Dietary patterns established during adolescence might play a role in adulthood disease. We examined the stability of dietary patterns (DPs) from childhood through adolescence and into young adulthood ...(from age 8 to 34 years). Data from 130 participants (53 females) of Saskatchewan Pediatric Bone Mineral Accrual Study (aged 8-15 years, at baseline) were included. Multiple 24-h recalls were collected annually from 1991 to 1997, 2002 to 2005, and 2010 and 2011. Using principal component analysis, "Vegetarian-style", "Western-like", "High-fat, high-protein", "Mixed", and "Snack" DPs were derived at baseline. Applied DP scores for all annual measurements were calculated using factor loading of baseline DPs and energy-adjusted food group intakes. We analyzed data using generalized estimating equations. The tracking coefficient represents correlation between baseline dietary pattern scores and all other follow-up dietary pattern scores. We found a moderate tracking for the "Vegetarian-style" (β = 0.44,
< 0.001) and "High-fat, high-protein" (β = 0.39,
< 0.001) DPs in females and "Vegetarian-style" DP (β = 0.30,
< 0.001) in males. The remaining DPs showed poor-to-fair tracking in both sexes. No tracking for "Western-like" DP in females was observed. Assessing overall change in DP scores from childhood to young adulthood showed an increasing trend in adherence to "Vegetarian-style" DP and decreasing trend in adherence to "High-fat, high-protein" DP by age in both sexes (
< 0.001), while "Western-like" and "Mixed" DP scores increased only in males (
< 0.001). These findings suggest that healthy dietary habits established during childhood and adolescence moderately continue into adulthood.
Although technical skills are a prerequisite for success in basketball, little is known about how they develop over time. In this study, we model the trajectories of technical skill development in ...young basketball players and investigate the effects of training experience, training volume, body composition, maturity status, physical performance, and club characteristics on skill development. A total of 264 male basketballers from five age-cohorts (11 to 15 years of age) were followed consecutively over three years using a mixed-longitudinal design. Technical skills, training experience and volume, basic anthropometrics, body composition, biological maturation and physical performance were assessed bi-annually. A multilevel hierarchical linear model was used for trajectory analysis. Non-linear trends (p < 0.01) were observed in speed shot shooting, control dribble, defensive movement, slalom sprint, and slalom dribble. Being more experienced and physically fitter had a significant (p < 0.05) positive effect on technical skill development; greater fat-free mass negatively affected skills demanding quick running and rapid changes of direction with or without the ball (p 0.05). Moreover, belonging to different clubs had no significant influence on the technical skills trajectories of players. Our findings highlight the important role that individual differences play, over and beyond club structure, in developing skills. Findings improve our understanding on how technical skills develop during adolescence through training, growth, and biological maturation.
The biological maturation (BM) analyzed by peak height velocity (PHV) and bone age (BA), and lean body mass has been associated with the strength and muscle power of young athletes. However, the ...ability of BM (PHV and BA) and LM markers to predict muscle strength and power in young athletes remains uncertain. The Aim was determine the predicting power of BM markers (PHV and BA) and LM in relation to muscle power of upper and lower limbs and muscle strength of upper limbs in adolescent athletes at puberty. Ninety-two adolescent athletes (both sexes; age 12.4 ± 1.02 years) were assessed for body composition by dual-energy X-ray absorptiometry (DXA). Power of upper limbs (ULP), force handgrip (HG), vertical jump (VJ) and countermovement jump (CMJ) were recorded. BM was predicted by mathematical models to estimate PHV and BA. Multilayer artificial neural network analyses (MLP's) were used to determine the power of prediction of LM, PHV and BA on muscle power and strength of upper- and lower-limbs of the athletes. LM, BA and PHV were associated with HG (r>0.74, p0.60, p0.55, p0.53, p0.60, p<0.05) with BA and with PHV (r<0.83, p 72% of probability to predict the muscle power of upper- and lower-limbs, and the strength of the upper limbs; whereas PHV provides > 43% and bone age >64% in both female and male adolescent athletes. We identified that, like PHV and BA, LM is a strong predictor of low cost of both upper limbs muscle strength and upper and lower limbs power in adolescent athletes.
Peak adolescent fracture incidence at the distal end of the radius coincides with a decline in size‐corrected BMD in both boys and girls. Peak gains in bone area preceded peak gains in BMC in a ...longitudinal sample of boys and girls, supporting the theory that the dissociation between skeletal expansion and skeletal mineralization results in a period of relative bone weakness.
Introduction: The high incidence of fracture in adolescence may be related to a period of relative skeletal fragility resulting from dissociation between bone expansion and bone mineralization during the growing years. The aim of this study was to examine the relationship between changes in size‐corrected BMD (BMDsc) and peak distal radius fracture incidence in boys and girls.
Materials and Methods: Subjects were 41 boys and 46 girls measured annually (DXA; Hologic 2000) over the adolescent growth period and again in young adulthood. Ages of peak height velocity (PHV), peak BMC velocity (PBMCV), and peak bone area (BA) velocity (PBAV) were determined for each child. To control for maturational differences, subjects were aligned on PHV. BMDsc was calculated by first regressing the natural logarithms of BMC and BA. The power coefficient (pc) values from this analysis were used as follows: BMDsc =BMC/BApc.
Results: BMDsc decreased significantly before the age of PHV and then increased until 4 years after PHV. The peak rates in radial fractures (reported from previous work) in both boys and girls coincided with the age of negative velocity in BMDsc; the age of peak BA velocity (PBAV) preceded the age of peak BMC velocity (PBMCV) by 0.5 years in both boys and girls.
Conclusions: There is a clear dissociation between PBMCV and PBAV in boys and girls. BMDsc declines before age of PHV before rebounding after PHV. The timing of these events coincides directly with reported fracture rates of the distal end of the radius. Thus, the results support the theory that there is a period of relative skeletal weakness during the adolescent growth period caused, in part, by a draw on cortical bone to meet the mineral demands of the expanding skeleton resulting in a temporary increased fracture risk.
Enhancing a Somatic Maturity Prediction Model Moore, Sarah A; McKay, Heather A; Macdonald, Heather ...
Medicine and science in sports and exercise,
2015-August, Letnik:
47, Številka:
8
Journal Article
Recenzirano
Assessing biological maturity in studies of children is challenging. Sex-specific regression equations developed using anthropometric measures are widely used to predict somatic maturity. However, ...prediction accuracy was not established in external samples. Thus, we aimed to evaluate the fit of these equations, assess for overfitting (adjusting as necessary), and calibrate using external samples.
We evaluated potential overfitting using the original Pediatric Bone Mineral Accrual Study (PBMAS; 79 boys and 72 girls; 7.5-17.5 yr). We assessed change in R and standard error of the estimate (SEE) with the addition of predictor variables. We determined the effect of within-subject correlation using cluster-robust variance and fivefold random splitting followed by forward-stepwise regression. We used dominant predictors from these splits to assess predictive abilities of various models. We calibrated using participants from the Healthy Bones Study III (HBS-III; 42 boys and 39 girls; 8.9-18.9 yr) and Harpenden Growth Study (HGS; 38 boys and 32 girls; 6.5-19.1 yr).
Change in R and SEE was negligible when later predictors were added during step-by-step refitting of the original equations, suggesting overfitting. After redevelopment, new models included age × sitting height for boys (R, 0.91; SEE, 0.51) and age × height for girls (R, 0.90; SEE, 0.52). These models calibrated well in external samples; HBS boys: b0, 0.04 (0.05); b1, 0.98 (0.03); RMSE, 0.89; HBS girls: b0, 0.35 (0.04); b1, 1.01 (0.02); RMSE, 0.65; HGS boys: b0, -0.20 (0.02); b1, 1.02 (0.01); RMSE, 0.85; HGS girls: b0, -0.02 (0.03); b1, 0.97 (0.02); RMSE, 0.70; where b0 equals calibration intercept (standard error (SE)) and b1 equals calibration slope (SE), and RMSE equals root mean squared error (of prediction). We subsequently developed an age × height alternate for boys, allowing for predictions without sitting height.
Our equations provided good fits in external samples and provide an alternative to commonly used models. Original prediction equations were simplified with no meaningful increase in estimation error.
The aim of this study was to examine the relationships among biological maturity, physical size, relative age (i.e. birth date), and selection into a male Canadian provincial age-banded ice hockey ...team. In 2003, 619 male ice hockey players aged 14 - 15 years attended Saskatchewan provincial team selection camps, 281 of whom participated in the present study. Data from 93 age-matched controls were obtained from the Saskatchewan Pediatric Bone Mineral Accrual Study (1991 - 1997). During the initial selection camps, birth dates, heights, sitting heights, and body masses were recorded. Age at peak height velocity, an indicator of biological maturity, was determined in the controls and predicted in the ice hockey players. Data were analysed using one-way analysis of variance, logistic regression, and a Kolmogorov-Smirnov test. The ice hockey players selected for the final team were taller, heavier, and more mature (P < 0.05) than both the unselected players and the age-matched controls. Furthermore, age at peak height velocity predicted (P < 0.05) being selected at the first and second selection camps. The birth dates of those players selected for the team were positively skewed, with the majority of those selected being born in the months January to June. In conclusion, team selectors appear to preferentially select early maturing male ice hockey players who have birth dates early in the selection year.
Physical activity in adolescence is beneficial for increasing bone mineral accrual; however, it's unclear whether these benefits persist into adulthood. This prospective study investigated whether ...physically active adolescents maintained their higher bone mineral content (BMC) into the third decade of life when compared to their less active peers. Data were from 154 subjects (82 females and 72 males) who participated in the University of Saskatchewan's Pediatric Bone Mineral Accrual Study (1991-1997), entry age 8 to 15 years. Participants returned for follow-up as young adults (2002-2006), follow-up age 23 to 30 years. Dual energy X-ray absorptiometry was used to measure BMC of total body (TB), lumbar spine (LS), total hip (TH) and femoral neck (FN) annually from 1991 to 1997 and from 2002 to 2006. Peak height velocity (PHV) was determined for each child as a measure of maturity. Age and gender-specific activity Z-scores were calculated for each participant based on the mean physical activity scores obtained from bi-annual questionnaire data during childhood and adolescence. Subjects were ranked into three adolescent activity groups: active, average and inactive (top, middle two, and bottom quartiles, respectively). Analysis of covariance (ANCOVA) was used to compare adjusted TB, LS, TH and FN BMC across the three adolescent activity groups at 1 year post PHV and in young adulthood. When compared to the inactive group, active males had 8% greater adjusted BMC at the TB, 13% at the LS and 11% at the TH (p<0.05) in adolescence. Active females also had 8% and 15% more adjusted BMC (p<0.05) at the TB and LS, respectively, during adolescence. In young adulthood the male and female adolescent active groups were still significantly more active than their peers (p>0.05). It was found that active adolescent males had 8-10% more adjusted BMC at the TB, TH and FN (p<0.05) in young adulthood and that active adolescent females had 9% and 10% more adjusted BMC at the TH and FN. These results suggest that the skeletal benefits of physically activity in adolescents are maintained into young adulthood.
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