Wireless heart rate (HR) monitors were originally developed in 1983 using electrocardiograph technology (27). Heart rate monitors have 2 key components: a transmitter, typically worn at the base of the sternum below the pectoralis major musculature, and a receiver usually constructed as a watch worn by the user, a nearby computer, or another electronic device. In all instances, HR monitors indicate the frequency of electrical heart activity (4). Previous literature supports HR as a measure of exercise intensity, with acceptable validity (29,63) and reliability (29,58) observed at various submaximal exercise intensities. Based on the established linear relationship between HR and oxygen uptake (V̇o2) (2), HR monitors can provide insight regarding oxidative metabolic recruitment during exercise. Furthermore, the relationship between HR and V̇o2 allows HR to be used as a predictor of maximal V̇o2 from responses taken during submaximal exercise tests (6,23). Subsequently, the relationship between HR and V̇o2 forms the basis of using HR monitors to estimate energy expenditure during exercise. Heart Rate responses have also been shown to positively correlate with blood lactate accumulation, an indicator of anaerobic metabolic recruitment (26). Thus, monitoring HR can provide useful insight regarding the metabolic requirements of tasks performed in intermittent sports, such as basketball (17).
Basketball is one of the most popular global, court-based team sports with 213 teams currently registered with the International Basketball Federation. Basketball is typically played on a 28 × 15-m court, and game-play is administered across 4 × 10-minute quarters, 4 × 12-minute quarters, or 2 × 20-minute halves. There are 5 on-court playing positions typically consisting of a point guard, shooting guard, small forward, power forward or stretch 4, and center. Although position-specific variations in game tasks exist, all players experience extensive intermittent activity during training and game-play, performing high-intensity movements interspersed throughout low-intensity activity (25,46,47). Playing level also influences the game demands imposed on basketball players with international players performing greater high-intensity activity than national players (9). Consequently, HR monitors have been used by basketball practitioners to measure the internal responses of players during training and game-play to make sound, evidence-based decisions regarding player management across the annual plan.
Although HR monitoring is commonly used in basketball settings, a dedicated review identifying the key uses and limitations associated with HR measurement, as well as reporting on the typical training and game-play responses in male and female players is lacking in the literature. Provision of this information will permit recommendations to be developed regarding HR monitoring in basketball, which will better enable basketball coaches, strength and conditioning professionals, and sport scientists to make informed decisions regarding the implementation of HR monitoring practices. Therefore, the aims of this review are to (a) identify the primary applications and limitations of HR measurement in basketball; (b) collate the reported HR responses in male and female basketball players during training and game-play; (c) evaluate use of current HR-based training load models in basketball; and (d) provide recommendations for future research and best practice regarding HR monitoring in basketball. The review was conducted through Central Queensland University, who approved the study. Ethical approval was not necessary.
Applications of Heart Rate Monitoring in Basketball
Measurement of player HR in basketball has several applications, which can be categorized as (a) monitoring exercise intensity; (b) assessing player fatigue status; and (c) quantifying internal training load using established models.
Monitoring Exercise Intensity
The application of HR monitors during basketball game-play provides an understanding of the exercise intensities experienced by players for the development of precise training approaches (36). Specifically, monitoring typical player responses during game-play allows basketball practitioners to better tailor training activities that meet or exceed the internal intensities evident during games (10). This approach enables optimal development of game-specific fitness capacities when preparing players during preparatory training phases of the annual plan. In addition, the utilization of HR to indicate playing intensities during games has the potential to assist with coaching tactical decisions. For example, recognizing players who are performing at near-maximal intensities for extended periods may enable coaching staff to better recognize opportunities to call time-outs, use substitutions, or use deliberate foul tactics to allow in-game recovery for their players (55).
Assess Fatigue Status
Measurement of player HR can provide insight regarding their state of fatigue in response to a single dose or repetitive bouts of exercise. Heart rate recovery responses are an established marker for determining cardiovascular adaptations to exercise (2). Specifically, an athlete who demonstrates a higher than usual HR pattern during preparatory training phases may be experiencing residual fatigue from the accumulated training stimulus (40). Moreover, large changes in HR from baseline or outlying results comparative to other players may be indicative of nonfunctional overreaching (61). Conversely, a lower HR during competition or taper phases compared with baseline may indicate successful cardiovascular adaptation through suppression of the sympathetic nervous system (28).
Quantifying Internal Training Load
During basketball training, HR monitoring can permit appropriate player management by ensuring players are responding appropriately to the imposed stimuli. Although exercise dosages are delivered in terms of the external loads administered, each player will respond in an individualized manner thus requiring an internal load monitoring system. To date, HR-based training load models have predominately been the most popular approach to monitor the internal loads experienced by basketball players (33,40,53,56,57). The use of HR-based training load models to monitor player responses across various training cycles can ensure each player has received an adequate internal stimulus and sufficient recovery to maintain or improve fitness capacities while being optimally prepared for competition. Similarly, a key determinant for successful teams in basketball is limiting the amount of injuries sustained by players and subsequent sideline time (13). In this regard, illness or injury occur when the physical demands exceed the ability of basketball players to adequately recover between training sessions and games (5). In turn, a high accumulated internal load, rapid increases in internal load, or an exacerbated internal response relative to a given external load (indicated by HR-based training load measures) might be accompanied by heightened fatigue, impaired decision-making ability, reduced coordination, diminished neuromuscular control, and decreased proprioception, which can predispose to injury, particularly in the lower body in basketball players (5,18,62).
Male Responses to Basketball Training and Game-Play
Although HR monitoring has varied applications in basketball, the majority of the existing research has focused on measuring player responses during training and game-play (7,9,10,14,22,36,41,45,64,65). A summary of the previous literature reporting HR data for male basketball players during training and game-play is presented in Table 1.
Training Heart Rate Responses in Male Players
Quantification of HR responses during training sessions allows basketball practitioners to identify whether players are adequately prepared for the physiological demands likely encountered during game-play. Research indicates that absolute HR responses during training activities vary from 133 ± 19 b·min−1 to 183 ± 6 b·min−1 equating to relative values of 65 ± 7% HRmax to 92 ± 4% HRmax in male players (14,64). The wide variation in responses is likely attributable to the different player numbers, court size, and type of activities administered in training scenarios.
Basketball training sessions often involve drills that alter player numbers or rules to manipulate the imposed demands (24). Sampaio et al. (45) examined HR responses during 3 vs. 3 (on 12 m2 court) and 4 vs. 4 (on 16.8 m2 court) scrimmage (training-based game-play) configurations in training settings reporting relative HR values >80% HRmax with no significant differences between approaches. To the contrary, Klusemann et al. (24) examined 2 vs. 2 and 4 vs. 4 half-court scrimmage activity and reported a moderate difference in relative HR response between the 2 configurations (2 vs. 2: 86 ± 4% HRmax; 4 vs. 4: 83 ± 5% HRmax). Furthermore, Castagna et al. (14) identified significant differences in player responses between 5 vs. 5, 3 vs. 3, and 2 vs. 2 full-court configurations with HR values increasing as the number of players on-court decreased. Although similarities in HR responses were evident between 3 vs. 3 and 4 vs. 4 training configurations, the collective evidence suggests that decreasing the number of players involved in scrimmage drills increases the cardiovascular stress imposed on players. This trend is likely explained by each player being required to travel greater distances to cover open-court space for offensive maneuvers and to defend opponents who would predispose them to greater running demands and exacerbate the HR response.
Another important factor in determining player HR responses in basketball is court size. Torres-Ronda et al. (64) examined friendly games as well as 5 vs. 5, 4 vs. 4, 3 vs. 3, 2 vs. 2, and 1 vs. 1 scrimmage scenarios across full- and half-court configurations and showed that full-court induced higher HR responses compared with half-court scrimmages. The increase in distance covered during full-court configurations suggested that less time was spent executing skill-based movements and more time undergoing running-based activity compared with half-court configurations (64). These altered movement patterns likely increase the cardiovascular workload placed on players to transition the ball across the court.
Although the chosen court size can influence player HR response during training, stoppages during training for coach instructions and feedback to provide learning opportunities for players can also impact player HR. Comparing 5 vs. 5 scrimmage scenarios in training settings with actual game-play, Montgomery et al. (37) reported similar absolute peak HR (scrimmage: 171 ± 12 b·min−1; game: 173 ± 6 b·min−1), with larger absolute mean HR during game-play (scrimmage: 147 ± 10 b·min−1; game: 162 ± 7 b·min−1). The lower mean HR responses observed during training were attributed to the intermittent pauses in activity to facilitate coaching intervention, allowing more low-intensity recovery opportunities for players.
Further to traditional training approaches, novel conditioning tools may ensure that players are expending maximal effort during training scenarios for optimal physiological adaptation. Simulations offer an alternative to traditional conditioning by eliciting basketball-specific movements (51,52). An example simulation is the Basketball Exercise Simulation Test (BEST), which replicates the demands of male basketball competition using a circuit-based, on-court design (51,52). Examining the BEST, Scanlan et al. (54) documented the highest HR response recorded in noncompetitive settings (relative mean: 91 ± 4% HRmax; relative peak: 98 ± 5% HRmax) in Australian, junior, state-level male players. The high HR response evident during the BEST is likely due to the absence of skill-based movements that are generally accompanied by lower movement intensities.
Overall, coaches prescribing training activities should consider the number of players on court as fewer players (3 vs. 3 and below) increase the cardiovascular intensity of scrimmage activity. Similarly, implementing full-court scrimmaging compared with reduced court sizes also increases the HR response during training. Finally, the application of novel conditioning exercises, such as simulations, has a role in training environments by stimulating physiological stress beyond typical game responses and offering more control over administration of the training dose for basketball practitioners.
Game-Play Heart Rate Responses in Male Players
To date, the majority of the existing literature has reported the HR responses of male players during game-play rather than training. The available male HR data during basketball game-play indicate that varied responses are experienced by male players ranging from 162 ± 7 b·min−1 to 176 ± 4 b·min−1 (10,37). Furthermore, many authors have reported player HR responses relative to playing position, playing level, and game period, which should be considered when interpreting the available data (9,10,22,25,36,37,41,65).
The physiological demands attributed to each position are dependent on the tasks undertaken during game-play. For example, centers are required to endure extensive physical contact when positioning themselves for scoring opportunities close to the basket, attaining rebounds, and contesting opponent shots. Conversely, guards control the offensive play and shifting the ball across the court. Accordingly, Vaquera Jimenez et al. (65) demonstrated mean and peak HR values for guards were significantly higher than centers and forwards in Spanish, professional players. Similarly, Ben Abdelkrim et al. (10) reported guards to exhibit significantly higher HR responses compared with forwards and centers in Tunisian, junior, national players. However, recent HR data from Puente et al. (41) detailed no significant positional differences between guards (90 ± 5% HRmax), forwards (88 ± 3% HRmax), and centers (93 ± 5% HRmax) during 20-minute outdoor games in Spanish, national players. The equivocal results are likely due to factors related to the game configuration and environment. Specifically, Puente et al. (41) examined 20-minute outdoor games in which ambient temperature was less controlled, possibly inducing greater heat gain from the environment (69). Consequently, to assist in reducing core body temperature, blood may have been redistributed peripherally resulting in less central return and a subsequent increase in HR across all players, masking positional variations (2). In addition, players can intermittently transition between playing positions during games resulting in altered task requirements and responses, which is not specified in many studies (47).
To date, only 1 study has directly compared HR recordings between playing levels in male players with Ben Abdelkrim et al. (9) Identifying Tunisian, junior, international players (94 ± 2% HRmax) produced a significantly higher relative mean HR response than Tunisian, junior, national players (92 ± 2% HRmax). An increase in aerobic fitness capacity noted in international players might underpin the greater frequency and duration of high-intensity activity demands and concomitant higher HR reported for these players. In addition, during exercise an increased state of arousal due to uncertain and exciting competitive environments may also promote higher HR responses as playing level increases (60).
Assessing the HR responses of players relative to game period allows an understanding of the distribution of cardiovascular workload across games and potential accumulation of fatigue. However, at present only 1 study has directly compared male player responses across game quarters. Ben Abdelkrim et al. (10) found a significant decrement in absolute mean HR in the fourth quarter compared with the first 3 quarters in Tunisian, junior, national players. The authors postulated that the reduced HR responses during the fourth quarter were reflective of depleted glycogen and high-energy phosphate stores contributing to player fatigue (10). Tactical aspects also likely contributed to a slowed game pace and concomitant reduction in HR response with game progression. Additional low-intensity periods during time-outs and fouls as well as teams controlling ball possessions for longer durations are encountered later in game-play, which likely allow for added recovery, thus diminishing the HR response (48).
Female Responses to Basketball Training and Game-Play
Comparative to male HR responses, literature focusing on female HR responses during training or game-play is sparse (7,34,42,50,66–68). A summary of the available studies reporting HR responses in female basketball players during training and game-play is presented in Table 2.
Training Heart Rate Responses in Female Players
To date, only 1 study has examined the HR responses of female basketball players during training scenarios. Atli et al. (7) recorded mean absolute and relative HR values during various scrimmage activities after different training configurations in Turkish, female high school players (15.5 ± 0.5 years). Specifically, Atli et al. (7) reported significantly higher HR responses during full-court (181 ± 6 b·min−1; 86 ± 3% HRmax) compared with half-court (162 ± 6 b·min−1; 76 ± 3% HRmax) 3 vs. 3 scrimmage configurations. The observed differences between configurations administered across different court sizes are comparable with male data and likely reflect the increased cardiovascular demands associated with heightened running demands for greater court coverage during offense and defense.
Game-Play Heart Rate Responses in Female Players
The majority of the available HR data for female players has been provided during competitive game-play with relative mean HR values ranging from 86 ± 2% HRmax to 93 ± 4% HRmax reported (34,68). Positional comparisons in player HR responses have yielded equivocal outcomes (42,50,68). For instance, no significant positional differences were reported for relative HR in Czech, junior, first-division players by Vencúrik et al. (68), indicating that guards (86 ± 3% HRmax), forwards (88 ± 3% HRmax), and centers (88 ± 3% HRmax) experienced similar cardiovascular demands. By contrast, Rodríguez-Alonso et al. (42) reported significantly higher HR in guards (186 ± 5 b·min−1) and forwards (179 ± 6 b·min−1) compared with centers (163 ± 10 b·min−1) in Spanish, national/international, female basketball players. Similarly, Scanlan et al. (50) showed that guards experienced significantly higher HR responses than forwards and centers during Australian, semiprofessional game-play. Discrepancies between studies may be due to variations in the players investigated. For instance, large differences in player age were apparent in the adult national (19.3 ± 2.8 years) and international (25.8 ± 2.1 years) Spanish female teams examined by Rodríguez-Alonso et al. (42), whereas Vencúrik et al. (68) investigated Czech, junior, female players (17.6 ± 0.9 years). Future research should seek to identify if position-specific variations in HR responses during game-play are dependent on player age and playing level. Nevertheless, the consistently higher HR responses seen in guards across sexes suggest that position-specific tasks influence the cardiovascular intensities experienced by players during game-play. For example, it is likely that transitioning the ball across the court during fast-break plays and also having a wider occupancy of space during offensive and defensive tasks on the perimeter induce a higher cardiovascular workload in guards compared with centers and forwards (46). Comparatively, centers may have a reduced HR response due to less dynamic work required for game tasks given they are typically performed in close vicinity to the basket (43).
Similar to the available male data, limited research has compared the HR responses of female players between playing levels. Rodríguez-Alonso et al. (42) identified Spanish, international players attained significantly higher relative mean HR compared with Spanish, national players (95% HRmax vs. 91% HRmax). These results emphasize the heightened internal demands imposed on players competing in international compared with national competition, which is consistent with the available male data (9). Furthermore, Vencúrik et al. (66) examined the HR responses of Czech, female players competing in junior first-division (17.4 ± 1.0 years) and senior second-division (20.6 ± 2.9 years) competitions, with no significant differences identified. However, it is difficult to conclude that playing level exerted no influence on player responses in this study given the variation in player age between the different samples.
At present, 4 studies have explored differences in HR responses across game halves or quarters during female basketball game-play, yielding conflicting results. Rodríguez-Alonso et al. (42) identified no significant differences between halves during international game-play in Spanish female players. Similarly, Scanlan et al. (50) detailed no significant interquarter differences during game-play in Australian, state-level female players. By contrast, Matthew and Delextrat (34) noted that HR responses were higher in the first half compared with the second half (166 ± 9 b·min−1 vs. 163 ± 9 b·min−1) in British university players. Confirming this finding, Vencúrik et al. (68) reported a significant reduction in relative HR in the second half compared with the first half (88 ± 3% HRmax vs. 87 ± 3% HRmax) in Czech, junior, first-division female players. These temporal trends might be underpinned by the depletion of high-energy phosphate stores and accumulation of fatigue markers, such as blood lactate (34). However, game situations may influence that the physiological responses of players given stoppages (and thus stationary or low-intensity actions) increase due to more frequent fouls and time-outs during latter game periods (55,66).
Overall, the paucity of HR data in female basketball players makes it difficult to establish a consensus among findings regarding game responses. Nevertheless, the cardiovascular demands imposed on female players seem to be dependent on playing level with the role of playing position and game period requiring further attention. Finally, limited literature is available regarding the HR responses experienced by female players during training (7), which makes it difficult to assess the congruence of training scenarios and game-play. Research addressing this gap would provide guidance in ensuring that female training practices provide adequate physiological preparation for game-play.
Limitations of Heart Rate Monitoring in Basketball
It is important to note there are factors that influence the outcome measures obtained with HR monitors in basketball settings. Table 3 provides a summative list of the factors and associated mechanisms that may influence HR responses during basketball training and game-play, which includes internal, environmental, technical, and activity-specific aspects.
Natural variation is the variability experienced by an individual on a day-to-day basis. During 2 identical submaximal exercise sessions, a 4.1% difference in HR can be observed in the same individual; however, this variation is reduced to 1.6% when performing maximal exercise (2). Natural variation may potentially be attributed to a combination of internal factors, such as hemodynamic differences, neuroendocrine levels, and psychological responses (15). Therefore, because most basketball studies collect HR data across multiple training sessions or games spanning several days or weeks, player responses may vary due to natural variation (2). Some variation in HR responses may also be attributed to the hydration status of players with hypohydrated states associated with reductions in body mass and blood-plasma volume, which may lead to decreases in stroke volume and a compensatory increase in HR to maintain cardiac output (44). Hypohydration accompanied by vasodilatory responses underpinning this cardiovascular drift response can increase HR by up to 11% during the first 60 minutes of submaximal exercise (2). In addition, insufficient or an unbalanced distribution of macronutrient intake may increase cellular respiration and glycolytic pathway reliance, resulting in an increased HR for a given workload (71). Finally, a state of anxiety before game-play can increase HR through activation of the sympathetic nervous system and subsequent inhibition of parasympathetic cardiac control (15).
From an external perspective, environmental limitations, such as ambient temperature and humidity, can impact HR responses. Exposure to hot environments requires the body to use heat loss mechanisms, such as evaporation (through sweating) to reduce core body temperature (2), which exacerbates the HR response. Environmental humidity levels influence HR through similar mechanisms to ambient temperature. High relative humidity levels impair the thermoregulatory ability of the body through reduced evaporative capacity, thus becoming more reliant on convection, conduction, and radiation to reduce core body temperature (70). Consequently, HR is elevated to facilitate peripheral blood flow and enzymatic changes accompanying high humidity (2). Finally, the hypoxic environments induced by higher altitudes alter HR responses during rest and exercise. The decreased partial pressure of oxygen at altitude reduces the volume of oxygen per unit of blood delivered to working musculature (35). To compensate for the reduced efficiency of blood flow, HR is elevated to assist in blood delivery to active tissues for adequate oxidative energy provision for continued exercise. Literature highlights that HR remains elevated after 3–5 days after initial exposure to altitude compensating for a decrease in stroke volume (35). With basketball played in a majority of countries characterized by hot and humid tropical settings (9,10,50) or at higher altitudes (22), HR data may be impacted by the geographical location and court environment (e.g., outdoor vs. indoor, air-conditioned vs. ambient air), which should be considered when monitoring HR.
Inherent hardware and software limitations are encountered with measuring player HR responses. Position of the belt, battery lifespan, location of receivers, software updates, storage capacity, and interference with other HR monitors are factors that can result in technical errors (58). In addition, an experienced technician is required to setup and ensure that all equipment is working correctly. Further to these technical aspects, each HR monitor model has different sampling rates with data supplied across predefined epochs potentially carrying a diminished sensitivity compared with beat-to-beat data. Although technical limitations need to be considered, basketball imposes unique activity demands on players who can also provide limitations regarding HR monitoring.
Rapid movements, such as repeated sprints, jumps, and changes in direction (<4 seconds), are interspersed throughout activities performed at lower intensities during basketball training and game-play (10,25,47,49). In turn, an inherent lag response is believed to possess some inaccuracies during such short or intermittent activities (2). Furthermore, examining mean responses does not necessarily provide information about proportions of time spent working at different HR intensities. For example, a mean HR of 160 b·min−1 can be attained by working consistently at steady state or alternatively by completing interval-style exercise characterized by high-intensity exertion with frequent recovery. An alternative approach to monitoring the high-intensity demands of basketball includes the implementation of mathematically derived training load models to calculate the internal responses of players across the annual plan.
Heart Rate–Based Training Load Approaches in Basketball
To best prepare players for the demands experienced during game-play, periodized training programs are implemented to induce specific cardiovascular, neuromuscular, and metabolic adaptations. Quantification of the internal training load is required to ensure sufficient player responses are attained. Consequently, HR-based training load models are used to (a) assess the efficacy of training practices; (b) monitor changes in responses that may indicate player fatigue, overreaching, or overtraining; and (c) ultimately adjust the external dose or training stimulus to ensure sufficient physiological responses in each player (40). Several internal training load models using HR data have been established (20), with recent studies documenting 3 common approaches in basketball settings: Banister's Training Impulse (TRIMP) (33,53,56,57), Lucia's TRIMP (53), and Edwards' Summated-Heart-Rate-Zones (SHRZ) (33,53,56,57).
Banister's Training Impulse Model
This model was first postulated by Banister in 1991 (8); Banister's TRIMP can be calculated in arbitrary units (AU) as follows:where b is the sex factor (1.67 for females and 1.92 for males); e = base of the natural logarithm (constant of 2.712); ΔHR ratio = (HRex − HRrest)/(HRmax − HRrest), with HRex indicating mean HR during the training session, HRrest indicating HR measured during pre-exercise rest, and HRmax indicating maximal HR achieved during a maximal exercise test.
To effectively manipulate the external training load to bring about an anticipated player response, the dose-response interaction of different HR-based training load models should be established relative to the external load prescribed (3,32). Accordingly, Scanlan et al. (57) explored the relationships between internal and external training load during basketball training activities in semiprofessional male players with a moderate, positive relationship between Banister's TRIMP and external load assessed using accelerometry (r = 0.38). Importantly, these findings suggest that Banister's TRIMP might not possess a strong dose-response relationship with the external load as quantified using accelerometers during basketball training.
Although a strong dose-response relationship is sought between external and internal training load approaches for effective training prescription, the relationships between different internal training load models provide insight into the overlap of outcomes provided by each approach. Accordingly, significant, very large relationships between Banister's TRIMP and Edwards' SHRZ have been reported across different basketball training modes (56). Scanlan et al. (56) detailed that Banister's TRIMP was significantly related to SHRZ across general conditioning, specific conditioning, and game-based training (r = 0.86–0.90), indicating that each model provides similar information about the internal load experienced by basketball players across different training modes. However, a key drawback of Banister's TRIMP is the requirement to accurately measure HRmax and HRrest, which can be determined using various approaches. For example, previous research indicates that HRmax responses attained during training or game-play can be higher than those evident during maximal exercise testing, and thus can be substituted to calculate Banister's TRIMP (1). Furthermore, Scanlan et al. (56) calculated HRrest after 2 minutes of preactivity rest, whereas Manzi et al. (33) determined HRrest as the mean resting HR during the pretraining briefing when applying Banister's TRIMP in semiprofessional and professional, male basketball players, respectively. Each approach to determine HRmax and HRrest likely introduces variability into the outcomes obtained, which must be considered when interpreting the available literature.
Lucia's Training Impulse Model
Lucia's TRIMP aims to quantify internal loading by weighting time spent at different intensities according to HR values corresponding to individualized blood lactate thresholds (30). Lucia's TRIMP is calculated as follows:where zone 1 = HR corresponding with blood lactate <2.5 mmol·L−1; zone 2 = HR corresponding with blood lactate between 2.5 mmol·L−1 and 4.0 mmol·L−1; zone 3 = HR corresponding with blood lactate >4 mmol·L−1.
At present, only 1 study has explored Lucia's TRIMP in basketball (53). Scanlan et al. (53) used a basketball simulation test in noncompetitive settings and reported Lucia's TRIMP to possess trivial to small relationships (r = −0.22 to 0.07) with Banister's TRIMP and Edwards' SHRZ across varied doses of basketball activity (10–40 minutes), indicating that it may provide different insights regarding the internal training load than other models. Despite the greater individualized approach adopted in Lucia's TRIMP, there are some inherent limitations in this model. Specifically, Lucia's TRIMP may be problematic for players experiencing extensive internal stress at intensities above the anaerobic threshold, which are not differentiated according to the weighting allocation (11). For example, if 2 players reached anaerobic threshold at 85% HRmax and were exercising at 90% HRmax and 95% HRmax, both players would attain the same internal load according to Lucia's TRIMP, despite experiencing different physiological stress. Furthermore, to formulate each zone, a trained practitioner must establish the blood lactate-HR relationship for each player through invasive laboratory-based testing, which is time consuming and requires costly, specialized infrastructure (e.g., metabolic analyzer, blood analyzer, and treadmill). Given that blood lactate thresholds fluctuate with training status, laboratory-based testing would need to be conducted periodically across the annual plan as players undergo physiological adaptations (31). Consequently, the paucity of research exploring Lucia's TRIMP compared with other HR-based models might be attributed to the greater methodological requirements prohibiting regular adoption by basketball teams in practice.
Edwards' Summated-Heart-Rate-Zones Model
The SHRZ model combines linearly weighted zones relative to HRmax (59). The SHRZ model is calculated using the following formula:where zone 1 = 50–59% HRmax; zone 2 = 60–69% HRmax; zone 3 = 70–79% HRmax; zone 4 = 80–89% HRmax; zone 5 = 90–100% HRmax.
Using the SHRZ model, Manzi et al. (33) reported that higher internal training loads were experienced across weekly microcycles when no games were played compared with weeks where 1 or 2 games were played in professional, male basketball players. The authors suggested the incorporation of tapering strategies before competition allows optimal performance coinciding with reduced total weekly internal training load (33). Furthermore, this research supports the sensitivity of the SHRZ model to detect changes in training load during weekly microcycles (33). Moreover, the SHRZ model has been shown to be more sensitive than Banister's TRIMP in detecting changes in internal training load across preseason training phases in semiprofessional, male players administered in a linear, periodized manner (56). The heightened sensitivity associated with the SHRZ compared with other models is likely due to the embedding of individualized HRmax responses and utilization of a greater number of HR zones when weighting exercise intensity (56). Moreover, Scanlan et al. (57) observed the SHRZ model to possess a stronger correlation (r = 0.61, large) with external load derived from accelerometers compared with Banister's TRIMP in semiprofessional, male players. This finding adds further support to the use of the SHRZ model in practice, given that a strong relationship between external and internal training loads permits practitioners to acquire a desired response in players with greater accuracy when adjusting the training dose administered.
The primary limitation of the SHRZ model is the utilization of a linear weighting system with large ranges (10% HRmax zones), which may produce similar variations in outcomes as those discussed for Lucia's TRIMP. For example, if 2 players of similar aerobic fitness were working at 81% HRmax and 89% HRmax they would record equivalent SHRZ weightings, despite exercising at noticeably different HR intensities.
Recommendations for Future Research in Basketball
Various applications of HR monitoring require further scientific investigation in basketball settings. First, the data presented in Tables 1 and 2 demonstrate the variability in the reporting of HR in the literature, with mean and peak responses presented in absolute and relative forms. Similarly, data have been reported relative to live time (excluding HR response recorded during game stoppages and substitutions) and total time (incorporating HR responses recorded during game stoppages and substitutions). Live time allows the understanding of peak demands experienced by players by removing in-game recovery periods, whereas total game time provides key information about recovery processes inherent during game-play. The variability in the reporting of HR values makes comparisons across studies difficult and limits the conclusions from previous studies. Consequently, future research needs a consensus approach in reporting HR responses during basketball training and game-play.
Second, the limited HR data reported relative to sex, age, playing position, and playing period in basketball players need to be addressed in future studies. To date, female HR responses remain largely unreported during different forms of training. Investigation is required to identify if training responses are appropriate in physiologically preparing female players for game-play. In addition, players used in the literature range broadly in age, which makes interpretations difficult between studies. Comparisons of HR responses between players of varied ages competing at the same playing level and following the same game configurations will provide greater evidence of developmental changes in HR responses during basketball and permit more precise development of age-specific training plans. Similarly, male and female HR responses relative to playing position and game period should be further examined to better develop position-specific approaches to training and recovery, and identify temporal fatigue patterns during game-play for more precise tactical management of players.
Third, only 1 study has directly compared the HR responses during training settings with those encountered during actual game-play in basketball players, showing heightened mean responses during game-play in male players (37). To establish training programs that are effective in adequately meeting or overloading players relative to the physiological demands of game-play, further research should quantify male and female player responses during different training modes and drills.
Fourth, HR-based training load approaches seem to be the most supported for quantifying the internal response of players across the annual plan (33,53,56,57). Practically, the SHRZ model provides a useful balance between adopting an individualized approach and requiring less time, expertise, and resources than other approaches for practical implementation. However, the determination of HRmax is important to standardize given the varied reported approaches across team sport research (12,16,19,21,38,39), with future research recommended to identify the influence of predictive, field-based, and laboratory-based methods to determine HRmax responses on training load outcomes. Furthermore, given the limitations around using zone intervals spanning 10% HRmax, future research should examine the utility of the refined SHRZ model embedding smaller zones (2.5% HRmax or 5% HRmax) carrying concomitant adjusted weightings.
Finally, HR-based training load data have only been reported for male players. In turn, it is important that more scientific inquiry be conducted regarding these approaches in female and junior basketball players. Examination of HR-based training load models in a wider variety of players will better assist in establishing optimal approaches across annual plans according to sex, age, and playing level.
Recommendations for Heart Rate Monitoring in Basketball
Based on the body of work presented in this review, it is important to provide evidence-based recommendations for basketball coaches, strength and conditioning professionals, and sport scientists using HR monitoring in basketball settings. First, the cardiovascular responses experienced during game-play seem to be higher in guards compared with forwards and centers, and in international players compared with national players during game-play. Practitioners should therefore develop appropriate position-specific training plans that adequately elicit heightened cardiovascular demands in guards compared with other positions to prepare players sufficiently for game demands. Furthermore, when transitioning players from national to international playing levels before specific competitions (e.g., World Championships, Olympic Games), added cardiovascular stress may need to be embedded when developing training drills for precompetition training camps.
Second, the collective evidence shows an overlap between male and female HR responses during game-play. These similarities in game responses across sexes indicate that similar drills eliciting game-specific internal intensities may be administered in both male and female players' training plans. When designing training scenarios for optimal physiological preparation, practitioners should look to manipulate the number of players on court and court size. Specifically, reducing player numbers to 3 vs. 3 or less during scrimmage drills promotes a heightened HR response close to those experienced during actual competition (14,24). Although practitioners often implement reduced court size to improve technical components, the application of full-court drill configurations promotes greater cardiovascular stress in response to more high-intensity activity transitioning the ball across the court (7,64). Accordingly, basketball practitioners should manipulate scrimmage configurations to promote the desired physiological stimulus and technical development for each player.
Third, from a technical perspective, HR monitoring approaches measuring beat-to-beat data should be incorporated to more precisely detect changes in player responses during short explosive and intermittent movements. In addition, monitoring of exercise intensity during basketball training activities and game-play should include relative HR values (%HRmax) and the distribution of intensities (time spent in different zones) to allow for individualized comparisons across players and provide a more comprehensive understanding of the cardiovascular stress experienced. Specifically, the reporting of time spent in HR zones allows coaches to understand the distribution of low-, moderate-, and high-intensity activity rather than solely relying on assumptions about the exercise bout from mean or peak HR values, which can be obtained through different stimuli. These data should also be provided during live and total playing time to encapsulate the physiological demands in light of the recovery periods encountered to better assist basketball practitioners to make informed decisions regarding individualized player training plans and recovery strategies, as well as in-game tactical decisions providing recovery opportunities for players.
Fourth, the existing basketball literature has predominantly reported use of HR-based models to monitor the internal training load in players across the annual plan (33,53,56,57). Consequently, practitioners should be cognizant of the limitations that each model possesses. Specifically, when implementing Banister's TRIMP, practitioners should be aware that the generic exponential relationship between blood lactate concentration and HR may not be accurately applied to all players. Although Lucia's TRIMP provides a more individualized approach to overcome this limitation, HR values above anaerobic threshold levels are indistinguishable and the intensive time and infrastructure requirements of testing may limit the use of this model by basketball teams. The determination of HRmax is a key drawback for the utilization of Banister's TRIMP and SHRZ as previous literature has implemented varied methodologies to measure this response with the precise implications on training load outcomes yet to be investigated. Based on the existing evidence, the SHRZ approach seems to be most practical HR-based model with limited time, expertise, and infrastructure required for the measurement. Furthermore, the SHRZ model has been shown to possess adequate sensitivity in detecting changes in training load across training microcycles (33) and mesocycles (56) in basketball settings, and also possesses the strongest relationship with external load (57) for heightened accuracy when planning an anticipated response in players to a prescribed training stimulus.
Finally, it should be noted that HR-based training load measurements provide information regarding the internal response. Accordingly, the external dose (or training stimulus) should be quantified through the use of appropriate technologies, such as microsensors or video analyses in conjunction with HR measures to provide an indication of player responses relative to the external load administered. These measures collected in tandem are important to ascertain if players are responding appropriately to changes in the external load and not placed at an increased risk of illness and injury. Practitioners should use internal and external load data to schedule training loads safely in an individualized manner while considering training phase, game scheduling, and travel requirements to ensure adequate recovery strategies are implemented, excessive loads are not applied, and increases in loading are progressive in nature (62).
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