Improving the risk management of Type 2 diabetes mellitus in China from the perspective of social relationships
Corresponding Author
Xiaojia Wang
Department of Information Management, School of Management, Hefei University of Technology, Hefei, China
Correspondence
Xiaojia Wang, Department of Information Management, School of Management, Hefei University of Technology, Tunxi Road, Hefei, Anhui 230009, China.
Email: [email protected]
Search for more papers by this authorMi Chen
Department of Information Management, School of Management, Hefei University of Technology, Hefei, China
Search for more papers by this authorWei Xia
Department of Information Management, School of Management, Hefei University of Technology, Hefei, China
Search for more papers by this authorKeyu Zhu
Department of Information Management, School of Management, Hefei University of Technology, Hefei, China
Search for more papers by this authorShanshan Zhang
Department of Clinical Teaching, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
National Health Commission of the People's Republic of China, The National Chinese Medicine Clinical Research Base-Key Disease of Diabetes Mellitus Study, Hefei, China
Search for more papers by this authorWeiqun Xu
Department of Endocrinology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
National Health Commission of the People's Republic of China, The National Chinese Medicine Clinical Research Base-Key Disease of Diabetes Mellitus Study, Hefei, China
Search for more papers by this authorYuxiang Guan
Department of Endocrinology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
National Health Commission of the People's Republic of China, The National Chinese Medicine Clinical Research Base-Key Disease of Diabetes Mellitus Study, Hefei, China
Search for more papers by this authorCorresponding Author
Xiaojia Wang
Department of Information Management, School of Management, Hefei University of Technology, Hefei, China
Correspondence
Xiaojia Wang, Department of Information Management, School of Management, Hefei University of Technology, Tunxi Road, Hefei, Anhui 230009, China.
Email: [email protected]
Search for more papers by this authorMi Chen
Department of Information Management, School of Management, Hefei University of Technology, Hefei, China
Search for more papers by this authorWei Xia
Department of Information Management, School of Management, Hefei University of Technology, Hefei, China
Search for more papers by this authorKeyu Zhu
Department of Information Management, School of Management, Hefei University of Technology, Hefei, China
Search for more papers by this authorShanshan Zhang
Department of Clinical Teaching, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
National Health Commission of the People's Republic of China, The National Chinese Medicine Clinical Research Base-Key Disease of Diabetes Mellitus Study, Hefei, China
Search for more papers by this authorWeiqun Xu
Department of Endocrinology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
National Health Commission of the People's Republic of China, The National Chinese Medicine Clinical Research Base-Key Disease of Diabetes Mellitus Study, Hefei, China
Search for more papers by this authorYuxiang Guan
Department of Endocrinology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
National Health Commission of the People's Republic of China, The National Chinese Medicine Clinical Research Base-Key Disease of Diabetes Mellitus Study, Hefei, China
Search for more papers by this authorAbstract
In China, Type 2 diabetes mellitus (T2DM) is increasingly affecting people's health. Although many risk factors related to T2DM have been researched, the association between social relationships and risk management of T2DM in China has not been fully researched. Therefore, we obtained 2,969 valid cases from the National Chinese Medicine Clinical Research Base-Key Disease of Diabetes Mellitus Study to evaluate the role of social relationships in the risk management of T2DM. We first establish an indicators system of social relationship factors and then propose a comprehensive method that integrates subjective (analytical network process) and objective (entropy weight method) evaluations to rank the importance of the 17 social relationship factors that were the most important and commonly used. The results suggest that different social relationship factors have different effects on the risk management of T2DM. Patients and health workers should pay more attention to the high-benefit factors and thus improve the efficiency of the risk management of T2DM. These findings provided theoretical support for patients and health workers by developing the positive effects of social relationships in improving the risk management of T2DM to the fullest degree.
CONFLICT OF INTEREST
The authors declare no conflicts of interest.
Supporting Information
| Filename | Description |
|---|---|
| EXSY12484-sup-0001-supplementary appendix.docxWord 2007 document , 82.9 KB |
Appindex S1. Social Support Measurement Questionnaire Social Network Measurement Questionnaire Sample Data Sub-criterion descriptions and references The meaning of 1-9 scales Pairwise comparison matrix on tangible support influence. Pairwise comparison matrix on emotional support influence. Pairwise comparison matrix on information support influence. Pairwise comparison matrix on network size influence. Pairwise comparison matrix on network diversity influence. The inner dependency matrix of tangible support The inner dependency matrix of emotional support The inner dependency matrix of information support The inner dependency matrix of network size The inner dependency matrix of network diversity Pairwise comparison matrix under healthy food support Pairwise comparison matrix under physical activities support Pairwise comparison matrix under medicine support Pairwise comparison matrix under financial support Pairwise comparison matrix under listening Pairwise comparison matrix under encouragement Pairwise comparison matrix under respect Pairwise comparison matrix under sympathy Pairwise comparison matrix under basic knowledge support Pairwise comparison matrix under guidance Pairwise comparison matrix under therapeutic agency Pairwise comparison matrix under frequency of contact Critical probit values for different commonly used grade numbers Conversion Table of Percentage and Probability Unit |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
REFERENCES
- Bajestani, N. S., Kamyad, A. V., Esfahani, E. N., & Zare, A. (2017). Prediction of retinopathy in diabetic patients using Type-2 fuzzy regression model. European Journal of Operational Research, 264(3), 859–869.
- Becker, J., Becker, A., Sulikowski, P., & Zdziebko, T. (2018). ANP-based analysis of ICT usage in Central European enterprises. Procedia Computer Science, 126, 2173–2183. https://doi.org/10.1016/j.procs.2018.07.231
10.1016/j.procs.2018.07.231 Google Scholar
- Brown, N., Critchley, J., Bogowicz, P., Mayige, M., & Unwin, N. (2012). Risk scores based on self-reported or available clinical data to detect undiagnosed Type 2 diabetes: A systematic review. Diabetes Research and Clinical Practice, 98(3), 369–385. https://doi.org/10.1016/j.diabres.2012.09.005
- Buijsse, B., Simmons, R. K., Griffin, S. J., & Schulze, M. B. (2011). Risk assessment tools for identifying individuals at risk of developing Type 2 diabetes. Epidemiologic Reviews, 33(1), 46–62. https://doi.org/10.1093/epirev/mxq019
- Cadre, H. L. (2014). Infrastructure topology optimization under competition through cross-entropy. Journal of the Operational Research Society, 65(6), 824–841. https://doi.org/10.1057/jors.2012.96
- Chemweno, P., Pintelon, L., Horenbeek, A. V., & Muchiri, P. (2015). Development of a risk assessment selection methodology for asset maintenance decision making: An analytic network process (ANP) approach. International Journal of Production Economics, 170, 663–676. https://doi.org/10.1016/j.ijpe.2015.03.017
- Chen, F., Wang, J., & Deng, Y. (2015). Road safety risk evaluation by means of improved entropy TOPSIS-RSR. Safety Science, 79, 39–54. https://doi.org/10.1016/j.ssci.2015.05.006
- Cho, N. H., Shaw, J. E., Karuranga, S., Huang, Y., Da, R. F. J., Ohlrogge, A. W., & Malanda, B. (2018). IDF diabetes atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Research & Clinical Practice, 138, 271–281. https://doi.org/10.1016/j.diabres.2018.02.023
- Cooke, B. D., Rossmann, M. M., Mccubbin, H. I., & Patterson, J. M. (1988). Examining the definition and assessment of social support: A resource for individuals and families. Family Relations, 37(2), 211–216. https://doi.org/10.2307/584322
- van Dam, H. A., Fg, V. D. H., Knoops, L., Ryckman, R. M., Crebolder, H. F., & Bh, V. D. B. (2005). Social support in diabetes: A systematic review of controlled intervention studies. Patient Education and Counseling, 59(1), 1–12. https://doi.org/10.1016/j.pec.2004.11.001
- Ghesquiere, A., Schwartz, T., Wang, Y., Mauro, C., Skritskaya, N., & Shear, M. K. (2017). Performance and psychometric properties of the Interpersonal Support Evaluation List (ISEL) in older adults with Complicated Grief. Journal of Affective Disorders, 218, 388–393. https://doi.org/10.1016/j.jad.2017.05.004
- Golcuk, I., & Baykasoglu, A. (2016). An analysis of DEMATEL approaches for criteria interaction handling within ANP. Expert Systems with Applications, 46, 346–366. https://doi.org/10.1016/j.eswa.2015.10.041
- Han, Y., Long, C., Geng, Z., & Zhang, K. (2017). Carbon emission analysis and evaluation of industrial departments in China: An improved environmental DEA cross model based on information entropy. Journal of Environmental Management, 205, 298–307. https://doi.org/10.1016/j.jenvman.2017.09.062
- Hempelmann, C. F., Sakoglu, U., Gurupur, V. P., & Jampana, S. (2016). An entropy-based evaluation method for knowledge bases of medical information systems. Expert Systems with Applications an International Journal, 46(C), 262–273. https://doi.org/10.1016/j.eswa.2015.10.023
- Hilding, A., Shen, C., & Ostenson, C. G. (2015). Social network and development of prediabetes and type 2 diabetes in middle-aged Swedish women and men. Diabetes Research & Clinical Practice, 107(1), 166–177. https://doi.org/10.1016/j.diabres.2014.09.057
- Jia, W. (2017). Diabetes research in China: Making progress. Lancet Diabetes & Endocrinology, 5(1), 9–10. https://doi.org/10.1016/S2213-8587(16)30094-8
- Flores, L. Jr., Cyranowski, J. M., Amole, M., & Swartz, H. A. (2017). Prospective assessment of social network quality among depressed mothers treated with brief psychotherapy: The Social Network Quality (SNQ) scales. Comprehensive Psychiatry, 78, 98–106. https://doi.org/10.1016/j.comppsych.2017.06.012
- Laursen, K. R., Hulman, A., Witte, D. R., & Maindal, H. T. (2017). Social relations, depressive symptoms, and incident Type 2 diabetes mellitus: The English longitudinal study of ageing. Diabetes Research and Clinical Practice, 126, 86–94. https://doi.org/10.1016/j.diabres.2017.01.006
- Laursen, K. R., Hulman, A., Witte, D. R., & Terkildsen, M. H. (2017). Social relations, depressive symptoms, and incident Type 2 diabetes mellitus: The English longitudinal study of ageing. Diabetes Research & Clinical Practice, 126, 86–94. https://doi.org/10.1016/j.diabres.2017.01.006
- Lee, C. M. Y., & Colagiuri, S. (2013). Risk scores for diabetes prediction: The International Diabetes Federation PREDICT-2 project. Diabetes Research & Clinical Practice, 100(2), 285–286. https://doi.org/10.1016/j.diabres.2013.01.024
- Liberatore, M. J., & Nydick, R. L. (2008). The analytic hierarchy process in medical and health care decision making: A literature review. European Journal of Operational Research, 189(1), 194–207. https://doi.org/10.1016/j.ejor.2007.05.001
- Ludwig, J., Sanbonmatsu, L., Gennetian, L., Adam, E., Duncan, G. J., Katz, L. F., … Whitaker, R. C. (2011). Neighborhoods, obesity, and diabetes—A randomized social experiment. The New England Journal of Medicine, 365(16), 1509–1519. https://doi.org/10.1056/NEJMsa1103216
- Mansourypoor, F., & Asadi, S. (2017). Development of a reinforcement learning-based evolutionary fuzzy rule-based system for diabetes diagnosis. Computers in Biology & Medicine, 91, 337–352. https://doi.org/10.1016/j.compbiomed.2017.10.024
- Nam, S., Redeker, N., & Whittemore, R. (2015). Social networks and future direction for obesity research: A scoping review. Nursing Outlook, 63(3), 299–317. https://doi.org/10.1016/j.outlook.2014.11.001
- Poplawska, J., Labib, A., & Reed, D. M. (2017). From vicious to virtuous circles: Problem structuring for quantified decision making in operationalization of corporate social responsibility. Journal of the Operational Research Society, 68(3), 1–17.
- Saaty, T. L. (2004). Fundamentals of the analytic network process. Proceedings of International Symposium on Analytical Hierarchy Process, 1-26.
- Santos, L. M., Amorim, L. D., Santos, D. N., & Barreto, M. L. (2015). Measuring the level of social support using latent class analysis. Social Science Research, 50, 139–146. https://doi.org/10.1016/j.ssresearch.2014.11.009
- Silarova, B., Douglas, F. E., Usher-Smith, J. A., Godino, J. G., & Griffin, S. J. (2018). Risk accuracy of Type 2 diabetes in middle aged adults: Associations with sociodemographic, clinical, psychological and behavioural factors. Patient Education and Counseling, 101(1), 43–51. https://doi.org/10.1016/j.pec.2017.07.023
- Strom, J. L., & Egede, L. E. (2012). The impact of social support on outcomes in adult patients with Type 2 diabetes: A Systematic Review. Current Diabetes Reports, 12(6), 769–781. https://doi.org/10.1007/s11892-012-0317-0
- Tian, F. t. (1993). Grading problem in RSR. Chinese Journal of Health Statistics, 2(5), 26–28.
- Valtorta, N. K., Kanaan, M., Gilbody, S., & Hanratty, B. (2016). Loneliness, social isolation and social relationships: What are we measuring? A novel framework for classifying and comparing tools. BMJ Open, 6(4), 1–7, e010799. https://doi.org/10.1136/bmjopen-2015-010799
- Wang, X., Chen, W., Shang, J., & Yang, S. (2018). Foreign markets expansion for air medical transport business. Journal of the Operational Research Society, 69(7), 1146–1159. https://doi.org/10.1080/01605682.2017.1390529
- Wang, X., Song, Y., Wei, X., Hui, L., & Yang, S. (2018). Promoting the development of the new energy automobile industry in China: Technology selection and evaluation perspective. Journal of Renewable & Sustainable Energy, 10(4), 1–17, 045901. https://doi.org/10.1063/1.5012116
- Xie, H., Peng, W., Yang, Y., Zhang, D., Sun, Y., Wu, M., … Su, Y. (2018). Social support as a mediator of physical disability and depressive symptoms in Chinese elderly. Archives of Psychiatric Nursing, 32(2), 256–262. https://doi.org/10.1016/j.apnu.2017.11.012
- Xu, P. B., Wang, P. L., Ding, P. L., Xu, P. Y., Jiang, P. Y., Zhang, M. M., … Ning, M. P. G. (2015). Prevalence, awareness, treatment, and control of diabetes in relation to hypertension status in Chinese adults: A nationwide cross-sectional survey. The Lancet, 386(1), S55–S55. https://doi.org/10.1016/S0140-6736(15)00636-4
10.1016/S0140-6736(15)00636-4 Google Scholar
- Zaim, S., Sevkli, M., Demirel, O. F., Yayla, A. Y., & Delen, D. (2014). Use of ANP weighted crisp and fuzzy QFD for product development. Expert Systems with Applications, 41(9), 4464–4474. https://doi.org/10.1016/j.eswa.2014.01.008
- Zdun-Ryżewska, A., Basiński, K., Majkowicz, M., Podolska, M., Landowski, J., & Walden-Gałuszko, K. (2018). Association between social support and quality of life in patients with affective disorders. The European Journal of Psychiatry, 32(3), 132–138. https://doi.org/10.1016/j.ejpsy.2018.05.002
- Zhang, F.-L., Xing, Y.-Q., Guo, Z.-N., Wu, Y.-H., Liu, H.-Y., & Yang, Y. (2018). Prevalence and risk factors for diabetes and impaired fasting glucose in Northeast China: Results from the 2016 China National Stroke Screening Survey. Diabetes Research and Clinical Practice, 25(11), 302-313.
