The benefits of regularly engaging in moderate-to-vigorous physical activity (MVPA) among children and adolescents are well established and include contributions to physical, mental, and social health outcomes (21). Public health guidelines state that children and adolescents should engage in 60 min of MVPA daily, which can be achieved cumulatively throughout the day in bouts (21). A considerable proportion of children and adolescents, however, fail to meet recommended levels of physical activity (PA) (5,12,22,25). This is particularly evident for girls; they are less physically active than boys (5,22), with the sharpest declines observed in adolescence (7,12).
Participation in organized sports (OS) has been recommended as an approach to increase PA (27). In Australia, yearly prevalence data indicate that approximately 69% of children (67% of girls) participate in at least one OS (including dance) outside of school hours (1). A recent systematic review found that children who participated in OS were more physically active than those who did not participate (13). However, the amount of PA that girls achieve during OS is unclear.
Of the few studies that have examined PA in OS, one study found that during soccer games, children were in MVPA for 33% of the match (18), and another study found that approximately 46% of practice time was spent in MVPA (9). However, children also spend high percentages of time during OS inactive or in light PA (6,9).
Despite these findings that children may be less than optimally active during OS practices and games, OS contributed close to 25% of daily MVPA (18,28). These studies provide evidence to suggest that OS can contribute substantially to MVPA levels; however, current literature has only examined PA in either OS practices or games. To date, a complete picture of OS is lacking, where PA has been examined in both practices and games for the same participants to provide comparisons between conditions. Furthermore, it remains unclear with respect to opportunity for PA how time is spent and how coaches conduct themselves during practices and games in OS.
The primary aim of this study was to objectively examine PA levels of girls during OS, and to compare the levels between games and practices for the same participants. The secondary aims of this study were to document lesson context and to coach behavior during practices and games.
A total of 94 girls aged between 11 and 17 yr (mean ± SD age = 13.4 ± 2.2 yr) participated in this study. Participants were recruited from OS clubs playing netball, basketball, and outdoor soccer in the western suburbs of Sydney, Australia. These three OS were chosen because of their popularity among girls in Australia (1). A convenience sample of 10 teams from the three sports was recruited (4 netball, 3 basketball, and 3 soccer). Initially, a member of the OS clubs executive committee was contacted by the primary investigator (JMG) to provide information about the study protocol. Interested clubs then provided the primary investigator with contact details of interested coaches. Detailed study information was then sent to coaches and parents. Participants were included based on their willingness to participate in the study. Before study commencement, informed consent and assent was obtained from coaches, parents, and athletes. The Human Research Ethics Committee of the University of Western Sydney approved this study.
Between May and August 2011, a team consisting of the primary investigator (JMG) and a female research assistant attended practices and games of all participating teams. The team observed one practice and one game, except netball. The netballers practiced twice a week, with one of those practices dedicated solely to fitness. Therefore, an additional practice was observed (one fitness session and one skill session) for this sport. There was an average of 13 and 15 d between observed practices and games for netball and soccer, respectively, and an average of 11 d between observed practices, games, and fitness sessions for netball, respectively. Before the practice, participating girls were taken to a semiprivate measurement area to be assessed on height, weight, and waist circumference. After anthropometric measurements, girls were fitted with an accelerometer that was placed on the right hip (described in the next section) and worn for the duration of the practice. Most girls on each team wore accelerometers across sports (netball, 38/39; basketball, 28/30; and soccer, 28/43). In addition to accelerometry, the System for Observing Fitness Instruction Time (SOFIT) (11) direct observation system was used by the primary investigator (described in the Direct Observation section). The protocol for games was the same as practice protocols, except that anthropometric measures were not taken.
Before measurement, girls were asked to remove shoes and heavy clothing. Standing height was measured to the nearest 0.1 cm using a portable stadiometer (PE87 portable stadiometer; Mentone Educational, Victoria, Australia). Weight was measured using a digital scale (EF 538 HealthStream digital scale; Aussie Fitness, Queensland, Australia) to the nearest 0.1 kg. Body mass index (BMI) was calculated and converted into age- and sex-specific percentiles using the Centers for Disease Control and Prevention growth charts (8). Waist circumference was measured on the right side of the body by finding the midpoint between the lowest rib and the iliac crest. A nonelastic tape measure (Myotape; Mentone Educational) was wrapped snugly around the waist, and measurement was taken at the end of exhalation to the nearest 0.1 cm. Measurements were conducted in duplicate for all assessments, and an average was recorded. A third measurement was taken if the first two measures differed by more than 0.5 cm or 0.5 kg, and the average was recorded. All anthropometric measures were used for descriptive purposes.
The ActiGraph GT3X accelerometer (ActiGraph, Pensacola, FL) was used to assess PA levels in this study. ActiGraph accelerometers are the most widely used accelerometers and have been shown to be valid and reliable devices for PA measurement in children and adolescents (16,23). Accelerometers were initialized to record counts and steps with 5-s epochs specified to capture efectively the intermittent activity patterns of children and adolescents (24).
Accelerometers were synchronized with an external clock and initialized to start recording a minimum of 30 min before and after the scheduled practice and/or game time. Start and finish times were recorded for every practice and game via direct observation to trim excess data outside the recorded start and finish time. Participating coaches and athletes were instructed by the research team not to change activities or the way they practiced or played games during observation.
After each practice or game, raw accelerometer counts were uploaded to a computer using ActiGraph software and saved to a Microsoft Excel file. Data outside the recorded start and finish time for given sessions were disregarded. Data that did not coincide with the direct observation records were checked for spurious values; all data between start and finish times for all practices and games were included in the analyses. Freedson’s MET prediction equation was used to determine PA intensity (4). The age-specific counts per minute were divided by 12 to account for our 5-s epochs. PA intensity was classified as follows: sedentary (SED) ≤100 counts per minute; light PA (LPA), ≥1.5 METs <4; moderate PA (MPA), ≥4 METs <7; and vigorous PA (VPA), ≥7 METs. Although a strong consensus does not exist regarding appropriate selection of MET intensity thresholds for children and adolescents (23), those selected for this study have been used in an adolescent female population (15).
To complement accelerometry, SOFIT was used in this study to provide contextual data on PA in OS. SOFIT is a widely used direct observation system that uses momentary time sampling to generate data on participant PA, lesson context, and instructor (or for our purposes, coach) behavior (11). SOFIT has demonstrated acceptable reliability and validity in a pediatric population (11,17). Typically, SOFIT is used for structured PA sessions such as physical education classes, and OS provides a similar environment, led by a coach instead of a teacher. Although SOFIT can be easily implemented in an OS setting, only one report that we are aware of has used the direct observation system in OS (14).
With SOFIT, four (plus one alternate) participants are quasi-randomly and furtively selected before session commencement by dividing the total number of participants attending a given session by five to inform selection order (e.g., 15/5 = 3, so every third participant is selected). On a rotational basis, the PA levels, lesson context, and coach behavior were coded and recorded on paper every 20 s via a looped voice recording that prompted the observer to observe and record. However, PA data from SOFIT were not used in this study because of the availability of accelerometer data that render PA data at the individual level.
The OS lesson context was coded into one of six mutually exclusive categories: management, knowledge delivery, fitness, skill practice, game play, and free play at the end of each 10-s observe interval. Coach behavior was coded using a hierarchical format and included the following (in hierarchal order): promotes PA (includes prompts of encouragement and praise) or discourages PA (includes prompts that are sarcastic and punitive in nature), demonstrates PA, and others. Therefore, promotes PA or discourages PA was recorded if it occurred at any time during the 10-s observe interval, whereas other was only scored if the other categories were not observed during the 10-s observe interval. Multiple coding was only permitted if promotes PA or discourages PA and demonstrates PA were observed at any time during the 10-s observe interval. SOFIT was used at each practice and game. The primary investigator (JMG) was fully trained to use the observation technique and conducted all direct observations.
The implementation of SOFIT is important to this study. SOFIT has not been used in these particular sports; therefore, important information regarding lesson context and coach behavior is unknown. In other words, how time is spent and how coaches conduct themselves concerning PA during practices and games in OS is unknown (13). Generating data on lesson context and coach behavior is best achieved through direct observation, as self-report data may be unreliable or otherwise biased (11).
All statistical analyses were analyzed using the Statistical Package for the Social Sciences (Version 18.0; Chicago, IL). The mean differences between practices and games for each PA intensity (SED, LPA, MPA, VPA, and MVPA), steps per hour, lesson context variables, and coach behavior variables were analyzed using paired samples t-tests. ANOVA was used to examine the differences in means for the anthropometric measures collected for girls in each OS. Descriptive statistics included mean and SD values. Statistical significance was set at P < 0.05.
Table 1 displays physical characteristics of participants by sport. Physical characteristics were assessed for 93 (98.9%) of 94 participants, as one participant was missing due to absence. The mean ± SD age of the participants was 13.4 ± 2.2 yr. On the basis of age- and sex-specific growth charts, the average height (164.1 ± 8.1 cm), weight (56.9 ± 10.9 kg), and BMI (21.0 ± 3.2 kg·m−2) for all participating athletes corresponded approximately to the 75th percentile (8). Significant mean differences were found among sports for age, weight, and BMI (P < 0.05).
PA intensity during practice and games
Table 2 displays PA intensity (percent time) and step counts (per hour) at practice and games for each OS. Participants with intact data (attended both the observed practice and game) were 82 (87.2%) of 94 participants. The mean ± SD duration values, across OS, for practice was 82.6 ± 22.6 and 90.8 ± 13.7 min for games. Across OS, the overall mean for percent time in MVPA during practices was significantly higher (t = 2.94, P < 0.05) than during games. Significant mean differences for percent time were found for each PA intensity across OS. During practices, the mean for percent time for VPA, MPA, and LPA were found to be significantly higher than that during games (VPA: t = 2.67, P < 0.05; MPA: t = 2.14, P < 0.05; LPA: t = 5.18, P < 0.001). The mean percent time for SED was significantly lower (t = −5.20, P < 0.001) during practice than that during games.
The percentage of time spent in MVPA during games was slightly more homogenous than during practice. The only significant mean difference for percent time in MVPA between practice and games was found in basketball (t = −2.34, P < 0.05). With regard to LPA and SED, across all OS, participants spent a greater percentage of time in LPA (t = 6.71, P < 0.001) and a lower percentage of time SED (t = −4.82, P < 0.001) in practice compared with games.
Out of the three OS observed, netball was the only sport that dedicated one whole practice solely to fitness. During fitness practices, the mean percentage of time spent in MVPA was significantly greater than regular (skill-based) netball practices (t = 10.10, P < 0.001) and games (t = 8.73, P < 0.001). Also, LPA and SED were significantly lower at fitness practices compared with regular practices (LPA: t = −7.68, P < 0.001; SED: t = −2.89, P < 0.05) and games (LPA: t = −7.38, P < 0.001; SED: t = −4.49, P < 0.001).
Steps counts during practice and games
Across OS, participants accumulated significantly more steps per hour during practice than during games (t = 2.15, P < 0.05) (See Table 2). Among sports, netball fitness practices provided significantly more steps per hour compared with regular netball practices (t = 10.10, P < 0.001) and games (t = 9.50, P < 0.001).
OS contribution to recommended levels of PA
On average, OS contributed 18.4 min·h−1 of MVPA during games (netball, 18.8 min·h−1; basketball, 18.3 min·h−1; soccer, 17.5 min·h−1) and 20.3 min·h−1 during practice (netball, 20.2 min·h−1; basketball, 21.3 min·h−1; soccer, 18.9 min·h−1). Large proportions of time, however, were spent in SED, on average 23.3 min·h−1 during games (netball, 20.9 min·h−1; basketball, 29.3 min·h−1; soccer, 19.3 min·h−1) and 18.1 min·h−1 during practice (netball, 19.0 min·h−1; basketball, 19.6 min·h−1; soccer, 14.1 min·h−1). Fitness practices provided approximately 27 min·h−1 of MVPA and 16.3 min·h−1 SED.
Participants across sports accumulated 22.6% and 24.2% of the recommended 12,000 daily steps (26) in 1 h of game play (netball, 21.5% daily steps; basketball, 20.0% daily steps; soccer, 28.4% daily steps) and practice time (netball, 22.6% daily steps; basketball, 22.9% daily steps; soccer, 29.0% daily steps), respectively. During netball fitness practices, approximately 34.3% of the recommended 12,000 steps per day were accumulated every hour.
A total of 20 sessions were observed: eight netball (four games and four practices), six basketball (three games and three practices), and six soccer sessions (three games and three practices). Four fitness practices were also observed but were not included in the overall comparison across OS because they were exclusive to netball. Table 3 displays lesson context as the percentage of a session that was spent in each category.
Across OS, the percentage of time spent in the SOFIT categories of management, knowledge delivery, and free play did not significantly differ between practice and games. The mean percentages for fitness and skill practice were significantly higher during practice compared with games (fitness: t = 2.92, P < 0.05; skill practice: t = 4.66, P < 0.05), and the mean percentages for game play were significantly higher during games compared with practice (t = 6.99, P < 0.001).
Although there tended to be more occurrences per hour of both promotion and discouragement of PA during games compared with practice, means were not significantly different. There were significantly fewer occurrences per hour of coaches demonstrating PA during games than during practice (t = −2.95, P < 0.05). These trends were consistent for across OS.
To our knowledge, this is the first study to examine PA in Australian OS and to compare mean proportions of PA levels of girls during practice and games in OS using the same participants. As far as we are aware, it is also the first to provide additional insight on lesson context and coach behaviors during OS through the inclusion of SOFIT in the peer-reviewed literature.
Our observations of the three sports showed that girls achieved significantly higher levels of MVPA during practice compared with games; accumulating approximately 20 min·h−1 (∼34% time) in MVPA during practice and approximately 18 min·h−1 (∼30% time) in MVPA during games. The girls also accumulated an average of 2904 and 2709 steps per hour during practice and games, respectively. Therefore, for every hour of game play or practice time, girls accumulated approximately one third of the recommended 60 min of MVPA (21), and approximately one quarter of the 12,000 steps girls are recommended to accumulate daily (26). For this population, OS seems to make a substantial contribution to the recommended levels of PA of participating girls.
Our findings are comparable with the findings of earlier studies. Sacheck et al. (18) found approximately 33% of soccer games were spent in MVPA, whereas Leek et al. (9) examined PA levels during soccer and baseball/softball practices and found children spent 46% of the practice time in MVPA across sports. Consistent with the present findings, practices may provide more MVPA compared with games. A possible explanation for this difference may be that coaches are better able to dictate the intensity of a practice compared with a game. Also, a larger proportion of the team can participate simultaneously and in smaller groups, which can provide increased opportunities for players to participate at a higher PA intensity during practice, compared with a game.
A study by Wickel and Eisenmann (28) sought to determine the contribution of OS (mean duration in OS = 65 min) to daily PA. Similar to our findings, OS contributed substantially to the amount of recommended MVPA on days where children participated in OS (∼23% or 26 min) (28). The authors, however, indicated that this additional PA was not maintained on days without OS. These findings indicate that although OS alone does not provide amounts of PA sufficient to meet daily recommendations, it does provide an ideal opportunity to be physically active and to contribute to daily MVPA of participating children. Furthermore, evidence indicates that children who participate in OS are more active than those who do not and are more likely to meet recommended PA guidelines (13,20).
Although OS provides a substantial proportion of the recommended amounts of MVPA, there may be potential for improvement in the contribution that OS makes to daily MVPA. In our study, a considerable proportion of practice and game time was spent insufficiently active (SED or LPA). Significantly higher proportions of time were spent SED (∼39% vs ∼30% time) during game time compared with practice and vice versa for LPA (∼31% vs 36% time). On average, girls were SED or in LPA approximately 42 min·h−1 (∼70% time) during games and approximately 40 min·h−1 (∼67% time) during practice. This finding is consistent with other studies that have observed sizeable proportions of game or practice time spent SED or in LPA (6,9,18,28). Thus, there are clearly opportunities to increase MVPA, particularly during practices, in OS.
With the inclusion of SOFIT in this study, not only does it provide the first glimpse of how time is spent (lesson context) and coach behavior during these OS, it may also assist in identifying opportunities to increase MVPA, particularly during practice. To our knowledge, there are no peer-reviewed studies reporting use of SOFIT in OS, and only one published report has used SOFIT in an OS setting, where rugby league and rugby union practices were observed (14). Rugby coaches spent similar percentages of practice time in fitness (9% vs ∼9%) and game play (∼20% vs ∼23%) compared with present study findings across OS. However, coaches in the present study spent a considerably higher percentage of practice time in management (15% vs 11%) and knowledge delivery (∼19% vs 12%) and considerably lower percentage of practice time in skill practice (∼35% vs 44%) compared with rugby coaches.
Rugby players in the earlier report (14) spent a considerably higher percentage of time in skill practice compared with our participants. It is likely that PA levels are higher during skill practice; therefore, it is probable that rugby players had more opportunities to be physically active during practice. It is also likely that children would be relatively inactive while in management and knowledge delivery. This has recently been exhibited in a physical education setting; the authors found a significant negative correlation between MVPA and time spent in management and knowledge delivery (2). Therefore, decreasing the percentage of time coaches spend in management and knowledge delivery may be a strategy to consider in helping create an environment that provides the most opportunity for PA.
Lastly, our findings indicate that coaches tended to promote PA (includes prompts of encouragement and praise) more frequently than they tended to discourage PA (includes prompts that are sarcastic and punitive in nature) during both games and practice. Coaches demonstrated PA more often during practice than during games. Although one report (14) has used SOFIT in OS, direct comparisons of coach behavior could not be made due to differences in coding made by the authors for this phase of SOFIT. However, comparisons for promoting PA can be made with physical education teachers. Compared with physical education teachers, higher rates of promoting PA were found with coaches in the present study, which may lead to increased PA (3,10,19).
A few potential limitations should be considered when interpreting the current findings. First, the present study was not designed for comparison between sports but rather to describe PA levels of these three OS and to compare PA levels during games and practices. Second, a convenience sample was used; therefore, there is the potential for selection bias. Third, PA findings were based on a single observation period for each team, that is, one game, one practice, and one fitness practice (for netball). Lastly, participants were recruited from only one club for each sport, and thus our ability to generalize the current findings may be limited. Despite these limitations, the present study used objective measures that allow for a rigorous description of the PA levels that girls achieved during practice and games in OS with some of the highest participation rates in Australia.
In conclusion, both games and practices in OS seem to have made a substantial contribution to the accumulation of recommended amounts of daily MVPA and steps of participating girls. However, OS alone did not provide a sufficient amount of PA to meet daily recommendations for adolescent girls. Across OS, large proportions of time were spent in SED or LPA. Also, considerable percentages of time were spent in management and knowledge delivery. Therefore, there is room for improvement with regard to optimizing PA levels in OS, particularly during practice, without compromising fundamental lessons and skills taught by coaches. This information on OS can be used as a platform on which to inform policies and to develop strategies to increase adolescent girls’ PA levels through OS. Because PA levels were not monitored on non-OS days in the current study, future research should examine the contribution OS has on PA levels during days of OS compared with non-OS days for these sports. Furthermore, support should be provided to coaches in an effort to increase MVPA and decrease SED time in OS, without interfering with fundamental learning opportunities and skill development that occur in OS.
The authors thank participating organizations and our research assistant. They thank Dr. Chris Lonsdale for his constructive criticism of the manuscript.
This study was supported by the School of Science and Health at the University of Western Sydney.
The authors have no conflict of interest to declare.
The results of the present study do not constitute endorsement by the American College of Sports Medicine.
1. Australian Bureau of Statistics. Children’s Participation in Organised Sport and Dancing
[cited 2012 January 16]. Available from: http://www.abs.gov.au/ausstats/abs@.nsf/Products/4177.0∼200910∼Main+Features∼Characteristics+of+participation?OpenDocument.
2. Dudley DA, Okely AD, Cotton WG, Pearson P, Caputi P. Physical activity levels and movement skill instruction in secondary school physical education. J Sci Med Sport
. 2012; 15 (3): 231–7.
3. Fairclough S, Stratton G. Improving health-enhancing physical activity in girls’ physical education. Health Educ Res
. 2005; 20: 448–57.
4. Freedson P, Pober D, Janz KF. Calibration of accelerometer
output for children
. Med Sci Sports Exerc
. 2005; 37 (11 Suppl): S523–30.
5. Hardy LL, Okely AD, Dobbins TA, Booth ML. Physical activity among adolescents
in New South Wales (Australia): 1997 and 2004. Med Sci Sports Exerc
. 2008; 40 (5): 835–41.
6. Katzmarzyk P, Walker P, Malina R. A time-motion study of organized youth sports
. J Hum Mov Stud
. 2001; 40: 325–34.
7. Kimm SYS, Glynn NW, Kriska AM, et al.. Decline in physical activity in black girls and white girls during adolescence. New Engl J Med
. 2002; 347: 709–15.
8. Kuczmarski RJ, Ogden CL, Grummer-Strawn LM, et al.. CDC growth charts: United States. Adv Data
. 2000; 314: 1.
9. Leek D, Carlson JA, Cain KL, et al.. Physical activity during youth sports
practices. Arch Pediatr Adol Med
. 2011; 165: 294–300.
10. McKenzie TL, Catellier DJ, Conway T, et al.. Girls’ activity levels and lesson contexts in middle school PE: TAAG baseline. Med Sci Sports Exerc.
2006; 38 (7): 1229–35.
11. McKenzie TL, Sallis JF, Nader PR. SOFIT
: System for Observing Fitness Instruction Time. J Teach Phys Educ
. 1991; 11: 195–205.
12. Nader PR, Bradley RH, Houts RM, McRitchie SL, O’Brien M. Moderate-to-vigorous physical activity from ages 9 to 15 years. JAMA
. 2008; 300: 295–305.
13. Nelson TF, Stovitz SD, Thomas M, LaVoi NM, Bauer KW, Neumark-Sztainer D. Do youth sports
prevent pediatric obesity? A systematic review and commentary. Cur Sports Med Rep
. 2011; 10 (6): 360–70.
14. O’ Connor D, Cotton W. Community Junior Sports Coaching
[cited 2012 February 23]. Available from: http://www.dsr.nsw.gov.au/assets/pubs/industry/community_junior_sport_coaching_report.pdf
15. Okely A, Cotton W, Lubans D, et al.. A school-based intervention to promote physical activity among adolescent girls: Rationale, design, and baseline data from the Girls in Sport group randomised controlled trial. BMC Public Health
. 2011; 11: 658.
16. Plasqui G, Westerterp KR. Physical activity assessment with accelerometers: an evaluation against doubly labeled water. Obesity
. 2007; 15: 2371–9.
17. Rowe P, Schuldheisz J, Van der Mars H. Measuring physical activity in physical education: validation of the SOFIT
direct observation instrument for use with first to eighth grade students. Pediatr Exerc Sci
. 1997; 9: 136–49.
18. Sacheck J, Nelson T, Ficker L, Kafka T, Kuder J, Economos C. Physical activity during soccer and its contribution to physical activity recommendations in normal weight and overweight children
. Pediatr Exerc Sci
. 2011; 23: 281–92.
19. Sallis JF, McKenzie TL, Alcaraz JE, Kolody B, Faucette N, Hovell MF. The effects of a 2-year physical education program (SPARK) on physical activity and fitness in elementary school students. Sports, play and active recreation for kids. Am J Public Health
. 1997; 87: 1328–34.
20. Silva P, Sousa M, Aires L, et al.. Physical activity patterns in Portuguese adolescents
: the contribution of extracurricular sports. Eur Phys Ed Rev
. 2010; 16: 171–81.
21. Strong WB, Malina RM, Blimkie CJR, et al.. Evidence based physical activity for school-age youth. J Pediatrics
. 2005; 146: 732–7.
22. Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer
. Med Sci Sports Exerc
. 2008; 40: 181–8.
23. Trost SG, Loprinzi PD, Moore R, Pfeiffer KA. Comparison of accelerometer
cut points for predicting activity intensity in youth. Med Sci Sports Exerc
. 2011; 43: 1360–8.
24. Trost SG, McIver KL, Pate RR. Conducting accelerometer
-based activity assessments in field-based research. Med Sci Sports Exerc
. 2005; 37 (11 Suppl): S531–43.
25. Trost SG, Pate RR, Sallis JF, et al.. Age and gender differences in objectively measured physical activity in youth. Med Sci Sports Exerc
. 2002; 34: 350–5.
26. Tudor-Locke C, Craig CL, Beets MW, et al.. How many steps/day are enough? For children
. Int J Behav Nutr Phys Act
. 2011; 8: 78.
27. Washington R, Bernhardt D, Gomez J, et al.. Organized sports for children
and preadolescents. Pediatrics
. 2001; 107: 1459–62.
28. Wickel EE, Eisenmann JC. Contribution of youth sport to total daily physical activity among 6-to 12-yr-old boys. Med Sci Sports Exerc
. 2007; 39: 1493–1500.