Relationship between tactics and energy expenditure according to level of experience in badminton.

Research on racket sports has traditionally focused on expert players and has treated energy expenditure and tactics as independent factors. These prior studies could not assess how energy expenditure and tactics changed as a function of experience and skill. Here, the specific relationship between playing tactics and energy expenditure in badminton were assessed. Participants were classified into five stages of badminton experience on the basis of conative criteria: structural (physical abilities), technical (technical skills), and functional (tactics). The physical activity of 99 players (47 beginners, 15 intermediates, 30 advanced, and 7 experts) was measured using a three-axis accelerometer during a badminton set (21 points, no extra scoring). The results showed that physical activity (counts/sec.) ranged between about 115 (Stage 1) and 155 (Stage 5), and differed significantly across the conative stages. For Stages 2 and 4, defined by an increase in use of tactics, physical activity increased substantially. For Stage 3, defined by a decrease in use of tactics, physical activity decreased significantly. Thus, tactically-oriented play appears to be closely related to physical activity.

Comparison of two ActiGraph accelerometer generations in the assessment of physical activity in free living conditions.

BACKGROUND: The aim of this study was to compare physical activity measured using GT1M ActiGraph and GT3X ActiGraph accelerometers in free living conditions. FINDINGS: Twenty-five adults wore GT1M and GT3X Actigraph accelerometers simultaneously during a typical weekday of activity. Data were uploaded from the monitor to a computer at the end of test (one day). Previously established thresholds were used for defining time spent at each level of physical activity, physical activity was assessed at varying intensities comparing data from the two accelerometers by ANOVA and Bland and Altman statistical analysis. The concordance correlation coefficient between accelerometers at each intensity level was 0.99. There were no significant differences between accelerometers at any of the activity levels. Differences between data obtained in minutes with the GT1M accelerometer and the GT3X monitor were to 0.56, 0.36, 0.52 and 0.44% for sedentary, light, moderate and vigorous, respectively. The Bland and Altman method showed good agreement between data obtained for the two accelerometers. CONCLUSIONS: Findings suggest that the two accelerometers provided similar results and therefore the GT3X may be used in clinical and epidemiological studies without additional calibration or validation studies.