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J Appl Physiol 105: 734-741, 2008. First published May 8, 2008; doi:10.1152/japplphysiol.00869.2007
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HIGHLIGHTED TOPIC
Biology of Physical Activity in Youth

The influence of physical activity on lean mass accrual during adolescence: a longitudinal analysis

Adam D. G. Baxter-Jones,1 Joey C. Eisenmann,2 Robert L. Mirwald,1 Robert A. Faulkner,1 and Donald A. Bailey1,3

1College of Kinesiology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada; 2Department of Kinesiology, Michigan State University, East Lansing, Michigan; and 3School of Human Movement Studies, The University of Queensland, Brisbane, Queensland, Australia

Submitted 13 August 2007 ; accepted in final form 7 May 2008


   ABSTRACT
TOP
 ABSTRACT
METHODS
 RESULTS
 DISCUSSION
 GRANTS
 ACKNOWLEDGMENTS
 REFERENCES
 
During childhood, physical activity is likely the most important modifiable factor for the development of lean mass. However, the effects of normal growth and maturation must be controlled. To distinguish effects of physical activity from normal growth, longitudinal data are required. One hundred nine boys and one hundred thirteen girls, participating in the Saskatchewan Pediatric Bone Mineral Accrual Study, were repeatedly assessed for 6 yr. Age at entry was 8–15 yr. Stature, body mass, and physical activity were assessed biannually. Body composition was assessed annually by dual-energy X-ray absorptiometry. Physical activity was determined using the physical activity questionnaires for children and adolescence. Biological age was defined as years from age of peak height velocity. Data were analyzed using multilevel random-effects models. In boys, it was found that physical activity had a significant time-dependent effect on lean mass accrual of the total body (484.7 ± 157.1 g), arms (69.6 ± 27.2 g), legs (197.7 ± 60.5 g), and trunk (249.1 ± 91.4 g) (P < 0.05). Although the physical activity effects were similar in the girls (total body: 306.9 ± 96.6 g, arms: 31.4 ± 15.5 g, legs: 162.9 ± 40.0 g, and trunk: 119.6 ± 58.2 g; P < 0.05), boys for the same level of activity accrued, depending on the site, between 21 and 120% more absolute lean mass (g). In conclusion, habitual physical activity had a significant independent influence on the growth of lean body mass during adolescence, once biological maturity and stature were controlled.

growth and development; exercise; longitudinal studies

SEVERAL FACTORS INFLUENCE the normal biological growth and maturation of the child and adolescent. Although genes, nutrients, and hormones are viewed as the chief determinants, the level of habitual physical activity (PA) is thought to be one of several ancillary factors contributing to the growth and maturation of body size and composition (11, 51). Recently, concern has been expressed regarding the low levels of habitual PA and associated negative health outcomes among contemporary youth (49).

Most of the previous studies examining the influence of PA on body composition of children and adolescents have focused on either adiposity (4) or bone accrual (2), given the concern of obesity and osteoporosis, respectively. In contrast, few studies have considered the influence of PA on lean body mass (LBM) in children and/or adolescents. LBM is often considered a surrogate for skeletal muscle mass, which is an important predictor of several physiological capacities expressed in absolute terms (e.g., maximal oxygen uptake, neuromuscular strength, anaerobic capacity, etc.) (1, 54) and health indicators (e.g., bone mass, insulin resistance, and obesity) (18, 56). Previous studies have shown that significant differences have been reported in LBM between athletes and nonathletes (35), changes following exercise (14–16), and longitudinal studies or exercise training studies of special populations (e.g., cystic fibrosis, Prader-Willi syndrome, etc.) (28, 42, 44, 50). However, few studies have examined the influence of free-living, habitual PA on LBM accrual during adolescence, a critical period of growth affecting body composition and those that have, for the most part, been cross-sectional in design and thus unable to tease out the independent effects of PA from those of growth and maturation (12, 21, 25).

To fully explore the influence of PA on the development of LBM during adolescence, longitudinal studies are required so that the individual trajectories of LBM can be examined by taking into consideration the timing and tempo of growth and maturation. This is important as there are known age- and sex-associated variations in lean mass development. During childhood, sex differences are minimal, but become more apparent during adolescence. Young adult values are reached earlier in girls (15–16 yr) compared with boys (19–20 yr). In late adolescence and young adulthood, boys have an average LBM that is about 1.5 times larger than that of girls. Partitioning the effects of PA on lean mass accrual from normal growth and maturation is, therefore, a major challenge (6). The introduction of multilevel statistical models (19) has enabled researchers to fit individual growth curves to measurements over time. Essentially, multilevel modeling is an extension of multiple regressions, which is appropriate for analyzing hierarchical data (20). In the multilevel framework, each individual has their own straight-line growth trajectory, with intercepts and slope coefficients varying between individuals. Using this technique, the independent effects of growth, maturation, and sex on LBM accrual can be identified, and the independent time-dependent effects of PA can be identified (5). To our knowledge, no studies have documented the independent influence of habitual free-living PA on LBM accrual in boys and girls. Clearly, further study is warranted to examine the effects of PA on lean mass development, taking into consideration normal growth and maturation and sex differences.

The purpose of this study was to investigate the independent effects of PA on total body and regional lean mass accrual, while accounting for the confounding effects of growth and maturation. It was hypothesized that PA would have a small but significant influence on the development of lean mass accrual during growth and maturation in both boys and girls. The uniqueness of this study is that serial measures of habitual PA and LBM were observed in a free-living group of boys and girls for 7 consecutive yr during the adolescent growth period.


   METHODS
TOP
 ABSTRACT
 METHODS
RESULTS
 DISCUSSION
 GRANTS
 ACKNOWLEDGMENTS
 REFERENCES
 
Subjects.   Subjects were drawn from the Saskatchewan Pediatric Bone Mineral Accrual Study (PBMAS) (2, 3). The study used a mixed-longitudinal cohort design. In 1991, seven age cohorts were recruited and followed for up to 6 consecutive yr. Because the cohorts overlapped, it is possible to establish developmental patterns from 8 to 20 yr of age. In 1991, of the 375 eligible students (ages 8–15 yr) attending two elementary schools in the city of Saskatoon (population 200,000), the parents of 228 students (113 boys and 115 girls) provided written consent for their children to be involved in this study, and 220 were scanned by dual-energy X-ray absorptiometry (DXA). From 1992 to 1993, an additional 31 subjects were recruited and scanned. After 6 yr of data collection, 109 boys and 113 girls had been measured on one or more occasions (median 6 occasions). These subjects represent the study population for the present investigation. Ninety-eight percent of subjects were of Caucasian descent. Before participation in the study, informed consent and child assent were obtained. The human subject's research protocol was approved by the University of Saskatchewan Biomedical Research Ethics Board (Bio no. 88-102).

Assessment of body composition and LBM.   DXA scans of the total body were performed in October or November of each year using a QDR2000 scanner (Hologic, Waltham, MA). The array mode was used for all scans, employing enhanced global software version 7.10. Total body scans were analyzed for body composition using software version 5.67A. Precision of the QDR2000 scanner was tested in vitro using a lumbar spine phantom (scanned daily) and in vivo using a test-retest design with 20 healthy male and female university students. The retest occurred either the same day (for short-term precision) or 4 wk later (for long-term precision); coefficients of variation of duplicate measurements were calculated (47). The short-term precision (%) in vivo was 0.54% for total body bone mineral free lean mass (BMFL), 4.09% for arm BMFL, 1.19% for leg BMFL, and 0.67% for trunk BMFL. The precision for total body fat mass (FM) and bone mineral content (BMC) was 2.95 and 0.60%, respectively. These values are in line with other studies utilizing the QDR 2000 in the array mode (48). Site-specific soft tissue (fat and lean mass) values were taken from the total body scans.

Anthropometry.   Anthropometric measurements were taken at 6-mo intervals by trained personnel following a standard protocol (40). Stature was recorded without shoes as stretch stature to 0.1 cm using a wall-mounted stadiometer. Body mass was measured to 0.01 kg on a calibrated electronic scale.

PA assessment.   A PA questionnaire was administered a minimum of three times per year (fall, winter, and spring) for the first 3 yr of the study and two times per year (fall and spring) thereafter for all subjects. The PA questionnaires for children (PAQ-C) and adolescence (PAQ-A) consist of nine items designed to provide a measure of a child's general PA level during the school year. PA is described as "sports, games, gym, dance or other activities that make you breathe harder, make your legs feel tired and make you sweat." Each item is scored on a 5-point scale, with higher scores indicating higher levels of activity. The mean of these items forms a composite activity score. In diverse samples of children, the scale has consistently demonstrated acceptable internal consistency. Validity has been examined by comparing results with teacher evaluation of activity, Caltrac motion sensors, 7-day activity recalls, step tests of fitness, and leisure time activity scales. Results have been generally favorable with moderate relationships reported (26, 27). In any 1 yr, a subject had either two or three activity assessments. The mean of these assessments was used as the activity score for that year.

Controlling for maturation.   A biological maturity age was determined for each individual to control for sex-related maturational differences. The age of peak linear growth [age at peak height velocity (APHV)] is an indicator of somatic maturity, representing the time of maximum growth in stature during adolescence. It occurs when linear growth is ~92% of adult height (36). To establish APHV for each child, whole-year velocity values were calculated for each subject by dividing the difference between the annual distance measurements by the age increment (the mean age increment was 0.998 ± 0.048 yr). A cubic spline fit was then applied to the whole-year velocity values for each child. A spline is interpolating polynomials, which uses information from neighboring points to obtain a degree of global smoothness. The cubic spline procedure was chosen over other curve-fitting protocols, because it maintains the integrity of the data without transforming or modifying the underlying growth characteristics. A biological maturity age (years) was calculated by subtracting the chronological age at the time of measurement from the chronological APHV. Lean mass values were considered in terms of time before and after APHV. Thus a continuous measure of biological age was generated. Biological age categories were constructed using 1-yr intervals, such that –1 APHV age group included observations between –0.49 and –1.50 yr from (i.e., before) APHV.

Statistical analysis.   Statistical analysis was performed using SPSS software version 15.0. Values are reported as means ± SE, unless otherwise noted, a level of significance of P < 0.05 was used, and all statistical tests were two-tailed. For the longitudinal analyses, hierarchical (multilevel) random-effects models were constructed using a multilevel modeling approach (MlwiN version 1.0, Multilevel Models Project; Institute of Education, University of London, UK) (5, 8, 9, 17, 19, 33). Detailed description of multilevel modeling, as applied to the PBMAS, has been previously reported (9, 17), and complete details of this approach are presented elsewhere (5). In brief, lean mass development was measured repeatedly in individuals (level 1 of the hierarchy) and between individuals (level 2 of the hierarchy). Analysis models that contain variables measured at different levels of the hierarchy are known as multilevel regression models. Specifically, the following additive, sex-specific, random-effects multilevel regression models were adopted to describe the developmental changes in lean mass parameters with biological age.

Formula
where y is the lean mass parameter on measurement occasion i in the jth individual; {alpha} is a constant; βjxij is the slope of the lean mass parameter with biological maturity age (years from APHV) for the jth individual; and k1 to kn are the coefficients of various explanatory variables (e.g., height, PA, etc.) at assessment occasion i in the jth individual. These are the fixed parameters in the model. Both µj and {varepsilon}ij are random quantities, whose means are equal to zero; they form the random parameters in the model. They are assumed to be uncorrelated and follow a normal distribution, and thus their variances can be estimated; µj is the level 2 (between-subjects variance) and {varepsilon}ij the level 1 residual (within-individual variance) for the ith assessment of lean mass in the jth individual. Models were built in a stepwise procedure, i.e., predictor variables ({kappa} fixed effects) were added one at a time, and likelihood ratio statistics were used to judge the effects of including further variables (5). Predictor variables ({kappa}) were accepted as significant if the estimated mean coefficient was greater than twice the standard error of the estimate (SEE) i.e., P < 0.05. If the retention criteria were not met, the predictor variable was discarded. To allow for the nonlinearity of growth, biological maturity age power functions were introduced into the linear models. The predictor variable (fixed variables in Tables 2 and 3) coefficients were used to predict total body lean mass at various ages from APHV [when peak height velocity (PHV) = 0]. Height was controlled in prediction equations using population averages at each biological age category (Table 1).


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  		Table 2. Multilevel regression models for total body, trunk, arm, and leg lean mass of boys aligned by biological age

 

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  		Table 3. Multilevel regression models for total body, trunk, arm, and leg lean mass of girls aligned by biological age

 

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  		Table 1. Descriptive statistics of biological age-related anthropometric, body composition, and physical activity data

 

   RESULTS
TOP
 ABSTRACT
 METHODS
 RESULTS
DISCUSSION
 GRANTS
 ACKNOWLEDGMENTS
 REFERENCES
 
Stature and body mass values were within normal reference standards (34) for all chronological ages in both sexes. Anthropometric, body composition, and PA data for the adolescents aligned by biological age (years from PHV) and sex are presented in Table 1. Boys were significantly older, taller, heavier, and had greater total body BMC than girls at all biological maturity ages, –4 yr pre-PHV to +4 yr post-PHV (P < 0.05). No significant sex differences were found in total body FM until 1 yr after the attainment of PHV (+1) (P > 0.05). Figure 1 shows the developmental curves for the lean mass parameters. When aligned by biological age, boys had greater lean mass than girls at all ages and at all body sites (P < 0.05) (Fig. 1 and Table 1). The mean differences between body weight and sum of DXA parts (bone mineral, fat and lean mass) (Table 1) was 0.7 ± 0.4 kg, when outliers (>3 SD) were removed. No significant differences in PA scores were found, apart from at –3 and –1 yr from PHV. For subsequent analysis, the sexes were split.


Figure 1
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  		Fig. 1. Development of boys' and girls' lean mass for total body (A), trunk (B), arms (C), and legs (D) aligned by biological maturity age [years from age at peak height velocity (PHV)]. Values are means ± SE.

 
Table 2 summarizes the results from the multilevel models for total body, trunk, arm, and leg lean mass development for boys. To shape the individual curves, and thus make the models nonlinear, power functions of biological age (biological age2 and biological age3) were added as fixed effects. The model for boys' total body lean mass (Table 2) indicates that, once biological age (1 yr predicts 1,119 g of lean mass) and height (1 cm predicts 738.7 g of lean mass) are controlled, a significant independent PA effect is found (a score of 1 predicts 484.7 g of lean mass) (P < 0.05). This indicates that a boy who had a PA score of 1 (1 * 484.7 = 484.7) had 1,938.8 g less total body lean mass than a boy of the same biological age and height with a PA score of 5 (5 * 484.7 = 2,423.5, 2,423.5 – 484.7 = 1,938.8). Once the effects of biological age and height were controlled at the other three sites, PA was also found to be a significant independent predictor of arm (69.6 ± 27.2 g, P < 0.05), leg (197.7 ± 60.5 g, P < 0.05), and trunk lean mass (249.1 ± 91.4 g, P < 0.05). A biological age by PA group interaction coefficient was added to the models, but was not significant (i.e., the estimated mean for the coefficient was <2 * SEE, P > 0.05), indicating that the PA group difference was the same at all biological ages.

Biological age was also added as a random coefficient (Table 2). The random effects coefficients describe the two levels of variance [within individuals (level 1 of the hierarchy) and between individuals (level 2 of the hierarchy)]. In boys, at all sites, the significant variances at level 1 of the models indicate that lean mass was increasing significantly at each measurement occasion within individuals (estimate < 2 * SEE; P < 0.05). The between-individuals variance matrix (level 2) for each model indicated that individuals had significantly different lean mass growth curves, both in terms of their intercepts (constant/constant, P < 0.05), and the slopes of their lines (biological age/biological age, P < 0.05). The variance of these intercepts and slopes was positively and significantly correlated (constant/biological age, P < 0.05) in all four models. The variance around the average line was, therefore, different at different biological ages.

Similar results were found in girls for both fixed and random effects (Table 3). The fixed effects indicated that PA was a significant independent predictor of total body (306.9 ± 96.6 g, P < 0.05), arm (31.4 ± 15.5 g, P < 0.05), leg (162.9 ± 40.0 g, P < 0.05), and trunk lean mass (119.6 ± 58.2 g, P < 0.05), although it is noted that the coefficients were between 18 and 55% lower than those observed in the boys. Similar to the boys, biological age by PA group interaction coefficients were not significant.

The significant effects of PA on total body lean mass models are illustrated in Fig. 2. In this figure, values from Tables 2 and 3 were used to predict the average growth curve for a boy A and girl C, who had an activity score of 5 and average statures at each biological age (Table 1). These data were compared with a boy B and girl D, who had an activity score of 1.0 and who had average statures at each biological age (data from Table 1). Both scenarios reflect the extremes of the observed distribution of these factors and serve to illustrate their influences on total body lean mass growth in adolescence. The significant difference between the two boys and girls is associated with PA levels.


Figure 2
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  		Fig. 2. Predicted total body lean mass accrual for a boy A and girl D, who had an activity score of 5 (high) at each biological age (years from PHV) compared with a boy B and girl C, who had an activity score of 1.0 (low) at each biological age (years from PHV). The height values are taken as the mean values shown in Table 1. Values are predicted means from models in Tables 2 and 3.

 

   DISCUSSION
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
GRANTS
 ACKNOWLEDGMENTS
 REFERENCES
 
The main finding of this study is that an increase of 1 SD in habitual PA score (SD = 0.71) will increase lean mass by 344 g in boys and 218 g in girls, when the confounders of biological maturity and stature are controlled. The results indicate a sex difference in that, with an increase in the PA score of 1 SD of PA score, there is >50% greater increase in total body lean mass accrual for boys compared with girls (344 vs. 218 g), when biological maturity and stature are controlled. The average PA level score for boys was 2.9 ± 0.71; thus increasing PA by 1 SD above the mean at each maturity age would add an additional 1.3% total body lean mass at –4 yr from PHV, 0.8% at PHV, and 0.6% 4 yr after PHV, when growth in stature is held constant. In contrast, the average PA level for girls was 2.7 ± 0.71, and increasing PA by 1 SD above the mean at each maturity age would add an additional 1.0% total body lean mass at –4 yr from PHV, 0.7% at PHV, and 0.5% 4 yr after PHV, when growth in stature is held constant.

Few longitudinal studies have examined the influence of habitual PA on lean mass accrual among free-living adolescence, with the majority only partitioning out fat mass (FM) from fat free mass (FFM). For example, a study of 40 Polish boys grouped the boys as regularly trained (>6 h/wk), moderately trained (4 h/wk but not on a regular basis), and untrained (<2 h of sport/wk) and followed them from 11 to 18 yr of age (35). The results showed that FFM was comparable at age 11 yr, but then increased more in the regularly trained boys compared with the other groups. The differences in FM remained minimal between the regularly active and untrained boys during adolescence. Others have shown the cross-sectional association between PA and lean mass or FFM in children (12, 21, 25). In general, these studies suggest that habitual PA explains a small portion (<5–10%) of the total variance in lean mass, which is consistent with our findings. Exercise training studies also show small changes in FFM in adolescent subjects. For example, Eliakim and colleagues (14–16) reported a consistent mean increase of 3–4% in thigh muscle volume following a 5-wk training program in boys and girls. However, it is important to consider that most children do not engage in highly structured training regimens such as those imposed in exercise training studies; therefore, the importance of considering the influence of habitual, free-living PA over a period of time (e.g., the adolescent growth spurt) is a novel and practical aspect of the present study. On another note, our findings showing a lack of difference in PA between boys and girls is inconsistent with the literature (53). Nevertheless, our findings suggest the importance of PA during the adolescent growth period on lean mass accrual. This finding is important, since adolescence represents not only the period of the lifespan when PA levels decrease substantially (41, 45, 52), but also a time when substantial changes in body composition are occurring, so it becomes more important to ascertain the positive influence of habitual PA. Furthermore, the development of lean mass has important implications for metabolic health (56). Given our previous work on bone mass, a discussion of the link between lean mass and bone mass development will be considered below. However, it should also be acknowledged that lean mass plays a potential role in obesity and insulin resistance.

The mechanisms by which PA or mechanical stress influences lean mass accrual during growth and maturation are uncertain (11). However, it is generally agreed that the hormonal milieu plays an important role in the anabolic effects of PA. The primary basis for skeletal muscle growth during childhood and adolescence is the growth hormone (GH)/insulin-like growth factor I (IGF-I) axis (11, 38), androgens (estrogen and testosterone) and their interaction with GH (39), and insulin (22). Cooper (11) has proposed a conceptual model of the exercise modulation of growth, which includes both central and local components. The central component includes mechanisms that affect cellular growth throughout the body, namely through the actions of GH-IGF-I. The local component includes mechanisms that stimulate growth of tissue specific to the exercised tissue. These mechanisms may act through either autocrine or paracrine actions of IGF-I or fibroblast growth factors in response to a variety of "signals" (i.e., stretch, changes in tissue PO2, etc.). However, in the growing and maturing child, the combination of relatively high levels of habitual PA and puberty-related increases in anabolic hormones make it difficult to specify the independent effects of PA and hormones on lean mass accrual. On the other hand, it is also possible that there are integrated mechanisms by which PA influences these metabolic processes (11).

As previously mentioned, the results indicate that only a modest portion of the total variance in lean mass can be independently accounted for by PA. It is known that lean mass is highly heritable, and previous studies in adults suggest that genetic factors account for as much as 80–90% of the variance in lean tissue (23, 32, 43). Limited genetic studies of children and adolescence are available. One study (29) of 105 twin pairs between 10 and 14 yr of age showed several important findings that may be related to the present study. First, univariate models revealed that the largest part (87–95%) of the variance for muscle circumferences at most ages was explained by additive genetic factors. Second, sex differences were observed for some age categories. Third, multivariate models showed age- and sex-specific patterns, which may suggest pubertal influences. Besides showing that muscle circumferences are highly heritable characteristics, this study also indicated that pubertal events in boys and girls explain some of the variation in lean mass accrual.

It is well known that muscle mass and bone mass are closely associated (13). The correlation between LBM and bone mineral content (BMC) is especially close during growth and maturation (30, 56). It has been postulated that the statistical association between LBM and BMC reflects a direct cause-and-effect relationship, the mechanostat theory (18). According to this hypothesis, the skeleton continually adapts its strength to the loads to which it is exposed to keep bone deformation within safe limits. The largest physiological loads on the skeleton result from muscle contraction, which puts severalfold larger stresses on the skeleton than the simple effect of gravity (10). Mechanostat theory, therefore, predicts that the increasing muscle mass (and thus force) during development creates the stimulus for bone to increase its mass (and thus its strength). We have previously shown in this cohort that the maximal rate of LBM accrual occurs a few months before the maximal increase in BMC (24) and that the peak rates of change in these two measures are closely correlated (37); these observations are in accordance with the mechanostat theory. In relation to PA, our laboratory has previously shown, when the PBMAS subjects were quartiled for activity, that the most active quartile had greater total body and femoral neck BMC peak accrual, and greater accrual over 2 yr around the peak, than their inactive peers, when the confounders of maturation and size were controlled (3). However, when the data were analyzed using the multilevel modeling procedure, an age-dependent effect of PA on bone mineral accrual was only found in girls at the total body (7). These findings suggest that the higher bone mineral accruals observed in the most active children are compatible with the view that bone development is driven by muscle development. Given the present findings, we further postulate that the muscle development driving bone development is driven in part by PA. However, it is important to note that the data do not exclude the hypothesis that the two processes are independently determined by genetic mechanisms.

A major limitation in this study pertains to the assessment of habitual PA. There are several tools to quantify PA, including subjective and objective measures (46). These tools also fall across a spectrum of feasibility and accuracy. In 1991 when the study was initiated, the technology with regards to objective measures was not as sophisticated as it is today; thus this study used a subjective survey to quantify habitual PA. This is a common limitation of longitudinal studies in that the instruments chosen at the beginning of the study are not necessarily the ones that would be chosen at the end of the study. Although this instrument (PAQ-C/A) has consistently demonstrated acceptable internal consistency in diverse samples of children and modest validity compared with a variety of other instruments, other objective instruments (e.g., accelerometry) would provide more accurate measures. However, two or three activity assessments on any given subject were provided in any one year. Thus the estimate of habitual PA was generally more representative of typical activity. In addition, it is also important to note that this study examined habitual, free-living PA as opposed to exercise training or sport. Here it is important to recognize that an exercise training program is brief in duration (e.g., 3–6 mo) in the lives of children and adolescence, and sport occupies only a portion of the total daily activity (55).

In summary, this study is unique in examining longitudinally the influence of PA on the development of lean mass during adolescence. The results of this study and previous studies from the PBMAS showing the influence of PA on bone mass (3) and FM (31) indicate the positive influence of PA on the growth of body composition during adolescence.


   GRANTS
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
ACKNOWLEDGMENTS
 REFERENCES
 
This study was supported in part by grants from the Canadian Institute of Health Research, the Saskatchewan Health Research Foundation, and the Canadian National Health and Research Development Program.


   ACKNOWLEDGMENTS
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 ACKNOWLEDGMENTS
REFERENCES
 
PBMAS group members include D. A. Bailey, A. D. G. Baxter-Jones, P. E. Crocker, K. S. Davison, D. T. Drinkwater, E. Dudzic, R. A. Faulkner, K. Kowalski, H. A. McKay, R. L. Mirwald, W. M. Wallace, and S. J. Whiting.


   FOOTNOTES
 

Address for reprint requests and other correspondence: A. D. G. Baxter-Jones, College of Kinesiology, Univ. of Saskatchewan, 87 Campus Dr., Saskatoon, SK S7N 5B2, Canada (e-mail: baxter.jones{at}usask.ca)

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.


   REFERENCES
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 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 ACKNOWLEDGMENTS
 REFERENCES
 

  1. Armstrong N, Welsman JR. Peak oxygen uptake in relation to growth and maturation in 11- to 17-year-old humans. Eur J Appl Physiol 85: 546–551, 2001.[Web of Science][Medline]
  2. Bailey DA, Faulkner RA, McKay HA. Growth, physical activity, and bone mineral acquisition. Exerc Sport Sci Rev 24: 233–266, 1996.[Medline]
  3. Bailey DA, McKay HA, Mirwald RL, Crocker PR, Faulkner RA. A six-year longitudinal study of the relationship of physical activity to bone mineral accrual in growing children: the University of Saskatchewan bone mineral accrual study. J Bone Miner Res 14: 1672–1679, 1999.[CrossRef][Web of Science][Medline]
  4. Bar-Or O, Baranowski T. Physical activity, adiposity, and obesity among adolescents. Pediatr Exerc Sci 6: 348–360, 1994.
  5. Baxter-Jones A, Mirwald R. Multilevel modeling. In: Methods in Human Growth Research, edited by Hauspie RC, Cameron N, Molinari L. Cambridge, UK: Cambridge University Press, 2004, p. 306–330.
  6. Baxter-Jones ADG, Eisenmann JC, Sherar LB. Controlling for maturation in pediatric exercise science. Pediatr Exerc Sci 17: 18–30, 2005.
  7. Baxter-Jones ADG, Faulkner RA, Whiting SJ. Interaction between nutrition, physical activity and skeletal health. In: Nutritional Aspects of Bone Health, edited by New SA, Bonjour JP. Cambridge, UK: The Royal Society of Chemistry, 2003, p. 545–564.
  8. Baxter-Jones A, Goldstein H, Helms P. The development of aerobic power in young athletes. J Appl Physiol 75: 1160–1167, 1993.[Abstract/Free Full Text]
  9. Baxter-Jones AD, Mirwald RL, McKay HA, Bailey DA. A longitudinal analysis of sex differences in bone mineral accrual in healthy 8–19-year-old boys and girls. Ann Hum Biol 30: 160–175, 2003.[CrossRef][Web of Science][Medline]
  10. Burr DB. Muscle strength, bone mass, and age-related bone loss. J Bone Miner Res 12: 1547–1551, 1997.[CrossRef][Web of Science][Medline]
  11. Cooper DM. Evidence for and mechanisms of exercise modulation of growth–an overview. Med Sci Sports Exerc 26: 733–740, 1994.[CrossRef][Web of Science][Medline]
  12. Deeheeger M, Rolland-Cachera MF, Fontvieille AM. Physical activity and body composition in 10 year old French children: linkages with nutritional intake? Int J Obes Relat Metab Disord 21: 372–379, 1997.[CrossRef][Web of Science][Medline]
  13. Doyle F, Brown J, Lachance C. Relation between bone mass and muscle weight. Lancet 1: 391–393, 1970.[Web of Science][Medline]
  14. Eliakim A, Scheett T, Allmendinger N, Brasel JA. Training, muscle volume, and energy expenditure in nonobese American girls. J Appl Physiol 90: 35–44, 2001.[Abstract/Free Full Text]
  15. Eliakim A, Brasel JA, Mohan S, Wong WL, Cooper DM. Increased physical activity and the growth hormone-IGF-I axis in adolescent males. Am J Physiol Regul Integr Comp Physiol 275: R308–R314, 1998.[Abstract/Free Full Text]
  16. Eliakim A, Barstow TJ, Brasel JA, Ajie H, Lee WN, Renslo R, Berman N, Cooper DM. Effect of exercise training on energy expenditure, muscle volume, and maximal oxygen uptake in female adolescents. J Pediatr 129: 537–543, 1996.[CrossRef][Web of Science][Medline]
  17. Forwood MR, Bailey DA, Beck TJ, Mirwald RL, Baxter-Jones AD, Uusi-Rasi K. Sexual dimorphism of the femoral neck during the adolescent growth spurt: a structural analysis. Bone 35: 973–981, 2004.[CrossRef][Web of Science][Medline]
  18. Frost HM. Muscle, bone, and the Utah paradigm: a 1999 overview. Med Sci Sports Exerc 32: 911–917, 2000.[Web of Science][Medline]
  19. Goldstein H. Multilevel Statistics Models. London: Arnold, 1995.
  20. Goldstein H, Browne W, Rasbash J. Multilevel modelling of medical data. Stat Med 21: 3291–3315, 2002.[CrossRef][Web of Science][Medline]
  21. Heelan KA, Eisenmann JC. Physical activity, media time, and body composition in young children. J Phys Act Health 3: 200–209, 2006.
  22. Horswill CA, Zipf WB, Kien L, Kahle EB. Insulin's contribution to growth in children and the potential for exercise to mediate insulin's action. Pediatr Exerc Sci 9: 18–32, 1997.
  23. Huygens W, Thomis MA, Peeters MW, Vlietinck RF, Beunen GP. Determinants and upper-limit heritabilities of skeletal muscle mass and strength. Can J Appl Physiol 29: 186–200, 2004.[Web of Science][Medline]
  24. Iuliano-Burns S, Mirwald RL, Bailey DA. Timing and magnitude of peak height velocity and peak tissue velocities for early, average, and late maturing boys and girls. Am J Hum Biol 13: 1–8, 2001.[CrossRef][Web of Science][Medline]
  25. Janz KF, Levy SM, Burns TL, Torner JC, Willing MC, Warren JJ. Fatness, physical activity, and television viewing in children during the adiposity rebound period: the Iowa Bone Development Study. Prev Med 35: 563–571, 2002.[CrossRef][Web of Science][Medline]
  26. Kowalski K, Crocker PR, Faulkner RA. Validation of the physical activity questionnaire for older children. Pediatr Exerc Sci 9: 174–186, 1997a.
  27. Kowalski K, Crocker PR, Faulkner RA. Validation of the physical activity questionnaire for adolescents. Pediatr Exerc Sci 9: 342–352, 1997b.
  28. Lazzer S, Boirie Y, Poissonnier C, Petit I, Duche P, Taillardat M, Meyer M, Vermorel M. Longitudinal changes in activity patterns, physical capacities, energy expenditure, and body composition in severely obese adolescents during a multidisciplinary weight-reduction program. Int J Obes (Lond) 29: 37–46, 2005.[CrossRef][Medline]
  29. Loos R, Thomis M, Maes HH, Beunen G, Claessens AL, Derom C, Legius E, Derom R, Vlietinck R. Gender-specific regional changes in genetic structure of muscularity in early adolescence. J Appl Physiol 82: 1802–1810, 1997.[Abstract/Free Full Text]
  30. Manzoni P, Brambilla P, Pietrobelli A, Beccaria L, Bianchessi A, Mora S, Chiumello G. Influence of body composition on bone mineral content in children and adolescents. Am J Clin Nutr 64: 603–607, 1996.[Abstract/Free Full Text]
  31. Mundt CA, Baxter-Jones ADG, Whiting SJ, Bailey DA, Faulkner RA, Mirwald RL. Relationships of activity and sugar drink intake on fat mass development in youths. Med Sci Sports Exerc 38: 1245–1254, 2006.[CrossRef][Web of Science][Medline]
  32. Nguyen TV, Howard GM, Kelly PJ, Eisman J. Bone mass, lean mass, and fat mass: same genes or same environments? Am J Epidemiol 147: 3–16, 1998.[Abstract/Free Full Text]
  33. Nurmi-Lawton JA, Baxter-Jones AD, Mirwald RL, Bishop JA, Taylor P, Cooper C, New SA. Evidence of sustained skeletal benefits from impact-loading exercise in young females: a 3-year longitudinal study. J Bone Miner Res 19: 314–322, 2004.[CrossRef][Web of Science][Medline]
  34. Ogden CL, Kuczmarski RJ, Flegal KM, Mei Z, Guo S, Wei R, Grummer-Strawn LM, Curtin LR, Roche AF, Johnson CL. Centers for Disease Control and Prevention 2000 growth charts for the United States: improvements to the 1977 National Center for Health Statistics version. Pediatrics 109: 45–60, 2002.[Abstract/Free Full Text]
  35. Parizkova J. Longitudinal study of the relationship between body composition and anthropometric characteristics in boys during growth and development. Antropoloskog Drustva Jugoslavije 7: 33–38, 1970.
  36. Preece MA, Baines MJ. A new family of mathematical models describing the human growth curve. Ann Hum Biol 5: 1–24, 1978.[CrossRef][Web of Science][Medline]
  37. Rauch F, Bailey DA, Baxter-Jones A, Mirwald R, Faulkner R. The ‘muscle-bone unit' during the pubertal growth spurt. Bone 34: 771–775, 2004.[CrossRef][Web of Science][Medline]
  38. Roemmich JN, Rogol AD. Exercise and growth hormone: does one affect the other? J Pediatr 131: S75–S80, 1997.[CrossRef][Web of Science][Medline]
  39. Rogol AD. Growth at puberty: interaction of androgens and growth hormone. Med Sci Sports Exerc 26: 767–770, 1994.[CrossRef][Web of Science][Medline]
  40. Ross WD, Marfell-Jones MJ. Kinanthropometry. In: Physiological Testing of the High Performance Athlete, edited by Green H. Champaign, IL: Human Kinetics, 1991, p. 223–308.
  41. Sallis JF. Age-related decline in physical activity: a synthesis of human and animal studies. Med Sci Sports Exerc 32: 1598–1600, 2000.[Web of Science][Medline]
  42. Schlumpf M, Eiholzer U, Gygax M, Schmid S, van der Sluis I, l'Allemand D. A daily comprehensive muscle training programme increases lean mass and spontaneous activity in children with Prader-Willi syndrome after 6 months. J Pediatr Endocrinol Metab 19: 65–74, 2006.[Web of Science][Medline]
  43. Seeman E, Hopper JL, Young NR, Formica C, Goss P, Tsalamandris C. Do genetic factors explain associations between muscle strength, lean mass, and bone density? A twin study. Am J Physiol Endocrinol Metab 270: E320–E327, 1996.[Abstract/Free Full Text]
  44. Selvadurai HC, Blimkie CJ, Meyers N, Mellis CM, Cooper PJ, Van Asperen PP. Randomized controlled study of in-hospital exercise training programs in children with cystic fibrosis. Pediatr Pulmonol 33: 194–200, 2002.[CrossRef][Web of Science][Medline]
  45. Sherar LB, Esliger DW, Baxter-Jones ADG, Tremblay MS. Age and gender related differences in childhood physical activity: does physical maturity matter? Med Sci Sports Exerc 39: 830–835, 2007.[CrossRef][Web of Science][Medline]
  46. Sirard JR, Pate RR. Physical activity assessment in children and adolescents. Sports Med 31: 439–454, 2001.[CrossRef][Web of Science][Medline]
  47. Slattery ML, Dyer A, Jacobs DR Jr, Hilner JE, Caan BJ, Bild DE, Liu K, McDonald A, Van Horn L, Hardin M. A comparison of two methods to ascertain dietary intake: the CARDIA Study. J Clin Epidemiol 47: 701–711, 1994.[CrossRef][Web of Science][Medline]
  48. Slosman D, Rizzoli R, Donath A, Bonjour JP. Vertebral bone mineral density measured laterally by dual energy x-ray absorptiometry. Osteoporos Int 1: 23–29, 1990.[CrossRef][Medline]
  49. Strong WB, Malina RM, Blimkie CJ, Daniels SR, Dishman RK, Gutin B, Hergenroeder AC, Must A, Nixon PA, Pivarnik JM, Rowland T, Trost S, Trudeau F. Evidence based physical activity for school-age youth. J Pediatr 146: 732–737, 2005.[CrossRef][Web of Science][Medline]
  50. Suman OE, Thomas SJ, Wilkins JP, Mlcak RP, Herndon DN. Effect of exogenous growth hormone and exercise on lean mass and muscle function in children with burns. J Appl Physiol 94: 2273–2281, 2003.[Abstract/Free Full Text]
  51. Tanner JM. Growth at Adolescence. Oxford: Blackwell, 1962.
  52. Trost SG, Pate RR, Sallis JF, Freedson PS, Taylor WC, Dowda M, Sirard J Age and gender differences in objectively measured physical activity in youth. Med Sci Sports Exerc 34: 350–355, 2002.[CrossRef][Web of Science][Medline]
  53. Welsman JR, Armstrong N, Kirby BJ, Winsley RJ, Parsons G, Sharpe P. Exercise performance and magnetic resonance imaging-determined thigh muscle volume in children. Eur J Appl Physiol 76: 92–97, 1997.[CrossRef][Web of Science]
  54. Wickel EE, Eisenmann JC. Contribution of youth sport to total daily physical activity among 6- to 12-yr-old boys. Med Sci Sports Exerc 39: 1493–1500, 2007.[CrossRef][Web of Science][Medline]
  55. Witzke KA, Snow CM. Lean body mass and leg power best predict bone mineral density in adolescent girls. Med Sci Sports Exerc 31: 1558–1563, 1999.[CrossRef][Web of Science][Medline]
  56. Wolfe RR. The underappreciated role of muscle in health and disease. Am J Clin Nutr 84: 475–82, 2006.[Abstract/Free Full Text]



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Six-year longitudinal analysis shows physical activity impacts on lean mass development in adolescence
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