C-mo System 1.0's Validation - Cough Monitoring (C-mo_01)

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: Cough is an extremely subjective symptom for patients (patients can't accurately describe and understand their cough's traits). 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.

Cough, Asthma, Chronic Obstructive Pulmonary Disease, Gastro Esophageal Reflux, Idiopathic Pulmonary Fibrosis

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 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 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 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 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 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 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 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 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 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 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 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 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 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.

Analyse the collected EMG signal to describe cough intensity, as percentage of maximum voluntary contraction (MVC).

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).

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.

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).

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).

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.