xml: <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/NCT06025838</url>
    </required_header>
    <id_info>
      <org_study_id>FeedbackGames</org_study_id>
      <nct_id>NCT06025838</nct_id>
    </id_info>
    <brief_title>Positive Feedback vs. No-Feedback Games for Behavioral Change</brief_title>
    <official_title>Positive Feedback vs. No-Feedback Games for Behavioral Change</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Bard College</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Bard College</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>
        We aim to investigate here whether we can develop a reinforcement learning game which&#xD;
        provides game-based feedback to encourage positive actions (behaviors) both inside and&#xD;
        outside of the game. Does providing positive reward when participants make decisions which&#xD;
        are associated with value-based actions (like those in BA) result in different game&#xD;
        decisions? We propose that it will increase positive actions in the game. And, secondly, how&#xD;
        does it affect short-term behavior (in one week)? We propose that it will increase pro-health&#xD;
        activities and may reduce depressive symptoms.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        We know that behavior influences mood -- our best interventions to improve mood rely upon the&#xD;
        relationship between these. Treatments like this are thought to work in part by helping&#xD;
        individuals to increase value-derived behaviors; participants are given guidance which&#xD;
        results in an increase of positive behaviors and a decrease of coping behaviors that don't&#xD;
        help -- that is, their health-seeking behavior is reinforced while behaviors that diminish&#xD;
        health are reduced. In past work, we showed that a text-based game could be used to explore&#xD;
        what sort of decisions people would make in certain environments. That game showed&#xD;
        associations between in-game behaviors and real-life depressive symptoms and actions. Such&#xD;
        work focuses on low-level symptoms of depression -- increasingly common, especially after the&#xD;
        onset of the covid-19 pandemic. We aim to investigate here whether we can develop a&#xD;
        reinforcement learning game which provides game-based feedback to encourage positive actions&#xD;
        (behaviors) both inside and outside of the game.&#xD;
  &#xD;
        Thus, the experiment described below and proposed in this application would test the role of&#xD;
        positive rewards (positive-feedback) in a dichotomous-choice game, compared to neutral&#xD;
        (no-feedback).&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">September 1, 2023</start_date>
    <completion_date type="Anticipated">November 1, 2023</completion_date>
    <primary_completion_date type="Anticipated">November 1, 2023</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>
      <intervention_model_description>Participants are either assigned to a control (no intervention) condition or to one where they receive positive feedback and are encouraged to specific, positive behavior.</intervention_model_description>
      <primary_purpose>Treatment</primary_purpose>
      <masking>Quadruple (Participant, Care Provider, Investigator, Outcomes Assessor)</masking>
      <masking_description>The intervention occurs entirely online. All assessment occurs via survey software. Participants do not interact with other participants and are not fully informed of the nature of the intervention and thus do not know if they are in an active condition.</masking_description>
    </study_design_info>
    <primary_outcome>
      <measure>Depressive symptoms (Patient Health Questionnaire-8 item)</measure>
      <time_frame>baseline (T0) - before intervention</time_frame>
      <description>8 items rated in a self-report questionnaire. Scores range from 0 to 24 with higher scores indicating more depression symptoms.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Depressive symptoms (Patient Health Questionnaire-8 item)</measure>
      <time_frame>one week following intervention (T1)</time_frame>
      <description>8 items rated in a self-report questionnaire. Scores range from 0 to 24 with higher scores indicating more depression symptoms.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Day Reconstruction Method (DRM)</measure>
      <time_frame>baseline (T0) - before intervention</time_frame>
      <description>The DRM measures how people spend their time and asks them how they felt while doing the activities they report. Ratings on a Likert scale indicate valence (how pleasant/unpleasant the activities were) and range from 0 (not at all) to 6 (very much), with higher pleasantness being better (and lower unpleasantness being better).</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Day Reconstruction Method (DRM)</measure>
      <time_frame>one week following intervention (T1)</time_frame>
      <description>The DRM measures how people spend their time and asks them how they felt while doing the activities they report. Ratings on a Likert scale indicate valence (how pleasant/unpleasant the activities were) and range from 0 (not at all) to 6 (very much), with higher pleasantness being better (and lower unpleasantness being better).</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Behavior in study-specific game</measure>
      <time_frame>baseline (T0)</time_frame>
      <description>For both conditions, behavior and choices made during the game will be used as data for analyses. Game choices allow participants to decide what next activities they will &quot;do&quot; in the game. Analyses incorporate decision probabilities (which of 2 choices are selected) and compare them between conditions. The game is completed at baseline following assessment.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Motivation to change</measure>
      <time_frame>baseline (T0) - after intervention</time_frame>
      <description>A 7-point item, &quot;It is important to me to do more positive activities in my life&quot;. Scores range from 0 [not at all] to 6 [very much]. Higher scores indicate more motivation to change.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Mood and Anxiety Symptoms Questionnaire (MASQ)</measure>
      <time_frame>baseline (T0) - before intervention</time_frame>
      <description>The MASQ measures mood and anxiety symptoms on three subscales: Anhedonic Depression, Anxious Arousal, and General Distress. Scores on each subscale range from 10 (low symptoms) to 50 (high symptoms). Higher scores indicate more depression, anxiety, and distress.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Mood Ratings</measure>
      <time_frame>baseline (T0) - before intervention</time_frame>
      <description>Mood will be rated on positive and negative scales: &quot;On the whole, how sad were you today?&quot; (Rated from 0 [not at all] to 6 [extremely]); &quot;On the whole, how happy were you today?&quot; (Rated from 0 [not at all] to 6 [extremely]). Higher scores indicate more sadness or happiness.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Mood Ratings</measure>
      <time_frame>one week following intervention (T1)</time_frame>
      <description>Mood will be rated on positive and negative scales: &quot;On the whole, how sad were you today?&quot; (Rated from 0 [not at all] to 6 [extremely]); &quot;On the whole, how happy were you today?&quot; (Rated from 0 [not at all] to 6 [extremely]). Higher scores indicate more sadness or happiness.</description>
    </secondary_outcome>
    <number_of_arms>2</number_of_arms>
    <enrollment type="Anticipated">160</enrollment>
    <condition>Major Depressive Disorder</condition>
    <condition>Low Mood</condition>
    <arm_group>
      <arm_group_label>positive feedback</arm_group_label>
      <arm_group_type>Experimental</arm_group_type>
      <description>Participants receive positive feedback and reward for their decisions taken during a small game similar to a normal person's life. For example, they are encouraged to exercise, make food, and interact with friends.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>no feedback</arm_group_label>
      <arm_group_type>No Intervention</arm_group_type>
      <description>Participants receive no feedback. While they may undertake the same decisions during a small game similar to a normal person's life, they receive no encouragement and are not given reward or positive feedback.</description>
    </arm_group>
    <intervention>
      <intervention_type>Behavioral</intervention_type>
      <intervention_name>positive feedback</intervention_name>
      <description>The positive feedback game uses techniques relating to established therapies like Behavioral Activation and Acceptance and Commitment Therapy's valued living to encourage positive action in game and outside of the game.</description>
      <arm_group_label>positive feedback</arm_group_label>
    </intervention>
    <eligibility>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  18-34 years old&#xD;
  &#xD;
            -  Fluent in English&#xD;
  &#xD;
            -  Based in the United States&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  None&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>34 Years</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Justin Dainer-Best, Ph.D.</last_name>
      <role>Principal Investigator</role>
      <affiliation>Bard College</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Justin Dainer-Best, Ph.D.</last_name>
      <phone>845-758-7223</phone>
      <email>jdainerbest@bard.edu</email>
    </overall_contact>
    <location>
      <facility>
        <name>Online research conducted through Bard College</name>
        <address>
          <city>Annandale-on-Hudson</city>
          <state>New York</state>
          <zip>12504</zip>
          <country>United States</country>
        </address>
      </facility>
      <contact>
        <last_name>Justin Dainer-Best, Ph.D.</last_name>
        <email>jdainerbest@bard.edu</email>
      </contact>
    </location>
    <location_countries>
      <country>United States</country>
    </location_countries>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>August 30, 2023</study_first_submitted>
    <study_first_submitted_qc>August 30, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 6, 2023</study_first_posted>
    <last_update_submitted>September 5, 2023</last_update_submitted>
    <last_update_submitted_qc>September 5, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 7, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Bard College</investigator_affiliation>
      <investigator_full_name>Justin Dainer-Best</investigator_full_name>
      <investigator_title>Assistant Professor of Psychology</investigator_title>
    </responsible_party>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Depressive Disorder</mesh_term>
      <mesh_term>Depressive Disorder, Major</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>Yes</sharing_ipd>
      <ipd_description>Data will be shared on the Open Science Framework, https://osf.io</ipd_description>
      <ipd_info_type>Study Protocol</ipd_info_type>
      <ipd_info_type>Statistical Analysis Plan (SAP)</ipd_info_type>
      <ipd_info_type>Analytic Code</ipd_info_type>
      <ipd_time_frame>Data will be available on or before a manuscript is submitted for publication, likely in 2024, and will be shared indefinitely.</ipd_time_frame>
      <ipd_access_criteria>Open access. Anyone will be able to access de-identified data and code.</ipd_access_criteria>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: 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/NCT06025838
  org study id: FeedbackGames
  nct id: NCT06025838
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  We aim to investigate here whether we can develop a reinforcement learning game which
        provides game-based feedback to encourage positive actions (behaviors) both inside and
        outside of the game. Does providing positive reward when participants make decisions which
        are associated with value-based actions (like those in BA) result in different game
        decisions? We propose that it will increase positive actions in the game. And, secondly, how
        does it affect short-term behavior (in one week)? We propose that it will increase pro-health
        activities and may reduce depressive symptoms.
  We know that behavior influences mood -- our best interventions to improve mood rely upon the
        relationship between these. Treatments like this are thought to work in part by helping
        individuals to increase value-derived behaviors; participants are given guidance which
        results in an increase of positive behaviors and a decrease of coping behaviors that don't
        help -- that is, their health-seeking behavior is reinforced while behaviors that diminish
        health are reduced. In past work, we showed that a text-based game could be used to explore
        what sort of decisions people would make in certain environments. That game showed
        associations between in-game behaviors and real-life depressive symptoms and actions. Such
        work focuses on low-level symptoms of depression -- increasingly common, especially after the
        onset of the covid-19 pandemic. We aim to investigate here whether we can develop a
        reinforcement learning game which provides game-based feedback to encourage positive actions
        (behaviors) both inside and outside of the game.
  
        Thus, the experiment described below and proposed in this application would test the role of
        positive rewards (positive-feedback) in a dichotomous-choice game, compared to neutral
        (no-feedback).
  allocation: Randomized
  intervention model: Parallel Assignment
  intervention model description: Participants are either assigned to a control (no intervention) condition or to one where they receive positive feedback and are encouraged to specific, positive behavior.
  primary purpose: Treatment
  masking: Quadruple (Participant, Care Provider, Investigator, Outcomes Assessor)
  masking description: The intervention occurs entirely online. All assessment occurs via survey software. Participants do not interact with other participants and are not fully informed of the nature of the intervention and thus do not know if they are in an active condition.
  measure: Depressive symptoms (Patient Health Questionnaire-8 item)
  time frame: baseline (T0) - before intervention
  description: 8 items rated in a self-report questionnaire. Scores range from 0 to 24 with higher scores indicating more depression symptoms.
  measure: Depressive symptoms (Patient Health Questionnaire-8 item)
  time frame: one week following intervention (T1)
  description: 8 items rated in a self-report questionnaire. Scores range from 0 to 24 with higher scores indicating more depression symptoms.
  measure: Day Reconstruction Method (DRM)
  time frame: baseline (T0) - before intervention
  description: The DRM measures how people spend their time and asks them how they felt while doing the activities they report. Ratings on a Likert scale indicate valence (how pleasant/unpleasant the activities were) and range from 0 (not at all) to 6 (very much), with higher pleasantness being better (and lower unpleasantness being better).
  measure: Day Reconstruction Method (DRM)
  time frame: one week following intervention (T1)
  description: The DRM measures how people spend their time and asks them how they felt while doing the activities they report. Ratings on a Likert scale indicate valence (how pleasant/unpleasant the activities were) and range from 0 (not at all) to 6 (very much), with higher pleasantness being better (and lower unpleasantness being better).
  measure: Behavior in study-specific game
  time frame: baseline (T0)
  description: For both conditions, behavior and choices made during the game will be used as data for analyses. Game choices allow participants to decide what next activities they will "do" in the game. Analyses incorporate decision probabilities (which of 2 choices are selected) and compare them between conditions. The game is completed at baseline following assessment.
  measure: Motivation to change
  time frame: baseline (T0) - after intervention
  description: A 7-point item, "It is important to me to do more positive activities in my life". Scores range from 0 [not at all] to 6 [very much]. Higher scores indicate more motivation to change.
  measure: Mood and Anxiety Symptoms Questionnaire (MASQ)
  time frame: baseline (T0) - before intervention
  description: The MASQ measures mood and anxiety symptoms on three subscales: Anhedonic Depression, Anxious Arousal, and General Distress. Scores on each subscale range from 10 (low symptoms) to 50 (high symptoms). Higher scores indicate more depression, anxiety, and distress.
  measure: Mood Ratings
  time frame: baseline (T0) - before intervention
  description: Mood will be rated on positive and negative scales: "On the whole, how sad were you today?" (Rated from 0 [not at all] to 6 [extremely]); "On the whole, how happy were you today?" (Rated from 0 [not at all] to 6 [extremely]). Higher scores indicate more sadness or happiness.
  measure: Mood Ratings
  time frame: one week following intervention (T1)
  description: Mood will be rated on positive and negative scales: "On the whole, how sad were you today?" (Rated from 0 [not at all] to 6 [extremely]); "On the whole, how happy were you today?" (Rated from 0 [not at all] to 6 [extremely]). Higher scores indicate more sadness or happiness.
  arm group label: positive feedback
  arm group type: Experimental
  description: Participants receive positive feedback and reward for their decisions taken during a small game similar to a normal person's life. For example, they are encouraged to exercise, make food, and interact with friends.
  arm group label: no feedback
  arm group type: No Intervention
  description: Participants receive no feedback. While they may undertake the same decisions during a small game similar to a normal person's life, they receive no encouragement and are not given reward or positive feedback.
  intervention type: Behavioral
  intervention name: positive feedback
  description: The positive feedback game uses techniques relating to established therapies like Behavioral Activation and Acceptance and Commitment Therapy's valued living to encourage positive action in game and outside of the game.
  arm group label: positive feedback
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: 34 Years
  healthy volunteers: Accepts Healthy Volunteers
  last name: Justin Dainer-Best, Ph.D.
  role: Principal Investigator
  affiliation: Bard College
  last name: Justin Dainer-Best, Ph.D.
  phone: 845-758-7223
  email: jdainerbest@bard.edu
  facility: 
  contact: 
  country: United States
  responsible party type: Principal Investigator
  investigator affiliation: Bard College
  investigator full name: Justin Dainer-Best
  investigator title: Assistant Professor of Psychology
  mesh term: Depressive Disorder
  mesh term: Depressive Disorder, Major
  sharing ipd: Yes
  ipd description: Data will be shared on the Open Science Framework, https://osf.io
  ipd info type: Study Protocol
  ipd info type: Statistical Analysis Plan (SAP)
  ipd info type: Analytic Code
  ipd time frame: Data will be available on or before a manuscript is submitted for publication, likely in 2024, and will be shared indefinitely.
  ipd access criteria: Open access. Anyone will be able to access de-identified data and code.