The Quantification of Acceleration Events in Elite Team Sport: a Systematic Review
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Delves et al. Sports Medicine - Open (2021) 7:45
https://doi.org/10.1186/s40798-021-00332-8
SYSTEMATIC REVIEW Open Access
The Quantification of Acceleration Events in
Elite Team Sport: a Systematic Review
Robert I. M. Delves1, Robert J. Aughey1* , Kevin Ball1 and Grant M. Duthie2
Abstract
Background: Wearable tracking devices are commonly utilised to quantify the external acceleration load of team
sport athletes during training and competition. The ability to accelerate is an important attribute for athletes in
many team sports. However, there are many different acceleration metrics that exist in team sport research. This
review aimed to provide researchers and practitioners with a clear reporting framework on acceleration variables by
outlining the different metrics and calculation processes that have been adopted to quantify acceleration loads in
team sport research.
Methods: A systematic review of three electronic databases (CINAHL, MEDLINE, SPORTDiscus), was performed to
identify peer-reviewed studies that published external acceleration load in elite team sports during training and/or
competition. Articles published between January 2010 and April 2020 were identified using Boolean search phrases
in relation to team sports (population), acceleration/deceleration (comparators), and competition and/or training
(outcome). The included studies were required to present external acceleration and/or deceleration load (of any
magnitude) from able-bodied athletes (mean age ≥ 18 years) via wearable technologies.
Results: A total of 124 research articles qualified for inclusion. In total, 113/124 studies utilised GPS/GNSS
technology to outline the external acceleration load of athletes. Count-based metrics of acceleration were
predominant of all metrics in this review (72%). There was a lack of information surrounding the calculation process
of acceleration with 13% of studies specifying the filter used in the processing of athlete data, whilst 32% outlined
the minimum effort duration (MED). Markers of GPS/GNSS data quality, including horizontal dilution of precision
(HDOP) and the average number of satellites connected, were outlined in 24% and 27% of studies respectively.
Conclusions: Team sport research has predominantly quantified external acceleration load in training and
competition with count-based metrics. Despite the influence of data filtering processes and MEDs upon
acceleration, this information is largely omitted from team sport research. Future research that outlines acceleration
load should present filtering processes, MEDs, HDOP, and the number of connected satellites. For GPS/GNSS
systems, satellite planning tools should document evidence of available satellites for data collection to analyse
tracking device performance. The development of a consistent acceleration filtering method should be established
to promote consistency in the research of external athlete acceleration loads.
Keywords: Acceleration, Data filtering, Activity profile, Deceleration, Wearable technology, Athlete tracking,
Microtechnology, Athlete loads
* Correspondence: Robert.Aughey@vu.edu.au
1
Institute for Health & Sport, Victoria University, Melbourne, VIC 3011,
Australia
Full list of author information is available at the end of the article
© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
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licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.Delves et al. Sports Medicine - Open (2021) 7:45 Page 2 of 35
Key Points and Australian football represent average match speeds
that would be considered low intensity at approximately
Acceleration in team sport research has largely been 80 to 140 m min− 1 (1.3–2.3 m s− 1) [7]. However, the
quantified via the use of count-based metrics. aforementioned sports can see peak intensities up to 170
There is a lack of information surrounding the to 210 m min− 1 during a 1-min moving average epoch
processing of acceleration data in team sport and have been shown to further increase to intensities
research. Very few studies in this review quantified up to 380 m min− 1 with smaller moving average window
the filtering processes used to calculate acceleration lengths (e.g., 5 s) [6, 9–12]. The wide range in intensities
and the minimum effort duration for these events. from match averages to competition peaks indicates that
For satellite-based tracking systems, inconsistency the ability to change velocity (acceleration) is important
seen in GPS/GNSS device reporting on horizontal to performance. In invasion/combat sports such as rugby
dilution of precision and satellite number informa- league, where general play is contested in tight confines,
tion has hindered the ability to objectively evaluate acceleration load is highest compared to other football
athlete acceleration and deceleration datasets. codes, indicating the ability to rapidly change velocity is
Future research should attempt to develop a important to successful performance in this code [6, 9–
common acceleration filtering/processing method to 11]. Similarly, in American football, where players are
allow for appropriate comparison in load between also actively trying to gain or negate yardage, skill
studies and between tracking manufacturers. A players such as wide receivers, defensive backs and line-
common process would help to alleviate concerns of backers accumulate substantial counts of high accelera-
technology-driven variations in athlete acceleration tions (> 3.5 m s− 2) per game (range 26–38 counts per
data. game) [13].
Whilst being able to perform accelerations is import-
Background ant to successful athletic performance, quantifying accel-
Through the continued development of athlete wearable erations is also important to practitioners for athlete
technology, team sport practitioners have increasingly load management [8]. Accelerations incorporate a sig-
elected to monitor their athlete’s external load during nificant portion of the total overall external load during
training and competition with player-tracking devices team sport training and competition [8, 14–17]. How-
[1]. Technologies, such as the Global Positioning System ever, the magnitude of acceleration efforts can provide
(GPS) and optical-based systems, are established player- different sources of load experienced by the athlete. For
tracking methods, whilst progressions have been made example, accelerations (positive velocity) will place a
in the development of local positioning systems (LPS) greater metabolic cost on the body compared to deceler-
and access to the Global Navigation Satellite System ation events, as accelerations require greater energy to
(GNSS). Regardless of the technology implemented, the fuel the change in velocity [4, 14, 15, 18]. Deceleration
aforementioned tracking systems allow for the relatively events however differ from accelerations with respect to
unobtrusive and objective collection of a player’s locomo- the mechanically demanding, eccentric loads placed
tion during training and match-play, with information ob- upon the body when braking (particularly at higher in-
tained on athlete distances and speeds [1, 2]. Tracking tensities). Athlete braking (decelerating) is dampened by
information allows for the creation of activity profiles for soft-tissue structures which attempt to attenuate the
respective sports, which details the different load placed force of each deceleration effort [8, 14–17, 19]. In team
upon athletes and positions played within that sport [1, 3]. sport athletes, an increased count of high-intensity accel-
For performance staff, an activity profile enables specific erations is associated with neuromuscular fatigue and
prescription of athlete training programs and rehabilita- muscle damage (marked by increased creatine kinase)
tion processes that are centred towards preparing the ath- post competition [7, 8, 14, 20]. Therefore, it is important
lete for the rigours of competition load [1]. that acceleration and deceleration can be appropriately
The ability to change speed and direction through ac- quantified and monitored during training and competi-
celeration and deceleration are important attributes for tion to ensure athletes are adequately prepared for this
successful performance in many team sports [4–7]. Sub- load [7, 9].
sequently, team sport research has produced a wide var- For team sport practitioners and researchers however,
iety of metrics to assess acceleration in training and the existing research on acceleration and how acceler-
competition [7, 8]. Given the stochastic nature of team ation load in competition and training is quantified, has
sport movement, the assessment of acceleration is im- varied greatly between studies [7, 8]. Currently, there are
perative in depicting the overall loads of competition [7]. a multitude of different methods in which to quantify
For example, team sport athletes across the football accelerations in team sport research [21]. Specifically, ac-
codes of rugby league, rugby union, association (soccer) celeration in applied team sports has been quantified viaDelves et al. Sports Medicine - Open (2021) 7:45 Page 3 of 35 threshold based counts, time or distance spent in certain acceleration/deceleration-based research [1, 26, 27]. Des- thresholds (e.g., > 3.5 m s− 2 threshold for “high-intensity pite the previously stated need for greater consistency in accelerations”) or more recently, by combining all abso- the reporting of wearable device specifications and pro- lute acceleration data (regardless of intensity) and aver- cesses, there are still large inconsistencies in reporting of aging over a defined time period [1, 7, 20–23]. acceleration in team sport research. Regardless of the metric chosen to quantify acceler- With the ongoing development of athlete-tracking sys- ation, the measurement of acceleration is subject to the tems as a measure of external athlete output and the ap- device quality and filtering settings of the tracking sys- proval to implement these devices during competition, tem. In GPS technology, there have been continual im- there is an increasing prevalence of the technology in provements in device capabilities, with 10-Hz devices team sport research [1, 8]. Additionally, with the exten- being deemed most valid and reliable for measuring ac- sive number of studies that have outlined activity pro- celeration [3, 7, 22, 24]. Varley et al. [22] determined files of respective sports during training and that 10-Hz devices could, at worst, detect an acceleration competition, numerous systematic reviews have been had occurred, but otherwise possessed acceptable valid- published [8, 28–30]. However, there is currently no sys- ity for accelerations at various starting velocities in tematic review that has outlined the different metrics straight running (CV 3.6–5.9%). However, deceleration and the calculation of the metrics used to quantify accel- at a starting velocity between 5 and 8 m s− 1 had greater erations in team sport research. The systematic review variability (CV 11.3%) which was attributed to the rapid from Harper et al. [8] outlined and compared high and change in speed during deceleration compared to accel- very high-intensity accelerations in competitive team eration [7, 22, 24]. sports but this study was dependent upon cut-off thresh- To analyse the quality of positional data in GPS/GNSS olds, which limited the overall scope of the study. The devices, the horizontal dilution of precision (HDOP) and introduction of metrics such as absolute acceleration the average number of connected satellites are extracted prompted this review to include all acceleration events/ [1, 25]. For GPS/GNSS devices, HDOP and the number metrics regardless of the magnitude, as ultimately all ac- of satellites provide an indication of the quality of device celeration and deceleration events carry a physiological connection and signal strength [2, 25]. However, despite cost [7]. With the inevitable further developments in the importance of HDOP and the number of satellite in- player-tracking technologies (e.g., optical systems) and formation, the reporting of these metrics has been in- the importance of accelerations in team sport activity consistent in team sport research [1]. With the profiles, it is pertinent to review and appraise the metrics development of online GNSS planning tools providing that have been used to quantify acceleration/deceler- evidence of the number of available satellites for a given ation. Therefore, the primary aim of this systematic re- period, researchers and practitioners should endeavour view is to outline and compare the different methods to compare the satellite tracking information from their that have been adopted to quantify acceleration and de- devices to website-based tools outlining satellite avail- celeration events in team sport research. A secondary ability. Extracting satellite quality information can then aim was to identify the processing methods used by re- aid in assessing the overall data quality of metrics sur- searchers in calculating acceleration/deceleration by way rounding acceleration events. Given the importance of of data filtering methods and minimum effort durations. device signal quality on athlete positioning data, the HDOP and the number of connected satellites are sig- Methods nificant variables that need to be reported upon in Study Design athlete-tracking research. In practice, the publishing of The current systematic review was undertaken in ac- HDOP and satellite data then aids practitioners to deter- cordance with the Preferred items for Systematic Re- mine what data they should include and exclude in their views and Meta-Analyses (PRISMA) statement on the athlete load monitoring systems, including acceleration transparent reporting of systematic reviews [31]. metrics. For example, HDOP values greater than one or satellite numbers less than 10 may be grounds for data Search Strategy exclusion in daily monitoring processes. Three electronic databases (CINAHL, Medline, and The processing or calculation of an acceleration event SPORTDiscus) were systematically reviewed in May may also influence the measurement of athlete acceler- 2020 by the lead author to identify articles that investi- ation [1]. It is believed that despite the similarities in de- gated the quantification of acceleration and/or deceler- vice hardware between manufacturers, the filtering and ation as a metric in the load monitoring of team sport minimum effort durations in the calculation of acceler- athletes in either training or competitive environments. ation/deceleration largely differ between devices, poten- Peer-reviewed research articles published in the English tially creating technology-driven differences in language between January 1, 2010, and April 2020 were
Delves et al. Sports Medicine - Open (2021) 7:45 Page 4 of 35
reviewed for selection into the study. The search terms identified in any other way other than the initial search
devised for this review were constructed using the PICO it would be subject to the same review process to deter-
framework, where population (team sport/team sport mine qualification.
athletes), interest (quantification of Acceleration/Decel-
eration metrics) and context (in competition or training) Data Extraction
were accounted for. Search terms and exclusion criteria All relevant search data were extracted into a custom-
(Table 1) relating to team sport athletes and the quanti- made Microsoft Excel spreadsheet by the lead author.
fication of acceleration and deceleration in competition The extracted data from each eligible study included
or training were then identified (Table 2). Boolean oper- athlete population (sport, competition, age, height,
ators “OR” and “AND” were used in the final search to weight), athlete-tracking system used (e.g., GPS, LPS or
combine all search terms together (Table 2). camera-based) and the associated properties (e.g., unit
sample rate, HDOP, number of satellites), acceleration
Screening Strategy and Study Selection metrics measured (e.g., counts, distance, or average ac-
Upon execution of the search, all returned studies were celeration), the filtering/processing method used to
collated and exported into a reference manager (End- quantify the acceleration and any relevant acceleration
Note X9, Thomson Reuters, Philadelphia, PA, USA) for findings. All acceleration events, regardless of the magni-
further review. The initial review process incorporated tude were included into the analysis. There were no ex-
three stages to identify qualifying articles. Firstly, all du- clusion criteria based on the velocity threshold of the
plicate articles were identified and removed from the ref- acceleration event. Similarly, all organised team activities
erence manager. Secondly, studies were scanned via (training and competition) were eligible for inclusion
their abstracts and keywords to establish relevance. If into the study. Studies that only presented information
studies were deemed to be irrelevant at this juncture, on athlete-tracking device reliability or validity in an ex-
they were excluded. If doubt remained after inspection perimental setting were excluded from analysis. Add-
of the abstract as to the relevance of the study, it would itionally, given the recent guidance on the reporting of
advance to the next stage for further scrutiny. The final GPS/GNSS device properties in research and similar sys-
stage consisted of reviewing the full-text documents of tematic review publications, all available GPS/GNSS de-
each study and excluding articles that were subject to vice information was extracted from each relevant study
the exclusion criteria (Table 1). If doubt remained as to [1, 8]. Specifically, the characteristics observed included
the eligibility of respective studies following this process, HDOP, number of satellites connected during activity,
the authors resolved the process through deliberation. If device sample rate, device model and device
an article was identified through this process or manufacturer.
Table 1 Search inclusion and exclusion criteria
Study inclusion/exclusion criteria
Inclusion criteria Exclusion criteria
Original research articles Systematic Reviews, Reviews, letters to the editors, non-peer reviewed
articles, editorial, books, periodicals, surveys, opinion pieces, conference
abstracts
Team-based sports Outdoor court games (tennis, volleyball) water-based, ice-based and
sand-based sports.
Participants with a mean age ≥ 18 years Research with the mean age of athletes below the age of ≤ 18 years.
Elite-level, able-bodied, participants playing at the elite domestic competi- Sub-elite-level, amateur and novice athletes or athletes not playing
tion for their respective team sport or international representation above within the top tier of their respective domestic league/competitions.
U/18 competition Athletes with a physical or mental disability. Referees & Officials
Official team activities: including competition/game/match observations Validation or reliability studies on wearable technologies using athletes
and training sessions (e.g., small sided games, match simulations, in an experimental setting
individual training drills)
GPS/GNSS-based trackers (sampling ≥ 5 Hz) Accelerometers
Optical/LPS-based Camera Systems
Acceleration or deceleration events measured during designated team Combined metrics (metabolic power, repeat high-intensity efforts,
activities of any magnitude and measured in any available metric (e.g., PlayerLoad)
counts, metres, time spent, average acceleration, acceleration load) that is
not combined with any separate metric (e.g., metabolic power)
Research available in English (full text) Research articles that are not published in English or cannot be
accessed in English.Delves et al. Sports Medicine - Open (2021) 7:45 Page 5 of 35
Table 2 Search terms and keywords used in each database. Searches 1, 2, 3 and 4 were combined with “AND”
Key search terms Related search terms
1. Acceleration/ accelerat* OR decelerat* OR metabolic power OR metabolic load OR energetic cost
Deceleration
2. Athlete tracking global positioning system* OR GPS OR global navigation satellite system* OR GNSS OR local positioning system* OR LPS OR
System microtechnology OR microsensor* OR tracking system* OR athlete tracking system OR notational analysis OR camera-based
tracking OR optical tracking system
3.Team sport team sport* OR team-sport* OR intermittent sport OR professional team sport OR elite sport OR elite team sport OR austra-
lian rules football OR australian rules OR australian football OR australian football league OR AFL OR australian football team
OR australian rules football team OR australian football club OR australian rules football club OR soccer OR soccer player OR
soccer team OR football OR footballer OR football player OR football team OR field hockey OR field hockey athlete OR field
hockey player OR rugby league OR rugby OR rugby league player OR rugby league team OR rugby football OR rugby league
competition OR rugby union OR rugby union player OR rugby union competition OR rugby union club OR rugby sevens OR
rugby sevens competition OR lacrosse OR lacrosse competition OR american football OR american football player OR na-
tional collegiate athletic association OR NCAA OR gaelic football OR gaelic football player OR hurling OR hurling player OR
cricket OR netball OR basketball
4.Training/competition movement demands OR movement pattern OR external load OR external demands OR physical workload OR physical
demand* OR activity demand* OR activity profile OR activit* profile* OR match profile OR match demand* OR match play
OR match-play OR match intensit* OR game load* OR game intensit* OR competit* demand* OR training OR training de-
mands OR practice OR small sided games OR match simulation OR game simulation
Results Team Sport Characteristics
Search Results The team sport characteristics of each of the 124 studies
The combined search of three databases returned 706 are featured in Table 4. Of the 124 articles, research
studies (SPORTSDiscus = 263, Medline = 272, CINAHL from Soccer provided the greatest contribution of stud-
= 171) for analysis. All 706 studies were exported into a ies to the review (33.9%), followed by Rugby League
reference manager where 357 articles were removed as (14.2%), Australian Football (11.8%) and Field Hockey
being duplicates. This resulted in the screening of 349 ti- (11.0%). Athlete sex was mixed in each sport contribu-
tles and abstracts. Of these titles and abstracts, 167 arti- tion, with the exception of Australian and American
cles were deemed well outside the scope of the review Football, Basketball, Hurling, Rugby League, Rugby
and were subsequently removed. In total, 182 full-text League Nines and Ultimate Frisbee.
articles were reviewed and assessed relative to the pa-
rameters of the inclusive criteria. Upon review of all full-
Tracking Device Characteristics
text articles, 62 were excluded based on athlete skill level
The wearable technology type, as well as respective man-
(n = 27), athlete age (n = 14), GPS device sample rate (n
ufacturers and devices, are outlined in Table 5. Global
= 12), inappropriate study type (n = 3) and other exclu-
Positioning System/GNSS-based studies were assessed
sions (including accelerometer derived acceleration and
on two data quality metrics. HDOP (mean ± SD) and
the use of combined metrics such as metabolic power)
the number of satellites (mean ± SD) in connection with
(n = 6). A total of 120 studies remained at the comple-
the GPS device during athlete tracking were observed in
tion of this process. Additionally, four studies were iden-
this review. Of the 113 eligible GPS/GNSS studies,
tified and included outside of the database search via the
23.9% (27/113 studies) of the included articles specified
review process for this research. Therefore, 124 studies
the mean HDOP for their research. For the number of
were included. Figure 1 identifies the classification of
satellite connections during the tracking period, 27.4%
studies and pathway of eligibility into the study.
(31/113) of studies specified the mean ± SD value. This
information is presented in Table 6.
Study Characteristics
The accepted studies in this review outlined acceleration Acceleration Processing Characteristics
load during an organised, elite team sport activity. This The processing methods studies implemented to calcu-
was measured through various player-tracking technolo- late acceleration events are outlined in Table 7. The vel-
gies, including GPS/GNSS, local positioning systems or ocity/acceleration filters that were implemented to
optical-based tracking systems. The results of this review process athlete movement data was specified by 12.9%
are focused on how acceleration was quantified in these (16/124 studies) of the studies included in this review.
studies and the metrics used to present the external ac- The minimum effort duration for the calculation of ac-
celeration load. The characteristics of each of the in- celeration metrics were specified in 32.3% (40/124 stud-
cluded studies are summarised in Table 3. ies) of the included studies. The specified minimumDelves et al. Sports Medicine - Open (2021) 7:45 Page 6 of 35
Fig. 1 Systematic review inclusion process for qualification into the review
effort duration of 0.5 s was most frequent in the in- to quantify acceleration events in previous team sport
cluded studies, followed by 0.4 s, 1 s and 0.2 s. research. The main finding in this review was that the
vast majority of included studies elected to quantify ac-
Acceleration Metrics celeration events using GPS/GNSS technology (113/124
Acceleration events in this review were quantified via studies) and via the use of count-based metrics (72% of
numerous different metrics. These metrics encompassed all studies featured counts). Whilst the aim to ascertain
counts, distance, time, load, intensity and ratio markers. how accelerations were quantified by way of metrics was
Of these metrics, count-based variables were predomin- achieved, this review could not achieve the secondary
ant. Acceleration counts were selected in 72% of the aim which was to determine how acceleration events
studies in this review. In total, 63% of studies included were commonly processed in team sport research. Spe-
absolute acceleration counts (regardless of magnitude), cifically, there was a lack of information provided by the
whilst 32% of studies implemented acceleration counts studies in this review that outlined the filtering processes
relative to the athlete or team’s time during the activity of acceleration events and the minimum effort duration
(counts per minute). Distance (m) was next highest in in which these events were designated. In this review,
terms of prevalence with 13.7% of the research in this only 13% of studies specified the filtering settings of
review opting to quantify acceleration events with re- their acceleration data whilst 32% outlined the minimum
spect to the distance attained in threshold bands. Met- effort duration. Moreover, for GPS/GNSS research, the
rics of acceleration intensity followed, with a combined reporting of HDOP and the number of satellites was
10.9% of studies (acceleration (m s− 2) 6.7%, deceleration only specified in approximately a quarter of all eligible
(m s− 2) 4.2%) opting to quantify acceleration with re- studies. Given the known influence of data quality met-
spect to the acceleration distance relative to the time rics, filtering techniques and calculation intervals on ac-
period. Similarly, absolute acceleration was selected in celeration/deceleration as it’s calculated, future team
9.2% of the included studies for this review. Statistics for sport research should endeavour to outline how acceler-
the acceleration metrics included are presented in ation and deceleration events are processed.
Table 8.
Variables Chosen to Quantify Acceleration
Discussion The results of this review overwhelmingly highlight the
The aim of this systematic review was to outline and use of counts to outline the external acceleration load of
compare the different methods that have been adopted team sport athletes. Counts and, to a lesser extent,Table 3 Tracking technology and acceleration/deceleration characteristics of each included study
Study Team Device Manufacturer Model Sample HDOP No. of Acc/ Filter Calculation Threshold Acc/dec Calculation of metric
sport rate satellites dec interval/ (m s− 2) metric
(Hz) MED
Akenhead Soccer GPS Catapult MinimaxX 10 Hz 0.9 ± 0.1 12 ± 1 Acc Smoothing 0.5 s Low: 1–2 Distance Distance attained in respective
et al. [32] Sports S4 Dec Filter 0.5 s Moderate: (m) threshold band.
2–3 Acc/dec also pooled at 1 and
High: > 3 3 m s−2
Total:
>1
Akenhead Soccer GPS Catapult MinimaxX 10 Hz 0.8 ± 0.1 13 ± 1 Acc N/S N/S Low: 1–2 Acc & Dec Threshold-based sum of acc/
Delves et al. Sports Medicine - Open
et al. [33] Sports Dec Moderate: Distance (m) dec distances
2–3
High: > 3
Total:
>1
Akiyama Lacrosse GPS Polar Electro Polar Team 10 Hz N/S N/S Acc N/S N/S Low: Counts Efforts in respective threshold
(2021) 7:45
et al. [34] Pro Dec 0–1.99 (n) band
Moderate:
2.0–3.99
High:
> 4
Altavilla et al. Soccer GPS K-Sport N/S 10 Hz N/S N/S Acc N/S N/S High: Distance Distance attained in respective
[35] Dec >2 (m) threshold band
Aughey Australian GPS Catapult MinimaxX 5 Hz N/S N/S Acc N/S 0.4 s Maximal: Counts At least two consecutive
[36] Football Sports Team > 2.78 (n) efforts at same rate of change
Sport Counts per in velocity (0.4 s) respective
2.0 minute (n/ threshold band
min−2)
Aughey Australian GPS Catapult MinimaxX 5 Hz N/S N/S Acc N/S 0.4 s Maximal: Counts At least two consecutive
[37] Football Sports Team > 2.78 (n) efforts at same rate of change
Sport Counts per in velocity (0.4 s) respective
2.0 minute (n/ threshold.
min−2) Efforts with respect to activity
time
Aughey Australian GPS Catapult MinimaxX 5 Hz 1.5 ± 0.9 7.5 ± 1.2 Acc N/S N/S Maximal: Counts Efforts with respect to activity
[38] Football Sports Team > 2.78 (n) time
Sport Counts per
2.0 minute
(n/min−2)
Aughey Australian GPS Catapult MinimaxX 5 Hz N/S N/S Acc N/S 0.4 s Maximal: Counts At least two consecutive
[39] Football Sports Team > 2.78 (n) efforts at same rate of change
Sport Counts per in velocity (0.4 s) respective
2.0 minute threshold band.
(n/min−2) Efforts with respect to activity
time
Bauer et al. Australian GPS Catapult MinimaxX 10 Hz 1.8 ± 0.4 N/S Acc N/S N/S Low: Counts Efforts in respective threshold
[40] Football Sports v4.0 0–2.77 (n) band.
Page 7 of 35Table 3 Tracking technology and acceleration/deceleration characteristics of each included study (Continued)
Study Team Device Manufacturer Model Sample HDOP No. of Acc/ Filter Calculation Threshold Acc/dec Calculation of metric
sport rate satellites dec interval/ (m s− 2) metric
(Hz) MED
Hard: Distance Distance attained in respective
≥ 2.78 (m) threshold band.
Bayliff et al. American GPS Catapult Optimeye 10 Hz N/S N/S Acc N/S N/S Band 1: Distance Metres attained in respective
[41] Football Sports S5 Dec 0–1 (m) threshold band
Band 2:
1–2
Band 3:
Delves et al. Sports Medicine - Open
2–3
Band 4:
3–10
Blair et al. Rugby GPS GPSports SPI Pro 10 10 Hz N/S N/S Acc N/S N/S Low: Counts Efforts in respective threshold
[42] Sevens Dec 1.5–2.5 (n) band
High:
(2021) 7:45
> 2.5-3.6
Bowen et al. Soccer GPS STATSports Viper 2 10 Hz N/S N/S Acc N/S 0.5 s All: Counts Efforts in respective threshold
[43] Optical ChyronHego TRACAB Dec > 0.5 (n) band lasting at least 0.5 s and
> 0.5 m s−2
Bradley et al. Soccer Optical ProZone ProZone N/A N/A N/A Acc N/S N/S Medium: Counts Efforts in respective threshold
[44] Sports Version 3.0 > 2.5-4 (n) band
High:
>4
Brooks et al. Netball LPS Catapult Catapult T6 10 Hz N/A N/A Acc N/S 0.2 s Z1: Acceleration Average acc
[45] Sports ClearSky Dec 0–2 Density: values across the specified
Z2: (Average period
2–3.5 Acc/Dec) Average acc performed per
Z3: (m s−2) 10 m of distance
3.5–6 Acceleration (Acc Load/Distance)
Z4: Density Sum of acc values across the
6–10 Index: analysed period (acc values
(avg Acc/Dec were calculated at 0.2 s
per intervals)
10 m; m s− 2) Distance attained in respective
Total threshold
Acceleration
Load:
(total Acc/
Dec;
m s− 2)
Distance
(m)
Campos- Soccer GPS Catapult MinimaxX 10 Hz N/S N/S Acc N/S N/S Moderate: Distance per Distance attained in respective
Vazquez et al. Sports S4 Dec 2–3 hour threshold
[46] High: > 3 (m h−1)
Chesher et al. Field GPS Catapult MinimaxX 10 Hz 0.88 ± 11 ± 0.59 Dec N/S N/S Low: Counts Efforts in respective threshold
Page 8 of 35
[47] Hockey Sports S4 0.03 − 3–−5.99 (n) band.Table 3 Tracking technology and acceleration/deceleration characteristics of each included study (Continued)
Study Team Device Manufacturer Model Sample HDOP No. of Acc/ Filter Calculation Threshold Acc/dec Calculation of metric
sport rate satellites dec interval/ (m s− 2) metric
(Hz) MED
Medium: Average Mean Dec in each competitive
-6–− 8.99 Deceleration match
High: (m s−2)
− 9–−
11.99
Very high:
3 (n) band
Couderc Rugby GPS Digital Sensor 8 Hz N/S N/S Acc Butterworth 0.5 s High: Counts Efforts in respective threshold
et al. Sevens Simulation Everywhere low pass > 2.5 (n) band
[49] 2nd order
Cut-off
(2021) 7:45
frequency:
1 Hz
Double
phase lag
filter
Coutts et al. Australian GPS Catapult MinimaxX 10 Hz N/S N/S Acc N/S 0.2 s > 2.78 Counts Two consecutive samples
[50] Football Sports Team Dec (n) exceeding 2.78 m s−2
Sport 2.5
Cummins Rugby GPS GPSports SPI Pro X 15 Hza N/S N/S Acc Butterworth N/S Moderate: Counts per Efforts in respective threshold
et al. [51] League Dec 4th order < 1.12 minute band with respect to activity
Cut-off High: (n/min−2) time
frequency: 1.13–2.78
1 Hz Very high:
> 2.78
Cummins Rugby GPS Catapult Optimeye 10 Hz N/S N/S Acc N/S N/S All: Counts Efforts in respective threshold
et al. [52] League Sports S5 Dec > 1.5 (n) band
Cummins Rugby GPS GPSports SPI Pro X 15 Hza N/S N/S Acc Butterworth N/S Moderate: Counts Efforts in respective threshold
et al. [53] League Dec 4th Order < 1.12 (n) band
Cut-off High:
frequency: 1.13–2.78
1 Hz Very high:
> 2.78
Cunningham Rugby GPS STATSports Viper 10 Hz N/S N/S Acc N/S N/S Moderate: Counts Efforts in respective threshold
et al. [54] Union Dec 2–3 (n) band
High:
3–4
Severe:
>4
Cunningham Rugby GPS STATSports Viper 10 Hz N/S 4 Best Acc N/S N/S Moderate: Counts Efforts in respective threshold
et al. [55] Union Satellites Dec 2–3 (n) band
High
Page 9 of 35Table 3 Tracking technology and acceleration/deceleration characteristics of each included study (Continued)
Study Team Device Manufacturer Model Sample HDOP No. of Acc/ Filter Calculation Threshold Acc/dec Calculation of metric
sport rate satellites dec interval/ (m s− 2) metric
(Hz) MED
intensity:
3–4
Severe:
>4
Dalen et al. Soccer Radio ZXY Sport RadioEye 20 Hz N/A N/A Acc N/S 0.5 s All: Counts per Efforts lasting for at 0.5 s in
[56] Freq. Tracking Sensors >2 minute respective threshold band.
Tracking (n/min−2) Efforts in respective threshold
Delves et al. Sports Medicine - Open
band with respect to activity
time
Dalen et al. Soccer Radio ZXY Sport RadioEye 20 Hz N/A N/A Acc N/S 0.5 s All: Counts (n) Efforts lasting for at 0.5 s in
[57] Freq. Tracking Sensors Dec >2 Distance (m) respective threshold band
Tracking
de Hoyo Soccer GPS GPSports SPI Elite 10 Hz N/S N/S Acc N/S N/S Moderate: Counts Efforts in respective threshold
(2021) 7:45
et al. [17] Dec 2–3 (n) band
High:
>3
Delaney et al. Rugby GPS GPSports SPI HPU 5 Hz N/S N/S Acc N/S N/S Low: Counts Efforts, time and/or distance in
[7] League Dec 1 (n) respective threshold band.
Moderate: Time Absolute values of acc
2 (s) averaged over given analysis
High: Distance period.
>3 (m) Absolute values of dec
Average Acc averaged over given analysis
(m s−2) period.
Average Dec Absolute values of acc/dec
(m s− 2) averaged over given analysis
Average Acc/ period
Dec
(m s− 2)
Delaney et al. Rugby GPS GPSports SPI HPU 15 Hza 1.1 ± 0.1 8.3 ± 1.4 Acc Butterworth N/S N/A Average Acc Absolute values of acc/dec
[9] League Dec 4th Order (m s−2) / min averaged over given analysis
Cut-off period
frequency:
1 Hz
Delaney et al. Australian GPS Catapult Optimeye 10 Hz N/S N/S Acc N/S N/S N/A Average Acc Absolute values of acc/dec
[10] Football Sports S5 Dec (m s−2) / min averaged over given analysis
period
Delaney et al. Rugby GPS GPSports SPI HPU 15 Hza N/S N/S Acc N/S N/S N/A Average Acc Absolute values of acc/dec
[11] Union Dec (m s−2) / min averaged over given analysis
period
Delaney et al. Soccer GPS Catapult Optimeye 10 Hz 0.86 ± 10.6 ± 1.7 Acc N/S N/S N/A Average Acc Absolute values of acc/dec
[6] Sports S5 0.28 Dec (m s−2) / min averaged over given analysis
period
Page 10 of 35Table 3 Tracking technology and acceleration/deceleration characteristics of each included study (Continued)
Study Team Device Manufacturer Model Sample HDOP No. of Acc/ Filter Calculation Threshold Acc/dec Calculation of metric
sport rate satellites dec interval/ (m s− 2) metric
(Hz) MED
Delves et al. Field GPS Catapult Optimeye 10 Hz N/S N/S Acc N/S N/S N/A Average Acc Absolute values of acc/dec
[58] Hockey Sports X4 Dec (m s−2) / min averaged over given analysis
MinimaxX Average Acc period
S4 (m s−2)
Dempsey Rugby GPS GPSports SPI Pro X 10 Hz N/S N/S Acc N/S N/S High: > 3.0 Counts Efforts in respective threshold
et al. [59] League Dec (n) band.
Counts per Efforts calculated in absolute
Delves et al. Sports Medicine - Open
minute terms with respect to activity
(n/min−2) time and threshold
Dubois et al. Rugby GPS GPSports SPI HPU 15 Hza N/S N/S Acc N/S N/S All: Counts Efforts in respective threshold
[60] Union Dec > 2.5 (n) band
Duthie et al. Field GPS Catapult Optimeye 10 Hz N/S N/S Acc N/S N/S N/A Average Acc Absolute values of acc/dec
[61] Hockey Sports X4 Dec (m s−2) / min averaged over given analysis
(2021) 7:45
period
Figueiredo Soccer GPS STATSports Viper Pod 10 Hz N/S N/S Acc N/S N/S N/S Counts Efforts in respective threshold
et al. Dec (n) band
[62]
Furlan et al. Rugby GPS GPSports SPI HPU 5 Hz N/S N/S Acc Butterworth N/S Moderate: Counts per Acc/Dec counts derived from
[63] Sevens Dec 4th Order 2–3 minute filtered 15 Hz data.
Cut-off High: (n/min−2) Efforts in respective threshold
frequency: .1–4 band with respect to activity
1 Hz Very high: time
>4
Gabbett Rugby GPS Catapult MinimaxX 5 Hz N/S N/S Acc N/S N/S Mild: Counts Efforts in respective threshold
[64] League Sports 0.55–1.11 (n) band
Moderate:
1.12–2.78
Maximal:
> 2.79
Gabbett Field GPS Catapult MinimaxX 5 Hz N/S N/S Acc N/S 2s High: Counts Efforts in respective threshold
[65] Hockey Sports > 0.5 (n) band lasting at least 2 s
Gabbett Rugby GPS Catapult MinimaxX 5 Hz N/S N/S Acc N/S N/S Maximal: Counts Efforts in respective threshold
[66] League Sports Team > 2.79 (n) band.
Sport Counts per Efforts in respective threshold
2.5 minute band with respect to activity
(n/min−2) time
Gabbett et al. Rugby GPS Catapult MinimaxX 5 Hz N/S N/S Acc N/S N/S Mild: Distance Distance in respective
[67] League Sports 0.55–1.11 (m) threshold band
Moderate:
1.12–2.78
Maximal:
> 2.79
Gabbett & Rugby GPS Catapult MinimaxX 5 Hz N/S N/S Acc N/S N/S Mild: Distance Distance in respective
Page 11 of 35Table 3 Tracking technology and acceleration/deceleration characteristics of each included study (Continued)
Study Team Device Manufacturer Model Sample HDOP No. of Acc/ Filter Calculation Threshold Acc/dec Calculation of metric
sport rate satellites dec interval/ (m s− 2) metric
(Hz) MED
Ullah League Sports 0.55–1.11 (m) threshold band
[68] Moderate:
1.12–2.78
Maximal:
> 2.79
Garvican Soccer GPS Catapult MinimaxX 10 Hz N/S N/S Acc N/S N/S Maximal: Counts Efforts in respective threshold
et al. Sports Team > 2.78 (n) band.
Delves et al. Sports Medicine - Open
[69] Sport Counts per Efforts in respective threshold
4.0 minute band with respect to activity
(n/min−2) time
Gaudino Soccer GPS GPSports SPI Pro X 15 Hza N/S Range: Acc N/S 1s Moderate: Counts Efforts in respective threshold
et al. [70] 8-11 Dec 2–3 (n) band lasting for at least 1 s.
Satellites High: Maximum Maximum acc & dec effort in
(2021) 7:45
>3 Acc/Dec analysed period.
(m s−2)
Gaudino Soccer GPS STATSports Viper 10 Hz N/S N/S Acc N/S 0.5 s Total: Counts Efforts in respective threshold
et al. [71] Dec >3 (n) band lasting for at least 0.5 s
Counts per and of magnitude > 0.5 m s− 2
minute
(n/min−2)
Hauer et al. Lacrosse GPS Polar Electro Polar Team 10 Hz N/S N/S Acc N/S N/S Z1: 0–1.0 Counts (n) Efforts in respective threshold
[72] Pro Dec Z2: 1.0–2.0 band
Z3: 2.0–3.0
Z4: > 3.0
Higham et al. Rugby GPS Catapult MinimaxX 5 Hz N/S N/S Acc N/S 0.4 s Moderate: Counts per Efforts in respective threshold
[73] Sevens Sports Team Dec 2–4 minute (n/ band with respect to activity
Sport 2.5 High: > 4 min− 2) time
Higham et al. Rugby GPS GPSports SPI Pro X 15 Hza N/S N/S Acc N/S 1s Total: > 1 Counts per Efforts in respective threshold
[74] Sevens Dec minute (n/ band with respect to activity
min−2 ) time lasting at least 1 s.
Hoppe et al. Soccer GPS Catapult MinimaxX 10 Hz 1.1 ± 0.1 11.8 ± 0.5 Acc Butterworth N/S High:> 3 Time (s) Time spent in respective
[75] Sports S4 Dec 2 Passes threshold band
Cut-off: 1 Hz
Ihsan et al. Field GPS Catapult MinimaxX 5 Hz N/S N/S Acc N/S N/S High: > 2 Counts (n) Efforts in respective threshold
[76] Hockey Sports Team Dec band
Sport 2.5
Ingebrigtsen Soccer Radio ZXY ZXY Sport 40 Hz N/A N/A Acc N/S 0.5 s Total: Counts 1) The start of Acc is marked
et al. [77] Tracking SportTracking Chip >2 (n) by the Acc reaching the
minimum limit (1 m s).
2) Acc has to reach 2 m s.
3) Acc must remain above the
2 m s for at least 0.5 s.
4) The duration of the Acc
Page 12 of 35Table 3 Tracking technology and acceleration/deceleration characteristics of each included study (Continued)
Study Team Device Manufacturer Model Sample HDOP No. of Acc/ Filter Calculation Threshold Acc/dec Calculation of metric
sport rate satellites dec interval/ (m s− 2) metric
(Hz) MED
lasts until it passes the
minimum Acc limit (1 m s)
Jackson et al. Field GPS Catapult MinimaxX 10 Hz MinimaxX: N/S Acc Smoothing 0.2 s–Calc Total: Counts Efforts in respective threshold
[78] Hockey Sports S4 (0.89 Dec Filter 0.6 s– > 1.46 (n) band.
Optimeye [0.04]) Minimum Maximum Maximum Max Acc/Dec recorded
S5 Optimeye effort count per Acc/Dec
S5: duration athlete (m s−2)
Delves et al. Sports Medicine - Open
(0.67
[0.05])
Jaspers et al. Soccer GPS Catapult Optimeye 10 Hz N/S N/S Acc N/S N/S Z1: Counts Efforts in respective threshold
[79] Sports S5 Dec 0–1 (n) band.
Z2: Distance Distance attained in respective
1–2 (m) threshold band
(2021) 7:45
Z3:
2–3.5
Z4:
> 3.5
Jaspers et al. Soccer GPS Catapult Optimeye 10 Hz < 1.5 ≥8 satellites Acc Smoothing 0.4 s Total: Counts Efforts in respective threshold
[80] Sports S5 Dec Filter >1 (n) band
MinimaxX 0.2 s
S4
Johnston Rugby GPS STATSports Apex 10 Hz 0.76 ± 17.7 ± 1.9 Acc N/S N/S N/A Average Acc Absolute values of acc/dec
et al. [81] League 0.25 Dec (m s−2) / min averaged over given analysis
period
Johnston Australian GPS AF: AF: 10 Hz AFL: AFL: Acc N/S N/S N/A Average Acc Absolute values of acc/dec
et al. [82] Football Catapult Optimeye 0.69 ± 10.5 ± 0.65 Dec (m s−2) / min averaged over given analysis
Rugby Sports S5 0.09 NRL: period
League RL: RL: NRL: 17.7 ± 1.90
STATSports Apex 0.76 ±
0.25
Johnston Rugby GPS Catapult Optimeye 10 Hz N/S N/S Acc N/S N/S N/A Average Acc Absolute values of acc/dec
et al. [83] League Sports S5 Dec (m s−2) / min averaged over given analysis
period
Johnston Australian GPS Catapult MinimaxX S3: 1.0 ± 0.3 12.2 ± 0.7 Acc N/S N/S Low: Counts per Efforts calculated in absolute
et al. [84] Football Sports S3 5 Hz Dec 0.65–1.46 minute terms with respect to activity
MinimaxX S4: Moderate: (n/min−2) time and threshold.
S4 10 Hz 1.47–2.77 Distance per Distance in respective
High: minute threshold band with respect
> 2.78 (m/min) to activity time and threshold.
Time Time spent as a percentage in
(%) respective threshold band
Johnston Australian GPS Catapult MinimaxX 5 Hz 1.0 ± 0.2 12.1 ± 0.7 Acc N/S N/S Low: Counts per Efforts in respective threshold
et al. [85] Football Sports S3 10 Hz Dec 0.65–1.46 minute (n/ band with respect to activity
MinimaxX Moderate: min−2) time and threshold.
Page 13 of 35Table 3 Tracking technology and acceleration/deceleration characteristics of each included study (Continued)
Study Team Device Manufacturer Model Sample HDOP No. of Acc/ Filter Calculation Threshold Acc/dec Calculation of metric
sport rate satellites dec interval/ (m s− 2) metric
(Hz) MED
S4 1.47–2.77 Distance per Distance attained in respective
High: minute threshold band with respect
> 2.78 (m/min−2) to activity time and threshold.
Time Percentage time spent in
(%. min−2) respective threshold band
with respect to activity time
Johnston Australian GPS Catapult MinimaxX 5 Hz 1.0 ± 0.1 12.2 ± 0.6 Acc N/S N/S Low: Counts per Efforts in respective threshold
Delves et al. Sports Medicine - Open
et al. [86] Football Sports S3 10 Hz Dec 0.65–1.46 minute band with respect to activity
MinimaxX Moderate: (n/min−2) time.
S4 1.47–2.77 Distance per Distance attained in respective
High: minute threshold band with respect
> 2.78 (m/min−2) to activity time.
Time Percentage time spent in
(%. min−2) respective threshold band
(2021) 7:45
with respect to activity time
Jones et al. Rugby GPS Catapult MinimaxX 10 Hz N/S N/S Acc N/S N/S Low: Distance Metres attained in respective
[87] Union Sports V4 Dec 1–2 (m) threshold band
Moderate:
2–3
High:
>3
Kempton & Rugby GPS GPSports SPI Pro X 15 Hza N/S N/S Acc N/S N/S Total: Counts Efforts in respective threshold
Coutts [88] League Dec > 2.78 (n) band.
Nines Counts per Efforts calculated in absolute
minute (n/ terms with respect to activity
min−2) time and threshold
Kempton Rugby GPS GPSports SPI Pro X 15 Hza N/S N/S Acc N/S N/S Total: Counts Efforts in respective threshold
et al. [89] League Dec > 2.78 (n) band.
Counts per Efforts calculated in absolute
minute terms with respect to activity
(n/min−2) time and threshold
Kempton Rugby GPS GPSports SPI Pro 5 Hz N/S 9.1 ± 1.4 Acc N/S 0.4 s Total: Counts Two consecutive samples
et al. [90] League Dec > 2.78 (n) exceeding 2.78 m s−2
Lacome et al. Rugby PC- Sport Amisco Pro 10 Hz N/A N/A Acc Butterworth 0.5 s Z1: Mean Values of acc averaged over
[91] Union based Universal Velocity low pass 1–2 acceleration given analysis period.
tracking Process 2nd order Z2: (m s−2) Distribution of acc values over
cut-off 2–3 given analysis period with
frequency: Z3: respect to thresholds
1 Hz >3
Double
phase-lag
filter
Malone et al. Soccer GPS Catapult Optimeye 10 Hz N/S N/S Acc N/S N/S High: Counts Efforts in respective threshold
[92] Sports G5 Dec >3 (n) band
Page 14 of 35Table 3 Tracking technology and acceleration/deceleration characteristics of each included study (Continued)
Study Team Device Manufacturer Model Sample HDOP No. of Acc/ Filter Calculation Threshold Acc/dec Calculation of metric
sport rate satellites dec interval/ (m s− 2) metric
(Hz) MED
Mara et al. Soccer GPS GPSports N/S 15 Hza N/S 5-8 Satellites Acc N/S N/S Efforts: Time Average time spent in acc in
[93] >2 (s) analysed period.
Distance Average distance accumulated
(m) in analysed period.
Max distance Average max distance
(m) accumulated in analysed
Max period.
acceleration Max acc effort in analysed
Delves et al. Sports Medicine - Open
(m s−2) period
Repeat Acc efforts performed with <
acceleration 21 s separation
Mara et al. Soccer GPS GPSports SPI HPU 15 Hza N/S N/S Acc N/S N/S High: Counts Efforts in respective threshold
[94] Dec >2 (n) band
(2021) 7:45
Marrier et al. Rugby GPS Digital Sensor 16 Hz < 2 7 [1] Acc N/S 0.5 s All: Counts (n) Efforts in respective threshold
[95] Sevens Simulation Everywhere > 2.5 band lasting for at least 0.5 s
V2
Martin-Garcia Soccer GPS STATSports Viper 10 Hz N/S N/S Acc N/S N/S High: Counts Efforts in respective threshold
et al. Dec >3 (n) band
[96]
Martin-Garcia Soccer GPS STATSports Viper 10 Hz N/S N/S Acc N/S 0.5 s High: Counts Efforts in threshold band
et al. Dec >3 (n) lasting for at least 0.5 s and of
[97] magnitude > 0.5 m s−2
Martin-Garcia Soccer GPS STATSports Viper 10 Hz N/S N/S Acc N/S N/S High: Counts Efforts in respective threshold
et al. Dec >3 (n) band
[98]
Modric et al. Soccer GPS Catapult Optimeye 10 Hz N/S N/S Acc N/S N/S Total Counts Efforts in respective threshold
[99] Sports S5 Dec events: (n) band
Optimeye > 0.5
X4 High:
>3
Montgomery 3×3 GPS Catapult Optimeye 10 Hz N/S N/S Acc N/S N/S Low: Intensity Average intensity in respective
& Maloney Basketball Sports S5 Dec < 2.5 (m s−2) threshold band.
[100] Medium:
2.5–3.5
High:
> 3.5
Morencos Field GPS GPSports SPI Elite 10 Hz N/S 10.6 ± 1.2 Acc N/S N/S Low: Counts Efforts in respective threshold
et al. [101] Hockey Dec 1–1.99 (n) band.
Moderate: Counts per Efforts calculated in absolute
2.0–2.99 minute terms with respect to activity
High: (n/min−2) time and threshold
>3
Morencos Field GPS GPSports SPI Elite 10 Hz N/S N/S Acc N/S N/S Low: Counts Efforts in respective threshold
Page 15 of 35Table 3 Tracking technology and acceleration/deceleration characteristics of each included study (Continued)
Study Team Device Manufacturer Model Sample HDOP No. of Acc/ Filter Calculation Threshold Acc/dec Calculation of metric
sport rate satellites dec interval/ (m s− 2) metric
(Hz) MED
et al. [102] Hockey Dec 1.0–1.9 (n) band.
Moderate: Counts per Efforts in respective threshold
2.0–2.9 minute (n/ band with respect to activity
High: min−2) time
> 3.0
Murray & Rugby GPS Catapult MinimaxX 10 Hz N/S 11.3 ± 1.4 Acc N/S 0.4 s Maximal: Counts Efforts in respective threshold
Varley [103] Sevens Sports S4 > 2.78 (n) band lasting at least 0.4 s.
Delves et al. Sports Medicine - Open
Counts per Efforts calculated in absolute
minute terms with respect to activity
(n/min−2) time and threshold lasting at
least 0.4 s
Newans et al. Soccer GPS Catapult Optimeye 10 Hz N/S N/S Acc N/S 0.5 s Moderate: Time Time spent in each respective
[104] Sports S5 Dec 1–2 (s) threshold lasting at least 0.5 s.
(2021) 7:45
Optimeye High: Ratio of Duration of Dec (High) and
X4 >2 Dec: Acc Dec (Mod)
divided by total Acc time
(High + Mod) in each period.
Determined a moderate and
high
Dec:Acc ratio
Owen et al. Soccer GPS STATSports Viper Pod 10 Hz N/S N/S Acc N/S N/S Total: Counts Efforts in respective threshold
[105] Dec > 3.3 (n) band
Owen et al. Rugby GPS GPSports SPI HPU 15 Hza N/S N/S Acc N/S N/S Light: Counts Efforts in respective threshold
[106] Union Dec 1–1.99 (n) band
Moderate:
2.0–2.99
Heavy:
3–5.99
Oxendale Rugby GPS Catapult MinimaxX 10 Hz N/S N/S Acc N/S N/S Maximal: Counts Efforts in respective threshold
et al. [107] League Sports Team Dec > 2.79 (n) band
Sport 2.5
Palmer et al. Ultimate GPS Catapult Optimeye 10 Hz 0.90 ± 13.7 ± 0.5 Acc Proprietary 0.6 s Total: Counts Efforts in respective threshold
[108] Frisbee Sports X4 0.10 Filter > 1.5 (n) band lasting for at least 0.6 s
Counts per and with respect to time
minute
(n/min−2)
Passos Soccer GPS Catapult MinimaxX 10 Hz N/S N/S Acc N/S N/S 1: Counts Efforts in respective threshold
Ramos et al. Sports Team S5 Dec − 1–1 (n) band and with respect to time
[109] 2: Counts per
1–2.5 minute (n/
3: min−2)
> 2.5
Passos Soccer GPS Catapult MinimaxX 10 Hz 0.75 ± 0.3 12.4 ± 0.5 Acc Exponential 0.5 s Total: Counts (n) Efforts in respective threshold
Ramos Sports Team S5 Dec Filter >1 band
Page 16 of 35Table 3 Tracking technology and acceleration/deceleration characteristics of each included study (Continued)
Study Team Device Manufacturer Model Sample HDOP No. of Acc/ Filter Calculation Threshold Acc/dec Calculation of metric
sport rate satellites dec interval/ (m s− 2) metric
(Hz) MED
et al. (Derived
[110] from GPS
Software)
Passos Soccer GPS Catapult MinimaxX 10 Hz 0.75 ± 0.3 15.5 ± 0.5 Acc N/S N/S Total: > 2 Counts (n) Efforts in respective threshold
Ramos Sports Team S5 Dec band
et al.
[111]
Delves et al. Sports Medicine - Open
Peeters et al. Rugby GPS Digital Sensor 16 Hz 1.35 ± 8±1 Acc N/S N/S Total: > Counts Efforts in respective threshold
[112] Sevens Simulation Everywhere 0.34 2.5 (n) band.
Counts per Efforts calculated in absolute
minute (n/ terms with respect to activity
min−2) time
Polgaze et al. Field GPS Catapult MinimaxX 10 Hz 1.00 ± 11.6 ± 0.5 Acc Proprietary 0.6 s Low: Counts Eligible Acc was determined
(2021) 7:45
[113] Hockey Sports S4 0.07 Filter < 2.0 (n) once a participant changed
High: Counts per speed by 2 m s for a
> 2.0 minute minimum within 0.6 s.
(n/min−2) Efforts in respective threshold
Time band.
(s) Efforts calculated in absolute
Distance terms with respect to activity
(m) time and threshold
Time spent in respective
threshold band.
Distance attained in respective
threshold band
Pollard et al. Rugby GPS STATSports Viper 10 Hz N/S N/S Acc N/S N/S Total: Counts per Efforts in respective threshold
[114] Union >3 minute band.
(n/min−2) Efforts in respective threshold
band with respect to activity
time
Polley et al. Lacrosse GPS Catapult MinimaxX 10 Hz N/S N/S Acc N/S N/S Low: Counts per Efforts in respective threshold
[115] Sports S4 Dec 0–1.11 minute band with respect to activity
Moderate: (n/min−2) time
1.11–2.78
High:
≥ 2.78
Portillo et al. Rugby GPS GPSports SPI Pro X 15 Hza N/S N/S Acc N/S N/S Z1: Counts Efforts in respective threshold
[116] Sevens > 1.5 (n) band
Z2:
> 2.0
Z3:
> 2.5
Z4:
> 2.75
Page 17 of 35
Rennie et al. Australian GPS Catapult Optimeye 10 Hz 1.1 ± 0.1 18.2 ± 1.1 Acc N/S 0.2 s Efforts: Counts Two consecutive samples inTable 3 Tracking technology and acceleration/deceleration characteristics of each included study (Continued)
Study Team Device Manufacturer Model Sample HDOP No. of Acc/ Filter Calculation Threshold Acc/dec Calculation of metric
sport rate satellites dec interval/ (m s− 2) metric
(Hz) MED
[117] Football Sports S5 Dec Two > 2.78 (n) respective threshold band
Samples
Romero- Field GPS GPSports SPI Elite 10 Hz N/S N/S Acc N/S N/S Low: Counts per Efforts in respective threshold
Moraleda Hockey Dec 1–1.9 minute band with respect to activity
et al. Moderate: (n/min−2) time
[118] 2–2.9
High: > 3
Delves et al. Sports Medicine - Open
Russell et al. Soccer GPS STATSports Viper 10 Hz N/S N/S Acc N/S N/S Total: > Counts Efforts in respective threshold
[119] Dec 0.5 (n) band
High:
>3
Russell et al. Soccer GPS STATSports Viper 10 Hz N/S N/S Acc N/S 0.5 s Total: Counts Efforts in respective threshold
[120] Dec > 0.5 (n) band
(2021) 7:45
High:
>3
Sangnier Soccer GPS K-Sport K-GPS 10 Hz N/S N/S Acc N/S 0.4 s (over 3 Distance: Counts per Efforts > 0.4 s (over 3 m s−2
et al. Dec s threshold) >2 minute threshold).
[121] Counts: (n/min−2) Distance in threshold band
>3 Distance per with respect to activity time
min (m/min)
Silva et al. Soccer GPS STATSports Viper 10 Hz N/S N/S Acc N/S 0.5 s Z1: > 2 Counts Efforts in respective threshold
[122] Dec Z2: (n) band lasting at least 0.5 s of
> 2.5 Counts per magnitude > 0.5 m s−2
Z3: minute (n/
>3 min−2)
Smpokos Soccer GPS STATSports Viper Pod 10 Hz N/S N/S Acc N/S 0.5 s Total: Counts (n) Efforts in respective threshold
et al. 2 Dec >2 and counts band lasting at least 0.5 s of
[123] per minute magnitude > 0.5 m s−2
(n/min−2)
Smpokos Soccer GPS STATSports Viper Pod 10 Hz N/S N/S Acc N/S 0.5 s Total: Counts (n) Efforts in respective threshold
et al. [124] 2 Dec >2 and counts band lasting at least 0.5 s of
per minute magnitude > 0.5 m s−2
(n/min−2)
Stevens et al. Soccer LPS Inmotio Inmotio 24 Hz N/A N/A Acc Weighted N/S >2 Distance Distance in respective
[125] LPS Gaussian (m) threshold band
Average
Stevens et al. Soccer LPS Inmotio LPS Inmotio 31 Hz N/A N/A Acc Weighted 0.5 s Medium: Counts Efforts in respective threshold
[126] LPS Dec Gaussian > 1.5 (n) band
Average High: > 3
Suarez- Rugby GPS GPSports SPI Pro X 15 Hza N/S N/S Acc N/S 1s Maximal: Counts 1-s at
Arrones Sevens Dec 2.78–4 (n) > 2.78 m s−2
et al. [127] Extremely or above
high:
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