BACKGROUND: The association of physical activity (PA), measured 3 ways, and biomarkers were compared in a sample of adolescents. METHODS: PA data were collected on 2 cohorts of adolescents (N = 700) in the Twin Cities, Minnesota, 2007-2008. PA was measured using 2 survey questions [Modified Activity Questionnaire (MAQ)], the 3-Day Physical Activity Recall (3DPAR), and accelerometers. Biomarkers included systolic (SBP) and diastolic blood pressure (DBP), lipids, percent body fat (%BF), and body mass index (BMI) percentile. Bivariate relationships among PA measures and biomarkers were examined followed by generalized estimating equations for multivariate analysis. RESULTS: The 3 measures were significantly correlated with each other (r = .22-.36, P < .001). Controlling for study, puberty, age, and gender, all 3 PA measures were associated with %BF (MAQ = -1.93, P < .001; 3DPAR = -1.64, P < .001; accelerometer = -1.06, P = .001). The MAQ and accelerometers were negatively associated with BMI percentile. None of the 3 PA measures were significantly associated with SBP or lipids. The percentage of adolescents meeting the national PA recommendations varied by instrument. CONCLUSIONS: All 3 instruments demonstrated consistent findings when estimating associations with %BF, but were different for prevalence estimates. Researchers must carefully consider the intended use of PA data when choosing a measurement instrument.
BACKGROUND: The association of physical activity (PA), measured 3 ways, and biomarkers were compared in a sample of adolescents. METHODS: PA data were collected on 2 cohorts of adolescents (N = 700) in the Twin Cities, Minnesota, 2007-2008. PA was measured using 2 survey questions [Modified Activity Questionnaire (MAQ)], the 3-Day Physical Activity Recall (3DPAR), and accelerometers. Biomarkers included systolic (SBP) and diastolic blood pressure (DBP), lipids, percent body fat (%BF), and body mass index (BMI) percentile. Bivariate relationships among PA measures and biomarkers were examined followed by generalized estimating equations for multivariate analysis. RESULTS: The 3 measures were significantly correlated with each other (r = .22-.36, P < .001). Controlling for study, puberty, age, and gender, all 3 PA measures were associated with %BF (MAQ = -1.93, P < .001; 3DPAR = -1.64, P < .001; accelerometer = -1.06, P = .001). The MAQ and accelerometers were negatively associated with BMI percentile. None of the 3 PA measures were significantly associated with SBP or lipids. The percentage of adolescents meeting the national PA recommendations varied by instrument. CONCLUSIONS: All 3 instruments demonstrated consistent findings when estimating associations with %BF, but were different for prevalence estimates. Researchers must carefully consider the intended use of PA data when choosing a measurement instrument.
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