Evidence map›Paper›PMID 38875693›Full record

ArticleJMIR biomedical engineering2023

Development and Testing of a Data Capture Device for Use With Clinical Incentive Spirometers: Testing and Usability Study.

Michael L Burns, Anik Sinha, Alexander Hoffmann, Zewen Wu, Tomas Medina Inchauste, Aaron Retsky, David Chesney, Sachin Kheterpal, Nirav Shah

Abstract read
In one paragraph

Article in JMIR biomedical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Michael L BurnsDepartment of Anesthesiology, University of Michigan Health System, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-5474-705X
Anik SinhaDepartment of Anesthesiology, University of Michigan Health System, Ann Arbor, MI, United States.ORCID https://orcid.org/0009-0001-9246-5942
Alexander HoffmannDepartment of Anesthesiology, University of Michigan Health System, Ann Arbor, MI, United States.ORCID https://orcid.org/0009-0003-9055-6992
Zewen WuDepartment of Anesthesiology, University of Michigan Health System, Ann Arbor, MI, United States.ORCID https://orcid.org/0009-0008-9651-4319
Tomas Medina InchausteDepartment of Anesthesiology, University of Michigan Health System, Ann Arbor, MI, United States.ORCID https://orcid.org/0009-0002-1971-3353
Aaron RetskyDepartment of Anesthesiology, University of Michigan Health System, Ann Arbor, MI, United States.ORCID https://orcid.org/0009-0000-5933-0395
David ChesneyDepartment of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States.ORCID https://orcid.org/0009-0003-6473-1953
Sachin KheterpalDepartment of Anesthesiology, University of Michigan Health System, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-6546-0273
Nirav ShahDepartment of Anesthesiology, University of Michigan Health System, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0001-7704-5141

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe incentive spirometer is a basic and common medical device from which electronic health care data cannot be directly collected. As a result, despite numerous studies investigating clinical use, there remains little consensus on optimal device use and sparse evidence supporting its intended benefits such as prevention of postoperative respiratory complications.

objectiveThe aim of the study is to develop and test an add-on hardware device for data capture of the incentive spirometer.

methodsAn add-on device was designed, built, and tested using reflective optical sensors to identify the real-time location of the volume piston and flow bobbin of a common incentive spirometer. Investigators manually tested sensor level accuracies and triggering range calibrations using a digital flowmeter. A valid breath classification algorithm was created and tested to determine valid from invalid breath attempts. To assess real-time use, a video game was developed using the incentive spirometer and add-on device as a controller using the Apple iPad.

resultsIn user testing, sensor locations were captured at an accuracy of 99% (SD 1.4%) for volume and 100% accuracy for flow. Median and average volumes were within 7.5% (SD 6%) of target volume sensor levels, and maximum sensor triggering values seldom exceeded intended sensor levels, showing a good correlation to placement on 2 similar but distinct incentive spirometer designs. The breath classification algorithm displayed a 100% sensitivity and a 99% specificity on user testing, and the device operated as a video game controller in real time without noticeable interference or delay.

conclusionsAn effective and reusable add-on device for the incentive spirometer was created to allow the collection of previously inaccessible incentive spirometer data and demonstrate Internet-of-Things use on a common hospital device. This design showed high sensor accuracies and the ability to use data in real-time applications, showing promise in the ability to capture currently inaccessible clinical data. Further use of this device could facilitate improved research into the incentive spirometer to improve adoption, incentivize adherence, and investigate the clinical effectiveness to help guide clinical care.

Indexed as

algorithmdata analysisdata collectioneffectivenesselectronic health recordsincentiveincentive spirometerInternet-of-Thingsmedical devicemedical toolspirometryspirometry dataweb-based intervention

Identifiers

PMID38875693
PMCPMC11041496

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.