Handheld shape discrimination hyperacuity test on a mobile device for remote monitoring of visual function in maculopathy

Abstract

Purpose: Frequency monitoring of age-related macular degeneration (AMD) and diabetic retinopathy (DR) is crucial for timely intervention. This study evaluated a handheld shape discrimination hyperacuity (hSDH) test iPhone app designed for visual function self-monitoring in patients with AMD and DR.

Methods: One hundred subjects (27 visually normal, 37 with AMD, and 36 with DR) were included based on clinical documentation and visual acuity of 20/100 or better. The hSDH test was implemented on the iOS platform. A cross-sectional study was conducted to compare the hSDH test with a previously established desktop SDH (dSDH) test and to assess the effect of disease severity on the hSDH test. A user survey was also conducted to assess the usability of the hSDH test on the mobile device.

Results: The hSDH test and dSDH test were highly correlated (r = 0.88, P < 0.0001). Bland-Altman analysis indicated no significant difference in hSDH and dSDH measurements. One-way ANOVA indicated that the mean hSDH measurement of the eyes with advanced AMD (n = 16) or with severe to very severe nonproliferative DR (NPDR) (n = 12) was significantly worse than that of the eyes with intermediate AMD (n = 11) or with mild to moderate NPDR (n = 11) (P < 0.0001). Ninety-eight percent of 46 patients (10 with AMD and 36 with DR) who completed the usability survey reported that the hSDH test was easy to use.

Conclusions: This study demonstrated that the hSDH test on a mobile device is comparable to PC-based testing methods. As a mobile app, it is intuitive to use, readily accessible, and sensitive to the severity of maculopathy. It has the potential to provide patients having maculopathy with a new tool to monitor their vision at home.

Keywords: age-related macular degeneration; diabetic retinopathy; remote vision self-testing; shape discrimination; visual acuity.

Figure 1.

Handheld SDH test on an iOS mobile platform.

Figure 2.

Left: Correlation of hSDH and dSDH testing protocols. The solid line is the linear regression. The dashed lines represent the band of 95% CI of the linear regression. Right: Bland-Altman analysis of hSDH and dSDH testing protocols. The dotted horizontal lines represent the 95% CI of the mean difference. The dashed horizontal lines represent the mean difference of ±1.96 SD.

Figure 3.

Correlation of hSDH with VA (left) or with CS (right) for data obtained from the visually normal subjects (open circles), AMD (closed red circles), and DR (closed blue diamonds) groups. The solid lines represent the linear fit for all data points. The cross indicates a point at the normal mean hSDH (−0.69 logMAR) and the normal mean VA (0.02 logMAR) (left) or the normal mean CS (−1.72 log unit) (right). The dotted lines go through the normal mean points (crosses) and have a slope of 1. The shaded area indicates a range of ±0.20 logMAR (estimated 95% CI) from the dotted line along both the vertical and horizontal axes.

Figure 4.

Handheld SDH and VA obtained from the visually normal subjects (open circles and squares), AMD (red closed circles and squares), and DR (blue closed diamonds and triangles) groups as a function of the average thickness of the central subfield of the ETDRS macular grid. The two vertical dotted lines represent the ±95% range (mean ± SD, 284 ± 23 mm) of the central subfield thickness obtained from the visually normal group in this study. In this plot, the eyes with a central subfield thinner than the normal ±95% range (<240 mm) were excluded. The dashed line is the linear fit for VA, while the solid line is the linear fit for hSDH.

Figure 5.

One-way ANOVA of hSDH (closed circles and diamonds) and VA (closed squares and triangles) versus the grading of AMD (red closed circles and squares) or DR (blue closed diamonds and triangles). Error bars denote 95% CI.