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Feasibility of Cough Monitoring in Children

Recruiting
Conditions
Cystic Fibrosis in Children
Interventions
Device: Curie Artificial Intelligence (AI) cough monitor
Registration Number
NCT06587126
Lead Sponsor
University of Colorado, Denver
Brief Summary

Cystic fibrosis (CF) is a disease characterized by chronic airway infection and impaired mucociliary clearance, which predisposes those affected to recurrent pulmonary exacerbations (PEx) and progressive decline in lung function. Treatment with elexacaftor/tezacaftor/ivacaftor (ETI) results in decreases in patient-reported cough and PEx. Despite this, increased cough remains the most common symptom associated with acute PEx and worsening lung disease. Cough frequency was historically difficult to measure due to reliance on human input. Recent advances in audio capture and signal processing have made automated cough detection possible. As a result there's been a surge in development of portable cough monitors, as cough is increasingly recognized as a measurable parameter of respiratory disease. The majority of cough monitors have been designed for use in adults, and little is known about the practicality of collecting cough data in the pediatric population. In this study investigators aim to assess the feasibility of using an in-home device to capture nighttime cough frequency in children with and without CF. Investigators plan to compare nighttime cough frequency between children with and without CF and, among children with CF, and determine the association between cough frequency and baseline lung function. Additionally, investigators aim to evaluate the changes in nighttime cough frequency in relationship to respiratory symptom scores surrounding clinician diagnosed pulmonary exacerbations. This study will provide important preliminary data needed for a larger study assessing the utility of home cough monitoring for clinical care and for use of cough as a clinical outcome measure in research studies.

Detailed Description

Not available

Recruitment & Eligibility

Status
RECRUITING
Sex
All
Target Recruitment
40
Inclusion Criteria

Not provided

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Exclusion Criteria
  • Shared bedroom with sibling
  • Underlying chronic respiratory or cardiac conditions including chronic cough, CF, asthma, obstructive sleep apnea, or congenital heart disease or other condition felt by the investigator to cause chronic nighttime symptoms
  • Shared custody (i.e., the participant is splitting time between time households)
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Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Arm && Interventions
GroupInterventionDescription
Children with Cystic FibrosisCurie Artificial Intelligence (AI) cough monitorChildrens ages 1-18 with a diagnosis of CF based on 2 known cystic fibrosis transmembrane conductance regulator (CFTR) mutations and/or sweat chloride \>60 mmol/L, thought to be clinically stable at the time of study consent.
Healthy ControlsCurie Artificial Intelligence (AI) cough monitorChildren ages 1-18 with no underlying respiratory of cardiac conditions including chronic cough, CF, asthma, obstructive sleep apnea, or congenital heart disease thought to cause chronic nighttime symptoms.
Primary Outcome Measures
NameTimeMethod
Feasibility of using an in-home cough monitoring deviceThrough study completion, an average of 3 months

Percent of nights over the study period during which 4 or more hours of analyzable data are collected over study period

Secondary Outcome Measures
NameTimeMethod
Comparison of nighttime cough between participants with CF and healthy controlsThrough study completion, an average of 3 months

Average cough seconds per hour per night over the study period

Comparison of nighttime cough in children with CF during clinician diagnosed pulmonary exacerbations7 days

Assess for changes in cough frequency before and after diagnosis of pulmonary exacerbation

Trial Locations

Locations (1)

Children's Hospital of Colorado

🇺🇸

Aurora, Colorado, United States

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