Mapping the Korean National Health Checkup Questionnaire to Standard Terminologies / 대한의료정보학회지

Objectives@#An increasing emphasis has been placed on the integration of clinical data and patient-generated health data (PGHD), which are generated outside of hospitals. This study explored the possibility of using standard terminologies to represent PGHD for data integration. @*Methods@#We chose the 2020 general health checkup questionnaire of the Korean Health Screening Program as a resource. We divided every component of the questionnaire into entities and values, which were mapped to standard terminologies—Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) version 2020-07-31 and Logical Observation Identifiers Names and Codes (LOINC) version 2.68. @*Results@#Eighty-nine items were derived from the 17 questions of the 2020 health examination questionnaire, of which 76 (85.4%) were mapped to standard terms. Fifty-two items were mapped to SNOMED CT and 24 items were mapped to LOINC. Among the items mapped to SNOMED CT, 35 were mapped to pre-coordinated expressions and 17 to post-coordinated expressions. Forty items had one-to-one relationships, and 17 items had one-to-many relationships. @*Conclusions@#We achieved a high mapping rate (85.4%) by using both SNOMED CT and LOINC. However, we noticed some issues while mapping the Korean general health checkup questionnaire (i.e., lack of explanations, vague questions, and overly narrow concepts). In particular, items combining two or more concepts into a single item were not appropriate for mapping using standard terminologies. Although it is not the case that all items need to be expressed in standard terminology, essential items should be presented in a way suitable for mapping to standard terminology by revising the questionnaire in the future.

Strategies for Adopting and Implementing SNOMED CT in Korea / 대한의료정보학회지

Objectives@#The objective of this study was to introduce the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), to describe use cases of SNOMED CT with the barriers and facilitators, and finally, to propose strategies for adopting and implementing SNOMED CT in Korea as a member of SNOMED International. @*Methods@#We reviewed a collection of SNOMED CT documents, such as introductory materials, practical guides, technical specifications, and reference materials provided by SNOMED International and the literature on SNOMED CT published by researchers to identify use cases of SNOMED CT with barriers and facilitators. We also surveyed the attendees of SNOMED CT education and training series offered by the Korea Human Resource Development Institute for Health and Welfare to identify perceived barriers to adopting SNOMED CT in Korea. @*Results@#We identified the barriers and facilitators to adopt SNOMED CT experienced by international terminology experts and prospective Korean users. They were related to governance and infrastructure, support services for use, education and training programs, use cases, and vendor capability to implement SNOMED CT. Based on these findings, we identified strategies for adopting and implementing SNOMED CT in Korea. They included the establishment of SNOMED CT management infrastructure, the development of SNOMED CT education and training programs for various user groups, the provision of support services for SNOMED CT use, and the development of SNOMED CT use cases. @*Conclusions@#These strategies for the adoption and implementation of SNOMED CT need to be executed step by step.

Analysis of Adverse Drug Reactions Identified in Nursing Notes Using Reinforcement Learning / 대한의료정보학회지

ObjectivesElectronic Health Records (EHRs)-based surveillance systems are being actively developed for detecting adverse drug reactions (ADRs), but this is being hindered by the difficulty of extracting data from unstructured records. This study performed the analysis of ADRs from nursing notes for drug safety surveillance using the temporal difference method in reinforcement learning (TD learning).MethodsNursing notes of 8,316 patients (4,158 ADR and 4,158 non-ADR cases) admitted to Ajou University Hospital were used for the ADR classification task. A TD(λ) model was used to estimate state values for indicating the ADR risk. For the TD learning, each nursing phrase was encoded into one of seven states, and the state values estimated during training were employed for the subsequent testing phase. We applied logistic regression to the state values from the TD(λ) model for the classification task.ResultsThe overall accuracy of TD-based logistic regression of 0.63 was comparable to that of two machine-learning methods (0.64 for a naïve Bayes classifier and 0.63 for a support vector machine), while it outperformed two deep learning-based methods (0.58 for a text convolutional neural network and 0.61 for a long short-term memory neural network). Most importantly, it was found that the TD-based method can estimate state values according to the context of nursing phrases.ConclusionsTD learning is a promising approach because it can exploit contextual, time-dependent aspects of the available data and provide an analysis of the severity of ADRs in a fully incremental manner.

Development and Evaluation of Electronic Health Record Data-Driven Predictive Models for Pressure Ulcers

PURPOSE@#The purpose of this study was to develop predictive models for pressure ulcer incidence using electronic health record (EHR) data and to compare their predictive validity performance indicators with that of the Braden Scale used in the study hospital.@*METHODS@#A retrospective case-control study was conducted in a tertiary teaching hospital in Korea. Data of 202 pressure ulcer patients and 14,705 non-pressure ulcer patients admitted between January 2015 and May 2016 were extracted from the EHRs. Three predictive models for pressure ulcer incidence were developed using logistic regression, Cox proportional hazards regression, and decision tree modeling. The predictive validity performance indicators of the three models were compared with those of the Braden Scale.@*RESULTS@#The logistic regression model was most efficient with a high area under the receiver operating characteristics curve (AUC) estimate of 0.97, followed by the decision tree model (AUC 0.95), Cox proportional hazards regression model (AUC 0.95), and the Braden Scale (AUC 0.82). Decreased mobility was the most significant factor in the logistic regression and Cox proportional hazards models, and the endotracheal tube was the most important factor in the decision tree model.@*CONCLUSION@#Predictive validity performance indicators of the Braden Scale were lower than those of the logistic regression, Cox proportional hazards regression, and decision tree models. The models developed in this study can be used to develop a clinical decision support system that automatically assesses risk for pressure ulcers to aid nurses.

Development and Evaluation of Electronic Health Record Data-Driven Predictive Models for Pressure Ulcers

PURPOSE: The purpose of this study was to develop predictive models for pressure ulcer incidence using electronic health record (EHR) data and to compare their predictive validity performance indicators with that of the Braden Scale used in the study hospital. METHODS: A retrospective case-control study was conducted in a tertiary teaching hospital in Korea. Data of 202 pressure ulcer patients and 14,705 non-pressure ulcer patients admitted between January 2015 and May 2016 were extracted from the EHRs. Three predictive models for pressure ulcer incidence were developed using logistic regression, Cox proportional hazards regression, and decision tree modeling. The predictive validity performance indicators of the three models were compared with those of the Braden Scale. RESULTS: The logistic regression model was most efficient with a high area under the receiver operating characteristics curve (AUC) estimate of 0.97, followed by the decision tree model (AUC 0.95), Cox proportional hazards regression model (AUC 0.95), and the Braden Scale (AUC 0.82). Decreased mobility was the most significant factor in the logistic regression and Cox proportional hazards models, and the endotracheal tube was the most important factor in the decision tree model. CONCLUSION: Predictive validity performance indicators of the Braden Scale were lower than those of the logistic regression, Cox proportional hazards regression, and decision tree models. The models developed in this study can be used to develop a clinical decision support system that automatically assesses risk for pressure ulcers to aid nurses.

Digital Epidemiology: Use of Digital Data Collected for Non-epidemiological Purposes in Epidemiological Studies / 대한의료정보학회지

OBJECTIVES: We reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method. METHODS: We reviewed research articles published within the last decade that used digital data to answer epidemiological research questions. Data were abstracted from these articles using a data collection tool that we developed. Finally, we summarized the characteristics of the digital epidemiological studies. RESULTS: We identified six main topic domains: infectious diseases (58.7%), non-communicable diseases (29.4%), mental health and substance use (8.3%), general population behavior (4.6%), environmental, dietary, and lifestyle (4.6%), and vital status (0.9%). We identified four categories for the study purpose: description (22.9%), exploration (34.9%), explanation (27.5%), and prediction and control (14.7%). We identified eight categories for the data sources: web search query (52.3%), social media posts (31.2%), web portal posts (11.9%), webpage access logs (7.3%), images (7.3%), mobile phone network data (1.8%), global positioning system data (1.8%), and others (2.8%). Of these, 50.5% used correlation analyses, 41.3% regression analyses, 25.6% machine learning, and 19.3% descriptive analyses. CONCLUSIONS: Digital data collected for non-epidemiological purposes are being used to study health phenomena in a variety of topic domains. Digital epidemiology requires access to large datasets and advanced analytics. Ensuring open access is clearly at odds with the desire to have as little personal data as possible in these large datasets to protect privacy. Establishment of data cooperatives with restricted access may be a solution to this dilemma.

Development of the IMB Model and an Evidence-Based Diabetes Self-management Mobile Application / 대한의료정보학회지

OBJECTIVES: This study developed a diabetes self-management mobile application based on the information-motivation-behavioral skills (IMB) model, evidence extracted from clinical practice guidelines, and requirements identified through focus group interviews (FGIs) with diabetes patients. METHODS: We developed a diabetes self-management (DSM) app in accordance with the following four stages of the system development life cycle. The functional and knowledge requirements of the users were extracted through FGIs with 19 diabetes patients. A system diagram, data models, a database, an algorithm, screens, and menus were designed. An Android app and server with an SSL protocol were developed. The DSM app algorithm and heuristics, as well as the usability of the DSM app were evaluated, and then the DSM app was modified based on heuristics and usability evaluation. RESULTS: A total of 11 requirement themes were identified through the FGIs. Sixteen functions and 49 knowledge rules were extracted. The system diagram consisted of a client part and server part, 78 data models, a database with 10 tables, an algorithm, and a menu structure with 6 main menus, and 40 user screens were developed. The DSM app was Android version 4.4 or higher for Bluetooth connectivity. The proficiency and efficiency scores of the algorithm were 90.96% and 92.39%, respectively. Fifteen issues were revealed through the heuristic evaluation, and the app was modified to address three of these issues. It was also modified to address five comments received by the researchers through the usability evaluation. CONCLUSIONS: The DSM app was developed based on behavioral change theory through IMB models. It was designed to be evidence-based, user-centered, and effective. It remains necessary to fully evaluate the effect of the DSM app on the DSM behavior changes of diabetes patients.

Correction: Development and Evaluation of an Obesity Ontology for Social Big Data Analysis / 대한의료정보학회지

The affiliation of the third author, Tae-Min Song, was changed during the submission and review process of the article. The third authors' affiliation should be corrected.

Characteristics and Risk Factors for Falls in Tertiary Hospital Inpatients

PURPOSE: The aim of this study was to explore characteristics of and risk factors for accidental inpatient falls. METHODS: Participants were classified as fallers or non-fallers based on the fall history of inpatients in a tertiary hospital in Seoul between June 2014 and May 2015. Data on falls were obtained from the fall report forms and data on risk factors were obtained from the electronic nursing records. Characteristics of fallers and non-fallers were analyzed using descriptive statistics. Risk factors for falls were identified using univariate analyses and logistic regression analysis. RESULTS: Average length of stay prior to the fall was 21.52 days and average age of fallers was 61.37 years. Most falls occurred during the night shifts and in the bedroom and were due to sudden leg weakness during ambulation. It was found that gender, BMI, physical problems such elimination, gait, vision and hearing and medications such as sleeping pills, antiarrhythmics, vasodilators, and muscle relaxant were statistically significant factors affecting falls. CONCLUSION: The findings show that there are significant risk factors such as BMI and history of surgery which are not part of fall assessment tools. There are also items on fall assessment tools which are not found to be significant such as mental status, emotional unstability, dizziness, and impairment of urination. Therefore, these various risk factors should be examined in the fall risk assessments and these risk factors should be considered in the development of fall assessment tools.

2016 Year-in-Review of Clinical and Consumer Informatics: Analysis and Visualization of Keywords and Topics / 대한의료정보학회지

OBJECTIVES: The objective of this study was to review and visualize the medical informatics field over the previous 12 months according to the frequencies of keywords and topics in papers published in the top four journals in the field and in Healthcare Informatics Research (HIR), an official journal of the Korean Society of Medical Informatics. METHODS: A six-person team conducted an extensive review of the literature on clinical and consumer informatics. The literature was searched using keywords employed in the American Medical Informatics Association year-in-review process and organized into 14 topics used in that process. Data were analyzed using word clouds, social network analysis, and association rules. RESULTS: The literature search yielded 370 references and 1,123 unique keywords. ‘Electronic Health Record’ (EHR) (78.6%) was the most frequently appearing keyword in the articles published in the five studied journals, followed by ‘telemedicine’ (2.1%). EHR (37.6%) was also the most frequently studied topic area, followed by clinical informatics (12.0%). However, ‘telemedicine’ (17.0%) was the most frequently appearing keyword in articles published in HIR, followed by ‘telecommunications’ (4.5%). Telemedicine (47.1%) was the most frequently studied topic area, followed by EHR (14.7%). CONCLUSIONS: The study findings reflect the Korean government's efforts to introduce telemedicine into the Korean healthcare system and reactions to this from the stakeholders associated with telemedicine.

Development and Evaluation of an Obesity Ontology for Social Big Data Analysis / 대한의료정보학회지

OBJECTIVES: The aim of this study was to develop and evaluate an obesity ontology as a framework for collecting and analyzing unstructured obesity-related social media posts. METHODS: The obesity ontology was developed according to the ‘Ontology Development 101’. The coverage rate of the developed ontology was examined by mapping concepts and terms of the ontology with concepts and terms extracted from obesity-related Twitter postings. The structure and representative ability of the ontology was evaluated by nurse experts. We applied the ontology to the density analysis of keywords related to obesity types and management strategies and to the sentiment analysis of obesity and diet using social big data. RESULTS: The developed obesity ontology was represented by 8 superclasses and 124 subordinate classes. The superclasses comprised ‘risk factors,’‘types,’‘symptoms,’‘complications,’‘assessment,’‘diagnosis,’‘management strategies,’ and ‘settings.’ The coverage rate of the ontology was 100% for the concepts and 87.8% for the terms. The evaluation scores for representative ability were higher than 4.0 out of 5.0 for all of the evaluation items. The density analysis of keywords revealed that the top-two posted types of obesity were abdomen and thigh, and the top-three posted management strategies were diet, exercise, and dietary supplements or drug therapy. Positive expressions of obesity-related postings has increased annually in the sentiment analysis. CONCLUSIONS: It was found that the developed obesity ontology was useful to identify the most frequently used terms on obesity and opinions and emotions toward obesity posted by the geneal population on social media.

Survey Study of Current Status of and Need for Mental Health Education Enhancing Protective Factors in the Elementary Schools

PURPOSE: The purpose of this study was to survey the current status of mental health education and need for mental health education enhancing protective factors in the elementary schools. METHODS: We surveyed 10 school health teachers and 328 fifth- and sixth-grade students using 19- and 20-item questionnaires, respectively. RESULTS: All of the teachers and 65.2% of the students replied that they were either teaching or being taught mental health in school. Topics covered suicide, depression, school violence, and Internet addiction. All of the teachers and 84.1% of the students expressed the need for mental health education enhancing protective factors in school. Both groups replied that two sessions are enough. The teachers preferred role play and discussion as teaching methods, and audiovisual materials and computer as instructional media. The students preferred lecture and role play as teaching methods, and audiovisual materials and smartphone as instructional media. Both groups ranked self-esteem, parent-child relationship, peer relationship, and emotional regulation as the most important topics to be covered in the education. CONCLUSION: There is a high demand for mental health education enhancing protective factors. Therefore, it is recommended to develop educational programs enhancing protective factors by enabling formal and informal learning using smartphone.

Current Status of Nursing Informatics Education in Korea / 대한의료정보학회지

OBJECTIVES: This study presents the current status of nursing informatics education, the content covered in nursing informatics courses, the faculty efficacy, and the barriers to and additional supports for teaching nursing informatics in Korea. METHODS: A set of questionnaires consisting of an 18-item questionnaire for nursing informatics education, a 6-item questionnaire for faculty efficacy, and 2 open-ended questions for barriers and additional supports were sent to 204 nursing schools via email and the postal service. Nursing schools offering nursing informatics were further asked to send their syllabuses. The subjects taught were analyzed using nursing informatics competency categories and other responses were tailed using descriptive statistics. RESULTS: A total of 72 schools (35.3%) responded to the survey, of which 38 reported that they offered nursing informatics courses in their undergraduate nursing programs. Nursing informatics courses at 11 schools were taught by a professor with a degree majoring in nursing informatics. Computer technology was the most frequently taught subject (27 schools), followed by information systems used for practice (25 schools). The faculty efficacy was 3.76 ± 0.86 (out of 5). The most frequently reported barrier to teaching nursing informatics (n = 9) was lack of awareness of the importance of nursing informatics. Training and educational opportunities was the most requested additional support. CONCLUSIONS: Nursing informatics education has increased during the last decade in Korea. However, the proportions of faculty with degrees in nursing informatics and number of schools offering nursing informatics courses have not increased much. Thus, a greater focus is needed on training faculty and developing the courses.

Current Status of Nursing Informatics Education in Korea / 대한의료정보학회지

OBJECTIVES: This study presents the current status of nursing informatics education, the content covered in nursing informatics courses, the faculty efficacy, and the barriers to and additional supports for teaching nursing informatics in Korea. METHODS: A set of questionnaires consisting of an 18-item questionnaire for nursing informatics education, a 6-item questionnaire for faculty efficacy, and 2 open-ended questions for barriers and additional supports were sent to 204 nursing schools via email and the postal service. Nursing schools offering nursing informatics were further asked to send their syllabuses. The subjects taught were analyzed using nursing informatics competency categories and other responses were tailed using descriptive statistics. RESULTS: A total of 72 schools (35.3%) responded to the survey, of which 38 reported that they offered nursing informatics courses in their undergraduate nursing programs. Nursing informatics courses at 11 schools were taught by a professor with a degree majoring in nursing informatics. Computer technology was the most frequently taught subject (27 schools), followed by information systems used for practice (25 schools). The faculty efficacy was 3.76 ± 0.86 (out of 5). The most frequently reported barrier to teaching nursing informatics (n = 9) was lack of awareness of the importance of nursing informatics. Training and educational opportunities was the most requested additional support. CONCLUSIONS: Nursing informatics education has increased during the last decade in Korea. However, the proportions of faculty with degrees in nursing informatics and number of schools offering nursing informatics courses have not increased much. Thus, a greater focus is needed on training faculty and developing the courses.

Development and Evaluation of a Smartphone Application for Managing Gestational Diabetes Mellitus / 대한의료정보학회지

OBJECTIVES: The purpose of this study was to develop and evaluate an application (app) that provides tailored recommendations based on lifestyle and clinical data entered by the user. METHODS: Knowledge and functions required for the gestational diabetes mellitus (GDM) management app were extracted from clinical practice guidelines and evaluated through an online survey. Common and tailored recommendations were developed and evaluated with a content validity index. Algorithms to link tailored recommendations with a patient's data were developed and evaluated by experts. An Android-based app was developed and evaluated by comparing the process of data entry and recommendation retrieval and the usability of the app. After the app was revised, the user acceptance of the app was evaluated. RESULTS: Six domains of knowledge and 14 functions were extracted. Seven common and 49 tailored recommendations were developed. Nine lifestyle and clinical data elements were modeled. Eight algorithms with 18 decision nodes presenting tailored recommendations based on patient's data and 12 user interface screens were developed. All recommendations obtained from the use of app concurred with recommendations derived by algorithms. The average usability score was 69.5 out of 100. The user acceptance score with behavioral intention to use was 5.5, intrinsic motivation 4.3, the perceived ease of use score was 4.6, and the perceived usefulness score was 5.0 out of 7, respectively. CONCLUSIONS: The GDM management knowledge and tailored recommendations obtained in this study could be of help in managing GDM.

Factors Affecting Acceptance of Smartphone Application for Management of Obesity / 대한의료정보학회지

OBJECTIVES: The factors affecting the acceptance of mobile obesity-management applications (apps) by the public were analyzed using a mobile healthcare system (MHS) technology acceptance model (TAM). METHODS: The subjects who participated in this study were Android smartphone users who had an intent to manage their weight. They used the obesity-management app for two weeks, and then completed an 18-item survey designed to determine the factors influencing the acceptance of the app. Three questions were asked pertaining to each of the following six factors: compatibility, self-efficacy, technical support and training, perceived usefulness, perceived ease of use, and behavior regarding intention to use. Cronbach's alpha was used to assess the reliability of the scales. Pathway analysis was also performed to evaluate the MHS acceptance model. RESULTS: A total of 94 subjects participated in this study. The results indicate that compatibility, perceived usefulness, and perceived ease of use significantly affected the behavioral intention to use the mobile obesity-management app. Technical support and training also significantly affected the perceived ease of use; however, the hypotheses that self-efficacy affects perceived usefulness and perceived ease of use were not supported in this study. CONCLUSIONS: This is the first attempt to analyze the factors influencing mobile obesity-management app acceptance using a TAM. Further studies should cover not only obesity but also other chronic diseases and should analyze the factors affecting the acceptance of apps among healthcare consumers in general.

Development of a Smartphone Application for Clinical-Guideline-Based Obesity Management / 대한의료정보학회지

OBJECTIVES: The purpose of the study was to develop and evaluate a clinical-guideline-based smartphone application ('app') for obesity management. METHODS: Obesity-related knowledge and functional requirements were extracted from clinical practice guidelines, a literature review, and consultations with experts. The extracted knowledge was used to design obesity-management algorithms, and the functions of the developed app are presented through a use case diagram and activity diagrams. The database and user interface were designed and then an app was developed. The proficiency and efficiency of the algorithm were evaluated using scenarios, while the user interface was assessed using a mobile heuristics evaluation tool, with its usability determined using the Post-Study System Usability Questionnaire. RESULTS: In total, 131 obesity-related knowledge statements and 11 functions for the app were extracted, and 5 algorithms (comprising 1 main algorithm and 4 subalgorithms) were developed. The database comprised 11 tables and 41 screens. The app was developed using the Android SDK platform 4.0.3, JDK 1.7.0, and Eclipse. The overall proficiency and efficiency scores of the algorithm were 88.0 and 69.1, respectively. In heuristics tests, 57 comments were made, and the mean usability score was 3.47 out of 5. Thirteen usability problems were identified by the heuristics and usability evaluations. CONCLUSIONS: The app developed in this study might be helpful for weight management because it can provide high-quality health information and intervention without spatial or temporal constraints. However, the clinical effectiveness of this app still requires further investigation.

Transtheoretical Model-based Nursing Intervention on Lifestyle Change: A Review Focused on Intervention Delivery Methods

PURPOSE: The transtheoretical model (TTM) was used to provide tailored nursing for lifestyle management such as diet, physical activity, and smoking cessation. The present study aims to assess the provision of intervention delivery methods, intervention elements, and stage-matched interventions, in order to identify ways in which information technology is used in the TTM-based research. METHODS: The relevant literature was selected by two researchers using inclusion criteria after searching for "TTM (transtheoretical or stage of change)" and "nursing" from the databases PubMed and CINAHL. The selected studies were categorized in terms of study characteristics, intervention delivery method, intervention element, and use and level of stage-matched intervention. RESULTS: A total of 35 studies were selected including eight studies that used information communication technology (ICT). Nine different intervention delivery methods were used, of which "face-to-face" was the most common at 24 times. Of the 35 studies, 26 provided stage-matched interventions. Seven different intervention elements were used, of which "counseling" was the most common at 27 times. Of all the intervention elements, tailored feedback used ICT the most at seven instances out of nine, and there was a significant difference in the rate of ICT usage among intervention elements. CONCLUSIONS: ICT is not yet actively used in the TTM-based nursing interventions. Stage-matched interventions and TTM concepts were shown to be in partial use also in the TTM-based interventions. Therefore, it is necessary to develop a variety of ways to use ICT in tailored nursing interventions and to use TTM frameworks and concepts.

Development of Health Information Search Engine Based on Metadata and Ontology / 대한의료정보학회지

OBJECTIVES: The aim of the study was to develop a metadata and ontology-based health information search engine ensuring semantic interoperability to collect and provide health information using different application programs. METHODS: Health information metadata ontology was developed using a distributed semantic Web content publishing model based on vocabularies used to index the contents generated by the information producers as well as those used to search the contents by the users. Vocabulary for health information ontology was mapped to the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), and a list of about 1,500 terms was proposed. The metadata schema used in this study was developed by adding an element describing the target audience to the Dublin Core Metadata Element Set. RESULTS: A metadata schema and an ontology ensuring interoperability of health information available on the internet were developed. The metadata and ontology-based health information search engine developed in this study produced a better search result compared to existing search engines. CONCLUSIONS: Health information search engine based on metadata and ontology will provide reliable health information to both information producer and information consumers.

Analysis of the Information Quality of Korean Obesity-Management Smartphone Applications / 대한의료정보학회지

OBJECTIVES: This study analyzed smartphone obesity-management applications developed in Korea and the quality of the information that they provide. METHODS: Obesity-management smartphone applications were searched using the keywords 'obesity + management,' 'weight + management,' 'weight + loss,' 'weight + exercise,' 'weight + diet,' 'weight + calories,' and 'diet,' with a search application programming interface (provided by Apple) between September 23 and September 27, 2013. These applications were then classified according to their main purpose, type of interventions used, price, type of developer, and user ratings. The information quality of the applications was analyzed using the Silberg scale. RESULTS: In total, 148 smartphone applications for obesity management were found. The main purpose of most of these applications (70.95%) was to provide information regarding weight control. The most frequently used intervention (34.62%) was to provide information on exercise management. More than half of the applications (58.78%) were free of charge. The mean of users' rating of these applications was 3.68 out of 5. The quality of information provided by these applications was evaluated as 4.55 out of 9: specifically, 1.79 out of 3 for authorship, 0.22 out of 2 for attribution, 1.29 out of 2 for disclosure, and 1.25 out of 2 for currency. Only three of the applications (2.88%) had a score on the Silberg scale greater than or equal to 7 points. CONCLUSIONS: The findings of this study suggest that the quality of information provided by smartphone applications in the healthcare domain urgently need to be evaluated to prevent users being misinformed by these applications.