<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/NCT01680380</url>
  </required_header>
  <id_info>
    <org_study_id>IRB00008533</org_study_id>
    <nct_id>NCT01680380</nct_id>
  </id_info>
  <brief_title>Tracking Breathing During Sleep With Non-contact Sensors</brief_title>
  <official_title>Tracking Breathing During Sleep With Non-contact Sensors</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Oregon Health and Science University</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Oregon Health and Science University</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
  </oversight_info>
  <brief_summary>
    <textblock>
      The purpose of this study is to evaluate the feasibility of tracking breathing during sleep&#xD;
      with non-contact sensors (for example, microphones or wireless movement sensors). The&#xD;
      investigators will use the data collected with these sensors to develop algorithms for&#xD;
      tracking breathing during sleep. The investigators will assess the performance of the&#xD;
      algorithms by comparing automatic output against manually-generated labels.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Subjects will be asked to place non-contact sensors (for example, ambient microphones,&#xD;
      wireless movement sensors) in their home sleep environment. No sensors will be attached to or&#xD;
      otherwise in contact with the subject's body. The subjects will start the data collection&#xD;
      before they fall asleep, and stop the data collection the next morning when they wake. The&#xD;
      subjects will then return the sensors to the investigator for analysis.&#xD;
&#xD;
      The investigators will study the data and associated manual labeling. The investigators will&#xD;
      develop algorithms that use statistical and machine-learning methods to train computer models&#xD;
      designed to track breathing automatically. The investigators will compare the automatic&#xD;
      output against manually generated labels to determine breath-tracking accuracy.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Withdrawn</overall_status>
  <why_stopped>
    Superseded by larger funded study&#xD;
  </why_stopped>
  <start_date>October 2012</start_date>
  <completion_date type="Anticipated">August 2016</completion_date>
  <primary_completion_date type="Anticipated">August 2016</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Case-Only</observational_model>
    <time_perspective>Cross-Sectional</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Breathing sounds are evident in overnight audio recordings</measure>
    <time_frame>Night of recording</time_frame>
    <description>This study aims to track breathing during sleep using a high-quality audio interface. Our primary objective is to determine if quiet breathing sounds are visible (in the spectral domain) to trained human labelers.</description>
  </primary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Actual">0</enrollment>
  <condition>Sleep Apnea Syndromes</condition>
  <condition>Snoring</condition>
  <arm_group>
    <arm_group_label>At-Home</arm_group_label>
    <description>Overnight sleep at home</description>
  </arm_group>
  <eligibility>
    <study_pop>
      <textblock>
        Participants will be recruited from the PI's department (OHSU faculty, Ph.D. students) by&#xD;
        email and the investigators' personal acquaintances. Participants must be age 21-100 and&#xD;
        have no self-reported sleep breathing problems.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Age 21-89&#xD;
&#xD;
          -  No self-reported sleep breathing problems&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Positive diagnosis for sleep breathing problem (e.g., obstructive sleep apnea)&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>21 Years</minimum_age>
    <maximum_age>89 Years</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Alexander Kain, Ph.D.</last_name>
    <role>Principal Investigator</role>
    <affiliation>Oregon Health and Science University</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Center for Spoken Language Understanding</name>
      <address>
        <city>Portland</city>
        <state>Oregon</state>
        <zip>97239</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <verification_date>July 2016</verification_date>
  <study_first_submitted>September 4, 2012</study_first_submitted>
  <study_first_submitted_qc>September 6, 2012</study_first_submitted_qc>
  <study_first_posted type="Estimate">September 7, 2012</study_first_posted>
  <last_update_submitted>July 5, 2016</last_update_submitted>
  <last_update_submitted_qc>July 5, 2016</last_update_submitted_qc>
  <last_update_posted type="Estimate">July 7, 2016</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Oregon Health and Science University</investigator_affiliation>
    <investigator_full_name>Alexander Kain</investigator_full_name>
    <investigator_title>Assistant Professor</investigator_title>
  </responsible_party>
  <keyword>sleep apnea</keyword>
  <keyword>snoring</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Sleep Apnea Syndromes</mesh_term>
    <mesh_term>Snoring</mesh_term>
  </condition_browse>
  <!-- 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/NCT01680380
org study id: IRB00008533
nct id: NCT01680380
lead sponsor: 
has dmc: No
The purpose of this study is to evaluate the feasibility of tracking breathing during sleep
      with non-contact sensors (for example, microphones or wireless movement sensors). The
      investigators will use the data collected with these sensors to develop algorithms for
      tracking breathing during sleep. The investigators will assess the performance of the
      algorithms by comparing automatic output against manually-generated labels.
Subjects will be asked to place non-contact sensors (for example, ambient microphones,
      wireless movement sensors) in their home sleep environment. No sensors will be attached to or
      otherwise in contact with the subject's body. The subjects will start the data collection
      before they fall asleep, and stop the data collection the next morning when they wake. The
      subjects will then return the sensors to the investigator for analysis.

      The investigators will study the data and associated manual labeling. The investigators will
      develop algorithms that use statistical and machine-learning methods to train computer models
      designed to track breathing automatically. The investigators will compare the automatic
      output against manually generated labels to determine breath-tracking accuracy.
observational model: Case-Only
time perspective: Cross-Sectional
measure: Breathing sounds are evident in overnight audio recordings
time frame: Night of recording
description: This study aims to track breathing during sleep using a high-quality audio interface. Our primary objective is to determine if quiet breathing sounds are visible (in the spectral domain) to trained human labelers.
arm group label: At-Home
description: Overnight sleep at home
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 21 Years
maximum age: 89 Years
healthy volunteers: Accepts Healthy Volunteers
last name: Alexander Kain, Ph.D.
role: Principal Investigator
affiliation: Oregon Health and Science University
facility: 
country: United States
responsible party type: Principal Investigator
investigator affiliation: Oregon Health and Science University
investigator full name: Alexander Kain
investigator title: Assistant Professor
mesh term: Sleep Apnea Syndromes
mesh term: Snoring