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Introduction: Calibration equations offer potential to improve the accuracy and utility of self-report measures of physical activity (PA) and sedentary behavior (SB) by rescaling 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 Study., Methods: Participants in the Physical Activity Measurement Study project completed replicate single-day trials that involved wearing a Sensewear armband (SWA) monitor for 24 h followed by a telephone administered 24-h PA 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 body mass index. 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., Conclusions: The results demonstrate that simple regression models can be used to statistically adjust for overestimation 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.

Purpose: To assess the relationship between self-reported and objectively measured physical activity (PA) and metabolic syndrome and its risk factors in U.S. adults., Design: A cross-sectional design was used for this study., Setting: The study was set among a nationally representative sample of U.S. adults., Subjects: Adults, ages 20 years and older, from the National Health and Nutrition Examination Survey (NHANES) 2003-2006 (n = 5580) participated in the study., Measures: PA measures included minutes per week of moderate plus vigorous PA estimated by self-report (MVPAsr), total 7-day accelerometry (MVPAa), and accelerometer-based MVPA performed in 10-minute bouts (MVPAb). Risk factors for metabolic syndrome included blood pressure, high-density lipoprotein cholesterol, triglycerides, glucose, and waist circumference., Analysis: Odds ratios (ORs) for having metabolic syndrome were calculated for men and women who met the Physical Activity Guidelines for Americans compared to those who did not., Results: Women who did not meet the PA guidelines had significantly greater odds of having metabolic syndrome according to MVPAsr (OR = 2.20; 95% confidence interval [CI] = 1.65-2.94), MVPAa (OR = 4.40; 95% CI = 2.65-7.31), and MVPAb (OR = 2.91; 95% CI = 1.42-5.96). Men had significantly higher odds of having metabolic syndrome according to MVPAa (OR = 2.57; 95% CI = 1.91-3.45) and MVPAb (OR = 2.83; 95% CI = 1.55-5.17), but not MVPAsr. These ORs remained significant after adjusting for all potential confounders except body mass index, after which only MVPAsr in women and MVPAb in men remained significant., Conclusion: 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.

Background: The 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)., Methods: A 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)., Results: A 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)., Conclusions: These results suggest that the calibrated PAQ may be a valid alternative tool to activity monitoring instruments for estimating %MVPA in groups of youth.

Background: 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., Methods: 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., Results: 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., Conclusions: Modeling measurement error in recall data can be used to provide more accurate estimates of long-term activity behavior.

Background: To date, no study has objectively measured physical activity levels among U.S. adults according to the 2008 Physical Activity Guidelines for Americans (PAGA)., Purpose: The purpose of this study was to assess self-reported and objectively measured physical activity among U.S. adults according to the PAGA., Methods: Using data from the NHANES 2005-2006, the PAGA were assessed using three physical activity calculations: moderate plus vigorous physical activity ≥150 minutes/week (MVPA); moderate plus two instances of vigorous physical activity ≥150 minutes/week (M2VPA); and time spent above 3 METs ≥500 MET-minutes/week (METPA). Self-reported physical activity included leisure, transportation, and household activities. Objective activity was measured using Actigraph accelerometers that were worn for 7 consecutive days. Analyses were conducted in 2009-2010., Results: U.S. adults reported 324.5 ± 18.6 minutes/week (M ± SE) of moderate physical activity and 73.6 ± 3.9 minutes/week of vigorous physical activity, although accelerometry estimates were 45.1 ± 4.6 minutes/week of moderate physical activity and 18.6 ± 6.6 minutes/week of vigorous physical activity. The proportion of adults meeting the PAGA according to M2VPA was 62.0% for self-report and 9.6% for accelerometry., Conclusions: According to the NHANES 2005-2006, fewer than 10% of U.S. adults met the PAGA according to accelerometry. However, physical activity estimates vary substantially depending on whether self-reported or measured via accelerometer., (Copyright © 2011 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

Background: 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., Methods: 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., Results: 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)., Conclusions: The prediction equation provides a valid and useful metric to aid in the interpretation of PDPAR results., (©2011 Human Kinetics, Inc.)

Purpose: The purpose of this study was to examine the validity of a computerized 24-hour physical activity recall instrument (24PAR)., Methods: 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., Results: 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., Conclusions: The results of this study support the overall utility of the 24PAR for group-level estimates of daily EE and MVPA.