Asthma, Chronic Obstructive Pulmonary Disease, Cough, Cough Frequency, Coughing, Cough Severity, Gastro Esophageal Reflux, Idiopathic Pulmonary Fibrosis
Conditions
Keywords
Cough monitoring, Cough detection, Cough assessment, C-mo
Brief summary
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.
Interventions
DEVICEC-mo System
Patients will use C-mo System for a period of 24h, to assess cough characteristics.
Sponsors
Cough Monitoring Medical Solutions
Universidade Nova de Lisboa
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
OTHER
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
2 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Cough detection (precision and recall) | 24 hours | 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. |
| Cough detection (F1-score) | 24 hours | 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. |
| Cough characterisation (precision, recall and global accuracy) | 24 hours | 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. |
| Cough characterisation (F1-score) | 24 hours | 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. |
| Cough characterisation (Matthews correlation coefficient) | 24 hours | 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. |
| Cough characterisation (Cohen's Kappa) | 24 hours | 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. |
| Wheezing detection (precision, recall, true negative rate, accuracy, and negative predictive value) | 24 hours | 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. |
| Wheezing detection (F1-score) | 24 hours | 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. |
| Cough frequency (Matthews correlation coefficient) | 24 hours | 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. |
| Cough frequency (Cohen's Kappa Index) | 24 hours | 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. |
| Cough type percentage (Matthews correlation coefficient) | 24 hours | 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. |
| Cough type percentage (Cohen's Kappa Index) | 24 hours | 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. |
| Wheezing detection (Matthews correlation coefficient) | 24 hours | 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. |
| Wheezing detection (Cohen's Kappa Index) | 24 hours | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Cough intensity | 24 hours | Analyse the collected EMG signal to describe cough intensity, as percentage of maximum voluntary contraction (MVC). |
| Cough patterns | 24 hours | 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). |
| Usability results | 24 hours | 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. |
| Cough perception vs. C-mo System analysis, in relation to gold standard (expert evaluation) | 24 hours | 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). |
Countries
Portugal
Contacts
CONTACTDiogo B Tecelão, MSc
CONTACTSara B Lobo
PRINCIPAL_INVESTIGATORNuno M Neuparth, PhD
NOVA Medical School | Faculdade de Ciências Médicas da Universidade Nova de Lisboa
Outcome results
None listed