Identification of Respiratory Profiles From Nasal Pressure Signals

NCT ID: NCT03753386

Last Updated: 2019-09-16

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

20 participants

Study Classification

INTERVENTIONAL

Study Start Date

2019-05-16

Study Completion Date

2019-05-28

Brief Summary

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Chronic Obstructive Pulmonary Disease (COPD) is a chronic respiratory disease caused by smoking and associated with significant morbidity due to recurrent exacerbations or even hospitalizations. It is one of the leading causes of death in rich countries.

Early detection of exacerbations of the disease is thus an economic and public health issue in the world. Finding tools for automatic and early detection of an exacerbation is the subject of the study. A continuous analysis of the frequency and shape of the patients' breathing will be used.

As a first step in this study, investigators want to measure the frequency and shape of breathing in healthy subjects as they execute standard activities. These activities include resting, drinking, coughing, speaking, breathing through the mouth, physical exercise and recuperation.

Detailed Description

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Conditions

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Healthy

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

BASIC_SCIENCE

Blinding Strategy

NONE

Study Groups

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Healthy subjects

Subjects not presenting any pulmonary pathology that will follow the intervention Oxygen therapy simulation.

Group Type EXPERIMENTAL

Oxygen therapy simulation

Intervention Type OTHER

The intervention consists of reproducing oxygen therapy situations in healthy individuals by replacing oxygen bottles with bottles of air. A TeleOx® device will be placed on the circuit between the source and the nasal cannula for respiratory signals recording.

The intervention will last 30 minutes, containing the following activities in the given order:

* rest (9 minutes);
* drinking water (1 minute);
* rest (1 minute);
* coughing (1 minute);
* rest (1 minute);
* reading aloud (1 minute);
* rest (1 minute);
* breathing by the mouth (1 minute);
* rest (1 minute);
* exercising (running) (3 minutes) and
* rest (10 minutes)

Recordings will be retrieved after each test.

Interventions

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Oxygen therapy simulation

The intervention consists of reproducing oxygen therapy situations in healthy individuals by replacing oxygen bottles with bottles of air. A TeleOx® device will be placed on the circuit between the source and the nasal cannula for respiratory signals recording.

The intervention will last 30 minutes, containing the following activities in the given order:

* rest (9 minutes);
* drinking water (1 minute);
* rest (1 minute);
* coughing (1 minute);
* rest (1 minute);
* reading aloud (1 minute);
* rest (1 minute);
* breathing by the mouth (1 minute);
* rest (1 minute);
* exercising (running) (3 minutes) and
* rest (10 minutes)

Recordings will be retrieved after each test.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Age of majority
* Health volunteers

Exclusion Criteria

* Pregnancy
* Age of minority
* Protected adult
* Person deprived of liberty
* Respiratory pathology
* Non affiliation to the health care system
Minimum Eligible Age

18 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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SRETT

UNKNOWN

Sponsor Role collaborator

Association pour le Développement et l'Organisation de la Recherche en Pneumologie et sur le Sommeil

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Jesus Gonzalez-Bermejo

Role: PRINCIPAL_INVESTIGATOR

Groupe Hospitalier Pitié-Salpêtrière

Locations

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INSERM-UPC UMR_S 1158, Service de Pneumologie et Réanimation, GH Pitié-Salpêtrière

Paris, , France

Site Status

Countries

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France

References

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Pegoraro JA, Lavault S, Wattiez N, Similowski T, Gonzalez-Bermejo J, Birmele E. Machine-learning based feature selection for a non-invasive breathing change detection. BioData Min. 2021 Jul 18;14(1):33. doi: 10.1186/s13040-021-00265-8.

Reference Type DERIVED
PMID: 34275469 (View on PubMed)

Other Identifiers

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2018-A01999-46

Identifier Type: -

Identifier Source: org_study_id

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