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Modeling measurement error in recall data can be used to provide more accurate estimates of long-term activity behavior.

BackgroundThe utility of self-report measures of physical activity (PA) in youth can be greatly enhanced by calibrating self-report output against objectively measured PA data.This study demonstrates the potential of calibrating self-report output against objectively measured physical activity (PA) in youth by using a commonly used self-report tool called the Physical Activity Questionnaire (PAQ).MethodsA total of 148 participants (grades 4 through 12) from 9 schools (during the 2009–2010 school year) wore an Actigraph accelerometer for 7 days and then completed the PAQ. Multiple linear regression modeling was used on 70% of the available sample to develop a calibration equation and this was cross validated on an independent sample of participants (30% of sample).ResultsA calibration model with age, gender, and PAQ scores explained 40% of the variance in values for the percentage of time in moderate-to-vigorous PA (%MVPA) measured from the accelerometers (%MVPA = 14.56 - (sex*0.98) - (0.84*age) + (1.01*PAQ)). When tested on an independent, hold-out sample, the model estimated %MVPA values that were highly correlated with the recorded accelerometer values (r = .63) and there was no significant difference between the estimated and recorded activity values (mean diff. = 25.3 ± 18.1 min; p = .17).ConclusionsThese results suggest that the calibrated PAQ may be a valid alternative tool to activity monitoring instruments for estimating %MVPA in groups of youth.

Individuals who do not meet the PA guidelines exhibited greater odds of having metabolic syndrome. This relationship tended to be stronger for objective PA measures than for self-report.

The results of this study support the overall utility of the 24PAR for group-level estimates of daily EE and MVPA.

The current findings suggest that Playworks had a significant impact on some measures of girls' physical activity, but no significant impact on measures of boys' physical activity.

Introduction Calibration equations offer potential to improve the accuracy and utility of self-report measures of physical activity (PA) and sedentary behavior (SB) by re-scaling potentially biased estimates. The present study evaluates calibration models designed to estimate PA and SB in a representative sample of adults from the Physical Activity Measurement Survey (PAMS). Methods Participants in the PAMS project completed replicate single day trials that involved wearing a Sensewear armband (SWA) monitor for 24 hours followed by a telephone administered 24-hour physical activity recall (PAR). Comprehensive statistical model selection and validation procedures were used to develop and test separate calibration models designed to predict objectively-measured SB and moderate to vigorous PA (MVPA from self-reported PAR data. Equivalence testing was used to evaluate the equivalence of the model-predicted values with the objective measures in a separate holdout sample. Results The final prediction model for both SB and MVPA included reported time spent in SB and MVPA, as well as terms capturing sex, age, education, and BMI. Cross-validation analyses on an independent sample exhibited high correlations with observed SB (r = 0.72) and MVPA (r = 0.75). Equivalence testing demonstrated that the model-predicted values were statistically equivalent to the corresponding objective values for both SB and MVPA. Conclusion The results demonstrate that simple regression models can be used to statistically adjust for over or underestimation in self-report measures among different segments of the population. The models produced group estimates from the PAR that were statistically equivalent to the observed time spent in SB and MVPA obtained from the objective SWA monitor; however additional work is needed to correct for estimates of individual behavior.

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