Cardiorespiratory fitness is an important marker of childhood health and low fitness levels are a risk factor for disease later in life. Levels of children's fitness have declined in recent decades. Whether school-based physical activity interventions can increase fitness at the population level remains unclear.
Objective
To evaluate the effect of an internet-based intervention on children's cardiorespiratory fitness across a large number of schools.
Design, Setting, and Participants
In this cluster randomized clinical trial, 22 government-funded elementary schools (from 137 providing consent) including 1188 students stratified from grades 3 and 4 in New South Wales, Australia, were randomized. The other schools received the intervention but were not included in the analysis. Eleven schools received the internet-based intervention and 11 received the control intervention. Recruitment and baseline testing began in 2016 and ended in 2017. Research assistants, blinded to treatment allocation, completed follow-up outcome assessments at 12 and 24 months. Data were analyzed from July to August 2020.
Interventions
The internet-based intervention included standardized online learning for teachers and minimal in-person support from a project mentor (9-10 months).
Main Outcomes and Measures
Multistage 20-m shuttle run test for cardiorespiratory fitness.
Results
Of 1219 participants (49% girls; mean [SD] age, 8.85 [0.71] years) from 22 schools, 1188 students provided baseline primary outcome data. At 12 months, the number of 20-m shuttle runs increased by 3.32 laps (95% CI, 2.44-4.20 laps) in the intervention schools and 2.11 laps (95% CI, 1.38-2.85 laps) in the control schools (adjusted difference = 1.20 laps; 95% CI, 0.17-2.24 laps). By 24 months, the adjusted difference was 2.22 laps (95% CI, 0.89-3.55 laps). The cost per student was AUD33 (USD26).
Conclusions and Relevance
In this study, a school-based intervention improved children's cardiorespiratory fitness when delivered in a large number of schools. The low cost and sustained effect over 24 months of the intervention suggests that it may have potential to be scaled at the population level.
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School‐based physical education (PE) provides opportunities to accumulate moderate‐to‐vigorous physical activity (MVPA), but many students are insufficiently active during PE lessons. Providing teachers with feedback regarding their students’ physical activity may increase the effectiveness of PE for achieving MVPA goals, but existing physical activity monitoring technologies have limitations in class environments. Therefore, the purpose of this study was to develop and validate a system capable of providing feedback on PE lesson MVPA. Equations for translating step counts to %MVPA were derived from measures in 492 students who concurrently wore an ActiGraph GT3X+ (ActiGraph) and Yamax pedometer (Yamax) during a PE lesson. To enhance feedback availability during PE lessons, we then developed a bespoke monitoring system using wireless tri‐axial pedometers (HMM) and a smart device app. After developing and testing the monitoring system, we assessed its validity and reliability in 100 students during a PE lesson. There was a strong correlation of 0.896 between step counts and accelerometer‐determined %MVPA and quantile regression equations showed good validity for translating step counts to %MVPA with a mean absolute difference of 5.3 (95% CI, 4.4‐6.2). The physical activity monitoring system was effective at providing %MVPA during PE lessons with a mean difference of 1.6 ± 7.1 compared with accelerometer‐determined %MVPA (7% difference between the two measurement methods). Teachers and students can use a smart device app and wireless pedometers to conveniently obtain feedback during PE lessons. Future studies should determine whether such technologies help teachers to increase physical activity during PE lessons.
Despite widespread encouragement for children to participate in sport, the efficacy of early sporting pathways remains underexplored. We compared a rotational junior-sport model combining skills from rugby, cricket, and netball with a modified games model. Motion analysis was used to quantify movement. Results revealed no differences between sporting models in relative percent time spent stationary ( p = .32), walking ( p = .89), jogging ( p = .45), and fast running ( p =.06). The rotational model had a greater number of skill-development opportunities per minute (median = 3.4) compared with the modified games model (median = 1.1, p = .001). Promising results from varied and rotational skill exposure warrant further elucidation.
The quantification and evaluation of training practices in youth rugby players, including exposure to competition and training loads, is important for supporting long-term athletic development. Training loads in youth rugby are highly variable within and between players, and the characteristics of training practices have been shown to differ by age category, playing standard and region. This chapter presents the research that has explored the training practices and training loads of youth rugby players, including periodisation frameworks with macro- and mesocycle overviews, weekly training loads, session characteristics and comparisons to match-play demands. The second part of the chapter provides a practical overview on methods of training load measurement, data analysis, visualisation and communication, as well as considerations on training to competition ratios in youth rugby. The chapter concludes with a range of recommendations to practically monitor training loads of youth rugby players, as well as practical implications regarding data interpretation, communication and additional considerations to improve decision-making for key stakeholders.
Context: Most available data on athletic development training models focus on adult or professional athletes, where increasing workload capacity and performance is a primary goal. Development pathways in youth athletes generally emphasize multisport participation rather than sport specialization to optimize motor skill acquisition and to minimize injury risk. Other models emphasize the need for accumulation of sport- and skill-specific hours to develop elite-level status. Despite recommendations against sport specialization, many youth athletes still specialize and need guidance on training and competition. Medical and sport professionals also recommend progressive, gradual increases in workloads to enhance resilience to the demands of high-level competition. There is no accepted model of risk stratification and return to play for training a specialized youth athlete through periods of injury and maturation. In this review, we present individualized training models for specialized youth athletes that (1) prioritize performance for healthy, resilient youth athletes and (2) are adaptable through vulnerable maturational periods and injury. Evidence Acquisition: Nonsystematic review with critical appraisal of existing literature. Study Design: Clinical review. Level of Evidence: Level 4. Results: A number of factors must be considered when developing training programs for young athletes: (1) the effect of sport specialization on athlete development and injury, (2) biological maturation, (3) motor and coordination deficits in specialized youth athletes, and (4) workload progressions and response to load. Conclusion: Load-sensitive athletes with multiple risk factors may need medical evaluation, frequent monitoring, and a program designed to restore local tissue and sport-specific capacity. Load-naive athletes, who are often skeletally immature, will likely benefit from serial monitoring and should train and compete with caution, while load-tolerant athletes may only need occasional monitoring and progress to optimum loads. Strength of Recommendation Taxonomy (SORT): B.
Hartwig, TB, Naughton, G, and Searl, J. Motion analyses of adolescent rugby union players: A comparison of training and game demands. J Strength Cond Res 25(4): 966-972, 2011-This research described the physiological demands of participation in adolescent rugby union including positional differences and the degree to which training practices replicate game demands. Between 2003 and 2008, 118 male adolescent rugby players aged 14 to 18 years were recruited from 10 teams representing 3 levels of adolescent rugby. Time-motion analyses using global positioning satellite tracking devices (SPI10; GPSports Systems Pty Ltd 2003) and computer-based tracking software (Trak Performance; Sports Tec Pty Ltd) applied to video footage determined player movement patterns 161 times during rugby training sessions and 53 times during rugby games. Compared with rugby training, rugby games were consistently characterized by more time spent jogging (14 vs. 8%), striding (3.2 vs. 1.3%), and sprinting (1.3 vs. 0.1%) (p < 0.001). Players also covered greater distances (4000 ± 500 vs. 2710 ± 770 m) and performed more sprints (21.8 vs. 1) during games compared with training (p < 0.001). The average sprint duration of 2 seconds was similar in games and training; however, the frequency of sprint efforts in training sessions was low (1 per hour). A major finding of this study is the disparity between physical game demands and on-field rugby training practices in adolescent players determined using time-motion analyses. Sprint pattern differences between games and training in particular could have important implications for player performance during competition. Results of this study should assist in the development of game-specific training sessions and drills that provide the kinds of physically demanding experiences observed in games. Additionally, coaches could assist in the management of adolescent players' participation loads by increasing the intensity and specificity and decreasing the volume of training.
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