Stratification of Individual Symptoms of Contact Lens-Associated Dry Eye Using the iPhone App DryEyeRhythm: Crowdsourced Cross-Sectional Study

Abstract

Background: Discontinuation of contact lens use is mainly caused by contact lens-associated dry eye. It is crucial to delineate contact lens-associated dry eye's multifaceted nature to tailor treatment to each patient's individual needs for future personalized medicine.

Objective: This paper aims to quantify and stratify individual subjective symptoms of contact lens-associated dry eye and clarify its risk factors for future personalized medicine using the smartphone app DryEyeRhythm (Juntendo University).

Methods: This cross-sectional study included iPhone (Apple Inc) users in Japan who downloaded DryEyeRhythm. DryEyeRhythm was used to collect medical big data related to contact lens-associated dry eye between November 2016 and January 2018. The main outcome measure was the incidence of contact lens-associated dry eye. Univariate and multivariate adjusted odds ratios of risk factors for contact lens-associated dry eye were determined by logistic regression analyses. The t-distributed Stochastic Neighbor Embedding algorithm was used to depict the stratification of subjective symptoms of contact lens-associated dry eye.

Results: The records of 4454 individuals (median age 27.9 years, SD 12.6), including 2972 female participants (66.73%), who completed all surveys were included in this study. Among the included participants, 1844 (41.40%) were using contact lenses, and among those who used contact lenses, 1447 (78.47%) had contact lens-associated dry eye. Multivariate adjusted odds ratios of risk factors for contact lens-associated dry eye were as follows: younger age, 0.98 (95% CI 0.96-0.99); female sex, 1.53 (95% CI 1.05-2.24); hay fever, 1.38 (95% CI 1.10-1.74); mental illness other than depression or schizophrenia, 2.51 (95% CI 1.13-5.57); past diagnosis of dry eye, 2.21 (95% CI 1.63-2.99); extended screen exposure time >8 hours, 1.61 (95% CI 1.13-2.28); and smoking, 2.07 (95% CI 1.49-2.88). The t-distributed Stochastic Neighbor Embedding analysis visualized and stratified 14 groups based on the subjective symptoms of contact lens-associated dry eye.

Conclusions: This study identified and stratified individuals with contact lens-associated dry eye and its risk factors. Data on subjective symptoms of contact lens-associated dry eye could be used for prospective prevention of contact lens-associated dry eye progression.

Keywords: DryEyeRhythm; ResearchKit; contact lens-associated dry eye; dry eye; mobile health; mobile phone; risk factors; smartphone app; stratification; subjective symptoms.

Figure 1

Trends in the number of downloads.

Figure 2

Flow chart of the enrollment process used in this study. CL: contact lens.

Figure 3

Stratification of subjective symptoms of CLADE. (A) The number of subjective symptoms in the non-CLADE and CLADE groups. (B) The frequency of subjective symptoms of DED based on each OSDI questionnaire. (C) The treemap shows the difference in the frequency of subjective symptoms of DED (the percentage of subjective symptoms in the CLADE group minus the percentage of subjective symptoms in the non-CLADE group) based on each OSDI question. (D) The Pearson correlation coefficients between subjective symptoms and CLADE are shown in the heatmap as a color gradient. (E) t-SNE projection shows the groups of CLADE based on the subjective symptoms. (F) The heatmap shows the correlation between the groups made by t-SNE projection and each OSDI item. CLADE: contact lens–associated dry eye. DED: dry eye disease. OSDI: Ocular Surface Disease Index. t-SNE: t-distributed Stochastic Neighbor Embedding.