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. 2021 Sep 22:15:3915-3929.
doi: 10.2147/OPTH.S314943. eCollection 2021.

Development and Validation of a Web-Based Reading Test for Normal and Low Vision Patients

Georgios Labiris  1 , Eirini-Kanella Panagiotopoulou  1 , Erald Duzha  2 , Maria Tzinava  2 , Asli Perente  1 , Aristeidis Konstantinidis  1 , Konstantinos Delibasis  2
Affiliations

Development and Validation of a Web-Based Reading Test for Normal and Low Vision Patients

Georgios Labiris et al. Clin Ophthalmol. .

Abstract

Purpose: To develop and validate a web-based reading test for normal and low vision patients.

Methods: This is a prospective, comparative trial. The web-based Democritus Digital Acuity Reading Test (wDDART) was developed. wDDART introduces a series of advanced characteristics (advanced text calibration, computer-vision-based estimation of patient's distance, and automatic calculation of patient's reading times) that facilitate the overall examination procedure. wDDART's reading parameters [reading acuity (RA), maximum reading speed (MRS), critical print size (CPS) and reading accessibility index (ACC)] were compared to the corresponding ones of its conventional Windows-based reading test (DDART) in a sample of normal and low vision participants. wDDART's test-retest reliability for all reading parameters was evaluated in a 15-day time-window.

Results: One hundred patients (normal vision group-NVG: 70; low vision group-LVG: 30 patients) responded to DDART and wDDART. Non-significant differences between the two reading tests were found for all parameters in NVG and LVG. Intraclass correlation coefficients (ICCs) between the two tests demonstrated good or excellent correlation for RA, MRS, ACC and moderate correlation for CPS. Test-retest reliability was excellent for RA and ACC, while ICCs were 0.715-0.895 for MRS and CPS.

Conclusion: The wDDART demonstrated sufficient validity and repeatability making it suitable for clinical and research settings.

Clinicaltrialsgov identifier: NCT04618224.

Keywords: automatic reading timing; computer vision distance estimation; critical print size; internet; presbyopia; reading; reading acuity; reading speed.

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Conflict of interest statement

The authors report no conflicts of interest in this work.

Figures

Figure 1
Figure 1
The initial screen of wDDART.
Figure 2
Figure 2
The control flow of the wDDART application (see the explanation of symbols used at the upper right corner of the figure).
Figure 3
Figure 3
The text calibration screen.
Figure 4
Figure 4
(A) The concept of face-camera distance calculation, (B) an exemplar screenshot of the camera calibration screen, with automatic face detection overlaid.
Figure 5
Figure 5
(A) Acquired sound x during patient read out of a random sentence. (B) The processed signal y (blue curve) and the signal values considered as non-reading (red color). The post-talk delay is graphically displayed.
Figure 6
Figure 6
A typical example of wDDART’s output (html format).
Figure 7
Figure 7
Typical examples of face detection at different distances (40, 70 and 100 cm), for two individuals with and without spectacles.
Figure 8
Figure 8
Comparison between wDDART and DDART for NVG and LVG using Bland–Altman plots: (A) RA, (B) MRS, (C) CPS, (D) ACC.
Figure 9
Figure 9
The average patient – camera distance estimation error, as a function of distance, with the 95% confidence intervals superimposed.
See this image and copyright information in PMC

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