Metabolic Adaptations to Marathon Training and Racing

Hawley, John A.; Spargo, Fiona J.

doi:10.2165/00007256-200737040-00014

Cited by 11 publications

(6 citation statements)

References 10 publications

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“…Several factors have been reported to influence the performance in marathon running such as the rate of aerobic metabolism, the availability of the limited stores of carbohydrate, and the velocity that can be maintained without developing excessive hyperthermia (7). The aim of marathon training is to increase the pace that can be sustained over the entire distance (13). Thus, marathon-specific research has made a significant contribution to researchers and practitioners developing new training strategies to enhance the physiological regulators of marathon performance.…”

Section: Introductionmentioning

confidence: 99%

Is It Time to Consider a New Performance Classification for High-Level Male Marathon Runners?

Torre

Vernillo²,

Agnello³

et al. 2011

Journal of Strength and Conditioning Research

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La Torre, A, Vernillo, G, Agnello, L, Berardelli, C, and Rampinini, E. Is it time to consider a new performance classification for high-level male marathon runners? J Strength Cond Res 25(12): 3242-3247, 2011-Studies have attempted to describe human running performances by the analysis of world-record times. However, to date, no study has analyzed the evolution of high-level marathon performances over time. Thus, the purpose of this study was to analyze these performances across the past 42 years with the aim of delineating a time-based classification. To identify the nature of the phenomenon represented by the sequence of observations, we examined the data collected (i.e., 8,400 times from 1969 to 2010) as a time series. The leading time (LT) and the mean 200 times (T200) per year underwent a nonlinear but significant decrement (r = -0.92, p < 0.001 and r = -0.98, p < 0.001, respectively). In fact, from 1969 to 2010, the mean time differences were 3 minutes 20 seconds ± 1 minute 59 seconds and 7 minutes 1 second ± 2 minutes 48 seconds, corresponding to an improvement of 5 and 10 seconds per year for LT and T200, respectively. Furthermore, trend analysis suggested a disruption in marathon time improvements, indicating the presence of 3 points in the time series in which the performance significantly improved with respect to that of the previous years, corresponding to the years 1983-1984 (p < 0.001), 1997-1998 (p < 0.003), and 2003 (p < 0.001). In conclusion, despite the trend in high-level marathon performances being better explained by a nonlinear tendency, significant improvements in the ability of the high-level marathon runners to complete the distance were observed. These improvements are likely to be related to sociological, environmental, physiological, and training-method factors. Researchers and coaches should take into account these enhancements by using the time classification proposed in this study to better reflect the marathon performance profile of their athletes.

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Section: Introductionmentioning

confidence: 99%

Is It Time to Consider a New Performance Classification for High-Level Male Marathon Runners?

Torre

Vernillo²,

Agnello³

et al. 2011

Journal of Strength and Conditioning Research

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“…The reducing equivalents are derived from the metabolism of fat and carbohydrate, which are mobilized from intramuscular stores, adipose tissue, liver and exogenously ingested carbohydrate (Spriet, 2007). Exercise training creates a cascade of signals (Coffee & Hawley, 2007) that put into motion an upregulation of defense systems that permit 1) an increase in the rate of energy production from both aerobic pathways, 2) a tighter metabolic control (i.e., matching ATP production with ATP hydrolysis), 3) an increased economy of motion, and therefore 4) less fatigue in working muscles during exercise (Hawley & Spargo, 2007). Kimber et al (2002) showed in recreational triathletes competing in the New Zealand Ironman event, that total energy expenditure approximated 10,000 kcal, but that energy intake (94% carbohydrate) was around 4000 kcal.…”

Section: Bioenergetic Goalsmentioning

confidence: 99%

Long distance triathlon: demands, preparation and performance

Laursen¹

2011

JHSE

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Laursen PB. Long distance triathlon: demands, preparation and performance. J. Hum. Sport Exerc. Vol. 6, No. 2, pp. 247-263, 2011. The rise in worldwide popularity of long distance triathlon racing comes with it an increased interest into how to train and prepare optimally for such an event. This paper examines the physiologic and bioenergetic demands of long distance triathlon racing, including energy requirements, muscle damage consequences, thermoregulatory demands and water turnover rates. In response to these physiological challenges, the second part of the paper describes the training goals and race practices that may assist to minimize these disturbances, in turn, optimizing performance and health for the long distance triathlete. Some of these race strategies include appropriate pacing, ensuring adequate fluid and carbohydrate consumption, acclimating to the heat, and consuming caffeine in appropriate quantities.

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“…Running economy studies have high test-retest reliability (1.5-5.0%) among a range of populations (Armstrong and Costill, 1985 Metabolic adaptations have been identified as critical contributors to marathon running performance (Hawley and Spargo, 2007). As exercise intensity increases, a shift away from fat and toward carbohydrate utilization occurs (Coyle, 1995).…”

Section: Introductionmentioning

confidence: 99%

“…VO2 -1 , provides a non-invasive, reliable measure of substrate metabolism (ratio of fat to carbohydrate utilization) during low to moderate intensity exercise (Jeukendrup and Wallis, 2005). Mitochondrial adaptations to endurance training enhance the capacity for fat metabolism, which, in turn, spares glycogen for use later in a long exercise bout such as a marathon (Hawley and Spargo, 2007). Some researchers have suggested that energy cost, which incorporates metabolic substrate (usually measured with RER), provides a more sensitive measure of exercise economy than oxygen cost (Shaw et al, 2013;Fletcher et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers have suggested that energy cost, which incorporates metabolic substrate (usually measured with RER), provides a more sensitive measure of exercise economy than oxygen cost (Shaw et al, 2013;Fletcher et al, 2009). Prolonged, intense endurance training leads to an increased capacity to utilize fat as fuel during exercise (Coyle, 2007;Hawley, 2002;Hawley and Spargo, 2007). This decreased reliance on glycogen stores may be a particularly important factor in events like the marathon, where glycogen depletion can be a limiting factor to performance (Coyle, 2007;Hawley and Spargo, 2007).…”

Section: Introductionmentioning

confidence: 99%

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Allometric scaling of body mass in running economy data: An important consideration in modeling marathon performance

Lundstrom

Biltz

Snyder

et al. 2017

jhse

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The purpose of this study was to compare metabolic variables during submaximal running as predictors of marathon performance. Running economy (RE) and respiratory exchange ratio (RER) data were gathered during a 30 min incremental treadmill run completed within 2 weeks prior to running a 42.2-km marathon. Paces during the treadmill run progressed every 5 min from 75-100% of 10-km race velocity. Variables at each stage were analyzed as predictors of relative marathon performance (RMP) in competitive (COMP) and recreational (REC) runners. Twenty-nine runners were classified as COMP (n = 12; age 30 ± 8 years) or REC (n =17; age 20 ± 1 year) based on performance in shorter races. RMP was calculated as percent difference from predicted marathon finish time. Two methods of calculating RE were used: unscaled (ml . kg -1. km -1 ) and with allometric scaling of body mass (ml . kg -0.75. km -1 ). The COMP runners were significantly more economical than REC (p=0.005; p=0.015 with scaling). For the whole population, RE with and without scaling was significantly correlated with RMP. Within groups, RMP was not significantly correlated with RE unless scaling was used: COMP runners at 75% (p=0.044), 80% (p=0.040), and REC runners at 85% (p=0.038). Runners classified as COMP were more economical than REC, but RER was not different. The use of allometric scaling is important when assessing homogeneous groups. In this study, allometrically-scaled RE at 80-85% of 10-km velocity was the best predictor of RMP within groups.

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Metabolic Adaptations to Marathon Training and Racing

Cited by 11 publications

References 10 publications

Is It Time to Consider a New Performance Classification for High-Level Male Marathon Runners?

Is It Time to Consider a New Performance Classification for High-Level Male Marathon Runners?

Long distance triathlon: demands, preparation and performance

Allometric scaling of body mass in running economy data: An important consideration in modeling marathon performance

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