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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.
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... 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]. ...
Full-text available
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.
... Furthermore, it is recognized that elite male 800 m runners possess biomechanical and neuromuscular abilities that underpin fast maximal sprinting speed (MSS), which is a requirement for competitive success (Sandford et al., 2018. Sandford et al. found that elite male 800 m runners have a large anaerobic speed reserve (ASR) -the difference between MSS and the velocity at VO 2 max (Sandford et al., 2018). ...
... Furthermore, it is recognized that elite male 800 m runners possess biomechanical and neuromuscular abilities that underpin fast maximal sprinting speed (MSS), which is a requirement for competitive success (Sandford et al., 2018. Sandford et al. found that elite male 800 m runners have a large anaerobic speed reserve (ASR) -the difference between MSS and the velocity at VO 2 max (Sandford et al., 2018). Possessing a large ASR may be advantageous because it signifies the athlete has a large "race pace" speed bandwidth in which they can adjust velocity to produce mid-race surges and end-kicks, as well as the force application abilities to execute very fast starting velocities . ...
... Perhaps females are "training smarter" in these longer events, leading to greater relative improvement compared to males. There may be scope for female 800 m runners to narrow the sex gap by tapping into the ASR domain, which is evidently a successful approach for male middle-distance runners (Sandford et al., 2018). There is a male sex bias in middle-distance running research (Mpholwane, 2007) which means that coaches tend to train female athletes using strategies that have been validated in males, but do not take into consideration aforementioned sex differences. ...
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Males consistently outperform females in athletic endeavors, including running events of standard Olympic distances (100 m to Marathon). The magnitude of this percentage sex difference, i.e., the sex gap, has evolved over time. Two clear trends in sex gap evolution are evident; a narrowing of the gap during the 20th century, followed by a period of stability thereafter. However, an updated perspective on the average sex gap from top 20 athlete performances over the past two decades reveals nuanced trends over time, indicating the sex gap is not fixed. Additionally, the sex gap varies with performance level; the difference in absolute running performance between males and females is lowest for world record/world lead performances and increases in lower-ranked elite athletes. This observation of an increased sex gap with world rank is evident in events 400 m and longer and indicates a lower depth in female competitive standards. Explanations for the sex difference in absolute performance and competition depth include physical (physiological, anatomical, neuromuscular, biomechanical), sociocultural, psychological, and sport-specific factors. It is apparent that females are the disadvantaged sex in sport; therefore, measures should be taken to reduce this discrepancy and enable both sexes to reach their biological performance potential. There is scope to narrow the sex performance gap by addressing inequalities between the sexes in opportunities, provisions, incentives, attitudes/perceptions, research, and media representation.
... During men's 800-m championship races that are characterized by a positive pacing strategy, the fastest 100-m split of the race occurs in the first 200 m and may exceed 9.0 m·s −1 (1,4). In more conservative 800-m races, a slow first lap (typically >53.0 s) is followed by an increase in speed from the 500-to 600-m mark, whereas medallists tend to further increase their speed to the finish line over the final 200-m, with the fastest 100-m split (~8.0-8.3 m·s −1 ) occurring in this final sector (2). ...
... and 200 m (r = −0.84), whereas Sandford et al. (4) reported that a greater maximal sprint speed (MAX SS ) was associated with faster 800-m times (r = −0.74) in elite 800-m runners (800-m PB times ≤1:47.50). Although these studies (4,11) have demonstrated that speed capabilities may be important for 800-m performance, similar to previous studies (6-10), 800-m performance was assessed under conditions where athletes are attempting to run the fastest time possible, such as a maximal time trial (6,9,10), simulated maximal time trial on a treadmill (7), or a "gun-to-tape" recent best performance (4,8,11). ...
... whereas Sandford et al. (4) reported that a greater maximal sprint speed (MAX SS ) was associated with faster 800-m times (r = −0.74) in elite 800-m runners (800-m PB times ≤1:47.50). Although these studies (4,11) have demonstrated that speed capabilities may be important for 800-m performance, similar to previous studies (6-10), 800-m performance was assessed under conditions where athletes are attempting to run the fastest time possible, such as a maximal time trial (6,9,10), simulated maximal time trial on a treadmill (7), or a "gun-to-tape" recent best performance (4,8,11). It is conceivable that the underpinning determinants of performance may differ when 800-m trials are completed with a positive pacing strategy compared to a slower first lap, but with an all-out last lap. ...
Purpose: We aimed to identify the underpinning physiological and speed/mechanical determinants of different types of 800-m running time trials (i.e., with a positive or negative pacing strategy) and key components within each 800-m time trial (i.e., first and final 200-m). Methods: Twenty trained male 800-m runners (800-m personal best time (min:s): 1:55.10 ± 0:04.44) completed a maximal 800-m time trial (800MAX) and one pacing trial, whereby runners were paced for the first lap and speed was reduced by 7.5% (800PACE) relative to 800MAX, while the last lap was completed in the fastest time possible. Anaerobic speed reserve, running economy, the velocity corresponding with VO2peak (VVO2peak), maximal sprint speed (MAXSS), maximal accumulated oxygen deficit and sprint force-velocity-power profiles were derived from laboratory and field testing. Carnosine content was quantified by proton magnetic resonance spectroscopy in the gastrocnemius and soleus and expressed as a carnosine aggregate Z-score (CAZ-score) to estimate muscle typology. Data were analysed using multiple stepwise regression analysis. Results: MAXSS and vVO2peak largely explained the variation in 800MAX time (r2 = 0.570; P = 0.020), while MAXSS was the best explanatory variable for the first 200-m time in 800MAX (adjusted r2 = 0.661, P < 0.001). Runners with a higher CAZ-score (i.e., higher estimated percentage of type II fibres) reduced their last lap time to a greater extent in 800PACE relative to 800MAX (adjusted r2 = 0.413, P < 0.001), while better maintenance of mechanical effectiveness during sprinting, a higher CAZ-score and vVO2peak was associated with a faster final 200-m time during 800PACE (adjusted r2 = 0.761, P = 0.001). Conclusion: These findings highlight that diversity in the physiological and speed/mechanical characteristics of male middle-distance runners may be associated with their suitability for different 800-m racing strategies in order to have the best chance of winning.
... Despite an increasing amount of research devoted to middle-distance training [e.g., [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17], it is reasonable to argue that the developments in these disciplines have not been driven by sport scientists [18]. Publicly available "recipe books" and training diaries based upon the practical experience and intuition of world-leading athletes and coaches have become important and popular sources of best practice training information and framework development for the international middle-distance community (Table 1). ...
... While traditional endurance disciplines can be described as maximization challenges (i.e., training that enhances VO 2max or fractional utilization is "always positive" for performance), we propose that the 800-m event in particular requires an energy release optimization strategy that respects the interactions and trade-offs between anaerobic and aerobic metabolism emerging in both training and performance. This complexity allows internationally successful middledistance runners to present a variety of physiological profiles [12][13][14][15]. For example, VO 2max ranges from ~ 65 to 85 ml·kg·min −1 in elite men [16,29,70,71]. ...
... Power output and technique are considered key underlying determinants for MSS [74]. Fast male world-class middle-distance runners may approach 10 m·s −1 [12,15], and if we assume a ~ 10% sex difference [75], corresponding females are capable of sprinting ≥ 9 m·s −1 . To achieve such running velocities, maximal horizontal power outputs of ~ 21 and ~ 19 W·kg −1 are required for men and women, respectively [76]. ...
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Despite an increasing amount of research devoted to middle-distance training (herein the 800 and 1500 m events), information regarding the training methodologies of world-class runners is limited. Therefore, the objective of this review was to integrate scientific and best practice literature and outline a novel framework for understanding the training and development of elite middle-distance performance. Herein, we describe how well-known training principles and fundamental training characteristics are applied by world-leading middle-distance coaches and athletes to meet the physiological and neuromus-cular demands of 800 and 1500 m. Large diversities in physiological profiles and training emerge among middle-distance runners, justifying a categorization into types across a continuum (400-800 m types, 800 m specialists, 800-1500 m types, 1500 m specialists and 1500-5000 m types). Larger running volumes (120-170 vs. 50-120 km·week −1 during the preparation period) and higher aerobic/anaerobic training distribution (90/10 vs. 60/40% of the annual running sessions below vs. at or above anaerobic threshold) distinguish 1500-and 800-m runners. Lactate tolerance and lactate production training are regularly included interval sessions by middle-distance runners, particularly among 800-m athletes. In addition, 800-m runners perform more strength, power and plyometric training than 1500-m runners. Although the literature is biased towards men and "long-distance thinking," this review provides a point of departure for scientists and practitioners to further explore and quantify the training and development of elite 800-and 1500-m running performance and serves as a position statement for outlining current state-of-the-art middle-distance training recommendations.
... faster 800-m runners have a larger ASR, related to their higher MSS. Importantly, in athletes with similar MSS, differences in MAS or ASR showed no relationship with 800-m performance time [9]. In athlete populations with lower performance times, aerobic markers (VO 2 max, running economy) were in fact related to 800-m performance [65], implying that determinants of performance may be different at the highest levels of elite sport. ...
... An athlete's locomotor profile reflects their individual propensity for dominance in speed versus endurance aptitude. Several studies in elite athletes reveal diversity of locomotor profiles exist within the same groups of individual [4,9,75] or team sport athletes [76][77][78]. Therefore, when beginning a training plan, locomotor profiling is recommended to calibrate the training approach relative to each individual athlete profile (Fig. 4, Sect. ...
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Many individual and team sport events require extended periods of exercise above the speed or power associated with maximal oxygen uptake (i.e., maximal aerobic speed/power, MAS/MAP). In the absence of valid and reliable measures of anaerobic metabolism, the anaerobic speed/power reserve (ASR/APR) concept, defined as the difference between an athlete’s MAS/MAP and their maximal sprinting speed (MSS)/peak power (MPP), advances our understanding of athlete tolerance to high speed/power efforts in this range. When exercising at speeds above MAS/MAP, what likely matters most, irrespective of athlete profile or locomotor mode, is the proportion of the ASR/APR used, rather than the more commonly used reference to percent MAS/MAP. The locomotor construct of ASR/APR offers numerous underexplored opportunities. In particular, how differences in underlying athlete profiles (e.g., fiber typology) impact the training response for different ‘speed’, ‘endurance’ or ‘hybrid’ profiles is now emerging. Such an individualized approach to athlete training may be necessary to avoid ‘maladaptive’ or ‘non-responses’. As a starting point for coaches and practitioners, we recommend upfront locomotor profiling to guide training content at both the macro (understanding athlete profile variability and training model selection, e.g., annual periodization) and micro levels (weekly daily planning of individual workouts, e.g., short vs long intervals vs repeated sprint training and recovery time between workouts). More specifically, we argue that high-intensity interval training formats should be tailored to the locomotor profile accordingly. New focus and appreciation for the ASR/APR is required to individualize training appropriately so as to maximize athlete preparation for elite competition.
... Training for the 800-m run entails a big challenge to middledistance coaches, probably due to the complexity of the event, as a variety of factors are related to performance over this distance. Performance in middle-distance events is characterized by both aerobic and anaerobic system contribution, 1,2 together with other factors such as biomechanics, muscle strength, and speed, 3,4 with the challenge being to run at high velocities while still maintaining economical movement. In addition, in the last decade, a changing on the run strategy has been observed during the men's championship 800-m event, showing predominantly a "gun-to-tape" type race tactic in the finals, which means running at a high pace from the start of the race to the end. ...
Purpose: To analyze the relationships between the evolution of training-load values and countermovement jump (CMJ) as an indicator of stress and fatigue in a high-level 800-m runner during a whole season, including indoor (ID) and outdoor season (OD). Methods: Over 42 weeks, daily training load was quantified as the result of the product of the intensity and volume, and it was termed load index (LI). CMJ was measured in every running session after warm-up and immediately after the last effort of the session. Other jump-related variables such as CMJ height loss, average weekly CMJ, initial CMJ of the next consecutive session, and initial CMJ of the following week were studied. Results: A significant negative relationship was observed between LI and weekly CMJ (ID: r = -.68, P < .001, common variance [CV] = 46%; OD: r = -.73, P < .001, CV = 53%), initial CMJ of the following week (OD: r = -.71, P < .01, CV = 50%), and CMJ height loss (ID: r = -.58, P < .01, CV = 34%; OD: r = -.52, P < .01, CV = 27%). A significant positive relationship was observed between LI and initial CMJ of the next consecutive session when LI values were <8 (OD: r = .72; P < .01, CV = 52%). However, from values ≥8, the relationship turned into a significant negative one (ID: r = -.74; P < .01, CV = 55%; OD: r = -64, P < .01, CV = 41%). Conclusions: CMJ may be a valid indicator of the degree of stress or fatigue generated by specific training sessions of a competitive athlete within a single session, a week, or even the following week. There could be an individual limit LI value from which the training volume does not allow a positive effect on high-speed actions such as a CMJ in the next consecutive session.
Background We tested the hypothesis that multiple obesity-related risk factors (obesity, physical activity, cardiopulmonary physical fitness, sleep-disorder breathing (SDB), and sleep quality) are associated with childhood asthma using a Mendelian randomization (MR) design. Furthermore, we aim to investigate whether these risk factors were associated with incident asthma prospectively. Methods In total, 7069 children aged 12 from the Taiwan Children Health Study were enrolled in the current study. Cross-sectional logistic regression, one-sample MR, summary-level MR sensitivity analyses, and prospective survival analyses were used to investigate each causal pathway. Results In MR analysis, three of the five risk factors (obesity, SDB, and sleep quality) were associated with asthma, with the highest effect sizes per interquartile range (IQR) increase observed for sleep quality (odds ratio [OR] =1.42; 95% confidence interval [CI]: 1.06 to 1.92) and the lowest for obesity (OR = 1.08; 95% CI: 1.00–1.16). In the prospective survival analysis, obesity showed the highest risk of incident asthma per IQR increase (hazard ratio [HR] = 1.28; 95% CI: 1.05 to 1.56), followed by SDB (HR = 1.18; 95% CI: 1.08 to 1.29) and sleep quality (HR = 1.10; 95% CI: 1.03 to 1.17). Conclusion Among the examined factors, the most plausible risk factors for asthma were obesity, SDB, and poor sleep quality. For the prevention of childhood asthma, relevant stakeholders should prioritize improving children’s sleep quality and preventing obesity comorbidities such as SDB.
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BACKGROUND: CrossFit is becoming popular over the past few years, and various supplementation ways have been utilized by exercise physiologists to enhance CrossFit athletes’ performance. This study aimed to evaluate the effects of consuming pre-workout carbohydrateprotein supplements on CrossFit athletes’ performance. METHODS: Well-trained CrossFit athletes (8 men; 25.62 ± 3.02 years) were randomized to a singleblind, placebo controlled, crossover design (7-day washout) to performed six bouts of two CrossFit workouts: Fight Gone Bad (FGB) and Cindy (CI). One hour and immediately before the onset of each bout, the subjects consumed carbohydrate-protein supplement in two ratios (2:2 or 3:1) or Placebo (P): FGB + 2:2, FGB + 3:1, FGB + P, CI + 2:2, CI + 3:1, and CI + P. To value the differentiation in performances, the performed each subject repetitions in FGB and CI were recorded in the bouts. RESULTS: Repeated measure analysis of variance was used to analyze the data, and the level of significance set for the study was p ≤ 0.05. No significant difference was observed in the total number of repetitions performed in FGB (p= 0.275) or CI (p= 0.789) workouts in supplements and placebo groups. CONCLUSIONS: These results indicate that acute consumption of pre-workout carbohydrateprotein supplement may not enhance the CrossFit athletes’ performance in FGB and CI workouts.
BACKGROUND: The aim of the present study was to provide physical and strength-related characteristics of 800 m athletes with regard to their initial performance level. METHODS: Fourteen male athletes of different levels in 800 m running (with personal best ranging from 1'43" s to 1'58") participated in this study and were divided into three groups according to their competitive level: High-level, Medium-level, and Low-level. Athletes performed a 800 m running trial and a battery of strength-related tests which involved sprint tests (10, 20, and 200 m), countermovement jump (CMJ), jump squat (JS), and full squat (SQ) tests. RESULTS: Significant differences between the High-level and the Low-level groups were observed in all the sprint variables measured: 10 and 20 m sprint (P<0.01), 200 m (P<0.01), showing the High-level group better performance in all the sprint tests. With regard to the strength-related tests (CMJ, JS, SQ V 1 load, SQ 1RM) although no significant differences between groups were found, a clear tendency to a better performance in all the strength-related variables for the High-level group can be observed (CMJ, P=0.08; JS, P=0.07; SQ V 1 load, P=0.1), as well as a better performance for the Medium-level group when compared to the Low-level group. CONCLUSIONS: The results of the present study indicate that the athletes with higher strength and sprint performance levels were those with a higher 800 m performance level. These findings suggest the importance of strength and sprint levels in 800 m running performance.
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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.