Using longitudinal data to understand children's activity patterns in an exposure context: Data from the Kanawha county health study

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.

Section: Evaluating the Activity Factors Typology In The Longitudinalsupporting

confidence: 86%

“…Large within-& between-individual variability in travel & outdoor locations, esp. for a subsample of the group Schwab et al (1992) #=Male;~=female. a Usually shown to be statistically significant in quantitative analyses, but often is used simply as a qualitative descriptor of organized data (without being tested for statistical difference).…”

Section: Discussionmentioning

confidence: 99%

Using human activity data in exposure models: Analysis of discriminating factors

McCurdy

Graham

2003

“…Finally, scientists involved in estimating and simulating human exposures to environmental pollutants became interested in activity pattern research in the 1980s ( Robinson, 1988;Robinson and Thomas, 1991;Robinson et al, 1989;Adair and Spendler, 1989;Lichtenstein and Wyzga, 1989;Schwab, 1989;Schwab et al, 1990aSchwab et al, , 1991Schwab et al, , 1992Stock and Morandi, 1989 ). They emphasize proximity to potential sources of pollution, and Ð to a limited extent Ð the amount of time spent in e's with open windows and in activities involving high breathing rates (Johnson, 1984) .…”

Section: Human Activity Pattern Research and Methodsmentioning

confidence: 99%

“…NERL's two carbon monoxide ( CO ) studies discussed below are good examples, as are the studies described in Schwab et al (1990b) and Stock et al ( 1985 ) . Numerous other monitoring studies obtain e-specific data on concentrations and activities that occur within a e, but usually do not attempt to capture a subject's complete activity pattern for the specified monitoring period.…”

Section: Human Activity Pattern Research and Methodsmentioning

confidence: 99%

The National Exposure Research Laboratory's Consolidated Human Activity Database

McCurdy

Glen²,

Smith³

et al. 2000

123

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.

“…Children, as a subset of the population, are of particular concern when investigating exposures to environmental pollutants because of their frequent hand and mouthing behaviors California Air Resources Board, 1991;Schwab et al, 1992;Eckerman, 1994;Lewis et al, 1994). Because of these contact and mouthing activities, children may have high exposures to dust-laden pollutants such as lead and pesticides through dermal contact or ingestion.…”

Section: Introductionmentioning

confidence: 99%

Quantification of children's hand and mouthing activities through a videotaping methodology

Reed

Jimenez²,

Freeman

et al. 1999

100

A videotaping methodology has been developed for use in quantifying the types and frequencies of children's hand and mouthing activities that could lead to exposure to environmental pollutants via dermal and ingestion pathways. Twenty children in day care, ages 3 ± 6 years and 10 children in residences, ages 2 ± 5 years, were videotaped during their waking hours for 1 day. Parents of each child completed questionnaires for the purpose of evaluating the accuracy of parental reports of hand-to-mouth rates. Videotapes were translated as quantifiable activities by two trained observers whose reporting reliability was checked throughout the investigation. Results determined that reliability of the videotaping method was very good, even over a year post-training. From videotape data, the average hand-to-mouth frequency rate was determined to be 9.5 contacts/h. These values are considerably higher than the current default value of 1.56 contacts/ h under consideration by the EPA.