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.

Reliability and Validity of Electronic Patient-Reported Outcomes Using the Smartphone App AllerSearch for Hay Fever: Prospective Observational Study.

BACKGROUND: Hay fever is a highly prevalent, heterogenous, and multifactorial disease. Patients may benefit from longitudinal assessments using mobile health (mHealth) principles. We have previously attempted to establish an effective mHealth platform for patients with hay fever through AllerSearch, our in-house smartphone app that assesses electronic patient-reported outcomes through a questionnaire on hay fever and provides evidence-based advice. To be used by the public, an investigation on its reliability and validity is necessary. OBJECTIVE: The aim of this paper is to assess the reliability and validity of subjective symptom data on hay fever collected through our app, AllerSearch. METHODS: This study used a prospective observational design. The participants were patients aged ≥20 years recruited from a single university hospital between June 2, 2021, and January 26, 2022. We excluded patients who could not use smartphones as well as those with incomplete data records and outlier data. All participants answered the Japanese Allergic Conjunctival Disease Standard Quality of Life Questionnaire (JACQLQ), first in the paper-and-pencil format and subsequently on AllerSearch on the same day. The JACQLQ comprises the following three domains: Domain I, with 9 items on ocular or nasal symptoms; Domain II, with 17 items on daily activity and psychological well-being; and Domain III, with 3 items on overall condition by face score. The concordance rate of each domain between the 2 platforms was calculated. The internal consistency of Domains I and II of the 2 platforms was assessed using Cronbach alpha coefficients, the concurrent validity of Domains I and II was assessed by calculating Pearson correlation coefficients, and the mean differences between the 2 platforms were assessed using Bland-Altman analysis. RESULTS: In total, 22 participants were recruited; the data of 20 (91%) participants were analyzed. The average age was 65.4 (SD 12.8) years, and 80% (16/20) of the participants were women. The concordance rate of Domains I, II, and III between the paper-based and app-based JACQLQ was 0.78, 0.85, and 0.90, respectively. The internal consistency of Domains I and II between the 2 platforms was satisfactory (Cronbach alpha of .964 and .919, respectively). Pearson correlation analysis yielded a significant positive correlation between Domains I and II across the 2 platforms (r=0.920 and r=0.968, respectively). The mean difference in Domains I and II between the 2 platforms was 3.35 units (95% limits of agreement: -6.51 to 13.2). CONCLUSIONS: Our findings indicate that AllerSearch is a valid and reliable tool for the collection of electronic patient-reported outcomes to assess hay fever, contributing to the advantages of the mHealth platform.