Susanne James-Burdumy, Nicholas Beyler, Kelley Borradaile, Martha Bleeker, Alyssa Maccarone and Jane Fortson
The Playworks program places coaches in low-income urban schools to engage students in physical activity during recess. The purpose of this study was to estimate the impact of Playworks on students’ physical activity separately for Hispanic, non-Hispanic black, and non-Hispanic white students.
Twenty-seven schools from 6 cities were randomly assigned to treatment and control groups. Accelerometers were used to measure the intensity of students’ physical activity, the number of steps taken, and the percentage of time in moderate-to-vigorous physical activity (MVPA) during recess. The impact of Playworks was estimated by comparing average physical activity outcomes in treatment and control groups.
Compared with non-Hispanic black students in control schools, non-Hispanic black students in Playworks schools recorded 338 more intensity counts per minute, 4.9 more steps per minute, and 6.3 percentage points more time in MVPA during recess. Playworks also had an impact on the number of steps per minute during recess for Hispanic students but no significant impact on the physical activity of non-Hispanic white students.
The impact of Playworks was larger among minority students than among non-Hispanic white students. One possible explanation is that minority students in non-Playworks schools typically engaged in less physical activity, suggesting that there is more room for improvement.
Jared M. Tucker, Greg Welk, Sarah M. Nusser, Nicholas K. Beyler and David Dzewaltowski
This study was designed to develop a prediction algorithm that would allow the Previous Day Physical Activity Recall (PDPAR) to be equated with temporally matched data from an accelerometer.
Participants (n = 121) from a large, school-based intervention wore a validated accelerometer and completed the PDPAR for 3 consecutive days. Physical activity estimates were obtained from PDPAR by totaling 30-minute bouts of activity coded as ≥4 METS. A regression equation was developed in a calibration sample (n = 91) to predict accelerometer minutes of moderate to vigorous physical activity (MVPA) from PDPAR bouts. The regression equation was then applied to a separate, holdout sample (n = 30) to evaluate the utility of the prediction algorithm.
Gender and PDPAR bouts accounted for 36.6% of the variance in accelerometer MVPA. The regression model showed that on average boys obtain 9.0 min of MVPA for each reported PDPAR bout, while girls obtain 4.8 min of MVPA per bout. When applied to the holdout sample, predicted minutes of MVPA from the models showed good agreement with accelerometer minutes (r = .81).
The prediction equation provides a valid and useful metric to aid in the interpretation of PDPAR results.
Miguel A. Calabro, Gregory J. Welk, Alicia L. Carriquiry, Sarah M. Nusser, Nicholas K. Beyler and Charles E. Matthews
The purpose of this study was to examine the validity of a computerized 24-hour physical activity recall instrument (24PAR).
Participants (n = 20) wore 2 pattern-recognition activity monitors (an IDEEA and a SenseWear Pro Armband) for a 24-hour period and then completed the 24PAR the following morning. Participants completed 2 trials, 1 while maintaining a prospective diary of their activities and 1 without a diary. The trials were counterbalanced and completed within a week from each other. Estimates of energy expenditure (EE) and minutes of moderate-to-vigorous physical activity (MVPA) were compared with the criterion measures using 3-way (method by gender by trial) mixed-model ANOVA analyses.
For EE, pairwise correlations were high (r > .88), and there were no differences in estimates across methods. Estimates of MVPA were more variable, but correlations were still in the moderate to high range (r > .57). Average activity levels were significantly higher on the logging trial, but there was no significant difference in the accuracy of self-report on days with and without logging.
The results of this study support the overall utility of the 24PAR for group-level estimates of daily EE and MVPA.
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.