Citation: Helwig, J.; Diels, J.; Röll, M.;
Mahler, H.; Gollhofer, A.; Roecker, K.;
Willwacher, S. Relationships between
External, Wearable Sensor-Based, and
Internal Parameters: A Systematic
Review. Sensors 2023, 23, 827.
https://doi.org/10.3390/s23020827
Academic Editor: George Grouios
Received: 26 October 2022
Revised: 5 January 2023
Accepted: 9 January 2023
Published: 11 January 2023
Copyright:
© 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sensors
Systematic Review
Relationships between External, Wearable Sensor-Based,
and Internal Parameters: A Systematic Review
Janina Helwig 1,2,*, Janik Diels 1, Mareike Röll 1, Hubert Mahler 1,3, Albert Gollhofer 1, Kai Roecker 1,4
and Steffen Willwacher 2
1
Institute of Sport and Sport Science, Albert-Ludwigs University Freiburg, 79117 Freiburg, Germany
2
Institute for Advanced Biomechanics and Motion Studies, Offenburg University, Max-Planck Straße 1,
77656 Offenburg, Germany
3
Sport-Club Freiburg e.V., Achim-Stocker-Str. 1, 79108 Freiburg, Germany
4
Institute for Applied Health Promotion and Exercise Medicine, Furtwangen University,
78120 Furtwangen, Germany
*
Correspondence: janina.helwig@hs-offenburg.de
Abstract: Micro electro-mechanical systems (MEMS) are used to record training and match play of
intermittent team sport athletes. Paired with estimates of internal responses or adaptations to exercise,
practitioners gain insight into players’ dose–response relationship which facilitates the prescription of
the training stimuli to optimize performance, prevent injuries, and to guide rehabilitation processes.
A systematic review on the relationship between external, wearable-based, and internal parameters in
team sport athletes, compliant with the PRISMA guidelines, was conducted. The literature research
was performed from earliest record to 1 September 2020 using the databases PubMed, Web of Science,
CINAHL, and SportDISCUS. A total of 66 full-text articles were reviewed encompassing 1541 athletes.
About 109 different relationships between variables have been reviewed. The most investigated
relationship across sports was found between (session) rating of perceived exertion ((session-)RPE)
and PlayerLoad™ (PL) with, predominantly, moderate to strong associations (r = 0.49–0.84). Relation-
ships between internal parameters and highly dynamic, anaerobic movements were heterogenous.
Relationships between average heart rate (HR), Edward’s and Banister’s training impulse (TRIMP)
seem to be reﬂected in parameters of overall activity such as PL and TD for running-intensive team
sports. PL may further be suitable to estimate the overall subjective perception. To identify high
ﬁne-structured loading—relative to a certain type of sport—more speciﬁc measures and devices are
needed. Individualization of parameters could be helpful to enhance practicality.
Keywords: player monitoring; external load; internal load; MEMS; wearable sensors
1. Introduction
Player monitoring in sports aims at optimizing training adaptations to improve per-
formance and reduce injury risk [1]. Adaptations occur based on psycho-physiological
responses to exercise. These internal responses are stimulated by the internal load expe-
rienced during exercise; they are difﬁcult to measure directly in a non-invasive way and
can only be estimated in typical sports settings. Estimates of internal load and an athlete’s
response to exercise are commonly provided by markers of cardiovascular, neuromuscular,
or metabolic functioning, e.g., measurements of heart rate (HR) or ratings of perceived
exertion (RPE) [2]. Adaptations to training and match demands may be estimated by
detecting a change in ﬁtness or fatigue state using, e.g., spiroergometry, cardiopulmonary
ﬁtness tests, immunological or hormonal blood markers. Adaptations may be positive
or negative, or the ﬁtness state may be maintained. Negative adaptations occur during
detraining phases (i.e., off-season) and overtraining, whereas positive adaptations occur
after optimal loading and adequate recovery periods.
Sensors 2023, 23, 827. https://doi.org/10.3390/s23020827
https://www.mdpi.com/journal/sensors
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Internal loading due to sports activities primarily results from movement-related force
application demands. Forces need to be applied to the environment to cover running
distances, perform changes in movement direction or accelerate or decelerate the body,
e.g., during acceleration, stopping, or jumping tasks. Applying forces to the environment
results in reaction forces acting on athletes’ bodies, determining the external load stimulus
applied to the biological system. Physical external loads applied over time result in different
types of internal loads (e.g., mechanical or physiological), which determine the body’s
adaptations. Knowledge of the internal response and adaptation to a given dose of external
load is crucial for optimal, injury-free training progress. The internal load is inﬂuenced by
individual factors such as age, gender, training experience, health status, and nutrition [3].
The link between individual characteristics, external load, internal load, exercise-induced
responses, and performance adaptations is depicted in Figure 1. In the context of this
paper, we refer to internal load, exercise-induced response and adaptations, and individual
characteristics as internal parameters. In the same ﬁgure, the possibilities to assess these
categories are displayed, as they are included in this systematic review.
Sensors 2023, 23, x FOR PEER REVIEW 
2 of 24 
 
 
may be positive or negative, or the fitness state may be maintained. Negative adaptations 
occur during detraining phases (i.e., off-season) and overtraining, whereas positive 
adaptations occur after optimal loading and adequate recovery periods. 
Internal loading due to sports activities primarily results from movement-related 
force application demands. Forces need to be applied to the environment to cover running 
distances, perform changes in movement direction or accelerate or decelerate the body, 
e.g., during acceleration, stopping, or jumping tasks. Applying forces to the environment 
results in reaction forces acting on athletes’ bodies, determining the external load stimulus 
applied to the biological system. Physical external loads applied over time result in 
different types of internal loads (e.g., mechanical or physiological), which determine the 
body’s adaptations. Knowledge of the internal response and adaptation to a given dose of 
external load is crucial for optimal, injury-free training progress. The internal load is 
influenced by individual factors such as age, gender, training experience, health status, 
and nutrition [3]. The link between individual characteristics, external load, internal load, 
exercise-induced responses, and performance adaptations is depicted in Figure 1. In the 
context of this paper, we refer to internal load, exercise-induced response and adaptations, 
and individual characteristics as internal parameters. In the same figure, the possibilities 
to assess these categories are displayed, as they are included in this systematic review. 
 
Figure 1. Interaction of external and internal parameters and possibilities to assess those parameters. 
In this framework, it can be distinguished between four different categories of 
internal parameter assessments: First, the internal load estimates collected during 
exercise, primarily made up of HR-based indices and RPE or session-RPE. Second, the 
exercise-induced responses measured post-exercise due to the delayed response of 
specific systems to activity, such as creatine kinase (CK), an indicator of muscle damage. 
Third, the body’s adaptations may be assessed over time (usually tested under 
standardized conditions, e.g., maximal oxygen uptake (VO2max) tests using ergometry). 
Fourth, the assessment of the current health and fitness status, which, among other 
parameters such as genetics, age, and gender, make up the individual characteristics. 
Parameters of each category are included in this systematic review if a relationship to an 
external load parameter, measured during training or match play using a MEMS device, 
was assessed. 
Most sports science research groups term the responses as exercise and the training 
or match stimuli as internal and external load, workload, or training load, respectively 
[1,4–7]. We acknowledged that this terminology might be misleading considering the 
Figure 1. Interaction of external and internal parameters and possibilities to assess those parameters.
In this framework, it can be distinguished between four different categories of internal
parameter assessments: First, the internal load estimates collected during exercise, primarily
made up of HR-based indices and RPE or session-RPE. Second, the exercise-induced
responses measured post-exercise due to the delayed response of speciﬁc systems to activity,
such as creatine kinase (CK), an indicator of muscle damage. Third, the body’s adaptations
may be assessed over time (usually tested under standardized conditions, e.g., maximal
oxygen uptake (VO2max) tests using ergometry). Fourth, the assessment of the current
health and ﬁtness status, which, among other parameters such as genetics, age, and gender,
make up the individual characteristics. Parameters of each category are included in this
systematic review if a relationship to an external load parameter, measured during training
or match play using a MEMS device, was assessed.
Most sports science research groups term the responses as exercise and the training or
match stimuli as internal and external load, workload, or training load, respectively [1,4–7].
We acknowledged that this terminology might be misleading considering the mechanical
concepts where the load is weight or resistance, which is expressed in Newtons (N), as
deﬁned by the Système International d’Unites (SI), as various other research groups have
indicated [8–11]. In order to cover the literature comprehensively, the terms external and
internal load were included during the search process and are further used throughout this
systematic review, but with their meaning as outlined in Figure 1.
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Internal parameters, such as biochemical, hormonal and immunological parameters,
are often impractical to collect during training sessions and competitions; doing so might
be time and cost-intensive [1]. External load variables can be measured more efﬁciently and
time-effectively. Thus, knowing the relationship between external and internal parameters
would be practical to learn about potential dose-response relationships [12,13].
External load variables can be tested in laboratory or ﬁeld settings. While labora-
tory settings offer access to accurate gold-standard approaches to quantify external load
(e.g., through direct measurements of ground reaction forces (GRFs) using force platforms
or the inverse dynamics-based calculation of external joint moments), ﬁeld settings offer
greater ecological validity and the potential to reach larger numbers of athletes.
In the ﬁeld setting, external load variables can be measured using lightweight, body-
worn sensors. With the introduction of global navigation satellite systems (GNSS) devices
into the player monitoring market, the research around workload quantiﬁcation and load
monitoring has increased exponentially in the last 15–20 years [14,15]. Next to GNSS,
wearable sensor-based load monitoring systems may consist of local positioning systems
(LPS), offering higher accuracy in the location of, e.g., team sports athletes in the ﬁeld of
play. Another promising combination of sensor technologies are inertial measurement units
(IMUs), commonly combining 3D accelerometers, 3D gyroscope, and 3D magnetometers.
Combined in one unit, these systems belong to the group of micro-electro-mechanical
systems (MEMS). Commercially available physical activity trackers have gained tremen-
dous interest in the recent decade. “Wearable technology was the top worldwide ﬁtness
trend in 2016 and 2017” [16]. Besides physical parameters (i.e., step count), wearables aim
to estimate the internal load a person is experiencing. Some smartwatches provide an
estimate of, e.g., the metabolic work and power [17]. However, the validity and reliability
of these parameters may be questionable and highly dependent on the hardware used, and
algorithms applied [17]. Thus, the factors mentioned above are not always clearly deﬁned
or explained; yet, the parameters are still widely used to quantify the general population’s
activity and the external load and internal parameters of team sport athletes.
However, keeping track of loading in team sports is a complex task: Running-based
team sports are intermittent sports, consisting of hundreds of brief and very intense actions,
such as jumps, tackles, changes of directions, accelerations, and decelerations [18]. These
movements are metabolically and physically demanding, more than the same distance
covered at a constant speed [19]; thus, speciﬁc approaches to quantifying loads for team
sport athletes are needed.
Consequently, sports scientists and tracking device manufacturers have created several
parameters such as “PlayerLoad™” (PL), “impact load”, or “leg stiffness”, intending to
capture load characteristics and their changes with, e.g., fatigue or training status. One of
the main challenges in developing load parameters is to capture the demands of accelerating
and decelerating, as well as turns and tackles. “Metabolic power”, for example, is one more
recently developed approach that attempts to capture the demands of accelerating based
on the assumption that this is comparable to the metabolic demands of running uphill [20].
Nevertheless, it does not capture the lateral movements, turns, and tackles.
New possibilities have been created using MEMS to quantify loading in team sports
athletes. Nevertheless, a consensus on quantifying the “internal” load of team sport athletes
by “external” locomotor measurements is still missing [1,21–23]. Consequently, common
ground for best practice in load monitoring of team sport athletes has not been established
so far [1,22]. In particular, detailed knowledge about the relationship between a recorded
external load and internal parameters is rare. A recent meta-analysis has analyzed the
relationship between external and internal load parameters in team sport athletes [24].
This work focused on the relationship between HR indices, RPE, and various external load
parameters. However, as outlined above, beyond internal load, a multitude of internal
processes are stimulated, which are relevant for the psycho-physiological response and
adaptation to exercise, as well as the risk of injury.
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Consequently, two main challenges regarding load monitoring in team sport athletes
have been identiﬁed: First, the complex task of quantifying the complex loading situation
of intermittent team sports, and second, the difﬁculty of knowing the relationship between
the given MEMS-based external load and the athlete’s individual internal loading and
consequently exercise-responses and adaptations within different domains. Identifying
these relationships offers great potential to improve the understanding of individual load-
response proﬁles.
Therefore, this systematic review addresses MEMS-based external load parameters and
their relationship to various internal parameters, encompassing biochemical, neuromus-
cular, subjective, cardiovascular, and further domains. This work could aid practitioners
in choosing and interpreting appropriate parameters to monitor load in a time- and cost-
effective manner to provide the appropriate stimuli to induce adaptations to improve sports
performance and decrease the risk of injury.
2. Materials and Methods
2.1. Article Search, Inclusion, Exclusion
A systematic literature review was conducted based on the preferred reporting items
for systematic reviews and meta-analyses (PRISMA) guidelines [25]. The following elec-
tronic databases were searched: PubMed, Web of Science, SPORTDiscus, and CINAHL.
The search term was created by linking four sections with the Boolean operator “AND”, en-
suring that at least one word from each section will appear in the results. Keywords within
one section were connected with the operator “OR”. The ﬁrst section contained various
team sports. The second section contained methods and systems used to monitor athletes.
The third and fourth sections contained numerous external and internal or performance
measurement parameters. Truncation searching was employed to ﬁnd variations of certain
words (see Table 1 for the complete search term). The databases were searched with no
restrictions from the earliest records available up to September 1, 2020. Results were stored
in a citation manager, and all duplicates were removed (search process see Figure 2). All
abstracts were then screened for eligibility regarding the inclusion and exclusion criteria
assessed. Any studies including athletes younger than 18 years were excluded as cognitive
development inﬂuences the accuracy of the RPE [26]. Articles were considered if they
showed a relationship measure between one external and one internal or performance
parameter obtained from able-bodied team sport athletes during regular training or match
play which did not include additional interventions, such as nutritional interventions or
manipulated play. For the complete list of inclusion and exclusion criteria, please refer
to Table 2. All data were independently extracted by two researchers (JH, JD). In case of
disagreement, a consensus was found by a third reviewer (KR). The study further adheres
to the ethical standards in sports and exercise science research [27].
Table 1. Search Term: Categories are connected with the Boolean operator “AND”; key words within
a category are connected with “OR”.
Category
Keywords
Team Sport
“Team Sport*” OR soccer OR football OR handball OR basketball OR rugby OR volleyball OR futsal
OR netball
Monitoring system
monitoring OR tracking OR GPS OR “Global Positioning System”[MeSH] OR LPS OR “Local
Positioning System”[MeSH] OR IMU OR “inertial measurement unit” OR acceleromet* OR MEMS
OR microsensor OR “time motion” OR TMA OR “motion analysis”[MeSH] OR “wearable
technologies”[MeSH]
External load
workload OR load OR speed OR ACWR OR “acute to chronic work ratio” OR “work:rest” OR
distance OR acceleration OR “metabolic power” OR “metabolic load” OR PlayerLoad OR intensit*
OR “energy expenditure” OR “high intensity burst*” OR “work ratio” OR “fatigue index” OR
“physical” OR “repeated sprintability
Internal load
“internal load” OR RPE OR “rate of perceived exertion” OR RPE OR sRPE OR “heart rate” OR HR
OR TRIMP OR questionnaire OR biochemical OR physiological OR neurological OR fatigue OR
blood OR lactate OR SPX OR Spiroergometry OR “breath gas analysis” OR CK OR “creatine kinase”
OR VO2 OR “anaerobic threshold”
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Figure 2. Flow diagram of the study selection process adopted from the PRISMA guidelines. 
Table 1. Search Term: Categories are connected with the Boolean operator “AND”; key words 
within a category are connected with “OR”. 
Category 
Keywords 
Figure 2. Flow diagram of the study selection process adopted from the PRISMA guidelines.
Table 2. Inclusion and exclusion criteria.
Inclusion
Exclusion
Topic of the article is human physical performance
Topic not related to physical performance or
non-human subjects
Original research
Surveys, opinions, books, case studies, non-academic text,
reviews, conference abstracts
Competitive ﬁeld- or court-based team sport athletes
Individual sports, ice-, sand-, or water-based team
sports, referees
Adult athletes
Athletes under 18 years of age
Able-bodied, non-injured athletes
Special populations (i.e., clinical), mentally or physically
impaired athletes, injured athletes
Training or match play
Laboratory settings, and ﬁeld-based settings coupled with an
intervention (i.e., nutritional intervention).
Report of at least one external and one internal load measure or
physiological ﬁtness assessment
Report of only internal or only external measures
Report of a relationship between internal and external measures
No relationship between internal and external
measures reported
Use of GNSS, MEMS, IMU, LPS
Use of timing gates, measuring tapes, video-based tracking
Good, very good, or excellent methodological quality based on
the checklist used for this review
Poor methodological quality based on the checklist used for
this review
2.2. Study Quality Assessment
After the ﬁnal selection was made, the quality of the selected studies was assessed
using a 16-item checklist developed by Law et al. [28] and modiﬁed by Sarmento et al. [29],
which has also been used in previous reviews [29,30]. The authors are aware that a risk
of bias assessment may be superior to a checklist summarizing components into a single
number, especially when concerned about randomized controlled trials. This systematic
review, however, is concerned with observational studies, and thus, the authors decided
Sensors 2023, 23, 827
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on a quality checklist that is applicable to the topic at hand. The items on the checklist
were scored on a binary scale (0 = no, 1 = yes). Items 6 and 13 included the option “not
applicable”. The sum of the scores for each study divided by the maximum value possible
for that study represented the quality score. Expressed as a percentage, the score then
indicated the methodological quality of the studies. The following meaning was associated
with the ﬁnal percentage: low methodological quality ≤ 50%; good methodological quality
51–75%, excellent methodological quality > 75%. The quality assessment was carried out
independently by two reviewers (JH and KR). Disagreement was solved by discussion.
2.3. Data Extraction
Data extraction was done using a custom-made sheet pilot tested on ﬁve randomly
chosen articles. The sheet was redeﬁned, and its ﬁnal version was used by one reviewer
(JH) who performed the data extraction. In case of unclear or missing data, corresponding
authors were contacted. The following data were extracted from the studies: (1.) The
type of team sport; (2.) the study sample (along with the number of participants, gender,
and level/league athletes competed in); (3.) the external parameters recorded or calcu-
lated; (4.) the internal parameters measured or calculated and/or the ﬁtness assessment;
(5.) the relationship between the external and internal parameters as indicated by statistical
association or predictive measures.
2.4. Data Synthesis
Data were categorized into groups consisting of the different team sports. Then,
subgroups according to the parameters analyzed were created. The subgroups are based on
Figure 1 and consist of: the assessment of the relationship between external load parameters
and internal load collected during exercise, exercise-induced responses, adaptations, and
individual characteristics.
A descriptive synthesis was undertaken with the data structured in a table contain-
ing the team sport, the studies included, the load parameters collected, including their
frequency of use per sport, and the statistical relational measures between the external
and internal parameters. The overall frequency of use of each external and each internal
parameter was visualized using pie charts.
3. Results
3.1. Search Results
The initial search returned 3573 articles. A total of 2234 records remained after re-
moving duplicates; these articles were screened by title and abstracts against the eligibility
criteria. After further exclusion of studies (n = 2178) that did not meet the criteria, 66 articles
remained for the ﬁnal analysis (Figure 2). The main reasons for exclusion were not using
MEMS-based parameters, not analyzing regular training or match play, and analyzing only
internal or only external parameters. The references of the included articles were screened,
but no further study met the inclusion criteria. The mean methodological quality score of
the included studies was 84.6% (+/−8.4%). No article was excluded due to low quality.
Ten studies scored between 51 and 75% as good methodological quality. The remainder
(n = 56) qualiﬁed as excellent regarding methodological quality. The most common item to
lose quality points on was item 5: justiﬁcation of the study sample size.
3.2. Basic Characteristics of Included Studies
The articles included in this systematic review ranged from 2011 to 2019. The sports
analyzed were: American football (n = 6), Australian football (n = 11), basketball (n = 4),
ﬁeld hockey (n = 1), rugby union and rugby league (n = 8), soccer (n = 35), and tag football
(n = 1). The participants were professional (n = 606), elite (n = 413), college/university
(n = 402), and semi-professional (n = 120) athletes. n = 62 studies included male participants,
totaling 1479 male athletes. Four studies studied female participants, accounting for n = 62
female athletes. The three most commonly external parameters recorded were distances in
Sensors 2023, 23, 827
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speed zones (n = 55), total distance (n = 46), and PL (n = 34), as depicted in Figure 3. The
most frequently recorded internal parameters were (session) RPE (this includes RPE as well
as session RPE and thus termed “(session-)RPE” going forward) (n = 29), HR-based indices
(n = 19), and well-being questionnaires (n = 17), as depicted in Figure 3.
 
The articles included in this systematic review ranged from 2011 to 2019. The sports 
analyzed were: American football (n = 6), Australian football (n = 11), basketball (n = 4), 
field hockey (n = 1), rugby union and rugby league (n = 8), soccer (n = 35), and tag football 
(n = 1). The participants were professional (n = 606), elite (n = 413), college/university (n = 
402), and semi-professional (n = 120) athletes. n = 62 studies included male participants, 
totaling 1479 male athletes. Four studies studied female participants, accounting for n = 
62 female athletes. The three most commonly external parameters recorded were 
distances in speed zones (n = 55), total distance (n = 46), and PL (n = 34), as depicted in 
Figure 3. The most frequently recorded internal parameters were (session) RPE (this 
includes RPE as well as session RPE and thus termed “(session-)RPE” going forward) (n 
= 29), HR-based indices (n = 19), and well-being questionnaires (n = 17), as depicted in 
Figure 3. 
 
Figure 3. External and internal parameters with the number of studies they are appearing in. 
Parameters occurring in one or two studies only are pooled under “Further”. Acc/Dec, acceleration 
and deceleration parameters; Avg., average; CK, creatine kinase; CMJ, countermovement jump; 
Dyn., dynamic; exp., expenditure; HR, heart rate; Im.gl., immunoglobulin parameters; Max., 
Figure 3. External and internal parameters with the number of studies they are appearing in.
Parameters occurring in one or two studies only are pooled under “Further”. Acc/Dec, acceleration
and deceleration parameters; Avg., average; CK, creatine kinase; CMJ, countermovement jump; Dyn.,
dynamic; exp., expenditure; HR, heart rate; Im.gl., immunoglobulin parameters; Max., maximal;
Met., metabolic; RHIE, repeated high-intensity efforts; (s)RPE (session) rating of perceived exertion;
Well-being, well-being questionnaires; YYIR, Yo-Yo intermittent recovery test.
3.3. External and Internal Parameters
About 34 external and 32 internal parameters and parameter groups were included
across all studies. Different HR-based and various (session-)RPE parameters were grouped
and displayed in Figures 3 and 4. The most often investigated external parameter was
distance covered in speciﬁc speed zones (n = 55) which was investigated in 82% of studies
included in this review, followed by total distance (n = 46), analyzed in 67% of the re-
search articles in this review, and PL (n = 34), occurring in 51%. (session-)RPE (n = 29) was
most often investigated amongst the internal parameters, followed by HR-based indices
(n = 19) and well-being questionnaires (n = 17), occurring in 45, 28, and 27% of research
articles included in this systematic review, respectively. From the 66 articles included,
109 different relationships between external and internal parameters have been extracted.
The most frequently analyzed relationship was between (session-)RPE and PL with pre-
dominantly moderate to strong associations (r = 0.49–0.84). The second most frequently
analyzed relationship was between (session-)RPE and distances in speed zones with het-
erogeneous results. All results for the 109 relationships can be found in Table S1 in the
supplementary material.
3.4. Summary of Individual Studies
Table 3 provides an overview of all studies included in this systematic review, grouped
by sport. It includes the number of participants, their playing level, and the collected
external and internal parameters. Study designs, participants, hard- and software used,
and outcome measures varied noticeably such that the authors focused on describing the
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results of the studies rather than performing a meta-analysis. Table S1 in the supplementary
material further shows the relationship measures between parameters.
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Table 3. Studies included in this systematic review sorted by type of team sport. The table includes information about the player level and the parameters collected.
Sport
Study
Player Level (n = Number
of Athletes)
External Parameters (n = Number
of Studies)
Internal Parameters (n = Number of Studies)
American football
[31–36]
University Divison I
(n = 225, male)
PL (AU) (n = 4)
Acceleration/Deceleration (m·s−2) (n = 4)
Distance in speed zones (m) (n = 2)
Impacts (n) (n = 2)
Stride variability (n = 1)
INTERNAL LOAD PARAMETERS
(session-)RPE (AU) (n = 1)
EXERCISE-INDUCED RESPONSES
Well-being questionnaire (5-point scale) (n = 4)
S100beta (pg/mL) (n = 1)
Tau concentration (pg/mL) (n = 1)
Australian football
[13,37–45]
Professional (n = 202, male)
Elite (n = 118, male)
Distance in speed zones (m) (n = 13)
PL (au) (n = 9)
Total/Relative distance (m, m/min) (n = 9)
Duration (min) (n = 5)
Average speed (m/s) (n = 4)
Acceleration/Deceleration (m·s−2) (n = 3)
Energy expenditure (kJ/kg) (n = 2)
Metabolic power concept (W/kg) (n = 2)
Distance load (m2/s) (distance x mean
speed) (n = 1)
Effort zones (n) (n = 1)
Equivalent distance (m) (n = 1)
Explosive efforts (n) (n = 1)
Impacts (n) (n = 1)
Match exercise intensity (AU) (n = 1)
INTERNAL LOAD PARAMETERS
(session-)RPE (AU) (n = 7)
Core temperature (C) (n = 1)
EXERCISE-INDUCED RESPONSES
Well-being questionnaire (5-point scale) (n = 3)
CMJ (cm) (n = 1)
CK (U/L) (n = 1)
INDIVIDUAL CHARACTERISTICS
Maximal aerobic speed (m/s) (n = 1)
YYIR (m) (n = 1)
Basketball
[46–49]
Elite (n = 12, male)
Professional (n = 26, male)
Semiprofessional (n = 8, male)
University (n = 5, female)
PL (AU) (n = 4)
Acceleration/Deceleration (m·s−2) (n = 4)
Jumps (n) (n = 2)
IMA™ (AU) (n = 1)
INTERNAL LOAD PARAMETERS
(session-)RPE (AU) (n = 3)
HR-based indices (n = 1)
EXERCISE-INDUCED RESPONSES
Tensiomyography (ms, mm) (n = 1)
Field Hockey
[50]
Elite (n = 12, male)
Acceleration/Deceleration (m·s−2) (n = 1)
Distances in speed zones (m) (n = 1)
Total/relative distance (m, m/min) (n = 1)
EXERCISE-INDUCED RESPONSES
Well-being questionnaire (5-point scale) (n = 1)
Rugby Sevens
[51,52]
Elite (n = 24, 12 female, 12 male)
Amateur (n = 10, female)
Total/relative distance (m, m/min) (n = 2)
Distance in speed zones (m) (n = 2)
Impacts (n) (n = 1)
EXERCISE-INDUCED RESPONSES
CK (U/L) (n =1)
Bicarbonate concentration (mmol/L) (n = 1)
Lactate concentration (mmol/L) (n = 1)
pH (n = 1)
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Table 3. Cont.
Sport
Study
Player Level (n = Number of Athletes)
External Parameters (n = Number of Studies)
Internal Parameters (n = Number of Studies)
Rugby League
[53–56]
Professional (n = 46, male)
Elite (n = 45, male)
Distance in speed zones (m) (n = 3)
Impacts (n) (n = 3)
Acceleration/Deceleration (m·s−2) (n = 2)
Total/Relative distance (m, m/min) (n = 2)
Duration (min) (n = 1)
PL (AU) (n = 1)
RHIE (n) (n = 1)
INTERNAL LOAD PARAMETERS
(session-)RPE (AU) (n = 2)
EXERCISE-INDUCED RESPONSES
Well-being questionnaire (5-point scale) (n = 1)
CK (U/L) (n = 2)
Salivary cortisol (nmol/L) (n = 1)
Repeated plyometric push-ups (n) (n = 1)
Sleep (h) (n = 1)
ADAPTATION PARAMETERS
Sleep (h) (n = 1)
Rugby Union
[57,58]
Professional (n = 51, male)
Distance in speed zones (m) (n = 2)
Impacts (n) (n = 2)
PL (au) (n = 1)
Total/Relative distance (m, m/min) (n = 1)
EXERCISE-INDUCED RESPONSES
CK (U/L) (n = 1)
Urinary n-terminal prohormone of brain natriuretic
peptide (pg/mL) (n = 1)
Soccer
[59–93]
Professional (n = 311, male)
Elite (n = 236, male)
Semi-professional (n = 61, male)
University (n = 114, 79 male, 35 female)
Distance in speed zones (m) (n = 31)
Total/Relative distance (m, m/min) (n = 30)
PL (AU) (n = 15)
Acceleration/Deceleration (m·s−2) (n = 13)
Duration (min) (n = 12)
Impacts (n) (n = 5)
Average Speed (m/s) (n = 4)
Dynamic stress load (AU) (n = 4)
Metabolic power concept (W/kg) (n = 4)
Maximal velocity (m/s) (n = 3)
Efﬁndex (AU) (n = 2)
RHIE (n) (n = 2)
Body load (AU) (n = 1)
Energy expenditure (kJ/kg) (n = 2)
Equivalent distance (m) (n = 1)
Explosive distance (m) (n = 1)
Impulse Load (Ns) (n = 1)
Force load (AU) (n = 1)
Mechanical work (AU) (n = 1)
Training load score by Polar (AU) (n = 1)
Total accelerometer load (AU) (n = 1)
Total forces (AU) (n = 1)
Velocity load (AU) (n = 1)
Work:rest ratio (n = 1)
INTERNAL LOAD PARAMETERS
HR-based indices (n = 17)
(session-)RPE (AU) (n = 16)
Efﬁndex (AU) (n = 2)
EXERCISE-INDUCED RESPONSES
Well-being questionnaire (5-point scale) (n = 8)
CMJ (cm) (n = 6)
CK (U/L) (n = 5)
Immunoglobulin (µg/mL) (n = 3)
C-reactive protein (mg/L) (n = 1)
HR-based indices (n = 1)
Myoglobin concentration (ng/mL) (n = 1)
Plasma lactate dehydrogenase (U/L) (n = 1)
Body mass measures (kg) (n = 1)
ADAPTATION PARAMETERS
HR-based indices (n = 2)
Body mass measures (kg) (n = 2)
Strength test (Nm) (n = 1)
VO2max (ml/kg/min) (n = 1)
30-15 intermittent ﬁtness test (m) (n = 1)
INDIVIDUAL CHARACTERISTICS
VO2max (ml/kg/min) (n = 1)
YYIR (m) (n = 1)
Repeated sprint ability (m) (n = 1)
Body mass measures (kg) (n = 1)
Muscle characteristics (cm) (n = 1)
Sprint test (s) (n = 1)
Sensors 2023, 23, 827
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Table 3. Cont.
Sport
Study
Player Level (n = Number of Athletes)
External Parameters (n = Number
of Studies)
Internal Parameters (n = Number of Studies)
Tag football
[94]
Regional (n = 16, male)
Acceleration/Deceleration (m·s−2) (n = 1)
Distance in speed zones (m) (n = 1)
RHIE (n) (n = 1)
Total/relative distance (m, m/min) (n = 1)
INDIVIDUAL CHARACTERISTICS
CMJ (cm) (n = 1)
Sprint test (m/s) (n = 1)
YYIR (m) (n = 1)
AU arbitrary unit, CK creatine kinase, CMJ countermovement jump, HRmax maximal heart rate, HR heart rate, IMA™ inertial movement analysis, PL player load, RHIE repeated
high-intensity events, RPE rating of perceived exertion, TRIMP training impulse, VO2max maximal oxygen uptake, YYIR Yo-Yo intermittent recovery test.
Sensors 2023, 23, 827
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4. Discussion
This systematic review aimed to enhance the knowledge around relationships between
external, wearable-based load parameters and internal load, exercise-induced responses,
adaptation parameters, and parameters of individual characteristics in running-based team
sports. Knowledge about these relationships may reduce time- and possibly cost-intensive
testing outside regular training. Acute ﬁtness and fatigue states may be drawn only based
on external load parameters. Additionally, the amount of data to be collected and analyzed
could be reduced by collecting fewer internal parameters.
Our systematic review is the ﬁrst to include a myriad of external and internal param-
eters, focusing on external parameters collected from wearables only. This is crucial to
enhance practicality and usability of parameters collected on-ﬁeld. As the amount of data
from wearable sensors and their use increase, it is inevitable to enhance the knowledge
around these parameters and understand the dose–response relationship of team sport
athletes. The ﬁndings are discussed in the following sections.
As some relationships have been examined by a minimal number of studies, results
are discussed only when a systematic synthesis of results is feasible. In the following,
results are discussed in categories of internal load, exercise-induced response, adaptation
parameters, and individual characteristics (Figure 1). Figure 4 additionally highlights the
ﬁndings of moderate to large relationships which are explored in the following sections,
separated by sports. Then, general aspects and future directions are discussed and outlined.
Sensors 2023, 23, x FOR PEER REVIEW 
12 of 24 
 
 
4. Discussion 
This systematic review aimed to enhance the knowledge around relationships be-
tween external, wearable-based load parameters and internal load, exercise-induced re-
sponses, adaptation parameters, and parameters of individual characteristics in running-
based team sports. Knowledge about these relationships may reduce time- and possibly 
cost-intensive testing outside regular training. Acute fitness and fatigue states may be 
drawn only based on external load parameters. Additionally, the amount of data to be 
collected and analyzed could be reduced by collecting fewer internal parameters. 
Our systematic review is the first to include a myriad of external and internal param-
eters, focusing on external parameters collected from wearables only. This is crucial to 
enhance practicality and usability of parameters collected on-field. As the amount of data 
from wearable sensors and their use increase, it is inevitable to enhance the knowledge 
around these parameters and understand the dose–response relationship of team sport 
athletes. The findings are discussed in the following sections. 
As some relationships have been examined by a minimal number of studies, results 
are discussed only when a systematic synthesis of results is feasible. In the following, re-
sults are discussed in categories of internal load, exercise-induced response, adaptation 
parameters, and individual characteristics (Figure 1). Figure 4 additionally highlights the 
findings of moderate to large relationships which are explored in the following sections, 
separated by sports. Then, general aspects and future directions are discussed and out-
lined. 
 
Figure 4. Displayed are parameters for which a systematic synthesis was feasible and that exhibited 
a moderate to strong relationship. Internal load parameters are sorted by sport and colored as indi-
cated in the legend. The relationship to an external load parameter is marked by an arrow. External 
parameters 
are 
displayed 
with 
the 
number 
of 
studies 
they 
are 
appearing 
in 
Figure 4. Displayed are parameters for which a systematic synthesis was feasible and that ex-
hibited a moderate to strong relationship. Internal load parameters are sorted by sport and col-
ored as indicated in the legend. The relationship to an external load parameter is marked by
an arrow.
External parameters are displayed with the number of studies they are appearing
in [13,31,32,38,39,43,47–49,51,56,57,59,60,69,80,91,93,94]. Acc/Dec acceleration/deceleration parame-
ters, CK creatine kinase, CMJ countermovement jump, HR heart rate, RPE rating of perceived exertion,
sIgA secretory immunoglobulin A, TRIMP training impulse, LI low intensity, MI medium intensity.
Sensors 2023, 23, 827
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4.1. Internal Load
4.1.1. (session-)RPE
Internal load parameters predominantly encompassed subjective ratings of exertion
and HR-based indices.
(session-)RPE had moderate to strong associations with total and relative distance
in Australian football [13,43] and soccer players [59,60,93]. Moderate to strong asso-
ciations were also present between (session-)RPE and PL in Australian football [43],
basketball [47,48], and soccer [59,60]. Similar strength of associations was detected be-
tween (session-)RPE and distances covered in different speed zones in Australian football
players [43], and acceleration and deceleration parameters in basketball players [48,49]. A
weak relationship between (session-)RPE and PL was only found in American football play-
ers [31], and heterogenous results were present for the relationship between (session-)RPE
and distances covered in speed zones and (session-)RPE and acceleration and deceleration
parameters in soccer players.
The weak relationship between (session-)RPE and PL in American football may be due
to different match demands compared to other team sports analyzed. American football
players generally cover lower overall distances in games (3000 to 5500 m in NCAA I
football [59]) than soccer players (male elite outﬁeld players 9000–14,000 m [95]), Australian
football players (elite level 12,939 ± 1145 m [96]), and even basketball players (4404 to
7558 m [97]). Bartlett et al. (2017) found collinearity between session duration, PL, and total
distance. This may suggest that the lower the overall distance, the lower the association
between PL and RPE or session-RPE.
Heterogenous results between (session-)RPE and distances in speed zones for soc-
cer players may be due to the larger volume of studies compared to other team sports
included and due to the different methods used: partial correlations [92], within-individual
correlations [73,92], Pearson product-moment correlations [59,60], and machine learning
techniques [61,64]. Furthermore, speed thresholds to deﬁne speed zones were either ﬁxed
or individualized, and distances were expressed in various ways (as absolute, percentage
of total distance, frequency, number of efforts, or distance per minute).
Generally, indicators of total volume seem to result in higher associations than those
expressed per minute or as a percentage of total volume. Rago et al. (2019) found a tendency
of increasing correlations when speed thresholds were individualized rather than identical
for all players.
Heterogenous results between (session-)RPE and parameters describing accelerations
and decelerations in soccer may be due to varying methods. Using partial and within-
individual correlations [92], small to moderate correlations were detected between those
parameters. Furthermore, correlations describing total acceleration were higher than
those describing accelerations per minute [92]. This supports the above ﬁndings that
(session-)RPE seems to have stronger associations with parameters describing total volume.
Machine learning techniques identiﬁed the number of acceleration efforts and decelerating
distances as the main contributors to RPE in soccer [64].
Correlations between (session-)RPE and acceleration and deceleration parameters may
be higher in basketball due to the high frequency of accelerations of 29.6 ± 3.9–32.7 ± 11.0 per
minute in professional male players [98] compared to 90 ± 21 total accelerations per match
in soccer players [99]. This places a greater total amount of accelerations and decelerations
on basketball players compared to i.e., soccer. Thus, accelerations and deceleration may
have a greater impact on perceived exertion compared to team sports in which they occur
less frequently.
For practitioners, this means that estimation of (session-)RPE may be done most
adequately with indicators of total volume such as total distance or PL in Australian
football, soccer, and basketball players. In indoor team sports such as basketball, where total
distance is not available from wearable sensors due to a lack of GNSS signal, parameters
describing acceleration and deceleration may be used instead of total distance. Omitting
Sensors 2023, 23, 827
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(session-)RPE scales would save practitioners and players the time to analyze and ﬁll out
the scales.
4.1.2. HR-Based Indices
The relationships of HR-based parameters of internal load to external load parameters
were analyzed in soccer players only. HR was divided in zones [79,80], predicted from
external load parameters [65], expressed as percentage of HRmax [79,84], and used for
calculations of Edward’s and Banister’s TRIMP [59,60,72,79]. TRIMP is a method, originally
proposed by Banister et al. [100,101] that integrates training duration, maximal, resting, and
average exercise HR, and a weighting factor to address high intensities [102]. Banister’s
TRMIP has further been modiﬁed, including a summated HR zone method proposed by
Edwards [103], here Edwards’ TRIMP. This method takes the time spent in predeﬁned HR
zones into account. The result of both methods is a training score per session indicating the
cardio-vascular demands experienced by the athlete. Moderate to large correlations existed
between time spent in the low- and medium-intensity velocity and the low- and medium-
intensity HR zones, respectively. Time spent in the low- and medium- intensity HR zones
also showed moderate to large correlations with PL [80]. Similar strength in associations
was found between total distance and Edwards’ TRIMP [60] and between PL and Edwards’
and Banister’s TRIMP [59,60]. Correlations were not signiﬁcant or weak between time spent
in the high-intensity velocity and the high-intensity HR zone [80], between high-speed
distance and number of efforts at sprinting speed and Edwards’ TRIMP [60], between
PL and the high-intensity HR zone [80], between the number of rapid accelerations per
minute and the time spent above 80% of HRmax [79], and between repeated high-intensity
events and Banister’s TRIMP [79] and percent time spent above 80% of HRmax [79]. With
increasing speed, correlations between HR-based parameters and distances in speed zones
seem to weaken. This ﬁnding is similar to (session-)RPE which may be due to the high
relationship between HR-based parameters and session-RPE [104,105]. Previous research
has highlighted that HR measures may not be appropriate for high-intensity interval
training or intermittent exercise due to the increase in anaerobic contribution [102,106]. For
practitioners, this means that high-intensity running parameters may not be an appropriate
indicator of HR. Low-intensity running and indicators of total volume such as total distance
or PL may be more suited for low- and medium-intensity HR parameter estimation.
Overall, among the internal load parameters, (session-)RPE, time spent in low- and
medium-intensity HR zones, and TRIMP are best estimated using parameters of total
volume such as PL and total distance. Time spent in high-intensity HR zones are not
represented adequately by the external load parameters examined and may be recorded
separately if of interest. Noteworthy is that HR-based indices do not adequately represent
anaerobic training [102,106]. The latter two points support the idea of collecting both
HR-based indices and external load parameters.
4.2. Exercise-Induced Responses
Exercise-induced response are short-term changes in parameters. To detect changes,
parameters are collected at two or more time points several hours apart. The ﬁrst time
point serves as a baseline measure, usually in a non-fatigued state. The next time point(s)
occur(s) following exercise when athletes may be fatigued. Exercise-induced responses
extracted from the studies included consist of well-being indicators, CK concentrations,
HR-based indices, neuromuscular functioning, and biomarkers, among others.
The relationships between well-being parameters and external load indicators were
heterogeneous, possibly due to studies using different questionnaires analyzing either
overall wellness [35,36,38,50,68,76] or single parameters of wellness (e.g., sleep, stress,
recovery, muscles soreness, or fatigue) [56,83,84,87,88] and related it to external load pa-
rameters of the same day [38,50,56,76], the previous day [35,36,63,83], the previous two to
four days [36,84], or the previous weeks [88]. Thus, practitioners may need to be careful
Sensors 2023, 23, 827
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when choosing the speciﬁc parameter and the time period being analyzed as this provides
different information about the athlete.
Results in Table S1 in the supplementary material indicate that especially high-intensity
distance parameters such as distance covered, time spent, or number of efforts at high
speeds may inform about potential muscle damage as indicated by CK levels. In col-
lision team sports such as Australian football, rugby league, rugby union, and rugby
sevens, moderate to strong relationships were found between CK levels and impact
parameters [39,51,56,57]. In these sports, impact parameters may additionally indicate
muscle damage. No study included addressed those parameters in American football
players. However, since American football belongs to the collision team sports, similar
associations to CK levels can be expected. This, however, needs further veriﬁcation.
Exercise-induced HR-based indices were found only in studies observing soccer play-
ers. Here, mostly negligible to small associations between several external load indicators
and heart rate variability (HRV) were found [71,83,84]. For practitioners, this means that
common external load indicators, as included in this review, may not serve as an estimation
of HRV. Thus, this metric should be recorded separately if it is of interest.
The relationship between change in countermovement jump (CMJ) parameters as
an indicator of neuromuscular fatigue and high-speed running parameters varied from
negligible and nonsigniﬁcant to large and was assessed in soccer and tag football players.
Three studies found negligible to small correlations [83,84,86], whereas another three
studies found moderate to large correlations [74,75,94]. This may be due to the different
CMJ parameters collected (jump height, GRFs, or power output) and different time points
when fatigued jumps were executed (24 to 72 h post-exercise).
CMJ parameters’ relationship to acceleration and deceleration parameters exhibited
varying results. One study assessed relationships to parameters of overall volume, such as
total distance, duration, and PL, only ﬁnding trivial or unclear effects in soccer players [86].
For practitioners, this means that a high volume of acceleration, deceleration, and high-
speed running efforts possibly negatively inﬂuence neuromuscular performances for up to
24 h. Further research is needed to assess CMJ parameters relationships.
In American football, the number of impacts and peak head accelerations may indi-
cate S100beta levels but not tau concentrations [32]. Here, data were collected from an
instrumented mouthguard. S100beta is a blood biomarker that may be useful in detecting
mechanical stress in the brain [32]. In the ﬁeld, symptom scores offer a quick and easy-to-
use method to detect symptoms of concussions [107]. Kawata et al. (2017) did not ﬁnd
higher symptom scores in players who sustained more impacts. This ﬁnding is particu-
larly important to recognize for practitioners as the easy-to-use method (symptom scores)
may fail to detect exposure to repeated sub-concussive (head) impacts. Accelerometer-
embedded mouth guards, however, seem to pose a reasonable method to detect elevated
S100beta levels. Thus, it may be beneﬁcial to implement external monitoring systems such
as accelerometers. Further studies are needed to analyze these relationships.
Concentrations of secretory immunoglobulin A (sIgA), a marker of immune function,
have been linked to high-intensity distance, total distance, and acceleration and deceleration
parameters in soccer [69,91]. Small to large negative relationships were observed. If the
overall volume is high, sIgA is reduced; thus, immune function and the risk of contracting
an upper respiratory tract infection (URTI) may be increased. This ﬁnding is in line with the
previous ﬁndings that reported reduced sIgA levels after interval runs [108] and in athletes
with a high workload [109]. For practitioners, this means that players are at a higher risk of
falling sick following a period of congested schedule or high volume in general. Players
may be at greatest risk 3 to 72 h post intense exercise according to the “open window
theory” of altered immunity [109].
Overall, numerous different exercise-induced responses were analyzed, as previously
depicted in Figure 3. Most of them, however, appeared in few studies such that a systematic
synthesis was not feasible. For the parameters discussed above, practitioners need to
carefully consider time point of collection and the speciﬁc parameter collected, as changes
Sensors 2023, 23, 827
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may result in varying outcomes. Consistent ﬁndings were present regarding muscle
damage in collision-based team sports which may be estimated using impact parameters.
Practitioners shall further consider that sIgA levels may be low following high total activity
volume and players may be at risk of attracting an URTI.
4.3. Adaptation Parameters
Adaptation parameters are assessed at a minimum of two time points several days,
weeks, or months apart and they are commonly carried out in a non-fatigued state. Changes
between measurements can be analyzed and viewed as adaptation. Adaptation parameters
extracted from the studies included were related to changes in body mass, HR-based indices,
intermittent and aerobic endurance capacities, sleep efﬁciency, and strength parameters.
Body mass seems to change in relation to 10-week sprinting distance, and total distance,
but not session duration or average speed [78,82]. Change in HRmax was positively related
to 10-week sprinting distance [82], and HRV negatively to acute-to-chronic workload ratio
(ACWR)-based session time during a season [77]. ACWR is commonly used for injury
prevention purposes. The model says that a greatly increased or decreased acute workload,
compared to the chronic workload, increases the risk of sustaining an injury [110]. Change
in hamstring peak torque, quadriceps to hamstring ratio, percent change in peak torque,
and quadriceps to hamstring ratio was positively related to 10-week accumulated PL and
acceleration sum (accumulated acceleration data in all three axes), sprinting distance, dura-
tion, and total distance, respectively [82]. Meaning volume and intensity improve strength
test performances and may thus reduce hamstring injuries and the risk of suffering an
anterior cruciate ligament (ACL) injury as imbalances between quadriceps and hamstring
constitute an ACL risk factor [111]. Monitoring loads long term to ensure they are sufﬁ-
ciently high to cause adaptations may be beneﬁcial. Further, team sports practitioners can
gain a better understanding of the individual dose-response patterns.
As indicated by a positive change in VO2max, improvements in aerobic endurance were
strongly correlated to 10-week accumulated PL and acceleration [82]. Session duration,
however, showed an inverse relationship to changes in VO2max [82]. The duration in this
study, however, decreased throughout the season. Meaning VO2max increased despite
decreasing duration. In this case, other factors, such as high mechanical loading, seem to
represent improvements in aerobic capacity better than training duration.
Changes in intermittent ﬁtness, as indicated by the 30-15 intermittent ﬁtness test, were
observed by one study only, which found unclear and large relationships to high-intensity
running, total distance, and PL, respectively [72]. More research is needed regarding these
relationships to synthesize results systematically.
Overall, ﬁndings suggest that intensity seems particularly important to improve
certain physiological capacities related to intermittent team sports. Volume and intensity
need to be well-balanced in training programs to cause optimal adaptations.
4.4. Individual Characteristics
Individual characteristics analyzed in relation to external parameters collected using
wearables include intermittent and aerobic endurance capacity, neuromuscular perfor-
mance parameters, and muscle architecture. As indicated by the Yo-Yo intermittent recov-
ery test (YYIR), players with larger intermittent endurance capacities covered greater total
distances in soccer and tag football [66,94]. Greater intensities, as indicated by high-speed
running meters per minute and the number of repeated high-intensity events, were covered
by tag football players with better YYIR performance [94]. Similar ﬁndings were present
for players with greater VO2max regarding total distance and intensity parameters in soccer
players [67]. For practitioners, this means that players who generally cover more distances
likely have greater endurance capacities. As such, rigorous and time-intensive testing in
the laboratory may not be necessary to ﬁnd out endurance deﬁcits and strengths in players.
Sensors 2023, 23, 827
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Parameters of neuromuscular performance and muscle architecture were analyzed
each by one study only; thus, a systematic synthesis is not feasible, and more research is
needed to draw conclusions regarding those parameter relationships.
4.5. General Aspects
Generally, based on the intensity and volume of external load experienced during
training and match play, internal bodily reactions take place, which, in the long term, lead to
adaptations and inﬂuence individual characteristics. Those characteristics determine how
well a player can handle the external load. However, no consensus exists on the parameters
encompassing internal load yet. Some researchers have a broad understanding of internal
load parameters, including biochemical, neuromuscular, and hormonal responses [1,2].
Others have a more narrow deﬁnition of internal load, including only measures that
can prescribe exercise intensity, comprising mainly HR and session-RPE [3,4,110]. In
this systematic review, parameters were categorized mainly based on the time point of
measurement, as depicted in Figure 1. This, however, was not always straightforward:
Couderc et al. (2017) collected blood from the ﬁngertip 3 min post-exercise to determine
lactate and bicarbonate concentration and pH levels. As those parameters are not monitored
continuously during exercise, they fall into the category of exercise-induced responses. RPE,
however, is commonly collected around 15–30 min after exercise [53,59,73,92] to reduce
bias that may result from particular easy or challenging segments during the ﬁnal exercise
period [112]. However, RPE is deemed an internal load parameter by both parties of broad
and narrow deﬁnitions of internal load. This might be due to the strong relationship of
RPE with internal load parameters (HR and blood lactate) [113,114].
Some limitations to this systematic review are acknowledged in the following. These
include the non-feasibility of synthesizing results for some parameter relationships. Given
the wide variety of parameters, some relationships were analyzed by fewer studies to
synthesize results systematically. Further, thresholds for speed zones differ across studies
such that results may vary due to varying absolute or individualized thresholds used.
Different hard- and software was implemented in the studies analyzed, which may cause a
discrepancy in results. Manufacturers apply ﬁlters to the data during post-processing such
that the same parameter could supposedly differ when obtained from another product.
Even a software update could result in inconsistent results.
Correlations found do not mean causality; parameters might correlate because of
other circumstances. Clemente et al. (2019) found a large negative correlation between
training duration and VO2max. This ﬁnding, however, likely does not mean shorter training
durations cause an increase in aerobic endurance, but rather other circumstances were in
place, such as high running volume and repeated high-intensity events, that may elicit
improved aerobic endurance.
Few studies (n = 4) included in this systematic review studied female athletes [46,51,67,93],
totaling 62 female athletes compared to 1479 male athletes. Even though the parameter rela-
tionships were comparable between males and females, this can only be said about the few
parameters’ relationships analyzed. Thus, more research, including female athletes, is needed.
4.6. Outlook and Future Work
Besides ever ongoing enhancements in hard- and software that will provide more
accurate and reliable data in the future, research around accelerometer- or inertial-based
GRF and moment estimation has shown promising results. Estimating GRFs and joint
moments during training and match play would provide valuable biomechanical insight.
Continuous monitoring of forces and moments acting on the player’s body could enhance
the knowledge of the optimal individual dose-response relationship, injury mechanisms,
and performance indicators from a biomechanical perspective. Research groups have
placed MEMS on the shank [115], the sacrum [116], and the trunk [117] or used a full-body
sensor suit [118]. So far, movements such as walking, jumping, running, and squatting,
have been analyzed separately. This approach needs further development for more complex
Sensors 2023, 23, 827
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and compound movements to be transferrable to team sports. Players will either have to
wear sensors in more locations (possibly embedded in clothing), or algorithms based on
one trunk-mounted sensor only will have to be developed further to gain valuable insights
into the said domains.
Recently, advancements in continuous lactate and glucose monitoring have been made.
This methodology will provide new insights into the external–internal load relationships
of those parameters which need to be studied in the future. More possibilities to monitor
internal load parameters continuously during training and match play as well as knowledge
about the relationships to external parameters will move testing and identiﬁcation of
adaptations and health and ﬁtness status away from the laboratory and more toward on-
ﬁeld assessments. Thus, separate time-consuming and fatiguing testing can be eliminated
and replaced with data collected during training and match play.
An ever-increasing amount of data will be collected, so the ability to analyze and
select data appropriately according to the context becomes increasingly important. Even
though some external parameters show strong relationships with, e.g., HR, it is essential
to use parameters according to their context. If a highly anaerobic training session takes
place, valid measures may differ from those of a more aerobic-based session. Despite strong
correlations between parameters, and even if both external and internal parameters are
considered, it is still essential to know what type of parameters to inspect depending on
the demands placed on the athletes and the stressed biological systems. Having a sound
understanding of those differences is inevitable for practitioners to harness the power of
the collected data. Besides selecting parameters, verbal and visual transfer of information
becomes increasingly relevant to create a common understanding between athletes, coaches,
data analysts, and medical staff to enhance performance.
Future work will need to validate novel methods of collecting internal parameters,
analyze relationships of those to external parameters, and include more female athletes.
With more reliable data, captured from highly dynamic movements, the impact of those
movements on players can be explored in more depth, as the parameter relationships in
this domain are currently ambiguous.
5. Conclusions
Strong correlations have been detected, especially between parameters of total activity
volume and the internal load parameters HR-indices and RPE or session-RPE. These pa-
rameter relationships, were analyzed most often making the state of evidence clearer than
less researched parameter relationships. Fitness tests assessing aerobic and intermittent
endurance, or (session-) RPE, and in collision-based team sports, additionally markers of
muscle damage may be omitted and replaced by external, on-ﬁeld measurements, facili-
tating the work of practitioners. Relationships between external load and the other three
internal parameter categories, exercise-induced responses, adaptations, and individual
characteristics, are mostly ambiguous and need further veriﬁcation. Until then, a holistic
picture of an athlete may best be obtained by collecting external and internal parameters for
those parameter groups. Due to the ever-increasing amount of data collected in both areas,
external and internal, a sound understanding of the data and their sport-speciﬁc context
becomes increasingly important. Good communication is crucial for all stakeholders to
attain a common understanding of the data. Studies including female athletes have been
noticeably little in number and should be increased in the future. Future work will need to
validate novel methods of collecting internal parameters and their relationships to external
parameters in order to understand the individual dose-response patterns.
Supplementary Materials:
The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/s23020827/s1. Table S1: Studies included in this systematic
review, the parameters collected and the relationship found between them. Results are listed by sport
in alphabetical order.
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Author Contributions: Conceptualization, K.R., H.M., S.W. and M.R.; methodology, J.H.; formal
analysis, J.H.; investigation, J.H., J.D.; data curation, J.H. and J.D.; writing—original draft preparation,
J.H.; writing—review and editing, A.G., K.R. and S.W.; supervision and editing, A.G., K.R. and S.W.
All authors have read and agreed to the published version of the manuscript.
Funding: We acknowledge support by the Open Access Publication Fund of the University of Freiburg.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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Relationships between External, Wearable Sensor-Based, and Internal Parameters: A Systematic Review.
Helwig, Janina,Diels, Janik,Röll, Mareike,Mahler, Hubert,Gollhofer, Albert,Roecker, Kai,Willwacher, Steffen