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Original Research
World-Class Long-Distance Running Performances
Are Best Predicted by Volume of Easy Runs and
Deliberate Practice of Short-Interval and
Tempo Runs
Arturo Casado,
1
Brian Hanley,
2
Jordan Santos-Concejero,
3
and Luis M. Ruiz-P ´erez
4
1
Faculty of Health Sciences, Isabel I University, Burgos, Spain;
2
Carnegie School of Sports, Leeds Beckett University, Leeds, United
Kingdom;
3
Department of Physical Education and Sport, University of the Basque Country UPV/EHU, Vitoria-Gasteiz, Spain; and
4
Department of Social Sciences of Physical Activity and Sports, Polit ´ecnica University of Madrid UPM, Madrid, Spain
Abstract
Casado, A, Hanley, B, Santos-Concejero, J, and Ruiz-P ´erez, LM. World-class long-distance running performances are best
predicted by volume of easy runs and deliberate practice of short-interval and tempo runs. J Strength Cond Res XX(X): 000–000,
2019—The aim of this novel study was to analyze the effect of deliberate practice (DP) and easy continuous runs completed by elite-
standard and world-class long-distance runners on competitive performances during the first 7 years of their sport careers. Eighty-
five male runners reported their best times in different running events and the amounts of different DP activities (tempo runs and
short- and long-interval sessions) and 1 non-DP activity (easy runs) after 3, 5, and 7 years of systematic training. Pearson’s
correlations were calculated between performances (calculated using the International Association of Athletics Federations’ scoring
tables) and the distances run for the different activities (and overall total). Simple and multiple linear regression analysis calculated
how well these activities predicted performance. Pearson’s correlations showed consistently large effects on performance of total
distance (r$0.75, p,0.001), easy runs (r$0.68, p,0.001), tempo runs (r$0.50, p,0.001), and short-interval training (r$
0.53, p,0.001). Long-interval training was not strongly correlated (r$0.22). Total distance accounted for significant variance in
performance (R
2
$0.57, p,0.001). Of the training modes, hierarchical regression analysis showed that easy runs and tempo runs
were the activities that accounted for significant variance in performance (p,0.01). Although DP activities, particularly tempo runs
and short-interval training, are important for improving performance, coaches should note that the non-DP activity of easy running
was crucial in better performances, partly because of its contribution to total distance run.
Key Words: elite-standard athletes, endurance training, long-distance events, track and field
Introduction
In global competitions such as the Olympic Games and In-
ternational Association of Athletics Federations (IAAF) World
Championships, the long-distance running races typically include
the 5,000, 10,000 m, and marathon events, with the IAAF also
holding separate cross country and half-marathon world cham-
pionships. Success in these events depends predominantly on
physiological determinants such as maximal oxygen uptake
(V
̇
O
2
max), running speed at V
̇
O
2
max (vVO
2
max), running
economy (RE), and lactate threshold (LA) (33), as well as an-
aerobic capacity and sprinting ability (16). High-intensity train-
ing sessions that are intended to improve these determinants in
long-distance runners include short intervals (between 200 and
1,000 m long and from 95 to 100% of maximum heart rate
[HRmax] in intensity), long intervals (between 1,000 and
2,000 m long and from 92 to 95% of HRmax), tempo running
(between 45 and 70 minutes in duration or running intervals from
1,000 to 5,000 m and from 82 to 92% of HRmax), and official
competitions or time trials at race pace (from 5,000 m to
marathon-distance long and from 82 to 95% of HRmax) (4,16).
Because these modes of training are physically and mentally
taxing, athletes usually supplement their training with easier
running sessions, conducted typically over distances between 8
and 40.5 km and from 62 to 82% of HRmax (16). These easy
running sessions can function as base training during the early
season, as active recovery sessions in between harder sessions or
as a continuous stimulus for cardiovascular conditioning and
development of connective tissue adaptations (16,32). Although
competitive club runners might alternate hard and easy sessions
daily, world-class athletes typically train more than once a day,
with their easy sessions taking place in the mornings (32). Because
of their less taxing nature, easy runs can be performed over longer
distances and are therefore important in accumulating greater
overall running distances. For the strength and conditioning
professionals, an understanding of how important these easy runs
are relative to more race-specific training sessions will assist in
developing programs that get the most out of the time spent
training.
A key principle of improving performance is that there should
be specificity in training (29). In terms of skills development,
deliberate practice (DP) is a theory of practice that is conducted
with the specific goal of improving performance, and in sports,
requires considerable effort, concentration, and enjoyment, and
should be considered by athletes as very relevant in improving
Address correspondence to Dr. Arturo Casado, arturocasado1500@gmail.com.
Journal of Strength and Conditioning Research 00(00)/1–7
ª2019 National Strength and Conditioning Association
1
Copyright © 2019 National Strength and Conditioning Association. Unauthorized reproduction of this article is prohibited.
their performance (14,15,40). Once DP has been identified
according to the accomplishment of these characteristics, it can be
quantified throughout a sports career through the use of training
logs and personal interviews (41). The original research on DP
focused on musicians (15), but its proposed potential for im-
provement has encouraged research across many domains to
determine what aspects of practice can be taught to expert
aspirants (13,19). Sport is one of these domains where there has
been considerable research in the past 25 years (12,23,24,26,38),
including the disciplines of middle- and long-distance running
(9,10,40,41). A recent study on elite-standard long-distance
runners (9) found that world-leading Kenyan runners reported
very high physical and mental effort and enjoyment in very in-
tense training activities (i.e., short- and long-interval workouts,
tempo runs, and races), similar to research on DP in elite-standard
Canadian middle-distance runners (40). By contrast, subjects in
both the Kenyan and Canadian cohorts did not consider easy runs
to require physical or mental effort and thus did not fulfill the
original definition of DP. How much of a contribution to per-
formance is made by specific DP activities has yet to be reported
for elite-standard long-distance runners, but a new study on these
types of training session will inform coaches and athletes of their
relative merits regarding the overall training volume.
Although DP has been proposed as an essential component of
improvement (simple experience in the task alone is not sufficient
(38)), recent scientific research has argued that differences in
performance variability were just partially explained by adopting
DP activities (20,30,31). Therefore, current trends in DP theory
claim that practice is not the only variable that explains different
standards of performance (31). This is clearly the case in excellent
long-distance runners, whose abilities and performances are af-
fected by physiology, genetics, biomechanics, nutrition, altitude,
training methods, and psychology (39). In this review (39), the
authors did not refer to the effect of training using DP. However,
given its importance in other sports, this effect may likely have
relevance on the success of long-distance runners. Deliberate
practice studies are typically retrospective, having been used with
musicians (15), triathletes and swimmers (25), and middle-
distance runners (41), with several studies reporting high re-
liability for long-term recall of physical and sports activities
(5,17,22,23). Baker et al. (2) demonstrated the reliability of the
“training journal method”for supporting recall for athletes in
individual sports, and training logs and schedules are very fre-
quently used by coaches and athletes to monitor progress and
make comparisons with previous running seasons (32). To date,
the importance of DP as a predictor of performance in long-
distance runners has yet to be explored, but the vast personal
training data that elite-standard athletes record will allow for in-
depth analysis of training volumes and the sessions completed.
The aim of this novel study was to analyze the amount and type of
DP, as well as the easy runs, completed by elite-standard and
world-class long-distance runners during the first 7 years of their
sport careers, with reference to their best performances in
competition.
Methods
Experimental Approach to the Problem
A Taxonomy of Training Activities Questionnaire (TTAQ) was
provided to 85 male long-distance runners to measure how much
each subject took part in different types of training sessions
during their sports career (1, 3, 5, and 7 years after beginning
systematic training). Modes of training (e.g., easy runs and tempo
runs) were analyzed using Pearson’s correlations and linear
regressions, with competition performances (converted into
points using the IAAF’s scoring system) used as the dependent
variable.
Subjects
Eighty-five male elite- and international-standard long-distance
runners took part. The age range was between 18 and 43 years,
with a mean age of 28 years (65). All subjects were specialists in
the 5,000, 10,000 m, half-marathon (21.195 km), or marathon
(42.195 km) events. At the time of data collection, all athletes
were competing in at least one of these events. Polit´
ecnica Uni-
versity of Madrid approved the details of the study, which was
conducted in accordance with the Declaration of Helsinki, in-
cluding consent documentation and information to subjects be-
fore commencement. In accordance with the Polit´
ecnica
University of Madrid’s policies for use of human subjects in re-
search, all subjects were informed of the benefits and possible
risks associated with participation before taking part and in-
formed of their right to withdraw at any point. All subjects were
older than 18 years and gave written informed consent to indicate
their voluntary participation. The subjects’performances ranged
from world-class to competitive national standard. Among the
best athletes, there were medalists from the IAAF World Cross
Country Championships, IAAF World Championships (mara-
thon), Olympic Games, Commonwealth Games, African Cham-
pionships, European Cross Country Championships, and
European Championships (track). These runners’best times
ranged from 2:03:23 (a former World Record holder) to 2:36:15
in the marathon, and from 58:54 to 1:08:48 in the half-marathon;
the current World Record holder for the 10-km road race (26:44)
was also included in the sample. The subjects’best times gave
scores ranging from 494 to 1,285 points in the IAAF scoring
tables (35). Only the time periods in which subjects reported both
training data and performance data (competition results) were
analyzed in this study.
Procedures
To explore how much each subject took part in practice or sys-
tematic training during his sports career, a TTAQ was developed
based on Young and Salmela’s (40,41) questionnaire developed
for middle-distance runners. This instrument was based on the
original study on musicians by Ericsson et al. (15), adapted for
long-distance running and validated by 3 experienced long-
distance running coaches.
Each subject received a 3-part questionnaire. The first part
asked subjects to provide biographical information, current age,
athletics event, and personal records. In the second part, subjects
were required to recall information for each 2-year interval from
the time they began systematic practice (i.e., at 1, 3, 5, and 7 years)
until the time of data collection. At each time interval, subjects
were asked to report how much time they had engaged in different
training activities (not running), and how much distance they had
run in different running activities during a typical week of training
10 weeks before their season goal race (e.g., Olympic Games,
World Championships, European Championships, and national
championships) (41). The relevant training activities included
were cross-training, flexibility training, weight training, work
with the coach, easy runs, tempo runs, long-interval training,
Deliberate Practice in Distance Running (2019) 00:00
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Copyright © 2019 National Strength and Conditioning Association. Unauthorized reproduction of this article is prohibited.
short-interval training, and competition and time trials (9,41).
For each of the latter 5 activities, subjects were further instructed
to account for total weekly distance (km). The latter 4 activities
(i.e., not including easy runs) were the activities that subjects
considered more important and, for this study, were considered
DP. This consideration was taken because the same subjects of
this study rated these activities with high values (mean superior to
7 in a 10-point Likert-type scale) and significantly higher than 5
on the scale for relevance, physical and mental effort, and en-
joyment (p,0.001, Cohen’sd$1.86) in 2 previous studies
(9,10). Easy runs were considered mentally effortless because its
rating was not significantly higher than 5 on the Likert scale for
concentration (p50.06, d50.27) (9,10).
Subsequently, subjects were asked to report their best finishing
times in competitions after 1, 3, 5, and 7 years of systematic
training. Because 70% of subjects did not report competition
times for the first year of systematic training, details reported for
this stage were not included.
In the third part of the questionnaire, subjects were required to
provide information on long periods of no training or full rest
weeks during their sports career. In this way, it was possible to
assess the number of training weeks per year. As conducted by
Young and Salmela (41), for the beginning year of systematic
training, typical hours of involvement per week were multiplied
by the number of weeks per year to derive the annual total. This
was then multiplied by 2 to calculate the 2-year interval total, and
these totals were then summed for accumulated amounts of
practice at 3 (i.e., including the “beginning year”value, plus the
interpolated value for the next 2 years), 5, and 7 years (15,26).
Because establishing the validity of data collected using retro-
spective recall methods can be problematic, recruiting high- and
elite- standard runners worked well because most possessed de-
tailed training logs covering years or even decades of training.
Seventy-two percent of the subjects indicated that they used
a training log to help them to complete the questionnaire. Some
athletes did not keep hold of the training information; rather,
their coaches had recorded these data.
Statistical Analyses
Statistical analyses of data were performed using SPSS 24.0
(Chicago, IL, USA). Data were screened for normality of distri-
bution and homogeneity of variances using a Shapiro-Wilk nor-
mality test and a Levene test, respectively. Simple linear
regressions and associated equations were calculated to analyze
the relationships between running performance scores after 3, 5,
and 7 years of systematic training (dependent variable) and total
distance accumulated after 3, 5, and 7 years of systematic training
(independent variables). Linear regression assumptions were
checked using residual vs. fitted, normal QQ, and Cook’s distance
plots. Pearson’s correlations were calculated between running
performance after 3, 5, and 7 years of systematic training and
total distance accumulated and different types of running activi-
ties after 3, 5, and 7 years of systematic training, respectively, and
starting age of systematic training. Correlation effects were
interpreted as small (rvalue of 0.10–0.29), moderate (0.30–0.49),
large (0.50–0.69), or very large ($0.70) (27).
A hierarchical linear regression analysis was conducted be-
tween subjects’performance scores after 3, 5, and 7 years of
systematic training (dependent variable) and running activities
and starting age of systematic training (independent variables).
Pearson’s correlations were used to set the order of the variables
introduced in the regression analysis. Only variables that were
correlated significantly with performance were introduced into
the hierarchical regression analysis. Nine athletes were excluded
from the 5-year analysis and 21 from the 7-year analysis because
at the time of data collection, they had not yet trained systemat-
ically for that period of time. Pearson’s multivariate coefficient of
determination (R
2
), unstandardized beta (regression) coefficient
(B), SE of B (B SE), standardized beta (regression) coefficient (b),
and Ffor change in R
2
were calculated. Significance for all
analyses was set at p#0.05.
Results
Table 1 shows the mean values and SDs of total distance accu-
mulated and total distance of DP activities and easy runs. The
mean starting age for systematic training was 18 years (65).
Simple linear regression analysis between performance scores and
total distance accumulated was significant after 3 years of sys-
tematic training (p,0.001, R
2
50.583); predicted performance
(IAAF score) is equal to 406.5 10.03 km. The same analysis for 5
years of systematic training also showed significance (p,0.001,
R
2
50.592); predicted performance (IAAF score) is equal to
502.2 10.2 km. Performance scores and total distance accu-
mulated were also significant after 7 years of systematic training
(p,0.001, R
2
50.566); predicted performance (IAAF score) is
equal to 593.0 10.01 km.
Pearson’s correlations between running performance scores
after 3, 5, and 7 years of systematic training and total volume and
different types of running activities after 3, 5, and 7 years of
systematic training and starting age are shown in Table 2. The
total volume of training showed the strongest correlation with
performance scores, explaining up to 59% of the total variability
between athletes (Figure 1). Easy runs were more correlated with
performance scores than DP activities (tempo runs, long-interval
training, and short-interval training). The total volume of dis-
tance run was also strongly related to performance after 3, 5, and
Table 1
Total accumulated distance run as deliberate practice runs and easy runs (mean 6SD).*
Variables
After 3 years of systematic
training (N585)
After 5 years of systematic
training (N577)
After 7 years of systematic
training (N565)
Performance (IAAF scores) 787 6250 882 6233 945 6207
Total distance (km) 12,933 66,503 23,069 610,676 33,446 614,822
Easy runs (km) 8,264 64,191 14,797 67,022 21,981 69,879
Tempo runs (km) 2,647 62,718 4,525 64,242 5,848 65,637
Long-interval training (km) 903 6759 1,731 61,116 2,816 61,529
Short-interval training (km) 981 6783 1,741 61,277 2,327 61,555
Competition and time trials (km) 224 6428 398 6716 602 61,062
*IAAF 5International Association of Athletics Federations.
Deliberate Practice in Distance Running (2019) 00:00 |www.nsca.com
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Copyright © 2019 National Strength and Conditioning Association. Unauthorized reproduction of this article is prohibited.
7 years of systematic training (r50.75–0.77) (Table 2). Accu-
mulated easy runs (r50.68–0.72), tempo runs (r50.50–0.58),
and short-interval training volumes (r50.53–0.56) had stronger
relationships with running performance scores after 3, 5, or 7
years of systematic training than long-interval training (r5
0.22–0.31), which was not significant after 7 years of training.
Competitions and time trials were not significantly correlated
with performance at any stage. The correlation score between
starting age and performance was only large after 7 years (r
50.52).
Hierarchical regression analysis was conducted between
subjects’performances and easy runs, tempo runs, short-
interval training, long-interval training, and starting age of
systematic training after 3, 5, and 7 years of systematic
training (Table 3). R
2
increased through the different models
in all stages, although the only variables that can be consid-
ered predictors of performance were easy runs and tempo
runs in all stages and models. The bvalue for easy runs de-
creased through all stages, although it always remained sig-
nificant (p,0.01).
Discussion
The aim of this study was to analyze the amount and type of
DP, as well as the easy runs, completed by elite-standard and
world-class long-distance runners during the first 7 years of
their sport careers, with reference to their performances as
scored using the IAAF tables. The first finding was that the
total volume of distance run in training was a strong predictor
of performance scores; indeed, just the total volume of training
itself explained up to 59% of performance score variability
between athletes (Figure 1). This is not hugely surprising, given
the long-distance events the athletes take part in, where
training for an event such as the marathon requires regular
running sessions that come close to its race distance
(42.195 km). Even for those athletes covering shorter dis-
tances, the regular racing and high-intensity training that is
undertaken requires a well-developed aerobic base (16). The
starting age of systematic training showed a moderate effect on
performance scores, similar to earlier research (31,41). How-
ever, starting age did not account for a significant variance in
performance (Table 3), but as most, if not all, athletes would
have completed some sort of running before beginning
systematic training, in that running is a universal practice
(unlike skills used in musicianship, for example) adopted from
an early age, many athletes would have developed some aer-
obic conditioning during their formative years. Indeed, it is
highly likely that those who decided to begin systematic
training already had an aptitude or liking for it, and so, their
systematic training was effectively refining many of the skills
already developed.
The main training contributor to the total distance run during
the athlete’s careers was the easy runs, accounting for approxi-
mately two-thirds of each 2-year accumulated total. Previous
studies on training intensity distribution in elite-standard endur-
ance sports similarly stated that most training is performed at low
intensities (28,36). As the hierarchical regression analysis showed
that easy runs were the training activity that most accounted for
variance in performance scores in all stages (Table 3), they are the
Table 2
Pearson’s correlation values between running performance
(measured as the IAAF score) and training starting age, total
volume of training, easy runs, tempo runs, long-interval training,
and short-interval training volumes (km) after 3, 5, and 7 years of
systematic training.*
After 3 years
(N585), r
After 5 years
(N577), r
After 7 years
(N565), r
Total volume 0.76† 0.77† 0.75†
Easy runs 0.72† 0.71† 0.68†
Tempo runs 0.50† 0.54† 0.58†
Long-interval training 0.27‡ 0.31§ 0.22
Short-interval training 0.55† 0.53† 0.56†
Competitions and time trials 20.06 20.07 0.03
Starting age 0.29‡ 0.45† 0.52†
*IAAF 5International Association of Athletics Federations.
†p,0.001.
‡p,0.05.
§p,0.01.
Figure 1. Simple linear regression analysis between perfor-
mance and total distance after 3 years of systematic training
(A), 5 years of systematic training (B), and 7 years of systematic
training (C). R
2
5Pearson’s multivariate coefficient of de-
termination; IAAF 5International Association of Athletics
Federations.
Deliberate Practice in Distance Running (2019) 00:00
4
Copyright © 2019 National Strength and Conditioning Association. Unauthorized reproduction of this article is prohibited.
single training activities that best predict the performance scores
of subjects. However, these cannot be considered a DP activity
because they do not require high physical effort and concentra-
tion (9,10,41), and it means that although DP is considered very
relevant by subjects to improve performance (9), the most fun-
damental practice required to succeed in long-distance running is
not DP. This might be because although DP plays an important role
in performance and talent development, it cannot explain by itself
variability of performance in absolute terms because other factors
also contribute to explain performance differences (31). This is not
to understate the importance of DP in a holistic training regimen,
which needs to have variety (16,29). Nevertheless, the original DP
theory claims that these forms of domain-relevant kinds of practice
are less important than DP as predictors of expertise (7,15) because
experience in itself is not enough to reach world-classperformances
(38). This assertion has been widely disputed by studies that
demonstrated the relevance of, for example, playful activities on
developing expertise (1,8,34), and what this novel study on a range
of running abilities shows is that there is a crucial role for long, easy
runs that contribute to greater volumes of running, allowing for
improving the efficiency of metabolic key components for energy
fueling (36) and facilitating enjoyment and camaraderie between
teammates (32).
Deliberate practice activities are built on the easier, “base-
forming”runs and allow athletes to develop other important
aspects of physiological functioning, such as RE and maximal
running speed (16). One key finding of this study was that some
forms of DP training (tempo running and short-interval training)
had large correlations with performance scores after 3, 5, and 7
years of systematic training (Table 2). Tempo runs were the most
important predictor within the DP activities, agreeing with Tjelta
(37) on the relevance of this kind of training, particularly with
regard to improving LA (4). Over the course of their careers, the
relevance of tempo runs seemed to increase, highlighting the im-
portance of progressive specialization from the most fundamental
training sessions (easy runs) to those most specific to long-
distance racing (tempo runs) (Table 2). These results were high-
lighted by the hierarchical regression analysis (bfor tempo runs
increased from 0.258 after 3 years of systematic training to 0.293
after 7 years in the 4th model, whereas bfor easy runs decreased
from 0.532 after 3 years to 0.366 after 7 years in the 4th model)
(Table 3). These results agree with classical talent development
studies (6,11) and show the importance of progressive overload
and specificity of training for athletic improvement (29). For each
2-year period analyzed, tempo runs accounted for about 20% of
total distance run, demonstrating again the importance of cov-
ering great distances in training. To a great extent, tempo running
replicates what happens in long-distance racing because it is at
a high intensity (unlike easy runs) and does not have rest periods
(that interval training typically does). Coaches should note the
importance of this type of training on performance and plan for
increased intensity of tempo runs as part of an athlete’s
progression.
Like tempo running, short-interval training showed a large
association with performance; previous research on world-
class Kenyan athletes highlighted its use in their training reg-
imens (4). Hence, although short-interval training did not ac-
count for significant variance in performance (Table 3), any
small effects it has on overall performance could be critical in
differentiating between very closely matched athletes (e.g.,
Table 3
Summary of hierarchical regression analysis for variables predicting performance (the IAAF score) after 3 years (N585), 5 years (N578),
and 7 years of systematic training (N565).*
Model 1 Model 2 Model 3 Model 4
BSE BbBSE BbBSE BbBSE Bb
After 3 years
Easy run 0.037 0.005 0.611† 0.031 0.006 0.524† 0.030 0.006 0.496† 0.032 0.006 0.532†
Tempo run 0.023 0.007 0.253‡ 0.022 0.007 0.240‡ 0.021 0.007 0.233‡ 0.024 0.008 0.258‡
Short interval 0.049 0.029 0.154 0.052 0.029 0.164 0.048 0.029 0.150
Long interval 0.035 0.024 0.106 0.034 0.024 0.104
Starting age 24.23 4.06 20.09
R
2
0.565 0.580 0.591 0.597
Ffor change in R
2
53.36 37.35 28.90 23.36
After 5 years
Easy run 0.019 0.003 0.588† 0.019 0.003 0.561† 0.017 0.003 0.511† 0.016 0.004 0.492†
Tempo run 0.016 0.005 0.292‡ 0.016 0.005 0.282‡ 0.016 0.005 0.282‡ 0.015 0.005 0.266‡
Short interval 0.009 0.019 0.049 0.012 0.018 0.066 0.013 0.019 0.070
Long interval 0.030 0.016 0.143 0.029 0.016 0.140
Starting age 2.67 5.32 0.048
R
2
0.576 0.577 0.596 0.597
Ffor change in R
2
50.21 33.20 26.55 21.07
After 7 years
Easy run 0.011 0.002 0.521† 0.010 0.002 0.459† 0.009 0.002 0.420‡ 0.008 0.003 0.366‡
Tempo run 0.013 0.003 0.354† 0.012 0.004 0.322‡ 0.012 0.004 0.340‡ 0.011 0.004 0.293‡
Short interval 0.016 0.015 0.124 0.017 0.015 0.129 0.019 0.015 0.143
Long interval 0.013 0.011 0.100 0.011 0.011 0.088
Starting age 6.87 5.12 0.137
R
2
0.560 0.568 0.577 0.590
Ffor change in R
2
39.39 26.77 20.48 16.96
*IAAF 5International Association of Athletics Federations; R
2
5Pearson’s multivariate coefficient of determination; B 5unstandardized beta (regression) coefficient; SE B5standard error of B;
b5standardized beta (regression) coefficient; Ffor change in R
2
5ANOVA Ffor change in the Pearson’s multivariate coefficient of determination.
†p,0.001.
‡p,0.01.
Deliberate Practice in Distance Running (2019) 00:00 |www.nsca.com
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Copyright © 2019 National Strength and Conditioning Association. Unauthorized reproduction of this article is prohibited.
during the end spurt). This is not to take away from the im-
portance of greater running volume or tempo runs but indi-
cates that short-interval training can be built on these
foundations. By contrast, long-interval training showed the
lowest effect on variability of performance (of the training
modes) and the smallest correlations. The DP activities of long-
and short-interval training accounted for 7–8% of total dis-
tance accumulated, but athletes might want to consider em-
phasizing short-interval training given the results shown in this
study. In terms of the highest intensity activities, competitions
are of course the truest reflection of a runner’s ability, but
taking part in them did not lead in turn to improvement. In
a sense, this might be because world-class athletes tend to focus
on winning, rather than achieving fast times, in championship
racing (21), and because peaking for competitions usually
occurs at the end of the season, with any small gains lost before
the start of the following season. The large variation in dis-
tance covered in races reflects the wide range of distances
covered in competition (5,000 m to marathon), which neces-
sitate different tapering and recovery periods. Nonetheless,
competitions throughout the season are an excellent method of
monitoring progress in training and learning racing and pacing
strategies. Although the use of logs and diaries for retrospec-
tive analysis of training loads and modes can present difficul-
ties with recall (and hence, this study was limited to those
world-class and high-standard athletes who had reliable data),
the study’s novel findings regarding training advice are made
based on long-term training regimens, actual results in com-
petition (rather than laboratory-based, for example), and from
a sizeable cohort of successful athletes.
Furthermore, 1 limitation in particular has to be ac-
knowledged. Deliberate practice theory aims to quantify the
amounts of practice at different activities using the same
methodology regardless of the field that is being studied. It
means that the same methodology is used for violinists and
endurance athletes. In this sense, the intensity of training
accounted for by DP theory is based on athletes’general
perceptions using a Likert scale at each training activity (15).
However, each field has its own characteristics and, even
more importantly, has displayed specific and accurate meth-
odologies of quantifying amounts of practice. In the case of
middle- and long-distance endurance events, different vali-
dated methodologies have been developed. Accordingly,
a training load objective quantification method based on
a HR index was evolved (3), developing the concept of
training impulse (Bannister’s TRIMPS). Subsequently, a sub-
jective method of training load quantification (session rating
of perceived exertion [RPE]) based on the perceived exertion
shown by athletes at each training session (intensity) and the
duration of the training session were also proposed and val-
idated (18). Therefore, the accuracy and reliability displayed
by session-RPE and Bannister’s TRIMPS when determining
activities such as easy runs, tempo runs, and short- and long-
interval training are considerably higher than the quantifi-
cation method used by DP theory, mainly because intensity is
not accounted for during training, but retrospectively and, in
general, perceived effort is rated when all the training sessions
have been conducted. Nonetheless, this methodology has
been shown to be accurate, valid, and reliable in a huge va-
riety of domains (12,14), and it represents the only way of
assessing the training conducted by this sample of athletes
with such high standards of performance.
Practical Applications
The first important finding that coaches should note was that
the strongest relationships found for performance scores were
with total distance run after 3, 5, and 7 years of systematic
practice. There is thus a fundamental need for athletes to run
over considerable distances (.100 km per week) to compete
with world-class athletes and even with those who are below
this highest standard. It is not possible to always train at high
intensities, particularly over these long distances, so the large
associations found between easy runs and performance scores
are welcome in terms of managing training intensity in long-
distance running regimens, notwithstanding their central role
in developing cardiovascular fitness. Tempo runs contribute
to performance by being both an important source of accu-
mulated distance run and in terms of their role in physiological
improvements and specificity to racing; similarly, short-
interval training seemed to be a key component of a varied
training schedule, although long intervals were less important.
Further DP studies should analyze not only the difference in
accumulated DP (by distance) in different performance groups
but more so the influence of DP on performance because it
very likely differs across domains. Taking into account this
novel study’s findings, there are nonetheless numerous other
factors that play an important role in performance and that
strength and conditioning professionals have to take into ac-
count, such as biomechanics, tactical acumen, and psycho-
logical responses to stress.
Acknowledgments
The authors acknowledge the outstanding contributions of the
coaches and the athletes who participated in this study. The
authors also gratefully thank the useful suggestions and advice
provided by Dr. Martin Hagger and Dr. Bradley Young in
conducting this study. There is no disclosure of funding to report
for this study. The authors report no conflict of interest. The
results of this study do not constitute endorsement of the product
by the authors of the National Strength and Conditioning
Association.
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