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Kate M. Luckin-Baldwin, Claire E. Badenhorst, Ashley J. Cripps, Grant J. Landers, Robert J. Merrells, Max K. Bulsara, and Gerard F. Hoyne

Purpose: The completion of concurrent strength and endurance training can improve exercise economy in cyclists and runners; however, the efficacy of strength training (ST) implementation to improve economy in long-distance (LD) triathletes has not yet been investigated. The purpose of this study was to investigate physiological outcomes in LD triathletes when ST was completed concurrently to endurance training. Methods: A total of 25 LD triathletes were randomly assigned to either 26 weeks of concurrent endurance and ST (n = 14) or endurance training only (n = 11). The ST program progressed from moderate (8–12 repetitions, ≤75% of 1-repetition maximum, weeks 0–12) to heavy loads (1–6 repetitions, ≥85% of 1-repetition maximum, weeks 14–26). Physiological and performance indicators (cycling and running economy, swim time, blood lactate, and heart rate) were measured during a simulated triathlon (1500-m swim, 60-min cycle, and 20-min run) at weeks 0, 14, and 26. Maximal strength and anthropometric measures (skinfolds and body mass) were also collected at these points. Results: The endurance strength group significantly improved maximal strength measures at weeks 14 and 26 (P < .05), cycling economy from weeks 0 to 14 (P < .05), and running economy from weeks 14 to 26 (P < .05) with no change in body mass (P > .05). The endurance-only group did not significantly improve any economy measures. Conclusions: The addition of progressive load ST to LD triathletes’ training programs can significantly improve running and cycling economy without an increase in body mass.

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Davide Ferioli, Aaron T. Scanlan, Daniele Conte, Emanuele Tibiletti, and Ermanno Rampinini

Purpose: To quantify and compare the internal workloads experienced during the playoffs and regular season in basketball. Methods: A total of 10 professional male basketball players competing in the Italian first division were monitored during the final 6 weeks of the regular season and the entire 6-week playoff phase. Internal workload was quantified using the session rating of perceived exertion (s-RPE) method for all training sessions and games. A 2-way repeated-measures analysis of variance (day type × period) was utilized to assess differences in daily s-RPE between game days, days within 24 hours of games, and days >24 hours from games during the playoffs and regular season. Comparisons in weekly training, game, and total workloads were made between the playoffs and regular season using paired t tests and effect sizes. Results: A significant interaction between day and competitive period for s-RPE was found (P = .003, moderate). Lower s-RPE was apparent during playoff and regular-season days within 24 hours of games than all other days (P < .001, very large). Furthermore, s-RPE across days >24 hours from playoff games was different than all other days (P ≤ .01, moderate–very large). Weekly training (P = .009, very large) and total (P < .001, moderate) s-RPE were greater during the regular season than playoffs, whereas weekly game s-RPE was greater during the playoffs than the regular season (P < .001, very large). Conclusions: This study presents an exploratory investigation of internal workload during the playoffs in professional basketball. Players experienced greater training and total weekly workloads during the regular season than during the playoffs with similar daily game workloads between periods.

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Sebastian Keller, Simon Kohne, Hannah L. Notbohm, Wilhelm Bloch, and Moritz Schumann

Purpose: This study assessed the effects of cooling during endurance cycling (percooling) on changes in core body temperature (Tcore), inflammatory, and metabolic responses. Methods: A total of 12 male cyclists (peak oxygen uptake 60 [4] mL·kg−1·min−1) completed a 60-minute constant workload trial (55% of peak power output and ambient temperature 30.4°C [0.6°C]) in a randomized order both with (ICE) and without (CON) an ice vest. An ingestible capsule was used to measure Tcore. Blood samples were collected immediately before and after each trial to determine concentrations of blood lactate, serum cortisol, interleukin-6, and reactive oxygen and nitrogen species. Results: Tcore increased statistically (P < .001) both in CON (7.0% [1.4%], effect size [ES] = 6.3) and ICE (5.1% [1.1%], ES = 5.7). The increase in CON was statistically larger compared with ICE (P = .006, ES = 1.4). Concentrations of blood lactate (CON: 163% [63%], ES = 1.3; ICE: 149% [91%], ES = 1.3), cortisol (CON: 138% [123%], ES = 1.7; ICE: 81% [102%], ES = 1.0), and interleukin-6 (CON: 661% [324%], ES = 2.1; ICE: 624% [368%], ES = 1.2) statistically increased in both conditions (P < .01) to a similar extent. In addition, reactive oxygen and nitrogen species statistically decreased in both conditions (CON: −19.2% [14.9%], P = .002, ES = 0.9; ICE: −15.1% [16.5%], P = .02, ES = 0.9). No correlations were found between the changes of Tcore and blood parameters across the conditions. Conclusions: Despite attenuated Tcore, similar inflammatory and metabolic responses were observed. Our findings suggest percooling to be a promising strategy to attenuate thermal strain without compromising physiological function.

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Alejandro Martínez-Cava, Alejandro Hernández-Belmonte, Javier Courel-Ibáñez, Elena Conesa-Ros, Ricardo Morán-Navarro, and Jesús G. Pallarés

Purpose: A variation of the traditional squat (SQ) rebound technique (REBOUND) including a momentary pause ∼2 seconds (PAUSE) between eccentric and concentric phases has been proposed. Although there is a consensus about the lower acute effects on performance of this PAUSE variant compared with traditional REBOUND technique, no information exists about the differences in longitudinal adaptations of these SQ executions. Methods: A total of 26 men were randomly assigned into the PAUSE (n = 13) or REBOUND (n = 13) groups and completed a 10-week velocity-based training using the SQ exercise, only differing in the technique. Neuromuscular adaptations were assessed by the changes in the 1-repetition maximum strength and mean propulsive velocity achieved against the absolute loads (in kilograms) common to pretest and posttest. Functional performance was evaluated by the following tests: countermovement jump, Wingate, and sprint time at 0 to 10, 10 to 20, and 0 to 20 m. Results: Whereas both groups showed significant increases in most of the neuromuscular tests (P < .05), the PAUSE (effect size [ES] = 0.76–1.12) presented greater enhancements than REBOUND (ES = 0.45–0.92). Although not significant, improvements in Wingate and sprint time at 0 to 10 and 0 to 20 m were higher for PAUSE (ES = 0.31–0.46) compared with REBOUND (ES = 0.10–0.29). Conversely, changes on countermovement jump and sprint time at 10 to 20 m were superior for REBOUND (ES = 0.17–0.88) than for PAUSE (ES = 0.09–0.75). Conclusion: Imposing a pause between eccentric and concentric phases in the SQ exercise could be an interesting strategy to increase neuromuscular and functional adaptations in sport actions that mainly depend on concentric contractions. Moreover, sport abilities highly dependent on the stretch-shortening cycle could benefit from the REBOUND or a combination of the 2 techniques.

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Adam J. Pinos, David J. Bentley, and Heather M. Logan-Sprenger

Purpose: The purpose of this study was to compare 4 weeks of pool-based sprint interval training with a similar ergometer training intervention on a maximal anaerobic lactate test (MANLT), 50-m (competition) freestyle performance, and 6- and 30-second maximal swimming ergometer performances. Methods: A total of 14 competitive adolescent swimmers (male, n = 8; female, n = 6) participated in this study. Swimmers were categorized into 2 sex-matched groups: swimming ergometer (ERG; n = 7) and pool-sprint training (n = 7) groups. Each athlete performed 4 preintervention and postintervention assessments consisting of a MANLT, a 50-m freestyle race, and 6- and 30-second maximal swim ERG performances. Results: Both groups demonstrated a significant effect (P < .05) of time for all assessments. Group differences were observed after 4 weeks of sprint interval training as follows: (1) The ERG group had a significantly faster speed in the fourth 50-m MANLT sprint (ERG 1.58 [0.05] vs pool-sprint training 1.48 [0.07] m/s, P < .01) and (2) The ERG group demonstrated enhanced Δblood lactate post-MANLT (ERG 2.4 [1.2] vs pool-sprint training 2.7 [0.9] mmol/L, P < .05). A significant correlation was found between the 30-second maximal ERG test and 50-m freestyle swimming velocity (r = .74, P < .01, effect size = 0.52). Conclusions: The results demonstrate significant physiological improvements to anaerobic sprint ability after 4 weeks of sprint interval training in both swim ERG and pool-based interventions. Thus, sprint ability may be improved through multiple modalities (pool and dry land) to elicit a positive training response.

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Sara R. Sherman, Clifton J. Holmes, Bjoern Hornikel, Hayley V. MacDonald, Michael V. Fedewa, and Michael R. Esco

Purpose: To assess the agreement of the root mean square of successive R-R interval (RMSSD) values when recorded immediately upon waking to values recorded later in the morning prior to practice, and to determine the associations of the RMSSD recordings with performance outcomes in female rowers. Methods: A total of 31 National Collegiate Athletic Association Division I rowers were monitored for 6 consecutive days. Two seated RMSSD measurements were obtained on at least 3 mornings using a smartphone-based photoplethysmography application. Each 1-minute RMSSD measure was recorded following a 1-minute stabilization period. The first (T1) measurement occurred at the athlete’s home following waking, while the second (T2) transpired upon arrival at the team’s boathouse immediately before practice. From the measures, the RMSSD mean and coefficient of variation were calculated. Two objective performance assessments were conducted on an indoor rowing ergometer on separate days: 2000-m time trial and distance covered in 30 minutes. Interteam rank was determined by the coaches, based on subjective and objective performance markers. Results: The RMSSD mean (intraclass correlation coefficient = .82; 95% CI, .63 to .92) and RMSSD coefficient of variation (intraclass correlation coefficient = .75; 95% CI, .48 to .88) were strongly correlated at T1 and T2, P < .001. The RMSSD mean at T1 and T2 was moderately associated with athlete rank (r = −.55 and r = −.46, respectively), 30-minute distance (r = .40 and r = .41, respectively), and 2000 m at T1 (r = −.37), P < .05. No significant correlations were observed for the RMSSD coefficient of variation. Conclusion: Ultrashort RMSSD measurements taken immediately upon waking show very strong agreement with those taken later in the morning, at the practice facility. Future research should more thoroughly investigate the relationship between specific performance indices and the RMSSD mean and coefficient of variation for female collegiate rowers.

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Eva Piatrikova, Nicholas J. Willsmer, Marco Altini, Mladen Jovanović, Lachlan J.G. Mitchell, Javier T. Gonzalez, Ana C. Sousa, and Sean Williams

Purpose: First, to examine whether heart rate variability (HRV) responses can be modeled effectively via the Banister impulse-response model when the session rating of perceived exertion (sRPE) alone, and in combination with subjective well-being measures, are utilized. Second, to describe seasonal HRV responses and their associations with changes in critical speed (CS) in competitive swimmers. Methods: A total of 10 highly trained swimmers collected daily 1-minute HRV recordings, sRPE training load, and subjective well-being scores via a novel smartphone application for 15 weeks. The impulse-response model was used to describe chronic root mean square of the successive differences (rMSSD) responses to training, with sRPE and subjective well-being measures used as systems inputs. Changes in CS were obtained from a 3-minute all-out test completed in weeks 1 and 14. Results: The level of agreement between predicted and actual HRV data was R 2 = .66 (.25) when sRPE alone was used. Model fits improved in the range of 4% to 21% when different subjective well-being measures were combined with sRPE, representing trivial-to-moderate improvements. There were no significant differences in weekly group averages of log-transformed (Ln) rMSSD (P = .34) or HRV coefficient of variation of Ln rMSSD (P = .12); however, small-to-large changes (d = 0.21–1.46) were observed in these parameters throughout the season. Large correlations were observed between seasonal changes in HRV measures and CS (changes in averages of Ln rMSSD: r = .51, P = .13; changes in coefficient of variation of Ln rMSSD: r = −.68, P = .03). Conclusion: The impulse-response model and data collected via a novel smartphone application can be used to model HRV responses to swimming training and nontraining-related stressors. Large relationships between seasonal changes in measured HRV parameters and CS provide further evidence for incorporating a HRV-guided training approach.

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Adam Mallett, Phillip Bellinger, Wim Derave, Eline Lievens, Ben Kennedy, Hal Rice, and Clare Minahan

Purpose: To determine the association between estimated muscle fiber typology and the start and turn phases of elite swimmers during competition. Methods: International and national competition racing performance was analyzed from 21 female (FINA points = 894 ± 39: 104.5 ± 1.8% world record ratio [WRR]) and 25 male (FINA points = 885 ± 54: 104.8 ± 2.1% WRR) elite swimmers. The start, turn, and turn out times were determined from each of the swimmers’ career best performance times (FINA points = 889 ± 48: 104.7 ± 2.0% WRR). Muscle carnosine concentration was quantified by proton magnetic resonance spectroscopy in the gastrocnemius and soleus and was expressed as a carnosine aggregate z score relative to an age- and gender-matched nonathlete control group to estimate muscle fiber typology. Linear mixed models were employed to determine the association between muscle fiber typology and the start and turn times. Results: While there was no significant influence of carnosine aggregate z score on the start and turn times when all strokes and distance events were entered into the model, the swimmers with a higher carnosine aggregate z score (ie, faster muscle typology) had a significantly faster start time in 100-m events compared with the swimmers with a lower carnosine aggregate z score (P = .02, F = 5.825). The start and turn times were significantly faster in the male compared with the female swimmers in the 100-m events compared with other distances, and between the 4 different swimming strokes (P < .001). Conclusion: This study suggests that start times in sprint events are partly determined (and limited) by muscle fiber typology, which is highly relevant when ∼12% of the overall performance time is determined from the start time.

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Enrico Perri, Carlo Simonelli, Alessio Rossi, Athos Trecroci, Giampietro Alberti, and F. Marcello Iaia

Purpose: To investigate the relationship between the training load (TL = rate of perceived exertion × training time) and wellness index (WI) in soccer. Methods: The WI and TL data were recorded from 28 subelite players (age = 20.9 [2.4] y; height = 181.0 [5.8] cm; body mass = 72.0 [4.4] kg) throughout the 2017/2018 season. Predictive models were constructed using a supervised machine learning method that predicts the WI according to the planned TL. The validity of our predictive model was assessed by comparing the classification’s accuracy with the one computed from a baseline that randomly assigns a class to an example by respecting the distribution of classes (B1). Results: A higher TL was reported after the games and during match day (MD)-5 and MD-4, while a higher WI was recorded on the following days (MD-6, MD-4, and MD-3, respectively). A significant correlation was reported between daily TL (TLMDi) and WI measured the day after (WIMDi+1) (r = .72, P < .001). Additionally, a similar weekly pattern seems to be repeating itself throughout the season in both TL and WI. Nevertheless, the higher accuracy of ordinal regression (39% [2%]) compared with the results obtained by baseline B1 (21% [1%]) demonstrated that the machine learning approach used in this study can predict the WI according to the TL performed the day before (MD<i). Conclusion: The machine learning technique can be used to predict the WI based on a targeted weekly TL. Such an approach may contribute to enhancing the training-induced adaptations, maximizing the players’ readiness and reducing the potential drops in performance associated with poor wellness scores.

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Teun van Erp, Marcel Kittel, and Robert P. Lamberts

Purpose: To describe the performance and tactical sprint characteristics of a world-class sprinter competing in the Tour de France. In addition, differences in the sprint tactics of 2 teams and won versus lost sprints are highlighted. Method: Power output (PO) and video footage of 21 sprints were analyzed. Position in the peloton and number of teammates supporting the sprinter at different times before the finish line together with PO for different time intervals were determined. Sprints were classified as team Shimano (2013–2014) and team Quick-step (2016–2017), as well as won or lost. Results: The sprinter was highly successful, winning 14 out of the 21 sprints. At time intervals 10 to 5, 3 to 2, and 1.5 to 1 minute, POs were significantly lower in team Quick-step compared with team Shimano, but the sprinter was positioned further away from the front at 10, 2, 1.5, 1, and 0.5 minutes at team Quick-step compared with team Shimano. The PO was higher at time interval 0.5 to 0.25 minutes before the finish line with team Quick-step when compared with team Shimano. The position of the sprinter in the peloton in lost sprints was further away from the front at 0.5 minutes before the finish compared with won sprints, while no differences were noted for PO and the number of teammates between won and lost sprints. Conclusions: Differences in sprint tactics (Shimano vs Quick-step) influence the PO and position in the peloton during the sprint preparation. In addition, the position at 0.5 minutes before the finish line influences the outcome (won or lost) of the sprint.