JMIR Publications

ABSTRACT

Background:

Dry eye disease (DED) is one of the most common ocular surface diseases and its prevalence is projected to increase in the digitalized society. Numerous patients with DED remain undiagnosed and inadequately treated, experiencing various subjective symptoms and decreased quality of life and work productivity. To provide non-intrusive and non-invasive screening devices in the context of an ongoing paradigm shift in the healthcare system, we developed a smartphone application (app) (DEA01) to assist in the diagnosis of DED.

Objective:

To evaluate the capabilities of DEA01, an mHealth app, for DED diagnosis.

Methods:

This study is designed as a multicenter, open-label, prospective, cross-sectional study. The participants will be initially evaluated for the presence of DED through the app-based Japanese version of Ocular Surface Disease Index (J-OSDI) and maximum blink interval (MBI) using DEA01 (test method), followed by paper-based J-OSDI evaluation and tear film breakup time (TFBUT) measurement in an in-person encounter (standard method). A total of 220 patients will be allocated to the DED and non-DED groups based on the standard method. The primary outcome will be the sensitivity and specificity of the DED diagnosis according to the test method. Secondary outcomes will be the validity and reliability of the test method. The concordance rate, positive predictive value, negative predictive value, and likelihood ratio between test method and standard method will be assessed. The area under the curve of the test method will be evaluated using the receiver operating characteristic curve. The internal consistency of the app-based J-OSDI and correlation between the app-based J-OSDI and paper-based J-OSDI will be assessed. The cutoff value of DED diagnosis of the app- based MBI will be determined using the receiver operating characteristic curve. The app- based MBI will be assessed using the correlation compared with slit lamp-based MBI and TFBUT. Adverse events and DEA01 failure will be accumulated. The operability and usability will be assessed by 5-Likert scale questionnaires.

Results:

This study has been approved by the Juntendo University Certified Review Board, Tokyo, Japan (approved protocol V.1.1.1, dated December 19, 2022; approval number: J22-003) and has been registered with the Japan Registry of Clinical Trials. Patients will be enrolled between February 2023 and July 2023, and the findings will be analyzed in August 2023. The results will be reported from December 2023 onward.

Conclusions:

This study may have implications in identifying a non-invasive and non-contact route to aid in the diagnosis of DED. DEA01 may enable a comprehensive diagnostic evaluation in the setting of telemedicine and allow for early intervention in patients who have not been properly diagnosed with DED due to barriers to healthcare. Clinical Trial: jRCTs032220524