Roland Renson

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.

A LONGITUDINAL STUDY OF YOUTH SPORT PARTICIPATION AND ADHERENCE TO SPORT IN ADULTHOOD

Youth sport programmes are often legitimized on their assumed contribution to continued sport involvement in adulthood. A longitudinal analysis was made of the sport involvement pattern of a sample (N = 236) of male subjects from 13 to 35 years of age, from a perspective of continued socialization into sport. The results of the quantitative analysis show that the continuation of sport participation from youth into adulthood is different according to the type of youth sport career. Tracking of sport participation patterns is moderate to high during youth, and low to moderate from youth to adulthood. Methodological issues are raised. It is concluded that youth sport programmes should be critically examined with regard to their contribution to continued sport participation in adulthood.

Social Sports Stratification in Flanders 1969-1999 Intergenerational Reproduction of Social Inequalities?

This article provides substantial empirical evidence that significant social differences exist in sports involvement in Flanders. A 30-year follow-up study of social stratification in sports was carried out to find out (i) if sports participation in Flanders is still socially stratified, and (ii) if social changes occurred in the status sports pyramid over the years of investigation. Based upon the educational status, the professional status and the geographical status of male and female adults in Flanders, social stratification pyramids in sport were set up for 1969, 1979, 1989 and 1999. Although the amount of sports participation from 1969 until 1999 has increased for each socioprofessional status, a significant difference persists between the high and the low professional levels (X² test for trend = 85.90; p<0.0001; df=1). The 1999 social sports pyramid continues to be socially stratified, and therefore confirms that sports participation still depends on socioprofessional status.

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.

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.

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.

Daily physical activity and physical fitness from adolescence to adulthood: A longitudinal study

The stability of physical fitness and physical activity in Flemish males from 18 to 40 years of age was investigated. In addition, effects of a consistently low‐activity or high‐activity level during the same age period on physical fitness were studied. The sample consisted of males who were followed longitudinally from age 13 to age 18 years, and were remeasured at the ages of 30, 35, and 40 years. Complete data about physical fitness and physical activity between 13 and 40 years were available for 130 subjects. Stability was measured using Pearson autocorrelations and simplex models. Multivariate analysis of variance (MANOVA) for repeated measurements was used to look for the effects of activity level on physical fitness. Simplex models showed higher stability coefficients than Pearson correlations, and stability of physical fitness was higher than stability of physical activity. Physical fitness showed the highest stability in flexibility (r = 0.91 between 18 and 30 years, r = 0.96 for both the 30–35 and 35–40 ages intervals), while physical activity showed the highest stability during work (r between 0.70 and 0.98 for the 5‐year intervals). Results from MANOVA indicated that for some fitness characteristics the high‐active subjects were more fit than their low‐active peers. Stability of physical activity was higher than assumed and, therefore, it is a useful and independent indicator for further research. Although possible confounding factors are present (e.g., heredity), a higher level of physical activity during work and leisure time on a regular basis benefits physical fitness considerably. Am. J. Hum. Biol. 12:487–497, 2000.

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.

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.

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

SummaryThis 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 (70% 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. This indicated that in addition to fitness scores, anthropometric dimensions, and skeletal maturation, behavioural characteristics also added significantly to the prediction of adult fitness levels.