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NCT03635710

Smartphone Enabled Detection of Nocturnal Cough Rate and Sleep Quality as a Prognostic Marker for Asthma Control

Completed Last updated 27 January 2020
What this trial tests

trial testing The patient will undergo no intervention in Asthma in 94 participants. Completed in 31 December 2019.

Timeline
1 January 2018
Primary endpoint
31 December 2019
31 December 2019

Quick facts

Lead sponsorCantonal Hospital of St. Gallen
StatusCompleted
Study typeOBSERVATIONAL
Enrollment94
Start date1 January 2018
Primary completion31 December 2019
Estimated completion31 December 2019
Sites3 locations across Switzerland

Drugs / interventions tested

Conditions studied

Sponsor

Cantonal Hospital of St. Gallen

Who can join

18 and older, any sex, with Asthma or Cough. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

The purpose of the study is to explore the value which cough rate might provide for asthma self-management. In this study, the focus will be specifically on nocturnal cough rate. The plan is to use a longitudinal study design, in order to investigate to which extent trends in the nocturnal cough rates might have meaningful implications for future asthma control and asthma exacerbations of patients. The incidence of nocturnal cough in asthmatics will be described and visualized over the course of one month in the first stage of the study. Additionally, the aim will be to identify and model trends in nocturnal cough rates. Measuring cough is very time-consuming. Currently, there are no cough frequency monitors available, which measure cough rates in a fully automated and unobtrusive way. Consequently, manual labeling of cough based on video or sound recordings is still considered to be the gold standard for measuring cough rates by medical guidelines. Recently, a machine learning algorithm was successfully designed to automatically detect cough in a proof of concept study. This machine learning algorithm will be further developed in order to provide robust results in the field. The focus of this study will be the cough during the night time due to the limited interfering noise, which greatly facilitates manual labeling and enables a more reliable detection rate of the machine learning algorithm. Apart from developing a machine learning algorithm for cough detection, data will be gathered for the assessment of patient's sleep quality based on data obtained from smartphone's sensors.

Publications & conference data

3 peer-reviewed publications reference this trial (live from Europe PMC):

  1. Nocturnal Cough and Sleep Quality to Assess Asthma Control and Predict Attacks.
    Tinschert P, Rassouli F, Barata F, Steurer-Stey C, et al · · 2020 · cited 23× · PMID 33363391 · DOI 10.2147/jaa.s278155
  2. Characteristics of Asthma-related Nocturnal Cough: A Potential New Digital Biomarker.
    Rassouli F, Tinschert P, Barata F, Steurer-Stey C, et al · · 2020 · cited 18× · PMID 33299332 · DOI 10.2147/jaa.s278119
  3. Prevalence of nocturnal cough in asthma and its potential as a marker for asthma control (MAC) in combination with sleep quality: protocol of a smartphone-based, multicentre, longitudinal observational study with two stages.
    Tinschert P, Rassouli F, Barata F, Steurer-Stey C, et al · · 2019 · cited 15× · PMID 30617104 · DOI 10.1136/bmjopen-2018-026323

Verify or expand the search:

Other recruiting trials for Asthma

Currently open trials in the same condition.

Other Cantonal Hospital of St. Gallen trials

Trials by the same sponsor.

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