Abstract
The rapidly increasing utilization of smartphones makes ophthalmic problems associated with their use an important issue. This prospective study aimed to determine whether using a smartphone to view visual material is associated with a change in the intraocular pressure (IOP), and to determine which groups of factors best predict the time-dependent increase in IOP with smartphone use. This study included 158 eyes (127 glaucomatous and 31 healthy eyes) recruited from Seoul National University Bundang Hospital. Participants performed a sustained fixation task consisting of watching a movie on a smartphone screen for 30 minutes continuously at a viewing distance of 30 cm. A small but statistically significant time-dependent increase in IOP was observed while viewing a movie on a smartphone, being 10.6 ± 3.1, 11.0 ± 3.3, 11.2 ± 3.4, and 11.6 ± 3.5 mmHg before and 5, 10, and 30 minutes after the fixation task, respectively (P < 0.0001). Recursive partitioning tree analysis revealed that a shallower anterior chamber (<2.32 mm) was the strongest predictive factor for faster time-dependent increase in IOP (0.68 mmHg/minute). A higher visual field mean deviation (≥–0.22 dB), and an older age (≥48 years) were the second and third most influential factors associated with the rate of IOP increase (0.59 and 0.15 mmHg/minute, respectively).
Highlights
The use of smartphones has been increasing rapidly since their introduction in the late 2000s
A small but significant increase in intraocular pressure (IOP) was observed while viewing visual material on a smartphone in the present study
A regression tree was created to predict an increase in IOP associated with smartphone use, which revealed that a smaller anterior chamber depth (ACD) was the most significant factor for the time-dependent increase in IOP, followed by a higher visual field (VF) mean deviation (MD), which was the most important factor in eyes with a larger ACD
Summary
The use of smartphones has been increasing rapidly since their introduction in the late 2000s. The effect of smartphone use on IOP needs to be determined. The study attempted to develop a prediction model for any time-dependent increase in IOP with smartphone use. To this end, recursive partitioning (rpart) tree analysis was applied[17]. The rpart is an interactive statistical tool that can separate a group into two subgroups repeatedly given some risk factors of interest
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