318 research outputs found

    Short and long-term lifestyle coaching approaches used to address diverse participant barriers to weight loss and physical activity adherence

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    Background: Individual barriers to weight loss and physical activity goals in the Diabetes Prevention Program, a randomized trial with 3.2 years average treatment duration, have not been previously reported. Evaluating barriers and the lifestyle coaching approaches used to improve adherence in a large, diverse participant cohort can inform dissemination efforts. Methods: Lifestyle coaches documented barriers and approaches after each session (mean session attendance = 50.3 ± 21.8). Subjects were 1076 intensive lifestyle participants (mean age = 50.6 years; mean BMI = 33.9 kg/m2; 68% female, 48% non-Caucasian). Barriers and approaches used to improve adherence were ranked by the percentage of the cohort for whom they applied. Barrier groupings were also analyzed in relation to baseline demographic characteristics. Results: Top weight loss barriers reported were problems with self-monitoring (58%); social cues (58%); holidays (54%); low activity (48%); and internal cues (thought/mood) (44%). Top activity barriers were holidays (51%); time management (50%); internal cues (30%); illness (29%), and motivation (26%). The percentage of the cohort having any type of barrier increased over the long-term intervention period. A majority of the weight loss barriers were significantly associated with younger age, greater obesity, and non-Caucasian race/ethnicity (p-values vary). Physical activity barriers, particularly thought and mood cues, social cues and time management, physical injury or illness and access/weather, were most significantly associated with being female and obese (p 90% long term) and regularly reviewed self-monitoring skills. More costly approaches were used infrequently during the first 16 sessions (≤10%) but increased over 3.2 years. Conclusion: Behavioral problem solving approaches have short and long term dissemination potential for many kinds of participant barriers. Given minimal resources, increased attention to training lifestyle coaches in the consistent use of these approaches appears warranted

    US pediatric population-level associations of DXA-measured percentage of body fat with four BMI metrics with cutoffs

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    ObjectiveFour body mass index (BMI) metrics—BMI, BMI z-score, BMI percentile, and BMI%—are commonly used as proxy measures for children's adiposity. We sought to determine a BMI metric that is most strongly associated with measured percentage of body fat (%BF) in the US pediatric population stratified by sex, age and race/ethnicity, and to determine cutoffs that maximize the association for each BMI metric.Subjects, Design and Methods%BF was measured by DXA among N=6120 US boys and girls aged 8.0 to 17.9 years old from NHANES 1999-2004. We fit piece-wise linear regression models with cutoffs to %BF data using each BMI metric as the predictor stratified by sex, race/ethnicity and age. The slopes were modeled differently before and after the cutoffs which were determined based on grid searches.ResultsBMI z-score was in general most strongly associated with %BF for both boys and girls. The associations of the four BMI metrics were lowest for boys aged 12-13.9 years and girls aged 16-17.9 years, and strongest for Mexican-American boys and for non-Hispanic black girls. Overall, the associations were stronger for girls than for boys. In boys, BMI had the lowest association with %BF (R2=0.39) for all ages combined. The fold changes in slopes before and after cutoffs were greatest in general for BMI percentiles regardless of age, sex and race/ethnicity. BMI z-score cutoffs were 0.4 for both boys and girls for all ages combined. Except for BMI, the slopes after the cutoffs were in general greater than those before.ConclusionsAll BMI metrics were strongly associated with %BF when stratified by age and race/ethnicity except that BMI was the least associated with %BF in boys for all ages combined. Overall, BMI z-score was superior for evaluation of %BF, and its cutoff of 0.4 can also serve as a threshold for careful monitoring of weight status

    Macronutrients, Food Groups, and Eating Patterns in the Management of Diabetes: A systematic review of the literature, 2010

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    The effectiveness of medical nutrition therapy (MNT) in the management of diabetes has been well established (1). Previous reviews have provided comprehensive recommendations for MNT in the management of diabetes (2,3). The goals of MNT are to 1 ) attain and maintain optimal blood glucose levels, a lipid and lipoprotein profile that reduces the risk of macrovascular disease, and blood pressure levels that reduce the risk for vascular disease; 2 ) prevent and treat the chronic complications of diabetes by modifying nutrient intake and lifestyle; 3 ) address individual nutrition needs, taking into account personal and cultural preferences and willingness to change; and 4 ) maintain the pleasure of eating by only limiting food choices when indicated by scientific evidence (4). The literature on nutrition as it relates to diabetes management is vast. We undertook the specific topic of the role of macronutrients, eating patterns, and individual foods in response to continued controversy over independent contributions of specific foods and macronutrients, independent of weight loss, in the management of diabetes. The position of the American Diabetes Association (ADA) on MNT is that each person with diabetes should receive an individualized eating plan (4). ADA has received numerous criticisms because it does not recommend one specific mix of macronutrients for everyone with diabetes. The previous literature review conducted by ADA in 2001 supported the idea that there was not one ideal macronutrient distribution for all people with diabetes. This review focuses on literature that has been published since that 2001 date (5). This systematic review will be one source of information considered when updating the current ADA Nutrition Position Statement (4). Other systematic reviews and key research studies that may not be included in this review will also be considered. When attempting to tease out the role of macronutrients from other dietary

    Documentation of body mass index and control of associated risk factors in a large primary care network

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    <p>Abstract</p> <p>Background</p> <p>Body mass index (BMI) will be a reportable health measure in the United States (US) through implementation of Healthcare Effectiveness Data and Information Set (HEDIS) guidelines. We evaluated current documentation of BMI, and documentation and control of associated risk factors by BMI category, based on electronic health records from a 12-clinic primary care network.</p> <p>Methods</p> <p>We conducted a cross-sectional analysis of 79,947 active network patients greater than 18 years of age seen between 7/05 - 12/06. We defined BMI category as normal weight (NW, 18-24.9 kg/m<sup>2</sup>), overweight (OW, 25-29.9), and obese (OB, ≥ 30). We measured documentation (yes/no) and control (above/below) of the following three risk factors: blood pressure (BP) ≤130/≤85 mmHg, low-density lipoprotein (LDL) ≤130 mg/dL (3.367 mmol/L), and fasting glucose <100 mg/dL (5.55 mmol/L) or casual glucose <200 mg/dL (11.1 mmol/L).</p> <p>Results</p> <p>BMI was documented in 48,376 patients (61%, range 34-94%), distributed as 30% OB, 34% OW, and 36% NW. Documentation of all three risk factors was higher in obesity (OB = 58%, OW = 54%, NW = 41%, p for trend <0.0001), but control of all three was lower (OB = 44%, OW = 49%, NW = 62%, p = 0.0001). The presence of cardiovascular disease (CVD) or diabetes modified some associations with obesity, and OB patients with CVD or diabetes had low rates of control of all three risk factors (CVD: OB = 49%, OW = 50%, NW = 56%; diabetes: OB = 42%, OW = 47%, NW = 48%, p < 0.0001 for adiposity-CVD or diabetes interaction).</p> <p>Conclusions</p> <p>In a large primary care network BMI documentation has been incomplete and for patients with BMI measured, risk factor control has been poorer in obese patients compared with NW, even in those with obesity and CVD or diabetes. Better knowledge of BMI could provide an opportunity for improved quality in obesity care.</p

    Long-term changes in dietary and food intake behaviour in the Diabetes Prevention Program Outcomes Study

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    To 1) compare change in dietary intake, with an emphasis on food groups and food behaviors, over time across treatment arms in a diabetes prevention trial and 2) assess differences in dietary intake across demographic groups within treatment arms

    Applying Recovery Biomarkers to Calibrate Self-Report Measures of Energy and Protein in the Hispanic Community Health Study/Study of Latinos

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    We investigated measurement error in the self-reported diets of US Hispanics/Latinos, who are prone to obesity and related comorbidities, by background (Central American, Cuban, Dominican, Mexican, Puerto Rican, and South American) in 2010–2012. In 477 participants aged 18–74 years, doubly labeled water and urinary nitrogen were used as objective recovery biomarkers of energy and protein intakes. Self-report was captured from two 24-hour dietary recalls. All measures were repeated in a subsample of 98 individuals. We examined the bias of dietary recalls and their associations with participant characteristics using generalized estimating equations. Energy intake was underestimated by 25.3% (men, 21.8%; women, 27.3%), and protein intake was underestimated by 18.5% (men, 14.7%; women, 20.7%). Protein density was overestimated by 10.7% (men, 11.3%; women, 10.1%). Higher body mass index and Hispanic/Latino background were associated with underestimation of energy (P < 0.05). For protein intake, higher body mass index, older age, nonsmoking, Spanish speaking, and Hispanic/Latino background were associated with underestimation (P < 0.05). Systematic underreporting of energy and protein intakes and overreporting of protein density were found to vary significantly by Hispanic/Latino background. We developed calibration equations that correct for subject-specific error in reporting that can be used to reduce bias in diet-disease association studies

    Validity of Resting Energy Expenditure Predictive Equations before and after an Energy-Restricted Diet Intervention in Obese Women

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    Background We investigated the validity of REE predictive equations before and after 12-week energy-restricted diet intervention in Spanish obese (30 kg/m2>BMI<40 kg/m2) women. Methods We measured REE (indirect calorimetry), body weight, height, and fat mass (FM) and fat free mass (FFM, dual X-ray absorptiometry) in 86 obese Caucasian premenopausal women aged 36.7±7.2 y, before and after (n = 78 women) the intervention. We investigated the accuracy of ten REE predictive equations using weight, height, age, FFM and FM. Results At baseline, the most accurate equation was the Mifflin et al. (Am J Clin Nutr 1990; 51: 241–247) when using weight (bias:−0.2%, P = 0.982), 74% of accurate predictions. This level of accuracy was not reached after the diet intervention (24% accurate prediction). After the intervention, the lowest bias was found with the Owen et al. (Am J Clin Nutr 1986; 44: 1–19) equation when using weight (bias:−1.7%, P = 0.044), 81% accurate prediction, yet it provided 53% accurate predictions at baseline. Conclusions There is a wide variation in the accuracy of REE predictive equations before and after weight loss in non-morbid obese women. The results acquire especial relevance in the context of the challenging weight regain phenomenon for the overweight/obese population.The present study was supported by the University of the Basque Country (UPV 05/80), Social Foundation of the Caja Vital- Kutxa and by the Department of Health of the Government of the Basque Country (2008/111062), and by the Spanish Ministry of Science and Innovation (RYC-2010-05957)
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