Bart Vanreusel

Stratification Patterns of Active Sport Involvement Among Adults: Social Change and Persistence

This study examines leisure-time sport participation among adults according to their socio-cultural background and from a time-trend perspective. Using stapled cross-sectional survey data, sport participation trends in Flanders (Belgium) are studied for the 1979-99 period. The study explores the extent to which social inequalities in leisure-time sport still prevail. The results indicate that age, gender and social class remain the most important factors influencing sport involvement. Differences according to age, gender and social class are not only found with respect to participation rates; even among sport participants, social differentiation takes place in terms of the organizational context and participation preferences. Using logistic regression modelling and canonical correlation analysis, stratification patterns and different sporting styles are identified in terms of activities. The findings are discussed in terms of three decades of the Sport for All movement in Flanders.

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.

Sports Participation Among Females From Adolescence To Adulthood A Longitudinal Study

It is widely believed that active participation in sports during youth is an important prerequisite for adult involvement in sports. However, data from reliable longitudinal studies tracking patterns of sports participation from youth into adulthood are scarce. This study addresses the leisure-time sports participation of adult women, 32—41 years of age, from a lifetime sports socialization perspective. Some 20 years after they participated in 1979 in the Leuven Growth Study on Flemish Girls, 257 female adults participated again in a comprehensive questionnaire and face-to-face interview. Inter-age correlations for sports participation are calculated from adolescence into adulthood. Logistic regression modeling and structural equation modeling are used to explain individual differences in adult sports participation. Outcomes indicate that tracking of sports involvement between late adolescence and adulthood is moderately high (r = .41; beta .42). The results from the multivariate analysis show that sport participation during adolescence is a better predictor of adults involvement in sports than educational level or parental socioeconomic status. The variances accounted for are rather small, indicating that sport experiences and social background characteristics only partially explain the sport participation behavior of adults. In the sports socialization process, late adolescent sports experience, along with the school program in which an adolescent is involved, appear to play a crucial role in sport involvement in later life. We recommend that youth sports programs need to be examined critically with regard to their contribution to lifetime sports participation.

Adolescent Correlates of adult physical activity: A 26-year follow-up

PURPOSEnIt 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.nnnMETHODSnSubjects 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.nnnRESULTSnWhen 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.nnnCONCLUSIONnAdolescent 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.

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

PURPOSEnThe present population study is conducted to examine the extent to which lifetime physical activity and lifestyle parameters contribute to bone mass.nnnMETHODSnThe 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).nnnRESULTSnResults 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.nnnCONCLUSIONnLifetime 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.

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

PURPOSEnTo 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.nnnMETHODSnFrom 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.nnnRESULTSnStability 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.nnnCONCLUSIONSnPhysical 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.

Social stratification patterns in adolescents’ active sports participation behaviour: a time trend analysis 1969-1999:

The purpose of this study was to examine (a) whether adolescents’ leisure-time sports participation is socially stratified, and (b) whether possible stratification patterns have changed over the last decades. The population for the study consisted of four random samples of high school boys and girls in Flanders who were exposed to a standardized questionnaire in 1969, 1979, 1989 or 1999. The results indicated that social back ground variables remain relevant to analyse constraints on leisure-time sports participation. Parental sports participation, gender and school programme still deter mine the respondents’ active involvement in sports. The impact of gender and school programme has intensified during the last decade. On the other hand, the adoles cents’ sports participation is no longer correlated with the socioeconomic status of the parents. Some explanations are discussed for linking the adolescents’ sports participation behaviour to the respondents’ social background.