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Ueno Y, Oda M, Yamaguchi T, Fukuoka H, Nejima R, Kitaguchi Y, Miyake M, Akiyama M, Miyata K, Kashiwagi K, Maeda N, Shimazaki J, Noma H, Mori K, Oshika T. Deep learning model for extensive smartphone-based diagnosis and triage of cataracts and multiple corneal diseases. Br J Ophthalmol 2024; 108:1406-1413. [PMID: 38242700 DOI: 10.1136/bjo-2023-324488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 12/17/2023] [Indexed: 01/21/2024]
Abstract
AIM To develop an artificial intelligence (AI) algorithm that diagnoses cataracts/corneal diseases from multiple conditions using smartphone images. METHODS This study included 6442 images that were captured using a slit-lamp microscope (6106 images) and smartphone (336 images). An AI algorithm was developed based on slit-lamp images to differentiate 36 major diseases (cataracts and corneal diseases) into 9 categories. To validate the AI model, smartphone images were used for the testing dataset. We evaluated AI performance that included sensitivity, specificity and receiver operating characteristic (ROC) curve for the diagnosis and triage of the diseases. RESULTS The AI algorithm achieved an area under the ROC curve of 0.998 (95% CI, 0.992 to 0.999) for normal eyes, 0.986 (95% CI, 0.978 to 0.997) for infectious keratitis, 0.960 (95% CI, 0.925 to 0.994) for immunological keratitis, 0.987 (95% CI, 0.978 to 0.996) for cornea scars, 0.997 (95% CI, 0.992 to 1.000) for ocular surface tumours, 0.993 (95% CI, 0.984 to 1.000) for corneal deposits, 1.000 (95% CI, 1.000 to 1.000) for acute angle-closure glaucoma, 0.992 (95% CI, 0.985 to 0.999) for cataracts and 0.993 (95% CI, 0.985 to 1.000) for bullous keratopathy. The triage of referral suggestion using the smartphone images exhibited high performance, in which the sensitivity and specificity were 1.00 (95% CI, 0.478 to 1.00) and 1.00 (95% CI, 0.976 to 1.000) for 'urgent', 0.867 (95% CI, 0.683 to 0.962) and 1.00 (95% CI, 0.971 to 1.000) for 'semi-urgent', 0.853 (95% CI, 0.689 to 0.950) and 0.983 (95% CI, 0.942 to 0.998) for 'routine' and 1.00 (95% CI, 0.958 to 1.00) and 0.896 (95% CI, 0.797 to 0.957) for 'observation', respectively. CONCLUSIONS The AI system achieved promising performance in the diagnosis of cataracts and corneal diseases.
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Affiliation(s)
- Yuta Ueno
- Department of Ophthalmology, University of Tsukuba, Tsukuba, Japan
| | - Masahiro Oda
- Information Technology Center, Nagoya University, Nagoya, Japan
- Graduate School of Informatics, Nagoya University, Nagoya, Japan
| | - Takefumi Yamaguchi
- Department of Ophthalmology, Tokyo Dental College Ichikawa General Hospital, Ichikawa, Japan
| | - Hideki Fukuoka
- Department of Ophthalmology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | | | - Yoshiyuki Kitaguchi
- Department of Ophthalmology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Masahiro Miyake
- Department of Ophthalmology and Vusual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Masato Akiyama
- Department of Ocular Pathology and Imaging Science, Kyushu University, Fukuoka, Japan
| | | | - Kenji Kashiwagi
- Department of Ophthalmology, University of Yamanashi, Kofu, Japan
| | - Naoyuki Maeda
- Department of Ophthalmology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Jun Shimazaki
- Department of Ophthalmology, Tokyo Dental College Ichikawa General Hospital, Ichikawa, Japan
| | - Hisashi Noma
- Department of Data Science, Institute of Statistical Mathematics, Tokyo, Japan
| | - Kensaku Mori
- Information Technology Center, Nagoya University, Nagoya, Japan
- Graduate School of Informatics, Nagoya University, Nagoya, Japan
- National Institute of Informatics, Tokyo, Japan
| | - Tetsuro Oshika
- Department of Ophthalmology, University of Tsukuba, Tsukuba, Japan
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Akasaki Y, Inomata T, Iwagami M, Sung J, Nagino K, Adachi T, Morita H, Tamari M, Kainuma K, Kan‐o K, Ogata H, Sakashita M, Futamura M, Kurashima Y, Nakajima S, Masaki K, Ogawa Y, Sato S, Miyagawa A, Midorikawa‐Inomata A, Fujimoto K, Okumura Y, Fujio K, Huang T, Hirosawa K, Morooka Y, Murakami A, Nakao S. The impact of COVID-19 on hay fever treatment in Japan: A retrospective cohort study based on the Japanese claims database. Clin Transl Allergy 2024; 14:e12394. [PMID: 39286886 PMCID: PMC11406147 DOI: 10.1002/clt2.12394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 08/10/2024] [Accepted: 09/05/2024] [Indexed: 09/19/2024] Open
Abstract
BACKGROUND Hay fever (HF) presents with various symptoms, including allergic conjunctivitis and rhinitis, and requires cross-organ treatment. This study assessed the impact of the coronavirus disease 2019 (COVID-19) pandemic on HF treatment trends. METHODS This retrospective cohort study utilized data from the JMDC database collected between January 2018 and May 2021. Patients with HF were identified based on the relevant International Classification of Diseases 10th Revision diagnosis codes and the prescription of HF-related medications. The treatment approaches were compared during the cedar and cypress pollen allergy season (January to May in Japan) before and during the COVID-19 pandemic (2018 and 2019, and 2020 and 2021, respectively). RESULTS This study included 2,598,178 patients with HF. The numbers of prescribed HF-related claims in 2018, 2019, 2020, and 2021 were 3,332,854, 3,534,198, 2,774,380, and 2,786,681 times, respectively. Oral second-generation antihistamine prescriptions decreased by >10% from 2019 to 2020, with a <10% change in the subsequent year. Anti-allergic eye drop prescriptions also decreased by >10% from 2019 to 2020 but increased by >10% from 2020 to 2021. Compared with 2018, 2019, and 2020, the number of claims in the rhinitis symptoms dominant group was significantly decreased in 2021 (p < 0.001, all). In contrast, the number of claims in the eye symptoms dominant group and the rhinitis and eye symptoms dominant group increased in 2021 compared with that in 2018, 2019, and 2020 (p < 0.001, all). CONCLUSION Changes in HF treatment and related outcomes could be attributed to lifestyle modifications resulting from the COVID-19 pandemic. Measures, such as limiting outdoor activities and adopting mask-wearing practices may have influenced HF symptoms, preventive behaviors, and the overall approach to treating HF.
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Fujio K, Sung J, Hirosawa K, Yamaguchi M, Toshida H, Inagaki K, Ishida G, Itoi M, Sado K, Hayatsu H, Nobutaka H, Ono J, Taniguchi H, Iwagami M, Nagino K, Okumura Y, Midorikawa-Inomata A, Akasaki Y, Huang T, Morooka Y, Okuyama T, Nakao S, Murakami A, Kobayashi H, Inomata T. Effect of antihistamine-releasing contact lenses on ocular symptoms and treatment behavior in patients with seasonal allergic conjunctivitis: A retrospective study. Heliyon 2024; 10:e33385. [PMID: 39027577 PMCID: PMC467065 DOI: 10.1016/j.heliyon.2024.e33385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 06/12/2024] [Accepted: 06/20/2024] [Indexed: 07/20/2024] Open
Abstract
Purpose This study aimed to compare subjective allergic conjunctivitis symptoms and anti-allergic eye drop use patterns between antihistamine-releasing contact lens users and daily disposable soft contact lens users during Japan's hay fever season. Methods This web-based retrospective cohort study included daily disposable soft contact lens or antihistamine-releasing contact lens users with a history of seasonal allergic conjunctivitis who regularly used daily disposable soft contact lenses since the previous year. The total ocular symptom score (range 0-20) based on 5-item questionnaire scores and time from the start of the hay fever season to the initiation of anti-allergic eye drop treatment were compared between antihistamine-releasing contact lens users and daily disposable soft contact lens users. Results The study included 24 participants: 17 using daily disposable soft contact lenses and 7 using antihistamine-releasing contact lenses. Antihistamine-releasing contact lens users experienced a greater reduction in total ocular symptom score from 2021 to 2022 compared with daily disposable soft contact lens users (mean total ocular symptom score [standard deviation]: daily disposable soft contact lens: -0.65 [1.4], antihistamine-releasing contact lens: -4.7 [3.6]; n = 24; Mann-Whitney U test, P = 0.010). Fourteen daily disposable soft contact lens users and five antihistamine-releasing contact lens users eventually required anti-allergic eye drops. Kaplan-Meier analysis revealed a significant delay in the initiation of anti-allergic eye drop treatment among those using antihistamine-releasing contact lenses compared with those using daily disposable soft contact lenses (median days, daily disposable soft contact lenses: 19 days, antihistamine-releasing contact lens: 57 days; n = 24; log-rank test, P = 0.045). Conclusions Antihistamine-releasing contact lenses can potentially mitigate worsening ocular allergic responses during the hay fever season when used appropriately as a preventive measure.
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Affiliation(s)
- Kenta Fujio
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Jaemyoung Sung
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Ophthalmology, Tulane University School of Medicine, 131 S. Robertson St., 12th Floor, New Orleans, LA, 70112, USA
| | - Kunihiko Hirosawa
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Masahiro Yamaguchi
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Hiroshi Toshida
- Department of Ophthalmology, Shizuoka Hospital, Juntendo University, Shizuoka, Japan
| | | | | | | | | | | | | | | | - Hidetaka Taniguchi
- Okachimachi Taniguchi Eye Clinic, Tokyo, Japan
- Shinshizuoka Taniguchi Eye Clinic, Shizuoka, Japan
| | - Masao Iwagami
- Department of Health Services Research, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Ken Nagino
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Telemedicine and Mobile Health, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuichi Okumura
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Telemedicine and Mobile Health, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akie Midorikawa-Inomata
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Telemedicine and Mobile Health, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yasutsugu Akasaki
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Tianxiang Huang
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuki Morooka
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Tomoko Okuyama
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Shintaro Nakao
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akira Murakami
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Hiroyuki Kobayashi
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Takenori Inomata
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Telemedicine and Mobile Health, Juntendo University Graduate School of Medicine, Tokyo, Japan
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4
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Akasaki Y, Iwagami M, Sung J, Nagino K, Adachi T, Morita H, Tamari M, Kainuma K, Kan-O K, Ogata H, Sakashita M, Futamura M, Kurashima Y, Nakajima S, Masaki K, Ogawa Y, Sato S, Miyagawa A, Midorikawa-Inomata A, Fujimoto K, Okumura Y, Fujio K, Huang T, Hirosawa K, Morooka Y, Nakao S, Murakami A, Kobayashi H, Inomata T. Impact of COVID-19 on care-seeking patterns for hay fever in Japan: A retrospective claims database cohort study. Allergy 2024; 79:1056-1060. [PMID: 37966466 DOI: 10.1111/all.15947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 10/02/2023] [Accepted: 11/05/2023] [Indexed: 11/16/2023]
Affiliation(s)
- Yasutsugu Akasaki
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- ENGAGE-Task Force, Tokyo, Japan
| | - Masao Iwagami
- Department of Health Services Research, Institute of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Jaemyoung Sung
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Ken Nagino
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Telemedicine and Mobile Health, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Takeya Adachi
- ENGAGE-Task Force, Tokyo, Japan
- Department of Dermatology, Keio University School of Medicine, Tokyo, Japan
- Department of Medical Regulatory Science, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Hideaki Morita
- ENGAGE-Task Force, Tokyo, Japan
- Department of Allergy and Clinical Immunology, National Research Institute for Child Health and Development, Tokyo, Japan
- Allergy Center, National Center for Child Health and Development, Tokyo, Japan
| | - Mayumi Tamari
- ENGAGE-Task Force, Tokyo, Japan
- Division of Molecular Genetics, Research Center for Medical Science, The Jikei University School of Medicine, Tokyo, Japan
| | - Keigo Kainuma
- ENGAGE-Task Force, Tokyo, Japan
- Institute for Clinical Research, National Hospital Organization, Mie National Hospital, Mie, Japan
| | - Keiko Kan-O
- ENGAGE-Task Force, Tokyo, Japan
- Department of Respiratory Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hiroaki Ogata
- ENGAGE-Task Force, Tokyo, Japan
- Department of Respiratory Medicine, NHO Fukuoka National Hospital, Fukuoka, Japan
| | - Masafumi Sakashita
- ENGAGE-Task Force, Tokyo, Japan
- Division of Otorhinolaryngology Head and Neck Surgery, Department of Sensory and Locomotor Medicine, University of Fukui, Fukui, Japan
| | - Masaki Futamura
- ENGAGE-Task Force, Tokyo, Japan
- Department of Pediatrics, National Hospital Organization Nagoya Medical Center, Aichi, Japan
| | - Yosuke Kurashima
- ENGAGE-Task Force, Tokyo, Japan
- Department of Innovative Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
- Institute for Advanced Academic Research, Chiba University, Chiba, Japan
- International Research and Development Center for Mucosal Vaccines, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Department of Pathology/Medicine, Allergy and Vaccines, CU-UCSD Center for Mucosal Immunology, University of California, San Diego, California, USA
- Mucosal Immunology and Allergy Therapeutics, Institute for Global Prominent Research, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Saeko Nakajima
- ENGAGE-Task Force, Tokyo, Japan
- Department of Drug Discovery for Inflammatory Skin Diseases, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Katsunori Masaki
- ENGAGE-Task Force, Tokyo, Japan
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Yasushi Ogawa
- ENGAGE-Task Force, Tokyo, Japan
- Department of Advanced Medicine, Nagoya University Hospital, Aichi, Japan
| | - Sakura Sato
- ENGAGE-Task Force, Tokyo, Japan
- Department of Allergy, Clinical Research Center for Allergy and Rheumatology, NHO Sagamihara National Hospital, Kanagawa, Japan
| | - Akihiro Miyagawa
- Department of Dermatology, Keio University School of Medicine, Tokyo, Japan
| | - Akie Midorikawa-Inomata
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Telemedicine and Mobile Health, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Keiichi Fujimoto
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuichi Okumura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Telemedicine and Mobile Health, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kenta Fujio
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Tianxiang Huang
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kunihiko Hirosawa
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuki Morooka
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Shintaro Nakao
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akira Murakami
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Hiroyuki Kobayashi
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Takenori Inomata
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- ENGAGE-Task Force, Tokyo, Japan
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Telemedicine and Mobile Health, Juntendo University Graduate School of Medicine, Tokyo, Japan
- AI Incubation Farm, Juntendo University Graduate School of Medicine, Tokyo, Japan
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5
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Inomata T, Sung J, Nakamura M, Iwagami M, Akasaki Y, Fujio K, Nakamura M, Ebihara N, Ide T, Nagao M, Okumura Y, Nagino K, Fujimoto K, Eguchi A, Hirosawa K, Midorikawa-Inomata A, Muto K, Fujisawa K, Kikuchi Y, Nojiri S, Murakami A. Using the AllerSearch Smartphone App to Assess the Association Between Dry Eye and Hay Fever: mHealth-Based Cross-Sectional Study. J Med Internet Res 2023; 25:e38481. [PMID: 37698897 PMCID: PMC10523221 DOI: 10.2196/38481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 09/21/2022] [Accepted: 08/17/2023] [Indexed: 09/13/2023] Open
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.
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Affiliation(s)
- Takenori Inomata
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Jaemyoung Sung
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Masahiro Nakamura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Bioengineering, Graduate School of Bioengineering, Precision Health, The University of Tokyo, Tokyo, Japan
| | - Masao Iwagami
- Department of Health Services Research, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Yasutsugu Akasaki
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kenta Fujio
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Masahiro Nakamura
- Department of Otorhinolaryngology, Head and Neck Surgery, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Nobuyuki Ebihara
- Department of Ophthalmology, Urayasu Hospital, Juntendo University, Chiba, Japan
| | - Takuma Ide
- Department of Otorhinolaryngology, Head and Neck Surgery, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Masashi Nagao
- Medical Technology Innovation Center, Juntendo University, Tokyo, Japan
- Department of Orthopedic Surgery, Juntendo University Faculty of Medicine, Tokyo, Japan
- Graduate School of Health and Sports Science, Juntendo University, Tokyo, Japan
| | - Yuichi Okumura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Ken Nagino
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Keiichi Fujimoto
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Atsuko Eguchi
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kunihiko Hirosawa
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akie Midorikawa-Inomata
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kaori Muto
- Department of Public Policy, The Institute of Medical Science, Human Genome Center, The University of Tokyo, Tokyo, Japan
| | - Kumiko Fujisawa
- Department of Public Policy, The Institute of Medical Science, Human Genome Center, The University of Tokyo, Tokyo, Japan
| | - Yota Kikuchi
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Shuko Nojiri
- Medical Technology Innovation Center, Juntendo University, Tokyo, Japan
| | - Akira Murakami
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
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6
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Nagino K, Okumura Y, Akasaki Y, Fujio K, Huang T, Sung J, Midorikawa-Inomata A, Fujimoto K, Eguchi A, Hurramhon S, Yee A, Miura M, Ohno M, Hirosawa K, Morooka Y, Murakami A, Kobayashi H, Inomata T. Smartphone App-Based and Paper-Based Patient-Reported Outcomes Using a Disease-Specific Questionnaire for Dry Eye Disease: Randomized Crossover Equivalence Study. J Med Internet Res 2023; 25:e42638. [PMID: 37535409 PMCID: PMC10436120 DOI: 10.2196/42638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 03/22/2023] [Accepted: 07/12/2023] [Indexed: 08/04/2023] Open
Abstract
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.
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Affiliation(s)
- Ken Nagino
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuichi Okumura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yasutsugu Akasaki
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- AI Incubation Farm, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kenta Fujio
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Tianxiang Huang
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Jaemyoung Sung
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akie Midorikawa-Inomata
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Keiichi Fujimoto
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Atsuko Eguchi
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Shokirova Hurramhon
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Alan Yee
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Maria Miura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Mizu Ohno
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kunihiko Hirosawa
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuki Morooka
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akira Murakami
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Hiroyuki Kobayashi
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Takenori Inomata
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- AI Incubation Farm, Juntendo University Graduate School of Medicine, Tokyo, Japan
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7
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Nagino K, Sung J, Midorikawa‐Inomata A, Eguchi A, Fujimoto K, Okumura Y, Yee A, Fujio K, Akasaki Y, Huang T, Miura M, Hurramhon S, Hirosawa K, Ohno M, Morooka Y, Kobayashi H, Inomata T. The minimal clinically important difference of app-based electronic patient-reported outcomes for hay fever. Clin Transl Allergy 2023; 13:e12244. [PMID: 37227421 PMCID: PMC10151605 DOI: 10.1002/clt2.12244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 03/06/2023] [Accepted: 04/03/2023] [Indexed: 05/26/2023] Open
Abstract
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.
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Affiliation(s)
- Ken Nagino
- Department of Hospital AdministrationJuntendo University Graduate School of MedicineTokyoJapan
- Department of OphthalmologyJuntendo University Graduate School of MedicineTokyoJapan
- Department of Digital MedicineJuntendo University Graduate School of MedicineTokyoJapan
| | - Jaemyoung Sung
- Department of OphthalmologyJuntendo University Graduate School of MedicineTokyoJapan
- University of South FloridaMorsani College of MedicineTampaFloridaUSA
| | - Akie Midorikawa‐Inomata
- Department of Hospital AdministrationJuntendo University Graduate School of MedicineTokyoJapan
| | - Atsuko Eguchi
- Department of Hospital AdministrationJuntendo University Graduate School of MedicineTokyoJapan
| | - Keiichi Fujimoto
- Department of OphthalmologyJuntendo University Graduate School of MedicineTokyoJapan
| | - Yuichi Okumura
- Department of OphthalmologyJuntendo University Graduate School of MedicineTokyoJapan
- Department of Digital MedicineJuntendo University Graduate School of MedicineTokyoJapan
| | - Alan Yee
- Department of OphthalmologyJuntendo University Graduate School of MedicineTokyoJapan
- Department of Digital MedicineJuntendo University Graduate School of MedicineTokyoJapan
| | - Kenta Fujio
- Department of OphthalmologyJuntendo University Graduate School of MedicineTokyoJapan
- Department of Digital MedicineJuntendo University Graduate School of MedicineTokyoJapan
| | - Yasutsugu Akasaki
- Department of OphthalmologyJuntendo University Graduate School of MedicineTokyoJapan
- Department of Digital MedicineJuntendo University Graduate School of MedicineTokyoJapan
| | - Tianxiang Huang
- Department of OphthalmologyJuntendo University Graduate School of MedicineTokyoJapan
- Department of Digital MedicineJuntendo University Graduate School of MedicineTokyoJapan
| | - Maria Miura
- Department of OphthalmologyJuntendo University Graduate School of MedicineTokyoJapan
- Department of Digital MedicineJuntendo University Graduate School of MedicineTokyoJapan
| | - Shokirova Hurramhon
- Department of OphthalmologyJuntendo University Graduate School of MedicineTokyoJapan
| | - Kunihiko Hirosawa
- Department of OphthalmologyJuntendo University Graduate School of MedicineTokyoJapan
- Department of Digital MedicineJuntendo University Graduate School of MedicineTokyoJapan
| | - Mizu Ohno
- Department of OphthalmologyJuntendo University Graduate School of MedicineTokyoJapan
- Department of Digital MedicineJuntendo University Graduate School of MedicineTokyoJapan
| | - Yuki Morooka
- Department of OphthalmologyJuntendo University Graduate School of MedicineTokyoJapan
- Department of Digital MedicineJuntendo University Graduate School of MedicineTokyoJapan
| | - Hiroyuki Kobayashi
- Department of Hospital AdministrationJuntendo University Graduate School of MedicineTokyoJapan
| | - Takenori Inomata
- Department of Hospital AdministrationJuntendo University Graduate School of MedicineTokyoJapan
- Department of OphthalmologyJuntendo University Graduate School of MedicineTokyoJapan
- Department of Digital MedicineJuntendo University Graduate School of MedicineTokyoJapan
- Juntendo University Graduate School of MedicineAI Incubation FarmTokyoJapan
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8
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Nagino K, Okumura Y, Yamaguchi M, Sung J, Nagao M, Fujio K, Akasaki Y, Huang T, Hirosawa K, Iwagami M, Midorikawa-Inomata A, Fujimoto K, Eguchi A, Okajima Y, Kakisu K, Tei Y, Yamaguchi T, Tomida D, Fukui M, Yagi-Yaguchi Y, Hori Y, Shimazaki J, Nojiri S, Morooka Y, Yee A, Miura M, Ohno M, Inomata T. Diagnostic Ability of a Smartphone App for Dry Eye Disease: Protocol for a Multicenter, Open-Label, Prospective, and Cross-sectional Study. JMIR Res Protoc 2023; 12:e45218. [PMID: 36912872 PMCID: PMC10131757 DOI: 10.2196/45218] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/31/2023] [Accepted: 01/31/2023] [Indexed: 03/14/2023] Open
Abstract
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.
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Affiliation(s)
- Ken Nagino
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuichi Okumura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Masahiro Yamaguchi
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Jaemyoung Sung
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Morsani College of Medicine, University of South Florida, Tampa, FL, United States
| | - Masashi Nagao
- Department of Orthopedics, Juntendo University Faculty of Medicine, Tokyo, Japan.,Medical Technology Innovation Center, Juntendo University, Tokyo, Japan.,Graduate School of Health and Sports Science, Juntendo University, Tokyo, Japan
| | - Kenta Fujio
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yasutsugu Akasaki
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Tianxiang Huang
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kunihiko Hirosawa
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Masao Iwagami
- Department of Health Services Research, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Akie Midorikawa-Inomata
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Keiichi Fujimoto
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Atsuko Eguchi
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yukinobu Okajima
- Department of Ophthalmology, Toho University Omori Medical Center, Tokyo, Japan
| | - Koji Kakisu
- Department of Ophthalmology, Toho University Omori Medical Center, Tokyo, Japan
| | - Yuto Tei
- Department of Ophthalmology, Toho University Omori Medical Center, Tokyo, Japan
| | - Takefumi Yamaguchi
- Department of Ophthalmology, Tokyo Dental College Ichikawa General Hospital, Chiba, Japan
| | - Daisuke Tomida
- Department of Ophthalmology, Tokyo Dental College Ichikawa General Hospital, Chiba, Japan
| | - Masaki Fukui
- Department of Ophthalmology, Tokyo Dental College Ichikawa General Hospital, Chiba, Japan
| | - Yukari Yagi-Yaguchi
- Department of Ophthalmology, Tokyo Dental College Ichikawa General Hospital, Chiba, Japan
| | - Yuichi Hori
- Department of Ophthalmology, Toho University Omori Medical Center, Tokyo, Japan
| | - Jun Shimazaki
- Department of Ophthalmology, Tokyo Dental College Ichikawa General Hospital, Chiba, Japan
| | - Shuko Nojiri
- Medical Technology Innovation Center, Juntendo University, Tokyo, Japan
| | - Yuki Morooka
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Alan Yee
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Maria Miura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Mizu Ohno
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Takenori Inomata
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan.,AI Incubation Farm, Juntendo University Graduate School of Medicine, Tokyo, Japan
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9
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Inomata T, Sung J, Fujio K, Nakamura M, Akasaki Y, Nagino K, Okumura Y, Iwagami M, Fujimoto K, Ebihara N, Nakamura M, Midorikawa-Inomata A, Shokirova H, Huang T, Hirosawa K, Miura M, Ohno M, Morooka Y, Iwata N, Iwasaki Y, Murakami A. Individual multidisciplinary clinical phenotypes of nasal and ocular symptoms in hay fever: Crowdsourced cross-sectional study using AllerSearch. Allergol Int 2023:S1323-8930(23)00001-1. [PMID: 36740498 DOI: 10.1016/j.alit.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 10/08/2022] [Accepted: 12/15/2022] [Indexed: 02/05/2023] Open
Abstract
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.
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Affiliation(s)
- Takenori Inomata
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan; AI Incubation Farm, Juntendo University Graduate School of Medicine, Tokyo, Japan.
| | - Jaemyoung Sung
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Kenta Fujio
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Masahiro Nakamura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan; Precision Health, Department of Bioengineering, Graduate School of Bioengineering, The University of Tokyo, Tokyo, Japan
| | - Yasutsugu Akasaki
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Ken Nagino
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuichi Okumura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Masao Iwagami
- Department of Health Services Research, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Keiichi Fujimoto
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Nobuyuki Ebihara
- Department of Ophthalmology, Urayasu Hospital, Juntendo University, Chiba, Japan
| | - Masahiro Nakamura
- Department of Otorhinolaryngology and Head and Neck Surgery, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Akie Midorikawa-Inomata
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Hurramhon Shokirova
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Tianxiang Huang
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kunihiko Hirosawa
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Maria Miura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Mizu Ohno
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuki Morooka
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Nanami Iwata
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuma Iwasaki
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akira Murakami
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
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10
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INOMATA TAKENORI, SUNG JAEMYOUNG, YEE ALAN, MURAKAMI AKIRA, OKUMURA YUICHI, NAGINO KEN, FUJIO KENTA, AKASAKI YASUTSUGU, MIDORIKAWA-INOMATA AKIE, EGUCHI ATSUKO, FUJIMOTO KEIICHI, HUANG TIANXIANG, MOROOKA YUKI, MIURA MARIA, SHOKIROVA HURRAMHON, HIROSAWA KUNIHIKO, OHNO MIZU, KOBAYASHI HIROYUKI. P4 Medicine for Heterogeneity of Dry Eye: A Mobile Health-based Digital Cohort Study. JUNTENDO IJI ZASSHI = JUNTENDO MEDICAL JOURNAL 2023; 69:2-13. [PMID: 38854846 PMCID: PMC11153075 DOI: 10.14789/jmj.jmj22-0032-r] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 10/11/2022] [Indexed: 06/11/2024]
Abstract
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.
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Affiliation(s)
- TAKENORI INOMATA
- Corresponding author: Takenori Inomata, Juntendo University Graduate School of Medicine, Department of Ophthalmology, 2-1-1 Hongo, Bunkyo-ku, Tokyo. 113-8431, Japan, TEL: +81-3-5802-1228 FAX: +81-3-5689-0394 E-mail:
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11
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Micera A, Di Zazzo A, De Piano M, Sharma S, Mori T, De Gregorio C, Coassin M, Fernandes M. Tissue remodeling in adult vernal keratoconjunctivitis. Exp Eye Res 2022; 225:109301. [PMID: 36336099 DOI: 10.1016/j.exer.2022.109301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 09/30/2022] [Accepted: 10/25/2022] [Indexed: 11/06/2022]
Abstract
Our aim is to describe local tissue remodeling in a cohort of adult VKC patients. Male patients diagnosed with active VKC were enrolled in an open pilot study into two groups according disease onset: childhood classic VKC and adult VKC. Visual acuity and ocular surface clinical examination focusing on chronic inflammatory sequelae and impression cytology were performed in all enrolled subjects. Conjunctival imprints were processed for molecular, biochemical and immunofluorescent analysis for tissue remodeling (TGFβ1,2,3 and αSMA) and epigenetic (DNMT3a, Keap1; Nrf2) markers as well as androgen receptors were investigated and compared between groups. Clinical assessment showed increased conjunctival scarring in adult VKC compared to classic VKC. Immunoreactivity for αSMA and expression of TGFβ were higher in adult VKC group. Significantly higher levels of TGFβ3 (3.44 ± 1.66; p < 0.05) were detected in adult VKC compared to childhood VKC, associated with an increasing trend of TGFβ1 (1.58 ± 0.25) and TGFβ2 (1.65 ± 0.20) isoforms levels. Molecular analysis showed a relative increase in tissue remodeling/fibrogenic transcripts (TGFβ isoforms and αSMA) associated to a significant increase of selective epigenetic targets (DNMT3, Nrf2 and keap1) in adult VKC phenotype. Increased local conjunctival androgen receptors was detected in patients with adult variants compared to classic childhood VKC and healthy subjects. Finally, a direct correlation between TGFβ and androgen receptor expression was also detected. A pro-fibrotic clinical and biomolecular trait was unveiled in adult variant of VKC, which causes ocular surface disease and visual impairment.
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Affiliation(s)
- Alessandra Micera
- Research and Development Laboratory for Biochemical, Molecular and Cellular Applications in Ophthalmological Sciences, IRCCS - Fondazione Bietti, Rome, Italy
| | - Antonio Di Zazzo
- Ophthalmology Operative Complex Unit, University Campus Bio-Medico, Rome, Italy
| | - Maria De Piano
- Research and Development Laboratory for Biochemical, Molecular and Cellular Applications in Ophthalmological Sciences, IRCCS - Fondazione Bietti, Rome, Italy
| | - Savitri Sharma
- Jhaveri Microbiology Centre, LV Prasad Eye Institute, Kallam Anji Reddy Campus, Hyderabad, India
| | - Tommaso Mori
- Ophthalmology Operative Complex Unit, University Campus Bio-Medico, Rome, Italy
| | - Chiara De Gregorio
- Ophthalmology Operative Complex Unit, University Campus Bio-Medico, Rome, Italy
| | - Marco Coassin
- Ophthalmology Operative Complex Unit, University Campus Bio-Medico, Rome, Italy
| | - Merle Fernandes
- Cornea and Anterior Segment Services, LV Prasad Eye Institute, GMR Varalakshmi Campus, Visakhapatnam, India.
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12
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Sousa‐Pinto B, Anto A, Berger M, Dramburg S, Pfaar O, Klimek L, Jutel M, Czarlewski W, Bedbrook A, Valiulis A, Agache I, Amaral R, Ansotegui IJ, Bastl K, Berger U, Bergmann KC, Bosnic‐Anticevich S, Braido F, Brussino L, Cardona V, Casale T, Canonica GW, Cecchi L, Charpin D, Chivato T, Chu DK, Cingi C, Costa EM, Cruz AA, Devillier P, Durham SR, Ebisawa M, Fiocchi A, Fokkens WJ, Gemicioğlu B, Gotua M, Guzmán M, Haahtela T, Ivancevich JC, Kuna P, Kaidashev I, Khaitov M, Kvedariene V, Larenas‐Linnemann DE, Lipworth B, Laune D, Matricardi PM, Morais‐Almeida M, Mullol J, Naclerio R, Neffen H, Nekam K, Niedoszytko M, Okamoto Y, Papadopoulos NG, Park H, Passalacqua G, Patella V, Pelosi S, Pham‐Thi N, Popov TA, Regateiro FS, Reitsma S, Rodriguez‐Gonzales M, Rosario N, Rouadi PW, Samolinski B, Sá‐Sousa A, Sastre J, Sheikh A, Ulrik CS, Taborda‐Barata L, Todo‐Bom A, Tomazic PV, Toppila‐Salmi S, Tripodi S, Tsiligianni I, Valovirta E, Ventura MT, Valero AA, Vieira RJ, Wallace D, Waserman S, Williams S, Yorgancioglu A, Zhang L, Zidarn M, Zuberbier J, Olze H, Antó JM, Zuberbier T, Fonseca JA, Bousquet J. Real-world data using mHealth apps in rhinitis, rhinosinusitis and their multimorbidities. Clin Transl Allergy 2022; 12:e12208. [PMID: 36434742 PMCID: PMC9673175 DOI: 10.1002/clt2.12208] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 09/02/2022] [Accepted: 09/16/2022] [Indexed: 11/19/2022] Open
Abstract
Digital health is an umbrella term which encompasses eHealth and benefits from areas such as advanced computer sciences. eHealth includes mHealth apps, which offer the potential to redesign aspects of healthcare delivery. The capacity of apps to collect large amounts of longitudinal, real-time, real-world data enables the progression of biomedical knowledge. Apps for rhinitis and rhinosinusitis were searched for in the Google Play and Apple App stores, via an automatic market research tool recently developed using JavaScript. Over 1500 apps for allergic rhinitis and rhinosinusitis were identified, some dealing with multimorbidity. However, only six apps for rhinitis (AirRater, AllergyMonitor, AllerSearch, Husteblume, MASK-air and Pollen App) and one for rhinosinusitis (Galenus Health) have so far published results in the scientific literature. These apps were reviewed for their validation, discovery of novel allergy phenotypes, optimisation of identifying the pollen season, novel approaches in diagnosis and management (pharmacotherapy and allergen immunotherapy) as well as adherence to treatment. Published evidence demonstrates the potential of mobile health apps to advance in the characterisation, diagnosis and management of rhinitis and rhinosinusitis patients.
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Affiliation(s)
- Bernardo Sousa‐Pinto
- MEDCIDS ‐ Department of Community Medicine, Information and Health Decision SciencesFaculty of MedicineUniversity of PortoPortoPortugal
- CINTESIS – Center for Health Technology and Services Research, University of PortoPortoPortugal
- RISE – Health Research NetworkUniversity of PortoPortoPortugal
| | | | - Markus Berger
- Department of Pathophysiology and Allergy ResearchCenter for Pathophysiology, Infectiology and ImmunologyMedical University of ViennaViennaAustria
- Department for Oto‐Rhino‐Laryngology, Head and Neck SurgeryMedical University of ViennaViennaAustria
| | - Stephanie Dramburg
- Pediatric Pulmonology, Immunology and Intensive Care MedicineCharité Universitätsmedizin BerlinBerlinGermany
| | - Oliver Pfaar
- Department of Otorhinolaryngology, Head and Neck SurgerySection of Rhinology and AllergyUniversity Hospital MarburgPhilipps‐Universität MarburgMarburgGermany
| | - Ludger Klimek
- Department of Otolaryngology, Head and Neck SurgeryUniversitätsmedizin MainzMainzGermany
- Center for Rhinology and AllergologyWiesbadenGermany
| | - Marek Jutel
- Department of Clinical ImmunologyWrocław Medical UniversityALL‐MED Medical Research InstituteWroclawPoland
| | | | - Anna Bedbrook
- MASK‐airMontpellierFrance
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Allergology and ImmunologyBerlinGermany
| | - Arunas Valiulis
- Institute of Clinical Medicine and Institute of Health SciencesMedical Faculty of Vilnius UniversityVilniusLithuania
| | | | - Rita Amaral
- MEDCIDS ‐ Department of Community Medicine, Information and Health Decision SciencesFaculty of MedicineUniversity of PortoPortoPortugal
- CINTESIS – Center for Health Technology and Services Research, University of PortoPortoPortugal
- RISE – Health Research NetworkUniversity of PortoPortoPortugal
| | | | - Katharina Bastl
- Department for Oto‐Rhino‐Laryngology, Head and Neck SurgeryMedical University of ViennaViennaAustria
| | - Uwe Berger
- Department for Oto‐Rhino‐Laryngology, Head and Neck SurgeryMedical University of ViennaViennaAustria
| | - Karl C. Bergmann
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Allergology and ImmunologyBerlinGermany
- Institute of AllergologyCharité – Universitätsmedizin BerlinCorporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
| | - Sinthia Bosnic‐Anticevich
- Quality Use of Respiratory Medicine GroupWoolcock Institute of Medical ResearchThe University of SydneySydneyNew South WalesAustralia
| | - Fulvio Braido
- Department of Internal Medicine (DiMI), University of GenoaIRCCS Ospedale Policlinico San MartinoGenovaItaly
| | - Luisa Brussino
- Department of Medical SciencesAllergy and Clinical Immunology UnitUniversity of Torino & Mauriziano HospitalTorinoItaly
| | - Victoria Cardona
- Allergy SectionDepartment of Internal MedicineHospital Vall d'Hebron & ARADyAL Research NetworkBarcelonaSpain
| | - Thomas Casale
- Division of Allergy/immunologyUniversity of South FloridaTampaFloridaUSA
| | - G. Walter Canonica
- Department of Biomedical SciencesHumanitas UniversityPieve Emanuele, Milan & Personalized Medicine, Asthma and Allergy, Humanitas Clinical and Research Center IRCCSRozzanoItaly
| | - Lorenzo Cecchi
- SOS Allergology and Clinical ImmunologyUSL Toscana CentroPratoItaly
| | - Denis Charpin
- Clinique des bronches, allergie et sommeilHôpital NordMarseilleFrance
| | - Tomás Chivato
- School of MedicineUniversity CEU San PabloMadridSpain
| | - Derek K. Chu
- Department of Health Research Methods, Evidence and Impact & Department of MedicineMcMaster UniversityHamiltonOntarioCanada
| | - Cemal Cingi
- Eskisehir Osmangazi UniversityMedical FacultyENT DepartmentEskisehirTurkey
| | - Elisio M. Costa
- UCIBIOREQUINTEFaculty of Pharmacy and Competence Center on Active and Healthy Ageing of University of Porto (Porto4Ageing)PortoPortugal
| | - Alvaro A. Cruz
- Fundaçao ProARFederal University of Bahia and GARD/WHO Planning GroupSalvadorBahiaBrazil
| | - Philippe Devillier
- VIM Suresnes, UMR 0892, Pôle des Maladies des Voies Respiratoires, Hôpital FochUniversité Paris‐SaclaySuresnesFrance
| | - Stephen R. Durham
- Allergy and Clinical ImmunologyNational Heart and Lung InstituteImperial College LondonLondonUK
| | - Motohiro Ebisawa
- Clinical Research Center for Allergy and RheumatologyNHO Sagamihara National HospitalSagamiharaJapan
| | - Alessandro Fiocchi
- Division of AllergyDepartment of Pediatric Medicine ‐ The Bambino Gesù Children's Research HospitalIRCCSRomeItaly
| | - Wytske J. Fokkens
- Department of OtorhinolaryngologyAmsterdam University Medical CentresAmsterdamThe Netherlands
| | - Bilun Gemicioğlu
- Department of Pulmonary DiseasesIstanbul University‐CerrahpasaCerrahpasa Faculty of MedicineIstanbulTurkey
| | - Maia Gotua
- Center of Allergy and ImmunologyGeorgian Association of Allergology and Clinical ImmunologyTbilisiGeorgia
| | | | - Tari Haahtela
- Skin and Allergy HospitalHelsinki University HospitalUniversity of HelsinkiHelsinkiFinland
| | | | - Piotr Kuna
- Division of Internal Medicine, Asthma and AllergyBarlicki University HospitalMedical University of LodzLodzPoland
| | | | - Musa Khaitov
- National Research CenterInstitute of ImmunologyFederal Medicobiological AgencyLaboratory of Molecular ImmunologyMoscowRussia
- Pirogov Russian National Research Medical UniversityMoscowRussia
| | - Violeta Kvedariene
- Institute of Biomedical SciencesDepartment of PathologyFaculty of MedicineVilnius University and Institute of Clinical Medicine, Clinic of Chest Diseases and Allergology, Faculty of Medicine, Vilnius UniversityVilniusLithuania
| | | | - Brian Lipworth
- Scottish Centre for Respiratory ResearchCardiovascular & Diabetes MedicineMedical Research InstituteNinewells HospitalUniversity of DundeeDundeeUK
| | | | - Paolo M. Matricardi
- Pediatric Pulmonology, Immunology and Intensive Care MedicineCharité Universitätsmedizin BerlinBerlinGermany
| | | | - Joaquim Mullol
- Rhinology Unit & Smell ClinicENT DepartmentHospital Clínicand Clinical & Experimental Respiratory Immunoallergy, IDIBAPS, CIBERES, University of BarcelonaBarcelonaSpain
| | | | - Hugo Neffen
- Director of Center of Allergy, Immunology and Respiratory DiseasesSanta FeArgentina
| | - Kristoff Nekam
- Hospital of the Hospitaller Brothers in BudaBudapestHungary
| | | | | | | | - Hae‐Sim Park
- Department of Allergy and Clinical ImmunologyAjou University School of MedicineSuwonSouth Korea
| | - Giovanni Passalacqua
- Allergy and Respiratory DiseasesIRCCS Polyclinic Hospital San MartinoUniversity of GenoaGenovaItaly
| | - Vincenzo Patella
- Division of Allergy and Clinical Immunology, Department of Medicine"Santa Maria della Speranza" Hospital, Battipagliaand Agency of Health ASLSalernoItaly
| | | | - Nhân Pham‐Thi
- Ecole Polytechnique PalaiseauIRBA (Institut de Recherche bio‐Médicale des Armées)BretignyFrance
| | - Ted A. Popov
- University Hospital 'Sv Ivan Rilski'SofiaBulgaria
| | - Frederico S. Regateiro
- Allergy and Clinical Immunology UnitCentro Hospitalar e Universitário de CoimbraCoimbra and Institute of ImmunologyFaculty of MedicineUniversity of CoimbraCoimbraPortugal
- Coimbra Institute for Clinical and Biomedical Research (iCBR)Faculty of MedicineUniversity of CoimbraCoimbraPortugal
| | - Sietze Reitsma
- Department of OtorhinolaryngologyAmsterdam University Medical CentresAmsterdamThe Netherlands
| | | | | | - Philip W. Rouadi
- Department of Otolaryngology‐Head and Neck SurgeryEye and Ear University HospitalBeirutLebanon
- Department of Otorhinolaryngology‐Head and Neck SurgeryDar Al Shifa HospitalSalmiyaKuwait
| | - Boleslaw Samolinski
- Department of Prevention of Environmental Hazards, Allergology and ImmunologyMedical University of WarsawWarsawPoland
| | - Ana Sá‐Sousa
- MEDCIDS ‐ Department of Community Medicine, Information and Health Decision SciencesFaculty of MedicineUniversity of PortoPortoPortugal
- CINTESIS – Center for Health Technology and Services Research, University of PortoPortoPortugal
- RISE – Health Research NetworkUniversity of PortoPortoPortugal
| | - Joaquin Sastre
- Fundacion Jimenez Diaz, CIBERESFaculty of MedicineAutonoma University of MadridMadridSpain
| | - Aziz Sheikh
- Usher InstituteThe University of EdinburghEdinburghUK
| | - Charlotte Suppli Ulrik
- Department of Respiratory MedicineCopenhagen University Hospital‐HvidovreCopenhagenDenmark
- Institute of Cinical MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Luis Taborda‐Barata
- Department of Immunoallergology, Cova da Beira University Hospital Centreand UBIAir ‐ Clinical & Experimental Lung Centre and CICS‐UBI Health Sciences Research CentreUniversity of Beira InteriorCovilhãPortugal
| | - Ana Todo‐Bom
- Allergy and Clinical Immunology UnitCentro Hospitalar e Universitário de CoimbraCoimbra and Institute of ImmunologyFaculty of MedicineUniversity of CoimbraCoimbraPortugal
| | - Peter Valentin Tomazic
- Department of General ORL, H&NSMedical University of GrazENT‐University Hospital GrazGrazAustria
| | - Sanna Toppila‐Salmi
- Skin and Allergy HospitalHelsinki University HospitalUniversity of HelsinkiHelsinkiFinland
| | | | - Ioanna Tsiligianni
- Health Planning UnitDepartment of Social MedicineFaculty of MedicineUniversity of CreteGreece and International Primary Care Respiratory Group IPCRGAberdeenScotland
| | - Erkka Valovirta
- Department of Lung Diseases and Clinical ImmunologyUniversity of Turku and Terveystalo Allergy ClinicTurkuFinland
| | | | - Antonio A. Valero
- Pneumology and Allergy Department CIBERES and Clinical & Experimental Respiratory ImmunoallergyIDIBAPSUniversity of BarcelonaBarcelonaSpain
| | - Rafael José Vieira
- MEDCIDS ‐ Department of Community Medicine, Information and Health Decision SciencesFaculty of MedicineUniversity of PortoPortoPortugal
- CINTESIS – Center for Health Technology and Services Research, University of PortoPortoPortugal
- RISE – Health Research NetworkUniversity of PortoPortoPortugal
| | - Dana Wallace
- Nova Southeastern UniversityFort LauderdaleFloridaUSA
| | - Susan Waserman
- Department of Medicine, Clinical Immunology and AllergyMcMaster UniversityHamiltonOntarioCanada
| | - Sian Williams
- International Primary Care Respiratory Group IPCRGLarbertScotland
| | - Arzu Yorgancioglu
- Department of Pulmonary DiseasesCelal Bayar University, Faculty of MedicineManisaTurkey
| | - Luo Zhang
- Department of Otolaryngology Head and Neck SurgeryBeijing TongRen Hospital and Beijing Institute of OtolaryngologyBeijingChina
| | - Mihaela Zidarn
- University Clinic of Respiratory and Allergic DiseasesGolnikSlovenia
- University of LjubljanaFaculty of MedicineLjubljanaSlovenia
| | - Jaron Zuberbier
- Department of OtorhinolaryngologyCharité‐Universitätsmedizin BerlinBerlinGermany
| | - Heidi Olze
- Department of OtorhinolaryngologyCharité‐Universitätsmedizin BerlinBerlinGermany
| | - Josep M. Antó
- ISGlobal, Barcelona Institute for Global HealthBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Universitat Pompeu Fabra (UPF)BarcelonaSpain
- CIBER Epidemiología y Salud Pública (CIBERESP)BarcelonaSpain
| | - Torsten Zuberbier
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Allergology and ImmunologyBerlinGermany
- Institute of AllergologyCharité – Universitätsmedizin BerlinCorporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
| | - João A. Fonseca
- MEDCIDS ‐ Department of Community Medicine, Information and Health Decision SciencesFaculty of MedicineUniversity of PortoPortoPortugal
- CINTESIS – Center for Health Technology and Services Research, University of PortoPortoPortugal
- RISE – Health Research NetworkUniversity of PortoPortoPortugal
| | - Jean Bousquet
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Allergology and ImmunologyBerlinGermany
- Institute of AllergologyCharité – Universitätsmedizin BerlinCorporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
- University Hospital MontpellierMontpellierFrance
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13
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Fujio K, Inomata T, Fujisawa K, Sung J, Nakamura M, Iwagami M, Muto K, Ebihara N, Nakamura M, Okano M, Akasaki Y, Okumura Y, Ide T, Nojiri S, Nagao M, Fujimoto K, Hirosawa K, Murakami A. Patient and public involvement in mobile health-based research for hay fever: a qualitative study of patient and public involvement implementation process. RESEARCH INVOLVEMENT AND ENGAGEMENT 2022; 8:45. [PMID: 36056430 PMCID: PMC9437402 DOI: 10.1186/s40900-022-00382-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 08/29/2022] [Indexed: 06/15/2023]
Abstract
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.
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Affiliation(s)
- Kenta Fujio
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0033, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Takenori Inomata
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0033, Japan.
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan.
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan.
| | - Kumiko Fujisawa
- Department of Public Policy, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Jaemyoung Sung
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0033, Japan
| | - Masahiro Nakamura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0033, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Masao Iwagami
- Department of Health Services Research, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Kaori Muto
- Department of Public Policy, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Nobuyuki Ebihara
- Department of Ophthalmology, Urayasu Hospital, Juntendo University, Chiba, Japan
| | - Masahiro Nakamura
- Department of Otorhinolaryngology, Head and Neck Surgery, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Mitsuhiro Okano
- Department of Otorhinolaryngology, International University of Health and Welfare, Narita, Japan
| | - Yasutsugu Akasaki
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0033, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuichi Okumura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0033, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Takuma Ide
- Department of Otorhinolaryngology, Head and Neck Surgery, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Shuko Nojiri
- Medical Technology Innovation Center, Juntendo University, Tokyo, Japan
| | - Masashi Nagao
- Medical Technology Innovation Center, Juntendo University, Tokyo, Japan
- Department of Orthopedic Surgery, Juntendo University Graduate School of Medicine, Tokyo, Japan
- School of Health and Sports Science, Juntendo University, Chiba, Japan
| | - Keiichi Fujimoto
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0033, Japan
| | - Kunihiko Hirosawa
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0033, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akira Murakami
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0033, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
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14
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Akasaki Y, Inomata T, Sung J, Okumura Y, Fujio K, Miura M, Hirosawa K, Iwagami M, Nakamura M, Ebihara N, Nakamura M, Ide T, Nagino K, Murakami A. Reliability and Validity of Electronic Patient-Reported Outcomes Using the Smartphone App AllerSearch for Hay Fever: Prospective Observational Study. JMIR Form Res 2022; 6:e38475. [PMID: 35998022 PMCID: PMC9449823 DOI: 10.2196/38475] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/08/2022] [Accepted: 08/10/2022] [Indexed: 01/20/2023] Open
Abstract
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.
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Affiliation(s)
- Yasutsugu Akasaki
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Takenori Inomata
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Jaemyoung Sung
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuichi Okumura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kenta Fujio
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Maria Miura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kunihiko Hirosawa
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Masao Iwagami
- Department of Health Services Research, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Masahiro Nakamura
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Nobuyuki Ebihara
- Department of Ophthalmology, Urayasu Hospital, Juntendo University, Chiba, Japan
| | - Masahiro Nakamura
- Department of Otorhinolaryngology, Head and Neck Surgery, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Takuma Ide
- Department of Otorhinolaryngology, Head and Neck Surgery, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Ken Nagino
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akira Murakami
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
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15
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Prevalence of Comorbidity between Dry Eye and Allergic Conjunctivitis: A Systematic Review and Meta-Analysis. J Clin Med 2022; 11:jcm11133643. [PMID: 35806928 PMCID: PMC9267454 DOI: 10.3390/jcm11133643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 06/16/2022] [Accepted: 06/21/2022] [Indexed: 12/07/2022] Open
Abstract
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.
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Changing Medical Paradigm on Inflammatory Eye Disease: Technology and Its Implications for P4 Medicine. J Clin Med 2022; 11:jcm11112964. [PMID: 35683352 PMCID: PMC9181649 DOI: 10.3390/jcm11112964] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 05/23/2022] [Indexed: 12/10/2022] Open
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Inomata T, Nakamura M, Iwagami M, Sung J, Nakamura M, Ebihara N, Fujisawa K, Muto K, Nojiri S, Ide T, Okano M, Okumura Y, Fujio K, Fujimoto K, Nagao M, Hirosawa K, Akasaki Y, Murakami A. Individual characteristics and associated factors of hay fever: A large-scale mHealth study using AllerSearch. Allergol Int 2022; 71:325-334. [PMID: 35105520 DOI: 10.1016/j.alit.2021.12.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 11/26/2021] [Accepted: 12/13/2021] [Indexed: 12/20/2022] Open
Abstract
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.
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18
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Inomata T, Nakamura M, Sung J, Midorikawa-Inomata A, Iwagami M, Fujio K, Akasaki Y, Okumura Y, Fujimoto K, Eguchi A, Miura M, Nagino K, Shokirova H, Zhu J, Kuwahara M, Hirosawa K, Dana R, Murakami A. Smartphone-based digital phenotyping for dry eye toward P4 medicine: a crowdsourced cross-sectional study. NPJ Digit Med 2021; 4:171. [PMID: 34931013 PMCID: PMC8688467 DOI: 10.1038/s41746-021-00540-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 11/28/2021] [Indexed: 01/01/2023] Open
Abstract
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.
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Affiliation(s)
- Takenori Inomata
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan. .,Juntendo University Graduate School of Medicine, Department of Strategic Operating Room Management and Improvement, Tokyo, Japan. .,Juntendo University Graduate School of Medicine, Department of Hospital Administration, Tokyo, Japan. .,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan.
| | - Masahiro Nakamura
- Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan.,Precision Health, Department of Engineering, Graduate School of Bioengineering, The University of Tokyo, Tokyo, Japan
| | - Jaemyoung Sung
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,University of South Florida, Morsani College of Medicine, Tampa, FL, USA
| | - Akie Midorikawa-Inomata
- Juntendo University Graduate School of Medicine, Department of Hospital Administration, Tokyo, Japan
| | - Masao Iwagami
- Department of Health Services Research, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Kenta Fujio
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
| | - Yasutsugu Akasaki
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
| | - Yuichi Okumura
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Strategic Operating Room Management and Improvement, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
| | - Keiichi Fujimoto
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan
| | - Atsuko Eguchi
- Juntendo University Graduate School of Medicine, Department of Hospital Administration, Tokyo, Japan
| | - Maria Miura
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
| | - Ken Nagino
- Juntendo University Graduate School of Medicine, Department of Hospital Administration, Tokyo, Japan
| | - Hurramhon Shokirova
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan
| | - Jun Zhu
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan
| | - Mizu Kuwahara
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
| | - Kunihiko Hirosawa
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
| | - Reza Dana
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Akira Murakami
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
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