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Purpose: This study aimed to examine the impact of different rest periods between short sprint interval training (SSIT) trials on the physiological and performance adaptations of female volleyball players. Methods: Twenty-four trained college athletes volunteered to participate in this study and were randomly assigned to 3 SSIT groups with different work-to-rest ratios (1:2 [5-s run:10-s rest], 1:4 [5-s run:20-s rest], and 1:6 [5-s work:30-s rest]). Before and after 6-week training, physiological parameters (maximum oxygen uptake, first and second ventilatory thresholds, and peak and mean power output) and physical performance measures (ie, countermovement vertical jump, 10-m sprint, and T-test change-of-direction speed) were evaluated. Results: After the training period, all groups improved (P = .001) their sport-related performance and physiological parameters, ranging from moderate to very large effect sizes. Comparative analysis of the magnitude of training effects indicated that the 1:6 SSIT group had in a significantly greater change in countermovement vertical jump (P = .007), 10-m sprint (P = .014), peak power output (P = .019), and mean power output (P = .05) compared with 1:2 SSIT group. By contrast, the 1:2 SSIT group demonstrated significantly (P = .022) greater changes in maximum oxygen uptake than the 1:6 SSIT group. However, the change-of-direction speed and changes in first and second ventilatory thresholds were the same among the groups (P > .05). Conclusions: When performing SSIT, longer rest intervals are suitable for physical and anaerobic performance, and shorter rest periods are appropriate for enhancing the cardiorespiratory fitness of female volleyball players’ performance.

Purpose: To profile the training characteristics of an elite team pursuit cycling squad and assess variations in training intensity and load accumulation across the 36-week period prior to a world-record performance at the 2018 Commonwealth Games. Methods: Training data of 5 male track endurance cyclists (mean [SD]; age 21.9 [3.52] y; 4.4 [0.16] W·kg⁻¹ at anaerobic threshold; 6.2 [0.28] W·kg⁻¹ maximal oxygen uptake 68.7 [2.99] mL kg·min⁻¹) were analyzed with weekly total training volume and heart rate, power output, and torque intensity distributions calculated with reference to their 3:49.804 min:s.ms performance requirements for a 4-km team pursuit. Results: Athletes completed 543 (37) h⁻¹ of training across 436 (16) sessions. On-bike activities accounted for 69.9% of all training sessions, with participants cycling 11,246 (1139) km⁻¹ in the training period of interest, whereas 12.7% of sessions involved gym/strength training. A pyramidal intensity distribution was evident with over 65% and 70% of training, respectively, performed at low-intensity zone heart rate and power output, whereas 5.3% and 7.7% of training was performed above anaerobic threshold. The athletes accumulated 4.4% of total training volume at, or above, their world-record team pursuit lead position torque (55 N·m). Conclusions: These data provide updated and novel insight to the power and torque demands and load accumulation contributing to world-record team pursuit performance. Although the observed pyramidal intensity distribution is common in endurance sports, the lack of shift toward a polarized intensity distribution during taper and competition peaking differs from previous research.

Purpose: To compare the training characteristics of an elite team pursuit cycling squad in the 3-month preparation phases prior to 2 successive world-record (WR) performances. Methods: Training data of 5 male track endurance cyclists (mean [SD]; age 23.4 [3.46] y; body mass 80.2 [2.74] kg; 4.5 [0.17] W·kg⁻¹ at LT₂; maximal aerobic power 6.2 [0.27] W·kg⁻¹; maximal oxygen uptake 65.9 [2.89] mL·kg⁻¹·min⁻¹) were analyzed with weekly total training volume by training type and heart rate, power output, and torque intensity distributions calculated with reference to the respective WRs’ performance requirements. Results: Athletes completed 805 (82.81) and 725 (68.40) min·wk^–1 of training, respectively, in each season. In the second season, there was a 32% increase in total track volume, although track sessions were shorter (ie, greater frequency) in the second season. A pyramidal intensity distribution was consistent across both seasons, with 81% of training, on average, performed below LT₁ power output each week, whereas 6% of training was performed above LT₂. Athletes accumulated greater volume above WR team pursuit lead power (2.4% vs 0.9%) and torque (6.2% vs 3.2%) in 2019. In one athlete, mean single-leg-press peak rate of force development was 71% and 46% higher at mid- and late-phases, respectively, during the preparation period. Conclusions: These findings provide novel insights into the common and contrasting methods contributing to successive WR team pursuit performances. Greater accumulation of volume above race-specific power and torque (eg, team pursuit lead), as well as improved neuromuscular force-generating capacities, may be worthy of investigation for implementation in training programs.

Purpose: Prediction of athlete wellness is difficult—or, many sports-medicine practitioners and scientists would argue, impossible. Instead, one settles for correlational relationships of variables gathered at fixed moments in time. The issue may be an inherent mismatch between usual methods of data collection and analysis and the complex nature of the variables governing athlete wellness. Variables such as external load, stress, muscle soreness, and sleep quality may affect each other and wellness in a dynamic, nonlinear, way over time. In such an environment, traditional data-collection methods and statistics will fail to capture causal effects. If we are to move this area of sport science forward, a different approach is required. Methods: We analyzed data from 2 different soccer teams that showed no significance between player load and wellness or among individual measures of wellness. Our analysis used methods of attractor reconstruction to examine possible causal relationships between GPS/accelerometer-measured external training load and wellness variables. Results: Our analysis showed that player self-rated stress, a component of wellness, seems a fundamental driving variable. The influence of stress is so great that stress can predict other components of athlete wellness, and, in turn, self-rated stress can be predicted by observing a player’s load data. Conclusion: We demonstrate the ability of nonlinear methods to identify interactions between and among variables to predict future athlete stress. These relationships are indicative of the causal relationships playing out in athlete wellness over the course of a soccer season.

Purpose: To describe the self-reported menstrual health, symptomatology, and perceived effects of the menstrual cycle on athletic performance for national and international Australian football (soccer) players. Methods: Players from national and domestic teams were invited to complete an online questionnaire regarding menstrual health, use of hormonal contraceptives (HCs), negative symptomatology, and perceived disruption of the menstrual cycle to performance. Descriptive statistics and binomial regressions with odds ratios (OR) were used to report the relationship of menstrual-related variables with perceived performance disruption. Results: A total of 199 players (20.9 [5.1] y) completed the questionnaire, with 18% of players reporting using HCs. One primary amenorrhea case was detected, and 26% of players reported menarche at age ≥15 years. For non-HC users, the prevalence of secondary amenorrhea was 2%, oligomenorrhea was 19%, and heavy menstrual bleeding was 11%. Ninety-seven percent of players reported experiencing physical or affective menstrual symptoms (5 [1.3] per player), and 40% of all players reported that menstrual symptoms impacted their ability to work, study, train, or compete. Furthermore, 40% of players perceived their training or performance to be disrupted by the menstrual cycle. Increasing number of menstrual symptoms (OR = 1.43; 95% CI, 1.28–1.62; P < .001), heavy menstrual bleeding (OR = 12.73; 95% CI, 3.4–82.8; P < .001), and pelvic pain (OR = 3.40; 95% CI, 1.7–7.2; P < .001) increased the likelihood of perceiving the menstrual cycle to disrupt performance. Conclusion: Heavy menstrual bleeding and HC use were low among this cohort of national and international footballers, whereas amenorrhea and oligomenorrhoea were comparable with other football populations. Nearly all players reported menstrual symptoms, and increased symptomatology was associated with greater perceived effects on performance.

Purpose: Travel fatigue impacts cognitive and physiologic systems, but its association with elite soccer match performance is unclear. In this retrospective observational study, we aimed to explore the association between travel and match outcomes in elite North American soccer. Methods: Travel data and match outcomes (team points or goals scored and conceded) and physical performance outcomes from 26 elite professional soccer teams and their players were analyzed (148 matches [team-based data] and 1252 player matches from 297 players; age 22.7 [4.5] y). Player- and match-level correlations between performance measures and both acute and cumulated travel metrics were analyzed. Results: Cumulative travel metrics were positively associated with team (travel distance [r = .20; 95% CI, .03–.25], travel time [r = .20; .06–.37], and time away [r = .20; .06–.37]) and individual player (travel distance, [r = .14; .08–.19], travel time [r = .17–.23], and time away [r = .13; .07–.18]) high-intensity running. Cumulative time away was negatively associated with team points (r = −.14; −.28 to −.001) and positively associated with goals conceded (r = .14; .01–.27); no clear association between acute travel metrics and match outcomes or physical performance was observed. Conclusions: As travel cumulated, away teams and their players ran more but for less reward (team points), although the magnitude of these associations was small. These data are exploratory and do not imply a causal relationship; however, further research should consider cumulation of travel.

Purpose: The study validated variables corresponding to lactate threshold (LT) in swimming. Speed (sLT), blood lactate concentration (BL_LT), oxygen uptake (VO_2LT), and heart rate (HR_LT) corresponding to LT were calculated by 2 different incremental protocols and validated in comparison with maximal lactate steady state (MLSS). Methods: Ten competitive swimmers performed a 7 × 200-m front-crawl incremental “step test” with 2 protocols: (1) with 30-second rests between repetitions (short-rest incremental protocols) and (2) on a 5-minute cycle (swim + rest time, long-rest incremental protocols). Five methods were used for the assessment of sLT and corresponding BL_LT, VO_2LT, and HR_LT: intersection of 2 lines, D_max, modified D_max, visual inspection, and intersection of combined linear and exponential regression lines. Subsequently, swimmers performed two to three 30-minute continuous efforts to identify speed (sMLSS) and physiological parameters corresponding to MLSS. Results: Both protocols resulted in similar sLT and corresponding physiological variables (P > .05). Bland–Altman plots showed agreement between protocols (sLT, bias: −0.017 [0.002] m·s⁻¹; BL_LT, bias: 0.0 [0.5] mmol·L⁻¹; VO_2LT, bias: −0.1 [2.2] mL·kg⁻¹·min⁻¹; HR_LT. bias: −2 [8] beats·min⁻¹). However, sLT calculated by modified D_max using short rest was higher compared with speed at MLSS (1.346 [0.076] vs 1.300 [0.101] m·s⁻¹; P < .05). Conclusions: Calculated sLT, BL_LT, VO_2LT, and HR_LT using all other methods in short-rest and long-rest incremental protocols were no different compared with MLSS (P > .05). Both 7 × 200-m protocols are valid for determination of sLT and corresponding physiological parameters, but the modified D_max method may overestimate sLT.