Clinical trial NCT05989698: C-mo System 1.0's Validation - Cough Monitoring
Official title: C-mo System 1.0's Validation - Cough Monitoring
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Cough
Asthma
Chronic Obstructive Pulmonary Disease
Gastro Esophageal Reflux
Idiopathic Pulmonary Fibrosis
Keywords: Cough monitoring
Cough detection
Cough assessment
C-mo
Lead sponsor: Cough Monitoring Medical Solutions
Sponsor class: INDUSTRY
Collaborators: name: Universidade Nova de Lisboa
First posted: 2023-08-14
Enrollment: 245
Sex: ALL
Minimum age (years): 2
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: OTHER

Brief 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.


Eligibility criteria: 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.

Primary outcomes:
measure: Cough detection (precision and recall)
timeFrame: 24 hours
measure: Cough detection (F1-score)
timeFrame: 24 hours
measure: Cough characterisation (precision, recall and global accuracy)
timeFrame: 24 hours
measure: Cough characterisation (F1-score)
timeFrame: 24 hours
measure: Cough characterisation (Matthews correlation coefficient)
timeFrame: 24 hours
measure: Cough characterisation (Cohen's Kappa)
timeFrame: 24 hours
measure: Wheezing detection (precision, recall, true negative rate, accuracy, and negative predictive value)
timeFrame: 24 hours
measure: Wheezing detection (F1-score)
timeFrame: 24 hours
measure: Cough frequency (Matthews correlation coefficient)
timeFrame: 24 hours
measure: Cough frequency (Cohen's Kappa Index)
timeFrame: 24 hours
measure: Cough type percentage (Matthews correlation coefficient)
timeFrame: 24 hours
measure: Cough type percentage (Cohen's Kappa Index)
timeFrame: 24 hours
measure: Wheezing detection (Matthews correlation coefficient)
timeFrame: 24 hours
measure: Wheezing detection (Cohen's Kappa Index)
timeFrame: 24 hours
Secondary outcomes:
measure: Cough intensity
timeFrame: 24 hours
measure: Cough patterns
timeFrame: 24 hours
measure: Usability results
timeFrame: 24 hours
measure: Cough perception vs. C-mo System analysis, in relation to gold standard (expert evaluation)
timeFrame: 24 hours
Other outcomes:
measure: Relation between cough characteristics and target diseases
timeFrame: 24 hours
Locations:
city: Amadora
country: Portugal
facility: HFF - Hospital Professor Doutor Fernando Fonseca
city: Lisboa
country: Portugal
facility: NMS Research - Laboratório de Exploração Funcional | Fisiopatologia
city: Porto
country: Portugal
facility: CHUSJ - Centro Hospitalar Universitário de São João
Study officials:
affiliation: NOVA Medical School | Faculdade de Ciências Médicas da Universidade Nova de Lisboa
name: Nuno M Neuparth, PhD
role: PRINCIPAL_INVESTIGATOR
References: