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

Abstract

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.

Keywords: Japanese Allergic Conjunctival Disease Standard Quality of Life Questionnaire; Ocular Surface Disease Index; allergic conjunctivitis; dry eye; hay fever; mobile health; mobile phone; nasal symptom score; nonnasal symptom score; personalized medicine; pollinosis; rhinitis; smartphone.

Figure 1

Screenshots of the AllerSearch app. Screenshots of the (A) top screen, (B) nasal symptom score, (C) nonnasal symptom score, (D) Ocular Surface Disease Index, and (E) Japanese Allergic Conjunctival Disease Quality of Life Questionnaire.

Figure 2

Study cohort description, with the number of participants at each enrollment stage. eConsent: electronic consent.

Figure 3

Stratification of the heterogeneous symptoms of dry eye (DE) and hay fever (HF). (A) An overview of the stratification process of heterogeneous and diverse subjective symptoms of DE and HF using AllerSearch. (B) The normalized maximum eigengap values were used to estimate the number of clusters during spectral clustering. Fourteen clusters were determined using the eigengaps of the normalized affinity matrix. (C) Dimension reduction of the included individuals—via Uniform Manifold Approximation and Projection (UMAP) with spectral clustering identified through unsupervised clustering analysis (n=11,284 individuals collected by AllerSearch)—depicted 14 clusters upon stratification for subjective symptoms based on the 12 Japanese version of the Ocular Surface Disease Index (J-OSDI) items and the 9 nasal symptom score (NSS) and non-NSS (NNSS) items. (D) The fraction of individuals within each cluster is visualized on the left-most panel, along with a corresponding heat map of the subjective symptoms of DE and HF in individuals within the identified clusters. The dendrogram clusters for each J-OSDI, NSS, and NNSS item are shown at the top of the heat map.

Figure 4

Symptoms and quality of life (QoL) of dry eye and hay fever in each stratified cluster. A bar graph of the (A) total nasal symptom score (NSS), (B) total non-NSS (NNSS), (C) total symptom score (TSS), (D) Japanese version of the Ocular Surface Disease Index (J-OSDI) total score, and (E) QoL total score in each stratified cluster.

Figure 5

The correlation between each item of Japanese version of the Ocular Surface Disease Index (J-OSDI) and nasal symptom score (NSS) and non-NSS (NNSS). Pearson correlation coefficients between the 12 J-OSDI items and the 9 NSS and NNSS items are shown in the heat map as a color gradient.