Relation between cardiovascular risk factors at adult age, and physical activity during youth and adulthood: the Leuven Longitudinal Study on Lifestyle, Fitness and Health.

This study investigated the relationship between sports participation/physical activity during youth (13 - 18 years of age) and adulthood (30 - 40 years of age), and cardiovascular risk factors (body fat and fat distribution, blood pressure, lipoprotein levels and cardiorespiratory fitness) at 40 years of age. Subjects were 166 Flemish males from "The Leuven Longitudinal Study on Lifestyle, Fitness and Health". Physical activity was assessed by means of a sports participation inventory and the Tecumseh community Health Study Questionnaire. In addition to correlation and multiple stepwise regression analyses, different groups (at risk, not at risk) were contrasted on sports participation/physical activity parameters using ANOVA. Long-term exposure during adulthood to daily physical activity was slightly related to a low/high risk profile for waist circumference, percent body fatness, triglycerides and peak VO(2). Sports participation during adolescence was not related to levels of cardiovascular risk factors at 40 years of age.

Adolescent physical performance and adult physical activity in Flemish males.

Limited information is available about the associations between adolescent fitness levels and adult physical activity. In the present study, these associations are investigated using different indicators of physical activity. It is hypothesized that both health- and performance-related fitness characteristics, observed during the adolescent period, contribute equally to the explained variance in adult physical activity levels. Subjects were 109 Flemish males followed over a period of 27 years from 13 to 40 years of age in the Leuven Longitudinal Study on Lifestyle Fitness and Health. Performance and health-related fitness characteristics were observed during the growth period and at 40 years of age. The Work Index, Leisure Time Index, and Sport Index of the Baecke questionnaire were used as indicators of physical activity together with triaxial accelerometry. Multiple regression and discriminant analyses contrasting extreme quintiles of activity groupings were used to analyse the associations. Only the Baecke Sport Index showed consistent significant associations (R2 = 0.03 to R2 = 0.23) with adolescent fitness levels observed at 13, 15, and 18 years. When upper and lower quintiles were contrasted, fitness characteristics observed at the three age levels during adolescence were significantly different for each of the three indices of the Baecke questionnaire at 40 years of age. Lowest associations (R2 = 0.09 to R2 = 0.17) were found for the Work Index, followed by the Leisure Time Index (R2 = 0.12 to R2 = 0.28) and Sport Index (R2 = 0.25 to R2 = 0.43). Highest associations were evident for the 18- to 40-year interval. Performance- and health-related fitness characteristics explain equally well the variance in physical activity indicators.

Tracking of physical fitness during adolescence: a panel study in boys.

PURPOSE: To investigate the tracking in physical fitness (PF) viewed as a whole, a multidimensional trait of the subject, and to establish the stability of each factor of PF in adolescence from the perspective of a panel study using the structural equation modeling approach. METHODS: From a sample of 454 boys followed from 12 to 18 yr of age of the Leuven Growth Study, we considered only three consecutive measurement occasions with a mean age of 12.76, 14.69, and 17.73 yr. Physical fitness was evaluated by means of a battery composed of the following tests: plate tapping, sit and reach, vertical jump, arm pull, leg lifts, bent arm hang, and shuttle run. Structural equation models were fitted to the data, namely autoregressive models with latent variables. These models were used to quantify the tracking of PF as a whole and also of the individual marker variables of fitness. RESULTS: Stability estimates of PF as a whole are rather high, beta21 = 0.86 and beta32 = 0.68, with an explained variance of 74% and 73%, respectively. Tracking coefficients represented by disattenuated autocorrelations among the fitness factor gave high results: r1,2 = 0.86; r1,3 = 0.78; and r2,3 = 0.85. CONCLUSIONS: Physical fitness as a whole is highly stable in adolescent years and very predictable from early years. The same is observed for each factor of fitness. Moreover, autoregressive models within the context of structural equation modeling are better suited than simple Pearson or Spearman autocorrelations to study the tracking problem of PF.

Skeletal maturation, somatic growth and physical fitness in girls 6-16 years of age.

The importance of chronological age (CA) and skeletal age (SA) in explaining variation in somatic dimensions, and the independent contributions of CA, SA, stature (ST) and weight (WT) to variability in physical fitness were investigated in a sample of 6593 girls 6-16 years of age. Body dimensions included lengths, breadths, circumferences, skinfolds, and Heath-Carter somatotype, while fitness tests included measures of health- and performance-related fitness, and cardiovascular and lung functions. Age-specific correlations were calculated between SA and anthropometric dimensions, fitness tests and cardiovascular and lung functions, while age-specific stepwise multiple regressions were used to investigate the relative importance of SA, CA, ST and WT in explaining fitness and cardiovascular and lung functions. SA is most highly correlated with lengths and then with breadths, circumferences and skinfolds in this order. SA per se or in interaction with CA is the only significant predictor of somatic characteristics. Among fitness items, physical working capacity and static strength correlate highest with SA. Bent arm hang, leg lifts and sit-ups correlate negatively with SA but values are low, while all other components correlate at non-significant or low levels. Results of the multiple regression analysis indicate that, with few exceptions, CA, SA, ST and WT and their interactions explain less than 10% of the variance in most physical fitness items. However, for PWC, arm pull strength, and bent arm hang, the interaction terms explain between 12% and 67% of the variance.

Development and tracking in fitness components: Leuven longtudinal study on lifestyle, fitness and health.

In the Leuven Growth Study of Belgian Boys the growth and physical performance of Belgian boys followed longitudinally between 12 and 19 years were studied. Subsequently, a subsample (n = 240) of Flemish-speaking males were reexamined at 30 and 35 years. A first question relates to the individual growth patterns in a variety of physical fitness characteristics. The three strength tests (static, functional, explosive) show curves that are qualitatively similar to those for height and weight. Their adolescent spurts occur after the height spurt. Flexibility and the two speed tests appear to reach maximum velocities prior to the height and weight spurts. Longitudinal principal component analysis was applied to the study of growth patterns of several somatic and motor characteristics. The results for height show three components sufficient to provide an adequate representation of the original information. The first component characterizes the general position of an individual growth curve. Components 2 and 3 reflect fluctuation in percentile level during the age period studied and can be conceived as indices of stability and are related to age at peak height velocity (APHV) and peak height velocity (PHV), respectively. Relationships between somatic characteristics, physical performance, and APHV have been studied in a sample of 173 Flemish boys, measured yearly between +/- 13 and +/- 18 years and again as adults at 30 years of age. The sample was divided into three contrasting maturity categories based on the APHV. There are consistent differences among boys of contrasting maturity status during adolescence in body weight, skeletal lengths and breadths, circumferences, and skinfolds on the trunk. There are no differences in skinfolds on the extremities. None of the differences in somatic dimensions and ratios among the three contrasting maturity groups are significant at 30 years of age except those for subscapular skinfold and the trunk/extremity skinfold ratio. During adolescence, speed of limb movement, explosive strength and static strength are negatively related to APHV; thus, early maturers performed better than late maturers. However, between late adolescence and adulthood (30 years), the late maturers not only caught up to the early maturers, but there were significant differences for explosive strength and functional strength in favor of late maturers. Finally, age-specific tracking, using inter-age correlations, of adult health- and performance-related fitness scores were investigated. In addition, the independent contribution of adolescent physical characteristics to the explanation of adult fitness scores was also studied. Tracking between age 13 and age 30 years was moderately high (46% of variance explained) for flexibility, low to moderate (between 19% and 27% of variance explained) for the other fitness parameters and low for pulse recovery and static strength (7% to 11% of variance explained). Between age 18 and age 30 years the tracking was high for flexibility, moderately high for explosive and static strength, and moderate for the other fitness parameters except for pulse recovery. The amount of variance of adult fitness levels explained increased significantly when other characteristics observed during adolescence entered the regressions or discriminant functions.

Prediction of adult stature and noninvasive assessment of biological maturation.

The Tanner-Whitehouse method to predict adult stature uses current stature, current skeletal age (SA), and chronological age (CA), and, if available, change (gain) in stature and SA over the previous year. Since assessment of SA requires invasive techniques, a method is proposed to predict adult stature noninvasively and to use percentage of adult stature as a maturity indicator. Age-specific multiple regression equations were calculated in a sample of 102 Flemish boys 13 through 16 yr who were followed during adolescence and remeasured at 30 yr of age. The proposed procedure, the Beunen-Malina method for prediction of adult stature, includes four somatic dimensions (current stature, sitting height, subscapular skinfold, triceps skinfold) and CA. In this age range multiple correlations (Rs between 0.70 and 0.87) and SEEs (between 3.0 and 4.2 cm) compare favorably with the original Tanner-Whitehouse method. Furthermore, when maturity groups based on percentage of adult stature calculated from the Beunen-Malina predictions are contrasted for somatic dimensions and performance characteristics, differences are similar to those observed when maturity grouping is based on skeletal maturity.

Fatness and physical fitness of girls 7 to 17 years.

A two-fold approach was used to investigate the association between fatness and fitness of girls 7 to 17 years of age: first, age-specific correlations between fatness and measures of health-related and motor fitness, and second, comparisons of fitness levels of girls classified as fat and lean. A representative sample of 6700 between 7 to 17 years was surveyed. Adiposity (fatness) was estimated as the sum of five skinfolds (biceps, triceps, subscapular, suprailiac, medial calf). Physical fitness included health-related items (step test, PWC170, the sit and reach, sit-ups and leg lifts, flexed arm hang) and motor performance items (standing long jump, vertical jump, arm pull strength, flamingo stand, shuttle run, plate tapping). Age-specific partial correlations between fatness and each fitness item, controlling for stature and weight, were calculated. In addition, in each age group the fattest 5% (presumably the obese) and the leanest 5% were compared on each fitness test. After controlling for stature and weight, subcutaneous fatness accounts for variable percentages of the variance in each fitness item. Estimates for health-related fitness items are: cardiorespiratory endurance-step test (3% to 5%) and PWC170 (0% to 16%), flexibility-sit and reach (3% to 8%), functional strength-flexed arm hand (6% to 17%) and abdominal strength-sit-ups/leg lifts (1% to 8%). Corresponding estimates for motor fitness items are more variable: speed of limb movement-plate tapping (0% to 3%), balance-flamingo stand (0% to 5%), speed and agility-shuttle run (2% to 12%), static strength-arm pull (4% to 12%), explosive strength-standing long jump/vertical jump (11% to 18%). At the extremes, the fattest girls have generally poorer levels of health-related and motor fitness.

Size, fatness and relative fat distribution of males of contrasting maturity status during adolescence and as adults.

The somatic characteristics of boys of contrasting biological maturity status during adolescence are compared from 13-18 years and at 30 years of age. Within the mixed longitudinal Leuven Growth Study of Belgian Boys, 173 boys were followed annually from 13-18 years and were subsequently measured at 30 years of age. Age at peak height velocity (PHV) was estimated for 149 boys and the sample was then divided into three contrasting maturity categories based on the age at PHV: early (PHV < 13.37 years), average (PHV between 13.85 and 14.80 years) and late (PHV > 15.27 years) maturers. Using ANOVA for repeated measures and one-way ANOVA, differences in 18 somatic dimensions and five ratios of body proportions and subcutaneous fat distribution among the three maturity groups were tested from 13-18 years and at 30 years of age. There are consistent differences among boys of contrasting breadths, circumferences,and skinfolds on the trunk. There are no differences in skinfolds on the extremities. None of the differences in somatic dimensions and ratios among the three contrasting maturity groups are significant at 30 years of age except those for subscapular skinfold and the trunk/extremity skinfold ratio. Thus, during adolescence and in adulthood, late maturing boys have a distribution of subcutaneous fat that is associated with lower risk for several adult degenerative diseases.

Adiposity and biological maturity in girls 6-16 years of age.

The aim of this study was to investigate the relationships between adiposity and skeletal maturity, relative skeletal maturity and percentage of predicted adult stature. A representative sample of 6,029 Flemish girls aged 6-16 years of age was investigated. Age specific correlations between adiposity and biological maturity indicators were calculated and in each age group the fattest 5% were compared with the leanest 5%. Adiposity was estimated from the sum of five skinfolds (biceps, triceps, subscapular, suprailiac and medial calf). Skeletal age was assessed according to the Tanner-Whitehouse technique. Relative skeletal age was calculated as the difference between skeletal age and chronological age and percentage of predicted adult stature was calculated according to the Tanner-Whitehouse Mark II regression technique. Correlations between adiposity and maturity indicators are positive, but vary between r = 0.00 and r = 0.39. When stature is statistically controlled, correlations are reduced slightly. The 5% fattest girls are equally advanced (0.2 to 1.2 years) as the 5% leanest girls are delayed (0.0 to 0.9 years) in skeletal maturation. Attained statures are consistent with the maturity data and indicate that size differences between fat and lean girls are primarily due to maturity differences. It was concluded that during childhood and adolescence, fatness is associated with advanced and leanness with delayed biological maturity status. This association seems to have long term effects that merit further study.

Fractures, physical activity, and growth velocity in adolescent Belgian boys.

The relationship of fractures to physical activity and growth velocity in stature and metacarpal II bone dimensions was investigated in adolescent Belgian boys. Peak fracture incidence occurred between 12 and 14 yr of age and preceded the age at peak height velocity. The peak fracture rate occurred during mid adolescence (+/- 2 SD of the age at peak height velocity) and was twice as high as the rates before and after this period. The majority of fractures occurred during active participation in sports and general physical activities. The age at peak growth velocity for metacarpal cortical thickness, an indirect measure of bone mineral content, was about 6 months later than the ages at peak height velocity and peak growth velocity for metacarpal length. Peak fracture incidence occurred during a period when the amount of time spent in sports physical activity was low compared with later years. A lag in cortical bone thickness and mineralization, relative to linear skeletal growth, and unknown factors associated with active participation in sports, rather than an increase in the amount of physical activity, appear to be the predominant factors associated with the increased fracture incidence in Belgian boys during the growth spurt.

Physical activity and growth, maturation and performance: a longitudinal study.

The effects of increased physical activity upon physical growth, maturation and performance were investigated in samples of 32 active and 32 nonactive Belgian boys followed longitudinally from 13 to 18 yr of age. Active boys participated in sports activities for more than 5 h.wk-1.yr-1 during each of the first 3 yr of the study, in addition to compulsory physical education. Nonactive boys participated in less than 1.5 h.wk-1.yr-1 during the first 3 yr of the study, but did participate in required school physical education. Anthropometric dimensions included lengths, breadths, circumferences, and skinfolds. A physical fitness test battery was administered at each observation including nine health- and performance-related tests. Skeletal maturation was assessed; sociocultural determinants and sports participation were obtained through written questionnaires verified by a control interview. No significant effects of increased physical activity were observed on growth in somatic dimensions, including skinfolds, age at peak height velocity, skeletal maturation, and most of the physical fitness components. More active boys obtained better results from 14 yr onward only for pulse recuperation and for bent arm hang. These results can be generalized to the average population but do not necessarily apply for highly trained and selected elite athletes.

Age-specific correlation analysis of longitudinal physical fitness levels in men.

This study investigated the age-specific tracking of adult health- and performance-related fitness scores. In addition, the independent contribution of adolescent physical characteristics to the explanation of adult fitness scores was also studied. The sample consisted of 173 adults observed at age 30 years. These subjects had been followed at annual intervals from age 13 to age 18 years and were remeasured at age 30 years. At each age nine fitness tests were administered together with the recording of anthropometric dimensions, biological maturation, sports participation and family characteristics. Tracking was measured by the inter-age correlations at each age between 13 and 18 years and the performance scores at 30 years. The independent contribution of characteristics observed during adolescence to the explanation of adult fitness was investigated through stepwise multiple regression analysis and discriminant analysis with the adult fitness scores as the dependent variables and the fitness, maturation, anthropometric characteristics, sports participation and family background as the independent variables. Tracking between age 13 and age 30 years was moderately high (46% of variance explained) for flexibility, low to moderate (between 19% and 27% of variance explained) for the other fitness parameters and low for pulse recovery and static strength (7% to 11% of variance explained). Between age 18 and age 30 years the tracking was high for flexibility, moderately high for explosive and static strength, and moderate for the other fitness parameters except for pulse recovery. The amount of variance of adult fitness levels explained increased significantly when other characteristics observed during adolescence entered the regressions or discriminant functions.(ABSTRACT TRUNCATED AT 250 WORDS)

Motor performance during adolescence and age thirty as related to age at peak height velocity.

Relationships between motor performance, as measured by various fitness tests, and age at peak height velocity have been studied in a sample of 173 Flemish boys, measured yearly between +/- 13 and +/- 18 years and again as adults at 30 years of age. In addition to correlation studies, comparisons were made between boys with an early, average and late age at peak height velocity. To summarize the successive measurements during adolescence, a longitudinal principal component analysis was carried out. The first component can be interpreted as an average percentile level component. During adolescence, three performance tasks, namely speed of limb movement, explosive strength and static strength, are negatively related to age at peak height velocity; thus early maturers performed significantly better than late maturers. However, between late adolescence and adulthood, a cross-over of the average distance curves between 18 and 30 years of age was noted for almost all motor tasks. The late maturers not only caught up the early maturers, but there were significant differences for explosive strength and functional strength in favour of late maturers. In order to predict performance in adulthood from measures during adolescence, the following hypothesis is suggested: the best results at adulthood are obtained by those men who were already good performers during adolescence and who were late maturers, while the worst results are obtained by poor performers during adolescence who were early maturers.

Skeletal maturity in Belgian youths assessed by the Tanner-Whitehouse method (TW2).

Reference data for skeletal maturity (TW2 method) of the hand and wrist are provided for large representative samples of Belgian boys and girls. The sample of Belgian boys consisted of 21,174 boys aged 12 to 20 years studied in a nationwide cross-sectional and longitudinal study on the physical fitness of secondary schoolboys (1969-1974). The girls' sample consisted of 9698 6-19-year-old Flemish girls studied cross-sectionally (1979-1980). Both samples were multi-stage stratified cluster samples of entire school classes. All skeletal maturity assessments of the boys were made by the same observer (GB). His estimations agreed quite closely with those of the originators of the method. The skeletal age assessments of the girls were made by two observers trained by GB. Both observers showed high intraobserver reliability after training, and during the assessments. Moreover their ratings compared favourably with those of GB and the originators of the method. Smoothed percentile curves of the maturity scores (TW2-20 bone, RUS and CARP scores) were calculated by means of cubic splines using a stepwise regression procedure for the selection of suitable knots. In the boys, the TW2 scores (20 bone and RUS) increase linearly between 12 and 14.5 years of age, slow down for a while, and then increase again, while the CARP scores increase linearly between 12 and 15 years of age. In girls, the 20-bone maturity scores increase nearly linearly from 6 through 9.5 years of age, accelerate until 11.0 years followed by a smaller increase; RUS scores increase curvilinearly from 6 years of age onwards; and Carp scores increase almost linearly between 6.0 and 12.5 years of age. Belgian boys are advanced in RUS scores but are delayed for the carpal bones as compared with the British standards. The Belgian girls show advancement for both scales as compared with the British reference data. The skeletal maturation of youths from several other continental European countries corresponds more closely with the Belgian than with the British data. The reference data presented herein most probably provide suitable standards for youths of West-European countries.

Patterns of TW-1 and TW-2 skeletal age differences in 12-19-year-old Belgian boys.

The pattern of differences between TW-1 and TW-2 skeletal ages was investigated in a mixed longitudinal sample of Belgian school boys aged about 12-19 years. The differences between the TW-1 and TW-2 skeletal ages decrease from 12 years until 15 years, then increase until they stabilize at 17 years. TW-1 skeletal ages are greater than TW-2 skeletal ages, except at 14 and 15 years. This trend confirms the findings in better-off black and white Philadelphia children and in disadvantaged Mexican children (Malina and Little 1981).

Fatness and skeletal maturity of Belgian boys 12 through 17 years of age.

Relationships between fatness and skeletal maturity are considered in a nationwide sample of 14,259 Belgian boys 12 through 17 years of age (The Leuven Growth Study of Belgian Boys). Absolute fatness was estimated from four skinfolds using the Drinkwater and Ross technique and from the sum of four skinfolds, and was related to skeletal maturity assessed by the Tanner-Whitehouse method (I and II). In addition, comparisons were made between the fattest 5% and leanest 5% of the boys at each age level. Correlations between the indices of fatness and skeletal age and relative skeletal age (the difference between skeletal and chronological ages) are positive and generally low, ranging from 0.12 to 0.39. They tend to decrease with age from 12 to 17 years. Comparisons between the extreme groups indicate that the leanest boys are more delayed in skeletal maturity, by about 0.8 years, than the fattest boys are advanced, by about 0.5 years. Stature data for the same boys are consistent with the skeletal maturity data and thus suggest that the size differences between the extreme groups are due in part to maturity differences. Over the age span 12 through 20 years, the leanest boys are reduced in stature by about -1.2 standard deviations, while the fattest boys are larger in stature by about +0.6 standard deviation units. The size differences, however, persist after skeletal maturity is attained so that there may be a specific role for fatness in influencing statural growth.

Chronological and biological age as related to physical fitness in boys 12 to 19 years.

The relative importance of skeletal age and chronological age in explaining body measurements and the relative importance of skeletal age, chronological age, height, weight, and their interactions in explaining motor fitness components are reported. Anthropometric, motor fitness, and skeletal maturity data have been collected in a mixed longitudinal study of Belgian school boys 12+/- - 19+/- years. At each age level multiple regression equations were calculated to evaluate the relative importance of the independent variables. Skeletal age was assessed by the TW2 method and the anthropometric measurements were taken following standard procedures. The motor fitness tests were selected on their factor loading and reliability in the same age range. Between 13 and 16 years a fairly high percentage of the variation in body dimensions is explained by skeletal age (+/-50% for stature). The percentage of explained variance reaches its maximum at 14-15 years. The highest percentage is found for linear dimensions and weight followed by bone width dimensions and circumferences. Triceps and calf skinfolds are not related to skeletal age. Chronological age as such does not contribute in the prediction of body measurements. The interaction between chronological age and skeletal age as such or in combination with height and/or weight have the highest predictive value except for trunk strength (leg lifting) and functional strength (bent arm hang). Except for static strength (arm pull), for which the explained variance ranged from 33% to 58%, the predictive value of body size, maturity, chronological age and their interactions is rather low, varying between 0% and 17%. As for body dimensions, the explained variance reaches its maximum for most motor tests at 14-15 years.