Shona L. Halson and Michele Lastella
Aaron T. Scanlan, Robert Stanton, Charli Sargent, Cody O’Grady, Michele Lastella, and Jordan L. Fox
Purpose: To quantify and compare internal and external workloads in regular and overtime games and examine changes in relative workloads during overtime compared with other periods in overtime games in male basketball players. Methods: Starting players for a semiprofessional male basketball team were monitored during 2 overtime games and 2 regular games (nonovertime) with similar contextual factors. Internal (rating of perceived exertion and heart-rate variables) and external (PlayerLoad and inertial movement analysis variables) workloads were quantified across games. Separate linear mixed-models and effect-size analyses were used to quantify differences in variables between regular and overtime games and between game periods in overtime games. Results: Session rating-of-perceived-exertion workload (P = .002, effect size 2.36, very large), heart-rate workload (P = .12, 1.13, moderate), low-intensity change-of-direction events to the left (P = .19, 0.95, moderate), medium-intensity accelerations (P = .12, 1.01, moderate), and medium-intensity change-of-direction events to the left (P = .10, 1.06, moderate) were higher during overtime games than during regular games. Overtime periods also exhibited reductions in relative PlayerLoad (first quarter P = .03, −1.46, large), low-intensity accelerations (first quarter P = .01, −1.45, large; second quarter P = .15, −1.22, large), and medium-intensity accelerations (first quarter P = .09, −1.32, large) compared with earlier periods. Conclusions: Overtime games disproportionately elevate perceptual, physiological, and acceleration workloads compared with regular games in starting basketball players. Players also perform at lower external intensities during overtime periods than earlier quarters during basketball games.
Michele Lastella, Gregory D. Roach, Grace E. Vincent, Aaron T. Scanlan, Shona L. Halson, and Charli Sargent
Purpose: To quantify the sleep/wake behaviors of adolescent, female basketball players and to examine the impact of daily training load on sleep/wake behaviors during a 14-day training camp. Methods: Elite, adolescent, female basketball players (N = 11) had their sleep/wake behaviors monitored using self-report sleep diaries and wrist-worn activity monitors during a 14-day training camp. Each day, players completed 1 to 5 training sessions (session duration: 114  min). Training load was determined using the session rating of perceived exertion model in arbitrary units. Daily training loads were summated across sessions on each day and split into tertiles corresponding to low, moderate, and high training load categories, with rest days included as a separate category. Separate linear mixed models and effect size analyses were conducted to assess differences in sleep/wake behaviors among daily training load categories. Results: Sleep onset and offset times were delayed (P < .05) on rest days compared with training days. Time in bed and total sleep time were longer (P < .05) on rest days compared with training days. Players did not obtain the recommended 8 to 10 hours of sleep per night on training days. A moderate increase in sleep efficiency was evident during days with high training loads compared with low. Conclusions: Elite, adolescent, female basketball players did not consistently meet the sleep duration recommendations of 8 to 10 hours per night during a 14-day training camp. Rest days delayed sleep onset and offset times, resulting in longer sleep durations compared with training days. Sleep/wake behaviors were not impacted by variations in the training load administered to players.
Spencer S.H. Roberts, Emma Falkenberg, Alysha Stevens, Brad Aisbett, Michele Lastella, and Dominique Condo
Purpose: Australian football has elite men’s (Australian Football League; AFL) and women’s (Australian Football League Women’s; AFLW) competitions. This study compared AFL and AFLW players’ sleep and characterized players’ sleep in the context of current sleep recommendations. Methods: A total of 70 players (36 AFL, 34 AFLW) had their sleep monitored via actigraphy over a 10-day preseason period. Sleep outcomes and their intraindividual variation, were compared between AFL and AFLW players using linear mixed models. Proportions of players sleeping ≥7 and ≥8 hours per night, and achieving ≥85% sleep efficiency, were compared using chi-square analyses. Results: Compared with AFL players, AFLW players slept less (7.9 [0.5] vs 7.1 [0.6] h, P = .000), had lower sleep efficiency (89.5% [2.8%] vs 84.0% [4.4%], P = .000), and greater intraindividual variation in sleep efficiency (3.1% [0.9%] vs 5.1% [2.1%], P = .000). A total of 47% of AFLW versus 3% of AFL players averaged <7 hours sleep (χ 2 = 18.6, P = .000). A total of 88% of AFLW versus 50% of AFL players averaged <8 hours sleep (χ 2 = 11.9, P = .001). A total of 53% of AFLW versus 14% of AFL players averaged <85% sleep efficiency (χ 2 = 12.1, P = .001). Conclusions: AFLW players slept less and had poorer sleep quality than AFL players. Many AFLW players do not meet current sleep duration or sleep quality recommendations. Research should test strategies to improve sleep among Australian rules footballers, particularly among elite women.
Aaron T. Scanlan, Neal Wen, Joshua H. Guy, Nathan Elsworthy, Michele Lastella, David B. Pyne, Daniele Conte, and Vincent J. Dalbo
Purpose: To examine correlations between peak force and impulse measures attained during the isometric midthigh pull (IMTP) and basketball-specific sprint and jump tests. Methods: Male, adolescent basketball players (N = 24) completed a battery of basketball-specific performance tests. Testing consisted of the IMTP (absolute and normalized peak force and impulse at 100 and 250 ms); 20-m sprint (time across 5, 10, and 20 m); countermovement jump (CMJ; absolute and normalized peak force and jump height); standing long jump (distance); and repeated lateral bound (distance). Correlation and regression analyses were conducted between IMTP measures and other attributes. Results: An almost perfect correlation was evident between absolute peak force attained during the IMTP and CMJ (r = .94, R2 = 56%, P < .05). Moderate to very large correlations (P < .05) were observed between IMTP normalized peak force and 5-m sprint time (r = −.44, R2 = 19%), 10-m sprint time (r = −.45, R2 = 20%), absolute (r = .57, R2 = 33%), normalized (r = .86, R2 = 73%) CMJ peak force, and standing long-jump distance (r = .51, R2 = 26%). Moderate to very large correlations were evident between impulse measures during the IMTP and 5-m sprint time (100 ms, r = −.40, R2 = 16%, P > .05) and CMJ absolute peak force (100 ms, r = .73, R2 = 54%; 250 ms, r = .68, R2 = 47%; P < .05). Conclusions: The IMTP may be used to assess maximal and rapid force expression important across a range of basketball-specific movements.