Earticle

Currently the regional festival for each city and province of Korea is a total of 555 on the basis of 2014. It reached the peak with 926 festivals in 2008, and since then all of them have tended to be merged. However, when all festivals that were excluded from the above data basis are combined, it is expected the total festival is exceeding around 2,000. In Japan, 7,400 festivals are held, and in Spain, more than one hundred thousand festivals are held. However, Korea has the demerit that too many festivals of similar themes are held unlike the cases of foreign countries. For example, the number of the festival related to cherry blossom is eight, and the number of the festival related to the movie is four. Due to this, sometimes disputes between each festival occur. However, the essential problem is that there are too many generous festivals that earn no profits. In this regard, this study tries to understand the problem of the culture tourism festival through analysis on the big data, and suggest the solution for this.

지능 정보화 사회에서 통계적 소양과 공학 도구 활용의 중요성이 강조되고 있으나, 실제 초등 통계 수업에서 학생들의 실질적인 도구 활용 과정을 심층적으로 분석한 연구는 부족하다. 이에 본 연구는 초등학교 4학년 학생을 대상으로 공학 도구를 활용한 통계 수업을 실시하고, 통계적 문제해결 과정의 자료 분석 단계에서 나타난 학생들의 공학 도구 활용 양상을 분석하였다. 이를 위해 수업에서 학생 발화, 화면 기록, 활동 결과물 등을 수집·분석하였다. 분석 결과, 학생들의 공학 도구 활용 양상은 초기 구성, 탐색적 조작, 표상 재구성, 도구 기반 추론, 도구 제약 인식, 협력적 도구 활용의 여섯 범주로 나타났다. 특히 학생들은 공학 도구를 단순한 그래프 작성 도구로 사용하는 데 그치지 않고, 축과 눈금을 조정하거나 그래프 유형을 전환하며 자료를 탐색적으로 해석하였으며, 도구의 기능적 한계를 인식하고 이를 보완하려는 모습도 보였다. 이러한 결과는 초등 통계 수업에서 공학 도구가 시각화 도구를 넘어 자료 탐색과 해석을 매개하는 탐구 도구로 기능할 수 있음을 시사한다.

Although the importance of statistical literacy and the use of technological tools have been emphasized in the intelligent information society, there is still a lack of research that closely examines students’ actual processes of using tools in elementary statistics classrooms. Accordingly, this study implemented a technology-integrated statistics lesson for fourth-grade elementary students and analyzed the patterns of students’ use of technological tools that appeared in the data analysis stage of the statistical problem-solving process. For this purpose, students’ utterances, screen recordings, and activity outcomes were collected and analyzed during the lesson. The results showed that students’ use of technological tools could be categorized into initial construction, exploratory manipulation, representational reconstruction, tool-based inference, recognition of tool constraints, and collaborative tool use. In particular, students did not merely use technological tools as tools to draw graphs; rather, they interpreted data in an exploratory manner by adjusting axes and scales or changing graph types, and they also showed attempts to recognize and compensate for the functional limitations of the tools. These results suggest that, in elementary statistics classrooms, technological tools can function not only as visualization tools but also as inquiry tools that mediate the processes of data exploration and interpretation.

본 연구는 지리정보시스템과 빅데이터 분석 시스템을 활용하여 관광객 유입동향 및 소비패턴 분석에 목적을 둔 연구이다. 인천광역시 주요 관광지 중 송도센트럴파크와 차이나타운을 선정하여 2017년 6월 1개월 동안 유동인구 분석, 카드매출 분석을 진행하였다. 전국 광역시도로부터 송도센트럴파크에 방문한 관광객은 인천광역시, 경기도, 서울 특별시 순으로 높게 나타났으며, 외국인 관광객 비중은 중국이 가장 높았다. 차이나타운 관광객의 카드 소비 이용건수는 남성이 여성보다 12.4% 높게 나타났고 카드소비 금액도 남성이 18% 높게 나타났다. 본 연구는 관광객들의 유입동향 및 소비패턴을 분석하여 관광정책 수립의 주요 쟁점들을 도출함으로써 관광정책의 전략적 방안을 제안하는데 시사점이 있다. 본 연구를 바탕으로 향후 관광 인프라 구축 개선에 도움이 될 수 있다고 기대된다.

This study aims to analyze tourist inflow trends and consumption patterns using a geographic information system and big data analysis system. Songdo Central Park and Chinatown were selected among the major tourist destinations in Incheon, and floating population analysis and card sales analysis were conducted for one month in June 2017. The number of tourists visiting Songdo Central Park from metropolitan cities across the country was highest in the order of Incheon Metropolitan City, Gyeonggi-do, and Seoul Metropolitan City, and the proportion of foreign tourists was the highest in China. The number of card consumption used by Chinatown tourists was 12.4% higher for men than for women, and the amount of card consumption was also higher for men by 18%. This study has implications for proposing a strategic plan for tourism policy by analyzing the inflow trend and consumption pattern of tourists and deriving major issues in the establishment of tourism policy. Based on this study, it is expected that it can be helpful in improving the construction of tourism infrastructure in the future.

Herbal prescriptions play a crucial role in traditional East Asian medicine on the aspects of disease treatment and knowledge understanding. However, the vast number of these prescriptions makes it challenging to unlock their hidden knowledge. To address this, we developed EveryFormula, a web-based application designed to facilitate the understanding of the relationships between herbal prescriptions, medicinal herbs, and diseases through data-driven search and analysis. EveryFormula features [SEARCH] for relevance analysis, [COWORD-NET] for term network analysis, and [SEMANTIC-MAP] for semantic analysis. These tools enable users to search for prescriptions, analyze related medicinal herbs and diseases, and trace similar terms, thereby making it easier to derive explicit knowledge from traditional East Asian medicine prescriptions. This application promises to aid anyone interested in understanding and utilizing traditional East Asian medicine prescriptions.

본 연구의 목적은 뉴스포츠 관련 미디어에 나타난 데이터를 파악함으로써 뉴스포츠의 질적 성장을 위한 기초자료를 제공하는 데 있다. 이를 위해 빅카인즈(BIGKinds)에서 제공하는 2020년에서 2023년 동안의 뉴스를 수집하여 분석하였으며, 수집 기사는 총 665건이다. 연구결과 첫째, 빈도 분석 결과 장애인이 가장 높은 빈도를 보였다. 이는 뉴스포츠가 가지고 있는 배우기 쉽고 공간 제약이 적으며, 운동기능에 덜 영향을 받는다는 점에서 장애인들의 뉴스포츠 프로그램 수요가 높았던 것으 로 보인다. 이에 따라 장애인들을 위한 규칙의 변화 그리고 장애인 전용 뉴스포츠 종목 개발이 필요할 것으로 보인다. 둘째, 연관어 분석 결과 뉴스포츠는 상대적으로 코로나19의 영향을 적게 받은 것으로 확인되었는데, 이는 소규모로 실내에서 진행 할 수 있는 뉴스포츠 종목이 다수 존재하기 때문으로 보인다. 이에 따라 실생활의 도구를 활용하거나 혹은 맨몸으로 할 수 있 는 다양한 뉴스포츠 종목이 개발될 필요성이 있다. 후속 연구에서는 보다 다양한 사람들이 관심있고 필요한 생활체육 종목들 에 대한 연구를 진행한다면 활용도가 높을 것이라고 판단된다.

This study aims to offer essential insights for qualitative development of new sports by finding the data from media. Total 665 news provided by BIGKinds were collected from 2020 to 2023 and analyzed. Results indicated individuals with disabilities exhibited the highest frequency. This suggests a high demand for new sports programs among individuals with disabilities, attributed to the ease of learning, spatial constraints, and lesser reliance on motor skills associated with new sports. Consequently, it appears necessary to modify the rules of existing new sports and develop new sports exclusively for individuals with disabilities. The analysis of related words indicated that the impact of COVID-19 on new sports was minimal. This is probably attributable to the presence of numerous new sports that can be conducted indoors on a small scale. Therefore, it is important to establish various new sports that can be performed using simple tools or with no equipment. Future research needs to extend the range to cover various life sports of interest to a wider audience.

본 논문은 도금 공정의 효율성과 품질을 향상시키기 위해 한국 AI 제조 플랫폼(KAMP)과 지능형 센싱 시스템의 통 합을 제안하였다. 연구 내용은 도금조와 스팀 보일러에 부착된 지능형 센서와 AI 기반 분석을 활용하여 운영 매개변 수를 최적화하고, 결함을 감소시키며, 전반적인 생산성을 개선하는 데 중점을 두었다. IoT 센서에서 수집된 실시간 데이터를 이용하여 예측 유지보수 및 자동 품질 관리를 가능하게 함으로써, 공정의 안정성, 에너지 효율성 및 환경 규정 준수에서 현저한 개선을 보여주며, 금속 도금 산업에서 스마트 제조 실천의 진전을 이루는 데 기여할 것으로 기대한다.

This paper proposes the integration of the Korea AI Manufacturing Platform(KAMP) and intelligent sensing systems to enhance the efficiency and quality of plating processes. The research focuses on optimizing operational parameters, reducing defects, and improving overall productivity using intelligent sensors attached to plating baths and steam boilers, along with AI-based analytics. Utilizing real-time data collected from IoT sensors enables predictive maintenance and automated quality control, significantly improving process stability, energy efficiency, and environmental compliance. These advancements are expected to contribute to the progress of smart manufacturing practices in the metal plating industry.

본 연구는 데이터 리터러시 교육에서 생성형 인공지능 활용의 가능성과 한계를 탐구하는 것을 목적으로 한다. 데이터 리터러시의 정의와 구성 요소를 체계적으로 분석하고, 생성형 인공지능이 데이터 분석 과정에서 교육적 도구로 어떻게 활용될 수 있는지 검토하였다. 이를 통해 생성형 인공지능이 데이터 분석의 각 단계에서 분석의 효율성을 높이는 동시에 학습자의 창의적 문제 해결 능력을 강화할 수 있는 잠재력을 확인하였다. 그러나 생성 형 인공지능의 기술적 측면과 학생들의 역량 증진 측면에서 다양한 요인들이 교육적 적용에 장애가 될 수 있음 이 보고되고 있어, 이에 본 연구에서는 이를 보완하기 위한 전략과 방안을 제안하였다. 본 연구는 데이터 리터 러시 교육 현장에서 생성형 인공지능 활용 가능성을 극대화하기 위한 기초 자료로 활용될 수 있을 것으로 기대 한다.

This study aims to explore the potential and limitations of utilizing generative artificial intelligence (AI) in data literacy education. By systematically analyzing the definition and components of data literacy, the study examines how generative AI can be utilized as an educational tool throughout various stages of the data analysis process. The findings highlight the potential of generative AI to enhance the efficiency of data analysis while simultaneously fostering students’ creative problem-solving abilities. However, it has been reported that various factors, such as technical limitations of generative AI and challenges related to students’ skill development, may hinder its effective educational application. Accordingly, this study proposes strategies and solutions to address these challenges. The research is expected to serve as a foundational resource for maximizing the potential use of generative AI in data literacy education settings.

본 연구에서는 k-평균 군집화 및 인공신경망 모형의 통합적 활용방식을 주택가격 추정에 적용하였다. 기존의 통합적 방식을 정리하고 새로운 통합방식을 제안하였으며, 주택가격 추정에 적용 및 추정력을 비교·평가하였다. 기존의 통합방식 모형을 주택가격 추정에 적용한 결과는 앞선 연구결과와 마찬가지로, 인공신경망으로만 구성한 모형보다 k-평균 군집결과를 인공신경망에 통합적으로 적용한 경우가 나은 추정력을 보이는 것으로 평가되었다. 이와 더불어, 본 논문에서는 개선된 통합방식을 제안하고자 하였다. 기존의 통합방식은 k-평균 군집화 과정이 마치 인공신경망의 입력자료를 유사성에 따라 나누고, 군집별 특성을 추가적으로 인공신경망에 반영하는 방식으로 고려된다. 제안 한 통합모형은 학습용 데이터로부터 분할한 훈련용 데이터의 k-평균 및 군집결과로부터 인공신경망을 구성하는 형태로 구성하였다. 그리고 검증용 및 테스트용 데이터는 훈련용 데이터의 군집중심과 최근접 기준으로 각각 동일한 수의 군집으로 분류하였다. MAE로써 평가한 결과, 주택가격 추정사례에서 기존의 통합방식과 비교하여 개선된 추정력을 보이는 것으로 평가되었다. 본 논문에서 제안된 k-평균 군집화 및 인공신경망의 통합모형은 주택가격 추정 뿐만 아니라, 다양한 추정모형으로 활용이 가능할 것으로 기대된다.

In this paper, the existing methods and a new proposed method of integration of k-means clustering and artificial neural network are compared and their estimation powers are evaluated for house price estimation. In previous studies, the integration methods were applied to areas other than house price, but the results of applying them to house price estimation are better than that of single usage of artificial neural network without k-means clustering. In the existing integration method, the k-means clustering is considered as a method in which the input data of the artificial neural network is divided according to similarity and the characteristics of each cluster are additionally reflected in the artificial neural network. The proposed integration model is configured in such a way that the training data is first divided from the learning data and the artificial neural network is constructed from k-means clustering results of the training data. In addition, the validation and test data are classified into the same number of clusters based on the closest criterion to the cluster centers of the training data groups. MAE is evaluated to show improved estimation power compared to the existing integration methods in the case of housing price estimation. The integration method proposed in this paper can be used not only for housing price estimation but also for various estimation models.

본 연구는 MZ 세대 직장인을 대상으로 OTT 서비스의 사용빈도와 삶의 만족도에 미치는 영향을 한국미디어패널 의 설문조사 데이터를 이용하여 다각적으로 분석하였다. 최근 OTT 서비스 시장이 급격하게 성장했지만 이러한 변화가 MZ 세대의 삶에 대한 어떠한 변화를 가져왔는지에 대한 연구는 부족했다. 본 연구에서는 MZ 세대 직장인의 특성에 따라 OTT 서비스의 사용빈도를 알아보고, 사용빈도가 삶의 만족도에 영향을 주는지 연구모형을 설정하여 검정하고자 하였다. 실제로 MZ 세대의 성별, 연령대에 따라 사용빈도가 달라졌고, 사용빈도도 삶의 만족도에 영향을 미치는 것으로 나타났다. 미디어 콘텐츠의 이용행태와 삶의 만족도의 관계를 처음으로 연구했다는 점에서 학문적인 기여를 가진다. 향후 사용빈도 뿐만 아니라 OTT 콘텐츠의 종류, OTT 서비스의 사용 시간대 등으로 추가적인 연구가 가능하다.

This research multilaterally analyzed the frequency of use of OTT service and its effect on life satisfaction for office workers of the MZ generation using the survey data of the Korean Media Panel. Recently, the OTT service market has grown rapidly, but there is a lack of research on what changes these changes have brought to the lives of the MZ generation. In this research, the frequency of OTT service use was investigated according to the characteristics of workers in the MZ generation, and a research model was set up to test whether the frequency of use affects life satisfaction. In fact, the frequency of use varied according to the gender and age group of the MZ generation, and it was found that the frequency of use also affected life satisfaction. It has an academic contribution in that it was the first to study the relationship between media content use patterns and life satisfaction. In the future, additional research is possible not only on the frequency of use, but also on the type of OTT content and the time of use of OTT service.

세계 각국이 코로나 19 확산을 막기 위한 영업 중지(business shutdown), 여행금지(travel ban), 자가 격리(self-isolation), 재택근무 (teleworking) 등의 억제책을 시행하였다. 이로 인한 코로나 19 조치는 기본적으로 대면접촉·이동을 금지하는 형태이기 때문에 필연적으로 관광에 막대한 피해를 준다(김형종, 2020a). 본 연구는 시계열 분석을 통해서 관광시장의 과거와 현재 및 미래 법칙성을 발견하고 이를 모형화하여 추정하고, 이 추정된 모형을 사용하여 미래에 관측된 값들을 예측하는 것이다(이우리, 2016). 수요예측은 관광시장의 역동성에 따른 환경변동에 대하여 대응을 위해 관광분야에서는 꾸준히 활용하고 있다. 본 연구의 목적은 포스트 코로나 시대를 대비하여 합리적인 관광진흥정책을 수립하고 바람직한 관광정책의 방향을 제시하기 위해 관광목적지의 미래 관광수요를 예측하고 분석하고자 하였다. 연구 목적을 달성하기 위하여 계량경제학에서 많이 사용하고 있는 ARIMA 시계열분석을 실시하였다. ARIMA, 즉 자기회귀이동평균 모형을 이용하여 얻은 예측치와 관측치를 비교하고 모형의 적합성과 정확성을 검정하며, 가장 적합한 모형을 선정하고자 한다. 본 연구에서 ARIMA(2,1,2)모델로 얻은 예측결과, 관광목적지 2022년 4월 88,669명, 2022년 5월 105,465명, 2022년 6월 94,402명, 2022년 7월 95,466명, 2022년 8월 102,276명, 2022년 9월 86,245명, 2022년 10월 99,875명, 2022년 11월 95,764명, 2022년 12월 84,075명, 2023년 1월 93,120명, 2023년 2월 103,905명, 2023년 3월 92,696명으로 예측되었다. 이러한 관점에서, 본 논문은 이론을 바탕으로 하여 관광목적 지의 관광수요를 예측함으로써 관광목적지는 정확하게 관광경제발전 정책과 방침을 만들어낼 수 있는 데에 큰 도움이 되리라 생각한다.

The purpose of this study is to forecast the number of tourists in Gamcheon Culture Village . For this, this study collected data from January 2015 to December 2021 from Tourism Knowledge & Information System. To forecast the number of tourists ARIMA time series analysis was performed. The predictions and observations obtained using the model were compared, the suitability and accuracy of the model were tested, and the best model for this study was selected. The results of tourists from January to December 2022 in Gamcheon Culture Village shows that there is no trend of a surge in the number of tourists visiting Gamcheon Culture Village, and about 148,442 tourists were expected to visit Gamcheon Culture Village every month. From this point of view, this study is thought to be of great help in accurately creating policies and policies for the development of the tourism economy by predicting the tourism demand of tourism destination based on theory.

In order to gain more profits from past to present, there is no time to analyze and predict the value of goods. Many efforts have been made to obtain information about the product to obtain it at a lower price and to sell it at a higher price. As computers have become commonplace in modern times, a number of methods have been developed. And, I was attracted to this method and collected the sales data of avocado and analyzed and predicted through the method, for example regression model and fbprophet. The source of the data is 'hass avocado board'.

Purpose: This study aimed to identify the main keywords, network structures, and topical themes in patient safety incident reports using text network analysis. Methods: The study analyzed patient safety incident reports from a general hospital in Seoul, covering a total of 3,576 cases reported over five years, from 2019 to 2023. Unstructured data were extracted from the text of the incident reports, detailing how the patient safety incidents occurred and how they were managed according to the six-part principles. The analysis was conducted in four steps: 1) word extraction and refinement, 2) keyword extraction and word network generation, 3) network connectivity and centrality analysis, and 4) topic modeling analysis. The NetMiner program was used for data analysis. Results: The analysis of degree, betweenness, and closeness centrality revealed that the most common keywords among the top five were "confirmation," "medication," "inpatient room," "caregiver," and "condition." Topic modeling analysis identified three main topic groups: 1) incidents caused by a lack of awareness of fall risk, 2) incidents of non-compliance with basic medication principles, and 3) incidents due to inaccurate patient identification. Conclusion: To prevent patient safety incidents, it is necessary to promote a culture of safety in hospitals, standardize patient identification procedures, and provide basic training in medication safety and fall prevention to healthcare staff. Furthermore, empirical research on patient safety practices is necessary to encourage active participation in patient safety activities by patients and family caregivers.

Purpose: Inpatient and residential smoking cessation programs in Korea have demonstrated relatively high quit rates, with residential programs consistently outperforming inpatient ones. However, simple comparisons are limited by differences in participant characteristics and eligibility criteria. This study aimed to determine whether program type independently influences quit rates, using both self-reported and biochemically verified outcomes.Methods: This descriptive study conducted a secondary analysis of data from 17,290 participants enrolled in national smoking cessation services across 18 regional tobacco control centers (2018-2020). Data included demographics, smoking history, and program participation. Quit status at 4 weeks, 12 weeks, and 6 months was assessed through self-report and biochemical verification. Multivariate logistic regression was used to evaluate the independent effect of program type on 6-month quit outcomes.Results: The mean age of participants was 54.8 ± 12.0 years, and 14.5% were women. At 6 months, self-reported quit rates ranged from 16.5% to 34.1% for the inpatient program and from 26.0% to 62.8% for the residential program. Biochemically verified rates ranged from 8.6% to 19.0% (inpatient) and 11.9% to 46.7% (residential). After adjusting for confounders, program type was significantly associated with self-reported quitting (aOR = 0.80; 95% CI = 0.72-0.89; p < .001), but not with biochemically verified quitting (aOR = 0.91; 95% CI = 0.82-1.01; p = .082).Conclusion: Although residential programs showed higher self-reported quit rates, program type did not predict verified cessation. This suggests differences in participant characteristics may drive outcomes. Further research should identify effective, evidence-based components for sustained quitting.

Revealing customer satisfaction through big social data has been an interesting research topic in tourism and hospitality. Big data analysis is an effective way to detect customers’ behaviors in their decision-making. This study aims to perform big social data analysis to reveal whether food quality impacts the relationship between hotel performance criteria and travelers’ satisfaction. A two-stage methodology is developed to address the objectives of this study. The findings demonstrated that there is a positive relationship between eco-friendly hotels’ performance criteria and satisfaction. The results and implications for managers and future research directions are discussed.

Purpose: Although digital solutions could mitigate the challenges faced by older adults living alone(OALA), only a few studies investigated the need for and acceptance of digital health interventions forself-management (DHISMs) among this demographic. Thus, we aim to explore this need and acceptance,along with the contextual factors, among OALA. Methods: A mixed-methods research approach was adopted. We conducted 1) a quantitative survey(n ¼ 191) to investigate the need for and acceptance of DHISMs using a numeric rating scale and 2) aqualitative study (n ¼ 24) based on focus group interviews to explore contextual factors related to thequantitative results. Qualitative data were analyzed using thematic analysis. Results: In the quantitative study, the mean scores for the need for and acceptance of DHISMs were 6.41and 6.53 out of 10, respectively. Emergency response systems had the highest need and acceptancescores, whereas digital interventions for behavioral change (medication adherence, sleep, stress, and dietmanagement) had relatively lower scores. The qualitative analysis revealed two themes and five subthemes:the need for inclusive support for independent living (environmental safety and diverseself-management challenges with limited support) and multidimensional factors related to DHISMacceptance (personal, technological, and relational barriers and facilitators). Conclusions: In the future, the unique and multidimensional factors influencing the need for andacceptance of DHISMs among OALA should be carefully considered to support their self-managementand independent living. Blended care, which involves integrating age-friendly technology withpersonalized human interaction, is pivotal for increasing DHISM acceptance in this population.

Purpose: Self-rated health in individuals with chronic diseases is influenced by various factors, including dietary adherence and physical activity. However, limited research has investigated how these factors relate to self-rated health among people with chronic kidney disease. Therefore, this study aimed to describe self-rated health and identify its associated factors in this population. Methods: This cross-sectional, secondary data analysis utilized datasets from the seventh Korea National Health and Nutrition Examination Survey, which were collected between 2016 and 2018. A total of 557 participants (mean age=74.8 years) with a glomerular filtration rate of <60 mL/min/1.73 m² were included. Data from health interviews and examinations were analyzed to assess self-rated health, dietary adherence, and physical activity. Descriptive and inferential statistical methods were employed for analysis. Results: Among the 557 participants, 42.6% rated their health as poor. Factors such as sex, age, income, smoking history, anxiety/depression, number of comorbidities, glomerular filtration rate, and physical activity were significantly associated with self-rated health. In contrast, dietary adherence did not exhibit a significant association. Conclusion: Understanding the factors associated with self-rated health can inform the development of nursing interventions aimed at improving self-rated health among patients with chronic kidney disease.

Purpose: This study aimed to investigate the association among various adolescent prob- lem behaviors in South Korea, focusing on gender differences. Methods: This secondary analysis was conducted using cross-sectional data from the 19th Korea Youth Risk Behavior Survey, conducted in 2023 which included 52,880 mid- dle and high school students in South Korea. They completed an anonymous self-admin- istered survey regarding problem behaviors (drinking alcohol, smoking, drugs use, and sexual experiences). Data were analyzed using complex-samples chi-square and multiple logistic regression models. Results: Among the 52,880 adolescents, the prevalence rates of alcohol use, smoking, drug use, and sexual experiences were 32.6%, 8.6%, 1.7%, and 6.5%, respectively. Gender differences were observed in alcohol use complex-samples multiple logistic regression models. However, no significant gender difference was found in drug use (p=.250). Four problem behaviors were significantly associated with one other when analyzed as inde- pendent variables (odds ratio [OR], 1.33?10.85). The strongest associations were found between alcohol use and smoking (OR, 10.49?10.85), and between smoking and sexual experiences (OR, 4.91?4.96). Conclusion: This study found significant gender differences in adolescent problem be- haviors, with male adolescents exhibiting higher rates of alcohol use, smoking, and sexu- al experience. Strong associations were observed between alcohol use and smoking, as well as between smoking and sexual experience. These findings suggest the need for in- tegrated intervention strategies that target multiple co-occurring problem behaviors.

Purpose: The majority of adolescents use smartphones, and their overdependence on smartphones has emerged as a serious social issue. Although studies have explored the effect of smartphone overdependence on adolescent problem behaviors, research on its influence on sexual behavior is scarce. This study aimed to examine the association be- tween smartphone overdependence and sexual behavior among adolescents. Methods: This study used data from the 19th Korea Youth Risk Behavior Web-Based Survey conducted in 2023. Smartphone overdependence was the independent variable, and sexual behaviors (sexual intercourse and contraceptive use) were the dependent variables. Multivariate regression analysis was performed to control for confounding variables. Results: The study participants included 52,880 adolescents aged 13?18 years. Among them, 28.0% (n=14,672) were classified as smartphone overdependent, 6.5% (n=3,349) had sexual experience, and 65% (n=2,182) of those with sexual experience reported using contraception. Smartphone overdependence was not significantly associated with sexual experience but was significantly associated with contraceptive use. Adolescents in the non-overdependent group were 1.27 times more likely to use contraception than those in the smartphone overdependent group (95% confidence interval, 1.07?1.52; p<.007). Conclusion: The findings highlight a significant association between smartphone over- dependence and contraceptive use among adolescents. Policy interventions and educa- tional strategies that consider adolescents’ smartphone usage patterns and trait factors are necessary.