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Factors affecting Runners' Marathon Performance

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... On the other hand, large decreases in air temperatures under the optimum also reduce performances. These optimal temperatures found in the present study are comprised in the optimal temperature range of 5 − 10 • C WBGT found in previous studies [92]; other studies stated that a weather of 10−12 • C WBGT is the norm for fast field performance and reported a decrease of performance with increasing WBGT [90,105,129,130]. Best marathon times and most marathon world records were achieved in cool environmental temperatures (10 − 15 • C) and have been run in the early morn-ing during spring and fall [90]. ...
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The development of sport performances in the future is a subject of myth and disagreement among experts. As arguments favoring and opposing such methodology were discussed, other publications empirically showed that the past development of performances followed a non linear trend. Other works, while deeply exploring the conditions leading to world records, highlighted that performance is tied to the economical and geopolitical context. Here we investigated the following human boundaries: development of performances with time in Olympic and non-Olympic events, development of sport performances with aging among humans and others species (greyhounds, thoroughbreds, mice). Development of performances from a broader point of view (demography & lifespan) in a specific sub-system centered on primary energy was also investigated. We show that the physiological developments are limited with time. Three major and direct determinants of sport performance are age, technology and climatic conditions (temperature). However, all observed developments are related to the international context including the efficient use of primary energies. This last parameter is a major indirect propeller of performance development. We show that when physiological and societal performance indicators such as lifespan and population density depend on primary energies, the energy source, competition and mobility are key parameters for achieving long term sustainable trajectories. Otherwise, the vast majority (98.7%) of the studied trajectories reaches 0 before 15 generations, due to the consumption of fossil energy and a low mobility rate. This led us to consider that in the present turbulent economical context and given the upcoming energy crisis, societal and physical performances are not expected to grow continuously.
... Likewise, March's study (March, Vanderburgh, Titlebaum, & Hoops, 2011) of 319 runners of the midwestern US marathon found that the faster runners displayed pacing strategies significantly closer to even than slower runners, holding other aspects such as gender and age constant. Similar results are reported in other large marathon (Buoncristiani & Martin, 1993) or ultra-marathon studies (Lambert, Dugas, Kirkman, Mokone, & Waldeck, 2004). Whereas, in shorter (<10 km) efforts, self-paced WR or high-performance runners adopt a declining (plus end-spurt) pacing strategy (Lima-Silva et al., 2009;Noakes, Lambert, & Hauman, 2009;Thiel, Foster, Banzer, & De Koning, 2012). ...
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Abstract We apply statistical analysis of high frequency (1 km) split data for the most recent two world-record marathon runs: Run 1 (2:03:59, 28 September 2008) and Run 2 (2:03:38, 25 September 2011). Based on studies in the endurance cycling literature, we develop two principles to approximate 'optimal' pacing in the field marathon. By utilising GPS and weather data, we test, and then de-trend, for each athlete's field response to gradient and headwind on course, recovering standardised proxies for power-based pacing traces. The resultant traces were analysed to ascertain if either runner followed optimal pacing principles; and characterise any deviations from optimality. Whereas gradient was insignificant, headwind was a significant factor in running speed variability for both runners, with Runner 2 targeting the (optimal) parallel variation principle, whilst Runner 1 did not. After adjusting for these responses, neither runner followed the (optimal) 'even' power pacing principle, with Runner 2's macro-pacing strategy fitting a sinusoidal oscillator with exponentially expanding envelope whilst Runner 1 followed a U-shaped, quadratic form. The study suggests that: (a) better pacing strategy could provide elite marathon runners with an economical pathway to significant performance improvements at world-record level; and (b) the data and analysis herein is consistent with a complex-adaptive model of power regulation.
... On the other hand, large decreases in air temperatures under the optimum also reduce performances. These optimal temperatures found in the present study are comprised in the optimal temperature range of 5–10uC WBGT found in previous studies [14]; other studies stated that a weather of 10–12uC WBGT is the norm for fast field performance and reported a decrease of performance with increasing WBGT [12,27,51,52]. Best marathon times and most marathon world records were achieved in cool environmental temperatures (10–15uC) and have been run in the early morning during spring and fall [12]. ...
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The objectives of this study were to describe the distribution of all runners' performances in the largest marathons worldwide and to determine which environmental parameters have the maximal impact. We analysed the results of six European (Paris, London, Berlin) and American (Boston, Chicago, New York) marathon races from 2001 to 2010 through 1,791,972 participants' performances (all finishers per year and race). Four environmental factors were gathered for each of the 60 races: temperature (°C), humidity (%), dew point (°C), and the atmospheric pressure at sea level (hPA); as well as the concentrations of four atmospheric pollutants: NO(2)-SO(2)-O(3) and PM(10) (μg x m(-3)). All performances per year and race are normally distributed with distribution parameters (mean and standard deviation) that differ according to environmental factors. Air temperature and performance are significantly correlated through a quadratic model. The optimal temperatures for maximal mean speed of all runners vary depending on the performance level. When temperature increases above these optima, running speed decreases and withdrawal rates increase. Ozone also impacts performance but its effect might be linked to temperature. The other environmental parameters do not have any significant impact. The large amount of data analyzed and the model developed in this study highlight the major influence of air temperature above all other climatic parameter on human running capacity and adaptation to race conditions.
... In spite of these limitations, however, the main conclusion of this data analysis was confirmed by later analyses. Buoncristiani and Martin (1983) showed that an ambient temperature (Ta) of 10– 12 1C is optimal. In a much larger data set involving records from seven marathon races for periods of 6– 36 years, Ely et al. (2007b) showed from a crosssectional analysis of performance data that there is a progressive slowing of marathon performance as the wet bulb globe temperature (WBGT) increases from 5 to 25 1C. ...
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Endurance performance is impaired in the heat, and a combination of high temperature and high humidity presents a major challenge to the elite marathon runner, who must sustain a high metabolic rate throughout the race. The optimum temperature for marathon performance is generally about 10-12 °C. The optimum temperature may be lower for faster runners than for slower runners. Sweat evaporation limits the rise in core temperature, but dehydration will impair cardiovascular function, leading to a fall in blood flow to muscle, skin and other tissues. There is growing evidence that the effects of high ambient temperature and dehydration on performance of exercise may be mediated by effects on the central nervous system. This seems to involve serotonergic and dopaminergic functions.
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The aim of the study was to investigate how women and men age group runners pace during a large city marathon. We analysed changes in running speed by splits of 5 km in 20,283 women and 28,282 men age group runners competing in the 2015 edition of the “New York City Marathon”. A moderate split×sex interaction on running speed (p < 0.001, η² = 0.108) was observed with men showing a larger decrease in speed from the fastest split (5–10 km) to the slowest one (35–40 km) than women (21.1 vs. 16.7%), and a different pattern was observed in the 25–30 km split (increase in women, decrease in men). A trivial split×age group interaction on speed was observed in women (p < 0.001, η² = 0.003) and men (p < 0.001, η² = 0.004). In summary, men and women of all age groups reduced running speed during the marathon with a final spurt in the last segment (i.e. 40–42.2 km).
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The objective of this study was to develop an empirical model relating human running performance to some characteristics of metabolic energy-yielding processes using A, the capacity of anaerobic metabolism (J/kg); MAP, the maximal aerobic power (W/kg); and E, the reduction in peak aerobic power with the natural logarithm of race duration T, when T greater than TMAP = 420 s. Accordingly, the model developed describes the average power output PT (W/kg) sustained over any T as PT = [S/T(1 - e-T/k2)] + 1/T integral of T O [BMR + B(1 - e-t/k1)]dt where S = A and B = MAP - BMR (basal metabolic rate) when T less than TMAP; and S = A + [Af ln(T/TMAP)] and B = (MAP - BMR) + [E ln(T/TMAP)] when T greater than TMAP; k1 = 30 s and k2 = 20 s are time constants describing the kinetics of aerobic and anaerobic metabolism, respectively, at the beginning of exercise; f is a constant describing the reduction in the amount of energy provided from anaerobic metabolism with increasing T; and t is the time from the onset of the race. This model accurately estimates actual power outputs sustained over a wide range of events, e.g., average absolute error between actual and estimated T for men's 1987 world records from 60 m to the marathon = 0.73%. In addition, satisfactory estimations of the metabolic characteristics of world-class male runners were made as follows: A = 1,658 J/kg; MAP = 83.5 ml O2.kg-1.min-1; 83.5% MAP sustained over the marathon distance. Application of the model to analysis of the evolution of A, MAP, and E, and of the progression of men's and women's world records over the years, is presented.
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