Pacing Strategy Research Articles

Pacing Strategy During 24-Hour Ultramarathon-Distance Running.

To describe pacing strategy in a 24-h running race and its interaction with sex, age group, athletes' performance group, and race edition. Data from 398 male and 103 female participants of 5 editions were obtained based on a minimum 19.2-h effective-running cutoff. Mean running speed from each hour was normalized to the 24-h mean speed for analyses. Mean overall performance was 135.6 ± 33.0 km with a mean effective-running time of 22.4 ± 1.3 h. Overall data showed a reverse J-shaped pacing strategy, with a significant reduction in speed from the second-to-last to the last hour. Two-way mixed ANOVAs showed significant interactions between racing time and both athlete performance group (F = 7.01, P < .001, ηp2 = .04) and race edition (F = 3.01, P < .001, ηp2 = .02) but not between racing time and either sex (F = 1.57, P = .058, ηp 2 < .01) or age group (F = 1.25, P = .053, ηp2 = .01). Pearson product-moment correlations showed an inverse moderate association between performance and normalized mean running speed in the first 2 h (r = -.58, P < .001) but not in the last 2 h (r = .03, P = .480). While the general behavior represents a rough reverse J-shaped pattern, the fastest runners start at lower relative intensities and display a more even pacing strategy than slower runners. The "herd behavior" seems to interfere with pacing strategy across editions, but not sex or age group of runners.

Impact of Fatigue on Positional Movements During Professional Rugby Union Match Play.

In team sports, fatigue is manifested by a self-regulated decrease in movement distance and intensity. There is currently limited information on the effect of fatigue on movement patterns in rugby union match play, particularly for players in different position groups (backs vs forwards). This study investigated the effect of different match periods on movement patterns of professional rugby union players. Global positioning system (GPS) data were collected from 46 professional match participations to determine temporal effects on movement patterns. Total relative distance (m/min) was decreased in the 2nd half for both forwards (-13%, ±8%, ES = very likely large) and backs (-9%, ±7%, ES = very likely large). A larger reduction in high-intensity-running distance in the 2nd half was observed for forwards (-27%, ±16%, ES = very likely medium) than for backs (-10%, ±15%; ES = unclear). Similar patterns were observed for sprint (>6 m/s) frequency (forwards -29%, ±29%, ES = likely small vs backs -13% ±18%, ES = possibly small) and acceleration (>2.75 m/s2) frequency (forwards -27%, ±24%, ES = likely medium vs backs -5%, ±46%, ES = unclear). Analysis of 1st- and 2nd-half quartiles revealed differing pacing strategies for forwards and backs. Forwards display a "slow-positive" pacing strategy, while the pacing strategy of backs is "flat." Forwards suffered progressively greater performance decrements over the course of the match, while backs were able to maintain performance intensity. These findings reflect differing physical demands, notably contact and running loads, of players in different positions.

Effect of the pacing strategy during half-duration resistance test on the mechanic, metabolic and cardio-respiratory response

Abstract Objective Changes in pacing rhythm are translated into functional and metabolic changes that can be significantly reflected in the final results of an athlete. Method Ten male subjects, with moderate performance level (age: 25.2 ± 2.2 years; VO 2max : 56.9 ± 5.7 ml kg −1 min −1 ), performed four 5-min races with different pacing strategies: constant-pace (CP), record-pace (RP), kicker-pace (KP), incremental-pace (IP). Results The cardio-respiratory response did not show statistically significant. There were statistically significant differences ( p ≤ 0.05) in the energetic efficiency among the protocols CP vs. RP, CP vs. KP and RP vs. IP. When results were analyzed by partials (1-min duration phases), significant differences were observed in the energetic efficiency during the 3rd-min among CP vs. KP, RP vs. KP and KP vs. IP. These significant differences were extended to the 4th-min when comparing CP vs. IP, CP vs. KP, KP vs. RP and KP vs. IP. In the last minute of the test, there were significant differences among CP vs. KP. No significant differences were found in any of the variables assessing anaerobic metabolism (accumulated oxygen deficit, oxygen debt, oxygen uptake kinetics and blood lactate) between both protocols. Conclusions Results suggest that the main functional systems response are significantly affected by the pacing strategy used by middle-level subjects during middle-distance running.

Cycling in the Absence of Task-Related Feedback: Effects on Pacing and Performance.

Introduction: To achieve personal goals in exercise task completion, exercisers have to regulate, distribute, and manage their effort. In endurance sports, it has become very commonplace for athletes to consult task-related feedback on external devices to do so. The aim of the present study was to explore the importance of the presence of this information by examining the influence of the absence of commonly available task-related feedback on effort distribution and performance in experienced endurance athletes.Methods: A 20-km cycling time trial was performed. Twenty Participants from a homogenous cyclist population were appointed to a group that did not receive any feedback (NoF), or a group that could consult task-related feedback (i.e., speed, heart rate, power output, cadence, elapsed time, and elapsed distance) continuously during their trial (FF).Results: The distribution of power output (PO) differed between groups. Most evident is the spurt at the end of the trial of FF, which was not incorporated by NoF. Nevertheless, no between-group differences were found in performance time (FF: 28.86 ± 3.68 vs. NoF: 30.95 ± 2.77 min) and mean PO controlled by body mass (FF: 3.61 ± 0.60 vs. NoF: 3.43 ± 0.38 W/kg). Also, no differences in rating of perceived exertion scores were found.Conclusion: The current study provides a first indication that prior knowledge of task demands together with reliance on bodily and environmental information can be sufficient for experienced athletes to come to comparable time trial performances. This questions the necessity of the presence of in-race instantaneous task-related feedback via external devices for maximizing performance. Moreover, it seems that different pacing strategies emerge depending on sources of information available to experienced athletes.

Pacing in age-group freestyle swimmers at The XV FINA World Masters Championships in Montreal 2014

ABSTRACTPacing strategies have been investigated for elite-standard freestyle swimmers, but little is known about pacing in age-group freestyle swimmers. We investigated changes in swimming time across distances in 4,481 women and men swimmers who competed in 100, 200, 400, and 800 m freestyle age groups from 25–29 years to 90–94 years in the FINA World Masters Championships 2014. In 100 to 800 m, there was a small lap×sex interaction (P < 0.001, 0.033 ≤ η2 ≤ 0.045) whereby women had larger lap-to-lap changes in swimming time than men. From 100 to 800 m, there were moderate to large lap×age group interactions (P < 0.001, 0.054 ≤ η2 ≤ 0.235), i.e., pacing patterns differed by age groups. There were small main effects of lap on time in 100, 200, 400 and 800 m freestyle events (P < 0.001, 0.033 ≤ η2 ≤ 0.045). In summary, (i) the largest increase in swimming time occurred during the second lap and a decrease in time occurred during the last lap, except in the 100 m, and (ii) the effect of participants’ sex on lap time indicated larger percentage changes of pacing in women than in men. These findings should help coaches to develop age- and event-tailored pacing strategies.

Seasonal Pacing - Match Importance Affects Activity in Professional Soccer

This research explores the influence of match importance on player activity in professional soccer. Therefore, we used an observational approach and analyzed 1,211 matches of German Bundesliga and 2nd Bundesliga. The importance measurement employed is based on post season consequences of teams involved in a match. This means, if a match result could potentially influence the final rank, and this rank would lead to different consequences for a team, such as qualification for Champions League opposed to qualification for Europe League, then this match is classified as important; otherwise not. Activity was quantified by TOTAL DISTANCE COVERED, SPRINTS, FAST RUNS, DUELS, FOULS and ATTEMPTS. Running parameters were recorded using a semi-automatic optical tracking system, while technical variables were collected by professional data loggers. Based on our importance classification, low important matches occurred at the beginning of round 29. A two-way ANOVA indicates significantly increased FAST RUNS (+4%, d = 0.3), DUELS (+16%, d = 1.0) and FOULS (+36%, d = 1.2) in important matches compared to low important ones. For FAST RUNS and FOULS, this effect only exists in Bundesliga. A comparison of the two leagues show that TOTAL DISTANCE COVERED (+3%, d = 0.9), SPRINTS (+25%, d = 1.4) and FAST RUNS (+15%, d = 1.4) are higher compared to 2nd Bundesliga, whilst FOULS is less in Bundesliga (-7%, d = 0.3). No difference in player activity was found between matches at the beginning of a season (round 1–6) and at the end of a season (round 29–34). We conclude that match importance influences player activity in German professional soccer. The most reasonable explanation is a conscious or unconscious pacing strategy, motivated by preserving abilities or preventing injury. Since this tendency mainly exists in Bundesliga, this may suggest that more skilled players show a higher awareness for the need of pacing.

The Anticipated Clinical and Economic Effects of 90-90-90 in South Africa.

The Joint United Nations Programme on HIV/AIDS (UNAIDS) 90-90-90 global treatment target aims to achieve 73% virologic suppression among HIV-infected persons worldwide by 2020. To estimate the clinical and economic value of reaching this ambitious goal in South Africa, by using a microsimulation model of HIV detection, disease, and treatment. Modeling of the "current pace" strategy, which simulates existing scale-up efforts and gradual increases in overall virologic suppression from 24% to 36% in 5 years, and the UNAIDS target strategy, which simulates 73% virologic suppression in 5 years. Published estimates and South African survey data on HIV transmission rates (0.16 to 9.03 per 100 person-years), HIV-specific age-stratified fertility rates (1.0 to 9.1 per 100 person-years), and costs of care ($11 to $31 per month for antiretroviral therapy and $20 to $157 per month for routine care). South African HIV-infected population, including incident infections over the next 10 years. Modified societal perspective, excluding time and productivity costs. 5 and 10 years. Aggressive HIV case detection, efficient linkage to care, rapid treatment scale-up, and adherence and retention interventions toward the UNAIDS target strategy. HIV transmissions, deaths, years of life saved, maternal orphans, costs (2014 U.S. dollars), and cost-effectiveness. Compared with the current pace strategy, over 5 years the UNAIDS target strategy would avert 873000 HIV transmissions, 1174000 deaths, and 726000 maternal orphans while saving 3002000 life-years; over 10 years, it would avert 2051000 HIV transmissions, 2478000 deaths, and 1689000 maternal orphans while saving 13340000 life-years. The additional budget required for the UNAIDS target strategy would be $7.965 billion over 5 years and $15.979 billion over 10 years, yielding an incremental cost-effectiveness ratio of $2720 and $1260 per year of life saved, respectively. Outcomes generally varied less than 20% from base-case outcomes when key input parameters were varied within plausible ranges. Several pathways may lead to 73% overall virologic suppression; these were examined in sensitivity analyses. Reaching the 90-90-90 HIV suppression target would be costly but very effective and cost-effective in South Africa. Global health policymakers should mobilize the political and economic support to realize this target. National Institutes of Health and the Steve and Deborah Gorlin MGH Research Scholars Award.

Temporal changes in physiological and performance responses across game-specific simulated basketball activity

PurposeThe aims of this study were to: (1) provide a comprehensive physiological profile of simulated basketball activity and (2) identify temporal changes in player responses in controlled settings. MethodsState-level male basketball players (n = 10) completed 4 × 10 min simulated quarters of basketball activity using a reliable and valid court-based test. A range of physiological (ratings of perceived exertion, blood lactate concentration ([BLa−]), blood glucose concentration ([BGlu]), heart rate (HR), and hydration) and physical (performance and fatigue indicators for sprint, circuit, and jump activity) measures were collected across testing. ResultsSignificantly reduced [BLa−] (6.19 ± 2.30 vs. 4.57 ± 2.33 mmol/L; p = 0.016) and [BGlu] (6.91 ± 1.57 vs. 5.25 ± 0.81 mmol/L; p = 0.009) were evident in the second half. A mean HR of 180.1 ± 5.7 beats/min (90.8% ± 4.0% HRmax) was observed, with a significant increase in vigorous activity (77%–95% HRmax) (11.31 ± 6.91 vs. 13.50 ± 6.75 min; p = 0.024) and moderate decrease in near-maximal activity (>95% HRmax) (7.24 ± 7.45 vs. 5.01 ± 7.20 min) in the second half. Small increases in performance times accompanied by a significantly lower circuit decrement (11.67% ± 5.55% vs. 7.30% ± 2.16%; p = 0.032) were apparent in the second half. ConclusionThese data indicate basketball activity imposes higher physiological demands than previously thought and temporal changes in responses might be due to adapted pacing strategies as well as fatigue-mediated mechanisms.

Laboratory predictors of uphill cycling performance in trained cyclists

ABSTRACTThis study aimed to assess the relationship between an uphill time-trial (TT) performance and both aerobic and anaerobic parameters obtained from laboratory tests. Fifteen cyclists performed a Wingate anaerobic test, a graded exercise test (GXT) and a field-based 20-min TT with 2.7% mean gradient. After a 5-week non-supervised training period, 10 of them performed a second TT for analysis of pacing reproducibility. Stepwise multiple regressions demonstrated that 91% of TT mean power output variation (W kg−1) could be explained by peak oxygen uptake (ml kg−1.min−1) and the respiratory compensation point (W kg−1), with standardised beta coefficients of 0.64 and 0.39, respectively. The agreement between mean power output and power at respiratory compensation point showed a bias ± random error of 16.2 ± 51.8 W or 5.7 ± 19.7%. One-way repeated-measures analysis of variance revealed a significant effect of the time interval (123.1 ± 8.7; 97.8 ± 1.2 and 94.0 ± 7.2% of mean power output, for epochs 0–2, 2–18 and 18–20 min, respectively; P < 0.001), characterising a positive pacing profile. This study indicates that an uphill, 20-min TT-type performance is correlated to aerobic physiological GXT variables and that cyclists adopt reproducible pacing strategies when they are tested 5 weeks apart (coefficients of variation of 6.3; 1 and 4%, for 0–2, 2–18 and 18–20 min, respectively).

On a bioenergetic four-compartment model for human exercise

Bioenergetic models for exercise performance simulations and pacing strategy optimizations currently lag behind empirical knowledge in human bioenergetics. Therefore, the objective of this study was the construction of a four-compartment hydraulic bioenergetic model that incorporates separate oxidative phosphorylation of lipids and carbohydrates and describes the regulation of these energy substrates’ utilization. Furthermore, the aim was also to model efficiency and the impact of muscle fatigue and the force–velocity relationship on the maximal attainable rate of energy expenditure. The model is formulated with five systems of differential equations that regulates the fluid levels in three of the compartments, while the fat compartment energy is kept constant. Regulations had to be imposed on the system of compartments to achieve the desired carbohydrate dependent functionality and efficiency of the model. Equilibrium equations are modeled for the alactic compound composition and a constraint is modeled for the maximal energy expenditure rate, dependent on the intramuscular inorganic phosphate. A separate force–velocity relationship is modeled to constrain power output at low speeds and efficiency is modeled with a linear but off-set relationship between power output and rate of energy expenditure. The relative aerobic contribution to total energy expenditure showed good congruence with empirical results, while time to exhaustion was overestimated due to the constraint on maximal rate of energy expenditure. Therefore, further experimental studies are necessary for complete validation of the model.

Influence of pacing on reliability of middle-distance cycling performance

The purpose of the present study was to examine the reliability of middle distance cycling time trials using fast-, even-, and slow-starts . Eighteen endurance-trained male cyclists [mean ± standard deviation; VO 2peak 63.1 ± 6.1 mL ⋅ kg -1 ⋅ min -1 ] performed nine cycling time trials where the total external work (96.5 ± 11.2 kJ) was identical to the better of two, 5-minute habituation time trials. Power output during the first quarter of the time-trials (24.1 ± 2.8 kJ) was fixed to induce fast-, even- or slow-starting strategies (60, 75 and 90 s, respectively). In consecutive sessions, participants performed three trials of each pacing condition although the order of these pacing conditions was counterbalanced. Average power output and performance time were unaffected by trial number in the fast- (P = 0.60), even- (P = 0.18) and slow-start (P = 0.53) trials. In all three pacing conditions, average power output was highly reliable and similar between trial 1 to 2 and trial 2 to 3 in fast- (standard error of measurement; SEM=8.3 and 8.2W), even (coefficient of variation; CV=2.8 and 2.4%) and slow-start (CV=2.4 and 1.5%) trials. In conclusion, the reproducibility of 5-min cycling time trials is unaffected by starting strategy and is acceptable following two self- paced habituation trials. Research examining the influence of pacing strategies may therefore be conducted without the need for familiarisation trials using each individual pacing condition.

The effect of trial familiarisation on the validity and reproducibility of a field-based self-paced VO2max test.

The self-paced maximal oxygen uptake (VO2max) test (SPV), which is based on the Borg 6-20 Ratings of Perceived Exertion (RPE) scale, allows participants to self-regulate their exercise intensity during a closed-loop incremental maximal exercise test. As previous research has assessed the utility of the SPV test within laboratory conditions, the purpose to this study was to assess the effect of trial familiarisation on the validity and reproducibility of a field-based, SPV test. In a cross-sectional study, fifteen men completed one laboratory-based graded exercise test (GXT) and three field-based SPV tests. The GXT was continuous and incremental until the attainment of VO2max. The SPV, which was completed on an outdoor 400m athletic track, consisted of five x 2 min perceptually-regulated (RPE11, 13, 15, 17 and 20) stages of incremental exercise. There were no differences in the VO2max reported between the GXT (63.5±10.1 ml·kg-1·min-1) and each SPV test (65.5±8.7, 65.4±7.0 and 66.7±7.7 ml·kg-1·min-1 for SPV1, SPV2 and SPV3, respectively; P>.05). Similar findings were observed when comparing VO2max between SPV tests (P>.05). High intraclass correlation coefficients were reported between the GXT and the SPV, and between each SPV test (≥.80). Although participants ran faster and further during SPV3, a similar pacing strategy was implemented during all tests. This study demonstrated that a field-based SPV is a valid and reliable VO2max test. As trial familiarisation did not moderate VO2max values from the SPV, the application of a single SPV test is an appropriate stand-alone protocol for gauging VO2max.

Effects Of Differing 2 Minute Pacing Strategies On VO2, Muscle Deoxygenation And Performance

PURPOSE: To examine the physiological and performance responses of 3 different pacing strategies during a 2 min cycling trial on well trained collegiate track athletes. METHODS: 5 Male (VO2peak = 54.81±7.49 ml/kg/min; Power output peak (POpeak) = 402.5±57.57 watts) and 6 female (VO2peak = 43.36±5.54 ml/kg/min; POpeak = 274.33±27.93 watts) varsity track athletes performed 3 pacing strategies on a cycle ergometer within a 10 day period. Test 1: A 3 min all out trial (AO) determined critical power and 2 min performance. Test 2: A constant power strategy (CON) calculated from the CP test with a final 30 s maximal effort. Test 3: A parabolic race strategy (PRS) utilising an initial 30 s period performed 5% above CON, followed by 60 s at 5% below CON and then a 30 s maximal effort finish. Breath by breath pulmonary VO2 and muscle deoxygenation (HHb) data from the vastus lateralis were collected from -10 to 120 s during the 3 pacing strategies. Blood lactate concentrations were collected pre and post all trials. RESULTS: Mean VO2 was different across the 3 trials (AO: 2.87±0.74L/min, CON: 2.56±0.68L/min, PRS: 2.71±0.71L/min, p<0.05). As was mean VO2 from -10 to 30 s, where AO elicited the highest VO2 and CON elicited the lowest (AO: 2.1±0.43L/min, CON: 1.61±0.46L/min, PRS: 1.76±0.47L/min, p<0.05). VO2 from 31 to 60 s in all trials was observed to be greater in AO than in both CON and PRS (AO: 3.06±0.82L/min, CON: 2.60±0.71L/min, PRS: 2.73±0.73L/min, p<0.05). Only AO VO2 was different from CON during 61 to 90 s of all trials (AO: 3.15±0.88L/min, CON: 2.95±0.79L/min, PRS: 3.06±0.82L/min, p<0.05), while there were no differences in mean VO2 over the final 30 seconds. Differences in % HHb change were only observed from -10 to 30 s of all trials. AO had a greater change than both CON and PRS from baseline (AO: 83.72±5.26%, CON: 74.42±8.27%, PRS: 73.39±10.41%, p<0.05). Mean PO of CON and PRS were higher than AO (AO: 335.88±88W, CON: 342.56±86.67W, PRS: 341.39±85.9W, p<0.05). Post-trial lactate levels for AO were higher than CON, but no differences were observed between CON and PRS (AO: 15.78±1.89mmol/L, CON: 13.84±2.1mmol/L, PRS: 14.19±2.34mmol/L, p<0.05). CONCLUSION: CON and PRS are superior strategies compared to AO for 2 min performance, despite the slower rate of adjustment of VO2 and HHb, and the lower glycolytic phosphorylation contributions.

Effects of Priming and Pacing Strategy on Oxygen-Uptake Kinetics and Cycling Performance.

To assess whether combining prior "priming" exercise with an all-out pacing strategy is more effective at improving oxygen-uptake (VO2) kinetics and cycling performance than either intervention administered independently. Nine men completed target-work cycling performance trials using a self-paced or all-out pacing strategy with or without prior severe-intensity (70%Δ) priming exercise. Breath-by-breath pulmonary VO2 and cycling power output were measured during all trials. Compared with the self-paced unprimed control trial (22 ± 5 s), the VO2 mean response time (MRT) was shorter (VO2 kinetics were faster) with all-out pacing (17 ± 4 s) and priming (17 ± 3 s), with the lowest VO2 MRT observed when all-out pacing and priming were combined (15 ± 4 s) (P < .05). However, total O2 consumed and end-exercise VO2 were only higher than the control condition in the primed trials (P < .05). Similarly, cycling performance was improved compared with control (98 ± 11 s) in the self-paced primed (93 ± 8 s) and all-out primed (92 ± 8 s) trials (P < .05) but not the all-out unprimed trial (97 ± 5 s; P > .05). These findings suggest that combining an all-out start with severe-intensity priming exercise additively improves VO2 MRT but not total O2 consumption and cycling performance since these were improved by a similar magnitude in both primed trials relative to the self-paced unprimed control condition. Therefore, these results support the use of priming exercise as a precompetition intervention to improve oxidative metabolism and performance during short-duration high-intensity cycling exercise, independent of the pacing strategy adopted.

Pace Versus Prediction

PURPOSE: The ability to regulate effort (pace) is ascribed to the ability to make prospective judgments regarding the metabolic demands of the exercise challenge against personal metabolic capacity. Thus pace modulations which are dependent on knowing an exercise end-point are a function of biologically and cognitively orchestrated afferent signals and the homeostatically orientated efferent responses are manifest to prevent a depletion of the finite anaerobic capacity and onset of fatigue. The purpose of this study was to examine the pacing strategies adopted during a marathon and to explore whether there was a difference in outcome between males and females. METHOD: Following local institutional ethical approval n= 777 runners competing in the 2015 London Marathon volunteered and agreed to participate of which n= 393 were females and n= 384 were males. Using an online survey, available for 12 weeks up to the marathon and opportunistic sampling at the pre-marathon registration, participants were also asked to predict race time. Additional information regarding age and experience (number of marathons) were also obtained. Prediction time (PT) served as a proxy of end target time. For each participant 5km splits and finish time (FT) were converted to speed and then normalised (%) to the final split time/speed (m.s-1). RESULTS: A significant difference (P= 0.0001) of 476s was observed between PT and FT for the whole group compared to differences of 531 s (p= 0.000) and 419s (P= 0.000) for the males and females respectively. Both males (P= 0.0001) and females (P= 0.0001) showed significant differences between PT and FT. Males exhibited differences in pace for all 5km splits (P= 0.0001) except 5-10km (P= 0.483), large ES between 25-30km (r= 0.319) and 30-35km (r= 0.426), pace decreased from 107.4 ± 7.8% (5km) to 91.2 ± 7.1% (40km), compared to 109.1 ± 8.6% (5km) to 93.2 ± 6.5% (40km) for females. Females exhibited differences across all 5km splits (P= 0.0001) except between 35-40km (P= 1.000) with medium ES for 20-25km (r= 0.243) and 30-35km (r= 0.313). CONCLUSIONS: There is no difference between male and female marathon pacing strategies suggesting that the pacing template is not sex specific. Furthermore these data lend support to the notion that there is not an unfair advantage for females to be paced or race with males.

The Effects of Exercise Modality on Pacing Strategy During Endurance Exercise

The Effects of Exercise Modality on Pacing Strategy During Endurance Exercise

Altering Pace Control and Pace Regulation: Attentional Focus Effects during Running.

To date, there are no published studies directly comparing self-controlled (SC) and externally controlled (EC) pace endurance tasks. However, previous research suggests pace control may impact on cognitive strategy use and effort perceptions. The primary aim of this study was to investigate the effects of manipulating perception of pace control on attentional focus, physiological, and psychological outcomes during running. The secondary aim was to determine the reproducibility of self-paced running performance when regulated by effort perceptions. Twenty experienced endurance runners completed four 3-km time trials on a treadmill. Subjects completed two SC pace trials, one perceived exertion clamped (PE) trial, and one EC pace time trial. PE and EC were completed in a counterbalanced order. Pacing strategy for EC and perceived exertion instructions for PE replicated the subjects' fastest SC time trial. Subjects reported a greater focus on cognitive strategies such as relaxing and optimizing running action during EC than during SC. The mean HR was 2% lower during EC than that during SC despite an identical pacing strategy. Perceived exertion did not differ between the three conditions. However, increased internal sensory monitoring coincided with elevated effort perceptions in some subjects during EC and a 10% slower completion time for PE (13.0 ± 1.6 min) than that for SC (11.8 ± 1.2 min). Altering pace control and pace regulation impacted on attentional focus. External control over pacing may facilitate performance, particularly when runners engage attentional strategies conducive to improved running efficiency. However, regulating pace based on effort perceptions alone may result in excessive monitoring of bodily sensations and a slower running speed. Accordingly, attentional focus interventions may prove beneficial for some athletes to adopt task-appropriate attentional strategies to optimize performance.

Pace Versus Prediction

PURPOSE: During closed-loop exercise, such as marathon running, the athlete adopts a pacing strategy to optimise performance. Exercise intensity (speed) is modulated in response to afferent signals from biological and psychological systems, which relay the responses of the exercise to the brain where efferent, homeostatic-orientated responses are issued. Thus a conscious perception of effort is continuously compared to a sub-conscious template which is derived from previous exposure to the sensations of pain and fatigue and expected exercise duration. The purpose of this study was to explore the association between pacing strategy/race outcome and biological age of the athlete. METHODS: Following local institutional ethical approval n= 777 runners who were competing in the 2015 London Marathon volunteered and agreed to participate. Age, gender and experience of the participants were ascertained using an online survey and opportunistic questionnaire surveying at the pre-marathon registration event. Age was stratified according to those adopted by the marathon organisers: 18-39yrs, 40-49yrs, 50-59yrs and >60yrs. Additionally participants were asked to predict their marathon finish time (PT) serving as a proxy for end-point and compared to actual finish time (FT). All participating runners 5km splits and FT were downloaded from the race website, converted to speed and then normalised (%) to the final split time/speed (m.s-1). RESULTS: Significant differences were observed for all age groups (P= 0.0001) between FT and PT except >60yrs (P= 0.153). Non-significant differences observed between age groups across all 5km splits (p> 0.05), but within group differences observed between 10-15km for all age groups (p< 0.05), with this being only difference for >60yrs group. Large effect sizes (ES) observed for 18-39yrs at 30-35km (r= 0.370), 40-49yrs at 30-35km (r= 0.337), 50-59yrs at 25-30km (r= 0.368) and 30-35km (r= 0.418) and >60yrs at 30-35km (r= 0.527). CONCLUSIONS: These data suggest that the biological age of the athlete is associated with the implementation of a successful pacing strategy and may be a function of the accrued training volume and/or emotional-event development. Athletes are encouraged to pace themselves with older (>60yrs) athletes with similar PT’s.

Pace Versus Prediction

PURPOSE: Pacing strategies during exercise are attributed to optimising the balance between the artefacts of fatigue and regulation of substrate metabolism. Pace judgement is set within a continuum of information from the ability to anticipate metabolic demands and select an appropriate strategy through to the accumulation of prior experience for completion of such a task that has a known end-point. Therefore the purpose of this study was evaluate the importance of athlete experience to successfully regulate pace and attain a predicted end time during a marathon. METHOD: Following local institutional ethical approval n= 777 runners competing in the 2015 London Marathon agreed to participate. Using an on-line survey and opportunistic questionnaires at a pre-marathon event participants were asked to predict their race time. Athlete experience (EXP) was established based on the number of previously completed marathons using a Likert scale from 0 to greater than 10 with increments of 1 race. Athlete age was also recorded. All race data was downloaded from the race website generating 5Km split times, then converted to speed and normalised (%) to the final split time/speed (m.s-1). Prediction time (PT) was used a proxy for end-point and compared to finish time (FT). RESULTS: FT for whole group (WG) was 15479 ± 3311s compared to the group PT 15003 ± 2972s a significant difference of 476s (P= 0.0001). An R2 of 0.863 observed for WG compared to 0.799 (0-EXP) and 0.852 (EXP-5) when comparing FT to PT. Significant differences observed between PT and FT for all EXP groups apart from EXP-5 (P= 0.0001). 0-EXP showed significant differences across all split times apart from 35-40 km (P=0.0001) with a decrease in normalised speed from 5km (109.0 ± 7.6) - 40km (89.9 ± 7.4%). The 5-EXP group showed significant changes in pace between 25-30 km (P= 0.001) (ES =0.35), 30-35 km (P= 0.0001) (ES= 0.44) and 35-40 km (P= 0.0001), decrease in pace from 5km (105.0 ± 5.7%) to 40km (93.7 ± 5.6%). CONCLUSIONS: These data suggest that successful marathon pacing is dependent on the experience of the athlete reflecting the development of the pacing template. Additionally experience is associated with better attainment of prediction time suggesting that less experienced runners should run with more experienced athletes with similar end-point targets.

Investigating the Effects of Typical Rowing Strength Training Practices on Strength and Power Development and 2,000 m Rowing Performance.

This study aimed to determine the effects of a short-term, strength training intervention, typically undertaken by club-standard rowers, on 2,000 m rowing performance and strength and power development. Twenty-eight male rowers were randomly assigned to intervention or control groups. All participants performed baseline testing involving assessments of muscle soreness, creatine kinase activity (CK), maximal voluntary contraction (leg-extensors) (MVC), static-squat jumps (SSJ), counter-movement jumps (CMJ), maximal rowing power strokes (PS) and a 2,000 m rowing ergometer time-trial (2,000 m) with accompanying respiratory-exchange and electromyography (EMG) analysis. Intervention group participants subsequently performed three identical strength training (ST) sessions, in the space of five days, repeating all assessments 24 h following the final ST. The control group completed the same testing procedure but with no ST. Following ST, the intervention group experienced significant elevations in soreness and CK activity, and decrements in MVC, SSJ, CMJ and PS (p < 0.01). However, 2,000 m rowing performance, pacing strategy and gas exchange were unchanged across trials in either condition. Following ST, significant increases occurred for EMG (p < 0.05), and there were non-significant trends for decreased blood lactate and anaerobic energy liberation (p = 0.063 – 0.086). In summary, club-standard rowers, following an intensive period of strength training, maintained their 2,000 m rowing performance despite suffering symptoms of muscle damage and disruption to muscle function. This disruption likely reflected the presence of acute residual fatigue, potentially in type II muscle fibres as strength and power development were affected.