Evidence map›Paper›PMID 29674844›Full record

ArticlePatient preference and adherence2018

Evaluation of performance, acceptance, and compliance of an auto-injector in healthy and rheumatoid arthritic subjects measured by a motion capture system.

Xiao Xiao, Wei Li, Corbin Clawson, David Karvani, Perceval Sondag, James K Hahn

Open access · goldAbstract read
In one paragraph

Article in Patient preference and adherence, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
3.0field-weighted citation impact, top 8% of its field
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

10 citing papers in PubMed, 18 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Autoinjector - A smart device for emergency cum personal therapy.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2021
    Review
  6. Article
  7. Observational
  8. Automated Assessment System for Neonatal Endotracheal Intubation Using Dilated Convolutional Neural Network.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020
    Article
  9. Automated Assessment of Neonatal Endotracheal Intubation Measured by a Virtual Reality Simulation System.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020
    Article
  10. A Physics-based Virtual Reality Simulation Framework for Neonatal Endotracheal Intubation.Proceedings. IEEE Conference on Virtual Reality and 3D User Interfaces · 2020
    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

6 authors at 2 institutions in 1 country.

Xiao XiaoDepartment of Computer Science, The George Washington University, Washington, DC.
Wei LiDepartment of Computer Science, The George Washington University, Washington, DC.
Corbin ClawsonMedImmune, LLC, Gaithersburg, MD.
David KarvaniMedImmune, LLC, Gaithersburg, MD.
Perceval SondagArlenda, Inc., Flemington, NJ.
James K HahnDepartment of Computer Science, The George Washington University, Washington, DC.
George Washington University · USArno Therapeutics (United States) · US

Funding

Calculation of Percent Body Fat by Analyzing Virtual Body ModelsR21HL124443 · NHLBI · GEORGE WASHINGTON UNIVERSITY · PI HAHN, JAMES K · 2015 to 2016
$422k
NHLBI NIH HHS R21 HL124443
6 · The paper itself

Abstract

purposeThe study aimed to develop a motion capture system that can track, visualize, and analyze the entire performance of self-injection with the auto-injector.

methodsEach of nine healthy subjects and 29 rheumatoid arthritic (RA) patients with different degrees of hand disability performed two simulated injections into an injection pad while six degrees of freedom (DOF) motions of the auto-injector and the injection pad were captured. We quantitatively measured the performance of the injection by calculating needle displacement from the motion trajectories. The max, mean, and SD of needle displacement were analyzed. Assessments of device acceptance and usability were evaluated by a survey questionnaire and independent observations of compliance with the device instruction for use (IFU).

resultsA total of 80 simulated injections were performed. Our results showed a similar level of performance among all the subjects with slightly larger, but not statistically significant, needle displacement in the RA group. In particular, no significant effects regarding previous experience in self-injection, grip method, pain in hand, and Cochin score in the RA group were found to have an impact on the mean needle displacement. Moreover, the analysis of needle displacement for different durations of injections indicated that most of the subjects reached their personal maximum displacement in 15 seconds and remained steady or exhibited a small amount of increase from 15 to 60 seconds. Device acceptance was high for most of the questions (ie, >4; >80%) based on a 0-5-point scale or percentage of acceptance. The overall compliance with the device IFU was high for the first injection (96.05%) and reached 98.02% for the second injection.

conclusionWe demonstrated the feasibility of tracking the motions of injection to measure the performance of simulated self-injection. The comparisons of needle displacement showed that even RA patients with severe hand disability could properly perform self-injection with this auto-injector at a similar level with the healthy subjects. Finally, the observed high device acceptance and compliance with device IFU suggest that the system is convenient and easy to use.

Indexed as

auto-injectionCochin scorehand disabilitymotion trackingrheumatoid arthritissubcutaneous injection

Identifiers

PMID29674844
PMCPMC5898887
OpenAlexW2795999530

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.