The incidence of dry eye disease has increased; the potential for crowdsource data to help identify undiagnosed dry eye in symptomatic individuals remains unknown.OBJECTIVE To assess the characteristics and risk factors associated with diagnosed and undiagnosed symptomatic dry eye using the smartphone app DryEyeRhythm.
DESIGN, SETTING, AND PARTICIPANTSA cross-sectional study using crowdsourced data was conducted including individuals in Japan who downloaded DryEyeRhythm and completed the entire questionnaire; duplicate users were excluded. DryEyeRhythm was released on November 2, 2016; the study was conducted from November 2, 2016, to January 12, 2018.EXPOSURES DryEyeRhythm data were collected on demographics, medical history, lifestyle, subjective symptoms, and disease-specific symptoms, using the Ocular Surface Disease Index (100-point scale; scores 0-12 indicate normal, healthy eyes; 13-22, mild dry eye; 23-32, moderate dry eye; 33-100, severe dry eye symptoms), and the Zung Self-Rating Depression Scale (total of 20 items, total score ranging from 20-80, with 諉40 highly suggestive of depression).
MAIN OUTCOMES AND MEASURESMultivariate-adjusted logistic regression analysis was used to identify risk factors for symptomatic dry eye and to identify risk factors for undiagnosed symptomatic dry eye.RESULTS A total of 21 394 records were identified in our database; 4454 users, included 899 participants (27.3%) with diagnosed and 2395 participants (72.7%) with undiagnosed symptomatic dry eye, completed all questionnaires and their data were analyzed. A total of 2972 participants (66.7%) were women; mean (SD) age was 27.9 (12.6) years. The identified risk factors for symptomatic vs no symptomatic dry eye included younger age (odds ratio [OR], 0.99; 95% CI, 0.987-0.999, P = .02), female sex (OR, 1.99; 95% CI, 1.61-2.46; P < .001), pollinosis (termed hay fever on the questionnaire) (