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ORIGINAL ARTICLE

Variables associated with odds of finishing and finish time

in a 161-km ultramarathon

Jacob A. Wegelin•Martin D. Hoffman

Accepted: 19 August 2010/Published online: 11 September 2010

? The Author(s) 2010. This article is published with open access at Springerlink.com

Abstract

age, sex, calendar year, previous event experience and

ambient race day temperature were associated with finish-

ing a 100-mile (161-km) trail running race and with finish

time in that race. We computed separate generalized linear

mixed-effects regression models for (1) odds of finishing

and (2) finish times of finishers. Every starter from 1986 to

2007 was used in computing the models for odds of fin-

ishing (8,282 starts by 3,956 individuals) and every finisher

in the same period was included in the models for finish

time (5,276 finishes). Factors associated with improved

odds of finishing included being a first-time starter and

advancing calendar year. Factors associated with reduced

odds of finishing included advancing age above 38 years

and warmer weather. Beyond 38 years of age, women had

worse odds of finishing than men. Warmer weather had a

similar effect on finish rates for men and women. Finish

times were slower with advancing age, slower for women

than men, and less affected by warm weather for women

than for men. Calendar year was not associated with finish

time after adjustment for other variables.

We sought to determine the degree to which

Keywords

exercise ? Running ? Sex ? Sport

Aerobic exercise ? Aging ? Endurance

Introduction

The Western States Endurance Run (WSER) is the premier

161-km trail running competition in the world. Although

no longer the largest running event of this distance, it

remains among the largest with nearly 400 runners par-

ticipating each year. It is also one of the most challenging

161-km runs with 5,500 m of climb, 7,000 m of descent,

altitude reaching 2,667 m, the possibility of encountering

snow in the early sections of the course, and the likelihood

of high temperatures later in the run.

The seemingly accidental origin of the WSER dates back

to 1974 when a horse race over a course similar to the

present-day WSER was completed on foot by a man who

would have participated on horseback had it not been for his

horse going lame (Ainsleigh 2004; Klein 1998). By 1979,

the run had become an international event and it had grown

to its present limit of nearly 400 runners by 1984. Since

1986, the course has been essentially unchanged and com-

plete data on starters and finishers have been maintained.

In preceding papers, we detailed some characteristics of

WSER participants (Hoffman 2008; Hoffman and Wegelin

2009) and the trends in participation and performance over

the history of the event (Hoffman and Wegelin 2009). We

have also analyzed the participation and performance

trends of all 161-km ultramarathons in North America

(Hoffman 2010; Hoffman et al. 2010b). From this work,

age (Hoffman 2010; Hoffman and Wegelin 2009), sex

(Hoffman 2010; Hoffman and Wegelin 2009), and body

composition (Hoffman 2008) have been shown to be

associated with performance in 161-km ultramarathons.

Communicated by Guido Ferretti.

J. A. Wegelin

Department of Biostatistics, Virginia Commonwealth

University, P. O. Box 980032, Richmond, VA 23298, USA

M. D. Hoffman (&)

Department of Physical Medicine and Rehabilitation,

Department of Veterans Affairs, Northern California Health

Care System, and University of California Davis Medical Center,

10535 Hospital Way (117), Sacramento, CA 95655-1200, USA

e-mail: martin.hoffman@va.gov

123

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DOI 10.1007/s00421-010-1633-1

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Ambient temperature is a factor that has been shown to

affect marathon performance (Ely et al. 2007a, b, 2008;

Trapasso and Cooper 1989; Zhang et al. 1992), but it has

not been examined for ultramarathons. Furthermore, a

large-scale and focused analysis to define other factors

associated with finishing and how fast one completes an

ultramarathon has not been performed. Such an analysis is

of interest in delineating factors affecting physiological

capacity in extreme endurance activities under conditions

that could not be replicated in a non-competitive laboratory

environment. And in an annual event continuing across a

time span of over two decades, the possibility of a histor-

ical trend must be acknowledged and its effect sifted out, if

possible, from the other variables.

Thus, in this paper, we assess the relationship of vari-

ables publicly available at the start of each year’s WSER

(age, sex, previous WSER experience and performance,

and calendar year), along with race day ambient tempera-

ture, with the odds of finishing the run. And, among those

who finished in any year, we assess the relationship

between these variables and finish time.

Methods

Data source

Race results posted on the WSER website were used to

compile a spreadsheet covering the races between 1986 and

2007 inclusive. Variables included the year of the event,

name, sex, and age of each starter, whether the starter

finished or dropped out, and finish time for those who

finished. In addition, the number of WSER races finished

by each starter from 1974 to 1985 was compiled. Infor-

mation about who had started the race but not finished was

not available for this time period. Discrepancies in name,

age, and sex were reconciled to the extent possible as

described earlier (Hoffman and Wegelin 2009).

Statistical analyses

A linear spline was computed for age with knots at the 25th

and 75th percentiles to permit the computation of piece-

wise linear relationships between outcomes (i.e., log odds

of finishing and finish time) and age, and thereby to

accommodate differing effects of age during youth, middle

and advanced age (Gould 1993).

AmbienttemperaturedataforAuburn,CA(thelocationof

the finish) from the start date of each yearly event were

obtained from the National Climatic Data Center. Ambient

temperaturedatawerenotavailableatothercourselocations,

and although minimum and maximum temperatures were

knowntobemoreextremeatotherlocationsonthecourse,it

was felt that the temperatures at the finish would reflect the

overall temperature trends. Minimum, maximum, and mean

temperaturesweretabulated,butregressionmodelsincluded

onlyoneofthesevariablesatatimebecauseofthelikelihood

that they were highly correlated with each other. The mea-

sure of temperature that yielded greatest statistical signifi-

cance was included in reported models.

Each starter was coded as a first-timer at his or her first

start after 1985. For those who started more than once after

1985, at the second and subsequent starts the individual’s

past finish rate (proportion of starts finished from 1986 to

the previous year inclusive) was computed. For finishers

who were not first-timers, if the finisher had failed to finish

his or her previous WSER start, the finisher was coded as

‘‘previous drop’’ for that year.

For those who finished after 1985, for each year when

they finished, a measure of performance relative to his or

her gender was computed from finish time (t) as

maxt

½? ? t

ð

where the maximum (max t) and minimum (min t) finish

times were computed separately each year for men and

women. Thus, the first finisher for each gender and each

year obtained a score of 1, the last finisher a score of 0, and

all other finishers obtained scores between 0 and 1 that

reflected their performance relative to their peers. Subse-

quently for each finisher, his or her ‘‘first-time’’ status,

‘‘previous drop’’ status, or previous relative finish perfor-

mance was recorded.

Generalized linear mixed-effects (GLME) regression

models were computed to assess the effects of individual

variables on the odds of a starter completing the race and

on the predicted finish time of a finisher. Regression

models were computed rather than statistics that include

only two variables at a time so that assessments of the

effects of variables would simultaneously take into account

and adjust for all other available variables. Models with

logit link were employed for odds of finishing and with

identity link for finish time (McCullagh and Nelder 1989).

Random effects for starter and year were included to

account for correlation that might be caused by the same

individual starting the WSER on more than one year or by

similarities within year not accounted for by available

covariates (Diggle et al. 2002).

In models for the odds of finishing, the following were

considered as possible predictors: sex and age of the starter,

whether or not the starter had finished a WSER event before

1986, starter’s ‘‘first-time’’ status or finish rate since 1986,

ambient temperature on the day of the run, and calendar

year. All starts between 1986 and 2007 inclusive were used

for computing these models. In models for finish time, in

addition to the above variables, the finisher’s ‘‘previous

drop’’ status or relative performance for the previous race in

Þ= maxt

½ ? ? mint

½?ðÞ;

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which the finisher had participated was considered. Thus, in

models for finish time, at each year, and for each finisher,

either the relative performance at the previous WSER was

defined or the finisher had ‘‘first-timer’’ or ‘‘previous drop’’

status. All finishes between 1986 and 2007 inclusive were

used for computing these models.

Statistical significance of fixed effects was assessed by

the Wald test, and of random effects by the likelihood ratio

test. Main effects were included in regression models if

p\0.05. The interaction of two variables was considered

only if both the main effects individually satisfied

p\0.05. If an interaction between two main effects sat-

isfied p\0.05, it was included in the model. Subsequently,

those main effects were retained even if inclusion of their

interaction caused a main effect to no longer satisfy

p\0.05. An indicator (yes/no) variable for each starter’s

‘‘first-time’’ status was included in all models regardless of

its p value and similarly an indicator variable for each

finisher’s ‘‘previous drop’’ status was included in all

models for finish time.

Coefficients, standard errors, Wald statistics, and p val-

ues from the final regression models are reported in tabular

format. From these models, average probabilities of fin-

ishing and average finish times were computed (Diggle

et al. 2002). Subsequently, graphical displays were con-

structed to illustrate the relationships between explanatory

variables and outcomes for typical levels of the explanatory

variables.

Results

From 1986 to 2007, there were 8,282 starts by 3,956 dif-

ferent individuals (3,253 men, 703 women) and 5,276

finishes by 2,933 different individuals (2,437 men, 496

women). Complete data were available for all starters and

finishers. Minimum temperatures ranged from 11.1 to

22.0?C, mean from 13.8 to 30.0?C, maximum from 15.5 to

37.8?C. Minimum, maximum, and mean temperatures were

highly correlated (minimum with mean, r = 0.89; mini-

mum with maximum, r = 0.65; mean with maximum,

r = 0.90). Both the mean and maximum were exception-

ally low in 1991, which skewed the distributions of these

variables. Minimum temperature, on the other hand, had no

outliers and yielded the smallest p values in regression

models. Consequently, the minimum temperature was used

in reported models.

For 13.3% of starts and 7.8% of starters, the starter had

finished the WSER before 1986; in 52.3% of starts, the

runner had started a previous WSER since 1985 and thus

possessed a past finish rate. 48.1% of finishes corresponded

to the finisher’s first start since 1985. In 12.6% of finishes,

the finisher had started the WSER since 1985, but had

dropped out of his or her most recent WSER event. Thus,

the remaining 39.3% of finishes had a previous finish time

for the same runner. For these, the relative performance at

previous WSER start was computed and employed in the

regression model.

Between the years 1986 and 2007, the youngest starter

was 18 years and the oldest was 75 years. The quartiles of

age were 38, 44, and 50 years. Accordingly, relationships

with age were modeled as piecewise curves with different

coefficientsforage intervals

50–75 years.

The coefficients of the regression model for odds of

finishing are presented in Table 1. Coefficients for first

starts after 1985 and for starters with a WSER history since

1985 represent differences from the constant term. Because

men constituted the majority of starters, main effects rep-

resent the effects for men, whereas the coefficients for

women represent differences from men.

Factors that improved the odds of finishing included

being a first-time starter at the WSER, having a higher

finish rate if one had previously started the WSER, and

starting in a later calendar year. Factors that adversely

affected the odds of finishing included being a woman,

advanced age above 38 years, and warmer weather.

The effects, on the predicted probability of finishing, of

calendar year, gender, and the starter’s first-time status or

proportion of past WSERs finished, are shown in Fig. 1.

The three panels provide snapshots of representative

years, indicating the effect of the starter’s sex and past

finish rate or first-time status at a point in WSER history.

The differences between the panels indicate the way these

effects changed across the history of the WSER. The

point symbols on the left of each panel indicate the finish

probability of a first-time starter (with no WSER experi-

ence since 1985), whereas the smooth curves in each

panel demonstrate the effect of an experienced starter’s

past finish rate on his or her predicted probability of

finishing. Year 1990 is the first year displayed, because

not until 1990 could finishers possess a diverse range of

finish records since 1986. Year 1998 is displayed because

it lies approximately halfway between 1990 and the latest

year under study.

The most marked effect in Fig. 1 is that, within each

panel, the smooth curves slope upward from left to right.

This reflects the fact that, as expected, the greater the

starter’s personal past WSER finish rate the more likely he

or she is to finish the current race. A comparison between

panels, however, illustrates a historical difference: a first-

time starter’s probability of finishing increased between

1990 and 2007, but the finish probability of a starter with a

100% past finish record increased more rapidly. In 1990, a

first-timer’s probability of finishing was roughly equal to

that of a starter with a 100% finish record whereas by 2007,

18–38,38–50,and

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those with a 100% finish record were substantially more

likely to finish than a first-timer. The change across panels

at the leftmost end of the smooth curve indicates that the

finish probability of a starter with a history of starting and

never finishing (zero on the horizontal axis) decreased

across the years. But for past finish rates above approxi-

mately 33%, the level of the curve increases across the

years, indicating that the finish probability of a starter with

a history of finishing the WSER at least one-third of the

time increased over the history of the WSER. The better

the starter’s past finish history, the more rapid the historical

increase in probability of finishing. Thus, the finish prob-

ability of starters with a history of finishing at least three-

fourth of the time increased more rapidly across the years

than did the finish probability of a first-timer. By 2007,

first-timers’ finish probabilities were no longer roughly

equal to that of starters with a perfect finish record, but

rather to those who had finished 76% of WSER events

since 1985. The effect of starter’s WSER history on the

starter’s predicted probability of finishing did not differ

between men and women.

The effects of age, temperature, and sex on a starter’s

predicted probability of finishing are shown in Fig. 2.

While warmer weather was associated with a reduced

probability of finishing, there was no evidence that heat

affected women differently from men. No effect of age was

found during the first quartile (18–38 years) and the dif-

ference in probability of finishing between men and women

in that quartile, although visible in the figure, was not

statistically significant. Between ages 38 and 50, however,

an increase in age was associated with a decreased prob-

ability of finishing, and women’s probability of finishing

Table 1 Coefficients of the regression model for odds of finishing

Effect Coefficient Standard error Coef/SE

p

Calendar year0.0520.011 4.823

\0.001

0.037

\0.001

0.267a

\0.001

\0.001

\0.001

0.022

\0.001

0.036

First time since 1985 0.1160.055 2.087

Proportion of previous starts finished since 19860.8770.0969.169

Female-0.118 0.106-1.109

One year greater age within 38–50-year interval-0.0390.007-5.546

One year greater age above 50 years

Minimum ambient temperature (?C)

One year greater age within 38–50-year interval 9 female

-0.0880.010-9.056

-0.0800.018-4.347

-0.036 0.016-2.293

Proportion of previous starts finished since 1986 9 calendar year0.069 0.0154.473

One year greater age within 38–50-year interval 9 calendar year-0.0020.001-2.097

Positive coefficients increase and negative coefficients decrease the odds of finishing. Specifically, the coefficient for a dichotomous variable

(first-timer or female) is added directly to the log odds of finishing. The coefficient for a quantitative variable is multiplied by a difference in the

variable’s value (e.g., a difference in years for an age variable or a difference between zero and one for proportion of previous starts finished

since 1986) and this product is added to the log odds of finishing. The log odds are related to the probability by log odds = log(p/(1 - p))

aThe main effect for female gender remains in the model because it was statistically significant before the addition of its interaction with age, as

explained in the ‘‘Methods’’

Starter's first−time status or finish rate since 1986 inclusive

Probability of finishing current year

0.5

0.6

0.7

0.8

F0 50 100

1990

F0 50100

1998

F050100

2007

Fig. 1 Predicted probability of

finishing as a function of

calendar year, sex, and a

starter’s first-time status (‘‘F’’)

or past finish rate. Squares and

solid curves indicate men;

triangles and dashed curves

represent women. Probabilities

are for an average male starter

aged 44 years at 16.3?C

minimum ambient temperature

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dropped more quickly over this interval. Probabilities of

finishing dropped more quickly after age 50, but across this

age range, men and women experienced the same decrease

with advancing age.

A very small and barely statistically significant inter-

action was found between the calendar year and the effect

of age during the 38–50-year interval. This relationship is

not displayed herewith, as we do not deem it practically

significant. In addition, we note that finishing or not fin-

ishing a WSER event before 1986 was not associated with

finishing an event from 1986 to 2007.

The coefficients of the regression model for finish time

are presented in Table 2. Factors related to a shorter finish

time included a better relative performance at the previous

WSER, having a WSER finish prior to 1986 and having

finished more of one’s previous WSER starts. Factors

associated with a longer finish time included being a

woman, advanced age, and warmer weather. Unlike the

odds of finishing, we found no relationship between cal-

endar year and finish time after adjustment for other vari-

ables, and no difference between the sexes in the

relationship between age and finish time.

Although women finished slower than men on the

average, the size of the difference varied with air temper-

ature, in that women were less affected by hot weather than

men. Average finish times by gender and air temperature

are shown in Fig. 3.

Increased age was associated with longer finish times,

but the relationship between age and finish time differed

according to two other variables: whether the finisher had

finished a WSER before 1986 and, for those who had

previously started the WSER since 1985, the proportion of

past WSERs that the finisher had finished. The complex

interaction of these variables with respect to finish time is

shown in Fig. 4.

For current finishers who had also finished their pre-

vious WSER start, better performance at the previous

finish was associated with a shorter finish time at the

current finish, as expected. But this relationship was

stronger for those who had finished a greater percentage

of their previous starts. This relationship is shown in

Fig. 5.

Although warmer weather was associated with slower

finish times, the effect was more marked in faster runners.

This is illustrated in Fig. 6. For current finishers who

finished last at their previous WSER, one additional

degree was associated with an approximately 0.34%

longer finish time, whereas for those who had finished

first at their previous WSER, the difference was approx-

imately 0.77%.

Age (years)

Probability of finishing

0.4

0.5

0.6

0.7

0.8

11°C (min)16.3°C (mean)

38 4450 3844 503844 50

22°C (max)

Fig. 2 Predicted probability of finishing as a function of age, sex, and

ambient temperature. Solid curves represent men and dashed curves

represent women. Vertical dotted lines mark the first and third

quartiles of age, where the model allowed the slope to change as

described in ‘‘Methods’’. Probabilities are for an average starter who

had not started since 1985 (a ‘‘first-timer’’). The three panels

correspond to the least, average, and greatest values of minimum

air temperature that occurred among the 22 yearly WSER events

under consideration. Probabilities are adjusted to 1996 rates; for other

years the pattern is adjusted up or down as shown in Fig. 1. The

display has been truncated after 60 years because extrapolation of

results to the small population of WSER starters above that age would

likely be inaccurate. Probabilities for starters who had previously

started since 1985 were similar, but adjusted up or down as shown in

Fig. 1

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Discussion

The present study made use of publicly available infor-

mation to model the odds of finishing and finish time in the

WSER. Every starter and finisher from 1986 to 2007 was

used in computing these models. While WSER is no longer

the largest running event of this distance, it is the premier

161-km ultramarathon and accounted for around 20% of all

161-km ultramarathon finishes in North America through

2008 (Hoffman et al. 2010b; Hoffman and Wegelin 2009).

Furthermore, around 35% of those who had finished a 161-

km ultramarathon in North America prior to 2009 had

completed the WSER (Hoffman et al. 2010b; Hoffman and

Wegelin 2009). Therefore, a focused analysis of this event

captures a sizable proportion of those participating in

running events of this distance.

Average annual finish rates at the WSER have ranged

from 51 to 80% since 1986 (Hoffman and Wegelin 2009).

Factors found to be associated with an enhanced likelihood

of finishing the WSER included being a first-time starter,

and advancing calendar years for first-timers and those who

had finished at least 75% of their previous starts. Factors

that were associated with a lower likelihood of finishing the

WSER included advancing age above 38 years and

Table 2 Coefficients of the regression model for finish time

Effect CoefficientStandard errorCoef/SE

p

First time since 1985 (constant) 24.0600.54544.131

\0.001

\0.001

\0.001

\0.001

\0.001

0.005

\0.001

\0.001

0.037

Previous drop (constant)18.4901.399 13.216

Finished previous start (constant)24.1600.561 43.030

Relative performance at previous start (for those who finished the previous start)-1.725 0.310-5.566

Female1.3470.123 10.968

One year greater age up to 38 years 0.043 0.0152.840

One year greater age within 38–50-year interval0.167 0.01016.375

One year greater age above 50 years

Minimum ambient temperature (?C)

Finished WSER before 1986

0.122 0.0186.857

0.081 0.0392.089

-4.302 1.509-2.8510.004

\0.001

0.0148

\0.001

0.0236

Proportion of previous starts finished since 1986-1.8210.317-5.750

Finished WSER before 1986 9 first time since 1985-0.533 0.219-2.436

One year greater age up to 38 years 9 previous drop

Relative performance at previous start 9 minimum ambient temperature (?C)

Relative performance at previous start 9 proportion of previous starts

finished since 1986

Female 9 minimum ambient temperature (?C)

One year greater age up to 38 years 9 finished WSER before 1986

0.1390.036 3.836

0.0700.0312.263

-2.1980.894-2.4590.0139

-0.047 0.023-2.0470.0407

0.0830.040 2.0880.0368

\0.001 One year greater age within 38–50-year interval 9 proportion

of previous starts finished since 1986

0.0990.0283.522

One year greater age above 50 years 9 finished WSER before 19860.0800.0332.4080.016

The constants apply to the three categories into which all finishes are sorted each year. Subsequent coefficients apply equally to all finishes except

where noted explicitly. Positive coefficients increase and negative coefficients decrease the average finish time. Specifically, the coefficient for a

dichotomous variable (female or finished WSER before 1986) is added directly to the average finish time. On the other hand, the coefficient for a

quantitative variable is multiplied by a difference in the variable’s value and this product is added to the average finish time

Minimum air temperature (°C)

Finish time (hours)

26.5

27.0

27.5

28.0

1214 161820 22

Fig. 3 Average finish time as a function of ambient temperature and

sex, for individuals finishing the first WSER event that they started

after 1985. Solid curves represent men and dashed curves represent

women. Estimates are adjusted to represent average men and women

aged 44 with no WSER finish before 1986

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increasing ambient temperature. Women and men under

38 years were equally likely to finish, but beyond 38 years

women had a lower likelihood of finishing than men.

Furthermore, advancing age between 38 and 50 years

decreased the odds of finishing at a more rapid rate among

women compared with men. While higher ambient

temperature was associated with a reduced probability of

finishing, there was no evidence that the higher tempera-

tures affected the likelihood of finishing differently for men

and women.

With regard to finish times at the WSER, it was dem-

onstrated that women finished slower than men on the

average, and women’s finish times were less affected by

increasing air temperature than men. There was no change

Age (years)

Finish time (hours)

22

24

26

28

3040 5060

No finish before 1986

3040 5060

At least one finish before 1986

Fig. 4 Average finish time as a

function of age, for finishers for

whom previous relative

performance was not defined.

Dotted curves represent ‘‘first-

timers,’’ dashed curves

represent those who had

finished 50% of their starts since

1986, but not their most recent

start, and solid curves represent

finishers who had started at least

once, but failed to finish each

start since 1986. Estimates are

adjusted to represent an average

man, subject to 16.3?C

minimum air temperature

Relative performance at previous WSER

Finish time (hours)

24

25

26

27

0.00.2 0.4 0.60.81.0

Fig. 5 Average finish time as a function of relative performance at

previous WSER start and percent previous starts finished since 1986,

for finishers for whom previous relative performance was defined.

The solid line represents those who had finished 100% of previous

starts, the dashed line those who had finished 50% of previous starts.

Finishers who finished 0% of their previous WSER starts since 1985

are represented in Fig. 4. Estimates are adjusted to represent an

average man aged 44 with no WSER finish before 1986, subject to

16.3?C minimum air temperature

Minimum air temperature (°C)

Finish time (hours)

19

20

21

22

23

24

12 1416 1820 22

Fig. 6 Interaction between previous relative performance and min-

imum air temperature in the prediction of finish time. The solid line

represents current finishers who finished last at their previous WSER,

and the dashed line represents current finishers who previously

finished first. Estimates are adjusted to represent an average man aged

44 who had finished all of his previous WSER starts

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in finish time across calendar years after adjustment for

other variables. Increased age was associated with slower

finish times, but this effect was affected by two other

variables. Having finished a WSER before 1986 and having

a higher WSER finish rate for those who had previously

started the WSER since 1985 were associated with faster

times. For runners who had finished their previous WSER

start, performance at the previous finish was strongly

associated with their current finish time, and this relation-

ship was stronger for finishers who had finished a greater

percentage of their previous starts.

Aging is known to adversely affect finish times in run-

ning competitions of the marathon distance (42 km) (Jokl

et al. 2004) and in long distance triathlons (Lepers and

Maffiuletti 2010; Lepers et al. 2010), so it is not surprising

to observe the same effect for 161-km runs. In fact, we

have previously demonstrated this finding from the same

WSER dataset (Hoffman and Wegelin 2009) as well as

from the results of participants in all 161-km ultramara-

thons in North America (Hoffman 2010) when considering

all finishers. Of course, the situation is different when

focusing on the fastest runners where we have shown that it

is the 30–39 and 40–49-year brackets that produce the

fastest finish times at the 161-km distance (Hoffman 2010;

Hoffman and Wegelin 2009).

The effect of aging on the likelihood of finishing an

extreme endurance event has received little attention. A

small analysis of the 1989 Leadville Trail 100 (161-km)

ultramarathon offered some suggestion that increasing age

was associated with a lower likelihood of finishing (Siguaw

1990). The present study provides solid support that

increasing age beyond 38 years adversely affects the likeli-

hoodoffinishingtheWSER,andthatthiseffectisgreaterfor

women than for men. We hypothesize that these findings are

due to an increasing difficulty at meeting check point cutoff

times with aging, and that this issue is more important for

women given that they are slower than men on the average.

The typical temperature conditions at the WSER would

be considered by most runners to include some relatively

hot sections given that only once between 1986 and 2007

did the maximum recorded temperature at the location of

the finish remain below 25?C. Yet, even with the WSER

typically including relatively hot temperatures, we were

able to demonstrate that increasing ambient temperatures

were associated with slower finish times and lower finish

rates. Several studies have shown finish times in the mar-

athon to be negatively impacted by warm environmental

temperatures (Ely et al. 2007a, b, 2008; Trapasso and

Cooper 1989; Zhang et al. 1992). Interestingly, the nega-

tive effect on marathon finish times from increases in

temperature has been shown to be similar for men and

women (Ely et al. 2007a, b). In contrast, we found that

increases in temperature had less effect on 161-km finish

times for women than for men. On the other hand, the

effect of hot weather on finish rates did not differ between

men and women.

In the present study, we also found that increasing

ambient temperatures adversely affected the finish times of

the faster runners slightly more than the slower runners.

This is in contrast to the situation in the marathon where it

has been demonstrated that the finish times of slower

runners are most affected by increases in temperature (Ely

et al. 2007b, 2008). The present finding of a greater effect

of temperature on faster runners is partially accounted for

by slower runners dropping out under hotter conditions, so

that their finish time did not enter into the model. In a

shorter run or a run without cutoff times, the slower runners

might have recorded a slower time, rather than dropped

out, because of the increased heat. In contrast, perhaps the

faster finishers were more likely to simply be slowed down

by the increased temperature rather than drop out. Since

women were generally slower than men, the same rationale

might explain why finish times for women were found to

be less affected by increasing temperatures than for men.

Although there was no effect of calendar year on finish

time, finish rates were shown to improve across time.

Examination of interaction effects revealed that the

improvement in finish rate with advancing calendar year

was for first-time WSER runners and for those who had

finished at least 75% of their previous starts. It should not

be surprising that previous success at finishing the WSER

would be associated with a greater likelihood of finishing

again. However, an explanation for the improved finish rate

across calendar years among first-time WSER runners may

not seem so obvious. This effect is likely reflective of these

runners being more experienced from other races now than

in the past, given that the number of such events has been

rising exponentially (Hoffman et al. 2010b).

Several variables are known to have associations with

performance in 161-km ultramarathons, such as age

(Hoffman 2010; Hoffman and Wegelin 2009), sex (Hoffman

2010; Hoffman and Wegelin 2009), and body composition

(Hoffman 2008; Hoffman et al. 2010a). Others are pre-

sumed to be related to performance, such as maximal

oxygen uptake, anaerobic threshold, heat acclimatization,

and training regimen. In light of these known and presumed

associations, one limitation in the current study is apparent.

Because we used only those variables that were publicly

available, we were unable to account for body composition,

anaerobic threshold, heat acclimatization, or training regi-

men. Most notably, we did not account for maximal oxy-

gen uptake. Thus, the current study does not in any way

report a true quantitative analysis of performance.

Another limitation is related to our choice of analytic

method. Since the model for finish time was based only on

finishers, starters who ran so slowly that they were unable

152Eur J Appl Physiol (2011) 111:145–153

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Page 9

to finish within the time limit were not accounted for in that

model. It would have been possible to treat the odds of

finishing and the finish time in a single analysis using

techniques of survival analysis (Therneau 2000). However,

we chose to analyze finish time based only on finishers

because that is more consistent with the reality of these

events in that one does not get credit for finishing if it is not

done within the time limits of the event. It is also recog-

nized that runners drop out of an event of this nature for

reasons besides being unable to finish within the allocated

time limit. For instance, some of the top performers may

drop out if they are having a poor day, hoping that they can

perform better in an upcoming event if they do not continue

in the race just to finish. It was not possible to identify such

situations in this analysis.

Conclusion

The current study investigated human capacity to perform a

particular kind of extreme endurance exercise: a 161-km

ultramarathon on mountain trails. Through the use of vari-

ables publicly available on all starters, phenomena were

assessed that could not have been reproduced in a labora-

tory setting. Among those attempting the WSER for the first

time, finish rates improved between 1986 and 2007. This

may have been because the population of individuals rep-

resented by WSER starters had become more experienced

through participation in other races. Advancing age and hot

weather adversely affected the ability to finish as well as

how fast one could finish. Women were typically less likely

to finish and slower than men, but their finish rates were no

more affected by hot weather than men’s finish rates.

Finally, hot weather adversely affected the finish times of

faster runners more than those of slower runners. This

stands in contrast to previous findings related to the mara-

thon, where increases in ambient temperature had a greater

effect on the slower runners.

Research reported in this article was performed in

compliance with the current laws of the United States and

of the states of California and Virginia. This study was

approved by our institutional review board with the

requirement for informed consent being waived, since all

data analyzed or reported were publicly available.

Acknowledgments

Number UL1 RR024146 from the National Center for Research

Resources (NCRR), a component of the National Institutes of Health

(NIH), and NIH Roadmap for Medical Research. This material is the

result of work supported with resources and the use of facilities at the

VA Northern California Health Care System. The work was also

supported by the Western States Endurance Run Foundation.

This publication was made possible by Grant

Conflict of interest

of interest.

The authors declare that they have no conflict

Open Access

Creative Commons Attribution Noncommercial License which per-

mits any noncommercial use, distribution, and reproduction in any

medium, provided the original author(s) and source are credited.

This article is distributed under the terms of the

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