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Eleven Years' Monitoring of the World's Most Successful Male Biathlete of the Last Decade

Authors:
  • National Ski-Nordic Centre, Premanon, France

Abstract and Figures

Purpose: To report the changes in the training characteristics, performance, and heart-rate variability (HRV) of the world's most successful male biathlete of the last decade. Method: During the analyzed 11-year (2009-2019) period, the participant won 7 big crystal globes, corresponding to the winner of the International Biathlon Union World Cup. The training characteristics are reported as yearly volume (in hours) of low-intensity training (LIT), moderate- and high-intensity training, and speed and strength training. Performance was quantified by the number of World Cup top-3 positions per season. HRV was expressed as low- and high-frequency spectral power (in milliseconds squared), root-mean-square difference of successive R-R interval (in milliseconds), and heart rate (in beats per minute). Results: The training volume increased from 530 to ∼700 hours per year in 2009-2019, with a large polarization in training intensity distribution (ie, LIT 86.3% [2.9%]; moderate-intensity training 3.4% [1.5%]; high-intensity training 4.0% [0.7%]; strength 6.3% [1.6%]). The number of top-3 positions increased from 2 to 24-26 in 2009-2018 but decreased to 6 in 2019. The mean supine values in the root-mean-square difference of successive R-R interval and high-frequency spectral power divided by heart rate increased until 2015, which were stable over 2016-2018 but decreased in 2019. The number of top-3 positions was related to the total (r = .66, P = .02) and LIT (r = .92, P < .001) volume and to several markers of supine parasympathetic activity. Conclusion: The improvement in performance of the participant was mainly determined by the progressive increase in training volume, especially performed at low intensity, and was correlated to parasympathetic activity markers. This case study confirms the effectiveness of the training method, with a large amount of LIT in an elite endurance athlete, and of regular HRV monitoring.
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Eleven YearsMonitoring of the Worlds Most Successful Male
Biathlete of the Last Decade
Laurent Schmitt, Stéphane Bouthiaux, and Grégoire P. Millet
Purpose:To report the changes in the training characteristics, performance, and heart-rate variability (HRV) of the worlds
most successful male biathlete of the last decade. Method:During the analyzed 11-year (20092019) period, the participant
won 7 big crystal globes, corresponding to the winner of the International Biathlon Union World Cup. The training
characteristics are reported as yearly volume (in hours) of low-intensity training (LIT), moderate- and high-intensity
training, and speed and strength training. Performance was quantied by the number of World Cup top-3 positions per
season. HRV was expressed as low- and high-frequency spectral power (in milliseconds squared), root-mean-square
difference of successive RR interval (in milliseconds), and heart rate (in beats per minute). Results:The training volume
increased from 530 to 700 hours per year in 20092019, with a large polarization in training intensity distribution (ie, LIT
86.3% [2.9%]; moderate-intensity training 3.4% [1.5%]; high-intensity training 4.0% [0.7%]; strength 6.3% [1.6%]). The
number of top-3 positions increased from 2 to 2426 in 20092018 but decreased to 6 in 2019. The mean supine values in the
root-mean-square difference of successive RR interval and high-frequency spectral power divided by heart rate increased
until 2015, which were stable over 20162018 but decreased in 2019. The number of top-3 positions was related to the total
(r= .66, P= .02) and LIT (r= .92, P<.001) volume and to several markers of supine parasympathetic activity. Conclusion:
The improvement in performance of the participant was mainly determined by the progressive increase in training volume,
especially performed at low intensity, and was correlated to parasympathetic activity markers. This case study conrms the
effectiveness of the training method, with a large amount of LIT in an elite endurance athlete, and of regular HRV
monitoring.
Keywords:biathlon, endurance performance, heart-rate variability, training intensity, training volume
For many years, the French Nordic-ski national teams period-
ized their training loads with the polarizedprinciple.
1
This
method emphasizes the major inuence of high training volume
performed at low intensity.
25
The polarizedprinciple,
1
with a
75-5-20training intensity distribution across 3 intensity zones
(low-intensity training [LIT], moderate-intensity training [MIT],
and high-intensity training [HIT]), separated by the rst and the
second lactate thresholds (LT1 and LT2), respectively, is preco-
nized in endurance disciplines.
6
For many years, the French
Nordic-ski national teams periodized their training loads with
even more than 75% of LIT. This method is in line with recent
studies that emphasized the major inuence of high training
volume performed at low intensity.
25
Such distribution has
been observed in many endurance sports and is commonly used
by elite cross-country skiers.
1,5,7,8
Biathlon combines cross-country skiing and rie shooting,
and requires considerable physiological demands, similar to those
associated with competitive cross-country skiing,
9,10
while also
requiring precise ne motor control for fast and accurate shooting
under mental pressure. World-class male biathletes demonstrate a
high maximal oxygen uptake of >80 mL·kg
1
·min
1
9
and perform
700 to 900 hours of physical training annually, including 80%
at LIT, 4% to 5% at MIT, 5% to 6% at HIT, and 10% of
strength and speed training.
10
This volume is slightly lower than
in cross-country skiers
8,11
due to the training time for shooting
(150200 h).
8
It is known that the training components (intensity and
volume) inuence heart-rate variability (HRV) responses, owing
to a modulation in autonomic nervous system activity.
12,13
Endur-
ance training stimulates the parasympathetic activity, represented
by the high-frequency spectral power (HF) and the root mean
square differences of successive RR intervals (RMSSD) in
frequency and time domains, respectively. HIT stimulates the
sympathetic activity, mainly represented by the low-frequency
spectral power (LF). Training at LIT is therefore generally
believed to enhance parasympathetic autonomic activity
14,15
and
generally associated to a good state of health and tness in
athletes. Moreover, our research group has previously reported
that HRV was related to training volume at LIT in elite Nordic-
skiers
1
and that the analysis of combined supine and standing
HRV spectral parameters led to the denition of 4 different
patterns of fatigue.
16
Case studies in world-class athletes are of high interest in
exercise physiology and provide unique data on their physiologi-
cal development and their training content.
7
However, to our
knowledge, there is no study reporting training characteristics and
HRV data over a long period (>10 y) in athletes of this perfor-
mance level. So, the aim of this study was to describe the overall
training, as well as the relationship between the development of
key load factors (volume and intensity distribution) in the training
and how these affect the heart variability and performance devel-
opment report of the worlds most successful male biathlete of the
last decade.
Schmitt is with the National Ski-Nordic Center, Premanon, France. Bouthiaux is
with the French Ski Federation, Annecy, France. Schmitt and Millet are with the Inst
of Sport Sciences, University of Lausanne, Lausanne, Switzerland. Schmitt (laurent.
schmitt@ensm.sports.gouv.fr) is corresponding author.
1
International Journal of Sports Physiology and Performance, (Ahead of Print)
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Methods
Participant
The participant (born in 1988; aged 2131 y; height 184 cm; weight
80 kg; VO
2
max 83 mL·mn
1
·kg
1
) is the most successful male
biathlete of the last decade. VO
2
max tests were performed in roller
ski at 1200 m of altitude in the French national training center of
Prémanon. He is the all-time biathlon record holder of overall
World Cup (WC) titles, with 7 big crystal globes in a row (2012
2018), corresponding to the winner of the International Biathlon
Union WC. Over an 11-year period (20092019), he won 76 WC
events and 140 top-3 WC positions. He is a 5-time Olympic
champion (Sochi 2014, PyeongChang 2018).
Overall Design
The period studied was from the 2009 to 2019 season.
Training and Performance Characteristics
The training characteristics were extracted from his training log and
are reported as yearly volume (in hours) of LIT, MIT, HIT, and
speed and strength training. These intensity zones were dened
regularly from incremental tests performed by the French team
under the supervision of the same physiologist (L.S.) and are
separated by the LT1 and LT2. The LT1 corresponds to the rst
increase in lactate above resting value and LT2 to the rst break-
point of the lactate curve during an incremental test. Roller-ski tests
to measure VO
2
max and LTs were performed every year on road or
treadmill at 1200 m of altitude in June and October at the French
national training center of Prémanon.
The performance level was quantied by the number of WC
top-3 positions per season.
Moreover, the shooting performance was quantied as the
percentage of targets hit, extracted from the International Biathlon
Union database. We decided not to extract and analyze the skiing
performance per se, since it is affected by too many factors (snow
quality, altitude, equipment, waxing, tactical concerns, etc).
Heart-Rate Variability
The HRV tests were scheduled regularly during each year to record
HRV data at least twice during each 4-week mesocycle: 1 test during
the rst 2 weeks and 1 at the end of the fourth week during the
recovery microcycle. No training session at an intensity equal to or
above the LT2 was performed 2 days preceding the HRV tests.
During the competition period, the tests were performed after 2 full
days of recovery (aerobic training), following the last competition.
The protocol has been previously described
1,16
: it was performed in
the same conditions at wake-up before breakfast, at rest in both the
supine and standing positions, and expressed as LF and HF (in
millisecond squared), RMSSD (in milliseconds), and heart rate (HR,
in beats per minute). During the competition period, the tests were
performed after 2 full days of recovery (aerobic training), following
the last competition. The head physiologist of the French national
Nordic ski teams (L.S.) was the sole investigator to analyze the data.
Statistical Analysis
All data from the 2009 to 2019 period are presented as mean (SD)
of each year. A 1-way analysis of variance was used to test the
statistical difference in the HR and HRV parameters between the
11 seasons. The Spearman rank-order correlation coefcient (r)
was used to analyze the relationship between season values
in performances, training characteristics, and HRV data. All
analyses were completed using SigmaStat (version 3.5; Systat
Software
®
, San Jose, CA). Statistical signicance was accepted at
P<.05.
Results
Performance
For the entire studied period, the number of top-3 positions in WC
events increased from 2 to 2426 in 20092018 but decreased to 6
in 2019 (Figure 1A).
Training volume increased from 530 to 700 hours per year in
20092019, with a large polarization in training intensity distribu-
tion (ie, LIT: 86.3% [2.9%]; MIT: 3.4% [1.5%]; HIT: 4.0% [0.7%];
strength: 6.3% [1.6%]; Figure 1B). MIT that ranged between 1.6%
and 3.6% from 2009 to 2018 was increased (7.4%) in 2019.
Figure 1C describes the training volume performed in the
different forms of training used year by year.
Two altitude training camps of 15 days at 1700 to 1850 m with
the living high-training high method were regularly performed in
August and November every year.
The shooting sessions were not quantied by their duration but
by the number of cartridges (ie, between 12,000 and 15,000) red
every year. The specic shooting-only sessions were estimated at
30% of the number of cartridges red per year.
The percentage of targets hit ranged between 85% in 2009 and
90.7% in 2017. The shooting performance was stabilized over the
last 3 seasons (90.7%, 90.2%, and 89.3% in 2017, 2018, and 2019,
respectively).
The shooting performance was correlated to the following
HRV parameters: HR in the supine (r=.83, P<.001) and stand-
ing (r=.79, P<.01) positions, as well as RMSSD (r= .72,
P<.01), LF (r= .73, P<.01), HF (r= .68, P<.05), and HF·HR
1
(r=0.73, P<.01) in the standing position.
The mean (SD) values of the HR and HRV parameters are
presented in Table 1.
The mean supine values in RMSSD (from 31 [12] to 114
[14] ms) and HF (from 817 [284] to 2910 [342] ms
2
) increased until
2015 and were stable over 20162018 but decreased in 2019 (94
[28] ms; 1815 [821] ms
2
). Similarly, the supine HR decreased from
44.4 [3.4] to 33.5 [1.7] bpm in 2018 but increased in 2019 (35.9
[1.8] bpm). The ratio of HF·HR
1
measured in the supine position is
shown in Figure 1D. Over the period, the HRV parameters measured
in the standing position increased continuously (Table 1).
Three different periods were characterized in the 11 years
studied, as follows:
From 2009 to 2013, the number of top-3 positions in WC events
increased gradually from 2 to 24. The annual training volume
increased regularly from 530 to 637 hours per year, with a
large polarization in training intensity distribution during this
period (ie, LIT: 88.5% [1.2%]; MIT: 2.5% [0.7%]; HIT: 3.9%
[0.4%]; strength: 5.1% [0.3%]). The mean supine values in
RMSSD (31 [12] to 90 [13] ms) and HF (817 [284] to 2234
[217] ms
2
) increased. Similarly, the supine HR decreased from
44.4 (3.4) to 37.3 (3.3) beats per minute.
From 2014 to 2018, the number of top-3 positions in WC events
decreased in 2014 and 2015 (16 and 15) and increased again in
2016, 2017, and 2018 (19, 26, and 24, respectively). The annual
training volume was stabilized from 680 to 711 hours per year,
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with a polarization in training intensity distribution over the
period (ie, LIT: 85.5% [0.6%]; MIT: 3.5% [0.6%]; HIT: 4.2%
[1.0%]; strength: 6.8% [0.6%]). The mean supine values in
RMSSD increased from 91 [14] to 108 [16] milliseconds. The
HF decreased moderately from 2649 (297) to 2187 (588) milli-
seconds squared. Similarly, the supine HR decreased from 36.5
(3.3) to 33.5 (1.7) beats per minute.
In 2019, performance showed an important decrease, with only
6 top-3 positions in WC events. The annual training volume
was similar to the 2 preceding seasons, with 691 hours per
year, but some changes were observed in the training intensity
distribution, with a decrease in LIT and increases in MIT and
strength training (ie, LIT: 79.1%; MIT: 7.4%; HIT: 3.4%;
strength: 10%). When compared with the previous season, the
Figure 1 From season 2009 to 2019: (A) number of top-3 positions in International Biathlon Union World Cup events (rst, second, and third
positions). (B) Training volume and intensity distribution: I, LIT (below rst lactate threshold); II, MIT (between rst and second lactate thresholds);
III, HIT (above second lactate threshold); and IV, speed and strength training. (C) Training volume in the different forms of activity (roller-ski, ski,
running, cycling, strength, and various). (D) HF (in milliseconds squared) divided by HR (in beats per minute); HF·HR
1
(in normalized units [n.u.]).
HF indicates high-frequency spectral power; HIT, high-intensity training; HR, heart rate; LIT, low-intensity training; MIT, moderate-intensity
training.
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mean supine values in RMSSD and HF decreased, while the
supine HR increased.
Relationships Between Performances, Training
Characteristics, and HRV Parameters
The number of top-3 positions per season was related to the yearly
total (r= .73, P= .009) and LIT (r= .92, P<.001; Figure 2A)
volume and to several markers of supine parasympathetic activity
as HF·HR
1
(r= .57, P= .05; Figure 2B), HR (r=.66, P= .02;
Figure 2C), and RMSSD (r= .58, P= .05; Figure 2D).
Shooting performance was correlated to the following HRV
parameters of the studied period: HR in the supine (r=.83,
P<.001) and standing (r=.79, P<.01) positions, as well as
RMSSD (r= .72, P<.01), LF (r= .73, P<.01), HF (r= .68, P<.05),
and HF·HR
1
(r=.73, P<.01) in the standing position.
Discussion
This study reports the changes in training volume in the most
successful male biathlete of the last decade, with a progressive
increase from 530 to 700 hours between 2009 and 2016 and a
plateau over the last 3 years. This training volume is lower than that
for a world-class cross-country skier (750900 h).
5
The total
training volume analyzed in this case study does not take into
account the specic shooting sessions, which represent 30% of
the number of cartridges red per year. From 2009 to 2018, the
intensity distribution remained quite similar across the seasons,
Figure 2 Correlations between the number of top-3 positions per season over the 11-year period (20092019). (A) The volume at LIT below rst
lactate threshold, (B) resting supine mean yearly values in HF·HR
1
(in normalized units [n.u.]), (C) resting supine mean yearly values in HR (in beats per
minute), and (D) resting supine mean yearly values in RMSSD (in milliseconds). HF·HR
1
indicates high-frequency spectral power divided by heart rate;
HR, heart rate; LIT, low-intensity training; RMSSD, root-mean-square differences of successive RR intervals.
Table 1 Yearly Mean Values of HR-Variability Parameters Measured at Rest in Supine and Standing Positions
Supine Standing
Season HR, bpm RMSSD, ms LF, ms
2
HF, ms
2
HR, bpm RMSSD, ms LF, ms
2
HF, ms
2
2009 44.4 (3.4) 31 (12) 1118 (275) 817 (284) 59.4 (4.6) 30 (11) 2439 (763) 281 (148)
2010 44.0 (3.1) 44 (13) 1083 (276) 1279 (298) 58.5 (4.1) 31 (11) 2611 (849) 297 (143)
2011 40.3 (3.3)** 51 (13) 2189 (417) 1485 (312) 57.7 (3.0) 34 (13) 2824 (1004) 356 (186)
2012 39.0 (3.3) 95 (13)** 2472 (391) 2412 (260) 58.1 (3.3) 34 (13) 2634 (1124) 335 (200)
2013 37.3 (3.3) 89 (13) 3421 (334) 2234 (217) 58.7 (2.3)*** 31 (11)* 2559 (1069) 307 (170)*
2014 36.5 (3.3) 91 (14) 3337 (326) 2649 (297) 59.0 (1.9)** 32 (11) 2741 (1265) 330 (175)
2015 34.6 (3.3)* 114 (14) 3653 (367) 2910 (342) 58.6 (2.0) 34 (11) 3011 (1274) 337 (177)
2016 34.9 (2.2) 99 (15)* 1841 (495)*** 2007 (418)** 58.8 (1.9) 35 (10) 3177 (1212)* 352 (172)**
2017 34.2 (1.6) 99 (17) 2211 (495) 2137 (455) 58.1 (2.1) 39 (11) 3670 (1237) 394 (155)
2018 33.5 (1.7) 108 (19) 1992 (497) 2187 (588) 58.4 (2.6) 40 (10) 3603 (1293) 398 (152)
2019 35.9 (1.8)** 94 (28) 1961 (489) 1815 (821)* 59.5 (3.8) 39 (11) 3346 (1496) 385 (164)
ANOVA,P *** *** ** ** *** *** ***
Abbreviations: ANOVA, analysis of variance; bpm, beats·min
1
; HF, high-frequency spectral power; HR, heart rate; LF, low-frequency spectral power; RMSSD, root-
mean-square differences of successive RR interval.
*P<.05, **P<.01, ***P<.001 for difference with previous season.
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with 87.0% (1.8%) at LIT. This large amount of LIT is higher than
the 75% usually associated to the polarized trainingparadigm
6
but is in line with previous reports in cross-country skiers.
5,7,8
The
correlation between his performance level and the total training
volume, as well as the LIT volume, emphasizes the need for a large
amount of aerobic training, not only during the early phase of the
career in an endurance athlete but also even when he has achieved
the highest level.
The improvement in shooting performance over the studied
period was related to a decrease in HR in both the supine and
standing positions, as well as an increase in the HRV parameters. It
may come from either direct (ie, improved HR recovery and larger
tachycardia facilitating the shooting when HF is higher) or indirect
(ie, lower fatigue and/or higher condence in shooting when his
physical tness was high, as associated with the HRV parameters)
mechanisms.
Of high interest is the change observed in 2019, when the
participant modied the training distribution by increasing the
volume in MIT and decreasing the volume carried out in LIT.
These changes in his intensity distribution were concomitant with a
clear decrease in performance, although the training volume re-
mained similar to the 3 previous years.
The HRV data also followed several phases over the period:
from 2009 to 2015, the markers of the temporal (RMSSD) and
spectral (LF, HF, LF + HF, and LF·HR
1
) parasympathetic activi-
ties when measured in the supine position were regularly increas-
ing. Then, there was a plateau, followed by a regression in the 2019
season, when these supine HRV parameters went back to the values
measured in 20112014.
The standing HRV parameters did not display a similar trend
and increased nonlinearly but continuously over the 11-year period.
Overall, the respective changes in the supine (mainly HF and
RMSSD, parasympathetic activity) andstanding (LF, predominantly
sympathetic activity) positions support the assumptions that (1) both
the supine and standing measurements of HRV are valuable and
(2) elite endurance performance requires a high level of both
parasympathetic and sympathetic activities. However, the relation-
ship between performance and HRV (especially HF, HF·HR
1
,and
RMSSD) appears to be stronger with markers of parasympathetic
activity that is enhanced mainly by LIT.
Practical Applications
One important contribution of this study is that the annual perfor-
mance development appears to be closely related to the training
being polarized,with an annual increase in the load in the form of
an increased number of hours at low intensity.
Most of the supine and standing HRV parameters increased or
remained very high over the period, except in 2019, which
corresponded to a training less polarizedand a decreased per-
formance level. Overall, this case study conrms the effectiveness
of a training method with a large amount of LIT in an elite
endurance athlete. Moreover, it demonstrates the benets of regular
HRV monitoring.
Conclusion
The improvement in performance in the most successful male
biathlete over the last decade is obviously the consequence of many
factors. One of the most important factors appears to be the
progressive increase in training volume, especially performed at
low intensity. Since HRV and HR are correlated to the variation in
performance, regular monitoring including these parameters ap-
pears to be effective. This case study conrms the effectiveness of a
training method with a large amount of LIT in an elite endurance
athlete and HRV monitoring in both the supine and standing
positions.
Acknowledgment
The authors warmly acknowledge Martin Fourcade for openly sharing his
training and HRV data.
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... In several professional and amateur sports, from cycling to football weekly and daily follow-up are used to make critical decisions regarding training plans and competitions. It is a point of debate whether athletes' monitoring can be limited to RMSSD (Schmitt et al., 2015b) analysis or shall be performed using both the time and frequency-domain analyses (Schmitt et al., 2021). ...
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Easy-to-use and accurate heart rate variability (HRV) assessments are essential in athletes’ follow-up, but artifacts may lead to erroneous analysis. Artifact detection and correction are the purpose of extensive literature and implemented in dedicated analysis programs. However, the effects of number and/or magnitude of artifacts on various time- or frequency-domain parameters remain unclear. The purpose of this study was to assess the effects of artifacts on HRV parameters. Root mean square of the successive differences (RMSSD), standard deviation of the normal to normal inter beat intervals (SDNN), power in the low- (LF) and high-frequency band (HF) were computed from two 4-min RR recordings in 178 participants in both supine and standing positions, respectively. RRs were modified by (1) randomly adding or subtracting 10, 30, 50 or 100 ms to the successive RRs; (2) a single artifact was manually inserted; (3) artifacts were automatically corrected from signal naturally containing artifacts. Finally, RR recordings were analyzed before and after automatic detection-correction of artifacts. Modifying each RR by 10, 30, 50 and 100 ms randomly did not significantly change HRV parameters (range -6%, +6%, supine). In contrast, by adding a single artifact, RMSSD increased by 413% and 269%, SDNN by 54% and 47% in supine and standing positions, respectively. LF and HF changed only between -3% and +8% (supine and standing) in the artifact condition. When more than 0.9% of the signal contained artifacts, RMSSD was significantly biased, whilst when more than 1.4% of the signal contained artifacts LF and HF were significantly biased. RMSSD and SDNN were more sensitive to a single artifact than LF and HF. This indicates that, when using RMSSD only, a single artifact may induce erroneous interpretation of HRV. Therefore, we recommend using both time- and frequency-domain parameters to minimize the errors in the diagnoses of health status or fatigue in athletes.
... Most retrospective analyses of the TID of athletes engaged in various endurance sports, such as rowing (Hartmann et al., 1990;Guellich et al., 2009;Plews et al., 2014), cycling (Lucía et al., 2000;Schumacher and Mueller, 2002;Zapico et al., 2007;Neal et al., 2011), swimming (Mujika et al., 1995;Baldassarre et al., 2019), running (Esteve-Lanao et al., 2005), triathlon (Neal et al., 2013) and cross-country skiing (Torvik et al., 2021), have revealed a pyramidal structure, with > 70% of the training being performed in Z1. However, some retrospective analyses do report utilization of a polarized TID by successful cross-country skiers (Seiler and Kjerland, 2006;Sandbakk et al., 2011;Tønnessen et al., 2014;Schmitt et al., 2020), runners (Billat et al., 2001) and rowers (Bourgois et al., 2014). ...
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Purpose: To evaluate retrospectively the training intensity distribution (TID) among highly trained canoe sprinters during a single season and to relate TID to changes in performance. Methods: The heart rates during on-water training by 11 German sprint kayakers (7 women, 4 men) and one male canoeist were monitored during preparation periods (PP) 1 and 2, as well as during the period of competition (CP) (total monitoring period: 37 weeks). The zones of training intensity (Z) were defined as Z1 [<80% of peak oxygen consumption (VO 2peak )], Z2 (81–87% VO 2peak ) and Z3 (>87% VO 2peak ), as determined by 4 × 1,500-m incremental testing on-water. Prior to and after each period, the time required to complete the last 1,500-m stage (all-out) of the incremental test (1,500-m time-trial), velocities associated with 2 and 4 mmol·L ⁻¹ blood lactate (v2 [BLa] , v4 [BLa] ) and VO 2peak were determined. Results: During each period, the mean TID for the entire group was pyramidal (PP1: 84/12/4%, PP2: 80/12/8% and CP: 91/5/4% for Z1, Z2, Z3) and total training time on-water increased from 5.0 ± 0.9 h (PP1) to 6.1 ± 0.9 h (PP2) and 6.5 ± 1.0 h (CP). The individual ranges for Z1, Z2 and Z3 were 61–96, 2–26 and 0–19%. During PP2 VO 2peak (25.5 ± 11.4%) markedly increased compared to PP1 and CP and during PP1 v2 [bla] (3.6 ± 3.4%) showed greater improvement compared to PP2, but not to CP. All variables related to performance improved as the season progressed, but no other effects were observed. With respect to time-trial performance, the time spent in Z1 ( r = 0.66, p = 0.01) and total time in all three zones ( r = 0.66, p = 0.01) showed positive correlations, while the time spent in Z2 ( r = −0.57, p = 0.04) was negatively correlated. Conclusions: This seasonal analysis of the effects of training revealed extensive inter-individual variability. Overall, TID was pyramidal during the entire period of observation, with a tendency toward improvement in VO 2peak , v2 [bla] , v4 [bla] and time-trial performance. During PP2, when the COVID-19 lockdown was in place, the proportion of time spent in Z3 doubled, while that spent in Z1 was lowered; the total time spent training on water increased; these changes may have accentuated the improvement in performance during this period. A further increase in total on-water training time during CP was made possible by reductions in the proportions of time spent in Z2 and Z3, so that more fractions of time was spent in Z1.
... Indeed, case studies are the more common research design to feature these athletes, yielding valuable information about the top-end of athletic performance. [30][31][32] In Figure 1, we show the interaction between these classification tiers, training volume/physical activity level, performance standards, research design, and the population density of each tier. ...
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Throughout the sport-science and sports-medicine literature, the term “elite” subjects might be one of the most overused and ill-defined terms. Currently, there is no common perspective or terminology to characterize the caliber and training status of an individual or cohort. This paper presents a 6-tiered Participant Classification Framework whereby all individuals across a spectrum of exercise backgrounds and athletic abilities can be classified. The Participant Classification Framework uses training volume and performance metrics to classify a participant to one of the following: Tier 0: Sedentary; Tier 1: Recreationally Active; Tier 2: Trained/Developmental; Tier 3: Highly Trained/National Level; Tier 4: Elite/International Level; or Tier 5: World Class. We suggest the Participant Classification Framework can be used to classify participants both prospectively (as part of study participant recruitment) and retrospectively (during systematic reviews and/or meta-analyses). Discussion around how the Participant Classification Framework can be tailored toward different sports, athletes, and/or events has occurred, and sport-specific examples provided. Additional nuances such as depth of sport participation, nationality differences, and gender parity within a sport are all discussed. Finally, chronological age with reference to the junior and masters athlete, as well as the Paralympic athlete, and their inclusion within the Participant Classification Framework has also been considered. It is our intention that this framework be widely implemented to systematically classify participants in research featuring exercise, sport, performance, health, and/or fitness outcomes going forward, providing the much-needed uniformity to classification practices.
... This is in no small part due to technological advances in recent years, such as the use of wearables, smartphone apps, and teambased tracking systems to enable time-efficient and cost-effective recording of beat-by-beat HR data in almost any situation and context. There is a variety of methods and metrics available that are well-established while being easily implemented into the busy schedules of today's elite athletes (Buchheit, 2014;Lacome et al., 2018;Lamberts, 2014;Plews et al., 2013;Schmitt et al., 2020). ...
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The monitoring of heart rate (HR) and heart rate variability (HRV) can contribute significantly to the individualization and optimization of training and recovery. However, the practical interpretation of longitudinal data is still challenging in some cases. The results of this dissertation can be summarized as follows: Practical interpretation of HR(V) data requires consideration of contextual factors such as training context and more detailed analysis of HR(V) time courses (Study 1). Orthostatic tests appear to be useful in identifying complex and training-specific HR(V) responses following short-term overload training and recovery (Study 2). Submaximal HR during standardized warm-up is sensitive to short-term periods of training and recovery, contrary to previous assumptions (Study 3). A future challenge is to effectively separate potential short-term from long-term effects. --- Das Monitoring von Herzfrequenz (HR) und Herzfrequenzvariabilität (HRV) wird zur Individualisierung und Optimierung von Training und Regeneration empfohlen. Die trainingspraktische Interpretation der Daten stellt jedoch nach wie vor eine Herausforderung dar. Die im Rahmen der Dissertation veröffentlichten Ergebnisse können wie folgt zusammengefasst werden: Die praxisnahe Interpretation von HR(V) Daten erfordert die Berücksichtigung kontextualer Faktoren wie die Trainingsstruktur und eine differenziertere Analyse von HR(V) Zeitverläufen (Studie 1). Orthostase Tests scheinen hilfreich zu sein, um die komple-xen, belastungsspezifischen HR(V) Reaktionen nach Kurzzeit-Überlastungstraining und Erholung identifizieren zu können (Studie 2). Die standardisiert im Training erfasste submaximale Belastungs-HR reagiert entgegen früherer Annahmen sensitiv auf kurze Trainings- und Erholungsphasen (Studie 3). Eine zukünftige Herausforderung besteht darin, Kurzzeit- von Langzeiteffekten zu isolieren.
... These observational studies, many of which lasted several years, included many of the original studies of Matveyev (120), Nadori (149,150), Verkoshansky (220), etc. More recent long-term observational and descriptive studies have dealt with a number of periodization related factors including performance-related variables, sport performance, and injuries and have included a variety of sports such as swimming (82), volleyball (181), orienteering (211), crosscountry skiing and biathlon (147,176,182,212), and weightlifting (21). These types of studies and observations are especially important because they were performed observing athletes in their normal environment, thus maintaining ecological validity. ...
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Periodization can be defined as a logical sequential, phasic method of manipulating fitness and recovery phases to increase the potential for achieving specific performance goals while minimizing the potential for nonfunctional overreaching, overtraining, and injury. Periodization deals with the micromanagement of timelines and fitness phases and is cyclic in nature. On the other hand, programming deals with the micromanagement of the training process and deals with exercise selection, volume, intensity, etc. Evidence indicates that a periodized training process coupled with appropriate programming can produce superior athletic enhancement compared with nonperiodized process. There are 2 models of periodization, traditional and block. Traditional can take different forms (i.e., reverse). Block periodization has 2 subtypes, single goal or factor (individual sports) and multiple goals or factors (team sports). Both models have strengths and weaknesses but can be “tailored” through creative programming to produce excellent results for specific sports.
... Being an elite athlete (i.e., performing at the highest international level) case studies report high training volumes. A female skier conducted on average 940 h a year across a 5year successful period (Solli et al., 2017) and a male biathlete up to 700 h per year (Schmitt et al., 2020). Thus, confusion in the distinction between commitment and addiction is obvious. ...
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Exaggerated exercise volumes, lack of control, withdrawal symptoms and conflicts with family and friends are core symptoms of exercise addiction. The condition can lead to health problems and social isolation because exercise is given the highest priority in any situation. The prevalence of the risk of exercise addiction has mostly been assessed in leisure time exercisers such as runners, fitness attendees and cyclists. The prevalence proportion ranges from 3 to 42% depending on the type of sport and the assessment tool. The proportion is greater among elite athletes, and increases with the level of competition. This study's primary aim was to assess the prevalence of exercise addiction among elite athletes competing at national level and its secondary aim was to evaluate the psychometric properties of the Exercise Addition Inventory (EAI) in elite sports. Participants (n = 417) from 15 sports disciplines and with 51% women completed an online survey. Results showed that 7.6% were at risk of exercise addiction. This group was younger, exhibited tendency to exercise despite pain and injury, felt guilty if not exercising enough, and reported substantial eating disorder symptoms. The reliability and validity of the EAI was good suggesting that the scale is appropriate for measuring the risk of exercise addiction in elite athletes.
... In order to analyze muscle oxygenation in the four most relevant muscle groups for XCS during a real race scenario, the present study was limited to one participant. As studies in elite athletes naturally come with a low number of participants with particular interest in the individual response to training and competition [31][32][33][34], equipment availability further limits the sample size in the development of new technologies [35]. For instance, Swarén and Eriksson [35] developed a power balance model to calculate propulsive power based on local positioning data of two participants during a XCS Scandinavian cup sprint race. ...
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The aims of the study were to assess the robustness and non-reactiveness of wearable near-infrared spectroscopy (NIRS) technology to monitor exercise intensity during a real race scenario, and to compare oxygenation between muscle groups important for cross-country skiing (XCS). In a single-case study, one former elite XCS (age: 39 years, peak oxygen uptake: 65.6 mL/kg/min) was equipped with four NIRS devices, a high-precision global navigation satellite system (GNSS), and a heart rate (HR) monitor during the Vasaloppet long-distance XCS race. All data were normalized to peak values measured during incremental laboratory roller skiing tests two weeks before the race. HR reflected changes in terrain and intensity, but showed a constant decrease of 0.098 beats per minute from start to finish. Triceps brachii (TRI) muscle oxygen saturation (SmO2) showed an interchangeable pattern with HR and seems to be less affected by drift across the competition (0.027% drop per minute). Additionally, TRI and vastus lateralis (VL) SmO2 revealed specific loading and unloading pattern of XCS in uphill and downhill sections, while rectus abdominus (RA) SmO2 (0.111% drop per minute) reflected fatigue patterns occurring during the race. In conclusion, the present preliminary study shows that NIRS provides a robust and non-reactive method to monitor exercise intensity and fatigue mechanisms when applied in an outdoor real race scenario. As local exercise intensity differed between muscle groups and central exercise intensity (i.e., HR) during whole-body endurance exercise such as XCS, NIRS data measured at various major muscle groups may be used for a more detailed analysis of kinetics of muscle activation and compare involvement of upper body and leg muscles. As TRI SmO2 seemed to be unaffected by central fatigue mechanisms, it may provide an alternative method to HR and GNSS data to monitor exercise intensity.
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Purpose: To analyze the effects of different training strategies (i.e., mainly intensity distribution) during living high – training low (LHTL) between elite cross-country skiers and Nordic-combined athletes. Methods: 12 cross-country skiers (XC) (7 men, 5 women), and 8 male Nordic combined (NC) of the French national teams were monitored during 15 days of LHTL. The distribution of training at low-intensity (LIT), below the first ventilatory threshold (VT1), was 80% and 55% in XC and NC respectively. Daily, they filled a questionnaire of fatigue, and performed a heart rate variability (HRV) test. Prior (Pre) and immediately after (Post), athletes performed a treadmill incremental running test for determination of V˙O2max and V˙O2 at the second ventilatory threshold (V˙O2V T2), a field roller-skiing test with blood lactate ([La-]) assessment. Results: The training volume was in XC and NC, respectively: at LIT: 45.9 ± 6.4 vs. 23.9 ± 2.8 h (p < 0.001), at moderate intensity: 1.9 ± 0.5 vs. 3.0 ± 0.4 h, (p < 0.001), at high intensity: 1.2 ± 0.9 vs. 1.4 ± 02 h (p = 0.05), in strength (and jump in NC): 7.1 ± 1.5 vs. 18.4 ± 2.7 h, (p < 0.001). Field roller-skiing performance was improved (-2.9 ± 1.6%, p < 0.001) in XC but decreased (4.1 ± 2.6%, p < 0.01) in NC. [La-] was unchanged (-4.1 ± 14.2%, p = 0.3) in XC but decreased (-27.0 ± 11.1%, p < 0.001) in NC. Changes in field roller-skiing performance and in [La-] were correlated (r = -0.77, p < 0.001). V˙O2max increased in both XC and NC (3.7 ± 4.2%, p = 0.01 vs. 3.7 ± 2.2%, p = 0.002) but V˙O2V T2 increased only in XC (7.3 ± 5.8%, p = 0.002). HRV analysis showed differences between XC and NC mainly in high spectral frequency in the supine position (HFSU). All NC skiers showed some signs of overreaching at Post. Conclusion: During LHTL, despite a higher training volume, XC improved specific performance and aerobic capacities, while NC did not. All NC skiers showed fatigue states. These findings suggest that a large amount of LIT with a moderate volume of strength and speed training is required during LHTL in endurance athletes.
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The biathlon, combining cross-country ski skating with rifle marksmanship, has been an Olympic event since the Winter Games in Squaw Valley, United States, in 1960. As a consequence of replacing the classical with the skating technique in the 1980s, as well as considerable improvements in equipment and preparation of ski tracks and more effective training, the average biathlon skiing speed has increased substantially. Moreover, the mass-start, pursuit, and sprint races have been introduced. Indeed, two of the four current individual Olympic biathlon competitions involve mass-starts, where tactics play a major role and the outcome is often decided during the last round of shooting or final sprint. Biathlon is a demanding endurance sport requiring extensive aerobic capacity. The wide range of speeds and slopes involved requires biathletes to alternate continuously between and adapt different skating sub-techniques during races, a technical complexity that places a premium on efficiency. Although the relative amounts of endurance training at different levels of intensity have remained essentially constant during recent decades, today’s biathletes perform more specific endurance training on roller skis on terrain similar to that used for competition, with more focus on the upper-body, systematic strength and power training and skiing at higher speeds. Success in the biathlon also requires accurate and rapid shooting while simultaneously recovering from high-intensity skiing. Many different factors, including body sway, triggering behavior, and even psychology, influence the shooting performance. Thus, the complexity of biathlon deserves a greater research focus on areas such as race tactics, skating techniques, or shooting process.
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The main aim of this study was to investigate the training characteristics of the most successful female cross-country skier ever during the best period of her career. The participant won six gold medals at the Olympic Games, 18 gold medals at the World Championship, and 110 World Cup victories. Day-to-day training diary data, interviews, and physiological tests were analyzed. Training data was systemized by training form (endurance, strength, and speed), intensity [low- (LIT), moderate- (MIT), and high-intensity training (HIT)], and mode (running, cycling, and skiing/roller skiing), followed by a division into different periodization phases. Specific sessions utilized in the various periodization periods and the day-to-day periodization of training, in connection with altitude camps and tapering toward major championships, were also analyzed. Following a 12-year nonlinear increase in training load, the annual training volume during the five consecutive successful years stabilized at 937 ± 25 h, distributed across 543 ± 9 sessions. During these 5 years, total training time was distributed as 90.6% endurance-, 8.0% strength-, and 1.4% speed-training, with endurance-training time consisting of 92.3 ± 0.3% LIT, 2.9 ± 0.5% MIT, and 4.8 ± 0.5% HIT. Total LIT-time consisted of 21% warm-up, 14% sessions <90 min, and 65% long-duration sessions >90 min. While the total number of LIT sessions remained stable across phases (32 sessions), total LIT-time was reduced from GP (76 h/month) to SP (68 h/month) and CP (55 h/month). MIT-time decreased from GP (2.8 h/month) to SP (2.2 h/month) and CP (1 h/month). HIT-time increased from GP (2.8 h/month) to SP (3.2 h/month) and CP (4.7 h/month). Altitude training accounted for 18–25% of annual training volume and performed across relatively short training camps (≤16 days) with a clear reduction of HIT training, but increased total and LIT volume compared to sea-level training. Training before international championships included a 2-week increase in LIT and strength volume followed by a gradual reduction of training volume and increased HIT during the last week. This study provides unique data on the world's most successful female cross-country skier's long-term training process, including novel information about the distribution of and interplay between sessions of different forms, intensities, and exercise modes throughout the annual season.
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Cross-country skiing is one of the most demanding of endurance sports, involving protracted competitions on varying terrain employing a variety of skiing techniques that require upper- and/or lower-body work to different extents. Through more effective training and extensive improvements in equipment and track preparation, the speed of cross-country ski races has increased more than that of any other winter Olympic sport and, in addition, new types of racing events have been introduced. To a certain extent this has altered the optimal physiological capacity required to win and the training routines of successful skiers have evolved accordingly. The longstanding tradition of researchers working closely with XC skiing coaches and athletes to monitor progress, improve training and refine skiing techniques has provided unique physiological insights revealing how these athletes are approaching the upper limits of human endurance. In the present review, we summarize current scientific knowledge concerning the demands involved in elite cross-country skiing, as well as the physiological capacity and training routines of the best athletes.
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Purpose: The aim of this study was to generate updated Olympic medal benchmarks for VO2max in winter endurance disciplines, examine possible differences in VO2max between medalists and non-medalists, and calculate gender difference in VO2max based on a homogeneous subset of world-leading endurance athletes. Methods: We identified 111 athletes who participated in winter Olympic Games/World Championships in the period 1990-2013. All identified athletes tested VO2max at the Norwegian Olympic Training Center within ±1 yr from their championship performance. Testing procedures were consistent throughout the entire period. Results: For medal winning athletes, the following relative VO2max values (mean: 95 % CIs) for men/women were observed (mL·min-1· kg-1): 84:87-81/72:77-68 for cross-country distance skiing, 78:81-75/68:73-64 for cross-country sprint skiing, 81:84-78/67:73-61 for biathlon and 77:80-75 for Nordic combined (men only). Similar benchmarks for absolute VO2max (L·min-1) in male/female athletes (mean: 95 % CIs) are 6.4:6.1-6.7/4.3:4.1-4.5 for cross-country distance skiers, 6.3:5.8-6.8/4.0:3.7-4.3 for cross-country sprint skiers, 6.2:5.7-6.4/4.0:3.7-4.3 for biathletes and 5.3:5.0-5.5 for Nordic combined (men only). The difference in relative VO2max between medalists and non-medalists was large for Nordic combined, moderate for cross-country distance and biathlon, and small/trivial for the other disciplines. Corresponding differences in absolute VO2max were small/trivial for all disciplines. Male cross-country medalists achieve 15% higher relative VO2max values than corresponding females. Conclusions: This study provides updated benchmark VO2max values for Olympic medal level performance in winter endurance disciplines, and can serve as a guideline of the requirements for future elite athletes.
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Purpose: To describe training variations across the annual cycle in Olympic and World Champion endurance athletes, and determine whether these athletes used tapering strategies in line with recommendations in the literature. Methods: Eleven elite XC skiers and biathletes (4 male; 28±1 yr, 85±5 mL x min(-1) x kg(-1) VO2max, 7 female, 25±4 yr, 73±3 mL x min(-1) x kg(-1) VO2max) reported one year of day-to-day training leading up to the most successful competition of their career. Training data were divided into periodization and peaking phases and distributed into training forms, intensity zones and endurance activity forms. Results: Athletes trained ∼800 h/500 sessions x year(-1), including ∼500 h x year(-1) of sport-specific training. Ninety-four percent of all training was executed as aerobic endurance training. Of this, ∼90% was low intensity training (LIT, below the first lactate threshold) and 10% high intensity training (HIT, above the first lactate threshold) by time. Categorically, 23% of training sessions were characterized as HIT with primary portions executed at or above the first lactate turn point. Training volume and specificity distribution conformed to a traditional periodization model, but absolute volume of HIT remained stable across phases. However, HIT training patterns tended to become more polarized in the competition phase. Training volume, frequency and intensity remained unchanged from pre-peaking to peaking period, but there was a 32±15% (P<.01) volume reduction from the preparation period to peaking phase. Conclusions: The annual training data for these Olympic and World champion XC skiers and biathletes conforms to previously reported training patterns of elite endurance athletes. During the competition phase, training became more sport-specific, with 92% performed as XC skiing. However, they did not follow suggested tapering practice derived from short-term experimental studies. Only three out of 11 athletes took a rest day during the final 5 days prior to their most successful competition.
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We aimed to analyse the relationship between training load/intensity and different heart rate variability (HRV) fatigue patterns in 57 elite Nordic-skiers. 1063 HRV tests were performed during 5 years. R-R intervals were recorded in resting supine (SU) and standing (ST) positions. Heart rate, low (LF), high (HF) frequency powers of HRV were determined. Training volume, training load (TL, a.u.) according to ventilatory threshold 1 (VT1) and VT2 were measured in zones I≤VT1; VT1<II≤VT2; III>VT2, IV for strength. TL was performed at 81.6±3.5% in zone I, 0.9±0.9% in zone II, 5.0±3.6% in zone III, 11.6±6.3% in zone IV. 172 HRV tests matched a fatigue state and four HRV fatigue patterns (F) were statistically characterized as F(HF-LF-)SU_ST for 121 tests, F(LF+SULF-ST) for 18 tests, F(HF-SUHF+ST) for 26 tests and F(HF+SU) for 7 tests. The occurrence of fatigue states increased substantially with the part of altitude training time (r2=0.52, p<0.001). This study evidenced that there is no causal relationship between training load/intensity and HRV fatigue patterns. Four fatigue-shifted HRV patterns were sorted. Altitude training periods appeared critical as they are likely to increase the overreaching risks.
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Purpose: To compare the physiological capacity and training characteristics of the world's six highest ranked female cross-country skiers (WC) with those of six competitors of national class (NC). Methods: Immediately before the start of the competition season, all skiers performed three 5-min submaximal stages of roller skiing on a treadmill for measurement of oxygen cost, as well as a 3-min self-paced performance test employing both the double poling (DP) and diagonal stride (DIA) techniques. During the 3-min performance tests, the total distance covered, peak oxygen uptake (VO2peak) and accumulated oxygen deficit were determined. Each skier documented the intensity and mode of their training during the preceding 6 months in a diary. Results: There were no differences between the groups with respect to oxygen cost or gross efficiency at the submaximal speeds. The WC skiers covered 6-7% longer distances during the 3-min tests and exhibited average VO2peak values of ~70 and ~65 mL·min·kg with DIA and DP, respectively, which were 10 and 7% higher than the NC skiers (all P<0.05). However, the accumulated oxygen deficit did not differ between groups. From May to October, the WC skiers trained a total of 532±73 hours (270±26 sessions) versus 411±62 hours (240±27 sessions) for the NC skiers. In addition, the WC skiers performed 26% more low-intensity and almost twice as much moderate-intensity endurance and speed training (all P<0.05). Conclusions: This study highlights the importance of a high oxygen uptake and the ability to utilize this while performing the different skiing techniques on varying terrain for female cross-country skiers to win international races. In addition, the training data documented here provide benchmark values for female endurance athletes aiming for medals.
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This study investigated changes in heart rate variability (HRV) in elite Nordic-skiers to characterize different types of "fatigue" in 27 men and 30 women surveyed from 2004 to 2008. R-R intervals were recorded at rest during 8 min supine (SU) followed by 7 min standing (ST). HRV parameters analysed were powers of low (LF), high (HF) frequencies, (LF+HF) (ms(2)) and heart rate (HR, bpm). In the 1 063 HRV tests performed, 172 corresponded to a "fatigue" state and the first were considered for analysis. 4 types of "fatigue" (F) were identified: 1. F(HF(-)LF(-))SU_ST for 42 tests: decrease in LFSU (- 46%), HFSU (- 70%), LFST (- 43%), HFST (- 53%) and increase in HRSU (+ 15%), HRST (+ 14%). 2. F(LF(+) SULF(-) ST) for 8 tests: increase in LFSU (+ 190%) decrease in LFST (- 84%) and increase in HRST (+ 21%). 3. F(HF(-) SUHF(+) ST) for 6 tests: decrease in HFSU (- 72%) and increase in HFST (+ 501%). 4. F(HF(+) SU) for only 1 test with an increase in HFSU (+ 2161%) and decrease in HRSU (- 15%). Supine and standing HRV patterns were independently modified by "fatigue". 4 "fatigue"-shifted HRV patterns were statistically sorted according to differently paired changes in the 2 postures. This characterization might be useful for further understanding autonomic rearrangements in different "fatigue" conditions. © Georg Thieme Verlag KG Stuttgart · New York.
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We examined heavy training-induced changes in baroreflex sensitivity, plasma volume and resting heart rate and blood pressure variability in female endurance athletes. Nine athletes (experimental training group, ETG) increased intense training (70–90% VO2max) volume by 130% and low-intensity training (<70% VO2max) volume by 100% during 6–9 weeks, whereas the corresponding increases in six control athletes (CG) were 5% and 10% respectively. Maximal oxygen uptake (VO2max) in the ETG and CG did not change, but in five ETG athletes VO2max decreased from 53·0 ± 2·2 (mean ± SEM) (CI 46·8–59·2) ml kg–1 min–1 to 50·2 ± 2·3 (43·8–56·6) ml kg–1 min–1 (P<0·01), indicating overtraining. Baroreflex sensitivity (BRS) measured using the phenylephrine technique and blood pressure variability (BPV) did not change, but the low-frequency power of the R–R interval variability increased in the ETG (P<0·05). The relative change in plasma volume was 7% in the ETG and 3% in the CG. The changes in BRS did not correlate with the changes in plasma volume, heart rate variability and BPV. We conclude that heavy endurance training and overtraining did not change baroreflex sensitivity or BPV but significantly increased the low-frequency power of the R–R interval variability during supine rest in female athletes as a marker of increased cardiac sympathetic modulation.