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Associations between the knowledge of different food categories and glycemia in Chinese adult patients with Type 2 diabetes

Associations between the knowledge of different food categories and glycemia in Chinese adult patients with Type 2 diabetes

Xue, Yuan, Zhang, Xiao-Yan, Zhou, Hui-Juan, Ojo, Omorogieva ORCID: 0000-0003-0071-3652, Wang, Qi, Wang, Li-Li, Jiang, Qing, Liu, Ting and Wang, Xiao-Hua (2019) Associations between the knowledge of different food categories and glycemia in Chinese adult patients with Type 2 diabetes. Clinical Nursing Research. ISSN 1054-7738 (Print), 1552-3799 (Online) (doi:https://doi.org/10.1177/1054773818822126)

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Abstract

This study aims to evaluate the levels of knowledge of different food categories and analyze the association between the levels of dietary knowledge and glycemia in Chinese adult patients with type 2 diabetes mellitus (T2DM). A cross-sectional study design was adopted. A total of 334 patients with T2DM were recruited from six hospitals from July to October 2014 in China. The Diabetes Dietary Knowledge Scale was utilized to obtain dietary knowledge. The results showed the item examining knowledge of carbohydrates received the highest score, whereas the item examining knowledge of healthy fats received the lowest score. The item “nuts” (B = −0.49; 95% confidence interval [CI] = [0.38, 0.99]) was associated with the incidence of hyperglycemia. The item “foods when hypoglycemic” (B = 0.42; 95% CI = [1.22, 2.07]) was associated with the incidence of hypoglycemia. There were different knowledge levels of different food categories in patients with T2DM, and these have implications for blood glucose control.

Item Type: Article
Uncontrolled Keywords: diabetes mellitus, diet, hyperglycemia, hypoglycemia
Subjects: R Medicine > RT Nursing
Faculty / Department / Research Group: Faculty of Education & Health
Faculty of Education & Health > Department of Adult Nursing & Paramedic Science
Faculty of Education & Health > Health & Society Research Group
Last Modified: 02 Oct 2019 10:41
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/22730

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