ABSTRACT The coronavirus disease (COVID-19) pandemic has emphasized the paucity of non-contact and non-invasive methods for the objective evaluation of dry eye disease (DED). However, robust

evidence to support the implementation of mHealth- and app-based biometrics for clinical use is lacking. This study aimed to evaluate the reliability and validity of app-based maximum blink

interval (MBI) measurements using DryEyeRhythm and equivalent traditional techniques in providing an accessible and convenient diagnosis. In this single-center, prospective,

cross-sectional, observational study, 83 participants, including 57 with DED, had measurements recorded including slit-lamp-based, app-based, and visually confirmed MBI. Internal consistency

coefficient, intraclass correlation coefficient, and Pearson correlation coefficient values, compared with visually confirmed MBI. The DED group had significantly shorter app-based MBIs,

compared with the non-DED group. Bland–Altman analysis revealed minimal biases between the app-based and visually confirmed MBIs. Our findings indicate that DryEyeRhythm is a reliable and

valid tool that can be used for non-invasive and non-contact collection of MBI measurements, which can assist in accessible DED detection and management. SIMILAR CONTENT BEING VIEWED BY

OTHERS DIAGNOSTIC ABILITY OF MAXIMUM BLINK INTERVAL TOGETHER WITH JAPANESE VERSION OF OCULAR SURFACE DISEASE INDEX SCORE FOR DRY EYE DISEASE Article Open access 22 October 2020

SMARTPHONE-BASED DIGITAL PHENOTYPING FOR DRY EYE TOWARD P4 MEDICINE: A CROWDSOURCED CROSS-SECTIONAL STUDY Article Open access 20 December 2021 OPTIMAL CUTOFF VALUE OF THE DRY EYE-RELATED

QUALITY-OF-LIFE SCORE FOR DIAGNOSING DRY EYE DISEASE Article Open access 26 February 2024 INTRODUCTION Dry eye disease (DED) is the most commonly occurring ocular surface disease, affecting

5–50% of the population globally1,2. Its prevalence is expected to increase with the ongoing digitalization and aging of the society2,3. Patients with DED present with a wide range of

symptoms, such as ocular pain, discomfort, and decreased visual acuity caused by decreased tear film breakup time (TFBUT) and kerato-conjunctival epithelial defects4,5. Hence, DED has a

negative impact on productivity and quality of vision, thereby impacting quality of life and resulting in economic loss6,7. A significant proportion of patients with DED may be undiagnosed

and do not seek treatment despite experiencing symptoms8, indicating the need for a novel approach that can expand the reach of DED screening, promote early diagnosis and intervention for

the prompt management of symptoms, prevent decreased quality of life, and reduce the societal costs of DED management5. The demand for non-invasive and non-contact examinations and the

incorporation of telemedicine in routine practice have rapidly increased with the novel coronavirus disease (COVID-19) pandemic9,10. DED is diagnosed by evaluating subjective symptoms and

objective findings on examinations, such as TFBUT and ocular surface staining11,12. Dry eye examinations require specialized equipment, such as slit-lamp microscopes and fluorescein dye;

moreover, the invasive nature of the examination disrupts the true in vivo tear composition12. Therefore, performing a comprehensive DED evaluation in a telehealth setting is impractical,

warranting the formal appraisal of various telehealth strategies to remotely diagnose DED and manage its symptoms5,13,14. The maximum blink interval (MBI), which is defined as the duration

that participants can keep their eyes open before blinking during each trial, correlates positively with TFBUT15. MBI can be measured non-invasively, without contact, under observation with

a slit-lamp microscope. The combined use of a slit-lamp microscope and DED-specific symptom questionnaire has shown a sensitivity and specificity of 75.4% and 92.9%, respectively, for DED

diagnosis16. By eliminating the requirement for slit-lamp-based MBI measurements, MBI could replace TFBUT in remote settings to enable non-invasive and non-contact DED diagnosis and

monitoring. In November 2016, we developed and released an in-house smartphone application (app), DryEyeRhythm, which is capable of measuring MBI and administering DED-specific symptom

questionnaires14,17,18 with positive and negative predictive values, sensitivity, and specificity of 91.3%, 69.1%, 50.0%, and 95.0%, respectively5. DryEyeRhythm could measure MBI by

biosensing blinking using smartphone-attached cameras. Additionally, recent data on various DED subtypes suggest that MBI monitoring is useful in determining the disease mechanism for

stratified and personalized treatment approaches18. The administration of DED-specific questionnaires through DryEyeRhythm provides reliable patient-reported data14,17; however, MBI

measurement through the app must be assessed for reliability and validity. Therefore, in this study, MBI data that were collected through the DryEyeRhythm app (app-based MBI) were formally

evaluated for their validity, reliability, and equivalence to visually confirmed MBI. RESULTS PARTICIPANTS’ CHARACTERISTICS Figure 1 shows the enrollment process. This study initially

included 94 participants. One patient was excluded due to refusal to participate after the MBI measurement. Among the 93 remaining participants, 10 (10.8%) were excluded owing to their

inability to obtain app-based MBI measurements. MBI was unobtainable in 10 participants—on both platforms in 4 participants, on the iPhone operating system (iOS) in 2 participants, and on

Android in 4 participants. Table 1 presents the characteristics of the 83 included participants. Supplementary Table S1 shows a comparison between the included and excluded individuals.

INTERNAL CONSISTENCY AND AGREEMENT OF APP-BASED MBI Table 2 shows the internal consistency of app-based MBIs, with Cronbach’s alpha coefficients and intraclass correlation coefficients

(ICCs). The Cronbach’s alpha coefficients of the correlation between app-based and visually confirmed MBIs were above 0.7 in the iOS (0.999) and Android (1.000) versions. The ICCs (95%

confidence intervals [CIs]) of the app-based (0.996 [0.994–0.998], iOS; and 0.998 [0.997–0.999], Android) and visually confirmed MBIs were above 0.7. DISCRIMINANT VALIDITY OF APP-BASED AND

SLIT-LAMP-BASED MBIS Table 3 shows the discriminant validity of app-based and slit-lamp-based MBIs. All MBIs were significantly shorter in the DED than in the non-DED groups (app-based MBI

[iOS], _P_ = 0.021; visually confirmed MBI [iOS], _P_ = 0.018; app-based MBI [Android], _P_ = 0.028; and visually confirmed MBI [Android], _P_ = 0.031). CONCURRENT VALIDITY AMONG APP-BASED,

VISUALLY CONFIRMED, AND SLIT-LAMP-BASED MBIS The concurrent validity among the app-based, visually confirmed, and slit-lamp-based MBIs was assessed using Pearson’s correlation analysis

(Table 4). Significant positive correlations were identified between the app-based (iOS) and visually confirmed MBIs (iOS) (_r_ = 0.999, _P_ < 0.001), app-based (Android) and visually

confirmed MBIs (Android) (_r_ = 0.999, _P_ < 0.001), and app-based (iOS) and app-based MBIs (Android) (_r_ = 0.824, _P_ < 0.001). BLAND–ALTMAN ANALYSIS Bland–Altman analysis for

agreement showed differences (biases) of − 0.08 s (Fig. 2a; 95% limits of agreement [LoA], − 0.76 to 0.60) between app-based (iOS) and visually confirmed MBIs (iOS), − 0.09 s (Fig. 2b; 95%

LoA, − 0.63 to 0.45) between app-based (Android) and visually confirmed MBIs (Android), and -0.43 s (Fig. 2c; 95% LoA, − 7.98 to 7.13) between app-based (iOS) and app-based MBIs (Android) [−

0.88 s (Fig. 2d; 95% LoA, − 10.3 to 8.58) and -0.88 s (Fig. 2e; 95% LoA, − 9.70 to 8.80), respectively]. SENSITIVITY, SPECIFICITY, AND CUT-OFF VALUES OF APP-BASED MBIS FOR DETECTING DED AND

TFBUT ≤ 5 S Table 5 shows the sensitivity, specificity, and cut-off values for detecting DED determined by app-based and slit-lamp-based MBIs. For app-based MBI (iOS), the sensitivity,

specificity, and area under the curve (AUC) for detecting DED were 80.4%, 50.0%, and 0.651, respectively, with an optimum cut-off value of 10.5 s. For the app-based MBI (Android), the

sensitivity, specificity, and AUC for detecting DED were 49.0%, 78.1%, and 0.644, respectively, with an optimum cut-off value of 7.0 s. For slit-lamp-based MBI, the sensitivity, specificity,

and AUC for detecting DED were 80.4%, 43.8%, and 0.638, respectively, with an optimum cut-off value of 11.7 s. Supplementary Tables S2–S4 show the cut-off values, sensitivity, specificity,

and Youden indices of the app-based (iOS), app-based (Android), and slit-lamp-based MBIs for detecting DED. Table 6 shows the sensitivity, specificity, and cut-off values for detecting a

TFBUT of ≤ 5 s determined by app-based and slit-lamp-based MBIs. For the app-based MBI (iOS), the sensitivity, specificity, and AUC for detecting DED were 44.3%, 76.9%, and 0.519,

respectively, with an optimum cut-off value of 6.8 s. For the app-based MBI (Android), the sensitivity, specificity, and AUC for detecting DED were 85.7%, 30.7%, and 0.543, respectively,

with an optimum cut-off value of 16.8 s. For the slit-lamp-based MBI, the sensitivity, specificity, and AUC for detecting DED were 72.9%, 38.5%, and 0.540, respectively, with an optimum

cut-off value of 11.7 s. Supplementary Tables S5–S7 show the cut-off values, sensitivity, specificity, and Youden indices of the app-based (iOS), app-based (Android), and slit-lamp-based

MBIs for detecting a TFBUT of ≤ 5 s. DISCUSSION The unmet medical need for non-invasive, non-contact DED evaluation has drastically increased with the increase in DED prevalence due to aging

and a digitalized society, as well as the COVID-19 pandemic. In this study, the performance of app-based MBI collected through the DryEyeRhythm app was compared with that of the

slit-lamp-based MBI to determine its validity and reliability compared to the traditional technique. The results reflected the feasibility of the DryEyeRhythm app-based MBI in DED diagnosis

and may help lay the foundation to implement digital phenotyping strategies and biometric data collection through mobile health (mHealth) apps. With a smartphone app for DED screening and

management, DED care may become possible in a longitudinal, day-to-day manner with minimal invasiveness and no requirement to visit a specialized facility. The synergy between mHealth and

biometric data collection has been gaining attention owing to its potential for creating a comprehensive dataset on patient pathophysiology and elucidating new digital phenotypes with

minimal intrusion18,19,20. Additionally, the push toward telemedicine has expanded substantially owing to the requirement of non-contact and non-invasive examinations during the COVID-19

pandemic9. However, robust evidence to support the implementation of mHealth- and app-based biometrics for clinical use is lacking21. In this study, we evaluated MBI biosensing using an

image recognition app programming interface as part of a smartphone app to assist in DED diagnosis. Numerous approaches in incorporating mHealth and biosensing techniques have been used in

ophthalmology, including visual acuity testing22, allergic conjunctivitis management23, pupillary reflex testing for amblyopia and strabismus detection24, and leukocoria recognition apps25.

Their use is expected to expand with the rapidly increasing capabilities of commonplace smart devices and attached sensors. The unique advantage of mHealth can be attributed to its alignment

with the paradigm shift from traditional facility-oriented medicine to non-intrusive, longitudinal care in a patient-centered manner18. The results of this study demonstrate the validity,

reliability, and equivalence of app-based MBI determination to its visually confirmed and slit-lamp-based counterparts. Good reliability values using Cronbach’s alpha coefficient and ICC

were shown by both app-based and visually confirmed MBIs for iOS and Android platforms, reflecting sufficient internal consistency. App-based MBI also showed satisfactory discriminant

validity and concurrent validity. Minimal biases were present between visually confirmed and app-based MBIs for both operating systems on Bland–Altman analysis. The discrepancy between the

iOS and Android MBI measurements was minimal. Notably, the AUC of the app-based MBI for detecting decreased TFBUT was 0.519 and 0.543 for iOS and Android, respectively, possibly due to the

temporal gap between app-based MBI and TFBUT measurements, which may be sufficient to affect the consistency of measurements. The demonstrated equivalence of app-based MBI with manually

measured MBI and its reliability and validity suggest that app-based MBI measurements may be useful in obtaining an objective finding to support the diagnosis of DED in a telemedicine

setting. Two major criteria must be met to confirm a diagnosis of DED: subjective symptoms and objective clinical findings6,12. The Asia Dry Eye Society characterizes the pathophysiology of

DED as a disease of tear film instability, which leads to visual decline12. TFBUT is a crucial component in assessing the tear film status26, and subjective symptoms quantified through

disease-specific questionnaires alone are insufficient to make the diagnosis. However, specialized equipment and procedures (i.e., slit-lamp microscopy and fluorescein dye administration)

are required to obtain TFBUT measurements, thus hindering remote assessment of DED status. Our previous efforts to find an appropriate substitute for TFBUT posited MBI as a possible

candidate based on the positive correlation between the two measurements15. Additionally, the diagnostic performance of concomitant Japanese version of Ocular Surface Disease Index (J-OSDI)

and MBI was satisfactory, with a sensitivity, specificity, and AUC of 75.4%, 92.9%, and 0.938, respectively16. The validity and reliability of the app-based J-OSDI were satisfactory, and its

performance was comparable with its paper-based counterpart5,14. Therefore, accurate measurement of MBI using a smartphone app can enable comprehensive assessment of tear film status in a

remote setting for DED diagnosis and progression monitoring. The results of this study demonstrate the validity, reliability, and equivalence of app-based MBI compared with the traditional

measurement methods. Assisting DED diagnosis in a telemedicine environment may be possible by administering J-OSDI and measuring MBI using a smartphone app. Our results indicate that the

optimal cutoff values for app-based MBI were shorter, compared with the visually confirmed MBI. This discrepancy in MBI cutoff likely stems from the difference in visual tasks during various

MBI measurements, with strong evidence supporting a significant decrease in blink rate and amplitude when using electronic monitors, such as smartphones and computers27. The observed

decrease in optimal MBI cutoff for app-based measurements, compared with visually confirmed measurements, is thought to be affected by the specific visual task of focusing on handheld

screens, which may ultimately elongate the physiological blink interval and subsequently the MBI when obtaining an app-based measurement. MBI cutoff values from prior studies were entirely

derived under slit lamp-based measurements15,16. To enhance the assessment of different diagnostic capabilities of MBI and encourage its utilization on mobile platforms, future research

should employ methodologies that effectively explore the optimal cutoff value for app-based MBI as a primary outcome10. Previous efforts to screen for DED using web-based administered

questionnaires lacked the objective component of DED diagnosis, such as TFBUT28,29. One notable strategy was to utilize an external infrared thermography device for smartphones, which showed

satisfactory sensitivity, specificity, and AUC values of 96%, 91%, and 0.79, respectively30. However, specialized external devices are not ideal for screening purposes. DryEyeRhythm is an

easily accessible software that can be executed by most commonplace smartphones to assess DED intermittently and longitudinally without the use of special devices or intrusive procedures. By

administering the J-OSDI and measuring MBI through a single mHealth app, DryEyeRhythm, a comprehensive assessment of DED using remotely collected subjective and objective data on a

patient’s tear film status may be possible. This study has few limitations. First, it may have been affected by selection bias owing to its single-center design. The average TFBUT of the

non-DED cohort participating in this study was 4.4 ± 2.5 s, lower than normal (range of normal TFBUT values: 7.6 ± 10.4 s to 9.1 ± 3.5 s)11,12,31,32. This may likely indicate that

specialized university facilities are frequented by patients with various underlying ocular diseases that may affect tear film stability, and our sample may have included participants who

may not accurately represent the larger population. Therefore, an ongoing multicenter, open-label, prospective, cross-sectional study is underway to determine the diagnostic ability of the

smartphone app for DED and a cutoff value for app-based MBI10. Second, this study did not evaluate several factors associated with DED, such as socioeconomic status, education level,

cultural background, lifestyle patterns, and systemic medications1. Third, as this study aimed to assess the reliability, validity, and equivalence of app-based MBI compared with traditional

measurements, several objective findings were not evaluated, including Rose Bengal staining scores, tear osmolality, meibomian gland dysfunction, and corneal sensations. Lastly, this study

excluded participants with ptosis or other palpebral dysfunctions that may physically disrupt normal blinking physiology. Therefore, the app may not accurately measure MBI in older patients

with dermatochalasis. Additionally, as the blink recognition function of DryEyeRhythm can be hindered for users wearing a mask, the app-based MBI was measured with masks removed. Another

factor that may affect the blink recognition function of DryEyeRhythm is the narrow palpebral fissure width of the participants33, due to which, 10 participants were unable to undergo

app-based MBI measurements in this study. Future studies and updates of the app should focus on enhancing the recognition algorithm, aiming to eliminate the necessity for users to remove

masks and adjusting for narrow palpebral fissure width. In summary, MBI measured through DryEyeRhythm, an app available on iOS and Android platforms, showed good reliability, validity, and

equivalence compared with slit-lamp-based MBI measurements, suggesting that app-based MBI could be a substitute for TFBUT in an mHealth environment. The results of this study indicate that

DryEyeRhythm may serve as a novel tool for assisting in DED diagnosis and progression monitoring in a remote setting. METHODS DRYEYERHYTHM SMARTPHONE APPLICATION The DryEyeRhythm smartphone

app was initially developed using the open-source framework ResearchKit of Apple Inc. (Cupertino, CA, USA)17. The app was released in November 2016 for iOS and September 2020 for Android

under a consignment contract with the Juntendo University Graduate School of Medicine, Tokyo, Japan, and InnoJin Inc., Tokyo, Japan. It is freely available on Apple’s App Store and Google

Play. The DryEyeRhythm app collects data regarding user demographics, medical history, lifestyle history, daily subjective symptoms, J-OSDI score, blink monitoring including blink frequency

and MBI biosensor data, depression data (Zung Self-Rating Depression Scale), and work productivity4,5,8,17,18,34,35,36. STUDY DESIGN AND PARTICIPANTS This single-center, prospective,

cross-sectional, observational study was conducted at the Juntendo University Hospital, Department of Ophthalmology, Tokyo, Japan37. Patients aged ≥ 20 years were recruited between February

16, 2022, and August 3, 2022. Written informed consent was obtained from all participants. This study was approved by the Independent Ethics Committee of the Juntendo University Faculty of

Medicine (approval number: 20-092) in accordance with the Declaration of Helsinki. Participants with a history of eyelid disorders, ptosis, psychiatric disease, Parkinson’s disease, or any

other disease affecting blinking were excluded5,15. Those with any missing data and whose MBI measurements could not be obtained with the DryEyeRhythm app were also excluded. DRY EYE DISEASE

DIAGNOSIS According to the 2016 Asia Dry Eye Society criteria12, participants with a TFBUT ≤ 5 s and J-OSDI ≥ 13 points were diagnosed with DED. The TFBUT was considered positive if the

average was ≤ 5 s in a severely affected eye. MBI MBI was defined as the time that patients could keep their eyes open before blinking15. It was measured in three ways: using a slit-lamp

microscope (slit-lamp-based MBI), DryEyeRhythm (app-based MBI [iOS and Android]), and a stopwatch (visually confirmed MBI). All MBIs were measured thrice. Slit-lamp-based MBI was calculated

using a stopwatch under light microscopy. App-based MBIs were measured using the iOS and Android versions of the DryEyeRhythm smartphone app installed on an iPhone 12 Pro MAX (Apple Inc.,

Cupertino, CA, USA) and an Xperia 5 II (Sony Corporation, Tokyo, Japan) and their embedded cameras, with a face recognition technology called ARCore for the iOS and Android interface38.

During the measurement of app-based MBIs, visually confirmed MBI was measured by the examiner by observing the user’s eyes with a stopwatch. The mean MBI was used in the analysis. Figure 3

shows a representative illustration (Fig. 3a) and screenshots of MBI measurement (Fig. 3b–e) using the DryEyeRhythm app. STUDY PROCEDURES Figure 1 shows the flowchart of this study. All

participants underwent visual acuity measurement; intraocular pressure measurement; and several DED evaluations, including TFBUT, corneal fluorescein staining (CFS), and slit-lamp-based MBI.

After completing these tests, the app-based and visually confirmed MBIs were measured simultaneously. A Schirmer test I was performed after the MBI measurement. CLINICAL ASSESSMENT OF DED

TFBUT was measured using fluorescein sodium staining (Ayumi Pharmaceutical Co., Tokyo, Japan) according to standard methodology12. The mean values of three measurements in the right eye were

used for the analysis. CFS was graded according to the van Bijsterveld grading score39, with a maximum potential score of 9. Schirmer’s test I was performed without topical anesthesia after

completing all examinations. RELIABILITY The internal consistency of the app-based MBIs was assessed using Cronbach’s alpha coefficient; an alpha value > 0.70 was considered

acceptable40. ICC was used to evaluate the agreement among the slit-lamp-based, app-based, and visually confirmed MBIs. An ICC value ≥ 0.70 was considered acceptable41. VALIDITY The

discriminant validity of each MBI was evaluated by comparing the non-DED and DED groups. Concurrent validity was assessed by calculating correlations (Pearson coefficients) between each

app-based MBI. Bland–Altman analysis42 was conducted to indicate systematic random error, heteroscedasticity of the data, and 95% LoA of each MBI. STATISTICAL ANALYSIS The sample size was

predetermined using the formula derived from hypothesis testing43. Using the following settings, the required sample size was determined to be 79 cases: power, 80%; significance level, 5%;

minimal acceptable ICC score, 0.5; expected ICC score, 0.7; and number of raters, 2. Considering a dropout rate of 15% possibly owing to missing data, unmeasurable app-based MBIs, and

withdrawal of consent, 94 participants were recruited. The characteristics of study participants were compared using an unpaired t test for continuous variables and an χ2 test for

categorical variables. Data were presented as mean ± standard deviation or percentage. Receiver operating characteristic curve analysis was performed to examine the diagnostic efficacy of

MBIs in detecting DED or TFBUT ≤ 5 s. The AUC was estimated using the trapezoidal rule4. The cut-off values of MBIs for detecting DED and TFBUT ≤ 5 s were determined using the Youden

index44. Statistical analyses were performed using STATA version 15 (StataCorp, College Station, TX, USA), and statistical significance was set at _P_ < 0.05. DATA AVAILABILITY All data

are available in the main text or the supplementary materials. Data access, responsibility, and analysis: Takenori Inomata, had full access to all the data in the study and takes

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https://doi.org/10.1002/bimj.200410135 (2005). Article  MathSciNet  PubMed  MATH  Google Scholar  Download references ACKNOWLEDGEMENTS We thank OHAKO, Inc. (Tokyo, Japan) and Medical Logue,

Inc. (Tokyo Japan) for developing the DryEyeRhythm application and extend our gratitude to Shiang T, Yoshimura Y, Hirastuka Y, Hori S, Uchino M, and Tsubota K for the initial development of

the application. FUNDING This research was supported by JST COI [Grant Number: JPMJCER02WD02] (T.I.)] and JSPS KAKENHI [Grant Numbers: 20KK0207 (T.I.), 20K23168 (A.M.I.), 21K17311 (A.M.I.),

21K20998 (A.E.)], 22K16983 (A.E.), and 23K16364 (A.M.I.)]. The Kondou Kinen Medical Foundation, Medical Research Encouragement Prize 2020 (T.I.), Charitable Trust Fund for Ophthalmic

Research in Commemoration of Santen Pharmaceutical’s Founder 2020 (T.I.), Nishikawa Medical Foundation, Medical Research Encouragement Prize 2020 (T.I.), Terumo Life Science Foundation

(T.I.), OTC Self-Medication Promotion Foundation 2020 (Y.O.) , 2021 (T.I.), and 2023 (T.I.), Medical Research Encouragement Prize 2022, Cell Science Research Foundation (T.I.), and Takeda

Science Foundation (T.I.) also helped fund this study. The sponsors had no role in the design or performance of the study; in data collection and management; in the analysis and

interpretation of the data; in the preparation, review, or approval of the manuscript; or in the decision to submit the manuscript for publication. AUTHOR INFORMATION AUTHORS AND

AFFILIATIONS * Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan Kenta Fujio, Ken Nagino, Tianxiang Huang, 

Jaemyoung Sung, Yasutsugu Akasaki, Yuichi Okumura, Keiichi Fujimoto, Maria Miura, Shokirova Hurramhon, Alan Yee, Kunihiko Hirosawa, Mizu Ohno, Yuki Morooka, Akira Murakami & Takenori

Inomata * Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan Kenta Fujio, Ken Nagino, Tianxiang Huang, Yasutsugu Akasaki, Yuichi Okumura, Keiichi

Fujimoto, Maria Miura, Kunihiko Hirosawa, Mizu Ohno, Yuki Morooka, Akira Murakami & Takenori Inomata * Department of Hospital Administration, Juntendo University Graduate School of

Medicine, Tokyo, Japan Ken Nagino, Akie Midorikawa-Inomata, Atsuko Eguchi, Hiroyuki Kobayashi & Takenori Inomata * AI Incubation Farm, Juntendo University Graduate School of Medicine,

Tokyo, Japan Takenori Inomata Authors * Kenta Fujio View author publications You can also search for this author inPubMed Google Scholar * Ken Nagino View author publications You can also

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Scholar CONTRIBUTIONS K.Fujio.: data curation, formal analysis, investigation, writing—original draft preparation, and writing—reviewing and editing; K.N.: methodology, formal analysis,

writing—original draft preparation, and writing—reviewing and editing; T.H.: data curation, writing—original draft preparation, and writing—reviewing and editing; J.S.: validation,

writing—original draft preparation, and writing—reviewing and editing; Y.A.: data curation, writing—original draft preparation, and writing—reviewing and editing; Y.O.: data curation,

writing—original draft preparation, and writing—reviewing and editing; A.M.I.: validation, writing—original draft preparation, and writing—reviewing and editing; K.Fujim.: validation,

writing—original draft preparation, and writing—reviewing and editing; A.E.: validation, writing—original draft preparation, and writing—reviewing and editing; M.M.: validation,

writing—original draft preparation, and writing—reviewing and editing; S.H.: validation, writing—original draft preparation, and writing—reviewing and editing; A.Y.: data curation,

writing—original draft preparation, and writing—reviewing and editing; K.H.: data curation, writing—original draft preparation, and writing—reviewing and editing; M.O.: validation,

writing—original draft preparation, and writing—reviewing and editing; Y.M.: validation, writing—original draft preparation, and writing—reviewing and editing; A.M.: methodology,

writing—original draft preparation, and writing—reviewing and editing; H.K.: methodology, writing—original draft preparation, and writing—reviewing and editing; T.I.: conceptualization,

methodology, funding acquisition, validation, investigation, writing—original draft preparation, and writing—reviewing and editing; all authors reviewed the manuscript. CORRESPONDING AUTHOR

Correspondence to Takenori Inomata. ETHICS DECLARATIONS COMPETING INTERESTS The DryEyeRhythm app was created using Apple’s Research Kit (Cupertino, CA, USA) along with OHAKO, Inc. (Tokyo,

Japan) and Medical Logue, Inc. (Tokyo, Japan). T.I., Y.O., and A.M.I are the owners of InnoJin, Inc., Tokyo, Japan, which helped develop DryEyeRhythm. T.I. received grants from Johnson &

Johnson Vision Care, SEED Co., Ltd., Novartis Pharma K.K., and Kowa Company, Ltd., outside the submitted work, as well as personal fees from Santen Pharmaceutical Co., Ltd. and InnoJin,

Inc. Y.O. and A. M. I. reported receiving personal fees from InnoJin, Inc. The remaining authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER'S NOTE Springer Nature

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copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Fujio, K., Nagino, K.,

Huang, T. _et al._ Clinical utility of maximum blink interval measured by smartphone application DryEyeRhythm to support dry eye disease diagnosis. _Sci Rep_ 13, 13583 (2023).

https://doi.org/10.1038/s41598-023-40968-y Download citation * Received: 22 January 2023 * Accepted: 19 August 2023 * Published: 21 August 2023 * DOI:

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