PURPOSE: To examine whether there are differences between demographic, behavioral, and biological variables for those with invalid accelerometry data (IAD) and those with valid accelerometry data (VAD). DESIGN: Cross-sectional. SETTING: Data from 2003-2006 National Health and Nutrition Examination Survey (NHANES) were used. SUBJECTS: Participants included 1,315 children (i.e., 6-11 years) with VAD and 534 children with IAD and 1,859 adolescents (i.e., 12-17 years) with VAD and 1,057 with IAD. MEASURES: Physical activity (PA) was measured using an accelerometer, with questionnaires used to assess demographic and behavioral variables and biological parameters assessed from a blood sample. ANALYSIS: Wald and design-based likelihood ratio tests and logistic regression were used to assess differences between those subjects with IAD and those with VAD. RESULTS: After adjustments, overweight children, compared to normal weight children, were 1.6 (odds ratio [OR] = 1.67; 95% confidence interval [CI]: 1.22-2.29) times more likely to have IAD. After adjustments, and as an example, adolescents engaging in 4 or more hours of computer use per day, compared to no computer use, were 2.6 (OR = 2.6; 95% CI: 1.38-5.18) times more likely to have IAD. CONCLUSION: Excluding youth with IAD may introduce bias, limit generalizability, and ultimately underestimate the association between PA and health outcomes. Future research is needed to identify reasons for poor monitoring compliance.
PURPOSE: To examine whether there are differences between demographic, behavioral, and biological variables for those with invalid accelerometry data (IAD) and those with valid accelerometry data (VAD). DESIGN: Cross-sectional. SETTING: Data from 2003-2006 National Health and Nutrition Examination Survey (NHANES) were used. SUBJECTS:Participants included 1,315 children (i.e., 6-11 years) with VAD and 534 children with IAD and 1,859 adolescents (i.e., 12-17 years) with VAD and 1,057 with IAD. MEASURES: Physical activity (PA) was measured using an accelerometer, with questionnaires used to assess demographic and behavioral variables and biological parameters assessed from a blood sample. ANALYSIS: Wald and design-based likelihood ratio tests and logistic regression were used to assess differences between those subjects with IAD and those with VAD. RESULTS: After adjustments, overweight children, compared to normal weight children, were 1.6 (odds ratio [OR] = 1.67; 95% confidence interval [CI]: 1.22-2.29) times more likely to have IAD. After adjustments, and as an example, adolescents engaging in 4 or more hours of computer use per day, compared to no computer use, were 2.6 (OR = 2.6; 95% CI: 1.38-5.18) times more likely to have IAD. CONCLUSION: Excluding youth with IAD may introduce bias, limit generalizability, and ultimately underestimate the association between PA and health outcomes. Future research is needed to identify reasons for poor monitoring compliance.
Authors: Cyrelle R Fermin; Arthur M Lee; Stephanie L Filipp; Matthew J Gurka; Mark D DeBoer Journal: Metab Syndr Relat Disord Date: 2017-05-18 Impact factor: 1.894
Authors: Erin R Hager; Margarita S Treuth; Candice Gormely; LaShawna Epps; Soren Snitker; Maureen M Black Journal: Res Q Exerc Sport Date: 2015-08-19 Impact factor: 2.500