A Wireless Bed Sensor for Monitoring Coughs

NCT03177694 | Unknown

• 1 other identifier: LPNE_01
• Study Type: observational
• Target: 20
• Locations: 0 countries
• Sites: N/A

Brief Summary

Aging of the population is dramatically increasing the number of hospitalized patients, with the consequent challenges of limited medical personnel and resources in hospitals. Wireless technologies that create highly connected healthcare environments are developed to help hospitals address these issues, once these technologies are perfectly integrated in the hospital environment with respect to IT infrastructure for big data storage. Such devices have proven remarkable efficiencies in monitoring patients with high patient safety, data accuracy and security, which are essential to provide high quality patient care, reduce health-related costs and optimize the management of high numbers of patients. Cough is the most common condition that results in a visit to the physician. Often coughs are benign, but sometimes can be the sign of exacerbations of a chronic respiratory disease. Exacerbations are defined in the Global Initiative for Chronic Obstructive Lung Disease (GOLD) document "as an acute event characterised by a worsening of the patient's respiratory symptoms that is beyond normal day-to-day variations and leads to a change in medication". It is assumed that, if coughs were remotely monitored, hospitals might be unburdened, patients would be empowered to self-manage their health, and that prevention of serious respiratory diseases might be facilitated, thus improving health outcomes. Unfortunately, remote monitoring for cough that rely on self-reporting is impractical, as patients do not record data very reliably. On the contrary, a bed sensor under the mattress connected to a medical data analysis platform might monitor patients' micro-movements at night and alert the medical staff as soon as there is a cough exacerbation.

Conditions

Chronic Obstructive Pulmonary Disease; Respiratory Disease

Keywords

cough; exacerbation; connected device

Outcome Measures

Primary Outcomes (1)

• Measurement of the clinical performance and accuracy of the bed sensor and of the analysis platform to detect coughs

  Comparison of the % of coughs detected by the connected device and the % of coughs detected by the AV system coupled to the polygraph recordings. Comparison of control and experimental groups will be performed using t-test for continuous variable. Difference with probability values \> 0.05 will be considered insignificant.

  2 nights

Secondary Outcomes (2)

• Patients' acceptance on the connected device

  2 nights

• Medical staff's satisfaction on both the device and the medical analysis platform (usability, data usage)

  5 days

Interventions

Wireless bed sensor DEVICE

Remote data collection

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

Male and female subjects aged ≥ 18 years fulfilling all of the inclusion criteria are eligible for the study. The presence of any one of the exclusion criteria will lead to exclusion of the participant.

You may qualify if:

• Suffering from chronic obstructive pulmonary disease (COPD I to IV) or other respiratory diseases generating dry or productive cough and hospitalized for a minimal duration of 5 days
• Living in the Canton of Neuchâtel
• Understanding French

You may not qualify if:

• Suffering from cognitive disorders
• Refractory to test new technologies

Sponsors & Collaborators

• Ligue Pulmonaire Neuchâteloise: lead
• Hopital Neuchatelois: collaborator
• Domo-Safety S.A.: collaborator
• Vivactis (Suisse) S.A.: collaborator

MeSH Terms

Conditions

Pulmonary Disease, Chronic Obstructive; Respiration Disorders; Cough

Condition Hierarchy (Ancestors)

Lung Diseases, Obstructive; Lung Diseases; Respiratory Tract Diseases; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Signs and Symptoms, Respiratory; Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 30, 2017
• First Posted: June 6, 2017
• Study Start: October 1, 2018
• Primary Completion: March 31, 2019
• Study Completion: May 1, 2019
• Last Updated: August 15, 2018

Record last verified: 2018-08