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/NCT05989698</url>
    </required_header>
    <id_info>
      <org_study_id>C-mo_01</org_study_id>
      <nct_id>NCT05989698</nct_id>
    </id_info>
    <brief_title>C-mo System 1.0's Validation - Cough Monitoring</brief_title>
    <acronym>C-mo_01</acronym>
    <official_title>C-mo System 1.0's Validation - Cough Monitoring</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Cough Monitoring Medical Solutions</agency>
        <agency_class>Industry</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>Universidade Nova de Lisboa</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>Cough Monitoring Medical Solutions</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>
        Cough is one of the most reported symptoms, especially associated with respiratory diseases.&#xD;
        Additionally, cough contains extremely insightful information regarding the patient's health.&#xD;
        It is a symptom full of physiopathological information, which can be extremely helpful in&#xD;
        clinical practice. However, cough is not currently used as a clinical biomarker given that:&#xD;
  &#xD;
          1. Cough is an extremely subjective symptom for patients (patients can't accurately&#xD;
             describe and understand their cough's traits).&#xD;
  &#xD;
          2. There is currently no tool available to evaluate cough objectively and thoroughly.&#xD;
  &#xD;
        As such, there is an unmet medical need: solutions for objective cough monitoring and&#xD;
        management.&#xD;
  &#xD;
        C-mo System is a novel non-invasive medical device, which performs an objective monitoring of&#xD;
        the patient's cough for long periods of time. The C-mo System consists of a wearable device&#xD;
        (C-mo wearable) and a desktop software (C-mo Medical Platform). C-mo System characterises&#xD;
        cough automatically through data collection and processing techniques (automatic&#xD;
        classification), and its base outputs include:&#xD;
  &#xD;
          -  Cough frequency (how many times the patient coughs)&#xD;
  &#xD;
          -  Cough intensity (how strong cough's expiratory effort is)&#xD;
  &#xD;
          -  Cough type (if the cough is dry, wet, or laryngeal)&#xD;
  &#xD;
          -  Identification of patterns (associations between cough characteristics and specific&#xD;
             events, namely the time of day, body position, physical exercising, and meals).&#xD;
  &#xD;
        It is extremely important to validate C-mo System in a wide and diverse population, given the&#xD;
        use of signal processing algorithms and artificial intelligence. C-mo System's base outputs&#xD;
        will allow healthcare professionals to improve significantly the medical care associated with&#xD;
        this symptom, namely:&#xD;
  &#xD;
          -  Speed-up and improve the accuracy of the diagnosis of several medical conditions,&#xD;
             especially respiratory diseases. C-mo System's ability to objectively monitor cough will&#xD;
             allow healthcare professionals to make associations between specific cough patterns and&#xD;
             specific medical conditions.&#xD;
  &#xD;
          -  Optimize treatment prescription and monitor their effectiveness. C-mo System's objective&#xD;
             assessment of cough will allow healthcare professionals to understand if a given therapy&#xD;
             is working as intended.&#xD;
  &#xD;
          -  Objectively monitor chronic disease progression. C-mo System's monitoring of cough will&#xD;
             allow healthcare professionals to objectively assess the progression of the patient's&#xD;
             cough.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">September 11, 2023</start_date>
    <completion_date type="Anticipated">March 11, 2025</completion_date>
    <primary_completion_date type="Anticipated">June 11, 2024</primary_completion_date>
    <phase>N/A</phase>
    <study_type>Interventional</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <allocation>N/A</allocation>
      <intervention_model>Single Group Assignment</intervention_model>
      <primary_purpose>Other</primary_purpose>
      <masking>None (Open Label)</masking>
    </study_design_info>
    <primary_outcome>
      <measure>Cough detection (precision and recall)</measure>
      <time_frame>24 hours</time_frame>
      <description>Measure C-mo System's performance and ability to automatically detect cough, using precision and recall (percentage - between 0% and 100%), higher scores mean a better outcome.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Cough detection (F1-score)</measure>
      <time_frame>24 hours</time_frame>
      <description>Measure C-mo System's performance and ability to automatically detect cough, using the F1-score (value between 0 and 1), higher scores mean a better outcome.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Cough characterisation (precision, recall and global accuracy)</measure>
      <time_frame>24 hours</time_frame>
      <description>Measure C-mo System's performance and ability to automatically characterise cough, using precision, recall, and global accuracy (percentage - between 0% and 100%), higher scores mean a better outcome.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Cough characterisation (F1-score)</measure>
      <time_frame>24 hours</time_frame>
      <description>Measure C-mo System's performance and ability to automatically characterise cough, using the F1-score (value between 0 and 1), higher scores mean a better outcome.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Cough characterisation (Matthews correlation coefficient)</measure>
      <time_frame>24 hours</time_frame>
      <description>Measure C-mo System's performance and ability to automatically characterise cough using Matthews correlation coefficient (MCC). The MCC value ranges from -1 to 1, and in this case it indicates the level of agreement between C-mo System's output and the result obtained from expert analysis (considered to be the gold standard in this study). A MCC value of -1 indicates total disagreement, 0 indicates that C-mo System's classification is no better than random guessing, and 1 represents a perfect classification (total agreement between C-mo System's output and the gold standard). Hence, higher scores mean a better outcome.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Cough characterisation (Cohen's Kappa)</measure>
      <time_frame>24 hours</time_frame>
      <description>Measure C-mo System's performance and ability to automatically characterise cough using Cohen's Kappa coefficient (κ). The κ value indicates the level of agreement between C-mo System's output and the result obtained from expert analysis (considered to be the gold standard in this study). It ranges from -1 (worst possible performance) to 1 (best possible performance). Hence, higher scores mean a better outcome.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Wheezing detection (precision, recall, true negative rate, accuracy, and negative predictive value)</measure>
      <time_frame>24 hours</time_frame>
      <description>Measure C-mo System's performance and ability to automatically detect wheezing in cough events, using precision, recall, true negative rate, accuracy, and negative predictive value (percentage - between 0% and 100%), higher scores mean a better outcome.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Wheezing detection (F1-score)</measure>
      <time_frame>24 hours</time_frame>
      <description>Measure C-mo System's performance and ability to automatically detect wheezing in cough events, using the F1-score (value between 0 and 1), higher scores mean a better outcome.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Cough frequency (Matthews correlation coefficient)</measure>
      <time_frame>24 hours</time_frame>
      <description>Measure C-mo System's performance and ability to automatically assess cough frequency, based on the average &quot;number of coughs per hour&quot;, using Matthews correlation coefficient (MCC). The MCC value ranges from -1 to 1, and in this case it indicates the level of agreement between C-mo System's output and the result obtained from expert analysis (considered to be the gold standard in this study). A MCC value of -1 indicates total disagreement, 0 indicates that C-mo System's classification is no better than random guessing, and 1 represents a perfect classification (total agreement between C-mo System's output and the gold standard). Hence, higher scores mean a better outcome.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Cough frequency (Cohen's Kappa Index)</measure>
      <time_frame>24 hours</time_frame>
      <description>Measure C-mo System's performance and ability to automatically assess cough frequency, based on the average &quot;number of coughs per hour&quot;, using Cohen's Kappa coefficient (κ). The κ value indicates the level of agreement between C-mo System's output and the result obtained from expert analysis (considered to be the gold standard in this study). It ranges from -1 (worst possible performance) to 1 (best possible performance). Hence, higher scores mean a better outcome.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Cough type percentage (Matthews correlation coefficient)</measure>
      <time_frame>24 hours</time_frame>
      <description>Measure C-mo System's performance and ability to automatically assess cough type, based on the percentage of each cough type, using Matthews correlation coefficient (MCC). The MCC value ranges from -1 to 1, and in this case it indicates the level of agreement between C-mo System's output and the result obtained from expert analysis (considered to be the gold standard in this study). A MCC value of -1 indicates total disagreement, 0 indicates that C-mo System's classification is no better than random guessing, and 1 represents a perfect classification (total agreement between C-mo System's output and the gold standard). Hence, higher scores mean a better outcome.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Cough type percentage (Cohen's Kappa Index)</measure>
      <time_frame>24 hours</time_frame>
      <description>Measure C-mo System's performance and ability to automatically assess cough type, based on the percentage of each cough type, using Cohen's Kappa coefficient (κ). The κ value indicates the level of agreement between C-mo System's output and the result obtained from expert analysis (considered to be the gold standard in this study). It ranges from -1 (worst possible performance) to 1 (best possible performance). Hence, higher scores mean a better outcome.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Wheezing detection (Matthews correlation coefficient)</measure>
      <time_frame>24 hours</time_frame>
      <description>Measure C-mo System's performance and ability to automatically assess wheeze in cough, based on the percentage of cough events in which wheezing was identified, using Matthews correlation coefficient (MCC). The MCC value ranges from -1 to 1, and in this case it indicates the level of agreement between C-mo System's output and the result obtained from expert analysis (considered to be the gold standard in this study). A MCC value of -1 indicates total disagreement, 0 indicates that C-mo System's classification is no better than random guessing, and 1 represents a perfect classification (total agreement between C-mo System's output and the gold standard). Hence, higher scores mean a better outcome.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Wheezing detection (Cohen's Kappa Index)</measure>
      <time_frame>24 hours</time_frame>
      <description>Measure C-mo System's performance and ability to automatically assess wheeze in cough, based on the percentage of cough events in which wheezing was identified, using Cohen's Kappa coefficient (κ). The κ value indicates the level of agreement between C-mo System's output and the result obtained from expert analysis (considered to be the gold standard in this study). It ranges from -1 (worst possible performance) to 1 (best possible performance). Hence, higher scores mean a better outcome.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Cough intensity</measure>
      <time_frame>24 hours</time_frame>
      <description>Analyse the collected EMG signal to describe cough intensity, as percentage of maximum voluntary contraction (MVC).</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Cough patterns</measure>
      <time_frame>24 hours</time_frame>
      <description>Describe cough patterns through the analysis of changes of cough characteristics (frequency, intensity, type and presence of wheeze) for each subject during their monitoring period, based on their post-monitoring questionnaire (if/how cough changes in relation to physical exercise, eating, resting, body position and time of day).</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Usability results</measure>
      <time_frame>24 hours</time_frame>
      <description>Analyse the results from usability questionnaires regarding the C-mo wearable, calculating average scores for each of the evaluated parameters. A 5-point Likert scale will be used for the overall satisfaction score, in which a higher rating corresponds to a better outcome.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Cough perception vs. C-mo System analysis, in relation to gold standard (expert evaluation)</measure>
      <time_frame>24 hours</time_frame>
      <description>Analyse the difference between the results obtained by the C-mo System and the results of the questionnaires filled out by the participants about their cough, comparing these obtained results to the gold standard. Differences between participants will also be analysed.&#xD;
  Statistical tests will be used to identify significant differences between groups (patient perception, C-mo System, and gold standard results).</description>
    </secondary_outcome>
    <other_outcome>
      <measure>Relation between cough characteristics and target diseases</measure>
      <time_frame>24 hours</time_frame>
      <description>Compare each indicator (cough frequency, type, intensity, presence of wheeze, and cough patterns) amongst the diseases observed in the study's sample. This will be performed using multivariate analysis of variance (MANOVA).</description>
    </other_outcome>
    <number_of_arms>1</number_of_arms>
    <enrollment type="Anticipated">245</enrollment>
    <condition>Cough</condition>
    <condition>Asthma</condition>
    <condition>Chronic Obstructive Pulmonary Disease</condition>
    <condition>Gastro Esophageal Reflux</condition>
    <condition>Idiopathic Pulmonary Fibrosis</condition>
    <arm_group>
      <arm_group_label>C-mo System</arm_group_label>
      <arm_group_type>Experimental</arm_group_type>
    </arm_group>
    <intervention>
      <intervention_type>Device</intervention_type>
      <intervention_name>C-mo System</intervention_name>
      <description>Patients will use C-mo System for a period of 24h, to assess cough characteristics.</description>
      <arm_group_label>C-mo System</arm_group_label>
    </intervention>
    <eligibility>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Patients aged 2 years or older;&#xD;
  &#xD;
            -  Patients with symptoms/complaints of cough;&#xD;
  &#xD;
            -  Signed Informed Consent (age ≥ 18 years), signed Informed Consent from the&#xD;
               parents/legal representative and the patient (16 and 17 years), or signed Informed&#xD;
               Assent and Consent (5 years ≤ age ≤ 15 years).&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Presence of musculoskeletal (e.g., severe scoliosis), neurological (e.g., post&#xD;
               stroke), cardiac (e.g., unstable angina), cognitive (e.g., dementia) changes, or other&#xD;
               significant conditions that hinder the participants from collaborating in the&#xD;
               collection of data.&#xD;
  &#xD;
            -  Damaged/weakened skin at the C-mo wearable device's placement area (epigastric&#xD;
               region).&#xD;
  &#xD;
            -  Absence of Informed Consent and/or Assent, as applicable.&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>2 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Nuno M Neuparth, PhD</last_name>
      <role>Principal Investigator</role>
      <affiliation>NOVA Medical School | Faculdade de Ciências Médicas da Universidade Nova de Lisboa</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Diogo B Tecelão, MSc</last_name>
      <phone>+351 917 935 447</phone>
      <email>diogo.tecelao@c-mo.solutions</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Sara B Lobo</last_name>
      <phone>+351 967 889 091</phone>
      <email>sara.lobo@c-mo.solutions</email>
    </overall_contact_backup>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>May 12, 2023</study_first_submitted>
    <study_first_submitted_qc>August 4, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 14, 2023</study_first_posted>
    <last_update_submitted>August 4, 2023</last_update_submitted>
    <last_update_submitted_qc>August 4, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 14, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>Cough monitoring</keyword>
    <keyword>Cough detection</keyword>
    <keyword>Cough assessment</keyword>
    <keyword>C-mo</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Gastroesophageal Reflux</mesh_term>
      <mesh_term>Lung Diseases, Obstructive</mesh_term>
      <mesh_term>Pulmonary Disease, Chronic Obstructive</mesh_term>
      <mesh_term>Pulmonary Fibrosis</mesh_term>
      <mesh_term>Cough</mesh_term>
      <mesh_term>Idiopathic Pulmonary Fibrosis</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/NCT05989698
  org study id: C-mo_01
  nct id: NCT05989698
  lead sponsor: 
  collaborator: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  Cough is one of the most reported symptoms, especially associated with respiratory diseases.
        Additionally, cough contains extremely insightful information regarding the patient's health.
        It is a symptom full of physiopathological information, which can be extremely helpful in
        clinical practice. However, cough is not currently used as a clinical biomarker given that:
  
          1. Cough is an extremely subjective symptom for patients (patients can't accurately
             describe and understand their cough's traits).
  
          2. There is currently no tool available to evaluate cough objectively and thoroughly.
  
        As such, there is an unmet medical need: solutions for objective cough monitoring and
        management.
  
        C-mo System is a novel non-invasive medical device, which performs an objective monitoring of
        the patient's cough for long periods of time. The C-mo System consists of a wearable device
        (C-mo wearable) and a desktop software (C-mo Medical Platform). C-mo System characterises
        cough automatically through data collection and processing techniques (automatic
        classification), and its base outputs include:
  
          -  Cough frequency (how many times the patient coughs)
  
          -  Cough intensity (how strong cough's expiratory effort is)
  
          -  Cough type (if the cough is dry, wet, or laryngeal)
  
          -  Identification of patterns (associations between cough characteristics and specific
             events, namely the time of day, body position, physical exercising, and meals).
  
        It is extremely important to validate C-mo System in a wide and diverse population, given the
        use of signal processing algorithms and artificial intelligence. C-mo System's base outputs
        will allow healthcare professionals to improve significantly the medical care associated with
        this symptom, namely:
  
          -  Speed-up and improve the accuracy of the diagnosis of several medical conditions,
             especially respiratory diseases. C-mo System's ability to objectively monitor cough will
             allow healthcare professionals to make associations between specific cough patterns and
             specific medical conditions.
  
          -  Optimize treatment prescription and monitor their effectiveness. C-mo System's objective
             assessment of cough will allow healthcare professionals to understand if a given therapy
             is working as intended.
  
          -  Objectively monitor chronic disease progression. C-mo System's monitoring of cough will
             allow healthcare professionals to objectively assess the progression of the patient's
             cough.
  allocation: N/A
  intervention model: Single Group Assignment
  primary purpose: Other
  masking: None (Open Label)
  measure: Cough detection (precision and recall)
  time frame: 24 hours
  description: Measure C-mo System's performance and ability to automatically detect cough, using precision and recall (percentage - between 0% and 100%), higher scores mean a better outcome.
  measure: Cough detection (F1-score)
  time frame: 24 hours
  description: Measure C-mo System's performance and ability to automatically detect cough, using the F1-score (value between 0 and 1), higher scores mean a better outcome.
  measure: Cough characterisation (precision, recall and global accuracy)
  time frame: 24 hours
  description: Measure C-mo System's performance and ability to automatically characterise cough, using precision, recall, and global accuracy (percentage - between 0% and 100%), higher scores mean a better outcome.
  measure: Cough characterisation (F1-score)
  time frame: 24 hours
  description: Measure C-mo System's performance and ability to automatically characterise cough, using the F1-score (value between 0 and 1), higher scores mean a better outcome.
  measure: Cough characterisation (Matthews correlation coefficient)
  time frame: 24 hours
  description: Measure C-mo System's performance and ability to automatically characterise cough using Matthews correlation coefficient (MCC). The MCC value ranges from -1 to 1, and in this case it indicates the level of agreement between C-mo System's output and the result obtained from expert analysis (considered to be the gold standard in this study). A MCC value of -1 indicates total disagreement, 0 indicates that C-mo System's classification is no better than random guessing, and 1 represents a perfect classification (total agreement between C-mo System's output and the gold standard). Hence, higher scores mean a better outcome.
  measure: Cough characterisation (Cohen's Kappa)
  time frame: 24 hours
  description: Measure C-mo System's performance and ability to automatically characterise cough using Cohen's Kappa coefficient (κ). The κ value indicates the level of agreement between C-mo System's output and the result obtained from expert analysis (considered to be the gold standard in this study). It ranges from -1 (worst possible performance) to 1 (best possible performance). Hence, higher scores mean a better outcome.
  measure: Wheezing detection (precision, recall, true negative rate, accuracy, and negative predictive value)
  time frame: 24 hours
  description: Measure C-mo System's performance and ability to automatically detect wheezing in cough events, using precision, recall, true negative rate, accuracy, and negative predictive value (percentage - between 0% and 100%), higher scores mean a better outcome.
  measure: Wheezing detection (F1-score)
  time frame: 24 hours
  description: Measure C-mo System's performance and ability to automatically detect wheezing in cough events, using the F1-score (value between 0 and 1), higher scores mean a better outcome.
  measure: Cough frequency (Matthews correlation coefficient)
  time frame: 24 hours
  description: Measure C-mo System's performance and ability to automatically assess cough frequency, based on the average "number of coughs per hour", using Matthews correlation coefficient (MCC). The MCC value ranges from -1 to 1, and in this case it indicates the level of agreement between C-mo System's output and the result obtained from expert analysis (considered to be the gold standard in this study). A MCC value of -1 indicates total disagreement, 0 indicates that C-mo System's classification is no better than random guessing, and 1 represents a perfect classification (total agreement between C-mo System's output and the gold standard). Hence, higher scores mean a better outcome.
  measure: Cough frequency (Cohen's Kappa Index)
  time frame: 24 hours
  description: Measure C-mo System's performance and ability to automatically assess cough frequency, based on the average "number of coughs per hour", using Cohen's Kappa coefficient (κ). The κ value indicates the level of agreement between C-mo System's output and the result obtained from expert analysis (considered to be the gold standard in this study). It ranges from -1 (worst possible performance) to 1 (best possible performance). Hence, higher scores mean a better outcome.
  measure: Cough type percentage (Matthews correlation coefficient)
  time frame: 24 hours
  description: Measure C-mo System's performance and ability to automatically assess cough type, based on the percentage of each cough type, using Matthews correlation coefficient (MCC). The MCC value ranges from -1 to 1, and in this case it indicates the level of agreement between C-mo System's output and the result obtained from expert analysis (considered to be the gold standard in this study). A MCC value of -1 indicates total disagreement, 0 indicates that C-mo System's classification is no better than random guessing, and 1 represents a perfect classification (total agreement between C-mo System's output and the gold standard). Hence, higher scores mean a better outcome.
  measure: Cough type percentage (Cohen's Kappa Index)
  time frame: 24 hours
  description: Measure C-mo System's performance and ability to automatically assess cough type, based on the percentage of each cough type, using Cohen's Kappa coefficient (κ). The κ value indicates the level of agreement between C-mo System's output and the result obtained from expert analysis (considered to be the gold standard in this study). It ranges from -1 (worst possible performance) to 1 (best possible performance). Hence, higher scores mean a better outcome.
  measure: Wheezing detection (Matthews correlation coefficient)
  time frame: 24 hours
  description: Measure C-mo System's performance and ability to automatically assess wheeze in cough, based on the percentage of cough events in which wheezing was identified, using Matthews correlation coefficient (MCC). The MCC value ranges from -1 to 1, and in this case it indicates the level of agreement between C-mo System's output and the result obtained from expert analysis (considered to be the gold standard in this study). A MCC value of -1 indicates total disagreement, 0 indicates that C-mo System's classification is no better than random guessing, and 1 represents a perfect classification (total agreement between C-mo System's output and the gold standard). Hence, higher scores mean a better outcome.
  measure: Wheezing detection (Cohen's Kappa Index)
  time frame: 24 hours
  description: Measure C-mo System's performance and ability to automatically assess wheeze in cough, based on the percentage of cough events in which wheezing was identified, using Cohen's Kappa coefficient (κ). The κ value indicates the level of agreement between C-mo System's output and the result obtained from expert analysis (considered to be the gold standard in this study). It ranges from -1 (worst possible performance) to 1 (best possible performance). Hence, higher scores mean a better outcome.
  measure: Cough intensity
  time frame: 24 hours
  description: Analyse the collected EMG signal to describe cough intensity, as percentage of maximum voluntary contraction (MVC).
  measure: Cough patterns
  time frame: 24 hours
  description: Describe cough patterns through the analysis of changes of cough characteristics (frequency, intensity, type and presence of wheeze) for each subject during their monitoring period, based on their post-monitoring questionnaire (if/how cough changes in relation to physical exercise, eating, resting, body position and time of day).
  measure: Usability results
  time frame: 24 hours
  description: Analyse the results from usability questionnaires regarding the C-mo wearable, calculating average scores for each of the evaluated parameters. A 5-point Likert scale will be used for the overall satisfaction score, in which a higher rating corresponds to a better outcome.
  measure: Cough perception vs. C-mo System analysis, in relation to gold standard (expert evaluation)
  time frame: 24 hours
  description: Analyse the difference between the results obtained by the C-mo System and the results of the questionnaires filled out by the participants about their cough, comparing these obtained results to the gold standard. Differences between participants will also be analysed.
  Statistical tests will be used to identify significant differences between groups (patient perception, C-mo System, and gold standard results).
  measure: Relation between cough characteristics and target diseases
  time frame: 24 hours
  description: Compare each indicator (cough frequency, type, intensity, presence of wheeze, and cough patterns) amongst the diseases observed in the study's sample. This will be performed using multivariate analysis of variance (MANOVA).
  arm group label: C-mo System
  arm group type: Experimental
  intervention type: Device
  intervention name: C-mo System
  description: Patients will use C-mo System for a period of 24h, to assess cough characteristics.
  arm group label: C-mo System
  criteria: 
  gender: All
  minimum age: 2 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Nuno M Neuparth, PhD
  role: Principal Investigator
  affiliation: NOVA Medical School | Faculdade de Ciências Médicas da Universidade Nova de Lisboa
  last name: Diogo B Tecelão, MSc
  phone: +351 917 935 447
  email: diogo.tecelao@c-mo.solutions
  last name: Sara B Lobo
  phone: +351 967 889 091
  email: sara.lobo@c-mo.solutions
  responsible party type: Sponsor
  mesh term: Gastroesophageal Reflux
  mesh term: Lung Diseases, Obstructive
  mesh term: Pulmonary Disease, Chronic Obstructive
  mesh term: Pulmonary Fibrosis
  mesh term: Cough
  mesh term: Idiopathic Pulmonary Fibrosis
  sharing ipd: No