<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/NCT03774433</url>
  </required_header>
  <id_info>
    <org_study_id>4UH3DA041713-04 D19035</org_study_id>
    <secondary_id>UH3DA041713</secondary_id>
    <nct_id>NCT03774433</nct_id>
  </id_info>
  <brief_title>Using Mobile Technology to Improve Self-Regulation</brief_title>
  <official_title>Applying Novel Technologies and Methods to Inform the Ontology of Self-Regulation - Aim 4 Dartmouth Study: Using Mobile Technology to Improve Self-Regulation</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Dartmouth-Hitchcock Medical Center</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>National Institutes of Health (NIH)</agency>
      <agency_class>NIH</agency_class>
    </collaborator>
    <collaborator>
      <agency>National Institute on Drug Abuse (NIDA)</agency>
      <agency_class>NIH</agency_class>
    </collaborator>
  </sponsors>
  <source>Dartmouth-Hitchcock Medical Center</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>
      This study will evaluate the degree to which engaging targets produces a desired change in&#xD;
      medical regimen adherence (across 4-week interventions) and health behavior among smokers&#xD;
      (n=50) and overweight/obese persons with binge eating disorder (n=50) (smoking in the former&#xD;
      sample and binge eating in the latter sample). The investigators will employ a novel mobile&#xD;
      behavioral assessment/intervention platform to engage targets in these samples, given that&#xD;
      (1) it offers self-regulation assessment and behavior change tools via an integrated platform&#xD;
      to a wide array of populations, and (2) content within the platform can be quickly modified&#xD;
      as needed to better impact targets. This is the fourth and final phase of a study that aims&#xD;
      to identify putative mechanisms of behavior change to develop an overarching &quot;ontology&quot; of&#xD;
      self-regulatory processes.&#xD;
&#xD;
      This trial builds on NCT03352713.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Health risk behavior, including poor diet, physical inactivity, tobacco and other substance&#xD;
      use, causes as much as 40% of the illness, suffering, and early death related to chronic&#xD;
      diseases. Non-adherence to medical regimens is an important exemplar of the challenges in&#xD;
      changing health behavior and its associated impact on health outcomes. Although an array of&#xD;
      interventions has been shown to be effective in promoting initiation and maintenance of&#xD;
      health behavior change, the mechanisms by which they actually work are infrequently&#xD;
      systematically examined. One promising domain of mechanisms to be examined across many&#xD;
      populations and types of health behavior is self-regulation. Self-regulation involves&#xD;
      identifying one's goals, and maintaining goal-directed behavior. A large scientific&#xD;
      literature has identified the role of self-regulation as a potential causal mechanism in&#xD;
      promoting health behavior.&#xD;
&#xD;
      Advances in digital technologies have created unprecedented opportunities to assess and&#xD;
      modify self-regulation and health behavior. In this project, the investigators plan to use a&#xD;
      systematic, empirical process to integrate concepts across the divergent self-regulation&#xD;
      literatures to identify putative mechanisms of behavior change to develop an overarching&#xD;
      &quot;ontology&quot; of self-regulatory processes.&#xD;
&#xD;
      This multi-year, multi-institution project aims to identify an array of putative&#xD;
      psychological and behavioral targets within the self-regulation domain implicated in medical&#xD;
      regimen adherence and health behavior. This is in service of developing an &quot;ontology&quot; of&#xD;
      self-regulation that will provide structure and integrate concepts across diverse&#xD;
      literatures. The investigators aim to examine the relationship between various constructs&#xD;
      within the self-regulation domain, the relationship among measures and constructs across&#xD;
      multiple levels of analysis, and the extent to which these patterns transcend population and&#xD;
      context. The project consists of four primary aims across two phases of funding (UH2 and UH3&#xD;
      phases). Note that Aims 1-3 were conducted under our prior UH2 phase, and the investigators&#xD;
      herein include the protocol for Aim 4 to be conducted in the UH3 phase:&#xD;
&#xD;
      Aim 1. Identify an array of putative targets within the self-regulation domain implicated in&#xD;
      medical regimen adherence and health behavior across these 3 levels of analysis. The&#xD;
      investigators will build on Multiple PI Poldrack's pioneering &quot;Cognitive Atlas&quot; ontology to&#xD;
      integrate concepts across divergent literatures to develop an &quot;ontology&quot; of self-regulatory&#xD;
      processes. The expert team will catalog tasks in the self-regulation literature, implement&#xD;
      tasks via online testing (Mechanical Turk) to rapidly obtain large datasets of&#xD;
      self-regulatory function, assess the initial ontology via confirmatory factor analysis and&#xD;
      structural equation modeling, and assess and revise the resulting ontology according to&#xD;
      neural similarity patterns across tasks (to identify tasks for Aim 2).&#xD;
&#xD;
      Aim 2. Evaluate the extent to which putative targets can be engaged and manipulated within&#xD;
      the self-regulation domain both within and outside of laboratory settings. Fifty smokers and&#xD;
      50 overweight/obese persons with binge eating disorder will participate in a lab study (led&#xD;
      by Poldrack) to complete the tasks identified under Aim 1. The investigators will&#xD;
      experimentally modulate engagement of targets (e.g., stimulus set of highly palatable foods&#xD;
      images or tobacco-related images as well as self-regulation interventions). A comparable&#xD;
      sampling of 100 persons will participate in a non-lab study (led by Multiple PI Marsch) in&#xD;
      which the investigators will leverage our novel mobile-based behavioral&#xD;
      assessment/intervention platform to modulate target engagement and collect data in real-world&#xD;
      conditions.&#xD;
&#xD;
      Aim 3. Identify or develop measures and methods to permit verification of target engagement&#xD;
      within the self-regulation domain. Led by Co-I MacKinnon, the investigators will examine&#xD;
      cross-assay validity and cross-context and cross-sample reliability of assays. The&#xD;
      investigators will employ discriminant and divergent validation methods and Bayesian modeling&#xD;
      to refine an empirically-based ontology of self-regulatory targets (to be used in Aim 4).&#xD;
&#xD;
      Aim 4. The investigators will evaluate the degree to which engaging targets produces a&#xD;
      desired change in medical regimen adherence (across 4-week interventions) and health behavior&#xD;
      among smokers (n=50 each at Dartmouth and Stanford) and overweight/obese persons with binge&#xD;
      eating disorder (n=50 each at Dartmouth and Stanford) (smoking in the former sample and binge&#xD;
      eating in the latter sample). The investigators will employ a novel mobile behavioral&#xD;
      assessment/intervention platform to engage targets in these samples, given that (1) it offers&#xD;
      self-regulation assessment and behavior change tools via an integrated platform to a wide&#xD;
      array of populations, and (2) content within the platform can be quickly modified as needed&#xD;
      to better impact targets. The proposed project is designed to identify valid and replicable&#xD;
      assays of mechanisms of self-regulation across populations to inform an ontology of&#xD;
      self-regulation that can ultimately inform development of health behavior interventions of&#xD;
      maximal efficacy and potency.&#xD;
&#xD;
      This protocol details the Aim 4 study at Dartmouth led by Multiple PI Marsch.&#xD;
&#xD;
      This phase of the study takes what the investigators learned about self-regulation in the&#xD;
      first three phases and applies it in two samples that are exemplary for &quot;lapses&quot; in&#xD;
      self-regulation: individuals who smoke and overweight/obese individuals with binge eating&#xD;
      disorder. The investigators learned in Aim 2 that many real-world conditions (e.g.,&#xD;
      temptation, negative affect) may decrease self-regulation, whereas training through the&#xD;
      mobile intervention described below may increase self-regulation. The primary purpose of this&#xD;
      Aim 4 study is to target self-regulation to impact health behaviors.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">February 27, 2019</start_date>
  <completion_date type="Actual">June 29, 2020</completion_date>
  <primary_completion_date type="Actual">June 29, 2020</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>Basic Science</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Rate of Binge Eating Episodes [Binge Eating Sample Only]</measure>
    <time_frame>28 days</time_frame>
    <description>[Binge eating sample only] Self-reported binge eating episodes are assessed four times daily (morning, early afternoon, late afternoon/evening, and night) over a 28-day period. A binge eating episode is defined as self-reported overeating and loss of control. Overeating is assessed by the question &quot;Since the last prompt, when you ate most recently, did you overeat?&quot; and is scored as 0 (no) or 1 (yes). Loss of control is assessed by the question &quot;When you ate most recently, did you lose control over your eating?&quot; and is scored as 1 (not at all) to 5 (totally), where a 4 or 5 is considered loss of control. We are reporting the mean and standard deviations of the target behavior for each sample by week (i.e., Week 1, Week 2, Week 3, and Week 4). Data were averaged for all assessments each week to calculate the number of binge-eating episodes per week.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Rate of Smoking Episodes [Smoking Sample Only]</measure>
    <time_frame>28 days</time_frame>
    <description>[Smoking sample only] Self-reported smoking is assessed four times daily (morning, early afternoon, late afternoon/evening, and night) over a 28-day period. A smoking episode is defined as self-reported smoking of more than zero cigarettes and is assessed by the question &quot;Since the last prompt, how many cigarettes have you smoked?&quot; Participants are asked to input a number into a number field. We are reporting the mean and standard deviations of the target behavior for each sample by week (i.e., Week 1, Week 2, Week 3, and Week 4). Data were averaged for all assessments each week to calculate the number of cigarettes smoked per week.</description>
  </primary_outcome>
  <number_of_arms>1</number_of_arms>
  <enrollment type="Actual">114</enrollment>
  <condition>Self-regulation</condition>
  <condition>Binge Eating</condition>
  <condition>Smoking</condition>
  <arm_group>
    <arm_group_label>Laddr</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>All participants in the study will be invited to use Laddr, described in the intervention section.</description>
  </arm_group>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>Laddr</intervention_name>
    <description>Laddr is an integrated, personalized, web-based self-regulation assessment and behavior change system. It integrates tools that have been shown to be effective for a wide array of behavioral phenomena ranging from substance use and abuse, mental health, risk-taking, chronic pain management, medication adherence, diet, exercise, diabetes and other chronic disease management, and smoking. The organizational structure, functionality and content within Laddr's system centrally embrace these fundamental aspects of behavior change; thus, the Laddr platform is not &quot;diagnosis-specific&quot; but rather enables integrated care for any combination of individuals' goals, needs, and preferences.</description>
    <arm_group_label>Laddr</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion criteria:&#xD;
&#xD;
          -  Age 18-50 years&#xD;
&#xD;
          -  Understand English sufficiently to provide informed consent&#xD;
&#xD;
          -  Access to a computer in a setting in which the participant is comfortable providing&#xD;
             sensitive information&#xD;
&#xD;
          -  Use a smartphone operating system compatible with Laddr&#xD;
&#xD;
        Additional inclusion criteria for binge eating sample:&#xD;
&#xD;
          -  27 ≤ BMI ≤ 45 kg/m2&#xD;
&#xD;
          -  Have binge eating disorder according to DSM-5 criteria&#xD;
&#xD;
          -  Non-smoking (defined as no cigarettes in past 12 months-this includes former and never&#xD;
             smokers)&#xD;
&#xD;
          -  Confirmed interest in an eating intervention&#xD;
&#xD;
          -  Use a smartphone compatible with Fitbit&#xD;
&#xD;
        Additional inclusion criteria for smoking sample:&#xD;
&#xD;
          -  Smoke 5 or more tobacco cigarettes/day for past year&#xD;
&#xD;
          -  17 ≤ BMI &lt; 27 kg/m2&#xD;
&#xD;
          -  Confirmed interest in a smoking quit attempt&#xD;
&#xD;
          -  Use a smartphone compatible with the iCO Smokerlyzer&#xD;
&#xD;
        Exclusion criteria:&#xD;
&#xD;
          -  Enrolled in Aim 2 study&#xD;
&#xD;
          -  Any current substance use disorder&#xD;
&#xD;
             o Will not exclude based on use of substances&#xD;
&#xD;
          -  Currently pregnant or plans to become pregnant in next 3 months&#xD;
&#xD;
          -  Lifetime history of mental disorder due to a medical condition&#xD;
&#xD;
          -  Lifetime history of major psychotic disorders (including schizophrenia and bipolar&#xD;
             disorder)&#xD;
&#xD;
          -  Current use of prescription pain medications (e.g., Vicodin, oxycodone)&#xD;
&#xD;
          -  Current use of any medication for smoking (e.g., Wellbutrin, varenicline)&#xD;
&#xD;
             o Exceptions: will not screen out for nicotine replacement therapy (e.g., patch, gum,&#xD;
             lozenge, nasal spray, inhaler)&#xD;
&#xD;
          -  Current use of any medication for weight loss&#xD;
&#xD;
          -  Have undergone weight-loss surgery (e.g., gastric bypass, lap band)&#xD;
&#xD;
          -  Current nighttime shift work or obstructive sleep apnea&#xD;
&#xD;
          -  Note: We will not exclude based on e-cigarette use.&#xD;
&#xD;
        Additional exclusion criteria for binge eating sample:&#xD;
&#xD;
          -  Compensatory behavior (e.g., purging, excessive exercise, fasting)&#xD;
&#xD;
             o Already excluded as part of the DSM-5 binge eating disorder criteria&#xD;
&#xD;
          -  Lost weight in recent past (&gt;10 pounds in past 6 months)&#xD;
&#xD;
          -  Currently in a weight-loss program (e.g., Weight Watchers, Jenny Craig)&#xD;
&#xD;
             o Will ask about, but won't exclude on, online/mobile app weight-loss programs as part&#xD;
             of the screener&#xD;
&#xD;
          -  Currently on a special diet for a serious health condition&#xD;
&#xD;
          -  Currently in therapy with a clinician for binge eating&#xD;
&#xD;
          -  Nickel allergy (because Fitbit band contains nickel)&#xD;
&#xD;
        Additional exclusion criteria for smoking sample:&#xD;
&#xD;
          -  Currently in therapy with a clinician for smoking&#xD;
&#xD;
          -  Binge eating behavior&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>50 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Lisa A Marsch, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Dartmouth College</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Center for Technology and Behavioral Health, Dartmouth College</name>
      <address>
        <city>Lebanon</city>
        <state>New Hampshire</state>
        <zip>03766</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <verification_date>July 2021</verification_date>
  <study_first_submitted>December 11, 2018</study_first_submitted>
  <study_first_submitted_qc>December 11, 2018</study_first_submitted_qc>
  <study_first_posted type="Actual">December 13, 2018</study_first_posted>
  <results_first_submitted>June 29, 2021</results_first_submitted>
  <results_first_submitted_qc>July 29, 2021</results_first_submitted_qc>
  <results_first_posted type="Actual">August 24, 2021</results_first_posted>
  <last_update_submitted>July 29, 2021</last_update_submitted>
  <last_update_submitted_qc>July 29, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">August 24, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Dartmouth-Hitchcock Medical Center</investigator_affiliation>
    <investigator_full_name>Lisa A. Marsch</investigator_full_name>
    <investigator_title>Andrew G. Wallace Professor; Director, Center for Technology and Behavioral Health</investigator_title>
  </responsible_party>
  <keyword>self-regulation</keyword>
  <keyword>binge eating</keyword>
  <keyword>smoking</keyword>
  <keyword>ecological momentary assessment</keyword>
  <keyword>mobile sensing</keyword>
  <keyword>mHealth</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Bulimia</mesh_term>
    <mesh_term>Binge-Eating Disorder</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Yes</sharing_ipd>
    <ipd_description>After completion of the study, a de-identified dataset (i.e., stripped of all codes and other information that could be linked back to an individual participant) will be generated and made available to the research community as a whole. Informed consent procedures will ensure that participants are aware that consenting to participate in the study means consenting to inclusion in this open data set.</ipd_description>
    <ipd_time_frame>Data will be available upon submission of the paper detailing the findings of the research.</ipd_time_frame>
    <ipd_access_criteria>All data will be shared openly with no restrictions on access.</ipd_access_criteria>
  </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>July 11, 2019</document_date>
      <document_url>https://ClinicalTrials.gov/ProvidedDocs/33/NCT03774433/Prot_SAP_003.pdf</document_url>
    </provided_document>
  </provided_document_section>

  <clinical_results>

    <participant_flow>
      <recruitment_details>Participants were recruited from online (e.g., Craigslist, Facebook, Google AdWords) between 2/27/19 and 6/29/20.</recruitment_details>
      <group_list>
        <group group_id="P1">
          <title>Laddr</title>
          <description>All participants in the study will be invited to use Laddr, described in the intervention section.&#xD;
Laddr: Laddr is an integrated, personalized, web-based self-regulation assessment and behavior change system. It integrates tools that have been shown to be effective for a wide array of behavioral phenomena ranging from substance use and abuse, mental health, risk-taking, chronic pain management, medication adherence, diet, exercise, diabetes and other chronic disease management, and smoking. The organizational structure, functionality and content within Laddr's system centrally embrace these fundamental aspects of behavior change; thus, the Laddr platform is not &quot;diagnosis-specific&quot; but rather enables integrated care for any combination of individuals' goals, needs, and preferences.</description>
        </group>
      </group_list>
      <period_list>
        <period>
          <title>Overall Study</title>
          <milestone_list>
            <milestone>
              <title>STARTED</title>
              <participants_list>
                <participants group_id="P1" count="114"/>
              </participants_list>
            </milestone>
            <milestone>
              <title>COMPLETED</title>
              <participants_list>
                <participants group_id="P1" count="100">Participants who completed at least 12 EMAs for inclusion in data analysis</participants>
              </participants_list>
            </milestone>
            <milestone>
              <title>NOT COMPLETED</title>
              <participants_list>
                <participants group_id="P1" count="14"/>
              </participants_list>
            </milestone>
          </milestone_list>
          <drop_withdraw_reason_list>
            <drop_withdraw_reason>
              <title>Participants who completed less than 12 EMAs are not included in the final analysis</title>
              <participants_list>
                <participants group_id="P1" count="14"/>
              </participants_list>
            </drop_withdraw_reason>
          </drop_withdraw_reason_list>
        </period>
      </period_list>
    </participant_flow>

    <baseline>
      <population>Total number of consented participants.</population>
      <group_list>
        <group group_id="B1">
          <title>Laddr</title>
          <description>All participants in the study will be invited to use Laddr, described in the intervention section.&#xD;
Laddr: Laddr is an integrated, personalized, web-based self-regulation assessment and behavior change system. It integrates tools that have been shown to be effective for a wide array of behavioral phenomena ranging from substance use and abuse, mental health, risk-taking, chronic pain management, medication adherence, diet, exercise, diabetes and other chronic disease management, and smoking. The organizational structure, functionality and content within Laddr's system centrally embrace these fundamental aspects of behavior change; thus, the Laddr platform is not &quot;diagnosis-specific&quot; but rather enables integrated care for any combination of individuals' goals, needs, and preferences.</description>
        </group>
      </group_list>
      <analyzed_list>
        <analyzed>
          <units>Participants</units>
          <scope>Overall</scope>
          <count_list>
            <count group_id="B1" value="114"/>
          </count_list>
        </analyzed>
      </analyzed_list>
      <measure_list>
        <measure>
          <title>Age</title>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <category_list>
                <category>
                  <title>&lt;=18 years</title>
                  <measurement_list>
                    <measurement group_id="B1" value="0"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Between 18 and 65 years</title>
                  <measurement_list>
                    <measurement group_id="B1" value="114"/>
                  </measurement_list>
                </category>
                <category>
                  <title>&gt;=65 years</title>
                  <measurement_list>
                    <measurement group_id="B1" value="0"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Age</title>
          <units>years</units>
          <param>Mean</param>
          <dispersion>Full Range</dispersion>
          <class_list>
            <class>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="33.83" lower_limit="18" upper_limit="50"/>
                  </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="65"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Male</title>
                  <measurement_list>
                    <measurement group_id="B1" value="49"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Ethnicity (NIH/OMB)</title>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <category_list>
                <category>
                  <title>Hispanic or Latino</title>
                  <measurement_list>
                    <measurement group_id="B1" value="9"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Not Hispanic or Latino</title>
                  <measurement_list>
                    <measurement group_id="B1" value="105"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Unknown or Not Reported</title>
                  <measurement_list>
                    <measurement group_id="B1" value="0"/>
                  </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_list>
                </category>
                <category>
                  <title>Asian</title>
                  <measurement_list>
                    <measurement group_id="B1" value="7"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Native Hawaiian or Other Pacific Islander</title>
                  <measurement_list>
                    <measurement group_id="B1" value="0"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Black or African American</title>
                  <measurement_list>
                    <measurement group_id="B1" value="14"/>
                  </measurement_list>
                </category>
                <category>
                  <title>White</title>
                  <measurement_list>
                    <measurement group_id="B1" value="83"/>
                  </measurement_list>
                </category>
                <category>
                  <title>More than one race</title>
                  <measurement_list>
                    <measurement group_id="B1" value="1"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Unknown or Not Reported</title>
                  <measurement_list>
                    <measurement group_id="B1" value="9"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Region of Enrollment</title>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <title>United States</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="114"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
      </measure_list>
    </baseline>

    <outcome_list>
      <outcome>
        <type>Primary</type>
        <title>Rate of Binge Eating Episodes [Binge Eating Sample Only]</title>
        <description>[Binge eating sample only] Self-reported binge eating episodes are assessed four times daily (morning, early afternoon, late afternoon/evening, and night) over a 28-day period. A binge eating episode is defined as self-reported overeating and loss of control. Overeating is assessed by the question &quot;Since the last prompt, when you ate most recently, did you overeat?&quot; and is scored as 0 (no) or 1 (yes). Loss of control is assessed by the question &quot;When you ate most recently, did you lose control over your eating?&quot; and is scored as 1 (not at all) to 5 (totally), where a 4 or 5 is considered loss of control. We are reporting the mean and standard deviations of the target behavior for each sample by week (i.e., Week 1, Week 2, Week 3, and Week 4). Data were averaged for all assessments each week to calculate the number of binge-eating episodes per week.</description>
        <time_frame>28 days</time_frame>
        <population>Participants included completed &gt;10% (at least 12) EMAs.</population>
        <group_list>
          <group group_id="O1">
            <title>Laddr</title>
            <description>All participants in the study will be invited to use Laddr, described in the intervention section.&#xD;
Laddr: Laddr is an integrated, personalized, web-based self-regulation assessment and behavior change system. It integrates tools that have been shown to be effective for a wide array of behavioral phenomena ranging from substance use and abuse, mental health, risk-taking, chronic pain management, medication adherence, diet, exercise, diabetes and other chronic disease management, and smoking. The organizational structure, functionality and content within Laddr's system centrally embrace these fundamental aspects of behavior change; thus, the Laddr platform is not &quot;diagnosis-specific&quot; but rather enables integrated care for any combination of individuals' goals, needs, and preferences.</description>
          </group>
        </group_list>
        <measure>
          <title>Rate of Binge Eating Episodes [Binge Eating Sample Only]</title>
          <description>[Binge eating sample only] Self-reported binge eating episodes are assessed four times daily (morning, early afternoon, late afternoon/evening, and night) over a 28-day period. A binge eating episode is defined as self-reported overeating and loss of control. Overeating is assessed by the question &quot;Since the last prompt, when you ate most recently, did you overeat?&quot; and is scored as 0 (no) or 1 (yes). Loss of control is assessed by the question &quot;When you ate most recently, did you lose control over your eating?&quot; and is scored as 1 (not at all) to 5 (totally), where a 4 or 5 is considered loss of control. We are reporting the mean and standard deviations of the target behavior for each sample by week (i.e., Week 1, Week 2, Week 3, and Week 4). Data were averaged for all assessments each week to calculate the number of binge-eating episodes per week.</description>
          <population>Participants included completed &gt;10% (at least 12) EMAs.</population>
          <units>binge eating episodes</units>
          <param>Mean</param>
          <dispersion>Standard Deviation</dispersion>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="O1" value="50"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <title>week 1 (self-reported overeating behavior)</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="0.256" spread="0.437"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>week 2 (self-reported overeating behavior)</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="0.210" spread="0.408"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>week 3 (self-reported overeating behavior)</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="0.219" spread="0.414"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>week 4 (self-reported overeating behavior)</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="0.205" spread="0.404"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>week 1 (self-reported lost control behavior)</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="0.170" spread="0.376"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>week 2 (self-reported lost control behavior)</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="0.167" spread="0.373"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>week 3 (self-reported lost control behavior)</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="0.135" spread="0.312"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>week 4 (self-reported lost control behavior)</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="0.148" spread="0.355"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
      </outcome>
      <outcome>
        <type>Primary</type>
        <title>Rate of Smoking Episodes [Smoking Sample Only]</title>
        <description>[Smoking sample only] Self-reported smoking is assessed four times daily (morning, early afternoon, late afternoon/evening, and night) over a 28-day period. A smoking episode is defined as self-reported smoking of more than zero cigarettes and is assessed by the question &quot;Since the last prompt, how many cigarettes have you smoked?&quot; Participants are asked to input a number into a number field. We are reporting the mean and standard deviations of the target behavior for each sample by week (i.e., Week 1, Week 2, Week 3, and Week 4). Data were averaged for all assessments each week to calculate the number of cigarettes smoked per week.</description>
        <time_frame>28 days</time_frame>
        <population>Participants included completed &gt;10% (at least 12) EMAs.</population>
        <group_list>
          <group group_id="O1">
            <title>Laddr</title>
            <description>All participants in the study will be invited to use Laddr, described in the intervention section.&#xD;
Laddr: Laddr is an integrated, personalized, web-based self-regulation assessment and behavior change system. It integrates tools that have been shown to be effective for a wide array of behavioral phenomena ranging from substance use and abuse, mental health, risk-taking, chronic pain management, medication adherence, diet, exercise, diabetes and other chronic disease management, and smoking. The organizational structure, functionality and content within Laddr's system centrally embrace these fundamental aspects of behavior change; thus, the Laddr platform is not &quot;diagnosis-specific&quot; but rather enables integrated care for any combination of individuals' goals, needs, and preferences.</description>
          </group>
        </group_list>
        <measure>
          <title>Rate of Smoking Episodes [Smoking Sample Only]</title>
          <description>[Smoking sample only] Self-reported smoking is assessed four times daily (morning, early afternoon, late afternoon/evening, and night) over a 28-day period. A smoking episode is defined as self-reported smoking of more than zero cigarettes and is assessed by the question &quot;Since the last prompt, how many cigarettes have you smoked?&quot; Participants are asked to input a number into a number field. We are reporting the mean and standard deviations of the target behavior for each sample by week (i.e., Week 1, Week 2, Week 3, and Week 4). Data were averaged for all assessments each week to calculate the number of cigarettes smoked per week.</description>
          <population>Participants included completed &gt;10% (at least 12) EMAs.</population>
          <units>cigarettes</units>
          <param>Mean</param>
          <dispersion>Standard Deviation</dispersion>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="O1" value="50"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <title>Week 1 (self-reported smoking of more than zero cigarettes)</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="0.841" spread="0.366"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Week 2 (self-reported smoking of more than zero cigarettes)</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="0.706" spread="0.456"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Week 3 (self-reported smoking of more than zero cigarettes)</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="0.640" spread="0.480"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Week 4 (self-reported smoking of more than zero cigarettes)</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="0.606" spread="0.489"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
      </outcome>
    </outcome_list>

    <reported_events>
      <time_frame>Adverse event data were collected between the Baseline and the Follow-Up Assessment. After a participant completed the Baseline Assessment, they would start their 28-day study period. At the end of their 28-day study period, they completed one Follow-Up Assessment.</time_frame>
      <group_list>
        <group group_id="E1">
          <title>Laddr</title>
          <description>All participants in the study will be invited to use Laddr, described in the intervention section.&#xD;
Laddr: Laddr is an integrated, personalized, web-based self-regulation assessment and behavior change system. It integrates tools that have been shown to be effective for a wide array of behavioral phenomena ranging from substance use and abuse, mental health, risk-taking, chronic pain management, medication adherence, diet, exercise, diabetes and other chronic disease management, and smoking. The organizational structure, functionality and content within Laddr's system centrally embrace these fundamental aspects of behavior change; thus, the Laddr platform is not &quot;diagnosis-specific&quot; but rather enables integrated care for any combination of individuals' goals, needs, and preferences.</description>
        </group>
      </group_list>
      <serious_events>
        <default_assessment>Non-systematic Assessment</default_assessment>
        <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="114"/>
              </event>
              <event>
                <sub_title>Total, serious adverse events</sub_title>
                <counts group_id="E1" subjects_affected="1" subjects_at_risk="114"/>
              </event>
            </event_list>
          </category>
          <category>
            <title>Injury, poisoning and procedural complications</title>
            <event_list>
              <event>
                <sub_title>Facial Fractures</sub_title>
                <counts group_id="E1" subjects_affected="1" subjects_at_risk="114"/>
              </event>
            </event_list>
          </category>
        </category_list>
      </serious_events>
      <other_events>
        <frequency_threshold>0</frequency_threshold>
        <default_assessment>Non-systematic Assessment</default_assessment>
        <category_list>
          <category>
            <title>Total</title>
            <event_list>
              <event>
                <sub_title>Total, other adverse events</sub_title>
                <counts group_id="E1" subjects_affected="1" subjects_at_risk="114"/>
              </event>
            </event_list>
          </category>
          <category>
            <title>Blood and lymphatic system disorders</title>
            <event_list>
              <event>
                <sub_title>Deep Vein Thrombosis</sub_title>
                <counts group_id="E1" subjects_affected="1" subjects_at_risk="114"/>
              </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>Dr. Lisa Marsch</name_or_title>
      <organization>Dartmouth College</organization>
      <phone>603-646-7092</phone>
      <email>Lisa.A.Marsch@dartmouth.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/NCT03774433
org study id: 4UH3DA041713-04 D19035
secondary id: UH3DA041713
nct id: NCT03774433
lead sponsor: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
This study will evaluate the degree to which engaging targets produces a desired change in
      medical regimen adherence (across 4-week interventions) and health behavior among smokers
      (n=50) and overweight/obese persons with binge eating disorder (n=50) (smoking in the former
      sample and binge eating in the latter sample). The investigators will employ a novel mobile
      behavioral assessment/intervention platform to engage targets in these samples, given that
      (1) it offers self-regulation assessment and behavior change tools via an integrated platform
      to a wide array of populations, and (2) content within the platform can be quickly modified
      as needed to better impact targets. This is the fourth and final phase of a study that aims
      to identify putative mechanisms of behavior change to develop an overarching "ontology" of
      self-regulatory processes.

      This trial builds on NCT03352713.
Health risk behavior, including poor diet, physical inactivity, tobacco and other substance
      use, causes as much as 40% of the illness, suffering, and early death related to chronic
      diseases. Non-adherence to medical regimens is an important exemplar of the challenges in
      changing health behavior and its associated impact on health outcomes. Although an array of
      interventions has been shown to be effective in promoting initiation and maintenance of
      health behavior change, the mechanisms by which they actually work are infrequently
      systematically examined. One promising domain of mechanisms to be examined across many
      populations and types of health behavior is self-regulation. Self-regulation involves
      identifying one's goals, and maintaining goal-directed behavior. A large scientific
      literature has identified the role of self-regulation as a potential causal mechanism in
      promoting health behavior.

      Advances in digital technologies have created unprecedented opportunities to assess and
      modify self-regulation and health behavior. In this project, the investigators plan to use a
      systematic, empirical process to integrate concepts across the divergent self-regulation
      literatures to identify putative mechanisms of behavior change to develop an overarching
      "ontology" of self-regulatory processes.

      This multi-year, multi-institution project aims to identify an array of putative
      psychological and behavioral targets within the self-regulation domain implicated in medical
      regimen adherence and health behavior. This is in service of developing an "ontology" of
      self-regulation that will provide structure and integrate concepts across diverse
      literatures. The investigators aim to examine the relationship between various constructs
      within the self-regulation domain, the relationship among measures and constructs across
      multiple levels of analysis, and the extent to which these patterns transcend population and
      context. The project consists of four primary aims across two phases of funding (UH2 and UH3
      phases). Note that Aims 1-3 were conducted under our prior UH2 phase, and the investigators
      herein include the protocol for Aim 4 to be conducted in the UH3 phase:

      Aim 1. Identify an array of putative targets within the self-regulation domain implicated in
      medical regimen adherence and health behavior across these 3 levels of analysis. The
      investigators will build on Multiple PI Poldrack's pioneering "Cognitive Atlas" ontology to
      integrate concepts across divergent literatures to develop an "ontology" of self-regulatory
      processes. The expert team will catalog tasks in the self-regulation literature, implement
      tasks via online testing (Mechanical Turk) to rapidly obtain large datasets of
      self-regulatory function, assess the initial ontology via confirmatory factor analysis and
      structural equation modeling, and assess and revise the resulting ontology according to
      neural similarity patterns across tasks (to identify tasks for Aim 2).

      Aim 2. Evaluate the extent to which putative targets can be engaged and manipulated within
      the self-regulation domain both within and outside of laboratory settings. Fifty smokers and
      50 overweight/obese persons with binge eating disorder will participate in a lab study (led
      by Poldrack) to complete the tasks identified under Aim 1. The investigators will
      experimentally modulate engagement of targets (e.g., stimulus set of highly palatable foods
      images or tobacco-related images as well as self-regulation interventions). A comparable
      sampling of 100 persons will participate in a non-lab study (led by Multiple PI Marsch) in
      which the investigators will leverage our novel mobile-based behavioral
      assessment/intervention platform to modulate target engagement and collect data in real-world
      conditions.

      Aim 3. Identify or develop measures and methods to permit verification of target engagement
      within the self-regulation domain. Led by Co-I MacKinnon, the investigators will examine
      cross-assay validity and cross-context and cross-sample reliability of assays. The
      investigators will employ discriminant and divergent validation methods and Bayesian modeling
      to refine an empirically-based ontology of self-regulatory targets (to be used in Aim 4).

      Aim 4. The investigators will evaluate the degree to which engaging targets produces a
      desired change in medical regimen adherence (across 4-week interventions) and health behavior
      among smokers (n=50 each at Dartmouth and Stanford) and overweight/obese persons with binge
      eating disorder (n=50 each at Dartmouth and Stanford) (smoking in the former sample and binge
      eating in the latter sample). The investigators will employ a novel mobile behavioral
      assessment/intervention platform to engage targets in these samples, given that (1) it offers
      self-regulation assessment and behavior change tools via an integrated platform to a wide
      array of populations, and (2) content within the platform can be quickly modified as needed
      to better impact targets. The proposed project is designed to identify valid and replicable
      assays of mechanisms of self-regulation across populations to inform an ontology of
      self-regulation that can ultimately inform development of health behavior interventions of
      maximal efficacy and potency.

      This protocol details the Aim 4 study at Dartmouth led by Multiple PI Marsch.

      This phase of the study takes what the investigators learned about self-regulation in the
      first three phases and applies it in two samples that are exemplary for "lapses" in
      self-regulation: individuals who smoke and overweight/obese individuals with binge eating
      disorder. The investigators learned in Aim 2 that many real-world conditions (e.g.,
      temptation, negative affect) may decrease self-regulation, whereas training through the
      mobile intervention described below may increase self-regulation. The primary purpose of this
      Aim 4 study is to target self-regulation to impact health behaviors.
allocation: N/A
intervention model: Single Group Assignment
primary purpose: Basic Science
masking: None (Open Label)
measure: Rate of Binge Eating Episodes [Binge Eating Sample Only]
time frame: 28 days
description: [Binge eating sample only] Self-reported binge eating episodes are assessed four times daily (morning, early afternoon, late afternoon/evening, and night) over a 28-day period. A binge eating episode is defined as self-reported overeating and loss of control. Overeating is assessed by the question "Since the last prompt, when you ate most recently, did you overeat?" and is scored as 0 (no) or 1 (yes). Loss of control is assessed by the question "When you ate most recently, did you lose control over your eating?" and is scored as 1 (not at all) to 5 (totally), where a 4 or 5 is considered loss of control. We are reporting the mean and standard deviations of the target behavior for each sample by week (i.e., Week 1, Week 2, Week 3, and Week 4). Data were averaged for all assessments each week to calculate the number of binge-eating episodes per week.
measure: Rate of Smoking Episodes [Smoking Sample Only]
time frame: 28 days
description: [Smoking sample only] Self-reported smoking is assessed four times daily (morning, early afternoon, late afternoon/evening, and night) over a 28-day period. A smoking episode is defined as self-reported smoking of more than zero cigarettes and is assessed by the question "Since the last prompt, how many cigarettes have you smoked?" Participants are asked to input a number into a number field. We are reporting the mean and standard deviations of the target behavior for each sample by week (i.e., Week 1, Week 2, Week 3, and Week 4). Data were averaged for all assessments each week to calculate the number of cigarettes smoked per week.
arm group label: Laddr
arm group type: Experimental
description: All participants in the study will be invited to use Laddr, described in the intervention section.
intervention type: Behavioral
intervention name: Laddr
description: Laddr is an integrated, personalized, web-based self-regulation assessment and behavior change system. It integrates tools that have been shown to be effective for a wide array of behavioral phenomena ranging from substance use and abuse, mental health, risk-taking, chronic pain management, medication adherence, diet, exercise, diabetes and other chronic disease management, and smoking. The organizational structure, functionality and content within Laddr's system centrally embrace these fundamental aspects of behavior change; thus, the Laddr platform is not "diagnosis-specific" but rather enables integrated care for any combination of individuals' goals, needs, and preferences.
arm group label: Laddr
criteria: 
gender: All
minimum age: 18 Years
maximum age: 50 Years
healthy volunteers: No
last name: Lisa A Marsch, PhD
role: Principal Investigator
affiliation: Dartmouth College
facility: 
country: United States
responsible party type: Principal Investigator
investigator affiliation: Dartmouth-Hitchcock Medical Center
investigator full name: Lisa A. Marsch
investigator title: Andrew G. Wallace Professor; Director, Center for Technology and Behavioral Health
mesh term: Bulimia
mesh term: Binge-Eating Disorder
sharing ipd: Yes
ipd description: After completion of the study, a de-identified dataset (i.e., stripped of all codes and other information that could be linked back to an individual participant) will be generated and made available to the research community as a whole. Informed consent procedures will ensure that participants are aware that consenting to participate in the study means consenting to inclusion in this open data set.
ipd time frame: Data will be available upon submission of the paper detailing the findings of the research.
ipd access criteria: All data will be shared openly with no restrictions on access.
provided document: 
participant flow: 
baseline: 
outcome list: 
reported events: 
certain agreements: 
point of contact: 