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.

Bone mass and lifetime physical activity in Flemish males: a 27-year follow-up study.

PURPOSE: The present population study is conducted to examine the extent to which lifetime physical activity and lifestyle parameters contribute to bone mass. METHODS: The design of the project is a 27-yr prospective follow-up study. Subjects are 126 males gathered from the Leuven Longitudinal Study on Lifestyle, Physical Fitness and Health, and aged 13 yr at the onset of the study and 40 at the end of the follow-up. Physical activity and lifestyle parameters are obtained with questionnaires. Bone mass is measured by means of dual-energy x-ray absorptiometry (DXA). RESULTS: Results from correlation and regression analyses show that the body mass index (BMI) is the most important parameter in relation to cortical and trabecular bone mass at every examination period. Longitudinally, static arm strength, running speed, and upper muscular endurance contribute significantly to the prediction of adult bone mass. The parameter "change in motor fitness" between 18 and 13 yr old was used to control for hereditary influences. The score for static arm strength and trunk muscle strength demonstrates a significant correlation with adult total bone mineral content (BMC) and lumbar bone mineral density (BMD), respectively. At the age of 40, the Baecke sports index is almost equally important as BMI in explaining the variance in BMD, and static arm strength is the most important parameter (after BMI) for BMC. CONCLUSION: Lifetime physical activity, physical fitness, and BMI all contribute to adult bone mass. The clinical relevance of these findings is emphasized by the fact that the observed patterns of physical activity and motor fitness pertain to customary lifestyle and are thus feasible targets.

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.

Evaluation of the influence of cardiorespiratory fitness on diverse health risk factors, independent of waist circumference, in 40-year-old Flemish males.

OBJECTIVE: To examine whether cardiorespiratory fitness is associated with health risk factors, independent of abdominal obesity (waist circumference). RESEARCH METHODS AND PROCEDURES: Data from 141 40-year-old Flemish male subjects were used in a cross-sectional design. The main outcome measure in this study was the health risk profile, composed of lipid parameters, blood pressure, and body composition parameters. Data were analyzed by regression analysis and by means of analysis of covariance with waist circumference (WC) as covariate and cardiorespiratory fitness as source of variance. RESULTS: The data demonstrated that cardiorespiratory fitness was significantly associated with triglycerides, with the ratio of total cholesterol/serum high density lipoprotein cholesterol, and with percent body fat, independent of an association with WC. DISCUSSION: Major findings of the present cross-sectional study indicate that a high WC is associated with adverse health-related lipid profile and body composition characteristics and that relatively high levels of aerobic fitness attenuate the health risk associated with a large WC.

Associations between physical activity, nutritional practices and health-related anthropometry in Flemish males: a 5-year follow-up study.

OBJECTIVE: To examine the extent to which extent health-related anthropometric characteristics such as body mass index, waist-hip ratio, waist girth, percentage of body fat and weight gain are associated with physical activity and nutritional habits in a prospective follow-up study. DESIGN: A 5-y prospective follow-up study. SUBJECTS: 132 healthy men from The Leuven Longitudinal Study on Lifestyle, Physical Fitness, and Health. MEASUREMENTS: The main outcome measures in this study were body mass index, waist-hip ratio, waist girth, percentage of body fat and significant weight gain at the ages of 35 and 40y. An unhealthy body composition was defined as a body mass index above 26, a waist-hip ratio above 0.95, a waist girth and body fat percentage above percentile 75 at the end of the 5-y follow-up period and a significant weight gain of 5 kg or more between the two examination periods. Anthropometric characteristics were derived from clinical examination. Physical activity at the age of 35 and 40 and nutritional habits at the age of 40 were determined from questionnaires. RESULTS: Physical activity was the most important predictor for the waist-hip ratio in this sample. In contrast, nutritional habits show a stronger relationship with body mass index. The absolute amount of energy intake is not as important as the dietary factors in predicting subjects with overweight or an unhealthy body composition. The index of occupational activities appears to be the most important physical activity variable. CONCLUSION: The relative contribution of physical activity and nutritional habits in health-related anthropometry varies with the characteristic considered. It is important to include all components of physical activity, and occupational activities in particular, in determining these relationships in a general population sample.

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.

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.

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)