Available via license: CC BY
Content may be subject to copyright.
EDITED BY
Jordan Turner Andersen,
Macquarie University, Australia
REVIEWED BY
Paul Stapley,
University of Wollongong, Australia
José Fernández Sáez,
University Institute for Primary Care Research
(IDIAP Jordi Gol), Spain
*CORRESPONDENCE
Christian Weich
christian.weich@uni-konstanz.de
SPECIALTY SECTION
This article was submitted to Biomechanics and
Control of Human Movement, a section of the
journal Frontiers in Sports and Active Living
RECEIVED 10 October 2022
ACCEPTED 22 November 2022
PUBLISHED 16 December 2022
CITATION
Weich C, Barth V, Killer N, Vleck V, Erich J and
Treiber T (2022) Discovering the sluggishness of
triathlon running - using the attractor method
to quantify the impact of the bike-run
transition.
Front. Sports Act. Living 4:1065741.
doi: 10.3389/fspor.2022.1065741
COPYRIGHT
© 2022 Weich, Barth, Killer, Vleck, Erich and
Treiber. This is an open-access article
distributed under the terms of the Creative
Commons Attribution License (CC BY). The use,
distribution or reproduction in other forums is
permitted, provided the original author(s) and
the copyright owner(s) are credited and that the
original publication in this journal is cited, in
accordance with accepted academic practice.
No use, distribution or reproduction is
permitted which does not comply with these
terms.
Discovering the sluggishness of
triathlon running - using the
attractor method to quantify the
impact of the bike-run transition
Christian Weich1*, Valentin Barth2, Nikolai Killer1,3,
Veronica Vleck4, Julian Erich1and Tobias Treiber1
1
Sports Science Department, University of Konstanz, Konstanz, Germany,
2
Physics Department,
University of Konstanz, Konstanz, Germany,
3
Computer Science Department, University of Konstanz,
Konstanz, Germany,
4
Interdisciplinary Centre for the Study of Human Performance (CIPER),
Faculdade de Motricidade Humana, University of Lisbon, Cruz Quebrada-Dafundo, Portugal
Running in a triathlon, a so-called brick run, is uniquely influenced by
accumulated load from its preceding disciplines. Crucially, however, and
irrespective of race type, the demands of a triathlon always exceed the sum
of its parts. Triathletes of all levels commonly report subjectively perceived
incoordination within the initial stages of the cycle run transition (T2).
Although minimizing it, and its influence on running kinematics, can
positively impact running and overall triathlon performance, the mechanisms
behind the T2 effect remain unclear. In the present study, we assessed the
influence of the pre-load exercise mode focusing on the biomechanical
perspective. To analyze inertial sensor-based raw data from both legs, the
so-called Attractor Method was applied. The latter represents a sensitive
approach, allowing to quantify subtle changes of cyclic motions to uncover
the transient effect, a potentially detrimental transient phase at the beginning
of a run. The purpose was to analyze the impact of a pre-load on the
biomechanics of a brick run during a simulated Olympic Distance triathlon
(without the swimming section). Therefore, we assessed the influence of
pre-load exercise mode on running pattern (δM) and precision (δD), and on
the length of the transient effect (t
T
) within a 10 km field-based run in 22
well-trained triathletes. We found that δD, but not δM, differed significantly
between an isolated run (I
Run
) and when it was preceded by a 40 km cycle
(T
Run
) or an energetically matched run (R
Run
). The average distance ran until
overcoming the transient phase (t
T
) was 679 m for T
Run
, 450 m for R
Run
, and
29 4 m for I
Run
. The results demonstrated that especially the first kilometer of
a triathlon run is prone to an uncoordinated running sensation, which is also
commonly reported by athletes. That is, i) the T2 effect appeared more
linked to variability in running style than to running style per se ii) run t
T
distance was influenced by preceding exercise load mode, being greater for
aT
Run
than for the R
Run
condition, and iii) the Attractor Method seemed to
be a potentially promising method of sensitively monitoring T2 adaptation
under ecologically valid conditions.
KEYWORDS
attractor method, human cyclic motion, triathlon, running biomechanics, brick run
TYPE Original Research
PUBLISHED 16 December 2022
|
DOI 10.3389/fspor.2022.1065741
Frontiers in Sports and Active living 01 frontiersin.org
Introduction
Triathlon involves consecutive swimming, cycling and
running over a variety of distances and formats (1,2). At the
elite, but not at the amateur (or so-called age-group) level of
competition, the cycle section is draft-legal. These factors in turn
affect both the level and the distribution of exercise intensity
that the athlete experiences within each individual discipline of a
race (3,4). Crucially, however, and irrespective of race type, the
demands of a triathlon always exceed the sum of its parts. Both,
triathlon cycling,and running are influenced by the demands of
their preceding discipline (s), with the most obvious effects of
this being manifested up to approximately seven minutes from
the bike dismount (5). Over the cycle-run transition (T2),
defined as the period from the last kilometer of the cycle section
through to the end of the first kilometer of the run, athletes
often sense a lack of coordination (5). This may result in altered
running kinematics, with adverse consequences for the athletés
running performance (6). The overall relative contribution of
this triathlon run to race performance generally differs with
event distance, within both non-drafting and draft-legal triathlon
(7–10). In draft legal triathlon, it has been demonstrated to
become increasingly decisive, the better the athletes (11–15).
This was shown clearly by the analysis that was carried out by
Piacentini et al. (16) of the performance, over the two Olympic
cycles from 2009 to 2016, of competitors in the World Triathlon
Series (WTS), i.e., the highest level of Sprint and Olympic
distance (OD) competition below the Olympic Games. Athletes
were divided into 4 groups according to their final race placing
(G1: 1
st
–3
rd
place; G2: 4–8
th
place; G3: 8–16
th
place and G4: ≥
17
th
place). For females, there were significant differences in the
swim and bike segment only between G4 and the other groups,
whilst for the run segment each group differed significantly
from each other. For males, there were significant differences in
swim only between G4 and the other groups, whilst for the
running segment each group differed significantly from the
others. Essentially, the athletés swimming ability affected how
many seconds they exited the swim behind the race leader, and
their capability to attain the leading bike pack(s)- within which
it was apparently important for overall success that a good
runner be positioned (17,18). Importantly, over all the years
and races that were analyzed, both the female and the male
winners had, on average, the 2nd run split; the second finisher
exhibited, on average, the 4
th
run split; whilst the third finisher
had, on average, the 5
th
run split -despite there being no
particular differences between the first three athletes in their
position at the exit from T2. Analysis, over six World
ChampionshipsandthreeOlympicGames,ofthetimelagsin
(1.5 km/40 km/10 km) OD competition between the first
triathlete who started running and the rest of the athletes who
arrived in the transition area with the same pack has also
confirmed time lost in T2 to be inversely related to performance
in males (19). The higher the level and performance density of
the race field,themoreimportantagoodT2became.Basedon
their results, Piacentini et al. (16) consequently suggested that
“both for males and females, it is worthwhile to train the actual
practice of T2 transitions.’This advice can also be applied to
age-group athletes –for whom prior cycling appeared to have
more of an adverse effect on subsequent running than it did in
elites (20). However, it is unclear how best to devise such brick
workouts, i.e., back-to-back training sessions in multiple exercise
modes (2,5), in an optimal manner, and so ensure a smooth
transition from cycling to running. Prior cycling (6,18,21)has
certainly been shown to elicit changes in neuromuscular (22),
physiological (5,23,24) and biomechanical (20,25,26)
parameters, especially during the initial minutes (or transient
phase) of the run, although energy availability may also play a
role (2,5,27). However, and in spite of the amount of research
that has been undertaken on this topic to date, it is not yet clear
why the phenomenon occurs. One out of various theories (e.g.,
26,27), is that cycling destroys the activity pattern of a
subsequent run as a result of differences in the working
conditions of the muscles between the two disciplines.
Presumably -given that the relevant neuronal and muscular
units were pre fatigued and needed time to adapt- the motor
program that had been established for cycling could not be
instantaneously switched to that of running (6,28). The
problem with elucidating which mechanism(s) underly the T2
response, however, is that it is not easy to examine the effects of
the bike-run transition (as compared to control running) in
detail. Conventional biomechanical approaches, such as the
analysis of step-characteristics (29), ground-contact time (30), or
lower limb range of motion (31), often lacked the ability to
quantitatively discriminate between subtle running differences
because they were focusing only on a part of the content of
running motion. Consequently, they ran the risk of disregarding
essential gait related information (32,33).
Nor, although the principle of specificity indicates that brick
workouts make sense, has much research to support them been
conducted (34–36). While assessment of the ability to run after
cycling within a triathlon specific test protocol (37), as well as on
its own, is important, neither task has been easy to conduct
under ecologically valid conditions (2). Added to that, exactly
what constitutes these ecologically valid conditions itself varies
with event distance and format. Of the five existing cycle-run
transition test protocols that were reviewed by Vleck and Alves
(37), for example, three (27,38,39) are laboratory- and two (40,
41)arefield-based. The former involved ergometer-based cycling
and treadmill running –both of which exercise modes differed
kinematically from their real world, field based, equivalents (42).
Although they appeared to be able to distinguish between
neuromuscular adaptors vs. non-adaptors to T2; and were
sensitive both to differences in T2 adaptation between both
genders, between short-distance and long-distance specialists, and
between Senior and Junior National Squad athletes, all the
laboratory-based tests were and are still somewhat time
Weich et al. 10.3389/fspor.2022.1065741
Frontiers in Sports and Active living 02 frontiersin.org
consuming. Nor may all age-groupathletes, as opposed to National
Squad level athletes, be able to complete them.As for the two field-
based methods, the level to which the specifics of their protocols
were appropriate proxies for the demands of actual racing, and/or
sensitive to training induced changes in a given athletés
adaptation to T2, may vary with athlete level and race format (40,
43). Furthermore, they have, as yet only been used to assess the
cardiorespiratory, biochemical, and/or pacing responses to bike
run transitions. Despite their potential for applied research with
real world implications, wearables have not yet been used to
assess the biomechanical response to a bike run transition in the
field (44,45). To put the potential interest of the development of
such a system into context, we note that ‘the shorter the race
distance and the higher the exercise intensity that is required, the
more important a good cycle-to-run transition (T2) is likely to be
to the athlete’soverallplacing”(4). It is not known to what extent
fast run starts occur within elite (0.75 km/20 km/5 km) Sprint
distance triathlons –although they can account for
approximately a quarter of WTS events- but they have been
recorded within the (0.3 km/8 km/2 km) Triathlon Mixed Team
Relay- which debuted in the recent Tokyo Olympic Games (46).
At the elite level the opportunity to evaluate the athletés response
to a cycle run transition under race conditions, in conjunction
with the ability to provide him/her with speedy feedback on the
potential relationship between this and his/her race performance,
is becoming increasingly desirable (2,47).
The Attractor Method, as introduced by Vieten, Sehle and
Jensen (32), which regards human motion as affected by
stochastic portions, may provide a solution to the problems of
sensitivity of the data so obtained, the need for ecological
validity of, and the ease providing feedback on adaptation to
the cycle-run transition. The outcomes of the method i.e.,
attractors–which are arrived from the computation of three-
dimensional acceleration data from sensors that are attached to
the athletés ankles, allow for very precise insights into
individual movement behavior of the lower kinematic chain. A
special role in research into the triathlon cycle-run transition
may possibly be attributed to the so-called “transient effect”
(48). The latter described a temporary variation in running
characteristics over less than ten minutes after the start of a
running session, before the motion stabilizes over time.
The aims of this study were to assess whether the Attractor
Method is indeed sensitive to the effects of a (simulated
Olympic Distance) cycle-run transition, by using it to quantify
the changes in running motion that occurred over an isolated
10 km run (I
Run
) and comparing the data so obtained with
those acquired for when the same 10 km (
1–10km
)runwas
preceded either by an endurance cycle (T
Run
), or by another
run (R
Run
) - the energetic load of which was matched to that of
the cycling load within T
Run
. The method essentially provided
two distinct parameters, how the running changed over, e.g.,
two time points during a race:
d
Mdescribedchangesinthe
running motion itself and
d
Dreflected the variability (or
precision) of the motion. Given that the movement
characteristics of cycling differ from that of endurance running,
we hypothesized that the magnitude to which both,
d
M (H1)
and
d
D(H2),withinT
Run
and R
Run
diverged from their values
at equivalent points (
1–10km
) within the isolated run (I
Run
).
Thereby, the following hypotheses were formed: H1 = (
d
M
1–10km
T
Run
-I
Run
)>(
d
M
1–10km
R
Run
-I
Run
) for running style as well
as H2 = (
d
D
1–10km
T
Run
-I
Run
)>(
d
D
1–10km
R
Run
-I
Run
)for
running precision. We also aimed to evaluate the potential use
of the duration of the transient effect (t
T
)thatoccurred
prominently over the initial minutes of running exercise (48)as
a marker of the extent to which an athlete had adapted to T2.
We therefore hypothesized (H3) that t
T
wouldbebiasedbythe
unfamiliar cycling motion within the preload of, and last
longest during the T
Run
. As we would expect the running
preload of the R
Run
to act as a run-specific warm up for the
10km run, we hypothesized that the next longest duration of
the transient effect, across our three experimental conditions,
wouldbeobservedfortheI
Run
, leading to the order t
T
:t
Tdistance
T
Run
>t
T distance
I
Run
>t
T distance
R
Run
for H3.
Lastly, and as a result of conducting the entire study outside
the laboratory environment, we aimed to explore the potential
of the Attractor Method to translate research into applied
practice, by providing rapid real world T2 training and/ or
racing related feedback in the field.
Materials and methods
Participants
A total of 22 well-trained, but non-elite, athletes (Table 1), 10 of
whom were female and 12 of whom were males, were tested over the
period June 2021 until October 2021, at the University of University
of Konstanz, Konstanz (Germany). In the preparation of the study,
the aim was to include a balanced proportion of male and female
triathletes. All the study participants were regularly physically
TABLE 1 Participant overview (F = female, M = Male, All = both, provided as mean (SD)).
N Age
(year)
Height
(cm)
Mass
(kg)
Triathlon
experience (years)
_
VO
2
max cycling
(ml/min/kg)
_
VO
2
max running
(ml/min/kg)
Speed brick run (% of
anaerobic threshold)
F 10 31 (9.2) 168 (6.4) 60.2 (6.7) 5 (4.7) 49.6 (3.6) 49.3 (4.1) 96.4 (6.3)
M 12 28 (6.1) 179 (5.1) 72.3 (5.0) 7 (3.8) 59.7 (7.0) 61.1 (4.2) 98.1 (3.9)
All 22 29 (7.8) 174 (8.1) 66.8 (8.4) 6 (5.6) 55.4 (7.4) 56.0 (6.9) 97.3 (5.1)
Weich et al. 10.3389/fspor.2022.1065741
Frontiers in Sports and Active living 03 frontiersin.org
active, and none of them was suffering from any injury that could
possibly have impeded their performance. They further were
actively training and competing in triathlon, having at least three
starts in a triathlon race. They agreed that they did not undergo an
intensive modification of their individual running technique within
the past three months. The study prerequisites were to be aged 18
years or older and be able to run 10 kilometers faster than 50 min
(for females)/45 min (for males) and to have a cycling anaerobic
threshold (AT) of at least 2.5 W/kg (for female)/3.5 W/kg (for
male). The above performance-relevant parameters were ensured
in advance using remote performance diagnostics (PPD-Cycling
and PPD-Running, INSCYD GmbH, Switzerland, see https://
inscyd.com/functions/power-performance-decoder). All
participants provided their written informed consent. The study
was approved by the local University of Konstanz Ethics
Committee (Ref. No.: IRB21KN008-01w).
Equipment & test setting
Sensor technology
To collect the necessary raw data, two inertial sensors were used
(SpoSens 2.0, Wille Engineering, Germany). The sensors had a size
of 77.5 × 37 × 34.5 mm and weighed 45 g each. They functioned as a
triaxial accelerometer with up to 400 G (16 G was used), a triaxial
Gyroscope with up to 2000°s (maximum was used) and a
magnetometer measuring with up to 16 Gauss (maximum was
used). The possible sampling rate could be set up to 1.000 Hz
(250 Hz was used) and they were constructed as a micro-electro-
mechanical system (MEMS). Further, the sensors were equipped
with GNSS (max. 5 Hz) technology. They collected and saved the
running motion data in three dimensions (x, y, z) on an internal
storage device (8192 GB). The sensors were attached to both
ankles above each lateral malleolus by a hook-and-loop fastener.
All data were received and stored on a GARMIN Forerunner 945
(GARMIN, Schaffhausen, Switzerland) during the entire test
session. Earlobe blood samples (each of 10 µl) were analyzed for
lactate concentration using a stationary laboratory device
(HITADO Super GL compact). Body weight, body fat percentage
and active muscle mass (required for the INSCYD analysis) were
measured with a Tanita BC-545N body analysis scale (Tanita
Europe B.V., Stuttgart, Germany).
Run course and bike setting
The runs were performed outside at the Graf Lennart
Bernadotte Allee in Konstanz (Germany). This is a cycling
path, between the sports facilities of the University Konstanz
FIGURE 1
Running course map (extracted from Garmin Connect App, GARMIN, Switzerland).
Weich et al. 10.3389/fspor.2022.1065741
Frontiers in Sports and Active living 04 frontiersin.org
and the Island of Mainau, that is generally closed to traffic (see
Figure 1).
This three-kilometer loop was completely flat and had to be
ran 3.5 times to achieve the 10 km run distance equivalent to
that which occurs within an Olympic distance triathlon. The
running preload, which had an individually calculated length
and intensity, was also carried out over this route. For the
triathlon test all participants had to undergo a 40 kilometer
cycling time trial on an indoor smart trainer (Tacx NEO 2T,
Tacx, Wassenaar, Netherlands) using their own road bike. As
the route to be cycled we chose the first 40 kilometer of the
IRONMAN Frankfurt (see Figure 2) which represented an
appropriate mixture of flat and hilly sections (accumulated
altitude meters 208 m). The simulation was undertaken with
the indoor cycling reality app Rouvy (VirtualTraining s.r.o.,
Vimperk, Czech Republic).
Experimental protocol and data
measurement
Determination of performance parameter
The study was conducted as a cross-over study with three
conditions: Triathlon Run (T
Run
), Run-Run (R
Run
) and an
Isolated Run (I
Run
) separated by at least 5 days. To set the
right power for the cycling simulation and the running pace
for the preload of the Run-Run condition each athlete had to
undergo a performance diagnostic in both disciplines in
advance. For both analyses the Power Performance Decoder
(PPD) of INSCYD (Switzerland, see https://inscyd.com/
functions/power-performance-decoder) was used. This
performance tool allowed remote performance testing, so that
all participants could gather their data on their own after
receiving prior instructions via video call. Finally, based on
these outcomes, we had access to the calculated (maximum)
oxygen uptake ( _
VO
2
max) and the (anaerobic) threshold
power/pace, needed for further use. Additional training status
related feedback parameters that the tool provided, such as
the lactate building rate ( _
V
La
max), substrate consumption or
derived training zones were offered to the participants after
they competed the entire test scenario.
The triathlon test (T
Run
)
The basic initial test session was always the triathlon
condition (T
Run
), as this was needed to get reference values
for both other experimental conditions. The T
Run
consisted of
a 40-kilometer time trial (Figures 2,3) on a smart trainer at
90%–95% of the individually determined threshold power, i.e.,
average power within the cycle section of an Olympic distance
triathlon (see Table 2 in 3,4). Due to the often draft-legal
racing formats, the pacing (in OD races) is rather
characterized by an intermittent character (49). To represent
this, athletes were instructed to ride significantly above the
given power range on two climbs (see Figure 3, blue area),
followed by sections below this range on the subsequent
descents (grey area). The entire ride was carried out in the
handlebar position with no use of aerobars being allowed (50).
The cycling part was followed by a 10km all-out outdoor
run (Figure 1). The pacing could be chosen individually. The
change between both disciplines had to be done as fast and
triathlon specific as possible. The transition time was noted.
Beforehand the athletes were equipped with several sensors
(see above) placed in such a way, that they did not interfere
with the sport-specific execution of running and cycling. All
the listed devices recorded the corresponding data during the
entire T
Run
. During the cycling section lactate samples were
FIGURE 2
Cycling route (extracted from TrainingsPeaks, Peaksware, United States of America).
Weich et al. 10.3389/fspor.2022.1065741
Frontiers in Sports and Active living 05 frontiersin.org
collected form the earlobe after both hills and descents
(Figure 3) to check if the athletes had followed instructions to
increase and lower their intensity. Further samples were taken
immediately after the bike session and right before the start of
the run. Finally, blood lactate concentrations were determined
immediately after the 10 kilometer run and at a further 1 min,
3 min, 5 min and 7 min post-finish. The participants were
instructed following an appropriate carbohydrate diet before
and during the test, as well as regarding an adequate training
load to present themselves for testing in a fully recovered
state, with replenished glycogen stores. The runners were
accompanied throughout the entire trial by a cycling member
of the scientific team in order both to provide them with
nutrition (when requested) and ensure their safety.
The run-run test (R
Run
)
The R
Run
condition matched the T
Run
but instead of the
cycling preload the athletes had to run an individually
tailored running session. This multiple-step calculation was
carried out by metabolic matching of cycling and running
loads, based on the performance diagnostic data (INSCYD
PPD, see https://inscyd.com/functions/power-performance-
decoder), as follows:
(1) Active muscle mass for cycling (60% of muscle mass) and
running (70% of muscle mass) were determined based on
individual weight (BW), body fat percentage and muscle
mass.
(2) The aerobic capacities ( _
VO
2max
) of both disciplines were
multiplied by BW and divided by the active muscle mass
for (1) to get the absolute amount of oxygen used by the
active muscle mass in millilitre per minute.
(3) The average power output (in watts) during the T
Run
cycling section could be attributed to an individual
oxygen consumption. The latter was taken from the
performance diagnostics and calculated as in (2) only for
the active muscle mass.
(4) The ratio between the active muscle mass used during the
cycling session (3) and the individual _
VO
2max
(2) resulted
in the proportional utilization of the _
VO
2max
.
(5) This ratio was transferred to the calculation of the running
effort. On this basis, steps (3) and (4) were performed
backwards, so that ultimately an oxygen uptake for the
running section was obtained that was metabolically
equivalent to the cycling load.
(6) The calculated oxygen consumption was then assigned to
an appropriate running pace using the data from the
performance diagnostics.
As a last step, the running duration was adjusted due to the
significantly higher orthopaedic stress during running (weight-
bearing vs. non-weight-bearing exercise). This weight-bearing-
factor based on the observations of Munro, Miller &
Fuglevand (51). The latter provided vertical ground reaction
forces (GRF) as an Impact maximum (See Table 3 in 51,
Table 3) relative to BW differentiated according to the
individual running speed. To determine the duration of the
preload run, the duration (in full minutes) of the cycling
section of the T
Run
condition, was divided by the running
pace-related weight-bearing-factor.
The duration between the preload and the brick run was
adjusted to the time the athlete spent in the T
Run
condition.
TABLE 2 Distance over which transient effects (TE) were observed in
meters. The cases who showed no transient effect are labelled as
“no TE”. f = female; m = Male subject.
Subject I
Run
[m]
T
Run
[m]
R
Run
[m]
Subject I
Run
[m]
T
Run
[m]
R
Run
[m]
1 (m) 380 200 232 12 (m) no TE 1600 940
2 (f) 60 20 no TE 13 (f) 180 no TE no TE
3 (m) 180 550 no TE 14 (f) no TE 1400 no TE
4 (m) no TE 338 no TE 15 (f) 228 no TE no TE
5 (m) no TE 238 18 16 (f) no TE 690 482
6 (m) 20 1180 no TE 17 (f ) no TE 462 354
7 (m) no TE 160 no TE 18 (f) no TE 28 760
8 (m) no TE no TE 280 19 (m) no TE no TE no TE
9 (m) no TE 250 600 20 (f) no TE 720 no TE
10 (m) no TE 540 no TE 21 (f) 1012 610 no TE
11 (m) no TE 2000 380 22 (f) no TE 1234 no TE
FIGURE 3
Cycling course profile with hills and descents.
Weich et al. 10.3389/fspor.2022.1065741
Frontiers in Sports and Active living 06 frontiersin.org
The brick run (R
Run
) was paced by a GARMIN sports watch
each kilometer, according to the kilometer splits that the
athlete had run during the baseline test (T
Run
) ± two seconds.
Again, the athletes were supported by a cyclist and the lactate
samples as well as the data collection were undertaken
similarly to as in the T
Run
.
The isolated run test (I
Run
)
The Isolated run (I
Run
) was paced by a GARMIN sports
watch each kilometer, according to the kilometer splits that
the athlete had had run during the baseline test (T
Run
). All
the study participants were allowed to warm up for 10 min
before I
Run
, although none of them did so. The athletes were
supported by a cyclist and the lactate samples as well as the
data collection were undertaken similarly to the T
Run
. The
order of I
Run
and R
Run
was set randomly.
Data analyses
To detect subtle changes in the running behavior after the
preloads, the Attractor Method was used to create an
individual attractor from each kilometer, which then served as
the basis for further processing steps. We evaluated whether
there was a statistical difference between the T
Run
and the
R
Run
conditions as compared to the basic run (I
Run)
, and if
the differences in transient time that we expected between
conditions could be seen. Furthermore, conventional running
analysis parameters, like stride frequency and ground contact
time were statistically examined.
Attractor parameter
Based on three-dimensional acceleration data, the Attractor
Method (see 33, p.3 for the complete mathematical derivation)
allowed the determination of two main parameters describing
changes in human cyclic motion. As a basis served the
calculation of an attractor ~
Arepresenting each measuring
interval, like one kilometer of running, (equation 1) and its
fluctuations D(equation 2).
~
Aa,C(
t
j)¼1
nX
n
i¼1
~
aa,C(i
t
j)þ1
nX
n
i¼1
~
ba,C(t¼i
t
j)
1
nX
n
i¼1
~
aa,C(i
t
j) (1)
Da,C(
t
j)¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1
n1X
n
i¼1
[~
Aa,C(
t
j)~
aa,C(i
t
j)]2
s(2)
with tbeing the time, athe right or left foot ankle (where
sensors were attached) and Crepresented two different time/
distance intervals of the compared run.
Subsequently, the two parameters, one describing changes
in the motion itself (
d
M, equation 3) and further, the
variability of the motion (
d
D, equation 4) can be calculated.
d
M¼1
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
X
3
i¼1
h(Ar,B,xiAr,E,xi)2iþ h(Al,B,xiAl,E,xi)2i
v
u
u
t(3)
with vbeing the running speed in m/s, rand lstood for right or
left foot ankle (where sensors were attached) and B = begin and
E = end represented two different time/distance intervals of the
compared run. < …> meant the average of the included
expression.
d
Mdescribed the velocity normalized difference between
two attractors (30), allowing to quantify the two time points
or the running conditions of this study, regarding changes in
the individual running motion.
d
D¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
h(Dr,BDr,E)2iþh(Dl,BDl,E)2
qi(4)
d
D corresponded to the difference between the variability
around each of the two attractors and was therefore a proxy
measure for the precision of a movement. Its calculation was
based on the absolute D(equation 2) of each attractor, which
described the average deviation of single gait cycles from the
attractor. For the calculation of both, the research group
provided an open access application (available online http://
www.uni-konstanz.de/FuF/SportWiss/vieten/CyclicMove/). In
the present study, based on previous work (for an overview
see 34),
d
M and
d
D values equal to or below 5 m/s
2
represented a very high similarity of the compared running
motion and the movement precision respectively.
Attractor analysis
All runs were split into single data sets, each of which
represented one kilometer within each of the three conditions
(T
Run,
I
Run
,R
Run
), on the basis of the athletés recorded GPS
signals. This allowed for fluctuations in pacing to be
considered in the subsequent evaluation. In additional to
being the normal cycle and run unit that has been considered
within triathlon cycle and run pacing research (17,18) a one-
kilometer-separation resulted in average interval lengths of
four to five minutes (per kilometer). This was an appropriate
duration for analyses of running motion to have sufficient
cycles for a meaningful attractor calculation. The
d
M and
d
D
values derived from comparisons of the single kilometers 1–
10 between the control I
Run
session, and each of the two brick
runs: T
Run
and R
Run
, served as the basis for further statistical
calculations.
Weich et al. 10.3389/fspor.2022.1065741
Frontiers in Sports and Active living 07 frontiersin.org
Transient effect analysis
In a second analysis process the running data from each
condition were split into fifty 200 m sections, so as to obtain
a more precise insight into when the transient effect of a
running session ended, and the athletes finally adapted a
smooth running motion. To perform the transient analysis,
the procedure that has been extensively described in Weich
et al. (48)as“I. Delta M (
d
M)”was followed, using a slightly
modified equation for
d
M:
d
Mtransient ¼1
vhTket
tTe
tE
tT
hi
þa0
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
(tEt)
tE
sþa1sin a22
p
(tEt)
tE
()
i(5)
with the given constants Tk,tT,a0,a1,a2, which are derived
from a curve fitting application of all measurements
(CurveExpert Professional (version 2.6.5, Hyams
Development)), using the Levenberg-Marquardt algorithm.
The constants a0,a1,a2represented a morphing process
whereas Tkand tTwere based on the transient oscillations at
the onset of a movement (see 52 for a full description of all
components of cyclic human motion). tTquantified the time
until the oscillation decreased to e1of its original starting
value T.
In the final result, the durations of the transient effect (tT)
were given as a distance in meters for each individual run of
each person. Runs which did not show a transient effect are
reported (Table 2) but were not included in the average
distance calculation.
Statistical analysis
The traditional stride data, stride frequency and ground
contact time, were analyzed using a two-way repeated
measures ANOVA (SPSS, IBM Version 28) with one factor
being the distance in ten-kilometer levels and the second the
three running conditions. The stride lengths were not
analyzed because they are inversely related to stride frequency.
The attractor-based data were evaluated with pairwise
Student’s t-tests - comparing the
d
M and
d
D outcomes of
both comparisons (I
Run
vs. T
Run
and I
Run
vs. R
Run
) within
each kilometer. To check whether the athletes’pacing targets,
based on the initial brick run, were met, running speeds (as a
percentage of the average time for each running condition)
were compared, on a kilometer-by-kilometer basis, between
the three experimental conditions using a repeated measures
ANOVA (SPSS, IBM Version 28). This included looking at
potential gender differences. The significance level was set at
p< 0.05.
Results
Conventional parameter and pacing
When examining the traditional parameter of stride
frequency (Figure 4) there were no statistically significant
differences (p> 0.05 between all kilometers, conditions, and
interaction) until kilometer 5. For kilometers 6 (p= 0.040), 8
(p= 0.002) and 10 ( p= 0.003) the R
Run
was significantly
different from the I
Run
condition (but not from the T
Run
).
One noticeable aspect was that the first kilometer initially
started with a low cadence but then increased over the second
and third kilometer in all running categories, and that the
stride frequency increased steadily over the entire distance in
the R
Run
condition. Another traditional parameter was the
ground contact time (GCT, Figure 5), where the conditions
and the interaction (distance*condition) showed no significant
difference (p> 0.05). Although the first kilometer in all
conditions initially started with a somewhat elevated GCT
before the second kilometer always indicated the lowest GCT.
For the within-factor distance there was an overall significant
difference with post-hoc tests (Bonferroni correction)
revealing kilometer 2 to be significantly different from
kilometers 6, 8 and 9.
In terms of pacing, we typically observed a u-shaped pattern,
i.e., a higher initial pace above 100% of the later average pace of
the whole run, followed by a gradual decrease in running speed to
about 98% of the average pace up to kilometer 8, before the last
two kilometers were covered significantly faster (at about 100% of
the average pace). This was true for both genders. There was no
significant difference in running speed between the running
categories (I
Run
,T
Run
,R
Run
) over any kilometer, indicating a
very good compliance with the pace prescriptions and allowing
a reliable comparability for the attractor-based motion analyses.
FIGURE 4
Kilometer-separated development of step frequency between the
three running conditions (Triathlon Run = dark grey dotted line;
Run Run = black solid line; Isolated Run = grey dashed line).
Weich et al. 10.3389/fspor.2022.1065741
Frontiers in Sports and Active living 08 frontiersin.org
The average transition time between cycling and running (T
Run
)
was 2.6 (SD 1.2) minutes. The runners were instructed to
replicate their transition time from the first test (T
Run
)during
the R
Run
.
Attractor-based motion analyses
In the pairwise
d
M comparison for each kilometer between
the I
Run
and both brick conditions (T
Run,
R
Run
) no statistically
significant differences (at the p> 0.05 level) could be seen
(Figure 6). Although within both conditions, T
Run
and R
Run
,
d
M increased slightly over the course of the runs, its absolute
size remained well below the cut-off of
d
M = 5 m/s
2
i.e., both
conditions were very similar in motion terms.
For
d
D, reflecting the difference in variation of the running
motion, we saw equally generally low
d
D scores clearly below
5 m/s
2
(Figure 7). Here, the T
Run
usually showed higher
values and thus a higher range of variation, for kilometer 1
(p= 0.026) and 10 ( p= 0.011) being significantly different to
the same kilometer within the R
Run
.
Transient effect
Table 2 and Figure 8 present an overview of all athletes’
occurrences of the transient effect, together with the distance
over which it was in effect. During both running conditions,
the I
Run
and the R
Run
, athletes experienced a transient phase
in only 32% and 41% of cases, respectively, whereas the
triathlon preload caused the initial effect in 82% of the group.
It was evident that both brick conditions took the longest way
for the runners to find their running rhythm: 450 m (min.
18 m, max. 940 m), a bit more than one lap on a usual track
in a stadium, during the R
Run
and almost 700 m (min. 20 m,
max. 2000 m) during the T
Run
. For the I
Run
, on the other
hand, the few participants who showed any transient effect at
all, did not even need 300 m (min. 20 m, max. 1012 m) to get
into their running rhythm.
Discussion and practical implications
Our first aim was to quantify the running behavior of
triathletes with and without preloads. We assessed multiple
biomechanical and pacing related parameters between an
isolated run (I
Run
) and two brick runs, one involving a
previous run (R
Run
) and the other, as per within a triathlon,
involving a prior bike load (T
Run
). In this way, the
FIGURE 6
d
M analysis for the control run vs. the T
Run
(black solid line with
rhombus) and the R
Run
(grey dotted line with circle). No statistical
difference can be seen.
FIGURE 7
d
D analysis for the control run vs. the T
Run
(black solid line with
rhombus) and the R
Run
(grey dotted line with circle). Kilometer 1
and 10 indicate a significant difference between both conditions.
FIGURE 5
Kilometer-separated development of ground contact time between
the three runn ing conditions (Triathlon Run = dark grey dotted line;
Run Run = black solid line; Isolated Run = grey dashed line).
Weich et al. 10.3389/fspor.2022.1065741
Frontiers in Sports and Active living 09 frontiersin.org
uncoordinative feeling of triathletes, which many experience
during the early phase of their run in a competition, was
objectively quantified under field conditions. Our intention
was further to improve on the methodology of our initial
triathlon paper (21). That meant both, an improved
evaluation algorithm of the Attractor Method, and an
improved study design i.e., one that used the additional Run-
Run condition to test whether cycling per se impairs running
or preloads in general. In the aforementioned study, the
authors noticed that the athletes’running behavior was quite
messy within the first five minutes after the run start, until
the athletes finally found their running rhythm. That this
effect applied, even independently of the preload, was also
confirmed by the results presented here. Moreover, it was not
surprising that this transient effect, which was described by
Weich, Vieten & Jensen (48) lasted longest after cycling (i.e,
679m, on average, in this study, confirming the t
T
:t
Tdistance
T
Run
>t
T distance
I
Run
section of our initial hypothesis H3). The
explanation for this phenomenon is likely the different
motion pattern of cycling compared to running (28,53).
Particularly remarkable compared to our work from 2020 (48)
was the finding that exceptionally high numbers of athletes
(indeed, almost 70%) did not exhibit a transient effect in their
I
Run
at all. A plausible reason for this, as already described
back in the 2020 paper on a case wise basis, could be the
above average experience and performance level of all the
participants in the present study (Table 1). Consequently, our
results contradicted our expectations (and the t
T distance
I
Run
>
t
T distance
R
Run
section of our hypothesis H3) to the extent
that the I
Run
not only presented quite few transient cases, but
also the shortest duration of all three conditions (294 m).
Thus, it seemed that preloads always caused a higher transient
duration compared to a stand-alone run started from a resting
state (and that t
T
:t
Tdistance
T
Run
>t
T distance
R
Run
>t
T distance
I
Run
). This was even true when the preload discipline
corresponded to the subsequent exercise mode (R
Run
),
although the preliminary runs in this study had always been
performed at a more moderate pace. That is, the data that
were obtained by this study clearly confirmed, yet again, that
a basic transient effect seems to be present in cyclic
movements such as running. This effect was prolonged
temporarily by a preceding load and was further intensified if
said preload involved a different exercise mode, such as
cycling. This finding should also encourage research in other
(cyclic) sports, especially those with alternating disciplines
(duathlon, biathlon, pentathlon etc.), to include the transient
effect in their test designs and access its applications.
Furthermore, the runs were also compared between each
other using the Attractor Method. Pairwise comparisons of
each brick run with the session without a preload were made
to highlight how much they differed in running pattern and
variability over the entire distance. The statistical analysis
showed that for
d
M, contrary to expectations, i.e., the possible
change in the running style itself, there was no significant
difference at any time (Figure 6). That attractors are highly
individual and stable has already been shown in previous
studies for walking (54,55) and also for treadmill running
(56) - which should have reduced the complexity of the
movement, especially due to the steady running speed and the
even ground of the treadmill belt. Although the
d
M-related
hypothesis of the current study (our hypotheses H1) must be
rejected, the results of previous works on the attractor
properties appeared to be confirmed even for more complex
test designs as applied here. This means that even, or
especially, in very trained athletes, that the running style itself
remained very stable and other factors must be considered for
FIGURE 8
Overview of the different distances that were covered to finish the transient process at the initial stage of a run. Only sessions with a measured
transient effect are included in the plot.
Weich et al. 10.3389/fspor.2022.1065741
Frontiers in Sports and Active living 10 frontiersin.org
the feeling of uncoordinated running and the accompanied
transient effect as reported above. One of the possible
explanations might be an increased variability of the
movement, thus an increased variation (
d
D) within the gait
cycles which are summarized in one attractor (32). This
parameter was examined in the same way as
d
M before,
however, it revealed a significant difference for the first and
the last kilometer (Figure 7). This difference might be
attributed to the R
Run
, especially within the first and last
kilometer, possessing a higher movement precision as a result
of its lower
d
D value. Accordingly, our initial hypothesis (H2)
concerning the motion variability (
d
D), at least partially, can
be confirmed. It was reasonable to assume that for the
discrepancy between R
Run
and T
Run
at the beginning
(kilometer 1) was caused by the transient effect described
above, which was probably driven by the variability of the
running motion. As noted in previous studies (21,29,57),
cycling itself appeared to have a very disruptive effect on the
subsequent run. This study showed, for the first time, that
this most likely influenced the precision and less of running
motion itself. This observation should therefore be more
closely examined in follow-up studies, in order to be able to
derive major practical consequences. That the tactics how to
run the first race kilometer could be of high importance has
already been shown by works on pacing (58,59) as well as
the contribution of the individual performances to the overall
performance in triathlon (17,18,60). Changes in running
behavior were also commonly seen in the last few meters of a
triathlon, as indicated by the statistically relevant difference in
d
D during the last kilometer, also in the present paper
(Figure 7, kilometer 10). Further, analyses of pacing strategies
showed, at least with respect to the running speed, a u-shaped
development, which reveals a re-increase of the pace towards
the end (16,18). The results of this study suggested that the
cycling preload affects the 10km run substantially more than
a running preload does. This was furthermore supported by
the fact that on the second half of the run, i.e., from
kilometer 5, the variability of the running behavior in the
triathlon run was permanently higher (although only
significantly different on the last kilometer; see Figure 7). In
general, it can also be stated that the absolute
d
M and
d
D
values in this study could be classified as very low (< 5 m/s
2
),
which speaks for an extremely similar running motion. This
underlined once again the stability of the individual attractors.
Furthermore, it should be highlighted that the sensitivity of
the Attractor Method to analyze human cyclic motion had
been demonstrated by the fact that conventional parameters,
such as stride frequency/-length or ground contact time, were
unable to represent these findings (Figures 4 and 5).
One limitation to be mentioned here is that the data that we
presented were derived from sample of well- to very well-trained
endurance athletes. The results are therefore not applicable to
untrained or professional athletes without further
investigation. The calculation of attractors was mathematically
based on mean values of many data points. In this study, the
algorithm was applied per kilometer, which lasted about five
minutes on average. This corresponded to an average step
frequency of 170 steps per minute, 85 complete step cycles
per minute and thus 425 cycles per kilometer. This means
that if only a few cycles are unrhythmic or distinctive, they
will disappear with the averaging process. In future studies, it
might be methodologically more appropriate to look at
smaller distances, such as 400m (= approx. 2 min and
170 cycles), to be more selective. On the other hand, as seen
within kilometers 1 and 10 for
d
D, if significant differences
show up due to the methodology used here, one can assume
that they are so severe that they occur for the majority of the
entire kilometer.
We already targeted to realize an evenly distributed sample
concerning male and female participants. The latter will allow
us to evaluate the important difference between the two in a
separate study in the future.
Future work should moreover focus on an application of the
methodology providing (live) feedback already during the
running session. The research group has already developed a
beta version of the software that allows live data
communication including analyses results within a marginal
time delay < 40 ms. A key and sport-practically relevant future
objective must be to find out whether the impairment during
the brick runs that has been described in this paper has a
negative impact on triathlon performance. If this is the case,
the Attractor Method may in itself present a promising
solution to determine which training and competition
strategies can best be used to reduce bike-run related deficits.
Summary and conclusions
This triathlon transition study used a hands-on
methodology and applied tool, based on attractors, to analyze
the motion pattern and variation of running after an
endurance sports related preload, in the field. While we could
not support our first hypothesis (H1), as we did not find
(
d
M
1–10km
T
Run
-I
Run
)>(
d
M
1–10km
R
Run
-I
Run
), we did
observe that (
d
D
1–10km
T
Run
-I
Run
)>(
d
D
1–10km
R
Run
-I
Run
),
confirming the second hypothesis (H2), at least for the first
and last kilometer. Moreover, rather than it being the case
that t
T
:t
Tdistance
T
Run
>t
T distance
I
Run
>t
T distance
R
Run
,aswe
had initially hypothesized (H3), we found t
Tdistance
T
Run
>t
T
distance
R
Run
>t
T distance
I
Run
. The data that we obtained
demonstrated that especially the first kilometer of a triathlon
run is prone to running in an uncoordinated manner, which
has been commonly reported by athletes. The results
indicated that the cause of this T2 effect is probably linked
not so much to running style itself, but, rather, to variability
in it. In a sport like triathlon, which is always striving for
Weich et al. 10.3389/fspor.2022.1065741
Frontiers in Sports and Active living 11 frontiersin.org
marginal gains, this finding could open another perspective
both to examine and improve the final discipline, and to be
able to do so under ecologically valid conditions. “At
minimum, easing (the cycle-run) transition can bring added
comfort to the athlete, and at maximum it could mean the
(…) difference between victory and defeat”(34).
Data availability statement
The raw data supporting the conclusions of this article will
be made available by the authors, without undue reservation.
Ethics statement
The studies involving human participants were reviewed
and approved by Ethics committee (IRB) of the University of
Konstanz (Ref. No.: IRB21KN008-01w). The patients/
participants provided their written informed consent to
participate in this study.
Author contributions
CW, VB, NK, VV, JE, TT.
The individual contributions are: Conceptualization, CW;
methodology, CW; software, CW, VB and NK; validation,
CW, VB and NK; data collection, CW, VB, NK, JE and TT;
formal analysis, CW, VB, NK and VV; writing—original draft
preparation, CW; writing—review and editing, CW, VB, NK,
VV, JE and TT; visualization, CW; supervision, CW; project
administration, CW; funding acquisition, CW; All authors
have read and agree to the published version of the
manuscript. All authors contributed to the article and
approved the submitted version.
Funding
Approved Ausschuss fuer Forschungsfragen (AFF)
application: Realization of two application studies using the
Attractor Method for the analysis of cyclic motion in cross-
country skiing and triathlon.
Acknowledgments
We would like to thank all athletes for their valuable time
and the sports psychology department of the University of
Konstanz for making their laboratory available to us for this
study. VV acknowledges the support of the Fundação para a
Ciência e Tecnologia –as expressed by Grant UIDB/00447/
2020 to CIPER - Centro Interdisciplinar para o Estudo da
Performance Humana (unit 447).
Conflict of interest
The authors declare that the research was conducted
in the absence of any commercial or financial
relationships that could be construed as a potential
conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of
the authors and do not necessarily represent those of
their affiliated organizations, or those of the publisher, the
editors and the reviewers. Any product that may be
evaluated in this article, or claim that may be made by its
manufacturer, is not guaranteed or endorsed by the
publisher.
Supplementary material
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fspor.
2022.1065741/full#supplementary-material.
References
1. Bentley DJ, Millet GP, Vleck VE, McNaughton LR. Specific aspects of
contemporary triathlon: implications for physiological analysis and performance.
Sports Med. (2002) 32:345–59. doi: 10.2165/00007256-200232060-00001
2. Walsh JA. The rise of elite short-course triathlon Re-emphasises the necessity
to transition efficiently from cycling to running. Sports. (2019) 7:99. doi: 10.3390/
sports7050099
3. Aoyagi A, Ishikura K, Nabekura Y. Exercise intensity during Olympic-
distance triathlon in well-trained age-group athletes: an observational study.
Sports. (2021) 9:18. doi: 10.3390/sports9020018
4. Vleck V, Millet GP, Alves FB. The impact of triathlon training and racing on Athletes’
general health. Sports Med. (2014) 44:1659–92. doi: 10.1007/s40279-014-0244-0
5. Millet GP, Vleck V. Physiological and biomechanical adaptations to the cycle
to run transition in Olympic triathlon: review and practical recommendations for
training. Br J Sports Med. (2000) 34:384–90. doi: 10.1136/bjsm.34.5.384
6. Fraeulin L, Maurer-Grubinger C, Holzgreve F, Groneberg DA, Ohlendorf D.
Comparison of joint kinematics in transition running and isolated running in elite
triathletes in overground conditions. Sensors. (2021) 21:4869. doi: 10.3390/s21144869
Weich et al. 10.3389/fspor.2022.1065741
Frontiers in Sports and Active living 12 frontiersin.org
7. Figueiredo P, Marques EA, Lepers R. Changes in contributions of swimming,
cycling, and running performances on overall triathlon performance over a 26-
year period. J Strength and Cond Res. (2016) 30:2406–15. doi: 10.1519/JSC.
0000000000001335
8. Horne MJ. The relationship of race discipline with overall performance in
sprint and standard distance triathlon age-group world championships. Int
J Sports Sci & Coach. (2017) 12:814–22. doi: 10.1177/1747954117738878
9. Scorcine C. Contribution of swimming, cycling and running in the final
performance in different distances of triathlon races. MOJSM. (2017) 1:125–8.
doi: 10.15406/mojsm.2017.01.00027
10. Sousa CV, Aguiar S, Olher RR, Cunha R, Nikolaidis PT, Villiger E,
Rosemann T, Knechtle B. What is the best discipline to predict overall triathlon
performance? An analysis of sprint, Olympic, ironman® 70.3, and ironman®
140.6. Front Physiol. (2021) 12:654552. doi: 10.3389/fphys.2021.654552
11. Fernández-Revelles AB, Ramírez-Granizo I, Castro-Sánchez M, Padial-Ruz
R. Men’s triathlon correlation between stages and final result in the London 2012
Olympic games.Journal of human sport and exercise - 2018 - spring conferences
of sports science. Universidad de Alicante (2018) doi: 10.14198/jhse.2018.13.
Proc2.35
12. Fröhlich M, Klein M, Pieter A, Emrich E, Gießing J. Consequences of the
three disciplines on the overall result in Olympic-distance triathlon.
International Journal of Sports Science and Engineering. 2(4):204–10.
13. Hoffmann M, Moeller T, Seidel I, Stein T. Predicting elite triathlon
performance: a comparison of multiple regressions and artificial neural
networks. Int J Comp Sci in Sport. (2017) 16:101–16. doi: 10.1515/ijcss-2017-0009
14. Olaya J, Fernández-Sáez J, Østerlie O, Ferriz-Valero A. Contribution of
segments to overall result in elite triathletes: sprint distance. IJERPH. (2021)
18:8422. doi: 10.3390/ijerph18168422
15. Fröhlich M, Balter J, Pieter A, Schwarz M, Emrich E. Model-theoretic
optimization approach to triathlon performance under comparative static
conditions –results based on the Olympic games 2012. Int J Kinesiol & Sports
Sci. (2013) 1:9–14. doi: 10.7575/aiac.ijkss.v.1n.3p.9
16. Piacentini M, Bianchini L, Minganti C, Sias M, Di Castro A, Vleck V. Is the
bike segment of modern Olympic triathlon more a transition towards running in
males than it is in females? Sports. (2019) 7:76. doi: 10.3390/sports7040076
17. Vleck VE, Bürgi A, Bentley DJ. The consequences of swim, cycle, and run
performance on overall result in elite Olympic distance triathlon. Int J Sports
Med. (2006) 27:43–8. doi: 10.1055/s-2005-837502
18. Vleck VE, Bentley DJ, Millet GP, Bürgi A. Pacing during an elite Olympic
distance triathlon: comparison between Male and female competitors. J Sci and
Med in Sport. (2008) 11:424–32. doi: 10.1016/j.jsams.2007.01.006
19. Cejuela R, Cala A, Pérez-Turpin JA, Villa JG, Cortell JM, Chinchilla JJ.
Temporal activity in particular segments and transitions in the Olympic
triathlon. J Hum Kinetics. (2013) 36:87–95. doi: 10.2478/hukin-2013-0009
20. Millet G, Millet G, Candau R. Duration and seriousness of running
mechanices alterations after maximal cycling in triathlets: influence of the
performance level. J Sports Med and Phy Fitness. (2001) 41:147.
21. Weich C, Jensen RL, Vieten M. Triathlon transition study: quantifying
differences in running movement pattern and precision after bike-run
transition. Sports Biomechanics. (2019) 18:215–28. doi: 10.1080/14763141.2017.
1391324
22. Bonacci J, Saunders PU, Alexander M, Blanch P, Vicenzino B.
Neuromuscular control and running economy is preserved in elite international
triathletes after cycling. Sports Biomech. (2011) 10:59–71. doi: 10.1080/
14763141.2010.547593
23. Hue O, Le Gallais D, Boussana A, Chollet D, Prefaut C. Ventilatory
responses during experimental cycle-run transition in triathletes: Med & Sci in
Sports & Exer. (1999) 31:1422. doi: 10.1097/00005768-199910000-00010
24. Millet GP, Dréano P, Bentley DJ. Physiological characteristics of elite short-
and long-distance triathletes. Eur J Appl Physiol. (2003) 88:427–30. doi: 10.1007/
s00421-002-0731-0
25. Bonacci J, Green D, Saunders PU, Blanch P, Franettovich M, Chapman AR,
Vicenzino B. Change in running kinematics after cycling are related to alterations
in running economy in triathletes. J Sci and Med in Sport. (2010) 13:460–4.
doi: 10.1016/j.jsams.2010.02.002
26. Millet GP, Millet GY, Hofmann M, Candau R. Alterations in running
economy and mechanics after maximal cycling in triathletes: influence of
performance level. Int J Sports Med. (2000) 21:127–32. doi: 10.1055/s-2000-
8866
27. Chapman AR, Vicenzino B, Hodges PW, Blanch P, Hahn AG, Milner TE. A
protocol for measuring the direct effect of cycling on neuromuscular control of
running in triathletes. J Sports Sci. (2009) 27:767–82. doi: 10.1080/
02640410902859100
28. Karniel A, Mussa-Ivaldi FA. Does the motor control system use multiple
models and context switching to cope with a variable environment? Exp Brain
Res. (2002) 143:520–4. doi: 10.1007/s00221-002-1054-4
29. Gohlitz D, Große S, Witt M. “Darstellungen von veränderungen der
schrittlänge und schrittfrequenz beim Übergang vom radfahren zum laufen zur
kennzeichnung der dauer von Übergangsphasen im duathlon
(pilotuntersuchung).,”In: M Engelhardt, B Franz, G Neumann, A Pfützner,
editors. Triathlon –medizinische und methodische probleme des trainings.
Triathlon und Sportwissenschaft. Hamburg: Czwalina (1994). p. 131–6.
30. Sterzing T, Brauner T, Milani TL. Laufen: barfuß vs. Schuh–kinetische und
kinematische adaptationen der unteren extremität. Biomechanik–
Grundlagenforschung und Anwendung. (2009) 4:26–32.
31. Connick MJ, Li F-X. Prolonged cycling alters stride time variability and
kinematics of a post-cycle transition run in triathletes. J Electromyogr and
Kinesiol. (2015) 25:34–9. doi: 10.1016/j.jelekin.2014.08.009
32. Vieten MM, Sehle A, Jensen RL. A novel approach to quantify time series
differences of gait data using attractor attributes. PLoS ONE. (2013) 8:e71824.
doi: 10.1371/journal.pone.0071824
33. Weich C. The attractor method and its application in running, bicycling and
nordic skiing. [Dissertation] Konstanz: University of Konstanz. (2021). 796
p. http://nbn-resolving.de/urn:nbn:de:bsz:352-2-1op56v75zqhjm0
34. Haworth J, Walsh M, Strang A, Hohl J, Spraets S, Wilson M, Brown C.
Training for the bike to run transition in triathlon. In ISBS-Conference
Proceedings Archive. (2010). Available from: https://ojs.ub.uni-konstanz.de/cpa/
article/view/4442/4131
35. Hue O, Valluet A, Blonc S, Hertogh C. Effects of multicycle-run training on
triathlete performance. Res Q Exerc and Sport. (2002) 73:289–95. doi: 10.1080/
02701367.2002.10609022
36. Walsh J, Peoples G, Lepers R, Stamenkovic A, Stapley P. Activation patterns
of leg muscles in trained triathletes are not variable during the early period of
running after cycling. Australian Biomechanics Conference (ABC9) 2014. (2014)
doi: 10.13140/2.1.1632.4164
37. Vleck V, Alves FB. Triathlon Transition Tests: Overview and
Recommendations for Future Research. RICYDE Revista Internacional de
Ciencias del Deporte doi: 105232/ricyde (2011) 7:I–III.
38. Vleck V, Millet GP, Alves FB, Bentley DJ. Reliability and validity of
physiological data obtained within a cycle-run transition test in age-group
triathletes. (2012)9.
39. Bentley D, Delextrat A, Vleck V, Reid AK. Reliability of a sequential
running-cycling-running test in trained triathletes. J Sports Sci. (2005) 23:202–10.
40. Díaz V, Peinado AB, Vleck VE, Alvarez-Sánchez M, Benito PJ, Alves FB,
Calderón FJ, Zapico AG. Longitudinal changes in response to a cycle-run field
test of young Male national “talent identification”and senior elite triathlon
squads. J Strength and Cond Res. (2012) 26:2209–19. doi: 10.1519/JSC.
0b013e31823a3c6b
41. Vleck V, Santos S, Bentley D, Alves F. Influence of prior cycling on the
OBLA measured during incremental running in triathletes. (2005). p. 93–223
42. Evans SA, James D, Rowlands D, Lee JB. Differences in torso kinematics
between ergometer cycling and outdoor cycling in triathletes - A preliminary
study. ISBS Proceedings Archive. (2021). p. 5 https://commons.nmu.edu/cgi/
viewcontent.cgi?article=2273&context=isbs
43. Alves M, Vleck V, Alves FB. Influence of event distance specialisation on
performance within a sequential running-cycling-running test in age-group
triathletes. Proceedings of the 13th Annual Congress of the European College of
Sports Science. Estroil (Portugal) (2008)
44. McDevitt S, Hernandez H, Hicks J, Lowell R, Bentahaikt H, Burch R, Ball J,
Chander H, Freeman C, Taylor C, et al. Wearables for biomechanical performance
optimization and risk assessment in industrial and sports applications. Bioeng.
(2022) 9:33. doi: 10.3390/bioengineering9010033
45. Zhang X, Shan G, Wang Y, Wan B, Li H. Wearables, biomechanical
feedback, and human motor-Skills’learning & optimization. Appl Sci. (2019)
9:226. doi: 10.3390/app9020226
46. Sharma AP, Périard JD. Physiological requirements of the different distances
of triathlon. In: S Migliorini, editor. Triathlon medicine. Cham: Springer
International Publishing (2020). p. 5–17 doi: 10.1007/978-3-030-22357-1_2
47. Cuba-Dorado A, Vleck V, Álvarez-Yates T, Garcia-Garcia O. Gender effect
on the relationship between talent identification tests and later world triathlon
series performance. Sports. (2021) 9:164. doi: 10.3390/sports9120164
Weich et al. 10.3389/fspor.2022.1065741
Frontiers in Sports and Active living 13 frontiersin.org
48. Weich C, Vieten MM, Jensen RL. Transient effect at the onset of human
running. Biosensors. (2020) 10:117. doi: 10.3390/bios10090117
49. Smith D, Lee H, Pickard R, Sutton B, Hunter E. Power demands of the cycle
leg during elite triathlon competition. Cahiers de l’INSEP. (1999) 24:224–30.
50. Silder A, Gleason K, Thelen DG. Influence of bicycle seat tube angle and
hand position on lower extremity kinematics and neuromuscular control:
implications for triathlon running performance. J Appl Biomech. (2011)
27:297–305. doi: 10.1123/jab.27.4.297
51. Munro C, Miller D, Fuglevand A. Ground reaction forces in running: a
reexamination. J Biomech. (1987) 20:9. doi: 10.1016/0021-9290(87)90306-X
52. Vieten MM, Weich C. The kinematics of cyclic human movement. PLoS
ONE. (2020) 15:e0225157. doi: 10.1371/journal.pone.0225157
53. Lepers R, Bigard AX, Diard J-P, Gouteyron J-F, Guezennec CY. Posture
control after prolonged exercise. Euro J Appl Physiol. (1997) 76:55–61. doi: 10.
1007/s004210050212
54. Broscheid K-C, Dettmers C, Vieten M. Is the limit-cycle-attractor an
(almost) invariable characteristic in human walking? Gait Posture. (2018)
63:242–7. doi: 10.1016/j.gaitpost.2018.05.015
55. Byrnes SK, Nüesch C, Loske S, Leuenberger A, Schären S, Netzer C,
Mündermann A. Inertial sensor-based gait and attractor analysis as clinical
measurement tool: functionality and sensitivity in healthy subjects and patients
with symptomatic lumbar spinal stenosis. Front Physiol. (2018) 9:1–8. doi: 10.
3389/fphys.2018.01095
56. Weich C, Vieten M. The gaitprint: identifying individuals by their running
style. Sensors. (2020) 20:3810. doi: 10.3390/s20143810
57. Witt M. “Biomechanische untersuchungen zum belastungswechsel im
triathlon.,”In: M. Engelhardt, B. Franz, G. Neumann, A. Pfützner, editors.
Triathlon –medizinische und methodische probleme des trainings. Triathlon und
Sportwissenschaft. Hamburg: Czwalina (1994).
58. Hausswirth C, Le Meur Y, Bieuzen F, Brisswalter J, Bernard T. Pacing
strategy during the initial phase of the run in triathlon: influence on overall
performance. Eur J Appl Physiol. (2010) 108:1115–23. doi: 10.1007/s00421-009-
1322-0
59. Skroce K, Tarperi C, Brasi I, Bertinato L, Schena F. Fast or slow start? The
role of running strategies in triathlon. J Sci and Med in Sport. (2022) 25:70–4.
doi: 10.1016/j.jsams.2021.07.013
60. Meur YL, Bernard T, Dorel S, Abbiss CR, Honnorat G, Brisswalter J,
Hausswirth C. Relationships between triathlon performance and pacing strategy
during the run in an international competition. Int J Sports Physiol and Perf.
(2011) 6:183–94. doi: 10.1123/ijspp.6.2.183
Weich et al. 10.3389/fspor.2022.1065741
Frontiers in Sports and Active living 14 frontiersin.org
