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Speed agility is considered as the whole assessment of speed of movement, agility and coordination. The 10x4m test has been broadly used to evaluate physical fitness and overall health in children ...

Children from developed and developing countries have different anthropometric characteristics which may affect their motor performance (MP).To use the allometric approach to model the relationship between body size and MP in youth from two countries differing in socio-economic status-Portugal and Mozambique.A total of 2946 subjects, 1280 Mozambicans (688 girls) and 1666 Portuguese (826 girls), aged 10-15 years were sampled. Height and weight were measured and the reciprocal ponderal index (RPI) was computed. MP included handgrip strength, 1-mile run/walk, curl-ups and standing long jump tests. A multiplicative allometric model was adopted to adjust for body size differences across countries.Differences in MP between Mozambican and Portuguese children exist, invariably favouring the latter. The allometric models used to adjust MP for differences in body size identified the optimal body shape to be either the RPI or even more linear, i.e. approximately (height/mass(0.25)). Having adjusted the MP variables for differences in body size, the differences between Mozambican and Portuguese children were invariably reduced and, in the case of grip strength, reversed.These results reinforce the notion that significant differences exist in MP across countries, even after adjusting for differences in body size.

The association between being overweight/obese and deprivation is a serious concern in English schoolchildren.To model this association incorporating known confounders and to discover whether physical fitness and physical activity may reduce or eliminate this association.Cross-sectional data were collected between 2007-2009, from 8053 10-16 year old children from the East-of-England Healthy Heart Study. Weight status was assessed using waist circumference (cm) and body mass (kg). Deprivation was measured using the Index of Multiple Deprivation (IMD). Confounding variables used in the proportional, allometric models were hip circumference, stature, age and sex. Children's fitness levels were assessed using predicted VOA strong association was found between both waist circumference and body mass and the IMD. These associations persisted after controlling for all confounding variables. When the children's physical activity and fitness levels were added to the models, the association was either greatly reduced or, in the case of body mass, absent.To reduce deprivation inequalities in children's weight-status, health practitioners should focus on increasing physical fitness via physical activity in areas of greater deprivation.

Objective: The present study analysed the allometric relationship (MR = a · Mb) between human metabolic rate (MR), ranging from resting to maximal metabolic conditions, and body mass (M ), both in athletes of different specialization and untrained individuals.Subjects and methods: Two hundred and seventy male athletes and 43 untrained men performed a continuous incremental test to volitional exhaustion on a motorized treadmill. Metabolic rate (i.e. ) was measured during resting (), sub-maximal (walking at 5 km h−1 ; running at 7.5 km h−1 ; ventilatory anaerobic threshold ) and maximal exercise conditions (maximum oxygen uptake ).Results: A significant difference ( p

The power function relationship, MR = a.m(b), between metabolic rate (MR) and body mass m has been the source of much controversy amongst biologists for many years. Various studies have reported mass exponents (b) greater than the anticipated 'surface-area' exponent 0.67, often closer to 0.75 originally identified by Kleiber.The study aimed to provide a biological explanation for these 'inflated' exponents when modelling maximum oxygen uptake (max), based on the observations from this and previous studies that larger individuals develop disproportionately more muscle mass in the arms and legs.A cross-sectional study of 119 professional soccer players from Croatia aged 18-34 was carried out.Here we confirm that the power function relationship between max and body mass of the professional soccer players results in an 'inflated' mass exponent of 0.75 (95% confidence interval from 0.56 to 0.93), but also the larger soccer players have disproportionately greater leg muscle girths. When the analysis was repeated incorporating the calf and thigh muscle girths rather than body mass as predictor variables, the analysis not only explained significantly more of the variance in max, but the sum of the exponents confirmed a surface-area law.These findings confirm the pitfalls of fitting body-mass power laws and suggest using muscle-girth methodology as a more appropriate way to scale or normalize metabolic variables such as max for individuals of different body sizes.

The purpose of the present review is to critically examine the various models used to describe health-related performance indices (e.g. grip-strength, lung function, arterial blood pressure, anaerobic and aerobic power, etc.).The majority of health-related performance indices are known to increase proportionally with body size and decline with age, although some, for example arterial blood pressure and cholesterol, increase with both age and excessive body weight. Historically, the confounding effects of body size and age have been controlled using multiple linear or polynomial regression models assuming a constant additive error variance. Based on maximum likelihood goodness-of-fit criterion, an alternative class of allometric models with a multiplicative error term arc shown to describe such proportional relationships more adequately in both cross-sectional and longitudinal studies.When modelling health-related performance indices, multiplicative, allometric models incorporate the concept of a proportional association as an integral part of its model form. Such models guarantee realistic values of health-related response variables both within and beyond the range of observations, ensuring biologically sound estimates of the response variables for subjects of all ages. After a logarithmic transformation, the models can be fitted using ordinary linear regression that will naturally help to overcome the heteroscedasticity observed in such data. However, if the logarithmic transformation fails to provide a constant error variance, an alternative approach is to model the error variance itself using log-linear regression. For longitudinal data, the use of multilevel modelling provides an alternative method of modelling error variation observed in health-related performance indices.

The development of elbow flexor (biceps) and knee extensor (quadriceps) strength has been followed in a mixed longitudinal study of 50 boys and 50 girls from the age of 8 to 17 years. Sex differences in strength emerged at the time of peak height velocity and were especially marked for the biceps. Data for individual children were aligned to the time of peak height velocity and associations between strength, height, weight and circulating testosterone were investigated using multi-level modelling. The results show that, for girls, quadriceps strength is proportional to height and weight while for boys there is an additional factor which can be fully attributed to increasing levels of testosterone. Testosterone is important in explaining differences in biceps strength between the sexes but an additional factor is also required. It is suggested that, in addition to a direct effect on muscle, testosterone could have a second indirect action on biceps strength by promoting growth in length of the humerus as part of the general development of the male upper limb girdle.

Percentage of body fat (BF%) is a known risk factor for a range of healthcare problems but is difficult to measure. An easy to measure proxy is the weight/height(2) ratio known as the Body Mass Index (BMI kg/m(2)). However, BMI does have some inherent weaknesses which are readily overcome by its inverse iBMI (1000/BMI, cm(2)/kg).The association between BF% and both BMI and iBMI together with their distributional properties was explored using previously published data from healthy (n = 2993) and diseased populations (n = 298).BMI is skewed whereas iBMI is symmetrical and so is better approximated by the normal distribution. The relationship between BF% and BMI is curved, but that of iBMI and BF% is linear and thus iBMI explains more of the variation in BF% than BMI. For example a unit increase in BMI for a group of thin women represents an increase of 2.3% in BF, but for obese women this represents only a 0.3% increase in BF-a 7-fold difference. The curvature stems from body mass being the numerator in BMI but the denominator in BF% resulting in a form of hyperbolic curve which is not the case with iBMI. Furthermore, BMI and iBMI have different relationships (interaction) with BF% for men and women, but these differences are less marked with iBMI.Overall, these characteristics of iBMI favour its use over BMI, especially in statistical models.

The present study analysed the allometric relationship (MR = a . M(b)) between human metabolic rate (MR), ranging from resting to maximal metabolic conditions, and body mass (M ), both in athletes of different specialization and untrained individuals.Two hundred and seventy male athletes and 43 untrained men performed a continuous incremental test to volitional exhaustion on a motorized treadmill. Metabolic rate (i.e. VO2) was measured during resting (VO2REST), sub-maximal (walking at 5 km h(-1) VO2WALK; running at 7.5 km h(-1) VO2RUN; ventilatory anaerobic threshold VO2VT) and maximal exercise conditions (maximum oxygen uptake VO2MAX).A significant difference (p0.001) in the MR-body mass relationships between athletes and controls was found. For the control group, the mass exponent b exhibited a non-significant (p = 0.37) increase with increasing metabolic demand (b = 0.69, 0.76, 0.76, 0.84, and 0.89, for VO2REST, VO2WALK, VO2RUN, VO2VT, and VO2MAX, respectively). In contrast, the corresponding mass exponent for the athletic group significantly (p0.01) decreased when moving from resting to maximal metabolic conditions (b = 0.98, 0.88, 0.80, 0.69, and 0.67).These results indicate that the recently proposed allometric cascade model may be valid in describing the scaling behaviour of MR in untrained individuals, but not in athletes of different specialization.

In order to identify valid population differences in lung function (e.g. occupational, ethnic), it is necessary to adjust for known confounding variables (e.g. age, body size). The present paper proposes appropriate methods for analysing forced expiratory volume (FEV1), forced vital capacity (FVC) and maximum oxygen uptake (VO2 max), recorded as part of the Allied Dunbar national fitness survey (ADNFS). The ADNFS randomly selected subjects from 30 regional sites throughout England. Measurements of FEV1, FVC and complete records of other relevant information were available on 2672 subjects. Traditional analyses of co-variance (ANCOVA) were found to be inappropriate to investigate population differences in FEV1 and FVC, due to a significant increase in error variance with age. However, by fitting a multiplicative model with allometric body size components to the FEV1 and FVC measurements using weighted log-linear regression, valid and plausible associations with body size, age, smoking, and physical activity, together with 'gender specific' regional differences in lung function were identified. Further insight was obtained when FEV1 and FVC were included into the multiplicative model to predict VO2 max. The apparent advantage of being taller when predicting VO2 max, was explained more accurately by the subjects' superior FVC. In summary, by fitting the multiplicative 'allometric' model using weighted log-linear regression, valid population differences in lung function were identified. Regions containing a higher proportion of working-class, unemployed or less affluent subjects were found to be associated with below average lung function performances.

In order to identify risk factors (e.g. physical inactivity, dietary composition) associated with blood pressure within a given population, it is necessary to adjust for differences in known associations (e.g. age, body weight) using a method such as the analysis of covariance. However, the blood pressure results from the Allied Dunbar National Fitness Survey (ADNFS) were found to be non-linear with age, positively skewed (with heteroscedastic errors) and therefore non-normally distributed. Hence, before valid inferences can be drawn from such data, there is a clear need to formulate an appropriate model for blood pressure that will overcome these undesirable characteristics. A multiplicative model (with allometric body size components) was proposed and fitted to the ADNFS blood pressure results. After a logarithmic transformation the parsimonious solution was able to confirm the association with BMI, the non-linear changes with age, and overcome the heteroscedastic and positively skewed errors, i.e. the residuals from the fitted log-linear models for both systolic and diastolic blood pressure were symmetric and normally distributed. Other factors were found to make a significant additional contribution to the prediction of blood pressure. Cyclists, participants in vigorous physical activity and those subjects who consumed more fresh fruit, rice or pasta, and wine were found to have significantly lower mean levels of blood pressure. Indeed, the gap in blood pressure between participants and non-participants in vigorous physical activity increased further with age. However, subjects who drank more beer tended to have significantly higher mean levels of blood pressure. Thus by developing an appropriate model for arterial blood pressure, some well known, and some less well known, associations with arterial blood pressure have been identified. The results suggest that physical activity and other lifestyle factors may protect against hypertension.