William G. Hopkins

Background: Although 24-hour movement behaviors are known to be interconnected, limited knowledge exists about whether change in one behavior during the COVID-19 pandemic (eg, increased screen time) was associated with change in another (eg, reduced physical activity or sleep). This review estimates mediational associations between changes in children’s physical activity, screen time, and sleep during the COVID-19 pandemic. Methods: We included studies published between January 1, 2020 and June 27, 2022, in the PubMed/MEDLINE, Embase, PsycINFO, SPORTDiscus, and Web of Science databases. Summary data were extracted from included studies and analyzed with random-effects meta-regression. Results: This review included 26 studies representing 18,959 children across 18 mid-high-income countries (53% male; mean age, 11.5 [2.9] y). There was very good evidence of decreased total daily physical activity (factor change, 0.62; 90% CI, 0.47–0.81) and strong evidence of increased screen time (1.56; 90% CI, 1.38–1.77). There was very good evidence of decreased moderate to vigorous physical activity (0.75; 90% CI, 0.62–0.90) and weak evidence of increased sleep (1.02; 90% CI, 1.00–1.04). Mediational analysis revealed strong evidence that most of the reduction in total daily physical activity from before, to during, the pandemic was associated with increased screen time (0.53; 90% CI, 0.42–0.67). We observed no further mediational associations. Conclusion: Increased reliance on and use of screen-based devices during the COVID-19 pandemic can be linked with reduced child and adolescent physical activity. This finding links COVID-related restrictions to potential displacement effects within child and adolescent 24-hour movement behavior.

Purpose: Rugby union is a contact team sport demanding high levels of physical capacity, and understanding the match workloads can be useful to inform training. In this study, the factors influencing locomotion and contact workloads for offensive and defensive ball-in-play periods are quantified. Methods: Locomotion and contact metrics were collected from global positioning system units and videos for 31 professional players of a Super Rugby team across 14 games in the 2021 season. Data were analyzed with a generalized mixed-model procedure that included effects for type of play, playing position, match outcome, and ball-in-play time. Magnitudes were assessed with standardization, and evidence for substantial magnitudes was derived from sampling uncertainty. Results: When offense was compared to defense, most metrics showed decisively substantial increases (small to moderate) for forwards and backs. There was decisive evidence that locomotion metrics were substantially lower (large differences) and contact metrics were higher (very large differences) when comparing forwards to backs on offense and defense. When winning was compared to losing, there was good evidence that forwards experienced small increases in overall workload on defense, and backs experienced a small increase in high-speed running and a moderate decrease in contacts on offense. Match-to-match changes associated with ball-in-play time, attributed to fatigue, were decisive (moderate to very large) across most metrics for forwards and backs in offense and defense. Conclusions: The increased locomotion and contact workloads in offensive periods and the differing physical requirements between positions and match outcomes for both types of play are novel findings that should aid practitioners in designing effective training.

The aim of this investigation was to quantify professional rugby union player activity profiles after the most intense (peak) passages of matches. Movement data were collected from 30 elite and 30 subelite professional rugby union athletes across respective competitive seasons. Accelerometer-derived PlayerLoad and global navigation satellite system–derived measures of mean speed and metabolic power were analyzed using a rolling-average method to identify the most intense 5- to 600-second passages (ie, worst-case scenarios) within matches. Player activity profiles immediately post their peak 5- to 600-second match intensity were identified using 5 epoch duration-matched intervals. Mean speed, metabolic power, and PlayerLoad declined sharply (∼29%–86%) after the most intense 5 to 600 seconds of matches. Following the most intense periods of rugby matches, exercise intensity declined below the average match-half intensity 81% of the time and seldom returned to or exceeded it, likely due to a host of individual physical and physiological characteristics, transient and/or accumulative fatigue, contextual factors, and pacing strategies. Typically, player exercise intensities after the most intense passages of matches were similar between match halves, positional groups, and levels of rugby competition. Accurate identification of the peak exercise intensities of matches and movement thereafter using novel methodologies has improved the limited understanding of professional rugby union player activity profiles following the worst-case scenarios of matches. Findings of the present study may inform match-representative training prescription, monitoring, and tactical match decisions (eg, substitutions and positional changes).

Training prescription and monitoring of team-sport athletes rely on accurate quantification of player movement. Our aim was to determine the sensitivity, reliability and construct validity of measures derived from a wearable device incorporating Global Positioning System (GPS) and accelerometer technology to quantify the peak periods of rugby competition. Match movement data were collected from 30 elite and 30 sub-elite rugby union players across respective competitive seasons. Accelerometer and GPS measures were analysed using a rolling average to identify peak movement for epochs ranging from 5 to 600 seconds. General linear mixed modelling was used to quantify the effects of playing position and match-half on the peak movement and variabilities within and between players represented reliability of each measure. Mean positional differences and match-half changes were assessed via standardisation and magnitude-based decisions. Sensitivity of measures was quantified via evaluation of ("signal") and typical error of measurement ("noise"). GPS and accelerometer measures had poor sensitivity for quantifying peak movement across all epochs and both levels of rugby union competition (noise 4× to 5× the signal). All measures displayed correspondingly low reliability across most epochs and both levels of competition (ICC<0.50). Construct validity was evident in mean differences between playing positions and match halves that were consistent with expected activity profiles in rugby union. However, it was clear from the pattern of differences across epoch durations and levels of competition that GPS and accelerometer measures provided different information about player movement. The poor sensitivity and low reliability of GPS and accelerometer measures of peak movement imply that rugby union players need to be monitored across many matches to obtain adequate precision for assessing individuals. Although all measures displayed construct validity, accelerometers provided meaningful information additional to that of GPS. We recommend using accelerometers alongside GPS to monitor and prescribe match respresentative training.

Skeletal muscle glycogen is an important energy source for muscle contraction and a key regulator of metabolic responses to exercise. Manipulation of muscle glycogen is therefore a strategy to improve performance in competitions and potentially adaptation to training. However, assessing muscle glycogen in the field is impractical, and there are no normative values for glycogen concentration at rest and during exercise.

Background Statistical methods for inferring the true magnitude of an effect from a sample should have acceptable error rates when the true effect is trivial (type I rates) or substantial (type II rates). Objective The objective of this study was to quantify the error rates, rates of decisive (publishable) outcomes and publication bias of five inferential methods commonly used in sports medicine and science. The methods were conventional null-hypothesis significance testing [NHST] (significant and non-significant imply substantial and trivial true effects, respectively); conservative NHST (the observed magnitude is interpreted as the true magnitude only for significant effects); non-clinical magnitude-based inference [MBI] (the true magnitude is interpreted as the magnitude range of the 90 % confidence interval only for intervals not spanning substantial values of the opposite sign); clinical MBI (a possibly beneficial effect is recommended for implementation only if it is most unlikely to be harmful); and odds-ratio clinical MBI (implementation is also recommended when the odds of benefit outweigh the odds of harm, with an odds ratio >66). Methods Simulation was used to quantify standardized mean effects in 500,000 randomized, controlled trials each for true standardized magnitudes ranging from null through marginally moderate with three sample sizes: suboptimal (10 + 10), optimal for MBI (50 + 50) and optimal for NHST (144 + 144). Results Type I rates for non-clinical MBI were always lower than for NHST. When type I rates for clinical MBI were higher, most errors were debatable, given the probabilistic qualification of those inferences (unlikely or possibly beneficial). NHST often had unacceptable rates for either type II errors or decisive outcomes, and it had substantial publication bias with the smallest sample size, whereas MBI had no such problems. Conclusion MBI is a trustworthy, nuanced alternative to NHST, which it outperforms in terms of the sample size, error rates, decision rates and publication bias.

Authors reporting physiological measures of an exceptionally talented individual should provide standard deviations as well as means of the measures in an appropriate normal population group. With reasonable assumptions, the individual's values can be converted to percentile scores, which allow some quantitative assessment of the extent to which the physiology accounts for the exceptional talent.