The influence of cadence on fatigue during maximal sprint cycling in world-class and elite sprint cyclists

Optimising cadence through appropriate gear selection is a key consideration for track sprint cycling performance, yet the influence of cadence on fatigue (i.e., decrement in power output) within a maximal sprint is not well understood. The aim of this study was to identify the influence of cadence on fatigue during maximal sprint cycling. Eleven world-class and elite track sprint cyclists (n = 6 men, maximal power output (Pmax) = 1894 ± 351 W, optimal cadence (Fopt) = 134 ± 8 rev∙min-1: n = 5 women, Pmax = 1114 ± 80 W, Fopt = 124 ± 8 rev∙min-1) completed two testing sessions where power-cadence profiles were constructed to determine the Fopt associated with Pmax. Cyclists also performed three maximal 15-s sprints (Fopt, ±15%Fopt) to identify fatigue per pedal stroke across these cadence ranges. There was no significant difference (p = 0.2) in the absolute fatigue per pedal stroke when cadence was fixed 15% above (16.7 ± 6.1 W∙stroke-1) and below (15.3 ± 5.1 W∙stroke-1) Fopt. Similarly, there was no significant difference in the relative fatigue per pedal stroke (% peak power∙stroke-1) across Fopt and ± 15%Fopt trials (p = 0.12). The relative decrement in power output is equivalent across the ± 15%Fopt cadence range. As such, a higher-geared, lower-cadence approach to maximal sprint cycling could be a viable method to minimise maximal pedal strokes and reduce the decrement in power output.

Behind enemy lines: Expressing locomotor movements of athletes in the National Rugby League Women's (NRLW) competition relative to opposition data

We provide a novel analysis of the locomotor movements of athletes in the National Rugby League Women's (NRLW) competition by presenting the data of opposing teams expressed as a relative (%) difference and explore the association with match outcome. 117 rugby league athletes from the four NRLW clubs participated in this study. Mean speed (m·min-1), mean high-speed running (>12 km·h-1; m·min-1), and mean acceleration (m·s-2) were measured in 12 matches (370 individual match files) using the Global Navigation Satellite System (GNSS). Individual GNSS-derived data from each match-half were summed across each team and the association with total points and the points differential in each match-half was determined using linear mixed models. Greater high-speed running and lower mean acceleration were associated with more points being scored. A greater relative difference in mean high-speed running between competing teams was associated with a higher points differential. That is, if a team completed 10% more high-speed running than their opposition, they were likely to score an average of 3.2 points more during a given match-half. This unique analysis of GNSS-derived data may assist coaches and performance support staff to interpret the locomotor movements of female rugby league players with the appropriate considerations for the opposition team.

Field- and Laboratory-derived Power-Cadence Profiles in World-Class and Elite Track Sprint Cyclists

Previous investigations comparing Torque-Cadence (T-C) and Power-Cadence (P-C) profiles derived from seated and standing positions and field and laboratory conditions are not congruent with current methodological recommendations. Consequently, the aim of this investigation was to compare seated and standing T-C and P-C profiles generated from field and laboratory testing. Thirteen world-class and elite track sprint cyclists (n = 7 males, maximal power output (Pmax) = 2112 ± 395 W; n = 6 females, Pmax = 1223 ± 102 W) completed two testing sessions in which field- and laboratory-derived T-C and P-C profiles were identified. Standing P-C profiles had significantly (p < 0.05) greater Pmax than seated profiles, however there were no significant differences in optimal cadence (Fopt) between seated and standing positions. Pmax and Fopt were significantly lower in field-derived profiles in both positions compared to laboratory-derived profiles. However, there was no significant difference in the goodness-of-fit (R2) of the P-C profiles between laboratory (0.985 ± 0.02) and field-testing (0.982 ± 0.02) in each position. Valid T-C and P-C profiles can be constructed from field and laboratory protocols; however, the mechanical variables derived from the seated and standing and field and laboratory profiles cannot be used interchangeably. Both field and laboratory-derived profiles provide meaningful information and provide complementary insights into cyclists' capacity to produce power output.

The balancing act: Identifying multivariate sports performance using Pareto frontiers

Athletes often require a mix of physical, physiological, psychological, and skill-based attributes that can be conflicting when competing at the highest level within their sport. When considering multiple variables in tandem, Pareto frontiers is a technique that can identify the observations that possess an optimal balance of the desired attributes, especially when these attributes are negatively correlated. This study presents Pareto frontiers as a tool to identify athletes who possess an optimal ranking when considering multiple metrics simultaneously. This study explores the trade-off relationship between batting average and strike rate as well as bowling strike rate, economy, and average in Twenty 20 cricket. Eight hundred ninety-one matches of Twenty 20 cricket from the men's (MBBL) and women's (WBBL) Australian Big Bash Leagues were compiled to determine the best batting and bowling performances, both within a single innings and across each player's Big Bash career. Pareto frontiers identified 12 and seven optimal batting innings performances in the MBBL and WBBL respectively, with nine and six optimal batting careers respectively. Pareto frontiers also identified three optimal bowling innings in both the MBBL and WBBL and five and six optimal bowling careers in MBBL and WBBL, respectively. Each frontier identified players that were not the highest ranked athlete in any metric when analyzed univariately. Pareto frontiers can be used when assessing talent across multiple metrics, especially when these metrics may be conflicting or uncorrelated. Using Pareto frontiers can identify athletes that may not have the highest ranking on a given metric but have an optimal balance across multiple metrics that are associated with success in a given sport.

Effects of Acute Sleep Loss on Physical Performance: A Systematic and Meta-Analytical Review

BACKGROUND

Sleep loss may influence subsequent physical performance. Quantifying the impact of sleep loss on physical performance is critical for individuals involved in athletic pursuits.

DESIGN

Systematic review and meta-analysis.

SEARCH AND INCLUSION

Studies were identified via the Web of Science, Scopus, and PsycINFO online databases. Investigations measuring exercise performance under 'control' (i.e., normal sleep, > 6 h in any 24 h period) and 'intervention' (i.e., sleep loss, ≤ 6 h sleep in any 24 h period) conditions were included. Performance tasks were classified into different exercise categories (anaerobic power, speed/power endurance, high-intensity interval exercise (HIIE), strength, endurance, strength-endurance, and skill). Multi-level random-effects meta-analyses and meta-regression analyses were conducted, including subgroup analyses to explore the influence of sleep-loss protocol (e.g., deprivation, restriction, early [delayed sleep onset] and late restriction [earlier than normal waking]), time of day the exercise task was performed (AM vs. PM) and body limb strength (upper vs. lower body).

RESULTS

Overall, 227 outcome measures (anaerobic power: n = 58; speed/power endurance: n = 32; HIIE: n = 27; strength: n = 66; endurance: n = 22; strength-endurance: n = 9; skill: n = 13) derived from 69 publications were included. Results indicated a negative impact of sleep loss on the percentage change (%_Δ) in exercise performance (n = 959 [89%] male; mean %_Δ =  - 7.56%, 95% CI - 11.9 to - 3.13, p = 0.001, I² = 98.1%). Effects were significant for all exercise categories. Subgroup analyses indicated that the pattern of sleep loss (i.e., deprivation, early and late restriction) preceding exercise is an important factor, with consistent negative effects only observed with deprivation and late-restriction protocols. A significant positive relationship was observed between time awake prior to the exercise task and %_Δ in performance for both deprivation and late-restriction protocols (~ 0.4% decrease for every hour awake prior to exercise). The negative effects of sleep loss on different exercise tasks performed in the PM were consistent, while tasks performed in the AM were largely unaffected.

CONCLUSIONS

Sleep loss appears to have a negative impact on exercise performance. If sleep loss is anticipated and unavoidable, individuals should avoid situations that lead to experiencing deprivation or late restriction, and prioritise morning exercise in an effort to maintain performance.

Strong, Fast, Fit, Lean, and Safe: A Positional Comparison of Physical and Physiological Qualities Within the 2020 Australian Women's Rugby League Team

ABSTRACT

Minahan, C, Newans, T, Quinn, K, Parsonage, J, Buxton, S, and Bellinger, P. Strong, Fast, Fit, Lean, and Safe: A positional comparison of physical and physiological qualities within the 2020 Australian Women's Rugby League team. J Strength Cond Res 35(12S): S11-S19, 2021-The purpose of the present study was to report the physical and physiological characteristics of elite women Rugby League (RL) players. Thirty-nine women (25.6 ± 4.3 years, 171.3 ± 7.7 cm, 83.5 ± 13.9 kg) from the 2020 Australian women's RL squad were recruited for this study. Players were categorized as adjustables (n = 7), backs (n = 15), or forwards (n = 17) for analysis. Each player was assessed for anthropometry, body composition (dual-energy X-ray absorptiometry), speed (5, 10 and 20 m sprint times), lower-body power (countermovement jump), upper-body power (medicine ball throw and explosive push up force), estimated one repetition maximum (e1RM) bench press, squat and bench pull, isometric mid-thigh pull strength, eccentric knee flexor strength, isometric hip abduction and adduction, and intermittent endurance performance (30-15 intermittent fitness test; 30-15 IFT). Linear mixed models were performed to compare positional groups. Forwards were significantly heavier and had greater fat mass, fat-free mass, and body fat percentage compared with backs and adjustables (P < 0.01). Backs were faster over 20 m compared with forwards (P = 0.025), whereas forwards had a lower 30-15 IFT peak velocity and estimated V̇o2peak compared with backs and adjustables. Nonetheless, when including body mass in the model, there were no differences between groups in 30-15 IFT peak velocity. There were no significant differences in other variables. These results provide contemporary benchmark physical, physiological, and anthropometric data for elite women RL players, which can inform recruitment, selection, training, and testing.

Determinants of Performance in Paced and Maximal 800-m Running Time Trials

PURPOSE

We aimed to identify the underpinning physiological and speed/mechanical determinants of different types of 800-m running time trials (i.e., with a positive or negative pacing strategy) and key components within each 800-m time trial (i.e., first and final 200-m).

METHODS

Twenty trained male 800-m runners (800-m personal best time (min:s): 1:55.10 ± 0:04.44) completed a maximal 800-m time trial (800MAX) and one pacing trial, whereby runners were paced for the first lap and speed was reduced by 7.5% (800PACE) relative to 800MAX, while the last lap was completed in the fastest time possible. Anaerobic speed reserve, running economy, the velocity corresponding with VO2peak (VVO2peak), maximal sprint speed (MAXSS), maximal accumulated oxygen deficit and sprint force-velocity-power profiles were derived from laboratory and field testing. Carnosine content was quantified by proton magnetic resonance spectroscopy in the gastrocnemius and soleus and expressed as a carnosine aggregate Z-score (CAZ-score) to estimate muscle typology. Data were analysed using multiple stepwise regression analysis.

RESULTS

MAXSS and vVO2peak largely explained the variation in 800MAX time (r2 = 0.570; P = 0.020), while MAXSS was the best explanatory variable for the first 200-m time in 800MAX (adjusted r2 = 0.661, P < 0.001). Runners with a higher CAZ-score (i.e., higher estimated percentage of type II fibres) reduced their last lap time to a greater extent in 800PACE relative to 800MAX (adjusted r2 = 0.413, P < 0.001), while better maintenance of mechanical effectiveness during sprinting, a higher CAZ-score and vVO2peak was associated with a faster final 200-m time during 800PACE (adjusted r2 = 0.761, P = 0.001).

CONCLUSION

These findings highlight that diversity in the physiological and speed/mechanical characteristics of male middle-distance runners may be associated with their suitability for different 800-m racing strategies in order to have the best chance of winning.

Relationships between Lower Limb Muscle Characteristics and Force-Velocity Profiles Derived during Sprinting and Jumping

PURPOSE

This study aimed to identify the relationships between lower limb muscle characteristics and mechanical variables derived from the vertical (jumping) and horizontal (sprinting) force-velocity-power (FVP) profiles.

METHODS

Nineteen subelite male rugby league players performed a series of squat jumps and linear 30-m sprints to derive the vertical and horizontal FVP profiles, respectively. The theoretical maximal force (F0), velocity (V0), and power (Pmax) were derived from both the vertical (i.e., vF0, vV0, and vPmax) and the horizontal (i.e., hF0, hV0, and hPmax) FVP profiles. Vastus lateralis (VL), biceps femoris long head, and gastrocnemius medialis (GM) and lateralis muscle fascicle length, pennation angle, and thickness were measured using B-mode ultrasonography. Magnetic resonance imaging was used to calculate volumes of major lower limb muscles, whereas proton magnetic resonance spectroscopy was used to quantify the carnosine content of the GM to estimate muscle fiber typology.

RESULTS

Variation in vPmax was best explained by GM muscle fiber typology (i.e., greater estimated proportion of Type II fibers) and VL volume (adjusted r2 = 0.440, P = 0.006), whereas adductor and vastus medialis volume and GM muscle fiber typology explained the most variation in hPmax (adjusted r2 = 0.634, P = 0.032). Rectus femoris and VL volume explained variation in vF0 (r2 = 0.430, P = 0.008), whereas adductor and vastus medialis volume explained variation in hF0 (r2 = 0.432, P = 0.007). Variations in vV0 and hV0 were best explained by GM muscle fiber typology (adjusted r2 = 0.580, P < 0.001) and GM muscle fiber typology and biceps femoris short head volume (adjusted r2 = 0.590, P < 0.001), respectively.

CONCLUSION

Muscle fiber typology and muscle volume are strong determinants of maximal muscle power in jumping and sprinting by influencing the velocity- and force-oriented mechanical variables.

The influence of exercise training volume alterations on the gut microbiome in highly-trained middle-distance runners

The aim of this study was to determine the influence of training volume alterations on diversity and composition of the gut microbiome in a free-living cohort of middle-distance runners. Fourteen highly-trained middle-distance runners (n = 8 men; [Formula: see text]O_2peak = 70.1 ± 4.3 ml·kg·min^-1; n = 6 women, [Formula: see text]O_2peak: 59.0 ± 3.2 ml·kg·min^-1) completed three weeks of normal training (NormTr), three weeks of high-volume training (HVolTr; a 10, 20 and 30% increase in training volume during each successive week from NormTr), and a one-week taper (TaperTr; 55% exponential reduction in training volume from HVolTr week three). Faecal samples were collected before and immediately after each training phase to quantify alpha-diversity and composition of the gut microbiome. A three-day diet record was collected during each training phase and a maximal incremental running test was completed after each training phase. Results showed no significant changes in nutritional intake, alpha-diversity, or global microbial composition following HVolTr or TaperTr compared to NormTr (p's > 0.05). Following HVolTr, there was a significant decrease in Pasterellaceae (p = 0.03), Lachnoclostridium (p = 0.02), Haemophilus (p = 0.03), S. parasagunis (p = 0.02), and H. parainfluenzae (p = 0.03), while R. callidus (p = 0.03) significantly increased. These changes did not return to NormTr levels following TaperTr. This study shows that the alpha-diversity and global composition of the gut microbiome were unaffected by changes in training volume. However, an increase in training volume led to several changes at the lower taxonomy levels that did not return to pre-HVolTr levels following a taper period.

Functional Overreaching in Endurance Athletes: A Necessity or Cause for Concern?

There are variable responses to short-term periods of increased training load in endurance athletes, whereby some athletes improve without deleterious effects on performance, while others show diminished exercise performance for a period of days to months. The time course of the decrement in performance and subsequent restoration, or super compensation, has been used to distinguish between the different stages of the fitness-fatigue adaptive continuum termed functional overreaching (FOR), non-functional overreaching (NFOR) or overtraining syndrome. The short-term transient training-induced decrements in performance elicited by increases in training load (i.e. FOR) are thought be a sufficient and necessary component of a training program and are often deliberately induced in training to promote meaningful physiological adaptations and performance super-compensation. Despite the supposition that deliberately inducing FOR in athletes may be necessary to achieve performance super-compensation, FOR has been associated with various negative cardiovascular, hormonal and metabolic consequences. Furthermore, recent studies have demonstrated dampened training and performance adaptations in FOR athletes compared to non-overreached athletes who completed the same training program or the same relative increase in training load. However, this is not always the case and a number of studies have also demonstrated substantial performance super-compensation in athletes who were classified as being FOR. It is possible that there are a number of contextual factors that may influence the metabolic consequences associated with FOR and classifying this training-induced state of fatigue based purely on a decrement in performance may be an oversimplification. Here, the most recent research on FOR in endurance athletes will be critically evaluated to determine (1) if there is sufficient evidence to indicate that inducing a state of FOR is necessary and required to induce a performance super-compensation; (2) the metabolic consequences that are associated with FOR; (3) strategies that may prevent the negative consequences of overreaching.

Muscle Typology of World-Class Cyclists across Various Disciplines and Events

PURPOSE

Classic track-and-field studies demonstrated that elite endurance athletes exhibit a slow muscle typology, whereas elite sprint athletes have a predominant fast muscle typology. In elite cycling, conclusive data on muscle typology are scarce, which may be due to the invasive nature of muscle biopsies. The noninvasive estimation of muscle typology through the measurement of muscle carnosine enabled to explore the muscle typology of 80 world-class cyclists of different disciplines.

METHODS

The muscle carnosine content of 80 cyclists (4 bicycle motor cross racing [BMX], 33 track, 8 cyclo-cross, 24 road, and 11 mountain bike) was measured in the soleus and gastrocnemius by proton magnetic resonance spectroscopy and expressed as a z-score relative to a reference population. Track cyclists were divided into track sprint and endurance cyclists based on their Union Cycliste Internationale (UCI) ranking. Moreover, road cyclists were further characterized based on the percentage of UCI points earned during either single and multistage races.

RESULTS

BMX cyclists (carnosine aggregate z-score of 1.33) are characterized by a faster muscle typology than track, cyclo-cross, road, and mountain bike cyclists (carnosine aggregate z-score of -0.08, -0.76, -0.96, and -1.02, respectively; P < 0.05). Track cyclists also possess a faster muscle typology compared with mountain bikers (P = 0.033) and road cyclists (P = 0.005). Moreover, track sprinters show a significant faster muscle typology (carnosine aggregate z-score of 0.87) compared with track endurance cyclists (carnosine aggregate z-score of -0.44) (P < 0.001). In road cyclists, the higher the carnosine aggregate z-score, the higher the percentage of UCI points gained during single-stage races (r = 0.517, P = 0.010).

CONCLUSIONS

Prominent differences in the noninvasively determined muscle typology exist between elite cyclists of various disciplines, which opens opportunities for application in talent orientation and transfer.

Movement Patterns and Match Statistics in the National Rugby League Women's (NRLW) Premiership

As women's rugby league grows, the need for understanding the movement patterns of the sport is essential for coaches and sports scientists. The aims of the present study were to quantify the position-specific demographics, technical match statistics, and movement patterns of the National Rugby League Women's (NRLW) Premiership and to identify whether there was a change in the intensity of play as a function of game time played. A retrospective observational study was conducted utilizing global positioning system, demographic, and match statistics collected from 117 players from all NRLW clubs across the full 2018 and 2019 seasons and were compared between the ten positions using generalized linear mixed models. The GPS data were separated into absolute (i.e., total distance, high-speed running distance, and acceleration load) and relative movement patterns (i.e., mean speed, mean high speed (> 12 km·h^-1), and mean acceleration). For absolute external outputs, fullbacks covered the greatest distance (5,504 m), greatest high-speed distance (1,081 m), and most ball-carry meters (97 m), while five-eighths recorded the greatest acceleration load (1,697 m·s^-2). For relative external outputs, there were no significant differences in mean speed and mean high speed between positions, while mean acceleration only significantly differed between wingers and interchanges. Only interchange players significantly decreased in mean speed as their number of minutes played increased. By understanding the load of NRLW matches, coaches, high-performance staff, and players can better prepare as the NRLW Premiership expands. These movement patterns and match statistics of NRLW matches can lay the foundation for future research as women's rugby league expands. Similarly, coaches, high-performance staff, and players can also refine conditioning practices with a greater understanding of the external output of NRLW players.

The Effect of Consuming Carbohydrate With and Without Protein on the Rate of Muscle Glycogen Re-synthesis During Short-Term Post-exercise Recovery: a Systematic Review and Meta-analysis

Background

Rapid restoration of muscle glycogen stores is imperative for athletes undertaking consecutive strenuous exercise sessions with limited recovery time (e.g. ≤ 8 h). Strategies to optimise muscle glycogen re-synthesis in this situation are essential. This two-part systematic review and meta-analysis investigated the effect of consuming carbohydrate (CHO) with and without protein (PRO) on the rate of muscle glycogen re-synthesis during short-term post-exercise recovery (≤ 8 h).

Methods

Studies were identified via the online databases Web of Science and Scopus. Investigations that measured muscle glycogen via needle biopsy during recovery (with the first measurement taken ≤ 30 min post-exercise and at least one additional measure taken ≤ 8 h post-exercise) following a standardised exercise bout (any type) under the following control vs. intervention conditions were included in the meta-analysis: part 1, water (or non-nutrient beverage) vs. CHO, and part 2, CHO vs. CHO+PRO. Publications were examined for methodological quality using the Rosendal scale. Random-effects meta-analyses and meta-regression analyses were conducted to evaluate intervention efficacy.

Results

Overall, 29 trials (n = 246 participants) derived from 21 publications were included in this review. The quality assessment yielded a Rosendal score of 61 ± 8% (mean ± standard deviation). Part 1: 10 trials (n = 86) were reviewed. Ingesting CHO during recovery (1.02 ± 0.4 g·kg body mass (BM)⁻¹ h⁻¹) improved the rate of muscle glycogen re-synthesis compared with water; change in muscle glycogen (MG_Δ) re-synthesis rate = 23.5 mmol·kg dm⁻¹ h⁻¹, 95% CI 19.0–27.9, p < 0.001; I² = 66.8%. A significant positive correlation (R² = 0.44, p = 0.027) was observed between interval of CHO administration (≤ hourly vs. > hourly) and the mean difference in rate of re-synthesis between treatments. Part 2: 19 trials (n = 160) were reviewed. Ingesting CHO+PRO (CHO: 0.86 ± 0.2 g·kg BM⁻¹ h⁻¹; PRO: 0.27 ± 0.1 g·kg BM⁻¹ h⁻¹) did not improve the rate of muscle glycogen re-synthesis compared to CHO alone (0.95 ± 0.3 g·kg BM⁻¹ h⁻¹); MG_Δ re-synthesis rate = 0.4 mmol·kg  dm⁻¹ h⁻¹, 95% CI −2.7 to 3.4, p = 0.805; I² = 56.4%.

Conclusions

Athletes with limited time for recovery between consecutive exercise sessions should prioritise regular intake of CHO, while co-ingesting PRO with CHO appears unlikely to enhance (or impede) the rate of muscle glycogen re-synthesis.

Trial Registration

Registered at the International Prospective Register of Systematic Reviews (PROSPERO) (identification code CRD42020156841).

Supplementary Information

The online version contains supplementary material available at 10.1186/s40798-020-00297-0.

Muscle fiber typology is associated with the incidence of overreaching in response to overload training

Variability in the performance responses following an overload training period and subsequent taper was associated with the variation in the muscle fiber typology of the gastrocnemius. Runners with an estimated higher proportion of type I fibers (i.e., lower carnosine z-score) were able to maintain performance in response to an overload training period and subsequently achieve a superior performance supercompensation. These findings show that muscle fiber typology contributes to the variability in performance responses following training.

Quantifying the Training-Intensity Distribution in Middle-Distance Runners: The Influence of Different Methods of Training-Intensity Quantification

PURPOSE

To compare the training-intensity distribution (TID) across an 8-week training period in a group of highly trained middle-distance runners employing 3 different methods of training-intensity quantification.

METHODS

A total of 14 highly trained middle-distance runners performed an incremental treadmill test to exhaustion to determine the heart rate (HR) and running speed corresponding to the ventilatory thresholds (gas-exchange threshold and respiratory-compensation threshold), as well as fixed rating of perceived exertion (RPE) values, which were used to demarcate 3 training-intensity zones. During the following 8 weeks, the TID (total and percentage of time spent in each training zone) of all running training sessions (N = 695) was quantified using continuous running speed, HR monitoring, and RPE.

RESULTS

Compared with the running-speed-derived TID (zone 1, 79.9% [7.3%]; zone 2, 5.3% [4.9%]; and zone 3, 14.7% [7.3%]), HR-demarcated TID (zone 1, 79.6% [7.2%]; zone 2, 17.0% [6.3%]; and zone 3, 3.4% [2.0%]) resulted in a substantially higher training time in zone 2 (effect size ± 95% confidence interval: -1.64 ± 0.53; P < .001) and lower training time in zone 3 (-1.59 ± 0.51; P < .001). RPE-derived TID (zone 1, 39.6% [8.4%]; zone 2, 31.9% [8.7%]; and zone 3, 28.5% [11.6%]) reduced time in zone 1 compared with both HR (-5.64 ± 1.40; P < .001) and running speed (-5.69 ± 1.9; P < .001), whereas time in RPE training zones 2 and 3 was substantially higher than both HR- and running-speed-derived zones.

CONCLUSION

The results show that the method of training-intensity quantification substantially affects computation of TID.

Modelling the Acceleration and Deceleration Profile of Elite-level Soccer Players

The ability to change velocity rapidly is a key element of field-based sports. This study quantified the acceleration and deceleration profiles of soccer players during match play. Global positioning system measures were collected from 20 male soccer players competing in the Australian Hyundai A-League during 58 matches. Match data were organized into ten 9 min periods (i. e., P1: 0-9 min) and the time spent at moderate (1-2 m·s^-2) and high (>2 m·s^-2) acceleration and deceleration thresholds were quantified. Additionally, a novel deceleration: acceleration ratio was quantified to identify the transient nature of deceleration activity. Linear mixed models were used to model the acceleration and deceleration profiles. All acceleration and deceleration metrics displayed negative logarithmic curves within each half. There was no change in the ratio of high deceleration: acceleration; however, a significant increase in the ratio of moderate deceleration:acceleration was evident. Using negative logarithmic curves to illustrate the acceleration and deceleration decay provides a novel methodological approach to quantify the high-intensity actions during match play. A decrease in the time spent decelerating throughout a match may be attributed to a lack of opportunity. Practitioners can use the coefficients, intercepts, and deceleration: acceleration ratios to monitor a player's deceleration profile in match play.