Clinical trial NCT06681467: Non-invasive Methods of Measuring Lung Volume
Official title: An Investigation Into the Measurement of Lung Volume by Analysis of Breathing Parameters Using Non-invasive Wearable Devices
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Respiration Disorders
Keywords: chest band
wearable monitoring device
pulmonary function test
tidal volume
machine learning
Lead sponsor: University Hospital Southampton NHS Foundation Trust
Sponsor class: OTHER
Collaborators: name: University of Southampton
First posted: 2024-11-08
Enrollment: 50
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: BASIC_SCIENCE

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.

Detailed description: The study design was chosen so that the investigators can directly compare results produced from wearable respiratory devices to spirometry. In order to do this in a protocolised manner, pulmonary function testing (PFT) clinic was chosen as the location of the study. Prior to the protocol being agreed, this was discussed and approved by the medical and nursing team in charge of the pulmonary function clinic. The investigators also approached patients in PFT clinic to discuss the proposed protocol and if they felt it was suitable for someone attending the clinic. The investigators received positive responses form this and any comments were considered in the final protocol.

Potential participants will be identified when they are booking into their clinic appointment. All patients will be attending PFT clinic as part of their clinical care; none will be attending as part of the study only. Patients will be asked if they would like to participate in the study after receiving the patient information leaflet and given time to ask any questions. The investigators aim to recruit 50 patients. This was deemed a suitable number to be able to undertake machine learning analysis with sufficient data. The investigators aim to recruit these 50 patients by attending PFT clinic weekly for approximately 3 months. They will vary the days of the week/clinic times they attend to gain a range of different patient groups as PFT clinics vary across the week.

After participants are fully consented, baseline data will be collected. This will involve a medical questionnaire that they will be asked to complete. The following data will be collected using the questionnaire:

Patient demographics: Age, sex, height, weight Comorbidities including past medical and surgical history Smoking status Alcohol status Current medications used Allergy status Reason for spirometry testing

Once the questionnaire is completed the participants will start the measurement section of the study. They will have two respiratory measurement devices attached. The Go Direct Respiration Sensor and a biosignal respiratory belts will both be fitted over light clothing so that participants will wear both sensors at once. They work in different ways but both use chest movement during breathing to determine respiratory effort and respiratory rate. They fit as bands around the chest. The investigators will aim to place the devices in similar positions in all participants (around the centre of the chest at the breast bone). Before confirming their placement, participants will be asked if the devices are comfortable and allow for normal breathing. These devices will track chest movement for the duration of the spirometry test and during timed activities of sitting, speaking, and walking. Once fitted, participants will continue with their spirometry appointment as planned. Some results from their spirometry testing will be noted in a secure database. This will include their inspiratory reserve volume, expiratory reserve volume, tidal volume and forced expiratory volume. The values alone will be recorded with no other patient data from the clinic appointment. At the end of the appointment, once they are back at baseline breathing, participants will be asked to read a short standardised script (in the form of a simple poem) over the course of 1-2 minutes that will allow us to analyse breathing patterns during speech. This will be coordinated with audio recordings of the speech which will be used in later data analysis of speech breathing. This may occur in the clinic room or another quiet room opposite the clinic depending on clinic timings. The final task before removal of the devices will be a short walk down the corridor outside of the clinical room to assess breathing during light exercise. This will be a set distance of 75 meters and should take around 1 minute to complete. Variability in timing of the walk will be accepted due to different patient walking speeds. The total data collection will take approximately 30 minutes. There should not be any additional clinic time required as the speech assessment and walking assessment can be undertaken outside of the clinic room. When the devices are removed, participants will be asked if they wish to comment on anything they felt during the study including discomfort from wearing the devices. The spirometry data will be collected by the PFT team as part of their routine testing and as such should not be influenced by the study. The researcher will not be involved in the spirometry testing portion of the study and therefore should not bias any results obtained.

Once the bands are removed the data will be downloaded onto a secure laptop with all patient identifying information removed. Following the collection of data for all 50 patients, the investigators aim to spend a further 1-2 months analysing the data and comparing it to the spirometry results. The final report will be available within 24 months of the study start date.

There will not be any planned interim analysis.

Eligibility criteria: Inclusion Criteria:

* 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

Exclusion Criteria:

* 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

Primary outcomes:
measure: Data extraction from respiratory band devices using machine learning modelling
timeFrame: From enrollment to one year post data collection to allow for data extraction and analysis time
measure: Measurement of Tidal volume (in mL) using machine learning techniques
timeFrame: From enrollment to one year post data collection to allow for data extraction and analysis time
measure: Measurement of Inspiratory Reserve Volume (in L) using machine learning techniques
timeFrame: From enrollment to one year post data collection to allow for data extraction and analysis time
measure: Measurement of Expiratory reserve volume (in L) using machine learning techniques
timeFrame: From enrollment to one year post data collection to allow for data extraction and analysis time
measure: Measurement of Forced vital capacity (in L) using machine learning techniques
timeFrame: From enrollment to one year post data collection to allow for data extraction and analysis time
Secondary outcomes:
measure: Accuracy and reliability of the respiratory parameters formed using machine learning techniques in comparison to spirometry
timeFrame: From enrolment to one year post data collection to allow for data extraction and analysis time
measure: Direct comparison of machine learning results formed from the two devices against spirometry
timeFrame: From enrollment to one year post data collection to allow for data extraction and analysis time
measure: Analysis of how breathing patterns and respiratory volumes change with speech using data collected from two wearable respiratory devices
timeFrame: From enrollment to one year post data collection to allow for data extraction and analysis time
measure: Analysis of different disease severities and patient demographics and their impact on non-invasive breathing measurement
timeFrame: From enrollment to one year post data collection to allow for data extraction and analysis time
Other outcomes:
Locations:
city: Southampton
country: United Kingdom
facility: PFT
Study officials:
References:
citation: Mateu-Mateus, M., et al., Camera-Based Method for Respiratory Rhythm Extraction From a Lateral Perspective. IEEE Access, 2020. 8: p. 154924-154939.
citation: Lin, Y.-A., et al., Respiration Monitoring using a Motion Tape Chest Band and Portable Wireless Sensing Node. Journal of Commercial Biotechnology, 2022. 27.
citation: 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.
citation: 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.
citation: 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.
citation: Pierce R. Spirometry: an essential clinical measurement. Aust Fam Physician. 2005 Jul;34(7):535-9.
citation: Flesch JD, Dine CJ. Lung volumes: measurement, clinical use, and coding. Chest. 2012 Aug;142(2):506-510. doi: 10.1378/chest.11-2964.
citation: 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.