Background: Digital dementia is a term that describes a possible decline in cognitive abilities, especially memory, attributed to the excessive use of digital technology such as smartphones, computers, and tablets. This concept has gained popularity in public discourse and media lately. With the increasing use of social media platforms such as Twitter (subsequently rebranded as X), discussions about digital dementia have become more widespread, which offer a rich source of information to understand public perceptions, concerns, and sentiments regarding this phenomenon. Objective: The aim of this research was to delve into a comprehensive content and sentiment analysis of Twitter discussions regarding digital dementia using the hashtag #digitaldementia. Methods: Retrospectively, publicly available English-language tweets with hashtag combinations related to the topic of digital dementia were extracted from Twitter. The tweets were collected over a period of 15 years, from January 1, 2008, to December 31, 2022. Content analysis was used to identify major themes within the tweets, and sentiment analysis was conducted to understand the positive and negative emotions associated with these themes in order to gain a better understanding of the issues surrounding digital dementia. A one-way ANOVA was performed to gather detailed statistical insights regarding the selected tweets from influencers within each theme. Results: This study was conducted on 26,290 tweets over 15 years by 5123 Twitter users, mostly female users in the United States. The influencers had followers ranging from 20,000 to 1,195,000 and an average of 214,878 subscribers. The study identified four themes regarding digital dementia after analyzing tweet content: (1) cognitive decline, (2) digital dependency, (3) technology overload, and (4) coping strategies. Categorized according to Glaser and Strauss’s classifications, most tweets (14,492/26,290, 55.12%) fell under the categories of wretched (purely negative) or bad (mostly negative). However, only a small proportion of tweets (3122/26,290, 11.86%) were classified as great (purely positive) or swell sentiment (mostly positive). The ANOVA results showed significant differences in mean sentiment scores among the themes (F3,3581=29.03; P<.001). The mean sentiment score was –0.1072 (SD 0.4276). Conclusions: Various negative tweets have raised concerns about the link between excessive use of digital devices and cognitive decline, often known as digital dementia. Of particular concern is the rapid increase in digital device use. However, some positive tweets have suggested coping strategies. Engaging in digital detox activities, such as increasing physical exercise and participating in yoga and meditation, could potentially help prevent cognitive decline.

Objective: Although some previous studies have investigated health in police officers, investigations of related factors have been limited to work-related associations or those affecting certain police officers. To address this gap, this study investigated relationships between sociodemographic factors, work-related factors, health behaviors, health conditions, and self-rated bad health (SRBH) in Korean police officers. Methods: In 2021, we conducted a cross-sectional survey in cooperation with the Korean National Police Agency (KNPA). The respondents were 6,591 police officers aged 21-60 years, all of whom agreed to complete the survey online using the police agency's intranet. After collecting data, we conducted a multiple logistic regression analysis to examine factors that were associated with SRBH, with calculations for adjusted odds ratios (ORs) and 95 % confidence intervals (CIs). The study model included a range of individual socioeconomic characteristics, work-related variables, health behaviors, and health conditions. Results: Although the associated factors varied according to age group, several factors generally related to SRBH, including the lack of exercise, stress, having one or more chronic diseases, and work-related pain. By contrast, neither sex nor sleep duration were associated with SRBH of respondents. Monthly night work, smoking, and alcohol consumption were only associated with SRBH of certain age groups. Conclusions: Several variables clearly related to SRBH of police officers. In this context, health-related associations, especially stress and chronic diseases, tended to differ according to age, including those that should be considered to improve health. These findings have important implications for relevant healthcare programs and interventions.

Objectives South Korea grapples with a disproportionately high incidence of unmet medical needs, a concern that is particularly acute among police officers, who are exposed to significant occupational risks. Given the pivotal role of police officers in upholding democratic values and public safety, their well-being holds critical societal implications. This study aims to determine the incidence of unmet medical needs among police officers and identify the influencing factors. Design This is a retrospective and cross-sectional study. Applying the Andersen behavioural model and multiple logistic regression analysis, we explored factors impacting unmet medical needs. Setting The study took place in South Korea and involved its total force of police officers. Participants Our analysis encompassed data from 6591 participants, representing 5.2% of South Korea’s total police officers. Outcome measures Unmet medical needs. Results Our findings revealed several influencing factors. First, predisposing factors included sex, with women experiencing a higher incidence of unmet medical needs. Second, enabling factors highlighted the significance of job positions and reduced annual leave guarantees in influencing unmet medical needs. Finally, need factors demonstrated the substantial impact of chronic diseases, heightened levels of depression, reduced subjective health assessments, increased stress levels and exposure to rough physical activity on driving unmet medical needs. Conclusions To mitigate and pre-empt the long-term health repercussions associated with unmet medical needs, intervention strategies should prioritise these identified factors. An integrated healthcare programme emerges as a critical necessity for addressing the healthcare challenges faced by police officers.

South Korea is reported to have higher levels of unmet medical needs (UMN) than other countries, particularly among the middle-aged adult population. Considering that this group constitutes a substantial portion of the country’s productive workforce, their health requires continuous management to ensure sustained productivity. The purpose of this study is to investigate the factors associated with UMN in economically active middle-aged adults and to develop a model to predict the occurrence of UMN. In this study, 3,575 middle-aged adults who are economically active were selected from the 2020 Korean Health Panel Survey data. Logistic regression, Random Forest, Naïve Bayes, Gradient Boosting Method, and Neural Network were applied to create the prediction model, and tenfold cross validation was performed by checking the reliability of the analysis. The model was evaluated based on the Area Under Receiver Operating Characteristics (AUROC) as well as accuracy, precision, recall, F-1 score and MCC. First, the prevalence of UMN in middle-aged adults was 15.6%. Second, random forest was found to be the model with the highest predictive power. It showed an AUROC of 0.831, Accuracy of 0.862, and F-1 score of 0.820. Third, the main factors influencing the occurrence of UMN were subjective stress and subjective health awareness. These findings suggest that psychological support is necessary in order to manage the occurrence of UMN among middle-aged adults, with regular stress management being especially important. However, the lower AUROC suggests that additional variables are needed to enhance the prediction model.

Introduction: Adolescent smoking can lead to various health problems including atherosclerosis and cardiovascular disease, making it more difficult to quit smoking during adulthood. This study aims to evaluate the effect of the ordinance by assessing adolescents’ smoking cessation behaviors and environmental conditions, according to the “Ordinance for the Prevention of Child and Adolescent Smoking and Support for Smoking Cessation”, which was enacted in 2019 in Gwangju City, South Korea, for the first time in the country. Methods: The data for the analysis were obtained from the 2018 and 2021 Korea Youth Risk Behavior Survey. Propensity score matching (PSM) ensured homogeneity between ordinance-adopted and non-adopted areas, followed by a difference-in-differences (DID) analysis to assess changes in adolescent smoking behavior, secondhand smoke exposure, and related advertisements. Results: The difference-in-differences analysis of the homogeneous treatment and control groups in 2018 and 2021 showed statistically significant reductions in the variables “experience of secondhand smoke indoors at school” in the secondhand smoke category, and “exposure to smoking advertisements in convenience stores” in the smoking advertising category. However, there were no statistically significant changes in the other eight variables, such as smoking behavior and smoking cessation advertising. Conclusions: The results of this study suggest that the ordinance enacted in Gwangju Metropolitan City in 2019 created a positive environment for smoking prevention in convenience stores and schools for adolescents. Furthermore, if the ordinance is supplemented with the management of smoking behaviors and smoking cessation advertisements, it is expected to achieve the main purpose of the policy, which is to protect adolescents from smoking.

Despite the National Health Insurance (NHI) system implemented in South Korea, concerns persist regarding access to health coverage for low-income households. To address this issue, this study aims to use machine learning-based data mining techniques to classify whether such households will face catastrophic health expenditures (CHEs). A total of 4,031 low-income people were extracted using 2019 data from the Korea Health Panel Survey. The classification model was developed using four machine learning algorithms: Random Forest, Gradient boosting, Decision tree, Ridge regression, Neural network, and AdaBoost. Ten-fold cross validation was carried out to ensure the reliability of the analysis results. The model was evaluated based on the Area Under Receiver Operating Characteristics (AUROC) as well as accuracy, precision, recall, and F-1 score. The study’s findings revealed that the incidence of CHE was 26.2% in low-income households. The AdaBoost model had the highest classifiable power. It showed AUROC of 89.8%, accuracy of 83.1%, precision of 82.4%, recall of 83.1, and F1 score of 82.1%. The study found that economic activity, chronic disease, and age were significant factors that could lead to CHEs. Therefore, individuals over 65, with chronic conditions, and unemployed had the highest likelihood of developing CHE. It is essential to identify low-income households that are at risk of CHEs in advance before facing the economic burden. This research is expected to provide fundamental data that can aid in developing an integrated support program to prevent and manage CHEs more effectively.