Dataset Information

Applying latent class assignments for accelerometry data to external populations: Data from the National Health and Nutrition Examination Survey 2003-2006.

ABSTRACT: Latent class analysis can identify unmeasured mutually exclusive categories (class membership) among participants for either observed categorical or continuous variables. More recently, latent class analysis has been applied to accelerometry to better understand the day-to-day patterns of physical activity and sedentary behavior. Typically, the class assignments are only relevant to the study for which they were derived and not made available for others to use. Using one-week accelerometry (ActiGraph #AM7164) data collected from the National Health and Nutrition Examination Survey during 2003-2006, latent classes of physical activity and sedentary behavior were derived separately for youths 6-17 years and adults >=18 years. The purpose of this article is to provide the latent class assignments developed on this source population (United States) available to others to apply to their studies using similarly collected accelerometry. This method will extend the usefulness of the latent class analysis and allow for comparisons across studies.

SUBMITTER: Evenson KR

PROVIDER: S-EPMC5118612 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Applying latent class assignments for accelerometry data to external populations: Data from the National Health and Nutrition Examination Survey 2003-2006.

Evenson Kelly R KR Wen Fang F Howard Annie Green AG Herring Amy H AH

Data in brief 20161109

Latent class analysis can identify unmeasured mutually exclusive categories (class membership) among participants for either observed categorical or continuous variables. More recently, latent class analysis has been applied to accelerometry to better understand the day-to-day patterns of physical activity and sedentary behavior. Typically, the class assignments are only relevant to the study for which they were derived and not made available for others to use. Using one-week accelerometry (Acti ...[more]

PMID: 27896298

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Project description:BACKGROUND:Recent detailed analyses of data on dietary sources of energy and nutrients in US children are lacking. The objective of this study was to identify food sources of energy and 28 nutrients for children in the United States. METHODS:Analyses of food sources were conducted using a single 24-h recall collected from children 2 to 18 years old (n = 7332) in the 2003-2006 National Health and Nutrition Examination Survey. Sources of nutrients contained in foods were determined using nutrient composition databases. Food grouping included ingredients from disaggregated mixtures. Mean energy and nutrient intakes from the total diet and from each food group were adjusted for the sample design using appropriate weights. Percentages of the total dietary intake that food sources contributed were tabulated by rank order. RESULTS:The two top ranked food/food group sources of energy and nutrients were: energy - milk (7% of energy) and cake/cookies/quick bread/pastry/pie (7%); protein - milk (13.2%) and poultry (12.8%); total carbohydrate - soft drinks/soda (10.5%) and yeast bread/rolls (9.1%); total sugars - soft drinks/soda (19.2%) and yeast breads and rolls (12.7%); added sugars - soft drinks/soda (29.7%) and candy/sugar/sugary foods (18.6%); dietary fiber - fruit (10.4%) and yeast bread/rolls (10.3%); total fat - cheese (9.3%) and crackers/popcorn/pretzels/chips (8.4%); saturated fatty acids - cheese (16.3%) and milk (13.3%); cholesterol - eggs (24.2%) and poultry (13.2%); vitamin D - milk (60.4%) and milk drinks (8.3%); calcium - milk (33.2%) and cheese (19.4%); potassium - milk (18.8%) and fruit juice (8.0%); and sodium - salt (18.5%) and yeast bread and rolls (8.4%). CONCLUSIONS:Results suggest that many foods/food groupings consumed by children were energy dense, nutrient poor. Awareness of dietary sources of energy and nutrients can help health professionals design effective strategies to reduce energy consumption and increase the nutrient density of children's diets.

Project description:Animal models suggest a protective role of antioxidants against the adverse effect of di-2-ethylhexyl phthalate (DEHP) on insulin resistance. However, no epidemiologic study has examined the effects observed in the animal model. We conduct a study to examine associations of urinary concentrations of phthalate metabolites (individually and as a mixture) with insulin resistance, along with potential effect modification by serum antioxidant concentrations. This cross-sectional study included 1605 participants (51% males) aged 12-85 from the National Health and Nutrition Examination Surveys (2003-2006). Urinary concentrations of 9 phthalate metabolites were measured from spot urine samples. Antioxidant (vitamin A, C, E, and carotenoids) concentrations were measured from a fasting serum sample. We used Bayesian Kernel Machine Regression (BKMR) to evaluate associations between phthalate metabolite mixtures and insulin resistance, and examined whether serum antioxidant levels modified these associations, while accounting for the correlations of multiple concurrent exposures. A change in urinary ΣDEHP concentrations from the 25th to the 75th percentile was associated with a higher log HOMA-IR of 0.07 (95% CI = 0.01, 0.14) (4.85% increase in HOMA-IR). In contrast, the same change in urinary monoethyl phthalate (MEP) was associated with a lower HOMA-IR of -0.07 (95% CI = -0.14, -0.02) (6.68% decrease in HOMA-IR). The positive association between ΣDEHP and HOMA-IR became weaker at higher concentrations of serum β-carotene. The relationship between MEP and HOMA-IR, however, was not modified by the serum antioxidants examined. The remaining phthalate metabolites were unrelated to HOMA-IR. In this cross-sectional study, the positive association between DEHP exposure and insulin resistance weakened among participants with higher concentrations of serum β-carotene. As this is the first human report on the protective role of serum β-carotene on DEHP induced insulin resistance, future studies are needed.

Dataset Information

Applying latent class assignments for accelerometry data to external populations: Data from the National Health and Nutrition Examination Survey 2003-2006.

Publications

Applying latent class assignments for accelerometry data to external populations: Data from the National Health and Nutrition Examination Survey 2003-2006.

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