Activity profiles of professional soccer,rugby league and Australian football match play

Abstract

We compared the match activity profiles of elite footballers from Australian football (AF), rugby league (RL) and soccer (SOC), using identical movement definitions. Ninety-four elite footballers from AF, RL or SOC clubs in Australia participated in this study. Movement data were collected using a 5-Hz global positioning system from matches during the 2008–2011 competitive seasons, including measures of velocity, distance, acceleration and bouts of repeat sprints (RS). Australian footballers covered the greatest relative running distances (129 ± 17 m.min^?1) compared to RL (97 ± 16 m.min^?1) and SOC (104 ± 10 m.min^?1) (effect size [ES]; 1.0–2.8). The relative distance covered (4.92 ± 2.10 m.min^?1 vs. 5.42 ± 2.49 m.min^?1; 0.74 ± 0.78 m.min^?1 vs. 0.97 ± 0.80 m.min^?1) and the number of high-velocity running (0.4 ± 0.2 no.min^?1 vs. 0.4 ± 0.2 no.min^?1) and sprint (0.06 ± 0.06 no.min^?1 vs. 0.08 ± 0.07 no.min^?1) efforts between RL and SOC players were similar (ES; 0.1–0.3). Rugby league players undertook the highest relative number of accelerations (1.10 ± 0.56 no.min^?1). RS bouts were uncommon for all codes. RL and SOC players perform less running than AF players, possibly due to limited open space as a consequence of field size and code specific rules. While training in football should be code specific, there may be some transference of conditioning drills across codes.     

Comparing ActiGraph equations for estimating energy expenditure in older adults

Accurate estimation of energy expenditure (EE) from accelerometer outputs remains a challenge in older adults. The aim of this study was to validate different ActiGraph (AG) equations for predicting EE in older adults. Forty older adults (age = 77.4 ± 8.1 yrs) completed a set of household/gardening activities in their residence, while wearing an AG at the hip (GT3X+) and a portable calorimeter (MetaMax 3B – criterion). Predicted EEs from AG were calculated using five equations (Freedson, refined Crouter, Sasaki and Santos-Lozano (vertical-axis, vectormagnitude)). Accuracy of equations was assessed using root-mean-square error (RMSE) and mean bias. The Sasaki equation showed the lowest RMSE for all activities (0.47 METs) and across physical activity intensities (PAIs) (range 0.18–0.48 METs). The Freedson and Santos-Lozano equations tended to overestimate EE for sedentary activities (range: 0.48 to 0.97 METs), while EEs for moderate-to-vigorous activities (MVPA) were underestimated (range: ?1.02 to ?0.64 METs). The refined Crouter and Sasaki equations showed no systematic bias, but they respectively overestimated and underestimated EE across PAIs. In conclusion, none of the equations was completely accurate for predicting EE across the range of PAIs. However, the refined Crouter and Sasaki equations showed better overall accuracy and precision when compared with the other methods.     

Validity of the Actical activity monitor for assessing steps and energy expenditure during walking

This study examined the validity of the Actical accelerometer step count and energy expenditure (EE) functions in healthy young adults. Forty-three participants participated in study 1. Actical step counts were compared to actual steps taken during a 200 m walk around an indoor track at self-selected pace and during treadmill walking at different speeds (0.894, 1.56 and 2.01 m · s^–1) for 5 min. The Actical was also compared to three pedometers. For study 2, 15 participants from study 1 walked on a treadmill at their predetermined self-selected pace for 15 min. Actical EE was compared to EE measured by indirect calorimetry. One-way analysis of variance and t-tests were used to examine differences. There were no statistical difference between Actical steps and actual steps in self-selected pace walking and during treadmill walking at moderate and fast speeds. During treadmill walking at slow speed, the Actical step counts significantly under predicted actual steps taken. For study 2, there was no statistical difference between measured EE and Actical-recorded EE. The Actical provides valid estimates of step counts at self-selected pace and walking at constant speeds of 1.56 and 2.01 m · s^–1. The Actical underestimates EE of walking at constants speeds ≥1.38 m · s^–1.     

Validity of wearable activity monitors for tracking steps and estimating energy expenditure during a graded maximal treadmill test

The purpose of this study was to assess the accuracy of energy expenditure (EE) estimation and step tracking abilities of six activity monitors (AMs) in relation to indirect calorimetry and hand counted steps and assess the accuracy of the AMs between high and low fit individuals in order to assess the impact of exercise intensity. Fifty participants wore the Basis watch, Fitbit Flex, Polar FT7, Jawbone, Omron pedometer, and Actigraph during a maximal graded treadmill test. Correlations, intra-class correlations, and t-tests determined accuracy and agreement between AMs and criterions. The results indicate that the Omron, Fitbit, and Actigraph were accurate for measuring steps while the Basis and Jawbone significantly underestimated steps. All AMs were significantly correlated with indirect calorimetry, however, no devices showed agreement (p < .05). When comparing low and high fit groups, correlations between AMs and indirect calorimetry improved for the low fit group, suggesting AMs may be better at measuring EE at lower intensity exercise.     

Comparing the standards of one metabolic equivalent of task in accurately estimating physical activity energy expenditure based on acceleration

The purpose of the study is to analyse how the standard of resting metabolic rate (RMR) affects estimation of the metabolic equivalent of task (MET) using an accelerometer. In order to investigate the effect on estimation according to intensity of activity, comparisons were conducted between the 3.5 ml O₂ · kg^?1 · min^?1 and individually measured resting VO₂ as the standard of 1 MET. MET was estimated by linear regression equations that were derived through five-fold cross-validation using 2 types of MET values and accelerations; the accuracy of estimation was analysed through cross-validation, Bland and Altman plot, and one-way ANOVA test. There were no significant differences in the RMS error after cross-validation. However, the individual RMR-based estimations had as many as 0.5 METs of mean difference in modified Bland and Altman plots than RMR of 3.5 ml O₂ · kg^?1 · min^?1. Finally, the results of an ANOVA test indicated that the individual RMR-based estimations had less significant differences between the reference and estimated values at each intensity of activity. In conclusion, the RMR standard is a factor that affects accurate estimation of METs by acceleration; therefore, RMR requires individual specification when it is used for estimation of METs using an accelerometer.     

Energy expenditure and dietary intake in professional football players in the Dutch Premier League: Implications for nutritional counselling

ABSTRACT

Selecting effective dietary strategies for professional football players requires comprehensive information on their energy expenditure (EE) and dietary intake. This observational study aimed to assess EE and dietary intake over a 14-day period in a representative group (n = 41) of professional football players playing in the Dutch Premier League (Eredivisie). Daily EE, as assessed by doubly labelled water, was 13.8 ± 1.5 MJ/day, representing a physical activity level (PAL) of 1.75 ± 0.13. Weighted mean energy intake (EI), as assessed by three face-to-face 24-h recalls, was 11.1 ± 2.9 MJ/day, indicating 18 ± 15% underreporting of EI. Daily EI was higher on match days (13.1 ± 4.1 MJ) compared with training (11.1 ± 3.4 MJ; P < 0.01) and rest days (10.5 ± 3.1 MJ; P < 0.001). Daily carbohydrate intake was significantly higher during match days (5.1 ± 1.7 g/kg body mass (BM)) compared with training (3.9 ± 1.5 g/kg BM; P < 0.001) and rest days (3.7 ± 1.4 g/kg BM; P < 0.001). Weighted mean protein intake was 1.7 ± 0.5 g/kg BM. Daytime distribution of protein intake was skewed, with lowest intakes at breakfast and highest at dinner. In conclusion, daily EE and PAL of professional football players are modest. Daily carbohydrate intake should be increased to maximize performance and recovery. Daily protein intake seems more than adequate, but could be distributed more evenly throughout the day.