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

NCT03635710 | Completed

• 1 other identifier: EKOS 17/164
• Study Type: observational
• Target: 94
• Locations: 1 country
• Sites: 3

Brief Summary

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.

Conditions

Asthma; Cough

Outcome Measures

Primary Outcomes (1)

• Coughs per night assessed by smartphone audio recording

  Number of coughs per night measured by means of smartphone audio recording

  28 days

Secondary Outcomes (2)

• Detection rates of two machine learning algorithms

  28 days

• Sleep quality (Pittsburgh sleep quality index)

  28 days

Interventions

The patient will undergo no intervention DEVICE

Night coughs will be monitored using smartphone a app and interpreted using machine learning algorithm.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The population investigated in this study are adult asthmatics.

You may qualify if:

• all patients with physician-diagnosed asthma (obtained through self-reports)
• minimum age 18 years
• proficient in using a smartphone (e.g. for the daily smartphone-based self-

You may not qualify if:

• patients with mental diseases resulting in cognitive impairments such as depression, dementia, and Alzheimer's disease
• patients for whom it would not be feasible to obtain reliable nighttime measurements (i.e. patients with severe insomnia or shift workers) or for whom we cannot ensure the correct allocation of nocturnal coughs to the patient in the rating process (i.e. patients who usually share the bed with a person from the same sex).

Sponsors & Collaborators

• Cantonal Hospital of St. Gallen: lead
• University of Zurich: collaborator
• University of St.Gallen: collaborator

Study Sites (3)

Cantonal Hospital St. Gallen

Sankt Gallen, Canton of St. Gallen, 9007, Switzerland

Location

University of Zürich

Zurich, Canton of Zurich, 8001, Switzerland

Location

Cantonal Hospital St. Gallen

Sankt Gallen, 9007, Switzerland

Location

Related Publications (1)

• Tinschert P, Rassouli F, Barata F, Steurer-Stey C, Fleisch E, Puhan MA, Brutsche M, Kowatsch T. 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. BMJ Open. 2019 Jan 7;9(1):e026323. doi: 10.1136/bmjopen-2018-026323.

  PMID: 30617104 DERIVED

MeSH Terms

Conditions

Asthma; Cough

Condition Hierarchy (Ancestors)

Bronchial Diseases; Respiratory Tract Diseases; Lung Diseases, Obstructive; Lung Diseases; Respiratory Hypersensitivity; Hypersensitivity, Immediate; Hypersensitivity; Immune System Diseases; Respiration Disorders; Signs and Symptoms, Respiratory; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Study Officials

• Frank Rassouli, MD

  Cantonal Hospital of St. Gallen

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Attending Physician, Lung Center

Study Record Dates

• First Submitted: August 9, 2018
• First Posted: August 17, 2018
• Study Start: January 1, 2018
• Primary Completion: December 31, 2019
• Study Completion: December 31, 2019
• Last Updated: January 27, 2020

Record last verified: 2020-01

Data Sharing

• IPD Sharing: Will not share

Locations

CH (3)