Evaluation of Athlete Monitoring Tools across 10 Weeks of Elite Youth Basketball Training: An Explorative Study.

The growth of sport science technology is enabling more sporting teams to implement athlete monitoring practices related to performance testing and load monitoring. Despite the increased emphasis on youth athlete development, the lack of longitudinal athlete monitoring literature in youth athletes is concerning, especially for indoor sports such as basketball. The aim of this study was to evaluate the effectiveness of six different athlete monitoring methods over 10 weeks of youth basketball training. Fourteen state-level youth basketball players (5 males and 9 females; 15.1 ± 1.0 years) completed this study during their pre-competition phase prior to their national basketball tournament. Daily wellness and activity surveys were completed using the OwnUrGoal mobile application, along with heart rate (HR) and inertial measurement unit (IMU) recordings at each state training session, and weekly performance testing (3x countermovement jumps [CMJs], and 3x isometric mid-thigh pulls [IMTPs]). All of the athlete monitoring methods demonstrated the coaching staff's training intent to maintain performance and avoid spikes in workload. Monitoring IMU data combined with PlayerLoad™ data analysis demonstrated more effectiveness for monitoring accumulated load (AL) compared to HR analysis. All six methods of athlete monitoring detected similar trends for all sessions despite small-trivial correlations between each method (Pearson's correlation: -0.24 < r < 0.28). The use of subjective monitoring questionnaire applications, such as OwnUrGoal, is recommended for youth sporting clubs, given its practicability and low-cost. Regular athlete education from coaches and support staff regarding the use of these questionnaires is required to gain the best data.

Percent body fat via DEXA: comparison with a four-compartment model.

This study compared body composition by dual-energy X-ray absorptiometry (DEXA; Lunar DPX-L) with that via a four-compartment (4C; water, bone mineral mass, fat, and residual) model. Relative body fat was determined for 152 healthy adults [30.0 +/- 11.1 (SD) yr; 75.10 +/- 14.88 kg; 176.3 +/- 8.7 cm] aged from 18 to 59 yr. The 4C approach [20.7% body fat (%BF)] resulted in a significantly (P < 0.001) higher mean %BF compared with DEXA (18.9% BF), with intraindividual variations ranging from -2.6 to 7.3% BF. Linear regression and a Bland and Altman plot demonstrated the tendency for DEXA to progressively underestimate the %BF of leaner individuals compared with the criterion 4C model (4C %BF = 0.862 x DEXA %BF + 4.417; r(2) = 0.952, standard error of estimate = 1.6% BF). This bias was not attributable to variations in fat-free mass hydration but may have been due to beam-hardening errors that resulted from differences in anterior-posterior tissue thickness.

CPX/D underestimates VO(2) in athletes compared with an automated Douglas bag system.

PURPOSE: Based on persistent reports of low oxygen consumption VO(2) from Medical Graphics CPX/D metabolic carts, we compared the CPX/D against an automated Douglas bag system. METHODS: Twelve male athletes completed three, randomized 25-min bouts (5 min at 100, 150, 200, 250, and 300 W) on a cycle ergometer with intervening 30-min rests. One bout was measured on each of the CPX/D, the CPX/D with altered software (CPX/DDelta), and an automated Douglas bag system at Flinders University (FU). The CPX/DDelta software alteration was an apparent lag time correction factor of 60 ms. RESULTS: For the CPX/D, both VO(2) and VCO(2) were significantly lower than the FU system at 100-300 W, and the relative differences ranged -10.7 to -12.0% and -7.7 to -8.2%, respectively. Altering the software approximately halved the VO(2) discrepancy between the CPX/DDelta and FU systems. When data from all five workloads were pooled, V(E) of the CPX/D (67.2 +/- 26.4 L x min-1) and CPX/DDelta (67.5 +/- 26.9 L x min-1) were significantly lower than for the FU system (70.5 +/- 27.1 L x min-1); and at 300 W, the relative differences were -4.0% and -3.4% for the CPX/D and CPX/DDelta, respectively. Altering the software changed the pooled %O(2) from 16.24 +/- 0.40% for the CPX/D to 16.04 +/- 0.39% for the CPX/DDelta, and these were significantly different than pooled data for the FU system (16.15 +/- 0.39%). CONCLUSIONS: During submaximal exercise, the CPX/D yields VO(2) values that are approximately 11% lower than the criterion system, and the source of the discrepancy does not appear to be primarily related to volume measurement. A disturbing observation is that factory defaults for the lag time use different correction factors, which vary by 60 ms and this significantly alters VO(2) and VCO(2).

Measurement and prediction of energy expenditure in males during household and garden tasks.

Participation in at least 30 min of moderate intensity activity on most days is assumed to confer health benefits. This study accordingly determined whether the more vigorous household and garden tasks (sweeping, window cleaning, vacuuming and lawn mowing) are performed by middle-aged men at a moderate intensity of 3-6 metabolic equivalents (METs) in the laboratory and at home. Measured energy expenditure during self-perceived moderate-paced walking was used as a marker of exercise intensity. Energy expenditure was also predicted via indirect methods. Thirty-six males [ X (SD): 40.0 (3.3) years; 179.5 (6.9) cm; 83.4 (14.0) kg] were measured for resting metabolic rate (RMR) and oxygen consumption ( VO(2)) during the five activities using the Douglas bag method. Heart rate, respiratory frequency, CSA (Computer Science Applications) movement counts, Borg scale ratings of perceived exertion and Quetelet's index were also recorded as potential predictors of exercise intensity. Except for vacuuming in the laboratory, which was not significantly different from 3.0 METs ( P=0.98), the MET means in the laboratory and home were all significantly greater than 3.0 ( P/=3.0 METs.