<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/NCT03264521</url>
  </required_header>
  <id_info>
    <org_study_id>1R21AT009124-01</org_study_id>
    <nct_id>NCT03264521</nct_id>
  </id_info>
  <brief_title>Patient Engagement Via Crowdsourcing</brief_title>
  <acronym>PECS</acronym>
  <official_title>Feasibility of Crowdsourcing for Eliciting Patient Experiences of Chronic Pain</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>RAND</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Claremont Graduate University</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>RAND</source>
  <oversight_info>
    <has_dmc>Yes</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 study aims to advance pain research by exploring feasibility of crowdsourcing patient&#xD;
      pain data via Amazon Mechanical Turk, the largest and most studied crowdsourcing platform in&#xD;
      the U.S. We will leverage an existing NIH/NCCIH grant as a comparison data (RAND Center of&#xD;
      Excellence in Research on CAM; CERC) to conduct a feasibility study of new methods for&#xD;
      gathering and analyzing data on chronic pain and engaging pain patients in health policy.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The study aims to advance the pain research by exploring the crowdsourcing approach for&#xD;
      eliciting and analyzing the way in which individuals experience and understand chronic pain.&#xD;
      Investigators will leverage an existing NIH/NCCIH grant (RAND Center of Excellence in&#xD;
      Research on CAM; CERC) to conduct a feasibility study of new methods for gathering and&#xD;
      analyzing data on chronic pain and engaging pain patients in health policy processes through&#xD;
      three specific aims:&#xD;
&#xD;
      Aim 1 (Inclusion): Gain access to chronic pain patients using crowdsourcing platform Amazon&#xD;
      Mechanical Turk (MTurk). This aim explores whether crowdsourcing provides a credible method&#xD;
      for patient inclusion. People with low back pain will be accessed via the crowdsourcing&#xD;
      platform MTurk and asked to take health surveys that were also administered to a national&#xD;
      clinical sample of chiropractic patients within a RAND study. Equivalency of validated,&#xD;
      self-reported measures of low back pain obtained from crowdsourced versus &quot;gold standard&quot;&#xD;
      data from the RAND study will be assessed. Similarities and differences between demographics&#xD;
      and other pain and function variables between crowdsourced and RAND data will be analyzed.&#xD;
      Subsamples of crowdsourced data will be analyzed to assess reliability of the extent to which&#xD;
      data yields the same results across repeated crowdsourced samples.&#xD;
&#xD;
      Aim 2 (Participation): Engage chronic pain patients in inclusion criteria setting for&#xD;
      national pain treatment programs. This aim will intends to facilitate patient participation&#xD;
      in NIH criteria-setting for program inclusion. Crowdsourced patients will assist with&#xD;
      qualitative coding of data responses to the question, &quot;What does chronic pain mean to you?&quot;&#xD;
      Investigators will explore whether crowdsourcing provides a valid method by which coding may&#xD;
      be conducted, first measuring reliability across crowd samples, second testing the accuracy&#xD;
      of participant coding as compared with expert coders at RAND Corporation. A method of&#xD;
      assessing face validity will be tested as participants may create additional codes and give&#xD;
      feedback by rating the importance of each dimension.&#xD;
&#xD;
      Aim 3: Assess efficiency and quality of crowdsourced data as compared to CERC data.&#xD;
      Investigators will draw quantitative comparisons of cost (labor/incentives), time, data&#xD;
      quality (amount of text, missing data) across online crowdsourced and CERC study samples.&#xD;
&#xD;
      The proposed study utilizes the resources of an existing NIH grant by exploring the&#xD;
      feasibility of using innovative online methods for eliciting patient perspectives on chronic&#xD;
      pain and for engaging patients in analyses procedures. The study provides an opportunity to&#xD;
      determine whether patient chronic pain experiences and perspectives can be gathered through&#xD;
      crowdsourcing using Amazon Mechanical Turk (MTurk), in a valid, replicable, and resource&#xD;
      efficient way. Although focused on chronic low back pain, the study findings will have broad&#xD;
      implications for patient engagement more generally. If the crowdsourcing methods produce data&#xD;
      that is comparable to &quot;gold standard&quot; methods used in the RAND Study entitled, &quot;Center of&#xD;
      Excellence in Research on Chiropractic&quot; (RAND/CERC Study), this new experimental system has&#xD;
      the potential to provide low-cost and time-efficient methods to advance&#xD;
      democratically-oriented research, evaluation, policy and ultimately patient-centered clinical&#xD;
      care.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">January 23, 2017</start_date>
  <completion_date type="Actual">May 30, 2018</completion_date>
  <primary_completion_date type="Actual">December 22, 2017</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Cross-Sectional</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>ODI Score</measure>
    <time_frame>day 1</time_frame>
    <description>Oswestry Disability Index: Validated low back pain function scale (0-100)</description>
  </primary_outcome>
  <primary_outcome>
    <measure>PROMIS-29</measure>
    <time_frame>day 1</time_frame>
    <description>Patient-Reported Outcomes Measurement Information System, 29 items, Version 2: Validated, self-reported measure of global, physical, mental, and social health for adults in the general population and those living with a chronic condition.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Average Pain NRS (0-10)</measure>
    <time_frame>day 1</time_frame>
    <description>Numeric Rating Scale for pain intensity: Average pain in past seven days (0-10)</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Worst Pain NRS (0-10)</measure>
    <time_frame>day 1</time_frame>
    <description>Numeric Rating Scale for pain intensity: worse pain in past seven days (0-10)</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Demographics</measure>
    <time_frame>day 1</time_frame>
    <description>Gender, Age, Race/Ethnicity, Education, Income, Employment Status,</description>
  </secondary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Actual">8193</enrollment>
  <condition>Chronic Low Back Pain</condition>
  <arm_group>
    <arm_group_label>MTurk Participants</arm_group_label>
    <description>Participants who are registered users on Amazon Mechanical Turk (crowdsourcing platform) who volunteer to take survey.</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Surveys</intervention_name>
    <description>Web surveys about experiences with chronic low back pain, demographics, coding qualitative text.</description>
    <arm_group_label>MTurk Participants</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Participants of Amazon Mechanical Turk crowdsourcing platform with chronic low back pain&#xD;
        who utilize chiropractic care.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  At least 3 months of low back pain or self-reported chronic low back pain&#xD;
&#xD;
          -  Utilized chiropractic care for treating back pain&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Under 21 years of age&#xD;
&#xD;
          -  No open legal or workers compensation case related to condition&#xD;
&#xD;
          -  No diagnosis from provider of medical condition, so must be non-specific low back&#xD;
             pain.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>21 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <location>
    <facility>
      <name>RAND Corporation</name>
      <address>
        <city>Santa Monica</city>
        <state>California</state>
        <zip>90407</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <verification_date>February 2021</verification_date>
  <study_first_submitted>July 25, 2017</study_first_submitted>
  <study_first_submitted_qc>August 25, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">August 29, 2017</study_first_posted>
  <last_update_submitted>February 25, 2021</last_update_submitted>
  <last_update_submitted_qc>February 25, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">February 26, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Crowdsourcing</keyword>
  <keyword>Patient Engagement</keyword>
  <keyword>Chiropractic</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Low Back Pain</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </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/NCT03264521
org study id: 1R21AT009124-01
nct id: NCT03264521
lead sponsor: 
collaborator: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
The study aims to advance pain research by exploring feasibility of crowdsourcing patient
      pain data via Amazon Mechanical Turk, the largest and most studied crowdsourcing platform in
      the U.S. We will leverage an existing NIH/NCCIH grant as a comparison data (RAND Center of
      Excellence in Research on CAM; CERC) to conduct a feasibility study of new methods for
      gathering and analyzing data on chronic pain and engaging pain patients in health policy.
The study aims to advance the pain research by exploring the crowdsourcing approach for
      eliciting and analyzing the way in which individuals experience and understand chronic pain.
      Investigators will leverage an existing NIH/NCCIH grant (RAND Center of Excellence in
      Research on CAM; CERC) to conduct a feasibility study of new methods for gathering and
      analyzing data on chronic pain and engaging pain patients in health policy processes through
      three specific aims:

      Aim 1 (Inclusion): Gain access to chronic pain patients using crowdsourcing platform Amazon
      Mechanical Turk (MTurk). This aim explores whether crowdsourcing provides a credible method
      for patient inclusion. People with low back pain will be accessed via the crowdsourcing
      platform MTurk and asked to take health surveys that were also administered to a national
      clinical sample of chiropractic patients within a RAND study. Equivalency of validated,
      self-reported measures of low back pain obtained from crowdsourced versus "gold standard"
      data from the RAND study will be assessed. Similarities and differences between demographics
      and other pain and function variables between crowdsourced and RAND data will be analyzed.
      Subsamples of crowdsourced data will be analyzed to assess reliability of the extent to which
      data yields the same results across repeated crowdsourced samples.

      Aim 2 (Participation): Engage chronic pain patients in inclusion criteria setting for
      national pain treatment programs. This aim will intends to facilitate patient participation
      in NIH criteria-setting for program inclusion. Crowdsourced patients will assist with
      qualitative coding of data responses to the question, "What does chronic pain mean to you?"
      Investigators will explore whether crowdsourcing provides a valid method by which coding may
      be conducted, first measuring reliability across crowd samples, second testing the accuracy
      of participant coding as compared with expert coders at RAND Corporation. A method of
      assessing face validity will be tested as participants may create additional codes and give
      feedback by rating the importance of each dimension.

      Aim 3: Assess efficiency and quality of crowdsourced data as compared to CERC data.
      Investigators will draw quantitative comparisons of cost (labor/incentives), time, data
      quality (amount of text, missing data) across online crowdsourced and CERC study samples.

      The proposed study utilizes the resources of an existing NIH grant by exploring the
      feasibility of using innovative online methods for eliciting patient perspectives on chronic
      pain and for engaging patients in analyses procedures. The study provides an opportunity to
      determine whether patient chronic pain experiences and perspectives can be gathered through
      crowdsourcing using Amazon Mechanical Turk (MTurk), in a valid, replicable, and resource
      efficient way. Although focused on chronic low back pain, the study findings will have broad
      implications for patient engagement more generally. If the crowdsourcing methods produce data
      that is comparable to "gold standard" methods used in the RAND Study entitled, "Center of
      Excellence in Research on Chiropractic" (RAND/CERC Study), this new experimental system has
      the potential to provide low-cost and time-efficient methods to advance
      democratically-oriented research, evaluation, policy and ultimately patient-centered clinical
      care.
observational model: Cohort
time perspective: Cross-Sectional
measure: ODI Score
time frame: day 1
description: Oswestry Disability Index: Validated low back pain function scale (0-100)
measure: PROMIS-29
time frame: day 1
description: Patient-Reported Outcomes Measurement Information System, 29 items, Version 2: Validated, self-reported measure of global, physical, mental, and social health for adults in the general population and those living with a chronic condition.
measure: Average Pain NRS (0-10)
time frame: day 1
description: Numeric Rating Scale for pain intensity: Average pain in past seven days (0-10)
measure: Worst Pain NRS (0-10)
time frame: day 1
description: Numeric Rating Scale for pain intensity: worse pain in past seven days (0-10)
measure: Demographics
time frame: day 1
description: Gender, Age, Race/Ethnicity, Education, Income, Employment Status,
arm group label: MTurk Participants
description: Participants who are registered users on Amazon Mechanical Turk (crowdsourcing platform) who volunteer to take survey.
intervention type: Other
intervention name: Surveys
description: Web surveys about experiences with chronic low back pain, demographics, coding qualitative text.
arm group label: MTurk Participants
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 21 Years
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
facility: 
country: United States
responsible party type: Sponsor
mesh term: Low Back Pain
sharing ipd: No