The purpose of the study was to describe the differences in the activity demands of sub-elite and elite Australian men's basketball competition. Ten elite (age 28.3 ± 4.9 years, mass 97.0 ± 13.9 kg, height 197.4 ± 8.3 cm) and 12 sub-elite (age 26.1 ± 5.3 years, mass 85.9 ± 13.2 kg, height 191.4 ± 7.6 cm) Australian basketball players participated in the study. Player activity was analysed using video-based time–motion analysis across multiple in-season matches. Customized analytical software was used to calculate player activity into frequencies, mean and total durations (s), and mean and total distances (m) for standing/walking, jogging, running, sprinting, low shuffling, high shuffling, and dribbling movements. Only movement frequency was calculated for jumping and upper body activity. Multivariate analysis of variance revealed that elite players performed significantly more total movement changes (P <0.001), and experienced greater activity workloads while jogging (P <0.01) and running (P <0.002). In contrast, sub-elite players performed significantly more standing/walking (P <0.023) and sprinting (P <0.003) activities. These data suggest that elite basketball competition requires a greater intermittent workload and more sustained activity demands, whereas sub-elite competition may involve greater bursts of activity and longer recovery periods. These differences are likely to reflect variations in player skill and fitness, as well as playing structure between playing standards.

Chronic kidney disease (CKD) is characterized by increased levels of oxidative stress and inflammation. Oxidative stress and inflammation promote renal injury via damage to molecular components of the kidney. Unfortunately, relationships between inflammation and oxidative stress are cyclical in that the inflammatory processes that exist to repair radical-mediated damage may be a source of additional free radicals, resulting in further damage to renal tissue. Oxidative stress and inflammation also have the ability to become systemic, serving to injure tissues distal to the site of original insult. This review describes select mediators in the exacerbatory relationship between oxidative stress, inflammation, and CKD. This review also discusses oxidative stress, inflammation, and CKD as they pertain to the development and progression of common CKD-associated comorbidities. Lastly, the utility of several widely accessible and cost-effective lifestyle interventions and their ability to reduce oxidative stress and inflammation are discussed and recommendations for future research are provided.

Fox, JL, Scanlan, AT, and Stanton, R. A review of player monitoring approaches in basketball: current trends and future directions. J Strength Cond Res 31(7): 2021–2029, 2017—Effective monitoring of players in team sports such as basketball requires an understanding of the external demands and internal responses, as they relate to training phases and competition. Monitoring of external demands and internal responses allows coaching staff to determine the dose-response associated with the imposed training load (TL), and subsequently, if players are adequately prepared for competition. This review discusses measures reported in the literature for monitoring the external demands and internal responses of basketball players during training and competition. The external demands of training and competition were primarily monitored using time-motion analysis, with limited use of microtechnology being reported. Internal responses during training were typically measured using hematological markers, heart rate, various TL models, and perceptual responses such as rating of perceived exertion (RPE). Heart rate was the most commonly reported indicator of internal responses during competition with limited reporting of hematological markers or RPE. These findings show a large discrepancy between the reporting of external and internal measures and training and competition demands. Microsensors, however, may be a practical and convenient method of player monitoring in basketball to overcome the limitations associated with current approaches while allowing for external demands and internal responses to be recorded simultaneously. The triaxial accelerometers of microsensors seem well suited for basketball and warrant validation to definitively determine their place in the monitoring of basketball players. Coaching staff should make use of this technology by tracking individual player responses across the annual plan and using real-time monitoring to minimize factors such as fatigue and injury risk.

The present investigation described and compared the internal and external training loads during basketball training. Eight semiprofessional male basketball players (mean ± SD, age: 26.3 ± 6.7 years; stature: 188.1 ± 6.2 cm; body mass: 92.0 ± 13.8 kg) were monitored across a 7-week period during the preparatory phase of the annual training plan. A total of 44 total sessions were monitored. Player session ratings of perceived exertion (sRPE), heart rate, and accelerometer data were collected across each training session. Internal training load was determined using the sRPE, training impulse (TRIMP), and summated-heart-rate-zones (SHRZ) training load models. External training load was calculated using an established accelerometer algorithm. Pearson product-moment correlations with 95% confidence intervals (CIs) were used to determine the relationships between internal and external training load models. Significant moderate relationships were observed between external training load and the sRPE (r42 = 0.49, 95% CI = 0.23-0.69, p < 0.001) and TRIMP models (r42 = 0.38, 95% CI = 0.09-0.61, p = 0.011). A significant large correlation was evident between external training load and the SHRZ model (r42 = 0.61, 95% CI = 0.38-0.77, p < 0.001). Although significant relationships were found between internal and external training load models, the magnitude of the correlations and low commonality suggest that internal training load models measure different constructs of the training process than the accelerometer training load model in basketball settings. Basketball coaching and conditioning professionals should not assume a linear dose-response between accelerometer and internal training load models during training and are recommended to combine internal and external approaches when monitoring training load in players.