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.
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.
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.
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.
Georgia D. Tsiotra, Alan M. Nevill, Andrew M. Lane and Yiannis Koutedakis
We investigated whether children with suspected Developmental Coordination Disorder (DCD+) demonstrate different physical fitness levels compared with their normal peers (DCD−). Randomly recruited Greek children (n = 177) were assessed for body mass index (BMI), flexibility (SR), vertical jump (VJ), hand strength (HS), 40m dash, aerobic power, and motor proficiency. ANCOVA revealed a motor proficiency (i.e., DCD group) effect for BMI (p < .01), VJ (p < .01), and 40m speed (p < .01), with DCD+ children demonstrating lower values than DCD−. Differences between DCD+ and DCD− were also obtained in log-transformed HS (p < .01). These findings suggest that intervention strategies for managing DCD should also aim at physical fitness increases.
Ciara Sinnott-O’Connor, Thomas M. Comyns, Alan M. Nevill and Giles D. Warrington
Context: Stress responses in athletes can be attributed to training and competition, where increased physiological and psychological stress may negatively affect performance and recovery. Purpose: To examine the relationship between training load (TL) and salivary biomarkers immunoglobulin A (IgA), alpha-amylase (AA), and cortisol across a 16-wk preparation phase and 10-d competition phase in Paralympic swimmers. Methods: Four Paralympic swimmers provided biweekly saliva samples during 3 training phases—(1) normal training, (2) intensified training, and (3) taper—as well as daily saliva samples in the 10-d Paralympic competition (2016 Paralympic Games). TL was measured using session rating of perceived exertion. Results: Multilevel analysis identified a significant increase in salivary immunoglobulin A (sIgA: 94.98 [27.69] μg·mL−1), salivary alpha-amylase (sAA: 45.78 [19.07] μg·mL−1), and salivary cortisol (7.92 [2.17] nM) during intensified training concurrent with a 38.3% increase in TL. During the taper phase, a 49.5% decrease in TL from the intensified training phase resulted in a decrease in sIgA, sAA, and salivary cortisol; however, all 3 remained higher than baseline levels. A further significant increase was observed during competition in sIgA (168.69 [24.19] μg·mL−1), sAA (35.86 [16.67] μg·mL−1), and salivary cortisol (10.49 [1.89] nM) despite a continued decrease (77.8%) in TL from the taper phase. Conclusions: Results demonstrate that performance in major competition such as Paralympic games, despite a noticeable reduction in TL, induces a stress response in athletes. Because of the elevated stress response observed, modifications to individual postrace recovery protocols may be required to enable athletes to maximize performance across all 10 d of competition.
Michael J. Duncan, Lorayne Woodfield, Yahya Al-Nakeeb and Alan M. Nevill
The purpose of this study was to compare physical activity levels between white and South Asian children in the UK. The data were obtained from 606, 11–14 year old schoolchildren (397 white; 209 Asian). Physical activity was assessed using the ‘four by one day’ recall questionnaire from which the time spent in moderate and vigorous physical activity was calculated. Boys were significantly more active than girls (p = .0001), and white children reported significantly greater physical activity than south Asian children (p = .001). Mean ± SD of time spent in moderate and vigorous activity was 90.2 ± 65.4 mins and 68.2 ± 49.3 mins for white and south Asian children and 103.5 ± 63.4 mins and 65.6 ± 53.5 mins for boys and girls respectively. These findings indicate that south Asian children are significantly less active than their white peers and there may be a need for specific interventions to target South Asian children particularly.
Stephen R. Bird, Simon C. Theakston, Andrew Owen and Alan M. Nevill
This study assessed physiological and cardiac factors associated with 10-km running performance in a group of highly trained endurance runners age 21–63 years. Participants (N = 37) underwent a resting echocardiograph and incremental treadmill running test. They also provided information on their recent 10-km races. Data were analyzed using “best subsets” multiple regression. Declines with age were found for 10-km running speed (0.26 m · s−1 · decade−1), maximum heart rate (4 beats/decade), VO2peak (6 ml · kg−1 · min−1 · decade−1), velocity at lactate threshold (1 m · s−1 · decade−1), and VO2 at lactate threshold (4 ml · kg−1 · min−1 · decade−1). The percentage of VO2peak at which lactate threshold occurred increased with age by 1.5% per decade. The rate of change of displacement of the atrioventricular plane at the left free wall and septum both declined by 1 cm · s−1 · decade−1. The best single predictor of 10-km running speed was velocity at lactate threshold.
Caoimhe Tiernan, Mark Lyons, Tom Comyns, Alan M. Nevill and Giles Warrington
Purpose: Insufficient recovery can lead to a decrease in performance and increase the risk of injury and illness. The aim of this study was to evaluate salivary cortisol as a marker of recovery in elite rugby union players. Method: Over a 10-wk preseason training period, 19 male elite rugby union players provided saliva swabs biweekly (Monday and Friday mornings). Subjective markers of recovery were collected every morning of each training day. Session rating of perceived exertion (sRPE) was taken after every training session, and training load was calculated (sRPE × session duration). Results: Multilevel analysis found no significant association between salivary cortisol and training load or subjective markers of recovery (all P > .05) over the training period. Compared with baseline (wk 1), Monday salivary cortisol significantly increased in wk 4 (14.94 [7.73] ng/mL; P = .04), wk 8 (16.39 [9.53] ng/mL; P = .01), and wk 9 (15.41 [9.82] ng/mL; P = .02), and Friday salivary cortisol significantly increased in wk 5 (14.81 [8.74] ng/mL; P = .04) and wk 10 (15.36 [11.30] ng/mL; P = .03). Conclusions: The significant increase in salivary cortisol on certain Mondays may indicate that players did not physically recover from the previous week of training or match at the weekend. The increased Friday cortisol levels and subjective marker of perceived fatigue indicated increased physiological stress from that week’s training. Regular monitoring of salivary cortisol combined with appropriate planning of training load may allow sufficient recovery to optimize training performance.
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.