Self-Reported Dietary Management Behaviors and Dietary Intake among Chinese Adults with Diabetes: A Population-Based Study

Abstract

Few studies have analyzed the implementation of dietary management in Chinese adults with diabetes. Thus, we assessed and compared dietary intake and diet quality between diabetic patients with and without dietary management behaviors (DPDM vs. NDPDM), and evaluated the adherence to dietary guidelines in both groups of patients. The data were obtained from the 2002, 2010–2013, and 2015 China National Nutrition Survey. A total of 69,583, 67,177, and 96,631 subjects participated in the 2002, 2010–2013, and 2015 survey rounds, respectively. The dietary intake data were measured using 3-day 24 h dietary recalls and weighed records of household condiments. The China Healthy Diet Index (CHDI) was used to evaluate diet quality. The study included 6229 patients with diabetes, of which 78% had dietary management behaviors. The diabetic patients with dietary management behaviors showed higher percentages of energy from high-quality carbohydrates, animal protein, saturated fatty acids, and unsaturated fatty acids and lower percentages from low-quality carbohydrates and plant protein than NDPDM. The diabetic patients with dietary management behaviors also had lower intakes of cereals and tubers and higher intakes of vegetables than NDPDM. The total CHDI score of DPDM was higher than NDPDM (56.3 ± 12.7 vs. 54.1 ± 12.3). The proportion of DPDM meeting the recommended intake for different food items ranged from 3.3% to 42.8% and from 3.0% to 39.2% in NDPDM. The diabetic patients with dietary management behaviors showed better adherence to dietary guidelines and higher diet quality scores than NDPDM, while the overall adherence was poor in both groups of patients. Our findings suggested that measures are needed to promote and refine dietary management behaviors, which can help to improve disease management in diabetic patients.

Protocol of a parallel, randomized controlled trial on the effects of a novel personalized nutrition approach by artificial intelligence in real world scenario

“…In addition, instant dietary record and automatic calculation can be completed through the applet. Compared to traditional dietary survey methods like 3-day 24-h diet record [ 27 ], our PN approach would probably avoid recall bias from misreporting individual dietary intake, improving the reliability and validity of study.…”