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5 "Hyeoun-Ae Park"
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Original Articles
Development and Evaluation of Electronic Health Record Data-Driven Predictive Models for Pressure Ulcers
Seul Ki Park, Hyeoun-Ae Park, Hee Hwang
J Korean Acad Nurs 2019;49(5):575-585.   Published online January 15, 2019
DOI: https://doi.org/10.4040/jkan.2019.49.5.575
AbstractAbstract PDF
Abstract 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.

Citations

Citations to this article as recorded by  
  • Development of a Pressure Injury Machine Learning Prediction Model and Integration into Clinical Practice: A Prediction Model Development and Validation Study
    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
    Korean Journal of Adult Nursing.2024; 36(3): 191.     CrossRef
  • Could we prove the nursing outcomes utilising clinical data warehouse? Effectiveness of pressure ulcer intervention in Korean tertiary hospital
    Moonsook Kim, Se Yeon Park, Meihua Piao, Earom Lim, Soon Hwa Yoo, Minju Ryu, Hyo Yeon Lee, Hyejin Won
    International Wound Journal.2023; 20(1): 201.     CrossRef
  • Data‐driven approach to predicting the risk of pressure injury: A retrospective analysis based on changes in patient conditions
    Yinji Jin, Ji‐Sun Back, Sun Ho Im, Jong Hyo Oh, Sun‐Mi Lee
    Journal of Clinical Nursing.2023; 32(19-20): 7273.     CrossRef
  • Factors Associated with Pressure Injury Among Critically Ill Patients in a Coronary Care Unit
    Eunji Ko, Seunghye Choi
    Advances in Skin & Wound Care.2022; 35(10): 1.     CrossRef
  • Data-Driven Learning Teaching Model of College English Based on Mega Data Analysis
    Jie Zhang, Tongguang Ni
    Scientific Programming.2022; 2022: 1.     CrossRef
  • 301 View
  • 8 Download
  • 5 Web of Science
  • 5 Crossref
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Characteristics and Risk Factors for Falls in Tertiary Hospital Inpatients
Eun-Ju Choi, Young-Shin Lee, Eun-Jung Yang, Ji-Hui Kim, Yeon-Hee Kim, Hyeoun-Ae Park
J Korean Acad Nurs 2017;47(3):420-430.   Published online January 15, 2017
DOI: https://doi.org/10.4040/jkan.2017.47.3.420
AbstractAbstract PDF
Abstract 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.

Citations

Citations to this article as recorded by  
  • Prevalence of Bed Falls among Inpatients in Iranian Hospitals: A Meta-Analysis
    Parvaneh Isfahani, Mohammad Sarani, Mina Salajegheh, Somayeh Samani, Aliyeh Bazi, Mahdieh Poodineh Moghadam, Fatemeh Boulagh, Mahnaz Afshari
    Human Factors in Healthcare.2025; : 100093.     CrossRef
  • Sensitivity of Fall Risk Perception and Associated Factors in Hospitalized Patients with Mental Disorders
    Ji Young Kim, Sung Reul Kim, Yusun Park, Jin Kyeong Ko, Eunmi Ra
    Asian Nursing Research.2024; 18(5): 443.     CrossRef
  • Psychometric Properties of the Fall Risk Perception Questionnaire-Short Version for Inpatients in Acute Care Hospitals
    Jeeeun Choi, Sujin Lee, Eunjin Park, Sangha Ku, Sunhwa Kim, Wonhye Yu, Eunmi Jeong, Sukhee Park, Yusun Park, Hye Young Kim, Sung Reul Kim
    Journal of Korean Academy of Nursing.2024; 54(2): 151.     CrossRef
  • The Impact of Physical Performance and Fear of Falling on Fall Risk in Hemodialysis Patients: A Cross-Sectional Study
    Jiwon Choi, Sun-Kyung Hwang
    Korean Journal of Adult Nursing.2024; 36(1): 63.     CrossRef
  • The Impact of Possible Sarcopenia and Obesity on the Risk of Falls in Hospitalized Older Patients
    Kahyun Kim, Dukyoo Jung
    The Korean Journal of Rehabilitation Nursing.2023; 26(1): 18.     CrossRef
  • Analysis of Data on Accidental Falls from the Hospital Incident Reporting in a General Hospital
    Yu-ri Jang, Jeong Yun Park
    Quality Improvement in Health Care.2023; 29(1): 15.     CrossRef
  • Predication of Falls in Hospitalized Cancer Patients
    Jun-Nyun Kim, Sun-Hwa Beak, Bo-Seop Lee, Mi-Ra Han
    Asian Oncology Nursing.2023; 23(2): 56.     CrossRef
  • Nurses’ Burden of Elimination Care: Sequential Explanatory Mixed-Methods Design
    Se Young Jung, Hui-Woun Moon, Da Som Me Park, Sumi Sung, Hyesil Jung
    International Journal of General Medicine.2023; Volume 16: 4067.     CrossRef
  • Clinical study of falls among inpatients with hematological diseases and exploration of risk prediction models
    Jing Wang, Bin Chen, Fang Xu, Qin Chen, Jing Yue, Jingjing Wen, Fang Zhao, Min Gou, Ya Zhang
    Frontiers in Public Health.2023;[Epub]     CrossRef
  • A Clinical Data Warehouse Analysis of Risk Factors for Inpatient Falls in a Tertiary Hospital: A Case-Control Study
    Eunok Kwon, Sun Ju Chang, Mikyung Kwon
    Journal of Patient Safety.2023; 19(8): 501.     CrossRef
  • Z-drugs and falls in nursing home patients: data from the INCUR study
    Sarah Damanti, Moreno Tresoldi, Philipe de Souto Barreto, Yves Rolland, Matteo Cesari
    Aging Clinical and Experimental Research.2022; 34(12): 3145.     CrossRef
  • The Fall Risk Screening Scale Is Suitable for Evaluating Adult Patient Fall
    Li-Chen Chen, Yung-Chao Shen, Lun-Hui Ho, Whei-Mei Shih
    Healthcare.2022; 10(3): 510.     CrossRef
  • Comparisons of Fall Prevention Activities Using Electronic Nursing Records: A Case-Control Study
    Hyesil Jung, Hyeoun-Ae Park, Ho-Young Lee
    Journal of Patient Safety.2022; 18(3): 145.     CrossRef
  • Risk Factors according to Fall Risk Level in General Hospital Inpatients
    Yeon Hwa Lee, Myo Sung Kim
    Journal of Korean Academy of Fundamentals of Nursing.2022; 29(1): 35.     CrossRef
  • Development and validation of the fall risk perception questionnaire for patients in acute care hospitals
    Jieun Choi, Se Min Choi, Jeong Sin Lee, Soon Seok Seo, Ja Yeon Kim, Hye Young Kim, Sung Reul Kim
    Journal of Clinical Nursing.2021; 30(3-4): 406.     CrossRef
  • Factors Affecting the Degree of Harm from Fall Incidents in Hospitals
    Shinae Ahn, Da Eun Kim
    Journal of Korean Academy of Nursing Administration.2021; 27(5): 334.     CrossRef
  • A Machine Learning–Based Fall Risk Assessment Model for Inpatients
    Chia-Hui Liu, Ya-Han Hu, Yu-Hsiu Lin
    CIN: Computers, Informatics, Nursing.2021; 39(8): 450.     CrossRef
  • Factors Influencing Falls in High- and Low-Risk Patients in a Tertiary Hospital in Korea
    Young-Shin Lee, Eun-Ju Choi, Yeon-Hee Kim, Hyeoun-Ae Park
    Journal of Patient Safety.2020; 16(4): e376.     CrossRef
  • Impact of Hearing Loss on Patient Falls in the Inpatient Setting
    Victoria L. Tiase, Kui Tang, David K. Vawdrey, Rosanne Raso, Jason S. Adelman, Shao Ping Yu, Jo R. Applebaum, Anil K. Lalwani
    American Journal of Preventive Medicine.2020; 58(6): 839.     CrossRef
  • Improving Prediction of Fall Risk Using Electronic Health Record Data With Various Types and Sources at Multiple Times
    Hyesil Jung, Hyeoun-Ae Park, Hee Hwang
    CIN: Computers, Informatics, Nursing.2020; 38(3): 157.     CrossRef
  • Triggers and Outcomes of Falls in Hematology Patients: Analysis of Electronic Health Records
    Min Kyung Jung, Sun-Mi Lee
    Journal of Korean Academy of Fundamentals of Nursing.2019; 26(1): 1.     CrossRef
  • Incidence of Falls and Risk Factors of Falls in Inpatients
    Soo-Jin Yoon, Chun-Kyon Lee, In-Sun Jin, Jung-Gu Kang
    Quality Improvement in Health Care.2018; 24(2): 2.     CrossRef
  • 258 View
  • 3 Download
  • 22 Crossref
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A Meta-analysis of the Effect of Walking Exercise on Lower Limb Muscle Endurance, Whole Body Endurance and Upper Body Flexibility in Elders
Kook-Hee Roh, Hyeoun-Ae Park
J Korean Acad Nurs 2013;43(4):536-546.   Published online August 30, 2013
DOI: https://doi.org/10.4040/jkan.2013.43.4.536
AbstractAbstract PDF
Purpose

The purpose of this study was to determine whether walking exercise improved physical function in elderly people using meta-analysis.

Methods

Medical and nursing literature databases were searched to identify the studies on the effectiveness of walking exercise on physical function. In the databases, there were 16 articles reporting 21 interventions. Overall effect sizes for three outcome variables, elders' physical function in lower limb muscle endurance, whole body endurance and upper body flexibility, were calculated. Effects of study characteristics on outcome variables were analyzed.

Results

The meta-analysis showed that walking exercise generally had positive effects on CST (chair stand test), 6MW (6 min walking), and SRT (standing or sitting reach test) with overall weighted effect sizes of 1.06, 0.41 and 0.29 respectively. This study also showed that the chronic disease status of the elders, intervention methods, and type of residence had different effects on CST, 6MW and SRT.

Conclusion

The results indicate that walking exercise improves physical function in elders. Walking exercise which can be done at any time and any location is indeed a very effective exercise for elderly people.

Citations

Citations to this article as recorded by  
  • Objectively measured the impact of ambient air pollution on physical activity for older adults
    Jiali Cheng, Yin Wu, Xiaoxin Wang, Hongjun Yu
    BMC Public Health.2024;[Epub]     CrossRef
  • Effects of walking exercise on cognitive and physical functions: ­meta-analysis of older adults
    Mi Jin Lee, Hee Ju Ro, Jung Kee Choi, So Yeon Kim
    Forest Science and Technology.2024; 20(2): 201.     CrossRef
  • A Meta-Analysis of the Effects of Walking Exercise on Depression
    Jonghwa Lee, Youngho Kim
    The Asian Journal of Kinesiology.2023; 25(4): 12.     CrossRef
  • Effect of Aerobic, Resistance, and Combined Exercise Training on Depressive Symptoms, Quality of Life, and Muscle Strength in Healthy Older Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
    Ahmad Mahmoudi, Farahnaz Amirshaghaghi, Reza Aminzadeh, Ehsan Mohamadi Turkmani
    Biological Research For Nursing.2022; 24(4): 541.     CrossRef
  • Multimorbidity and associations with clinical outcomes in a middle-aged population in Iran: a longitudinal cohort study
    Maria Lisa Odland, Samiha Ismail, Sadaf G Sepanlou, Hossein Poustchi, Alireza Sadjadi, Akram Pourshams, Tom Marshall, Miles D Witham, Reza Malekzadeh, Justine I Davies
    BMJ Global Health.2022; 7(5): e007278.     CrossRef
  • Effects of a Physical Exercise Program on Physiological, Psychological, and Physical Function of Older Adults in Rural Areas
    Sunmi Kim, Eun-Jee Lee, Hyeon-Ok Kim
    International Journal of Environmental Research and Public Health.2021; 18(16): 8487.     CrossRef
  • Comparison of the Effects of Education Only and Exercise Training Combined with Education on Fall Prevention in Adults Aged 70 Years or Older Residing in Elderly Residential Facilities
    Chahwa Hong, Haejung Lee, Misoon Lee
    Journal of Korean Academy of Nursing.2021; 51(2): 173.     CrossRef
  • The Effects of Forest Healing Anti-aging Program on Physical Health of the Elderly: A Pilot Study
    Ji-Eun Baek, Ho-jin Shin, Sung-Hyeon Kim, Jae Yeon Kim, Sujin Park, Si-Yoon Sung, Hwi-young Cho, Suk-Chan Hahm, Min-Goo Lee
    Journal of The Korean Society of Physical Medicine.2021; 16(2): 81.     CrossRef
  • Factors Influencing Health-Related Quality of Life in the Korean Seniors with Lower Education Level: Focusing on Physical Activity Types
    Hye Young Choi, Guna Lee
    Korean Journal of Adult Nursing.2020; 32(3): 292.     CrossRef
  • Evaluation of the Interaction Model of Client Health Behavior‐based multifaceted intervention on patient activation and osteoarthritis symptoms
    Yang Heui Ahn, Ok Kyung Ham
    Japan Journal of Nursing Science.2020;[Epub]     CrossRef
  • The Effects of Sand Exercise Program on Balance Capability, Extremity Muscle Activity, and Inflammatory Markers in Older Women
    Sung-Soo Lee, Yong-Seok So
    Exercise Science.2019; 28(2): 131.     CrossRef
  • Physical activity in older age: perspectives for healthy ageing and frailty
    Jamie S. McPhee, David P. French, Dean Jackson, James Nazroo, Neil Pendleton, Hans Degens
    Biogerontology.2016; 17(3): 567.     CrossRef
  • Literature Review for the Effects of Physical Activity on Musculoskeletal Outcomes in Community-dwelling Older Adults
    Kyung Choon Lim, Jeung-Im Kim, Young Ran Chae
    Korean Journal of Women Health Nursing.2014; 20(4): 297.     CrossRef
  • 114 View
  • 1 Download
  • 13 Crossref
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An Introduction to Logistic Regression: From Basic Concepts to Interpretation with Particular Attention to Nursing Domain
Hyeoun-Ae Park
J Korean Acad Nurs 2013;43(2):154-164.   Published online April 30, 2013
DOI: https://doi.org/10.4040/jkan.2013.43.2.154
AbstractAbstract PDF
Purpose

The purpose of this article is twofold: 1) introducing logistic regression (LR), a multivariable method for modeling the relationship between multiple independent variables and a categorical dependent variable, and 2) examining use and reporting of LR in the nursing literature.

Methods

Text books on LR and research articles employing LR as main statistical analysis were reviewed. Twenty-three articles published between 2010 and 2011 in the Journal of Korean Academy of Nursing were analyzed for proper use and reporting of LR models.

Results

Logistic regression from basic concepts such as odds, odds ratio, logit transformation and logistic curve, assumption, fitting, reporting and interpreting to cautions were presented. Substantial shortcomings were found in both use of LR and reporting of results. For many studies, sample size was not sufficiently large to call into question the accuracy of the regression model. Additionally, only one study reported validation analysis.

Conclusion

Nursing researchers need to pay greater attention to guidelines concerning the use and reporting of LR models.

Citations

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    Nasrin Alostaz, Jiajie Mo, Margaret Walton‐Roberts, Ruth Chen, Maria Pratt, Olive Wahoush
    Journal of Advanced Nursing.2024;[Epub]     CrossRef
  • Longitudinal Risk Analysis of Second Primary Cancer after Curative Treatment in Patients with Rectal Cancer
    Jiun-Yi Hsia, Chi-Chang Chang, Chung-Feng Liu, Chia-Lin Chou, Ching-Chieh Yang
    Diagnostics.2024; 14(13): 1461.     CrossRef
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    Kufa Journal of Engineering.2024; 15(2): 74.     CrossRef
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    Chuol Jock Ruey
    KnE Social Sciences.2024;[Epub]     CrossRef
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    Geocarto International.2024;[Epub]     CrossRef
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Development and Evaluation of a Web-based Education Program to Prevent Secondary Stroke
Chul-Gyu Kim, Hyeoun-Ae Park
J Korean Acad Nurs 2011;41(1):47-60.   Published online February 28, 2011
DOI: https://doi.org/10.4040/jkan.2011.41.1.47
AbstractAbstract PDF
Purpose

This study was conducted to develop and evaluate a web-based education program for secondary stroke prevention.

Methods

A web-based secondary stroke prevention education program was developed using the system's life cycle methods and evaluated by comparing the effects of education among three groups, a web group, a booklet group and a control group.

Results

Knowledge level of both patients and family, as well as some health behavior compliance in the web-based and booklet education groups were significantly higher than those of the control group. Family support in the web-based and booklet education groups was significantly higher than that of the control group after 12 weeks. The urine cotinine level in the web-based education group was significantly lower than that of the control group after 12 weeks. Medication adherence, blood pressure and perceived health status were not statistically different among the three groups at any time.

Conclusion

Web-based and booklet education programs were equally effective regarding the level of knowledge of patients and their families, family support, health behavior compliance, and urine cotinine level. These results demonstrate the potential use of a web-based education program for secondary stroke prevention.

Citations

Citations to this article as recorded by  
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