A Structured Approach to the Development and Evaluation of a Virtual Reality Eye Examination Simulation

Figures & data

Figure 1. Examples of the VR app and educational scenes [A] app, phone and Google Cardboard^TM, [B] red reflex, [C] haemorrhage and [D] quiz.

Table 1. SUS statements.

Positive	Negative
S1. I think I would like to use this technology frequently	S2. I found the technology unnecessarily complex
S3. I thought that the technology was easy to use	S4. I think that I would need support of a technical person to use this  technology
S5. I found the various functions were well integrated	S6. I thought there was too much inconsistency in this technology
S7. I would imagine most people would learn to use this technology very  quickly	S8. I found this technology to be cumbersome/awkward to use
S9. I felt very confident using the technology	S10. I needed to learn a lot of things before I could get going with this  technology

Table 2. TA statements.

Ability to Perform Task	Ease of Use
T1. The app improves my understanding of the processes involved in ophthalmoscopy	T5. Learning to use the app would be easy for me
T2. The app improves my ability to identify the main landmarks in the eye	T6. I would find it easy to control the app so it will do what I want it to
T3. The app improves my ability to recognise abnormalities within the eye	T7. I would find it easy to understand how to use the app in future
T4. The app will give me the confidence to perform this task on a person in future	T8. Overall I find it easy to use the app

Table 3. Model summary table.

Model Summary^b
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Change Statistics
					R Square Change	F Change	df1	df2	Sig. F Change	Durbin-Watson
1	0.627^a	0.394	0.329	0.51442	0.394	6.058	3	28	0.003	1.635

^a Predictors: (Constant), EOU_sum, SUS_sum, APT_sum

^bDependent Variable: VR ACCEPTANCE.

Table 4. Regression model.

ANOVA^a
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	4.809	3	1.603	6.058	.003^b
	Residual	7.410	28	0.265
	Total	12.219	31

^a Dependent Variable: VR ACCEPTANCE ^bPredictors: (Constant), EOU_sum, SUS_sum, APT_sum.

Table 5. Coefficients.

Coefficients^a
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	95.0% Confidence Interval for B
		B	Std. Error	Beta			Lower Bound	Upper Bound
1	(Constant)	−0.107	1.357		−0.079	0.938	−2.887	2.674
	SUS_sum	0.310	0.347	0.132	0.893	0.379	−0.401	1.021
	PU_sum	0.071	0.204	0.058	0.345	0.732	−0.348	0.490
	EOU_sum	0.727	0.212	0.580	3.431	0.002	0.293	1.161

^a Dependent Variable: VR ACCEPTANCE.

Table 6. Breakdown of test scores.

Group	Pre-test score	Post-test score
All (n:27)	6.3 ± 1.5	8.4 ± 0.8***
Medical students (n:20)	6.2 ± 1.5	8.5 ± 0.9***
Doctors (n:7)	6.4 ± 1.5	8.3 ± 0.8*
Digital literacy
Weak (n:3)	6.3 ± 1.5	8.7 ± 0.6
Neutral (n:6)	6.7 ± 1.0	8.5 ± 1.0**
Strong & very strong (n: 18)	6.1 ± 1.6	8.4 ± 0.8***
Play video games
Never (n:18)	6.3 ± 1.3	8.3 ± 0.9***
Monthly or less (n:5)	6.0 ± 2.1	8.8 ± 0.8*
More than monthly (n:4)	6.3 ± 1.5	8.5 ± 0.6*
Previous experience of VR education apps
Yes (n:4)	5.0 ± 1.4	8.0 ± 0.8**
No (n:23)	6.5 ± 1.4	8.5 ± 0.8***
Enthusiasm for leaning new technology in clinical skills training
Neutral (n:3)
	4.7 ± 0.6	7.7 ± 0.6
Agree/Strongly agree (n:24)
	6.5 ± 1.4	8.5 ± 0.83***
Age
18–21 (n:10)	5.6 ± 1.5	8.3 ± 0.8***
22–24 (n:7)	7.1 ± 1.2	8.9 ± 1.9**
25–27 (n:8)	6.3 ± 1.5	8.3 ± 0.9**
28+ (n:2)	6.5 ± 0.7	8.5 ± 0.7
Gender
Male (n:11)	6.2 ± 1.8	8.6 ± 0.7***
Female (n:16)	6.3 ± 1.3	8.3 ± 0.9***

* p < 0.05.

** p < 0.01 .

*** p < 0.001.

Figure 2. Flow diagram of development process for medical simulations.

Table 7. Comparison and conformance for software development frameworks (Hesari et al., 2010).

Criteria for Software Development Frameworks	Conformance of the framework to these criteria
A sequence of software development activities	Present
Coverage of requirements, analysis, design, implementation, and testing	Present
Seamless transition between phases above	Present, with varying overlap between phases
Modelling language	Present through MVPs and physical entities
Managing and directing team efforts	Present through maintaining documentation
Risk, people and project management	Present
Monitoring and assessing project activities	Product tangibility, testability and visibility are supported
Quality management	Present through continuous user involvement
Configuration management	Not present

Hesari, S., Mashayekhi, H., & Ramsin, R. (2010). Towards a General Framework for Evaluating Software Development Methodologies [Paper presentation].34th COMPSAC, (pp 208–217). IEEE, Seoul. https://doi.org/10.1109/COMPSAC.2010.69

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Data availability statement

All data generated or analysed during this study are included in this published article.