Accelerometry-Derived Relative Exercise Intensities in Elite Women’s Basketball

Staunton, Craig A; Wundersitz, Daniel; Gordon, Brett; Kingsley, Michael

doi:10.1055/a-0637-9484

Cited by 26 publications

(62 citation statements)

References 19 publications

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“…Forward is a playing position that presents medium values in physical and technical performance, with a prevalence of offensive tasks and three-point field-goals [36,37]. The center is the tallest player, plays near the basket and performs most of the static high-intensity impacts, contacts, and collisions with opponents [38]; because of their role they need to perform specific actions in the free throw lane [3]. Therefore, for amateur basketball players, the specific physical profile of each playing position is similar to that for elite-level basketball players.…”

Section: Discussionmentioning

confidence: 99%

Influence of Contextual Variables on Physical and Technical Performance in Male Amateur Basketball: A Case Study

Fernández-Leo

Gómez-Carmona

García-Rubio

et al. 2020

IJERPH

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Currently, most basketball research is focused on professional and elite players. Studies at the amateur level are important to explain the physical and technical demands of competition and thus improve players’ and teams’ performance. The purpose of the present study was to describe the competitive demands of an amateur-level basketball team and to analyze the influence of different situational variables on the physical and technical performance indicators. Eleven amateur senior basketball players participated in six official final-round games during the 2018/2019 season. External, internal load, and notational analysis were registered by inertial devices, heart rate bands, and video analysis. The Kruskal-Wallis H-test was applied for comparisons based on playing positions, periods, and final quarter game outcome, with the post hoc comparison accomplished by a Mann-Whitney U test. The Spearman correlation coefficient was realized for the relational analysis. The results showed that: (a) guards covered more volume of displacements (effective on-court time: p < 0.01, E R 2 = 0.05; steps/min: p < 0.01, E R 2 = 0.28) and the centers performed competitive actions of higher load ([>8G] Imp/min: p < 0.01, E R 2 = 0.20; jumps/min: p < 0.01, E R 2 = 0.33); (b) a performance decreasing was found between the first and second half of the game; (c) in balanced matches there was the most individual technical performance (PIR/min: p < 0.98, E R 2 = 0.01), while in the unbalanced games more high-intensity impacts were seen ([>8G] Imp/min: p < 0.01, E R 2 = 0.07). The situational variables analyzed had an influence on athletic performance in amateur senior basketball players and should be considered for designing training sessions and planning strategies during official matches.

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Section: Discussionmentioning

confidence: 99%

Influence of Contextual Variables on Physical and Technical Performance in Male Amateur Basketball: A Case Study

Fernández-Leo

Gómez-Carmona

García-Rubio

et al. 2020

IJERPH

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“…This may allow researchers and users to take decisions around how to interpret and compare data between companies´equipment and device versions and software. Based on this limitation, we based our analysis in the available information already published in other high-quality peer review studies: a(t) [22,23], PL RT [25], PL TM [11], IL [26], PL RE [17] and TL [28]; and the open documents of companies specifications.…”

Section: Discussionmentioning

confidence: 99%

“…• a(t) (developing companies: ActiGraph LLC and GENEActiv; units: g force, g) [22,23]: Square root of the sum of the accelerations in the three accelerometer orthogonal axes (x, y and z), measuring the combination of gravity and changes in vertical, medio-lateral and anterior-posterior motions of a body segment to which the accelerometer is attached (Equation (1)).…”

Section: Accelerometry-based External Load Indicators (Abeli)mentioning

confidence: 99%

“…), with high reliability and validity [19][20][21]. Other variables have been created with the same purpose, such as vectorial sum of acceleration (a(t)) [22][23][24] used by almost all the companies, Player Load developed by the company RealTrack Systems (Player Load RT ) [25], Impulse Load created by Zephyr TM [26], New Body Load by GPS Sports [27], Player Load by the manufacturer ZXY SportTracking (Player Load RE ) [17] and Total Load by StatSports [28]. All the companies use accelerometer data from the vertical, horizontal and medio-lateral planes but the calculations made to extract the final external workload are quite different, complicating comparison among them.In this respect, two important aspects of accelerometry-based external load indicators (ABELIs) have been considered: (a) the difference among the algorithms used to calculate the ABELIs and the resulting magnitude and unit variability among them hinder their practical application by team staff and sport scientists due to the different devices used [29]; and (b) different technical aspects such as variety of sampling frequencies, chip sets, filtering methods and data-processing algorithms also influence interdevice comparison among ABELIs.…”

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confidence: 99%

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Accelerometry-Based External Load Indicators in Sport: Too Many Options, Same Practical Outcome?

Gómez-Carmona

Pino-Ortega

Sánchez-Ureña

et al. 2019

IJERPH

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With the development of new microsensor technology to assess load in sports, some indicators of external load through accelerometry-based data have been created by sport technology companies. Thus, the study aim was to analyze the agreement between different accelerometry-based external load indicators (ABELIs) available in sport science. A U-16 male soccer team was assessed during three official matches, divided by periods, to obtain 3-D accelerometry data (x, y and z axes). An average of 1,420,000 data points was analyzed per axis per player. The ABELIs were calculated using this information, and the agreement between them was explored. The following ABELIs were considered after a literature review: AcelT, Player Load RT , PlayerLoad TM , Impulse Load, Player Load RE and Total Load. In order to compare ABELIs, two analyses were performed using:(1) absolute data; and (2) normalized and centered data (Z-scores). In absolute and centered data, very large to nearly perfect correlations (1st period: r > 0.803, p > 0.01; 2nd period: r > 0.919; p > 0.01) were found. Instead, very large differences were found in absolute values (bias = −579,226.6 to 285,931.1; t = −224.66 to 213.91, p < 0.01), and no differences in scaled and centered values (bias = 0; t = 1; p = 1). In conclusion, considering the different output (magnitude and units) among ABELIs, the standardization of a universal index to calculate accelerometer load is needed in order to make possible between-study comparison.(heart rate telemetry, muscle oxygen saturation, internal temperature, power, cadence, among others) with excellent accuracy and reliability [7][8][9][10][11][12].One of these sensors is the accelerometer. Accelerometers were first introduced in sports science in the beginning of the 21st century thanks to the Project 2.5 "Technology of Communication to Athletes Monitoring" carried out by the Australian Centre of Microtechnological Research for designing a unique nonintrusive device for sport monitoring in real time [13]. Since its appearance, there has been enormous technological, technical and methodological development in the use of accelerometry to quantify external workload in sports [14][15][16]. The sensitivity and precision of accelerometry-based variables are higher compared to other tracking systems that underestimate load demands. This phenomenon is due to the fact that static high-intensity actions without covering ground (jumps, collisions, falls, tackles, etc.) cannot be recorded by time-motion systems, but can be measured with high accuracy by accelerometers [6,17].A large quantity of variables has been developed using data recorded by this sensor. The analysis of this information has tended to be more complex because the different companies use different algorithms to classify the actions and this limits comparability among studies [18]. The most used variable is PlayerLoad TM , designed by Catapult Sports company [11]. This variable was created to quantify the total load players are exposed to and is obtained from the acceler...

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Measuring Physical Demands in Basketball: An Explorative Systematic Review of Practices

et al. 2020

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Background Measuring the physical work and resultant acute psychobiological responses of basketball can help to better understand and inform physical preparation models and improve overall athlete health and performance. Recent advancements in training load monitoring solutions have coincided with increases in the literature describing the physical demands of basketball, but there are currently no reviews that summarize all the available basketball research. Additionally, a thorough appraisal of the load monitoring methodologies and measures used in basketball is lacking in the current literature. This type of critical analysis would allow for consistent comparison between studies to better understand physical demands across the sport. Objectives The objective of this systematic review was to assess and critically evaluate the methods and technologies used for monitoring physical demands in competitive basketball athletes. We used the term 'training load' to encompass the physical demands of both training and game activities, with the latter assumed to provide a training stimulus as well. This review aimed to critique methodological inconsistencies, establish operational definitions specific to the sport, and make recommendations for basketball training load monitoring practice and reporting within the literature. Methods A systematic review of the literature was performed using EBSCO, PubMed, SCOPUS, and Web of Science to identify studies through March 2020. Electronic databases were searched using terms related to basketball and training load. Records were included if they used a competitive basketball population and incorporated a measure of training load. This systematic review was registered with the International Prospective Register of Systematic Reviews (PROSPERO Registration # CRD42019123603), and approved under the National Basketball Association (NBA) Health Related Research Policy. Results Electronic and manual searches identified 122 papers that met the inclusion criteria. These studies reported the physical demands of basketball during training (n = 56), competition (n = 36), and both training and competition (n = 30). Physical demands were quantified with a measure of internal training load (n = 52), external training load (n = 29), or both internal and external measures (n = 41). These studies examined males (n = 76), females (n = 34), both male and female (n = 9), and a combination of youth (i.e. under 18 years, n = 37), adults (i.e. 18 years or older, n = 77), and both adults and youth (n = 4). Inconsistencies related to the reporting of competition level, methodology for recording duration, participant inclusion criteria, and validity of measurement systems were identified as key factors relating to the reporting of physical demands in basketball and summarized for each study. Conclusions This review comprehensively evaluated the current body of literature related to training load monitoring in basketball. Within this literature, there is a clear lack of alignment in applied practices and metho...

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Accelerometry-Derived Relative Exercise Intensities in Elite Women’s Basketball

Cited by 26 publications

References 19 publications

Influence of Contextual Variables on Physical and Technical Performance in Male Amateur Basketball: A Case Study

Influence of Contextual Variables on Physical and Technical Performance in Male Amateur Basketball: A Case Study

Accelerometry-Based External Load Indicators in Sport: Too Many Options, Same Practical Outcome?

Measuring Physical Demands in Basketball: An Explorative Systematic Review of Practices

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