Understanding variability in time spent in selected locations for 7–12-year old children

Abstract

This paper summarizes a series of analyses of clustered, sequential activity/location data collected by Harvard University for 160 children aged 7-12 years in Southern California (Geyh et al., 2000). The main purpose of the paper is to understand intra-and inter-variability in the time spent by the sample in the outdoor location, the location exhibiting the most variability of the ones evaluated. The data were analyzed using distribution-free hypothesis-testing (K-S tests of the distributions), generalized linear modeling techniques, and random-sampling schemes that produced ''cohorts'' whose descriptive statistical characteristics were evaluated against the original dataset. Most importantly, our analyses indicate that subdividing the population into appropriate cohorts better replicates parameters of the original data, including the interclass correlation coefficient (ICC), which is a relative measure of the intra-and inter-individual variability inherent in the original data. While the findings of our analyses are consistent with previous assessments of ''time budget'' and physical activity data, they are constrained by the rather homogeneous sample available to us. Owing to a general lack of longitudinal human activity/location data available for other age/gender cohorts, we are unable to generalize our findings to other population subgroups.

A new method of longitudinal diary assembly for human exposure modeling

“…The children in this study were fairly homogeneous geographically and culturally, which suggests a relatively low D statistic (note that D does not exceed 0.21 in Table 1). A more disparate population would likely exhibit a larger D. Xue et al (2004) calculated the Pearson correlation between outdoor times on consecutive days, and found an average of 0.45 for this study. This is much higher than the autocorrelation in outdoor time seen in Table 1.…”

“…A study of children conducted in southern California (Geyh et al, 2000;Xue et al, 2004) provides about 60 days each of data for 163 children. The time series are not continuous, as monitoring consisted of 12 five-day periods, one period per month over a year.…”

Indoor time–microenvironment–activity patterns in seven regions of Europe

“…The estimated variance components for city, individual, and day were transformed into percentages. Furthermore, we calculated the intraclass correlation statistic (ICC) according to the Spearman-Brown formula to assess the reliability of the 2-day mean (Shrout and Fleiss, 1979;McGraw and Wong, 1996;Baranowski and de Moor, 2000;Xue et al, 2004). An ICC expresses the between subject variability as a fraction of the variability between and within subjects.…”

Passive sampling methods to determine household and personal care product use

“…ICC is defined as the ratio of between-subject variance to total variance, ranging from 0 to 1 (Xue et al, 2004). The higher the ICC is, the greater the proportion of total variability that is attributed to differences from one individual to the next.…”