R Renson

KU Leuven, Leuven, VLG, Belgium

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Publications (38)75.14 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: 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.
    International Review for the Sociology of Sport 01/2006; 41:413-430. · 0.83 Impact Factor
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    ABSTRACT: Three aspects of physical fitness—somatic characteristics, motor fitness, and sport participation—of girls from different sociogeographic origins of Belgium were contrasted. The sample consisted of a cross-sectional sample of 4,528 Flemish girls 13 to 18 years of age, who were classified by dwelling area as rural, semi-urban, and urban. Somatic characteristics included 16 anthropometric dimensions, skeletal maturity, and somatotype. Motor fitness was assessed by 10 tests. Sociocultural background information and the level of sport participation were investigated by questionnaire and interview. Data were analysed via one-way analyses of variance and growth curves were plotted to compare the sociogeographic differentiation patterns in physical fitness variables. The results show small motor and somatic differences between rural and urban youngsters, which is explained by the process of conurbation. However, urban girls were significantly more involved in sports than their rural counterparts. This is most probably due to greater sport involvement of parents from urban girls and/or the differences in available sport facilities.
    American Journal of Human Biology 05/2005; 3(5):503 - 513. · 2.34 Impact Factor
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    ABSTRACT: Three issues are put forward with respect to the active participation of young people in leisure-time sports styles. It is considered (a) whether sports participation styles can be detected over the last three decades, (b) whether they have changed in this period of time, and (c) whether traditional parameters understood as structuring and positioning young people's lifestyles are still relevant with respect to their sports participation preferences. Youth sports participation data were retrieved from four large-scale surveys in 1969, 1979, 1989 and 1999 (Ntotal=22,424 high school boys and girls). These data allow for a time trend analysis of youth sports participation styles. Results from component and regression analyses indicate that different participation styles can be distinguished for each period of time and that these styles have been developing and differentiating through a growing responsiveness to wider social trends. Structural and positioning variables such as age, sex and education remain significant determinants for young people's active participation in leisure-time sports styles over the observed period of time. Although these social structures continue to shape youth sports participation styles, it is suggested that there is more variety within social groups than among social groups partially due to processes of individualisation and homogenisation.
    Sport Education and Society 01/2005; 10(3):321-341. · 1.17 Impact Factor
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    ABSTRACT: 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.
    European Physical Education Review 01/2005; 11(1):5-27. · 0.50 Impact Factor
  • Medicine and Science in Sports and Exercise - MED SCI SPORT EXERCISE. 01/2005; 37.
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    ABSTRACT: 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. 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. 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. 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.
    Medicine &amp Science in Sports &amp Exercise 12/2004; 36(11):1930-6. · 4.48 Impact Factor
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    ABSTRACT: 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.
    American Journal of Epidemiology 10/2003; 158(6):525-33. · 4.78 Impact Factor
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    ABSTRACT: The purpose of this study was to investigate stability and change in different expressions of strength development in adolescent boys using structural equation modeling. Three models were used: Markov simplex to study stability or tracking, Wiener or random walk to investigate fanning-out or spread effects in change, and latent growth to study differences in individual pathways of change as well as group changes. In the Leuven Growth Study, 588 male subjects were followed for 6 years with a mean age of 12.7 years at the onset of the study. Vertical jump, arm pull, and bent arm hang were used to mark the following strength factors: explosive strength, static strength, and functional strength. All models were tested with robust estimation procedures based on the software EQS 6.0. Main results and conclusions are as follows: 1) all strength factors showed moderate to high tracking, with low values of instability in relative position of the subjects in their developmental channels; 2) the fanning-out effect is not obvious, although some evidence showed a spread effect in functional and explosive strength; 3) there are marked interindividual differences in developmental pathways of strength manifestations; 4) strength development is linear and also has some curvilinearity, something akin to a breaking effect; 5) linear trend is negatively correlated with initial status and the leveling-off effect is also negatively correlated with the linear change.
    American Journal of Human Biology 06/2003; 15(4):579-91. · 2.34 Impact Factor
  • Medicine and Science in Sports and Exercise - MED SCI SPORT EXERCISE. 01/2003; 35.
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    ABSTRACT: 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.
    International Journal of Sports Medicine 06/2002; 23 Suppl 1:S32-8. · 2.27 Impact Factor
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    ABSTRACT: 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
    International Review for the Sociology of Sport 01/2002; 37(2):219-245. · 0.83 Impact Factor
  • Medicine and Science in Sports and Exercise - MED SCI SPORT EXERCISE. 01/2002; 34(5).
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    ABSTRACT: 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. 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. 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. 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.
    Medicine &amp Science in Sports &amp Exercise 06/2001; 33(5):765-71. · 4.48 Impact Factor
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    ABSTRACT: 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.
    American Journal of Human Biology 01/2001; 13(2):173-9. · 2.34 Impact Factor
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    ABSTRACT: 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. Copyright 2000 Wiley-Liss, Inc.
    American Journal of Human Biology 08/2000; 12(4):487-497. · 2.34 Impact Factor
  • Medicine and Science in Sports and Exercise - MED SCI SPORT EXERCISE. 01/1998; 30.
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    ABSTRACT: 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.
    International Journal of Sports Medicine 09/1997; 18(6):413-9. · 2.27 Impact Factor
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    ABSTRACT: 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.
    International Journal of Sports Medicine 08/1997; 18 Suppl 3:S171-8. · 2.27 Impact Factor
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    ABSTRACT: 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.
    Medicine &amp Science in Sports &amp Exercise 01/1997; 29(2):225-230. · 4.48 Impact Factor
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    ABSTRACT: 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.
    International Review for the Sociology of Sport 01/1997; 32(4):373-387. · 0.83 Impact Factor

Publication Stats

585 Citations
75.14 Total Impact Points

Institutions

  • 1997–2005
    • KU Leuven
      • • Section of Nuclear and Radiation Physics (IKS)
      • • Faculty of Kinesiology and Rehabilitation Science (FaBeR)
      • • Department of Biomedical Kinesiology
      Leuven, VLG, Belgium
  • 2003
    • University of Porto
      • Faculty of Sports
      Oporto, Porto, Portugal
  • 1995
    • University of Texas at Austin
      • Department of Kinesiology and Health Education
      Texas City, TX, United States