Abstract
Background
Despite widespread use of intensity zones to quantify external load variables in basketball research, the consistency in identifying zones and accompanying intensity thresholds using predominant monitoring approaches in training and games remains unclear.
Objectives
The purpose of this work was to examine the external load intensity zones and thresholds adopted across basketball studies using video-based time-motion analysis (TMA), microsensors, and local positioning systems (LPS).
Methods
PubMed, MEDLINE, and SPORTDiscus databases were searched from inception until 31 January 2023 for studies using intensity zones to quantify external load during basketball training sessions or games. Studies were excluded if they examined players participating in recreational or wheelchair basketball, were reviews or meta-analyses, or utilized monitoring approaches other than video-based TMA, microsensors, or LPS.
Results
Following screening, 86 studies were included. Video-based TMA studies consistently classified jogging, running, sprinting, and jumping as intensity zones, but demonstrated considerable variation in classifying low-intensity (standing and walking) and basketball-specific activities. Microsensor studies mostly utilized a single, and rather consistent, threshold to identify only high-intensity activities (> 3.5 m·s−2 for accelerations, decelerations, and changes-in-direction or > 40 cm for jumps), not separately quantifying lower intensity zones. Similarly, LPS studies predominantly quantified only high-intensity activities in a relatively consistent manner for speed (> 18.0 m·s−1) and acceleration/deceleration zones (> 2.0 m·s−2); however, the thresholds adopted for various intensity zones differed greatly to those used in TMA and microsensor research.
Conclusions
Notable inconsistencies were mostly evident for low-intensity activities, basketball-specific activities, and between the different monitoring approaches. Accordingly, we recommend further research to inform the development of consensus guidelines outlining suitable approaches when setting external load intensity zones and accompanying thresholds in research and practice.
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Avoid common mistakes on your manuscript.
This systematic review identified inconsistencies in the intensity zones and intensity thresholds adopted within and between popular monitoring technologies (i.e., video-based TMA, microsensors, and LPS) in the basketball literature. |
Across the existing research using different technologies to monitor external load using intensity zones in basketball, studies have frequently quantified only high-intensity activities, relied on manufacturer settings to set intensity zone thresholds, and sparingly included relative intensity zones. |
Further research encompassing expert input alongside validation work is needed to identify best-practice approaches that guide end-users in setting external load intensity zones and intensity thresholds in research and practice. |
1 Introduction
Basketball is a court-based team sport that requires players to perform intermittent, high-intensity activity including sprinting, cutting, shuffling, and jumping during training and games [1]. These demands can impose high external loads on players, whereby the external load represents the physical stimuli experienced during training and games [2, 3]. In turn, quantifying external load variables can be useful for basketball coaches to confirm they are delivering the desired stimuli to players to optimally prepare them for competition and safeguard them against excessive physical stress [4]. In this regard, external load variables have primarily been quantified in basketball players using video-based time-motion analysis (TMA), microsensors, and local positioning systems (LPS).
Video-based TMA utilizes video footage of training sessions and games to track player movements and identify the type and intensity of movement to obtain several external load variables such as movement frequencies, durations, distances, and speeds [5]. Historically, manual player tracking involving subjective identification of movement types via visual recognition has been adopted when using video-based TMA systems in basketball. However, this approach is labor- and time-intensive, restricting the ability to provide real-time feedback to coaches in practice [2, 6]. In turn, technological advancements have allowed for more efficient measurement of the same external load variables using video-based TMA systems, with various software platforms now possessing automated player tracking capabilities. Automated tracking in video-based TMA systems has removed the subjectivity accompanying visual interpretation of footage inherent in manual tracking systems, providing variables with quicker turnaround times for coaches to use [7]. Likewise, technological advancements underpin the increased use of microsensors and LPS to measure external load variables in basketball players over the past decade. In this regard, microsensors are small devices worn by players during training sessions and games, which typically contain accelerometers, gyroscopes, and magnetometers to quantify player movements in multiple directions [8]. In turn, microsensors provide efficient data collection and processing, can be easily transported for use in various locations, and provide several variables including accelerations, decelerations, changes-in-direction, jumps, and PlayerLoad [9]. In this regard, PlayerLoad is a composite variable that combines the instantaneous rates of change in acceleration across the three movement axes via the following formula: √((Ac1n − Ac1n–1)2 + (Ac2n − Ac2n–1)2 + (Ac3n − Ac3n–1)2) [10]. Moreover, LPS utilize radio frequency, Bluetooth, or ultra-wideband technologies to track player position on the court via worn sensors [11]. LPS allow for efficient data collection and processing, while providing distance-, speed-, and acceleration-derived variables [12].
Despite the various technologies and processes underpinning TMA, microsensors, and LPS, each provides distinct and overlapping variables indicative of exercise intensity. Intensity is generally considered as the physical work completed per unit of time in regard to external load monitoring [13]. Intensity is an important component of external load given it is often manipulated by coaches during periodized training plans across different seasonal cycles in basketball players [14,15,16,17], while also being a key contributing factor in obtaining desired physiological adaptations in response to prescribed training [17, 18]. Furthermore, quantification of the external load intensities encountered during training and games is essential to understanding the specificity of prescribed drills in relation to competition demands. In turn, external load intensity has been frequently quantified with intensity zones predicated on distinct thresholds to delineate these zones for specific variables (e.g., speed, acceleration, deceleration) in basketball research. However, the extent of consistency in methodologies to demarcate intensity zones for external load variables measured within and between popular monitoring approaches remains unclear in the basketball literature. If inconsistent approaches to define intensity zones and thresholds have been applied in basketball research, such as that identified systematically across various field-based team sports [19,20,21,22], meaningful comparisons across studies and a definitive consensus regarding the external load intensities experienced by basketball players cannot be effectively elucidated. Therefore, this systematic review aims to determine whether variation exists in approaches to classify external load intensity zones and thresholds for each predominant monitoring technology (i.e., video-based TMA, microsensors, and LPS) in basketball research.
2 Methods
2.1 Search Strategy
Studies were identified via PubMed, MEDLINE, and SPORTDiscus databases using the search terms presented in Table 1. Studies published online or in print from database inception until 31 January 2023 were considered for inclusion.
2.2 Selection Criteria
Screening of studies retrieved from database searches was undertaken in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [23]. Studies considered for inclusion were original peer-reviewed research, published in English, and reporting external load variables identified using intensity zones in competitive basketball players with video-based TMA, microsensors, or LPS. No restrictions were made on the basis of the age or sex of the players examined, study design adopted, or the context of data collection (i.e., games versus training). Exclusion criteria included studies examining players participating in recreational basketball or wheelchair basketball due to varied demands encountered [24], reviews or meta-analyses, and studies using monitoring approaches other than video-based TMA, microsensors, and LPS to measure external load variables. In this regard, while research has utilized global positioning system (GPS) technology to quantify external load variables in basketball players [25], GPS technologies are not readily implemented by basketball coaching staff in practice due to the signal interference encountered when indoors [26]. Likewise, optical tracking systems have been sparsely used to quantify external load variables in basketball players across the literature [4, 27, 28], with their uptake also currently limited in practice [4]. Some of the reasons for the limited uptake of optical tracking systems in research and practice may be the significant costs associated with their use, lengthy data processing time delaying feedback to coaches, and permanence of camera placement restricting data to games at home venues rather than also in training and away venues [29]. For these reasons, studies utilizing GPS or optical tracking systems to quantify external load variables with associated intensity zones were excluded from this review.
Following the removal of duplicates, the titles and abstracts of identified studies in the search were reviewed independently by two authors (M.C.T. and C.J.P.). Any disagreements between the two authors regarding study inclusion after review of the title and abstract were discussed, and if no consensus was reached, a third author (A.T.S.) provided a consensus decision on inclusion. Following the review of titles and abstracts, full-text versions of relevant studies were obtained and screened by two authors (M.C.T. and C.J.P.) to further determine their eligibility for inclusion. Any disagreements between the two authors regarding study inclusion after review of the full-text versions were discussed, and if no consensus was reached, a third author (A.T.S.) established a consensus decision on inclusion.
2.3 Data Extraction and Analysis
Data were extracted from each study by the lead author (M.C.T.), with co-authors reviewing extracted data for accuracy and completeness. The following data were extracted from each study: number of players recruited, mean ± standard deviation (SD) player age, player sex, playing level at which players competed, monitoring approach used for measuring external load variables, hardware and software used to quantify external load variables, number of intensity zones used (e.g., delineating speed into walking, jogging, running, and sprinting zones would equate to four intensity zones), and intensity zone thresholds applied [e.g., speed thresholds applied for jogging may be between 1 m·s−1 (lower threshold) and 3 m·s−1 (higher threshold)]. Intensity zone and threshold data for all external load variables reported in the included studies were extracted and analyzed. Playing level was categorized from lowest to highest as club, high school, college/university, representative (trained athletes selected into a representative team), semi-professional (some players are full-time and/or contracted athletes), or professional (all players are full-time, contracted athletes) [30]. When at least two studies reported thresholds for the same intensity zone using the same technology (i.e., video-based TMA, microsensors, or LPS), the mean ± SD thresholds were calculated across those studies. Of note, given most studies (5 out of 6, 83%) implemented numeric intensity zone thresholds measured in m·s−1 when using video-based TMA, the single study implementing thresholds in km × h−1 [31] was manually converted to m·s−1 by multiplying threshold values by 5/18 for consistency in reporting. Likewise, given that deceleration intensity zone thresholds were incorrectly reported as positive values instead of negative values in some studies using microsensors [15, 32,33,34,35,36,37,38] and LPS [39,40,41,42,43,44], all deceleration intensity zone thresholds were treated as negative values when calculating mean ± SD thresholds across studies. If a study included multiple player samples with different intensity zone thresholds applied for each sample (e.g., separate player samples competing at different playing levels with different intensity zone thresholds adopted for each playing level) [45], each set of intensity zones was included separately when calculating mean ± SD thresholds across studies.
2.4 Assessment of Methodological Quality
A modified version of the Downs and Black checklist was utilized to evaluate methodological quality of the included studies [46] (Table 2). The modified Downs and Black checklist was chosen as its validity has been established for evaluating experimental [47] and observational studies [48], and it has been adopted previously in other reviews focused on load monitoring in basketball [30, 49]. For this review, 11 of the 27 items from the original checklist were used, which has been previously used in other reviews examining external load in basketball [30, 46, 50]. The quality of each included study was assessed independently by two authors (M.C.T. and C.J.P.) with each item scored as “1” (yes) or “0” (no/unable to determine). The scores for each of the 11 items were then summed to provide the total quality score. Any disagreement in the outcome of the quality appraisal for individual studies was reviewed by a third author (A.T.S.) for a consensus decision.
3 Results
3.1 Study Selection and Methodological Quality
The results of the search process are presented in Fig. 1. The initial search across databases retrieved 1145 studies, with 369 studies identified as duplicates and a further 636 studies excluded on the basis of their title or abstract. Accordingly, 140 full-text studies were screened, with 58 of these studies being excluded, leaving 82 studies for further analysis. An additional four studies were retrieved from the reference lists of the 82 included studies, resulting in 86 studies being included in our analysis. Methodological quality and risk of bias scores ranged from 7 to 11 out of 11 (mean ± SD: 9.0 ± 0.9), with no studies being excluded on the basis of quality (Table 3).
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram showing the search results for this review
3.2 Player Characteristics
The characteristics of players monitored in the included studies are presented in Table 4. Sample sizes across studies ranged from six to 104 players. The mean age of players monitored across studies ranged from 14 to 55 years. Considering player sex, 61 studies monitored only male players, 18 studies monitored only female players, and seven studies monitored male and female players in combination. Regarding playing level, some studies included players competing at different playing levels, resulting in seven studies monitoring club players, eight studies monitoring collegiate/university players, 19 studies monitoring representative players, 18 studies monitoring semi-professional players, and 39 studies monitoring professional players.
3.3 Video-Based Time-Motion Analysis: Non-numeric Thresholds to Identify Intensity Zones
Studies utilizing video-based TMA with non-numeric thresholds to identify intensity zones (n = 18) are presented in Table 5. Across studies, 10 different cameras were used for filming, with three studies not specifying the camera used for filming. Further, 12 studies stated the software utilized to analyze recorded video, with seven different software programs utilized across studies. Moreover, six studies did not specify any software to analyze video or replayed footage on a video recorder.
Movement zones were delineated into locomotion-based movements and basketball-specific movements. Locomotion-based movements were defined as standing and any ambulation strategies ranging in intensity from walking to sprinting with a mean of 3.9 ± 1.0 (range 3–5) zones reported across studies. Basketball-specific movements were defined as non-locomotion-based movements that are commonly performed during basketball gameplay, including jumping, shuffling, dribbling, and screening/picking, with a mean of 4.0 ± 1.6 (range 1–7) zones reported across studies. A mean of 7.7 ± 2.5 (range 3–11) total movement zones (locomotion-based and basketball-specific movements combined) were reported across studies. One study [51] combined all locomotion-based and basketball-specific movements together and thus was not included in the mean zone calculations for each of these categories given they were not included.
3.4 Video-Based Time-Motion Analysis: Numeric Thresholds to Identify Intensity Zones
Studies utilizing video-based TMA with numeric thresholds to identify intensity zones (n = 6) are presented in Table 6. Across all six studies, four different cameras were used for filming, with one study not specifying the camera used for filming. All studies stated the software utilized to analyze recorded video, with four different software programs being utilized across studies. Across studies, the mean total intensity zones used was 7.3 ± 2.9 (range 3–11), the mean locomotion-based movement intensity zones used was 4.3 ± 0.5 (range 4–6), and the mean basketball-specific movement intensity zones used was 3.3 ± 2.4 (range 0–5). One study [52] combined all locomotion-based and basketball-specific movements together and thus was not included in the mean zone calculations for each of these categories given they were not included.
Across studies (n = 6), standing and walking were considered as separate intensity zones in two studies, where thresholds used to identify standing were 0 m·s−1 [31] and 0–0.1 m·s−1 [53], and thresholds used to identify walking were < 1.7 m·s−1 [31] and 0.2–2.0 m·s−1 [53]. In contrast, three studies [54,55,56] included walking and standing together in the same intensity zone, with thresholds of 0 m·s−1 to 1.0 m·s−1 used consistently. Further, one study [52] grouped all low-intensity movements (i.e., inactivity, walking and jogging) as < 3.0 m·s−1.
Across the six studies using video-based TMA with numeric thresholds, five of them [31, 53,54,55,56] assigned intensity zone thresholds to jogging, running, and sprinting activity. The mean intensity zone thresholds assigned to jogging were 1.4 ± 0.5 m·s−1 (range 1.1–2.1 m·s−1) to 3.2 ± 0.3 m·s−1 (range 3.0–3.7 m·s−1) across studies. The mean intensity zone thresholds assigned to running were 3.3 ± 0.3 m·s−1 (range 3.1–3.8 m·s−1) to 6.4 ± 0.9 m·s−1 (range 5.0–7.0 m·s−1) across studies. Additionally, one study [31] included striding as a locomotion-based movement intensity zone positioned between running and sprinting using thresholds of 5.0–6.7 m·s−1. Intensity zone thresholds for sprinting involved only an initial threshold with a mean of 6.8 ± 0.4 m·s−1 (range > 6.1 to > 7.0 m·s−1) across studies. A further study [52] combined all moderate- (3.1–5.0 m·s−1) and high-intensity (> 5.10 m·s−1) movements into separate categories.
Basketball-specific movement intensity zones were included in four studies, with three of them including intensity thresholds for low- and high-intensity shuffling activity [54,55,56] and one study [31] including intensity thresholds for low-, medium-, and high-intensity shuffling. Intensity zone thresholds for low-intensity shuffling involved only an upper threshold, with a mean of ≤ 1.9 ± 0.1 m·s−1 (range 1.7–2.0 m·s−1) across studies. The medium-intensity shuffling intensity zone utilized in a study [31] had thresholds of 1.7–2.5 m·s−1, with this study also including sideways running with a single threshold of > 3.3 m·s−1 as an additional basketball-specific movement. Intensity zone thresholds for high-intensity shuffling included only an initial threshold with a mean of > 2.1 ± 0.2 m·s−1 (range > 2.0 to > 2.5 m·s−1) across studies. In addition, four studies contained movements that did not have numeric intensity thresholds including jumping [31, 54, 56], dribbling [54,55,56], and upper-body movements [54, 56].
3.5 Microsensors
Studies utilizing microsensors (n = 33) to determine intensity zones are detailed in Table 7. Across studies, eight different microsensors were used to collect data and seven different software programs were used to analyze data, with three studies not specifying the software used to analyze data. The external load variables and the intensity zones used for each variable differed across studies. The mean total number of intensity zones (i.e., number of intensity zones defined) was 2.4 ± 2.1 (range 1–11) across all studies. In turn, 1.9 ± 1.1 (range 1–4) acceleration intensity zones were utilized across 20 studies, 1.5 ± 0.9 (range 1–3) deceleration intensity zones were utilized across 14 studies, 5.7 ± 0.6 (range 5–6) PlayerLoad intensity zones were utilized across three studies, 1.4 ± 0.9 (range 1–3) jump intensity zones were utilized across 13 studies, 1.8 ± 1.0 (range 1–3) change-of-direction (COD) intensity zones were utilized across eight studies, and 5.9 ± 1.1 (range 5–7) average net force intensity zones were utilized across seven studies.
In studies that measured accelerations (n = 20), 15 studies [15, 32,33,34,35,36,37,38, 57,58,59,60,61,62,63] used m·s−2 while five studies [64,65,66,67,68] used g-forces as the measurement unit (Table 7). In the 15 studies using m·s−2, 11 studies [15, 32,33,34,35,36,37,38, 57, 61, 62] adopted only an initial threshold to identify high-intensity accelerations, with a mean of > 3.3 ± 0.3 m·s−2 (range > 2.5 to > 3.5 m·s−2) across studies. Mean ± SD thresholds were not able to be calculated across studies using g-forces due to the wide variation in intensity zones adopted. All studies measuring decelerations (n = 14) utilized m·s−2 as the measurement unit, of which 10 studies [15, 32,33,34,35,36,37,38, 61, 62] only cited an initial threshold to identify high-intensity decelerations with a mean of < − 3.5 ± 0.2 m·s−2 (range < − 3.0 to < − 3.5 m·s−2) across studies. In turn, three studies [58,59,60] included low-, medium-, and high-intensity zones separately for accelerations and decelerations, with identical thresholds consisting of 1.5–2.5 m·s−2, 2.5–3.5 m·s−2, and > 3.5 m·s−2, respectively, while one study [63] included low- and high-intensity zones separately for accelerations and decelerations with thresholds of < 3.0 m·s−2 and > 3.0 m·s−2, respectively.
All studies (n = 3) [60, 69, 70] including intensity zones for PlayerLoad used arbitrary units (AU) as the measurement unit. Five intensity zones were consistently employed across all studies [60, 69, 70] with thresholds of 0–1 AU, 1–2 AU, 2–3 AU, 3–4 AU, and 4–6 AU, with two studies [60, 70] also including a sixth intensity zone with thresholds of 6–10 AU. Further, a study [70] calculated PlayerLoad intensity zones relative (%) to the individualized peak instantaneous PlayerLoad with thresholds of 0–10%, 10–20%, 20–30%, 30–40%, 40–60%, and 60–100% of the peak PlayerLoad achieved during any training session or game across the study being assigned to each player.
All 13 studies [15, 32,33,34,35,36,37,38, 58,59,60,61,62] that measured jumps utilized cm as the measurement unit. In this regard, ten studies [15, 32,33,34,35,36,37,38, 61, 62] provided only an initial threshold (> 40 cm) to identify high-intensity jumps, while three studies [58,59,60] incorporated three intensity zones with thresholds of < 20 cm, 20–40 cm, and > 40 cm used to represent low-, medium-, and high-intensity jumps, respectively.
All eight studies [15, 37, 38, 58,59,60,61,62] that measured COD utilized m·s−2 as the measurement unit. In this regard, five studies [15, 37, 38, 61, 62] provided only an initial threshold (> 3.5 m·s−2) to identify high-intensity COD while three studies [58,59,60] incorporated three intensity zones with thresholds of 1.5–2.5 m·s−2, 2.5–3.5 m·s−2, and > 3.5 m·s−2 to represent low-, medium-, and high-intensity COD, respectively.
All seven studies [71,72,73,74,75,76,77] that measured average net force used relative (%) intensity zones on the basis of individualized oxygen uptake reserve (VO2R) calculated from metabolic measurements taken at rest and during a modified Yo–Yo Intermittent Recovery Test. In turn, four studies [71,72,73,74] used five intensity zones consisting of ≤ 10% (inactive), > 10–40% (light), > 40–90% (moderate–vigorous), > 90–100% (maximal), and > 100% (supramaximal), while three studies [75,76,77] used seven intensity zones consisting of < 20% (sedentary), 20% to < 30% (very light), 30–40% (light), 40% to < 60% (moderate), 60% to < 90% (vigorous), 90% to < 100% (maximal), and ≥ 100% (supramaximal).
3.6 Local positioning systems (LPS)
Studies utilizing LPS (n = 25) to determine speed-based intensity zones are presented in Table 8. All studies used km × h−1 as the measurement unit for speed-based intensity zones, with a mean of 3.1 ± 1.8 (range 1–5) zones across studies. Studies utilizing LPS (n = 22) to determine acceleration and deceleration intensity zones are presented in Table 9. All studies utilized m·s−2 as the measurement unit for acceleration and deceleration zones, with a mean of 1.4 ± 1.0 (range 1–5) zones used each for accelerations and decelerations across studies. Across all studies, two different LPS were used to collect data and two different software programs were used to analyze data.
Among studies including speed-based intensity zones (n = 25), eight studies [45, 78,79,80,81,82,83,84] utilized five intensity zones with mean thresholds of 0.0–4.6 ± 1.8 km × h−1 (range 2.1–6.0 km × h−1), 4.7 ± 1.8 km × h−1 (range 2.1–6.0 km × h−1) to 9.4 ± 3.4 km × h−1 (range 4.9–12.0 km × h−1), 9.5 ± 3.4 km × h−1 (range 4.9–12.1 km × h−1) to 14.5 ± 4.5 km × h−1 (range 8.7–18.0 km × h−1), 14.6 ± 4.6 km × h−1 (range 8.7–18.1 km × h−1) to 19.7 ± 5.6 km × h−1 (range 12.6–24.0 km × h−1), and > 19.7 ± 5.6 km × h−1 (range 12.6 to > 24.1 km × h−1) across studies. In turn, three studies [39, 40, 85] utilized four speed-based intensity zones with identical thresholds adopted, including < 7.0 km × h−1, 7.01–14.0 km × h−1, 14.01–18.0 km × h−1, and > 18.01 km × h−1. Moreover, two studies [86, 87] used three speed-based intensity zones with different zones and thresholds adopted. A further three studies [43, 88, 89] utilized two speed-based intensity zones; however, only movements at higher intensities (> 14.0 km × h−1 [88, 89] or > 18.0 km × h−1 [43]) were captured. In this regard, the first zone had mean thresholds of 15.3 ± 2.3 km × h−1 (range 14.0–18.0 km × h−1) to 21.0 ± 0.0 km × h−1, while the second zone had a mean initial threshold of > 21.0 ± 0.0 km × h−1, with only one study including an upper threshold of 31 km × h−1 [89]. Lastly, nine studies utilized one intensity zone to categorize only high-intensity movements, with only a mean initial threshold of > 17.4 ± 1.2 km × h−1 (range > 14.4 to > 18.0 km × h−1) across studies.
Across studies reporting intensity zones for accelerations and decelerations (n = 22), 18 studies [39,40,41,42, 44, 80, 82,83,84, 86, 87, 90,91,92,93,94,95,96] only measured high-intensity accelerations and decelerations with an initial mean threshold of > 2.0 ± 0.6 m·s−2 (range > 0 to > 3.0 m·s−2) across studies. A further two studies [43, 78] utilized two intensity zones for accelerations and decelerations, with one of them including thresholds of < 2.0 m·s−2 and > 2.0 m·s−2 for accelerations and < − 2.0 m·s−2 and > − 2.0 m·s−2 for decelerations, and the other using thresholds of > 2.0 m·s−2 and > 3.0 m·s−2 for accelerations and decelerations. Finally, one study utilized five intensity zones to delineate acceleration and deceleration intensities [45], while another study utilized three intensity zones to delineate acceleration and deceleration intensities [97].
4 Discussion
Our review is the first to systematically examine variations in the intensity zones and associated thresholds to demarcate zones when using the predominant external load monitoring approaches (i.e., video-based TMA, microsensors, and LPS) utilized in existing basketball research. Our results demonstrate systemic variations exist in the number of zones utilized and the thresholds adopted for many intensity zones within and between monitoring approaches, which limits the ability to make meaningful comparisons across studies or draw definitive conclusions across the collective research published on this topic. Variations in the intensity zones adopted and associated thresholds used to demarcate intensity zones were bound to occur given that no guidelines have been developed to provide researchers with best-practice recommendations. Accordingly, our review emphasizes a clear need for guidance when monitoring and reporting external load variables using intensity zones in future basketball research.
4.1 Video-Based Time-Motion Analysis: Non-numeric Thresholds to Identify Intensity Zones
Variation was evident in the intensity zones adopted across studies using video-based TMA with non-numeric thresholds. More precisely, for locomotion-based movements, most studies (14 out of 18, 78%) included standing, walking, jogging, running, and sprinting, demonstrating these movements are recognized as fundamental in basketball. However, variation predominantly emerged for locomotion-based movements among studies, with standing and walking either included as separate zones (four out of 14 studies, 29%) or combined in the same zone (10 out of 14 studies, 71%). Given that standing and walking are both low-intensity movements that elicit minimal energy expenditure in players compared with running [98], it may be argued that separating them creates additional data to handle and interpret without any further meaningful insight into the external loads experienced. Consequently, including standing and walking in the same intensity zone may reduce the data burden on end-users, allowing for increased focus on movements that evoke greater stress in players.
Wider variation in intensity zones was evident for basketball-specific movements than for locomotion-based movements across TMA studies using non-numeric thresholds. In this regard, although shuffling movements were mostly categorized into low-, moderate-, and high-intensity zones, they were identified differently across studies. Specifically, shuffling movements were identified as shuffling actions of the feet in sideways or backwards directions (six out of 18 studies, 33%) or grouped with various other multi-directional activities in an inconsistent manner as specific movements (seven out of 18 studies, 39%). Consequently, the collective literature indicates shuffling activity is essential to consider, but it appears movements such as backwards and sideways running have been categorized as specific movements alongside shuffling, or grouped collectively with forwards running as locomotion-based movements (i.e., walking, jogging, running, or sprinting). Given that movement direction impacts the external load encountered during locomotor tasks [98,99,100], and different training approaches are needed to improve backwards, sideways, and forwards running abilities, movements should likely be measured specific to the direction in which they are performed (i.e., backwards, sideways, and forwards) in research for greatest specificity in the reported data. Identification of basketball-specific movements becomes more inconsistent across the literature as some studies included picking/screening (four out of 18, 22%) and positioning (three out of 18, 17%) as separate basketball-specific movement zones, while other studies grouped picking/screening and positioning movements within broader specific zones (five out of 18, 28%). Furthermore, some studies (13 out of 18, 72%) did not quantify picking, screening, or positioning movements, meaning they were likely absorbed in standing, walking, or basketball-specific activity, resulting in underreported player demands. While there are variations across studies regarding the inclusion of picking/screening and positioning, it is likely important to quantify and identify different intensity descriptors for these movements to understand the complete external loads experienced during basketball training and games, given that each of these actions involve extensive player-to-player contact and isometric muscular contractions, which add to the demands experienced [101, 102]. In contrast, jumping movement zones were included in almost all studies (17 out of 18, 94%), emphasizing the widespread recognition of jumping as a fundamental movement to consider when quantifying external load in basketball. Indeed, basketball players execute jumping maneuvers each minute during gameplay [1], which is among the most frequent across a wide range of team sports [103]. The almost unanimous inclusion of jumping as a movement zone may be attributed to the ease of identifying this movement using video-based TMA.
Despite the inconsistencies in categorization of movement zones across studies using video-based TMA with non-numeric thresholds, 14 out of 18 studies (78%) cited the first study published on this topic by McInnes et al. [104]. However, McInnes et al. did not provide any rationale for the approach adopted, making it difficult to identify the underlying logic, processes, and evidence involved in developing their movement zones. Moreover, many authors [105,106,107,108,109,110,111,112,113,114,115,116,117], who since cited McInnes et al. [104] in support of their approach, modified the original movement zones, particularly for basketball-specific movements. Modifications to the methods used by McInnes et al. [104] across time may reflect them as being somewhat outdated, as they were published almost three decades ago. Consequently, adaptations to the original methods proposed by McInnes et al. [104] without accompanying justification have likely contributed to inconsistent movement zones being used across studies, emphasizing the need for guidance in establishing fundamental movements that should be considered when quantifying external load with video-based TMA in modern basketball.
4.2 Video-Based Time-Motion Analysis: Numeric Thresholds to Identify Intensity Zones
The included studies utilizing video-based TMA with numeric thresholds were published by four different author groups, with each research group adopting different intensity zones in their research. In this regard, while three of the four author groups (five out of six studies, 83%) identified standing, walking, jogging, running, and sprinting as key locomotion-based movement intensity zones, variations emerged across studies in other ways. Specifically, two author groups separated standing and walking [31, 53], whereas other author groups combined standing and walking in the same zone [54,55,56] or broadly combined movements as low-, medium-, or high-intensity activity [52]. Moreover, the thresholds used to demarcate locomotion-based movement intensity zones differed across author groups. For instance, jogging was detected using initial thresholds with almost a twofold difference (i.e., from 1.1 m·s−1 to 2.1 m·s−1), while sprints were detected using thresholds from 6.1 m·s−1 to 7.0 m·s−1. Moreover, only two author groups (four out of six studies, 67%) included basketball-specific movements [31, 54,55,56]. Although shuffling activity was included by both author groups that included basketball-specific movements [31, 54,55,56], it was inconsistently separated into three (low, moderate, and high) [31] or two (low and high) [54,55,56] intensity zones, with different thresholds adopted between groups. Furthermore, jumping was included as a movement zone by both author groups that included basketball-specific movements, but dribbling [54,55,56], upper-body movements [54, 56], and sideways running [31] were each included by only one author group.
The extensive variation in the intensity zones included across studies using video-based TMA with numeric thresholds likely stems from the lack of consensus foundation research regarding best practices for using external load intensity zones in basketball research. In this way, most studies adapted key movement zones contained in the original video-based TMA study using non-numeric thresholds by McInnes et al. [104], and then applied different approaches to assign numeric thresholds for these zones. Specifically, some studies adopted numeric thresholds for intensity zones developed in research examining other court-based (i.e., futsal) [52, 54, 56, 118] or field-based team sports (i.e., soccer) [53, 119], diminishing the specificity to basketball, while other studies did not include any reasoning for the numeric thresholds assigned [31, 104]. Consequently, although limited studies have included intensity zones using video-based TMA with numeric thresholds to delineate intensity zones, there is still a need for consistency in approaches, given that automated video analyses may still be used in practice and other newer monitoring approaches utilize similar speed-based zone thresholds.
4.3 Microsensors
The studies utilizing microsensors in this review demonstrated relatively consistent approaches to delineating intensity zones, given that many adopted a single threshold to capture only high-intensity movements (13 out of 33 studies, 39%). More precisely, among studies including only high-intensity zones, most studies used thresholds of > 3.5 m·s−2 for accelerations (nine out of 11 studies, 82%), decelerations (nine out of 10 studies, 90%), and COD (five out of five studies, 100%), and used a threshold of > 40 cm for jumps (10 out of 10 studies, 100%). Likewise, consistent thresholds were used across the three studies [58,59,60] utilizing three intensity zones for accelerations, decelerations, and COD (1.5–2.5 m·s−2, 2.5–3.5 m·s−2, and > 3.5 m·s−2, respectively) as well as for jumps (0–20 cm, 20–40 cm, and > 40 cm, respectively). Despite these similarities in the intensity zones adopted across studies, variations were introduced in the measurement units used between studies. Specifically, five studies measured accelerations using intensity zones defined in g-forces [64,65,66,67,68], while the other 15 studies [15, 32,33,34,35,36,37,38, 57,58,59,60,61,62,63] utilized m·s−2. Furthermore, among the five studies that measured accelerations using intensity zones defined in g-forces, three studies only recorded accelerations > 4 g-forces [66,67,68], while the other two studies only recorded accelerations up to 2 g-forces [64, 65]. In turn, only reporting accelerations < 2 g-forces [64, 65] and > 4 g-forces [66,67,68] separately across studies introduces notable variation in the acceleration data reported and may omit important accelerative demands at high or low intensities. Given the five studies that reported accelerations using g-forces were all conducted in basketball players of similar ages (mean age of 13.7–20.0 years) and playing levels (club, college/university, and representative), who were therefore likely to experience similar g-forces during accelerations, the variation in intensity zones adopted by researchers may be attributed to the differing hardware, software, and data processing procedures used across studies, as well as differing justifications for the intensity zones selected. In regard to the differing justification, one study [66] cited badminton and rugby union as the source for the initial intensity zones selected. Additionally, in one study by Bredt et al. [65], the authors stated that selected intensity zones were not based on any prior recommendations or research. The authors' rationale for not utilizing previously published research was because they did not find any existing recommendations for intensity zone classification in basketball. Subsequently, in a second study [64] by Bredt et al., the authors cited their previous paper [65] as justification for the intensity zones despite not providing any rationale for them. The other two studies [67, 68] did not provide any justification. Specifically regarding the hardware and software used, research has noted that discrepancies in microsensor outputs may occur due to variability in software versions, sampling rates, data filtering and smoothing techniques [29], firmware updates [29], and minimum effort durations chosen for detecting movements [120]. Consequently, detailed information encompassing the hardware and software, as well as data sampling, cleaning, and detection processes, should be provided in studies when microsensors and other monitoring approaches such as video-based TMA and LPS are used to measure external load variables in basketball players.
Limited studies (three out of 26, 12%) included intensity zones for PlayerLoad, which is one of the most widely used variables to quantify external load in basketball research [50, 121]. Although these three studies followed manufacturer settings in assigning PlayerLoad intensity zones, it appears that the understanding and acceptance of universal intensity zones are yet to be established for PlayerLoad in basketball contexts across the literature. This may be due to researchers perceiving greater practical utility in quantifying high-intensity accelerations and decelerations, researchers limiting the volume of variables reported, or the arbitrary nature of the unit used to quantify this variable. Nevertheless, all studies [60, 69, 70] that reported intensity zones for PlayerLoad were quantified in AU per minute and consistently applied across five [69] or six fixed intensity zones [60, 70]. However, PlayerLoad intensity zone thresholds were also quantified using relative intensity zones (in addition to fixed intensity zones) that were calculated relative to the individualized peak PlayerLoad achieved in AU per min during training or games in a study [70], which has also been applied in other court-based team sports [122]. Likewise, the seven studies [71,72,73,74,75,76,77] measuring average net force with microsensors included in our review also delineated intensity zones using relative thresholds stratified using different percentages of VO2R, which were calculated on the basis of individualized associations between the average net force and average VO2R across different stages of a modified Yo–Yo Intermittent Recovery Test. Surprisingly, these eight studies [70,71,72,73,74,75,76,77] were the only instances in which relative intensity zone thresholds were adopted in our review, with most studies (n = 78) utilizing fixed intensity zones thresholds.
Use of intensity zone thresholds relative to peak outputs attained during training sessions and games [123, 124], maximal speed attained during linear sprint assessments [125, 126], and intensity markers such as speed at lactate [127] and ventilatory thresholds [128] attained during physiological fitness assessments have been widely adopted for monitoring approaches in field-based team sports. Although fixed intensity zone thresholds may permit players to be benchmarked against desired criteria or one another, and involve simpler processes during analyses in software for end-users [20], use of relative intensity zone thresholds accounts for variations in fitness status [129] and performance capacities [123, 125, 130] across players to provide external load data that may allow for greater individualization in developing training plans and player recovery strategies [128]. However, the added practical requirement to continually measure and adjust individualized thresholds due to longitudinal changes in fitness capacities among players when using relative intensity zones needs to be considered [131]. Likewise, the translation of relative intensity zone thresholds derived from continuous running assessments to competitive gameplay has been questioned given the extensive COD and accelerative requirements in team sports [19], particularly in basketball [103]. Consequently, there appears to be logical utility in using both relative and absolute intensity zone thresholds in basketball dependent on the intended application, with considerably more research needed to identify optimal approaches.
The greater consistency in intensity zone thresholds across studies using microsensors compared with studies using video-based TMA likely stems from a clear reliance on the use of manufacturer settings. Most studies (22 out of 26, 85%) used Catapult devices to capture external load data, with the included intensity zone thresholds being rather consistent given they align directly with those specified within predefined manufacturer settings. This reliance is somewhat concerning given that the rationale and evidence used to define the intensity thresholds set by manufacturers remain undisclosed, creating a need for greater transparency alongside further independent research and expert input to understand whether manufacturer-derived intensity zones are appropriate to implement in practice.
4.4 Local Positioning Systems
Similar to studies utilizing video-based TMA with numeric thresholds, variation in speed-based intensity zone thresholds among LPS studies (n = 25) were mostly evident across lower intensity zones. For instance, considerable variance in intensity thresholds was apparent between studies, including low-intensity zones, with the initial thresholds ranging from < 2.14 km × h−1 to < 7 km × h−1 across studies. This variation may be due to two studies including standing as a separate zone [45, 79], with eight studies combining standing and walking in the same zone and two studies not referring to “standing” activity at all [78, 86]. These findings emphasize the notion that it may be preferable to combine standing and walking activity rather than consider them as separate zones. Considering higher intensity zones, several studies (12 out of 25, 48%) only reported movements above 14 km × h−1. Moreover, most studies (16 out of 20, 80%) that specifically referred to “high-speed running” or “high-intensity running” utilized relatively consistent thresholds of ~ 18 km × h−1 on its own or with an upper limit of 24 km × h−1 (Table 8). Likewise, for sprinting activity, most studies (seven out of 12, 58%) utilized a threshold of > 24.1 km × h−1, with some (three out of 12, 25%) adopting a threshold of > 21 km × h−1. Regarding the use of LPS encompassing acceleration and deceleration intensity zones, most studies (14 out of 22, 64%) utilized a single intensity zone threshold of > 2 m·s−2, with four studies (18%) using an alternative threshold for the single zone (ranging from 0–3 m·s−2) and four studies (18%) including multiple intensity zones (ranging from 2 to 5 zones).
The high consistency in approaches to demarcating intensity zones across studies may be attributed to the collective LPS research being predominantly published by six author groups (21 out of 25 studies for speed-based intensity zones, 84%; 21 out of 22 for acceleration and deceleration intensity zones, 98%) who used either Catapult ClearSky or WIMU PRO monitoring systems. Importantly, no studies utilized > 3.5 m·s−2 as a threshold for high-intensity accelerations and decelerations, which was consistently utilized across the microsensor studies included in our review. This discrepancy raises a clear concern when attempting to compare reported acceleration and deceleration data between studies using microsensors and LPS to collect data. Nevertheless, the collective findings across microsensor and LPS studies indicate researchers are mostly interested in quantifying high-intensity accelerations and decelerations during training and games, potentially due to the high biomechanical demands they elicit [132].
Of note, one study [45] developed specific intensity zone thresholds using LPS for different playing levels (i.e., first division versus second division of Liga Femenina) to account for the varied external loads players experience. More precisely, the authors used monitoring data collected during training scrimmage scenarios and k-means clustering to develop intensity zones for speed-based and acceleration/deceleration variables for each playing level, which were all higher in the first division group [45]. Given the variations in external game loads reported between playing levels [101, 105, 112, 133], as well as between competitions involving different sexes [55] and age groups [87], such approaches may hold merit for developing intensity zone thresholds in basketball research to better categorize acquired data. Similarly, it has been suggested that universal fixed intensity zone thresholds of equal bandwidth may be used, but the corresponding label assigned to each zone (e.g., jog, run, sprint) could be adjusted dependent upon the playing level, sex, and/or age of the sample to account for differences in external loading [131], or expanding on this idea by using consistent fixed zones without labels. A further noteworthy observation across LPS studies was thresholds for acceleration and deceleration data were inconsistently, and often inaccurately, reported using positive (+) or negative (−) values, as well as less-than (<) or greater-than (>) signs. Although it appears that authors used similar intensity zone threshold values across studies, it is recommended that deceleration data be identified using negative values and less-than signs to indicate increased deceleration intensity for correctness.
4.5 Limitations
While our review is the first to systematically identify the external load intensity zones and thresholds adopted in basketball literature using popular monitoring approaches, some limitations should be acknowledged. Firstly, syntheses of intensity zone thresholds specific to the sex, age, and playing level of players for each monitoring approach could not be made due to the limited scope of player samples recruited in studies. For instance, limited studies exclusively examined female players (19 of 86, 22%), most studies examined player samples with mean ages between 18 and 27 years (51 out of 86, 59%), and most studies examined athletes competing at the semi-professional or professional level (53 out of 86, 62%). Secondly, optical tracking systems were not included as a predominant external load monitoring approach given they are an emerging technology with limited uptake in basketball research to date. As basketball studies using optical tracking systems become more prevalent, any recommendations developed concerning the development of external load intensity zones and thresholds should encompass this technology. Thirdly, the provided outcomes are specific to basketball and should not be extrapolated to other court-based sports such as netball [134], handball [103], or wheelchair basketball [135] given their different external demands.
4.6 Future Research Directions
The overarching future recommendation stemming from our review is the need to develop consensus guidelines that promote more sound and consistent approaches when monitoring external load variables using intensity zones. In this regard, an initial step in creating such guidelines could involve a Delphi study to elucidate current consensus on developing external load intensity zones and intensity thresholds among experts working within this field [136]. Validation studies are then needed to support the application of consensus approaches in setting external load intensity zones and thresholds for their intended practical functions. For instance, studies could explore the associations between external load data derived using suitable fixed and relative intensity zone thresholds for various practical functions, such as quantifying changes in fitness attributes, injury incidence, and in-game performance among different player samples [137]. Regarding relative intensity zone thresholds, various methods to set zone thresholds should be explored to identify the most effective approaches that are also practical to implement in basketball teams given the apparent lack of basketball research using them to date. Moreover, the reliance on manufacturer settings to set intensity zone thresholds using microsensors and LPS across the literature emphasizes the important role that manufacturers play in determining reported external load data and the need to ensure that transparent, consistent, evidence-informed approaches are adopted across different software platforms. A stronger evidence base established from practitioner expertise and scientific data could potentially inform the development of a consensus statement that guides researchers and practitioners in appropriately setting external load intensity zones. Such guidance could entail minimum reporting standards in describing and justifying the adopted methods as well as in providing appropriate absolute and relative intensity zones and thresholds for application in specific player samples considering their age, sex, and playing level when monitoring external load with different technologies.
5 Conclusions
This is the first review to examine the approaches adopted to classify external load intensity zones and thresholds in basketball research, showing that systemic variations exist across studies using the same or different (i.e., microsensors versus LPS or video-based TMA versus LPS) external load variable monitoring approaches. Although research using video-based TMA with non-numeric and numeric thresholds somewhat consistently accounted for jogging, running, sprinting, and jumping movements, some notable inconsistencies were evident in the approaches adopted. Namely, studies inconsistently combined or separated standing and walking activity, included varied basketball-specific movements, and did not uniformly or comprehensively account for the range of multi-directional and isometric activities performed. In research using microsensors, many studies included zones for accelerations, decelerations, COD, and jumps only performed at high intensities with relatively consistent thresholds adopted that aligned with manufacturer software settings. Surprisingly, despite the popularity of using PlayerLoad to measure total external load volume in the basketball literature, limited research has demarcated PlayerLoad into intensity zones when quantifying external load with microsensors. Like microsensor research, studies using LPS predominantly included zones only encompassing high-intensity movements but in a consistent manner and in line with manufacturer software settings. However, the intensity zone thresholds set to detect accelerations and decelerations using LPS (2–3 m·s−2) were notably lower than those adopted in studies using microsensors (3.5 m·s−2), highlighting discrepancies between monitoring approaches. Of note, limited research has explored the use of relative intensity zone thresholds when quantifying external load variables in basketball research, emphasizing an important area in need of further research attention given the popularity of using relative intensity zones in wider team sports [19,20,21,22]. The inconsistencies in the number and type of intensity zones utilized, as well as the associated thresholds for a given intensity zone among basketball studies using popular monitoring approaches (i.e., video-based TMA, microsensors, and LPS) support the need for further research to inform the development of consensus guidelines that outline best-practice approaches when implementing external load intensity zones and intensity thresholds in research and practice.
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This research project was supported under the Australian Commonwealth Government’s Research Training Program.
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Data and calculations for mean ± standard deviation intensity zones and thresholds reported in our review can be provided upon request.
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Matthew Tuttle is supported by an International Excellence Award from CQUniversity. No sources of external funding were used to assist in the preparation of this study.
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The study was conceived and designed by A.T.S., M.C.T., and V.J.D. Searching, screening, data extraction, and quality assessment procedures were performed by M.C.T., C.J.P., and A.T.S. Data interpretation was undertaken by M.C.T. and A.T.S. Manuscript preparation was performed by M.C.T., A.T.S., V.J.D., and C.J.P. All authors approved the final written version of the manuscript.
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Tuttle, M.C., Power, C.J., Dalbo, V.J. et al. Intensity Zones and Intensity Thresholds Used to Quantify External Load in Competitive Basketball: A Systematic Review. Sports Med (2024). https://doi.org/10.1007/s40279-024-02058-5
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DOI: https://doi.org/10.1007/s40279-024-02058-5