ouci.dntb.gov.ua Open in urlscan Pro
212.26.146.83  Public Scan

Form analysis
0 forms found in the DOM

Text Content

 * Search
 * Analytics
 * About
 * Українською
   


REAL-WORLD APPLICATION OF A SMARTPHONE-BASED VISUAL ACUITY TEST (WHOEYES) WITH
AUTOMATIC DISTANCE CALIBRATION

https://doi.org/10.1136/bjo-2023-324913 ·

Journal: British Journal of Ophthalmology, 2024, № 11, p. 1613-1620

Publisher: BMJ

Authors:

 1. Yi Wu
 2. Stuart Keel
 3. Vera Lúcia Alves Carneiro
 4. Shiran Zhang
 5. Wei Wang
 6. Chi Liu
 7. Xuanzhang Tang
 8. Xiaotong Han
 9. Mingguang He


ABSTRACT

<jats:sec> Background To develop and assess the usability of a smartphone-based
visual acuity (VA) test with an automatic distance calibration (ADC) function,
the iOS version of WHOeyes. </jats:sec> <jats:sec> Methods The WHOeyes was an
upgraded version with a distinct feature of ADC of an existing validated VA
testing app called V@home. Three groups of Chinese participants with different
ages (≤20, 20–40, &gt;40 years) were recruited for distance and near VA testing
using both an Early Treatment Diabetic Retinopathy Study (ETDRS) chart and the
WHOeyes. The ADC function would determine the testing distance. Infrared
rangefinder was used to determine the testing distance for the ETDRS, and actual
testing distance for the WHOeyes. A questionnaire-based interview was
administered to assess the satisfaction. </jats:sec> <jats:sec> Results The
actual testing distance determined by the WHOeyes ADC showed an overall good
agreement with the desired testing distance in all three age groups (p&gt;0.50).
Regarding the distance and near VA testing, the accuracy of WHOeyes was
equivalent to ETDRS. The mean difference between the WHOeyes and ETDRS ranged
from −0.084 to 0.012 logMAR, and the quadratic weighted kappa (QWK) values were
&gt;0.75 across all groups. The test–retest reliability of WHOeyes was high for
both near and distance VA, with a mean difference ranging from −0.040 to 0.004
logMAR and QWK all &gt;0.85. The questionnaire revealed an excellent user
experience and acceptance of WHOeyes. </jats:sec> <jats:sec> Conclusions WHOeyes
could provide accurate measurement of the testing distance as well as the
distance and near VA when compared to the gold standard ETDRS chart. </jats:sec>


FUNDERS

 1. National Natural Science Foundation of China
 2. Global STEM Professorship Scheme


LIST OF REFERENCES

 1.  Eckert, A simple method for estimating the economic cost of productivity
     loss due to blindness and moderate to severe visual impairment, Ophthalmic
     Epidemiol, № 22, с. 349
     https://doi.org/10.3109/09286586.2015.1066394
 2.  Bourne, Trends in prevalence of blindness and distance and near vision
     impairment over 30 years: an analysis for the global burden of disease
     study, Lancet Glob Health, № 9, с. e130
     https://doi.org/10.1016/S2214-109X(20)30425-3
 3.  Tsou, Visual acuity reporting in clinical research publications, JAMA
     Ophthalmol, № 135, с. 651
     https://doi.org/10.1001/jamaophthalmol.2017.0932
 4.  Suo, Use of mobile apps for visual acuity assessment: systematic review and
     meta-analysis, JMIR Mhealth Uhealth, № 10
     https://doi.org/10.2196/26275
 5.  Li, Prospective evaluation of teleophthalmology in screening and recurrence
     monitoring of neovascular age-related macular degeneration: a randomized
     clinical trial, JAMA Ophthalmol, № 133, с. 276
     https://doi.org/10.1001/jamaophthalmol.2014.5014
 6.  Alawa, Alleviating an increasingly burdened healthcare system with
     telemedicine: anterior segment, Ophthalmol Ther, № 10, с. 225
     https://doi.org/10.1007/s40123-021-00337-2
 7.  Zhao, Visual acuity assessment and vision screening using a novel
     smartphone application, J Pediatr, № 213, с. 203
     https://doi.org/10.1016/j.jpeds.2019.06.021
 8.  Steren, Visual acuity testing for telehealth using mobile applications,
     JAMA Ophthalmol, № 139, с. 344
     https://doi.org/10.1001/jamaophthalmol.2020.6177
 9.  Bastawrous, Development and validation of a smartphone-based visual acuity
     test (peek acuity) for clinical practice and community-based fieldwork,
     JAMA Ophthalmol, № 133, с. 930
     https://doi.org/10.1001/jamaophthalmol.2015.1468
 10. Han, Development and validation of a smartphone-based visual acuity test
     (vision at home), Transl Vis Sci Technol, № 8
     https://doi.org/10.1167/tvst.8.4.27
 11. Karampatakis, Design and validation of a novel smartphone-based visual
     acuity test: the K-VA test, Ophthalmol Ther, № 12, с. 1657
     https://doi.org/10.1007/s40123-023-00697-x
 12. Bellsmith, Validation of home visual acuity tests for telehealth in the
     COVID-19 era, JAMA Ophthalmol, № 140, с. 465
     https://doi.org/10.1001/jamaophthalmol.2022.0396
 13. Brady, Smartphone-based visual acuity measurement for screening and
     clinical assessment, JAMA, № 314, с. 2682
     https://doi.org/10.1001/jama.2015.15855
 14. WHO . Available:
     https://www.who.int/teams/noncommunicable-diseases/sensory-functions-disability-and-rehabilitation/whoeyes
 15. WHO . Available:
     https://www.who.int/multi-media/details/how-to-check-your-vision--use-the-whoeyes-app
 16. Mosa, A systematic review of healthcare applications for smartphones, BMC
     Med Inform Decis Mak, № 12
     https://doi.org/10.1186/1472-6947-12-67
 17. Martínez-Pérez, Mobile health applications for the most prevalent
     conditions by the world health organization: review and analysis, J Med
     Internet Res, № 15
     https://doi.org/10.2196/jmir.2600
 18. Andersen, Implementing a school vision screening program in Botswana using
     smartphone technology, Telemed J E Health, № 26, с. 255
     https://doi.org/10.1089/tmj.2018.0213
 19. Samanta, Is your vision blurry? A systematic review of home-based visual
     acuity for telemedicine, J Telemed Telecare, № 29, с. 81
     https://doi.org/10.1177/1357633X20970398
 20. Agarwal, Mobile app for improved self-management of type 2 diabetes:
     multicenter pragmatic randomized controlled trial, JMIR Mhealth Uhealth, №
     7
     https://doi.org/10.2196/10321
 21. Jiang, Automated calibration system for length measurement of lateral
     cephalometry based on deep learning, Phys Med Biol, № 67, с. 22
     https://doi.org/10.1088/1361-6560/ac9880
 22. Kawamoto, Visual acuity apps for rapid integration in teleconsultation
     services in all resource settings: a review, Asia Pac J Ophthalmol (Phila),
     № 10, с. 350
     https://doi.org/10.1097/APO.0000000000000384


PUBLICATIONS THAT CITE THIS PUBLICATION

STABLE trial of spectacle provision and driving safety among myopic motorcycle
users in Vietnam: study protocol for a stepped-wedge, cluster randomised trial

Vinh Chi Le, Kien Gia To, Van Dat Le, Le Nguyen, Graeme MacKenzie, Lovemore
Nyasha Sigwadhi, Prabhath Piyasena, Mai Tran, Ving Fai Chan, Rohit C. Khanna,
Mike Clarke, Lynne Lohfeld, Heather Dickey, Augusto Azuara-Blanco, Asha Latha
Mettla, Sridevi Rayasam, Han Thi Ngoc Doan, Dung Van Do, Phuoc Hong Le, Charlie
Klauer, Richard Hanowski, Zeb Bowden, Lynn Murphy, Joanne Thompson, Susan
McMullan, Cliona McDowell, Raja Narayanan, Julie-Anne Little, Huong Thu Ha,
Sangchul Yoon, Rahul Goel, Lan Luong, Xuan Nguyen, Nathan Congdon

https://doi.org/10.1186/s13063-024-08644-2 ·

2024, Trials, № 1

Scopus
WoS
Crossref citations: 0
Find all citations of the publication
About this publication

Publication type Журнальна стаття Number of citations 0 Number of works in the
list of references 22 Journal indexed in Scopus Yes Journal indexed in Web of
Science Yes

--------------------------------------------------------------------------------

Plum Print visual indicator of research metrics
Captures
 * Readers: 4

Social Media
 * Shares, Likes & Comments: 1


View details