bstractThe distribution of usual intakes of dietary components is important to individuals formulating food policy and to persons designing nutrition education programs. Usual intake of a dietary component for a person is the long run average of daily intakes of that component for that person. Because it is impossible to directly observe usual intake of an individual, it is necessary to develop an estimator of the distribution of usual intakes based on a sample of individuals with a small number of daily observations on each individual. Daily intake data for individuals are nonnegative and often very skewed. Also, there is large day-to-day variation relative to the individual-to-individual variation and the within-individual variance is correlated with the individual means. We suggest a methodology for estimating usual intake distributions that allows for varying degrees of departure from normality and recognizes the measurement error associated with daily dietary intakes. The estimation method contains four steps. First, the original data are standardized by adjusting for weekday and interview sequence effects. Second, the daily intake data are transformed to normality using a combination of power and grafted polynomial transformations. Third, using a normal components-of-variance model, the distribution of usual intakes is constructed for the transformed data. Finally, a transformation of normal usual intakes to the original scale is defined. The approach works well for a set of dietary components selected from the [1985][1986]

bstractThe distribution of usual intakes of dietary components is important to individuals formulating food policy and to persons designing nutrition education programs. Usual intake of a dietary component for a person is the long run average of daily intakes of that component for that person. Because it is impossible to directly observe usual intake of an individual, it is necessary to develop an estimator of the distribution of usual intakes based on a sample of individuals with a small number of daily observations on each individual. Daily intake data for individuals are nonnegative and often very skewed. Also, there is large day-to-day variation relative to the individual-to-individual variation and the within-individual variance is correlated with the individual means. We suggest a methodology for estimating usual intake distributions that allows for varying degrees of departure from normality and recognizes the measurement error associated with daily dietary intakes. The estimation method contains four steps. First, the original data are standardized by adjusting for weekday and interview sequence effects. Second, the daily intake data are transformed to normality using a combination of power and grafted polynomial transformations. Third, using a normal components-of-variance model, the distribution of usual intakes is constructed for the transformed data. Finally, a transformation of normal usual intakes to the original scale is defined. The approach works well for a set of dietary components selected from the [1985][1986]

Background: Parents directly influence children's physical activity and nutrition behaviors and also dictate the physical and social environments that are available to their children. This paper summarizes the development of an easy to use screening tool (The Family Nutrition and Physical Activity (FNPA) Screening Tool) designed to assess family environmental and behavioral factors that may predispose a child to becoming overweight.

Modeling measurement error in recall data can be used to provide more accurate estimates of long-term activity behavior.

Disease surveillance in wildlife populations involves detecting the presence of a disease, characterizing its prevalence and spread, and subsequent monitoring. A probability sample of animals selected from the population and corresponding estimators of disease prevalence and detection provide estimates with quantifiable statistical properties, but this approach is rarely used. Although wildlife scientists often assume probability sampling and random disease distributions to calculate sample sizes, convenience samples (i.e., samples of readily available animals) are typically used, and disease distributions are rarely random. We demonstrate how landscape-based simulation can be used to explore properties of estimators from convenience samples in relation to probability samples. We used simulation methods to model what is known about the habitat preferences of the wildlife population, the disease distribution, and the potential biases of the convenience-sample approach. Using chronic wasting disease in free-ranging deer (Odocoileus virginianus) as a simple illustration, we show that using probability sample designs with appropriate estimators provides unbiased surveillance parameter estimates but that the selection bias and coverage errors associated with convenience samples can lead to biased and misleading results. We also suggest practical alternatives to convenience samples that mix probability and convenience sampling. For example, a sample of land areas can be selected using a probability design that oversamples areas with larger animal populations, followed by harvesting of individual animals within sampled areas using a convenience sampling method. KeywordsEcology Evolution and Organismal Biology, chronic wasting disease, disease detection, disease prevalance, sample design, surveillance, waiting time distribution RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. ABSTRACT Disease surveillance in wildlife populations involves detecting the presence of a disease, characterizing its prevalence and spread, and subsequent monitoring. A probability sample of animals selected from the population and corresponding estimators of disease prevalence and detection provide estimates with quantifiable statistical properties, but this approach is rarely used. Although wildlife scientists often assume probability sampling and random disease distributions to calculate sample sizes, convenience samples (i.e., samples of readily available animals) are typically used, and disease distributions are rarely random. We demonstrate how landscape-based simulation can be used to explore properties of estimators from convenience samples in relation to probability samples. We used simulation methods to model what is known about the habitat preferences of the wildlife population, the disease distribution, and the potential biases of the convenience-sample approach. Using chronic wasting disease in free-r...

This study demonstrates the potential validity of a simple, easy-to-use screening tool for identifying children that may be at risk for becoming overweight.

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Purpose The primary purpose of this study was to evaluate the validity of an interviewer-administered, 24-hour physical activity recall (PAR) compared to the SenseWear Armband (SWA) for estimation of energy expenditure (EE) and moderate-to-vigorous physical activity (MVPA) in a representative sample of adults. A secondary goal was to compare measurement errors for various demographic sub-groups (gender, age and weight status). Methods A sample of 1347 adults (20–71yrs; 786 females) wore an SWA for a single day and then completed a PAR recalling that previous day’s physical activity. The participants each performed two trials on two randomly selected days across a 2 year time span. The EE and MVPA values for each participant were averaged across the two days. Group-level and individual-level agreement were evaluated using 95% equivalence testing and mean absolute percent error (MAPE), respectively. Results were further examined for sub-groups by gender, age and body mass index (BMI). Results The PAR yielded equivalent estimates of EE (compared to the SWA) for almost all demographic subgroups but none of the comparisons for MVPA were equivalent. Smaller MAPE values were observed for EE (ranges from 10.3% to 15.0%) than for MVPA (ranges from 68.6% to 269.5%), across all comparisons. The PAR yielded underestimates of MVPA for younger, less obese people but overestimates for older, more obese people. Conclusions For EE measurement, the PAR demonstrated good agreement relative to the SWA. However, the use of PAR may result in biased estimates of MVPA both at the group and individual level in adults.