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Science and Medicine in Football
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/rsmf20
Assessing the usefulness of submaximal exercise
heart rates for monitoring cardiorespiratory
fitness changes in elite youth soccer players
Stefan Altmann, Ludwig Ruf, Rainer Neumann, Sascha Härtel, Alexander
Woll & Martin Buchheit
To cite this article: Stefan Altmann, Ludwig Ruf, Rainer Neumann, Sascha Härtel, Alexander
Woll & Martin Buchheit (2022): Assessing the usefulness of submaximal exercise heart rates for
monitoring cardiorespiratory fitness changes in elite youth soccer players, Science and Medicine in
Football, DOI: 10.1080/24733938.2022.2060520
To link to this article: https://doi.org/10.1080/24733938.2022.2060520
Accepted author version posted online: 29
Mar 2022.
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Publisher: Taylor & Francis & Informa UK Limited, trading as Taylor & Francis Group
Journal: Science and Medicine in Football
DOI: 10.1080/24733938.2022.2060520
Assessing the usefulness of submaximal exercise heart rates for
monitoring cardiorespiratory fitness changes in elite youth soccer players
Submission Type:
Short Communication
Authors:
Stefan Altmann1,2
Ludwig Ruf1,4
Rainer Neumann2,3
Sascha Härtel4
Alexander Woll5
Martin Buchheit6,7,8,9
1TSG ResearchLab gGmbH, Zuzenhausen, Germany
2Department for Performance Analysis, Institute of Sports and Sports Science,
Karlsruhe Institute of Technology, Karlsruhe, Germany
3Institute of Movement and Sport, University of Education Karlsruhe, Karlsruhe,
Germany
4TSG 1899 Hoffenheim, Zuzenhausen, Germany
5Department for Social and Health Sciences in Sport, Institute of Sports and
Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
6HIITScience, Revelstoke, BC, Canada
7Institute for Health and Sport, Victoria University, Melbourne, VIC, Australia
8Kitman Labs, Performance Research Intelligence Initiative, Dublin, Ireland
9French National Institute of Sport (INSEP), Laboratory of Sport, Expertise and
Performance (EA 7370), Paris, France
For submission to: Science and Medicine in Football
Submission Date: October 26, 2021
Corresponding Author:
Stefan Altmann, PhD
TSG ResearchLab gGmbH
Horrenberger Str. 58, 74939 Zuzenhausen, Germany
Phone: +49-7261-9493-170
Email: stefan.altmann@tsg-researchlab.de
Monitoring fitness changes in soccer players 2
Running Head: Monitoring fitness changes in youth soccer players
Abstract Word Count: 212
Word Count: 1,990
Tables: 2
Figures: 1
Keywords:
youth soccer; player monitoring; fitness; heart rate; lactate; aerobic
Monitoring fitness changes in soccer players 3
Abstract
This study aimed to assess the value of monitoring fitness in elite youth soccer
players (U15 to U19 age groups) by analyzing the concomitant changes in heart
rate at submaximal intensity (HR12km/h) and the velocity at a lactate concentration
of 4 mmol/l (v4mmol/l).
Players were tested by means of an incremental treadmill test on two occasions
during the summer pre-season in two consecutive seasons. Based on data from a
total of 170 test comparisons from the U15 (n = 48 test comparisons), U16 (n =
40 test comparisons), U17 (n = 46 test comparisons), and U19 (n = 36 test
comparisons) age groups, the agreement between substantial changes in
HR12km/h and v4mmol/l was analyzed using the threshold combination of HR12km/h =
4.5% and v4mmol/l = 6.0%.
Results revealed 2% full mismatches, 36% partial agreements, and 62% full
agreements for the whole sample in terms of fitness change interpretation
between both variables. The respective values for the U15 to U19 age groups
ranged between 0–5% full mismatches, 28–44% partial agreements, and 56–68%
full agreements with no meaningful differences between age groups.
In conclusion, our findings confirm the practical value of using HR12km/h to monitor
fitness changes in elite youth soccer players when lactate sampling during
incremental tests is not possible.
Monitoring fitness changes in soccer players 4
Introduction
Youth soccer players cover up to 8–9 km per match, consisting of
moderate- and high-intensity running bouts interspersed by low-intensity
movements and breaks. These match-running characteristics increase with age,
thereby placing marked demands on the cardiorespiratory system of the players.1
Therefore, testing cardiorespiratory fitness plays an integral role in the overall
monitoring process of such players. While multistage incremental tests with heart
rate (HR) and blood lactate measures are considered a gold standard method in
this regard,2,3 this method has several drawbacks (e.g., time-labor, poor player
buy-in, expensive equipment for data collection and analysis) which limits its use
for regular testing during the competitive season. As a result, monitoring exercise
HR during a submaximal run in the field has emerged as an easy-to-implement
method in this setting.4 Indicated by large correlations between the changes in
exercise HR and maximal (aerobically-oriented) performance, the validity and
sensitivity of this simple practice have been confirmed in a number of studies.4–11
In this context, Buchheit et al.10 analyzed the change in HR at a running velocity
of 12 km/h (HR12km/h) and the corresponding change of the running speed at the
anaerobic threshold (i.e., blood lactate concentration of 4 mmol/l; v4mmol/l), both
determined during incremental treadmill tests during two testing occasions. They
found that substantial individual changes in HR12km/h were associated with
similarly substantial changes in v4mmol/l in more than 90% of the 23 test
comparisons in a professional soccer team. Based on a large sample (225 test
comparisons in professional soccer players), Altmann et al.11 replicated this
methodology and reported that substantial changes of the two abovementioned
parameters match in 60–78% of cases, depending on the context (e.g., timing
during the season).
However, these studies were conducted in adult soccer players and whether
these results could be generalized to youth players of different age groups
remains to be confirmed.
Therefore, the aim of this study was to assess the value of monitoring changes in
fitness in elite youth soccer players (U15 to U19 age groups) by analyzing the
Monitoring fitness changes in soccer players 5
concomitant changes in HR at submaximal intensity (HR12km/h) and the velocity at
a lactate concentration of 4 mmol/l (v4mmol/l).
Materials and Methods
Design
Observational, cross-sectional.
Population
A total of 170 test comparisons from the male U15 (age = 14.2 ± 0.3 years; n = 48
test comparisons), U16 (age = 15.3 ± 0.3 years; n = 40 test comparisons), U17
(age = 16.0 ± 0.5 years; n = 46 test comparisons), and U19 (age = 17.7 ± 0.7
years; n = 36 test comparisons) age groups of three professional German soccer
teams competing in the highest division of their respective age groups were used
for the purpose of this study. Data were collected during the routine fitness
assessments of the teams so that ethical approval was not required.12 The
players provided informed consent prior to participating in the fitness
assessments. All players were free from injuries at the time of testing and did not
train the day before testing.
Methodology
All players were tested by means of an incremental treadmill test on two
occasions during the summer pre-season in two consecutive seasons.
The incremental treadmill test (Woodway GmbH, Weil am Rhein, Germany)
started at 6 km/h and increased by 2 km/h every 3 min until volitional exhaustion
of the players (vmax). Rest between stages was passive and lasted for 30 sec. HR
(H7 and H10, Polar Electro Oy, Kempele, Finland) was measured during and
blood lactate (capillary blood samples from the earlobe, Biosen C-Line Sport,
EKF-diagnostic GmbH, Barleben, Germany) collected after each stage.13,14
Data Analysis
The HR at a running velocity of 12 km/h (% of maximum HR; HR12km/h) and the
running velocity at a lactate concentration of 4 mmol/l (as an indicator of the fixed
Monitoring fitness changes in soccer players 6
anaerobic threshold; v4mmol/l) were used for further analysis. V4mmol/l was
automatically determined using the Ergonizer Software (K. Roecker, Freiburg,
Germany); for a graphical illustration, see Buchheit et al.10 Regarding HR12km/h,
the average HR during the last 30 s of the 12-km/h stage was used. Between-
testing session percentage changes in HR12km/h and v4mmol/l were calculated.
Lastly, the changes in HR12km/h and v4mmol/l of a single player over a 3.5-year
period (case study; n = 10 test comparisons) were also examined. For this player,
not only pre-season data were used but also data obtained at different moments
during the competitive season (e.g., summer pre-season, autumn in-season,
winter pre-season, spring in-season).
Statistical Analysis
The data were analyzed using SPSS statistical software version 26.0 (SPSS,
Chicago, USA) and Microsoft Excel (Microsoft, Redmond, USA).
The agreement between changes in HR12km/h and v4mmol/l was analyzed for the
whole sample, each age group separately as well as for the case study on the
individual player. First, Pearson product-moment correlations with 95%
confidence intervals (95% CI) were applied to analyze the relationships between
the change in HR12km/h and v4mmol/l. Moreover, the concomitant changes in both
variables were examined using the following classification:
- Full agreement (e.g., improved (↑) fitness from HR12km/h / improved (↑)
fitness from v4mmol/l)
- Partial agreement (e.g., unclear change (↔) in fitness from HR12km/h /
improved (↑) fitness from v4mmol/l)
- Full mismatch (e.g., impaired (↓) fitness from HR12km/h / improved (↑) fitness
from v4mmol/l)
Based on the recommendations of Altmann et al.,11 the threshold combination of
HR12km/h = 4.5% and v4mmol/l = 6.0% was used to indicate substantial changes in
the respective variables. Data of all variables were normally distributed.
Monitoring fitness changes in soccer players 7
Results
For the U15 age group, v4mmol/l and vmax were 13.7 ± 1.2 km/h, 95% confidence
interval (CI) 13.6–14.2 km/h and 16.7 ± 1.2 km/h, CI 16.4–17.1 km/h, respectively.
For the U16 age group, v4mmol/l and vmax were 14.0 ± 1.4 km/h, CI 13.4–14.2 km/h
and 17.1 ± 1.3 km/h, CI 16.5–17.2 km/h, respectively. For the U17 age group,
v4mmol/l and vmax were 14.4 ± 1.2 km/h, CI 14.0–14.7 km/h and 17.7 ± 1.1, CI 17.3–
18.0 km/h, respectively. For the U19 age group, v4mmol/l and vmax were 14.2 ± 1.2
km/h, CI 13.6–14.3 km/h and 17.5 ± 1.1 km/h, CI 16.9–17.6 km/h, respectively.
Pearson correlations and 95% CI between changes in HR12km/h and v4mmol/l for the
whole sample, each group, and the case study are shown in Table 1. All response
classifications between changes in fitness from both variables (HR12km/h: threshold
of 4.5%; v4mmol/l: threshold of 6.0%) can be found in Table 2. Absolute values as
well as percentage changes for HR12km/h and v4mmol/l for a single player over a 3.5-
year period are illustrated in Figure 1.
+++ Please insert Tables 1–2 and Figure 1 about here +++
Discussion
The aim of the present study was to assess the agreement between changes in
HR12km/h and v4mmol/l to estimate changes in fitness in elite youth soccer players
(U15 to U19 age groups).
The correlation regarding between-season changes in HR12km/h and v4mmol/l was r
= -0.40 for the whole sample and ranged between r = -0.31 and -0.48 for the U15
to U19 age groups (Table 1). Using the threshold combination of HR12km/h = 4.5%
and v4mmol/l = 6.0% for analyzing concomitant changes in both variables, we found
2% full mismatches, 36% partial agreements, and 62% full agreements for the
whole sample. The respective values for the U15 to U19 age groups ranged
between 0–5% full mismatches, 28–44% partial agreements, and 56–68% full
agreements (Table 2).
Monitoring fitness changes in soccer players 8
While the magnitude of correlation provides an overall impression on the
agreement between changes in HR12km/h and v4mmol/l on a team level, the ability of
the two variables to determine substantial fitness changes at the individual level is
likely what coaches and practitioners are most interested in as these information
can be used to inform decisions about the training process (e.g., return-to-play
programs, fitness top-ups). The full agreements of 62% in the present study for
the whole sample are somewhat lower than the 71% reported by Altmann et al.,11
who also compared changes between two consecutive seasons. Since in the
latter study, we investigated adult professional soccer players, the present results
might indicate that the practical value of HR monitoring to assess fitness changes
during short submaximal runs could be slightly reduced in youth as examined in
the present study. In this specific population for example, Buchheit et al.7 showed
changes in HR12km/h to better reflect true improvements than decrements of
physical performance. However, there is no evidence that exercise HR should be
less reliable or less indicative of fitness changes in youth players per se.7,15 A
possible explanation for the present finding could rather be related to the
proportion of substantial fitness changes observed (as indicated by changes in
v4mmol/l) in relation to the overall changes comparisons, which was greater in the
present study (i.e., 34% (57 out of 170 comparisons) vs 23% (8 out of 35
comparisons) in the study of Altmann et al.11). In fact, since the level of full
agreement is lower within this specific change category (42% and 38% of
agreement in the current vs the previous study, respectively) than for all the other
test comparisons (i.e., unclear changes, 62% and 71% of full agreement), the
young players of the present study displayed a “mechanically” reduced level of
overall agreement.
Of note, the higher proportion of substantial fitness changes (which were
predominantly improvements) of the U15 to U19 players in our study reflect the
different training foci between youth and adult soccer. In youth soccer, the main
aim is to develop (physical) performance levels of players in order to optimally
prepare them for the increased demands of adult soccer.16 As a consequence,
physical performance (in our case cardiorespiratory fitness) might frequently
change, both in adaption to targeted training programs17 and biological maturation
Monitoring fitness changes in soccer players 9
towards adulthood.18 By contrast, in adult soccer, the main aims are to maintain
the players’ already developed physical performance levels and to prepare for or
recover from weekly matches.17
We also sought to identify patterns in terms of agreement between changes in
HR12km/h and v4mmol/l across the four age groups investigated. However, no clear
age-related trend was evident. We also believe that the slight between-group
differences in terms of full agreements (i.e., 54% (U15) and 68% (U17)) might only
stem from other influencing factors of HR12km/h and v4mmol/l such as time of day,19
load over the previous days,20 hydration4 or nutritional status21 (that were not
systematically controlled in our study). Importantly, full mismatches between
changes in HR12km/h and v4mmol/l were very uncommon for all age groups (0–5%; 3
out of 170 comparisons). Consequently, coaches and practitioners are very
unlikely to misinterpret changes in a player’s fitness based on the threshold
combination used in this study.
Lastly, we included a case study in our analyses where an individual player was
monitored 11 times over a 3.5-year period from the age of 14.6 to 18.1 years
(Figure 1). The results (correlation of r = -0.68; 0% full mismatches, 20% partial
agreements, and 80% full agreements) are higher than those of the whole
sample. Indeed, only one substantial fitness change as indicated by v4mmol/l
occurred within this time period which facilitates the high proportion of full
agreements (see explanation regarding proportion of substantial fitness changes
above). However, while the changes from the first to the second testing occasion
are less comparable (large increase in v4mmol/l by 13.2%, rather small decrease in
HR12km/h by -3.0%), the changes in both variables follow a similar pattern for all
following testing occasions.
Given that these analyses also included within-season tests, where influencing
factors of HR12km/h and v4mmol/l are commonly even less controlled,11 the results of
the case study confirm the practical value of monitoring HR12km/h on an individual
basis.
Monitoring fitness changes in soccer players 10
The findings of this study should be interpreted in light of its limitations. As
previously mentioned, influencing factors of HR12km/h and v4mmol/l were not
systematically controlled for. In addition, all data were obtained on a treadmill in a
laboratory, and therefore the transfer of the findings to the soccer pitch remains to
be investigated.
Practical Applications
Based on their specific purpose and a cost-benefit approach, coaches can decide
if the 62% of agreement between changes in HR12km/h and v4mmol/l are high
enough. For example, incremental tests with HR and blood lactate sampling
together should be preferred, if they aim to prescribe lactate-threshold based
training intensities and are not only interested in the direction of possible changes
in fitness but also their magnitude. When using HR12km/h only, coaches can on
average expect a 6.0%-change in v4mmol/l to yield a 1.0-km/h change in vmax for a
given player.11 Such a change commonly leads to the assignment to a different
player group for HIIT drills.
Conclusions
Our study showed that between-season changes in HR12km/h (threshold of 4.5%)
using a simple 3-min run match changes in v4mmol/l (threshold of 6.0%) in 62% of
the time in elite youth soccer players with no meaningful differences between U15
to U19 age groups. Moreover, opposite interpretations are extremely unlikely to
occur. Taken together, these findings confirm the practical value of using HR12km/h
to monitor fitness changes in this population. To further interpret the agreements
or mismatches between changes in HR12km/h and v4mmol/l, future research could
benefit from investigating influencing factors of these two variables. In doing so,
longitudinal measurements using within-subject correlations seem promising.22
Monitoring fitness changes in soccer players 11
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Monitoring fitness changes in soccer players 13
Disclosure of interest
All contributing authors declare that they have no conflicts of interest relevant to
the content of this study.
Figure Legends
Figure 1. Absolute HR12km/h and v4mmol/l values (upper graph; n = 11) as well as
percentage changes in HR12km/h and v4mmol/l (lower graph; n = 10) for a single
player over a 3.5-year period. The smallest worthwhile changes (SWC) are shown
with grey (HR12km/h) and light grey (v4mmol/l) areas. Error bars represent the typical
errors (TE) of each variable. To be considered as substantial (i.e., positive, +, or
negative, -, estimated change in fitness), changes had to be greater than the
SWC + TE. Unclear changes in the lower graph are indicated with an “u”.
Twitter Handle
@StefanAltmann3
@RufLudwig
@mart1buch
Monitoring fitness changes in soccer players 14
Acknowledgments
The authors would like to thank Christian Kloss and Frederik Nockemann for their
support during data collection.
Tables Captions
Table 1. Pearson correlations and 95% confidence intervals (95% CI) between changes
in HR12km/h and v4mmol/l regarding the whole sample, all age groups, and the case study.
Table 2. Response classifications between changes in HR12km/h and v4mmol/l along with
full mismatches, partial agreements, and full agreements regarding the whole sample,
all age groups, and the case study.
Monitoring fitness changes in soccer players 15
Table 1. Pearson correlations and 95% confidence intervals (95% CI) between changes
in HR12km/h and v4mmol/l regarding the whole sample, all age groups, and the case study.
Pearson's r (95% CI)
Whole sample (n = 170) r = -0.40 (-0.26 to -0.53)
U15 (n = 48) r = -0.48 (-0.31 to -0.62)
U16 (n = 40) r = -0.32 (0.03 to -0.70)
U17 (n = 46) r = -0.41 (-0.19 to -0.60)
U19 (n = 36) r = -0.31 (0.01 to -0.60)
Case study (n = 10) r = -0.68 (-0.38 to -0.98)
Table 2. Response classifications between changes in HR12km/h and v4mmol/l along with
full mismatches, partial agreements, and full agreements regarding the whole sample,
all age groups, and the case study.
Whole Sample (n =
170) U15 (n = 48)
↓ fitness
from
v4mmol/l
↔ fitness
from
v4mmol/l
↑ fitness
from
v4mmol/l
↓ fitness
from
v4mmol/l
↔ fitness
from
v4mmol/l
↑ fitness
from
v4mmol/l
↓ fitness
from
HR12km/h
6 17 3
Full
mismat
ch
2
%
↓ fitness
from
HR12km/h
1 8 0
Full
mismat
ch
0
%
↔ fitness
from
HR12km/h
10 82 20
Partial
agreem
ent
3
6
%
↔ fitness
from
HR12km/h
3 21 6
Partial
agreem
ent
4
4
%
↑ fitness
from
HR12km/h
0 14 18
Full
agreem
ent
6
2
%
↑ fitness
from
HR12km/h
0 4 5
Full
agreem
ent
5
6
%
U16 (n = 40) U17 (n = 46)
↓fitness
from
v4mmol/l
↔ fitness
from
v4mmol/l
↑ fitness
from
v4mmol/l
↓ fitness
from
v4mmol/l
↔ fitness
from
v4mmol/l
↑ fitness
from
v4mmol/l
↓ fitness
from
HR12km/h
1 4 2
Full
mismat
ch
5
%
↓ fitness
from
HR12km/h
4 5 1
Full
mismat
ch
2
%
↔ fitness
from
HR12km/h
2 21 3
Partial
agreem
ent
2
8
%
↔ fitness
from
HR12km/h
3 23 4
Partial
agreem
ent
3
3
%
↑ fitness
from
HR12km/h
0 2 5
Full
agreem
ent
6
8
%
↑ fitness
from
HR12km/h
0 3 3
Full
agreem
ent
6
5
%
U19 (n = 36)
Case Study (n =
10)
↓fitness
from
v4mmol/l
↔ fitness
from
v4mmol/l
↑ fitness
from
v4mmol/l
↓ fitness
from
v4mmol/l
↔ fitness
from
v4mmol/l
↑ fitness
from
v4mmol/l
↓ fitness
from
HR12km/h
0 0 0
Full
mismat
ch
0
%
↓ fitness
from
HR12km/h
1 1 0
Full
mismat
ch
0
%
↔ fitness
from
HR12km/h
2 17 7
Partial
agreem
ent
3
9
%
↔ fitness
from
HR12km/h
1 7 0
Partial
agreem
ent
2
0
%
↑ fitness
from
HR12km/h
0 5 5
Full
agreem
ent
6
1
%
↑ fitness
from
HR12km/h
0 0 0
Full
agreem
ent
8
0
%
Monitoring fitness changes in youth soccer players 17
Monitoring fitness changes in youth soccer players 18
FIG 1
