xml: <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/NCT05988840</url>
    </required_header>
    <id_info>
      <org_study_id>2023-A01589-36</org_study_id>
      <nct_id>NCT05988840</nct_id>
    </id_info>
    <brief_title>Acquisition and Analysis of Relationships Between Longitudinal Emotional Signals Produced by an Artificial Intelligence Algorithm and Self-questionnaires Used in the Psychiatric Follow-up of Patients With Mood and/or Anxiety Disorders: a Real-Environment Study.</brief_title>
    <acronym>EMOACQ-1</acronym>
    <official_title>Acquisition and Analysis of Relationships Between Longitudinal Emotional Signals Produced by an Artificial Intelligence Algorithm and Self-questionnaires Used in the Psychiatric Follow-up of Patients With Mood and/or Anxiety Disorders: a Real-Environment Study.</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Emobot</agency>
        <agency_class>Industry</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Emobot</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 worldwide prevalence of anxiety and depression increased massively during the pandemic,&#xD;
        with a 25% rise in the number of patients suffering from psychological distress.&#xD;
        Psychiatrists, and even more so general practitioners, need measurement tools that enable&#xD;
        them to remotely monitor their patients' psychological state of health, and to be&#xD;
        automatically alerted in the event of a break in behavior.&#xD;
  &#xD;
        In this study, the investigators propose to collect clinical data along with longitudinal&#xD;
        measurement of patients' emotions. Emobot proposes to analyze the evolution of mood disorders&#xD;
        over time by passively studying people's emotional behavior. The aim of EMOACQ-1 is to&#xD;
        acquire knowledge and produce a quantitative link between emotional expression and mood&#xD;
        disorders, ultimately facilitating the understanding and management of these disorders.&#xD;
  &#xD;
        Through this study, could be developed a technological solution to support healthcare&#xD;
        professionals and patients in psychiatry, a field known as the &quot;poor relation of medicine&quot;&#xD;
        and lacking in resources. Such a solution would enable better understanding, disorders remote&#xD;
        &amp; continuous monitoring and, ultimately, better treatment of these disorders.&#xD;
  &#xD;
        The investigators will process the data by carrying out a number of analyses, including&#xD;
        descriptive, comparative and correlation studies of the data from the self-questionnaire&#xD;
        results and the emotional signals captured by the devices.&#xD;
  &#xD;
        Finally, the aim will be to predict questionnaire scores from the emotional signals produced.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">October 17, 2023</start_date>
    <completion_date type="Anticipated">October 17, 2024</completion_date>
    <primary_completion_date type="Anticipated">August 17, 2024</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>Repeated measurements Correlations between emotional signals and studied disorders standardized tests.</measure>
      <time_frame>10 months</time_frame>
      <description>Repeated measurements Correlations between emotional signals and studied disorders standardized tests.</description>
    </primary_outcome>
    <number_of_groups>2</number_of_groups>
    <enrollment type="Anticipated">50</enrollment>
    <condition>Anxiety Disorders</condition>
    <condition>Major Depressive Disorder</condition>
    <arm_group>
      <arm_group_label>The hardware group (on-board camera)</arm_group_label>
      <description>A physical device equipped with a camera and embedding the acquisition/monitoring software. Positioned in the living space, it will be possible to capture the facial expressions of the person in ecology, for example when watching a TV program or reading.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>The software-only group (running on a PC or tablet and using the available webcam)</arm_group_label>
      <description>Software running on a computer, connected to the computer's camera (webcam). If the person is teleworking on a PC, it is expected that images will be captured during videoconferencing-type interactions.</description>
    </arm_group>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>Acquisition and analysis of relationships between Longitudinal Emotional Signals produced by a software and Self-questionnaires.</intervention_name>
      <description>Using the tool developed by Emobot, EMOACQ-1 is a study that passively and non-interventionaly collects data by capturing patients' facial expressions throughout the day, and then measures the correlation between emotional signals and the results of measurement questionnaires used in psychiatry.</description>
      <arm_group_label>The hardware group (on-board camera)</arm_group_label>
      <arm_group_label>The software-only group (running on a PC or tablet and using the available webcam)</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          The first cohort of 20 participants will be provided with the 20 measuring embedded device,&#xD;
          we will be able to collect specific data at targeted times and for specific activities.&#xD;
  &#xD;
          The second cohort formed with the remaining participants will receive the software on their&#xD;
          personal computer, enabling us to capture more general data and analyze behavior patterns&#xD;
          in less controlled contexts.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Persons over the age of 18 who volunteer to take part in research&#xD;
  &#xD;
            -  Must have access to a computer with an Internet connection,&#xD;
  &#xD;
            -  Written comprehension of French.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  N/A&#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_contact>
      <last_name>Tanel Petelot</last_name>
      <phone>+33 51 44 26 67</phone>
      <phone_ext>+33</phone_ext>
      <email>tanel.petelot@emobot.fr</email>
    </overall_contact>
    <link>
      <url>https://hal.science/hal-03389126/document</url>
      <description>D. Agarwal et al. From Multimodal to Unimodal Attention in Transformers using Knowledge Distillation. Nov 2021, Virtual, United States.</description>
    </link>
    <link>
      <url>https://ieeexplore.ieee.org/document/10178722</url>
      <description>Mamadou Dia et al. A Novel Stochastic Transformer-based Approach for Post-Traumatic Stress Disorder Detection using Audio Recording of Clinical Interviews, CBMS, June 2023.</description>
    </link>
    <link>
      <url>https://dl.acm.org/doi/10.1145/3204949.3208125</url>
      <description>E. Garcia-Ceja et al. Depression: A Motor Activity Database of Depression Episodes in Unipolar and Bipolar Patients</description>
    </link>
    <link>
      <url>https://aclanthology.org/L14-1421/</url>
      <description>J. Gratch et al. The Distress Analysis Interview Corpus of Human and Computer Interviews.</description>
    </link>
    <link>
      <url>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9281460/</url>
      <description>X. Kong et al. Automatic Identification of Depression Using Facial Images with Deep Convolutional Neural Network.</description>
    </link>
    <link>
      <url>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3409931/</url>
      <description>Mundt JC, Vogel AP, Feltner DE, Lenderking WR. Vocal acoustic biomarkers of depression severity and treatment response.</description>
    </link>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 3, 2023</study_first_submitted>
    <study_first_submitted_qc>August 3, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 14, 2023</study_first_posted>
    <last_update_submitted>August 11, 2023</last_update_submitted>
    <last_update_submitted_qc>August 11, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 15, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Anxiety Disorders</mesh_term>
      <mesh_term>Depressive Disorder</mesh_term>
      <mesh_term>Depressive Disorder, Major</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>No</sharing_ipd>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: 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/NCT05988840
  org study id: 2023-A01589-36
  nct id: NCT05988840
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  The worldwide prevalence of anxiety and depression increased massively during the pandemic,
        with a 25% rise in the number of patients suffering from psychological distress.
        Psychiatrists, and even more so general practitioners, need measurement tools that enable
        them to remotely monitor their patients' psychological state of health, and to be
        automatically alerted in the event of a break in behavior.
  
        In this study, the investigators propose to collect clinical data along with longitudinal
        measurement of patients' emotions. Emobot proposes to analyze the evolution of mood disorders
        over time by passively studying people's emotional behavior. The aim of EMOACQ-1 is to
        acquire knowledge and produce a quantitative link between emotional expression and mood
        disorders, ultimately facilitating the understanding and management of these disorders.
  
        Through this study, could be developed a technological solution to support healthcare
        professionals and patients in psychiatry, a field known as the "poor relation of medicine"
        and lacking in resources. Such a solution would enable better understanding, disorders remote
        & continuous monitoring and, ultimately, better treatment of these disorders.
  
        The investigators will process the data by carrying out a number of analyses, including
        descriptive, comparative and correlation studies of the data from the self-questionnaire
        results and the emotional signals captured by the devices.
  
        Finally, the aim will be to predict questionnaire scores from the emotional signals produced.
  observational model: Cohort
  time perspective: Prospective
  measure: Repeated measurements Correlations between emotional signals and studied disorders standardized tests.
  time frame: 10 months
  description: Repeated measurements Correlations between emotional signals and studied disorders standardized tests.
  arm group label: The hardware group (on-board camera)
  description: A physical device equipped with a camera and embedding the acquisition/monitoring software. Positioned in the living space, it will be possible to capture the facial expressions of the person in ecology, for example when watching a TV program or reading.
  arm group label: The software-only group (running on a PC or tablet and using the available webcam)
  description: Software running on a computer, connected to the computer's camera (webcam). If the person is teleworking on a PC, it is expected that images will be captured during videoconferencing-type interactions.
  intervention type: Other
  intervention name: Acquisition and analysis of relationships between Longitudinal Emotional Signals produced by a software and Self-questionnaires.
  description: Using the tool developed by Emobot, EMOACQ-1 is a study that passively and non-interventionaly collects data by capturing patients' facial expressions throughout the day, and then measures the correlation between emotional signals and the results of measurement questionnaires used in psychiatry.
  arm group label: The hardware group (on-board camera)
  arm group label: The software-only group (running on a PC or tablet and using the available webcam)
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: Accepts Healthy Volunteers
  last name: Tanel Petelot
  phone: +33 51 44 26 67
  phone ext: +33
  email: tanel.petelot@emobot.fr
  url: https://hal.science/hal-03389126/document
  description: D. Agarwal et al. From Multimodal to Unimodal Attention in Transformers using Knowledge Distillation. Nov 2021, Virtual, United States.
  url: https://ieeexplore.ieee.org/document/10178722
  description: Mamadou Dia et al. A Novel Stochastic Transformer-based Approach for Post-Traumatic Stress Disorder Detection using Audio Recording of Clinical Interviews, CBMS, June 2023.
  url: https://dl.acm.org/doi/10.1145/3204949.3208125
  description: E. Garcia-Ceja et al. Depression: A Motor Activity Database of Depression Episodes in Unipolar and Bipolar Patients
  url: https://aclanthology.org/L14-1421/
  description: J. Gratch et al. The Distress Analysis Interview Corpus of Human and Computer Interviews.
  url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9281460/
  description: X. Kong et al. Automatic Identification of Depression Using Facial Images with Deep Convolutional Neural Network.
  url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3409931/
  description: Mundt JC, Vogel AP, Feltner DE, Lenderking WR. Vocal acoustic biomarkers of depression severity and treatment response.
  responsible party type: Sponsor
  mesh term: Anxiety Disorders
  mesh term: Depressive Disorder
  mesh term: Depressive Disorder, Major
  sharing ipd: No