An important consideration in pediatric exercise sciences is how researchers can identify the effects of risk factors (e.g., physical inactivity, dietary composition, social class, and ethnic origin) on health-related fitness variables (e.g., peak VO2, lung function, grip strength, leg power, and arterial blood pressure) in the presence of confounding effects (e.g., differences in age, body size, and maturation). Allometric scaling provides an elegant method of identifying such risk factors while adjusting for the confounding effects of body size, age, and maturation while at the same time overcoming the undesirable distributional characteristics of such data (i.e., skewness [nonnormal] with heteroscedastic error variances). In contrast, the simple ratio standard (e.g., peak oxygen uptake [ml · kg-1 · min-1], and peak and mean power [W · kg-1]), although not a truly scaled ratio or index, is still able to provide the best prediction of weight-bearing athletic (e.g., running) performance.
Alan Nevill and Richard F. Burton
Louise Martin, Alan M. Nevill and Kevin G. Thompson
Fast swim times in morning rounds are essential to ensure qualification in evening finals. A significant time-of-day effect in swimming performance has consistently been observed, although physical activity early in the day has been postulated to reduce this effect. The aim of this study was to compare intradaily variation in race-pace performance of swimmers routinely undertaking morning and evening training (MEG) with those routinely undertaking evening training only (EOG).
Each group consisted of 8 swimmers (mean ± SD: age = 15.2 ± 1.0 and 15.4 ± 1.4 y, 200-m freestyle time 132.8 ± 8.4 and 136.3 ± 9.1 s) who completed morning and evening trials in a randomized order with 48 h in between on 2 separate occasions. Oral temperature, heart rate, and blood lactate were assessed at rest, after a warm-up, after a 150-m race-pace swim, and after a 100-m time trial. Stroke rate, stroke count, and time were recorded for each length of the 150-m and 100-m swims.
Both training groups recorded significantly slower morning 100-m performances (MEG = +1.7 s, EOG = +1.4 s; P < .05) along with persistently lower morning temperatures that on average were –0.47°C and –0.60°C, respectively (P < .05). No differences were found in blood-lactate, heart-rate, and stroke-count responses (P > .05). All results were found to be reproducible (P > .05).
The long-term use of morning training does not appear to significantly reduce intradaily variation in race-pace swimming or body temperature.
Frank Nugent, Thomas Comyns, Alan Nevill and Giles D. Warrington
Purpose: To assess the effects of a 7-wk low-volume, high-intensity training (HIT) intervention on performance parameters in national-level youth swimmers. Methods: Sixteen swimmers (age 15.8 [1.0] y, age at peak height velocity 12.9 [0.6] y, 100-m freestyle 61.4 [4.1] s) were randomly assigned to an HIT group or a low-intensity, high-volume training (HVT) group that acted as a control. The HIT group reduced their weekly training volume of zone 1 (low-intensity) training by 50% but increased zone 3 (high-intensity) training by 200%. The HVT group performed training as normal. Pretest to posttest measures of physiological performance (velocity at 2.5- and 4-mM blood lactate [velocity2.5mM and velocity4mM] and peak blood lactate), biomechanical performance (stroke rate, stroke length [SL], and stroke index [SI] over a 50- and 400-m freestyle), and swimming performance (50-, 200-, and 400-m freestyle) were assessed. Results: There were no significant 3-way interactions between time, group, and sex for all performance parameters (P > .05). There was a significant 2-way interaction between time and group for velocity4mM (P = .02,
Alan Nevill, Paul Donnelly, Simon Shibli, Charlie Foster and Marie Murphy
The association between health and deprivation is of serious concern to many health promotion agencies. The purpose of the current study was to assess whether modifiable behaviors of physical activity (PA), sports participation, diet, smoking and body mass index (BMI) can help to explain these inequalities in a sample of 4653 respondents from Northern Ireland.
The study is based on a cross-sectional survey of Northern Irish adults. Responses to a self-rated health question were dichotomized and binary logistic regression was used to identify the health inequalities between areas of high, middle or low deprivation. These differences were further adjusted for other sociodemographic factors and subsequently for various modifiable behaviors of PA, sports participation, diet, smoking, and BMI.
Respondents from high and middle areas of deprivation are more likely to report poorer health. As soon as sociodemographic factors and other modifiable behaviors were included, these inequalities either disappeared or were greatly reduced.
Many inequalities in health in NI can be explained by the respondents’ sociodemographic characteristics that can be further explained by introducing information about respondents who meet the recommended PA guidelines, play sport, eat 5 portions of fruit and vegetables, and maintain an optimal BMI.
Dan Weaving, Phil Marshall, Keith Earle, Alan Nevill and Grant Abt
This study investigated the effect of training mode on the relationships between measures of training load in professional rugby league players.
Five measures of training load (internal: individualized training impulse, session rating of perceived exertion; external—body load, high-speed distance, total impacts) were collected from 17 professional male rugby league players over the course of two 12-wk preseason periods. Training was categorized by mode (small-sided games, conditioning, skills, speed, strongman, and wrestle) and subsequently subjected to a principal-component analysis. Extraction criteria were set at an eigenvalue of greater than 1. Modes that extracted more than 1 principal component were subjected to a varimax rotation.
Small-sided games and conditioning extracted 1 principal component, explaining 68% and 52% of the variance, respectively. Skills, wrestle, strongman, and speed extracted 2 principal components each explaining 68%, 71%, 72%, and 67% of the variance, respectively.
In certain training modes the inclusion of both internal and external training-load measures explained a greater proportion of the variance than any 1 individual measure. This would suggest that in training modes where 2 principal components were identified, the use of only a single internal or external training-load measure could potentially lead to an underestimation of the training dose. Consequently, a combination of internal- and external-load measures is required during certain training modes.
Pavle Mikulic, Tomislav Blazina, Alan M. Nevill and Goran Markovic
The purpose of the current study was to examine the effect of age and body size upon maximal-intensity exercise performance in young rowing athletes. Male participants n = 171) aged 12–18 years were assessed using an “all-out” 30-s rowing ergometer test, and reassessed after 12 months. The highest rate of performance development, which amounts to [mean(SD)] +34%(23%) and +32%(23%) for mean and maximal power output, respectively, is observed between the ages of 12 and 13, while this rate of development gradually declines as the athletes mature through adolescence. Performance increases with body size, and mass, stature and chronological age all proved to be significant (all p < .05) explanatory variables of mean power output, with respective exponents [mean(SE)] of 0.56(0.08), 1.84(0.30) and 0.07(0.01), and of maximal power output, with respective exponents of 0.54(0.09), 1.76(0.32) and 0.06(0.01). These findings may help coaches better understand the progression of rowing performance during adolescence.
Michael J. Duncan, Joanne Hankey and Alan M. Nevill
This study examined the efficacy of peak-power estimation equations in children using force platform data and determined whether allometric modeling offers a sounder alternative to estimating peak power in pediatric samples. Ninety one boys and girls aged 12–16 years performed 3 countermovement jumps (CMJ) on a force platform. Estimated peak power (PPest) was determined using the Harman et al., Sayers SJ, Sayers CMJ, and Canavan and Vescovi equations. All 4 equations were associated with actual peak power (r = 0.893−0.909, all p < .01). There were significant differences between PPest using the Harman et al., Sayers SJ, and Sayers CMJ equations (p < .05) and actual peak power (PPactual). ANCOVA also indicated sex and age effect for PPactual (p < .01). Following a random two-thirds to one-third split of participants, an additive linear model (p = .0001) predicted PPactual (adjusted R 2 = .866) from body mass and CMJ height in the two-thirds split (n = 60). An allometric model using CMJ height, body mass, and age was then developed with this sample, which predicted 88.8% of the variance in PPactual (p < .0001, adjusted R 2 = .888). The regression equations were cross-validated using the one-third split sample (n = 31), evidencing a significant positive relationship (r = .910, p = .001) and no significant difference (p = .151) between PPactual and PPest using this equation. The allometric and linear models determined from this study provide accurate models to estimate peak power in children.
Alan Nevill, Georgia Tsiotra, Panagiotis Tsimeas and Yiannis Koutedakis
We adopted allometric models to identify the most appropriate body size/shape characteristics associated with physical performance activities of Greek school children. Children underwent assessments for aerobic and anaerobic fitness, flexibility and hand-grip strength. Results suggest that the inverse Ponderal index and not BMI is the most appropriate body-shape indicator associated with running and jumping activities. Height was negatively associated with flexibility, but both height and weight were positively associated with hand-grip strength. In conclusion, allometric models provide a valuable insight into the most appropriate body size and shape characteristics associated with children’s physical performances and at the same time ensure valid inference when investigating group/population differences (e.g., between gender and maturation status).
Joseph J. Murphy, Ciaran MacDonncha, Marie H. Murphy, Niamh Murphy, Alan M. Nevill and Catherine B. Woods
Background: Although levels of physical activity (PA) have been researched, no information on how university students organize their PA across different life domains is available. The purpose of this study is to explore if and how students organize their PA across transport and recreational domains, and to identify the psychosocial factors related to these patterns. Methods: Students from 31 Irish universities completed a supervised online survey measuring participant characteristics, psychosocial factors, and PA. Two-step cluster analysis was used to identify specific PA patterns in students. Binary logistic regressions identified factors associated with cluster membership while controlling for age, sex, household income, and perceived travel time to a university. Results: Analysis was performed on 6951 students (50.7% male; 21.51 [5.55] y). One Low Active cluster emerged. Four clusters containing a form of PA emerged including Active Commuters, Active in University, Active Outside University, and High Active. Increases in motivation and planning improved the likelihood of students being categorized in a cluster containing PA. Conclusion: One size does not fit all when it comes to students PA engagement, with 5 patterns identified. Health professionals are advised to incorporate strategies for increasing students’ motivation, action planning, and coping planning into future PA promotion efforts.