<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/NCT02265354</url>
  </required_header>
  <id_info>
    <org_study_id>H00002005</org_study_id>
    <secondary_id>K07CA172677</secondary_id>
    <nct_id>NCT02265354</nct_id>
  </id_info>
  <brief_title>Developing Smokers for Smoker (S4S): A Collective Intelligence Tailoring System</brief_title>
  <acronym>S4S</acronym>
  <official_title>Developing Smokers for Smoker (S4S): A Collective Intelligence Tailoring System</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>University of Massachusetts, Worcester</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>University of Massachusetts, Amherst</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>National Cancer Institute (NCI)</agency>
      <agency_class>NIH</agency_class>
    </collaborator>
  </sponsors>
  <source>University of Massachusetts, Worcester</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 advance computer tailoring by adapting machine learning collective&#xD;
      intelligence algorithms that have been used outside healthcare by companies like Amazon and&#xD;
      Google to enhance the personal relevance of the health communication.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Smoking is still the number one preventable cause of cancer death. New approaches are needed&#xD;
      to engage smokers in the 21st century in smoking cessation. I propose to develop S4S (Smokers&#xD;
      for Smoker), a next-generation patient-centered computer tailored health communication (CTHC)&#xD;
      system. Unlike current rule-based CTHCs, S4S will replace rules with complex machine learning&#xD;
      algorithms, and use the collective experiences of thousands of smokers engaged in a&#xD;
      web-assisted tobacco intervention to enhance personally-relevant tailoring for new smokers&#xD;
      entering the system. The investigators will adapt collective intelligence algorithms that&#xD;
      have been used outside healthcare by companies like Amazon and Google to enhance CTHC. Using&#xD;
      knowledge from scientific experts, current CTHC collect baseline patient &quot;profiles&quot; and then&#xD;
      use expert-written, rule-based systems to tailor messages to patient subsets. Such&#xD;
      theory-based &quot;market segmentation has been effective in helping patients reach lifestyle&#xD;
      goals. However, there is a natural limit in the ability of a rule-based system to truly&#xD;
      personalize content, and adapt personalization over time. Current CTHC have reached this&#xD;
      limit, and the investigators propose to go beyond. The investigators first aim is to develop&#xD;
      the Web 2.0 &quot;S4S&quot; recommender system. The investigators second aim is to evaluate S4S within&#xD;
      the context of a NCI funded web-assisted tobacco intervention (Decide2Quit.org).&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">January 11, 2017</start_date>
  <completion_date type="Actual">June 28, 2020</completion_date>
  <primary_completion_date type="Actual">December 28, 2017</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>Treatment</primary_purpose>
    <masking>Double (Participant, Investigator)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Repeated Use of website measure</measure>
    <time_frame>Every Login for 6 months</time_frame>
    <description>This measure is an ordinal scale of the number of functions used after the first visit to the Decide2Quit.org website (0: use of no functions, 1: use of 1-2 functions, 2: use of 2-4 functions, see Table 9 list of functions). We will use scripts on the website to assess this information</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>30-day point prevalent smoking cessation at six months</measure>
    <time_frame>At 6 months</time_frame>
    <description>Did you smoke any cigarettes during the past 30 days? This will be assessed using a follow-up Telephone or Internet survey</description>
  </secondary_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Actual">260</enrollment>
  <condition>Tobacco Smoking</condition>
  <arm_group>
    <arm_group_label>Collective-Intelligence computer tailored health communication</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Smokers will have access to all Decide2quit.org website functions and will receive 4 tailored emails per week based on a collective intelligence recommender systems algorithm for up to 6 months</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Rule-based computer tailored health communication</arm_group_label>
    <arm_group_type>Active Comparator</arm_group_type>
    <description>Smokers will have access to all Decide2quit.org website functions and will receive 4 tailored emails per week based on a rule-based algorithm for up to 6 months</description>
  </arm_group>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>Collective-Intelligence computer tailored health communication</intervention_name>
    <arm_group_label>Collective-Intelligence computer tailored health communication</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>Rule-based computer tailored health communication</intervention_name>
    <arm_group_label>Rule-based computer tailored health communication</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Current Smokers&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
        -&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>19 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Rajani S Sadasivam, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>UMass Medical School</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>UMass Medical School</name>
      <address>
        <city>Worcester</city>
        <state>Massachusetts</state>
        <zip>01605</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <verification_date>October 2020</verification_date>
  <study_first_submitted>October 9, 2014</study_first_submitted>
  <study_first_submitted_qc>October 14, 2014</study_first_submitted_qc>
  <study_first_posted type="Estimate">October 15, 2014</study_first_posted>
  <last_update_submitted>October 6, 2020</last_update_submitted>
  <last_update_submitted_qc>October 6, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">October 8, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>University of Massachusetts, Worcester</investigator_affiliation>
    <investigator_full_name>Rajani Sadasivam</investigator_full_name>
    <investigator_title>Assistant Professor</investigator_title>
  </responsible_party>
  <keyword>Tobacco Smoking Cessation</keyword>
  <keyword>Computer tailored health communication</keyword>
  <keyword>messaging</keyword>
  <keyword>collective intelligence</keyword>
  <keyword>recommender systems</keyword>
  <keyword>machine learning</keyword>
  <!-- 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/NCT02265354
org study id: H00002005
secondary id: K07CA172677
nct id: NCT02265354
lead sponsor: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
This study will advance computer tailoring by adapting machine learning collective
      intelligence algorithms that have been used outside healthcare by companies like Amazon and
      Google to enhance the personal relevance of the health communication.
Smoking is still the number one preventable cause of cancer death. New approaches are needed
      to engage smokers in the 21st century in smoking cessation. I propose to develop S4S (Smokers
      for Smoker), a next-generation patient-centered computer tailored health communication (CTHC)
      system. Unlike current rule-based CTHCs, S4S will replace rules with complex machine learning
      algorithms, and use the collective experiences of thousands of smokers engaged in a
      web-assisted tobacco intervention to enhance personally-relevant tailoring for new smokers
      entering the system. The investigators will adapt collective intelligence algorithms that
      have been used outside healthcare by companies like Amazon and Google to enhance CTHC. Using
      knowledge from scientific experts, current CTHC collect baseline patient "profiles" and then
      use expert-written, rule-based systems to tailor messages to patient subsets. Such
      theory-based "market segmentation has been effective in helping patients reach lifestyle
      goals. However, there is a natural limit in the ability of a rule-based system to truly
      personalize content, and adapt personalization over time. Current CTHC have reached this
      limit, and the investigators propose to go beyond. The investigators first aim is to develop
      the Web 2.0 "S4S" recommender system. The investigators second aim is to evaluate S4S within
      the context of a NCI funded web-assisted tobacco intervention (Decide2Quit.org).
allocation: Randomized
intervention model: Parallel Assignment
primary purpose: Treatment
masking: Double (Participant, Investigator)
measure: Repeated Use of website measure
time frame: Every Login for 6 months
description: This measure is an ordinal scale of the number of functions used after the first visit to the Decide2Quit.org website (0: use of no functions, 1: use of 1-2 functions, 2: use of 2-4 functions, see Table 9 list of functions). We will use scripts on the website to assess this information
measure: 30-day point prevalent smoking cessation at six months
time frame: At 6 months
description: Did you smoke any cigarettes during the past 30 days? This will be assessed using a follow-up Telephone or Internet survey
arm group label: Collective-Intelligence computer tailored health communication
arm group type: Experimental
description: Smokers will have access to all Decide2quit.org website functions and will receive 4 tailored emails per week based on a collective intelligence recommender systems algorithm for up to 6 months
arm group label: Rule-based computer tailored health communication
arm group type: Active Comparator
description: Smokers will have access to all Decide2quit.org website functions and will receive 4 tailored emails per week based on a rule-based algorithm for up to 6 months
intervention type: Behavioral
intervention name: Collective-Intelligence computer tailored health communication
arm group label: Collective-Intelligence computer tailored health communication
intervention type: Behavioral
intervention name: Rule-based computer tailored health communication
arm group label: Rule-based computer tailored health communication
criteria: 
gender: All
minimum age: 19 Years
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
last name: Rajani S Sadasivam, PhD
role: Principal Investigator
affiliation: UMass Medical School
facility: 
country: United States
responsible party type: Principal Investigator
investigator affiliation: University of Massachusetts, Worcester
investigator full name: Rajani Sadasivam
investigator title: Assistant Professor