Smartphone App-Based and Paper-Based Patient-Reported Outcomes Using a Disease-Specific Questionnaire for Dry Eye Disease: Randomized Crossover Equivalence Study.

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. OBJECTIVE: The purpose of this study is to assess the equivalence between smartphone app-based and paper-based questionnaires for DED. METHODS: This prospective, nonblinded, randomized crossover study enrolled 34 participants between April 2022 and June 2022 at a university hospital in Japan. The participants were allocated randomly into 2 groups in a 1:1 ratio. The paper-app group initially responded to the paper-based Japanese version of the OSDI (J-OSDI), followed by the app-based J-OSDI. The app-paper group responded to similar questionnaires but in reverse order. We performed an equivalence test based on minimal clinically important differences to assess the equivalence of the J-OSDI total scores between the 2 platforms (paper-based vs app-based). A 95% CI of the mean difference between the J-OSDI total scores within the ±7.0 range between the 2 platforms indicated equivalence. The internal consistency and agreement of the app-based J-OSDI were assessed with Cronbach α coefficients and intraclass correlation coefficient values. RESULTS: A total of 33 participants were included in this study. The total scores for the app- and paper-based J-OSDI indicated satisfactory equivalence per our study definition (mean difference 1.8, 95% CI -1.4 to 5.0). Moreover, the app-based J-OSDI total score demonstrated good internal consistency and agreement (Cronbach α=.958; intraclass correlation=0.919; 95% CI 0.842 to 0.959) and was significantly correlated with its paper-based counterpart (Pearson correlation=0.932, P<.001). CONCLUSIONS: This study demonstrated the equivalence of PROs between the app- and paper-based J-OSDI. Implementing the app-based J-OSDI in various scenarios, including telehealth, may have implications for the early diagnosis of DED and longitudinal monitoring of PROs.

Individual multidisciplinary clinical phenotypes of nasal and ocular symptoms in hay fever: Crowdsourced cross-sectional study using AllerSearch.

BACKGROUND: Multidisciplinary efforts to prospectively collect and analyze symptoms of hay fever are limited. We aimed to identify the characteristics of nasal and ocular symptoms of hay fever, using the AllerSearch smartphone application. METHODS: This mobile health-based prospective observational study using the AllerSearch smartphone application was conducted between February 1, 2018, and May 1, 2020. Individuals who downloaded AllerSearch from Japan and provided comprehensive self-assessments (including 17 items related to quality of life [QoL]-related items) were included. The characteristics and risk factors for allergic rhinitis (AR) and allergic conjunctivitis (AC) were identified using hierarchical heat maps and multivariate logistic regression. RESULTS: Of the 9041 participants with hay fever, 58.8% had AR and AC, 22.2% had AR, and 5.7% had AC. The AR-AC comorbid cohort showed worse symptoms of hay fever and QoL scores than the other cohorts. Factors (odds ratio, 95% confidence interval) associated with AR-AC included a lower age (0.98, 0.97-0.98), female sex (1.31, 1.19-1.45), liver disease (1.58, 1.26-2.35), dry eye disease (1.45, 1.30-1.63), unknown dry eye disease status (1.46, 1.31-1.62), contact lens use discontinuation during the hay fever season (1.69, 1.28-2.23), and bedroom flooring material other than hardwood, carpet, tatami, or vinyl (1.91, 1.16-3.14). CONCLUSIONS: Analysis of medical big data for hay fever performed using a mobile health app helped identify risk factors and characteristics of AC, AR, and AR-AC. Phenotyping of highly variable symptoms of hay fever, such as nasal and ocular symptoms, can facilitate better-quality clinical care.

Clinical utility of maximum blink interval measured by smartphone application DryEyeRhythm to support dry eye disease diagnosis.

The coronavirus disease (COVID-19) pandemic has emphasized the paucity of non-contact and non-invasive methods for the objective evaluation of dry eye disease (DED). However, robust evidence to support the implementation of mHealth- and app-based biometrics for clinical use is lacking. This study aimed to evaluate the reliability and validity of app-based maximum blink interval (MBI) measurements using DryEyeRhythm and equivalent traditional techniques in providing an accessible and convenient diagnosis. In this single-center, prospective, cross-sectional, observational study, 83 participants, including 57 with DED, had measurements recorded including slit-lamp-based, app-based, and visually confirmed MBI. Internal consistency and reliability were assessed using Cronbach's alpha and intraclass correlation coefficients. Discriminant and concurrent validity were assessed by comparing the MBIs from the DED and non-DED groups and Pearson's tests for each platform pair. Bland-Altman analysis was performed to assess the agreement between platforms. App-based MBI showed good Cronbach's alpha coefficient, intraclass correlation coefficient, and Pearson correlation coefficient values, compared with visually confirmed MBI. The DED group had significantly shorter app-based MBIs, compared with the non-DED group. Bland-Altman analysis revealed minimal biases between the app-based and visually confirmed MBIs. Our findings indicate that DryEyeRhythm is a reliable and valid tool that can be used for non-invasive and non-contact collection of MBI measurements, which can assist in accessible DED detection and management.

The minimal clinically important difference of app-based electronic patient-reported outcomes for hay fever.

BACKGROUND: Hay fever is a common allergic disease, with an estimated worldwide prevalence of 14.4% and a variety of symptoms. This study assessed the minimal clinically important difference (MCID) of nasal symptom score (NSS), non-nasal symptom score (NNSS), and total symptoms score (TSS) for app-based hay-fever monitoring. METHODS: MCIDs were calculated based on the data from a previous large-scale, crowdsourced, cross-sectional study using AllerSearch, an in-house smartphone application. MCIDs were determined with anchor-based and distribution-based methods. The face scale score of the Japanese Allergic Conjunctival Disease Standard Quality of Life Questionnaire Domain III and the daily stress level due to hay fever were used as anchors for determining MCIDs. The MCID estimates were summarized as MCID ranges. RESULTS: A total of 7590 participants were included in the analysis (mean age: 35.3 years, 57.1% women). The anchor-based method produced a range of MCID values (median, interquartile range) for NSS (2.0, 1.5-2.1), NNSS (1.0, 0.9-1.2), and TSS (2.9, 2.4-3.3). The distribution-based method produced two MCIDs (based on half a standard deviation, based on a standard error of measurement) for NSS (2.0, 1.8), NNSS (1.3, 1.2), and TSS (3.0, 2.3). The final suggested MCID ranges for NSS, NNSS, and TSS were 1.8-2.1, 1.2-1.3, and 2.4-3.3, respectively. CONCLUSIONS: MCID ranges for app-based hay-fever symptom assessment were obtained from the data collected through a smartphone application, AllerSearch. These estimates may be useful for monitoring the subjective symptoms of Japanese patients with hay fever through mobile platforms.

P4 Medicine for Heterogeneity of Dry Eye: A Mobile Health-based Digital Cohort Study.

During the 5th Science, Technology, and Innovation Basic Plan, the Japanese government proposed a novel societal concept -Society 5.0- that promoted a healthcare system characterized by its capability to provide unintrusive, predictive, longitudinal care through the integration of cyber and physical space. The role of Society 5.0 in managing our quality of vision will become more important in the modern digitalized and aging society, both of which are known risk factors for developing dry eye. Dry eye is the most common ocular surface disease encountered in Japan with symptoms including increased dryness, eye discomfort, and decreased visual acuity. Owing to its complexity, implementation of P4 (predictive, preventive, personalized, participatory) medicine in managing dry eye requires a comprehensive understanding of its pathology, as well as a strategy to visualize and stratify its risk factors. Using DryEyeRhythm®, a mobile health (mHealth) smartphone software (app), we established a route to collect holistic medical big data on dry eye, such as the subjective symptoms and lifestyle data for each individual. The studies to date aided in determining the risk factors for severe dry eye, the association between major depressive disorder and dry eye exacerbation, eye drop treatment adherence, app-based stratification algorithms based on symptomology, blink detection biosensoring as a dry eye-related digital phenotype, and effectiveness of app-based dry eye diagnosis support compared to traditional methods. These results contribute to elucidating disease pathophysiology and promoting preventive and effective measures to counteract dry eye through mHealth.

Symptom-based stratification algorithm for heterogeneous symptoms of dry eye disease: a feasibility study.

BACKGROUND/OBJECTIVE: To test the feasibility of a dry eye disease (DED) symptom stratification algorithm previously established for the general population among patients visiting ophthalmologists. SUBJECT/METHODS: This retrospective cross-sectional study was conducted between December 2015 and October 2021 at a university hospital in Japan; participants who underwent a comprehensive DED examination and completed the Japanese version of the Ocular Surface Disease Index (J-OSDI) were included. Patients diagnosed with DED were stratified into seven clusters using a previously established symptom-based stratification algorithm for DED. Characteristics of the patients in stratified clusters were compared. RESULTS: In total, 426 participants were included (median age [interquartile range]; 63 [48-72] years; 357 (83.8%) women). Among them, 291 (68.3%) participants were diagnosed with DED and successfully stratified into seven clusters. The J-OSDI total score was highest in cluster 1 (61.4 [52.2-75.0]), followed by cluster 5 (44.1 [38.8-47.9]). The tear film breakup time was the shortest in cluster 1 (1.5 [1.1-2.1]), followed by cluster 3 (1.6 [1.0-2.5]). The J-OSDI total scores from the stratified clusters in this study and those from the clusters identified in the previous study showed a significant correlation (r = 0.991, P < 0.001). CONCLUSIONS: The patients with DED who visited ophthalmologists were successfully stratified by the previously established algorithm for the general population, uncovering patterns for their seemingly heterogeneous and variable clinical characteristics of DED. The results have important implications for promoting treatment interventions tailored to individual patients and implementing smartphone-based clinical data collection in the future.

Diagnostic Ability of a Smartphone App for Dry Eye Disease: Protocol for a Multicenter, Open-Label, Prospective, and Cross-sectional Study.

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. OBJECTIVE: This study aimed to evaluate the capabilities of the DEA01 smartphone app to facilitate a DED diagnosis. METHODS: In this multicenter, open-label, prospective, and cross-sectional study, the test method will involve using the DEA01 smartphone app to collect and evaluate DED symptoms, based on the Japanese version of the Ocular Surface Disease Index (J-OSDI), and to measure the maximum blink interval (MBI). The standard method will then involve a paper-based J-OSDI evaluation of subjective symptoms of DED and tear film breakup time (TFBUT) measurement in an in-person encounter. We will allocate 220 patients to 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 and negative predictive values, and the likelihood ratio between the test and standard methods will be assessed. The area under the curve of the test method will be evaluated using a receiver operating characteristic curve. The internal consistency of the app-based J-OSDI and the correlation between the app-based J-OSDI and paper-based J-OSDI will be assessed. A DED diagnosis cutoff value for the app-based MBI will be determined using a receiver operating characteristic curve. The app-based MBI will be assessed to determine a correlation between a slit lamp-based MBI and TFBUT. Adverse events and DEA01 failure data will be collected. Operability and usability will be assessed using a 5-point Likert scale questionnaire. RESULTS: Patient enrollment will start in February 2023 and end in July 2023. The findings will be analyzed in August 2023, and the results will be reported from March 2024 onward. CONCLUSIONS: This study may have implications in identifying a noninvasive, noncontact route to facilitate a diagnosis of DED. The DEA01 may enable a comprehensive diagnostic evaluation within a telemedicine setting and facilitate early intervention for undiagnosed patients with DED confronting health care access barriers. TRIAL REGISTRATION: Japan Registry of Clinical Trials jRCTs032220524; https://jrct.niph.go.jp/latest-detail/jRCTs032220524. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/45218.

Prevalence and characteristics of dry eye disease in Parkinson's disease: a systematic review and meta-analysis.

We investigated and characterized the prevalence of dry eye disease (DED) in Parkinson's disease (PD). PubMed and EMBASE databases were searched for relevant studies between January 1, 1979 and March 10, 2022. Quality was assessed using the Joanna Briggs Institute Critical Appraisal Checklist. Study-specific estimates were combined using the DerSimonian-Laird random-effects model. Prevalence of subjective DED symptoms in patients with PD and mean differences in blink rate, corneal thickness, tear film breakup time, and tear secretion volume on Schirmer test I were compared to those in controls. Of 383 studies, 13 (1519 patients with PD) and 12 were included in qualitative and quantitative syntheses, respectively. Meta-analysis revealed a 61.1% prevalence of subjective DED symptoms in PD and that, compared with controls, patients with PD had significantly lower blink rate, thinner corneal thickness, shorter tear film breakup time, and lower tear secretion volumes on Schirmer test I, without and with anesthesia.

Changes in Distribution of Dry Eye Disease by the New 2016 Diagnostic Criteria from the Asia Dry Eye Society.

Dry eye disease (DED) is a disorder of the tear film. Here, we delineate the changes in distribution of DED after diagnostic criteria changes from the 2006 Japanese Diagnostic Criteria to the 2016 Asia Dry Eye Society criteria. We included 250 right eyes of 250 patients and all patients completed ophthalmic assessments for DED. The 2006 criteria classified patients into definite DED, probable DED, and non-DED based on subjective symptoms, tear function, and/or vital staining. The 2016 criteria eliminated probable DED and classified patients into definite DED or non-DED based on subjective symptoms and decreased tear break-up time. We examined how probable DED patients were reclassified by the 2016 criteria. By the 2006 criteria, 38.8% (97/250) of patients had definite DED, 35.6% (89/250) had probable DED, and 25.6% (64/250) had non-DED. By the 2016 criteria, 66.8% (167/250) had definite DED and 31.2% (83/250) had non-DED. Among patients with probable DED using the 2006 criteria, 79.8% (71/89) were reclassified as definite DED and 20.2% (18/89) were reclassified as non-DED using the 2016 criteria. Our data revealed that prevalence of definite DED increased because most probable DED patients were reclassified as definite DED after changes in the diagnostic criteria.

Author Correction: Changes in Distribution of Dry Eye Disease by the New 2016 Diagnostic Criteria from the Asia Dry Eye Society.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.