Non-invasive Methods of Measuring Lung Volume

NCT06681467 | Completed

• 2 other identifiers: RHM CRI0461349993
• Study Type: interventional
• Target: 50
• Locations: 1 country
• Sites: 1

Brief Summary

Each breath humans take can be split into different measurements that clinicians can use to see how well a patient's lungs are working. Clinicians take these measurements to see how the lungs of patients with conditions such as asthma, chronic obstructive pulmonary disease or other muscle problems are affected. This also allows us to monitor how a patient's disease changes over time. At present, to measure lung volumes patients need to attend a clinic appointment and complete a test called spirometry. This takes both time and effort for patients and not all will be able to attend. There are simple devices available that can be attached to patients which measure breathing parameters such as breathing rate. Many different devices are available to do this; a common version is a chest band. These comprise of a tight-fitting band that is placed around the centre of the chest and as patients breathe in and out, the band stretches and contracts. The force of this stretching and contraction can be measured and turned in to a continuous breathing rate. Although this is useful, there is no device that can currently measure lung volumes as well as spirometry can. Therefore, the investigators will use software analysis to change data collected from two different chest bands to make the measurements comparable to spirometry testing. Doing this could mean that patients could test their breathing at home and any problems be picked up sooner. It would also help patients be more involved in the care of their breathing and may lead to earlier treatments. Our study is the first stage in developing this device, but the investigators hope that it will help with other research later.

Conditions

Respiration Disorders

Keywords

chest band; wearable monitoring device; pulmonary function test; tidal volume; machine learning

Outcome Measures

Primary Outcomes (5)

• Data extraction from respiratory band devices using machine learning modelling

  Data will be extracted using machine learning modelling to form respiratory parameters

  From enrollment to one year post data collection to allow for data extraction and analysis time

• Measurement of Tidal volume (in mL) using machine learning techniques

  Tidal volume will be extracted from respiratory band devices using machine learning techniques

  From enrollment to one year post data collection to allow for data extraction and analysis time

• Measurement of Inspiratory Reserve Volume (in L) using machine learning techniques

  Inspiratory reserve volume will be extracted from respiratory band devices using machine learning techniques

  From enrollment to one year post data collection to allow for data extraction and analysis time

• Measurement of Expiratory reserve volume (in L) using machine learning techniques

  Expiratory reserve volume will be extracted from respiratory band devices using machine learning techniques

  From enrollment to one year post data collection to allow for data extraction and analysis time

• Measurement of Forced vital capacity (in L) using machine learning techniques

  Forced vital capacity will be extracted from respiratory band devices using machine learning techniques

  From enrollment to one year post data collection to allow for data extraction and analysis time

Secondary Outcomes (4)

• Accuracy and reliability of the respiratory parameters formed using machine learning techniques in comparison to spirometry

  From enrolment to one year post data collection to allow for data extraction and analysis time

• Direct comparison of machine learning results formed from the two devices against spirometry

  From enrollment to one year post data collection to allow for data extraction and analysis time

• Analysis of how breathing patterns and respiratory volumes change with speech using data collected from two wearable respiratory devices

  From enrollment to one year post data collection to allow for data extraction and analysis time

• Analysis of different disease severities and patient demographics and their impact on non-invasive breathing measurement

  From enrollment to one year post data collection to allow for data extraction and analysis time

Study Arms (1)

Patients undergoing planned pulmonary function testing

EXPERIMENTAL

Participants will be recruited from patients attending a planned pulmonary function clinic appointment. They will be invited to participate when they are booking into clinic. Should they agree to participate the will have the following interventions: 1. Basic medical questionnaire 2. 2x chest band devices fitted over clothing They will then undertake their planned clinic appointment. No additional resources will be required for this. Once they have finished their clinic appointment, the investigators will ask them to read a short script whilst recording their speech. Following this the investigators will ask them to walk 75m down the hall. The devices will then be removed and the study time is over.

Other: Respiratory monitoring band

Interventions

Respiratory monitoring band OTHER

The only intervention in this study is the application of two CE marked study approved chest bands- the Go direct respiratory sensor and a biosignal respiration belt

Also known as: Go Direct Respiration Sensor, biosignal respiratory belt

Patients undergoing planned pulmonary function testing

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Subject: Human participants
• Gender: Any
• Aged 18 years and over.
• Able to give informed consent in English.
• Physically able to take part including a simple walking exercise
• Either in a asymptomatic participant group or planned for spirometry testing

You may not qualify if:

• Significant chest deformity or having a medical device fitted in (e.g. Implantable cardioverter defibrillator (ICD), Spinal cord stimulator, Pacemaker, etc)
• Pregnant
• Unable/uncomfortable to use a chest belt device for any reason.
• Patients \<18 years old
• Unable to read and speak in English to an understandable level
• Unable to walk (aided or unaided) for 1 minute

Sponsors & Collaborators

• University Hospital Southampton NHS Foundation Trust: lead
• University of Southampton: collaborator

Study Sites (1)

PFT

Southampton, United Kingdom

Location

Related Publications (8)

• Mateu-Mateus, M., et al., Camera-Based Method for Respiratory Rhythm Extraction From a Lateral Perspective. IEEE Access, 2020. 8: p. 154924-154939.

  BACKGROUND

• Lin, Y.-A., et al., Respiration Monitoring using a Motion Tape Chest Band and Portable Wireless Sensing Node. Journal of Commercial Biotechnology, 2022. 27.

  BACKGROUND

• Ross R, Mongan WM, O'Neill P, Rasheed I, Fontecchio A, Dion G, Dandekar KR. An Adaptively Parameterized Algorithm Estimating Respiratory Rate from a Passive Wearable RFID Smart Garment. Proc COMPSAC. 2021 Jul;2021:774-784. doi: 10.1109/COMPSAC51774.2021.00110. Epub 2021 Sep 9.

  PMID: 34568878 BACKGROUND

• Vitazkova D, Foltan E, Kosnacova H, Micjan M, Donoval M, Kuzma A, Kopani M, Vavrinsky E. Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies. Biosensors (Basel). 2024 Feb 6;14(2):90. doi: 10.3390/bios14020090.

  PMID: 38392009 BACKGROUND

• Brochard L, Martin GS, Blanch L, Pelosi P, Belda FJ, Jubran A, Gattinoni L, Mancebo J, Ranieri VM, Richard JC, Gommers D, Vieillard-Baron A, Pesenti A, Jaber S, Stenqvist O, Vincent JL. Clinical review: Respiratory monitoring in the ICU - a consensus of 16. Crit Care. 2012 Dec 12;16(2):219. doi: 10.1186/cc11146.

  PMID: 22546221 BACKGROUND

• Pierce R. Spirometry: an essential clinical measurement. Aust Fam Physician. 2005 Jul;34(7):535-9.

  PMID: 15999163 BACKGROUND

• Flesch JD, Dine CJ. Lung volumes: measurement, clinical use, and coding. Chest. 2012 Aug;142(2):506-510. doi: 10.1378/chest.11-2964.

  PMID: 22871760 BACKGROUND

• Bhakta NR, McGowan A, Ramsey KA, Borg B, Kivastik J, Knight SL, Sylvester K, Burgos F, Swenson ER, McCarthy K, Cooper BG, Garcia-Rio F, Skloot G, McCormack M, Mottram C, Irvin CG, Steenbruggen I, Coates AL, Kaminsky DA. European Respiratory Society/American Thoracic Society technical statement: standardisation of the measurement of lung volumes, 2023 update. Eur Respir J. 2023 Oct 12;62(4):2201519. doi: 10.1183/13993003.01519-2022. Print 2023 Oct.

  PMID: 37500112 BACKGROUND

Related Links

• Go direct respiration belt
• Biosignal sensor belt

MeSH Terms

Conditions

Respiration Disorders

Condition Hierarchy (Ancestors)

Respiratory Tract Diseases

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: BASIC SCIENCE
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 4, 2024
• First Posted: November 8, 2024
• Study Start: November 20, 2024
• Primary Completion: May 19, 2025
• Study Completion: May 19, 2025
• Last Updated: May 22, 2025

Record last verified: 2024-11

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)