Literature DB >> 10925827

Measurement issues in the assessment of physical activity in children.

G J Welk1, C B Corbin, D Dale.   

Abstract

This paper reviewed the nature of children's physical activity patterns and how the unique nature of children can impact the assessment of physical activity. To accurately assess children's activity patterns, an instrument must be sensitive enough to detect, code, or record sporadic and intermittent activity. Care also must be used to select criterion measures that reflect appropriate physical activity guidelines for children. A number of different measurement approaches have been described for assessing children's activity, but no specific method can be identified as the best option for all studies. Selection of an appropriate instrument depends on the specific research question being addressed as well as the relative importance of accuracy and practicality (Baranowski & Simons-Morton, 1991). For example, accurate measures of energy expenditure using doubly-labeled water, indirect calorimetry, or heart rate calibration equations may be needed for certain clinical studies, but the cost and inconvenience would make them impractical for field-based assessments on larger samples. The "accuracy-practicality" trade-off presents a more challenging predicament with children than for adults. In adults, a number of self-report instruments have been found useful for large epidemiological studies or interventions where less precision is needed. Because of developmental differences, especially in ability to think abstractly and perform detailed recall (Going et al., 1999; Sallis, 1991), children are less likely to make accurate self-report assessment than adults. Though self-report methods are still likely to be a principal source of information for many studies, other approaches (or the use of combined measures) may be needed to better characterize children's activity levels. While objective instruments (e.g., direct observation or activity monitoring) require more time and resources than self-report, there are options available to simplify data collection. One approach may be to focus assessments on key times or places that allow children to be active. The time after school, for example, appears to be a critical period that defines their propensity for physical activity (Hager, 1999). Monitoring of entire groups for discrete periods of time (e.g., recess or physical education) may also be useful to understand variability in activity patterns since children would all be exposed to the same stimulus or opportunity to be active. Proxy measures may also be useful in studying activity in children. For example, several studies (Baranowski, Thompson, DuRant, Baranowski, & Puhl, 1993; Sallis et al., 1993) have demonstrated that time spent outside is strongly predictive of activity in children. Involvement in community sports programs may also be a useful proxy measure as sports programs have been found to account for approximately 55-65% of children's moderate to vigorous activity (Katzmarzyk & Malina, 1999). Another option for improving assessments in children is to employ multiple measures of physical activity. A number of studies (Coleman, Saelens, Wiedrich-Smith, Finn, & Epstein, 1997; McMurray et al., 1998; Sallis et al., 1998; Simons-Morton et al., 1994) have reported differences in levels of activity when activity monitors were compared with self-report data. The method of measurement has also been shown to influence the results of studies on the determinants of physical activity in children (Epstein, Paluch, Coleman, Vito, & Anderson, 1996). While we do not currently know which measure is most accurate, reporting the results with different instruments provides a more complete description of children's activity and permit a triangulation of outcomes. In summary, there remains no single way of obtaining a highly accurate account of physical activity or energy expenditure in children. The nature of children's movement patterns, the various types of activities engaged in, and the inherent limitations of each assessment tool limit the ultima

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Year:  2000        PMID: 10925827

Source DB:  PubMed          Journal:  Res Q Exerc Sport        ISSN: 0270-1367            Impact factor:   2.500


  102 in total

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