AI Article Synopsis
- The study aimed to evaluate the DryEyeRhythm smartphone app for diagnosing dry eye disease (DED), focusing on its reliability, validity, and feasibility.
- It involved 82 participants, including 42 with DED, who underwent various tests, with results showing strong correlation and consistency between the app and clinical assessments.
- The app demonstrated high predictive values and accuracy, indicating it is a reliable and non-invasive tool for assessing DED.
Article Abstract
Purpose: Undiagnosed 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.
Methods: This 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.
Results: The 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.
Conclusions: DryEyeRhythm app is a novel, non-invasive, reliable, and valid instrument for assessing DED.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.jtos.2022.04.005 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Smartphone App-Based and Paper-Based Patient-Reported Outcomes Using a Disease-Specific Questionnaire for Dry Eye Disease: Randomized Crossover Equivalence Study.
J Med Internet Res
August 2023
Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Background: Using traditional patient-reported outcomes (PROs), such as paper-based questionnaires, is cumbersome in the era of web-based medical consultation and telemedicine. Electronic PROs may reduce the burden on patients if implemented widely. Considering promising reports of DryEyeRhythm, our in-house mHealth smartphone app for investigating dry eye disease (DED) and the electronic and paper-based Ocular Surface Disease Index (OSDI) should be evaluated and compared to determine their equivalency.
View Article and Find Full Text PDFDiagnostic Ability of a Smartphone App for Dry Eye Disease: Protocol for a Multicenter, Open-Label, Prospective, and Cross-sectional Study.
JMIR Res Protoc
March 2023
Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Background: Dry eye disease (DED) is one of the most common ocular surface diseases. Numerous patients with DED remain undiagnosed and inadequately treated, experiencing various subjective symptoms and a decrease in quality of life and work productivity. A mobile health smartphone app, namely, the DEA01, has been developed as a noninvasive, noncontact, and remote screening device, in the context of an ongoing paradigm shift in the health care system, to facilitate a diagnosis of DED.
View Article and Find Full Text PDFDryEyeRhythm: A reliable and valid smartphone application for the diagnosis assistance of dry eye.
Ocul Surf
July 2022
Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Article Synopsis
- The study aimed to evaluate the DryEyeRhythm smartphone app for diagnosing dry eye disease (DED), focusing on its reliability, validity, and feasibility.
- It involved 82 participants, including 42 with DED, who underwent various tests, with results showing strong correlation and consistency between the app and clinical assessments.
- The app demonstrated high predictive values and accuracy, indicating it is a reliable and non-invasive tool for assessing DED.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!