The Interunit Reliability of Global Navigation Satellite Systems Apex (STATSports) Metrics During a Standardized Intermittent Running Activity

Abstract

Beato, M, Wren, C, and de Keijzer, KL. The interunit reliability of global navigation satellite systems Apex (STATSports) metrics during a standardized intermittent running activity. J Strength Cond Res XX(X): 000–000, 2023—This study aimed to evaluate the interunit reliability of global navigation satellite systems (GNSS) STATSports Apex metrics and to assess which metrics can be used by practitioners for the monitoring of short-distance intermittent running activities. Fifty-four male soccer players were enrolled (age = 20.7 ± 1.9 years, body mass = 73.2 ± 9.5 kg, and height = 1.76 ± 0.07 m) in this observational study. 10-Hz GNSS Apex (STATSports, Northern Ireland, Newry) units recorded total distance, high speed running (HSR), accelerations, decelerations, peak speed, average metabolic power, metabolic distance, dynamic stress load (DSL), relative distance, and speed intensity. The standardized intermitted running protocol used was a Yo-Yo intermittent recovery level 1. This study reported that Apex interunit analysis did not show any significant difference (delta difference and 95% confidence interval [CIs]) in total distance = 2.6 (−2.6; 7.9) m, HSR = 3.2 (−0.2; 6.8) m, accelerations = 0.09 (−0.9; 1.1), decelerations = 0.3 (−0.4; 1.1), peak speed = 0.02 (−0.03; 0.07) m·s ⁻¹ , average metabolic power = 0.01 (−0.02; 0.04) W·kg ⁻¹ , metabolic distance = 0.9 (−6.2; 8.0) m, DSL = 2.8 (−5.6; 10.7) au, relative distance = 0.14 (−0.19; 0.47) m·min ⁻¹ , and speed intensity = 0.21 (−0.21; 0.64) au. All metrics presented a delta d between trivial to small. The interunit intraclass correlation coefficient (ICC) was good or excellent for all metrics, with the exception of DSL, which was considered questionable . In conclusion, this study reports that all the metrics analysis in this study presents a low interunit bias and high reliability (ICC), with the exception of DSL.

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Original Research
The Interunit Reliability of Global Navigation
Satellite Systems Apex (STATSports) Metrics
During a Standardized Intermittent Running Activity
Marco Beato, Cion Wren, and Kevin L. de Keijzer
School of Health and Sports Sciences, University of Suffolk, Ipswich, United Kingdom
Abstract
Beato, M, Wren, C, and de Keijzer, KL. The interunit reliability of global navigation satellite systems Apex (STATSports) metrics
during a standardized intermittent running activity. J Strength Cond Res XX(X): 000–000, 2023—This study aimed to evaluate the
interunit reliability of global navigation satellite systems (GNSS) STATSports Apex metrics and to assess which metrics can be used
by practitioners for the monitoring of short-distance intermittent running activities. Fifty-four male soccer players were enrolled (age
520.7 61.9 years, body mass 573.2 69.5 kg, and height 51.76 60.07 m) in this observational study. 10-Hz GNSS Apex
(STATSports, Northern Ireland, Newry) units recorded total distance, high speed running (HSR), accelerations, decelerations, peak
speed, average metabolic power, metabolic distance, dynamic stress load (DSL), relative distance, and speed intensity. The
standardized intermitted running protocol used was a Yo-Yo intermittent recovery level 1. This study reported that Apex interunit
analysis did not show any significant difference (delta difference and 95% confidence interval [CIs]) in total distance 52.6 (22.6; 7.9)
m, HSR 53.2 (20.2; 6.8) m, accelerations 50.09 (20.9; 1.1), decelerations 50.3 (20.4; 1.1), peak speed 50.02 (20.03; 0.07)
m·s
21
, average metabolic power 50.01 (20.02; 0.04) W·kg
21
, metabolic distance 50.9 (26.2; 8.0) m, DSL 52.8 (25.6; 10.7) au,
relative distance 50.14 (20.19; 0.47) m·min
21
, and speed intensity 50.21 (20.21; 0.64) au. All metrics presented a delta
dbetween trivial to small. The interunit intraclass correlation coefficient (ICC) was good or excellent for all metrics, with the exception
of DSL, which was considered questionable. In conclusion, this study reports that all the metrics analysis in this study presents a low
interunit bias and high reliability (ICC), with the exception of DSL.
Key Words: GNSS, GPS, team sports, sprint, performance, football
Introduction
Wearable technology is commonly used to assess amateur and
professional team sport athletes’training and match load (2,32).
Of the wearable devices available, global navigation satellite
systems (GNSS) are the most commonly used (25,29). Global
navigation satellite systems units allow for the analysis of ex-
ternal load metrics such as total distance, accelerations, decel-
erations, sprinting distance, and so on (1,6,23,24). Such
informationisusedtohelpcoachesandsportscientistsmake
informed decisions around modifying training sessions, evalu-
ating the intensity of drills, and adapting training load at the
individual level (23,28,37).
A multi-GNSS augmented unit acquires and tracks multiple
satellite systems (e.g., global positioning system [GPS],
GLONASS, Galileo, and BeiDou) concurrently and thus pro-
vides more accurate positional information compared with
GPS alone (7). In fact, previous research reports that several
activities can be accurately monitored using GNSS units
(13,28,33). For example, when GNSS units were compared
with a criterion measure for peak speed during linear activi-
ties, they reported only a negligible error and obtained high
interunit reliability (5,7). Moreover, in different GNSS mod-
els, the total distance during linear and sport-specific activities
was also found accurate (error ,5%) and reliable (7,8,30).
However, large variability in accuracy between manufac-
turers’models and units has been previously identified (8,36),
and such differences may undermine practitioners’ability to
monitor and plan training effectively (34). To reduce possible
intermodel biases, players should be monitored using the same
GNSS technology, whereas to reduce interunit biases, the same
GNSS unit should be used with the same player during each
session (17,22). However, this is very unpractical, and this
approach does not sort out the possible bias of the interplayers
comparison, which is a core component of the training and
match load analysis because coaches need to compare the
training load among players during sessions (23). For such a
reason, an interunit analysis should be performed for each
GNSS manufacturer to verify its reliability (36). Previous re-
search reported that GNSS Apex (STATSports) units have
excellent interunit reliability and a coefficient of variation
ranging from 1.64 to 2.91% for the analysis of peak speed
during short sprints (between 5 and 30 m) (5). However, in-
terunit reliability analysis of this GNSS model does not cur-
rently exist for the most common metrics used in team sports,
which is a critical limitation for the use of this technology in
the sport industry and for research purposes.
Most of the activities performed by team sport players are
intermittent in nature, frequently requiring changes of direction,
and are usually performed over short distances (14,15,25). Such
short distances and constantly changing running characteristics
may indeed affect the reliability of the GNSS units (6). It is,
therefore, paramount to verify the interunit reliability of the most
Address correspondence to Marco Beato, m.beato@UOS.ac.uk.
Journal of Strength and Conditioning Research 00(00)/1–7
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commonly used GNSS Apex metrics (i.e., total distance, high-
speed running distance (HSR), accelerations, decelerations, peak
speed, average metabolic power, metabolic distance, dynamic
stress load (DSL), relative distance, and speed intensity) during
standardized intermittent exercise (i.e., Yo-Yo intermittent re-
covery level 1 [YYIRL1]) (4). Such information is currently
missing and could have very important practical applications for
sport scientists and coaches working in amateur and professional
team sports. Therefore, this study aims, first, to evaluate the in-
terunit reliability of GNSS STATSports Apex metrics, and sec-
ond, to assess which of those metrics can be used by practitioners
for monitoring short-distance intermittent running activities.
Methods
Experimental Approach to the Problem
Global navigation satellite systems Apex (STATSports, Northern
Ireland, Newry) have the following characteristics: wide 5
30 mm, high 580 mm, and mass 548 g. The 10 Hz GNSS device
is equipped with a 100 Hz gyroscope, a 100 Hz triaxial acceler-
ometer, and a 10 Hz magnetometer (7). Apex data were collected
on an outdoor soccer pitch in the absence of high buildings. Data
collection was only performed in good meteorological conditions
to enhance satellite reception, following the recommendations of
recent investigations (7,8). Before each session, a standardized
warm-up was led by an accredited strength and conditioning
coach to reduce the risk of muscle injuries.
The Apex units were turned on 20 minutes before the begin-
ning of the protocol; the satellites connected to each unit ranged
between 18 and 21. Each player used 2 units, which were placed
in a manufacturer-provided vest on the subject’s back about 3 cm
from each other, midway between the scapulas, to permit equal
exposure to the embedded antenna (6,7). Before the protocol,
subjects were required to stand still for 10 seconds at the starting
point to facilitate data analysis; then, they were required to follow
the audio indications related to the standardized test used in this
protocol. Apex data were downloaded and further analyzed by
the respective software (Apex 10 Hz, Sonra v4.2.1).
The interunit reliability between Apex vs. Apex was performed
for several metrics such as total distance, HSR, accelerations,
decelerations, peak speed, average metabolic power, metabolic
distance, DSL, relative distance, and speed intensity (9,10,32,35),
whose detailed explanation is given below. The standardized in-
termittent running protocol was performed using a YYIRL1,
which is a commonly used and validated test in team sports (4). In
brief, players performed a 20 m 120 m course, with a change of
direction of 180°, with a 10-second active break after each 40 m,
with the speed increasing at set intervals until the players were
unable to continue.
Subjects
Fifty-four male amateur soccer players were enrolled (age 520.7
61.9 years, range: 18.5–23.5 years, body mass 573.2 69.5 kg,
and height 51.76 60.07 m) in this observational study. The
subjects had a soccer experience ranging from 11 to 16 years, and
they were regularly training 2–3 times a week (including
matches). The sample size power was calculated a priori using G
power (Dusseldorf, Germany) and indicated that a total sample of
26 subjects would be required to detect a large (r50.50) corre-
lation with 80% band a5%, resulting in a power of 0.806.
However, this study enrolled a larger sample consisting of 54
subjects to reduce the chances of type 2 errors in the interunit
analysis, which resulted in an actual power (1-berror prob) of
.0.95 (12). The study was performed in accordance with the
Declaration of Helsinki for studies on human subjects. The In-
stitutional Ethics Board of the University of Suffolk (Ipswich,
United Kingdom) approved the experimental protocol
(SREC20023/RT). A written informed consent form was
obtained from all subjects of the current investigation.
Procedures
Global Navigation Satellite Systems Metrics. Total distance was
the overall distance covered by the subjects during the
YYIRL1, HSR was the total distance covered at a speed of over
5.5 m·s
21
(19.8 km·h
21
) (10), and accelerations and deceler-
ations were quantified as the number of events with intensity
.3m·s
22
and ,3m·s
22
for a minimum duration of 0.5 sec-
onds, respectively (35). Peak speed (m·s
21
)wasthehigher
velocity recorded by each subject during the YYIRL1 protocol
(5). Average metabolic power (W·kg
21
) and metabolic dis-
tance (m) were calculated using di Prampero’s model; further
details about the used formula can be found in the following
paper (31). Dynamic stress load is an accelerometer-derived
metric which aggregates the rates of accelerations on its 3 or-
thogonal axes (X, Y, and Z planes) to form a composite
magnitude vector (expressed as gforce) which this inputted to
a curved weighted function to get a value in arbitrary units
(au); further details about its formula can be found in the fol-
lowing paper (9). Relative distance is the total distance per unit
of time (m·min
21
), whereas speed intensity (au) is a measure of
total exertion calculated as the sum of a convexly weighted
measure of instantaneous speed (32).
Statistical Analyses
All descriptive data were presented as means 6SDs. Between-
units analysis was performed using a paired ttest. Normality
assumption was checked using a Shapiro-Wilk test, and if vio-
lated, ttest results were compared with a nonparametric Wil-
coxon signed-rank test. Statistical significance was set at p,
0.05, and confidence intervals (CIs) at 95% were reported (12).
Cohen’sdeffect size was reported and interpreted with the fol-
lowing scale of magnitudes: d,0.20 5trivial, 0.20–0.59 5
small, 0.60–1.19 5moderate, 1.20–1.99 5large, and d.2.00 5
very large (27). The interunit reliability was calculated using a 2-
way mixed model intraclass correlation coefficient (ICC), which
was interpreted accordingly: ICC $0.9 5excellent; 0.9 .ICC $
0.8 5good; 0.8 .ICC $0.7 5acceptable; 0.7 .ICC $0.6 5
questionable; 0.6 .ICC $0.5 5poor; and ICC ,0.5 5un-
acceptable (3).Technical error of measurement (TE) was calcu-
lated using the following formula: TE 5SD.√(1-ICC) (3,26).
Statistical analysis was performed using JASP (Amsterdam,
Netherlands) software version 0.16.3.
Results
A total of 1,050 individual data points were analyzed in the
current investigation to test Apex interunit reliability, which was
divided into 10 metrics, 54 subjects, and 2 GNSS units per subject.
Each parameter had 54 data points except for HSR and speed
intensity that had 46 and 47, respectively. Descriptive analysis
and test-retest analysis were reported in Table 1.
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Interunit reliability analysis was reported in Table 2. Interunit
reliability was rated as good or excellent for most of the metrics
analyzed except for DSL, which was rated as questionable.
Between units did not report any significant difference (delta
difference, 95% CIs) in total distance 52.6 (22.6; 7.9) m, HSR
53.2 (20.2; 6.8) m, accelerations 50.09 (20.9; 1.1) number,
decelerations 50.3 (20.4; 1.1) number, peak speed 50.02 (2
0.03; 0.07) m·s
21
, average metabolic power 50.01 (20.02;
0.04) W·kg
21
, metabolic distance 50.9 (26.2; 8.0) m, DSL 5
2.8 (25.6; 10.7) au, relative distance 50.14 (20.19; 0.47)
m·min
21
, and speed intensity 50.21 (20.21; 0.64) au. A
graphical representation of the between units’analysis for each
subject and for each metric was reported in Figures 1–3.
Discussion
This study aimed to evaluate the interunit reliability of GNSS
STATSports Apex metrics and to assess which metrics can be used
by practitioners to monitor short-distance intermittent running
activities. A total of 1,050 individual data points were analyzed in
the current investigation to test Apex interunit reliability. No
significant between unit difference was found (p.0.05), with a
dranging from trivial to small. In addition, the interunit reliability
was excellent for the following metrics: total distance, HSR, ac-
celerations, decelerations, average metabolic power, metabolic
distance, relative distance, and speed intensity, with the exception
for peak speed that was good and DSL which was considered
questionable.
Although GNSS Apex units are among the most common
wearablesusedtomonitortrainingandcompetitionforteam
sports (11,18,21), this is the first study to evaluate the interunit
reliability of typically used GNSS and accelerometer metrics.
Total distance reported a nonsignificant (p50.327) trivial
difference, with a delta difference of only 2.6 m in the interunit
analysis; HSR reported a nonsignificant (p50.068) small dif-
ference, with a delta difference of only 3.2 m in the interunit
analysis (Figure 1). Furthermore, accelerations, decelerations,
and peak speed reported nonsignificant (p50.862, p50.375,
and 0.471, respectively) trivial differences (Table 1). These re-
sults show that these GNSS metrics are consistent and can be
used to monitor team sport athletes during intermittent running
activities. The peak speed results reported in this study are
supported by previous research that compared peak speed re-
liability during maximal linear sprints from 5 to 30 m. Specifi-
cally, Beato and de Keijzer (5) did not find any statistically
significant difference during sprinting activities at any distance
interval such as 5–10 m (p50.162), 10–15 m (p50.793),
15–20 m (p50.998), and 20–30 m (p50.207). These results
highlight that peak speed assessed with Apex units can be used
for the analysis of speed data in team sports, as recently sug-
gested to practitioners (10). However, practitioners should
avoid using interchangeably different GNSS manufacturers (or
models) because statistically significant differences were pre-
viously found (5,36).
The analysis of derived metabolic metrics from GNSS such
as average metabolic power and metabolic distance reported
nonsignificant trivial differences (d50.076 and 0.034, re-
spectively). These metrics have been used in several papers to
assess metabolic power and energy cost indirectly (19,20,32);
therefore, because of their common use for both sport and
research purposes, the assessment of the interunit analysis of
those parameters was needed. This study demonstrates for the
Table 1
Differences between data recorded during the Yo-Yo intermittent recovery level 1 (54 players).*
Variables Apex units Apex units 2 Delta difference (CIs) pd(Interpretation)
Total distance (m) 1794.6 6874.9 1792.0 6870.9 2.6 (22.6; 7.9) 0.327 0.135 (Trivial)
High speed running (m) 46.9 655.2 43.6 651.4 3.2 (20.2; 6.8) 0.068 0.276 (Small)
Accelerations (n) 59.6 631.9 59.5 631.7 0.09 (20.9; 1.1) 0.862 0.024 (Trivial)
Decelerations (n) 37.7 621.4 37.4 621.6 0.3 (20.4; 1.1) 0.375 0.122 (Trivial)
Peak speed (m·s
21
) 5.98 60.43 5.97 60.41 0.02 (20.03; 0.07) 0.471 0.099 (Trivial)
Average metabolic power (W·kg
21
) 13.62 63.5 13.61 63.5 0.01 (20.02; 0.04) 0.579 0.076 (Trivial)
Metabolic distance (m) 1,142.6 6556.7 1,141.7 6553.2 0.9 (26.2; 8.0) 0.804 0.034 (Trivial)
Dynamic stress load (au) 58.2 639.7 55.3 632.2 2.8 (25.6; 10.7) 0.472 0.098 (Trivial)
Relative distance (m·min
21
) 125.4 630.3 125.3 630.2 0.14 (20.19; 0.47) 0.407 0.114 (Trivial)
Speed intensity (au) 102.0 648.5 101.7 628.2 0.21 (20.21; 0.64) 0.323 0.146 (Trivial)
*d5Cohen’s d;CIs5confidence intervals, m 5meters, s 5seconds, Au 5arbitrary units.
Table 2
Reliability data recorded during the Yo-Yo intermittent recovery level 1 (54 players).*
Variables Apex interunit reliability ICC (95% CI) Reliability qualitative interpretation Technical error of measurement
Total distance (m) 0.999 (0.999, 1.00) Excellent 2.76
High speed running (m) 0.974 (0.955, 0.985) Excellent 8.90
Accelerations (n) 0.993 (0.987, 0.996) Excellent 2.67
Decelerations (n) 0.991 (0.985, 0.995) Excellent 2.03
Peak speed (m·s
21
) 0.898 (0.831, 0.939) Good 0.14
Average metabolic power (W·kg
21
) 0.999 (0.999,1.000) Excellent 0.11
Metabolic distance (m) 0.999 (0.998, 0.999) Excellent 17.58
Dynamic stress load (au) 0.681 (0.510, 0.801) Questionable 22.4
Relative distance (m·min
21
) 0.999 (0.999, 1.000) Excellent 0.96
Speed intensity (au) 0.999 (0.999, 1.000) Excellent 1.50
*ICC 5intraclass correlation coefficient, CIs 5confidence intervals, m 5meters, s 5seconds, Au 5arbitrary units.
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first time that the interunit error between GNSS units is ex-
tremely small and cannot undermine the analysis of these
metricsinsuchcontexts(Figure 2). Dynamic stress load, an
accelerometer derived metric, is calculated by the aggregation
of the rates of acceleration on its 3 orthogonal axes to form a
composite magnitude vector,whichisinputtedtoacurved
weighted function and provide a value in arbitrary units (au)
(9). Dynamic stress load can be used to assess variation in
running style and the acute or chronic fatigue effect on running
pattern (9). Dynamic stress load was also used to compare
players of different levels, reporting greater DSL values for first
team players compared with U23 and U18 players (32). In this
study, DSL obtained a nonsignificant (p50.472) trivial dif-
ference between units at a group level, which highlight the
potential use of this metric for between-group comparisons.
Finally, relative intensity and speed intensity reported non-
significant (p50.407 and p50.323, respectively) trivial
differences, which highlights that interunit error of those
metrics is low and they can be used to assess intensity in team
sports (Figure 3).
The interunit analysis performed in this study found that the
total distance has a negligible TE (2.76 m) and an excellent re-
liability; HSR also presented an excellent ICC of 0.974 95% CIs
(0.955, 0.985). These reliability scores are much higher than
previously reported for other GPS devices such as Catapult 10 Hz
minimax, which reported an ICC 50.51 and ICC 50.88, for
total distance and HSR, respectively (30). Moreover, GPSports 15
Hz reported a lower reliability for total distance than both
STATSports Apex and Catapult 10 Hz minimax. Instead,
GPSports 15 Hz reported a higher interunit reliability for HSR
(ICC 50.94) than Catapult 10 Hz minimax but lower than
STATSports Apex (30).
In this study, STATSports Apex showed a negligible in-
terunit bias for the accelerations and decelerations, with a TE
of 2.67 and 2.03, respectively, and an excellent reliability
(Table 2). These results are in contrast with what was pre-
viously reported by other GPS manufacturers about acceler-
ations and decelerations interunit reliability, which ranged
from poor to good (13).The differences found between pre-
vious GPS units and the current GNSS STATSports could be
due to recent technological advances, which consist of units
with higher acquisition frequency (i.e., from 1 or 5–10 Hz)
and the use of multisatellites systems (i.e., from GPS to GNSS)
(7). This point is particularly relevant for accelerations and
decelerations, which are calculated using the 10 Hz-GNSS
data, instead of using triaxial accelerometer data, which is a
common misconception. Finally, previous research reported
that because of the inadequate interunit reliability found be-
tween devices, accelerations interunit (therefore inter-subject)
comparisons should not be recommended (34). However, this
recommendation was made based on studies published before
2016, and although correct at that time, such recommenda-
tions are not suitable anymore. The current study clearly
demonstrated that Apex interunit reliability for accelerations
and decelerations is suitable for such a comparison (Figure 1
and Table 1).
The peak speed recorded during the YYIRL1 test was con-
sidered good to excellent, with an ICC of 0.898 95% CIs
(0.831, 0.939), which is similar to the excellent interunit re-
liability obtained during maximal linear sprints from 5 to 30 m
Figure 1. A graphical representation of the between units’ analysis for each subject. The global navigation satellite systems
metrics are total distance, high-speed running distance, accelerations, and decelerations.
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previously reported (5). In addition, the TE of the present (TE
50.14 [Table 2]) and the previous study, when compared for a
similar distance such as 15 and 20 m (TE 50.14 [0.12 to
0.15]), was equivalent, which highlights the consistency of this
interunit reliability between studies. The STATSports Apex
units analyzed in this study report a very high reliability when
compared with Catapult S5 OptimEye GNSS system evaluated
in previous research (16). Chahal et al. (16), reported that
these units have an ICC of 0.131 and 0.323 for total distance
and peak speed, respectively. They conclude that the tested
GNSS units (n58) are not sufficiently consistent among
themselves during straight-line sprint running (16).
The interunit analysis for average metabolic power and
metabolic distance was nearly perfect, with ICC of 0.999 for
both metrics (Table 2). The DSL was the only parameter to
report an ICC below expectation, with a score of 0.681, 95%
CIs (0.510, 0.801), which is interpreted as questionable. This
result is in contrast with previous research on the same topic,
which reported that this metric could be consistently used to
assess running activities (9). Because of these contrasting re-
sults, it is necessary to perform further investigations before
making final conclusions. Finally, relative distance and speed
intensity reported an excellent reliability score, with a nearly
perfect ICC 50.999 (0.999, 1.000), which highlight the con-
sistency between units.
This study is not without limitation; first, we have used a
single GNSS manufacturer (STATSports Apex) in this re-
search; therefore, the current evidence reported cannot be
translated to other GNSS devices, which need to be in-
dependently analyzed. Second, this study analyzed a sample of
metrics that are commonly recorded with the GNSS Apex
model; however, other less commonly used metrics should be
analyzed in future studies. Third, to perform the interunit re-
liability, units were placed in a manufacturer-provided vest on
the subject’s back about 3 cm from each other, midway be-
tween the scapulas, to permit equal exposure to the embedded
antenna. For such a reason, the units were not placed exactly 1
above the other; therefore, some difference between units
could be related to this. However, it is very important to
highlight that the positioning of 1 unit above the other can
decrease the quality of the signal and, therefore, decrease their
interunit reliability; this is the motivation because the re-
searchers involved in this study placed the units on the subject’s
in this way, as suggested in previous studies (5,7). Last, the
interunit reliability of this study was performed during a
standardized YYIRL1 test, which consists of short shuttle
running activities (20 m) where changes of direction were
present. Previous research reported that GPS technology pre-
sentsagreaterbiasduringsuchactivities (6), so the protocol
used in this study could be considered as a worst-case scenario.
Figure 2. A graphical representation of the between units’ analysis for each subject. The global navigation satellite systems
metrics are average metabolic power, metabolic distance, and peak speed.
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Considering this, future studies could assess the interunit re-
liability in ecological scenarios (e.g., during matches or stan-
dardized training sessions) to further verify their consistency.
Practical Applications
This study evaluated the interunit reliability of GNSS STAT-
Sports Apex metrics and assessed which of those metrics can
be used by practitioners for the monitoring of short-distance
intermittent running activities. Apex units did not show any
significant between unit difference (p.0.05), with a dbe-
tween trivial to small. The interunit reliability was interpreted
as excellent for total distance, HSR, accelerations, decelera-
tions, average metabolic power, metabolic distance, relative
distance, and speed intensity, whereas it was interpreted as
good for peak speed. However, this study found a question-
able ICC score for DSL, requiring further research to verify if
its use is appropriate for the monitoring of team sport activi-
ties. In conclusion, coaches and sport scientists can confidently
use GNSS Apex (STATSports) units for training load moni-
toring and they can perform interunit (interplayers) compar-
isons using the parameters assessed in this study.
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