Purpose: Critical speed (CS) and supra-CS distance capacity (D′) are useful metrics for monitoring changes in swimmers’ physiological and performance capacities. However, the utility of these metrics across a season has not been systematically evaluated in high-level swimmers. Methods: A total of 27 swimmers (mean [SD]: 18 females, age = 19.1 [2.9] y, and 9 males, age = 19.5 [1.9] y) completed the 12 × 25-m swimming test multiple times (4  tests/swimmer) across a 2-y period. Season-best times in all distances for the test stroke were sourced from publicly available databases. Swimmers’ distance speciality was determined as the event with the time closest to world record. Four metrics were calculated from the 12 × 25-m test: CS, D′, peak speed, and drop-off %. Results: Guyatt responsiveness index values were calculated to ascertain the practically relevant sensitivity of each 12 × 25-m metric: CS = 1.5, peak speed = 2.3, D′ = 2.1, and drop-off % = 2.6. These values are modified effect sizes; all are large effects. Bayesian mixed modeling showed substantial between-subjects differences between genders and strokes for each variable but minimal within-subject changes across the season. Drop-off % was lower in 200-m swimmers (14.0% [3.3%]) than in 100-m swimmers (18.1% [4.1%], P = .003, effect size = 1.10). Conclusions: The 12 × 25-m test is best suited to differentiating between swimmers of different strokes and events. Further development is needed to improve its utility in quantifying meaningful changes over a season for individual swimmers.
Lachlan J.G. Mitchell, Ben Rattray, Paul Wu, Philo U. Saunders and David B. Pyne
Francesco Campa, Hannes Gatterer, Henry Lukaski and Stefania Toselli
Purpose: The exercise-induced increase in skin and body temperature, cutaneous blood flow, and electrolyte accumulation on the skin affects the validity of bioimpedance analysis to assess postexercise changes in hydration. This study aimed to assess the influence of a 10-min cold (22°C) shower on the time course of impedance measurements after controlled exercise. Methods: In total, 10 male athletes (age 26.2 [4.1] y and body mass index 23.9 [1.7] kg/m2) were tested on 2 different days. During both trials, athletes ran for 30 min on a treadmill in a room at 23°C. In a randomized crossover trial, the participants underwent a 10-min cold shower on the trial occasion and did not shower in the control trial. Bioimpedance analysis variables were measured before running (ie, baseline [T0]), immediately after exercising (T1), and 20 (T2), 40 (T3), and 60 min (T4) after the exercise. The shower was performed after T1 in the shower trial. Results: Body weight decreased similarly in both trials (−0.4% [0.1%], P < .001; −0.4% [0.1%], P < .001). Resistance and vector length returned to baseline at T2 in the shower trial, whereas baseline values were achieved at T3 in the control trial (P > .05). In the control trial, reactance remained at a lower level for the entire testing period (38.1 [6.9] vs 37.3 [6.7], P < .001). Forehead skin temperature returned to baseline values at T2 with shower, whereas it was still high at T4 without shower (P < .001). Conclusions: The present data show that a 10-min cold shower enables the stabilization of bioimpedance analysis measurements within 20 min after exercise, which might facilitate the assessment of hydration change after exercise.
Brian Hanley, Trent Stellingwerff and Florentina J. Hettinga
Purpose: This was the first study to analyze high-resolution pacing data from multiple global championships, allowing for deeper and rigorous analysis of pacing and tactical profiles in elite-standard middle-distance racing. The aim of this study was to analyze successful and unsuccessful middle-distance pacing profiles and variability across qualifying rounds and finals. Methods: Finishing and 100-m-split speeds and season’s best times were collected for 265 men and 218 women competing in 800- and 1500-m races, with pace variability expressed using coefficient of variation. Results: In both events, successful athletes generally separated themselves from slower athletes in the final 200 m, not by speeding up but by avoiding slowing compared with competitors. This was despite different pacing profiles between events in the earlier part of the race preceding the end spurt. Approximately 10% of athletes ran season’s best times, showing a tactical approach to elite-standard middle-distance racing and possible fatigue across rounds. Men’s and women’s pacing profiles were remarkably similar within each event, but the previously undescribed seahorse-shaped profile in the 800-m (predominantly positive pacing) differed from the J-shaped negative pacing of the 1500-m. Pacing variability was high compared with world records, especially in the finals (coefficient of variation: 5.2–9.1%), showing that athletes need to be able to vary pace and cope with surges. Conclusions: The best athletes had the physiological capacity to vary pace and respond to surges through successive competition rounds. In competition-specific training, coaches should incorporate several sessions in which pace changes frequently and sometimes unexpectedly.
Aitor Iturricastillo, Cristina Granados, Raúl Reina, José Manuel Sarabia, Ander Romarate and Javier Yanci
Purpose: To analyze the relationship between mean propulsive velocity (MPV) of the bar and relative load (percentage of the 1-repetition maximum [%1RM]) in the bench-press (BP) exercise and to determine the relationship of power variables (ie, mean concentric power [MP], mean propulsive power [MPP], and peak power [PP]) in change-of-direction ability, linear sprint, and repeated-sprint ability. Methods: A total of 9 Spanish First Division wheelchair basketball players participated in the study. All participants performed an isoinertial BP test in free execution mode, a 505 change-of-direction ability test, linear sprint test (20 m), and repeated-sprint ability test. Results: A nearly perfect and inverse relationship was observed for the BP exercise between the %1RM and MPV (r = −.97, R 2 = .945, P < .001). The maximum loads for MP, MPP, and PP were obtained between 48.1% and 59.4% of the 1RM. However, no significant correlations were observed between strength and wheelchair performance. Conclusions: Wheelchair basketball players with different functional impairments showed a nearly perfect and inverse relationship for the BP exercise between the %1RM and MPV; thus the MPV could be used to estimate the %1RM. This finding has important practical applications for velocity-based resistance training in that coaches would be able to prescribe and monitor training load. Conversely, the absence of association between BP performance and field tests might be due to other factors such as the wheelchair–user interface, trunk-muscle activity, or propulsion technique, apart from strength variables.
Leah S. Goudy, Brandon Rhett Rigby, Lisa Silliman-French and Kevin A. Becker
The purpose of this study was to determine changes in balance, postural sway, and quality of life after 6 wk of simulated horseback riding in adults diagnosed with Parkinson’s disease. Eight older adults completed two 60-min riding sessions weekly for 6 wk. Variables of balance, postural sway, and quality of life were measured 6 wks before and within 1 wk before and after the intervention. Berg Balance Scale scores decreased from baseline to preintervention (48.36 ± 5.97 vs. 45.86 ± 6.42, p = .050) and increased from preintervention to postintervention (45.86 ± 6.42 vs. 50.00 ± 4.38, p = .002). Cognitive impairment, a dimension of quality of life, improved from baseline to postintervention (37.5 ± 20.5 vs. 21.5 ± 14.4, p = .007). Six weeks of simulated horseback riding may improve balance and cognitive impairment in older adults with Parkinson’s disease.
Xiaomin Sun, Zhen-Bo Cao, Kumpei Tanisawa, Satomi Oshima and Mitsuru Higuchi
Low serum 25-hydroxyvitamin D [25(OH)D] concentrations are associated with a high risk of insulin resistance and Type 2 diabetes mellitus in adults. However, it is unknown whether this is the case for American collegiate football and rugby football athletes. This study investigated the associations between serum 25(OH)D concentrations and glucose profiles in male collegiate football athletes. Thirty-four collegiate athletes (13 American football players and 21 rugby football players) aged 21 years were recruited. Their body fat percent and visceral fat area were measured by dual-energy X-ray absorptiometry and magnetic resonance imaging, respectively. The participants completed an oral glucose tolerance test (75 g glucose) with venous blood samples obtained at time points 0, 30, 60, 90, and 120 min for the determination of plasma glucose and serum insulin concentrations. Fasting serum 25(OH)D concentrations were also measured. The prevalence of vitamin D deficiency and insufficiency was 17.6% and 58.8%, respectively. The serum 25(OH)D concentrations were negatively associated with the increments in the areas under the curve (iAUC) for glucose (r = −.429, p = .011) and were borderline significantly correlated with the Matsuda index (r = −.303, p = .082). No relationships were observed between the serum 25(OH)D concentrations and other glucose profiles. Multiple stepwise regression analysis of glucose iAUC concentrations as the dependent variable indicated that the serum 25(OH)D concentrations, but not body fat indicators, were independently associated with glucose iAUC (β = −0.390, p = .025). The serum 25(OH)D concentrations were only an independent predictor for glucose iAUC in male collegiate football athletes, suggesting that increased 25(OH)D concentrations would be helpful for maintaining glucose homeostasis.
Ben Desbrow, Katelyn Barnes, Gregory R. Cox, Elizaveta Iudakhina, Danielle McCartney, Sierra Skepper, Caroline Young and Chris Irwin
This study assessed voluntary dietary intake when different beverages were provided within a recovery area following recreational exercise. Participants completed two 10-km runs 1 week apart. Immediately after the first run, “beer drinkers” (n = 54; mean ± SD: age = 23.9 ± 5.8 years, body mass [BM] = 76 ± 13 kg) randomly received low-alcohol beer (Hahn Ultra® [Lion Co.], 0.9% alcohol by volume) or sports drink (SD; Gatorade® [PepsiCo]), whereas “nonbeer drinkers” (n = 78; age = 21.8 ± 2.2 years, BM = 71 ± 13 kg) received water or SD. Participants remained in a recovery area for 30–60 min with fluid consumption monitored. The following week, participants received the alternate beverage. Participants recorded all food/fluid consumed for the remainder of both trial days (diary and photographs). Fluid balance was assessed via BM change and urine specific gravity. Paired t tests were used to assess differences in hydration and dietary variables. No differences were observed in preexercise urine specific gravity (∼1.01) or BM loss (∼2%) between intervention groups (ps > .05). Water versus SD: No difference in acute fluid intake was noted (water = 751 ± 259 ml, SD = 805 ± 308 ml, p = .157). SD availability influenced total energy and carbohydrate intakes (water = 5.7 ± 2.5 MJ and 151 ± 77 g, SD = 6.5 ± 2.7 MJ and 187 ± 87 g, energy p = .002, carbohydrate p < .001). SD versus beer: SD availability resulted in greater acute fluid intake (SD = 1,047 ± 393 ml, beer = 850 ± 630 ml; p = .004), which remained evident at the end of trial days (SD = 3,337 ± 1,100 ml, beer = 2,982 ± 1,191 ml; p < .01). No differences in dietary variables were observed. Next day, urine specific gravity values were not different between water versus SD. However, a small difference was detected between SD versus beer (SD = 1.021 ± 0.009, beer = 1.016 ± 0.008, p = .002). Consuming calorie-containing drinks postexercise appears to increase daily energy and carbohydrate intake but has minimal impact on next-day hydration.
Nils Haller, Tobias Ehlert, Sebastian Schmidt, David Ochmann, Björn Sterzing, Franz Grus and Perikles Simon
Purpose: Player monitoring in elite sport settings is becoming increasingly important. Questionnaire-based methods and biomarkers such as circulating, cell-free DNA (cfDNA) are suggested for load monitoring. cfDNA concentrations were shown to increase depending on total distance covered in football and were associated with overtraining in weight lifters. Thus, the objective of this study was to examine whether cfDNA is feasible as a monitoring tool in elite football players. Methods: Capillary blood samples from 22 male elite football players were collected over 4 mo of a regular season. Sampling was conducted the day before, 1 day after, or several days after regular-season games and/or training. In addition, each player filled in a visual analogue scale (VAS) questionnaire including the items “general perceived exertion,” “muscular fatigue,” and “mental fatigue.” Performance during training and games was tracked by the Catapult system and with the OPTA system, respectively. Results: cfDNA values were significantly elevated in players the day after regular-season games (1.4-fold; P = .0004) in line with the scores of the VAS. Both parameters showed significantly higher values during midweek-game weeks. cfDNA concentrations correlated with training data, and VAS was correlated with the tracking of the season games. However, cfDNA and VAS did not correlate with each other. Conclusions: cfDNA concentrations at rest and VAS scores are influenced by previous load in professional football players. Future studies will reveal whether cfDNA might serve as a practically applicable marker for player load in football players.
Heidi R. Thornton, Jace A. Delaney, Grant M. Duthie and Ben J. Dascombe
In professional team sports, the collection and analysis of athlete-monitoring data are common practice, with the aim of assessing fatigue and subsequent adaptation responses, examining performance potential, and minimizing the risk of injury and/or illness. Athlete-monitoring systems should be underpinned by appropriate data analysis and interpretation, to enable the rapid reporting of simple and scientifically valid feedback. Using the correct scientific and statistical approaches can improve the confidence of decisions made from athlete-monitoring data. However, little research has discussed and proposed an outline of the process involved in the planning, development, analysis, and interpretation of athlete-monitoring systems. This review discusses a range of methods often employed to analyze athlete-monitoring data to facilitate and inform decision-making processes. There is a wide range of analytical methods and tools that practitioners may employ in athlete-monitoring systems, as well as several factors that should be considered when collecting these data, methods of determining meaningful changes, and various data-visualization approaches. Underpinning a successful athlete-monitoring system is the ability of practitioners to communicate and present important information to coaches, ultimately resulting in enhanced athletic performance.