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

Clinical Utility of Smartphone Applications in Ophthalmology: A Systematic Review

Topic

Numerous smartphone applications have been devised for diagnosis, treatment, and symptom management in ophthalmology. Despite the importance of systematic evaluation of the purpose, target disease, effectiveness, and utility of smartphone applications to their effective utilization, few studies have formally evaluated their validity, reliability, and clinical utility.

Clinical Relevance

This report identifies smartphone applications with potential for clinical implementation in ophthalmology and summarizes the evidence on their practical utility.

Methods

We searched PubMed and EMBASE on July 28, 2022, for articles reporting original data on the effectiveness of treatment, disease detection, diagnostic accuracy, disease monitoring, and usability of smartphone applications in ophthalmology published between January 1, 1987, and July 25, 2022. Their quality was assessed using the Joanna Briggs Institute Critical Appraisal Checklist.

Results

The initial search yielded 510 articles. After removing 115 duplicates and 285 articles based on inclusion and exclusion criteria, the full texts of the remaining 110 articles were reviewed. Furthermore, 71 articles were included in the final qualitative synthesis. All studies were determined to be of high (87.3%) or moderate (12.7%) quality. In terms of respective application of interest, 24 (33.8%) studies assessed diagnostic accuracy, 17 (23.9%) assessed disease detection, and 3 (4.2%) assessed intervention efficacy. A total of 48 smartphone applications were identified, of which 27 (56.3%) were publicly available. Seventeen (35.4%) applications included functions for ophthalmic examinations, 13 (27.1%) included functions aimed at disease detection, 10 (20.8%) included functions to support medical personnel, five (10.4%) included functions related to disease education, and three (6.3%) included functions to promote treatment adherence for patients. The largest number of applications targeted amblyopia (18.8%), followed by retinal disease (10.4%). Two (4.2%) smartphone applications reported significant efficacy in treating diseases.

Conclusion

In this systematic review, a comprehensive appraisal is presented on studies related to diagnostic accuracy, disease detectability, and efficacy of smartphone applications in ophthalmology. Forty-eight applications with potential clinical utility are identified. Appropriate smartphone applications are expected to enable early detection of undiagnosed diseases via telemedicine and prevent visual dysfunction via remote monitoring of chronic diseases.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

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.

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.

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.

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.

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.

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.

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.

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.

Evaluation of dry eye symptoms and risk factors among medical students in Serbia

BACKGROUND

Dry eye is a multifactorial disease defined less than 30 years ago. It is a relatively common disorder, affected by a number of well-known risk factors. Dry eye can be challenging to diagnose because of the possible discrepancy between patients' symptoms and clinical signs, and its overlap with other ocular surface diseases. Literature-wise, dry eye is usually associated with age and therefore investigated within older populations. Recently, studies focusing on young adult and student populations have demonstrated a higher prevalence of dry eye than previously expected.

AIM

The study aims to determine the frequency of dry eye symptoms in the student population, and the impact of students' activities and habits as potential risk factors.

METHODOLOGY

Our study involved 397 students from the medical school at the University of Belgrade, Serbia. Students were asked to complete an online survey that addressed general information, health, habits, and routine in everyday use of electronic devices. In addition, students completed a standard Ocular Surface Disease Index questionnaire.

RESULTS

The prevalence of dry eye was 60.5% (240/397) in our study population. Contact lens wear (p<0.001), allergies (p = 0.049) and increased number of hours per day using VD devices for studying purposes (p = 0.014) were associtated with a higher risk of dry eye disease. Risk factors that did not significantly impact dry eye were the use of oral contraceptives, smoking, systemic diseases, year of study and sex.

CONCLUSION

In our study, the prevalence of dry eye disease was similar or slightly higher than in previous studies among young adults. In addition, contact lenses, allergies and visual display devices were associated with the development of the dry eye.

Patient and public involvement in mobile health-based research for hay fever: a qualitative study of patient and public involvement implementation process

BACKGROUND

Smartphones are being increasingly used for research owing to their multifunctionality and flexibility, and crowdsourced research using smartphone applications (apps) is effective in the early detection and management of chronic diseases. We developed the AllerSearch app to gather real-world data on individual subjective symptoms and lifestyle factors related to hay fever. This study established a foundation for interactive research by adopting novel, diverse perspectives accrued through implementing the principles of patient and public involvement (PPI) in the development of our app.

METHODS

Patients and members of the public with a history or family history of hay fever were recruited from November 2019 to December 2021 through a dedicated website, social networking services, and web briefing according to the PPI Guidebook 2019 by the Japan Agency for Medical Research and Development. Nine opinion exchange meetings were held from February 2020 to December 2021 to collect opinions and suggestions for updating the app. After each meeting, interactive evaluations from PPI contributors and researchers were collected. The compiled suggestions were then incorporated into the app, establishing an active feedback loop fed by the consistently interactive infrastructure.

RESULTS

Four PPI contributors (one man and three women) were recruited, and 93 items were added/changed in the in-app survey questionnaire in accordance with discussions from the exchange meetings. The exchange meetings emphasized an atmosphere and opportunity for participants to speak up, ensuring frequent opportunities for them to contribute to the research. In March 2020, a public website was created to display real-time outcomes of the number of participants and users' hay-fever-preventative behaviors. In August 2020, a new PPI-implemented AllerSearch app was released.

CONCLUSIONS

This study marks the first research on clinical smartphone apps for hay fever in Japan that implements PPI throughout its timeline from research and development to the publication of research results. Taking advantage of the distinct perspectives offered by PPI contributors, a step was taken toward actualizing a foundation for an interactive research environment. These results should promote future PPI research and foster the establishment of a social construct that enables PPI efforts in various fields.

Prevalence of Comorbidity between Dry Eye and Allergic Conjunctivitis: A Systematic Review and Meta-Analysis

This systematic review aimed to determine the comorbid dry eye (DE) and allergic conjunctivitis (AC) prevalence. We searched PubMed and EMBASE for articles published until 22 March 2022, combining the terms “(dry eye OR keratoconjunctivitis sicca) AND allergic conjunctivitis.” Study-specific estimates (DE and AC incidence rates among patients with AC and DE, respectively) were combined using the one-group meta-analysis in a random-effects model. The initial search yielded 700 studies. Five articles reporting AC incidence among individuals with DE and six articles reporting DE incidence among individuals with AC were included in the qualitative synthesis. In these nine articles, the total sample size was 7254 patients. The DE incidence among individuals with AC was 0.9–97.5%; the AC incidence among individuals with DE was 6.2–38.0%. One-group meta-analysis using a random-effects model showed that 47.2% (95% confidence interval: 0.165–0.779; 320/1932 cases) of patients with AC had comorbid DE and 17.8% (95% confidence interval: 0.120–0.236; 793/4855 cases) of patients with DE had comorbid AC, as defined by each article. Complimentary screening and treatment for patients with DE and AC may improve long-term outcomes and prevent chronic ocular damage in highly susceptible populations.

Assessing the Risk Factors For Diagnosed Symptomatic Dry Eye Using a Smartphone App: Cross-sectional Study

Background

Dry eye (DE) is a chronic inflammatory disease of the ocular surface of the eye that affects millions of people throughout the world. Smartphone use as an effective health care tool has grown exponentially. The “Dry eye or not?” app was created to evaluate the prevalence of symptomatic DE, screen for its occurrence, and provide feedback to users with symptomatic DE throughout Thailand.

Objective

The purpose of this study was to compare the prevalence of symptomatic dry eye (DE), blink rate, maximum blink interval (MBI), and best spectacle-corrected visual acuity (BSCVA) between people with and without symptomatic DE and to identify risk factors for symptomatic DE in Thailand.

Methods

This cross-sectional study sourced data from the “Dry eye or not?” smartphone app between November 2019 and July 2020. This app collected demographic data, Ocular Surface Disease Index (OSDI) score, blink rate, MBI, BSCVA, and visual display terminal (VDT) use data. The criterion for symptomatic DE was OSDI score ≥13.

Results

The prevalence of symptomatic DE among individuals using this smartphone app in Thailand was 85.8% (8131/9482), with the Northeastern region of Thailand having the highest prevalence, followed by the Northern region. Worse BSCVA (median 0.20, IQR 0.40; P=.02), increased blink rate (median 18, IQR 16; P<.001), reduced MBI (median 8.90, IQR 10.80; P<.001), female sex (adjusted OR 1.83; 95% CI 1.59-2.09; P<.001), more than 6 hours of VDT use (adjusted OR 1.59; 95% CI 1.15-2.19; P=.004), and lower than bachelor’s degree (adjusted OR 1.30; 95% CI 1.03-1.64; P=.02) were significantly associated with symptomatic DE. An age over 50 years (adjusted OR 0.77; 95% CI 0.60-0.99) was significantly less associated with symptomatic DE (P=.04).

Conclusions

This smartphone DE app showed that the prevalence of symptomatic DE in Thailand was 85.8%. Signs and risk factors could be also evaluated with this smartphone DE app. Screening for DE by this app may allow for the development of strategic plans for health care systems in Thailand.

Association of dry eye disease with smoking: A systematic review and meta-analysis

There is conflicting evidence for the association between smoking and dry eye disease (DED). We conducted a meta-analysis to determine the true relationship between smoking and DED. A systematic literature search was performed using electronic databases, including PubMed, Embase and Cochrane Library, till August 2021 to identify observational studies with data on smoking as risk factor of DED. Quality assessment of the included studies was conducted using Joanna Briggs Institute (JBI) critical appraisal checklists. The random-effects model was used to calculate the pooled odds ratio (OR). Heterogeneity was evaluated by Cochrane Q and I² index; in addition, subgroup, sensitivity, and meta-regression analyses were performed. Publication bias was assessed using funnel plot and Egger’s regression test. A total of 22 studies (4 cohort and 18 cross-sectional studies) with 160,217 subjects met the inclusion criteria and were included in this meta-analysis. There is no statistically significant relationship between current smokers (OR_adjusted = 1.14; 95% CI: 0.95–1.36; P = 0.15; I² = 84%) and former smokers (OR_adjusted = 1.06; 95% CI: 0.93–1.20; P = 0.38; I² = 26.7%) for the risk of DED. The results remained consistent across various subgroups. No risk of publication bias was detected by funnel plot and Eggers’s test (P > 0.05). No source of heterogeneity was observed in the meta-regression analysis. Our meta-analysis suggest current or former smoking may not be involved in the risk of dry eye disease. Further studies to understand the mechanism of interaction between current smokers and formers smokers with DED are recommended.

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.

Individual characteristics and associated factors of hay fever: A large-scale mHealth study using AllerSearch

BACKGROUND

The prevalence of hay fever, a multifactorial allergic disease, is increasing. Identifying individual characteristics and associated factors of hay fever is essential for predictive, preventive, personalized, and participatory (P4) medicine. This study aimed to identify individual characteristics and associated factors of hay fever using an iPhone application AllerSearch.

METHODS

This large-scale mobile health-based cross-sectional study was conducted between February 2018 and May 2020. Individuals who downloaded AllerSearch in Japan and provided a comprehensive self-assessment (general characteristics, medical history, lifestyle habits, and hay fever symptoms [score range 0-36]) were included. Associated factors of hay fever (vs. non-hay fever) and severe hay fever symptoms were identified using multivariate logistic and linear regression analyses, respectively.

RESULTS

Of the included 11,284 individuals, 9041 had hay fever. Factors associated with hay fever (odds ratio) included age (0.98), female sex (1.33), atopic dermatitis (1.40), history of dry eye diagnosis (1.36), discontinuation of contact lens use during hay fever season (3.34), frequent bowel movements (1.03), and less sleep duration (0.91). The factors associated with severe hay fever symptoms among individuals with hay fever (coefficient) included age (-0.104), female sex (1.329), history of respiratory disease (1.539), history of dry eye diagnosis (0.824), tomato allergy (1.346), discontinuation of contact lens use during hay fever season (1.479), smoking habit (0.614), and having a pet (0.303).

CONCLUSIONS

Our large-scale mobile health-based study using AllerSearch elucidated distinct hay fever presentation patterns, characteristics, and factors associated with hay fever. Our study establishes the groundwork for effective individualized interventions for P4 medicine.

Smartphone-based digital phenotyping for dry eye toward P4 medicine: a crowdsourced cross-sectional study

Multidimensional integrative data analysis of digital phenotyping is crucial for elucidating the pathologies of multifactorial and heterogeneous diseases, such as the dry eye (DE). This crowdsourced cross-sectional study explored a novel smartphone-based digital phenotyping strategy to stratify and visualize the heterogenous DE symptoms into distinct subgroups. Multidimensional integrative data were collected from 3,593 participants between November 2016 and September 2019. Dimension reduction via Uniform Manifold Approximation and Projection stratified the collected data into seven clusters of symptomatic DE. Symptom profiles and risk factors in each cluster were identified by hierarchical heatmaps and multivariate logistic regressions. Stratified DE subgroups were visualized by chord diagrams, co-occurrence networks, and Circos plot analyses to improve interpretability. Maximum blink interval was reduced in clusters 1, 2, and 5 compared to non-symptomatic DE. Clusters 1 and 5 had severe DE symptoms. A data-driven multidimensional analysis with digital phenotyping may establish predictive, preventive, personalized, and participatory medicine.

A Cooperative Management App for Parents with Myopic Children Wearing Orthokeratology Lenses: Mixed Methods Pilot Study

Orthokeratology (OK) lens wear is an effective modality to inhibit axial elongation in myopic children. Willingness for commitment from both parents and children contributes to the success of OK treatment. We aimed to develop and assess the usability of a mobile application on OK lens wear by quantitatively and qualitatively evaluating parents with myopic children and eye care professionals (ECPs). Moreover, the preliminary outcome was also evaluated in this study. The app was developed and tested using a co-design approach involving key stakeholders. Two prototype tests were conducted during the feasibility and utility assessment. The app features include self-reported compliance documentation, analytics, and personalized and generalized messages for compliance behaviors of OK lenses. After the trial period, the full usage of app functions ranged from 40% to 60% among the enrolled parents. After app implementation, the compliance with follow-up visits substantially improved. Qualitative data show that the high-satisfaction app functions reported by parents were the app’s reminder and axial length recording, although it was recommended that the number of compliance questions should be reduced to minimize the survey completion time. Additionally, who should complete the recording of the axial length data as well as the management and reminder for the follow-up visit remained controversial. This is the first app developed to improve parents of myopic children’s compliance with OK lens wear and to assist ECPs and parents in collaboratively monitoring and managing the use and care of OK lenses among myopic children. This study highlights the importance of interdisciplinary collaboration in the design, development, and validation of such an app.

Design Guidelines of a Computer-Based Intervention for Computer Vision Syndrome: Focus Group Study and Real-World Deployment

Background

Prolonged time of computer use increases the prevalence of ocular problems, including eye strain, tired eyes, irritation, redness, blurred vision, and double vision, which are collectively referred to as computer vision syndrome (CVS). Approximately 70% of computer users have vision-related problems. For these reasons, properly designed interventions for users with CVS are required. To design an effective screen intervention for preventing or improving CVS, we must understand the effective interfaces of computer-based interventions.

Objective

In this study, we aimed to explore the interface elements of computer-based interventions for CVS to set design guidelines based on the pros and cons of each interface element.

Methods

We conducted an iterative user study to achieve our research objective. First, we conducted a workshop to evaluate the overall interface elements that were included in previous systems for CVS (n=7). Through the workshop, participants evaluated existing interface elements. Based on the evaluation results, we eliminated the elements that negatively affect intervention outcomes. Second, we designed our prototype system LiquidEye that includes multiple interface options (n=11). Interface options included interface elements that were positively evaluated in the workshop study. Lastly, we deployed LiquidEye in the real world to see how the included elements affected the intervention outcomes. Participants used LiquidEye for 14 days, and during this period, we collected participants’ daily logs (n=680). Additionally, we conducted prestudy and poststudy surveys, and poststudy interviews to explore how each interface element affects participation in the system.

Results

User data logs collected from the 14 days of deployment were analyzed with multiple regression analysis to explore the interface elements affecting user participation in the intervention (LiquidEye). Statistically significant elements were the instruction page of the eye resting strategy (P=.01), goal setting of the resting period (P=.009), compliment feedback after completing resting (P<.001), a mid-size popup window (P=.02), and CVS symptom-like effects (P=.004).

Conclusions

Based on the study results, we suggested design implications to consider when designing computer-based interventions for CVS. The sophisticated design of the customization interface can make it possible for users to use the system more interactively, which can result in higher engagement in managing eye conditions. There are important technical challenges that still need to be addressed, but given the fact that this study was able to clarify the various factors related to computer-based interventions, the findings are expected to contribute greatly to the research of various computer-based intervention designs in the future.

Diagnostic ability of maximum blink interval together with Japanese version of Ocular Surface Disease Index score for dry eye disease

Various symptoms of the dry eye disease (DED) interfere with the quality of life and reduce work productivity. Therefore, screening, prevention, and treatment of DED are important. We aimed to investigate the potential diagnostic ability of the maximum blink interval (MBI) (the length of time participants could keep their eyes open) with disease-specific questionnaire for DED. This cross-sectional study included 365 patients (252 with DED and 113 without DED) recruited between September 2017 and December 2019. Discriminant validity was assessed by comparing the non-DED and DED groups based on the MBI with a Japanese version of the Ocular Surface Disease Index (J-OSDI) and tear film breakup time (TFBUT) with J-OSDI classifications. The MBI with J-OSDI showed good discriminant validity by known-group comparisons. The positive and predictive values of MBI with J-OSDI were 96.0% (190/198 individuals) and 37.1% (62/167 individuals), respectively. The area under the receiver operating characteristic curve (AUC) of MBI with J-OSDI was 0.938 (95% confidence interval 0.904–0.971), the sensitivity was 75.4% (190/252 individuals), and the specificity was 92.9% (105/113 individuals), which are similar to the diagnostic ability of TFBUT with J-OSDI (AUC 0.954). In conclusion, MBI with J-OSDI may be a simple, non-invasive screening test for DED.