Skip to main content
Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Accelerometry as a method for external workload monitoring in invasion team sports. A systematic review

  • Carlos D. Gómez-Carmona ,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Writing – original draft, Writing – review & editing

    Affiliation Training Optimization and Sports Performance Research Group (GOERD), Didactics of Music, Plastic and Body Expression Department, University of Extremadura, Caceres, Spain

  • Alejandro Bastida-Castillo,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Writing – original draft, Writing – review & editing

    Affiliations Department of Physical Activity and Sports, International Excellence Campus “Mare Nostrum”, Faculty of Sport Sciences, University of Murcia, San Javier, Spain, University Isabel I, Burgos, Spain

  • Sergio J. Ibáñez,

    Roles Conceptualization, Funding acquisition, Methodology, Resources, Supervision, Writing – review & editing

    Affiliation Training Optimization and Sports Performance Research Group (GOERD), Didactics of Music, Plastic and Body Expression Department, University of Extremadura, Caceres, Spain

  • José Pino-Ortega

    Roles Conceptualization, Investigation, Methodology, Project administration, Supervision, Writing – review & editing

    Affiliation Department of Physical Activity and Sports, International Excellence Campus “Mare Nostrum”, Faculty of Sport Sciences, University of Murcia, San Javier, Spain


Accelerometry is a recent method used to quantify workload in team sports. A rapidly increasing number of studies supports the practical implementation of accelerometry monitoring to regulate and optimize training schemes. Therefore, the purposes of this study were: (1) to reflect the current state of knowledge about accelerometry as a method of workload monitoring in invasion team sports according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, and (2) to conclude recommendations for application and scientific investigations. The Web of Science, PubMed and Scopus databases were searched for relevant published studies according to the following keywords: “accelerometry” or “accelerometer” or “microtechnology” or “inertial devices”, and “load” or “workload”, and “sport”. Of the 1383 studies initially identified, 118 were selected for a full review. The main results indicate that the most frequent findings were (i) devices’ body location: scapulae; (b) devices brand: Catapult Sports; (iii) variables: PlayerLoadTM and its variations; (iv) sports: rugby, Australian football, soccer and basketball; (v) sex: male; (vi) competition level: professional and elite; and (vii) context: separate training or competition. A great number of variables and devices from various companies make the comparability between findings difficult; unification is required. Although the most common location is at scapulae because of its optimal signal reception for time-motion analysis, new methods for multi-location skills and locomotion assessment without losing tracking accuracy should be developed.


Workload quantification is defined as the process of recording training and competition workload demands to regulate training volumes and intensities in athletes and to decrease the risk of injuries and overtraining [1]. These demands should not only be assessed overall but also individually as each player will respond differently to the same training workloads [2, 3]. Concerning workload quantification, sport science research differentiates between internal and external workload [4]. The internal workload is defined as the biological reaction of the athlete’s organism, both physiological and psychological, as a consequence of the external workload performed during exercise and it is measured through different variables like heart rate telemetry, blood lactate, oxygen consumption or rating of perceived exertion (RPE) [5]. In contrast, the external workload is defined as the mechanical and locomotor actions performed by an athlete, measured through various variables like power, speed, changes of speed, changes of direction or impacts [6]. Therefore, current literature suggests adopting strategies for quantifying and monitoring internal and external workload can enable team staff to assess fatigue and fitness level of players in real-time throughout the season [4, 7, 8].

At high levels in sports performance, coaches and sports scientists are constantly trying to find new ways for measuring athletes’ performance to obtain an advantage over their opponents [9]. However, training and competition activity and the developments of performance are extremely difficult to measure directly [10]. For this reason, sports professionals have found different methods for measuring the players’ workloads indirectly such as inertial measurement units (IMUs) for recording in a reliable and valid way compared to other instruments considered as “gold standard” or “criterion measures” [1113]. These instruments or diagnostic tests are considered the best available and most accurate under reasonable conditions (e.g. the gold standard for players tracking is video analysis but indirect methods can detect it with high accuracy as Global Navigation Satellite Systems, GNSS, Local Position Measurements, LPM or accelerometry).

In this sense, technological advances have allowed the development of different devices to obtain objective data in indoor and outdoor sports. Since 2001, the Australian Centre of Microtechnological Research through Project 2.5 “Technology of Communication to Athletes Monitoring” has been designing a unique and non-intrusive device for sports monitoring in real-time [14]. These devices are able to record external workload demands such as (a) total distance, (b) work zones concerning velocity or changes of speed, or (c) impacts performed by the athletes [15]. The incorporation of tri-axial accelerometers into these units has provided the opportunity to analyze new load parameters such as three axes acceleration recorded during sports movements, measured in arbitrary units (a.u.) [16].

The validity of accumulated accelerometry-based workload in the three planes of movement has been compared with other internal workload variables such as session RPE (sRPE) or the Edwards method, finding high correlations among indexes [17], and also with muscle oxygen saturation [18] or maximal oxygen uptake [19]. Previous research has also found satisfactory reliability results both in accelerometry-based workload [20, 21]. However, the workload recorded by accelerometers could be affected by the individualized profile of gait biomechanics or the speed of the athlete’s locomotion [22]. Nonetheless, accelerometry-based indexes have been used for workload monitoring in invasion team sports [23] such as netball [24, 25], soccer [17, 26], basketball [2730] and Australian football [31, 32], among others. Carey et al. [31] mentioned in a recent investigation that a multi-variable analysis should be carried out, where accelerometry-based indexes are incorporated with other external and internal workload indicators such as total distance covered, sRPE or high-intensity locomotion.

Since its appearance, the use of accelerometry as a method of workload monitoring has developed greatly. Although accelerometers do not provide information about static actions when an effort is performed without an acceleration (e.g. screenings or a prolonged stance position), their reliability, precision and sensitivity are greater compared to other automatic and semiautomatic time-motion analysis (TMA) technologies such as video-tracking, GNSS or LPM [26, 30]. Automatic and semiautomatic TMA may underestimate the workload demands because high-intensity actions where there is no locomotion (jumps, collisions, etc.) are classified in the group of low-intensity actions [26]. For these reasons, recent investigations identified that microtechnologies (e.g. wearable microsensors and accelerometers) may represent a valid and practical alternative to TMA and offer distinct advantages compared with TMA such as the relative simplification to analyze data using either proprietary or used-defined algorithms that quantify movement [30, 33]. Given this background, the purposes of the present study were to reflect the current state of knowledge, outline best practices and conclude recommendations about the use of accelerometry as a method of workload monitoring in invasion team sports.


Study design and search strategy

This manuscript is a systematic review [34] about peer-reviewed, scientific papers related to workload monitoring via accelerometry in sports. The Web of Science (Web of Science Core Collection, MEDLINE, Current Contents Connect, Derwent Innovations Index, KCI-Korean Journal Database, Russian Science Citation Index and Scielo Citation Index), PubMed electronics and Scopus electronic databases were searched on 1st May 2020 for relevant articles published between 1st January 2010 and 30th April 2020 using the keywords “accelerometer” or “accelerometry” or “microtechnology” or “inertial devices”, and “load” or “workload”, and “sport”. Reference lists of included articles were scanned to identify relevant studies. Any disagreements were resolved by consensus between two investigators and arbitration by a third investigator.

One investigator conducted electronic searches, identified relevant studies, and extracted data in an unblended, standardized manner. The database search was limited to peer-reviewed journal articles published in English. A systematic review of the available literature was undertaken in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [35] (Fig 1).

Fig 1. PRISMA flow diagram displaying the identification, screening, and selection of relevant studies in this systematic review.

In the present review the inclusion criteria for these articles were: (1) cross-sectional and longitudinal studies written in English, (2) participants were healthy players irrespective of competition level (amateur, well-trained, professional, elite, junior, senior) and sex (male and female), and (3) about invasion team sports following the classification of Read and Edwards [36] divided into three sports modalities: (a) goal throwing games (netball, basketball, handball and lacrosse), (b) try-scoring games (rugby, rugby union, Australian football and American football) and (c) goal striking games (hockey and soccer). Analysis during training or competition was not selected as an exclusion criterion. All included studies were deemed to have suitable ethical approval by a relevant review board.

Studies were excluded if (1) the type of document was case studies, doctoral thesis, books or book chapters, congress communications, patents or reviews, (2) they involved animals, (3) the workload monitoring was performed without accelerometry-based indexes, (4) the study context was outside competitive sports, and (5) they only assessed the reliability and validity of accelerometer raw data or accelerometry-based indexes.

Data extraction and analyzed variables

The Cochrane Consumers and Communication Review Group’s data extraction protocol [35] was used to extract the following information about studies that monitored external workload by accelerometry-based indexes in invasion team sports: (1) authors and date, (2) participant data (including sex and sample size), (3) description of the sport and competition level, ((4) type of session or sport context (training, competition, or both), (5) device and body location, (6) accelerometry-based indexes, (7) technical features of accelerometers (sample frequency, number of accelerometers 3D vs 2D, output range, and previous validity or reliability results), (8) main results and (9) referential values. This process was developed and tested with 10 randomly selected studies. First, one researcher extracted the data from the included studies and a second researcher then checked the extracted data. Disagreements were resolved by consensus.

Quality of the studies

The quality of the studies was evaluated with a risk-of-bias quality form used for quantitative studies developed by Law et al. [37] (S4 Table) and composed of 16 items in an evaluation process performed by five university full professors with a PhD in sport science and a large number of publications in the field of technology to monitoring sports performance in team sports. Cohen’s Kappa was calculated with 95% confidence interval to evaluate the inter-coders reliability and interpreted as: <0.20 poor, 0.21–0.40 fair, 0.41–0.60 moderate, 0.61–0.80 good, >0.80 very good [38].

Articles were assessed based on purpose (item 1), relevance of background literature (item 2), appropriateness of study design (item 3), sample studied (items 4 and 5), use of informed consent procedure (item 6), outcome measures (items 7 and 8), method description (item 9), significance of results (item 10), analysis (item 11), practical importance (item 12), description of dropouts (item 13), conclusions (item 14), practical implications (item 15), and limitations (item 16). All 16 quality criteria were rated on a binary scale (0/1), wherein two of those criteria (items 6 and 13) presented the option: ‘If not applicable, assume N/A’. The introduction of this option for items 6 ‘Was informed consent obtained?’ and 13 ‘Were any dropouts reported?’ was included because, in some studies, the investigators were not required to obtain informed consent (item 6) or report dropouts (item 13). The introduction of the option ‘not applicable’ allowed an appropriate score for the article, eliminating the negative effect of assuming the value ‘0’ on a binary scale, when in fact that specific item did not apply to that study. For this, the sum of the score of all items was divided by the number of relevant scored items for that specific research design. All articles were classified as (1) low methodological quality (<50%); (2) good methodological quality (51–75%), and (3) excellent methodological quality (>75%).


Search, selection and inclusion of publications

1371 articles were identified from the Web of Science (n = 566), PubMed (n = 443) and Scopus (n = 443) database search. In addition, 12 articles identified and selected in previous database searching (30th June 2019) and not found on 1st May 2020 were included, being a total of 1383 articles. These studies were then exported to reference manager software (Zotero), and any duplicates (818 articles) were eliminated automatically. From the remaining 565 articles, 243 did not fulfill the inclusion criteria and were removed after revision of the abstract and another 204 after full-text assessment. At the end of the screening procedure, 118 articles remained for the systematic review related to the invasion team sports modality: (a) goal striking games (soccer and hockey; n = 25) [6, 26, 3961] (S1 Table), (b) goal throwing games (basketball, netball, lacrosse and handball; n = 33) (S2 Table) [8, 24, 25, 2729, 6288] and (c) try-scoring games (rugby, rugby union, rugby seven, Australian and American football; n = 60) [32, 89147] (S3 Table).

The main reasons for exclusion were individual sports (n = 59), reliability and validity analyses of raw data and workload indexes through accelerometry (n = 38), monitoring external workload without accelerometry-based indexes (n = 37) and non-competitive sports contexts (n = 33). Other reasons for exclusion included studies that analyzed physical conditioning tests (n = 16) and non-invasion team sports (n = 14).

Quality of the studies

To analyze the quality of the selected studies, the classification designed by Law et al. [37] that is shown as S4 Table was utilized. Previously to quality assessment, an inter-coder reliability analysis was performed, obtaining a value of 0.93 that represents a very good agreement between observers (Confidence interval 95%: 0.89 to 0.96). The main results of the quality indicators for the selected studies were as follows: (1) the average methodological quality score was 82.3%; (2) Two articles reached the maximum score of 100%; (3) no study obtained a score below 50%; (4) 33 studies obtained a score between 50% and 75% (good methodological quality); and (5), 83 articles reached a rating of >75% (excellent methodological quality).

Four items were mainly related with methodological deficiencies in the selected studies: (1) Criterion 5 where 84.6% of studies did not show an explicit justification of the study sample size; (2) Criterion 16 where 60.7% of articles did not clearly acknowledge the limitations of the study; (3) Criterion 8 where 66.9% did not report the validity of the accelerometry-based index of the device; and (4) Criterion 7 where 42.4% did not report the reliability of the device for accelerometry-based index measurement.

Scientific journals, sports context, competition level, sex and publication years

Fig 2 shows the scientific journals, sports context, sports level, sex and publication years of the selected studies that use accelerometry-based indexes for workload monitoring in invasion team sports. The trends of topic publications are shown in Fig 2A, where there exists an increasing number of publications with an exponential evolution from 2015. The 118 papers included in the systematic review were published in 27 different journals, with 59.3% appearing in 4 journals, each publishing at least ten articles (Fig 2B).

Fig 2.

(a) Research evolution, (b) scientific journals, (c) type of session, (d) competition level and (e) sex of participants in the selected studies that use accelerometry-based indexes for monitoring workload in sport.

Most of the studies analyzed competition (58 articles, 49%) or training (32 articles, 27%) separately, with only 24% of selected studies that analyzed both contexts (Fig 2C). Most of the reviewed papers analyzed elite (46 articles, 39%) and professional-level (37 articles, 31%) athletes, although studies in other levels as junior (19%), university (8%) and amateur (2%), and referees (1%) were also carried out (Fig 2D). Finally, 86% of selected studies were performed with males compared to 14% with females (Fig 2E).

Body location, devices and invasion team sports analyzed

Fig 3 shows the body location, devices and companies, indexes and invasion team sports analyzed by scientific studies through accelerometry-based workload indexes. The most common body location for evaluation was the scapulae (95.8%, 113/118), through MinimaxX (23.7%, 28/118) and Optimeye (33.9%, 40/118) devices in their different versions, developed by the Australian company Catapult Sports (64.4%, 76/118). Most papers were descriptive and assessed maximum accelerations (impacts, collisions) (21/118, 17,8%) or accumulated workload accelerometry-based indexes expressed as arbitrary units (a.u.) through different indexes related to the developer company of the device (16 indexes). Australian football (18.6%, 22/118), soccer (20.3%, 24/118), rugby (13.6%, 16/118) and basketball (16.9%, 20/118) were the most frequently investigated invasion team sports. In addition, impact and collisions were mostly assessed in try-scoring games, and Dynamic Stress Load, Locomotion efficiency, Impulse Load and PLRE in goal striking games (see Table 1 for definitions).

Fig 3. Classification of selected studies related to body location, device model and company, accelerometry-based workload indexes and invasion team sport.

Table 1. Accelerometry-based external workload variables utilized in the selected studies in this systematic review.

Accelerometry technical features and based workload indexes

About technical features, 102 articles (86.4%) mentioned the sampling frequency of the accelerometers, being in all cases of 100 Hz, and the triaxial properties of the accelerometers (89.8%, 106/118). Instead, only 9 studies showed the number of accelerometers that composed the devices (4 accelerometers, 6.8%, 8/118; 3 accelerometers, 1/118). Respect to the reproducibility and the accuracy of the accelerometers, 41 articles not reported both aspects (34.75%), 37 articles reported the reliability (31.4%) and 10 reported the validity to measure the accelerometry-based workload index (8.5%). Only 30 articles reported the validity and reliability of the accelerometers that composed the inertial devices (25.4%).

Finally, Table 1 shows the accelerometry-based indexes utilized for workload monitoring with the developer company, description, measurement unit and formula for its calculation. The most frequently used is PlayerLoadTM (PLTM) developed by Catapult Sports (77 studies, 65.3%). Also, variations of the original formula such as accelerometry workload at low intensity (PLslow, 11 studies, 9.3%) and divided by axis such as PLx (14 studies, 11.9%), PLz (13 studies, 11.1%) and PLy (14 studies, 11.9%) were utilized (more details in Fig 3).


This manuscript showed a general overview of the use of accelerometry as a method of workload monitoring in invasion team sports, including research evolution, journals, sport modalities and contexts, competition level, sex, device location, accelerometry-based variables and technical features. For this purpose, a systematic review was carried out of the articles related to the study topic [34, 35]. The main results show a rapidly increasing number of publications about accelerometry-based workload monitoring, where training and competition were analyzed separately, in elite and professional-level men, placing the device at the scapulae level and using the PLTM index in invasion team sports in outdoor and indoor conditions.

Competition vs. training

Most studies analyzed training and competition contexts separately (78%) so that only a limited number compared both contexts (22%). The interrelation between training and competition during sports seasons is essential to achieve the appropriate adaptations, maintain optimal players’ physical fitness and avoid the occurrence of injuries due to an irregular workload dynamic between both sports contexts [7, 28]. Therefore, the sports tasks selection concerning the purpose of the training sessions and workload planning during competitive microcycles is fundamental for sports performance [39, 148].

In this review, a total number of 28 publications were found that performed an external workload analysis through accelerometry-based indexes in training and competition. Most studies analyzed the overall weekly workload (training and competition) and did not provide distinct training and competition hours so that the normalization is not possible making their comparison difficult [32, 62, 63, 8992]. To solve this problem, different researches contain the workload related to playing time [39, 40, 6466]. Therefore, future researches should provide training and competition hours or present the workload indexes both total and relative to playing time to allow for comparison between sports contexts.

Four studies that compared both sport contexts found a higher workload in training than in competition [25, 40, 64, 65]; four other articles reported the opposite [39, 41, 66, 93]. Higher competition workload reported in some studies may be the consequence of differences in weekly schedules, not accounting for conditions (e.g. day after game (starting vs substitutes), strength and power capabilities, technical-tactical elements, activation drills) for further analysis. Gentles et al. [41] analyzed the average training session workload in comparison with competition in university-level female soccer players through Impulse Load (20120±8609 vs. 12410±4067). Montgomery et al. [66] assessed the differences between 5vs5 game-based tasks in training in comparison with competition through PL/min (2.79±0.58 vs 1.71±0.84). Ritchie et al. [93] found a greater workload in training compared with matches during the pre-season (PL: 1985±745 vs. 1010±290), and the opposite during the competitive season (PL: 1014±383 vs. 1320±195).

On the other hand, if each training session is analyzed specifically and is not masked by overall weekly workload demands, a higher workload than the competition can be found depending on the training session purpose. In this sense, Beenham et al. [40] found higher demands in 2vs2, 3vs3 and 4vs4 small-sided games in comparison with official matches in youth soccer measured by PL/min. Chandler et al. [65] and Fox et al. [64] showed a greater workload when the purpose of the training sessions was physical conditioning or game-based training in comparison with competition in women’s netball and men’s basketball respectively.

It is necessary for the best preparation of the player to understand physical and physiological stress during both training and competition [28, 66, 93]. A correct training session design related to the technical-tactical-physical purpose and the competition is important for workload managing during competitive microcycles [4, 5, 64]. In this sense, the use of effective strategies can help to anticipate the higher peak of performance in competition [67, 68, 94]. Also, it is important to monitor the player during all the training phases to assure the efficacy of the training effectiveness [9597]. For this purpose, combined monitoring of internal responses with external workload demands through different variables based on tracking systems or accelerometry allow workload monitoring in an objective way [4, 15], being fundamental the selection of suitable workload indexes crucial for their control and also a clear presentation of the results for better decision-making by the team staff [149].

Device location

In most of the analyzed studies, the inertial devices composed of tri-axial accelerometers for external workload monitoring in invasion team sports have been placed on the scapulae using an anatomically adjusted harness [8, 17, 26, 69, 93, 98, 99], except in a few studies where the companies recommend the location on the center of mass [2426, 29] or the sternum [41].

The device location in team sports has been on the scapulae as this place is the most acceptable for detecting position coordinates by GNSS (latitude/longitude) in outdoor conditions [15, 40], or horizontal LPM using radio-frequency systems in indoor conditions [150153]. Placing the device in a different location from the scapulae is sometimes selected because a body-worn accelerometer only measures the acceleration of the segment to which it is attached [154]. Thus, to detect more accurately the specific skills and workload of each sports discipline, accelerometers have been placed on different locations like the wrist in tennis [155], the head in swimming [156], the cockpit in kayak [157], the handlebar, seat or bike shank in cycling XCO-MTB [158, 159], or the tibia during running [19, 154, 160].

Therefore, if the aim is to record and measure specific events or skills, the device location should be the closest to the segment that performs the movement/action to achieve the highest accuracy [154]. Conversely, if the aim is to record and measure player tracking, the device must be placed on the scapulae to achieve the highest accuracy both in indoor and outdoor conditions [161164]. To combine both measures and achieve the highest accuracy in both aims, the proposed solution is the development of a system composed of two interconnected parts: (a) an inertial device or HUB (signal concentrator) located on the scapulae for tracking location and receiving the signal from (b) different micro-sensors (accelerometers, gyroscopes, etc.) placed on different anatomical locations to detect the specific accelerometry-based workload of each segment. These micro-sensors would send the signal to the HUB by wireless technology (Ant+, Bluetooth, among others) where it would be stored for subsequent analysis. In this way, a recent study proposed the monitoring of different body locations simultaneously (scapulae, lumbar region, knees and ankles) through multiple inertial devices that could be attached to the body with elastic bands and harness or with a specific one-piece sport jumpsuit with pockets [165].

Accelerometry-based workload indexes

Currently, from the raw data obtained by the accelerometer, the analysis of external workload is carried out from two main variables: impacts as a function of intensity ranges and PLTM in its different variants (2D, x-axis, y-axis, z-axis, slow). Workload quantification about the intensity of impacts has been used predominantly in rugby [92, 100103], soccer [42] and American football [99]. In rugby and American football the detection threshold is 10G [91, 92, 99, 103]. In soccer, the detection threshold is 5G, and the number of impacts ranges from 490±309.5 to 613.1±329.4 number of impacts [42]. In trying score games, a greater number of >5G impacts were found in rugby 895±325 [102], rugby union 1222±607 [103] and American football 951±192 [99]. This difference could be due to the lower intensity of physical contacts in soccer (disputes, tackles, charges) compared with rugby or American football (collisions, scrum, rucks, etc.), or different game duration and format. Therefore, it is important to analyze the specific demands of each sport modality and to adapt the indexes or threshold detection for an accuracy workload monitoring during training and competition contexts.

The most frequently used accelerometry-based index is PLTM, which represents the accumulative workload in the three axes of movement during all sessions. For the comparison between sports disciplines, the variable PLTM is normalized by total session time [min]. Studies that included time normalized PLTM reported different workloads across sports as soccer 10.18±2.12 [40] or 10.6–13.2±1.5–2.5 [43], netball 9.4–10.6±2.4–3.6 [70], MMA 15.37±1.71 [166], handball 9.18–9.76±0.6–1.4 [69], rugby union 7.6±0.6 [104], hockey 13.8–12.5±1.6–1.0 [44], lacrosse 7.6–9.9±1.5–2.7 [71], Australian football 15.1–16.3±0.9–1.4 [105] and rugby 7.2–10.4±0.8–2.0 [100]. These data confirm that each sport has specific demands regarding external workload, being the ranges as a consequence of the different playing positions. Therefore, it is necessary to analyze the competition workload to design the optimal stimulus during training sessions for sports performance enhancement.

Different investigations also use other PLTM-dependent variables such as their segmentation by axes (PLx, PLy, PLz) to analyze the specific contribution of each axis in the total workload on the technical-tactical skills or which axis is more related to fatigue during competition [40, 62, 65, 70, 106, 167]. Otherwise, PLslow quantifies the contribution of low-intensity workload (<2G) to the total workload of the players [90, 100, 104, 107, 108]. These two indexes allow higher accuracy and individualization of the demands performed by the athletes. The highest contribution to the external workload suffered by the athletes is from the vertical axis of movement, being over 50% of the total workload (y-axis > x-axis > z-axis). Also, the low-intensity workload represented between 35 and 50% of the cumulative PL. Therefore, the assessment of both indexes will be important for designing individualized technical-tactical-physical workloads and making possible the objective detection of players’ deficiencies and optimum performance value enhancement.

Finally, concerning the company that develops each device, other variables are found in sport sciences area such as Dynamic Stress Load [45], Body Load [109], Total Load [46], Force Load [32], Impulse Load [41], PLRT [27] or PLRE [26] to quantify the cumulative workload during training sessions or official matches in team sports. These indexes are based on the accelerometry raw data in the 3-axes of movement applying different algorithms and scaled values. This makes the comparability of data from different devices difficult [168]. The result is a very high to perfect correlation between accelerometry-based workload indexes with very large differences in absolute values [47].

Thanks to this great number of variables, it is possible to specifically analyze the accelerometry load in each sports discipline, both the accumulative load and the specific demands of skills/abilities, with the aim of individualizing the specific load in each sport in relation with player position or roles in competition. However, a consensus is necessary to be able to compare data among devices.

Accelerometer technical features

Most studies with the purpose to detect movement patterns in invasion team sports through accelerometers presented a sampling frequency of 100 Hz. This technical feature is important to ensure high data quality during data collection [169]. A lower sampling rate is related to lower accuracy [153]. For this reason, Migueles et al. [170] recommended the use of a minimum of 90 Hz when researchers are using the manufacturer methods, or 100 Hz when researchers are filtering and processing the signal on their own. Therefore, a sampling frequency of 100 Hz is enough to detect external workload in the three-axis of movement through accelerometers in team sports.

Other important technical features that should be considered are the planes of movement (2 planes x-y vs 3 planes x-y-z), the number of accelerometers that compose the device and the output range of each accelerometer. Most of the studies shown that triaxial accelerometers composed the inertial device used. This characteristic is fundamental to detect three-dimensional movement and, consequently, to calculate the external workload index, which requires the acceleration in the three axes [15, 47, 170]. On the other hand, only 9 studies specified the number of accelerometers used in the devices. The number of accelerometers is only important if the output range of each accelerometer is considered. WIMU PRO is composed of four accelerometers with specific output ranges ±16g, ±16g, ±32g and ± 400g [47, 48, 72] while Optimeye S5 is composed of three ±16g accelerometers [49]. This technical feature is very important due to the second device cannot detect the peak of force generated when a collision is over than 16g. Therefore, the number of accelerometers cannot be considered as a quality criterion without the output range of the accelerometers that compose the device. For this reason, both technical aspects should be described in the methods section to identify if the accelerometers can detect with high accuracy all movements or events evaluated (total workload and peak workload) during training and competition.

Finally, the most important technical feature is the validity and reliability of accelerometers. The reliability is the consistency of measure between devices and across time that allows the workload comparison between devices and between sessions, while the validity is the extent to which the scores actually represent the variable they are intended to [171]. In this systematic review, it is worrying that only 25.4% of the selected studies reported both validity and reliability, 31.4% only reliability and 8.5% only validity of accelerometers. Specifically, the validity and reliability of PL and MinimaxX [16, 19], PLRT and WIMU PRO [18, 21], Body Load and SPI-PRO [172] and Impulse Load and Zephyr Bioharness [173] have been evaluated previously. All devices and accelerometry-based variables presented satisfactory results, except BodyLoad [172].

Among studies that cited the reliability and validity of accelerometers, 15 investigations (i.e. 12.7%) cited the reliability and validity of other devices that were not used in their respective research. Investigations measured with Optimeye and Team 2.5 devices (Catapult Sports) [62, 67, 69, 73, 74, 104, 110112], ZXY Sportracking (Radionor Communications) [26], X8-mini (Gulfcoast Data Concept) [24, 25, 29], Actilife v12 (ActiGraph) [75], and Viper V2 (StatSport) [76] cited the validity study of Barrett et al. [19] and reliability of Boyd et al. [16] realized with MinimaxX devices (Catapult Sports). Noteworthy, 34.7% of the studies did not report the validity or reliability and did also not refer to literature findings for this purpose.

Therefore, the validity and reliability of the accelerometer-derived outcomes to determine how they can be effectively applied to individual and team sports is necessary. A consensus in this aspect should be reached for that companies need to assess their devices through an independent and standardized protocol that assure the accuracy and reproducibility of accelerometer-derived outcomes in different context and sports.

Sports modalities, sex and category

Most of the selected studies have been on Australian football, rugby, soccer and basketball. The rest of invasion team sports have aroused low research interest. Thanks to the Australian Centre of Microtechnological Research through the Project 2.5 “Technology of Communication to Athletes Monitoring” beginning to design a unique and non-intrusive device for sports monitoring in real-time in 2001 [14], the research topic has been centered on the most popular sports in this region (Australian football and rugby), developing specific variables such as impact/collision detection [92, 100103]. Later, from the results obtained and the high socioeconomic impact, this technology began to be used in the most popular sports in Europe and the United States such as soccer and basketball [6, 27, 29, 40, 50, 51, 63, 64].

This socioeconomic aspect is also found in the sports category and sex. The majority of studies were performed at the elite and professional-level (77%) with men players (87%). This has meant that numerous studies have analyzed the relationship between accelerometry-based workload indexes and low-cost objective and subjective monitoring methods due to the low economic resources in the rest of the categories. Different research has related the accelerometry-based indexes with heart rate workload indexes such as training impulse (TRIMPS), and Edwards or summated heart rate zones (SHRZ) finding very high to almost perfect validity values [8, 17, 64]. Besides, it also has been related to sRPE [17, 52, 63] or subjective tools such as Integral System of Training Task Analysis (SIATE) [53, 174] with high to very high correlation values. Therefore, due to the low economic resources in non-professional categories and women’s sports, these alternative methods could be used for workload monitoring subjectively, both at internal and external workload levels. In addition to finding alternative methods for workload monitoring, it is the task of researchers and professional teams to help knowledge development through research in these sports populations where the largest number of athletes and licenses are to be found.

Although there are existing correlations between accelerometry-based workload with external subjective (SIATE) and internal subjective (sRPE) and objective (TRIMPS, Edwards and SHRZ) workload indexes, the use of accelerometers is recommendable to quantify external workloads objectively. Their reliability, precision and sensitivity are greater compared to other external workload quantification systems such as automatic and semiautomatic time-motion analysis (video tracking, GNSS or LPM) [26, 30]. Automatic and semiautomatic TMA systems may underestimate the external workload demands because static high-intensity actions (jumps, collisions, etc.) are classified in the low-intensity actions group [26]. Therefore, recent investigations identified that microtechnologies (e.g. wearable microsensors and accelerometers) may represent a valid and practical alternative to TMA and offer distinct advantages compared with TMA such as the relative simplification to analyze data using either proprietary or used-defined algorithms that quantify movement, detect forces generated by the athlete related to gravity, the non-invasiveness, the measuring of internal and external workload simultaneously and the real-time feedback to minimize fatigue and injury risk while ultimately improving performance [18, 30, 33, 47, 149].


While the results of this systematic review have provided a global overview of accelerometry-based workload demands in invasion team sports, considering multiple factors such as journals, context, categories, sex, body locations, brands and devices, technical features of accelerometers, variables and specific sports, some limitations to the study must be acknowledged. Firstly, only studies from Web of Science databases, PubMed and Scopus wrote in English were included, thereby potentially overlooking other relevant publications in other languages. Besides, although the study topic was invasion team sports, it would be interesting to include in a future systematic review all team and individual sports to achieve a better overview.

Conclusions and practical applications

This systematic review shows all studies that carried out workload monitoring through accelerometry-based indexes in invasion team sports during training and competition contexts. From the findings of the present systematic review, different conclusions could be shown:

  1. There has been an increase in workload monitoring through accelerometry-based indexes in training and competition, for which previous validity and reliability analysis is necessary both to evaluate the accuracy and allow comparison among and within units.
  2. A large number of accelerometry-based workload indexes were found depending on the device manufacturing companies. The most widely used is PLTM, but index unification among companies is required to be able to compare results among studies.
  3. The upper back (scapulae) is the most common body location used to place the inertial device on the players due to the better tracking signal reception by Global Navigation Satellite Systems in outdoor and Local Position Measurement in indoor conditions. New research should quantify the workload not only on the scapulae but in different body segments simultaneously in training and competition contexts in order to identify the real workload of the athlete during skill performance and sport locomotion more accurately.

Supporting information

S1 Table. Selected articles in goal striking games.


S2 Table. Selected articles in goal throwing games.


S3 Table. Selected articles in try-scoring games.


S4 Table. Quality criteria used to analyze the quantitative publications (extracted from Law et al. [37]).



  1. 1. Bourdon PC, Cardinale M, Murray A, Gastin P, Kellmann M, Varley MC, et al. Monitoring Athlete Training Loads: Consensus Statement. Int J Sports Physiol Perform. 2017;12:S2-161–S2-170.
  2. 2. Brink MS, Nederhof E, Visscher C, Schmikli SL, Lemmink KA. Monitoring load, recovery, and performance in young elite soccer players. J Strength Cond Res. 2010;24:597–603. pmid:20145570
  3. 3. Paulson TAW, Mason B, Rhodes J, Goosey-Tolfrey VL. Individualized Internal and External Training Load Relationships in Elite Wheelchair Rugby Players. Front Physiol. 2015;6:388. pmid:26733881
  4. 4. Akubat I, Barrett S, Abt G. Integrating the internal and external training loads in soccer. Int J Sports Physiol Perform. 2014;9:457–62. pmid:23475154
  5. 5. Halson SL. Monitoring Training Load to Understand Fatigue in Athletes. Sports Med. 2014;44:139–47.
  6. 6. Buchheit M, Lacome M, Cholley Y, Simpson BM. Neuromuscular Responses to Conditioned Soccer Sessions Assessed via GPS-Embedded Accelerometers: Insights Into Tactical Periodization. Int J Sports Physiol Perform. 2018;13:577–83. pmid:28872370
  7. 7. Gabbett TJ. The training—injury prevention paradox: should athletes be training smarter and harder? Br J Sports Med. 2016;50:273–280. pmid:26758673
  8. 8. Scanlan AT, Wen N, Tucker PS, Dalbo VJ. The relationships between internal and external training load models during basketball training. J Strength Cond Res. 2014;28:2397–2405. pmid:24662233
  9. 9. Barris S, Button C. A review of vision-based motion analysis in sport. Sports Med. 2008;38:1025–1043. pmid:19026019
  10. 10. Carling C, Reilly T, Williams AM. Performance assessment for field sports. London; New York: Routledge; 2009.
  11. 11. Atkinson G, Nevill AM. Statistical methods for assessing measurement error (reliability) in variables relevant to sports medicine. Sports Med. 1998;26:217–238. pmid:9820922
  12. 12. Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res. 1999;8:135–160. pmid:10501650
  13. 13. O’Donoghue P. Research methods for sports performance analysis. London: Routledge; 2010.
  14. 14. Wu F, Zhang K, Zhu M, Mackintosh C, Rice T, Gore C, et al. An Investigation of an Integrated Low-cost GPS, INS and Magnetometer System for Sport Applications. Fort Worth, TX; 2007. p. 113–20.
  15. 15. Cummins C, Orr R, O’Connor H, West C. Global Positioning Systems (GPS) and Microtechnology Sensors in Team Sports: A Systematic Review. Sports Med. 2013;43:1025–42. pmid:23812857
  16. 16. Boyd LJ, Ball K, Aughey RJ. The reliability of MinimaxX accelerometers for measuring physical activity in Australian football. Int J Sports Physiol Perform. 2011;6:311–321. pmid:21911857
  17. 17. Casamichana D, Castellano J, Calleja-Gonzalez J, San Román J, Castagna C. Relationship Between Indicators of Training Load in Soccer Players. J Strength Cond Res. 2013;27:369–74. pmid:22465992
  18. 18. Gomez-Carmona CD, Bastida-Castillo A, González-Custodio A, Olcina G, Pino-Ortega J. Using an inertial device (WIMU PROTM) to quantify neuromuscular load in running: Reliability, convergent validity and the influence of type of surface and device location. J Strength Cond Res. 2019;34:365–373.
  19. 19. Barrett S, Midgley A, Lovell R. PlayerLoadTM: Reliability, Convergent Validity, and Influence of Unit Position during Treadmill Running. Int J Sports Physiol Perform. 2014;9:945–52. pmid:24622625
  20. 20. Johnston RJ, Watsford ML, Pine MJ, Spurrs RW, Murphy AJ, Pruyn EC. The validity and reliability of 5-Hz global positioning system units to measure team sport movement demands. J Strength Cond Res. 2012;26:758–765. pmid:22310508
  21. 21. Gómez-Carmona CD, Bastida-Castillo A, García-Rubio J, Ibáñez SJ, Pino-Ortega J. Static and dynamic reliability of WIMU PROTM accelerometers according to anatomical placement. Proc Inst Mech Eng Part P J Sports Eng Technol. 2019;233:238–48.
  22. 22. Barreira P, Robinson MA, Drust B, Nedergaard N, Raja Azidin RMF, Vanrenterghem J. Mechanical Player Load using trunk-mounted accelerometry in football: Is it a reliable, task- and player-specific observation? J Sports Sci. 2017;35:1674–81. pmid:27598850
  23. 23. Read B, Edwards P. Blue Section. Introducing Formal Games. Teach Child Play Games. 1st edition. Leeds, UK: White Line Publishing Services; 1992. p. 61–5.
  24. 24. Bailey JA, Gastin PB, Mackey L, Dwyer DB. The Player Load Associated with Typical Activities in Elite Netball. Int J Sports Physiol Perform. 2017;12:1218–1223. pmid:28182504
  25. 25. Young CM, Gastin PB, Sanders N, Mackey L, Dwyer DB. Player Load in Elite Netball: Match, Training, and Positional Comparisons. Int J Sports Physiol Perform. 2016;11:1074–1079. pmid:27001768
  26. 26. Dalen T, Jørgen I, Gertjan E, Havard HG, Ulrik W. Player Load, Acceleration, and Deceleration During Forty-Five Competitive Matches of Elite Soccer. J Strength Cond Res. 2016;30:351–359. pmid:26057190
  27. 27. Pino-Ortega J, Rojas-Valverde D, Gómez-Carmona CD, Bastida-Castillo A, Hernández-Belmonte A, García-Rubio J, et al. Impact of Contextual Factors on External Load During a Congested-Fixture Tournament in Elite U’18 Basketball Players. Front Psychol. 2019;10:1100. pmid:31156514
  28. 28. Reina M, García-Rubio J, Feu S, Ibáñez SJ. Training and competition load monitoring and analysis of women’s amateur basketball by playing position: approach study. Front Psychol. 2018;9:2689. pmid:30687163
  29. 29. Schelling X, Torres L. Accelerometer Load Profiles for Basketball-Specific Drills in Elite Players. J Sports Sci Med. 2016;15:585–591. pmid:27928203
  30. 30. Fox JL, Scanlan AT, Stanton R. A Review of Player Monitoring Approaches in Basketball: Current Trends and Future Directions. J Strength Cond Res. 2017;31:2021–9. pmid:28445227
  31. 31. Carey DL, Blanch P, Ong K-L, Crossley KM, Crow J, Morris ME. Training loads and injury risk in Australian football—differing acute: chronic workload ratios influence match injury risk. Br J Sports Med. 2017;51:1215–1220. pmid:27789430
  32. 32. Colby MJ, Dawson B, Heasman J, Rogalski B, Gabbett TJ. Accelerometer and GPS-derived running loads and injury risk in elite Australian footballers. J Strength Cond Res. 2014;28:2244–2252. pmid:25054573
  33. 33. Ferioli D, Schelling X, Bosio A, La Torre A, Rucco D, Rampinini E. Match Activities in Basketball Games: Comparison Between Different Competitive Levels. J Strength Cond Res. 2020;34:172–182. pmid:30741861
  34. 34. Ato M, López-García JJ, Benavente A. Un sistema de clasificación de los diseños de investigación en psicología. An Psicol. 2013;29:1038–1059.
  35. 35. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4:1. pmid:25554246
  36. 36. Read B, Edwards P. Blue Section. Invasion Games. Teach Child Play Games. 1st edition. Leeds, UK: White Line Publishing Services; 1992. p. 91–139.
  37. 37. Law M, Stewart D, Pollock N, Letts L, Bosch J, Westmorland M. Critical review form: quantitative studies. Hamilton: MacMaster University; 1998.
  38. 38. Landis JR, Koch GG. The Measurement of Observer Agreement for Categorical Data. Biometrics. 1977;33:159–74. pmid:843571
  39. 39. Gómez-Carmona C, Gamonales J, Pino-Ortega J, Ibáñez S. Comparative Analysis of Load Profile between Small-Sided Games and Official Matches in Youth Soccer Players. Sports. 2018;6:173.
  40. 40. Beenham M, Barron DJ, Fry J, Hurst HH, Figueirdo A, Atkins S. A Comparison of GPS Workload Demands in Match Play and Small-Sided Games by the Positional Role in Youth Soccer. J Hum Kinet. 2017;22:129–137.
  41. 41. Gentles J, Coniglio C, Besemer M, Morgan J, Mahnken M. The Demands of a Women’s College Soccer Season. Sports. 2018;6:16.
  42. 42. Abade EA, Gonçalves BV, Leite NM, Sampaio JE. Time-Motion and Physiological Profile of Football Training Sessions Performed by Under-15, Under-17, and Under-19 Elite Portuguese Players. Int J Sports Physiol Perform. 2014;9:463–70. pmid:23920425
  43. 43. Trewin J, Meylan C, Varley MC, Cronin J. The match-to-match variation of match-running in elite female soccer. J Sci Med Sport. 2018;21:196–201. pmid:28595867
  44. 44. Polglaze T, Dawson B, Hiscock DJ, Peeling P. A Comparative Analysis of Accelerometer and Time–Motion Data in Elite Men’s Hockey Training and Competition. Int J Sports Physiol Perform. 2015;10:446–451. pmid:25364940
  45. 45. Gaudino P, Iaia FM, Strudwick AJ, Hawkins RD, Alberti G, Atkinson G, et al. Factors Influencing Perception of Effort (Session Rating of Perceived Exertion) during Elite Soccer Training. Int J Sports Physiol Perform. 2015;10:860–864. pmid:25671338
  46. 46. Bowen L, Gross AS, Gimpel M, Li F-X. Accumulated workloads and the acute:chronic workload ratio relate to injury risk in elite youth football players. Br J Sports Med. 2017;51:452–459. pmid:27450360
  47. 47. Gómez-Carmona CD, Pino-Ortega J, Sánchez-Ureña B, Ibáñez SJ, Rojas-Valverde D. Accelerometry-Based External Load Indicators in Sport: Too Many Options, Same Practical Outcome? Int J Environ Res Public Health. 2019;16:5101.
  48. 48. Oliva-Lozano JM, Rojas-Valverde D, Gómez-Carmona CD, Fortes V, Pino-Ortega J. Impact Of Contextual Variables On The Representative External Load Profile Of Spanish Professional Soccer Match-Play: A Full Season Study. Eur J Sport Sci. 2020;Epub:Ahead of print.
  49. 49. Enes A, Oneda G, Alves DL, Palumbo D de P, Cruz R, Moiano Junior JVM, et al. Determinant Factors of the Match-Based Internal Load in Elite Soccer Players. Res Q Exerc Sport. 2020;Epub:Ahead of print.
  50. 50. Jones RN, Greig M, Mawéné Y, Barrow J, Page RM. The influence of short-term fixture congestion on position specific match running performance and external loading patterns in English professional soccer. J Sports Sci. 2019;37:1338–1346. pmid:30563419
  51. 51. Rabbani A, Kargarfard M, Castagna C, Clemente FM, Twist C. Associations Between Selected Training Stress Measures and Fitness Changes in Male Soccer Players. Int J Sports Physiol Perform. 2019;14:1050–1057. pmid:30676148
  52. 52. Winder N, Russell M, Naughton R, Harper L. The Impact of 120 Minutes of Match-Play on Recovery and Subsequent Match Performance: A Case Report in Professional Soccer Players. Sports. 2018;6:22.
  53. 53. Gómez-Carmona CD, Gamonales-Puerto JM, Feu S, Ibáñez SJ. Study of internal and external load by different instruments. A case study in grassroots. Sport Sci J Sch Sport Phys Educ Psychomot. 2019;5:444–468.
  54. 54. Barrett S, Midgley A, Reeves M, Joel T, Franklin E, Heyworth R, et al. The within-match patterns of locomotor efficiency during Professional Soccer match play: Implications for Injury risk? J Sci Med Sport. 2016;19:810–815. pmid:26787341
  55. 55. Barron DJ, Atkins S, Edmundson C, Fewtrell D. Accelerometer derived load according to playing position in competitive youth soccer. Int J Perform Anal Sport. 2014;14:734–743.
  56. 56. Clemente FM. Associations between wellness and internal and external load variables in two intermittent small-sided soccer games. Physiol Behav. 2018;197:9–14. pmid:30236526
  57. 57. Clemente FM, Praça GM, Bredt S da GT, Linden CMI van der, Serra-Olivares J. External Load Variations Between Medium- and Large-Sided Soccer Games: Ball Possession Games vs Regular Games with Small Goals. J Hum Kinet. 2019;70:191–198. pmid:31915489
  58. 58. Clemente FM, Rabbani A, Conte D, Castillo D, Afonso J, Truman Clark CC, et al. Training/Match External Load Ratios in Professional Soccer Players: A Full-Season Study. Int J Environ Res Public Health. 2019;16:3057.
  59. 59. García-Ceberino JM, Antúnez A, Feu S, Ibáñez SJ. Quantification of Internal and External Load in School Football According to Gender and Teaching Methodology. Int J Environ Res Public Health. 2020;17:344.
  60. 60. Reche-Soto P, Cardona-Nieto D, Diaz-Suarez A, Bastida-Castillo A, Gomez-Carmona C, Garcia-Rubio J, et al. Player Load and Metabolic Power Dynamics as Load Quantifiers in Soccer. J Hum Kinet. 2019;69:259–269. pmid:31666908
  61. 61. Strauss A, Sparks M, Pienaar C. The Use of GPS Analysis to Quantify the Internal and External Match Demands of Semi-Elite Level Female Soccer Players during a Tournament. J Sports Sci Med. 2019;11:73–81.
  62. 62. Peterson KD, Quiggle GT. Tensiomyographical responses to accelerometer loads in female collegiate basketball players. J Sports Sci. 2017;35:2334–2341. pmid:27937967
  63. 63. Svilar L, Castellano J, Jukic I. Load monitoring system in top-level basketball team: relationship between external and internal training load. Kinesiology. 2018;50:25–33.
  64. 64. Fox JL, Stanton R, Scanlan AT. A Comparison of Training and Competition Demands in Semiprofessional Male Basketball Players. Res Q Exerc Sport. 2018;89:103–111. pmid:29334021
  65. 65. Chandler PT, Pinder SJ, Curran JD, Gabbett TJ. Physical demands of training and competition in collegiate netball players. J Strength Cond Res. 2014;28:2732–2737. pmid:24983848
  66. 66. Montgomery PG, Pyne DB, Minahan CL. The Physical and Physiological Demands of Basketball Training and Competition. Int J Sports Physiol Perform. 2010;5:75–86. pmid:20308698
  67. 67. Alonso E, Miranda N, Zhang S, Sosa C, Trapero J, Lorenzo J, et al. Peak Match Demands in Young Basketball Players: Approach and Applications. Int J Environ Res Public Health. 2020;17:2256.
  68. 68. Graham S, Zois J, Aughey R, Duthie G. The peak player loadTM of state-level netball matches. J Sci Med Sport. 2020;23:189–193. pmid:31704025
  69. 69. Wik EH, Luteberget LS, Spencer M. Activity Profiles in International Women’s Team Handball Using PlayerLoad. Int J Sports Physiol Perform. 2017;12:934–942. pmid:27967272
  70. 70. Cormack SJ, Smith RL, Mooney MM, Young WB, O’Brien BJ. Accelerometer Load as a Measure of Activity Profile in Different Standards of Netball Match Play. Int J Sports Physiol Perform. 2014;9:283–291. pmid:23799824
  71. 71. Polley CS, Cormack SJ, Gabbett TJ, Polglaze T. Activity Profile of High-Level Australian Lacrosse Players: J Strength Cond Res. 2015;29:126–136. pmid:25264672
  72. 72. Fernández-Leo A, Gómez-Carmona CD, García-Rubio J, Ibáñez SJ. Influence of Contextual Variables on Physical and Technical Performance in Male Amateur Basketball: A Case Study. Int J Environ Res Public Health. 2020;17:1193.
  73. 73. Fox J, O’Grady C, Scanlan AT. Game schedule congestion affects weekly workloads but not individual game demands in semi-professional basketball. Biol Sport. 2020;37:59–67. pmid:32205911
  74. 74. Heishman A, Miller RM, Freitas EDS, Brown BS, Daub BD, Bemben MG. Monitoring External Training Loads and Neuromuscular Performance For Division I Basketball Players Over the Pre-Season. J Sports Sci Med. 2020;19:204–212. pmid:32132844
  75. 75. Staunton C, Wundersitz D, Gordon B, Kingsley M. Accelerometry-Derived Relative Exercise Intensities in Elite Women’s Basketball. Int J Sports Med. 2018;39:822–827. pmid:29986346
  76. 76. Vázquez-Guerrero J, Suarez-Arrones L, Casamichana Gómez D, Rodas G. Comparing external total load, acceleration and deceleration outputs in elite basketball players across positions during match play. Kinesiology. 2018;50:228–234.
  77. 77. Birdsey LP, Weston M, Russell M, Johnston M, Cook CJ, Kilduff LP. Neuromuscular, physiological and perceptual responses to an elite netball tournament. J Sports Sci. 2019;37:2169–2174. pmid:31159643
  78. 78. Brooks ER, Benson AC, Fox AS, Bruce LM. Physical movement demands of elite-level netball match-play as measured by an indoor positioning system. J Sports Sci. 2020;Epub:ahead of print.
  79. 79. Fox JL, Stanton R, Sargent C, O’Grady CJ, Scanlan AT. The Impact of Contextual Factors on Game Demands in Starting, Semiprofessional, Male Basketball Players. Int J Sports Physiol Perform. 2020;15:450–456.
  80. 80. García-Santos D, Pino-Ortega J, García-Rubio J, Vaquera A, Ibáñez SJ. Internal and External Demands in Basketball Referees during the U-16 European Women’s Championship. Int J Environ Res Public Health. 2019;16:3421.
  81. 81. Heishman A, Peak K, Miller R, Brown B, Daub B, Freitas E, et al. Associations Between Two Athlete Monitoring Systems Used to Quantify External Training Loads in Basketball Players. Sports. 2020;8:33.
  82. 82. King DA, Cummins C, Hume PA, Clark TN. Physical Demands of Amateur Domestic and Representative Netball in One Season in New Zealand Assessed Using Heart Rate and Movement Analysis. J Strength Cond Res. 2018;Epub:ahead of print.
  83. 83. Kniubaite A, Skarbalius A, Clemente FM, Conte D. Quantification of external and internal match loads in elite female team handball. Biol Sport. 2019;36:311–316. pmid:31938001
  84. 84. Mancha-Triguero D, Reina M, Baquero B, García-Rubio J, Ibáñez SJ. Analysis of the competitive load in u16 handballers as a function of the final result. E-Balonmano Com Rev Cienc Deporte. 2018;14:99–108.
  85. 85. O’Grady CJ, Dalbo VJ, Teramoto M, Fox JL, Scanlan AT. External Workload Can Be Anticipated During 5 vs. 5 Games-Based Drills in Basketball Players: An Exploratory Study. Int J Environ Res Public Health. 2020;17:2103.
  86. 86. Portes R, Jiménez SL, Navarro RM, Scanlan AT, Gómez M-Á. Comparing the External Loads Encountered during Competition between Elite, Junior Male and Female Basketball Players. Int J Environ Res Public Health. 2020;17:1456.
  87. 87. van Gogh MJ, Wallace LK, Coutts AJ. Positional Demands and Physical Activity Profiles of Netball: J Strength Cond Res. 2020;34:1422–1430. pmid:32329990
  88. 88. Vázquez-Guerrero J, Fernández-Valdés B, Gonçalves B, Sampaio JE. Changes in Locomotor Ratio During Basketball Game Quarters From Elite Under-18 Teams. Front Psychol. 2019;10:2163. pmid:31616351
  89. 89. Graham SR, Cormack S, Parfitt G, Eston R. Relationships Between Model Predicted and Actual Match Performance in Professional Australian Footballers During an In-Season Training Macrocycle. Int J Sports Physiol Perform. 2018;13:339–346. pmid:28714739
  90. 90. Boyd LJ, Ball K, Aughey RJ. Quantifying external load in Australian football matches and training using accelerometers. Int J Sports Physiol Perform. 2013;8:44–51. pmid:22869637
  91. 91. Gabbett T, Jenkins D, Abernethy B. Physical collisions and injury during professional rugby league skills training. J Sci Med Sport. 2010;13:578–583. pmid:20483661
  92. 92. Gabbett T. Quantifying the Physical Demands of Collision Sports: Does Microsensor Technology Measure What It Claims to Measure? J Strength Cond Res. 2013;27:2319–2322. pmid:23090320
  93. 93. Ritchie D, Hopkins WG, Buchheit M, Cordy J, Bartlett JD. Quantification of Training and Competition Load across a Season in an Elite Australian Football Club. Int J Sports Physiol Perform. 2016;11:474–479. pmid:26355304
  94. 94. Johnston RD, Murray NB, Austin DJ, Duthie G. Peak Movement and Technical Demands of Professional Australian Football Competition. J Strength Cond Res. 2019;Epub:Ahead of Print.
  95. 95. Wellman AD, Coad SC, Flynn PJ, Siam TK, McLellan CP. Comparison of Preseason and In-Season Practice and Game Loads in National Collegiate Athletic Association Division I Football Players: J Strength Cond Res. 2019;33:1020–1027.
  96. 96. Cummins C, McLean B, Halaki M, Orr R. Positional Differences in External On-Field Load During Specific Drill Classifications Over a Professional Rugby League Preseason. Int J Sports Physiol Perform. 2017;12:764–776. pmid:27834500
  97. 97. Johnston RD, Murray NB, Austin DJ. The influence of pre-season training loads on in-season match activities in professional Australian football players. Sci Med Footb. 2019;3:143–149.
  98. 98. Gibson NE, Boyd AJ, Murray AM. Countermovement jump is not affected during final competition preparation periods in elite rugby sevens players. J Strength Cond Res. 2016;30:777–783. pmid:26332780
  99. 99. Wellman AD, Coad SC, Goulet GC, Coffey VG, McLellan CP. Quantification of Accelerometer Derived Impacts Associated With Competitive Games in NCAA Division I College Football Players. J Strength Cond Res. 2017;31:330–338. pmid:27227790
  100. 100. Gabbett T. Relationship Between Accelerometer Load, Collisions, and Repeated High-Intensity Effort Activity in Rugby League Players: J Strength Cond Res. 2015;29:3424–3431. pmid:26196661
  101. 101. Gabbett TJ, Seibold AJ. Relationship between tests of physical qualities, team selection, and physical match performance in semiprofessional rugby league players. J Strength Cond Res. 2013;27:3259–3265. pmid:23442268
  102. 102. McLellan CP, Lovell DI. Neuromuscular responses to impact and collision during elite rugby league match play. J Strength Cond Res. 2012;26:1431–1440. pmid:22516913
  103. 103. Suárez-Arrones LJ, Portillo LJ, González-Ravé JM, Muñoz VE, Sanchez F. Match running performance in Spanish elite male rugby union using global positioning system. Isokinet Exerc Sci. 2012;20:77–83.
  104. 104. Read DB, Jones B, Phibbs PJ, Roe GAB, Darrall-Jones JD, Weakley JJS, et al. Physical Demands of Representative Match-Play in Adolescent Rugby Union: J Strength Cond Res. 2017;31:1290–1296. pmid:27548792
  105. 105. Mooney M, Cormack S, O’Brien B, Coutts AJ. Do physical capacity and interchange rest periods influence match exercise-intensity profile in Australian football? Int J Sports Physiol Perform. 2013;8:165–172. pmid:23428488
  106. 106. Cormack SJ, Mooney MG, Morgan W, McGuigan MR. Influence of neuromuscular fatigue on accelerometer load in elite Australian football players. Int J Sports Physiol Perform. 2013;8:373–378. pmid:23170747
  107. 107. Gallo T, Cormack S, Gabbett T, Williams M, Lorenzen C. Characteristics impacting on session rating of perceived exertion training load in Australian footballers. J Sports Sci. 2015;33:467–475. pmid:25113820
  108. 108. Roe G, Halkier M, Beggs C, Till K, Jones B. The Use of Accelerometers to Quantify Collisions and Running Demands of Rugby Union Match-Play. Int J Perform Anal Sport. 2016;16:590–601.
  109. 109. Cunniffe B, Proctor W, Baker JS, Davies B. An evaluation of the physiological demands of elite rugby union using global positioning system tracking software. J Strength Cond Res. 2009;23:1195–1203. pmid:19528840
  110. 110. Johnston RD, Gabbett TJ, Jenkins DG, Hulin BT. Influence of physical qualities on post-match fatigue in rugby league players. J Sci Med Sport. 2015;18:209–213. pmid:24594214
  111. 111. Rowell AE, Aughey RJ, Clubb J, Cormack SJ. A Standardized Small Sided Game Can Be Used to Monitor Neuromuscular Fatigue in Professional A-League Football Players. Front Physiol. 2018;9:1011. pmid:30131704
  112. 112. Phibbs PJ, Jones B, Read DB, Roe GAB, Darrall-Jones J, Weakley JJS, et al. The appropriateness of training exposures for match-play preparation in adolescent schoolboy and academy rugby union players. J Sports Sci. 2018;36:704–709. pmid:28562186
  113. 113. Bayliff GE, Jacobson BH, Moghaddam M, Estrada C. Global Positioning System Monitoring of Selected Physical Demands of NCAA Division I Football Players During Games: J Strength Cond Res. 2019;33:1185–1191. pmid:30908375
  114. 114. Carey DL, Ong K, Whiteley R, Crossley KM, Crow J, Morris ME. Predictive Modelling of Training Loads and Injury in Australian Football. Int J Comput Sci Sport. 2018;17:49–66.
  115. 115. Coad S, Gray B, McLellan C. Seasonal Analysis of Mucosal Immunological Function and Physical Demands in Professional Australian Rules Footballers. Int J Sports Physiol Perform. 2016;11:574–580. pmid:26389779
  116. 116. Coad S, Gray B, Wehbe G, McLellan C. Physical Demands and Salivary Immunoglobulin a Responses of Elite Australian Rules Football Athletes to Match Play. Int J Sports Physiol Perform. 2015;10:613–617. pmid:25561572
  117. 117. Cummins C, Welch M, Inkster B, Cupples B, Weaving D, Jones B, et al. Modelling the relationships between volume, intensity and injury-risk in professional rugby league players. J Sci Med Sport. 2019;22:653–660. pmid:30651223
  118. 118. Davies MJ, Young W, Farrow D, Bahnert A. Comparison of agility demands of small-sided games in elite Australian football. Int J Sports Physiol Perform. 2013;8:139–147. pmid:22869639
  119. 119. Esmaeili A, Hopkins WG, Stewart AM, Elias GP, Lazarus BH, Aughey RJ. The Individual and Combined Effects of Multiple Factors on the Risk of Soft Tissue Non-contact Injuries in Elite Team Sport Athletes. Front Physiol. 2018;9:1280. pmid:30333756
  120. 120. Fleay B, Joyce C, Banyard H, Woods CT. Manipulating Field Dimensions During Small-sided Games Impacts the Technical and Physical Profiles of Australian Footballers: J Strength Cond Res. 2018;32:2039–2044. pmid:29337834
  121. 121. Gallo TF, Cormack SJ, Gabbett TJ, Lorenzen CH. Pre-training perceived wellness impacts training output in Australian football players. J Sports Sci. 2016;34:1445–1451. pmid:26637525
  122. 122. Gastin PB, McLean O, Spittle M, Breed RVP. Quantification of tackling demands in professional Australian football using integrated wearable athlete tracking technology. J Sci Med Sport. 2013;16:589–593. pmid:23433634
  123. 123. Garrett J, Graham SR, Eston RG, Burgess DJ, Garrett LJ, Jakeman J, et al. A Novel Method of Assessment for Monitoring Neuromuscular Fatigue Within Australian Rules Football Players. Int J Sports Physiol Perform. 2019;14:598–605. pmid:30427228
  124. 124. Goodale TL, Gabbett TJ, Tsai M-C, Stellingwerff T, Sheppard J. The Effect of Contextual Factors on Physiological and Activity Profiles in International Women’s Rugby Sevens. Int J Sports Physiol Perform. 2017;12:370–376. pmid:27347729
  125. 125. Govus AD, Coutts A, Duffield R, Murray A, Fullagar H. Relationship Between Pretraining Subjective Wellness Measures, Player Load, and Rating-of-Perceived-Exertion Training Load in American College Football. Int J Sports Physiol Perform. 2018;13:95–101. pmid:28488913
  126. 126. Highton J, Mullen T, Norris J, Oxendale C, Twist C. The Unsuitability of Energy Expenditure Derived From Microtechnology for Assessing Internal Load in Collision-Based Activities. Int J Sports Physiol Perform. 2017;12:264–267. pmid:27193085
  127. 127. Hogarth LW, Burkett BJ, McKean MR. Influence of Yo-Yo IR2 Scores on Internal and External Workloads and Fatigue Responses of Tag Football Players during Tournament Competition. Fisher G, editor. PLOS ONE. 2015;10:e0140547. pmid:26465599
  128. 128. Johnston RD, Devlin P, Wade JA, Duthie GM. There Is Little Difference in the Peak Movement Demands of Professional and Semi-Professional Rugby League Competition. Front Physiol. 2019;10:1285. pmid:31681000
  129. 129. Jones M, West D, Crewther B, Cook C, Kilduf L. Quantifying positional and temporal movement patterns in professional rugby union using global positioning system. Eur J Sport Sci. 2015; 15:488–496. pmid:25675258
  130. 130. Kempton T, Sullivan C, Bilsborough JC, Cordy J, Coutts AJ. Match-to-match variation in physical activity and technical skill measures in professional Australian Football. J Sci Med Sport. 2015;18:109–113. pmid:24444753
  131. 131. Li RT, Salata MJ, Rambhia S, Sheehan J, Voos JE. Does Overexertion Correlate With Increased Injury? The Relationship Between Player Workload and Soft Tissue Injury in Professional American Football Players Using Wearable Technology. Sports Health Multidiscip Approach. 2020;12:66–73.
  132. 132. McLaren SJ, Weston M, Smith A, Cramb R, Portas MD. Variability of physical performance and player match loads in professional rugby union. J Sci Med Sport. 2016;19:493–497. pmid:26118848
  133. 133. Murray A, Buttfield A, Simpkin A, Sproule J, Turner AP. Variability of within-step acceleration and daily wellness monitoring in Collegiate American Football. J Sci Med Sport. 2019;22:488–493. pmid:30442548
  134. 134. Owen SM, Venter RE, du Toit S, Kraak WJ. Acceleratory match-play demands of a Super Rugby team over a competitive season. J Sports Sci. 2015;33:2061–2069. pmid:25846204
  135. 135. Phibbs PJ, Jones B, Roe GA, Read DB, Darrall-Jones J, Weakley JJ, et al. We know they train, but what do they do? Implications for coaches working with adolescent rugby union players. Int J Sports Sci Coach. 2017;12:175–182.
  136. 136. Phibbs PJ, Jones B, Roe G, Read DB, Darrall-Jones J, Weakley J, et al. Organized Chaos in Late Specialization Team Sports: Weekly Training Loads of Elite Adolescent Rugby Union Players. J Strength Cond Res. 2018;32:1316–1323. pmid:29683915
  137. 137. Read DB, Jones B, Phibbs PJ, Roe GAB, Darrall-Jones J, Weakley JJS, et al. The physical characteristics of match-play in English schoolboy and academy rugby union. J Sports Sci. 2018;36:645–650. pmid:28514202
  138. 138. Roe G, Darrall-Jones J, Till K, Phibbs P, Read D, Weakley J, et al. The effect of physical contact on changes in fatigue markers following rugby union field-based training. Eur J Sport Sci. 2017;17:647–655. pmid:28276911
  139. 139. Rowell AE, Aughey RJ, Hopkins WG, Stewart AM, Cormack SJ. Identification of Sensitive Measures of Recovery After External Load From Football Match Play. Int J Sports Physiol Perform. 2017;12:969–976. pmid:27967334
  140. 140. Sullivan C, Bilsborough JC, Cianciosi M, Hocking J, Cordy J, Coutts AJ. Match score affects activity profile and skill performance in professional Australian Football players. J Sci Med Sport. 2014;17:326–331. pmid:23770325
  141. 141. Tee JC, Coopoo Y, Lambert M. Pacing characteristics of whole and part-game players in professional rugby union. Eur J Sport Sci. 2019;Epub:ahead of print.
  142. 142. Twist C, Highton J, Daniels M, Mill N, Close G. Player Responses to Match and Training Demands During an Intensified Fixture Schedule in Professional Rugby League: A Case Study. Int J Sports Physiol Perform. 2017;12:1093–1099. pmid:28095070
  143. 143. Ward PA, Ramsden S, Coutts AJ, Hulton AT, Drust B. Positional Differences in Running and Nonrunning Activities During Elite American Football Training: J Strength Cond Res. 2018;32:2072–2084. pmid:29176385
  144. 144. Weaving D, Marshall P, Earle K, Nevill A, Abt G. Combining Internal- and External-Training-Load Measures in Professional Rugby League. Int J Sports Physiol Perform. 2014;9:905–912. pmid:24589469
  145. 145. Weaving D, Jones B, Marshall P, Till K, Abt G. Multiple Measures are Needed to Quantify Training Loads in Professional Rugby League. Int J Sports Med. 2017;38:735–740. pmid:28783849
  146. 146. Weaving D, Dalton NE, Black C, Darrall-Jones J, Phibbs PJ, Gray M, et al. The Same Story or a Unique Novel? Within-Participant Principal-Component Analysis of Measures of Training Load in Professional Rugby Union Skills Training. Int J Sports Physiol Perform. 2018;13:1175–1181. pmid:29584514
  147. 147. Yamamoto H, Takemura M, Iguchi J, Tachibana M, Tsujita J, Hojo T. In-match physical demands on elite Japanese rugby union players using a global positioning system. BMJ Open Sport Exerc Med. 2020;6:e000659. pmid:32095269
  148. 148. Martin-Garcia AS, Diaz AGM, Bradley PS, Morera F, Casamichana D. Quantification of a professional football team’s external load using a microcycle structure. J Strength Cond Res. 2018;32:3511–3518. pmid:30199452
  149. 149. Rojas-Valverde D, Gómez-Carmona CD, Gutiérrez-Vargas R, Pino-Ortega J. From big data mining to technical sport reports: the case of inertial measurement units. BMJ Open Sport Exerc Med. 2019;5:e000565. pmid:31673403
  150. 150. Bastida Castillo A, Gómez Carmona CD, De la Cruz Sánchez E, Pino Ortega J. Accuracy, intra- and inter-unit reliability, and comparison between GPS and UWB-based position-tracking systems used for time–motion analyses in soccer. Eur J Sport Sci. 2018;18:450–457. pmid:29385963
  151. 151. Frencken WGP, Lemmink KAPM, Delleman NJ. Soccer-specific accuracy and validity of the local position measurement (LPM) system. J Sci Med Sport. 2010;13:641–645. pmid:20594910
  152. 152. Leser R, Schleindlhuber A, Lyons K, Baca A. Accuracy of an UWB-based position tracking system used for time-motion analyses in game sports. Eur J Sport Sci. 2014;14:635–642. pmid:24512176
  153. 153. Stevens TGA, de Ruiter CJ, van Niel C, van de Rhee R, Beek PJ, Savelsbergh GJP. Measuring Acceleration and Deceleration in Soccer-Specific Movements Using a Local Position Measurement (LPM) System. Int J Sports Physiol Perform. 2014;9:446–456. pmid:24509777
  154. 154. Nedergaard NJ, Robinson MA, Eusterwiemann E, Drust B, Lisboa PJ, Vanrenterghem J. The Relationship Between Whole-Body External Loading and Body-Worn Accelerometry During Team-Sport Movements. Int J Sports Physiol Perform. 2017;12:18–26. pmid:27002795
  155. 155. Whiteside D, Cant O, Connolly M, Reid M. Monitoring Hitting Load in Tennis Using Inertial Sensors and Machine Learning. Int J Sports Physiol Perform. 2017;12:1212–1217. pmid:28182523
  156. 156. Beanland E, Main LC, Aisbett B, Gastin P, Netto K. Validation of GPS and accelerometer technology in swimming. J Sci Med Sport. 2014;17:234–238. pmid:23707140
  157. 157. Janssen I, Sachlikidis A. Validity and reliability of intra-stroke kayak velocity and acceleration using a GPS-based accelerometer. Sports Biomech. 2010;9:47–56. pmid:20446639
  158. 158. Macdermid PW, Fink PW, Miller MC, Stannard S. The impact of uphill cycling and bicycle suspension on downhill performance during cross-country mountain biking. J Sports Sci. 2017;35:1355–1363. pmid:27484102
  159. 159. Macdermid PW, Fink PW, Stannard SR. Transference of 3D accelerations during cross country mountain biking. J Biomech. 2014;47:1829–1837. pmid:24735505
  160. 160. Sinclair J, Fau-Goodwin J, Richards J, Shore H. The influence of minimalist and maximalist footwear on the kinetics and kinematics of running. Footwear Sci. 2016;8:33–39.
  161. 161. Bastida-Castillo A, Gómez-Carmona CD, de la Cruz Sánchez E, Pino-Ortega J. Comparing Accuracy between Global Positioning Systems and Ultra-Wideband-Based Position Tracking Systems Used for Tactical Analyses in Soccer. Eur J Sport Sci. 2019;19:1157–1165. pmid:30922175
  162. 162. Bastida-Castillo A, Gómez-Carmona CD, De la Cruz Sánchez E, Reche-Royo X, Ibáñez SJ, Pino-Ortega J. Accuracy and Inter-Unit Reliability of Ultra-Wide-Band Tracking System in Indoor Exercise. Appl Sci. 2019;9:939.
  163. 163. Aughey RJ. Applications of GPS Technologies to Field Sports. Int J Sports Physiol Perform. 2011;6:295–310. pmid:21911856
  164. 164. Buchheit M, Simpson BM. Player Tracking Technology: Half-Full or Half-Empty Glass? Int J Sports Physiol Perform. 2016;12:S235–S241. pmid:27967285
  165. 165. Gómez-Carmona CD, Pino-Ortega J, Ibáñez SJ. Design and validity of a field test battery for assessing multi-location external load profile in invasion team sports. J Sport Sci. 2020;16:23–48.
  166. 166. Kirk C, Hurst HT, Atkins S. Measuring the Workload of Mixed Martial Arts using Accelerometry, Time Motion Analysis and Lactate. Int J Perform Anal Sport. 2015;15:359–370.
  167. 167. Barrett S, Midgley AW, Towlson C, Garrett A, Portas M, Lovell R. Within-Match PlayerLoadTM Patterns during a Simulated Soccer Match: Potential Implications for Unit Positioning and Fatigue Management. Int J Sports Physiol Perform. 2016;11:135–140. pmid:26114855
  168. 168. Buchheit M, Haddad HA, Simpson BM, Palazzi D, Bourdon PC, Salvo VD, et al. Monitoring Accelerations with GPS in Football: Time to Slow Down? Int J Sports Physiol Perform. 2014;9:442–445. pmid:23916989
  169. 169. Malone JJ, Lovell R, Varley MC, Coutts AJ. Unpacking the Black Box: Applications and Considerations for Using GPS Devices in Sport. Int J Sports Physiol Perform. 2017;12:S2-18–S2-26.
  170. 170. Migueles JH, Cadenas-Sanchez C, Ekelund U, Delisle Nyström C, Mora-Gonzalez J, Löf M, et al. Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations. Sports Med. 2017;47:1821–1845. pmid:28303543
  171. 171. Currell K, Jeukendrup AE. Validity, reliability and sensitivity of measures of sporting performance. Sports Med. 2008;38:297–316. pmid:18348590
  172. 172. Kelly SJ, Murphy AJ, Watsford ML, Austin D, Rennie M. Reliability and Validity of Sports Accelerometers during Static and Dynamic Testing. Int J Sports Physiol Perform. 2015;10:106–111. pmid:24911138
  173. 173. Johnstone JA, Ford PA, Hughes G, Watson T, Mitchell ACS, Garrett AT. Field based reliability and validity of the BioharnessTM multivariable monitoring device. J Sports Sci Med. 11:643–652. pmid:24150074
  174. 174. Reina M, Mancha-Triguero D, García-Santos D, García-Rubio J, Ibáñez SJ. Comparison of three methods of quantifying the training load in basketball. RICYDE Rev Int Cienc Deporte. 2019;15:368–382.