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“Relationship between Pre-Training Subjective Wellness Measures, Player Load and Rating of Perceived Exertion Training
Load in American College Football” by Govus AD et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Note. This article will be published in a forthcoming issue of the
International Journal of Sports Physiology and Performance. The
article appears here in its accepted, peer-reviewed form, as it was
provided by the submitting author. It has not been copyedited,
proofread, or formatted by the publisher.
Section: Original Investigation
Article Title: Relationship between Pre-Training Subjective Wellness Measures, Player Load
and Rating of Perceived Exertion Training Load in American College Football
Authors: Andrew D. Govus1, Aaron Coutts3, Rob Duffield3, Andrew Murray2, and Hugh
Fullagar2,3
Affiliations: 1Swedish Winter Sports Research Centre, Department of Health Sciences, Mid
Sweden University, Östersund, Sweden. 2Department of Athletics, University of Oregon,
Eugene, OR, United States. 3Sport & Exercise Discipline Group, University of Technology
(UTS), Sydney, Moore Park, Australia.
Journal: International Journal of Sports Physiology and Performance
Acceptance Date: April 19, 2017
©2017 Human Kinetics, Inc.
DOI: https://doi.org/10.1123/ijspp.2016-0714
“Relationship between Pre-Training Subjective Wellness Measures, Player Load and Rating of Perceived Exertion Training
Load in American College Football” by Govus AD et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Original Article
Relationship between Pre-Training Subjective Wellness Measures, Player Load and
Rating of Perceived Exertion Training Load in American College Football
Andrew D. Govus1, Aaron Coutts3, Rob Duffield3, Andrew Murray2, Hugh Fullagar2,3
1Swedish Winter Sports Research Centre, Department of Health Sciences, Mid Sweden
University, Östersund, Sweden.
2Department of Athletics, University of Oregon, Eugene, OR, United States.
3Sport & Exercise Discipline Group, University of Technology (UTS), Sydney, Moore Park,
Australia
Running Head: Monitoring of s-RPE training load in American College football
Abstract word count: 250 words
Text-only word count: 3,601 words
Corresponding Author:
Hugh Fullagar
Department of Athletics (Football)
University of Oregon
Marcus Marriota Sports Performance Center
Eugene, OR
United States of America
Email: hughf@uoregon.edu
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“Relationship between Pre-Training Subjective Wellness Measures, Player Load and Rating of Perceived Exertion Training
Load in American College Football” by Govus AD et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
ABSTRACT
Purpose: The relationship between pre-training subjective wellness, external and internal
training load in American College football is unclear. This study examined the relationship
between pre-training subjective wellness (sleep quality, muscle soreness, energy, wellness Z
score) on 1) player load and 2) session rating of perceived exertion (s-RPE-TL) in American
College footballers. Methods: Subjective wellness (measured using 5-point, Likert scale
questionnaires); external load (derived from global position systems [GPS] and accelerometry)
and s-RPE-TL were collected during three typical training sessions per week for the second
half of an American collegiate football season (eight weeks). The relationship between pre-
training subjective wellness and 1) player load and 2) s-RPE training load were analysed using
linear mixed models with a random intercept for athlete and a random slope for training session.
Standardised mean differences (SMD) denote the effect magnitude. Results: A one unit
increase in wellness Z score and energy were associated with a trivial 2.3% (90% confidence
interval (CI): 0.5, 4.2; SMD: 0.12) and 2.6% (90% CI: 0.1, 5.2; SMD: 0.13) increase in player
load. A one unit increase in muscle soreness (players felt less sore) corresponded to a trivial
4.4% (90% CI: -8.4, -0.3; SMD: -0.05) decrease in s-RPE training load. Conclusion:
Measuring pre-training subjective wellness may provide information about players’ capacity
to perform within a training session and could be a key determinant of their response to the
imposed training demands American College football. Hence, monitoring subjective wellness
may assist the individualisation of training prescription in American College footballers.
Keywords: Fatigue, sleep, recovery, monitoring, GPS.
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“Relationship between Pre-Training Subjective Wellness Measures, Player Load and Rating of Perceived Exertion Training
Load in American College Football” by Govus AD et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
INTRODUCTION
Daily monitoring of a player’s internal and external training loads are critical in
American College football since a high training load coupled with inadequate recovery can
result in injury, illness or overtraining (1). One commonly used, non-invasive method of
monitoring an athlete’s psychobiological training load is the session rating of perceived
exertion training load [s-RPE training load: session duration (in minutes) × RPE (using either
CR-10, CR-100 or 6-20 scales)] (2). Several early studies have established the construct
validity of s-RPE training load against other forms of internal load (such as heart rate and blood
lactate) and external load measures derived from microtechnologies such as global positioning
systems (GPS) and accelerometers (3-5). Consequently, s-RPE training load is used
extensively alongside GPS-derived metrics of training load (such as player load and total
distance run) in football codes to monitor changes in players’ training and match performance
throughout a season.
In addition to monitoring external load via GPS and accelerometers, monitoring
subjective ratings of wellness and mood states before each training session may provide
information about a player’s psychological response to the global training load in team sports
(6). For example, pre-training wellness questionnaires are considered valid and reliable tools
to imply changes in mood states in athletes (7) despite their unclear relationship with s-RPE
training load in a team sport context. Recently, Gallo et al. (8) investigated the relationship
between pre-training subjective wellness (sleep quality, fatigue, stress, mood and muscle
soreness) and external load in Australian Footballers. These authors found that a one unit
decrease in wellness Z score resulted in a 4.9% (95% confidence interval (CI): ± 3.1) and 8.6%
(95% CI: ± 3.9) decrease in player load and player load slow (running activity < 2 m.s-1),
respectively. In essence, these results suggest lower pre-training subjective wellness scores
may precede a decrease in external load during a training session, indicating that training
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“Relationship between Pre-Training Subjective Wellness Measures, Player Load and Rating of Perceived Exertion Training
Load in American College Football” by Govus AD et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
prescription parameters may need to be altered to reduce an athlete’s risk of injury, illness or
fatigue. Although the relationship between pre-training subjective wellness, external load and
s-RPE training load is established in other football codes, there is currently a limited
understanding of these relationships in American College football. Further research is therefore
required to describe such relationships between these training load parameters in American
College football owing to the differing physical and psychological demands, playing positions,
anthropometric characteristics (body mass may vary from 70-150 kg) and training/match
commitments compared with other team sports (9).
To this end, this study investigated the relationship between pre-training subjective
wellness measures (soreness, sleep, energy, wellness Z score) and 1) external load (player load)
and 2) s-RPE training load in American College footballers.
METHODS
Participants
Fifty-eight American College footballers participated in this study [mean ± standard
deviation (SD); age: 20.1 ± 1.1 y, mass: 103.9 ± 19.3 kg, height: 188.7 ± 7.0 cm]. All players
were members of the same Division I National Collegiate Athletic Association (NCAA)
football team. Data collection was implemented as part of the institution’s athletic department
performance procedures. Players provided written informed consent indicating that de-
identified, wellness or performance data may be used for research. The University’s Research
Compliance Services approved all experimental procedures for this retrospective analysis.
Study Design
This study retrospectively analysed pre-training subjective wellness and training load
data from training sessions performed during the 2015 NCAA football season. Specifically,
data collation occurred for the second half of the NCAA Division 1 football season (eight-
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“Relationship between Pre-Training Subjective Wellness Measures, Player Load and Rating of Perceived Exertion Training
Load in American College Football” by Govus AD et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
weeks). Each training week included five field-training sessions and four weight-training
sessions. Measures herein pertain only to field-based training measures and wellness data.
Training load measures included microtechonology (derived from GPS and accelerometry) and
internal load (s-RPE training load), whilst subjective wellness (perceived muscle soreness,
sleep, energy) were also collected. Since external load was only collected on three main field-
training sessions per week (excluding two walk through sessions), the following analyses
include daily external load, internal load and subjective wellness data from three sessions
(session 1, 2 and 3) each week.
Session 1 typically occurred two days following a game (GD+2) and consisted of
predominantly low volume, moderate intensity exercise. Session 2 occurred three days after
the game (GD+3) and consisted of moderate volume and intensity. Finally, session 3 occurred
four days following a game (GD+4; or three days before the next game) and was the main
session of the week, focussing on high volume and intensity training. Only team training
sessions were included; individual, rehabilitation and recovery sessions were excluded due to
their differing modes and intensities.
Data Collection
Subjective wellness
Players rated three questionnaire items (muscle soreness, sleep and energy) on 1-5
Likert scale each morning ~2 h before field training commenced. These were collected
individually via players inputting into a desktop computer database within a private area in the
weight room. All scales were anchored on a 1-5 scale. The soreness scale asked, “How SORE
were you when you woke up this morning?” (1 = terribly sore, 5 = no soreness at all). The sleep
scale asked: “How did you SLEEP last night?” (1 = terrible sleep, 5 = excellent sleep); the
energy scale asked: “How ENERGISED do you feel today?” (1 = no energy at all, 5 = totally
Downloaded by Mittuniversitetet on 05/11/17, Volume 0, Article Number 0
“Relationship between Pre-Training Subjective Wellness Measures, Player Load and Rating of Perceived Exertion Training
Load in American College Football” by Govus AD et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
energised). The players were familiarised with all scales two weeks before the commencement
of the study. In addition, a wellness Z score was calculated for each player using the following
formula: player’s session subjective wellness score – player’s mean subjective wellness
score/standard deviation (SD) of players’ subjective wellness score (8).
Internal load
Internal load was determined using the s-RPE method. Players were asked “how
physically exerting did you find the training session?” ~30 minutes after each training session
and rated their response on Borg’s CR-10 scale (10), with 0 = rest and 10 = maximal. Session
RPE was then multiplied by session duration (in minutes) to calculate s-RPE training load (2).
Players were familiarised with the scale two weeks before the commencement of the study.
External load
Players wore a GPS unit during training and match activities (Optimeye S5; Catapult
Innovations, Melbourne, Australia). The Optimeye device includes a 10 Hz GPS, a 100 Hz
accelerometer and a 100 Hz gyroscope, which have previously been shown to have acceptable
reliability and validity during team-sport activity (11, 12). Devices were inserted into a custom-
made pouch and attached between the scapulae of the players’ shoulder pads. Each player used
the same GPS device each day to maintain consistency between sessions (13). Sessions were
coded for individual periods but ran for the full duration of the session without omission of
time. Data were uploaded post-session using Catapult’s OpenField 1.11 software (Catapult
Innovations, Melbourne, Australia) and collated into Microsoft Excel. Player load was
calculated for each training session using a customised algorithm within the software provided
by the manufacturers (OpenField 1.11 software, Catapult Innovations, Melbourne, Australia).
Briefly, this parameter is collected through tri-axial accelerometers and represents the square
root of the sum of the squared instantaneous rate of change in acceleration within the three
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“Relationship between Pre-Training Subjective Wellness Measures, Player Load and Rating of Perceived Exertion Training
Load in American College Football” by Govus AD et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
planes divided by 100 (Catapult Innovations, Melbourne, Australia). The accelerometers
measuring player load possess high inter- and intra-device reliability and are a valid tool for
assessing changes in activity and direction in team sport (12).
Statistical Analysis
Player load and s-RPE training load were log-transformed before the analysis, and
back-transformed to allow the results to be expressed as a percentage change in the dependent
variable for each unit change in the predictor variable. Linear mixed models were fit using the
nlme package in the R statistics programme to examine 1) the relationship between subjective
wellness (sleep, soreness, energy, wellness Z score) and player load 2) the relationship between
player load and s-RPE training load and 3) the relationship between pre-training subjective
wellness and s-RPE training load. All models were fit with a random intercept for athlete (to
calculate the between-athlete SD) and a random slope for training session (to model a separate
slope for each type of training session) using an unstructured covariance matrix. Combined
models (including each subjective wellness metric both separately and collectively) were
compared using the Bayesian information criteria (BIC) and the model with the lowest BIC
score considered parsimonious. The imprecision of parameter estimates are expressed with
90% profile CIs. The standardised mean difference (SMD) was calculated by dividing the
parameter estimate by the between-subject SD. The magnitude of the SMD was interpreted
using the following qualitative descriptors: < 0.2 trivial, 0.2-0.6 small, 0.6-1.2 moderate, 1.2-
2.0 large, 2.0-4.0 very large (14). The smallest worthwhile change (SWC) in each variable was
calculated as 0.2 of the between-athlete SD (15).
Downloaded by Mittuniversitetet on 05/11/17, Volume 0, Article Number 0
“Relationship between Pre-Training Subjective Wellness Measures, Player Load and Rating of Perceived Exertion Training
Load in American College Football” by Govus AD et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
RESULTS
Relationship between subjective wellness and player load
Linear mixed model parameter estimates for the effect of subjective wellness on player
load is presented in Table 1. A one unit increase in wellness Z score was associated with a
trivial, 2.3% (90% CI: 0.5, 4.2) increase in player load (χ2 (1) = 4.40, P = 0.04, BIC = 332.4,
SMD: 0.12). Pre-training energy was also trivially related player load (χ2 (1) =3.03, P = 0.08,
BIC = 335.3, SMD = 0.13), with a one unit increase in energy corresponding to a 2.6% (90%
CI: 0.1, 5.2) increase in player load. The SWC was 4.4% (90% CI: 2.5, 7.9). In comparison,
muscle soreness (χ2 (1) =1.81, P = 0.18, BIC = 336.8) and sleep (χ2 (1) = 2.24, P = 0.13, BIC
= 336.0) were not related to player load.
Relationship between s-RPE training load and player load
Player load was trivially related to s-RPE training load (χ2 (1) = 137.5, P < 0.01, SMD:
0.01). Specifically, a one unit increase in player load was associated with a 0.3% (90% CI: [0.2,
0.3]) increase in s-RPE training load.
Relationship between subjective wellness and s-RPE training load
Linear mixed model parameter estimates for the effect of subjective wellness on session
RPE training load is presented in Table 2. The model containing all subjective wellness
variables revealed neither perceived muscle soreness (χ2 (1) = 1.97 P = 0.16), energy (χ2 (1) =
0.03, P = 0.86) or sleep (χ2 (1) = 0.00, P = 0.99) were related to s-RPE training load. When
modelled individually, a one unit increase in muscle soreness rating (i.e. participants perceived
less muscle soreness) corresponded to a trivial, -4.4% (90% CI: -8.4, -0.3) decrease in s-RPE
training load (χ2 (1) = 3.09, P = 0.08, SMD = -0.05). Neither sleep (χ2 (1) = 0.48, P = 0.49) nor
energy (χ2 (1) = 1.07, P = 0.30) were related to s-RPE training load. Of the three subjective
wellness variables, muscle soreness showed the lowest BIC score, indicating the model
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“Relationship between Pre-Training Subjective Wellness Measures, Player Load and Rating of Perceived Exertion Training
Load in American College Football” by Govus AD et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
containing muscle soreness (BIC: 956.2) was more parsimonious than energy (BIC: 957.7) and
sleep respectively (BIC: 958.2).
DISCUSSION
This study investigated the relationship between pre-training subjective wellness
(soreness, sleep, energy, wellness Z score) and 1) external load (i.e. player load, derived from
GPS and accelerometers) and 2) internal load (i.e. s-RPE training load) in American College
footballers. Pre-training wellness Z score and energy were trivially related to player load,
whereas pre-training muscle soreness was trivially related to s-RPE training load. Pre-training
subjective ratings of muscle soreness and sleep were not related to player load, whereas energy
and sleep were not related to s-RPE-training load. Firstly, the outcomes of this study provide
evidence to support the measurement of pre-exercise subjective wellness measures in addition
to accelerometer and GPS-derived external load measures and s-RPE training load as important
foundations of a holistic player monitoring system in American College football. Secondly,
these results suggest pre-training subjective wellness ratings, such as wellness Z score and
energy may influence the exercise output of American Collegiate footballers during in-season
training sessions, whilst muscle soreness may influence a player’s response to training, as
indicated by the relationship between s-RPE training load and pre-training muscle soreness. As
such, practitioners should consider an athletes’ pre-training subjective wellness scores when
prescribing training and/or recovery.
Some pre-exercise wellness questionnaires are valid and reliable tools that may be
useful to imply changes in mood states and perceptual fatigue in athletes (7). However, there
is currently no consensus on how they should be used to assist with training prescription or
how they are associated with training output variables. The trivial, albeit significant
relationship, between wellness Z score and player load observed in the current study suggest
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“Relationship between Pre-Training Subjective Wellness Measures, Player Load and Rating of Perceived Exertion Training
Load in American College Football” by Govus AD et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
that a higher wellness Z score (i.e. the player felt better overall) was associated with a higher
player load during training. Such findings support those of Gallo et al. (16), who investigated
pre-training subjective wellness score (a combination of sleep quality, fatigue, stress, mood
and muscle soreness) and s-RPE training load in Australian Footballers and also observed a
trivial relationship between wellness Z score of −1 (d = 0.06 ± 95% CI: 0.28) and s-RPE
training load. These authors speculated that players with low wellness scores might have
decreased their external load to maintain their RPE during a training session. In addition,
Crowcroft et al. (17) concluded that general health was a more sensitive diagnostic tool to
measure performance changes in National-level swimmers than any individual pre-training
wellness variable (i.e. soreness, motivation, total quality recovery and fatigue) alone. Hence,
averaging a player’s pre-training subjective wellness measured across several wellness variable
may yield superior information about their potential training performance (as indicated by their
player load) than any single wellness variable measured in isolation. By contrast, one limitation
of using average measures is a loss of sensitivity, although such a loss in information can be
overcome by calculating a correct weighting factor for each wellness measure in relation to a
global wellness state. Furthermore, the development of a “wellness passport”, based on the
adaptive Bayesian network approach currently utilised in athlete biological passport (18) could
help to integrate information from both subjective wellness and external load measures to
estimate global wellness. Such a model may also be useful in providing dynamic,
individualised reference ranges for both internal and external load measures, consequently
improving our ability to estimate an athlete’s risk of injury, illness or fatigue given their
historical training and match data. However, further research is necessary to establish the utility
of potential objective and subjective markers and their respective importance to global wellness
before being able to identify which variables should be included in a potential “wellness
passport”.
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“Relationship between Pre-Training Subjective Wellness Measures, Player Load and Rating of Perceived Exertion Training
Load in American College Football” by Govus AD et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Given wellness is a multidimensional construct (19), one limitation of averaging pre-
training subjective wellness across several variables to represent global wellness is that it may
restrict the ability to identify specific relationships between individual wellness components
and different global, external and internal load variables (8). In the current study, in comparison
to external load, muscle soreness, but not wellness Z score was related to s-RPE training load.
Accordingly, muscle soreness may specifically contribute towards a player’s response to a
training stimulus (i.e. internal load measured by s-RPE training load) in American College
football. Measuring pre-training muscle soreness may therefore be of particular importance in
American College football to determine whether players are able to cope with both the
physiological and psychological stress of training.
The relationship between pre-training ratings of muscle soreness and s-RPE training
load may be explained by the impacts derived from physical contact associated with American
Football. For instance, perceived muscle soreness could take longer than 4 days to return to
pre-game levels in DI players (20). This suggests perceptual muscle soreness responds to short
term reductions in muscle damage and power and peak force incurred from the loading
demands of American Football (resulting either from training or games) (21). Indeed, Wellman
and colleagues analysed the intensity, number and distribution of impact forces experienced by
football players during competition and showed that wide receivers sustained more 5.0-6.5 G
force impacts (moderate-to-light) than other position groups, whereas running backs endured
the most severe (>10 G force) impacts (other than the quarter backs) (9). Muscle tissue damage
following game impacts may therefore explain the relationship of muscle soreness and s-RPE
training load observed in the present study. However, many studies to date have examined
changes in player wellness and/or external load between games, but additional training-based
investigations of the relationship between these variables in American College football are
necessary to confirm our findings. Moreover, several variables affect s-RPE training load, such
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“Relationship between Pre-Training Subjective Wellness Measures, Player Load and Rating of Perceived Exertion Training
Load in American College Football” by Govus AD et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
as playing experience, position and markers of fitness (22). Future studies analysing the
relationship between pre-exercise subjective wellness measures and s-RPE training load should
account for these factors and assess the how the magnitude of the relationship changes between
different periods of the season, where accumulated fatigue may uncouple the relationship
between pre-exercise subjective wellness measures and s-RPE training load.
We chose player load rather than total distance as an explanatory variable since we
believe accelerometer-based metrics (such as player load) may better represent the physical
demands of American Football (9). For instance, many positions within American Football,
such as the trench positions, cover very short total distances and perform a high amount of
impacts, collisions accelerations and decelerations that are captured by the player load metric.
s-RPE training load is reportedly highly correlated with total distance run (r = 0.80 [0.72, 0.86])
and player load (r = 0.84, [0.77-0.89]) in soccer players (23). In contrast to Scott et al. (23) we
observed a trivial, albeit significant, relationship between s-RPE-TL and player load in the
current study. Such differences in the relationship between s-RPE-TL and player load may
result from the different match demands of American College football and soccer and the
different statistical modelling procedures (i.e. random slopes and intercepts model used in the
current study compared with ordinary least squares regression procedures elsewhere). For
example, Bartlett et al. (24) analysed s-RPE training load data from a training season in
Australian Rules Footballers using a neural network and an individualised GEE analysis
finding that total distance exhibited the best relationship with s-RPE training load, rather than
high speed running and player load. Similarly, a possible difference between Bartlett et al. (24)
and the current study may be the manner in which the player load variable is accumulated in
Australian Football versus American football. For instance, Cormack and colleagues (25)
reported that in Australian Football, fatigued players had a lower contribution of vertical
acceleration to player load, yet were able to maintain high-speed running and total distance
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“Relationship between Pre-Training Subjective Wellness Measures, Player Load and Rating of Perceived Exertion Training
Load in American College Football” by Govus AD et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
over the course of a game. Since the player load variable has been suggested to incorporate
changes in speed, direction and impacts (26), it is possible that the player load variable is more
representative of the nature of NCAA football where less distance and high-speed running is
performed compared to Australian Football. Indeed, NCAA football is comprised of short
periods of explosive plays with long rest periods between plays (to accommodate for television
advertising). As a result, during NCAA football the demands may be as high as 38 high
acceleration efforts and impact forces of up to 10 G-force units, along with total distances of
5,530 m and 655 m of high intensity running respectively (9). These characteristics of
collegiate football may contribute towards the association between player load and s-RPE
training load in the current study. Hence, in the absence of microtechnology, s-RPE training
load may provide a useful measure of external training load in American College football.
Limitations
This manuscript has several limitations. The subjective wellness questionnaire used in
this study reflects custom measures widely used in practice, however, they have not undergone
a rigorous process of development and evaluation to ensure responses have an acceptable
degree of validity and reliability.. Secondly, some training information was unsuitable for
analysis since these sessions were conducted indoors (thus removing the ability to collect GPS
measures). Finally, data collection was conducted in the middle and the end of the regular
season. More research is therefore required to confirm whether our findings are valid at the
beginning of the season and whether such results are consistent across multiple seasons.
PRACTICAL APPLICATION
Collectively, our findings support the measurement of pre-training subjective wellness
measures in addition to GPS-derived external and s-RPE training load as important foundations
of a player monitoring system in American College football. These data are important for
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“Relationship between Pre-Training Subjective Wellness Measures, Player Load and Rating of Perceived Exertion Training
Load in American College Football” by Govus AD et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
practitioners since they show that perceived pre-training muscle soreness may be a key
determinant of a player’s s-RPE training load in American College football players.
Furthermore, longitudinal monitoring of an athlete’s s-RPE training load in tandem with pre-
training subjective wellness measures provide a simple, global measure of an athlete’s wellness
state in American College football, which may assist coaches and sport scientists to more
accurately anticipate a student athlete’s risk of injury or illness. Finally, since pre-training
perceived muscle soreness was associated with fluctuations in s-RPE training load, developing
effective methods to recover from muscle soreness and to accurately quantify the training stress
resulting from collisions may help to improve the monitoring of global wellness in American
College football.
CONCLUSION
This study investigated the relationship between external load measures and pre-
training subjective wellness on s-RPE training load in American College footballers.
Subjective wellness Z score and energy were related to player load indicating that pre-training
wellness state may partially determine performance in training. Additionally, perceived muscle
soreness was related to s-RPE training load, perhaps highlighting that muscle soreness is a key
contributor of a player’s response to the imposed training demands. The outcomes of this study
could be used to provide evidence supporting the measurement of pre-exercise subjective
wellness measures in addition to GPS-derived external load and s-RPE training load as
important foundations of an holistic player monitoring system in American College football.
ACKNOWLEDGEMENTS
The authors would like to thank all players, staff and interns whom partook or helped in the
study that without them this study would not have been possible.
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“Relationship between Pre-Training Subjective Wellness Measures, Player Load and Rating of Perceived Exertion Training
Load in American College Football” by Govus AD et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
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“Relationship between Pre-Training Subjective Wellness Measures, Player Load and Rating of Perceived Exertion Training Load in American College Football” by Govus AD et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Table 1: Linear mixed model parameter estimates and 90% confidence intervals (CI) for the relationship between external load (dependent variable
- player load) and internal (session rating of perceived exertion training load) and pre-training subjective wellness measures (soreness, energy,
sleep, wellness Z score).
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Fixed
Effects
Est.
90% CI
Est.
90% CI
Est.
90% CI
Est.
90% CI
Est.
90% CI
Est.
90% CI
Intercept
141.2
[122.8,
162.3]
288.0
[265.8,
311.9]
280.7
[256.5,
307.2]
283.8
[258.9,
311.1]
302.4
[290.4,
314.9]
273.1
[245.7,
303.5]
s-RPE-TL
0.3
[0.2, 0.3]
Soreness
1.9
[-0.4, 4.2]
0.9
[3.5, 146]
Energy
2.6
[0.1, 5.2]
1.1
[4.2, 200.4]
Sleep
2.3
[-0.2, 4.9]
1.6
[4.9, 395.3]
Wellness
2.3
[0.5, 4.2]
SWC (%)
S1
10.8
[7.1, 16.9]
4.3
[2.5, 7.7]
4.4
[2.5, 7.9]
4.4
[2.5, 7.8]
4.3
[2.5, 7.6]
4.4
[2.3, 8.9]
S2
22.9
[17.6, 30.3]
8.6
[6.8, 10.9]
8.7
[6.9, 11.0]
8.7
[6.9, 11.0]
8.9
[7.0, 11.2]
8.8
[6.7, 11.7]
S3
23.5
[17.9, 31.4]
10.1
[8.0, 12.7]
10.2
[8.1, 12.9]
10.1
[8.1, 12.8]
10.2
[8.1, 12.9]
10.3
[7.8, 13.6]
Model Fit
BIC
963.5
7
336.8
0
335.3
2
336.0
4
332.3
7
360.2
5
Abbreviations: s-RPE-TL = Session rating of perceived exertion training load; SWC = Smallest worthwhile change; S1 = session 1; S2 = session 2; S3 = session 3, BIC =
Bayesian information criteria.
Downloaded by Mittuniversitetet on 05/11/17, Volume 0, Article Number 0
“Relationship between Pre-Training Subjective Wellness Measures, Player Load and Rating of Perceived Exertion Training Load in American College Football” by Govus AD et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Table 2: Linear mixed model parameter estimates and 90% confidence intervals (CI) for the relationship between internal load (dependent variable
- session rating of perceived exertion training load) and external load (player load) and pre-training subjective wellness (soreness, energy, sleep,
wellness Z score) measures.
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Fixed
Effects
Est.
90% CI
Est.
90% CI
Est.
90% CI
Est.
90% CI
Est.
90% CI
Est.
90% CI
Intercept
141.2
[122.8,
162.3]
608.5
[532.3,
695.7]
589.5
[508.6,
683.3]
573.1
[490.2,
669.9]
558.7
[526.8,
592.6]
615.0
[515.2,
734.0]
Player Load
0.3
[0.2, 0.3]
Soreness
-4.4
[-8.4, -0.3]
-4.1
[-8.8, 0.7]
Energy
-2.7
[-6.9, 1.7]
-0.6
[-6.3, 5.4]
Sleep
-2.0
[-6.5, 2.8]
0.0
[-5.6, 6.0]
Wellness
-0.1
[-3.5, 3.5]
SWC (%)
S1
10.8
[7.1, 16.9]
31.5
[19.7, 53.6]
32.40
[20.1, 55.9]
32.4
[21.0, 52.7]
35.2
[22.9, 57.1]
31.5
[21.3, 48.6]
S2
22.9
[17.6, 30.3]
34.4
[24.6, 49.8]
34.60
[24.4, 50.7]
34.8
[26.2, 47.4]
35.1
[26.0, 48.7]
34.4
[26.2, 46.2]
S3
23.5
[17.9, 31.4]
41.5
[29.3, 60.8]
41.50
[29.0, 61.8]
41.7
[31.2, 57.4]
42.3
[31.0, 59.4]
41.4
[31.2, 56.3]
Model Fit
BIC
963.5
7
956.2
1
957.7
2
958.1
8
960.6
2
978.8
2
Abbreviations: SWC = Smallest worthwhile change; S1 = session 1; S2 = session 2; S3 = session 3, BIC = Bayesian information criteria.
Downloaded by Mittuniversitetet on 05/11/17, Volume 0, Article Number 0
