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Young children may be more likely than adults to be exposed to pesticides following a residential application as a result of hand -and object -to -mouth contacts in contaminated areas. However, relatively few studies have specifically evaluated mouthing behavior in children less than 5 years of age. Previously unpublished data collected by the Fred Hutchinson Cancer Research Center ( FHCRC ) were analyzed to assess the mouthing behavior of 72 children ( 37 males / 35 females ). Total mouthing behavior data included the daily frequency of both mouth and tongue contacts with hands, other body parts, surfaces, natural objects, and toys. Eating events were excluded. Children ranged in age from 11 to 60 months. Observations for more than 1 day were available for 78% of the children. The total data set was disaggregated by gender into five age groups ( 10 -20, 20 -30, 30 -40, 40 -50, 50 -60 months ). Statistical analyses of the data were then undertaken to determine if significant differences existed among the age / gender subgroups in the sample. A mixed effects linear model was used to test the associations among age, gender, and mouthing frequencies. Subjects were treated as random and independent, and intrasubject variability was accounted for with an autocorrelation function. Results indicated that there was no association between mouthing frequency and gender. However, a clear relationship was observed between mouthing frequency and age. Using a tree analysis, two distinct groups could be identified: children 24 and children >24 months of age. Children 24 months exhibited the highest frequency of mouthing behavior with 81 ± 7 events / h ( mean ± SE ) ( n = 28 subjects, 69 observations ). Children >24 months exhibited the lowest frequency of mouthing behavior with 42 ± 4 events / h ( n = 44 subjects, 117 observations ). These results suggest that children are less likely to place objects into their mouths as they age. These changes in mouthing behavior as a child ages should be accounted for when assessing aggregate exposure to pesticides in the residential environment.

Purpose There has not been a recent comprehensive effort to examine existing studies on the resting metabolic rate (RMR) of adults to identify the effect of common population demographic and anthropometric characteristics. Thus, we reviewed the literature on RMR (kcal·kg−1·h−1) to determine the relationship of age, sex, and obesity status to RMR as compared with the commonly accepted value for the metabolic equivalent (MET; e.g., 1.0 kcal·kg−1·h−1). Methods Using several databases, scientific articles published from 1980 to 2011 were identified that measured RMR, and from those, others dating back to 1920 were identified. One hundred and ninety-seven studies were identified, resulting in 397 publication estimates of RMR that could represent a population subgroup. Inverse variance weighting technique was applied to compute means and 95% confidence intervals (CI). Results The mean value for RMR was 0.863 kcal·kg−1·h−1 (95% CI = 0.852–0.874), higher for men than women, decreasing with increasing age, and less in overweight than normal weight adults. Regardless of sex, adults with BMI ≥ 30 kg·m−2 had the lowest RMR (<0.741 kcal·kg−1·h−1). Conclusions No single value for RMR is appropriate for all adults. Adhering to the nearly universally accepted MET convention may lead to the overestimation of the RMR of approximately 10%for men and almost 15% for women and be as high as 20%–30% for some demographic and anthropometric combinations. These large errors raise questions about the longstanding adherence to the conventional MET value for RMR. Failure to recognize this discrepancy may result in important miscalculations of energy expended from interventions using physical activity for diabetes and other chronic disease prevention efforts.

This paper tests factors thought to be important in explaining the choices people make in where they spend time. Three aggregate locations are analyzed: outdoors, indoors, and in-vehicles for two different sample groups: a year-long (longitudinal) sample of one individual and a cross-sectional sample of 169 individuals from the US Environmental Protection Agency's Consolidated Human Activity Database (CHAD). The cross-sectional sample consists of persons similar to the longitudinal subject in terms of age, work status, education, and residential type. The sample groups are remarkably similar in the time spent per day in the tested locations, although there are differences in participation rates: the percentage of days frequenting a particular location. Time spent outdoors exhibits the most relative variability of any location tested, with in-vehicle time being the next. The factors found to be most important in explaining daily time usage in both sample groups are: season of the year, season/temperature combinations, precipitation levels, and daytype (work/nonwork is the most distinct, but weekday/weekend is also significant). Season, season/temperature, and day-type are also important for explaining time spent indoors. None of the variables tested are consistent in explaining in-vehicle time in either the cross-sectional or longitudinal samples. Given these findings, we recommend that exposure modelers subdivide their population activity data into at least season/temperature, precipitation, and day-type ''cohorts'' as these factors are important discriminating variables affecting where people spend their time.

EPA's National Exposure Research Laboratory ( NERL ) has combined data from 12 U.S. studies related to human activities into one comprehensive data system that can be accessed via the Internet. The data system is called the Consolidated Human Activity Database ( CHAD ) and is available at http: / / www.epa.gov / nerl / . CHAD contains 22,968 person days of activity and is designed to assist exposure assessors and modelers in constructing populatioǹ`c ohorts'' of people with specified characteristics that are suitable for subsequent analysis or modeling. This paper describes the studies comprising CHAD and the various intellectual foundations that underlay the gathering of human activity pattern data. Next, it provides a brief overview of the Internet version of CHAD, and discusses how the program was formulated. Emphasis is placed on how activity -specific energy expenditure estimates were developed. Finally, the paper recommends steps that should be taken to improve the collection of activity data that would improve energy expenditure estimates and related information needed for physiologically based exposure ± dose modeling efforts.

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This paper summarizes numerous statistical analyses focused on the US Environmental Protection Agency's Consolidated Human Activity Database (CHAD), used by many exposure modelers as the basis for data on what people do and where they spend their time. In doing so, modelers tend to divide the total population being analyzed into ''cohorts'', to reduce extraneous interindividual variability by focusing on people with common characteristics. Age and gender are typically used as the primary cohort-defining attributes, but more complex exposure models also use weather, day-of-the-week, and employment attributes for this purpose. We analyzed all of these attributes and others to determine if statistically significant differences exist among them to warrant their being used to define distinct cohort groups. We focused our attention mostly on the relationship between cohort attributes and the time spent outdoors, indoors, and in motor vehicles. Our results indicate that besides age and gender, other important attributes for defining cohorts are the physical activity level of individuals, weather factors such as daily maximum temperature in combination with months of the year, and combined weekday/weekend with employment status. Less important are precipitation and ethnic data. While statistically significant, the collective set of attributes does not explain a large amount of variance in outdoor, indoor, or in-vehicle locational decisions. Based on other research, parameters such as lifestyle and life stages that are absent from CHAD might have reduced the amount of unexplained variance. At this time, we recommend that exposure modelers use age and gender as ''first-order'' attributes to define cohorts followed by physical activity level, daily maximum temperature or other suitable weather parameters, and day type possibly beyond a simple weekday/weekend classification.

A novel source-to-dose modeling study of population exposures to fine particulate matter (PM 2.5 ) and ozone (O 3 ) was conducted for urban Philadelphia. The study focused on a 2-week episode, 11-24 July 1999, and employed the new integrated and mechanistically consistent source-to-dose modeling framework of MENTOR/SHEDS (Modeling Environment for Total Risk studies/Stochastic Human Exposure and Dose Simulation). The MENTOR/ SHEDS application presented here consists of four components involved in estimating population exposure/dose: (1) calculation of ambient outdoor concentrations using emission-based photochemical modeling, (2) spatiotemporal interpolation for developing census-tract level outdoor concentration fields, (3) calculation of microenvironmental concentrations that match activity patterns of the individuals in the population of each census tract in the study area, and (4) population-based dosimetry modeling. It was found that the 50th percentiles of calculated microenvironmental concentrations of PM 2.5 and O 3 were significantly correlated with census-tract level outdoor concentrations, respectively. However, while the 95th percentiles of O 3 microenvironmental concentrations were strongly correlated with outdoor concentrations, this was not the case for PM 2.5 . By further examining the modeled estimates of the 24-h aggregated PM 2.5 and O 3 doses, it was found that indoor PM 2.5 sources dominated the contributions to the total PM 2.5 doses for the upper 5 percentiles, Environmental Tobacco Smoking (ETS) being the most significant source while O 3 doses due to time spent outdoors dominated the contributions to the total O 3 doses for the upper 5 percentiles. The MENTOR/SHEDS system presented in this study is capable of estimating intake dose based on activity level and inhalation rate, thus completing the source-to-dose modeling sequence. The MENTOR/SHEDS system also utilizes a consistent basis of source characterization, exposure factors, and human activity patterns in conducting population exposure assessment of multiple co-occurring air pollutants, and this constitutes a primary distinction from previous studies of population exposure assessment, where different exposure factors and activity patterns would be used for different pollutants. Future work will focus on incorporating the effects of commuting patterns on population exposure/dose assessments as well as on extending the MENTOR/SHEDS applications to seasonal/annual studies and to other areas in the U.S.