Stability and Predictability of Video Throughout Childhood: Iowa Bone Development Longitudinal Study

Abstract

540accounting for 36% and 32% of daily wear time, respectively. Linear regression (controlling for testcenter, sex and wear time) showed that physical inactivity increased with increasing age (B=0.7, p<0.001), while light physical activity declined with increasing age (B=-0.5, p<0.001). CONCLUSIONS:Adults and elderly spend the majority of their awake time being physically inactive. Males are more physically inactive compared to females and inactivity increases with increasing age. Knowing the health risks of inactivity, the amount of physical inactivity among adults and elderly is too large to neglect and efforts need to be encouraged in order to reduce inactive time in the general population.(No relationships reported) PURPOSE: Sedentary behaviors are generally assumed to be related to the development of obesity. Tracking studies can help determine the stability and predictability of a sedentary behavior. This information is important to the development of effective intervention strategies. The current study examined tracking of video game use (Video) from middle childhood through early adolescence. METHODS:Video was measured at ages 5, 8, 11, and 13 yr (n = 434) via parental-and self-report. In addition, we measured moderate and vigorous physical activity (MVPA) using the Actigraph, % BF using Hologic dual-energy x-ray absorptiometry, and maturity via Mirwald predictive equations. Unadjusted and adjusted Generalized Estimating Equations (GEE) were used to assess stability and logistic regression to predict children "at risk" for maintaining high Video (1 hr or more video/d) compared to children playing Video less than 1 hr/d. RESULTS:At baseline (age 5 yr) 10.7% boys and 5.3% girls spent at least 1 hr/d playing Video. Time spent in Video increased for both boys and girls over the four measurement periods. At age 13 yr, 66% boys and 18% girls spent at least one hr/d playing video games. Unadjusted GEE coefficients were 0 .15 (95% CI = 0.05, 0.25) for boys, 0.24 (0.09, 0.39) for girls. GEE coefficients adjusted for MVPA, % BF, and maturity were 0.14 (0.05, 0.24) for boys and 0.24 (0.10, 0.38) for girls. The unadjusted odds ratios (OR) were 1.87 (1.1, 3.2) for boys and 3.43 (2.1, 5.8) for girls. The adjusted OR were 1.83 (1.1, 3.1) for boys and 3.46 (2.1, 5.8) for girls. CONCLUSION:Throughout childhood and into early adolescence more boys play Video than girls; however, playing Video tracks better in girls (when compared to boys). In both boys and girls, the tracking of Video is not influenced by physical activity, adiposity, or maturity. In addition, girls who play 1 hr or more of Video at age 5 are 3.5 times more likely than peers to play Video later in life. These results suggest that the effectiveness of interventions for the reduction of Video use may need to be tailored by gender.Recent epidemiological studies suggest that regularly 'breaking up' sedentary (sitting) time, even with short duration, light-intensity activities, is beneficially associated with several cardio-metabolic biomarkers. However, interv...