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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT05989698
Other study ID # C-mo_01
Secondary ID
Status Recruiting
Phase N/A
First received
Last updated
Start date December 11, 2023
Est. completion date June 11, 2025

Study information

Verified date February 2024
Source Cough Monitoring Medical Solutions
Contact Diogo B Tecelão, MSc
Phone +351 917 935 447
Email diogo.tecelao@c-mo.solutions
Is FDA regulated No
Health authority
Study type Interventional

Clinical Trial Summary

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.


Recruitment information / eligibility

Status Recruiting
Enrollment 245
Est. completion date June 11, 2025
Est. primary completion date September 11, 2024
Accepts healthy volunteers No
Gender All
Age group 2 Years and older
Eligibility Inclusion Criteria: - Patients aged 2 years or older; - Patients with symptoms/complaints of cough; - Signed Informed Consent (age = 18 years), signed Informed Consent from the parents/legal representative and the patient (16 and 17 years), or signed Informed Assent and Consent (5 years = age = 15 years). Exclusion Criteria: - Presence of musculoskeletal (e.g., severe scoliosis), neurological (e.g., post stroke), cardiac (e.g., unstable angina), cognitive (e.g., dementia) changes, or other significant conditions that hinder the participants from collaborating in the collection of data. - Damaged/weakened skin at the C-mo wearable device's placement area (epigastric region). - Absence of Informed Consent and/or Assent, as applicable.

Study Design


Intervention

Device:
C-mo System
Patients will use C-mo System for a period of 24h, to assess cough characteristics.

Locations

Country Name City State
Portugal HFF - Hospital Professor Doutor Fernando Fonseca Amadora
Portugal NMS Research - Laboratório de Exploração Funcional | Fisiopatologia Lisboa
Portugal CHUSJ - Centro Hospitalar Universitário de São João Porto

Sponsors (2)

Lead Sponsor Collaborator
Cough Monitoring Medical Solutions Universidade Nova de Lisboa

Country where clinical trial is conducted

Portugal, 

Outcome

Type Measure Description Time frame Safety issue
Other Relation between cough characteristics and target diseases 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). 24 hours
Primary Cough detection (precision and recall) 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. 24 hours
Primary Cough detection (F1-score) 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. 24 hours
Primary Cough characterisation (precision, recall and global accuracy) 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. 24 hours
Primary Cough characterisation (F1-score) 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. 24 hours
Primary Cough characterisation (Matthews correlation coefficient) 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. 24 hours
Primary Cough characterisation (Cohen's Kappa) Measure C-mo System's performance and ability to automatically characterise cough using Cohen's Kappa coefficient (?). The ? 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. 24 hours
Primary Wheezing detection (precision, recall, true negative rate, accuracy, and negative predictive value) 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. 24 hours
Primary Wheezing detection (F1-score) 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. 24 hours
Primary Cough frequency (Matthews correlation coefficient) 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. 24 hours
Primary Cough frequency (Cohen's Kappa Index) 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 (?). The ? 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. 24 hours
Primary Cough type percentage (Matthews correlation coefficient) 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. 24 hours
Primary Cough type percentage (Cohen's Kappa Index) 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 (?). The ? 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. 24 hours
Primary Wheezing detection (Matthews correlation coefficient) 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. 24 hours
Primary Wheezing detection (Cohen's Kappa Index) 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 (?). The ? 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. 24 hours
Secondary Cough intensity Analyse the collected EMG signal to describe cough intensity, as percentage of maximum voluntary contraction (MVC). 24 hours
Secondary Cough patterns 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). 24 hours
Secondary Usability results 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. 24 hours
Secondary Cough perception vs. C-mo System analysis, in relation to gold standard (expert evaluation) 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).
24 hours
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