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Steven H. Doeven, Michel S. Brink, Barbara C.H. Huijgen, Johan de Jong and Koen A.P.M. Lemmink

In elite basketball, players are exposed to intensified competition periods when participating in both national and international competitions. How coaches manage training between matches and in reference to match scheduling for a full season is not yet known. Purpose: First, to compare load during short-term match congestion (ie, ≥2-match weeks) with regular competition (ie, 1-match weeks) in elite male professional basketball players. Second, to determine changes in well-being, recovery, neuromuscular performance, and injuries and illnesses between short-term match congestion and regular competition. Methods: Sixteen basketball players (age 24.8 [2.0] y, height 195.8 [7.5] cm, weight 94.8 [14.0] kg, body fat 11.9% [5.0%], VO2max 51.9 [5.3] mL·kg−1·min−1) were monitored during a full season. Session rating of perceived exertion (s-RPE) was obtained, and load was calculated (s-RPE × duration) for each training session or match. Perceived well-being (fatigue, sleep quality, general muscle soreness, stress levels, and mood) and total quality of recovery were assessed each training day. Countermovement jump height was measured, and a list of injuries and illnesses was collected weekly using the adapted Oslo Sports Trauma Research Center Questionnaire on Health Problems. Results: Total load (training sessions and matches; P < .001) and training load (P < .001) were significantly lower for ≥2-match weeks. Significantly higher well-being (P = .01) and less fatigue (P = .001) were found during ≥2-match weeks compared with 1-match weeks. Conclusion: Total load and training load were lower during short-term match congestion compared with regular competition. Furthermore, better well-being and less fatigue were demonstrated within short-term match congestion. This might indicate that coaches tend to overcompensate training load in intensified competition.

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Martin Buchheit, Ben M. Simpson and Mathieu Lacome

Purpose: To compare between-tests changes in submaximal exercise heart rate (HRex, 3 min, 12 km/h) and the speed associated with 4 mmol/L of blood lactate (V4mmol) in soccer players to get insight into their level of agreement and respective sensitivity to changes in players’ fitness. Methods: A total of 19 elite professional players (23 [3] y) performed 2 to 3 graded incremental treadmill tests (3-min stages interspersed with 1 min of passive recovery, starting speed 8 km/h, increment 2 km/h until exhaustion or 18 km/h if exhaustion was not reached before) over 1.5 seasons. The correlation between the changes in HRex and V4mmol was examined. Individual changes in the 2 variables were compared (>2 × typical error considered “clear”). Results: The changes in HRex and V4mmol were largely correlated (r = .82; 90% confidence interval, .65–.91). In more than 90% of the cases, when a clear individual change in HRex was observed, it was associated with a similar clear change in V4mmol (the same direction, improvement, or impairment of fitness) and conversely. Conclusions: When it comes to testing players submaximally, the present results suggest that practitioners can use HRex or V4mmol interchangeably with confidence. However, in comparison with a field-based standardized warm-up run (3–4 min, all players together), the value of a multistage incremental test with repeated blood lactate samplings is questionable for a monitoring purpose given its time, labor, cost, and poorer player buy-in.

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Jac Orie, Nico Hofman, Laurentius A. Meerhoff and Arno Knobbe

At the Olympic level, optimally distributing training intensity is crucial for maximizing performance. Purpose: The authors evaluated the effect of training-intensity distribution on anaerobic power as a substitute for 1500-m speed-skating performance in the 4 y leading up to an Olympic gold medal. Methods: During the preparation phase of the speed-skating season, anaerobic power was recorded periodically (n = 15) using the mean power (in watts) with a 30-s Wingate test. For each training session in the 4 wk prior to each Wingate test, the volume (in hours), training type (specific, simulation, nonspecific, and strength training), and the rating of perceived exertion (RPE; CR-10) were recorded. Results: Compared with the 8 lowest, the 7 highest-scoring tests were preceded by a significantly (P < .01) higher volume of strength training. Furthermore, the RPE distribution of the number of nonspecific training sessions was significantly different (P < .01). Significant (P < .05) correlations highlighted that a larger nonspecific training volume in the lower intensities RPE 2 (r = .735) and 3 (r = .592) was associated positively and the medium intensities RPE 4 (r = −.750) and 5 (r = −.579) negatively with Wingate performance. Conclusion: For the subject, the best results were attained with a high volume of strength training and the bulk of nonspecific training at RPE 2 and 3, and specifically not at the adjoining RPE 4 and 5. These findings are surprising given the aerobic nature of training at RPE 2 and 3 and the importance of anaerobic capacity in this middle-distance event.

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Fabio Bertapelli, Stamatis Agiovlasitis, Robert W. Motl, Roberto A. Soares, Marcos M. de Barros-Filho, Wilson D. do Amaral-Junior and Gil Guerra-Junior

The purpose of this study was to develop and cross-validate an equation for estimating percentage body fat (%BF) from body mass index and other potential independent variables among young persons with intellectual disability. Participants were 128 persons with intellectual disability (62 women; age 16–24 years) split between development (n = 98) and cross-validation (n = 30) samples. Dual-energy X-ray absorptiometry served as the reference method for %BF. An equation including 1/body mass index and sex (0 = male; 1 = female) was highly accurate in estimating %BF (p < .001; R 2 = .82; standard error of estimate  = 5.22%). Mean absolute and root mean square errors were small (3.1% and 3.9%, respectively). A Bland–Altman plot indicated nearly zero mean difference between actual and predicted %BF with modest 95% confidence intervals. The prediction equation was %BF = 56.708 − (729.200 × [1/body mass index]) + (12.134 × sex). Health care professionals may use the prediction equation for monitoring %BF among young people with intellectual disability.