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Augmented learning environment for wound care simulation

#eden16 EDEN Annual conference . Budapest, 15 June 2016
by

Nelson Jorge

on 15 June 2016

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Transcript of Augmented learning environment for wound care simulation

Augmented learning environment for wound care simulation
Student's performance
quasi-experimental study
e-Fer platform
e-Fer platform
Nelson Jorge, Delft University of Technology
Lina Morgado, Universidade Aberta
Pedro Gaspar, Politécnico de Leiria

EDEN Annual Conference
Budapest, 15 June 2016
Data extracted from e-FER
e-Fer platform
e-Fer platform
with AR
Grupo de controlo (n=24)
Grupo de experimental (n=30)
To investigate the effect of Augmented Reality (AR) in nursing student’s decision making skills about wound care, by comparing the usage of the e-FER simulator with and without the support of AR to visualize the wounds.
Research goal
1st moment
(2 weeks)
Mann-Whitney U test
GC
GC
GE
GE
Mann-Whitney U test
GC
GC
GE
GE
Correct answers in the diagnosis
Correct answers in the treatment
Wilcoxin test
Control group
1ºM
2ºM
1ºM
2ºM
Wilcoxin test
Experimental group
1ºM
2ºM
1ºM
2ºM
Conclusion
AR improved participants performance in the diagnosis, when compared to using the traditional version of e-FER, with statistically significant differences (p<0,01) in the Mann-Whitney U and Wilcoxon tests.
Thank you.
n.ribeirojorge@tudelft.nl

http://prezi.com/user/nelsonrjorge/
AUGMENTED REALITY
Technology that allows the integration of virtual objects into the physical real world, supplementing it in a way that they seem to coexist in the same space (Zhou, Duh & Billinghurst, 2008).

The combination of real with virtual, real time interactivity and three dimensional (3D) virtual content are the three commonly accepted characteristics of AR systems (Azuma, 1997).
Usability

Motivation
Assessment
Questionnaire
Questionnaire
6 new clinical cases produced using:

Autodesk® 123D® Catch, a software that generates 3D objects based on several pictures taken from different angles.
Control group (n=24)
Experimental group (n=30)
2nd moment
(2 weeks)
1st moment (p=0,89)
2nd moment (p<0,01)
1st moment (p=0,63)
2nd moment (p=0,09)
Diagnosis (p=0,06)
Treatment (p=0,23)
Diagnosis (p<0,01)
Treatment (p=0,44)
In general, AR has shown to be an effective tool to develop clinical skills when compared with other methods, with a greater impact on inexperienced learners, and its transfer to real world scenarios.
Literature review
The main goal is to promote the healing of the patient’s wound by selecting the best diagnosis and treatment solution, in this order. The effectiveness of e-FER was demonstrated by Costa (2010).
Online clinical decision-making simulator used in the initial training of nurses, allowing to simulate the diagnosis and treatment of virtual clinical cases of chronic wounds.
e-FER
How would you use AR?
ViewAR, a software that uses printed markers to show the 3D objects in AR, when detected by an iPad with the application installed.
Full transcript