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Accuracy of Tracking Devices’ Ability to Assess Exercise Energy Expenditure in Professional Female Soccer Players: Implications for Quantifying Energy Availability

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The purpose of the study was to assess the accuracy of commonly used GPS/accelerometer-based tracking devices in the estimation of exercise energy expenditure (EEE) during high-intensity intermittent exercise. A total of 13 female soccer players competing at the highest level in Norway (age 20.5 ± 4.3 years; height 168.4 ± 5.1 cm; weight 64.1 ± 5.3 kg; fat free mass 49.7 ± 4.2 kg) completed a single visit test protocol on an artificial grass surface. The test course consisted of walking, jogging, high-speed running, and sprinting, mimicking the physical requirements in soccer. Three commonly used tracking devices were compared against indirect calorimetry as the criterion measure to determine their accuracy in estimating the total energy expenditure. The anaerobic energy consumption (i.e., excess post-exercise oxygen consumption, EPOC) and resting time were examined as adjustment factors possibly improving accuracy. All three devices significantly underestimated the total energy consumption, as compared to the criterion measure (p = 0.022, p = 0.002, p = 0.017; absolute ICC = 0.39, 0.24 and 0.30, respectively), and showed a systematic pattern with increasing underestimation for higher energy consumption. Excluding EPOC from EEE reduced the bias substantially (all p’s becoming non-significant; absolute ICC = 0.49, 0.54 and 0.49, respectively); however, bias was still present for all tracking devices. All GPS trackers were biased by showing a general tendency to underestimate the exercise energy consumption during high intensity intermittent exercising, which in addition showed a systematic pattern by over- or underestimation during lower or higher exercising intensity. Adjusting for EPOC reduced the bias and provided a more acceptable accuracy. For a more correct EEE estimation further calibration of these devices by the manufacturers is strongly advised by possibly addressing biases caused by EPOC.
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Citation: Dasa, M.S.; Friborg, O.;
Kristoffersen, M.; Pettersen, G.;
Sundgot-Borgen, J.; Rosenvinge, J.H.
Accuracy of Tracking Devices’ Ability
to Assess Exercise Energy
Expenditure in Professional Female
Soccer Players: Implications for
Quantifying Energy Availability. Int.
J. Environ. Res. Public Health 2022,19,
4770. https://doi.org/10.3390/
ijerph19084770
Academic Editor: Lynda B Ransdell
Received: 22 February 2022
Accepted: 11 April 2022
Published: 14 April 2022
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4.0/).
International Journal of
Environmental Research
and Public Health
Article
Accuracy of Tracking Devices’ Ability to Assess Exercise Energy
Expenditure in Professional Female Soccer Players:
Implications for Quantifying Energy Availability
Marcus S. Dasa 1, * , Oddgeir Friborg 2, Morten Kristoffersen 3, Gunn Pettersen 1, Jorunn Sundgot-Borgen 4
and Jan H. Rosenvinge 2
1Department of Health and Care Sciences, UiT, The Arctic University of Norway, 9019 Tromso, Norway;
gunn.pettersen@uit.no
2Department of Psychology, UiT, The Arctic University of Norway, 9019 Tromso, Norway;
oddgeir.friborg@uit.no (O.F.); jan.rosenvinge@uit.no (J.H.R.)
3Department of Sport, Food and Natural Sciences, Western Norway University of Applied Sciences,
5063 Bergen, Norway; morten.kristoffersen@hvl.no
4Department of Sports Medicine, Norwegian School of Sport Sciences, 0863 Oslo, Norway; jorunnsb@nih.no
*Correspondence: marcus.smavik.dasa@uit.no
Abstract:
The purpose of the study was to assess the accuracy of commonly used GPS/accelerometer-
based tracking devices in the estimation of exercise energy expenditure (EEE) during high-intensity
intermittent exercise. A total of 13 female soccer players competing at the highest level in Norway
(age 20.5
±
4.3 years; height 168.4
±
5.1 cm; weight 64.1
±
5.3 kg; fat free mass 49.7
±
4.2 kg)
completed a single visit test protocol on an artificial grass surface. The test course consisted of
walking, jogging, high-speed running, and sprinting, mimicking the physical requirements in soccer.
Three commonly used tracking devices were compared against indirect calorimetry as the criterion
measure to determine their accuracy in estimating the total energy expenditure. The anaerobic energy
consumption (i.e., excess post-exercise oxygen consumption, EPOC) and resting time were examined
as adjustment factors possibly improving accuracy. All three devices significantly underestimated
the total energy consumption, as compared to the criterion measure (p= 0.022, p= 0.002, p= 0.017;
absolute ICC = 0.39, 0.24 and 0.30, respectively), and showed a systematic pattern with increasing
underestimation for higher energy consumption. Excluding EPOC from EEE reduced the bias
substantially (all p’s becoming non-significant; absolute ICC = 0.49, 0.54 and 0.49, respectively);
however, bias was still present for all tracking devices. All GPS trackers were biased by showing a
general tendency to underestimate the exercise energy consumption during high intensity intermittent
exercising, which in addition showed a systematic pattern by over- or underestimation during lower
or higher exercising intensity. Adjusting for EPOC reduced the bias and provided a more acceptable
accuracy. For a more correct EEE estimation further calibration of these devices by the manufacturers
is strongly advised by possibly addressing biases caused by EPOC.
Keywords:
female athlete; exercise expenditure; energy availability; team sport; exercise metabolism;
technology
1. Introduction
To support basic physiological functions and aid adaptations to training, an athlete’s
energy intake (EI) should be matched against the energetic needs a given sport activity
require. An athlete’s energy availability (EA) is quantified as the residual energy after
subtracting the exercise energy expenditure (EEE) from the EI, divided by fat-free mass
(FFM) [
1
]. In soccer, nutritional intake may impact a player’s body composition, resulting in
performance alterations. As such, sport nutrition experts can assist players in manipulating
EI to meet the desired goals [
2
]. Nevertheless, athletes should be warned against the
Int. J. Environ. Res. Public Health 2022,19, 4770. https://doi.org/10.3390/ijerph19084770 https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2022,19, 4770 2 of 11
accidental or deliberate mismatch of EI and energy expenditure (EE), resulting in EA below
125 kilojoules (kJ) (30 kcal) per kg FFM. Such a low energy availability (LEA) may cause
disturbances to hormonal, metabolic, and immune functions [
3
5
]. Although monitoring of
body mass can provide insight into an athlete’ s energy balance, long term LEA may result
in “metabolic adaptation” causing weight stability, despite inadequate energy balance [
6
].
Thus, body mass is not sufficient to detect LEA in athletes. Despite this, limited knowledge
exists regarding the occurrence and implications of LEA in female soccer players.
In soccer and intermittent field sports, tracking devices (GPS or accelerometer-based) is
the most commonly used microtechnology to quantify physical activity [
7
], and to estimate
EA [
8
10
]. Such devices may provide valid estimates of EEE within sports characterized
by steady-state exercise such as running and cycling [
11
]. However, their accuracy is
questionable within sports characterized by intermittent exercises, notably soccer, with high
amounts of directional changes, accelerations, and deaccelerations [
12
]. In part, this is due
to the anaerobic energy production, resulting in lactate accumulation and oxidation, which
is not accounted for in aerobic energy production and therefore difficult to measure [
13
].
Further, different manufacturers may operate with disparate algorithms when processing
data, potentially resulting in dissimilar output of EEE [14,15].
Some tracking devices, which are specifically developed for intermittent sports such
as soccer, build on the metabolic power concept [
16
,
17
], yet several studies [
12
,
18
20
] still
report underestimations of EEE. Responding to such findings, the original authors have
argued that inappropriate usage of the concept could contribute to this underestimation [
21
].
Others have proposed alternatives to the original model [
22
]. Nevertheless, the validity of
studies reporting EEE in intermittent sports based on tracking devices is ambiguous. This
needs to be further investigated in female athletes and soccer players, as previous work on
metabolic power is based on male athletes. Disparities in physiological factors between
sexes such as work economy, efficiency, and body composition are also contradictory [
23
]
and have not been addressed in the development of the metabolic power concept.
In a recent study [
24
] of female endurance athletes, only a slight caloric surplus of
<200 kcal·d1
in energy balance was associated with increased performance. However,
failure to achieve a caloric surplus was associated with impairments to performance. This
finding highlights the importance of accurately measuring EEE, as it may have direct
consequences for the nutritional periodization of athletes, influencing performance, re-
covery, and health status. In summary, there is a need to identify the accuracy of current
tracking devices being utilized to quantify EEE, offering implications for measuring LEA.
Further, the investigation of female athletes is warranted to examine potential differences
in estimating EEE and enable between study comparisons, regardless of sex.
Therefore, the aim of the present study was to examine the accuracy of three commonly
used tracking devices utilizing metabolic power to quantify EEE during intermittent
exercise in high-level female soccer players. Based on the current literature, we expected
that the tracking devices underestimate the caloric expenditure as compared with indirect
calorimetry as the criterion measure. we also examined whether EPOC during rest could
be used to improve agreement and explain potential discrepancies in the results.
2. Materials and Methods
2.1. Study Design
Participants completed a single visit test protocol on artificial grass surface instru-
mented with a portable O
2
analyzer, and three different tracking devices. A pre-determined
course consisting of walking, jogging, shuttle run/stride, and sprinting was designed to
model the physical requirements in women’s soccer [
25
,
26
]. The course length was 549.5 m
and was repeated five times (total distance 2747.5 m) to ensure data sufficiency to measure
movement and EEE (Figure 1). Participants were instructed to complete each part of the
course at self-selected speeds, guided by movement descriptors. However, the 20 m sprint
was instructed to be completed at maximal effort. Before starting the testing protocol,
participants were instructed in how to use the Rated Perceived Exertion (RPE) [
27
] scale
Int. J. Environ. Res. Public Health 2022,19, 4770 3 of 11
ranging from 0–10. All participants completed a standardized guided warm up, consisting
of three rounds, corresponding to RPE 4, 6 and 8, respectively, the latter being the desired
intensity for the completion of the 5-round protocol (self-selected speed corresponding
to RPE 8). After each round, they rested standstill for 1 min. Here capillary blood lactate
samples were collected, and the participants were asked to rate their RPE of the preceding
round. Additionally, after the completion of the protocol, excess post exercise oxygen
consumption (EPOC) was measured for 120 s to account for EEE derived work above VO
2
max and total session RPE was stated.
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 3 of 12
2. Materials and Methods
2.1. Study Design
Participants completed a single visit test protocol on artificial grass surface instru-
mented with a portable O2 analyzer, and three different tracking devices. A pre-deter-
mined course consisting of walking, jogging, shuttle run/stride, and sprinting was de-
signed to model the physical requirements in women’s soccer [25,26]. The course length
was 549.5 m and was repeated five times (total distance 2747.5 m) to ensure data suffi-
ciency to measure movement and EEE (Figure 1). Participants were instructed to complete
each part of the course at self-selected speeds, g uided by movem ent desc ripto rs. Ho wever,
the 20 m sprint was instructed to be completed at maximal effort. Before starting the test-
ing protocol, participants were instructed in how to use the Rated Perceived Exertion
(RPE) [27] scale ranging from 0–10. All participants completed a standardized guided
warm up, consisting of three rounds, corresponding to RPE 4, 6 and 8, respectively, the
latter being the desired intensity for the completion of the 5-round protocol (self-selected
speed corresponding to RPE 8). After each round, they rested standstill for 1 min. Here
capillary blood lactate samples were collected, and the participants were asked to rate
their RPE of the preceding round. Additionally, after the completion of the protocol, ex-
cess post exercise oxygen consumption (EPOC) was measured for 120 s to account for EEE
derived work above VO2 max and total session RPE was stated.
Figure 1. Illustration of the intermittent exercise protocol, indicating the type of movement and
length of segment numbered A–J.
Figure 1.
Illustration of the intermittent exercise protocol, indicating the type of movement and
length of segment numbered A–J.
2.2. Participants
Eligibility criteria for the study were defined as (i) female competing at the highest
level in Norway, (ii) >16 years of age, and (iii) absence of injuries or illnesses. In addition,
participants were asked to abstain from caffeine intake on the day of testing, as well as
ingesting their last meal approximately 2 hours before testing. A total of 13 professional
female soccer players (age 20.5
±
4.3 years; height 168.4
±
5.1 cm; weight 64.1
±
5.3 kg;
fat free mass 49.7
±
4.2 kg) completed the study. Two players declined the invitation
to participate due to self-reported injury and time commitment. Following the Helsinki
declaration, all participants were informed about the project both orally and in writing
and signed an informed consent document. The project was approved by the Norwegian
Center for Research Data (Reference: 807592).
2.3. Tracking Measures
Participants were equipped with an 18 Hz GPS device with 952 Hz tri-axel accelerom-
eter, gyroscope, and magnetometer (GPS
1
, Apex, StatSport, Newry, Northern Ireland,
UK), a 10 Hz GPS device with 1 kHz tri-axel accelerometer, gyroscope and magnetometer
Int. J. Environ. Res. Public Health 2022,19, 4770 4 of 11
(GPS
2
, Vector, Catapult innovations, Melbourne, Australia), and a 1000 Hz inertial sen-
sor device, with accelerometer, gyroscope and multi-chip motion tracking module (IMU,
Playermaker
TM
, Tel Aviv, Israel). All devices were mounted and used according to manu-
facturers’ guidelines. The GPS devices were securely positioned in a custom-made vest
2–3 cm apart, between the participants’ scapulae. Both GPS devices were placed outside in
record mode at least 20 min prior to testing, to ensure adequate satellite connection. The
inertial sensor was mounted on the participants boots, using the manufacturers boot strap
designed for this purpose. After completion, data were uploaded to the device-specific
software and analyzed to calculate EEE for the whole period, before being exported to
Microsoft Excel. After reaching out to the manufacturers of GPS
1
and GPS
2
, both confirmed
that the calculation of metabolic power builds on previous work by Osgnach et al. [
16
],
utilizing acceleration and velocity data for the calculation. For the inertial measuring device
(IMU), EEE calculations were done by the manufacturer as the software lacked this feature.
The technical properties of this device is explained elsewhere [
28
] and it applies the same
metabolic power method [
16
]. Specifically, speed and acceleration were calculated in 10 Hz,
together with the formula and constants provided in the algorithm by [
16
]. All tracking
data were also edited post hoc, by synchronizing the start and cessation of the protocol
with the oxygen consumption (VO
2
)-derived data, ensuring the same measurement time
for the various devices.
2.4. Indirect Calorimetry
Indirect calorimetry (VO
2
Master Health Sensors Inc., Vernon, BC, Canada) was used
to establish VO
2
-derived EEE and served as criterion measure against the tracking systems.
The VO
2
master have previously been validated [
29
] resulting in a difference ranging from
0.17–0.27 VO
2
(L/min) during different intensities, compared to the Parvomedics trueOne
2400 metabolic cart (Parvomedics, Inc., Salt Lake City, UT, USA). Participants wore the
VO
2
master for the entire protocol, including rest periods, as well as 120 s following the
last round to account for EPOC, following the intermittent exercise protocol. To establish
resting energy expenditure, participants wore the VO
2
master for 10 min, after arriving
at the facility, laying down in supine position. The mean VO
2
(L/min)-derived EE value
from the last 5 minutes was subtracted from the total EE during the test post hoc for each
individual, consistent with previous literature [
20
,
30
]. The VO
2
master was calibrated
according to the manufacturer’s guidelines prior to each testing session. After completion,
breath by breath analysis of VO
2
(L/min) was analyzed in 30 s intervals, before being
converted to kJ to establish VO
2
derived EEE using a respiratory exchange ratio (RER) of
1.00, indicating mainly glycolytic energy production [
31
]. This was done as the usage of
RER assumes constant oxygen content and that CO
2
exchange in the lungs reflects that
of the cells [
32
]. As this is not the case during intermittent exercise, lactate measurements
served as confirmation of the appropriate RER level chosen.
2.5. Lactate Measurement
Blood lactate (mmol/L) was measured using the lactate plus (Lactate Plus, Nova
Biomedical, Waltham, MA, USA), which have previously been validated [
33
]. Samples
were taken at rest from the index finger, following resting energy expenditure measurement
and after each round of the protocol, indicating the level of intensity for the completed
work. Thus, blood lactate measures were used to verify the RER used to calculate EEE, as
blood lactate is associated with substrate metabolism during exercise [34].
2.6. Statistical Analyses
The statistical analyses and preparation were conducted using SPSS 26 (IBM, Armonk,
NY, USA) and Microsoft Excel (Microsoft corporation, Redmond, WA, USA). Descriptive
statistics from the participants are given for total running distance, inter-device distance,
percentage difference, as well as the lactate levels for the separate circuit rounds. All
devices were directly compared to the criterion measure (VO
2
derived EEE) and intraclass
Int. J. Environ. Res. Public Health 2022,19, 4770 5 of 11
correlation coefficients (ICC) were estimated to determine the level of agreement between
the individual tracking devices and the criterion measure. We estimated two-way mixed
ICC models with the subjects and method factors as random and fixed, respectively [
35
].
ICC estimates are presented based on the single measure formula since a single device
score represents the EEE score. ICC estimates for both relative (consistency) and absolute
agreement are given along with their 95% confidence intervals.
Paired sample t-tests were used to examine for mean differences between the mea-
surement methods, thus indicating the general level of bias. Effect sizes (ES) for these
differences were calculated by dividing on the standard deviation of the difference scores
corrected for their correlation. We report Hedge’s g, which additionally corrects bias
related to smaller samples, thus reducing overestimation according to the formula [
36
]:
g=M1M2
(s2
1+s2
22rs1s2)/2(1r)×J
(with Jas the Hedge’s gcorrection factor according to [
37
].
The a priori alpha level was set to p< 0.05. We added linear regression analyses to examine
the level and direction of systematic biases between the methods. The tracking device in
question was used as a predictor with VO
2
as the outcome. Unstandardized residuals,
which represent the difference between the predicted and the actual VO
2
energy consump-
tion score was saved and plotted on the y-axis against VO
2
data on the x-axis. Systematic
biases would be present if the residual scores showed a non-flat increasing or decreasing
pattern depending on the actual VO
2
levels. Lastly, we examined if adjusting the EEE
calorimetry scores by subtracting the resting time or the EPOC scores could reduce bias
and yield better agreement according to new paired sample t-tests, smaller residual scores
and improved ICC estimates. All results are presented as mean ±SD, unless specified.
3. Results
The total distance and mean energy expenditure measured for the tracking devices
GPS
1
, GPS
2
, and IMU are presented in Table 1. Compared to the manually measured track,
results for distance displayed a percentage difference of 4.4%, 3.7%, and 0.7%, respectively.
Mean lactate measurement was significantly elevated compared to baseline resting values
(1.3 ±0.4 mmol/L) during round 1–5 (Figure 2).
Table 1.
Upper panel displays descriptive data for the criterion measure EEE
VO2
, distance measured
by devices, and total EEE for the individual tracking devices. Middle panel display ICC measures for
absolute and consistency measures, percentage error (EEE), as well as paired sample t-test between
specific devices and criterion measure for total energy expenditure. Lower panel of the table display
the values adjusted, by removing EPOC measurement from the criterion measure value (VO
2
),
only analyzing moving time. Exercise energy expenditure = EEE; kilojoule = kJ; effect size = ES;
post-exercise energy consumption = EPOC.
GPS1GPS2IMU
N 13 11 11
VO2EEE (kJ) 1038 ±183 1043 ±198 1016 ±191
Distance (% error) 2625 ±25 (4.4%) 2644 ±73 (3.7%) 2767 ±207 (0.7%)
EEE (kJ) 933 ±83 843 ±73 879 ±82
ICCABS 0.39 0.24 0.30
ICCCON 0.48 0.44 0.42
Percentage error 10.7% 20.6% 14.5%
pvalue 0.022 0.002 0.017
ES 0.60 0.96 0.77
Values adjusted for
EPOC
VO2-EPOC (kJ) 868 ±156 875 ±168 847 ±161
ICCABS 0.49 0.54 0.49
ICCCON 0.54 0.53 0.48
Percentage error 7.2% 3.1% 3.7%
pvalue >0.05 >0.05 >0.05
ES 0.44 0.15 0.21
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Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 6 of 12
Table 1. Upper panel displays descriptive data for the criterion measure EEE
VO2
, distance measured
by devices, and total EEE for the individual tracking devices. Middle panel display ICC measures
for absolute and consistency measures, percentage error (EEE), as well as paired sample t-test be-
tween specific devices and criterion measure for total energy expenditure. Lower panel of the table
display the values adjusted, by removing EPOC measurement from the criterion measure value
(VO
2
), only analyzing moving time. Exercise energy expenditure = EEE; kilojoule = kJ; effect size =
ES; post-exercise energy consumption = EPOC.
GPS
1
GPS
2
IMU
N 13 11 11
VO
2EEE
(kJ) 1038 ± 183 1043 ± 198 1016 ± 191
Distance (% error) 2625 ± 25 (4.4%) 2644 ± 73 (3.7%) 2767 ± 207 (0.7%)
EEE (kJ) 933 ± 83 843 ± 73 879 ± 82
ICC
ABS
0.39 0.24 0.30
ICC
CON
0.48 0.44 0.42
Percentage error 10.7% 20.6% 14.5%
p value 0.022 0.002 0.017
ES 0.60 0.96 0.77
Values adjusted for EPOC
VO
2-EPOC
(kJ) 868 ± 156 875 ± 168 847 ± 161
ICC
ABS
0.49 0.54 0.49
ICC
CON
0.54 0.53 0.48
Percentage error 7.2% 3.1% 3.7%
p value >0.05 >0.05 >0.05
ES 0.44 0.15 0.21
Figure 2. Time plot of the lactate measurement levels during the protocol, at baseline (rest) and the
following each completed round.
Figure 2.
Time plot of the lactate measurement levels during the protocol, at baseline (rest) and the
following each completed round.
EEE measured as indirect calorimetry (criterion measure) compared to EEE estimated
by GPS
1
, GPS
2
, and IMU (EEE
IMU
) is presented in Table 1. Compared to the indirect
calorimetry, all tracking devices significantly underestimated the caloric expenditure during
intermittent bouts of exercise (GPS
1
,p= 0.022, ES = 0.60, GPS
2
,p= 0.002, ES = 0.96 and IMU,
p= 0.017, ES = 0.77). When adjusting EEE by subtracting EPOC (EEE measured during
standstill resting periods) from the measurement, no differences were found (EEEGPS
1
p> 0.05, ES = 0.44, EEEGPS2 p> 0.05, ES = 0.15 and EEEIMU p> 0.05, ES = 0.21) (Table 1).
The ICC values for total EEE ranged between 0.48 and 0.21 based on consistency
estimation, and between 0.39 and 0.24 based on absolute agreement estimation, respectively.
Adjusting calculations by excluding EPOC measurements, only analyzing moving time,
ICC values ranged between 0.54 and 0.48 and between 0.54 and 0.49 based on consistency
and absolute agreement estimation, respectively (See Table 1for specific values).
Series of regression analyses with VO
2
as outcome and the specific tracking device
as predictors are given in Table 2. An unstandardized beta coefficient above or below
1 indicates under- versus overestimation, respectively. Using mean centered values as
predictors, the unstandardized coefficients for EEE
GPS1
were 1.42. The GPS
1
device thus un-
derestimated true calorimetry usage with 0.42 kJ per unit increase in the VO
2
measurement.
For EEE
GPS2
, the unstandardized coefficient was 1.82; hence, underestimating estimated
caloric expenditure with a mean of 0.82 kJ, compared to the criterion measure. Lastly for
EEEIMU, unstandardized coefficient was 1.28, yielding a mean underestimation of 0.28 kJ.
Int. J. Environ. Res. Public Health 2022,19, 4770 7 of 11
Table 2.
Display regression coefficients representing the mean change for specific devices as predicted
by the regression model, compared against the criterion measure VO
2
. The first part of the table
displays the values for total EEE, with the second part showing values adjusted by removing EPOC
measurements, only analyzing moving time.
GPS1GPS2IMU
N 13 11 11
Intercept 1038.5 1043.1 1016.2
Beta 1.42 1.82 1.28
t 2.75 2.75 2.1
Absolute residual error
(kJ) 112.3 ±78.7 109.1 ±89.4 121.6 ±92.3
pvalue 0.019 0.022 0.077
95% CI 0.3–2.5 0.3–3.3 0–2.6
Values adjusted for EPOC are presented below
Intercept 867.8 875.4 846.8
Beta 1.23 1.66 1.11
T 2.86 3.16 2.1
Absolute residual error
(kJ) 97.3 ±59.9 89.1 ±67.2 103.6 ±72.1
pvalue 0.015 0.011 0.69
95% CI 0.3–2.2 0.5–2.8 0–2.2
Adjusting the caloric estimation by removing EPOC from the estimated EEE
VO2
score, produced lower unstandardized coefficients, hence yielding lower mean differences
between estimated and true caloric expenditure values (0.23 kJ for GPS
1
, 0.66 kJ for GPS
2
and 0.11 kJ for IMU respectively (Table 2). The regression analysis also displays increased
disagreement between predicted EEE values for tracking devices, compared to the observed
EEE, as caloric expenditure increases. Thus, increased bias is expected when the caloric
expenditure increases during intermittent activity (Figure 3).
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 8 of 12
Figure 3. Residual plot indicating the disagreement between the predicted EEE (residuals) and
measured EEEVO2 in the upper panel and EEEVO2-EPOC in the bottom panel (values displayed in kJ).
Negative values indicate overestimation and positive values indicate underestimation. Exercise en-
ergy expenditure = EEE; kilojoules = kJ; oxygen consumption = VO2; excess post-exercise energy
consumption = EPOC.
4. Discussion
This is the first study to quantify the accuracy of the latest tracking devices in female
athletes. Our results show that all tracking devices underestimated the EEE and, thus,
failing to adequately estimate caloric expenditure, yet GPS1 provided the most accurate
results. The level of underestimation shrunk for all devices when the criterion measure
(indirect calorimetry) was adjusted by subtracting EPOC (the standstill rest periods) from
the estimation of energy expenditure. Furthermore, the tracking devices displayed a sys-
tematic pattern of bias by overestimating EEE at lower levels of caloric expenditure and
underestimating EEE at higher levels of caloric expenditure.
These findings align with several previous studies conducted among male athletes,
reporting that GPS and accelerometer-based tracking devices fail to accurately estimate
total EEE in intermittent team sports [12,18,20]. Albeit with older technology, Brown et al.
[16] reported that GPS units displayed reasonable accuracy during steady-state jogging
and running [20]. However, substantial underestimation was observed during intermit-
tent movements and high-intensity actions. Stevens et al. [16] reported that metabolic
power [16] overestimated EEE during continuous, steady-state running. Conversely, these
authors also found that metabolic power underestimated EEE during aerobic shuttle run-
ning. As such, it is possible that the metabolic power algorithm systematically overesti-
mate EEE during walking and jogging and underestimate during high-intensity circuit
movements, resulting in total underestimation of EEE during high-intensity intermittent
exercise. Recently, Savoia et al. [16] proposed an alternative metabolic power algorithm
based on the original work [22]. Here the authors claim that previous studies demonstrat-
ing underestimation of EEE using metabolic power, utilize prolonged periods of rest, not
reflecting the actual demands of the game. This may be of importance in the interpretation
of the present, and the previous studies, as metabolic power primarily relies on locomo-
tion when estimating EEE. Nevertheless, our results demonstrate that three of the latest
Figure 3.
Residual plot indicating the disagreement between the predicted EEE (residuals) and
measured EEE
VO2
in the upper panel and EEE
VO2
-EPOC in the bottom panel (values displayed in
kJ). Negative values indicate overestimation and positive values indicate underestimation. Exercise
energy expenditure = EEE; kilojoules = kJ; oxygen consumption = VO
2
; excess post-exercise energy
consumption = EPOC.
Int. J. Environ. Res. Public Health 2022,19, 4770 8 of 11
4. Discussion
This is the first study to quantify the accuracy of the latest tracking devices in female
athletes. Our results show that all tracking devices underestimated the EEE and, thus,
failing to adequately estimate caloric expenditure, yet GPS
1
provided the most accurate
results. The level of underestimation shrunk for all devices when the criterion measure
(indirect calorimetry) was adjusted by subtracting EPOC (the standstill rest periods) from
the estimation of energy expenditure. Furthermore, the tracking devices displayed a
systematic pattern of bias by overestimating EEE at lower levels of caloric expenditure and
underestimating EEE at higher levels of caloric expenditure.
These findings align with several previous studies conducted among male athletes,
reporting that GPS and accelerometer-based tracking devices fail to accurately estimate total
EEE in intermittent team sports [
12
,
18
,
20
]. Albeit with older technology, Brown et al. [
16
]
reported that GPS units displayed reasonable accuracy during steady-state jogging and
running [
20
]. However, substantial underestimation was observed during intermittent
movements and high-intensity actions. Stevens et al. [
16
] reported that metabolic power [
16
]
overestimated EEE during continuous, steady-state running. Conversely, these authors also
found that metabolic power underestimated EEE during aerobic shuttle running. As such,
it is possible that the metabolic power algorithm systematically overestimate EEE during
walking and jogging and underestimate during high-intensity circuit movements, resulting
in total underestimation of EEE during high-intensity intermittent exercise. Recently,
Savoia et al. [
16
] proposed an alternative metabolic power algorithm based on the original
work [
22
]. Here the authors claim that previous studies demonstrating underestimation
of EEE using metabolic power, utilize prolonged periods of rest, not reflecting the actual
demands of the game. This may be of importance in the interpretation of the present, and
the previous studies, as metabolic power primarily relies on locomotion when estimating
EEE. Nevertheless, our results demonstrate that three of the latest and most-used tracking
devices in modern soccer all underestimate EEE with its current technology.
In the present study, all three devices displayed total distances within 4.4% of the
manually measured track. However, the metabolic power equation bases its calculations
on velocity and accelerations; thus, the inability of tracking devices to accurately estimate
this will influence the total estimated EEE. Previous research has found interindividual
differences between devices utilizing metabolic power, although displacement measures
were relatively similar, indicating disparities in the filtering of the GPS data [
15
]. Nonethe-
less, acceptable validity and reliability for GPS devices of 10 and 18 Hz, as used in this
study, have been reported [
38
]. Further, the inertial sensor used have been compared
against high sampling GPS units [
28
]. Although SD varied between the specific devices,
this alone is unlikely to explain the discrepancy in EEE. Hence, the underestimation seen
during intermittent exercise might be highly influenced by the algorithm applied by the
devices, rather than inaccurate sampling rates or measurements of velocity/acceleration.
This assumption is strengthened when investigating the results with and without EPOC
measures. Several assumptions are made in the modeling of metabolic power, especially
during high intensity running, including running efficiency [
17
,
39
]. Further, surface may
also play an influential role on the energetic cost of running [
40
], together with individual
running economy. As the metabolic power model is based on well-trained male endurance
athletes and this study was done in female soccer players on an artificial surface, these
factors may well be partially responsible for some of the observed discrepancies. Future
studies could therefore consider tailoring the metabolic power equation for females. Fur-
ther, practitioners may consider applying individual data for running cost to the equation
responsible for the EEE output. This would require substantial testing of each athlete, as
well as post session/match editing of the GPS derived data to calculate the EEE using
the athlete specific data as constants in the metabolic power equation. Since individuals
clearly differ in response to estimates of metabolic power based on average data, this could
be of interest in athletes where accurate measurement of energy availability is of special
importance for health and performance outcomes.
Int. J. Environ. Res. Public Health 2022,19, 4770 9 of 11
Our results show an inverse relationship as EEE
VO2
increases. In addition, individual
lactate measurements increase together with EEE
VO2
. As such, it appears that the metabolic
power estimates are somewhat correlated with exercise intensity. This is confirmed when
adjusting the analysis by subtracting EPOC measurements, resulting in non-statistically
significant differences between all devices and EEE
VO2
. These results indicates that tracking
devices are unable to sufficiently account for anerobic energy metabolism, manifested by
elevated VO
2
levels during rest (e.g., replenishing substrate stores, repaying O
2
debt from
the previous high-intensity action) [13], similar to previous research [18].
Several studies investigating LEA in female athletes have used devices relying on
metabolic estimates for EEE, to calculate EA [
9
,
10
,
41
,
42
]. Nonetheless, based on the findings
of our study, caution should be taken when interpreting results from studies utilizing
algorithmical estimates to quantify EEE. Undoubtedly, the main challenge when identifying
LEA in athletes is the definition of EA itself, as it relies heavily on measures of EI and EEE,
both fragile for significant error [
11
]. As such, more objective physiological markers have
been proposed going forward within the field of LEA [
43
]. These include the usage of
hormonal data; however, more research is needed regarding the sensitivity and specificity
of distinctive markers.
The sample size may raise concerns with response to statistical power. This could have
been increased with repeated measures, which was not possible due to the heavy schedule
of the players. Nevertheless, anthropometric characteristics are similar to those reported in
international and high domestic leagues elsewhere [
41
]. As such, we will argue that the
present results are applicable to other elite female soccer players. Moreover, power is not
critical as the main aim of the study was not to test specific hypotheses, but to test the level
of accuracy against indirect calorimetry for each tracking device. Conversely, power is a
concern in the sense that increased power would have allowed us to test accuracy across
the different tracking devices. We chose extended periods of rest between rounds and post
activity, to account for EPOC. During actual gameplay, prolonged periods of total rest are
sparser and, thus, may have contributed to the deviation between tracking devices and
VO
2
measurements. We also acknowledge that 120 s of EPOC measurement post-test is
not sufficient to return to baseline levels, hence, expanding the time would likely increase
discrepancy. However, given the slope of EPOC [
42
], 120 s will encompass the majority of
significant elevation in energy expenditure.
5. Conclusions
To our knowledge, this is the first all-female study exploring estimated EEE, as
well as comparing the latest model of three widely used tracking devices. The GPS and
accelerometer-based tracking devices tested generally underestimate the caloric expendi-
ture during intermittent exercise in professional female soccer players. This is primarily
because such devices cannot account for anaerobic energy production seen during high-
intensity exercise. Furthermore, the observed differences between manufacturers could be
of importance for practitioners and their choice of equipment. Therefore, caution should be
taken when utilizing estimated EEE in calculations of EA and nutritional calculations. This
is of special importance in training situations, where increased rest times between drills and
play is likely to produce greater underestimation of total EEE. Nevertheless, as calculations
based on EEE is the only method for assessing players EA at this point, the usage of devices
applying metabolic power is presumably superior to standard GPS and heart rate measures.
However, the deviations seen in caloric expenditure must be considered by practitioners
and researchers depending on the need of accuracy. Future studies should also aim to
include female players in the validation of the algorithms, as well as individualizing the
algorithm. Despite generally underestimating EEE, the devices tested can still provide
useful information in quantifying EA, taking the highlighted limitations in consideration.
Int. J. Environ. Res. Public Health 2022,19, 4770 10 of 11
Author Contributions:
Conceptualization, M.S.D., M.K., J.H.R., O.F. and G.P.; data curation, M.S.D.
and M.K.; formal analysis, M.S.D. and O.F.; methodology, M.S.D. and M.K.; supervision, J.H.R.;
writing—original draft, M.S.D.; writing—reviewing and editing, O.F., G.P., J.S.-B. and J.H.R. All
authors have read and agreed to the published version of the manuscript.
Funding:
This research was conducted by the Female Football Research Center, UiT, The Arctic
University of Norway. The research was funded by Tromso Research Foundation, Tromso, Norway.
Institutional Review Board Statement:
This study was conducted according to the guidelines of the
deceleration of Helsinki and approved by the Norwegian Center for Research Data (807592).
Informed Consent Statement:
Written and verbal informed consent was obtained by all participants
involved in the study.
Data Availability Statement:
Data presented are available on request from the corresponding author.
Acknowledgments:
The authors would like to thank the participants for their time, patience, and effort.
Conflicts of Interest: The authors declare no conflict of interest.
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Decades of laboratory research have shown impairments to several body systems after only 4-5 days of strictly controlled consistent low energy availability (LEA); where energy availability (EA) = Energy Intake (EI) – Exercise Energy Expenditure (EEE) / Fat-Free Mass. Meanwhile, cross-sectional reports exist on the interrelatedness of LEA, menstrual dysfunction and impaired bone health in females (the Female Athlete Triad). These findings have demonstrated that LEA is the key underpinning factor behind a broader set of health and performance outcomes, recently termed as Relative Energy Deficiency in Sport (RED-S). There is utmost importance of early screening and diagnosis of RED-S to avoid the development of severe negative health and performance outcomes. However, a significant gap exists between short-term laboratory studies and cross-sectional reports, or clinically field-based situations, of long-term/chronic LEA and no definitive, validated diagnostic tests for RED-S exist. This review aims to highlight methodological challenges related to the assessment of the components of EA equation in the field (e.g. challenges with EI and EEE measures). Due to the uncertainty of these parameters, we propose the use of more chronic “objective” markers of LEA (i.e. blood markers). However, we note that direct extrapolations of laboratory-based outcomes into the field are likely to be problematic due to potentially poor ecological validity and the extreme variability in most athlete’s daily EI and EEE. Therefore, we provide a critical appraisal of the scientific literature, highlighting research gaps, and a potential set of leading objective RED-S markers while working in the field.
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This study assessed validity and reliability of the VO2 Master Pro portable metabolic analyzer for assessment of oxygen consumption (VO2) and minute ventilation (VE). In Protocol 1, eight male participants (height: 182.6 ± 5.8 cm, weight: 79.6 ± 8.3 kg, age: 41.0 ± 12.3 years) with previous competitive cycling experience completed an hour-long stationary cycling protocol twice, progressing from 100-300 Watts every 10 minutes while wearing the VO2 Master and a criterion measure (Parvomedics) for five minutes each, at each stage. In Protocol 2, 16 recreationally active male participants (height: 168.2 ± 8.4 cm, weight: 76.5 ± 13.3 kg, age: 23.0 ± 9.4 years) completed three incremental, maximal stationary cycling tests wearing one of three analyzers for each test (VO2 Master version 1.1.1, VO2 Master version 1.2.1, Parvomedics). For Protocol 1 and convergent validity, the VO2 Master had mean absolute differences from the Parvomedics of <0.3 L/min for absolute VO2 and <5 L/min for VE overall and at each exercise stage. Mean absolute percent differences (MAPD) for VO2 and VE were <9% overall and <12% at each stage. Test-retest reliability of the VO2 Master (MAPD: 8.9-10.9%) was somewhat poorer than the Parvomedics (MAPD: 5.3-7.6%). For Protocol 2, validity was similar for both VO2 Master models (MAPD ~12% overall) compared to the Parvomedics for VO2 and VE. The VO2 Master had an acceptable validity and test-retest reliability for most intensities tested and may be an appealing option for field-based VO2 and VE analysis.
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This study aimed to assess energy availability (EA), alongside possible risk factors of reduced or low EA of professional female soccer players during a competitive season. Thirteen players (age: 23.7 ± 3.4 y, stature: 1.69 ± 0.08 m, body mass: 63.7 ± 7.0 kg) engaged in a 5-day (two rest days, one light training, heavy training and match day) monitoring period. Energy intake (EI) and expenditure during exercise (EEE) were measured. EA was calculated and categorised as optimal, reduced or low (>45, 30-45, <30 kcal·kg FFM-1·day-1, respectively). Relationships between EA and bone mineral density, resting metabolic rate (RMR), plasma micronutrient status, biochemical markers and survey data were assessed. EA was optimal for 15%, reduced for 62% and low for 23% of players. Higher EA was observed on rest days compared to others (P<0.05). EA was higher for the light compared to the heavy training day (P<0.001). EEE differed significantly between days (P<0.05). EI (2124 ± 444 kcal), carbohydrate (3.31 ± 0.64 g·kg·day-1) and protein (1.83 ± 0.41 g·kg·day-1) intake remained similar (P>0.05). Survey data revealed 23% scored ≥ 8 on the Low Energy Availability in Females Questionnaire and met criteria for low RMR (ratio <0.90). Relationships between EA and risk factors were inconclusive. Most players displayed reduced EA and did not alter EI or carbohydrate intake according to training or match demands. Although cases of low EA were identified, further work is needed to investigate possible long-term effects and risk factors of low and reduced EA separately to inform player recommendations.