| Institution: | 1. Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan;2. Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan;3. Department of Strategic Operating Room Management and Improvement, Juntendo University Graduate School of Medicine, Tokyo, Japan;4. Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan;5. Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan;6. Department of Health Services Research, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan;7. Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA;1. Rothschild Foundation Hospital, Paris, France;2. Paris University, Paris, France;1. Eye Program, Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA;2. Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA;3. Department of Medicine, University of Southern California, Los Angeles, CA, USA;4. David Geffen School of Medicine at UCLA, Los Angeles, CA, USA;1. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510060, China;2. Department of Hematology, Nanfang Hospital, Southern Medical University, No. 1838, North Guangzhou Avenue, Guangzhou, Guangdong, 510515, China;1. Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller, School of Medicine, Miami, USA;2. Department of Ophthalmology, Miami Veterans Administration Medical Center, Miami, FL, USA;3. Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT, USA;4. Ophthalmic Center, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China |
| Abstract: | PurposeUndiagnosed or inadequately treated dry eye disease (DED) decreases the quality of life. We aimed to investigate the reliability, validity, and feasibility of the DryEyeRhythm smartphone application (app) for the diagnosis assistance of DED.MethodsThis prospective, cross-sectional, observational, single-center study recruited 82 participants (42 with DED) aged ≥20 years (July 2020–May 2021). Patients with a history of eyelid disorder, ptosis, mental disease, Parkinson's disease, or any other disease affecting blinking were excluded. Participants underwent DED examinations, including the Japanese version of the Ocular Surface Disease Index (J-OSDI) and maximum blink interval (MBI). We analyzed their app-based J-OSDI and MBI results. Internal consistency reliability and concurrent validity were evaluated using Cronbach's alpha coefficients and Pearson's test, respectively. The discriminant validity of the app-based DED diagnosis was assessed by comparing the results of the clinical-based J-OSDI and MBI. The app feasibility and screening performance were evaluated using the precision rate and receiver operating characteristic curve analysis.ResultsThe app-based J-OSDI showed good internal consistency (Cronbach's α = 0.874). The app-based J-OSDI and MBI were positively correlated with their clinical-based counterparts (r = 0.891 and r = 0.329, respectively). Discriminant validity of the app-based J-OSDI and MBI yielded significantly higher total scores for the DED cohort (8.6 ± 9.3 vs. 28.4 ± 14.9, P < 0.001; 19.0 ± 11.1 vs. 13.2 ± 9.3, P < 0.001). The app's positive and negative predictive values were 91.3% and 69.1%, respectively. The area under the curve (95% confidence interval) was 0.910 (0.846–0.973) with concurrent use of the app-based J-OSDI and MBI.ConclusionsDryEyeRhythm app is a novel, non-invasive, reliable, and valid instrument for assessing DED. |
