Jung-Min Lee, Pedro F. Saint-Maurice, Youngwon Kim, Glenn A. Gaesser and Gregory Welk
The assessment of physical activity (PA) and energy expenditure (EE) in youth is complicated by inherent variability in growth and maturation during childhood and adolescence. This study provides descriptive summaries of the EE of a diverse range of activities in children ages 7 to 13.
A sample of 105 7- to 13-year-old children (boys: 57%, girls: 43%, and Age: 9.9 ± 1.9) performed a series of 12 activities from a pool of 24 activities while being monitored with an indirect calorimetry system.
Across physical activities, averages of VO2 ml·kg·min-1, VO2 L·min-1, EE, and METs ranged from 3.3 to 53.7 ml·kg·min-1, from 0.15 to 3.2 L·min-1, from 0.7 to 15.9 kcal·min-1, 1.5 MET to 7.8 MET, respectively.
The energy costs of the activities varied by age, sex, and BMI status reinforcing the need to consider adjustments when examining the relative intensity of PA in youth.
Richard P. Troiano, Kelley K. Pettee Gabriel, Gregory J. Welk, Neville Owen and Barbara Sternfeld
Advances in device-based measures have led researchers to question the value of reported measures of physical activity or sedentary behavior. The premise of the Workshop on Measurement of Active and Sedentary Behaviors: Closing the Gaps in Self-Report Methods, held in July 2010, was that assessment of behavior by self-report is a valuable approach.
To provide suggestions to optimize the value of reported physical activity and sedentary behavior, we 1) discuss the constructs that devices and reports of behavior can measure, 2) develop a framework to help guide decision-making about the best approach to physical activity and sedentary behavior assessment in a given situation, and 3) address the potential for combining reported behavior methods with device-based monitoring to enhance both approaches.
After participation in a workshop breakout session, coauthors summarized the ideas presented and reached consensus on the material presented here.
To select appropriate physical activity assessment methods and correctly interpret the measures obtained, researchers should carefully consider the purpose for assessment, physical activity constructs of interest, characteristics of the population and measurement tool, and the theoretical link between the exposure and outcome of interest.
Sarah M. Nusser, Nicholas K. Beyler, Gregory J. Welk, Alicia L. Carriquiry, Wayne A. Fuller and Benjamin M.N. King
Physical activity recall instruments provide an inexpensive method of collecting physical activity patterns on a sample of individuals, but they are subject to systematic and random measurement error. Statistical models can be used to estimate measurement error in activity recalls and provide more accurate estimates of usual activity parameters for a population.
We develop a measurement error model for a short-term activity recall that describes the relationship between the recall and an individual’s usual activity over a long period of time. The model includes terms for systematic and random measurement errors. To estimate model parameters, the design should include replicate observations of a concurrent activity recall and an objective monitor measurement on a subsample of respondents.
We illustrate the approach with preliminary data from the Iowa Physical Activity Measurement Study. In this dataset, recalls tend to overestimate actual activity, and measurement errors greatly increase the variance of recalls relative to the person-to-person variation in usual activity. Statistical adjustments are used to remove bias and extraneous variation in estimating the usual activity distribution.
Modeling measurement error in recall data can be used to provide more accurate estimates of long-term activity behavior.