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Murray F. Mitchell, Hal A. Lawson, Hans van der Mars, and Phillip Ward

This special issue was designed to facilitate futures-oriented planning, focused on identical, similar, and unique practice and policy priorities. Formal planning aimed at desirable futures is a practical necessity for every helping profession because rapid, sometimes dramatic, societal change continues nonstop. Like all futures-oriented analyses, ours is unavoidably selective. Selectivity, once recognized, is a strength because readers are not asked to view the main claims and recommendations as a final authority. Selective research and scholarship focused on the creation and safeguarding of desirable futures has generative propensities that can provide the impetus for subsequent proposals aimed at the common good. In this chapter, the authors offer an integrative summary of the work in this special issue. Our summary invites readers’ special attention to distinctive features in their respective home contexts. This perspective stands in stark contrast to 20th Century models often described as “one best system” and “one ideal physical education model.” Justifiable variability—where “justifiable” means evidence-based and harmonized values—is the new norm for the 21st Century. The authors conclude that the physical education profession will benefit to the extent that it adopts the theme offered in this special issue. Unity founded on diversity—an idea whose time has come in a field known for fierce competition over curricula and programs.

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Julian Alcazar, Pedro J. Cornejo-Daza, Juan Sánchez-Valdepeñas, Luis M. Alegre, and Fernando Pareja-Blanco

Purpose: This study aimed to compare the adaptations provoked by various velocity loss (VL) thresholds used in resistance training on the squat force–velocity (F–V) relationship. Methods: Sixty-four resistance-trained young men were randomly assigned to one of four 8-week resistance training programs (all 70%–85% 1-repetition maximum) using different VL thresholds (VL0 = 0%, VL10 = 10%, VL20 = 20%, and VL40 = 40%) in the squat exercise. The F–V relationship was assessed under unloaded and loaded conditions in squat. Linear and hyperbolic (Hill) F–V equations were used to calculate force at zero velocity (F 0), velocity at zero force (V 0), maximum muscle power (P max), and force produced at mean velocities ranging from 0.0 to 2.0 m·s−1. Changes in parameters derived from the F–V relationship were compared among groups using linear mixed models. Results: Linear equations showed increases in F 0 (120.7 N [89.4 to 152.1]) and P max (76.2 W [45.3 to 107.2]) and no changes in V 0 (−0.02 m·s−1 [−0.11 to 0.06]) regardless of VL. Hyperbolic equations depicted increases in F 0 (120.7 N [89.4 to 152.1]), V 0 (1.13 m·s−1 [0.78 to 1.48]), and P max (198.5 W [160.5 to 236.6]) with changes in V 0 being greater in VL0 and VL10 versus VL40 (both P < .001). All groups similarly improved force at 0.0 to 2.0 m·s−1 (all P < .001), although in general, effect sizes were greater in VL10 and VL20 versus VL0 and VL40 at velocities ≤0.5 m·s−1. Conclusions: All groups improved linear and hyperbolic F 0 and P max and hyperbolic V 0 (except VL40). The dose–response relationship exhibited an inverted U-shape pattern at velocities ≤0.5 m·s−1 with VL10 and VL20 showing the greatest standardized changes.

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Isabella Russo, Paul A. Della Gatta, Andrew Garnham, Judi Porter, Louise M. Burke, and Ricardo J.S. Costa

Purpose: This study aimed to determine the effects of an acute “train-low” nutritional protocol on markers of recovery optimization compared to standard recovery nutrition protocol. Methods: After completing a 2-hour high-intensity interval running protocol, 8 male endurance athletes consumed a standard dairy milk recovery beverage (CHO; 1.2 g/kg body mass [BM] of carbohydrate and 0.4 g/kg BM of protein) and a low-carbohydrate (L-CHO; isovolumetric with 0.35 g/kg BM of carbohydrate and 0.5 g/kg BM of protein) dairy milk beverage in a double-blind randomized crossover design. Venous blood and breath samples, nude BM, body water, and gastrointestinal symptom measurements were collected preexercise and during recovery. Muscle biopsy was performed at 0 hour and 2 hours of recovery. Participants returned to the laboratory the following morning to measure energy substrate oxidation and perform a 1-hour distance test. Results: The exercise protocol resulted in depletion of muscle glycogen stores (250 mmol/kg dry weight) and mild body-water losses (BM loss = 1.8%). Neither recovery beverage replenished muscle glycogen stores (279 mmol/kg dry weight) or prevented a decrease in bacterially stimulated neutrophil function (−21%). Both recovery beverages increased phosphorylation of mTORSer2448 (main effect of time = P < .001) and returned hydration status to baseline. A greater fold increase in p-GSK-3βSer9/total-GSK-3β occurred on CHO (P = .012). Blood glucose (P = .005) and insulin (P = .012) responses were significantly greater on CHO (618 mmol/L per 2 h and 3507 μIU/mL per 2 h, respectively) compared to L-CHO (559 mmol/L per 2 h and 1147 μIU/mL per 2 h, respectively). Rates of total fat oxidation were greater on CHO, but performance was not affected. Conclusion: A lower-carbohydrate recovery beverage consumed after exercise in a “train-low” nutritional protocol does not negatively impact recovery optimization outcomes.

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Tim J. Mosey and Lachlan J.G. Mitchell

Objectives: The purpose of this study was to document the longitudinal strength and power characteristic changes and race performance changes of a skeleton athlete. Method: Longitudinal strength and power changes were assessed with strength and power diagnostic testing over a 9-year period. Trends over 9 years for relative strength were analyzed using a linear model. Push-start time was recorded across multiple tracks. Trends over 9 years for start performance at each track were assessed using a mixed-effects linear model to account for the impact of different tracks. Lower-body strength and power changes were assessed via a 1-repetition-maximum squat and a body-weight countermovement jump. The relationship between strength and power changes was assessed over time. The relationship between strength changes and start performance was determined by assessing the fixed effect of relative strength changes on push-start time. Results: Relative lower-body strength ranged from 1.6 kg per body weight to 1.9 kg per body weight and showed a significant mean improvement of 0.05 kg per body weight per year (R 2 = .71, P < .01). A negative correlation (R 2 = .79) between relative strength changes and push-start performance across multiple tracks was found. The mixed-effects model indicated that push-start time improved significantly year to year (0.02 s; P < .001; R 2 = .74) when controlling for the effect of track. Conclusions: The longitudinal analysis of push-start time and the associations with changes in strength suggest that training this quality can have a positive effect on push-start performance.

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Even Brøndbo Dahl, Eivind Øygard, Gøran Paulsen, Bjarne Rud, and Thomas Losnegard

Purpose: Preconditioning exercise is a widely used strategy believed to enhance performance later the same day. The authors examined the influence of preconditioning exercises 6 hours prior to a time-to-exhaustion (TTE) test during treadmill running. Methods: Ten male competitive runners (age = 26 [3] y, height = 184 [8] cm, weight = 73 [9] kg, maximum oxygen consumption = 72 [7] mL·kg−1·min−1) did a preconditioning session of running (RUN) or resistance exercise (RES) or no morning exercise (NoEx) in a randomized order, separated by >72 hours. The RUN consisted of 15 minutes of low-intensity running and 4 × 15 seconds at race pace (21–24 km·h−1) on a treadmill; RES involved 5 minutes of low-intensity running and 2 × 3 repetitions of isokinetic 1-leg shallow squats with maximal mobilization. Following a 6-hour break, electrically evoked force (m. vastus medialis), countermovement jump, running economy, and a TTE of approximately 2 minutes were examined. Results: Relative to NoEx, no difference was seen for RUN or RES in TTE (mean ± 95% CI: −1.3% ± 3.4% and −0.5% ± 6.0%) or running economy (0.2% ± 1.6% and 1.9% ± 2.7%; all Ps > .05). Jump height was not different for the RUN condition (1.0% ± 2.7%]) but tended to be higher in RES than in the NoEx condition (1.5% ± 1.6%, P = .07). The electrically evoked force tended to reveal low-frequency fatigue (reduced 20:50-Hz peak force ratio) only after RES compared to NoEx (−4.5% ± 4.6%, P = .06). Conclusion: The RUN or RES 6 hours prior to approximately 2 minutes of TTE running test did not improve performance in competitive runners.

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Kolbjørn Lindberg, Ingrid Eythorsdottir, Paul Solberg, Øyvind Gløersen, Olivier Seynnes, Thomas Bjørnsen, and Gøran Paulsen

Purpose: The aim of this study was to examine the concurrent validity of force–velocity (FV) variables assessed across 5 Keiser leg press devices. Methods: A linear encoder and 2 independent force plates (MuscleLab devices) were mounted on each of the 5 leg press devices. A total of 997 leg press executions, covering a wide range of forces and velocities, were performed by 14 participants (29 [7] y, 181 [5] cm, 82 [8] kg) across the 5 devices. Average and peak force, velocity, and power values were collected simultaneously from the Keiser and MuscleLab devices for each repetition. Individual FV profiles were fitted to each participant from peak and average force and velocity measurements. Theoretical maximal force, velocity, and power were deduced from the FV relationship. Results: Average and peak force and velocity had a coefficient of variation of 1.5% to 8.6%, near-perfect correlations (.994–.999), and a systematic bias of 0.7% to 7.1% when compared with reference measurements. Average and peak power showed larger coefficient of variations (11.6% and 17.2%), despite excellent correlations (.977 and .952), and trivial to small biases (3.9% and 8.4%). Extrapolated FV variables showed near-perfect correlations (.983–.997) with trivial to small biases (1.4%–11.2%) and a coefficient of variation of 1.4% to 5.9%. Conclusions: The Keiser leg press device can obtain valid measurements over a wide range of forces and velocities across different devices. To accurately measure power, theoretical maximal power calculated from the FV profile is recommended over average and peak power values from single repetitions, due to the lower random error observed for theoretical maximal power.

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Mark J. Kilgallon, Michael J. Johnston, Liam P. Kilduff, and Mark L. Watsford

Purpose: To compare resistance training using a velocity loss threshold with training to repetition failure on upper-body strength parameters in professional Australian footballers. Methods: A total of 26 professional Australian footballers (23.9 [4.2] y, 189.9 [7.8] cm, 88.2 [8.8] kg) tested 1-repetition-maximum strength (FPmax) and mean barbell velocity at 85% of 1-repetition maximum on floor press (FPvel). They were then assigned to 2 training groups: 20% velocity loss threshold training (VL; n = 12, maximum-effort lift velocity) or training to repetition failure (TF; n = 14, self-selected lift velocity). Subjects trained twice per week for 3 weeks before being reassessed on FPmax and FPvel. Training volume (total repetitions) was recorded for all training sessions. No differences were present between groups on any pretraining measure. Results: The TF group significantly improved FPmax (105.2–110.9 kg, +5.4%), while the VL group did not (107.5–109.2 kg, +1.6%) (P > .05). Both groups significantly increased FPvel (0.38–0.46 m·s−1, +19.1% and 0.37–0.42 m·s−1, +16.7%, respectively) with no between-groups differences evident (P > .05). The TF group performed significantly more training volume (12.2 vs 6.8 repetitions per session, P > .05). Conclusions: Training to repetition failure improved FPmax, while training using a velocity loss threshold of 20% did not. Both groups demonstrated similar improvements in FPvel despite the VL group completing 45% less total training volume than the TF group. The reduction in training volume associated with implementing a 20% velocity loss threshold may negatively impact the development of upper-body maximum strength while still enhancing submaximal movement velocity.

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Andrew A. Flatt, Jeff R. Allen, Clay M. Keith, Matthew W. Martinez, and Michael R. Esco

Purpose: To track cardiac-autonomic functioning, indexed by heart-rate variability, in American college football players throughout a competitive period. Methods: Resting heart rate (RHR) and the natural logarithm root mean square of successive differences (LnRMSSD) were obtained throughout preseason and ∼3 times weekly leading up to the national championship among 8 linemen and 12 nonlinemen. Seated 1-minute recordings were performed via mobile device and standardized for time of day and proximity to training. Results: Relative to preseason, linemen exhibited suppressed LnRMSSD during camp-style preparation for the playoffs (P = .041, effect size [ES] = −1.01), the week of the national semifinal (P < .001, ES = −1.27), and the week of the national championship (P = .005, ES = −1.16). As a combined group, increases in RHR (P < .001) were observed at the same time points (nonlinemen ES = 0.48–0.59, linemen ES = 1.03–1.10). For all linemen, RHR trended upward (positive slopes, R 2 = .02–.77) while LnRMSSD trended downward (negative slopes, R 2 = .02–.62) throughout the season. Preseason to postseason changes in RHR (r = .50, P = .025) and LnRMSSD (r = −.68, P < .001) were associated with body mass. Conclusions: Heart-rate variability tracking revealed progressive autonomic imbalance in the lineman position group, with individual players showing suppressed values by midseason. Attenuated parasympathetic activation is a hallmark of impaired recovery and may contribute to cardiovascular maladaptations reported to occur in linemen following a competitive season. Thus, a descending pattern may serve as an easily identifiable red flag requiring attention from performance and medical staff.

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Murray F. Mitchell, Sue Sutherland, and Jennifer Walton-Fisette

Neglecting to adapt physical education programs, or resisting and worse ignoring the changing needs of students has created an environment where the reproduction of inequities prevails. An examination of the role physical education teacher education faculty in the physical education system begins with consideration of eight key factors that influence their performance: (a) society, (b) higher education institutions, (c) PK–12 schools, (d) PK–12 and preservice student teachers (PST) students, (e) the purpose of physical education, (f) kinesiology, (g) professional associations, and (h) personal life circumstances. The authors draw attention to lessons learned and future directions tied to these eight influences. A critical reflection on social identity and how it influences practice is provided with suggestions on how to begin this work. Undertaking a program equity audit is discussed as a tool to highlight areas within physical education teacher education programs that influence socially just and equitable practice. Engaging in self-study (either individually, collaboratively, or programmatically) is suggested as a means to explore pedagogical practices or programmatic decisions that promote socially just and equitable physical education teacher education and physical education. Attention to policy engagement at the local, state, and national levels is noted as a potentially powerful contribution to change.

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Antonis Kesisoglou, Andrea Nicolò, Lucinda Howland, and Louis Passfield

Purpose: To examine the effect of continuous (CON) and intermittent (INT) running training sessions of different durations and intensities on subsequent performance and calculated training load (TL). Methods: Runners (N = 11) performed a 1500-m time trial as a baseline and after completing 4 different running training sessions. The training sessions were performed in a randomized order and were either maximal for 10 minutes (10CON and 10INT) or submaximal for 25 minutes (25CON and 25INT). An acute performance decrement (APD) was calculated as the percentage change in 1500-m time-trial speed measured after training compared with baseline. The pattern of APD response was compared with that for several TL metrics (bTRIMP, eTRIMP, iTRIMP, running training stress score, and session rating of perceived exertion) for the respective training sessions. Results: Average speed (P < .001, ηp2=.924) was different for each of the initial training sessions, which all resulted in a significant APD. This APD was similar when compared across the sessions except for a greater APD found after 10INT versus 25CON (P = .02). In contrast, most TL metrics were different and showed the opposite response to APD, being higher for CON versus INT and lower for 10- versus 25-minute sessions (P < .001, ηp2>.563). Conclusion: An APD was observed consistently after running training sessions, but it was not consistent with most of the calculated TL metrics. The lack of agreement found between APD and TL suggests that current methods for quantifying TL are flawed when used to compare CON and INT running training sessions of different durations and intensities.