Adolescent correlates of adult physical activity: a 26-year follow-up.

PURPOSE: It is hypothesized that adolescent physical activity, fitness, anthropometric dimensions, fatness, biological maturity, and family characteristics contribute to the variation in physical activity at 40 yr of age, and that these associations vary with age. METHODS: Subjects were 166 males followed from 1969 to 1996, between the ages of 14 and 40 yr from the Leuven Longitudinal Study on Lifestyle, Fitness and Health. Sports participation, fitness, anthropometric dimensions, fatness, and biological maturity were observed during the growth period. Also, sociocultural characteristics of the family were examined. The work, leisure time, and sport activity index of the Baecke Questionnaire and activity counts of a triaxial accelerometer were used as outcome variables at 40 yr. RESULTS: When upper and lower activity groups (quintiles) at 40 yr were contrasted, moderate associations were found (R2c varied between 0.1419 and 0.3736). No or low associations were found with the leisure time index. Body dimensions, fitness scores, sports practice, and family characteristics contributed to the explained variance in work, sport index, and activity counts. Multiple correlations were low (R2 = 0.037-0.085) for the work and leisure time activities, and were somewhat higher (R2 = 0.06-0.156) for the sport index and the activity counts in the total sample. CONCLUSION: Adolescent somatic dimensions, fitness, sports participation, parental sociocultural characteristics, and sport participation contributed to a small-to-moderate extent to the contrast between high and low active adults.

Growth in peak aerobic power during adolescence.

PURPOSE: To model the growth of peak aerobic power during adolescence in both sexes followed longitudinally from 10 to 18 yr. METHODS: Peak aerobic power (peak VO2) was measured annually during a maximal treadmill test with the Bruce protocol. Height and weight were measured semiannually. The Preece-Baines Model I growth function was used to fit curves to data for individuals with >/= six observations for peak aerobic power to estimate age at peak velocity (PV) for peak VO2 (age at PVPVO2), PVPVO2 (L x min(-1) x yr(-1)), and value at PVPVO2 (L x min(-1)) for each individual. Curves were successfully fitted for 83 individuals (48 males, 35 females). The model was also fitted to individual data for height and weight to estimate ages at peak height velocity (PHV) and peak weight velocity (PWV). Age at PVPVO2 was compared with ages at PHV and PWV. Pearson correlation coefficients were calculated between ages at PV and PV for peak VO2, height, and weight. RESULTS: Mean ages at PVPVO2 are 12.3 +/- 1.2 yr for females and 14.1 +/- 1.2 yr for males. Peak VO2 increases in both sexes throughout adolescence, with males having higher values than females at all ages. Age at PVPVO2 occurs nearly coincident with PHV and before PWV in both sexes. Correlation coefficients among ages at PHV, PWV, and PVPVO2 suggest a general maturity factor for body size and aerobic power. CONCLUSION: Growth in peak VO2 exhibits a clear growth spurt in both sexes during adolescence. The growth spurt occurs earlier in females but is of greater magnitude in males.

Influence of participation in high-impact sports during adolescence and adulthood on bone mineral density in middle-aged men: a 27-year follow-up study.

This study examined whether participation in high-impact sports during adolescence and adulthood contributes to bone health in males aged 40 years. Data were analyzed on 154 Belgian men aged 13 years at study onset in 1969 and aged 40 years at the end of the 27-year follow-up. In a second analysis, subjects were divided into three groups according to their sports participation history: participation during adolescence and adulthood in high-impact sports (HH; n=18), participation during adolescence in high-impact sports and during adulthood in nonimpact sports or no sports (HN; n=15), and participation during adolescence and adulthood in nonimpact sports or no sports (NN; n=14). Body mass and impact loading during adulthood were significant predictors of total body bone mineral density (BMD) and lumbar spine BMD. Analysis of variance revealed significant differences for lumbar spine BMD between the HH (1.12 g/cm2) group and the HN (1.01 g/cm2) and NN (0.99 g/cm2) groups (F=5.07, p=0.01). Total body BMD was also higher in the HH group at age 40 years, but not significantly (F=3.17, p=0.0515). Covariance analyses for total body BMD and lumbar spine BMD, with body mass and time spent participating in sports as covariates, confirmed these results. Continued participation in impact sports is beneficial for the skeletal health of males aged 40 years.

Association between bone mineral density (DXA), body structure, and body composition in middle-aged men.

The association between bone mass, body structure, and body composition was explored in 156 men, 40 years of AGE. Bone mineral density (total body, lumbar spine, left arm, and left leg) was obtained by dual-energy X-ray absorptiometry (DXA; Hologic QDR 4500A). Body structure was determined from a battery of anthropometric dimensions with a principal components analysis. Body composition was estimated with DXA. From the 24 anthropometric dimensions, three components were extracted and identified as muscle, fat, and skeletal length. Significant correlations between the muscle component and all BMD measurements (r = 0.28-0.44) were obtained. Except for BMD of the left arm, which correlated significantly, but negatively, with the fat component (r = -0.16), no significant relations were found between the fat component and BMD. There were significant correlations between lean mass, fat mass, and BMD measurements. The correlations were higher between lean mass and BMD (r = 0.22-0.44) than between fat mass and BMD (r = 0.08-0.24). The multiple regression analysis revealed that except for BMD of the left arm only lean mass or the muscle component, and not fat mass or the fat component, were independent predictors of BMD. It is concluded that the principal anthropometric determinant of BMD in middle-aged men is lean mass or muscle.

Associations between daily physical activity and physical fitness in Flemish males: A cross-sectional analysis.

The relationship of physical activity to several components of physical fitness was investigated in a sample of 166 males 40 years of age. In addition to Pearson correlations, multivariate canonical correlations were calculated. Physical activity during work (work index), sport (sport index), and leisure time (leisure time index) was assessed by the Baecke questionnaire. Physical fitness included cardiorespiratory fitness measures, the body mass index (BMI), the sum of seven skinfold thicknesses (SKI), percentage body fat (PFAT), balance, and several tests of muscle strength and endurance, flexibility, and speed of limb movement. More than 86% of the variance was shared by the two first canonical variables. The first canonical variable can be interpreted as a health-related fitness function. Carciorespiratory fitness, balance, speed of limb movement, explosive strength, and trunk muscle strength are clearly related to this function. From the physical activity measures, the Baecke sport index correlated significantly with this health-related fitness function. The second canonical variable can be explained as a fatness function, since body weight, BMI, SKI, and PFAT showed the highest correlations with the variable. The Baecke work index was inversely related to this canonical variable. The sample was also divided into physical activity groups in order to look for differences in physical fitness. The data indicate that physical activity during work was modestly, but inversely related to adiposity. Sport activity was beneficially associated to several fitness components, including cardiorespiratory fitness, trunk muscle strength, and upper body muscular endurance. Am. J. Hum. Biol. 11:587-597, 1999. Copyright 1999 Wiley-Liss, Inc.