Risk Factors for Severe Dry Eye Disease: Crowdsourced Research

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Risk Factors for Severe Dry Eye Disease: Crowdsourced Research Ophthalmology Volume 126, Number 5, May 2019 Risk Factors for Severe Dry Eye framework “ResearchKit,” which allows researchers and de- Disease: Crowdsourced velopers to create powerful applications for clinical research. Research Using DryEyeRhythm DryEyeRhythm might help identify patients who experience DED- type symptoms but remain undiagnosed. This application might be Dry eye disease (DED), a disorder of the tear film, potentially particularly relevant to millennials and later generations who live in causes various symptoms that interfere with quality of life and a heavily digitalized world. vision and reduce work productivity.1,2 Dry eye disease is the most DryEyeRhythm was released in the Apple App Store in Japan common eye disease, affecting tens of millions of people world- on November 2, 2016, and in the United States in April 2018. wide.3 Therefore, it is necessary to prevent DED symptoms by Electronic informed consent for participation was obtained from identifying pertinent risk factors. all users. This study was conducted between November 2, 2016, Because of their multifunctionality and flexibility, smartphones and November 2, 2017, with approval from the Independent are being increasingly used for research.4 To elucidate the Ethics Committee of Juntendo University Faculty of Medicine contribution of certain risk factors to the progression of severe (Approval Number, 16-078) and adhered to the tenets of the DED-type symptoms, a DED application, “DryEyeRhythm,” was Declaration of Helsinki. Through DryEyeRhythm, data were designed using Apple Inc.’s (Cupertino, CA) open-source obtained regarding participant demographic characteristics Table 1. Age- and Sex-Adjusted and Fully Adjusted Odds Ratios for Severe Dry Eye DiseaseeType Symptoms (Ocular Surface Disease Index Score 33) Risk Factor Age- and Sex-Adjusted OR (95% CI) Fully Adjusted* OR (95% CI) Demographic Characteristics Age (every 1 yr) - 0.99 (0.98e0.99) Sex (women vs. men) - 1.85 (1.60-2.14) Medical History Hypertension No 1 (reference) - Medicated 1.22 (0.81e1.81) - Unmedicated 1.44 (0.94e2.22) - Unknown 1.06 (0.92e1.22) - Diabetes (yes vs. no) 1.39 (0.82e2.36) - Systemic diseases (yes vs. no) Blood disease 2.13 (1.10e4.10) 1.81 (0.92e3.57) Brain disease 1.72 (0.83e3.58) - Collagen disease 3.43 (1.68e7.02) 2.81 (1.34e5.90) Heart disease 1.52 (0.95e2.44) - Kidney disease 0.68 (0.38e1.22) - Liver disease 1.60 (0.87e2.93) - Malignant tumor 1.12 (0.48e2.59) - Respiratory disease 1.35 (1.06e1.72) 1.19 (0.92e1.53) Hay fever (yes vs. no) 1.19 (1.06e1.34) 1.18 (1.04e1.33) Mental illness (yes vs. no) Depression 1.86 (1.37e2.51) 1.68 (1.23e2.29) Schizophrenia 1.61 (0.84e3.09) - Others 1.58 (1.19e2.10) 1.31 (0.97e1.75) Ophthalmic surgery (yes vs. no) Cataract surgery 1.10 (0.43e2.81) - LASIK 0.95 (0.54e1.66) - Others 1.24 (0.87e1.77) - Lifestyle Habits Coffee (every 1 cup per day increase) 1.03 (0.98e1.08) - Contact lens use No 1 (reference) 1 (reference) Current use 1.28 (1.13e1.46) 1.24 (1.09e1.41) Past use 1.30 (1.05e1.60) 1.23 (0.99e1.52) Screen time (every 1 hr per day increase) 1.03 (1.01e1.04) 1.02 (1.01e1.03) Periodic exercise (yes vs. no) 0.84 (0.74e0.95) 0.90 (0.79e1.03) Sleeping time (every 1 hr per day increase) 0.98 (0.97e1.00) - Smoking (yes vs. no) 1.63 (1.40e1.90) 1.53 (1.31e1.79) Water intake (ml per day) 1.00 (1.00e1.00) - CI ¼ confidence interval; DED ¼ dry eye disease; OR ¼ odds ratio; OSDI ¼ Ocular Surface Disease Index. *In addition to age and sex, the fully adjusted logistic regression model included factors that were significantly associated with severe DED-type symptoms (OSDI total score 33) in the age- and sex-adjusted models. 766 Reports and medical and lifestyle histories (Table S1, available at risk factors that contribute to DED development and those that www.aaojournal.org), referring to previous studies on DED and contribute to DED deterioration. associated risk factors. This study has several limitations. First, recall bias might be The Ocular Surface Disease Index (OSDI) questionnaire was present in this questionnaire survey, resulting in over-reporting. incorporated in DryEyeRhythm to ensure the study’s validity. The The OSDI is suitable for DED screening because of its sensi- OSDI total score defined symptom severity, with scores of 0 to 12 tivity; however, it may contain false-positives because of its representing healthy eyes; 13 to 22 representing mild DED; 23 to moderate specificity, indicating the possibility of overestimating 32 representing moderate DED; and 33 to 100 representing severe the DED population. Second, this study could have involved se- DED.5 The OSDI measurements obtained using DryEyeRhythm lection bias. Here, the elderly or people lacking access to smart had good validity compared with those obtained using the paper- devices or mobile-service providers may have been underrepre- based questionnaire (Fig S1, available at www.aaojournal.org). sented, and younger people (who are more technologically savvy) Each risk factor’s odds ratio for severe DED-type symptoms and people who are socioeconomically able to afford a smartphone (OSDI total score 33e100) was determined by logistic regression may have been overrepresented. Indeed, it should be acknowl- analyses. In addition to age and sex, factors significantly associated edged that the study participants were substantially younger than with severe DED-type symptoms in age- and sex-adjusted models patients with DED generally are. Therefore, the generalizability of were included in the fully adjusted logistic regression models. Data the findings to older people may be limited. Furthermore, partici- are presented as mean Æ standard deviation, median (interquartile pants in this study were limited to iPhone users in Japan, who range) (for factors not normally distributed), or percentage. All data might exhibit different behavioral and consumer habits compared were analyzed with STATA version 15 (StataCorp, LP, College with people in other countries. For future studies, providing Station, TX). android-based applications could reach a larger part of the popu- DryEyeRhythm was downloaded by 18 225 users during the lation. Finally, there may be some important unmeasured factors, study period. Although 10 961 users (60.1%) completed the such as thyroid disease, Meibomian gland dysfunction, and facial demographics and medical history survey, 5265 (28.9%) also palsy, which should be included in the updated version of the completed the questionnaire on lifestyle habits and the OSDI. application. Table S1 summarizes participant demographics, medical history, However, DryEyeRhythm, represents a new digitalized manner lifestyle habits, and the OSDI total score. to conduct research and collect real-world data, and this integration Table 1 shows the relevant risk factors for severe DED-type of technology and medical research might eventually become symptoms identified by the age- and sex-adjusted, and fully mainstream.4 DryEyeRhythm also presents a unique opportunity adjusted logistic regression models. Briefly, the fully adjusted odds for preventive care by identifying individuals at risk for DED ratios (95% confidence intervals) were 0.99 (0.98e0.99) for age, earlier than currently possible. The application can be used to 1.85 (1.60e2.14) for female sex, 2.81 (1.34e5.90) for collagen supplement traditional hospital-based research. disease, 1.18 (1.04e1.33) for hay fever, 1.68 (1.23e2.29) for DryEyeRhythm was effectively used and showed that risk depression, 1.24 (1.09e1.41) for current contact lens use, 1.02 factors such as female sex, collagen disease, hay fever, depression, (1.01e1.03) for extended screen time, and 1.53 (1.31e1.79) for current contact lens use, extended screen time, and smoking smoking. contribute to severe DED-type symptoms. DryEyeRhythm facilitated participation in this study, and its 1,2 ease of access from an iPhone allowed recruitment of participants. TAKENORI INOMATA, MD, PHD 3 This recruitment model is also more inclusive of younger people, MASAHIRO NAKAMURA, MD, PHD 4 because they are relatively healthy and do not visit hospitals often. MASAO IWAGAMI, MD, PHD 5 The average participant age was 27.2Æ12.4 years, with only 2.0% TINA SHIANG,MD 6 of participants being older than 60 years. This might partially YUSUKE YOSHIMURA reflect the overwhelming presence of smartphone technology 1 KEIICHI FUJIMOTO,MD among the younger generation. 1,2 YUICHI OKUMURA,MD This study identified the risk factors contributing to severe 7 ATSUKO EGUCHI DED-type symptoms using real-world data (Table 1). Aging, 1 3 NANAMI IWATA although typically regarded as a DED risk factor, was inversely 1 MARIA MIURA,MD correlated to the severity of DED-type symptoms, suggesting that 8 younger people may experience DED symptoms while remaining SATOSHI HORI, MD, PHD 1 undiagnosed. It was confirmed that the risk factors suggested by YOSHIMUNE HIRATSUKA, MD, PHD 9 previous studies, such as female sex, collagen disease, hay fever, MIKI UCHINO, MD, PHD 9 depression, current contact lens use, extended screen time, and KAZUO TSUBOTA, MD, PHD 10 smoking, were significantly associated with severe DED symp- REZA DANA, MD, MPH 3 1 toms. These risk factors are modifiable and can be improved by AKIRA MURAKAMI,MD,PHD careful medical condition management and by avoiding lifestyle 1Juntendo University Faculty of Medicine, Department of choices that might exacerbate DED-type symptoms. Other previ- Ophthalmology, Tokyo, Japan; 2Juntendo University Faculty of ously suggested DED risk factors, such as hypertension, diabetes, Medicine, Department of Strategic Operating Room Management and blood diseases, past use of contact lenses, refractive surgery, and Improvement, Tokyo, Japan; 3Department of Prevention of Diabetes caffeine intake,3 were not corroborated as DED risk factors in this and Lifestyle-Related Diseases, The University of Tokyo, Tokyo, Japan; study; this discrepancy might reflect a difference between defining 4Department of Health Services Research, Faculty of Medicine, 767 Ophthalmology Volume 126, Number 5, May 2019 University of Tsukuba, Ibaraki, Japan; 5University of Massachusetts response to the available medical treatment.
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