Factors Affecting Abdominal Obesity: Analyzing National Data.

The purpose of this study is to understand the factors affecting abdominal obesity. A secondary data analysis was conducted to analyze 5262 individuals' data from the 2020 Korea National Health and Nutrition Examination Survey. The prevalence of obesity was slightly higher in men than women, while abdominal obesity was more prevalent in women. A higher correlation with obesity was observed in young and middle-aged individuals, unmarried individuals, urban residents, those with good subjective health, low-stress perception, moderate alcohol consumption, nonsmokers, regular aerobic exercisers, and those getting more than seven hours of sleep. In contrast, middle-aged and elderly individuals, married individuals, rural residents, those with an elementary school or lower education level, those with low-to-moderate income, those with fair or poor subjective health, high stress perception, nondrinkers, smokers, nonregular aerobic exercisers, and those getting less than seven hours of sleep had a higher correlation with abdominal obesity. Health education suggests that everyone should maintain healthy lifestyle habits, such as getting sufficient sleep, exercise, smoking cessation, and moderate drinking. Specifically, diverse health management support focusing on population groups with demographic factors related to the risk of obesity and abdominal obesity is necessary.

Effective data quality management for electronic medical record data using SMART DATA.

OBJECTIVES: In the medical field, we face many challenges, including the high cost of data collection and processing, difficult standards issues, and complex preprocessing techniques. It is necessary to establish an objective and systematic data quality management system that ensures data reliability, mitigates risks caused by incorrect data, reduces data management costs, and increases data utilization. We introduce the concept of SMART data in a data quality management system and conducted a case study using real-world data on colorectal cancer. METHODS: We defined the data quality management system from three aspects (Construction - Operation - Utilization) based on the life cycle of medical data. Based on this, we proposed the "SMART DATA" concept and tested it on colorectal cancer data, which is actual real-world data. RESULTS: We define "SMART DATA" as systematized, high-quality data collected based on the life cycle of data construction, operation, and utilization through quality control activities for medical data. In this study, we selected a scenario using data on colorectal cancer patients from a single medical institution provided by the Clinical Oncology Network (CONNECT). As SMART DATA, we curated 1,724 learning data and 27 Clinically Critical Set (CCS) data for colorectal cancer prediction. These datasets contributed to the development and fine-tuning of the colorectal cancer prediction model, and it was determined that CCS cases had unique characteristics and patterns that warranted additional clinical review and consideration in the context of colorectal cancer prediction. CONCLUSIONS: In this study, we conducted primary research to develop a medical data quality management system. This will standardize medical data extraction and quality control methods and increase the utilization of medical data. Ultimately, we aim to provide an opportunity to develop a medical data quality management methodology and contribute to the establishment of a medical data quality management system.

Brain-based medical education model for expert's clinical decision making.

PURPOSE: The expertise of medicians in clinical decision-making is very important since it improves the quality of medical services provided to patients. This study analyzed the characteristics of the decision-making process and confirmed clinicians' electroencephalography (EEG) characteristics by measuring their cerebral activity during clinical decision-making. This study aims to present learning directions for brain-based clinical decision-making to develop medical experts. METHODS: This study was performed in the following two projects: (1) a qualitative study of clinical decision-making in a clinical scenario and (2) an analysis of differences in cortical activity of experts and novices through EEG. RESULTS: In the 1st study, this study found that "confirmation of the patient's chief complaints," "physical examination," and "radiography reading" steps, which showed the most prominent differences in the experts' and novices' decision making, were set as the significant steps of dentists' clinical decision making. In the 2nd study, the experts' and novices' cortical activities were measured through a 32-channel EEG. In task 6, which had the lowest accuracy of diagnoses made by the experts, the brain activities in both groups were higher than in other tasks. CONCLUSION: This study developed and suggested a model of the decision-making process for experts and novices and suggested the basic directions for brain-based learning needed to raise experts based on brain activity.

Menstrual Tracking Mobile App Review by Consumers and Health Care Providers: Quality Evaluations Study.

BACKGROUND: Women's menstrual cycle is an important component of their overall health. Physiological cycles and associated symptoms can be monitored continuously and used as indicators in various fields. Menstrual apps are accessible and can be used to promote overall female health. However, no study has evaluated these apps' functionality from both consumers' and health care providers' perspectives. As such, the evidence indicating whether the menstrual apps available on the market provide user satisfaction is insufficient. OBJECTIVE: This study was performed to investigate the key content and quality of menstrual apps from the perspectives of health care providers and consumers. We also analyzed the correlations between health care provider and consumer evaluation scores. On the basis of this analysis, we offer technical and policy recommendations that could increase the usability and convenience of future app. METHODS: We searched the Google Play Store and iOS App Store using the keywords "period" and "menstrual cycle" in English and Korean and identified relevant apps. An app that met the following inclusion criteria was selected as a research app: nonduplicate; with >10,000 reviews; last updated ≤180 days ago; relevant to this topic; written in Korean or English; available free of charge; and currently operational. App quality was evaluated by 6 consumers and 4 health care providers using Mobile Application Rating Scale (MARS) and user version of the Mobile Application Rating Scale (uMARS). We then analyzed the correlations among MARS scores, uMARS scores, star ratings, and the number of reviews. RESULTS: Of the 34 apps, 31 (91%) apps could be used to predict the menstrual cycle, and 2 (6%) apps provided information pertinent to health screening. All apps that scored highly in the MARS evaluation offer a symptom logging function and provide the user with personalized notifications. The "Bom Calendar" app had the highest MARS (4.51) and uMARS (4.23) scores. The MARS (2.22) and uMARS (4.15) scores for the "Menstrual calendar-ovulation & pregnancy calendar" app were different. In addition, there was no relationship between MARS and uMARS scores (r=0.32; P=.06). CONCLUSIONS: We compared consumer and health care provider ratings for menstrual apps. Continuous monitoring of app quality from consumer and health care provider perspectives is necessary to guide their development and update content.

Drug_SNSMiner: standard pharmacovigilance pipeline for detection of adverse drug reaction using SNS data.

As society continues to age, it is becoming increasingly important to monitor drug use in the elderly. Social media data have been used for monitoring adverse drug reactions. The aim of this study was to determine whether social network studies (SNS) are useful sources of drug side effects information. We propose a method for utilizing SNS data to plot the known side effects of geriatric drugs in a dosing map. We developed a lexicon of drug terms associated with side effects and mapped patterns from social media data. We confirmed that well-known side effects may be obtained by utilizing SNS data. Based on these results, we propose a pharmacovigilance pipeline that can be extended to unknown side effects. We propose the standard analysis pipeline Drug_SNSMiner for monitoring side effects using SNS data and evaluated it as a drug prescription platform for the elderly. We confirmed that side effects may be monitored from the consumer's perspective based on SNS data using only drug information. SNS data were deemed good sources of information to determine ADRs and obtain other complementary data. We established that these learning data are invaluable for AI requiring the acquisition of ADR posts on efficacious drugs.

Time to surgery and survival in breast cancer.

BACKGROUND: This study aimed to investigate the effect of the time from diagnosis to breast cancer surgery on breast cancer patients' prognosis. METHODS: Of the 1900 patients diagnosed with invasive (stage 1-3) breast cancer who underwent surgery in KUH between 2012 and 2019, 279 patients were enrolled in this study. All patients, including those who received neoadjuvant chemotherapy, were classified as Model 1 subjects, and those who received immediate surgical treatment were classified as Model 2 subjects. We conducted a Cox regression analysis to identify prognostic factors of breast cancer associated with the time from diagnosis to surgery. RESULTS: The univariate results indicated a sharp drop in both groups' survival rates when the time to surgery was delayed for more than 8 weeks (Model 1 p = 0.000; Model 2 p = 0.001). In the multivariate analysis, the hazard ratio (HR) of Model 1increased (HR = 6.84, 95% CI 1.06-44.25) in response to a delay in surgery of more than 8 weeks. Smoking and the American Joint Committee on Cancer (AJCC) staging system had a negative effect on breast cancer prognosis, while hormone therapy had a positive effect. CONCLUSION: For all patients, a delay in breast cancer surgery of more than 8 weeks was inversely associated with survival.

Insights from Review and Content Analysis of Current COVID-19 Mobile Apps and Recommendations for Future Pandemics.

A number of mobile health apps related to coronavirus infectious disease 2019 (COVID-19) have been developed, but research into app content analytics for effective surveillance and management is still in its preliminary stages. The present study aimed to identify the purpose and functions of the currently available COVID-19 apps using content analysis. The secondary aim was to propose directions for the future development of apps that aid infectious disease surveillance and control with a focus on enhancing the app content and quality. Prior to conducting an app search in the App Store and the Google Play Store, we reviewed previous studies on COVID-19 apps found in Google Scholar and PubMed to examine the main purposes of the apps. Using the five selected keywords based on the review, we searched the two app stores to retrieve eligible COVID-19 apps including those already addressed in the reviewed literature. We conducted descriptive and content analyses of the selected apps. We classified the purpose types of the COVID-19 apps into the following five categories: Information provision, tracking, monitoring, mental health management, and engagement. We identified 890 apps from the review articles and the app stores: 47 apps met the selection criteria and were included in the content analysis. Among the selected apps, iOS apps outnumbered Android apps, 27 apps were government-developed, and most of the apps were created in the United States. The most common function for the iOS apps (63.6%) and Android apps (62.5%) was to provide COVID-19-related knowledge. The most common function among the tracking apps was to notify users of contact with infected people by the iOS apps (40.9%) and Android apps (37.5%). About 29.5% of the iOS apps and 25.0% of the Android apps were used to record symptoms and self-diagnose. Significantly fewer apps targeted mental health management and engagement. Six iOS apps (6/44, 13.6%) and four Android apps (4/24, 16.7%) provided behavioral guidelines about the pandemic. Two iOS apps (2/44, 4.5%) and two Android apps (2/24, 8.3%) featured communication functions. The present content analysis revealed that most of the apps provided unilateral information and contact tracing or location tracking. Several apps malfunctioned. Future research and development of COVID-19 apps or apps for other emerging infectious diseases should address the quality and functional improvements, which should begin with continuous monitoring and actions to mitigate any technical errors.

Marriage Intention among Korean Young Adults: Trends and Influencing Factors.

The recent increase in the tendency of people to marry late or to opt out of marriage entirely is among the key contributors to Korea's low fertility rate. One possible cause of this tendency may be a change in how marriage is valued among Korea's youth. The marriage intentions of young adults can be classified into "positive", "negative", and "neutral". Over time, positive marriage intentions have declined across all age groups (2010: 61% → 2020: 39%; ages 25-29), with no significant change in negative marriage intentions. In contrast, neutral marriage intentions have increased significantly (2010: 36% → 2020: 53%; ages 25-29). This phenomenon may be attributable to the increase in the number of young adults who prioritize survival over thinking about the future. However, neutral marriage values can be changed into positive values at any time. A holistic overview of Korean society is necessary to determine how the values of young adults might be influenced to align with a traditional life process.

Associations between Awareness of the Risk of Exposure to Pollutants Occurring at Fire Scenes and Health Beliefs among Metropolitan Firefighters in the Republic of Korea.

Firefighters are repeatedly exposed to various pollutants that occur at fire scenes. There are three levels of exposure: primary exposure to pollutants, secondary exposure to pollutants on personal protective equipment (PPE), and tertiary exposure to contaminated fire stations and fire engines due to pollutants on PPE. Therefore, it is important for firefighters to be aware of the risk of exposure to pollutants and to practice health behaviors such as appropriate PPE management. No clear association has been established in the existing literature between firefighters' risk perception level and their health beliefs about the health impact of awareness of exposure to hazardous substances at fire scenes. This study aims to evaluate the relationship between awareness of the exposure risk to primary, secondary, and tertiary pollutants and health beliefs. It was designed as a cross-sectional study, in which a web-based survey was conducted from 13 May to 31 May 2021. The analysis was conducted in 1940 firefighters working in the Seoul metropolis who agreed to participate in the research. Participants who perceived susceptibility were likely to be aware of the primary (adjusted odds ratio (AOR) = 2.10, 95% confidence interval (CI) 1.16-3.80), secondary (AOR = 2.77, 95% CI 1.77-4.32), and tertiary (AOR = 2.73, 95% CI 1.85-4.03) exposure risks. Participants who perceived barriers were unlikely to be aware of the risk of exposure to primary (AOR = 0.67, 95% CI 0.49-0.91), secondary (AOR = 0.77, 95% CI 0.61-0.96), and tertiary (AOR = 0.75, 95% CI 0.62-0.91) pollutants. Educational intervention is recommended to improve perceived susceptibility and awareness of the risk of exposure to pollutants and reduce perceived barriers. Consequently, educational intervention is expected to positively impact firefighters' management of appropriate PPE. We confirmed an association between occupational exposure risk and firefighters' health beliefs. In the health belief model (HBM), health beliefs that affect health behavior also affect awareness of the exposure risk level. Therefore, an intervention for health beliefs can also be used to raise job-related exposure risk awareness. Regular training on the health impacts of fire scenes is necessary for both newcomers and incumbents to enable firefighters to better recognize the risks of each occupational exposure level. Additionally, laws and regulations are necessary for the removal of harmful substances that contaminate PPE, such as self-contained breathing apparatus (SCBA), during exposure to a fire scene. Our research can be used as a basis for improving fire policies and education programs in the future.

Examination of Factors Influencing SCBA Washing Behavior among Firefighters in Metropolitan.

Field-washing decontamination of equipment is an effective way for firefighters to reduce their risk of secondary contamination. No study has yet clarified the factors influencing effective field decontamination of equipment such as self-contained breathing apparatuses (SCBAs). This study sought to examine factors that influence the SCBA washing and decontamination behavior of firefighters. We conducted a questionnaire using the web-based Seoul Metropolitan Electronic Questionnaire System. As of May 2021, the survey had been sent to 3626 of 7198 Seoul career firefighters, and 1940 subjects were selected to participate in the study. Binomial logistic regression and χ2-test analyses were performed. We confirmed that previous training in SCBA washing was an important factor in effective field decontamination of SCBAs. Firefighters should be trained to perform field decontamination procedures systematically and regulations to perform field decontamination before leaving the scene of a fire should be introduced.

Diet-Related Mobile Apps to Promote Healthy Eating and Proper Nutrition: A Content Analysis and Quality Assessment.

Dietary mobile applications (apps) continue to hold promise for facilitating a healthy diet and managing nutrition. However, few studies have objectively evaluated the content and quality of such apps in Korea. The present study assessed the content and quality of dietary mobile apps using the Mobile App Rating Scale (MARS). We selected 29 dietary apps based on keywords and eligibility criteria for inclusion in the analyses. We conducted regression analyses to examine the association between app content and MARS scores. Most of the apps featured a tracking tool, while few featured rewards or follow-up management. Our quality assessment revealed that the top-rated apps have distinct levels of quality in terms of MARS scores. The regression analyses showed that the ways in which the apps provide information and motivate the users are statistically significant predictors of app quality. Our findings may facilitate the selection of dietary apps in Korea and provide guidelines for app developers regarding potential improvements in terms of content and quality.

mHealth Approaches in Managing Skin Cancer: Systematic Review of Evidence-Based Research Using Integrative Mapping.

BACKGROUND: mHealth, which encompasses mobile health technologies and interventions, is rapidly evolving in various medical specialties, and its impact is evident in oncology. In particular, mHealth has established itself as a prominent part of dermatology for cancer screening. Intensified research to seek its use and effectiveness in each phase of the skin cancer continuum is needed in this fast-growing field of teledermatology. OBJECTIVE: The purpose of this review was to describe current trends in research addressing the integration of mHealth and its contributions across the skin cancer continuum. METHODS: A systematic review framework was applied to the search using three electronic databases: PubMed, Web of Science, and Embase. We extensively reviewed appropriate studies regarding skin cancer and mobile technology published between 2007 and 2017. Studies of the role and impact of mobile technology in the prevention and management of skin cancer were included. We selected 18 studies adhering to the inclusion and exclusion criteria for analysis. RESULTS: Of the 18 studies, 5 (28%) evaluated prevention interventions, 6 (33%) assessed diagnostic accuracy, and 7 (39%) pertained to feasibility in the context of mHealth approaches for skin cancer care. These studies portray the potential of mobile teledermatology in the prevention and management of skin cancer. However, not all phases of skin cancer involve mHealth, and not all have been addressed by research. CONCLUSIONS: This review extends our knowledge not only on the contributions of mHealth technologies, but also on their integration in different phases of skin cancer care. To optimize the effectiveness of mHealth in dermatology, larger numbers of robust, evidence-based studies on teledermatology implementations, distributed evenly across the care continuum, should be conducted so that research can be expanded to systematic reviews.

Identification of Keywords From Twitter and Web Blog Posts to Detect Influenza Epidemics in Korea.

OBJECTIVE: Social media data are a highly contextual health information source. The objective of this study was to identify Korean keywords for detecting influenza epidemics from social media data. METHODS: We included data from Twitter and online blog posts to obtain a sufficient number of candidate indicators and to represent a larger proportion of the Korean population. We performed the following steps: initial keyword selection; generation of a keyword time series using a preprocessing approach; optimal feature selection; model building and validation using least absolute shrinkage and selection operator, support vector machine (SVM), and random forest regression (RFR). RESULTS: A total of 15 keywords optimally detected the influenza epidemic, evenly distributed across Twitter and blog data sources. Model estimates generated using our SVM model were highly correlated with recent influenza incidence data. CONCLUSIONS: The basic principles underpinning our approach could be applied to other countries, languages, infectious diseases, and social media sources. Social media monitoring using our approach may support and extend the capacity of traditional surveillance systems for detecting emerging influenza. (Disaster Med Public Health Preparedness. 2018; 12: 352-359).

Web-based infectious disease surveillance systems and public health perspectives: a systematic review.

BACKGROUND: Emerging and re-emerging infectious diseases are a significant public health concern, and early detection and immediate response is crucial for disease control. These challenges have led to the need for new approaches and technologies to reinforce the capacity of traditional surveillance systems for detecting emerging infectious diseases. In the last few years, the availability of novel web-based data sources has contributed substantially to infectious disease surveillance. This study explores the burgeoning field of web-based infectious disease surveillance systems by examining their current status, importance, and potential challenges. METHODS: A systematic review framework was applied to the search, screening, and analysis of web-based infectious disease surveillance systems. We searched PubMed, Web of Science, and Embase databases to extensively review the English literature published between 2000 and 2015. Eleven surveillance systems were chosen for evaluation according to their high frequency of application. Relevant terms, including newly coined terms, development and classification of the surveillance systems, and various characteristics associated with the systems were studied. RESULTS: Based on a detailed and informative review of the 11 web-based infectious disease surveillance systems, it was evident that these systems exhibited clear strengths, as compared to traditional surveillance systems, but with some limitations yet to be overcome. The major strengths of the newly emerging surveillance systems are that they are intuitive, adaptable, low-cost, and operated in real-time, all of which are necessary features of an effective public health tool. The most apparent potential challenges of the web-based systems are those of inaccurate interpretation and prediction of health status, and privacy issues, based on an individual's internet activity. CONCLUSION: Despite being in a nascent stage with further modification needed, web-based surveillance systems have evolved to complement traditional national surveillance systems. This review highlights ways in which the strengths of existing systems can be maintained and weaknesses alleviated to implement optimal web surveillance systems.

Designing a Sustainable Noise Mapping System Based on Citizen Scientists Smartphone Sensor Data.

In this study, we attempted to assess the feasibility of collecting population health data via mobile devices. Specifically, we constructed noise maps based on sound information monitored by individuals' smartphones. We designed a sustainable way of creating noise maps that can overcome the shortcomings of existing station-based noise-monitoring systems. Three hundred and nine Seoul residents aged 20-49 years who used Android-based smartphones were recruited, and the subjects installed a special application that we developed for this study. This application collected information on sound and geographical location every 10 min for 7 days. Using GIS, we were able to construct various types of noise maps of Seoul (e.g., daytime/nighttime and weekdays/weekends) using the information on sound and geographical location obtained via the users' smartphones. Despite the public health importance of noise management, a number of countries and cities lack a sustainable system to monitor noise. This pilot study showed the possibility of using the smartphones of citizen scientists as an economical and sustainable way of monitoring noise, particularly in an urban context in developing countries.

Estimating Influenza Outbreaks Using Both Search Engine Query Data and Social Media Data in South Korea.

BACKGROUND: As suggested as early as in 2006, logs of queries submitted to search engines seeking information could be a source for detection of emerging influenza epidemics if changes in the volume of search queries are monitored (infodemiology). However, selecting queries that are most likely to be associated with influenza epidemics is a particular challenge when it comes to generating better predictions. OBJECTIVE: In this study, we describe a methodological extension for detecting influenza outbreaks using search query data; we provide a new approach for query selection through the exploration of contextual information gleaned from social media data. Additionally, we evaluate whether it is possible to use these queries for monitoring and predicting influenza epidemics in South Korea. METHODS: Our study was based on freely available weekly influenza incidence data and query data originating from the search engine on the Korean website Daum between April 3, 2011 and April 5, 2014. To select queries related to influenza epidemics, several approaches were applied: (1) exploring influenza-related words in social media data, (2) identifying the chief concerns related to influenza, and (3) using Web query recommendations. Optimal feature selection by least absolute shrinkage and selection operator (Lasso) and support vector machine for regression (SVR) were used to construct a model predicting influenza epidemics. RESULTS: In total, 146 queries related to influenza were generated through our initial query selection approach. A considerable proportion of optimal features for final models were derived from queries with reference to the social media data. The SVR model performed well: the prediction values were highly correlated with the recent observed influenza-like illness (r=.956; P<.001) and virological incidence rate (r=.963; P<.001). CONCLUSIONS: These results demonstrate the feasibility of using search queries to enhance influenza surveillance in South Korea. In addition, an approach for query selection using social media data seems ideal for supporting influenza surveillance based on search query data.

Public Trauma after the Sewol Ferry Disaster: The Role of Social Media in Understanding the Public Mood.

The Sewol ferry disaster severely shocked Korean society. The objective of this study was to explore how the public mood in Korea changed following the Sewol disaster using Twitter data. Data were collected from daily Twitter posts from 1 January 2011 to 31 December 2013 and from 1 March 2014 to 30 June 2014 using natural language-processing and text-mining technologies. We investigated the emotional utterances in reaction to the disaster by analyzing the appearance of keywords, the human-made disaster-related keywords and suicide-related keywords. This disaster elicited immediate emotional reactions from the public, including anger directed at various social and political events occurring in the aftermath of the disaster. We also found that although the frequency of Twitter keywords fluctuated greatly during the month after the Sewol disaster, keywords associated with suicide were common in the general population. Policy makers should recognize that both those directly affected and the general public still suffers from the effects of this traumatic event and its aftermath. The mood changes experienced by the general population should be monitored after a disaster, and social media data can be useful for this purpose.