<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/NCT04473599</url>
  </required_header>
  <id_info>
    <org_study_id>2020-04-13162</org_study_id>
    <nct_id>NCT04473599</nct_id>
  </id_info>
  <brief_title>Stay Well at Home: a Text-messaging Study Social Distancing</brief_title>
  <official_title>Stay Well at Home: A Text-messaging Study to Improve Mood and Help Cope With Social Distancing</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>University of California, Berkeley</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>University of California, Berkeley</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>
      The investigators have developed supportive text-messages in English and Spanish to help&#xD;
      people cope with the stress and anxiety of COVID-19 social distancing. The purpose of this&#xD;
      study is to examine if automated text-messages will improve depression and anxiety symptoms&#xD;
      and enhance positive mood.&#xD;
&#xD;
      Additionally, the investigators will compare the effectiveness of sending messages on a&#xD;
      random schedule (using a micro-randomized trial design) or sent by a reinforcement learning&#xD;
      policy on overall change in depression and anxiety symptoms and daily mood during the 8-week&#xD;
      study.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The investigators will send participants supportive text-messages for a period of 2 months.&#xD;
      These text-messages will include tips about behavioral activation and coping skills to deal&#xD;
      with worries and stress. The investigators generated a message bank balanced such that 50% of&#xD;
      all messages are related to behavioral activation (BA) and 50% messages involve different&#xD;
      coping skills. Participants will receive one of these messages per day between 9:00 am and&#xD;
      6:00 pm. Participants will also receive a message asking them to rate their mood on a scale&#xD;
      of 1-9 once a day 3 hours after the BA or coping message.&#xD;
&#xD;
      Participants will be randomized to:&#xD;
&#xD;
        1. a uniform random messaging group (micro-randomized trial design).&#xD;
&#xD;
        2. a reinforcement learning group with a learned decision mechanism for the timing and type&#xD;
           of text-message. The algorithm learns from previous data (which messages were sent, what&#xD;
           was the participants' mood) to maximize an increase in participants mood. No other data&#xD;
           are collected from participants' phones.&#xD;
&#xD;
      The investigators will compare the effect of sending text-messages by a random schedule, and&#xD;
      text-messaging chosen by the RL algorithm. This allows to both evaluate the effect of the&#xD;
      individual intervention components over time within a micro-randomized trial design, and&#xD;
      assess the added value of using RL to adapt the messaging scheme.&#xD;
&#xD;
      The investigators hypothesize that:&#xD;
&#xD;
        -  Participant will show improvements in depression, anxiety symptoms and mood during the&#xD;
           60 day study.&#xD;
&#xD;
        -  The participants in the group receiving reinforcement learning will have a greater&#xD;
           improvement in depressive symptoms, anxiety and positive mood during the study than&#xD;
           participants in the micro-randomized group.&#xD;
&#xD;
        -  The investigators will find differential effects on mood ratings for the two categories&#xD;
           of messages&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">April 17, 2020</start_date>
  <completion_date type="Actual">January 10, 2023</completion_date>
  <primary_completion_date type="Actual">July 25, 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>Prevention</primary_purpose>
    <masking>Single (Participant)</masking>
    <masking_description>Participants are randomized to receive messages according to a random schedule (within a mico-randomized trial), delivered via a reinforcement learning policy or a control with mood ratings only.</masking_description>
  </study_design_info>
  <primary_outcome>
    <measure>Depression scores</measure>
    <time_frame>Change from baseline to 8 week follow-up</time_frame>
    <description>Patient Health Questionnaire 9 item (PHQ-9). The PHQ-9 has scores from 0 to 27. Higher scores mean a worse outcome.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Anxiety scores</measure>
    <time_frame>Change from baseline to 8 week follow-up</time_frame>
    <description>General Anxiety Disorder 7 item (GAD-7). The GAD-7 has scores from 0 to 21. Higher scores mean a worse outcome.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Self reported mood ratings</measure>
    <time_frame>8 weeks</time_frame>
    <description>The self-reported mood rating 3 hours after receiving a message. The score is from 0-9. A higher mood rating indicates a better outcome.</description>
  </secondary_outcome>
  <number_of_arms>3</number_of_arms>
  <enrollment type="Actual">1000</enrollment>
  <condition>Depressive Symptoms</condition>
  <condition>Anxiety</condition>
  <condition>COVID-19</condition>
  <arm_group>
    <arm_group_label>Uniform Random</arm_group_label>
    <arm_group_type>Active Comparator</arm_group_type>
    <description>Participants will receive supportive text-messages for a period of 2 months. These text-messages have two categories: behavioral activation (BA) and coping skills. In this arm, participants will receive one of these types of messages daily on a random schedule in random time periods throughout the day.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Reinforcement Learning</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>In this arm we will test a reinforcement learning (RL) algorithm with a learned decision mechanism for the timing and type of text-messages. The algorithm learns from previous data (which messages were sent, what was the participants' mood) to maximize an increase in participants' mood.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Mood ratings only</arm_group_label>
    <arm_group_type>Active Comparator</arm_group_type>
    <description>In this arm, participants will monitor their mood and receive random feedback based on mood responses.</description>
  </arm_group>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>Uniform random message delivery</intervention_name>
    <description>In this arm, the categories and timings of text-messages will be delivered to participants using a random schedule</description>
    <arm_group_label>Uniform Random</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>Reinforcement learning message delivery</intervention_name>
    <description>In this arm, the categories and timings of text-messages will be chosen by a reinforcement learning algorithm</description>
    <arm_group_label>Reinforcement Learning</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>Mood ratings only</intervention_name>
    <description>In this arm, participants will monitor their mood daily and receive feedback on that mood randomly</description>
    <arm_group_label>Mood ratings only</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion criteria:&#xD;
&#xD;
          -  Over 18 years old&#xD;
&#xD;
          -  Own a mobile phone&#xD;
&#xD;
          -  Speak English or Spanish&#xD;
&#xD;
        Exclusion criteria:&#xD;
&#xD;
          -  Not owning a mobile phone&#xD;
&#xD;
          -  Under 18 years old&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Adrian Aguilera, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>UC Berkeley</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>University of California Berkeley</name>
      <address>
        <city>Berkeley</city>
        <state>California</state>
        <zip>94709</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <verification_date>May 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>
  <last_update_submitted>May 19, 2023</last_update_submitted>
  <last_update_submitted_qc>May 19, 2023</last_update_submitted_qc>
  <last_update_posted type="Actual">May 23, 2023</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Mobile health</keyword>
  <keyword>Text-messaging</keyword>
  <keyword>Covid-19</keyword>
  <keyword>Mental health</keyword>
  <keyword>Coping</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>COVID-19</mesh_term>
    <mesh_term>Depression</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Yes</sharing_ipd>
    <ipd_description>Individual participant data that underlies the results reported in the articles will be made available to researchers on request after deidentification.</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>After publication of the data, no end date</ipd_time_frame>
    <ipd_access_criteria>Anyone with a methodologically sound proposal.</ipd_access_criteria>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04473599
org study id: 2020-04-13162
nct id: NCT04473599
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
The investigators have developed supportive text-messages in English and Spanish to help
      people cope with the stress and anxiety of COVID-19 social distancing. The purpose of this
      study is to examine if automated text-messages will improve depression and anxiety symptoms
      and enhance positive mood.

      Additionally, the investigators will compare the effectiveness of sending messages on a
      random schedule (using a micro-randomized trial design) or sent by a reinforcement learning
      policy on overall change in depression and anxiety symptoms and daily mood during the 8-week
      study.
The investigators will send participants supportive text-messages for a period of 2 months.
      These text-messages will include tips about behavioral activation and coping skills to deal
      with worries and stress. The investigators generated a message bank balanced such that 50% of
      all messages are related to behavioral activation (BA) and 50% messages involve different
      coping skills. Participants will receive one of these messages per day between 9:00 am and
      6:00 pm. Participants will also receive a message asking them to rate their mood on a scale
      of 1-9 once a day 3 hours after the BA or coping message.

      Participants will be randomized to:

        1. a uniform random messaging group (micro-randomized trial design).

        2. a reinforcement learning group with a learned decision mechanism for the timing and type
           of text-message. The algorithm learns from previous data (which messages were sent, what
           was the participants' mood) to maximize an increase in participants mood. No other data
           are collected from participants' phones.

      The investigators will compare the effect of sending text-messages by a random schedule, and
      text-messaging chosen by the RL algorithm. This allows to both evaluate the effect of the
      individual intervention components over time within a micro-randomized trial design, and
      assess the added value of using RL to adapt the messaging scheme.

      The investigators hypothesize that:

        -  Participant will show improvements in depression, anxiety symptoms and mood during the
           60 day study.

        -  The participants in the group receiving reinforcement learning will have a greater
           improvement in depressive symptoms, anxiety and positive mood during the study than
           participants in the micro-randomized group.

        -  The investigators will find differential effects on mood ratings for the two categories
           of messages
allocation: Randomized
intervention model: Parallel Assignment
primary purpose: Prevention
masking: Single (Participant)
masking description: Participants are randomized to receive messages according to a random schedule (within a mico-randomized trial), delivered via a reinforcement learning policy or a control with mood ratings only.
measure: Depression scores
time frame: Change from baseline to 8 week follow-up
description: Patient Health Questionnaire 9 item (PHQ-9). The PHQ-9 has scores from 0 to 27. Higher scores mean a worse outcome.
measure: Anxiety scores
time frame: Change from baseline to 8 week follow-up
description: General Anxiety Disorder 7 item (GAD-7). The GAD-7 has scores from 0 to 21. Higher scores mean a worse outcome.
measure: Self reported mood ratings
time frame: 8 weeks
description: The self-reported mood rating 3 hours after receiving a message. The score is from 0-9. A higher mood rating indicates a better outcome.
arm group label: Uniform Random
arm group type: Active Comparator
description: Participants will receive supportive text-messages for a period of 2 months. These text-messages have two categories: behavioral activation (BA) and coping skills. In this arm, participants will receive one of these types of messages daily on a random schedule in random time periods throughout the day.
arm group label: Reinforcement Learning
arm group type: Experimental
description: In this arm we will test a reinforcement learning (RL) algorithm with a learned decision mechanism for the timing and type of text-messages. The algorithm learns from previous data (which messages were sent, what was the participants' mood) to maximize an increase in participants' mood.
arm group label: Mood ratings only
arm group type: Active Comparator
description: In this arm, participants will monitor their mood and receive random feedback based on mood responses.
intervention type: Behavioral
intervention name: Uniform random message delivery
description: In this arm, the categories and timings of text-messages will be delivered to participants using a random schedule
arm group label: Uniform Random
intervention type: Behavioral
intervention name: Reinforcement learning message delivery
description: In this arm, the categories and timings of text-messages will be chosen by a reinforcement learning algorithm
arm group label: Reinforcement Learning
intervention type: Behavioral
intervention name: Mood ratings only
description: In this arm, participants will monitor their mood daily and receive feedback on that mood randomly
arm group label: Mood ratings only
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
last name: Adrian Aguilera, PhD
role: Principal Investigator
affiliation: UC Berkeley
facility: 
country: United States
responsible party type: Sponsor
mesh term: COVID-19
mesh term: Depression
sharing ipd: Yes
ipd description: Individual participant data that underlies the results reported in the articles will be made available to researchers on request after deidentification.
ipd info type: Study Protocol
ipd info type: Statistical Analysis Plan (SAP)
ipd info type: Analytic Code
ipd time frame: After publication of the data, no end date
ipd access criteria: Anyone with a methodologically sound proposal.