Prevalence of Major Behavioral Risk Factors for Type 2 Diabetes

Siegel, Karen R.; Bullard, Kai McKeever; Imperatore, Giuseppina; Ali, Mohammed K.; Albright, Albright; Mercado, Carla; Li, Rui; Gregg, Edward W.

doi:10.2337/dc17-1775

Cited by 37 publications

(35 citation statements)

References 36 publications

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“…13 For example, Siegel et al 10 reported that, in a nationally representative survey, higher education was associated with meeting dietrelated diabetes prevention goals for the intake of vegetables, whole grains, meats, and healthy oils. 13 For example, Siegel et al 10 reported that, in a nationally representative survey, higher education was associated with meeting dietrelated diabetes prevention goals for the intake of vegetables, whole grains, meats, and healthy oils.…”

Section: Epidemiologic Overviewmentioning

confidence: 99%

“…The effects of education, income, and other indices of SES among people with or at risk for diabetes are often mediated by behavioral risk factors, including dietary patterns, levels of physical activity, and smoking. 13 For example, Siegel et al 10 reported that, in a nationally representative survey, higher education was associated with meeting dietrelated diabetes prevention goals for the intake of vegetables, whole grains, meats, and healthy oils. Lower SES has historically been associated with worse glycemic control among adults with type 2 diabetes, particularly younger adults.…”

Section: Epidemiologic Overviewmentioning

confidence: 99%

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New research directions on disparities in obesity and type 2 diabetes

Thornton

Kumanyika

Gregg

et al. 2019

Annals of the New York Academy of Sciences

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Obesity and type 2 diabetes disproportionately impact U.S. racial and ethnic minority communities and low-income populations. Improvements in implementing efficacious interventions to reduce the incidence of type 2 diabetes are underway (i.e., the National Diabetes Prevention Program), but challenges in effectively scaling-up successful interventions and reaching at-risk populations remain. In October 2017, the National Institutes of Health convened a workshop to understand how to (1) address socioeconomic and other environmental conditions that perpetuate disparities in the burden of obesity and type 2 diabetes; (2) design effective prevention and treatment strategies that are accessible, feasible, culturally relevant, and acceptable to diverse population groups; and (3) achieve sustainable health improvement approaches in communities with the greatest burden of these diseases. Common features of guiding frameworks to understand and address disparities and promote health equity were described. Promising research directions were identified in numerous areas, including study design, methodology, and core metrics; program implementation and scalability; the integration of medical care and social services; strategies to enhance patient empowerment; and understanding and addressing the impact of psychosocial stress on disease onset and progression in addition to factors that support resiliency and health.Directions on disparities in obesity and diabetes Thornton et al.

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Section: Epidemiologic Overviewmentioning

confidence: 99%

Section: Epidemiologic Overviewmentioning

confidence: 99%

New research directions on disparities in obesity and type 2 diabetes

Thornton

Kumanyika

Gregg

et al. 2019

Annals of the New York Academy of Sciences

Self Cite

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“…As age increases, the elderly need to strengthen their self-management of diabetes [13,16]. Besides, educational level [8,14] was an influential factor in community management of diabetes, and the higher the educational level, the better the self-glycemic control. In terms of public medical care, we also found that compared with those who did not receive public medical care, those who had public medical care had a slight advantage in blood glucose management.…”

Section: Community Management Of People With Diabetes Affected By Mulmentioning

confidence: 99%

“…Moreover, younger age and lower educational attainment were associated with lower probability of meeting the goals of diabetes management [8]. However, the influencing factors of diabetic patients based on community management are complex, and how to choose the most effective influencing factors requires in-depth research.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Influencing Factors of Community Management of Diabetes based on Adaptive-Lasso Logistic Regression Model

Lin¹,

Huang²,

Cao³

et al. 2020

Preprint

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Background: Influencing factors of community management of diabetes are complex and controversial, and how to select the most effective multiple influencing factors requires in-depth research. This study aims to analyse multiple influencing factors by adaptive-lasso logistic regression model for the effectiveness of diabetes management of the community to improve the efficiency and reduce the burden of diabetes. Methods: A cross-sectional survey (N=1,127) was adopted to establish the adaptive-lasso logistic regression model of influencing factors for community management of diabetic patients based on cluster sampling data of diabetic patients in Chengdu city, China. By comparing with the full-variable logistic model and the ridge logistic model to find the advantages of the adaptive lasso-logistic regression model in community diabetes management. Results: A total of 1,127 diabetic patients were included in the cross-sectional survey. The latest fasting blood glucose was included in the analysis. Among the included population, 90.6% of them had a fasting glucose level higher than 6.1mol/L, and 9.4% of them were below 6.1mol/L. By cross-validation, after folding eight times, the variables involved in the Adaptive lasso-logistic regression model include age, education level, main source of income, marital status, average monthly income, free medical service, basic medical insurance for residents, hospital history, number of follow-up evaluations by family doctor team, voluntary participation in community blood glucose measurement. The AIC and BIC criteria of adaptive lasso-logistic regression model were 2062 and 1981, which were lower than the full-variable logistic model (2349, 2023) and the ridge logistic model (2312, 2013). From the perspective of time cost, the adaptive-lasso logistic regression model was better than the other two models. Conclusions: The adaptive-lasso logistic regression model can be used to analyse the influencing factors of community management in patients with diabetes. Community intervention and intensive management measures can significantly improve the blood glucose status of patients with diabetes.

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“…Community‐based lifestyle interventions have attracted criticism due to their potential to increase health inequalities. Women and those with a more affluent life situation tend to be overrepresented, even though men, ethnic minorities, as well as those from lower social‐economic background tend to have higher risk of developing, for example, T2D (Gavarkovs, Burke, & Petrella, 2016; Harreiter & Kautzky‐Willer, 2018; Siegel et al, 2018; Sortsø, Lauridsen, Emneus, Green, & Jensen, 2018). Similarly, higher lifestyle intervention attrition has been associated with sociodemographic and cognitive factors such as lower educational achievement, ethnicity, younger age, higher BMI, unemployment, and lower self‐efficacy (Burgess, Hassmén, & Pumpa, 2017; Goode et al, 2016; Haughton et al, 2018; Leung, Chan, Sea, & Woo, 2017).…”

Section: Introductionmentioning

confidence: 99%

Sociocognitive factors associated with lifestyle intervention attrition after successful weight loss among participants with prediabetes—The PREVIEW study

Huttunen‐Lenz

Raben

Meinert‐Larsen

et al. 2020

Public Health Nursing

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Introduction: Major risk factors for type 2 diabetes are lifestyle choices such as lack of physical activity (PA) and poor diet. Many individuals either do not take part or struggle to complete interventions supporting lifestyle changes. Demographic and theory-based sociocognitive factors associated with PREVIEW intervention attrition after successful weight loss were examined. Methods: Participants (1,856) who started the weight maintenance phase after completion of low-energy diet were retrospectively divided into three clusters depending on the point they left the trial. Discriminant analysis examined which demographic and theory-based sociocognitive variables were associated with cluster membership.Results: Most of the participants were women and well-educated. Two discriminant functions were calculated (χ 2 (24) = 247.0, p ≥ .05, d = 0.78). The demographic variables, such as age and ethnicity, and the social cognitive variable outcome expectancies on the other side were associated with cluster membership. Older age,

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Prevalence of Major Behavioral Risk Factors for Type 2 Diabetes

Abstract: A small proportion of U.S. adults engages in risk reduction behaviors. Research and interventions targeted at young and less-educated segments of the population may help close gaps in risk reduction behaviors.

Cited by 37 publications

References 36 publications

New research directions on disparities in obesity and type 2 diabetes

New research directions on disparities in obesity and type 2 diabetes

Analysis of Influencing Factors of Community Management of Diabetes based on Adaptive-Lasso Logistic Regression Model

Sociocognitive factors associated with lifestyle intervention attrition after successful weight loss among participants with prediabetes—The PREVIEW study

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