Postpartum Weight Loss: Weight Struggles, Eating, Exercise, and Breast-Feeding.

Postpartum Weight Loss: Weight Struggles, Eating, Exercise, and Breast-Feeding.

Filed under: Eating Disorders

J Holist Nurs. 2012 Nov 21;
Montgomery KS, Best M, Aniello TB, Phillips JD, Hatmaker-Flanigan E

Twenty-four women with children 5 years old or younger were interviewed regarding their experiences in losing weight during the postpartum period. Phenomenological interviews were conducted according to Husserl’s perspective. Women who participated in the study revealed the issues related to postpartum weight loss: weight struggles, exercise, breast-feeding, eating, and pregnancy contributions to weight gain. The overall theme that resulted from these in-depth interviews was that women struggle to balance their successes and setbacks in losing weight during the postpartum period.
HubMed – eating

Nighttime snacking reduces whole body fat oxidation and increases LDL cholesterol in healthy young women.

Filed under: Eating Disorders

Am J Physiol Regul Integr Comp Physiol. 2012 Nov 21;
Hibi M, Masumoto A, Naito Y, Kiuchi K, Yoshimoto Y, Matsumoto M, Katashima M, Oka J, Ikemoto S

Background: The increase in obesity and lipid disorders in industrialized countries may be due to irregular eating patterns. Few studies have investigated the effects of nighttime snacking on energy metabolism. Objective: We examined the effects of nighttime snacking for 13-d on energy metabolism. Methods: Eleven healthy women (mean ± SD; age: 23±1 y; body mass index: 20.6 ± 2.6 kg/m2) participated in this randomized-crossover trial for a 13-d intervention period. Subjects consumed a specified snack (192.4 ± 18.3 kcal) either during the daytime (10:00) or the night (23:00) for 13 days. On day 14, energy metabolism was measured in a respiratory chamber without snack consumption. An oral glucose tolerance test was performed on day 15. Results: Relative to daytime snacking, nighttime snacking significantly decreased fat oxidation (daytime snacking: 52.0 ± 13.6 g/d; nighttime snacking: 45.8 ± 14.0 g/d; P = 0.02) and tended to increase the respiratory quotient (daytime snacking: 0.878 ± 0.022; nighttime snacking: 0.888 ± 0.021; P = 0.09). The frequency of snack intake and energy intake, body weight, and energy expenditure were not affected. Total and LDL cholesterol significantly increased after nighttime snacking (152 ± 26 mg/dL and 161 ± 29 mg/dL; P = 0.03 and 76 ± 20 mg/dL and 83 ± 24 mg/dL; P = 0.01, respectively), but glucose and insulin levels after the glucose load were not affected. Conclusion: Nighttime snacking increased total and LDL cholesterol and reduced fat oxidation, suggesting that eating at night changes fat metabolism and increases the risk of obesity.
HubMed – eating

A novel approach to selecting and weighting nutrients for nutrient profiling of foods and diets.

Filed under: Eating Disorders

J Acad Nutr Diet. 2012 Dec; 112(12): 1968-75
Arsenault JE, Fulgoni VL, Hersey JC, Muth MK

Nutrient profiling of foods is the science of ranking or classifying foods based on their nutrient composition. Most profiling systems use similar weighting factors across nutrients due to lack of scientific evidence to assign levels of importance to nutrients.Our aim was to use a statistical approach to determine the nutrients that best explain variation in Healthy Eating Index (HEI) scores and to obtain ?-coefficients for the nutrients for use as weighting factors for a nutrient-profiling algorithm.We used a cross-sectional analysis of nutrient intakes and HEI scores.Our subjects included 16,587 individuals from the National Health and Nutrition Examination Survey 2005-2008 who were 2 years of age or older and not pregnant.Our main outcome measure was variation (R(2)) in HEI scores.Linear regression analyses were conducted with HEI scores as the dependent variable and all possible combinations of 16 nutrients of interest as independent variables, with covariates age, sex, and ethnicity. The analyses identified the best 1-nutrient variable model (with the highest R(2)), the best 2-nutrient variable model, and up to the best 16-nutrient variable model.The model with 8 nutrients explained 65% of the variance in HEI scores, similar to the models with 9 to 16 nutrients, but substantially higher than previous algorithms reported in the literature. The model contained five nutrients with positive ?-coefficients (ie, protein, fiber, calcium, unsaturated fat, and vitamin C) and three nutrients with negative coefficients (ie, saturated fat, sodium, and added sugar). ?-coefficients from the model were used as weighting factors to create an algorithm that generated a weighted nutrient density score representing the overall nutritional quality of a food.The weighted nutrient density score can be easily calculated and is useful for describing the overall nutrient quality of both foods and diets.
HubMed – eating

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