Kastelein, TE, Duffield, R, Crowcroft, S & Marino, FE 2017, 'Cerebral oxygenation and sympathetic responses to smoking in young and middle-aged smokers.', Human and Experimental Toxicology, vol. 36, no. 2, pp. 184-194.View/Download from: UTS OPUS or Publisher's site
This study examined the effects of acute tobacco smoking on cerebral oxygenation and autonomic function in 28 male, habitual smokers of shorter young smokers (YSM) or longer middle-aged smokers (MSM) smoking history. Following baseline testing, participants undertook a smoking protocol involving the consumption of two cigarettes within 15 min. Measures of cerebral oxygenation and autonomic function were collected before, during, and 0 min, 30 min, 1 h, and 4 h post-smoking. Tissue saturation index (TSI) for MSM was greater than YSM during cigarette consumption (p< 0.05). Moreover, MSM observed significant within-group changes for TSI during and post-cigarette consumption (p< 0.05). Further, MSM observed an increase in low frequency (LF) band from 30 min to 1 h post-consumption, followed by a decline, whereas elevations above MSM were observed in YSM at 4 h (p< 0.05). Both MSM and YSM showed a decrease in high-frequency (HF) band post-cigarette, while increased LF/HF ratio post-consumption was observed in YSM. A decline in the standard deviation of RR intervals, post-cigarette consumption was evident in MSM (p< 0.05). Moreover, the root mean square of RR interval in both groups similarly decreased following cigarette consumption (p< 0.05). Acute smoking affects heart rate variability, suggestive of vagal withdrawal, and maybe indicate an effect of smoking history. Additionally, prolonged smoking history alters cerebral microcirculatory responses to acute tobacco exposure in MSM.
McCleave, EL, Slattery, KM, Duffield, R, Saunders, PU, Sharma, AP, Crowcroft, SJ & Coutts, AJ 2017, 'Temperate Performance Benefits after Heat, but Not Combined Heat and Hypoxic Training.', Medicine and Science in Sports and Exercise, vol. 49, no. 3, pp. 509-517.View/Download from: UTS OPUS or Publisher's site
PURPOSE: Independent heat and hypoxic exposure can enhance temperate endurance performance in trained athletes, although their combined effects remain unknown. This study examined whether the addition of heat interval training during "live high, train low" (LHTL) hypoxic exposure would result in enhanced performance and physiological adaptations as compared with heat or temperate training. METHODS: Twenty-six well-trained runners completed 3 wk of interval training assigned to one of three conditions: 1) LHTL hypoxic exposure plus heat training (H + H; 3000 m for 13 h·d, train at 33°C, 60% relative humidity [RH]), 2) heat training with no hypoxic exposure (HOT, live at <600 m and train at 33°C, 60% RH), or 3) temperate training with no hypoxic exposure (CONT; live at <600 m and train at 14°C, 55% RH). Performance 3-km time-trials (3-km TT), running economy, hemoglobin mass, and plasma volume were assessed using magnitude-based inferences statistical approach before (Baseline), after (Post), and 3 wk (3wkP) after exposure. RESULTS: Compared with Baseline, 3-km TT performance was likely increased in HOT at 3wkP (-3.3% ± 1.3%; mean ± 90% confidence interval), with no performance improvement in either H + H or CONT. Hemoglobin mass increased by 3.8% ± 1.8% at Post in H + H only. Plasma volume in HOT was possibly elevated above H + H and CONT at Post but not at 3wkP. Correlations between changes in 3-km TT performance and physiological adaptations were unclear. CONCLUSION: Incorporating heat-based training into a 3-wk training block can improve temperate performance at 3 wk after exposure, with athlete psychology, physiology, and environmental dose all important considerations. Despite hematological adaptations, the addition of LHTL to heat interval training has no greater 3-km TT performance benefit than temperate training alone.
Crowcroft, S, McCleave, E, Slattery, K & Coutts, AJ 2017, 'Assessing the Measurement Sensitivity and Diagnostic Characteristics of Athlete-Monitoring Tools in National Swimmers.', International Journal of Sports Physiology and Performance, vol. 12, no. Suppl 2, pp. S295-S2100.View/Download from: UTS OPUS or Publisher's site
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.
Fowler, PM, Knez, W, Crowcroft, S, Mendham, AE, Miller, J, Sargent, C, Halson, S & Duffield, R 2017, 'Greater effect of east vs. west travel on jet-lag, sleep and team-sport performance.', Medicine and Science in Sports and Exercise.View/Download from: UTS OPUS or Publisher's site
PURPOSE: Determine the recovery timeline of sleep, subjective jet-lag and fatigue, and team-sport physical performance following east and west long-haul travel. METHODS: Ten, physically-trained males underwent testing at 09:00 (AM) and 17:00 (PM) local time on four consecutive days two weeks prior to outbound travel (BASE), and the first four days following 21 h of outbound (WEST) and return (EAST) air travel across eight time-zones between Australia and Qatar. Data collection included performance (countermovement jump [CMJ], 20-m sprint and Yo-Yo Intermittent Recovery level 1 [YYIR1] test) and perceptual (jet-lag, motivation, perceived exertion and physical feeling) measures. In addition, sleep was measured via wrist activity monitors and self-report diaries throughout the aforementioned data collection periods. RESULTS: Compared to the corresponding day at BASE, the reduction in YYIR1 distance following EAST was significantly different to the increase WEST on day 1 post-travel (p<0.001). On day 2, significantly slower 20-m sprint times were detected in EAST compared to WEST (p=0.03), with large effect sizes also indicating a greater reduction in YYIR1 distance in EAST compared to WEST (d=1.06). Mean sleep onset and offset were significantly later and mean time in bed and sleep duration were significantly reduced across the four days in EAST compared to BASE and WEST (p<0.05). Lastly, mean jet-lag, fatigue and motivation ratings across the four days were significantly worse in EAST compared to BASE and WEST (p<0.05), and WEST compared to BASE (p<0.05). CONCLUSIONS: Long-haul transmeridian travel can impede team-sport physical performance. Specifically, travel east has a greater detrimental effect on sleep, subjective jet-lag, fatigue and motivation. Consequently, maximal- and intermittent-sprint performance is also reduced following travel east, particularly within 72 h following arrival.
Crowcroft, S, Duffield, R, McCleave, E, Slattery, K, Wallace, LK & Coutts, AJ 2015, 'Monitoring training to assess changes in fitness and fatigue: The effects of training in heat and hypoxia', SCANDINAVIAN JOURNAL OF MEDICINE & SCIENCE IN SPORTS, vol. 25, pp. 287-295.View/Download from: UTS OPUS or Publisher's site
Coutts, AJ, Crowcroft, S & Kempton, T 2017, 'Developing athlete monitoring systems: Theoretical basis and practical applications1' in Sport, Recovery, and Performance: Interdisciplinary Insights, Routledge, UK, pp. 19-32.View/Download from: UTS OPUS or Publisher's site
Athlete monitoring is now common practise in high-performance sport. Fundamentally, athlete monitoring involves quantifying the athletes training load and their responses to that training. The main reasons for monitoring athletes are that it can provide information to refine the training process, increase athlete performance readiness and reduce risk of injury and illness. Through a systematic approach to athlete monitoring an improved understanding of the complex relationships between training, performance, and injury can be obtained. The purpose of this chapter is to examine the training theory that underpins athlete monitoring and discuss the key components of an athlete monitoring system. Additionally, a discussion of methods used to analyse and interpret these data will also be provided.