TY  - JOUR
AU  - Inomata, Takenori
AU  - Nakamura, Masahiro
AU  - Iwagami, Masao
AU  - Midorikawa-Inomata, Akie
AU  - Sung, Jaemyoung
AU  - Fujimoto, Keiichi
AU  - Okumura, Yuichi
AU  - Eguchi, Atsuko
AU  - Iwata, Nanami
AU  - Miura, Maria
AU  - Fujio, Kenta
AU  - Nagino, Ken
AU  - Hori, Satoshi
AU  - Tsubota, Kazuo
AU  - Dana, Reza
AU  - Murakami, Akira
PY  - 2020
DA  - 2020/6/26
TI  - Stratification of Individual Symptoms of Contact Lens–Associated Dry Eye Using the iPhone App DryEyeRhythm: Crowdsourced Cross-Sectional Study
JO  - J Med Internet Res
SP  - e18996
VL  - 22
IS  - 6
KW  - contact lens-associated dry eye
KW  - mobile health
KW  - ResearchKit
KW  - smartphone app
KW  - DryEyeRhythm
KW  - subjective symptoms
KW  - risk factors
KW  - dry eye
KW  - stratification
KW  - mobile phone
AB  - 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. 
SN  - 1438-8871
UR  - http://www.jmir.org/2020/6/e18996/
UR  - https://doi.org/10.2196/18996
UR  - http://www.ncbi.nlm.nih.gov/pubmed/32589162
DO  - 10.2196/18996
ID  - info:doi/10.2196/18996
ER  -