Validation of the C-mo System - Cough Monitoring (C-mo_01)

The goal of this clinical study is to validate C-mo System's ability to automatically detect and characterise cough, in patients over 2 years old with cough as a key or refractory symptom.

The main questions it aims to answer are:

1. Can C-mo System detect cough events? (automatic cough detection)
2. Can C-mo System characterise cough events? (calculation of cough intensity, identification of cough type and presence of wheeze in detected coughs)

Participants will be asked to:

• Wear the C-mo Wearable device for 24 hours (1 day);
• Complete a diary with relevant activities throughout the monitoring period;
• Fill-out questionnaires related to coughing frequency and intensity, usability of the device, and impact of cough on quality of life.

Study Overview

• Status: Recruiting
• Conditions: Chronic Obstructive Pulmonary Disease; Cough; Asthma; Gastro Esophageal Reflux; Idiopathic Pulmonary Fibrosis; Coughing; Cough Severity; Cough Frequency
• Intervention / Treatment: Device: C-mo System

• Study Type: Interventional
• Enrollment (Estimated): 300
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Diogo B Tecelão, MSc; Phone Number: +351 917 935 447; Email: diogo.tecelao@c-mo.solutions
• Study Contact Backup: Name: Sara B Lobo; Phone Number: +351 967 889 091; Email: sara.lobo@c-mo.solutions

Study Locations

• Portugal
  • Alfena, Portugal
    • Recruiting
    • HPAV - Trofa Saúde Hospital de Alfena
    • Contact: Daniela Rodrigues
  • Amadora, Portugal
    • Recruiting
    • HFF - Hospital Professor Doutor Fernando Fonseca
    • Contact: José Pedro Boléo-Tomé
  • Aveiro, Portugal
    • Completed
    • Lab3R - Laboratório de Investigação e Reabilitação Respiratória da Escola Superior de Saúde da Universidade de Aveiro
  • Coimbra, Portugal
    • Recruiting
    • CHUC - Centro Hospitalar e Universitário de Coimbra
    • Contact: Pedro Gonçalo Ferreira
  • Lisbon, Portugal
    • Recruiting
    • HDE - Hospital Dona Estefânia
    • Contact: Raquel Lopes de Bragança
  • Lisbon, Portugal
    • Recruiting
    • NMS Research - Laboratório de Exploração Funcional | Fisiopatologia
    • Contact: Nuno Neuparth
  • Porto, Portugal
    • Recruiting
    • CHUSJ - Centro Hospitalar Universitário de São João
    • Contact: David Araújo
  • Porto, Portugal
    • Recruiting
    • ICUFP - Instituto CUF Porto
    • Contact: João Carlos Winck

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

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 Plan

How is the study designed?

Design Details

• Primary Purpose: Other
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: C-mo System	Device: C-mo System; Patients will use C-mo System for a period of 24h, to assess cough characteristics.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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
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
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
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
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
Cough characterisation (Cohen's Kappa)	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.	24 hours
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
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
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
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 (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.	24 hours
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
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 (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.	24 hours
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
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 (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.	24 hours

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Cough intensity	Analyse the collected EMG signal to describe cough intensity, as percentage of maximum voluntary contraction (MVC).	24 hours
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
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
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

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Cough Monitoring Medical Solutions
• Collaborators: Universidade Nova de Lisboa
• Investigators: Principal Investigator: Nuno M Neuparth, PhD, NOVA Medical School | Faculdade de Ciências Médicas da Universidade Nova de Lisboa

Study record dates

Study Major Dates

• Study Start (Actual): December 11, 2023
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): September 1, 2026

Study Registration Dates

• First Submitted: May 12, 2023
• First Submitted That Met QC Criteria: August 4, 2023
• First Posted (Actual): August 14, 2023

Study Record Updates

• Last Update Posted (Actual): April 8, 2026
• Last Update Submitted That Met QC Criteria: April 2, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Keywords: Cough monitoring; Cough detection; Cough assessment; C-mo
• Additional Relevant MeSH Terms: Pathologic Processes; Chronic Disease; Disease Attributes; Immune System Diseases; Respiratory Tract Diseases; Digestive System Diseases; Gastrointestinal Diseases; Lung Diseases; Respiration Disorders; Bronchial Diseases; Lung Diseases, Obstructive; Respiratory Hypersensitivity; Hypersensitivity, Immediate; Hypersensitivity; Esophageal Diseases; Signs and Symptoms, Respiratory; Lung Diseases, Interstitial; Esophageal Motility Disorders; Deglutition Disorders; Pulmonary Fibrosis; Pathological Conditions, Signs and Symptoms; Signs and Symptoms; Pulmonary Disease, Chronic Obstructive; Asthma; Idiopathic Pulmonary Fibrosis; Cough; Gastroesophageal Reflux
• Other Study ID Numbers: C-mo_01

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No