</title>
                  <measurement_list>
                    <measurement group_id="B1" value="21"/>
                    <measurement group_id="B2" value="25"/>
                    <measurement group_id="B3" value="46"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Number of Pillsy bottles, n(%)</title>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <category_list>
                <category>
                  <title>1</title>
                  <measurement_list>
                    <measurement group_id="B1" value="20"/>
                    <measurement group_id="B2" value="25"/>
                    <measurement group_id="B3" value="45"/>
                  </measurement_list>
                </category>
                <category>
                  <title>2+</title>
                  <measurement_list>
                    <measurement group_id="B1" value="9"/>
                    <measurement group_id="B2" value="6"/>
                    <measurement group_id="B3" value="15"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Level of non-adherence, self-reported number of doses missed in past month, n(%)</title>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <category_list>
                <category>
                  <title>0</title>
                  <measurement_list>
                    <measurement group_id="B1" value="12"/>
                    <measurement group_id="B2" value="14"/>
                    <measurement group_id="B3" value="26"/>
                  </measurement_list>
                </category>
                <category>
                  <title>1+</title>
                  <measurement_list>
                    <measurement group_id="B1" value="17"/>
                    <measurement group_id="B2" value="17"/>
                    <measurement group_id="B3" value="34"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
      </measure_list>
    </baseline>

    <outcome_list>
      <outcome>
        <type>Primary</type>
        <title>Medication Adherence</title>
        <description>Medication adherence to type 2 diabetes oral medications (averaged) as measured by the number of dates and times of pillbottle openings in the electronic pill bottles</description>
        <time_frame>6 months</time_frame>
        <population>Participants were included in the analysis until they no longer participated in the study.</population>
        <group_list>
          <group group_id="O1">
            <title>Reinforcement Learning Intervention Arm</title>
            <description>Up to daily, tailored text messages.&#xD;
Reinforcement Learning: Participants in the intervention arm will receive up to daily, tailored text messages based on their electronic pill bottle-measured adherence. Given the participants' baseline characteristics and time-varying responses to the messages, a reinforcement learning algorithm will deliver different text messages and adapt over time to determine which type of messaging works best for each individual participant.</description>
          </group>
          <group group_id="O2">
            <title>Control Arm</title>
            <description>Up to daily, untailored text messages.</description>
          </group>
        </group_list>
        <measure>
          <title>Medication Adherence</title>
          <description>Medication adherence to type 2 diabetes oral medications (averaged) as measured by the number of dates and times of pillbottle openings in the electronic pill bottles</description>
          <population>Participants were included in the analysis until they no longer participated in the study.</population>
          <units>pill bottle openings</units>
          <param>Mean</param>
          <dispersion>Standard Deviation</dispersion>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="O1" value="29"/>
                <count group_id="O2" value="31"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="74" spread="31"/>
                    <measurement group_id="O2" value="68" spread="29"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
      </outcome>
      <outcome>
        <type>Secondary</type>
        <title>Glycemic Control</title>
        <description>Change in glycated hemoglobin A1c from baseline to end of the 6-month follow-up</description>
        <time_frame>6 months</time_frame>
        <population>Participants were included in the analysis until they no longer participated in the study. Differences were calculated among those who had follow-up HbA1c values recorded.</population>
        <group_list>
          <group group_id="O1">
            <title>Reinforcement Learning Intervention Arm</title>
            <description>Up to daily, tailored text messages.&#xD;
Reinforcement Learning: Participants in the intervention arm will receive up to daily, tailored text messages based on their electronic pill bottle-measured adherence. Given the participants' baseline characteristics and time-varying responses to the messages, a reinforcement learning algorithm will deliver different text messages and adapt over time to determine which type of messaging works best for each individual participant.</description>
          </group>
          <group group_id="O2">
            <title>Control Arm</title>
            <description>Up to daily, untailored text messages.</description>
          </group>
        </group_list>
        <measure>
          <title>Glycemic Control</title>
          <description>Change in glycated hemoglobin A1c from baseline to end of the 6-month follow-up</description>
          <population>Participants were included in the analysis until they no longer participated in the study. Differences were calculated among those who had follow-up HbA1c values recorded.</population>
          <units>percentage glycated hemoglobin</units>
          <param>Mean</param>
          <dispersion>Standard Deviation</dispersion>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="O1" value="29"/>
                <count group_id="O2" value="31"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="-0.82" spread="1.19"/>
                    <measurement group_id="O2" value="-1.20" spread="2.28"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
      </outcome>
    </outcome_list>

    <reported_events>
      <time_frame>Adverse events were collected from enrollment through the 6 month study follow-up period.</time_frame>
      <group_list>
        <group group_id="E1">
          <title>Reinforcement Learning Intervention Arm</title>
          <description>Up to daily, tailored text messages.&#xD;
Reinforcement Learning: Participants in the intervention arm will receive up to daily, tailored text messages based on their electronic pill bottle-measured adherence. Given the participants' baseline characteristics and time-varying responses to the messages, a reinforcement learning algorithm will deliver different text messages and adapt over time to determine which type of messaging works best for each individual participant.</description>
        </group>
        <group group_id="E2">
          <title>Control Arm</title>
          <description>Up to daily, untailored text messages.</description>
        </group>
      </group_list>
      <serious_events>
        <category_list>
          <category>
            <title>Total</title>
            <event_list>
              <event>
                <sub_title>Total, all-cause mortality</sub_title>
                <counts group_id="E1" subjects_affected="0" subjects_at_risk="29"/>
                <counts group_id="E2" subjects_affected="1" subjects_at_risk="31"/>
              </event>
              <event>
                <sub_title>Total, serious adverse events</sub_title>
                <counts group_id="E1" subjects_affected="0" subjects_at_risk="29"/>
                <counts group_id="E2" subjects_affected="0" subjects_at_risk="31"/>
              </event>
            </event_list>
          </category>
        </category_list>
      </serious_events>
      <other_events>
        <frequency_threshold>0</frequency_threshold>
        <category_list>
          <category>
            <title>Total</title>
            <event_list>
              <event>
                <sub_title>Total, other adverse events</sub_title>
                <counts group_id="E1" subjects_affected="0" subjects_at_risk="29"/>
                <counts group_id="E2" subjects_affected="0" subjects_at_risk="31"/>
              </event>
            </event_list>
          </category>
        </category_list>
      </other_events>
    </reported_events>

    <certain_agreements>
      <pi_employee>Principal Investigators are NOT employed by the organization sponsoring the study.</pi_employee>
      <restrictive_agreement>There is NOT an agreement between Principal Investigators and the Sponsor (or its agents) that restricts the PI's rights to discuss or publish trial results after the trial is completed. </restrictive_agreement>
    </certain_agreements>
    <point_of_contact>
      <name_or_title>Julie Lauffenburger, PharmD, PhD</name_or_title>
      <organization>Brigham and Women's Hospital</organization>
      <phone>617-525-8865</phone>
      <email>jlauffenburger@bwh.harvard.edu</email>
    </point_of_contact>
  </clinical_results>
</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/NCT04473326
org study id: 2020P000846
secondary id: P30AG064199-01
nct id: NCT04473326
lead sponsor: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
Reinforcement learning is an advanced analytic method that discovers each individual's
      pattern of responsiveness by observing their actions and then implements a personalized
      strategy to optimize individuals' behaviors using trial and error. The goal of this pilot
      study is to develop and test a novel reinforcement learning-enhanced text messaging program
      to support medication adherence in patients with type 2 diabetes. Type 2 diabetes is an
      optimal condition in which to test this program, as it is one of the most prevalent chronic
      conditions in the US adult population and requires most patients to be on daily or twice
      daily doses of medications. This pilot study will be a parallel randomized pragmatic trial
      comparing medication adherence and clinical outcomes for adults aged 18-84 with type 2
      diabetes who are prescribed 1-3 daily oral medications for this disease. Participants will be
      randomized to one of two arms for the duration of the study period: (1) a reinforcement
      learning intervention arm with up to daily, tailored text messages based on time-varying
      treatment-response patterns; or (2) a control arm with up to daily, un-tailored text
      messages. Our outcomes of interest will be medication adherence, as measured by electronic
      pill bottles, and HbA1c levels.
The goal of this pilot study is to develop and test a novel reinforcement learning-enhanced
      text messaging program to support medication adherence in patients with type 2 diabetes. This
      pilot study will be a parallel randomized pragmatic trial comparing medication adherence and
      clinical outcomes for adults aged 18-84 with type 2 diabetes who are prescribed 1-3 daily
      oral medications for this disease. Participants will be randomized to one of two arms for the
      duration of the study period: 1) a reinforcement learning intervention arm with up to daily,
      tailored text messages based on time-varying treatment response patterns, or 2) a control arm
      with up to daily, untailored text messages. Our outcomes of interest will be medication
      adherence, as measured by electronic pill bottles, and HbA1c levels.
allocation: Randomized
intervention model: Parallel Assignment
primary purpose: Health Services Research
masking: Double (Investigator, Outcomes Assessor)
measure: Medication Adherence
time frame: 6 months
description: Medication adherence to type 2 diabetes oral medications (averaged) as measured by the number of dates and times of pillbottle openings in the electronic pill bottles
measure: Glycemic Control
time frame: 6 months
description: Change in glycated hemoglobin A1c from baseline to end of the 6-month follow-up
arm group label: Reinforcement Learning Intervention Arm
arm group type: Experimental
description: Up to daily, tailored text messages.
arm group label: Control Arm
arm group type: No Intervention
description: Up to daily, untailored text messages.
intervention type: Behavioral
intervention name: Reinforcement Learning
description: Participants in the intervention arm will receive up to daily, tailored text messages based on their electronic pill bottle-measured adherence. Given the participants' baseline characteristics and time-varying responses to the messages, a reinforcement learning algorithm will deliver different text messages and adapt over time to determine which type of messaging works best for each individual participant.
arm group label: Reinforcement Learning Intervention Arm
criteria: 
gender: All
minimum age: 18 Years
maximum age: 84 Years
healthy volunteers: No
facility: 
country: United States
responsible party type: Principal Investigator
investigator affiliation: Brigham and Women's Hospital
investigator full name: Julie Lauffenburger
investigator title: Assistant Professor
mesh term: Diabetes Mellitus
mesh term: Diabetes Mellitus, Type 2
sharing ipd: No
provided document: 
provided document: 
participant flow: 
baseline: 
outcome list: 
reported events: 
certain agreements: 
point of contact: 