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김영아(Young A Kim),서윤암(Yun Am Seo),윤상후(Sanghoo Yoon) 한국데이터정보과학회 2018 한국데이터정보과학회지 Vol.29 No.3
본 연구의 목적은 청소년 대상 비만 중재프로그램의 현황을 파악하고 그 효과를 알아보기 위함이다. 검색어는 ‘청소년비만과 효과’ 또는 ‘학생비만과 효과’를 사용하였고, 1993년부터 2016년까지 보고된 국내 논문 총 39편을 분석하였다. 가장 많이 측정된 변수 5가지는 체중, 체지방률, 중성지방, 고밀도지단백 콜레스테롤, 체질량지수 순이었다. 대부분의 연구에서 중재 전과 비교하여 중재 후에 긍정적이고 유의한 효과가 확인되었고, 교정된 표준화된 평균효과크기는 모두 중간 수준이었다. 추가로 메타회귀분석을 수행하여 통계적 이질성을 확인하고, 주요 종속변수들의 사전 효과크기가 사후 효과크기에 영향을 미치고 있음을 확인하였다. 본 연구는 청소년기 비만 중재 프로그램의 효과를 높이기 위해 프로그램들의 전반적인 구성요소와 형태를 고찰하였다. 이는 청소년기 비만을 예방하고 관리할 수 있는 중재 프로그램의 효과를 최적화시키기 위해 필요한 근거기반의 지침을 제공할 것이다. The purpose of this study was to investigate the current state of the experimental obesity intervention study in Korea and to examine the effect of the intervention program. The key words used ’adolescent obesity and effect’ or ’student obesity and effect’, and in this study 39 articles from 1993 to 2016 were used. The five most frequently measured variables were body weight, body fat percentage, triglyceride, HDL cholesterol, and body mass index. The positive and significant effects were confirmed after the intervention compared with before the intervention, and the corrected standardized mean effect sizes were medium. In addition, meta-regression analysis was performed, and pre-effect size was found to influence post-effect size of the major dependent variables. The results of this study specifically examined the overall components and forms of the programs to enhance the effectiveness of the adolescent obesity intervention program.
김영아(Young A Kim),서윤암(Yun Am Seo),정향인(Hyang-In Cho Chung),윤상후(Sanghoo Yoon) 한국데이터정보과학회 2019 한국데이터정보과학회지 Vol.30 No.4
본 연구의 목적은 취약가족을 대상으로 가족 탄력성을 증진시키고자 수행된 국내 실험연구들의 현황을 파악하고 지금까지의 연구결과를 체계적으로 정리하는데 있다. 2006년부터 2017년까지 가족 탄력성에 대해 출판된 연구의 질을 평가하여 최종 15편을 선정하여 분석하였다. 모든 연구는 비동등성대조군전후설계였고, 표본크기는 총 454명이었다. 연구대상자의 대부분은 장애 아동, 암환자, 만성조현병 환자, 치매, 다문화가정 및 이혼가정과 같은 취약가족의 일원이었다. 가족의 기능을 회복하기 위한 중재프로그램은 4~12주/4~12회기/1회 45~180분으로 구성되어 있었다. 관련연구에서 측정된 주요 종속변수는 가족 탄력성, 의사소통, 가족관계, 가족적응 및 양육효능감이다. 연구간 동질성 여부를 확인하여 동질성을 만족하지 않은 종속변수인 가족 탄력성과 의사소통은 소그룹 분석을 추가 실시하였다. 분석결과 의사소통과 가족관계를 제외한 종속변수의 통합 효과크기는 큰 수준이었다. 본 연구의 결과는 향후 가족 탄력성 증진 중재전략을 위한 구체적인 근거기반자료를 제공하였다는 데에 의의가 있다. The purpose of this study was to identify the status and effectiveness of domestic experimental programs conducted to enhance resilience to families. Fifteen studies reported from 2006 to 2017 were finally analyzed. All studies were designed nonequivalent control group pre-post test designs, and the sample size was 454 in total. Most of the subjects were members of vulnerable families such as disabled children, cancer patients, chronic schizophrenia, dementia, multicultural families, and divorced families. The intervention program consisted of 4~12 weeks/4~12 sessions/45~180 minutes per session. Major dependent variables measured in these studies were family resilience, communication, family relation, family adaptation, and parenting efficacy. The standardized effect sizes of the measured variables excluding the communication and family relation were above the high level. This study is meaningful in that it systematically analyzes the domestic research results that are used to improve the resilience of the family and objectively presents the size of the effect, and provide specific evidence-based data for future intervention strategies to enhance family resilience.
박준상 ( Jun Sang Park ),서윤암 ( Yun Am Seo ),김규랑 ( Kyu Rang Kim ),하종철 ( Jong-chul Ha ) 한국농림기상학회 2018 한국농림기상학회지 Vol.20 No.3
Models to predict Leaf Wetness Duration (LWD) were evaluated using the observed meteorological and dew data at the 11 citrus orchards in Jeju, South Korea from 2016 to 2017. The sensitivity and the prediction accuracy were evaluated with four models (i.e., Number of Hours of Relative Humidity (NHRH), Classification And Regression Tree/Stepwise Linear Discriminant (CART/SLD), Penman-Monteith (PM), Deep-learning Neural Network (DNN)). The sensitivity of models was evaluated with rainfall and seasonal changes. When the data in rainy days were excluded from the whole data set, the LWD models had smaller average error (Root Mean Square Error (RMSE) about 1.5hours). The seasonal error of the DNN model had the similar magnitude (RMSE about 3 hours) among all seasons excluding winter. The other models had the greatest error in summer (RMSE about 9.6 hours) and the lowest error in winter (RMSE about 3.3 hours). These models were also evaluated by the statistical error analysis method and the regression analysis method of mean squared deviation. The DNN model had the best performance by statistical error whereas the CART/SLD model had the worst prediction accuracy. The Mean Square Deviation (MSD) is a method of analyzing the linearity of a model with three components: squared bias (SB), nonunity slope (NU), and lack of correlation (LC). Better model performance was determined by lower SB and LC and higher NU. The results of MSD analysis indicated that the DNN model would provide the best performance and followed by the PM, the NHRH and the CART/SLD in order. This result suggested that the machine learning model would be useful to improve the accuracy of agricultural information using meteorological data.