Effectiveness and intention to use a COVID-19 self-management app for epidemiological investigation: a web-based survey study.

Introduction: Numerous COVID-19-related apps were widely used during the COVID-19 pandemic. Among them, those supporting epidemiological investigations were particularly useful. This study explored the effectiveness of apps that support epidemiological investigations, factors influencing users' intention to use them, and ways to encourage their use. Methods: We developed and evaluated the KODARI app to demonstrate its importance in epidemiological investigations. After adapting a questionnaire based on an existing evaluation framework for COVID-19-related apps, we collected data from 276 participants through an online survey conducted between April 28 and May 25, 2023. We conducted two independent sample t-tests to determine the differences between each variable according to demographic characteristics and a multiple regression analysis to identify factors affecting intention to use. Results: Users were generally satisfied with the KODARI. We observed differences in sex, age, marital status, occupational characteristics, and experience with epidemiological investigation. Females rated the app's information accuracy higher than males. Males had a higher intention to use than females. Participants aged under 35 years rated information accuracy and transparency highly, whereas single participants rated information accuracy higher than married participants. Occupational groups with frequent interactions with others evaluated their self-determination regarding the application. The app's self-determination was highly valued among participants with experience in epidemiological investigations. By investigating the factors affecting the intention to use the app, we confirmed that effectiveness, self-determination, and usability significantly affected the intention to use. Discussion: This study demonstrated the effectiveness of app supporting epidemiological investigations, identified meaningful factors that influence intention to use, and confirmed the applicability of our new framework by considering the specificity of infectious disease situations such as COVID-19. This study provides a new basis for future epidemiological studies.

Ambient particulate matter exposure and urologic cancer: a longitudinal nationwide cohort study.

Increased particulate matter (PM) exposure is positively associated with increased incidence and mortality of many human malignancies. However, evidence of urologic cancer is limited. We aimed to evaluate the association between PM10 exposure and the relative risk of urologic cancer. This nationwide longitudinal cohort study included 231,997 participants who underwent a baseline health examination in 2008 from the National Health Information Database of Korea. The primary endpoint was newly diagnosed urologic cancer according to PM10 exposure. Of the total 231,99 participants, 50,677 developed urologic cancer during a median follow-up of 6.7 years. After controlling for confounding factors, participants in the high PM10 exposure group had a higher risk of kidney cancer (hazard ratio [HR] 1.111, 95% confidence interval [CI] 1.068-1.157) and prostate cancer (HR 1.083, 95% CI 1.058-1.109) than those in the low PM10 exposure group. However, in urothelial cell carcinoma, there was no significant increase in the HRs in the high PM10 exposure group. For kidney cancer, participants with the following characteristics were more susceptible: age < 65 years, female sex, decreased regular physical activity, current smoking, no diabetes, no hypertension, normal body mass index, and desirable total cholesterol level. For prostate cancer, participants with the following characteristics were more susceptible: decreased regular physical activity, current smoking, and no hypertension. High PM10 exposure is associated with an increased risk of overall urologic cancers, especially kidney and prostate cancer.

Factors Influencing the Acceptance of Distributed Research Networks in Korea: Data Accessibility and Data Security Risk.

OBJECTIVES: Distributed research networks (DRNs) facilitate multicenter research by enabling the use of multicenter data; therefore, they are increasingly utilized in healthcare fields. Despite the numerous advantages of DRNs, it is crucial to understand researchers' acceptance of these networks to ensure their effective application in multicenter research. In this study, we sought to identify the factors influencing the adoption of DRNs among researchers in Korea. METHODS: We used snowball sampling to collect data from 149 researchers between July 7 and August 28, 2020. Five factors were used to formulate the hypotheses and research model: data accessibility, usefulness, ease of use, data security risk, and intention to use DRNs. We applied a structural equation model to identify relationships within the research model. RESULTS: Data accessibility and data security were critical to the acceptance and use of DRNs. The usefulness of DRNs partially mediated the relationship between data accessibility and the intention to use DRNs. Interestingly, ease of use did not influence the intention to use DRNs, but it was affected by data accessibility. Furthermore, ease of use impacted the perceived usefulness of DRNs. CONCLUSIONS: This study highlighted major factors that can promote the broader adoption and utilization of DRNs. Consequently, these findings can contribute to the expansion of active multicenter research using DRNs in the field of healthcare research.

An Investigation of Factors Influencing the Postponement of the Use of Distributed Research Networks in South Korea: Web-Based Users' Survey Study.

BACKGROUND: Distributed research networks (DRNs) offer researchers the advantage of using various big data sets that are difficult to access and use. In addition, since the data are not physically exposed to the outside, it is possible to conduct research using medical data safely without data exposure. However, researchers still have difficulties and are concerned about using DRNs. Few studies involving DRNs have been conducted from the user's viewpoint. Therefore, it is necessary to look at DRNs from the researcher's point of view and find ways to facilitate the active use of DRNs. OBJECTIVE: This study aimed to identify the factors that made researchers hesitate to use DRNs and to derive a method to facilitate active DRN use. METHODS: We conducted a web-based survey of people working in the medical fields, such as hospitals and universities. We used 131 respondents' data from a survey from December 6 to 17, 2021. We conducted multiple regression analyses to determine the factors affecting the postponement of using DRNs. In addition, 2 independent sample t tests were conducted to analyze the difference between the 2 groups according to the following factors: organization, gender, experience with DRNs, length of the research career, position, and age. RESULTS: Performance risk (t5=2.725, P=.007) and workload from DRNs (t5=3.543, P=.001) were significantly associated with users' postponement of DRN use. Researchers working at hospitals were found to feel more burdened by DRN use than researchers working at universities (t129=1.975, P=.05). It was also found that women perceived a higher privacy risk of DRNs than men (t129=-2.303, P=.02) and that those who had experience using DRNs delayed their use less than those without experience (t129=-4.215, P<.001). CONCLUSIONS: It is necessary to simplify the research and approval processes to reduce the performance risk and workload of research using DRNs. To optimize the process, DRN providers should develop a way to improve users' experiences. More user-friendly functionalities should be developed from the researcher's point of view. It is necessary to continuously promote effective functionalities for DRNs to reduce concerns about privacy risks. This study identified the concerns of DRN users in terms of DRN use and suggested ways to actively use DRNs. The derived results can be reflected in planning and developing DRNs. Our research will be helpful to prepare an activation plan for DRNs.

Cancer Research Line (CAREL): Development of Expanded Distributed Research Networks for Prostate Cancer and Lung Cancer.

Objectives: Big data-based multicenter medical research is expected to bring significant advances to cancer treatment worldwide. However, there are concerns related to data sharing among multicenter networks. Clinical data can be shielded by firewalls using distributed research networks (DRNs). We attempted to develop DRNs for multicenter research that can be easily installed and used by any institution. Patients and Methods: We propose a DRN for multicenter cancer research called the cancer research line (CAREL) and present a data catalog based on a common data model (CDM). CAREL was validated using 1723 patients with prostate cancer and 14 990 patients with lung cancer in a retrospective study. We used the attribute-value pairs and array data type JavaScript object notation (JSON) format to interface third-party security solutions such as blockchain. Results: We developed visualized data catalogs of prostate and lung cancer based on the observational medical outcomes partnership (OMOP) CDM, from which researchers can easily browse and select relevant data. We made the CAREL source code readily available for download and application for relevant purposes. In addition, it is possible to realize a multicenter research network using CAREL development sources. Conclusion: CAREL source can enable medical institutions to participate in multicenter cancer research. Our technology is open source, so small institutions that cannot afford to spend high costs can use it to develop a platform for multicenter research.

Dr. Answer AI for prostate cancer: Intention to use, expected effects, performance, and concerns of urologists.

Objectives: To efficiently implement artificial intelligence (AI) software for medical applications, it is crucial to understand the acceptance, expected effects, expected performance, and concerns of software users. In this study, we examine the acceptance and expectation of the Dr. Answer AI software for prostate cancer. Methods: We conducted an online survey for urologists from August 13 to September 18, 2020. The target software is an AI-based clinical software called Dr. Answer AI software, used for prostate cancer diagnosis. We collected data from 86 urologists and conducted a basic statistical and multiple regression analysis using the R package. Results: The compatibility was significantly associated with the intention to use the Dr. Answer AI software. The expected average accuracy for the software ranges from 86.91% to 87.51%, and the urologists perceived that the cloud method is suitable to introduce the software. The most desirable function of the software for the specialists is predicting the occurrence of extracapsular extension, seminal vesicle invasion, and lymph node metastasis after radical prostatectomy. Finally, the primary concerns involved the cost, compatibility with existing systems, and obtaining accurate information from the software. Conclusions: Our results present an understanding of the acceptance, expected effects, expected performance, and concerns of software users. The results provide a guide to help AI software be properly developed and implemented in medical applications.

Behavioral Intention to Use a Smartphone Usage Management Application Between a Non-Problematic Smartphone Use Group and a Problematic Use Group.

Despite the many advantages of smartphone in daily life, there are significant concerns regarding their problematic use. Therefore, several smartphone usage management applications have been developed to prevent problematic smartphone use. The purpose of this study is to investigate the factors of users' behavioral intention to use smartphone usage management applications. Participants were divided into a smartphone use control group and a problematic use group to find significant intergroup path differences. The research model of this study is fundamentally based on the Technology Acceptance Model and Expectation-Confirmation Theory. Based on this theorem, models were modified to best suit the case of problematic smartphone use intervention by smartphone application. We conducted online surveys on 511 randomly selected smartphone users aged 20-60 in South Korea, in 2018. The Smartphone Addiction Proneness Scale was used to measure participants' smartphone dependency. Descriptive statistics were used for the demographic analysis and collected data were analyzed using IBM SPSS Statistics 24.0 and Amos 24.0. We found that in both non-problematic smartphone use group and problematic smartphone use group, facilitating factors and perceived security positively affect the intentions of users to use the application. One distinct difference between the groups was that the latter attributed a lower importance to perceived security than the former. Some of our highlighted unique points are envisioned to provide intensive insights for broadening knowledge about technology acceptance in the field of e-Addictology.

Dr. Answer AI for Prostate Cancer: Predicting Biochemical Recurrence Following Radical Prostatectomy.

OBJECTIVES: To develop a model to predict biochemical recurrence (BCR) after radical prostatectomy (RP), using artificial intelligence (AI) techniques. PATIENTS AND METHODS: This study collected data from 7,128 patients with prostate cancer (PCa) who received RP at 3 tertiary hospitals. After preprocessing, we used the data of 6,755 cases to generate the BCR prediction model. There were 16 input variables with BCR as the outcome variable. We used a random forest to develop the model. Several sampling techniques were used to address class imbalances. RESULTS: We achieved good performance using a random forest with synthetic minority oversampling technique (SMOTE) using Tomek links, edited nearest neighbors (ENN), and random oversampling: accuracy = 96.59%, recall = 95.49%, precision = 97.66%, F1 score = 96.59%, and ROC AUC = 98.83%. CONCLUSION: We developed a BCR prediction model for RP. The Dr. Answer AI project, which was developed based on our BCR prediction model, helps physicians and patients to make treatment decisions in the clinical follow-up process as a clinical decision support system.

Development, Acceptance, and Concerns Surrounding App-Based Services to Overcome the COVID-19 Outbreak in South Korea: Web-Based Survey Study.

BACKGROUND: Since the COVID-19 outbreak, South Korea has been engaged in various efforts to overcome the pandemic. One of them is to provide app-based COVID-19-related services to the public. As the pandemic continues, a need for various apps has emerged, including COVID-19 apps that can support activities aimed at overcoming the COVID-19 pandemic. OBJECTIVE: We aimed to determine which apps were considered the most necessary according to users and evaluate the current status of the development of COVID-19-related apps in South Korea. We also aimed to determine users' acceptance and concerns related to using apps to support activities to combat COVID-19. METHODS: We collected data from 1148 users from a web-based survey conducted between November 11 and December 6, 2020. Basic statistical analysis, multiple response analysis, and the Wilcoxon rank sum test were performed using R software. We then manually classified the current status of the development of COVID-19-related apps. RESULTS: In total, 68.4% (785/1148) of the respondents showed high willingness to protect themselves from COVID-19 by using related apps. Users considered the epidemiological investigation app to be the most necessary app (709/1148, 61.8%) overall, followed by the self-management app for self-isolation (613/1148, 53.4%), self-route management app (605/1148, 52.7%), COVID-19 symptom management app (483/1148, 42.1%), COVID-19-related information provision app (339/1148, 29.5%), and mental health management app (270/1148, 23.5%). Despite the high intention to use these apps, users were also concerned about privacy issues and media exposure. Those who had an underlying disease and had experience using COVID-19-related apps showed significantly higher intentions to use those apps (P=.05 and P=.01, respectively). CONCLUSIONS: Targeting users is very important in order to design and develop the most necessary apps. Furthermore, to gain the public's trust and make the apps available to as many people as possible, it is vital to develop diverse apps in which privacy protection is maximized.

Prostate cancer trajectory-map: clinical decision support system for prognosis management of radical prostatectomy.

PURPOSE: Prostate cancer has a low mortality rate and requires persistent treatment; however, treatment decisions are challenging. Because prostate cancer is complex, the outcomes warrant thorough follow-up evaluation for appropriate treatment. Electronic health records (EHRs) do not present intuitive information. This study aimed to develop a Clinical Decision Support System (CDSS) for prognosis management of radical prostatectomy. METHODS: We used data from 5,199 prostate cancer patients from three hospitals' EHRs in South Korea, comprising laboratory results, surgery, medication, and radiation therapy. We used open source R for data preprocessing and development of web-based visualization system. We also used R for automatic calculation functionalities of two factors to visualize the data, e.g., Prostate-Specific Antigen Doubling Time (PSADT), and four Biochemical Recurrence (BCR) definitions: American Society of Therapeutic Radiology and Oncology (ASTRO), Phoenix, consecutive PSA > 0.2 ng/mL, and PSA > 0.2 ng/mL. RESULTS: We developed the Prostate Cancer Trajectory Map (PCT-Map) as a CDSS for intuitive visualization of serial data of PSA, testosterone, surgery, medication, radiation therapy, BCR, and PSADT. CONCLUSIONS: The PCT-Map comprises functionalities for BCR and PSADT and calculates and visualizes the newly added patient data automatically in a PCT-Map data format, thus optimizing the visualization of patient data and allowing clinicians to promptly access patient data to decide the appropriate treatment.

Predictors of Habitual and Addictive Smartphone Behavior in Problematic Smartphone Use.

OBJECTIVE: Smartphones have become common, and problematic smartphone use (PSU) is increasing. Predictors of PSU should be identified to prevent it. Little is known about the role of content types of smartphone use as predictors of PSU. Therefore, we aimed to evaluate the predictors of two proposed concepts of PSU, namely habitual smartphone behavior (SB) and addictive SB, within the context of the application (app) categories. METHODS: We studied 1,039 smartphone users using online surveys conducted between January 2 and 31, 2019. We employed multiple regression analysis to identify the predictors of habitual and addictive SB. We controlled for sex and age (mean=39.20). RESULTS: Common predictors of habitual and addictive SB were the use of social networking services, games, entertainment apps, and average weekend smartphone usage time. The predictors of habitual SB were the use of web and lifestyle apps, weekly usage frequency, and sex (female) and the predictors of addictive SB were the use of shopping apps and sleep duration. CONCLUSION: This study revealed the need to consider habitual and addictive SB in evaluating PSU. The predictors in terms of the content types of smartphone usage can be used to develop monitoring and prevention services for PSU.

Development of the Smartphone Addiction Risk Rating Score for a Smartphone Addiction Management Application.

Smartphone usage characteristics are useful for identification of the risk factors for smartphone addiction. Risk rating scores can be developed based on smartphone usage characteristics. This study aimed to investigate the smartphone addiction risk rating (SARR) score using smartphone usage characteristics. We evaluated 593 smartphone users using online surveys conducted between January 2 and January 31, 2019. We identified 102 smartphone users who were addicted to smartphones and 491 normal users based on the Korean Smartphone Addiction Proneness Scale for Adults. A multivariate logistic regression analysis was used to identify significant risk factors for smartphone addiction. The SARR score was calculated using a nomogram based on the significant risk factors. Weekend average usage time, habitual smartphone behavior, addictive smartphone behavior, social usage, and process usage were the significant risk factors associated with smartphone addiction. Furthermore, we developed the SARR score based on these factors. The SARR score ranged between 0 and 221 points, with the cut-off being 116.5 points. We developed a smartphone addiction management application using the SARR score. The SARR score provided insights for the development of monitoring, prevention, and prompt intervention services for smartphone addiction.

Dr. Answer AI for prostate cancer: Clinical outcome prediction model and service.

OBJECTIVES: The importance of clinical outcome prediction models using artificial intelligence (AI) is being emphasized owing to the increasing necessity of developing a clinical decision support system (CDSS) employing AI. Therefore, in this study, we proposed a "Dr. Answer" AI software based on the clinical outcome prediction model for prostate cancer treated with radical prostatectomy. METHODS: The Dr. Answer AI was developed based on a clinical outcome prediction model, with a user-friendly interface. We used 7,128 clinical data of prostate cancer treated with radical prostatectomy from three hospitals. An outcome prediction model was developed to calculate the probability of occurrence of 1) tumor, node, and metastasis (TNM) staging, 2) extracapsular extension, 3) seminal vesicle invasion, and 4) lymph node metastasis. Random forest and k-nearest neighbors algorithms were used, and the proposed system was compared with previous algorithms. RESULTS: Random forest exhibited good performance for TNM staging (recall value: 76.98%), while k-nearest neighbors exhibited good performance for extracapsular extension, seminal vesicle invasion, and lymph node metastasis (80.24%, 98.67%, and 95.45%, respectively). The Dr. Answer AI software consisted of three primary service structures: 1) patient information, 2) clinical outcome prediction, and outcomes according to the National Comprehensive Cancer Network guideline. CONCLUSION: The proposed clinical outcome prediction model could function as an effective CDSS, supporting the decisions of the physicians, while enabling the patients to understand their treatment outcomes. The Dr. Answer AI software for prostate cancer helps the doctors to explain the treatment outcomes to the patients, allowing the patients to be more confident about their treatment plans.

Introduction to digital pathology and computer-aided pathology.

Digital pathology (DP) is no longer an unfamiliar term for pathologists, but it is still difficult for many pathologists to understand the engineering and mathematics concepts involved in DP. Computer-aided pathology (CAP) aids pathologists in diagnosis. However, some consider CAP a threat to the existence of pathologists and are skeptical of its clinical utility. Implementation of DP is very burdensome for pathologists because technical factors, impact on workflow, and information technology infrastructure must be considered. In this paper, various terms related to DP and computer-aided pathologic diagnosis are defined, current applications of DP are discussed, and various issues related to implementation of DP are outlined. The development of computer-aided pathologic diagnostic tools and their limitations are also discussed.

Corrigendum: Factors Affecting User Acceptance in Overuse of Smartphones in Mobile Health Services: An Empirical Study Testing a Modified Integrated Model in South Korea.

[This corrects the article DOI: 10.3389/fpsyt.2018.00658.].

Types of problematic smartphone use based on psychiatric symptoms.

To provide appropriate solutions for problematic smartphone use, we need to first understand its types. This study aimed to identify types of problematic smartphone use based on psychiatric symptoms, using the decision tree method. We recruited 5,372 smartphone users from online surveys conducted between February 3 and February 22, 2016. Based on scores on the Korean Smartphone Addiction Proneness Scale for Adults (S-Scale), 974 smartphone users were assigned to the smartphone-dependent group and 4398 users were assigned to the normal group. The data-mining technique of C5.0 decision tree was applied. We used 15 input variables, including demographic and psychological factors. Four psychiatric variables emerged as the most important predictors: self-control (Sc; 66%), anxiety (Anx; 25%), depression (Dep; 7%), and dysfunctional impulsivities (Imp; 3%). We identified the following five types of problematic smartphone use: (1) non-comorbid, (2) self-control, (3) Sc + Anx, (4) Sc + Anx + Dep, and (5) Sc + Anx + Dep + Imp. We found that 74% of smartphone-dependent users had psychiatric symptoms. The ratio of participants belonging to the non-comorbid and self-control types was 64%. We proposed that these types of problematic smartphone use may be used for the development of an appropriate service for controlling and preventing such behaviors in adults.

Prevalence Risk of Metabolic Syndrome Associated with Alcohol Use Behavior in Korean Women.

OBJECTIVE: Considerable research has been conducted on the relationship between alcohol consumption and metabolic syndrome. Although various standards for the amount and frequency of alcohol consumption have been suggested, a tool to measure individual alcohol use behavior against a consistent standard is required. Moreover, the association of alcohol use behavior with health should be examined on the basis of such a standard. In this study, we examined the relationships between alcohol use behavior according to the Alcohol Use Disorders Identification Test (AUDIT) and metabolic syndrome and its components in Korean women. METHODS: This study utilized data from the fifth Korean National Health and Nutrition Examination Survey, which was administered from 2010 through 2012. We investigated the relationships between alcohol use behavior and metabolic syndrome and its components in a sample of 2,906 women by using analysis of covariance and logistic regression analysis. RESULTS: After adjusting for confounding variables, alcohol use behavior was significantly associated with metabolic syndrome [odds ratio (OR) 2.877; 95% confidence interval (CI) 1.523-5.435 in the problem use group]. AUDIT score also was significantly related to abdominal obesity (OR 2.263; 95% CI 1.704-4.459 in the problem use group), hypertension (OR 3.377; 95% CI 1.871-6.095 in the problem use group), hypertriglyceridemia (OR 3.204; 95% CI 1.800-5.702 in the problem use group), and impaired fasting glucose (OR 3.034; 95% CI 1.721-5.348 in the problem use group). CONCLUSION: In this study, positive associations were observed between AUDIT score and risk of metabolic syndrome and its components.

Factors Affecting User Acceptance in Overuse of Smartphones in Mobile Health Services: An Empirical Study Testing a Modified Integrated Model in South Korea.

Smartphones have become crucial in people's everyday lives, including in the medical field. However, as people become close to their smartphones, this leads easily to overuse. Overuse leads to fatigue due to lack of sleep, depressive symptoms, and social relationship failure, and in the case of adolescents, it hinders academic achievement. Self-control solutions are needed, and effective tools can be developed through behavioral analysis. Therefore, the aim of this study was to investigate the determinants of users' intentions to use m-Health for smartphone overuse interventions. A research model was based on TAM and UTAUT, which were modified to be applied to the case of smartphone overuse. The studied population consisted of 400 randomly selected smartphone users aged from 19 to 60 years in South Korea. Structural equation modeling was conducted between variables to test the hypotheses using a 95% confidence interval. Perceived ease of use had a very strong direct positive association with perceived usefulness, and perceived usefulness had a very strong direct positive association with behavioral intention to use. Resistance to change had a direct positive association with behavioral intention to use and, lastly, social norm had a very strong direct positive association with behavioral intention to use. The findings that perceived ease of use influenced perceived usefulness, that perceived usefulness influenced behavioral intention to use, and social norm influenced behavioral intention to use were in accordance with prior related research. Other results that were not consistent with previous research imply that these are unique behavioral findings regarding smartphone overuse. This research identifies the critical factors that need to be considered when implementing systems or solutions in the future for tackling the issue of smartphone overuse.

Risk Factors for Internet Gaming Disorder: Psychological Factors and Internet Gaming Characteristics.

Background: Understanding the risk factors associated with Internet gaming disorder (IGD) is important to predict and diagnose the condition. The purpose of this study is to identify risk factors that predict IGD based on psychological factors and Internet gaming characteristics; Methods: Online surveys were conducted between 26 November and 26 December 2014. There were 3568 Korean Internet game users among a total of 5003 respondents. We identified 481 IGD gamers and 3087 normal Internet gamers, based on Diagnostic and Statistical Manual for Mental Disorders (DSM-5) criteria. Logistic regression analysis was applied to identify significant risk factors for IGD; Results: The following eight risk factors were found to be significantly associated with IGD: functional and dysfunctional impulsivity (odds ratio: 1.138), belief self-control (1.034), anxiety (1.086), pursuit of desired appetitive goals (1.105), money spent on gaming (1.005), weekday game time (1.081), offline community meeting attendance (2.060), and game community membership (1.393; p < 0.05 for all eight risk factors); Conclusions: These risk factors allow for the prediction and diagnosis of IGD. In the future, these risk factors could also be used to inform clinical services for IGD diagnosis and treatment.

Comparison of the Acceptance of Telemonitoring for Glucose Management Between South Korea and China.

BACKGROUND: Telemonitoring is used for glucose management and support in many countries. A better understanding of the differences in telemonitoring acceptance based on regional characteristics is needed. Therefore, we compared the acceptance of telemonitoring for glucose management among patients in South Korea and China. MATERIALS AND METHODS: This study used data from Korean (n = 81) and Chinese (n = 92) subjects with type 2 diabetes. We used two independent sample t-tests to compare patients' perceptions of telemonitoring and multiple regression analysis to determine the factors that affected their behavioral intentions to use telemonitoring. We conducted Wilcoxon signed rank tests to assess the differences in hemoglobin A1c (HbA1c) levels from baseline to follow-up. RESULTS: Although Korean and Chinese patients had positive perceptions of the services, different factors influenced their behavioral intentions to use them. In South Korea, performance expectations and social influences were significantly associated with intention to use telemonitoring. Patients younger than 50 years showed a significant decrease in HbA1c levels at month 6 (p < 0.05). In China, effort expectancy and facilitating conditions were significantly associated with intention to use. In addition, subjects in all age groups exhibited a significant decrease in HbA1c levels at all follow-up points (p < 0.001). CONCLUSION: Telemonitoring was a supportive intervention in improving blood sugar levels among patients with diabetes in South Korea and China, but the factors influencing its use varied. We provide practical guidance for developing telemonitoring for glucose management that considers the distinct characteristics of different countries.