Journal of Korean Academy of Nursing

Research Papers

Influence of Social Capital on Depression of Older Adults Living in Rural Area: A Cross-Sectional Study Using the 2019 Korea Community Health Survey: Jung, Minho , Kim, Jinhyun; J Korean Acad Nurs 2022;52(2):144-156. Published online April 30, 2022; DOI: https://doi.org/10.4040/jkan.21239

Purpose
This study aimed to investigate the influence of social capital on the depression of older adults living in rural areas.
Methods
Data sets were obtained from the 2019 Korea Community Health Survey. The participants were 39,390 older adults over 65 years old living in rural areas. Indicators of social capital included trust, reciprocity, network, and social participation. Depression—the dependent variable—was measured using the Patient Health Questionnaire-9 (PHQ-9). Hierarchical ordinal logistic regression was conducted to identify factors associated with depression after adjusting the data numbers to 102,601 by applying the Synthetic Minority Oversampling Technique (SMOTE).
Results
The independent variables—indicators of social capital—exhibited significant association with the depression of older adults. The odds ratios of depression were higher in groups without social capital variables.
Conclusion
To reduce depression, we recommend increasing social capital. Factors identified in this study need to be considered in older adult depression intervention programs and policies.

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The Effects of Perceived Stress on Depression among Middle-aged Adults with Diabetes Mellitus in Korea: Exploring the Mediating Role of Social Capital through a Descriptive Correlational Study
Kyung Ae Kim, Mi Ran Bang
Korean Journal of Adult Nursing.2025; 37(1): 50. CrossRef
An Observational Study on the Association Between Nutritional Intake and Mental Health Among Older Adults in Rural Areas
Kyeongmin Jang
Nursing & Health Sciences.2025;[Epub] CrossRef
Prediction model of weight control experience in men with obesity in their 30 s and 40 s using decision tree analysis
Myeunghee Han
Scientific Reports.2024;[Epub] CrossRef
The relationship between human capital and depression among middle-aged rural adults: The multiple-parallel mediating effects of social capital
Soo Mi Jang, Hyung Mi Ha
Korean Journal of Health Education and Promotion.2023; 40(1): 33. CrossRef

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Development of a Diabetic Foot Ulceration Prediction Model and Nomogram: Lee, Eun Joo , Jeong, Ihn Sook , Woo, Seung Hun , Jung, Hyuk Jae , Han, Eun Jin , Kang, Chang Wan , Hyun, Sookyung; J Korean Acad Nurs 2021;51(3):280-293. Published online June 30, 2021; DOI: https://doi.org/10.4040/jkan.20257

Abstract PDF

Purpose
This study aimed to identify the risk factors for diabetic foot ulceration (DFU) to develop and evaluate the performance of a DFU prediction model and nomogram among people with diabetes mellitus (DM).
Methods
This unmatched case-control study was conducted with 379 adult patients (118 patients with DM and 261 controls) from four general hospitals in South Korea. Data were collected through a structured questionnaire, foot examination, and review of patients’ electronic health records. Multiple logistic regression analysis was performed to build the DFU prediction model and nomogram. Further, their performance was analyzed using the Lemeshow–Hosmer test, concordance statistic (C-statistic), and sensitivity/specificity analyses in training and test samples.
Results
The prediction model was based on risk factors including previous foot ulcer or amputation, peripheral vascular disease, peripheral neuropathy, current smoking, and chronic kidney disease. The calibration of the DFU nomogram was appropriate (χ² = 5.85, p = .321). The C-statistic of the DFU nomogram was .95 (95% confidence interval .93~.97) for both the training and test samples. For clinical usefulness, the sensitivity and specificity obtained were 88.5% and 85.7%, respectively at 110 points in the training sample. The performance of the nomogram was better in male patients or those having DM for more than 10 years.
Conclusion
The nomogram of the DFU prediction model shows good performance, and is thereby recommended for monitoring the risk of DFU and preventing the occurrence of DFU in people with DM.

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A Simple Nomogram for Predicting Stroke-Associated Pneumonia in Patients with Acute Ischemic Stroke
Youn-Jung Lee, Hee Jung Jang
Healthcare.2023; 11(23): 3015. CrossRef
Establishment of a Nomogram Model for Predicting Cardiovascular and Cerebrovascular Events in Diabetic Nephropathy Patients Receiving Maintenance Hemodialysis
Xiaobing Liu, Caili Yan, Xiuxiu Niu, Jiechun Zeng, Fahd Abd Algalil
Applied Bionics and Biomechanics.2022; 2022: 1. CrossRef
Prognostic factors in diabetes: Comparison of Chi-square automatic interaction detector (CHAID) decision tree technology and logistic regression
Hae-Young Choi, Eun-Yeob Kim, Jaeyoung Kim
Medicine.2022; 101(42): e31343. CrossRef

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Comparison of the Prediction Model of Adolescents’ Suicide Attempt Using Logistic Regression and Decision Tree: Secondary Data Analysis of the 2019 Youth Health Risk Behavior Web-Based Survey: Lee, Yoonju , Kim, Heejin , Lee, Yesul , Jeong, Hyesun; J Korean Acad Nurs 2021;51(1):40-53. Published online February 28, 2021; DOI: https://doi.org/10.4040/jkan.20207

Abstract PDF

Purpose
The purpose of this study was to develop and compare the prediction model for suicide attempts by Korean adolescents using logistic regression and decision tree analysis. Methods: This study utilized secondary data drawn from the 2019 Youth Health Risk Behavior web-based survey. A total of 20 items were selected as the explanatory variables (5 of sociodemographic characteristics, 10 of health-related behaviors, and 5 of psychosocial characteristics). For data analysis, descriptive statistics and logistic regression with complex samples and decision tree analysis were performed using IBM SPSS ver. 25.0 and Stata ver. 16.0.
Results
A total of 1,731 participants (3.0%) out of 57,303 responded that they had attempted suicide. The most significant predictors of suicide attempts as determined using the logistic regression model were experience of sadness and hopelessness, substance abuse, and violent victimization. Girls who have experience of sadness and hopelessness, and experience of substance abuse have been identified as the most vulnerable group in suicide attempts in the decision tree model.
Conclusion
Experiences of sadness and hopelessness, experiences of substance abuse, and experiences of violent victimization are the common major predictors of suicide attempts in both logistic regression and decision tree models, and the predict rates of both models were similar. We suggest to provide programs considering combination of high-risk predictors for adolescents to prevent suicide attempt.

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Factors Influencing Suicidal Ideation in Female Adolescents With Smartphone Overdependence
Hyeongyeong Yoon
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Meekang Sung, Jihye Han, Carrie G. Wade, Vaughan W. Rees
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Ruitong Li, Yuchuan Yue, Xujie Gu, Lingling Xiong, Meiqi Luo, Ling Li
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Nibene H. Somé, Pardis Noormohammadpour, Shannon Lange
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A prediction model for adolescents’ skipping breakfast using the CART algorithm for decision trees: 7th (2016–2018) Korea National Health and Nutrition Examination Survey
Sun A Choi, Sung Suk Chung, Jeong Ok Rho
Journal of Nutrition and Health.2023; 56(3): 300. CrossRef
Development of Prediction Model for Suicide Attempts Using the Korean Youth Health Behavior Web-Based Survey in Korean Middle and High School Students
Younggeun Kim, Sung-Il Woo, Sang Woo Hahn, Yeon Jung Lee, Minjae Kim, Hyeonseo Jin, Jiyeon Kim, Jaeuk Hwang
Journal of Korean Neuropsychiatric Association.2023; 62(3): 95. CrossRef
Effects of Stress on Suicide Behavior among Adolescents: An Analysis of Online Survey Data on Youth Health Behavior Using Propensity Score Matching
Chung Hee Woo, Ju Young Park
Korean Journal of Stress Research.2021; 29(3): 199. CrossRef

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Original Articles

Prediction Models of Mild Cognitive Impairment Using the Korea Longitudinal Study of Ageing: Park, Hyojin , Ha, Juyoung; J Korean Acad Nurs 2020;50(2):191-199. Published online April 30, 2020; DOI: https://doi.org/10.4040/jkan.2020.50.2.191

Abstract PDF

Purpose
The purpose of this study was to compare sociodemographic characteristics of a normal cognitive group and mild cognitive impairment group, and establish prediction models of Mild Cognitive Impairment (MCI).
Methods
This study was a secondary data analysis research using data from “the 4th Korea Longitudinal Study of Ageing” of the Korea Employment Information Service. A total of 6,405 individuals, including 1,329 individuals with MCI and 5,076 individuals with normal cognitive abilities, were part of the study. Based on the panel survey items, the research used 28 variables. The methods of analysis included a c2-test, logistic regression analysis, decision tree analysis, predicted error rate, and an ROC curve calculated using SPSS 23.0 and SAS 13.2.
Results
In the MCI group, the mean age was 71.4 and 65.8% of the participants was women. There were statistically significant differences in gender, age, and education in both groups. Predictors of MCI determined by using a logistic regression analysis were gender, age, education, instrumental activity of daily living (IADL), perceived health status, participation group, cultural activities, and life satisfaction. Decision tree analysis of predictors of MCI identified education, age, life satisfaction, and IADL as predictors.
Conclusion
The accuracy of logistic regression model for MCI is slightly higher than that of decision tree model. The implementation of the prediction model for MCI established in this study may be utilized to identify middle-aged and elderly people with risks of MCI. Therefore, this study may contribute to the prevention and reduction of dementia.

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Nomogram for predicting changes in cognitive function in community dwelling older adults with mild cognitive impairment based on Korea Longitudinal Study of Ageing Panel Data: a retrospective study
Hyuk Joon Kim, Hye Young Kim
Journal of Korean Academy of Nursing.2025; 55(1): 50. CrossRef
Cognitive Dysfunction Prediction Model with Lifelog Dataset based on Random Forest and SHAP
Myeongjin Lee, Jongun Lee, Hanjun Lee
The Journal of Korean Institute of Information Technology.2024; 22(1): 1. CrossRef
Sociodemographic Factors Predict Incident Mild Cognitive Impairment: A Brief Review and Empirical Study
Shuyi Jin, Chenxi Li, Jiani Miao, Jingyi Sun, Zhenqing Yang, Xingqi Cao, Kaili Sun, Xiaoting Liu, Lina Ma, Xin Xu, Zuyun Liu
Journal of the American Medical Directors Association.2023; 24(12): 1959. CrossRef
Characteristics and Factors Associated with Cognitive Decline of Elderly with Mild Cognitive Impairment
Eul Hee Roh
Journal of Health Informatics and Statistics.2023; 48(3): 179. CrossRef
Detection and Intervention of Subjective Cognitive Decline in Pre-Alzheimer’s Disease
雅红何
International Journal of Psychiatry and Neurology.2022; 11(04): 65. CrossRef
Influencing Factors of Subjective Cognitive Impairment in Middle-Aged and Older Adults
Min Roh, Hyunju Dan, Oksoo Kim
International Journal of Environmental Research and Public Health.2021; 18(21): 11488. CrossRef

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Factors Influencing Adolescent Lifetime Smoking and Current Smoking in South Korea: Using data from the 10^th (2014) Korea Youth Risk Behavior Web-Based Survey: Seok Hyun Gwon, Suyong Jeong; J Korean Acad Nurs 2016;46(4):552-561. Published online August 31, 2016; DOI: https://doi.org/10.4040/jkan.2016.46.4.552

Abstract PDF

Purpose

The purpose of this study was to investigate factors influencing lifetime smoking and current smoking among adolescents in South Korea.

Methods

Hierarchical logistic regression was conducted based on complex sample analysis using statistics from the 10th (2014) Korea Youth Risk Behavior Web-Based Survey. The study sample comprised 72,060 adolescents aged 12 to 18.

Results

The significant factors influencing adolescent lifetime smoking were female gender, older age, higher stress, higher weekly allowance, lower economic status, living apart from parents, parental smoking, sibling smoking, peer smoking, observation of school personnel smoking, and coed school compared to boys' school. The significant factors influencing adolescent current smoking were female gender, older age, higher stress, higher weekly allowance, both higher and lower economic status compared to middle economic status, living apart from parents, parental smoking, sibling smoking, peer smoking, observation of school personnel smoking, and coed school compared to boys' school.

Conclusion

Factors identified in this study need to be considered in programs directed at prevention of adolescent smoking and smoking cessation programs, as well as policies.

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A Study on Adolescent Smoking Prevention and Cessation Policies: Based on the Propensity Score Matching–Difference-in-Differences Method
Seokmin Ji, Byungchan Moon, Younggyu Kwon, Kyumin Kim
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Estimated prevalence and trends in smoking among adolescents in South Korea, 2005–2021: a nationwide serial study
Hyoin Shin, Sangil Park, Hyunju Yon, Chae Yeon Ban, Stephen Turner, Seong Ho Cho, Youn Ho Shin, Jung U. Shin, Ai Koyanagi, Louis Jacob, Lee Smith, Chanyang Min, Young Joo Lee, So Young Kim, Jinseok Lee, Rosie Kwon, Min Ji Koo, Guillaume Fond, Laurent Boye
World Journal of Pediatrics.2023; 19(4): 366. CrossRef
Health-Related Behavior and Psychosocial Characteristics of Adolescent Female Smokers in Korea, Compared with Adolescent Male Smokers
Yong-Sook Eo, Yeon-Hee Lee, Myo-Sung Kim
Healthcare.2023; 11(12): 1707. CrossRef
Disparity between Subjective Health Perception and Lifestyle Practices among Korean Adolescents: A National Representative Sample
Aniceto Echalico Braza, Jinsoo Jason Kim, Sun Hee Kim
Journal of Lifestyle Medicine.2022; 12(3): 153. CrossRef
Comparison of the Prediction Model of Adolescents' Suicide Attempt Using Logistic Regression and Decision Tree: Secondary Data Analysis of the 2019 Youth Health Risk Behavior Web-Based Survey
Yoonju Lee, Heejin Kim, Yesul Lee, Hyesun Jeong
Journal of Korean Academy of Nursing.2021; 51(1): 40. CrossRef
Factors Associated with Cigarette, E-Cigarette, and Dual Use among South Korean Adolescents
Myong Sun Cho
Healthcare.2021; 9(10): 1252. CrossRef
The Effect of Neighborhood Characteristics and Friends' Smoking Status on the Habitual Smoking Onset in Adolescents
You-Jung Choi, Gwang Suk Kim
Journal of Korean Academy of Nursing.2021; 51(1): 54. CrossRef
Sex Differences in Multilevel Factors of Smoking Experimentation and Age of Initiation in Korean Adolescents
Eun-Mi Kim, Eunhee Park, Heejung Kim
The Journal of School Nursing.2020; 36(5): 348. CrossRef
Multidisciplinary Approach to Smoking Cessation in Late Adolescence: A Pilot Study
Jae Suk Park, Sang Hyung Lee, Ga Hye Lee, Mi Ra Yang, Inhyuk Park, Bumjo Oh
Global Pediatric Health.2020;[Epub] CrossRef
Factors associated with maintenance of smoking cessation in adolescents after implementation of tobacco pricing policy in South Korea: Evidence from the 11th Youth Health Behavior Survey
Eun Gyeong Kim, Sook Kyoung Park, Young‐Me Lee, Mi Yeol Hyun, Laren (Riesche) Narapareddy
Research in Nursing & Health.2020; 43(1): 40. CrossRef
The Association Between Part-time Job Experience and Tobacco Smoking in Adolescents: Analysis on Korea Youth Risk Behavior Survey Data 2017
Kyoungmi Ku, Keum Ji Jung, San Kang, Yoonjeong Choi, Su Hyun Lee, Jakyoung Lee, Heejin Kimm
Journal of the Korean Society for Research on Nicotine and Tobacco.2020; 11(2): 56. CrossRef
Adolescent Abuse and Smoking Behavior Among High School Students in the Republic of Korea
Junghee Kim, Sunhee Park
Journal of Child and Family Studies.2019; 28(1): 52. CrossRef
The influence and interaction of exposure to pro-smoking and anti-smoking messaging on youth smoking behaviour and susceptibility
Jingfen Zhu*, Jiahui Li*, Yaping He^#, Na Li, Gang Xu^#, Jinming Yu
Tobacco Induced Diseases.2019;[Epub] CrossRef
Association Between Socioeconomic Status and Early Initiation of Smoking, Alcohol Drinking, and Sexual Behavior Among Korean Adolescents
Mei Lin C. Valencia, Binh Thang Tran, Min Kyung Lim, Kui Son Choi, Jin-Kyoung Oh
Asia Pacific Journal of Public Health.2019; 31(5): 443. CrossRef
Parent educators for teenage smoking behavior
Lucky Herawati, Johan Arief Budiman, Choirul Hadi, Abdul Khair
International Journal of Adolescent Medicine and Health.2019;[Epub] CrossRef
A Meta-Analytic Study on Differences between Middle and High School Students in Cognitive, Emotional and Social Variables Related to Adolescent Smoking
김인용, 강정석
Locality and Globality: Korean Journal of Social Sciences.2018; 42(3): 83. CrossRef
Impact of nearby smoking on adolescent smoking behavior in Korea
Dong Jun Kim, Sun Jung Kim
Medicine.2018; 97(45): e13125. CrossRef
Trend Analysis for the Choice and Cost of Lung Cancer Treatment in South Korea, 2003-2013
Dohun Kim, So Young Kim, Beomseok Suh, Jong Hyock Park
Cancer Research and Treatment.2018; 50(3): 757. CrossRef
Concept analysis of impressionability among adolescents and young adults
Seok Hyun Gwon, Suyong Jeong
Nursing Open.2018; 5(4): 601. CrossRef
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Kim Eun Soo, 정민수
Health and Social Welfare Review.2017; 37(3): 568. CrossRef

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Maternal and Hospital Factors Impacting the Utilization of Rooming-in Care in South Korea: Secondary Analysis of National Health Data: Yunmi Kim, Eun-Young Kim; J Korean Acad Nurs 2011;41(5):593-602. Published online October 31, 2011; DOI: https://doi.org/10.4040/jkan.2011.41.5.593

Abstract PDF

Purpose

Purpose: In this study analysis was done of utilization of rooming-in care in South Korean hospitals in order to examine the factors related to mothers and hospitals that affect rooming-in care.

Methods

With the involvement of 254,414 mothers who gave birth across 953 hospitals, the analysis used the health insurance qualification data of the National Health Insurance Corporations and Health Insurance Review and Assessment Service (2006). Factors associated with rooming-in care were analyzed using a GEE logistic regression analysis to consider factors related to both mothers and hospitals.

Results

Only 45.1% of the mothers used rooming-in care. The results of the regression analysis revealed that individual factors of the mothers were not associated with rooming-in care, whereas group factors of the hospitals were. Rooming-in care use was primarily related to small hospital, location of hospital, and higher nurse staffing level.

Conclusion

The findings of this study indicate that the utilization of rooming-in care is not associated with factors an individual mother, but rather with the group factors of the hospitals. Thus, a policy-based approach considering both of these types of factors is required to enhance the utilization of rooming-in care.

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Neonatal seizures and white matter injury: Role of rotavirus infection and probiotics
Jung Sook Yeom, Ji Sook Park, Young-Soo Kim, Rock Bum Kim, Dae-Sup Choi, Ju-Young Chung, Tae-Hee Han, Ji-Hyun Seo, Eun Sil Park, Jae-Young Lim, Hyang-Ok Woo, Hee-Shang Youn, Chan-Hoo Park
Brain and Development.2019; 41(1): 19. CrossRef

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Predictive Bayesian Network Model Using Electronic Patient Records for Prevention of Hospital-Acquired Pressure Ulcers: In Sook Cho, Eunja Chung; J Korean Acad Nurs 2011;41(3):423-431. Published online June 13, 2011; DOI: https://doi.org/10.4040/jkan.2011.41.3.423

Abstract PDF

Purpose

The study was designed to determine the discriminating ability of a Bayesian network (BN) for predicting risk for pressure ulcers.

Methods

Analysis was done using a retrospective cohort, nursing records representing 21,114 hospital days, 3,348 patients at risk for ulcers, admitted to the intensive care unit of a tertiary teaching hospital between January 2004 and January 2007. A BN model and two logistic regression (LR) versions, model-I and -II, were compared, varying the nature, number and quality of input variables. Classification competence and case coverage of the models were tested and compared using a threefold cross validation method.

Results

Average incidence of ulcers was 6.12%. Of the two LR models, model-I demonstrated better indexes of statistical model fits. The BN model had a sensitivity of 81.95%, specificity of 75.63%, positive and negative predictive values of 35.62% and 96.22% respectively. The area under the receiver operating characteristic (AUROC) was 85.01% implying moderate to good overall performance, which was similar to LR model-I. However, regarding case coverage, the BN model was 100% compared to 15.88% of LR.

Conclusion

Discriminating ability of the BN model was found to be acceptable and case coverage proved to be excellent for clinical use.

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Ju Hee Lee, Jae Yong Yu, So Yun Shim, Kyung Mi Yeom, Hyun A Ha, Se Yong Jekal, Ki Tae Moon, Joo Hee Park, Sook Hyun Park, Jeong Hee Hong, Mi Ra Song, Won Chul Cha
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Yuxia Ma, Xiang He, Tingting Yang, Yifang Yang, Ziyan Yang, Tian Gao, Fanghong Yan, Boling Yan, Juan Wang, Lin Han
Journal of Clinical Nursing.2024;[Epub] CrossRef
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Chaoran Qu, Weixiang Luo, Zhixiong Zeng, Xiaoxu Lin, Xuemei Gong, Xiujuan Wang, Yu Zhang, Yun Li
Heliyon.2022; 8(11): e11361. CrossRef
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Mireia Ladios-Martin, José Fernández-de-Maya, Francisco-Javier Ballesta-López, Adrián Belso-Garzas, Manuel Mas-Asencio, María José Cabañero-Martínez
American Journal of Critical Care.2020; 29(4): e70. CrossRef
Development and Evaluation of Electronic Health Record Data-Driven Predictive Models for Pressure Ulcers
Seul Ki Park, Hyeoun-Ae Park, Hee Hwang
Journal of Korean Academy of Nursing.2019; 49(5): 575. CrossRef
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Seul Ki Park, Hyeoun-Ae Park, Hee Hwang
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Recommendation of Personalized Surveillance Interval of Colonoscopy via Survival Analysis
Jayeon Gu, Eun Sun Kim, Seoung Bum Kim
Journal of Korean Institute of Industrial Engineers.2016; 42(2): 129. CrossRef
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Hanna Park, In Ho Bae, Yong Oock Kim
The Journal of Korea Information and Communications Society.2014; 39C(6): 497. CrossRef
Reusability of EMR Data for Applying Cubbin and Jackson Pressure Ulcer Risk Assessment Scale in Critical Care Patients
Eunkyung Kim, Mona Choi, JuHee Lee, Young Ah Kim
Healthcare Informatics Research.2013; 19(4): 261. CrossRef
Using EHR data to predict hospital-acquired pressure ulcers: A prospective study of a Bayesian Network model
Insook Cho, Ihnsook Park, Eunman Kim, Eunjoon Lee, David W. Bates
International Journal of Medical Informatics.2013; 82(11): 1059. CrossRef

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