<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/NCT03262337</url>
  </required_header>
  <id_info>
    <org_study_id>PB_2017_00412</org_study_id>
    <nct_id>NCT03262337</nct_id>
  </id_info>
  <brief_title>TOURIST 2: Tracking Of Urgent Risks In Swiss Travelers</brief_title>
  <acronym>TOURIST2</acronym>
  <official_title>An Observational Study Using a Smartphone-app Supported Electronic Survey and Localization Tracking to Map Risk Behaviors and Symptoms of Swiss Travelers to Thailand, China, India, Brazil, Peru and Tanzania From 2 Swiss Travel Clinics</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>University of Zurich</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Swiss Tropical &amp; Public Health Institute</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>University of Zurich</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>
      New mobile Health (mHealth) technology creates an opportunity to approach travel medicine&#xD;
      research in a different way, revolutionising our understanding of risks to travellers. Using&#xD;
      mHealth technology, the Epidemiology, Biostatistics and Prevention Institute (EBPI),&#xD;
      University of Zurich (UZH), developed a TRAVEL app in collaboration with the Eidgnössische&#xD;
      Technische Hochschule (ETH) Zurich.&#xD;
&#xD;
      By using this new technology, an extensive collection of data (prospective collection of&#xD;
      individual travel behaviour and experienced health events, mapping the travel itinerary via&#xD;
      global positioning system (GPS), linking to publicly available local weather data and data on&#xD;
      disease endemicity) can be combined and an unprecedented abundance of information on travel&#xD;
      behaviour and experienced risks can be obtained. These data will allow a much better&#xD;
      understanding of travel risk profiles using cluster analysis. By simultaneously recording&#xD;
      health outcomes, the relationship between travel risk profiles and health events can be&#xD;
      assessed. In this study, the investigators will address several major shortcomings in travel&#xD;
      health in tropical and subtropical destinations by improving the understanding of poorly&#xD;
      assessed and potentially underestimated health threats (e.g. risk of accidents and injury,&#xD;
      mental health disorders), and travel risks specific to elderly travellers and travellers with&#xD;
      chronic conditions. These findings will directly feed back into individual travel advice&#xD;
      given by practitioners in Switzerland and finally world-wide.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      New mobile Health (mHealth) technology creates an opportunity to approach travel medicine&#xD;
      research in a different way, revolutionising our understanding of risks to travellers. Using&#xD;
      mHealth technology, the Epidemiology, Biostatistics and Prevention Institute (EBPI),&#xD;
      University of Zurich (UZH), developed a TRAVEL app in collaboration with the Eidgnössische&#xD;
      Technische Hochschule (ETH) Zurich.&#xD;
&#xD;
      By using this new technology, an extensive collection of data (prospective collection of&#xD;
      individual travel behaviour and experienced health events, mapping the travel itinerary via&#xD;
      global positioning system (GPS), linking to publicly available local weather data and data on&#xD;
      disease endemicity) can be combined and an unprecedented abundance of information on travel&#xD;
      behaviour and experienced risks can be obtained. These data will allow a much better&#xD;
      understanding of travel risk profiles using cluster analysis. By simultaneously recording&#xD;
      health outcomes, the relationship between travel risk profiles and health events can be&#xD;
      assessed. In this study, the investigators will address several major shortcomings in travel&#xD;
      health in tropical and subtropical destinations by improving the understanding of poorly&#xD;
      assessed and potentially underestimated health threats (e.g. risk of accidents and injury,&#xD;
      mental health disorders), and travel risks specific to elderly travellers and travellers with&#xD;
      chronic conditions.&#xD;
&#xD;
      1000 clients traveling to Thailand, China, India, Brazil, Peru or Tanzania will be recruited&#xD;
      from the Travel Clinics in Zurich and Basel and through advertising at local travel agents&#xD;
      and the universities of ETH and UZH from September 2017 until February 2019. After completing&#xD;
      an intake at the clinic, participants will be given the option to use their own Smartphone&#xD;
      for data collection, or to use a phone owned by the UZH-EBPI during travel. Participants will&#xD;
      use a data collection app for questionnaire responses during and after travel including: a&#xD;
      daily questionnaire on travel behaviors, daily symptoms questionnaire, and localization&#xD;
      tracking, showing the participants' travel path and locations.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">September 18, 2017</start_date>
  <completion_date type="Actual">March 31, 2021</completion_date>
  <primary_completion_date type="Actual">March 31, 2019</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>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Self-reported risk behaviors</measure>
    <time_frame>for a maximum of 4 weeks during travel</time_frame>
    <description>The study participant will report on daily risk behaviors prior to, during and after travel in a daily app-based questionnaire</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Self-reported symptoms</measure>
    <time_frame>for a maximum of 4 weeks during travel</time_frame>
    <description>The study participant will report on experienced symptoms/health events prior to, during and after travel in a daily app-based questionnaire</description>
  </secondary_outcome>
  <number_of_groups>3</number_of_groups>
  <enrollment type="Actual">793</enrollment>
  <condition>Travel Health</condition>
  <condition>Mobile Health Technology (mHealth)</condition>
  <condition>Elderly Travelers</condition>
  <condition>Chronic Diseased Travelers</condition>
  <condition>Risk Behavior</condition>
  <arm_group>
    <arm_group_label>Elderly Travelers</arm_group_label>
    <description>approximately 135 travelers with an age &gt;= 60 years will be enrolled</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Chronic diseased travelers</arm_group_label>
    <description>approximately 225 travelers with a chronic disease will be enrolled</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Healthy travelers</arm_group_label>
    <description>approximately 640 healthy travelers with be enrolled</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>none, this is an observational study</intervention_name>
    <description>none, this is an observational study</description>
    <arm_group_label>Chronic diseased travelers</arm_group_label>
    <arm_group_label>Elderly Travelers</arm_group_label>
    <arm_group_label>Healthy travelers</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Study recruitment will be conducted at the University of Zurich Travel Clinic and the&#xD;
        Travel Clinic at the Swiss Tropical and Public Health Institute.&#xD;
&#xD;
        That means that individuals seeking pre-travel advice at one of the two Travel Clinics will&#xD;
        be asked to participate based inclusion criteria defined above.&#xD;
&#xD;
        Furthermore, patients with chronic diseases will be referred to the Travel Clinics by&#xD;
        various specialized clinics at the University Hospitals of Zurich and Basel&#xD;
        (Gastroenterology, Rheumatology, Pneumology, Cardiology)&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Informed Consent&#xD;
&#xD;
          -  Traveler to Thailand, China, India, Brazil, Peru or Tanzania&#xD;
&#xD;
          -  German-speaking (level 3 or higher)&#xD;
&#xD;
          -  Literate (able to read and write in German - level 3 or higher)&#xD;
&#xD;
          -  Capable of operating Smartphone application for data collection&#xD;
&#xD;
          -  Traveling to Thailand, China, India, Brazil, Peru or Tanzania for ≤ 4 weeks&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
        - not fulfilling inclusion criteria&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
  </eligibility>
  <overall_official>
    <last_name>Silja Bühler, Dr.</last_name>
    <role>Principal Investigator</role>
    <affiliation>University of Zurich</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Andreas Neumayr</name>
      <address>
        <city>Basel</city>
        <state>Basel Stadt</state>
        <zip>4051</zip>
        <country>Switzerland</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>University of Zurich, Epidemiology, Biostatistics and Prevention Institute, Travel Clinic</name>
      <address>
        <city>Zürich</city>
        <zip>8001</zip>
        <country>Switzerland</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Switzerland</country>
  </location_countries>
  <reference>
    <citation>Farnham A, Blanke U, Stone E, Puhan MA, Hatz C. Travel medicine and mHealth technology: a study using smartphones to collect health data during travel. J Travel Med. 2016 Sep 4;23(6):taw056. doi: 10.1093/jtm/taw056. Print 2016 Jun.</citation>
    <PMID>27592821</PMID>
  </reference>
  <verification_date>October 2017</verification_date>
  <study_first_submitted>August 16, 2017</study_first_submitted>
  <study_first_submitted_qc>August 21, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">August 25, 2017</study_first_posted>
  <last_update_submitted>April 27, 2021</last_update_submitted>
  <last_update_submitted_qc>April 27, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">April 29, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <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/NCT03262337
org study id: PB_2017_00412
nct id: NCT03262337
lead sponsor: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
New mobile Health (mHealth) technology creates an opportunity to approach travel medicine
      research in a different way, revolutionising our understanding of risks to travellers. Using
      mHealth technology, the Epidemiology, Biostatistics and Prevention Institute (EBPI),
      University of Zurich (UZH), developed a TRAVEL app in collaboration with the Eidgnössische
      Technische Hochschule (ETH) Zurich.

      By using this new technology, an extensive collection of data (prospective collection of
      individual travel behaviour and experienced health events, mapping the travel itinerary via
      global positioning system (GPS), linking to publicly available local weather data and data on
      disease endemicity) can be combined and an unprecedented abundance of information on travel
      behaviour and experienced risks can be obtained. These data will allow a much better
      understanding of travel risk profiles using cluster analysis. By simultaneously recording
      health outcomes, the relationship between travel risk profiles and health events can be
      assessed. In this study, the investigators will address several major shortcomings in travel
      health in tropical and subtropical destinations by improving the understanding of poorly
      assessed and potentially underestimated health threats (e.g. risk of accidents and injury,
      mental health disorders), and travel risks specific to elderly travellers and travellers with
      chronic conditions. These findings will directly feed back into individual travel advice
      given by practitioners in Switzerland and finally world-wide.
New mobile Health (mHealth) technology creates an opportunity to approach travel medicine
      research in a different way, revolutionising our understanding of risks to travellers. Using
      mHealth technology, the Epidemiology, Biostatistics and Prevention Institute (EBPI),
      University of Zurich (UZH), developed a TRAVEL app in collaboration with the Eidgnössische
      Technische Hochschule (ETH) Zurich.

      By using this new technology, an extensive collection of data (prospective collection of
      individual travel behaviour and experienced health events, mapping the travel itinerary via
      global positioning system (GPS), linking to publicly available local weather data and data on
      disease endemicity) can be combined and an unprecedented abundance of information on travel
      behaviour and experienced risks can be obtained. These data will allow a much better
      understanding of travel risk profiles using cluster analysis. By simultaneously recording
      health outcomes, the relationship between travel risk profiles and health events can be
      assessed. In this study, the investigators will address several major shortcomings in travel
      health in tropical and subtropical destinations by improving the understanding of poorly
      assessed and potentially underestimated health threats (e.g. risk of accidents and injury,
      mental health disorders), and travel risks specific to elderly travellers and travellers with
      chronic conditions.

      1000 clients traveling to Thailand, China, India, Brazil, Peru or Tanzania will be recruited
      from the Travel Clinics in Zurich and Basel and through advertising at local travel agents
      and the universities of ETH and UZH from September 2017 until February 2019. After completing
      an intake at the clinic, participants will be given the option to use their own Smartphone
      for data collection, or to use a phone owned by the UZH-EBPI during travel. Participants will
      use a data collection app for questionnaire responses during and after travel including: a
      daily questionnaire on travel behaviors, daily symptoms questionnaire, and localization
      tracking, showing the participants' travel path and locations.
observational model: Cohort
time perspective: Prospective
measure: Self-reported risk behaviors
time frame: for a maximum of 4 weeks during travel
description: The study participant will report on daily risk behaviors prior to, during and after travel in a daily app-based questionnaire
measure: Self-reported symptoms
time frame: for a maximum of 4 weeks during travel
description: The study participant will report on experienced symptoms/health events prior to, during and after travel in a daily app-based questionnaire
arm group label: Elderly Travelers
description: approximately 135 travelers with an age >= 60 years will be enrolled
arm group label: Chronic diseased travelers
description: approximately 225 travelers with a chronic disease will be enrolled
arm group label: Healthy travelers
description: approximately 640 healthy travelers with be enrolled
intervention type: Other
intervention name: none, this is an observational study
description: none, this is an observational study
arm group label: Chronic diseased travelers
arm group label: Elderly Travelers
arm group label: Healthy travelers
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
last name: Silja Bühler, Dr.
role: Principal Investigator
affiliation: University of Zurich
facility: 
facility: 
country: Switzerland
citation: Farnham A, Blanke U, Stone E, Puhan MA, Hatz C. Travel medicine and mHealth technology: a study using smartphones to collect health data during travel. J Travel Med. 2016 Sep 4;23(6):taw056. doi: 10.1093/jtm/taw056. Print 2016 Jun.
PMID: 27592821
responsible party type: Sponsor
sharing ipd: No