BackgroundThis study examined if family and friend support predicted adolescent physical activity (PA) across a five-year time span.MethodsThe Iowa Bone Development Study collected objective measures of physical activity and self-report of physical activity psychosocial factors at ages 13 (n = 306), 15 (n = 356), and 17 yr (n = 317). Total moderate and vigorous-intensity PA (MVPA) and MVPA after 3 pm on weekdays (MVPA-PM Weekday) were measured using ActiGraph accelerometers. Family Support for PA and Friend Support for PA scales were measured using the Choices questionnaire. Models were adjusted for SES (mother’s education) and somatic maturity (Mirwald predictive equations for maturity offset). Spearman correlation coefficients examined tracking of scales at ages 13, 15 and 17. Logistic regression estimated the odds ratio for being in the lowest tertile of each scale at age 17 if in the lowest tertile at age 13. Linear mixed regression models investigated associations between these scales and MVPA outcomes over time.ResultsTwo- and five-year intra-variable tracking associations for Family Support and Friend Support scales were moderate (r = 0.32–0.58), except for the comparison between age 13 and age 17 Friend Support for girls, which resulted in a low association (r = 0.26). Boys and girls in the lowest tertile for support at age 13 were more likely to remain in the lowest tertile at age 17 compared to those in the middle and upper tertiles. The regression models indicated that when all other factors were held constant, an increase in family and/or friend support resulted in an increase in both MVPA outcomesConclusionsFrom early to late adolescence, support for PA from the family and/or support from friends results in higher levels of total and discretionary MVPA. However, the importance of support in predicting MVPA decreased with age.

Purpose To assess association between lower body muscle power and bone strength, as well as the mediating effect of muscle cross-sectional area (MCSA) on that association. Methods Participants (N=141 males; 162 females) were approximately 17 years. Muscle power was predicted using vertical jump and the Sayers equation. Using peripheral quantitative computed tomography (pQCT), bone strength indices were obtained at two locations of the tibia, corresponding to primary stressors acting upon each site: bone strength index for compression (BSI) at the distal 4% site; density-weighted polar section modulus strength-strain index [SSIp] and cortical bone area (CoA) at the 66% mid-shaft site for torsion. Muscle cross-sectional area (MCSA) was measured at the 66% site. Pearson bivariate and partial correlation coefficients were estimated to quantify the strength of the associations among variables. Direct and indirect mediation model effects were estimated and 95% bootstrap confidence intervals were constructed to test the causal hypothesis. Height and maturity were examined as covariates. Results Pearson correlation coefficients among muscle power, MCSA, and bone strength were statistically significant (p<0.01) and ranged from r=0.54 to 0.78. After adjustment for covariates, associations were reduced (r=0.37 to 0.69) (p<0.01). Mediation models for males for BSI, SSIp, and CoA accounted for 38%, 66%, and 54% of the variance in bone strength, respectively. Models for females for BSI, SSIp, and CoA accounted for 46%, 77%, and 66% of the variance, respectively. Conclusions We found strong and consistent associations, as well as direct and indirect pathways, among muscle power, MCSA, and tibia strength. These results support the use of muscle power as a component of health-related fitness in bone health interventions for older adolescents.

BackgroundTracking studies determine the stability and predictability of specific phenomena. This study examined tracking of TV viewing (TV) and video game use (VG) from middle childhood through early adolescence after adjusting for moderate and vigorous physical activity (MVPA), percentage of body fat (% BF), and maturity.MethodsTV viewing and VG use were measured at ages 5, 8, 11, and 13 (n = 434) via parental- and self-report. MVPA was measured using the Actigraph, % BF using dual-energy x-ray absorptiometry, and maturity via Mirwald predictive equations. Generalized Estimating Equations (GEE) were used to assess stability and logistic regression was used to predict children "at risk" for maintaining sedentary behaviors. Additional models examined tracking only in overfat children (boys ≥ 25% BF; girls ≥ 32% BF). Data were collected from 1998 to 2007 and analyzed in 2010.ResultsThe adjusted stability coefficients (GEE) for TV viewing were 0.35 (95% CI = 0.26, 0.44) for boys, 0.32 (0.23, 0.40) for girls, and 0.45 (0.27, 0.64) for overfat. For VG use, the adjusted stability coefficients were 0.14 (0.05, 0.24) for boys, 0.24 (0.10, 0.38) for girls, and 0.29 (0.08, 0.50) for overfat. The adjusted odds ratios (OR) for TV viewing were 3.2 (2.0, 5.2) for boys, 2.9 (1.9, 4.6) for girls, and 6.2 (2.2, 17.2) for overfat. For VG use, the OR were 1.8 (1.1, 3.1) for boys, 3.5 (2.1, 5.8) for girls, and 1.9 (0.6, 6.1) for overfat.ConclusionsTV viewing and VG use are moderately stable throughout childhood and predictive of later behavior. TV viewing appears to be more stable in younger children than VG use and more predictive of later behavior. Since habitual patterns of sedentarism in young children tend to continue to adolescence, early intervention strategies, particularly to reduce TV viewing, are warranted.

This study examined the association between physical activity (PA) and bone mineral content (BMC; gram) from middle childhood to middle adolescence and compared the impact of vigorous-intensity PA (VPA) over moderate- to vigorous-intensity PA (MVPA). Participants from the Iowa bone development study were examined at ages 5, 8, 11, 13, and 15 years (n = 369, 449, 452, 410, and 307, respectively). MVPA and VPA (minutes per day) were measured using ActiGraph accelerometers. Anthropometry was used to measure body size and somatic maturity. Spine BMC and hip BMC were measured via dual-energy x-ray absorptiometry. Sex-specific multi-level linear models were fit for spine BMC and hip BMC, adjusted for weight (kilogram), height (centimeter), linear age (year), non-linear age (year2), and maturity (pre peak height velocity vs. at/post peak height velocity). The interaction effects of PA × maturity and PA × age were tested. We also examined differences in spine BMC and hip BMC between the least (10th percentile) and most (90th percentile) active participants at each examination period. Results indicated that PA added to prediction of BMC throughout the 10-year follow-up, except MVPA, did not predict spine BMC in females. Maturity and age neither modify the PA effect for males nor females. At age 5, the males at the 90th percentile for VPA had 8.5% more hip BMC than males in the 10th percentile for VPA. At age 15, this difference was 2.0%. Females at age 5 in the 90th percentile for VPA had 6.1% more hip BMC than those in the 10th percentile for VPA. The age 15 difference was 1.8%. VPA was associated with BMC at weight-bearing skeletal sites from childhood to adolescence, and the effect was not modified by maturity or age. Our findings indicate the importance of early and sustained interventions that focus on VPA. Approaches focused on MVPA may be inadequate for optimal bone health, particularly for females.

BackgroundSelf-report questionnaires are a valuable method of physical activity measurement in public health research; however, accuracy is often lacking. The purpose of this study is to improve the validity of the Global Physical Activity Questionnaire by calibrating it to 7 days of accelerometer measured physical activity and sedentary behavior.MethodsParticipants (n = 108) wore an ActiGraph GT9X Link on their non-dominant wrist for 7 days. Following the accelerometer wear period, participants completed a telephone Global Physical Activity Questionnaire with a research assistant. Data were split into training and testing samples, and multivariable linear regression models built using functions of the GPAQ self-report data to predict ActiGraph measured physical activity and sedentary behavior. Models were evaluated with the testing sample and an independent validation sample (n = 120) using Mean Squared Prediction Errors.ResultsThe prediction models utilized sedentary behavior, and moderate- and vigorous-intensity physical activity self-reported scores from the questionnaire, and participant age. Transformations of each variable, as well as break point analysis were considered. Prediction errors were reduced by 77.7–80.6% for sedentary behavior and 61.3–98.6% for physical activity by using the multivariable linear regression models over raw questionnaire scores.ConclusionsThis research demonstrates the utility of calibrating self-report questionnaire data to objective measures to improve estimates of physical activity and sedentary behavior. It provides an understanding of the divide between objective and subjective measures, and provides a means to utilize the two methods as a unified measure.