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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.

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Piotr A. Piasecki, Todd M. Loughead, Kyle F. Paradis, and Krista J. Munroe-Chandler

In an effort to increase perceptions of cohesion among intercollegiate soccer players, a team-based mindfulness meditation program was undertaken. This team-building program was delivered by using a personal-disclosure mutual-sharing approach. A total of 31 female intercollegiate soccer players from two teams participated. Assigned to the intervention condition was a Canadian intercollegiate team (U Sports), while the control condition was an American intercollegiate team (NCAA, Division II). The participants completed a measure of cohesion (Group Environment Questionnaire) pre- and postintervention. Controlling for the preintervention scores, the 8-week team-based mindfulness meditation program resulted in significantly higher perceptions of social cohesion for the intervention group compared with the control group at postintervention. However, there were no significant differences in task cohesion between the intervention and control groups at postintervention. Using personal disclosure, mutual sharing seems a viable approach by which to deliver a team-based mindfulness meditation program to enhance a team’s social cohesion.

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Luke Hogarth, Mark McKean, Max McKenzie, and Tyler Collings

Purpose: This study established the relationship between isometric midthigh pull (IMTP) peak force and court-based jumping, sprinting, and change of direction (COD) performance in professional netball players. The change in IMTP peak force in response to sport-specific training was also examined. Methods: IMTP peak force and court-based jumping, sprinting, and COD were collected in 18 female athletes contracted to a Suncorp Super Netball team. Linear regression models established the relationship between absolute and normalized strength values and court-based performance measures in the participant cohort. Changes in IMTP peak force and court-based performance measures were examined following 2 consecutive preseason training blocks in a subset of participants. Results: The IMTP peak force values normalized to body mass were found to be determinants of court-based jumping, sprinting, and COD performance in the participant cohort (R 2 = .34–.65, P ≤ .016). The participants showed increases in absolute (mean ± SE = 398 ± 68.5 N, P < .001, Hedge g = 0.70 [−0.05 to 1.35]) and normalized IMTP peak force (mean ± SE = 4.6 ± 0.78 N·kg−1, P < .001, Hedge g = 0.47 [−0.04 to 0.97]) over 2 consecutive training blocks that coincided with improvements in jumping, sprinting, and COD performances. Conclusion: IMTP peak force is a determinant of court-based jumping, sprinting, and COD performance and is sensitive to training in professional netball players. These results support the utility of the IMTP test to monitor the development and maintenance of maximal lower body muscular strength in these athletes.

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Jonathan R. Males, John H. Kerr, and Joanne Hudson

This case study examines the personal experiences of an elite athlete, coach, and sport psychology consultant (SPC) during the athlete’s preparation and performance in a recent Olympic Games. The qualitative research details how the consultancy process was affected by the athlete’s late admission of the deteriorating relationship with his coach. The concepts of closeness, commitment, complementarity, and co-orientation provided a theoretical perspective to the SPC’s interpretation of athlete performance and the interpersonal conflict that developed between athlete and coach. The basic performance demand model provided an applied perspective. The SPC’s commentary adopts a reflexive discursive style that also focuses on the SPC’s role in the consultancy process and the effectiveness of the performance demand model materials. Five important recommendations arise from the case study, and these might inform other SPCs’ future athlete–coach consultancies and interventions.

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Markus N.C. Williams, Vincent J. Dalbo, Jordan L. Fox, Cody J. O’Grady, and Aaron T. Scanlan

Purpose: To compare weekly training and game demands according to playing position in basketball players. Methods: A longitudinal, observational study was adopted. Semiprofessional, male basketball players categorized as backcourt (guards; n = 4) and frontcourt players (forwards/centers; n = 4) had their weekly workloads monitored across an entire season. External workload was determined using microsensors and included PlayerLoad (PL) and inertial movement analysis variables. Internal workload was determined using heart rate to calculate absolute and relative summated-heart-rate-zones workload and rating of perceived exertion (RPE) to calculate session-RPE workload. Comparisons between weekly training and game demands were made using linear mixed models and effect sizes in each positional group. Results: In backcourt players, higher relative PL (P = .04, very large) and relative summated-heart-rate-zones workload (P = .007, very large) were evident during training, while greater session-RPE workload (P = .001, very large) was apparent during games. In frontcourt players, greater PL (P < .001, very large), relative PL (P = .019, very large), peak PL intensities (P < .001, moderate), high-intensity inertial movement analysis events (P = .002, very large), total inertial movement analysis events (P < .001, very large), summated-heart-rate-zones workload (P < .001, very large), RPE (P < .001, very large), and session-RPE workload (P < .001, very large) were evident during games. Conclusions: Backcourt players experienced similar demands between training and games across several variables, with higher average workload intensities during training. Frontcourt players experienced greater demands across all variables during games than training. These findings emphasize the need for position-specific preparation strategies leading into games in basketball teams.