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Purpose:: In recent years (2011-2016), men's 800m championship running performances have required greater speed than previous eras (2000-2009). The "Anaerobic speed reserve" (ASR) may be a key differentiator of this performance, but profiles of elite 800m runners and its relationship to performance time have yet to be determined. Methods:: The ASR - determined as the difference between maximal sprint speed (MSS) and predicted maximal aerobic speed (MAS) - of 19 elite 800m and 1500m runners was assessed using 50m sprint and 1500m race performance times. Profiles of three athlete sub-groups were examined using cluster analysis and the speed reserve ratio (SRR), defined as MSS/MAS. Results:: For the same MAS, MSS and ASR showed very large negative (both r=-0.74±0.30, ±90% confidence limits; very likely) relationships with 800m performance time. In contrast, for the same MSS, ASR and MAS had small negative relationships (both r=-0.16±0.54), possibly) with 800m performance. ASR, 800m personal best, and SRR best defined the three sub-groups along a continuum of 800m runners, with SRR values as follows: 400-800m ≥1.58, 800m ≤1.57 to ≥1.47, and 800-1500m as ≤1.47 to ≥ 1.36. Conclusions:: MSS had the strongest relationship with 800m performance, whereby for the same MSS, MAS and ASR showed only small relationships to differences in 800m time. Further, our findings support coaching observation of three 800m sub-groups, with the SRR potentially representing a useful and practical tool for identifying an athlete's 800m profile. Future investigations should consider the SRR framework and its application for individualised training approaches in this event.
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Anaerobic Speed Reserve: A Key Component
of Elite Male 800-m Running
Gareth N. Sandford, Sian V. Allen, Andrew E. Kilding, Angus Ross, and Paul B. Laursen
Purpose:In recent years (20112016), mens 800-m championship running performances have required greater speed than
previous eras (20002009). The anaerobic speed reserve(ASR) may be a key differentiator of this performance, but proles of
elite 800-m runners and their relationship to performance time have yet to be determined. Methods:The ASRdetermined as the
difference between maximal sprint speed (MSS) and predicted maximal aerobic speed (MAS)of 19 elite 800- and 1500-m
runners was assessed using 50-m sprint and 1500-m race performance times. Proles of 3 athlete subgroups were examined using
cluster analysis and the speed reserve ratio (SRR), dened as MSS/MAS. Results:For the same MAS, MSS and ASR showed
very large negative (both r=.74 ± .30, ±90% condence limits; very likely) relationships with 800-m performance time. In
contrast, for the same MSS, ASR and MAS had small negative relationships (both r=.16 ± .54; possibly) with 800-m
performance. ASR, 800-m personal best, and SRR best dened the 3 subgroups along a continuum of 800-m runners, with SRR
values as follows: 400800 m 1.58, 800 m 1.57 to 1.48, and 8001500 m 1.47 to 1.36. Conclusion:MSS had the
strongest relationship with 800-m performance, whereby for the same MSS, MAS and ASR showed only small relationships to
differences in 800-m time. Furthermore, the ndings support the coaching observation of three 800-m subgroups, with the SRR
potentially representing a useful and practical tool for identifying an athletes 800-m prole. Future investigations should
consider the SRR framework and its application for individualized training approaches in this event.
Keywords:middle distance running, training science, maximal sprint speed, maximal aerobic speed
Preparation for 800-m running represents a unique challenge
to the middle-distance coach. With close interplay required
between aerobic and anaerobic/neuromuscular physiology, ath-
letes with distinctly different proles have an opportunity for
success in the event. Recently, a changing of the guardin the
mens championship 800-m event was revealed, whereby from
2011 onwards World and Olympic medalists were shown to run
predominantly a gun-to-tapetype race tactic in the nal,
requiring 100-m sectors that are 0.5 m/s faster than in 2000
2009. This raises important questions pertaining to the mechani-
cal speed range required in top athletes relative to their aerobic
Previous studies of national and international caliber 800-
and 1500-m runners reveal opposing ndings regarding the phys-
iological requirements of 800-m running. For example, Ingham
et al
reported that VO
max and running economy explained
95.9% of running performance in 800- and 1500-m runners;
however, no speed and power measures were collected. In contrast,
Bachero-Mena et al
showed very large relationships between
800-m performance and sprints over 20 m (r=.72) and 200 m
(r=.84), yet aerobic markers were not reported. Several reasons
may explain these contrasting outcomes. First, athletes with diverse
physiological proles may be competing in the same event. For
example, Schumacher and Muller
showed in Olympic gold medal-
winning team pursuit cyclists that the rst and second position
riders presented with markedly different anaerobic and aerobic
physiological proles, yet produced similar individual pursuit
performance times (4:18 vs 4:19). Accordingly, it is possible
that more aerobic-based 800-m runners demonstrate stronger
relationships between aerobic-measured variables and perfor-
mance, whereas more sprint-based 800-m athletes present stronger
correlations with anaerobic and neuromuscular qualities, depend-
ing on the random phenotype predominance of the sample. Second,
it is possible that a cultural endurance-focused training approach
bias has contributed to a production of studies from the more
endurance-based 800-m running subgroup. Third, although the 800
and 1500 m have historically been considered as similar events,
this belief may require reassessment in light of recent tactical
Indeed, heterogeneity of performance standard within
elitesamples may be misleading when it comes to differentiating
elite athlete makeup. For example, conclusions are often drawn on
elite performance when samples may contain only 1 or 2 truly elite
runners (800-m performance 1:46).
Middle-distance coaches have long spoken of 3 subgroups
of middle-distance runners. These include: (1) speed types (400- to
800-m specialists), (2) 800-m specialists, and (3) endurance types
(800- to 1500-m specialists).
Understanding the athlete subgroup
has substantial inuence on the coachs plan, training program, and
coaching approach. In contrast, the sport science literature has
traditionally treated the 800-m cohort as a single athlete type,
without assessing individual characteristics that might form a
coachs subgroup. Although a minimum level of both aerobic and
neuromuscular qualities would be required for success in any elite
800-m runner, a deciency in either component is likely balanced
by a strength in the other to achieve a 2-lap performance.
ever, without information dening this variability, clarity of train-
ing methods for this event group cannot be established to the degree
that they have been with runners training for the 1500- to 10,000-m
Sandford, Allen, Kilding, and Laursen are with the Sport Performance Research
Inst New Zealand (SPRINZ), Auckland University of Technology, Auckland,
New Zealand. Sandford and Ross are with High Performance Sport New Zealand
and Athletics New Zealand, Auckland, New Zealand. Sandford is also with the
Millennium Inst of Sport & Health in Auckland. Sandford (gareth.sandford@hpsnz. is corresponding author.
International Journal of Sports Physiology and Performance, 2019, 14, 501-508
© 2019 Human Kinetics, Inc. ORIGINAL INVESTIGATION
Sanders et al
recently showed the usefulness of the anaero-
bic power reserve construct for predicting sustainable power
performance across 4 professional road cyclists with largely
diverse peak power proles (range =10361525 W). Therefore,
the anaerobic speed reserve (ASR), dened as the speed range an
athlete possesses between velocity at VO
max (vVO
max) in the
laboratory (or maximal aerobic speed [MAS] in the eld
) and
maximal sprint speed (MSS),
may likewise prove a useful tool to
better understand the apparent diversity of mechanical proles
across the 800-m event group. In addition, ASR may provide the
coach and sport scientist a prole for assessing an athletes
mechanical limits supported by their metabolic systems (aerobic
and anaerobic) as well as for tracking progress in training.
Therefore, the aims of the present study were to (1) determine
MSS and ASR and their relationship with 800-m performance in an
elite middle-distance cohort and (2) using the ASR construct,
investigate the athlete proles within the event and offer possible
solutions for coaches and scientists to be able to better categorize
800-m athletes.
Study Overview
To perform this study, the primary researcher traveled to locations
around the world to test participants at their local training venue
during the late precompetition/competition phase of the 2017
athletics season. At each training location, athletes were tested
for their MSS. Within 6 weeks of the MSS assessment, an
outdoor 1500-m race used to estimate MAS, was performed in
A total of 19 elite 800- and 1500-m specialists representing 5
different continents (Africa, Europe, North America, Oceania, and
South America) participated in this study (Table 1). The study
inclusion criteria included an 800-m personal best (PB) of
1:47.50, and/or a 1500-m PB of 3:40, as guided by USA track
and eld World Championship trial standards (2017). Seasons
bests (SB) were used throughout the analysis to better reect an
athletescurrent shape (eg, PB could be up to 3 years prior). Each
athlete provided written informed consent to participate in the
study, which was approved by the Auckland University of Tech-
nology Ethics Committee.
Performance Testing
Maximal Aerobic Speed. For MAS assessment, a gun-to-tape
1500-m race performed within 6 weeks of MSS assessment was
reective of an athletes absolute time-trial capacity and current
aerobic tness. In line with the periodization phase described,
data collection aligned well with the period where athletes were
targeting qualifying times and gun-to-tapestyle races led by a
pacemaker to 1000 to 1200 m; an aspect that would also have
likely enhanced the reliability of 1500-m times. From these times,
MAS was predicted using the 1500-m race performance equation
of Bellenger et al
MAS =TTsð0.766 þ0.117 ½TTdÞ
where TT
was the athletes average 1500-m speed (in kilometer
per hour) and TT
was 1.5 km.
Maximal Sprint Speed. Upon arrival at the track, participants
were informed of the rationale for the 50-m MSS assessment and
maximal nature of the testing protocol. Athlete performance was
measured using a sports radar device (Stalker ATS II System; Radar
Sales, Richardson, TX) over a 50-m sector on the track straight. The
device was placed in the middle of 2 lanes, 2 m behind the start
line, and on a tripod resting 1.5 m from the ground. For live capture
of the athletes acceleration trace, the radar was operated remotely
from a laptop to remove the possibility of manual use variability,
using a method that has been shown to be highly reliable (CV =
1.1%) against gold-standard force plates.
Instantaneous radar was
used to extract MSS (in meter per second) and split time (in seconds)
from each effort, sampling at 46.9 Hz. Custom-built software
(Goldmine, HPSNZ, NZ), was used to remove postprocessing error
of the acceleration trace from manual inspection of erroneous data
points. Previous investigations have shown the reliability limitation
of postprocessing with LabVIEW(Build version: 11.0, National
Instruments Corp, Austin, TX) software.
Owing to the experienced status of the athletes, and cultural
differences in warm-up, a semistructured framework was used to
provide consistency across sites. Here, instructions were to take 10
to 15 minutes to prepare for a maximal effort, incorporating
individual needs to feel ready to go. Athletes were familiarized
with the standing start position and instructed to place 1 foot in
front of the other (athletes preference), with no backward oscilla-
tion, though a forward lean into the movement was permitted into
the rst forward step.
Boundaries for the warm-up includedsome pulse-raisingactivity
(jogging), some drills (A skips, B skips, etc), time for any other
exercises athletes required and 2 to 3 progressive strides in ats,
before transitioning into race spikes, where athletes were asked to
rehearse the standing start in 1 to 2 maximal efforts to 30 m. Athletes
performed the assessment in spikes (n =17)orraceats (n =2).
Once ready to go, an instruction of on youwas provided for
the athlete to accelerate in their own time maximally through to the
end of the cones, along with the line at the center of the 2 lanes.
Athletes performed 2 to 3 maximal efforts with 3 minutes rest on
rotation at the end of the athlete testing line. The primary variable of
interest taken from the radar for analysis was the MSS, representing
the ceiling of the athletes ASR. MSS assessments were conducted
where possible in an indoor location, but where not possible
(6 sites), a wind gauge (Kestrel 5100; Nielsen-Kellerman Com-
pany, Boothwyn, PA) was used to measure wind speed. All MSS
assessments outdoors were captured with 0.5 ± 0.3 m/s tailwind.
Environmental conditions indoors (25.2°C ± 3.5°C, 51.4 ± 10.7%
RH) and outdoors (26.2°C ± 5.0°C, 42.7 ± 22.8%RH) were similar.
One site was at 580-m altitude with all others at sea level.
Speed Reserve Ratio
The speed reserve ratio (SRR) was developed from the ASR
construct as a potentially practical tool for coaches to display
individual athlete proles in 1 variable, as used in team sports.
SRR =MSS ðkm=hÞ=MAS ðkm=hÞ
Data Analysis
Data are presented as mean ± 90% condence limits (CL) unless
otherwise stated. The relationships between 800-m SB perfor-
mance and MSS, MAS, and ASR (n =10) were determined
from partial correlations using SAS 9.4 (SAS Institute, Cary,
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Table 1 Description of the Participants in the Study
Category of athlete Regions represented Highest competition representation Other details
800-m PB,
(mean [SD])
1500-m PB,
(mean [SD])
International, n =8 Europe, North America,
South America, and Oceania
Olympic Games/World Championships, n =6
World Indoor Championships, n =1
World Relay Championships, n =1
Includes 1 ×world-record
holder, 2 ×national-record
1:45.55 (1.18) 3:46.69 (8.20)
European, n =3 Europe European U23 Cross Country
European U23 Outdoor Championships
European U20 Outdoor Championships
1×medalist 1:47.07 (0.15) 3:39.93 (3.53)
National, n =2 Oceania National Championship 5 ×national champion
1×national medalist
1:47.80 (1.13) 3:42.34 (5.04)
Collegiate, n =6 Africa, Europe, and North America World University Games, n =3
NCAA Outdoor Championship, n =3
World University Games
Includes African
championship nalist
1:47.90 (1.84) 3:41.40 (3.04)
Abbreviation: PB, personal best; NCAA, National Collegiate Athletic Association.
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NC) and described by magnitude-based inferences.
As alluded to
in the introduction, the merging of elite and subelite athlete data
into the same analysis
may explain disparate outcomes reported
previously. Therefore, two 1500-m specialists who did not have an
800-m SB on record were removed from this part of the analysis.
Five athletes with a SB of >1:47.50 for 800 m were also removed
with times outside cutoffs for elite status.
Ak-means cluster analysis was performed using SAS 9.4 to
investigate the variation in physiological and performance char-
acteristics of world-class 800-m runners. Athletes excluded from
partial-correlation analysis (due to the 800-m focus) were included
for subgroup clustering (n =19). Instruction was given to t the
collected variables into 3 clusters, as per the aforementioned coach
observations. MAS, MSS, ASR, SRR, body mass, 800-m PB and
SB, 1500-m PB and SB obtained through testing, questionnaire, or
competition data collection were standardized and run through the
cluster analysis to understand which best explained any differences
between groups.
Variables were excluded based on their ability to explain
variation between clusters, with the strongest relationships (highest
value) used for nal subgroup determination. Differences
between clusters were determined using magnitude-based infer-
ences. The following threshold values used for effect size statistics
were 0.2 (small), 0.6 (moderate), 1.2 (large), and 2.0 (very
large). The smallest worthwhile change for maximum velocity was
determined as the SD of all 19 athletesMSS, multiplied by the
effect size.
Moderate thresholds were applied across all variables
to acknowledge variability in the MAS equation.
To explore the individual variation specically in the 800 m,
the SRR was used. For this analysis, 10 athletes with SB 1:47.50,
who also had a MAS marker within the 6-week window were
assessed. 800-m SB times of these participants ranged from 1:44.50
to 1:47.36.
ASR and 800-m Performance Relationships
Participant details are described in Table 1. MSS and ASR
showed very large negative (both r=.74 ± .30; very likely)
relationships with 800-m performance time for the same MAS.
In contrast, ASR and MAS had small negative relationships (both
r=.16 ± .54, possibly) with 800 m when MSS was constant
(Figures 1A1C).
800-m Subgroup Variation
The ASR, SRR, and 800-m PB accounted for the greatest variation
between the 3 clusters of 800-m athletes (R
=.87; very large).
Subgroup performance characteristics are shown in Table 2.
The MSS of 400- to 800-m athletes was faster than the 800-m
specialists (1.8 ± 0.6 km/h, moderate, very likely), and 800- to
1500-m athletes (4.0 ± 0.4 km/h, very large, very likely). The 800-
m specialists had faster MSS than 800- to 1500-m athletes (2.2 ±
1.5 km/h, large, likely). MAS in 400- to 800-m athletes was slower
than both 800-m specialists (0.5 ± 0.5 km/h, moderate, likely) and
800- to 1500-m athletes (0.8 ± 0.4, large, very likely). MAS of
800-m specialists was slower than 800- to 1500-m athletes (0.3 ±
0.3, moderate, possibly). ASR of 400- to 800-m athletes was larger
than 800-m specialists (2.3 ± 0.7 km/h, large, very likely) and
800- to 1500-m athletes (4.3 ± 1.2 km/h, very large, most likely).
ASR of 800 specialists was larger than 800- to 1500-m athletes
(2.0 ± 1.2 km/h, moderate, likely).
The SRR had a large relationship with 800-m performance
(r=.53 ± .43, likely) whereby faster 800-m athletes had a higher
ratio. In addition, body mass showed a large relationship with SRR
(r=.62 ± .34, very likely). 400- to 800-m athletes were heavier
than 800-m specialists (6.4 ± 7.8 kg, moderate, possibly) and 800-
to 1500-m athletes (5.8 ± 11.2 kg, moderate, possibly), with trivial
differences between 800-m specialists and 800- to 1500-m athletes
(0.6 ± 11.1 kg, possibly).
In the present study, we examined for the rst time, the role of the
ASR in elite male 800-m running performance, in an era where
faster top speed appears to be a critical performance requirement.
Our ndings conrm this observation, with a greater MSS (and
therefore ASR) strongly correlated with a faster 800 m. Impor-
tantly, for the same MSS, having a greater MAS or ASR was not
strongly related to changes in 800-m time. These results support the
notion that at an elite level, faster 800-m runners have a larger ASR,
which is related to a higher MSS (Figures 1A and 1C), along with
an already established minimum level of estimated MAS. In
addition, in agreement with longstanding coaching observations,
we reveal the proles of three 800-m athlete subgroups, described
along a continuum herein as 400 to 800 m (speed types), 800 m
(specialists), and 800 to 1500 m (endurance types; Table 1).
Finally, we present the SRR construct (Figure 2) as a practical
and easily implemented tool to support a coachs identication of
the 800-m athlete subgroup, which may aid training approaches
and event specialization.
The unique nature of the global elite study sample (Table 1)
represents a critical addition to the middle-distance literature, with
high relevance to coaches, athletes, scientists, and sports federa-
tions. Importantly, we conrm the role of ASR (through the
function of larger MSS) as a key performance indicator of elite
male 800-m running. Indeed, for the same MSS, having a greater
MAS or ASR was not strongly related to changes in 800-m time.
The paradigm offered by our analysis should consider that once a
certain aerobic standard is reached, MSS becomes a differentiating
factor in elite 800-m runners. In agreement with Bachero-Mena
et al,
we found a very large relationship between MSS and 800-m
running performance (Figure 1A). The small relationship with
MAS contrasts the study by Ingham et al,
who studied athletes
with slower performance times (1:48.9 ± 2.4), where perhaps the
aerobic component may be a more important differentiator of
slower (1:47.50) performance times. As we have shown, in an
elite 800-m running cohort, the strong relationships between 800-m
performance times and MSS (Figure 1A) and ASR (Figure 1B)
demonstrate the importance of possessing advanced speed char-
acteristics alongside an already well-developed aerobic capability.
It appears that to be competitive in the modern-day elite 800 m
era, an MSS of 10 m/s is required to cope with the high-speed
demands in the rst 200 m of the race (Table 2).
the complex phenomenon of any middle-distance performance,
amid tactics, trips, mistimed training, injury, illness, and other
uncertainties that occur,
our data suggest that at the elite level, a
baseline level of MSS/MAS characteristics are required to handle
surging in slow, fast, or moderate paced 800-m events. Critically,
considering the energetic demands of the mens 800-m (66%
neither aerobic nor anaerobic/neuromuscular compo-
nents of training can be neglected.
Historically, the most common coaching approaches for dif-
ferentiating 800-m athletes into subgroups involves using 400-m
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Figure 1 Relationship between (A) MSS and (B) ASR with 800-m seasons-best race performance in 10 elite-class male 800-m
runners. (C) Partial correlation magnitudes with 90% condence limits; gray area represents trivial relationship. Change *possibly
substantial, **likely substantial, and ***very likely substantial. ASR indicates anaerobic speed reserve; MAS, maximal aerobic
speed; MSS, maximal sprint speed.
IJSPP Vol. 14, No. 4, 2019 505
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and 1500-m PB times to segregate an athlete into 400- to 800-m or
800- to 1500-m subgroups.
The present investigation offers the
SRR as an additional tool for classifying athletes into subgroups
(Table 2; Figure 2). MAS differences between 400 to 800 m and
800-m specialists, and 800-m and 800- to 1500-m subgroups were
moderate, whereas they were large between 400- to 800-m and
800- to 1500-m athletes. However, the differences in MSS were
much greater between subgroups (Table 2). Therefore, SRR may be
the most effective metric for easily identifying an athletes sub-
group, as evidenced by the cluster analysis, which revealed that
ASR, SRR, and 800-m PB accounted for the greatest amount of
subgroup variation. Further studies investigating middle distance
running should consider stating the distribution of athlete sub-
groups in the methodology sections and perform data analysis per
subgroup to provide readership with outcomes of interventions
(eg, training or nutritional) on specic subgroups with similar
proles (Table 2).
Many questions remain across 800-m subgroup characteriza-
tion, in terms of how mechanical and metabolic components may
explain these results. The critical speed describes the divide
between steady state and nonsteady state exercise, with the nite
work capacity above critical speed termed Dprime (D).
Dis our
current best estimate of an athletes so-called anaerobic capacity, a
measurement that has challenged physiologists for years.
it was shown that Finnish national 800- and 1500-m
distance runners and US 400-m athletes (PB range: 4452.5 s)
had superior anaerobic work capacity (as dened from the maximal
anaerobic running test) compared with long-distance runners and
control (sprinters and jumpers) groups. The 400-m athletes had
superior anaerobic work capacity and the highest MSS in compari-
son to the national Finnish 800- and 1500-m athletes; however,
individual event comparisons were not provided. A fast MSS
determines the proportion of ASR an athlete can work at and
may relate to high-intensity training tolerance.
Body mass showed a large positive relationship with SRR
(r=.62), which may be explained by the underlying ground force
characteristics, with MSS ultimately limited by the impulse an
athlete can produce.
Van Der Swaard
showed that fast-twitch
muscle ber composition and vastus lateralis muscle volume
explained 65% of the normalized peak power output in cycling.
Therefore, greater muscle mass differences (inferred from body
mass measurement) between subgroups may explain part of their
different MSS capability (Table 2). Furthermore, muscle composi-
tion differences between the subgroups have implications for VO
kinetics, with slower oxygen ux through type IIa and IIx bers,
as well as smaller capillary density and electron transport chain
enzymes versus type I bers.
Differences in metabolic efciency
(lower efciency in type II bers) may have implications for
metabolic perturbation during exercise intensities above critical
such as in 800-m racing, therefore, reiterating the need to
characterize D(alongside MSS) in 800-m subgroups. We postu-
late that perhaps 800-m specialist athletes are event experts, in part
due to a predominance of IIa ber types, which provide the unique
blend of higher force generation characteristics than type I bers,
Table 2 Performance ( and Prole Characteristics of the 800-m Subgroups (N =19), Mean (SD)
400- to 800-m athletes (n =10) 800-m specialist (n =6) 800- to 1500-m athletes (n =3)
800-m personal best 1:46.21 (1.16) 1:46.37 (1.43) 1:49.53 (1.28)
1500-m personal best 3:44.05 (4.33) 3:42.13 (3.87) 3:38.89 (0.87)
Body mass, kg 72.2 (8.3) 65.8 (8.3)
66.4 (6.9)
MSS, km/h 35.48 (0.30)
33.68 (0.63)
31.49 (0.99)
MAS, km/h 22.41 (0.62 22.76 (0.50)
23.21 (0.06)
ASR, km/h 14.46 (1.00)
12.12 (0.61)
10.13 (0.76)
SRR 1.58 1.57 to 1.48 1.47 to 1.36
Abbreviations: ASR, anaerobic speed reserve; MAS, maximal aerobic speed; MSS, maximal sprint speed; SRR, speed reserve ratio.
Possibly substantial difference from 400800 m.
Likely substantial difference from 400800 m.
Very likely substantial difference from 400800 m.
Most likely
substantial difference from 400800 m.
Possibly substantial difference from 800-m specialist.
Likely substantial difference from 800-m specialist.
Very likely
substantial difference from 800-m specialist.
Figure 2 Speed reserve ratio (maximum sprint speed [km/h]/maximal aerobic speed [km/h]) of 10 elite male 800-m runners. Lines depict 800-m
subgroups from cluster analysis.
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and greater metabolic efciency than type IIx, though this warrants
conrmation using noninvasive muscle ber-type estimation
alongside SRR.
Our current methods used a 1500-m race for MAS prediction,
potentially creating bias toward a better MAS prediction in 800 m
rather than 1500-m specialists. With the current methods and
logistics of the study collection, a race (that was already in
most athletes calendars), was deemed as the most practical
method for capturing the MAS estimate in this elite sample during
competition. However, specialization in competition today means
that some 800-m athletes rarely, if ever, perform 1500-m races.
The present sample also represents a distribution that unintention-
ally reects clustering around the qualifying mark for nationals
(1:47.50), similar to the Bannister 4-minute mile phenomenon,
in addition to the difculty of capturing World-Class participants
for a research study. As such, this should be kept in mind with
study interpretation.
The unique nature of this investigation into the speed char-
acteristics of elite athletes in their various locations meant that
laboratory assessment of vVO
max was not practically possible, so
a sound prediction method was a necessity. In this regard, it is also
important to highlight that the variability of elite middle-distance
racing is only 1%,
far less than typical metabolic cart measure-
ments (coefcient of variation VO
). In addition, athletes
may not always produce true maximumresults in laboratory
Although unique, we believe our methodology provides
a robust level of ecological validity and practicality for athletes and
Furthermore, the scientic literature has a comprehen-
sive understanding of the aerobic determinants of elite middle-
distance running, but data are scarce with respect to the MSS
characteristics of elite 800-m runners.
Practical Application
An athletes ASR and SRR showed the greatest variation between
the 3 subgroups of elite male 800-m runners, and these variables,
therefore, represent practical markers that coaches can easily
measure to categorize their athletessubgroup proles. Impor-
tantly, many training studies show large individual variation in
response to a given intervention, with the responder and nonre-
sponder conceptoften attributed to the outcome.
explanation may be that for some athlete proles, the stimulus
provided could be inappropriate. For example, it is unlikely an
anaerobic/neuromuscular-based athlete would respond to high
densities of continuous aerobic work. The SRR framework
(Table 2; Figure 2) could advance the proling of athletes into
subgroups based on their ASR characteristics, which may allow
precise selection of more favorable training content. Such an
approach has been successfully used in team sports,
and thus
provides a fruitful opportunity for further understanding the indi-
vidual training response required for different 800-m subgroups.
A larger ASR through the function of a faster MSS had the strongest
relationship with elite 800-m performance. When MSS was held
constant, MAS and ASR had only small relationships to differences
in 800-m time. In addition, the SRR, dened as the MSS/MAS, may
represent a useful tool to identify an athletes 800-m subgroup.
Future investigations should consider the SRR framework and its
application for individualized training approaches in this event.
The authors thank the athletes, coaches, and scientists who participated in
the project. We are also grateful to the funding partnersHigh Perfor-
mance Sport New Zealand, Athletics New Zealand, and Sport Performance
Research InstituteAUT University.
1. Sandford GN, Pearson S, Allen SV, et al. Tactical behaviors in mens
800-m Olympic and world-championship medalists: a changing
of the guard. Int J Sports Physiol Perform. 2018;13(2):246249.
PubMed ID: 28488905 doi:10.1123/ijspp.2016-0780
2. Ingham SA, Whyte GP, Pedlar C, Bailey DM, Dunman N, Nevill
AM. Determinants of 800-m and 1500-m running performance using
allometric models. Med Sci Sports Exerc. 2008;40(2):345350.
PubMed ID: 18202566 doi:10.1249/mss.0b013e31815a83dc
3. Bachero-Mena B, Pareja-Blanco F, Rodríguez-Rosell D, ˜nez-
García JM, Mora-Custodio R, González-Badillo JJ. Relationships
between sprint, jumping and strength abilities, and 800 m perfor-
mance in male athletes of national and international levels. J Hum
Kinet. 2017;58(1):187195. doi:10.1515/hukin-2017-0076
4. Schumacher YO, Mueller P. The 4000-m team pursuit cycling world
record: theoretical and practical aspects. Med Sci Sports Exerc. 2002;
34(6):10291036. PubMed ID: 12048333 doi:10.1097/00005768-
5. Ingham SA, Fudge BW, Pringle JS. Training distribution, physiolog-
ical prole, and performance for a male international 1500-m runner.
Int J Sports Physiol Perform. 2012;7(2):193195. PubMed ID:
22634971 doi:10.1123/ijspp.7.2.193
6. Horwill FJ. Solving the 800 m puzzle. In: Modern Athlete and Coach
(vol 343). Adelaide; 1996;4851.
7. Gamboa J, Elrick R, Mora A, et al. NSA Round tableSpeed in the
800 metres. New Stud Athl. 1996;11(4):722.
8. Daniels J. 800 meters. In: Martin B, Julie R, Carla Z. Daniels Running
Formula. 2nd ed. Human Kinetics; 2005:201212.
9. Tjelta LI. The training of international level distance runners. Int J Sport
Sci Coach. 2016;11(1):122134. doi:10.1177/1747954115624813
10. Sanders D, Heijboer M, Akubat I, Meijer K, Hesselink M. Predicting
high-power performance in professional cyclists. Int J Sports Physiol
Perform. 2017;12(3):410413. PubMed ID: 27248365 doi:10.1123/
11. Bellenger CR, Fuller JT, Nelson MJ, Hartland M, Buckley JD,
Debenedictis TA. Predicting maximal aerobic speed through set
distance time-trials. Eur J Appl Physiol. 2015;115(12):25932598.
PubMed ID: 26242778 doi:10.1007/s00421-015-3233-6
12. Blondel N, Berthoin S, Billat V, Lensel G. Relationship between run
times to exhaustion at 90, 100, 120, and 140% of vVO
max and
velocity expressed relatively to critical velocity and maximal veloc-
ity. Int J Sports Med. 2001;22(1):2733. PubMed ID: 11258638
13. Weyand P, Lin J, Bundle M. Sprint performance-duration relation-
ships are set by the fractional duration of external force application.
AJP Regul Integr Comp Physiol. 2006;290:R758R765. doi:10.1152/
IJSPP Vol. 14, No. 4, 2019
Unlocking the Complexity of 800-m Running 507
Downloaded by UBC LIBRARY CENTRAL on 04/19/19
14. Samozino P, Rabita G, Dorel S, et al. A simple method for measuring
power, force, velocity properties, and mechanical effectiveness in
sprint running: simple method to compute sprint mechanics. Scand J
Med Sci Sports. 2015;26:648658. PubMed ID: 25996964 doi:10.
15. Simperingham KD, Cronin JB, Pearson SN, Ross A. Reliability of
horizontal force-velocity-power proling during short sprint-running
accelerations using radar technology. Sport Biomech. 2017;3141:
112. doi:10.1080/14763141.2017.1386707
16. Mendez-Villanueva A, Buchheit M, Kuitunen S, Poon TK, Simpson
B, Peltola E. Is the relationship between sprinting and maximal
aerobic speeds in young soccer players affected by maturation?
Pediatr Exerc Sci. 2010;22:497510. PubMed ID: 21242600
17. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive
statistics for studies in sports medicine and exercise science. Med Sci
Sports Exerc. 2009;41(1):313. PubMed ID: 19092709 doi:10.1249/
18. Glazier PS. Towards a grand unied theory of sports performance.
Hum Mov Sci. 2015;56:118. doi:10.1016/j.humov.2015.08.001
19. Raysmith BP, Drew MK. Performance success or failure is inuenced
by weeks lost to injury and illness in elite Australian track and
eld athletes: a 5-year prospective study. J Sci Med Sport. 2016;
19(10):778783. PubMed ID: 26839047 doi:10.1016/j.jsams.2015.
20. Jones A, Whipp B. Bioenergetic constraints on tactical decision
making in middle distance running. Br J Sports Med. 2002;36:
102104. PubMed ID: 11916890 doi:10.1136/bjsm.36.2.102
21. Spencer MR, Gastin PB. Energy system contribution during 200- to
1500-m running in highly trained athletes. Med Sci Sports Exerc.
2001;33(1):157162. PubMed ID: 11194103 doi:10.1097/00005768-
22. Vanhatalo A, Jones AM, Burnley M. Application of critical
power in sport: what is the critical power concept? Int J Sports
Physiol Perform. 2011;6:128136. PubMed ID: 21487156 doi:10.
23. Davison RR, van Someren KA, Jones AM. Physiological monitoring
of the Olympic athlete. J Sports Sci. 2009;27(13):14331442.
PubMed ID: 19813137 doi:10.1080/02640410903045337
24. Nummela A, Mero A, Stray-Gundersen J, Rusko H. Important
determinants of anaerobic running performance in male athletes
and non-athletes. Int J Sports Med. 1996;17(suppl 2):S91S96.
25. Buchheit M, Hader K, Mendez-Villanueva A. Tolerance to high-
intensity intermittent running exercise: do oxygen uptake kinetics
really matter? Front Exerc Physiol. 2012;3:406. doi:10.3389/fphys.
26. Weyand PG, Davis J. Running performance has a structural basis.
J Exp Biol. 2005;208(14):26252631. doi:10.1242/jeb.01609
27. van der Zwaard S, van der Laarse WJ, Weide G, et al. Critical
determinants of combined sprint and endurance performance: an
integrative analysis from muscle ber to the human body. FASEB
J. 2018;32(4):21102123. doi:10.1096/fj.201700827R
28. Crow MT, Kushmerick MJ. Chemical energetics of slow- and fast-
twitch muscles of the mouse. J Gen Physiol. 1982;79(1):147166.
PubMed ID: 7061985 doi:10.1085/jgp.79.1.147
29. Jackman MR, Willis WT. Characteristics of mitochondria isolated
from type I and type IIb skeletal muscle characteristics of mitochon-
dria isolated from type I and type IIb skeletal muscle. Am J Physiol.
1996;270(39):C673C678. doi:10.1152/ajpcell.1996.270.2.C673
30. Pringle JS, Doust JH, Carter H, et al. Oxygen uptake kinetics during
moderate, heavy and severe intensity submaximalexercise in
humans: the inuence of muscle bre type and capillarisation. Eur
J Appl Physiol. 2003;89(3-4):289300. PubMed ID: 12736837
31. Baguet A, Everaert I, Hespel P, Petrovic M, Achten E, Derave W. A
new method for non-invasive estimation of human muscle ber type
composition. PLoS ONE. 2011;6(7):e21956. doi:10.1371/journal.
32. Denison J. Inhibiting progress: the record of the four-minute mile.
Sport Hist. 2006;26(2):280288. doi:10.1080/17460260600786930
33. Hopkins WG. Competitive performance of elite track and eld
athletes. Variability and smallest worthwhile enhancements.
Sportscience. 2005;9:1720.
34. Crouter SE, Antczak A, Hudak JR, DellaValle DM, Haas JD.
Accuracy and reliability of the ParvoMedics TrueOne 2400 and
MedGraphics VO2000 metabolic systems. Eur J Appl Physiol.
2006;98(2):139151. PubMed ID: 16896734 doi:10.1007/s00421-
35. Galbraith A, Hopker J, Lelliott S, Diddams L, Passeld L. A single-
visit eld test of critical speed. Int J Sports Physiol Perform. 2014;9(6):
931935. PubMed ID: 24622815 doi:10.1123/ijspp.2013-0507
36. Mann TN, Lamberts RP, Lambert MI. High responders and low
responders: factors associated with individual variation in response to
standardized training. Sport Med. 2014;44(8):11131124. doi:10.
37. Buchheit M. The 3015 intermittent tness test: 10 year review.
Myorobie J. 2010;1(Top 14):19.
IJSPP Vol. 14, No. 4, 2019
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... Of these subjects, 101 were male and 31 were female. The mean age was 21.6 years, although this was not accounted for in 3 cases (8,33,35). Regarding the samples, 4 included male runners (7,26,33,35), 1 included female runners (21), and 2 were mixed samples (8,40). Furthermore, the level of the participants was not homogeneous, with 2 studies conducted with elitelevel subjects (26,33), 4 with experienced middle-distance athletes (7,8,21,35), and 1 with healthy, active students (40). ...
... The mean age was 21.6 years, although this was not accounted for in 3 cases (8,33,35). Regarding the samples, 4 included male runners (7,26,33,35), 1 included female runners (21), and 2 were mixed samples (8,40). Furthermore, the level of the participants was not homogeneous, with 2 studies conducted with elitelevel subjects (26,33), 4 with experienced middle-distance athletes (7,8,21,35), and 1 with healthy, active students (40). ...
... Regarding the samples, 4 included male runners (7,26,33,35), 1 included female runners (21), and 2 were mixed samples (8,40). Furthermore, the level of the participants was not homogeneous, with 2 studies conducted with elitelevel subjects (26,33), 4 with experienced middle-distance athletes (7,8,21,35), and 1 with healthy, active students (40). Table 1 shows the characteristics of the participants and the instruments used to measure the variables analyzed. ...
Anaerobic speed reserve (ASR) allows us to measure an athlete’s metabolic and neuromuscular capacities and to profile the different types of middle-distance runners. The main objective of this systematic review was to investigate the relationship between ASR and performance in middle-distance events. Five databases were consulted, and after the screening and selection process, 7 studies were selected. The results show that ASR has no relationship with performance. However, it may do so when one of its variables is equalized or considered as an interaction with its edges. Nonetheless, both maximal sprint speed and maximal aerobic speed influence performance in 800 and 1500 m, with major implications for pacing behavior or tactical decisions.
... Furthermore, little is known about the neuromuscular and mechanical requirements [15]. Sprint ability is a technical variable limited by force application and mechanics, as opposed to anaerobic energy supply [13] which has been related to male 800 m performance [16,17]. Moreover, speed application is considered a skill involving technical adjustments of stride length and frequency, allowing for smooth speed transitions at minimal energetic cost [2]. ...
... The anaerobic speed reserve (ASR) is a novel concept within the field of middle-distance running performance [17]; ASR is defined as the speed range between maximal aerobic speed (MAS -the minimal speed required to elicit VO 2 max, or vVO 2 max [18]) and maximal sprint speed (MSS -top end speed) [12,19,20]. Since middle-distance races are run at velocities within this speed bandwidth, i.e., above vVO 2 max, the ASR could provide a framework to understand the physiological, mechanical and neuromuscular profiles of middle-distance runners [15]. ...
... Sandford et al. [17] found that possessing a larger ASR, as a function of faster MSS, is a key factor that differentiates elite male 800 m performers. In theory, for the same absolute running velocity above VO 2 max, an athlete with a faster MSS will be working at a lower portion of their ASR; this represents a lower physiological load compared to an athlete with a smaller ASR and slower MSS [20]. ...
Objectives Middle-distance running represents a complex interplay of metabolic and mechanical factors. A better understanding of the requirements of male 800 m running has been proposed using the anaerobic speed reserve construct. However, the anaerobic speed reserve is yet to be investigated within female middle-distance running. Methods The anaerobic speed reserve, defined as the difference between maximal sprint speed and maximal aerobic speed, was assessed in 12 sub-elite female middle-distance runners using fastest 15 m sprint times and a maximal incremental treadmill test, respectively. Participants were allocated to either 400–800 m or 800 m–Mile subgroups. Comparisons between groups were made for anaerobic speed reserve, maximal sprint speed, maximal aerobic speed and the speed reserve ratio, defined as maximal sprint speed divided by maximal aerobic speed. The relationships between the anaerobic speed reserve components and 800 m season's best race times were assessed. Results Female 400–800 m middle-distance runners had a significantly larger anaerobic speed reserve (P = 0.013), faster maximal sprint speed (P = 0.001) and greater speed reserve ratio (P = 0.042) than runners in the 800 m–Mile group. There was a significant negative correlation between maximal aerobic speed and 800 m time (P = 0.012), but no statistically significant relationship was observed for anaerobic speed reserve (P = 0.900), speed reserve ratio (P = 0.558) or maximal sprint speed (P = 0.057). Conclusions Female middle-distance subgroups can be distinguished using the speed reserve ratio, with implications for coaches and physiologists to use the speed reserve ratio as a tool to characterize athletes and advise individualized training prescription. Aerobic power appears to underpin female 800 m performance as opposed to anaerobic or sprint abilities in these sub-elite athletes.
... Athletes need to sustain running velocities at and above maximal aerobic speed (MAS), deemed as the minimum speed at which maximum oxygen uptake is attained, and develop their sprinting ability to a great extent in order to achieve successful performances at major championships [3,4]. Although running economy and MAS are considered main middledistance running performance determinants [5], recent studies also highlight the important role of anaerobic qualities [6,7] such as anaerobic speed reserve (ASR), which is the speed zone ranging from MAS to maximal sprint speed (MSS) [8,9]. Given that elite middledistance runners display high levels of MAS [5] and anaerobic capacity, it seems that ASR should be considered to understand the underpinning mechanisms explaining their performance. ...
... However, the influence of all these mechanical parameters on running performance in middle-distance running events has not been explored sufficiently yet. Since increasing evidence suggests that performance in these events could be strongly connected to anaerobic characteristics and sprint ability [7,17], it would be useful for athletes and coaches to describe the aforementioned mechanical parameters in middle-distance runners and elucidate the relationship to performance determinants. ...
... The large correlation observed between MSS and 800 m performance is in line with findings from Sandford et al., who reported an influence of ASR on the variability of running performance in elite 800 m runners when assuming similar MAS values and, therefore, the ASR was determined by MSS [7]. Although results from the present study indicate that ASR was not significantly correlated with 800 m performance, the correlation was higher than that observed with 1500 m performance. ...
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This study aimed to compare sprint, jump performance, and sprint mechanical variables between endurance-adapted milers (EAM, specialized in 1500–3000-m) and speed-adapted milers (SAM, specialized in 800–1500 m) and to examine the relationships between maximal sprint speed (MSS), anaerobic speed reserve (ASR), sprint, jump performance, and sprint mechanical characteristics of elite middle-distance runners. Fifteen participants (8 EAM; 7 SAM) were evaluated to obtain their maximal aerobic speed, sprint mechanical characteristics (force–velocity profile and kinematic variables), jump, and sprint performance. SAM displayed greater MSS, ASR, horizontal jump, sprint performance, and mechanical ability than EAM (p < 0.05). SAM also showed higher stiffness in the 40-m sprint (p = 0.026) and a higher ratio of horizontal-to-resultant force (RF) at 10 m (p = 0.003) and RFpeak (p = 0.024). MSS and ASR correlated with horizontal (r = 0.76) and vertical (r = 0.64) jumps, all sprint split times (r ≤ −0.85), stiffness (r = 0.86), and mechanical characteristics (r ≥ 0.56) during the 100-m sprint, and physical qualities during acceleration (r ≥ 0.66) and sprint mechanical effectiveness from the force–velocity profile (r ≥ 0.69). Season-best times in the 800 m were significantly correlated with MSS (r = −0.86). Sprint ability has a crucial relevance in middle-distance runners’ performance, especially for SAM.
... 17 Instruction was given to fit the collected variables into 3 clusters, as per the coaches' observations and similarly to what has been done in middle-distance running. 2 The mean and SD of MSS-ice, MAS, and PB were calculated for each of the ASR profiles. The smallest worthwhile change was calculated as 0.2 × SD to allow the calculation of the effect size and magnitude-based inferences. ...
Full-text available
Purpose: Short-track speed skating race distances of 500, 1000, and 1500 m that last ∼40 seconds to ∼2.5 minutes and require a maximal intensity at speeds beyond maximal oxygen uptake (VO2max). Recently, the anaerobic speed reserve (ASR) has been applied by scientists and coaches in middle-distance sports to deepen understanding of 1- to 5-minute event performance where different physiological profiles (speed, hybrid, and endurance) can have success. Methods: World-class (women, n = 2; men, n = 3) and international-level (women, n = 4; men, n = 5) short-track speed skaters completed maximal aerobic speed and maximal skating speed tests. ASR characteristics were compared between profiles and associated with on-ice performance. Results: World-class athletes raced at a lower %ASR in the 1000- (3.1%; large; almost certainly) and 1500-m (1.8%; large; possibly) events than international athletes. Men's and women's speed profiles operated at a higher %ASR in the 500-m than hybrid and endurance profiles, whereas in the 1500-m, endurance profiles worked at a substantially lower %ASR than hybrid and speed profiles. Women's 500-m performance is very largely associated with maximal skating speed, while women's maximal aerobic speed appears to be a key determining factor in the 1000- and 1500-m performance. Conclusion: World-class short-track speed skaters can be developed in speed, hybrid, and endurance profiles but achieve their performance differently by leveraging their strongest characteristics. These results show nuanced differences between men's and women's 500-, 1000- and 1500-m event performance across ASR profile that unlock new insights for individualizing athlete performance in these disciplines.
... The average speed of an ideal distance TT would be in agreement with or closely approximate the GXT-derived measure of the MAS (Souza et al., 2014;Bellenger et al., 2015). Several researchers have tried to estimate the MAS based on multiple TT distances such as 1,200 m (Rick et al., 2016), 1,500 m (Gareth et al., 2018), 2,000 m (Bellenger et al., 2015), and 3,000 m (Paul et al., 1997). Among these distances, 2,000 and 3,000 m TTs were especially related to the MAS and most favored by previous studies (Daniel and Nathan 2015;Bellenger et al., 2015;Paul et al., 1997, 5;Jose et al., 2010;William et al., 2018;Huerta Ojeda et al., 2020). ...
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Considered to be a lesser resource burden, 2,000 and 3,000 m time trials (TTs) have been recognized as alternatives to accurately estimate the maximal aerobic speed (MAS) derived from laboratory-graded exercise testing (GXT). Previous studies have commonly used ordinary least squares linear regression and the Bland–Altman method to compare the agreement between MAS and TT performance. The agreement analysis aimed to identify the systematic bias between the results of the two methods, rather than to identify similarities. The model II regression technique (ordinary least product regression) is increasingly favored by researchers in the field of physiology. Thus, we aimed to 1) use the ordinary least product (OLP) and bootstrap methods to determine the agreement between the average speed of 2,000 m TT (S2000) and the average speed of 3,000 m TT (S3000) and 2) determine whether S2000 or S3000 can accurately approximate the GXT-derived MAS. It is used as an alternative to estimate the MAS and prescribe training intensity. Thirty-five Beijing Sport University recreational male runners completed an MAS test in laboratory settings, followed by 2,000 and 3,000 m TTs randomly, with a 7-day interval. OLP regression was used to analyze the agreement between the GXT-derived MAS and S2000 and S3000. The bootstrap method was used to calibrate the equations. Differences between the GXT-derived MAS and S2000 and S3000 were compared using a one-way repeated measure analysis of variance (ANOVA) and a post hoc analysis (Bonferroni). The significance level was p < 0.05. The results showed that before calibration, the 95% CI of the OLP regression intercept and slope between the GXT-derived MAS and S2000 and S3000 did not include 0 and 1.00, respectively. These values, after calibration, included 0 and 1.00, respectively. Post hoc analysis revealed that S3000 closely approximated the GXT-derived MAS and underestimated 0.46% (0.06 km h ⁻¹ and p > 0.05), and S2000 overestimated 5.49% (0.81 km h ⁻¹ and p < 0.05) by the MAS. It concluded that the 3,000 m TT performance approximated the GXT-derived MAS compared to the 2,000 m TT performance. There exist fixed bias and proportional bias between the GXT-derived MAS and TT performance. More attention should be applied to calibration when using the TT performance to estimate the MAS.
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Purpose.Locomotor profiling using anaerobic speed reserve (ASR) enables insights into athletes’ physiological and neuromuscular contributing factors and prescription of high-intensity training beyond maximal aerobic speed (MAS). Several methods have been developed to assess characteristics of ASR, i.e., methods to assess MAS and maximal sprinting speed (MSS). This systematic review aimed to determine the validity and reliability of different methods to assess MAS and MSS in running-based sports. Methods. A comprehensive search of the PubMed and Web of Science databases was conducted according to the PRISMA guidelines. Studies were included if they reported data on validity and/or reliability for methods to assess MAS or MSS. Results. 58 studies were included with 28 studies referring to MAS and 30 studies to MSS. Regarding MAS, cardio-pulmonary exercise testing (CPET; n=19), time trials (n=20), incremental continuous field tests (n=12) and shuttle runs (n=10) were examined. Different methods for CPET yielded different values (four out of seven studies) of MAS (Cohen’s d (ES)=0.83–2.8; Pearson’s r/intraclass correlation coefficient (r/ICC)=0.46–0.85). Criterion validity (against CPET) of different field tests showed heterogeneous results (ES=0–3.57; r/ICC=0.40–0.96). Intraday and interday reliability was mostly acceptable for the investigated methods (ICC/r>0.76; CV<16.9%). The studied methods to assess MSS included global or local positioning systems (GPS; LPS) (n=18), timing gates (n=8), radar/laser (n=3), treadmills (n=2), and video analysis (n=2). Radar and laser measurements (one out of one studies), timing gates (two out of two studies), and video analysis showed mostly good criterion validity (two out of two studies) (ES=0.02–0.53; r/ICC=0.93–0.98) and reliability (r/ICC>0.83; CV<2.43%). Criterion validity (ES=0.02–7.11) and reliability (r/ICC=0.14–0.97; CV=0.7–9.77%) for GPS or LPS (seven out of nine studies) and treadmill sprinting (one out of one studies) was not acceptable in most studies. Conclusion. The criterion validity of incremental field tests or shuttle runs to examine MAS cannot be confirmed. Results on time trials indicate that distances adapted to the participants’ sporting background, fitness, or sex might be suitable to estimate MAS. Regarding MSS, only sprints with radar or laser measures, timing gates, or video analysis provide valid and reliable results for linear sprints of 20 to 50 m.
Purpose: To determine whether i) a plasma acidosis contributes to a reduction of mechanical performance and ii) bicarbonate supplementation blunts plasma acidosis and arterial oxygen desaturation to resist fatigue during the end-spurt of a supramaximal trial in elite sprint and endurance cyclists. Methods: Elite/world-class cyclists (n = 6 sprint, n = 6 endurance) completed two randomized, double-blind, crossover trials at 105%V̇O2peak simulating 3-min of a 4-km individual pursuit, 90 min after ingestion of 0.3 g/kgBM sodium bicarbonate (BIC) or placebo (PLA). Peak power output (PPO), optimal cadence and optimal peak torque, and fatigue were assessed using a 6-s "all-out sprint" before (PPO1) and after (PPO2) each trial. Plasma pH, bicarbonate, lactate-, K+, Na+, Ca2+ and arterial hemoglobin saturation (SpO2(%)), were measured. Results: Sprint cyclists exhibited a higher PPO, optimal pedal torque, and anaerobic power reserve (APR) than endurance cyclists. The trial reduced PPO (PLA) more for sprint (to 47%initial) than endurance cyclists (to 61%initial). Optimal cadence fell from ~151 to 92rev/min and cyclists with higher APR exhibited a reduced optimal peak torque. Plasma pH fell from 7.35 to 7.13 and plasma [lactate-] increased from 1.2 to 19.6 mM (PLA), yet neither correlated with PPO loss. Sprint cyclists displayed a lesser plasma acidosis but greater fatigue than endurance cyclists. BIC increased plasma [HCO3-] (+6.8 mM) and plasma pH after PPO1 (+0.09) and PPO2 (+0.07) yet failed to influence mechanical performance. SpO2 fell from 99% to 96% but was unrelated to the plasma acidosis and unaltered with BIC. Conclusions: Plasma acidosis was not associated with the decline of PPO in a supramaximal trial with elite cyclists. BIC attenuated acid-base disturbances yet did not improve arterial oxygen desaturation or mechanical performance at the end-spurt stage.
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Purpose: Aim of this thesis was the identification and critical review of traditional and novel physiological and performance parameters for different threshold concepts and 1-, 2-, 3-km time-trial (TT) running. Methods: Physiological tests and TTs were carried out in a group of sprinters (n = 6), middle- and long-distance (n = 16) and ultra-runners (n = 3). Relationship between TT performance and physiological (V̇O2max, RE, %V̇O2max, MFO, V̇Lamax, dLa100) as well as performance parameters (vV̇O2max, vMLSS, CV, Fatmax, D’, ASR, SRR) was assessed, Additionally, correlations between all investigated parameters and agreement between velocity at different threshold concepts (vOBLA, vMLSS and CV) was analyzed. Results: V̇O2max and CV presented the strongest positive relationship with 2- (r = 0.81, r = 0.84) and 3-km (r = 0.89, r = 0.98) TT performance among physiological and performance parameters respectively. V̇Lamax, La100, D’, ASR and SRR were positively correlated with sprint performance (r = 0.73, r = 0.54, r = 0.69, r = 0.56, r = 0.43) and negatively with 2- (r = -0.41, r = -0.46, r = -0.37, r = -0.71, r = -0.81) an 3-km (r = -0.50, r = -0.53, r = -0.62, r = -0.85, r = -0.91) TT performance and vMLSS r = -0.48, r = -0.51, r = -0.62, r = -0.79, r = -0.86). Correlations coefficients for 1-km TT were lower compared to 2- and 3-km. Strong agreement was found between threshold concepts (vMLSS – vOBLA: R2 = 0.94; vMLSS – CV: R2 = 0.83) and mean differences amounted to -0.08 and -0.49 m·s-1. Conclusion: Parameters linked to aerobic metabolism displayed the strongest relationship with TTs. While anaerobic variables correlated positively with sprint performance the relationships became increasingly negative with increasing distance of TT. It can be hypothesized that influence of anaerobic metabolism is in balance for maximal running efforts around three minutes. Efforts slower than this balance point might tend to benefit from anaerobic metabolism while longer efforts might be affected in a detrimental way. Prediction of TT and threshold velocity was more accurate through performance than physiological parameters. Based on these findings, novel parameters can complement traditional test variables in running. Deliberate and differential selection of test parameters is advised for performance prediction or physiological training prescription in running and depending on race distance.
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The aim of this study was to investigate physiological and performance adaptations to high-intensity interval training (HIIT) prescribed as a proportion of anaerobic speed reserve (ASR) compared to HIIT prescribed using maximal aerobic speed (MAS). Twenty-four highly trained sprint kayak athletes were randomly allocated to one of three 4-weak conditions (N = 8) (ASR-HIIT) two sets of 6 × 60 s intervals at ∆%20ASR (MAS-HIIT) six 2 min paddling intervals at 100% maximal aerobic speed (MAS); or controls (CON) who performed six sessions/week of 1-h traditional endurance paddling at 70%–80% maximum HR. A graded exercise test was performed on a kayak ergometer to determine peak oxygen uptake (V̇O2peak), MAS, V̇O2/HR, and ventilatory threshold. Also, participants completed four consecutive upper-body wingate tests to asses peak and average power output. Significant increases in V̇O2peak (ASR-HIIT = 6.9%, MAS-HIIT = 4.8%), MAS (ASR-HIIT = 7.2%, MAS-HIIT = 4.8%), ASR (ASR-HIIT = −25.1%, MAS-HIIT = −15.9%), upper-body Wingate peak power output and average power output (p < 0.05 for both HIIT groups) were seen compared with pre-training. Also, ASR-HIIT resulted in a significant decrease in 500-m − 1.9 % , and 1,000 − m − 1.5 % paddling time. Lower coefficient of variation values were observed for the percent changes of the aforementioned factors in response to ASR-HIIT compared to MAS-HIIT. Overall, a short period of ASR-HIIT improves 500-m and 1,000-m paddling performances in highly trained sprint kayak athletes. Importantly, inter-subject variability (CV) of physiological adaptations to ASR-HIIT was lower than MAS-HIIT. Individualized prescription of HIIT using ASR ensures similar physiological demands across individuals and potentially facilitates similar degrees of physiological adaptation.
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Optimizing physical performance is a major goal in current physiology. However, basic understanding of combining high sprint and endurance performance is currently lacking. This study identifies critical determinants of combined sprint and endurance performance using multiple regression analyses of physiologic determinants at different biologic levels. Cyclists, including 6 international sprint, 8 team pursuit, and 14 road cyclists, completed a Wingate test and 15-km time trial to obtain sprint and endurance performance results, respectively. Performance was normalized to lean body mass2/3 to eliminate the influence of body size. Performance determinants were obtained from whole-body oxygen consumption, blood sampling, knee-extensor maximal force, muscle oxygenation, whole-muscle morphology, and muscle fiber histochemistry of musculusvastus lateralis Normalized sprint performance was explained by percentage of fast-type fibers and muscle volume (R2 = 0.65; P < 0.001) and normalized endurance performance, by performance oxygen consumption (V̇o2), mean corpuscular hemoglobin concentration, and muscle oxygenation (R2 = 0.92; P < 0.001). Combined sprint and endurance performance was explained by gross efficiency, performance V̇o2, and likely by muscle volume and fascicle length (P = 0.056; P = 0.059). High performance V̇o2 related to a high oxidative capacity, high capillarization × myoglobin, and small physiologic cross-sectional area (R2 = 0.67; P < 0.001). Results suggest that fascicle length and capillarization are important targets for training to optimize sprint and endurance performance simultaneously.-Van der Zwaard, S., van der Laarse, W. J., Weide, G., Bloemers, F. W., Hofmijster, M. J., Levels, K., Noordhof, D. A., de Koning, J. J., de Ruiter, C. J., Jaspers, R. T. Critical determinants of combined sprint and endurance performance: an integrative analysis from muscle fiber to the human body.
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This study analysed the relationships between sprinting, jumping and strength abilities, with regard to 800 m running performance. Fourteen athletes of national and international levels in 800 m (personal best: 1:43-1:58 min:ss) completed sprint tests (20 m and 200 m), a countermovement jump, jump squat and full squat test as well as an 800 m race. Significant relationships (p < 0.01) were observed between 800 m performance and sprint tests: 20 m (r = 0.72) and 200 m (r = 0.84). Analysing the 200 m run, the magnitude of the relationship between the first to the last 50 m interval times and the 800 m time tended to increase (1st 50 m: r = 0.71; 2nd 50 m: r = 0.72; 3rd 50 m: r = 0.81; 4th 50 m: r = 0.85). Performance in 800 m also correlated significantly (p < 0.01-0.05) with strength variables: the countermovement jump (r = -0.69), jump squat (r = -0.65), and full squat test (r = -0.58). Performance of 800 m in high-level athletes was related to sprint, strength and jumping abilities, with 200 m and the latest 50 m of the 200 m being the variables that most explained the variance of the 800 m performance.
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Purpose: To assess the longitudinal evolution of tactical behaviours used to medal in Men's 800m (M800) Olympic Games (OG) or World Championship (WC) events in the recent competition era (2000-2016). Methods: Thirteen OG and WC events were characterised for first and second lap splits using available footage from YouTube. Positive pacing strategies were defined as a faster first lap. Season's best M800 time and world ranking, reflective of an athlete's 'peak condition', was obtained to determine relationships between adopted tactics and physical condition prior to the championships. Seven championship events provided coverage of all medallists to enable determination of average 100m speed and sector pacing of medallists. Results: From 2011 onwards, M800 OG and WC medallists showed a faster first lap by 2.2 ±1.1s (mean, ±90% confidence limits; large difference, very likely), contrasting a possibly faster second lap in 2000-2009 (0.5, ±0.4s; moderate difference). A positive pacing strategy was related to a higher world ranking prior to the championships (r=0.94, 0.84 to 0.98; extremely large, most likely). After 2011, the fastest 100m sector from M800 OG and WC medallists was faster than before 2009 by 0.5, ±0.2m/s (large difference, most likely). Conclusions: A secular change in tactical racing behaviour appears evident in M800 championships; since 2011, medallists have largely run faster first laps and have faster 100m sector speed requirements. This finding may be pertinent for training, tactical preparation and talent identification of athletes preparing for M800 running at OG and WC.
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Purpose: To assess if short duration (5 to ~300 s) high-power performance can accurately be predicted using the anaerobic power reserve (APR) model, in professional cyclists. Method: Data from four professional cyclists from a World Tour cycling team were used. Using the maximal aerobic power, sprint peak power output and an exponential constant describing the decrement in power over time a power-duration relationship was established for each participant. To test the predictive accuracy of the model, several all-out field trials of different durations were performed by each cyclist. The power output achieved during the all-out trials was compared to the predicted power output by the APR model. Results: The power output predicted by the model showed very large to nearly perfect correlations to the actual power output obtained during the all-out trials for each cyclist (r= 0.88±0.21; 0.92±0.17; 0.95±0.13; 0.97±0.09). Power output during the all-out trials remained within an average of 6.6% (53W) of the predicted power output by the model. Conclusions: This preliminary pilot study presents four case studies on the applicability of the APR model in professional cyclists using a field-based approach. The decrement in all-out performance during high-intensity exercise seems to conform to a general relationship with a single exponential decay model describing the decrement in power versus increasing duration. These results are in line with previous studies using the APR model to predict performance during brief all-out trials. Future research should evaluate the APR model with a larger sample size of elite cyclists.
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A limited number of studies have examined the distribution of training at different intensities during longer training periods among elite runners. Runners who want to reach international level in distance running should run ≥110 km/week at the age of 18–19 years. For senior runners, it appears that training volumes around 150–200 km/week are appropriate for 5000 and 10,000 m runners and 120–160 km/week for 1500 m runners. It also appears to be beneficial to combine these weekly training volumes with two to four sessions per week at the velocity at the anaerobic threshold pace, and one to two sessions per week above velocity at the anaerobic threshold pace during the preparation period. For runners who compete over distances from 1500 to 10,000 m, it seems appropriate to reduce the number of sessions carried out at velocity at the anaerobic threshold pace and to increase the number of sessions at specific race pace in the pre-competition period and during the competition period. Top results for the marathon can be achieved by a “low volume/high intensity model” (150–200 km/week), as well as by a “high volume/low intensity model” (180–260 km/week).
Radar technology can be used to perform horizontal force–velocity–power profiling during sprint-running. The aim of this study was to determine the reliability of radar-derived profiling results from short sprint accelerations. Twenty-seven participants completed three 30 m sprints (intra-day analysis), and nine participants completed the testing session on four separate days (inter-day analysis). The majority of radar-derived kinematic and kinetic descriptors of short sprint performance had acceptable intra-day and inter-day reliability [intraclass correlation coefficient (ICC) ≥ 0.75 and coefficient of variation (CV) ≤ 10%], but split times over the initial 10 m and some variables that include a horizontal force component had only moderate relative reliability (ICC = 0.49–0.74). Comparing the average of two sprint trials between days resulted in acceptable reliability for all variables except the relative slope of the force–velocity relationship (SFvrel; ICC = 0.74). Practitioners should average sprint test results over at least two trials to reduce measurement variability, particularly for outcome variables with a horizontal force component and for sprint distances of less than 10 m from the start.
Objectives To investigate the impact of training modification on achieving performance goals. Previous research demonstrates an inverse relationship between injury burden and success in team sports. It is unknown whether this relationship exists within individual sport such as athletics. Design A prospective, cohort study (n = 33 International Track and Field Athletes; 76 athlete seasons) across five international competition seasons. Methods Athlete training status was recorded weekly over a 5-year period. Over the 6-month preparation season, relationships between training weeks completed, the number of injury/illness events and the success or failure of a performance goal at major championships was investigated. Two-by-two table were constructed and attributable risks in the exposed (AFE) calculated. A mixed-model, logistic regression was used to determine the relationship between failure and burden per injury/illness. Receiver Operator Curve (ROC) analysis was performed to ascertain the optimal threshold of training week completion to maximise the chance of success. Results Likelihood of achieving a performance goal increased by 7-times in those that completed >80% of planned training weeks (AUC, 0.72; 95%CI 0.64-0.81). Training availability accounted for 86% of successful seasons (AFE = 0.86, 95%CI, 0.46 to 0.96). The majority of new injuries occurred within the first month of the preparation season (30%) and most illnesses occurred within 2-months of the event (50%). For every modified training week the chance of success significantly reduced (OR = 0.74, 95%CI 0.58 to 0.94). Conclusions Injuries and illnesses, and their influence on training availability, during preparation are major determinants of an athlete's chance of performance goal success or failure at the international level.