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Educational Needs Associated with the Level of Complication and Comparative Risk Perceptions in People with Type 2 Diabetes
Youngji Hwang, Dongsuk Lee, Yeon Sook Kim
Osong Public Health Res Perspect. 2020;11(4):170-176.   Published online August 31, 2020
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  • 144 Download
AbstractAbstract PDF

This study aimed to identify the educational needs of people with type 2 diabetes according to risk perceptions and the level of severity of complications.


There were 177 study participants who were outpatients of the internal medicine department at a university hospital located in the Republic of Korea, who consented to participate in the survey from December 10, 2016 to February 10, 2017. The data were analyzed using descriptive statistics, Pearson correlation, ANOVA with post-hoc comparison, and multiple regression analysis. Type 2 diabetes complications were classified into 3 groups: no complications, common complications, and severe complications.


There were statistically significant positive correlations between educational needs and comparative risk perceptions, and the level of complication and comparative risk perception. Multiple regression analysis revealed that the factor predicting educational needs of type 2 diabetes people was their comparative risk perceptions, rather than the severity of diabetes complications or sociodemographic variables.


Since risk perception is the factor that indicates the educational needs of people with type 2 diabetes, there is a need to explore factors which increase risk perception, in order to meet educational needs. The findings suggest that a more specific and individualized educational program, which focuses on each person's risk perceptions, should be developed.

Depression among Korean Adults with Type 2 Diabetes Mellitus: Ansan-Community-Based Epidemiological Study
Chan Young Park, So Young Kim, Jong Won Gil, Min Hee Park, Jong-Hyock Park, Yeonjung Kim
Osong Public Health Res Perspect. 2015;6(4):224-232.   Published online August 31, 2015
  • 2,042 View
  • 12 Download
  • 22 Citations
AbstractAbstract PDF
There are an increasing number of studies being carried out on depression in patients with diabetes. Individuals with diabetes have been reported as having a higher prevalence of depression compared to those without diabetes. However, only a few studies involving Korean patients have been conducted. The aims of this study were to examine the prevalence of depression and to find various risk factors according to the degree of depression among Korean patients with Type 2 diabetes mellitus (T2DM).
An Ansan-community-based epidemiological study was conducted from 2005 to 2012. The total number of participants in this study was 3,540, from which patients with diabetes (n = 753) have been selected. The presence of depression was evaluated using the Beck Depression Inventory total score.
The prevalence of depression was 28.8%. The mean age of participants was 55.5 ± 8.2 years. We divided the participants into three groups (without-depression, moderate-depression, and severe-depression groups) to examine the depression prevalence among Korean T2DM patients. The unemployed participants had 2.40 [95% confidence interval (CI) 1.21–4.76], the low-income participants had 2.57 (95% CI 1.52–4.35), the participants using an oral diabetes medicine or insulin had 2.03 (95% CI 1.25–3.32), the participants who are currently smoking had 2.03 (95% CI 1.10–3.73), and those without regular exercise had 1.91 (95% CI 1.17–3.14) times higher odds of depression in the severe-depression group, compared with the without-depression group.
There was a significant association between depression prevalence and diabetes, and we found various risk factors according to the degree of depression in Korean patients with T2DM.


Citations to this article as recorded by  
  • Psychological Health and Diabetes Self-Management among Patients with Type 2 Diabetes during COVID-19 in the Southwest of Saudi Arabia
    Abdulrhman H. Alkhormi, Mohamed Salih Mahfouz, Najim Z. Alshahrani, Abdulrahman Hummadi, Wali A. Hakami, Doha H. Alattas, Hassan Q. Alhafaf, Leena E. Kardly, Mulook A. Mashhoor
    Medicina.2022; 58(5): 675.     CrossRef
  • Higher risk of depression in individuals with type 2 diabetes and obesity: Results of a meta-analysis
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    The Open Public Health Journal.2021; 14(1): 104.     CrossRef
  • Association between the level of adherence to dietary guidelines and depression among Korean patients with type 2 diabetes mellitus
    Seonghee Park, Kyong Park
    Journal of Psychosomatic Research.2021; 145: 110463.     CrossRef
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    Nhu Minh Hang Tran, Quang Ngoc Linh Nguyen, Thi Han Vo, Tran Tuan Anh Le, Ngoc Ha Ngo
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  • Factors Associated with Depressive Symptoms in Korean Adults with Diabetes Mellitus: A Cross-Sectional Study
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  • Spiritual intelligence, mindfulness, emotional dysregulation, depression relationship with mental well-being among persons with diabetes during COVID-19 pandemic
    Wojujutari Kenni Ajele, Teslim Alabi Oladejo, Abimbola A. Akanni, Oyeyemi Bukola Babalola
    Journal of Diabetes & Metabolic Disorders.2021; 20(2): 1705.     CrossRef
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    Bereket Beyene Gebre, Suzan Anand, Zebene Mekonnen Assefa
    Journal of Diabetes Research.2020; 2020: 1.     CrossRef
  • Effect of Study Design and Survey Instrument to Identify the Association Between Depressive Symptoms and Physical Activity in Type 2 Diabetes, 2000-2018: A Systematic Review
    Jusung Lee, Timothy Callaghan, Marcia Ory, Hongwei Zhao, Margaret Foster, Jane N. Bolin
    The Diabetes Educator.2020; 46(1): 28.     CrossRef
  • Genetic Overlap Between Type 2 Diabetes and Depression in a Sri Lankan Population Twin Sample
    Carol Kan, Kaushalya Jayaweera, Anushka Adikari, Sisira Siribaddana, Helena M.S. Zavos, Lisa Harber-Aschan, Athula Sumathipala, Matthew Hotopf, Khalida Ismail, Frühling Rijsdijk
    Psychosomatic Medicine.2020; 82(2): 247.     CrossRef
  • Depression in Iranian Children with Diabetes and Related Factors
    Azadeh Sayarifard, Fatemeh Sayarifard, Maryam Nazari, Morteza Nikzadian, Mona Amrollahinia, Javad Mahmoudi-Gharaei
    Iranian Journal of Pediatrics.2020;[Epub]     CrossRef
  • Prevalence of Undiagnosed Depression in Patients With Type 2 Diabetes
    Dina Siddiq Abdulhadi Alajmani, Amna Mohamad Alkaabi, Mariam Waleed Alhosani, Ayesha Abdulaziz Folad, Fawzia Ahmed Abdouli, Frederick Robert Carrick, Mahera Abdulrahman
    Frontiers in Endocrinology.2019;[Epub]     CrossRef
  • Risk and protective factors of co-morbid depression in patients with type 2 diabetes mellitus: a meta analysis
    Aidibai Simayi, Patamu Mohemaiti
    Endocrine Journal.2019; 66(9): 793.     CrossRef
  • The prevalence of comorbid depression in patients with type 2 diabetes: an updated systematic review and meta-analysis on huge number of observational studies
    Mohammad Khaledi, Fahimeh Haghighatdoost, Awat Feizi, Ashraf Aminorroaya
    Acta Diabetologica.2019; 56(6): 631.     CrossRef
  • Effect of walking and aerobic exercise on physical performance and depression in cases of type 2 diabetes mellitus
    Manal K. Youssef
    The Egyptian Journal of Internal Medicine.2019; 31(2): 142.     CrossRef
  • Premorbid risk perception, lifestyle, adherence and coping strategies of people with diabetes mellitus: A phenomenological study in the Brong Ahafo Region of Ghana
    Philip Teg-Nefaah Tabong, Vitalis Bawontuo, Doris Ningwiebe Dumah, Joseph Maaminu Kyilleh, Tolgou Yempabe, Noël C. Barengo
    PLOS ONE.2018; 13(6): e0198915.     CrossRef
  • Past and Current Status of Adult Type 2 Diabetes Mellitus Management in Korea: A National Health Insurance Service Database Analysis
    Seung-Hyun Ko, Kyungdo Han, Yong-ho Lee, Junghyun Noh, Cheol-Young Park, Dae-Jung Kim, Chang Hee Jung, Ki-Up Lee, Kyung-Soo Ko
    Diabetes & Metabolism Journal.2018; 42(2): 93.     CrossRef
  • Why Early Psychological Attention for Type 2 Diabetics Could Contribute to Metabolic Control
    Alfredo Briones-Aranda, Manuela Castellanos-Pérez, Raquel Gómez-Pliego
    Romanian Journal of Diabetes Nutrition and Metabol.2018; 25(3): 329.     CrossRef
  • Depression and Mortality in People with Type 2 Diabetes Mellitus, 2003 to 2013: A Nationwide Population-Based Cohort Study
    Jong-Hyun Jeong, Yoo Hyun Um, Seung-Hyun Ko, Jong-Heon Park, Joong-Yeol Park, Kyungdo Han, Kyung-Soo Ko
    Diabetes & Metabolism Journal.2017; 41(4): 296.     CrossRef
  • Diabetes-related distress and its associated factors among patients with type 2 diabetes mellitus in China
    Huanhuan Zhou, Junya Zhu, Lin Liu, Fan Li, Anne F. Fish, Tao Chen, Qingqing Lou
    Psychiatry Research.2017; 252: 45.     CrossRef
  • Comorbidity of depression and diabetes: an application of biopsychosocial model
    Tesfa Dejenie Habtewold, Md. Atiqul Islam, Yosef Tsige Radie, Balewgizie Sileshi Tegegne
    International Journal of Mental Health Systems.2016;[Epub]     CrossRef
  • Differences in depression between unknown diabetes and known diabetes: results from China health and retirement longitudinal study
    Huaqing Liu, Xiaoyue Xu, John J. Hall, Xuesen Wu, Min Zhang
    International Psychogeriatrics.2016; 28(7): 1191.     CrossRef
Development of a Predictive Model for Type 2 Diabetes Mellitus Using Genetic and Clinical Data
Juyoung Lee, Bhumsuk Keam, Eun Jung Jang, Mi Sun Park, Ji Young Lee, Dan Bi Kim, Chang-Hoon Lee, Tak Kim, Bermseok Oh, Heon Jin Park, Kyu-Bum Kwack, Chaeshin Chu, Hyung-Lae Kim
Osong Public Health Res Perspect. 2011;2(2):75-82.   Published online June 30, 2011
  • 2,044 View
  • 15 Download
  • 11 Citations
AbstractAbstract PDFSupplementary Material
Recent genetic association studies have provided convincing evidence that several novel loci and single nucleotide polymorphisms (SNPs) are associated with the risk of developing type 2 diabetes mellitus (T2DM). The aims of this study were: 1) to develop a predictive model of T2DM using genetic and clinical data; and 2) to compare misclassification rates of different models.
We selected 212 individuals with newly diagnosed T2DM and 472 controls aged in their 60s from the Korean Genome and Epidemiology Study. A total of 499 known SNPs from 87 T2DM-related genes were genotyped using germline DNA. SNPs were analyzed for significant association with T2DM using various classification algorithms including Quest (Quick, Unbiased, Efficient, Statistical tree), Support Vector Machine, C4.5, logistic regression, and K-nearest neighbor.
We tested these models using the complete Korean Genome and Epidemiology Study cohort (n = 10,038) and computed the T2DM misclassification rates for each model. Average misclassification rates ranged at 28.2–52.7%. The misclassification rates for the logistic and machine-learning algorithms were lower than the statistical tree algorithms. Using 1-to-1 matched data, the misclassification rate of the statistical tree QUEST algorithm using body mass index and SNP variables was the lowest, but overall the logistic regression performed best.
The K-nearest neighbor method exhibited more robust results than other algorithms. For clinical and genetic data, our “multistage adjustment” model outperformed other models in yielding lower rates of misclassification. To improve the performance of these models, further studies using warranted, strategies to estimate better classifiers for the quantification of SNPs need to be developed.


Citations to this article as recorded by  
  • Population stratification in type 2 diabetes mellitus: A systematic review
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  • The Prediction of Diabetes
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  • Hypertension: Constraining the Expression of ACE-II by Adopting Optimal Macronutrients Diet Predicted via Support Vector Machine
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  • Supervised and unsupervised algorithms for bioinformatics and data science
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  • Perspective: Advancing Understanding of Population Nutrient–Health Relations via Metabolomics and Precision Phenotypes
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    Advances in Nutrition.2019; 10(6): 944.     CrossRef
  • Stacked classifiers for individualized prediction of glycemic control following initiation of metformin therapy in type 2 diabetes
    Dennis H. Murphree, Elaheh Arabmakki, Che Ngufor, Curtis B. Storlie, Rozalina G. McCoy
    Computers in Biology and Medicine.2018; 103: 109.     CrossRef
  • Machine Learning and Data Mining Methods in Diabetes Research
    Ioannis Kavakiotis, Olga Tsave, Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas, Ioanna Chouvarda
    Computational and Structural Biotechnology Journal.2017; 15: 104.     CrossRef
  • Survey on clinical prediction models for diabetes prediction
    N. Jayanthi, B. Vijaya Babu, N. Sambasiva Rao
    Journal of Big Data.2017;[Epub]     CrossRef
  • Rule Extraction From Support Vector Machines Using Ensemble Learning Approach: An Application for Diagnosis of Diabetes
    Longfei Han, Senlin Luo, Jianmin Yu, Limin Pan, Songjing Chen
    IEEE Journal of Biomedical and Health Informatics.2015; 19(2): 728.     CrossRef
  • Depression among Korean Adults with Type 2 Diabetes Mellitus: Ansan-Community-Based Epidemiological Study
    Chan Young Park, So Young Kim, Jong Won Gil, Min Hee Park, Jong-Hyock Park, Yeonjung Kim
    Osong Public Health and Research Perspectives.2015; 6(4): 224.     CrossRef

PHRP : Osong Public Health and Research Perspectives