Demographic characteristics and food choices of participants in the Special Diabetes Program for American Indians Diabetes Prevention Demonstration Project

Teufel-Shone, Nicolette I.; Jiang, Luohua; Beals, Janette; Henderson, William G.; Zhang, Lijing; Acton, Kelly J.; Roubideaux, Yvette; Manson, Spero M.

doi:10.1080/13557858.2014.921890

Cited by 27 publications

(29 citation statements)

References 28 publications

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“…These variables also indicate consumption preferences. Food habits such as consumption of aerated drinks, fast-foods, fried foods, etc., increases the risk of obesity and insulin resistance [26,27,28]. Gulati and Misra [29] find that an increase in per capita sugar consumption leads to development of insulin resistance, abdominal adiposity and risk of diabetes.…”

Section: Methodsmentioning

confidence: 99%

Does a rise in BMI cause an increased risk of diabetes?: Evidence from India

Gupta

Bansal

2020

PLoS ONE

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BackgroundOvernutrition increases the risk of diabetes. Evidence on the causal impact of overnutrition on diabetes is scarce for India. Considering a representative sample from India, this study examines the causal effect of a rise in the Body Mass Index (BMI) of an individual on the likelihood of being diabetic while addressing the issue of unobserved endogeneity between overnutrition and diabetes. MethodsThe study considers individual level data from Demographic and Health Surveys (DHS) of India, namely, National Family Health Survey (NFHS) for the year 2015-16. The NFHS is a large-scale, multi-round survey conducted in a representative sample of households throughout India. The survey covers females having age 15-49 years and males having age 15-54 years. The instrument variable approach is used to address the potential endogeneity in the relationship between BMI and diabetes. We instrument BMI of an individual by BMI of a non-biologically related household member. Ordered Probit, Probit and IV-Probit models are estimated using two alternative definitions for measuring diabetes-self-reported diabetes status and blood glucose levels (ordinal measure). ResultsThe coefficients obtained from the Ordered Probit and Probit models are much smaller than those estimated by an IV-Probit model. The latter estimates the causal impact of a rise in BMI on diabetes by taking into account the effect of the unobserved genetic and other related factors. The likelihood of being diabetic is twice or more among the overweight and obese individuals as compared to non-overweight individuals in all the specifications. With a unit increase in BMI the probability of being diabetic increases by about 1.5% among overweight and obese individuals and by 0.5% among the non-overweight individuals in the IV-Probit model. Similar results from the Ordered Probit model show that on average, the overweight and obese individuals experience about 0.2% increase in the probability of being diabetic and about 0.4% increase in the probability of being prediabetic. PLOS ONEPLOS ONE | https://doi.org/10.1371/journal.pone. ConclusionOur study demonstrates that the likelihood of being both prediabetic and diabetic is higher among the overweight and obese individuals as compared to the non-overweight individuals. We also find that the level of risk of being prediabetic or diabetic differs across gender, wealth quintiles and regions and the effects are more severe among population in the urban areas, belonging to the richest wealth quintile and men. Our findings have significant implications for the policy formulation as diabetes has a substantial health and economic burden associated with it. Future studies may investigate the effect of abdominal obesity on diabetes. PLOS ONEDoes a rise in BMI cause an increased risk of diabetes? PLOS ONE | https://doi.

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Section: Methodsmentioning

confidence: 99%

Does a rise in BMI cause an increased risk of diabetes?: Evidence from India

Gupta

Bansal

2020

PLoS ONE

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“…Diet information was acquired using a set of culturally adapted questions for self-reported frequency of eating a variety of foods ( 22 ). The healthy diet score (α = 0.70) was constructed by averaging the frequency of consuming six kinds of relatively healthy foods (e.g., whole grain bread, fruit, and vegetables), with responses for each question ranging from 1 (less than once a month) to 6 (more than once a day).…”

Section: Methodsmentioning

confidence: 99%

Socioeconomic Disparities in Weight and Behavioral Outcomes Among American Indian and Alaska Native Participants of a Translational Lifestyle Intervention Project

et al. 2015

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OBJECTIVETo investigate possible socioeconomic disparities in weight and behavioral outcomes among American Indian and Alaska Native (AI/AN) participants in a translational diabetes prevention project.RESEARCH DESIGN AND METHODSWe analyzed data from the Special Diabetes Program for Indians Diabetes Prevention (SDPI-DP) Program, an evidence-based lifestyle intervention to prevent diabetes in 36 AI/AN grantee sites. A total of 2,553 participants started the 16-session Lifestyle Balance Curriculum between 1 January 2006 and 31 July 2008. Linear mixed models were used to evaluate the relationships of participant and staff socioeconomic characteristics with weight and behavioral outcomes at the end of the curriculum.RESULTSA strong, graded association existed between lower household income and less BMI reduction, which remained significant after adjusting for other socioeconomic characteristics. Compared with others, participants with annual income <$15,000 also had less improvement in physical activity and unhealthy food consumption in bivariate models, but the relationships were only marginally significant in multivariate regressions. Furthermore, grantee sites with fewer professionally prepared staff were less successful at improving participant BMI and healthy food consumption than the other sites. The strong association between income and BMI reduction was reduced by 20–30% in the models with changes in diet variables but was unrelated to changes in physical activity.CONCLUSIONSSignificant socioeconomic disparities exist in weight outcomes of lifestyle intervention at both participant and site staff levels. Helping low-income participants choose more affordable healthy foods and increasing the proportion of professionally trained staff might be practical ways to maximize the effectiveness of lifestyle interventions implemented in “real-world” settings.

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“…Details about the dietary choice variables are described elsewhere [14]. Briefly, participants were asked to recall the intake of 18 different types of foods over the last 30 days using a self-administered food frequency questionnaire.…”

Section: Methodsmentioning

confidence: 99%

Recruitment and effectiveness by cohort in a case management intervention among American Indians and Alaska Natives with diabetes

Pratte

Beals

Johnson

et al. 2018

Translational Behavioral Medicine

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In real-world settings, eligible populations and intervention effectiveness for a translational intervention likely vary across time. To determine the optimal strategies for effective large-scale implementation of evidence-based interventions, it is critical to investigate these potential variabilities. The purpose of this study is to evaluate whether patient characteristics and intervention effectiveness differed by year of enrollment in a multiyear evidence-based translational intervention. The Special Diabetes Program for Indians Healthy Heart (SDPI-HH) Demonstration Project is an intensive case management intervention designed to reduce cardiovascular disease risk among American Indians and Alaska Natives with diabetes. SDPI-HH participants recruited from 2006 through 2008 were included. Baseline characteristics were compared by year of enrollment. We also evaluated the differences in improvements in clinical and behavioral risk factors for cardiovascular disease among participants recruited in different years. The baseline characteristics of the three cohorts significantly differed in demographics, diabetes duration, health behaviors, level of motivation, and clinical measures. Improvements in 13 clinical and behavioral outcomes also differed by enrollment year with the 2006 cohort having the greatest number of significant improvements and the highest rates of participation and retention. Further investigation into the ways to modify the intensive case management model to address differences in levels of motivation and participation is warranted to improve the management of chronic disease in Indian health. Given the evolving nature of translational initiatives of this kind, our analysis results highlight the need to understand and adapt during the natural progression of health behavioral interventions.

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Demographic characteristics and food choices of participants in the Special Diabetes Program for American Indians Diabetes Prevention Demonstration Project

Cited by 27 publications

References 28 publications

Does a rise in BMI cause an increased risk of diabetes?: Evidence from India

Does a rise in BMI cause an increased risk of diabetes?: Evidence from India

Socioeconomic Disparities in Weight and Behavioral Outcomes Among American Indian and Alaska Native Participants of a Translational Lifestyle Intervention Project

Recruitment and effectiveness by cohort in a case management intervention among American Indians and Alaska Natives with diabetes

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