Jacqueline Tran

Sleep restriction during simulated wildfire suppression: Effect on physical task performance

Objectives To examine the effects of sleep restriction on firefighters’ physical task performance during simulated wildfire suppression. Methods Thirty-five firefighters were matched and randomly allocated to either a control condition (8-hour sleep opportunity, n = 18) or a sleep restricted condition (4-hour sleep opportunity, n = 17). Performance on physical work tasks was evaluated across three days. In addition, heart rate, core temperature, and worker activity were measured continuously. Rate of perceived and exertion and effort sensation were evaluated during the physical work periods. Results There were no differences between the sleep-restricted and control groups in firefighters’ task performance, heart rate, core temperature, or perceptual responses during self-paced simulated firefighting work tasks. However, the sleep-restricted group were less active during periods of non-physical work compared to the control group. Conclusions Under self-paced work conditions, 4 h of sleep restriction did not adversely affect firefighters’ performance on physical work tasks. However, the sleep-restricted group were less physically active throughout the simulation. This may indicate that sleep-restricted participants adapted their behaviour to conserve effort during rest periods, to subsequently ensure they were able to maintain performance during the firefighter work tasks. This work contributes new knowledge to inform fire agencies of firefighters’ operational capabilities when their sleep is restricted during multi-day wildfire events. The work also highlights the need for further research to explore how sleep restriction affects physical performance during tasks of varying duration, intensity, and complexity.

Multiple Days of Heat Exposure on Firefighters’ Work Performance and Physiology

This study assessed the accumulated effect of ambient heat on the performance of, and physiological and perceptual responses to, intermittent, simulated wildfire fighting tasks over three consecutive days. Firefighters (n = 36) were matched and allocated to either the CON (19°C) or HOT (33°C) condition. They performed three days of intermittent, self-paced simulated firefighting work, interspersed with physiological testing. Task repetitions were counted (and converted to distance or area) to determine work performance. Participants were asked to rate their perceived exertion and thermal sensation after each task. Heart rate, core temperature (Tc), and skin temperature (Tsk) were recorded continuously throughout the simulation. Fluids were consumed ad libitum. Urine volume was measured throughout, and urine specific gravity (USG) analysed, to estimate hydration. All food and fluid consumption was recorded. There was no difference in work output between experimental conditions. However, significant variation in performance responses between individuals was observed. All measures of thermal stress were elevated in the HOT, with core and skin temperature reaching, on average, 0.24 ± 0.08°C and 2.81 ± 0.20°C higher than the CON group. Participants’ doubled their fluid intake in the HOT condition, and this was reflected in the USG scores, where the HOT participants reported significantly lower values. Heart rate was comparable between conditions at nearly all time points, however the peak heart rate reached each circuit was 7 ± 3% higher in the CON trial. Likewise, RPE was slightly elevated in the CON trial for the majority of tasks. Participants’ work output was comparable between the CON and HOT conditions, however the performance change over time varied significantly between individuals. It is likely that the increased fluid replacement in the heat, in concert with frequent rest breaks and task rotation, assisted with the regulation of physiological responses (e.g., heart rate, core temperature).

Convergent validity of a novel method for quantifying rowing training loads

Abstract Elite rowers complete rowing-specific and non-specific training, incorporating continuous and interval-like efforts spanning the intensity spectrum. However, established training load measures are unsuitable for use in some modes and intensities. Consequently, a new measure known as the T2minute method was created. The method quantifies load as the time spent in a range of training zones (time-in-zone), multiplied by intensity- and mode-specific weighting factors that scale the relative stress of different intensities and modes to the demands of on-water rowing. The purpose of this study was to examine the convergent validity of the T2minute method with Banister’s training impulse (TRIMP), Lucia’s TRIMP and Session-RPE when quantifying elite rowing training. Fourteen elite rowers (12 males, 2 females) were monitored during four weeks of routine training. Unadjusted T2minute loads (using coaches’ estimates of time-in-zone) demonstrated moderate-to-strong correlations with Banister’s TRIMP, Lucia’s TRIMP and Session-RPE (rho: 0.58, 0.55 and 0.42, respectively). Adjusting T2minute loads by using actual time-in-zone data resulted in stronger correlations between the T2minute method and Banister’s TRIMP and Lucia’s TRIMP (rho: 0.85 and 0.81, respectively). The T2minute method is an appropriate in-field measure of elite rowing training loads, particularly when actual time-in-zone values are used to quantify load.

No Compromise of Competition Sleep Compared With Habitual Sleep in Elite Australian Footballers

PURPOSE To assess the impact of match-start time and days relative to match compared with the habitual sleep characteristics of elite Australian Football (AF) players. METHODS 45 elite male AF players were assessed during the preseason (habitual) and across 4 home matches during the season. Players wore an activity monitor the night before (-1), night of (0), 1 night after (+1), and 2 nights (+2) after each match and completed a self-reported rating of sleep quality. A 2-way ANOVA with Tukey post hoc was used to determine differences in sleep characteristics between match-start times and days relative to the match. Two-way nested ANOVA was conducted to examine differences between competition and habitual phases. Effect size ± 90% confidence interval (ES ± 90% CI) was calculated to quantify the magnitude of pairwise differences. RESULTS Differences observed in sleep-onset latency (ES = 0.11 ± 0.16), sleep rating (ES = 0.08 ± 0.14), and sleep duration (ES = 0.08 ± 0.01) between competition and habitual periods were trivial. Sleep efficiency was almost certainly higher during competition than habitual, but this was not reflected in the subjective rating of sleep quality. CONCLUSIONS Elite AF competition does not cause substantial disruption to sleep characteristics compared with habitual sleep. While match-start time has some impact on sleep variables, it appears that the match itself is more of a disruption than the start time. Subjective ratings of sleep from well-being questionnaires appear limited in their ability to accurately provide an indication of sleep quality.

Consensus on measurement properties and feasibility of performance tests for the exercise and sport sciences: a Delphi study

BackgroundPerformance tests are used for multiple purposes in exercise and sport science. Ensuring that a test displays an appropriate level of measurement properties for use within a population is important to ensure confidence in test findings.The aim of this study was to obtain subject matter expert consensus on the measurement and feasibility properties that should be considered for performance tests used in the exercise and sport sciences and how these should be defined. This information was used to develop a checklist for broader dissemination.MethodsA two-round Delphi study was undertaken including 33 exercise scientists, academics and sport scientists. Participants were asked to rate the importance of a range of measurement properties relevant to performance tests in exercise and sport science. Responses were obtained in binary and Likert-scale formats, with consensus defined as achieving 67% agreement on each question.ResultsConsensus was reached on definitions and terminology for all items. Ten level 1 items (those that achieved consensus on all four questions) and nine level 2 items (those achieving consensus on ≥2 questions) were included. Both levels were included in the final checklist.ConclusionsThe checklist developed from this study can be used to inform decision-making and test selection for practitioners and researchers in the exercise and sport sciences. This can facilitate knowledge sharing and performance comparisons across sub-disciplines, thereby improving existing field practice and research methodological quality.

Development and implementation of a novel measure for quantifying training loads in rowing: the T2minute method.

Abstract Tran, J, Rice, AJ, Main, LC, and Gastin, PB. Development and implementation of a novel measure for quantifying training loads in rowing: The T2minute method. J Strength Cond Res 28(4): 1172–1180, 2014—The systematic management of training requires accurate training load measurement. However, quantifying the training of elite Australian rowers is challenging because of (a) the multicenter, multistate structure of the national program; (b) the variety of training undertaken; and (c) the limitations of existing methods for quantifying the loads accumulated from varied training formats. Therefore, the purpose of this project was to develop a new measure for quantifying training loads in rowing (the T2minute method). Sport scientists and senior coaches at the National Rowing Center of Excellence collaborated to develop the measure, which incorporates training duration, intensity, and mode to quantify a single index of training load. To account for training at different intensities, the method uses standardized intensity zones (T zones) established at the Australian Institute of Sport. Each zone was assigned a weighting factor according to the curvilinear relationship between power output and blood lactate response. Each training mode was assigned a weighting factor based on whether coaches perceived it to be “harder” or “easier” than on-water rowing. A common measurement unit, the T2minute, was defined to normalize sessions in different modes to a single index of load; one T2minute is equivalent to 1 minute of on-water single scull rowing at T2 intensity (approximately 60–72% V[Combining Dot Above]O2max). The T2minute method was successfully implemented to support national training strategies in Australian high performance rowing. By incorporating duration, intensity, and mode, the T2minute method extends the concepts that underpin current load measures, providing 1 consistent system to quantify loads from varied training formats.

The relationship between match performance indicators and outcome in Australian Football

OBJECTIVES To identify novel insights about performance in Australian Football (AF), by modelling the relationships between player actions and match outcomes. This study extends and improves on previous studies by utilising a wider range of performance indicators (PIs) and a longer time frame for the development of predictive models. DESIGN Observational. METHODS Ninety-one team PIs from the 2001 to 2016 Australian Football League seasons were used as independent variables. The categorical Win-Loss and continuous Score Margin match outcome measures were used as dependent variables. Decision tree and Generalised Linear Models were created to describe the relationships between the values of the PIs and match outcome. RESULTS Decision tree models predicted Win-Loss and Score Margin with up to 88.9% and 70.3% accuracy, respectively. The Generalised Linear Models predicted Score Margin to within 6.8 points (RMSE) and Win-Loss with up to 95.1% accuracy. The PIs that are most predictive of match outcome include; Turnovers Forced score, Inside 50s per shot, Metres Gained and Time in Possession, all in their relative (to opposition) form. The decision trees illustrate how combinations of the values of these PIs are associated with match outcome, and they indicate target values for these PIs. CONCLUSIONS This work used a wider range of PIs and more historical data than previous reports and consequently demonstrated higher prediction accuracies and additional insights about important indicators of performance. The methods used in this work can be implemented by other sport analysts to generate further insights that support the strategic decision-making processes of coaches.