Accepted for/Published in: JMIR Research Protocols
Date Submitted: Jul 29, 2022
Open Peer Review Period: Jul 29, 2022 - Aug 10, 2022
Date Accepted: Sep 6, 2022
(closed for review but you can still tweet)
Measurement of Vital Signs by Lifelight® Software in Comparison to Standard of Care Multi-site Development (VISION-MD): observational study protocol
ABSTRACT
Background:
Measuring vital signs (VS) is an important aspect of clinical care but is time consuming and requires multiple pieces of equipment and trained staff. Interest in the contactless measurement of VS has grown since the COVID-19 pandemic, including for non-clinical situations. Lifelight® is an app being developed as a medical device for the contactless measurement of VS using remote photoplethysmography via the camera on smart devices such as phones and tablets. The VISION-D (Measurement of Vital Signs by Lifelight® Software in Comparison to the Standard of Care – Development )and -V (Validation) studies demonstrated the accuracy of Lifelight compared with standard of care measurement of blood pressure, pulse rate and respiratory rate, supporting certification of Lifelight as a class I Conformité Européenne (CE) medical device.
Objective:
To support further development of the Lifelight app, the observational VISION-MD (Multi-site Development) study (clinicaltrials.gov identifier NCTNCT04763746) is collecting high-quality data from a broad range of patients, including those with VS measurements outside the normal healthy range and critically ill patients.general practice; vital signs/methods; vital signs/standards; photoplethysmography; remote photoplethysmography; rPPG; Lifelight; contactless; software
Methods:
High-resolution videos of the face are being recorded using Lifelight software whilst simultaneously measuring VS using standard of care techniques (sphygmomanometer for blood pressure and pulse rate; manual counting of respiratory rate). Training data are being collected first and used to train the Lifelight algorithms to identify small areas of high-quality signal; the accuracy of the algorithms will then be evaluated using separate test data.
Results:
Recruitment began in May 2021 but was hindered by the restrictions instigated during the COVID-19 pandemic. The aim is to collect measurements from approximately 1950 participants (driven by the rate of machine learning). The analyses are expected to be completed in early 2023.
Conclusions:
Lifelight has the potential to bring about a paradigm shift in the way VS are monitored, in the clinic and in community settings. This study will support refinement of data collection and processing, in order to develop a robust app suitable for routine clinical use. Clinical Trial: https://clinicaltrials.gov/ct2/show/NCT04763746
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.