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.

Using the AllerSearch Smartphone App to Assess the Association Between Dry Eye and Hay Fever: mHealth-Based Cross-Sectional Study.

BACKGROUND: Dry eye (DE) and hay fever (HF) show synergistic exacerbation of each other's pathology through inflammatory pathways. OBJECTIVE: This study aimed to investigate the association between DE and HF comorbidity and the related risk factors. METHODS: A cross-sectional observational study was conducted using crowdsourced multidimensional data from individuals who downloaded the AllerSearch smartphone app in Japan between February 2018 and May 2020. AllerSearch collected the demographics, medical history, lifestyle and residential information, HF status, DE symptoms, and HF-related quality of life. HF symptoms were evaluated using the nasal symptom score (0-15 points) and nonnasal symptom score (0-12 points). HF was defined by the participants' responses to the questionnaire as HF, non-HF, or unknown. Symptomatic DE was defined as an Ocular Surface Disease Index total score (0-100 points), with a threshold score of 13 points. HF-related quality of life was assessed using the Japanese Allergic Conjunctival Disease Standard Quality of Life Questionnaire (0-68 points). We conducted a multivariable linear regression analysis to examine the association between the severity of DE and HF symptoms. We subsequently conducted a multivariable logistic regression analysis to identify the factors associated with symptomatic DE (vs nonsymptomatic DE) among individuals with HF. Dimension reduction via Uniform Manifold Approximation and Projection stratified the comorbid DE and HF symptoms. The symptom profiles in each cluster were identified using hierarchical heat maps. RESULTS: This study included 11,284 participants, classified into experiencing HF (9041 participants), non-HF (720 participants), and unknown (1523 participants) groups. The prevalence of symptomatic DE among individuals with HF was 49.99% (4429/9041). Severe DE symptoms were significantly associated with severe HF symptoms: coefficient 1.33 (95% CI 1.10-1.57; P<.001) for mild DE, coefficient 2.16 (95% CI 1.84-2.48; P<.001) for moderate DE, and coefficient 3.80 (95% CI 3.50-4.11; P<.001) for severe DE. The risk factors for comorbid symptomatic DE among individuals with HF were identified as female sex; lower BMI; medicated hypertension; history of hematologic, collagen, heart, liver, respiratory, or atopic disease; tomato allergy; current and previous mental illness; pet ownership; living room and bedrooms furnished with materials other than hardwood, carpet, tatami, and vinyl; discontinuation of contact lens use during the HF season; current contact lens use; smoking habits; and sleep duration of <6 hours per day. Uniform Manifold Approximation and Projection stratified the heterogeneous comorbid DE and HF symptoms into 14 clusters. In the hierarchical heat map, cluster 9 was comorbid with the most severe HF and DE symptoms, and cluster 1 showed severe HF symptoms with minimal DE-related symptoms. CONCLUSIONS: This crowdsourced study suggested a significant association between severe DE and HF symptoms. Detecting DE among individuals with HF could allow effective prevention and interventions through concurrent treatment for ocular surface management along with HF treatment.

A Survey of Near-Miss Dispensing Errors in Hospital Pharmacies in Japan: DEPP-J Study-Multi-Center Prospective Observational Study-

The aim of this study was to determine the proportion of near-miss dispensing errors in hospital pharmacies in Japan. A prospective multi-center observational study was conducted between December 2018 and March 2019. The primary objective was to determine the proportion of near-miss dispensing errors in hospital pharmacy departments. The secondary objective was to determine the predictive factors for near-miss dispensing errors using multiple logistic regression analysis. The study was approved by the ethical committee at The Institute of Medical Sciences, University of Tokyo, Japan. A multi-center prospective observational study was conducted in 20 hospitals comprising 8862 beds. Across the 20 hospitals, we assessed data from 553 pharmacists and 53039 prescriptions. A near-miss dispensing error proportion of 0.87% (n = 461) was observed in the study. We found predictive factors for dispensing errors in day-time shifts: a higher number of drugs in a prescription, higher number of quantified drugs, such as liquid or powder formula, in a prescription, and higher number of topical agents in a prescription; but we did not observe for career experience level for clinical pharmacists. For night-time and weekend shifts, we observed a negative correlation of near-miss dispensing errors with clinical pharmacist experience level. We found an overall incidence of near-miss dispensing errors of 0.87%. Predictive factors for errors in night-time and weekend shifts was inexperienced pharmacists. We recommended that pharmacy managers should consider education or improved work flow to avoid near-miss dispensing errors by younger pharmacists, especially those working night or weekend shifts.

Changes in objectively measured lifestyle factors during the COVID-19 pandemic in community-dwelling older adults.

BACKGROUND: In this manuscript, we investigate whether objectively measured lifestyle factors, including walking steps, sedentary time, amount of unforced physical activity, level of slight and energetic physical activity, conversation time, and sleep parameters, were altered before and during the COVID-19 pandemic among community-dwelling older adults. METHODS: Data were obtained from a prospective cohort study conducted from 2015 to 2019 and a subsequent dementia prevention study undertaken in September 2020. Community-dwelling adults aged ≥ 65 years wore wearable sensors before and during the pandemic. RESULTS: A total of 56 adults were enrolled in this study. The mean age was 74.2 ± 3.9 years, and 58.9% (n = 33) of the participants were female. Moderate and vigorous physical activity time significantly decreased, and sedentary time significantly increased during the pandemic. CONCLUSIONS: This is the first study to demonstrate differences in objectively assessed lifestyle factors before and during the COVID-19 pandemic among community-dwelling older adults. The findings show that the pandemic has adversely affected physical activity among older adults living on their own in Japan.

Stratification of Individual Symptoms of Contact Lens-Associated Dry Eye Using the iPhone App DryEyeRhythm: Crowdsourced Cross-Sectional Study.

BACKGROUND: Discontinuation of contact lens use is mainly caused by contact lens-associated dry eye. It is crucial to delineate contact lens-associated dry eye's multifaceted nature to tailor treatment to each patient's individual needs for future personalized medicine. OBJECTIVE: This paper aims to quantify and stratify individual subjective symptoms of contact lens-associated dry eye and clarify its risk factors for future personalized medicine using the smartphone app DryEyeRhythm (Juntendo University). METHODS: This cross-sectional study included iPhone (Apple Inc) users in Japan who downloaded DryEyeRhythm. DryEyeRhythm was used to collect medical big data related to contact lens-associated dry eye between November 2016 and January 2018. The main outcome measure was the incidence of contact lens-associated dry eye. Univariate and multivariate adjusted odds ratios of risk factors for contact lens-associated dry eye were determined by logistic regression analyses. The t-distributed Stochastic Neighbor Embedding algorithm was used to depict the stratification of subjective symptoms of contact lens-associated dry eye. RESULTS: The records of 4454 individuals (median age 27.9 years, SD 12.6), including 2972 female participants (66.73%), who completed all surveys were included in this study. Among the included participants, 1844 (41.40%) were using contact lenses, and among those who used contact lenses, 1447 (78.47%) had contact lens-associated dry eye. Multivariate adjusted odds ratios of risk factors for contact lens-associated dry eye were as follows: younger age, 0.98 (95% CI 0.96-0.99); female sex, 1.53 (95% CI 1.05-2.24); hay fever, 1.38 (95% CI 1.10-1.74); mental illness other than depression or schizophrenia, 2.51 (95% CI 1.13-5.57); past diagnosis of dry eye, 2.21 (95% CI 1.63-2.99); extended screen exposure time >8 hours, 1.61 (95% CI 1.13-2.28); and smoking, 2.07 (95% CI 1.49-2.88). The t-distributed Stochastic Neighbor Embedding analysis visualized and stratified 14 groups based on the subjective symptoms of contact lens-associated dry eye. CONCLUSIONS: This study identified and stratified individuals with contact lens-associated dry eye and its risk factors. Data on subjective symptoms of contact lens-associated dry eye could be used for prospective prevention of contact lens-associated dry eye progression.

Relationship between dry eye disease and myopia: A systematic review and meta-analysis.

Background/objectives: Dry eye disease (DED) and myopia are common ocular disorders. This systematic review and meta-analysis investigated the association between DED and myopia. Methods: PubMed and EMBASE were searched for articles published between 1984 and 2022. Study quality was assessed using the Joanna Briggs Institute Critical Appraisal Checklist, and analysis was conducted using the DerSimonian-Laird random-effects model. Results: Of the 1,313 studies identified, 15 studies on DED and myopia were included. The meta-analysis revealed that the overall prevalence of subjective DED symptoms in the myopia population was 45.1 % (95 % confidence interval: 0.287-0.616). There was a significant association between DED and myopia. The myopia population had higher Ocular Surface Disease Index scores and shorter tear film breakup times than the non-myopia population. Additionally, the meta-regression analysis showed that spherical equivalent was significantly associated with the prevalence of DED symptoms in adults with myopia. Conclusion: Interventions to prevent DED are required in the myopia population. Enhancing patient awareness and self-management for DED, in addition to early screening and detection, is especially critical for younger populations who are at a higher risk of developing myopia.

Impact of the Coronavirus Disease 2019 Pandemic on Outpatient Visits for Diabetic Retinopathy in Japan: A Retrospective Cohort Study.

Purpose: Long-term ramifications of the coronavirus disease 2019 pandemic on various care-seeking characteristics of patients with diabetic retinopathy remain unclear. This study aimed to identify risk factors for dropout from regular fundus examinations (RFEs) in patients with diabetic retinopathy in Japan. Methods: We extracted demographic and health checkup data (April 2018 to March 2021) from the JMDC database. Patients with diabetes identified using diagnosis-related and medication codes were included. The dropout and continuation groups included patients who discontinued and continued to undergo RFEs during the coronavirus disease 2019 pandemic, respectively. Results: The number of RFEs was significantly lower during the mild lockdown period (April and May 2020) than during the prepandemic period. Of the 14,845 patients with diabetes, 2333 (15.7%) dropped out of RFEs during the pandemic, whereas before the pandemic, of the 11,536 patients with diabetes, 1666 (14.4%) dropped out of RFEs (P = 0.004). Factors associated with dropout in the multivariate logistic regression analysis included younger age, male sex, high triglyceride levels, high γ-glutamyl transpeptidase levels, smoking habit, alcohol consumption, weight gain of more than 10 kg since the age of 20 years, and certain stages of lifestyle improvement. Factors associated with continuation included low body mass index and high glycosylated hemoglobin levels. Conclusions: Our findings can assist in identifying patients with diabetes at risk of dropout. Translational Relevance: These results have implications for public health and identifying patients with diabetes at risk of dropout. Education and tailored monitoring regimens could be pivotal role in fostering adherence.

Optimal cutoff value of the dry eye-related quality-of-life score for diagnosing dry eye disease.

This retrospective study aimed to determine the optimal cutoff values of the Dry Eye-Related Quality-of-Life Score (DEQS) questionnaire for diagnosing dry eye disease (DED) and classifying DED severities. Participants completed the DEQS questionnaire, the Japanese version of the Ocular Surface Disease Index (J-OSDI) questionnaire, and DED examinations. DED was diagnosed according to the 2016 Asia Dry Eye Society diagnostic criteria based on DED symptoms (J-OSDI ≥ 13 points) and tear film breakup time ≤ 5 s. Receiver operating characteristic (ROC) analysis was used to calculate the optimal cutoff values of the DEQS summary score for detecting DED and grading its severity. Among 427 patients, 296 (69.3%) and 131 (30.7%) were diagnosed with DED and non-DED, respectively. ROC analysis determined an optimal cutoff value of 15.0 points for DED diagnosis, with 83.5% sensitivity, 87.0% specificity, and an area under the curve of 0.915. The positive and negative predictive values for DEQS ≥ 15.0 points were 93.6% and 69.9%, respectively. DEQS cutoff values of 15.0, 20.0, and 26.8 points could be accepted for severity classification of DED subjective symptoms in clinical use and represent mild, moderate, and severe DED, respectively. Conclusively, the optimal cutoff values of DEQS enable DED detection and subjective symptom severity classification.

Lifestyle factors that affect cognitive function-a longitudinal objective analysis.

Background: Identifying lifestyle factors associated with cognitive decline has critical clinical and public health implications for dementia prevention in later life. The longitudinal associations of sleep and physical activity with cognitive function remain unclear. This study examined whether objectively measured sleep and physical activity were longitudinally associated with cognitive function in older adults over a three-year period. Methods: This prospective cohort study enrolled 855 community-dwelling adults aged 65 and older, who were followed from 2015 to 2019. All participants were required to wear a wearable sensor for 7 consecutive days every 3 months and had annual cognitive assessments. Wearable sensor data (August 2015-September 2019) and Mini-Mental State Examination (MMSE) scores (August 2015-April 2019) were collected over 3 years of follow-up. First, principal component analysis was conducted to reduce the dimensions of the sleep and physical activity variables to two principal components for inclusion in a mixed-effects model. The sleep index consisted of sleep efficiency, time awake after sleep onset, and waking frequency. The physical activity index was composed of walking comprised steps per day and time devoted to light or moderate-to-vigorous physical activity. A higher sleep index indicated poor sleep quality, whereas a lower physical activity index indicated less physical activity. Second, a linear mixed effect model was used to examine the longitudinal association of sleep and physical activity indices with cognitive decline over time. Results: In total, 855 adults were recruited for this study at baseline. Of these, 729 adults (85.3%) completed a measurement of lifestyle factors and an annual cognitive testing, whereas 126 were excluded because of death or loss during follow-up. After adjusting for age, sex, education level, and time, the sleep index was inversely associated with MMSE scores (estimate, -0.06229; standard error, 0.02202; p = 0.0047) and the physical activity index was positively associated with MMSE scores (estimate, 0.06699; standard error, 0.03343; p = 0.0453). Conclusion: Poor sleep quality and lower physical activity were significant risk factors for subsequent cognitive decline in older adults. The present study facilitates the development of novel evidence-based interventions for physical activity and sleep quality to delay cognitive decline.

Objective sleep was longitudinally associated with brain amyloid burden in mild cognitive impairment.

OBJECTIVE: Understanding the longitudinal association of objective sleep and physical activity with brain amyloid burden and cortical glucose metabolism has critical clinical and public health implications for dementia prevention in later life. METHODS: We enrolled 118 individuals aged ≥65 years with mild cognitive impairment, who were followed up on from August 2015 to September 2019. All participants continuously wore an accelerometer sensor for 7 consecutive days every 3 months and received annual 11 C-Pittsburgh compound-B and 18 F-fluorodeoxyglucose positron emission tomography (PET). Sleep and physical activity parameters were assessed using accelerometer sensor data and PET imaging was quantified using a standardized uptake-value ratio. Fifty-seven participants (48.3%) completed a lifestyle factor assessment and PET imaging over the 3-year period. A linear mixed-effects model was applied to examine the longitudinal association of sleep and physical activity parameters with PET imaging over the 3-year period, controlling for potential confounders. RESULTS: Sleep efficiency was inversely associated with amyloid uptake in the frontal lobe. Although sleep duration was positively associated with global amyloid uptake, particularly in the frontal lobe, their impact was extremely small. However, physical activity parameters were not significantly associated with the 11 C-Pittsburgh compound-B-uptake. Furthermore, sleep and physical activity parameters were not significantly associated with cortical glucose metabolism. INTERPRETATION: Lower sleep efficiency could be an early symptom of greater brain amyloid burden at the mild cognitive impairment stage. Therefore, the assessment of sleep may be useful for identifying individuals at higher risk for brain amyloid burden. Future longer term observational studies are required to confirm these findings.

Predicting positron emission tomography brain amyloid positivity using interpretable machine learning models with wearable sensor data and lifestyle factors.

BACKGROUND: Developing a screening method for identifying individuals at higher risk of elevated brain amyloid burden is important to reduce costs and burden to patients in clinical trials on Alzheimer's disease or the clinical setting. We developed machine learning models using objectively measured lifestyle factors to predict elevated brain amyloid burden on positron emission tomography. METHODS: Our prospective cohort study of non-demented, community-dwelling older adults aged ≥ 65 years was conducted from August 2015 to September 2019 in Usuki, Oita Prefecture, Japan. One hundred and twenty-two individuals with mild cognitive impairment or subjective memory complaints (54 men and 68 women, median age: 75.50 years) wore wearable sensors and completed self-reported questionnaires, cognitive test, and positron emission tomography imaging at baseline. Moreover, 99 individuals in the second year and 61 individuals in the third year were followed up. In total, 282 eligible records with valid wearable sensors, cognitive test results, and amyloid imaging and data on demographic characteristics, living environments, and health behaviors were used in the machine learning models. Amyloid positivity was defined as a standardized uptake value ratio of ≥ 1.4. Models were constructed using kernel support vector machine, Elastic Net, and logistic regression for predicting amyloid positivity. The mean score among 10 times fivefold cross-validation repeats was utilized for evaluation. RESULTS: In Elastic Net, the mean area under the receiver operating characteristic curve of the model using objectively measured lifestyle factors alone was 0.70, whereas that of the models using wearable sensors in combination with demographic characteristics and health and life environment questionnaires was 0.79. Moreover, 22 variables were common to all machine learning models. CONCLUSION: Our machine learning models are useful for predicting elevated brain amyloid burden using readily-available and noninvasive variables without the need to visit a hospital. TRIAL REGISTRATION: This prospective study was conducted in accordance with the Declaration of Helsinki and was approved by the local ethics committee of Oita University Hospital (UMIN000017442). A written informed consent was obtained from all participants. This research was performed based on the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline.

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.

DryEyeRhythm: A reliable and valid smartphone application for the diagnosis assistance of dry eye.

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.

Prevalence and Characteristics of Dry Eye Disease After Cataract Surgery: A Systematic Review and Meta-Analysis.

Dry eye disease (DED) after cataract surgery is associated with various risk factors, while causing a wide range of heterogeneous symptoms including decreased quality of vision. This systematic review and meta-analysis aimed to determine the prevalence and characteristics of DED after cataract surgery. We searched PubMed and EMBASE and included studies on patients with DED after cataract surgery, between January 2011 and June 2020. Study-specific estimates (DED prevalence rates after cataract surgery in patients without preexisting DED) were combined using one-group meta-analysis in a random-effects model. We included 36 studies published between 2013 and 2020. We included nine of these in the meta-analysis of DED prevalence after cataract surgery. Overall 37.4% (95% CI 22.6-52.3; 206/775) of patients without preexisting DED developed DED after cataract surgery. The risk factors for DED after cataract surgery included age, female sex, systemic diseases, systemic medications, psychiatric conditions, preexisting DED, meibomian gland dysfunction, preservatives in eye drops, surgery techniques, and lifestyle. DED severity peak occurred 1 day postoperatively and persisted for at least 1-12 months following cataract surgery; therefore, consistent follow-up for DED is warranted for at least 1 month after cataract surgery. Topical administration of preservative-free diquafosol tetrasodium solution and preoperative meibomian gland treatment were effective in preventing and treating DED following cataract surgery. As more than one-third of patients develop DED after cataract surgery, careful DED management and treatment is needed after cataract surgery to improve satisfaction and vision quality.

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.