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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: Quantifying training intensity provides a comprehensive understanding of the training stimulus. Recent technological advances may have improved the feasibility of using heart-rate (HR) monitoring in swimming. However, the implementation of HR monitoring is yet to be assessed longitudinally in the daily training environment of swimmers. This study aimed to assess the implementation of HR by comparing the training-intensity distribution from an external measure, planned volume at set intensities (PVSI), with the internal training-intensity distribution measured using time in HR zones. Methods: Using a longitudinal observational design, 10 competitive swimmers (8 male and 2 female, age: 22.0 [2.3] y, Fédération Internationale de Natation point score: 842.9 [58.5], mean [SD]) were monitored daily for 6 months. Each session, HR data, and coached-planned and athlete-reported session rating of perceived exertion (Modified Category Ratio 10 scale) were recorded. Based on previously determined training zones from an incremental step test, PVSI was calculated using the planned distance and planned intensity of each swim bout. Training-intensity distributions were analyzed using a linear mixed model (lme4). Results: The model revealed a small to moderate relationship between PVSI and time in HR zone, based on the Nakagawa R-squared value (range.14–.42). Conclusions: Training-intensity distribution differed between the internal measure (ie, HR) and the external measure of intensity (ie, PVSI). This demonstrates that internal and planned external measures of intensity cannot be used interchangeably to monitor training. Further research should explore how to best integrate these measures to better understand training in swimming. [ABSTRACT FROM AUTHOR]

On-water rowing performance consists of the integration of physical and technical attributes. This exploratory study aimed to describe key physical and technical variables for elite and junior rowers and examine the associations and predictive capacity of these variables with on-water rowing performance outcomes. Twenty-eight junior (16 females, 16 ± 0.8 years and 12 males, 17 ± 0.7 years) and 24 elite rowers (12 females, 24 ± 2.7 years and 12 males, 27 ± 2.6 years) completed an on-water, single sculling biomechanics assessment combined with a series of physical, strength and power tests. Elite men and women were superior in mean gate force, distance per stroke and recovery distance compared to junior groups as determined by independent t-tests and effect size appraisal (p < 0.017, d > 1.2). Large associations (p < 0.01) were evident between anthropometry, strength and power assessments with the on-water measures of catch angle, mean gate force, recovery distance and boat speed. Differences in ROM and flexibility attributes did not distinguish between elite and junior rowers. Linear discriminant analysis revealed that individual rowers can be appropriately categorised by sex and performance level based on their physical and technical attributes. This battery of testing with world-class athletes represents an excellent level of ecological validity for the assessment of rowers pertinent to on-water performance. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to independently validate a wearable inertial sensor designed to monitor training and performance metrics in swimmers. Methods: A total of 4 male (21 [4] y, 1 national and 3 international) and 6 female (22 [3] y, 1 national and 5 international) swimmers completed 15 training sessions in an outdoor 50-m pool. Swimmers were fitted with a wearable device (TritonWear, 9-axis inertial measurement unit with triaxial accelerometer, gyroscope, and magnetometer), placed under the swim cap on top of the occipital protuberance. Video footage was captured for each session to establish criterion values. Absolute error, standardized effect, and Pearson correlation coefficient were used to determine the validity of the wearable device against video footage for total swim distance, total stroke count, mean stroke count, and mean velocity. A Fisher exact test was used to analyze the accuracy of stroke-type identification. Results: Total swim distance was underestimated by the device relative to video analysis. Absolute error was consistently higher for total and mean stroke count, and mean velocity, relative to video analysis. Across all sessions, the device incorrectly detected total time spent in backstroke, breaststroke, butterfly, and freestyle by 51% (15%). The device did not detect time spent in drill. Intraclass correlation coefficient results demonstrated excellent intrarater reliability between repeated measures across all swimming metrics. Conclusions: The wearable device investigated in this study does not accurately measure distance, stroke count, and velocity swimming metrics or detect stroke type. Its use as a training monitoring tool in swimming is limited. [ABSTRACT FROM AUTHOR]

Purpose: To examine whether concurrent heat and intermittent hypoxic training can improve endurance performance and physiological responses relative to independent heat or temperate interval training. Methods: Well-trained male cyclists (N = 29) completed 3 weeks of moderate- to high-intensity interval training (4 × 60 min·wk−1) in 1 of 3 conditions: (1) heat (HOT: 32°C, 50% relative humidity, 20.8% fraction of inspired oxygen, (2) heat + hypoxia (H+H: 32°C, 50% relative humidity, 16.2% fraction of inspired oxygen), or (3) temperate environment (CONT: 22°C, 50% relative humidity, 20.8% fraction of inspired oxygen). Performance 20-km time trials (TTs) were conducted in both temperate (TTtemperate) and assigned condition (TTenvironment) before (base), immediately after (mid), and after a 3-week taper (end). Measures of hemoglobin mass, plasma volume, and blood volume were also assessed. Results: There was improved 20-km TT performance to a similar extent across all groups in both TTtemperate (mean ±90% confidence interval HOT, −2.8% ±1.8%; H+H, −2.0% ±1.5%; CONT, −2.0% ±1.8%) and TTenvironment (HOT, −3.3% ±1.7%; H+H, −3.1% ±1.6%; CONT, −3.2% ±1.1%). Plasma volume (HOT, 3.8% ±4.7%; H+H, 3.3% ±4.7%) and blood volume (HOT, 3.0% ±4.1%; H+H, 4.6% ±3.9%) were both increased at mid in HOT and H+H over CONT. Increased hemoglobin mass was observed in H+H only (3.0% ±1.8%). Conclusion: Three weeks of interval training in heat, concurrent heat and hypoxia, or temperate environments improve 20-km TT performance to the same extent. Despite indications of physiological adaptations, the addition of independent heat or concurrent heat and hypoxia provided no greater performance benefits in a temperate environment than temperate training alone. [ABSTRACT FROM AUTHOR]

Purpose: To assess a coach's subjective assessment of their athletes' performances and whether the use of athlete-monitoring tools could improve on the coach's prediction to identify performance changes. Methods: Eight highly trained swimmers (7 male and l female, age 21.6 [2.0] y) recorded perceived fatigue, total quality recovery, and heart-rate variability over a 9-month period. Prior to each race of the swimmers' main 2 events, the coach (n = 1) was presented with their previous race results and asked to predict their race time. All race results (n = 93) with aligning coach's predictions were recorded and classified as a dichotomous outcome (0 = no change; 1 = performance decrement or improvement [change +/- > or < smallest meaningful change]). A generalized estimating equation was used to assess the coach's accuracy and the contribution of monitoring variables to the model fit. The probability from generalized estimating equation models was assessed with receiver operating characteristic curves to identify the model s accuracy from the area under the curve analysis. Results: The coach's predictions had the highest diagnostic accuracy to identify both decrements (area under the curve: 0.93; 95% confidence interval, 0.88-0.99) and improvements (area under the curve: 0.89; 95% confidence interval, 0.83-0.96) in performance. Conclusions: These findings highlight the high accuracy of a coach's subjective assessment of performance. Furthermore, the findings provide a future benchmark for athlete-monitoring systems to be able to improve on a coach's existing understanding of swimming performance. [ABSTRACT FROM AUTHOR]

This study aimed to define, develop, and validate a subjective scale of training quality. Two related studies were used to 1) define training quality and 2) develop and validate a subjective scale. Part One: a purposive sample of 15 sub-elite (i.e. national) and elite (i.e. international) swimmers participated in one, 20-30-min semi-structured interview. Thematic analysis of interview responses established three constructs to define training quality. These were the physical, technical, and mental aspects of training. Part Two: development of the Subjective Training Quality (STQ) scale based on the three constructs identified in Part One. 252 sub-elite and elite athletes, across eight sports completed the STQ scale. Cronbach's alpha (α) assessed internal consistency, histogram plot analysis assessed face validity, and confirmatory factor analysis (CFA) compared physical, technical, and mental constructs with training quality. Root mean square error of approximation (RMSEA) and standardised root mean square residual (SRMR) evaluated CFA quality of fit. Physical, technical, and mental constructs demonstrated a high "acceptable" level of internal consistency (α = 0.85) and excellent face validity. Comparatively, the CFA quality of fit was "excellent" (RMSEA = <0.01 "good", SRMR = 0.00 "perfect"). The STQ scale demonstrated excellent internal consistency and face validity, establishing capacity to monitor training quality. The STQ scale could be used in conjunction with traditional training monitoring tools to provide additional insight into athlete's training quality. Further investigation is required to determine how the STQ scale may interact with subjective and objective training performance measures, and how it could be incorporated into daily training monitoring. Highlights Athletes perceive the subjective training quality (STQ) scale adequately represents the physical, technical, and mental constructs of training quality. Excellent internal consistency and confirmatory factor analysis fit demonstrates the STQ scale is an effective tool to monitor training quality. With additional validation, the STQ scale could be used in conjunction with traditional load monitoring tools to provide greater insight to an athlete's training response, and subsequently inform training prescription. [ABSTRACT FROM AUTHOR]

Purpose: To determine whether combining training in heat with "Live High, Train Low" hypoxia (LHTL) further improves thermoregulatory and cardiovascular responses to a heat-tolerance test compared with independent heat training. Methods: A total of 25 trained runners (peak oxygen uptake = 64.1 [8.0] mL·min−1·kg−1) completed 3-wk training in 1 of 3 conditions: (1) heat training combined with "LHTL" hypoxia (H+H; FiO2 = 14.4% [3000 m], 13 h·d−1; train at <600 m, 33°C, 55% relative humidity [RH]), (2) heat training (HOT; live and train <600 m, 33°C, 55% RH), and (3) temperate training (CONT; live and train <600 m, 13°C, 55% RH). Heat adaptations were determined from a 45-min heat-response test (33°C, 55% RH, 65% velocity corresponding to the peak oxygen uptake) at baseline and immediately and 1 and 3 wk postexposure (baseline, post, 1 wkP, and 3 wkP, respectively). Core temperature, heart rate, sweat rate, sodium concentration, plasma volume, and perceptual responses were analyzed using magnitude-based inferences. Results: Submaximal heart rate (effect size [ES] = −0.60 [−0.89; −0.32]) and core temperature (ES = −0.55 [−0.99; −0.10]) were reduced in HOT until 1 wkP. Sweat rate (ES = 0.36 [0.12; 0.59]) and sweat sodium concentration (ES = −0.82 [−1.48; −0.16]) were, respectively, increased and decreased until 3 wkP in HOT. Submaximal heart rate (ES = −0.38 [−0.85; 0.08]) was likely reduced in H+H at 3 wkP, whereas CONT had unclear physiological changes. Perceived exertion and thermal sensation were reduced across all groups. Conclusions: Despite greater physiological stress from combined heat training and "LHTL" hypoxia, thermoregulatory adaptations are limited in comparison with independent heat training. The combined stimuli provide no additional physiological benefit during exercise in hot environments. [ABSTRACT FROM AUTHOR]

There are few practical methods available for evaluating training loads (TL) during swimming. The purpose of this study was to examine the ecological validity of the session-rating of perceived exertion (RPE) method for quantifying internal TL in competitive swimmers using heart rate (HR)based methods and distance as criterion measures. This study also examined the correspondence between athlete and coach perceptions of internal TL using the session-RPE method. Twelve (six male, six female) well-trained swimmers (mean [+ or -] SD: age 22.3 [+ or -] 3.1 years, weight 71.8 [+ or -] 11.6 kg, height 175.0 [+ or -] 9.0 cm) participated in this study. All subjects completed a swimming step test to evaluate individual HR zones and blood lactate profile before undertaking 20 swim training sessions where RPE, HR, and distance covered were recorded. Training load was then calculated for each session using the session-RPE, HR-based methods, and session distance. The session-RPE scores were correlated to HR-based methods for measuring internal TL as well as training distance for each swimmer. All individual correlations between session-RPE, HR-based methods (r = 0.55-0.94; p < 0.05), and distance measures (r= 0.37-0.81 ;p < 0.05) were significant. Two-way ANOVA showed that there was a significant interaction for training intensity x coach-athlete perception, indicating that coach RPE was lower than athlete RPE for low-intensity sessions and higher than athlete RPE at high-intensity sessions. The results of this study suggest that session-RPE may provide a practical, noninvasive method for quantifying internal TL in competitive swimmers. Key Words swimmers, training load, monitoring

Slattery, K.M., L.K. Wallace, A.J. Murphy, and A.J. Coutts. Physiological determinants of three-kilometer running performance in experienced triathletes. J. Strength Cond. Res. 20(1):47-52. 2006.--The present investigation examined the physiological parameters that contribute to 3-km running performance. Following 2 familiarization sessions, 16 experienced male triathletes ([VO.sub.2]max = 55.7 [+ or -] 4.9 ml x kg 1 x min 1, age = 31.3 [+ or -] 11.7 years) performed a 3-km time trial (3kmTT) and were assessed for selected physiological and anthropometrical characteristics. Stepwise multiple regression and correlation analysis was used to determine the variables that significantly related to 3kmTT. The analysis revealed that 82.3% of the adjusted variance in 3kmTT performance could be explained by peak treadmill running velocity during a Vo2max test (Vmax) alone. The addition of the running velocity at lactate threshold ([LT.sub.vel]) and peak lactate concentration ([[Bla-].sub.peak]) to the prediction equation allowed for 93.6% of the adjusted variante in 3kmTT to be predicted (Y = 13.64 Vmax - 25.61 [LT.sub.vel] - 5.40 [[BLa -].sub.peak] + 1358.5). Correlation analysis revealed that Vmax (r = -0.91), [LT.sub.vel], (r = -0.90), and [VO.sub.2]max (r = - 0.80) were significantly related to running performance. These results show that Vmax was the single best predictor of 3-km running performance in experienced male triathletes and that both aerobic and anaerobic abilities are related to improved 3kmTT performance. Since the assessment of Vmax is relatively simple to implement, we suggest that determining Vmax may be a practical method for monitoring performance changes in short-term endurance running events. KEY WORDS. endurance, peak running velocity, lactate threshold

Purpose: To assess measurement sensitivity and diagnostic characteristics of athlete-monitoring tools to identify performance change. Methods: Fourteen nationally competitive swimmers (11 male, 3 female; age 21.2 ± 3.2 y) recorded daily monitoring over 15 mo. The self-report group (n = 7) reported general health, energy levels, motivation, stress, recovery, soreness, and wellness. The combined group (n = 7) recorded sleep quality, perceived fatigue, total quality recovery (TQR), and heart-rate variability. The week-to-week change in mean weekly values was presented as coefficient of variance (CV%). Reliability was assessed on 3 occasions and expressed as the typical error CV%. Week-to-week change was divided by the reliability of each measure to calculate the signal-to-noise ratio. The diagnostic characteristics for both groups were assessed with receiver-operating-curve analysis, where area under the curve (AUC), Youden index, sensitivity, and specificity of measures were reported. A minimum AUC of .70 and lower confidence interval (CI) >.50 classified a "good" diagnostic tool to assess performance change. Results: Week-to-week variability was greater than reliability for soreness (3.1), general health (3.0), wellness% (2.0), motivation (1.6), sleep (2.6), TQR (1.8), fatigue (1.4), R-R interval (2.5), and LnRMSSD:RR (1.3). Only general health was a "good" diagnostic tool to assess decreased performance (AUC -.70, 95% CI, .61-.80). Conclusion: Many monitoring variables are sensitive to changes in fitness and fatigue. However, no single monitoring variable could discriminate performance change. As such the use of a multidimensional system that may be able to better account for variations in fitness and fatigue should be considered. [ABSTRACT FROM AUTHOR]