Validation of the New York University Langone Eye Test Application, a Smartphone-Based Visual Acuity Test

Mina Iskander, Galen Hu, Shefali Sood, Noah Heilenbach, Victor Sanchez, Titilola Ogunsola, Dinah Chen, Ceyhun Elgin, Vipul Patel, Andrew Wronka, Lama A Al-Aswad, Mina Iskander, Galen Hu, Shefali Sood, Noah Heilenbach, Victor Sanchez, Titilola Ogunsola, Dinah Chen, Ceyhun Elgin, Vipul Patel, Andrew Wronka, Lama A Al-Aswad

Abstract

Purpose: To validate and assess user satisfaction and usability of the New York University (NYU) Langone Eye Test application, a smartphone-based visual acuity (VA) test.

Design: Mixed-methods cross-sectional cohort study.

Participants: Two hundred forty-four eyes of 125 participants were included. All participants were adults 18 years of age or older. Participants' eyes with a VA of 20/400 (1.3 logarithm of the minimum angle of resolution [logMAR]) or worse were excluded.

Methods: Patients were tested using the clinical standard Rosenbaum near card and the NYU Langone Eye Test application on an iPhone and Android device. Each test was performed twice to measure reliability. Ten patients were selected randomly for subsequent semistructured qualitative interviews with thematic analysis.

Main outcome measures: Visual acuity was the parameter measured. Bland-Altman analysis was used to measure agreement between the results of the NYU Langone Eye Test application and Rosenbaum card, as well as test-retest reliability of each VA. The correlation between results was calculated using the intraclass correlation coefficient. Satisfaction survey and semistructured interview questions were developed to measure usability and acceptability.

Results: Bland-Altman analysis revealed an agreement between the application and the Rosenbaum near card of 0.017 ± 0.28 logMAR (iPhone) and 0.009 ± 0.29 logMAR (Android). The correlation between the application and the Rosenbaum near card was 0.74 for both the iPhone and Android. Test-retest reliability was 0.003 ± 0.22 logMAR (iPhone), 0.01 ± 0.25 logMAR (Android), and 0.01 ± 0.23 logMAR (Rosenbaum card). Of the 125 participants, 97.6% found the application easy to use, and 94.3% were overall satisfied with the application. Thematic analysis yielded 6 key themes: (1) weaknesses of application, (2) benefits of the application, (3) tips for application improvement, (4) difficulties faced while using the application, (5) ideal patient for application, and (6) comparing application with traditional VA testing.

Conclusions: The NYU Langone Eye Test application is a user-friendly, accurate, and reliable measure of near VA. The application's integration with the electronic health record, accessibility, and easy interpretation of results, among other features, make it ideal for telemedicine use.

Keywords: NYU, New York University; Ophthalmology; Smartphone-based visual acuity test; Telemedicine; Teleophthalmology; VA, visual acuity; Visual acuity; logMAR, logarithm of the minimum angle of resolution.

© 2022 by the American Academy of Ophthalmology.

Figures

Figure 3
Figure 3
Diagram showing visual acuity testing workflow.
Figure 4
Figure 4
A, Bland–Altman plot showing agreement between the New York University Langone Eye Test application on iPhone and the Rosenbaum card of 0.017 ± 0.28 logarithm of the minimum angle of resolution (logMAR). B, Correlation graph between iPhone and Rosenbaum visual acuity measurements. Intraclass correlation coefficient was 0.74.
Figure 5
Figure 5
A, Bland–Altman plot showing agreement between the New York University Langone Eye Test application on Android and the Rosenbaum card of 0.009 ± 0.29 logarithm of the minimum angle of resolution (logMAR). B, Correlation graph between Android and Rosenbaum visual acuity measurements. Intraclass correlation coefficient was 0.74.
Figure 6
Figure 6
A, Bland–Altman plot showing agreement between the Rosenbaum card and its retest of 0.01 ± 0.23 logarithm of the minimum angle of resolution (logMAR). B, Correlation graph between Rosenbaum card and retest visual acuity measurements. Intraclass correlation coefficient was 0.85.
Figure 7
Figure 7
A, Bland-Altman plot showing test–retest variability of the New York University (NYU) Langone Eye Test on iPhone of 0.003 ± 0.22 logarithm of the minimum angle of resolution (logMAR). B, Bland–Altman plot showing test–retest variability of the NYU Langone Eye Test on Android of 0.01 ± 0.25 logMAR. C, Bland–Altman plot showing test–retest variability of the Rosenbaum card of 0.01 ± 0.22 logMAR.

References

    1. Kniestedt C., Stamper R.L. Visual acuity and its measurement. Ophthalmol Clin North Am. 2003;16(2):155–170. , v.
    1. Han X., Scheetz J., Keel S., et al. Development and validation of a smartphone-based visual acuity test (vision at home) Transl Vis Sci Technol. 2019;8(4):27.
    1. Tofigh S., Shortridge E., Elkeeb A., Godley B.F. Effectiveness of a smartphone application for testing near visual acuity. Eye (Lond) 2015;29(11):1464–1468.
    1. Benjamin W.J. Elsevier Health Sciences; 2006. Borish’s Clinical Refraction-E-Book.
    1. Phung L., Gregori N.Z., Ortiz A., et al. Reproducibility and comparison of visual acuity obtained with sightbook mobile application to near card and Snellen chart. Retina. 2016;36(5):1009–1020.
    1. Perera C., Chakrabarti R., Islam F.M., Crowston J. The Eye Phone Study: reliability and accuracy of assessing Snellen visual acuity using smartphone technology. Eye (Lond) 2015;29(7):888–894.
    1. Steren B.J., Young B., Chow J. Visual acuity testing for telehealth using mobile applications. JAMA Ophthalmol. 2021;139(3):344–347.
    1. Samanta A., Mauntana S., Barsi Z., et al. Is your vision blurry? A systematic review of home-based visual acuity for telemedicine. J Telemed Telecare. 2020
    1. Zhou L., Bao J., Setiawan I.M.A., et al. The mHealth App Usability Questionnaire (MAUQ): development and validation study. JMIR mHealth and uHealth. 2019;7(4):e11500–e.
    1. Michie S., van Stralen M.M., West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. 2011;6:42.
    1. Braun V., Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101.
    1. Rosser D.A., Cousens S.N., Murdoch I.E., et al. How sensitive to clinical change are ETDRS logMAR visual acuity measurements? Invest Ophthalmol Vis Sci. 2003;44(8):3278–3281.
    1. Tiraset N., Poonyathalang A., Padungkiatsagul T., et al. Comparison of visual acuity measurement using three methods: standard ETDRS chart, near chart and a smartphone-based eye chart application. Clin Ophthalmol. 2021;15:859–869.
    1. Cooke M.D., Winter P.A., McKenney K.C., et al. An innovative visual acuity chart for urgent and primary care settings: validation of the Runge near vision card. Eye (Lond) 2019;33(7):1104–1110.
    1. Lim L.A., Frost N.A., Powell R.J., Hewson P. Comparison of the ETDRS logMAR, ‘compact reduced logMar’ and Snellen charts in routine clinical practice. Eye (Lond). 2010;24(4):673–677. doi: 10.1038/eye.2009.147.
    1. Ruamviboonsuk P., Tiensuwan M., Kunawut C., Masayaanon P. Repeatability of an automated Landolt C test, compared with the early treatment of diabetic retinopathy study (ETDRS) chart testing. Am J Ophthalmol. 2003;136(4):662–669.
    1. Lovie-Kitchin J.E. Validity and reliability of visual acuity measurements. Ophthalmic Physiol Opt. 1988;8(4):363–370.
    1. Chaikitmongkol V., Nanegrungsunk O., Patikulsila D., et al. Repeatability and agreement of visual acuity using the ETDRS number chart, Landolt C chart, or ETDRS alphabet chart in eyes with or without sight-threatening diseases. JAMA Ophthalmol. 2018;136(3):286–290.

Source: PubMed

3
Subscribe