Heart Rate Variability in Basketball: The Golden Nugget of Holistic Adaptation?

Citation: Sánchez, R.P.; AlonsoPérez-Chao, E.; Calleja-González, J.; Jiménez Sáiz, S.L. Heart Rate Variability in Basketball: The Golden Nugget of Holistic Adaptation? Appl. Sci. 2024,14, 10013. https://doi.org/ 10.3390/app142110013 Academic Editors: Dana Badau, Adela Badau and Philip X. Fuchs Received: 25 August 2024 Revised: 14 October 2024 Accepted: 21 October 2024 Published: 2 November 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Review Heart Rate Variability in Basketball: The Golden Nugget of Holistic Adaptation? Rubén Portes Sánchez 1 , Enrique Alonso-Pérez-Chao 2,3 , Julio Calleja-González 4 and Sergio L. Jiménez Sáiz 5, * 1Valencia Basket Performance Department, Pabellón Fuente de San Luis, Avenida dels Germans Maristes 16, Quatre Carreres, 46013 Valencia, Spain; [email protected] 2Department of Physical Activity and Sports Science, University Alfonso X el Sabio, 28691 Madrid, Spain; [email protected] 3Faculty of Sports Science, European University of Madrid, 28670 Villaviciosa de Odón, Spain 4Department of Physical Education and Sports, Faculty of Education and Sport, University of the Basque Country, (UPV/EHU), 01007 Vitoria-Gasteiz, Spain; [email protected] 5Sport Sciences Research Centre, Universidad Rey Juan Carlos, 28943 Madrid, Spain *Correspondence: ser[email protected] Abstract: The main aim of this narrative review is to assess the existing body of scientific literature on heart rate variability (HRV) in relation to basketball, focusing on its use as a measure of internal load and vagal nerve responses. Monitoring HRV offers insights into the autonomic function and traininginduced adaptations of basketball players. Various HRV measurement protocols, ranging from short-term to longer durations, can be conducted in different positions and conditions, such as rest, training, and sleep, to determine this key metric. Consistency and individualization in measurement protocols, responding to the athlete’s specific characteristics, is crucial for reliable HRV data and their interpretation. Studies on HRV in basketball have explored psychological adaptation, training effects, individual differences, recovery, and sleep quality. Biofeedback techniques show positive effects on HRV and anxiety reduction, potentially enhancing performance and stress management. The scientific literature on HRV in basketball could benefit from studies involving longer monitoring periods to identify significant trends and results related to training and recovery. Longitudinal HRV monitoring in teams with intense travel schedules could reveal the impact on athletes of all levels and ages, and, in this regard, individualized interpretation, considering the subjective recovery and fitness levels of athletes, is recommended to optimize training programs and performance. HRV provides insights into training and competitive loads, aiding in determining exercise intensities and training status. Additionally, HRV is linked to recovery and sleep quality, offering valuable information for optimizing player performance and well-being. Overall, HRV is a reliable tool for adjusting training programs to meet the specific needs of basketball players. Keywords: HRV; basketball; recovery; adaptation; heart rate 1. Introduction Heart rate variability (HRV) measures the fluctuation in time intervals between heartbeats [ 1 ] and is associated with adaptability and resilience. Therefore, high HRV can enhance executive function performance [ 2 ]. In particular, a 5 min HRV measurement at rest effectively interprets responses to training [ 3 ]. In fact, sympathetic activity increases heart rate (HR), while parasympathetic activity decreases it (Figure 1), leading to psychophysiological changes in HRV [ 2 ]. After exercise, the autonomic nervous system (ANS) shifts towards sympathetic activity to manage training stress, reducing parasympathetic activity and HRV [ 4 ]. Consequently, monitoring HRV can effectively determine the crucial balance between these two ANS responses [ 5 ]. In this regard, circadian variations in metabolism, sleep cycles, and body temperature affect HRV measurements [ 6 ]. Specifically, Appl. Sci. 2024,14, 10013. https://doi.org/10.3390/app142110013 https://www.mdpi.com/journal/applsci Appl. Sci. 2024,14, 10013 2 of 17 changes in athletes’ ANS patterns can alter HRV patterns [ 7 ], aiding training professionals in managing fatigue and adjusting the exercise intensity [ 7 ], thus enabling dynamic periodization [8]. Appl. Sci. 2024, 14, x FOR PEER REVIEW 2 of 16 changes in athletes’ ANS patterns can alter HRV patterns [7], aiding training professionals in managing fatigue and adjusting the exercise intensity [7], thus enabling dynamic periodization [8]. Figure 1. Adapted from (Aubert, 2003) [2]. BP = blood pressure; CO = cardiac output; HR = heart rate; n. vagus = nervus vagus; SV = stroke volume; TPR = total peripheral resistance. HRV monitoring has gained significant interest in team sports for reflecting changes in the autonomic function and training adaptations during the competitive season [9]. In basketball [10], a sport characterized by high-intensity efforts [11], HRV is crucial for managing executive functions during practice and competition [12], considering that effective basketball training aims to improve the physical and physiological condition of athletes by accurately recording, interpreting, and adjusting training loads [13]. Concretely, load demands can be categorized as the external load (EL), related to the volume and intensity, and the internal load (IL), which is the athlete’s physiological response to these demands [14]. Specifically, the IL measures can be classified as subjective (the athletes’ mental interpretation of stress) and objective (the monitored physiological responses) [15]. Among them, HRV (an objective measure) is typically monitored using HR sensors, straps, watches, wristbands, or rings that register beat-to-beat timing [16], indicating how athletes adapt to training, and has gained attention due to technological advancements in data collection. As a consequence, consistent and validated protocols are essential for effective decision-making using these data [17]. The impact of physical activity on HRV is well documented in numerous team sports [18–28]. Previous studies have investigated the role of HRV on recovery and readiness and as a load indicator. For instance, Ayuso-Moreno et al. [18] found differences in the HRV of female soccer players before high-demand matches. On the other hand, D’ascenzi et al. [19] observed increased HRV in female volleyball players in the days leading up to competition. Conversely, Holguín-Ramírez et al. [21] found no effect of mindfulness training on the HRV of female soccer players. Laborde et al. [22] linked well-being to baseline HRV in female handball players. Móra et al. [23] found decreased HRV after psychological stress in soccer players, suggesting strategies to enhance athletic performance. In another study, centered on field sport, Parrado et al. [25] identified a negative correlation between perceived tiredness and HRV. Finally, Ke-Tien et al. [29] found correlations between POMS questionnaires and HRV in rugby players. In summary, the variability in studies highlights the need for specificity and contextualization when interpreting HRV data across different athlete groups. Figure 1. Adapted from (Aubert, 2003) [ 2 ]. BP = blood pressure; CO = cardiac output; HR = heart rate; n. vagus = nervus vagus; SV = stroke volume; TPR = total peripheral resistance. HRV monitoring has gained significant interest in team sports for reflecting changes in the autonomic function and training adaptations during the competitive season [ 9 ]. In basketball [ 10 ], a sport characterized by high-intensity efforts [ 11 ], HRV is crucial for managing executive functions during practice and competition [ 12 ], considering that effective basketball training aims to improve the physical and physiological condition of athletes by accurately recording, interpreting, and adjusting training loads [13]. Concretely, load demands can be categorized as the external load (EL), related to the volume and intensity, and the internal load (IL), which is the athlete’s physiological response to these demands [ 14 ]. Specifically, the IL measures can be classified as subjective (the athletes’ mental interpretation of stress) and objective (the monitored physiological responses) [ 15 ]. Among them, HRV (an objective measure) is typically monitored using HR sensors, straps, watches, wristbands, or rings that register beat-to-beat timing [ 16 ], indicating how athletes adapt to training, and has gained attention due to technological advancements in data collection. As a consequence, consistent and validated protocols are essential for effective decision-making using these data [17]. The impact of physical activity on HRV is well documented in numerous team sports [ 18 – 28 ]. Previous studies have investigated the role of HRV on recovery and readiness and as a load indicator. For instance, Ayuso-Moreno et al. [ 18 ] found differences in the HRV of female soccer players before high-demand matches. On the other hand, D’ascenzi et al. [ 19 ] observed increased HRV in female volleyball players in the days leading up to competition. Conversely, Holguín-Ramírez et al. [ 21 ] found no effect of mindfulness training on the HRV of female soccer players. Laborde et al. [ 22 ] linked well-being to baseline HRV in female handball players. Móra et al. [ 23 ] found decreased HRV after psychological stress in soccer players, suggesting strategies to enhance athletic performance. In another study, centered on field sport, Parrado et al. [ 25 ] identified a negative correlation between perceived tiredness and HRV. Finally, Ke-Tien et al. [ 29 ] found correlations between POMS questionnaires and HRV in rugby players. In summary, the variability in studies highlights the need for specificity and contextualization when interpreting HRV data across different athlete groups. In a concrete way, to the best of the author’s knowledge, research on HRV in basketball is limited. Current existing studies have mainly focused on HRV responses to basketballspecific stress activities [ 9 , 10 , 30 – 32 ] and physical tests [ 33 – 35 ], but no previous specific Appl. Sci. 2024,14, 10013 3 of 17 reviews have been published on the outcomes of these studies. Therefore, this narrative review aims to examine the scientific literature on HRV in basketball to assess the evidence related to internal load and vagal nerve responses. 2. HRV Measurement Protocols in Basketball Measurements of HRV in the basketball-related literature include short term measurements (ST, 5 min) [ 10 , 36 – 41 ], specific test and in-session measures [ 31 , 32 , 36 , 42 ], sleep measures [ 43 , 44 ], and ultra-short-term measures [ 30 , 45 ]. Messina et al. [ 37 ] measured the PSA of HRV through an electrocardiogram (ECG) for 5 min, analyzing the signal through an electrocardiograph connected to a PC (Personal Computer) at 100 s/s and analyzing the obtained data through specific software. In addition, Paul et al. [ 38 ] analyzed the role of biofeedback in training through the measurement of HRV in a seated position, with the participants seated on a chair with their eyes closed, while Moreno et al. [ 39 ] measured ACB (Spain’s First League) players over the same time duration but in a different body position; in this case, the participants were resting in a supine position for the measurement, a protocol that the same author later used for a posterior study with LEB Gold (Spain’s Second Division) players [ 40 ]. Khan et al. [ 46 ] used the same time frame (5 min) to measure the HRV of collegiate players, but in this case, the measurements were recorded during an incremental running protocol, as well as after the test. García-Ceberino et al. [ 33 ] studied the HRV in a younger population (U-16), with a protocol of 5 min in a seated position before and after competition. The 5 min protocol can be placed within the short-term group of HRV measurements [ 6 ], which are the product of two processes: (a) mainly concerns the complex and dynamic relationship between the sympathetic and parasympathetic branches, while (b) relates to the regulatory mechanisms that control HR via respiratory sinus arrythmia (RSA) and the baroreceptor reflex and rhythmic changes in the vascular tone [ 47 ]. Longer periods of recording provide information about cardiac variations in response to a wider variety of environmental and stress factors [ 6 ], such as training load variations produced by conditioning tests or training sessions. Longer durations of measurement were utilized by Morales et al. [ 34 ] in a study where participants were assessed in an orthostatic position for 10 min, both before and after a vertical jump assessment. Similarly, Abad et al. [ 35 ] employed the same duration to investigate the effects of sub-maximal tests and a Yo-Yo IR1 test on participants positioned in a resting posture, as outlined by the authors, to gather HR data. In another study focusing on workload effects, Ramos-Campo et al. [ 36 ] conducted an incremental test to volitional fatigue while simultaneously recording gas-exchange measurements and the HRV of the subjects during the tests. Additionally, Nakamura et al. [ 9 ] utilized 10 min measurements in a seated position, before and after practice, to examine the impact of training sessions. Piedra et al. [ 42 ], Zamora et al. [ 32 ], and Jin et al. [ 10 ] collected HRV measurements of amateur basketball players during training sessions and specific ball drills (5 vs. 5) to assess their response to training. Moreover, García et al. [ 33 ] and Senbel at el. [ 44 ] analyzed HRV measurements obtained during sleep, using wearable devices to understand the variations influenced by factors affecting sleep quality. Regarding ultra-short-term measures, Lukonaitiene et al. [ 30 ] studied female under-18 and under-20 players before a European championship, using a 90 seconds morning measurement in a seated position. In conclusion, various protocols have been used in basketball studies, with different results found. Nevertheless, to ensure the validity and applicability of the results obtained when monitoring the heart rate responses within a group of athletes, consistency in measurement protocols is needed for the reliability and subsequent interpretation of the results obtained. 3. Impact of HRV on Fatigue and Recovery Recovery is an essential component of any training program, facilitating the body’s adaptation and enhancement. An athlete’s ability to recover is influenced by various factors, the foremost among them being fatigue [ 48 , 49 ]. Fatigue is defined as a suboptimal Appl. Sci. 2024,14, 10013 4 of 17 psychophysiological state, encompassing mental and physical mechanisms, concurrent to physical exertion [15]. Multiple factors contribute to fatigue, including glycogen depletion, muscle damage, action potential interruption, and excitation–contraction coupling failure [ 50 – 52 ]. These factors, resulting from basketball activity, can impede a player’s capacity to maintain high-intensity activity levels during games [ 51 ]. Additionally, since exercise performance is centrally regulated by the central nervous system (CNS), fatigue should no longer be perceived solely as a physical event. Instead, it should be seen as a sensation or emotion, distinct from any overt physical manifestation, such as the reduction in the force output of active muscles [ 53 ]. Physical activity is controlled by a central governor in the brain, with the human body operating as a complex system during exercise. Using feed-forward control in response to afferent feedback from various physiological systems, the extent of skeletal muscle recruitment is modulated as part of a pacing strategy. The sensation of fatigue represents the conscious interpretation of these homoeostatic central governor control mechanisms [ 52 ]. Given that studying muscle fatigue in isolation, without CNS control [ 52 ], is unlikely to advance the understanding of this phenomenon, HRV emerges as a measure that can effectively monitor an athlete’s recovery and fatigue levels [50]. The autonomic nervous system regulates numerous physiological processes, including heart rate, respiration, and blood pressure [ 6 ]. HRV serves as an indirect gauge of autonomic nervous system activity, with higher HRV indicating a healthier and more adaptable nervous system. Therefore, HRV is invaluable in monitoring an athlete’s fatigue level [ 17 ]. A decrease in HRV could be a sign of fatigue in the athlete, also signifying reduced activity in the parasympathetic nervous system, which is responsible for rest and recovery [ 50 , 54 ]. Conversely, an increase in HRV suggests that the parasympathetic nervous system is more active when an athlete is well rested. A low HRV in an athlete may signal the need for a training break or adjustments to the training program to allow adequate recovery time [ 55 ]. Consequently, HRV serves as a valuable tool in monitoring an athlete’s recovery and fatigue levels. By tracking HRV, athletes and their coaches can make well-informed decisions regarding training and recovery strategies [ 10 , 50 , 56 ]. Future studies on recovery should consider these variables in other basketball populations. 4. Discussion of the Present Evidence This narrative review aimed to explore the scientific literature on HRV in basketball to assess the evidence related to internal load and vagal nerve responses. The main conclusions described that HRV provides insights into training and competitive loads, aiding in determining exercise intensities and training status. Additionally, HRV is linked to recovery and sleep quality, offering valuable information for optimizing player performance and well-being. Overall, HRV is a reliable tool for adjusting training programs to meet the specific needs of basketball players. A summary of the main investigations can be seen in Table 1. Many diverse coaching strategies should be adapted by players, and, consequently, performance coaches must adapt to these. The monitorization and interpretation of HRV in basketball can provide these professionals with a comprehensive and holistic understanding of the shift from vagal to sympathetic cardiac modulation [ 7 ]. This insight allows coaches to tailor workloads and rest periods to better match the individual needs of each player. Given the nature of basketball as a high-intensity intermittent physical activity, which is highly dependent on both the aerobic and anaerobic components [ 57 ], preparation for these actions and the recovery [ 58 ] from exposure to them seems crucial to a welldeveloped training program. In this sense, studies related to HRV in basketball have aimed for different goals and objectives, as previously mentioned in this review, but the main and overall interest seems to be the different and specific branches or components that this variable can provide to interpret a holistic adaptation to the training process [59,60]. One of these specific branches of adaptation is the psychological one [ 37 , 61 ], where studies investigated the subjective or emotional status of an athlete to find out its impact Appl. Sci. 2024,14, 10013 5 of 17 on HRV. Authors found that biofeedback had a positive effect on HRV by a reduction in both the trait and state anxiety, probably due to the stimulation of baroreflexes through the regulation of breathing, which occurred due to specific HRV biofeedback [ 37 ]. Results showed a reduction in the reaction and movement time after biofeedback training, as was confirmed by another study about the effect of biofeedback on HRV [ 61 ]. It is possible that these results are due to a greater balance between the sympathetic and parasympathetic systems. However, additional evidence related to these protocols is needed to confirm their efficacy for a broader spectrum of the population. These findings are of high relevance to practitioners who want to improve specific athlete training programs related to situations of high stress, resembling competition, as the high levels of arousal that are produced during competitive events could lead to a performance reduction [ 62 ]. Thus, by improving the control of these variables, performance could be positively affected. Paced breathing could help athletes to cope with stress through the production of high-amplitude oscillations in the cardiovascular system. In addition, this will help maintain focus and improve decisionmaking during these specific moments, characteristic of competition [60]. The addition of psychological measures could also help to interpret HRV measures and vice versa in elite basketball players [ 38 ]. This could bring a more holistic approach to psychophysiological measures and our understanding of the training process by considering the intangible load affecting the players through the psychological taxation of travel or team results. Further studies are required to gather more evidence regarding this topic. Training adaptation has also gathered interest through the HRV-related studies in basketball. Physical activity seems to have a positive impact in basketball players, as female professional basketball players seemed to improve their parasympathetic activity through basketball training compared to a sedentary group [ 36 ]. Although these results might be specific, since gender, age, and athletic conditioning influence an athlete’s autonomic control [ 7 ], it is confirmed that team-based athletic activities induce parasympathetic modulation [ 63 ], therefore improving HRV in athletes involved in such activities. Maximum jumps seem to reduce HRV and 5 ′ seems to be a resting time that can onset the vagal tone in elite basketball players [ 33 ]. This could also be confirmation of the cardiac flexibility of highly trained participants, a fact that could affect the considerations for recovery in these populations [64,65]. Although there are some common considerations regarding the monitoring of HRV, the personalization of the monitoring process seems to be recommended for athletes; in particular, professional basketball players seem to present significant differences between individuals regarding subjective recovery and HRV data [ 39 ]. For example, HRV measures in basketball players are related to the subjects’ athletic fitness [ 66 ] and can be influenced by the intensity, duration, and content of the exercise intervention [ 40 ], which suggests the need for personalization in the interpretation of heart rate-derived data. On account of this, the implementation of both subjective and objective methods in the evaluation of recovery will bring more individualized results to practitioners. Female basketball players have shown disparities in subjective well-being measures and HRV monitoring data, with both of these methods recommended to determine player status [ 30 ]. These principles could also be applied when monitoring stress in basketball players, as a multivariable model seems to be more desirable to determine the effect of the stress imposed during training periods, even at the amateur level [36]. Appl. Sci. 2024,14, 10013 6 of 17 Table 1. Summary of the studies analyzed regarding HRV in basketball. Title Journal Year/Quartil Authors Participants/ Level Methodology Dependent Variables Methodological Procedures Results Conclusions Practical Applications Study Strengths Study Limitations The Effect of Heart Rate Variability Biofeedback on Performance Psychology of Basketball Players Applied Psychophysiology biofeedback 2012 Paul & Garg [38] 17 M and 13 F Different levels of competition (university, state, national) Experimental double-blind HRV HRV, RR analyzed with BFB software Variation anxiety/time significant (F = 66.503, p\0.001). HRV improves with biofeedback training Biofeedback training →> performance “zone of excellence” Placebo group, inclusion male/female Small sample group Enhanced parasympathetic activity of sportive women is paradoxically associated to enhanced resting energy expenditure Basic & Clinical neuroscience 2012/Q3 Messina et al. [37] 20 F/10 Sedentary & 10 Professional Observational retrospective PSA of HRV (LF, HF, LF/HF) HRV evaluated through electrocardiogram during 5′ Significant differences between groups on HF of HRV Increases of parasympathetic activity at rest induced by training Basketball does not modify the sympathetic discharge as no changes were observed in LF in this population Number of physiological measures included in the study No performance results included in study, limited age range of the sample Role of biofeedback in optimizing psychomotor performance in sports Asian Journal of Sport Medicine 2012/Q4 Paul et al. [61] 16 M & 14 F/University, State, National level (China) Experimental double blind HRV, RR, LF, HF, Concentration (60 seconds), Choice Reaction Time, Movement Time, Shooting (number shots in 90′′) HRV (5′seated with closed eyes on a chair) Significant variation in HRV in both groups Changes in HRV are sensitive to emotional state Biofeedback helps regulate HRV and positively impacts physiological and psychological states of the athlete Inclusion of placebo group Small sample size Heart Rate Variability and Psychophysiological Profiles in HighPerformance Team Sports Revista de Psicología del Deporte 2013/Q3 Moreno et al. [39] 53 M (14 basketball players)/ACB (Spain), Grass Hockey, Football Observational retrospective and descriptive HRV, POMS HRV measured 5′resting in supine position Basketball and grass hockey players present higher HRV than football players POMS and HRV variables together could be used to determine psychophysiological profiles in elite athletes Addition of psychological measures to HRV interpretation could help on the understanding of both parameters Elite level and number of the subjects selected for the study Only one evaluation of the selected athletes Heart Rate Variability Responses in Vertical Jump Performance of Basketball Players International Journal of Sports Science 2014/Q3 Morales et al. [34] 8 M/National Basketball League (Brazil) Observational, retrospective, and descriptive HRV & VJ HRV (10’ standing orthostatic position before and after the vertical jump assessment) Relationships VJ SympParasymp modulation Sympathetic modulation (LF; LF: HF) = 5VJ test> 60SVJ protocol. 5′rest =onset of vagal tone→ higher fatigue >5′rest for onset of vagal tone Sample playing level, comparison of external and internal measures Results related only to high training load Appl. Sci. 2024,14, 10013 7 of 17 Table 1. Cont. Title Journal Year/Quartil Authors Participants/ Level Methodology Dependent Variables Methodological Procedures Results Conclusions Practical Applications Study Strengths Study Limitations Individual Recovery Profiles in Basketball Players Spanish Journal of Psychology 2015/Q3 Moreno et al. [40] 6 M/LEB Oro (Spain) Experimental, retrospective descriptive HRV & Subjective Recovery TQart (6-20) & HRV (5′in supine lying) Significant differences between subjects No consistent relationship between TQart & HRV Individual recovery profiles are recommended Objective/ subjective measurements Interdisciplinary perspective Heart rate and heart rate variability of Yo-Yo IR1 and simulated match in young female basketball athletes: A comparative study International Journal of Performance Analysis in Sport 2016/Q3 Abad et al. [35] 15 F (Voluntary participants 3–4 years of playing experience) Quantitative, descriptive, and comparative study Yo-Yo IR1, Submaximal efforts (SM), HRmax & HRV HRV measured in resting condition for 10 ´ (last 5 ´ only registered for the measurement) after the Yo-Yo IR1 and the SM tests Small effect size of HR yo-yo IR1 & SM (177 ±9 and 173 ±10 beats.min−1; ES = 0.2) SM > HRmax, Yo-Yo IR1 < HRV Specificity of stimulus to enhance players performance Stableness, related to fatigue and fitness levels, of the sample Long term adaptations not studied Heart Rate Variability to Assess Ventilatory Thresholds in Professional Basketball Players Journal of Sport and Health Science 2017/Q1 Ramos-Campo et al. [36] 24 M/ACB league (Spain) Experimental, descriptive, and comparative HRV & VTs (1&2) Incremental test to exhaustion collecting gas exchange (breath by breath analysis) and HR (R-R intervals) VT1 = Speed>HRV VT2 = No differences VO2/HR/Speed HRV→VT2 estimation HRV→ exercise intensity Objective and qualitative data collection Small group of subjects, specific timing < applicability Adequacy of the Ultra-ShortTerm HRV to Assess Adaptive Processes in Youth Female Basketball Players Journal of Human Kinetics 2017/Q3 Nakamura et al. [67] 17 F/Brazil State U-17 & U-19 Observational, retrospective and descriptive HRV (1nRMSSD) HRV pre and post training 10´in seated position Agreement of 1nRMSSD between ultra-short term 1’ and criterion 5′ measures 1nRMSSD→ 2´ measurement protocol (1st minute discarded) Ultra-shortterm HRV measure responds to training→ simplifies adaptation monitoring Validation of ultra-short1nRMSSD measures Isolated 1nRMSSD Measures Non-invasive assessment of internal and external player load: implications for optimizing athletic performance The Journal of Strength and Conditioning Research 2018/Q1 Heishman et al. [68] 10 M/NCAA (USA) Retrospective analysis HRV, TRIMP, PL HRV measured in supine position with by mobile app All performance indicators improved with higher CNS readiness values Lower training loads correlate with higher physical performance indicators Non-invasive strategies can effectively monitor internal and external stress Technology utilized, inclusion of internal and external load measures Period of measurement affects interpretation of PL and consequent results derived from it Appl. Sci. 2024,14, 10013 8 of 17 Table 1. Cont. Title Journal Year/Quartil Authors Participants/ Level Methodology Dependent Variables Methodological Procedures Results Conclusions Practical Applications Study Strengths Study Limitations Investigating the workload, readiness and physical performance changes during intensified 3-week preparation periods in female national Under18 and Under20 basketball teams Journal of Sport Sciences 2020/Q2 Lukonaitien˙ e et al. [45] 24 F/U18 & U20 Lithuanian league Observational, retrospective and descriptive PL, TRIMP, sRPE-TL, HRV, 20mSprt, CMJ, Yo-Yo IRT1 HRV 90′′ morning measurement in seated position Differences between groups in all variables (workload, readiness, WB) Short overload and taper periods → <HRV during short and intensified training periods Adopting constant and adequate workload→ >HRV values in short and intensified training periods Level of competition of sample, measures during official competition Tests limited to physical qualities, different time-lapse of players exposure Investigation of readiness and perceived workload in junior female basketball players during a congested match schedule Biology of Sport 2021/Q1 Lukonaitien˙ e et al. [30] 24 F/U18 & U20 Lituanian national teams (European national tournaments) Observational, retrospective and descriptive HRV, sRPE, WB, Match Performance (box score) HRV 90′′ morning measurement in seated position Significant between-day differences in all variables HRV (constant)= WB (decreased) during tournament Objective & subjective methods→ determine player status Level of competition of sample, measures during official competition No objective external load measures or internal biochemical markers Internal and External Load Control in Team Sports through a Multivariable Model Journal of Sport Science and Medicine 2021/Q2 Piedra et al. [42] 10 M/EBA League (Spain) Observational, retrospective and descriptive HRV (RMSSD), CMJ, SHRZ, RPE, Total Acc. & Dec. HRV obtained during the session Physiological markers related to the onset of neuromuscular physiological fatigue Multivariable model seems to be reliable for the measurement of physiological stress on this level Applying internal and external load variables to the monitoring process increases reliability of decision making based on the data obtained The objective multivariable approach of the study Specific competition context, small sample Heart Rate Variability and Accelerometry: Workload Control Management in Men’s Basketball Apunts 2021/Q2 Zamora et al. [32] 12 M/Copa Catalunya (Spain) Observational, retrospective and descriptive IL(HRV), EL(Acc-Dec) 10 sessions HRV 10 sessions Acc-Dec Causation correlation between RR mean & EL, % SHRZ and specificity of exercises IL correlations with EL Predictability of load variables registering one of them Measurements of IL & EL of specific drills Different time recordings of IL and El Appl. Sci. 2024,14, 10013 9 of 17 Table 1. Cont. Title Journal Year/Quartil Authors Participants/ Level Methodology Dependent Variables Methodological Procedures Results Conclusions Practical Applications Study Strengths Study Limitations Effects of visual search task on attentional bias and stress response under pressure WORK 2021 Jin et al. [69] 62 M/ Shenyang Sports Institute, State and National level (China) Experimental double blind HRV (TP, VLF, LF, HF, LF/HF) HRV recorded during a selective attention test No significant differences in HRV before and after training VST improved AB of the subjects VST monitored through HRV can help improve athletes’ decision making Large sample, physiologicalpsychological measures included No follow up measures for the athletes Intermittent pneumatic compression changes heart rate recovery and heart rate variability after short term submaximal exercise in collegiate basketball players: a cross-over study Sport Sciences for Health 2021/Q4 Khan et al. [46] 16 M/ Collegiate level (India) Randomized cross-over experimental design HR, HRV & IPC 5′HRV protocol HR incremental running test protocol IPC therapy 15 ′ after (5′) incremental test No significant differences found between pre and post IPC group in session one to pre and post IPC group in session two, respectively, for all measures of HRV IPC = effective o lowering HR in 1st min of recovery & + effect on HRV IPC→+ effects on recovery Two group intervention Small sample size, no sympathetic activity measures A validation study of heart variability index in monitoring basketball training load Frontiers In Physiology 2022/Q1 Jin et al. [10] 10 M/ Collegiate level (China) Observational within-subjects HRV(TLHRV), TRIMP, RPE, Speed & Distance All variables recorded during 5 half-court ball-drills Significant TL differences between the five drills TLHRV valid objective TL measure Usage of TLHRV as valid tool for training adaptation Measurement based on specific ball drills No biochemical indicators, validity of TLHRV Impact of Basketball Match on the PreCompetitive Anxiety and HRV of Youth Female Players IJERPH 2022/Q2 GarciaCeberino et al. [33] 12 F/U-16 (Spain) Cross sectional study HRV 5′HRV seated protocol measurement pre and post competition was followed <HRV postcompetition → baseline & precompetition No > of precompetitive anxiety, No change in HRV or cognitivesomatic anxiety between baseline and pre-match measures, <HRV after match Monitoring of variables can bring <anxiety & >joy→ competition→ -<sport quitting in young females Exploring effects of preand postcompetition Small sample size Appl. 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