Browse

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 mTOR^Ser2448 (main effect of time = P < .001) and returned hydration status to baseline. A greater fold increase in p-GSK-3β^Ser9/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.

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.

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.

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.

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 (FP_max) and mean barbell velocity at 85% of 1-repetition maximum on floor press (FP_vel). 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 FP_max and FP_vel. 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 FP_max (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 FP_vel (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 FP_max, while training using a velocity loss threshold of 20% did not. Both groups demonstrated similar improvements in FP_vel 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.

Along with digestion and absorption of nutrients, the gastrointestinal epithelium acts as a primary intestinal defense layer, preventing luminal pathogens from entering the circulation. During exercise in the heat, epithelial integrity can become compromised, allowing bacteria and bacterial endotoxins to translocate into circulation, triggering a systemic inflammatory response and exacerbating gastrointestinal damage. While this relationship seems clear in the general population in endurance/ultraendurance exercise, the aim of this systematic review was to evaluate the effect of exercise in the heat on blood markers of gastrointestinal epithelial disturbance in well-trained individuals. Following the 2009 Preferred Reporting Items for Systematic Reviewed and Meta-Analyses guidelines, five electronic databases were searched for appropriate research, and 1,885 studies were identified. Five studies met the inclusion criteria and were subject to full methodological appraisal by two reviewers. Critical appraisal of the studies was conducted using the McMasters Critical Review Form. The studies investigated changes in markers of gastrointestinal damage (intestinal fatty acid–binding protein, endotoxin, and/or lipopolysaccharide-binding protein) following acute exercise in warm to hot conditions (≥ 30 °C) and included trained or well-trained participants with direct comparisons to a control temperate condition (≤ 22 °C). The studies found that prolonged submaximal and strenuous exercise in hot environmental conditions can acutely increase epithelial disturbance compared with exercise in cooler conditions, with disturbances not being clinically relevant. However, trained and well-trained populations appear to tolerate exercise-induced gastrointestinal disturbance in the heat. Whether this is an acquired tolerance related to regular training remains to be investigated.

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, R² = .02–.77) while LnRMSSD trended downward (negative slopes, R² = .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.

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, ${\eta }_{\mathrm{p}}^2=.924$) 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, ${\eta }_{\mathrm{p}}^2>.563$). 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.