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1.
Med Sci Sports Exerc ; 36(11): 1930-6, 2004 Nov.
Article in English | MEDLINE | ID: mdl-15514509

ABSTRACT

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.


Subject(s)
Adolescent Behavior , Life Style , Motor Activity , Adolescent , Adult , Belgium/epidemiology , Body Composition , Body Size , Family Characteristics , Follow-Up Studies , Health Surveys , Humans , Longitudinal Studies , Male , Socioeconomic Factors , Sports/statistics & numerical data
2.
Am J Epidemiol ; 158(6): 525-33, 2003 Sep 15.
Article in English | MEDLINE | ID: mdl-12965878

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.


Subject(s)
Bone Density/physiology , Sports/physiology , Absorptiometry, Photon , Adolescent , Analysis of Variance , Anthropometry , Belgium/epidemiology , Follow-Up Studies , Humans , Male , Regression Analysis
3.
Am J Hum Biol ; 14(6): 735-42, 2002.
Article in English | MEDLINE | ID: mdl-12400034

ABSTRACT

The association between bone mass, body structure, and body composition was explored in 156 men, 40 years of AGE. Bone mineral density (total body, lumbar spine, left arm, and left leg) was obtained by dual-energy X-ray absorptiometry (DXA; Hologic QDR 4500A). Body structure was determined from a battery of anthropometric dimensions with a principal components analysis. Body composition was estimated with DXA. From the 24 anthropometric dimensions, three components were extracted and identified as muscle, fat, and skeletal length. Significant correlations between the muscle component and all BMD measurements (r = 0.28-0.44) were obtained. Except for BMD of the left arm, which correlated significantly, but negatively, with the fat component (r = -0.16), no significant relations were found between the fat component and BMD. There were significant correlations between lean mass, fat mass, and BMD measurements. The correlations were higher between lean mass and BMD (r = 0.22-0.44) than between fat mass and BMD (r = 0.08-0.24). The multiple regression analysis revealed that except for BMD of the left arm only lean mass or the muscle component, and not fat mass or the fat component, were independent predictors of BMD. It is concluded that the principal anthropometric determinant of BMD in middle-aged men is lean mass or muscle.


Subject(s)
Body Composition/physiology , Body Constitution/physiology , Bone Density/physiology , Osteoporosis/etiology , Absorptiometry, Photon , Adult , Anthropometry , Belgium , Body Mass Index , Humans , Longitudinal Studies , Male , Middle Aged , Multivariate Analysis , Osteoporosis/physiopathology , Predictive Value of Tests , Probability , Regression Analysis , Risk Assessment , Sampling Studies
4.
Am J Hum Biol ; 12(4): 487-497, 2000 Jul.
Article in English | MEDLINE | ID: mdl-11534040

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.

5.
Am J Hum Biol ; 11(5): 587-597, 1999 Sep.
Article in English | MEDLINE | ID: mdl-11533977

ABSTRACT

The relationship of physical activity to several components of physical fitness was investigated in a sample of 166 males 40 years of age. In addition to Pearson correlations, multivariate canonical correlations were calculated. Physical activity during work (work index), sport (sport index), and leisure time (leisure time index) was assessed by the Baecke questionnaire. Physical fitness included cardiorespiratory fitness measures, the body mass index (BMI), the sum of seven skinfold thicknesses (SKI), percentage body fat (PFAT), balance, and several tests of muscle strength and endurance, flexibility, and speed of limb movement. More than 86% of the variance was shared by the two first canonical variables. The first canonical variable can be interpreted as a health-related fitness function. Carciorespiratory fitness, balance, speed of limb movement, explosive strength, and trunk muscle strength are clearly related to this function. From the physical activity measures, the Baecke sport index correlated significantly with this health-related fitness function. The second canonical variable can be explained as a fatness function, since body weight, BMI, SKI, and PFAT showed the highest correlations with the variable. The Baecke work index was inversely related to this canonical variable. The sample was also divided into physical activity groups in order to look for differences in physical fitness. The data indicate that physical activity during work was modestly, but inversely related to adiposity. Sport activity was beneficially associated to several fitness components, including cardiorespiratory fitness, trunk muscle strength, and upper body muscular endurance. Am. J. Hum. Biol. 11:587-597, 1999. Copyright 1999 Wiley-Liss, Inc.

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