Key word
training load - periodisation - soccer - rugby - hockey
Introduction
At any sporting level, training involves the manipulation of loads (e. g., intensity
and time) to promote positive adaptation (i. e., improved fitness) whilst guarding
against potentially negative consequences (i. e., non-functional overreaching, injury
and illness) [6]. Increases in training workload, characterised by increases in training volume,
intensity and frequency have typically been shown to lead to beneficial adaptations
and performance improvement [34]. On the other hand, a “more is better” approach may be too simplistic, with higher
training volumes shown to increase the risk of overuse injuries in multiple sports
[15]
[36].
The above factors are relevant for youth athletes who participate in multiple sports
[11] or across multiple age groups and playing standards within a sport [29] leading to an escalated training load. Youth athletes who encounter a high ratio
of workload-to-recovery time are at risk of overuse injuries and overtraining with
20% of school and club level athletes suffering from non-functional overreaching at
some point in their sporting careers [28]. Both non-functional overreaching and overuse injuries can lead to burnout and withdrawal
from sport, circumventing the potential benefits of sporting participation such as
improvements in physical fitness, reduced metabolic disease risk, and development
of self-esteem [13]. The “Sport England” organisation recently estimated that 3.83 million 16–25 year
olds participate in sport on a weekly basis. Therefore, the appropriate monitoring
and prescription of training load is as much of an issue for schoolteachers and local
club coaches as it is to those coaches working with elite level athletes.
To optimise physiological responses to training, monitoring internal and external
training loads has been recommended [23]. External training load represents the work performed by an athlete on the court,
field or track (e. g., actions, distance covered, high-speed running), whereas internal
training load is the physiological stress response to the external load experienced
by an athlete. Individual differences such as physical fitness, training age, genotype,
phenotype and playing experience can influence perceptions of session intensity (i. e.,
internal load) to a given external training stimulus [19]. Consequently, a prescribed training load may prove to be inadequate or excessive
for individual athletes within a team sport context, which may result in some athletes
under- or overtraining. To effectively periodise a training program and ensure intended
loads are being achieved at an individual level, coaches must incorporate measures
of internal load monitoring within their coaching and training practices.
Before methods of quantifying internal training loads can be implemented into practice,
coaches must be confident that the data collection methods accurately represent the
internal response of the athlete. Edwards [14] developed the summated heart rate (HR) zone method (sHRz) whereby the training session
is divided into the duration spent in five heart rate zones with time in each zone
multiplied by a different weighting factor (50–59% x 1, 60–69% x 2, 70–79% x 3, 80–89%
x 4, 90–100% x 5). The adjusted scores are then summated. Although this method has
proven useful in monitoring internal training load [6], a high level of technical expertise is required to collect and collate heart rate
information for an entire team. Additionally, the purchase and maintenance of telemetric
heart rate systems have large cost implications, confining this method to sports teams
or athletes with sufficient financial backing, as opposed to athletes competing below
this level, where participation numbers are greater (e. g., school sport).
Foster et al. [18] developed a cost-effective, quick and practical method of quantifying internal training
load through the Session Rating of Perceived Exertion method (s-RPE). Large correlations
between s-RPE and the sHRz method have been demonstrated within adult professional
tennis [20], swimming [37] and male and female soccer [2]
[8]
[16]
[25]
[27] but are currently lacking within youth sport athletics. Although such findings appear
to suggest s-RPE is an accurate measure of internal training load, only one study
in professional soccer [27] has calculated within-participant correlations. Within-participant correlations
offer a higher level of statistical precision than calculating correlations for individual
players or pooling data by utilising the correct degrees of freedom [3]
[5].
Despite the apparent advantages of using the s-RPE method to quantify internal training
load, there is currently a scarcity of research investigating the validity of s-RPE
in comparison to heart rate-derived training loads in youth sport. At present, research
in youth soccer [25] is limited by the lack of within-participant correlation analysis. Therefore, the
present study aimed to quantify the within-participant correlation between the s-RPE
and sHRz methods of monitoring internal training load in youth sports and to determine
the influence of sport (rugby, soccer, field hockey) on the magnitude of the correlation.
Methods
Subjects
Twenty-nine adolescent athletes including nine female field hockey (age 16.7±0.8 years,
height 164.7±6.4 cm, body mass 60.0±6.3 kg), 10 male rugby union (age 17.2±0.4 years,
height 179.9±5.4 cm, body mass 83.6±11.5 kg) and 10 male soccer (age 17.2±0.8 years,
height 174±0.05 cm, body mass 73.6±7.1 kg) players were recruited from an independent
school in the United Kingdom. All players and parents provided informed written consent
prior to participation. Ethics approval was granted by the University’s ethics committee
with ethical standards meeting those for sport and exercise science research.
Design
The study used an observational and longitudinal research design, whereby data were
collected over a 14-week in-season training period from September to December, 2016.
Coaches were instructed to carry out their training sessions as normal with no interference
from the researcher.
Each participant was assigned a portable heart rate belt (T31c, Polar Electro, Kempele,
Finland) and prior to data collection, participants completed the 30:15 intermittent
fitness test whilst wearing their assigned heart rate monitor to elicit a maximum
heart rate [7]. Maximum heart rates were required for each participant to calculate individual
heart rate zones [14].
All participants typically completed four training sessions per week structured around
a competitive mid-week fixture. Due to the unsuitability of heart rate to quantify
training load during resistance training [6]
[12], only data obtained from field-based training sessions with a clean heart rate trace
were analysed. A total of 397 training sessions were observed (rugby n=170, soccer
n=114 and field hockey n=113) with a median of 18 sessions per rugby player (range
10–24), 12 sessions per soccer (range 5–18) and 10 sessions per field hockey player
(range 4–23). Matches, rehabilitation and gym sessions were not analysed.
Procedures
Following all field-based training sessions, participants provided an RPE measure
as well as a session duration to the nearest minute to the lead researcher. The RPE
selection was made non-verbally by pointing to the desired text descriptor on a modified
Borg category-ratio 10 (CR-10) scale, in isolation from other participants to avoid
external influence on selection. Measures of RPE were taken approximately 30 min following
each training session to avoid any influence the activities completed towards the
end of each training session had on RPE [18]. The RPE anchor was then multiplied by the session duration to give an s-RPE in
arbitrary units.
Participants wore their assigned heart rate monitors throughout all field-based training
sessions with heart rate recorded at a sampling frequency of 1 Hz. Following the training
session, all participants’ heart rate data was downloaded using the software provided
by the manufacturer (Catapult Sprint 5.17, Catapult Innovations, Melbourne, Australia).
Each file was cut so only data representing the actual training session were analysed,
reconciling with session duration. The five heart rate zones were set at 50–59%, 60–69%,
70–79%, 80–89% and 90–100% of an individual’s max heart rate in keeping with the sHRz
method [14]. Time spent in each of the heart rate zones was multiplied by a factor relevant
to each zone (50–59% x 1, 60–69% x 2, 70–79% x 3, 80–89% x 4 and 90–100% x 5) with
the results summated to provide a measure of internal training load in arbitrary units.
Statistical analyses
Within-participant correlations and associated 95% confidence intervals (95% CI) were
calculated between s-RPE and the sHRz method [5]. In previous research [2]
[20]
[25], the correlation between the two methods has been calculated mainly by pooling data
over time points, or by calculating Pearson’s correlation coefficients separately
for individual participants. Such approaches lead to a lower level of statistical
precision and/or the problem of “pseudoreplication” in data analysis [27]. The magnitude of the correlation was labelled according to the following thresholds;
r=0.1–0.29=small, 0.3–0.49=moderate, 0.5–0.69=large, 0.7–0.89=very large, 0.9–0.99=nearly perfect, 1=perfect [24]. Differences between the independent correlation coefficients for each sport were
assessed by converting each correlation coefficient into a z score using Fisher’s
r-to-z transformation [9]. Statistical analyses were carried out using the SPSS statistical analysis software
for Mac (version 24.0, SPSS Inc., Chicago, IL, USA).
Discussion
The purpose of the current study was to quantify the correlation between s–RPE and
the sHRz method of quantifying internal training load in youth athletes, whilst also
assessing the influence of sport on the magnitude of the correlation. Analyses demonstrated
a large correlation when all athletes were considered together, whilst within-participant
correlations for individual sports showed large correlations for rugby and field hockey and a very large correlation for soccer. Therefore, coaches and schoolteachers alike can confidently
use s-RPE as a measure of internal training load in youth athletes of these sports.
The findings of this study demonstrating the large and very large correlations between s-RPE and sHRz are lower than the mean magnitude of correlation
found in tennis (r=0.74) [20], swimming (r=0.75) [37], and female soccer (r=0.85) [2]. However, the magnitude of correlation found within youth soccer in the present
study is comparable to the range found in a similar cohort (r=0.54 to 0.78) [25]. The lack of research within youth sport makes it difficult to conclusively identify
an explanation for the smaller correlations found in the present study. One potential
explanation is the age and experience of the cohorts investigated. Both age [22] and experience [21] have been suggested to influence RPE response, and the cohorts investigated in tennis
(18.5±0.4), swimming (22.3±3.1), and female soccer (19.3±2) were all older than the
cohort investigated in this study (16.7±0.8).
Previous attempts to assess the association between the s-RPE and sHRz methods of
monitoring internal training load have predominantly utilised a restricted method
of statistical analysis. Pooling the data fails to consider case independence and
is associated with exaggerated degrees of freedom, whereas calculating the mean of
the range of individual correlations compromises statistical power [3]. The present study controls for subjects as a factor, subsequently providing a more
precise assessment. To the authors’ knowledge, only one other study has assessed the
relationship between s-RPE and sHRz using within-participant correlations that found
a magnitude of correlation (r=0.75; 95% CI 0.71–0.78) in senior male soccer [27] similar to that of the youth soccer players investigated in this study. This would
suggest that the association between s-RPE and heart rate remains consistent from
youth- to senior-level soccer.
Although heart rate-derived training loads have been shown to be suitable in quantifying
internal load during endurance training [17], they may not be as valid during high-intensity intermittent exercise due to the
influence of muscular acidosis [2]. Previous research has demonstrated a combination of blood lactate and heart rate
measures were better related to RPE in comparison to blood lactate and heart rate
measures alone [10]. Rugby [30], soccer [4] and field hockey [35] are all team sports characterised by low-intensity locomotion interspersed with
bouts of high-intensity activity. Such high-intensity activities may have led to increases
in participant’s blood lactate concentration increasing perceptions of exertion and
restricting the magnitude of correlation between s-RPE and sHRz. Additionally, heart
rate time-in-zone methods tend to underestimate session intensity during recovery
[32]. Following an intense period of training, heart rate will return to a lower zone,
increasing the time spent at lower intensity when summating the heart rate score,
failing to depict the accumulated metabolic distress and likely misrepresenting the
perceived effort and blood lactate profile of the session. Therefore, s-RPE may encapsulate
factors influencing effort which are not represented by heart rate, restricting the
correlation between the two methods of internal load quantification.
Conversely, s-RPE may underestimate load during short but intense training sessions
when ratings of exertion are multiplied by session duration. Previous research [33] has demonstrated 4×4-minute bouts of intermittent exercise to produce the greatest
heart rate, lactate and RPE responses in comparison to 4×8-minute and 2×16-minute
bouts of matched exercise. Despite this, when ratings of intensity were multiplied
by duration, the 4×4-minute condition yielded the lowest s-RPE load. Therefore, although
s-RPE appears to offer an accurate measure of internal training load, some precautions
must be taken particularly during the quantification of short and intense training.
A secondary aim of the present study was to assess the influence of sport on the magnitude
of correlation, with Fisher’s r-to-z transformation revealing no significant differences
between the correlations for each sport. Adolescent rugby union is characterised by
frequent bouts of physical contact [30]. The physical contact associated with rugby union play can lead to subsequent muscle
damage [31], whilst research in rugby league has demonstrated increased perceptions of effort
together with increases in physical contact [26]. Due to the fatigue induced through physical contact, potentially not represented
by a linear increase in heart rate, it may have been expected that the magnitude of
correlation between s-RPE and the sHRz method was reduced for youth rugby in comparison
to soccer and field hockey. A potential reason for the lack of difference in correlation
between rugby, soccer and field hockey is that perceptions of effort and heart rate
were quantified only during training sessions. Due to the fatigue induced through
physical contact, it may be that bouts of physical contact were actively reduced by
the coaches during training sessions to maintain player freshness prior to match day.
Further research should seek to investigate the relationship between s-RPE and the
sHRz method of quantifying internal training load between different sports during
match play when the characteristics of each sport are accurately represented.
Conclusion
The accurate quantification of internal training load is essential to facilitate the
assessment of how the athlete is responding to the prescribed training load, potentially
reducing the negative implications associated with over- and undertraining. Heart
rate monitoring has long been established as a popular method of quantifying internal
exercise intensity [1]. Despite this, the potential for incomplete heart rate traces and subsequent missing
data alongside the time and cost associated with this method means that it may not
be the most efficient method of quantifying internal training load. The s-RPE method
offers a practical and cost-efficient solution, with the present study highlighting
the validity of s-RPE in comparison to the sHRz method. Very large and large correlations
were found for youth soccer, rugby and field hockey, respectively, meaning coaches
and practitioners can confidently utilise the s-RPE method for quantifying internal
load in these sports.
