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

Journal of Exposure Science & Environmental Epidemiology volume 14, pages 222–233 (2004)Cite this article

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.

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Notes

  1. Such as weekday/weekend distinctions (often called a day-type) and a daily ambient temperature indicator of season-of-the-year. See, for example, Johnson et al. (1996), Graham and McCurdy (2003), and McCurdy and Graham (2003). Other discriminating factors can be used, such as employment status, housing type, and physical activity level. We call any such discriminating approach a “day-block” method.

  2. These include the NEM (NAAQS Exposure Model) and the probabilistic NEM (pNEM) models (Johnson, 1995; McCurdy, 1995), and the Air Pollution Exposure (APEX) model currently under development (Richmond et al., 2002). The approach also is used on a modified basis by non-EPA modelers (see, for example: Lurmann and Korc, 1994; Freijer et al., 1997).

  3. Standard deviation is shown in parentheses.

  4. The findings related to tracking are not consistent. Most studies find that it occurs, but others do not (both relative to some criterion measure). It is hard to reconcile the disparate findings since there are (1) multiple physical activity metrics used as the dependent variable, and (2) multiple criteria of a significant association used to determine if the phenomenon exists or not. Tracking usually is measured by an “interperiod correlation coefficient” (IPC). See Van Mechelen and Kemper (1995).

  5. Monitoring using an instrumental measure of PA: oxygen consumption, heart rate, accelerometer/pedometer movement “counts,” or carbon dioxide production. All of these monitoring techniques require additional physiological data and/or relationships to calibrate the objective measure to a particular individual.

  6. This is the term used by exercise physiologists for the more generally applicable interclass correlation coefficient (ICC) to assess reliability or consistency in PA over multiple days. See the text below.

  7. Summer=June–August; Autumn=September–November; Winter =December–February; Spring=March–May. Because the South Coast of California has a climatology associated with cold ocean/land interface phenomena that greatly affects monthly temperature/precipitation characteristics which do not fit a school-calendar based seasonal typology, a 3-season breakdown was also used for some analyses (December–March; April–July; August–November).

  8. Mean h/day estimates: indoors — 20.5; outdoors — 2.4; and in-transit — 1.1 (n=1,200).

  9. Mean h/day estimates: indoors — 20.7; outdoors — 2.2; and in-transit — 1.0 (n=1292).

  10. MacIntosh (2001) provides correlation coefficients for lags up to 6 days, but they are not calculated in the same manner as ours so they cannot be compared.

  11. The PROC GLM procedure of SAS® was used for this modeling (Version 6: Cary, NC: SAS Institute, 2002). Basically, the procedure used was analysis of variance, with and without repeated measures.

References

  • Akland G.G., Hartwell T.D., Johnson T.R., and Whitmore R.W. Measuring human exposure to carbon monoxide in Washington, D.C. and Denver, Colorado during the Winter of 1982–83. Environ Sci Technol 1985: 19: 911–918.

    Article  CAS  Google Scholar 

  • Anderssen N., Jacobs Jr. D.R., Sidney S., Bild D.E., et al. Change and secular trends in physical activity patterns in young adults: a seven-year longitudinal follow-up in the Coronary Artery Risk Development in Young Adults Study (CARDIA). Am J Epidemiol 1996: 143: 351–362.

    Article  CAS  Google Scholar 

  • Baranowski T., Smith M., Thompson W.O., et al. Intraindividual variability and reliability in a 7-day exercise record. Med Sci Sports Exer 1999: 31: 1619–1622.

    Article  CAS  Google Scholar 

  • Baranowski T., and de Moor C. How many days was that? Intra-individual variability and physical activity assessment. Res Q Exer Sport 2000: 71: 74–78.

    Article  Google Scholar 

  • Burke J.M., Zufall M.J., and Ozkaynak H. A population exposure model for particulate matter: Case study results for PM2.5 in Philadelphia, PA. J Exp Anal Environ Epidemiol 2001: 11: 470–489.

    Article  CAS  Google Scholar 

  • Butcher J. Longitudinal analysis of adolescent girls' participation in physical activity. Sociol Sport J 1985: 2: 130–143.

    Article  Google Scholar 

  • DuRant R.H., Baranowski T., Davis H., et al. Reliability and variability of heart rate monitoring in 3-, 4-, or 5-yr-old children. Med Sci Sports Exer 1992: 24: 265–271.

    Article  CAS  Google Scholar 

  • Eaton C.B., Feldman H., Reynes J., et al. Predicting physical activity change in men and women in two New England communities. Am J Prev Med 1993: 9: 209–219.

    Article  CAS  Google Scholar 

  • Fortier M.D., Katzmarzk P.T., Malina R.M., and Bouchard C. Seven-year stability of physical activity and musculoskeletal fitness in the Canadian population. Med Sci Sports Exer 2001: 33: 1905–1911.

    Article  CAS  Google Scholar 

  • Freijer J.I., Bloemen H.J.T., de Loos S., Marra M., et al. AirPEx: Air Pollution Exposure Model. Rijksinstituut Voor Volksgezondheid en Milieu, Bilthoven Netherlands, 1997.

    Google Scholar 

  • Geyh A.S., Roberts P.T., Lurmann F.W., Schoell B.M., and Avol E.L. Initial field evaluation of the Harvard active ozone sampler for personal ozone monitoring. J Exp Anal Environ Epidemiol 1999: 9: 143–149.

    Article  CAS  Google Scholar 

  • Geyh A.S., Xue J., Özkaynak H., and Spengler J.D. The Harvard Southern California chronic ozone exposure study: Assessing ozone exposure of grade-school-age children in two Southern California communities. Environ Health Persp 2000: 108: 265–270.

    Article  CAS  Google Scholar 

  • Glen G. Options for Longitudinal Activity Diary Selection in Exposure Modeling. Mantech Environmental Technologies, Inc., Research Triangle Park NC, 2000.

    Google Scholar 

  • Glen G., and Shadwick D. Final Technical Report on the Analysis of Carbon Monoxide Exposures for Fourteen Cities Using HAPEM-MS3. ManTech Environmental Technology, Research Triangle Park, NC, 1998.

    Google Scholar 

  • Graham S.E., and McCurdy T. Developing meaningful cohorts for human exposure models. J Exp Anal Environ Epidemiol 2003: 13, (accepted).

  • Jenkins P.L., Phillips T.J., Mulberg E.J., and Hui S.P. Activity patterns of Californians: Use of and proximity to indoor pollutant sources. Atmos Environ 1992: 26A: 2141–2148.

    Article  CAS  Google Scholar 

  • Johnson T. A Study of Personal Exposure to Carbon Monoxide in Denver, Colorado. U.S. Environmental Protection Agency (EPA 600/S4-84-014), Research Triangle Park, NC, 1984.

    Google Scholar 

  • Johnson T. Recent advances in the estimation of population exposure to mobile source pollutants. J Exp Anal Environ Epidemiol 1995: 5: 551–571.

    CAS  Google Scholar 

  • Johnson T., Capel J., McCoy M., and Warnasch J. Estimation of Ozone Exposures Experienced by Outdoor Children in Nine Urban Areas Using a Probabilistic Version of NEM. IT Corporation, Durham, NC, 1996.

    Google Scholar 

  • Kelder S.H., Perry C.L., Klepp K.-I., and Lytle L.L. Longitudinal tracking of adolescent smoking, physical activity, and food choice behaviors. Am J Pub Health 1994: 84: 1121–1126.

    Article  CAS  Google Scholar 

  • Kemper H.C.G., Verschuur R., and deMey L. Longitudinal changes of aerobic fitness in youth ages 12 to 23. Pediatr Exer Sci 1989: 1: 257–270.

    Article  Google Scholar 

  • Kimm S.Y.S., Glynn N.W., Kriska A.M., et al. Longitudinal changes in physical activity in a biracial cohort during adolescence. Med Sci Sports Exer 2000: 32: 1445–1454.

    Article  CAS  Google Scholar 

  • Klepeis N.E., Nelson W.C., Ott W.R., et al. The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants. J Exp Anal Environ Epidemiol 2001: 11: 231–252.

    Article  CAS  Google Scholar 

  • Klepeis N., Tsang A., and Behar J.V. Analysis of the National Human Activity Pattern Survey (NHAPS) Respondents from a Standpoint of Exposure Assessment. National Exposure Research Laboratory, US Environmental Protection Agency (EPA-600-R-96/074), Las Vegas NV, 1996.

    Google Scholar 

  • Kohl III H.W., and Hobbs K.E. Development of physical activity behaviors among children and adolescents. Pediatrics 1998: 101(Suppl. 3): 549–554.

    PubMed  Google Scholar 

  • Lee I.-M., Paffenbarger Jr R.S., and Hsieh C.-C. Time trends in physical activity among college alumni, 1962–1988. Am J Epidemiol 1992: 135: 915–925.

    Article  CAS  Google Scholar 

  • Lefevre J., Philappaerts R.M., Delvaux K., et al. Daily physical activity and physical fitness from adolescence to adulthood: a longitudinal study. Am J Hum Biol 2000: 12: 487–497.

    Article  Google Scholar 

  • Lurmann F.W., and Korc M.E. User's Guide to the Regional Human Exposure (REHEX) model. Draft report. Bay Area Air Quality Management District; San Francisco, CA, report no. STI–93150–1414–DR, 1994.

  • MacIntosh D.L. Refinements to EPA/NERL's Aggregate SHEDS-Pesticide Model. US Environmental Protection Agency, Research Triangle Park NC, 2001.

    Google Scholar 

  • Matthews C.E., Hebert J.R., Freedson P.S., et al. Sources of variance in daily physical activity levels in the Seasonal Variation of Blood Cholesterol Study. Am J Epidemiol 2001: 153: 987–995.

    Article  CAS  Google Scholar 

  • McCurdy T. Estimating human exposure to selected motor vehicle pollutants using the NEM series of models: Lessons to be learned. J Exp Anal Environ Epidemiol 1995: 5: 533–550.

    CAS  Google Scholar 

  • McCurdy T. Modeling the dose profile in human exposure assessments: ozone as an example. Rev Tox: In Vivo Tox. Risk Assess 1997: 1: 3–23.

    CAS  Google Scholar 

  • McCurdy T., Glen G., Smith L., and Lakkadi Y. The National Exposure Research Laboratory's Consolidated Human Activity Database. J Exp Anal Environ Epidemiol 2000: 10: 566–578.

    Article  CAS  Google Scholar 

  • McCurdy T., and Graham S. Using human activity data in exposure models: analysis of discriminating factors. J Exp Anal Environ Epidemiol 2003: 13: 294–317.

    Article  Google Scholar 

  • NCEA National Center for Environmental Assessment. Exposure Factors Handbook I. General Factors. U.S. Environmental Protection Agency, Washington DC, EPA/600/P-95/002Fa, 1997.

  • Palma T., Riley M., and Capel J.E. Development and Evaluation of Enhancements to the Hazardous Air Pollutant Exposure Model (HAPEM-MS3). International Technology Corporation, Cary, NC, 1996.

    Google Scholar 

  • Pate R.R., Heath G.W., Dowda M., and Trost S.G. Associations between physical activity and other health behaviors in a representative sample of US adolescents. Am J Pub Health 1996: 86: 1577–1581.

    Article  CAS  Google Scholar 

  • Pate R.R., Trost S.G., Dowda M., et al. Tracking of physical activity, physical inactivity, and heath-related physical fitness in rural youth. Pediat Exer Sci 1999: 11: 364–376.

    Article  Google Scholar 

  • Raitakari O.T., Porkka K.V.K., Taimela S., et al. Effects of persistent physical activity and inactivity on coronary risk factors in children and young adults. (The Cardiovascular Risk in Young Finns Study). Am J Epidemiol 1994: 140: 195–205.

    Article  CAS  Google Scholar 

  • Richmond H.M., Palma T., Langstaff J., et al. Further refinements and testing of APEX3.0: EPA's population exposure model for criteria and air toxic inhalation exposures. Poster presented at the Annual Meeting of the International Society of Exposure Analysis, Vancouver, Canada, August, 2002.

  • SAB. Science Advisory Board. NATA–Evaluating the National Scale Air Toxics Assessment: 1996 Data–An SAB Advisory. SAB (EPA-SAB-EC-ADV-02-001), Washington DC. Available at: www.epa.gov/science1/fiscal02.htm, 2001.

  • Schwab M., Colomé S.D., Spengler J.D., et al. Activity patterns applied to pollutant exposure assessment: data from a personal monitoring study in Los Angeles. Tox Ind Health 1990: 6: 517–532.

    CAS  Google Scholar 

  • Schwab M., McDermott A., and Spengler J.D. Using longitudinal data to understand children's activity patterns in an exposure context: data from the Kanawha County health study. Environ Int 1992: 18: 173–189.

    Article  CAS  Google Scholar 

  • Schwab M., Terblanche A.P.S., and Spengler J.D. Self-reported exertion levels on time/activity diaries: application to exposure assessment. J Exp Anal Environ Epidemiol 1991: 1: 339–356.

    CAS  Google Scholar 

  • Sternfeld B., Sidney S., Jacobs Jr D.R., et al. Seven-year changes in physical fitness, physical activity, and lipid profile in the CARDIA Study. Ann Epidemiol 1999: 9: 25–33.

    Article  CAS  Google Scholar 

  • Stofan J.R., DiPietro L., Davis D., et al. Physical activity patterns associated with cardiorespiratory fitness and reduced mortality: The Aerobics Center Longitudinal Study. Amer J Public Health 1998: 88: 1807–1813.

    Article  CAS  Google Scholar 

  • Trost S.G., Pate R.R., Freedson P.S., et al. Using objective physical activity measures with youth: how many days of monitoring are needed? Med Sci Sports Exer 2000: 32: 426–431.

    Article  CAS  Google Scholar 

  • Michigan U . Available at: www.isr.umich.edu/frc/childevelopment/home.html, 1997.

  • Van Mechelen W., and Kemper H.C.G. Habitual physical activity in longitudinal perspective. In: Kemper H.C.G. (Ed.) The Amsterdam Growth Study: A Longitudinal Analysis of Health, Fitness, and Lifestyle. Champaign, IL, Human Kinetics, 1995, pp 135–158.

    Google Scholar 

  • Wiley J.A., Robinson J.P., Cheng Y.-T., et al. Study of Children's Activity Patterns. Survey Research Center, University of California, Berkeley, CA, 1991.

    Google Scholar 

  • Zartarian V.G., Xue J., Ozkaynak H., Glen G., et al. Using the SHEDS model to assess children's exposure and dose from treated wood preservatives on playsets and residential decks. Presentation at EPA's Science Advisory Panel to the Office of Pollution Prevention meeting; Washington DC, August 30, 2002.

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Acknowledgements

We thank the children and parents who participated in the Harvard Southern California Chronic Ozone Exposure Study, as well as the following Harvard University staff members: A. Geyh, J. Arnold, C. Bench, D. Burlow, D. Belliveau, L. Kole, M. Palmer-Rhea, L. Sanchez, N. Scopen, and M. Simun. We also thank unidentified staff of the Rim of the World and Upland Unified School Districts who provided us with supplemental information concerning the school calendars for the time period of interest. The work was supported by the National Institute of Environmental Health Sciences Grant RO1-ES06370 and, in part, by the National Institute of Environmental Health Sciences Harvard Center for Environmental Health grant ES000002. In response to a reviewer's comments, we reorganized the paper and undertook additional analyses that hopefully improved it and broadened its applicability.

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  1. Human Exposure and Atmospheric Sciences Division, National Exposure Research Laboratory, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA

    Jianping Xue, Thomas McCurdy & Hâluk Özkaynak

  2. School of Public Health, Harvard University, Boston, Massachusetts, USA

    John Spengler

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  1. Jianping Xue
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Correspondence to Thomas McCurdy.

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Xue, J., McCurdy, T., Spengler, J. et al. Understanding variability in time spent in selected locations for 7–12-year old children. J Expo Sci Environ Epidemiol 14, 222–233 (2004). https://doi.org/10.1038/sj.jea.7500319

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