In elite sports, athletic performance is determined by a combination of factors [1
], reflecting physical, psychological and organizational domains. Substantial investment goes into intense training intended to integrate physical preparation (e.g., aerobic conditioning to sustain power output [2
], nutrition titrated to individual athletes [3
]), psychological/emotional maturation (e.g., resilience to stress) [4
] and technical/tactical skills (particularly in team sports) [5
] optimizing preparedness to competitions [6
]. In addition, each sport may call for unique details of training, according to characteristic differences. Personalized intensity training might, e.g., be particularly beneficial in team sports, such as basketball [7
], because the different playing positions define a unique combination of dynamic and static efforts throughout the competition [8
], that (as in soccer [9
]) might lead to individual differences.
Performance monitoring, which is highly dependent upon availability of individual metrics of training, may be facilitated by the recent observation that subjective self-reported measures may be trusted as evidence for athlete’s well-being, in response to training loads [10
Within the multidimension determinants of athlete’s performance the autonomic nervous system plays a special role [1
] as a bridge between physical and psychological elements.
Accordingly, a potentially useful method to follow individual changes in training, particularly with elite athletes [11
], may be offered by Heart Rate Variability (HRV) [12
]. This technique [13
] has gained popularity (as documented by more than 50,000 hits in the MedLine database) for a combination of advantages. They are both practical (the technique is simple, inexpensive, totally non-invasive and non-intrusive and can be replicated frequently) and empirical (as a means to assess cardiac autonomic regulation, it is also obtaining growing attention in the sports area: there are now more than 4600 specific hits).
HRV (or the inverse RR interval V) and particularly its frequency domain indices [14
] seem to track faithfully cardiac neural remodeling attending variations in training volumes [15
], quality of preparedness to compete [16
] and subjective well-being [1
Aerobic training, being associated to increased vagal and reduced sympathetic tonic drive, leads to lower resting HR and greater beat-by-beat variability, which is assessable with time domain measures, such as RR variance [17
]. Its reduction, conversely, might suggest overtraining [18
]. In addition the sympathetic-vagal (excitatory-inhibitory) balance is approximated by the normalized power of, respectively, the Low Frequency (LF) and High Frequency (HF) spectral components of HRV, synthetized in the LF/HF ratio [19
]. A similar LF-HF balance is observed in directly recorded efferent sympathetic nerve activity [20
] or in the activity of central neural nuclei [21
], suggesting a common integrated closed loop organization from central to peripheral neural visceral circuits [20
]. This behavior seems to replicate the arousal/performance umbrella-like Yerkes-Dodson [22
] relationship of the noradrenergic nucleus ceruleus [23
], which also supports the model of amplitude and frequency neural codes of autonomic functional organization [20
In athletes and in aerobic specialties, with submaximal work-out training volumes, far from competition, there is a clear predominance of vagal modulation (higher HFnu) [25
], while in proximity of the competition, with maximal volumes of activity, there is a shift towards sympathetic predominance, reflected by an increase in LFnu.
Regarding the proposal to use HRV as a means to follow the various steps of training [11
], the presence of multiple HRV derived indices and lack of exercise-focused standards represents a potentially serious barrier to a widespread use of this approach [12
To address this issue, after pondering various data reduction approaches, we observed that the combination of six HRV indices obtained at rest and during an ergometric test, after % ranking and controlling for age and sex, could be integrated by a radar plot into a % rank unitary proxy of cardiac autonomic regulation, named Autonomic Nervous System Index for sports (ANSIs) [27
More recently we tested the use a further downsized Autonomic Nervous System Index (ANSI) and found it useful to recapitulate major linear indices (RR, RR variance, rest-stand change in low frequency spectral component in normalized units, i.e., LFnu) [28
]. ANSI is by design insensitive to age and gender and easy to interpret thanks to the presentation as % rank (range 0–100, higher indicates better) based on a relatively large benchmark population [29
]. We have utilized this approach with elite athletes [9
], noncompeting individuals [32
], as well as in ambulatory patient populations [33
It should be added that in the last decade interest on non-linear algorithms to investigate complexity of HRV [34
] increased considerably, paving the way to novel applications, particularly in conditions far from physiological rest and with low HRV signal. Recently it has been suggested that the combination of numerical indices with physical measures (e.g., HR) should be considered complementary and, particularly in cardiovascular pathophysiology [35
], capable of providing additional informative value.
Goal of the present, proof of concept, study on a group of elite basket players is to verify whether ANSI [28
] can describe the autonomic changes expected to occur following the two weeks long yearly athletic retreat in an Alpine village. As a secondary goal we assess the concomitant effects on selected short term nonlinear indices related to entropy and pattern classification [36
Summary data from HR and RR interval beat by beat variability, employing spectral analysis and non-linear measures (Ro and pattern classification), together with subjective stress indices are presented in Table 2
. It should be noted that these data refer to a small group of elite athletes and represent the ANS profile at the end of a few weeks period of free holiday, after the year-long competition season, without scheduled training: thus they are as much as possible, detraining values.
It should be remarked that at epoch 1, HR is less than 60 beats/min; RR variance over 5000 msec2; the HFnu component is 60, and LF/HF 0.86, overall still clearly depicting the vagal prevalence typical of yearlong intense aerobic training.
This picture is even emphasized after the aerobic training period at the alpine retreat (HR is significantly reduced to 50 beat/min, RR variance increased to more than 8000 msec2; HF nu slightly increased to about 64 nu and LF/HF slightly reduced to 0.68).
Resumption of competitions, considering that in this initial period matches were only friendly and not valid for championship points, is not accompanied by significant deviations from the overall picture observed at T2 (e.g., ANSI is 58.83 ± 32.56 at T1; 81.07 ± 27.50, p
= 0.015 at T2; 77.44 ± 26.86, p
= 0.004 vs. T1 and p
= 0.925 vs. T2) (see Figure 3
However a congruent profile of changes is observed with non-linear indices (regularity: RR Ro is 0.29 ± 0.10 at T1, it is 0.17 ± 0.08 at T2, p = 0.010 and does not change [p = 0.273] at T3; pattern classification: P_2uv increases from 21.23 ± 11.05 at T1 to 36.21 ± 17.17, p = 0.06 and does not change further at T3 p = 0.898 vs. T2; P_0v has a reverse profile, p = 0.028).
Regarding stress symptoms profile it should be remarked that all indices are at T1 at low values (stress 1.75 ± 1.82 au; tiredness 2.33 ± 1.30 au; 4SQ 14.42 ± 13.65 au). Stress and tiredness scores do not vary at T2 or T3; conversely 4SQ is selectively reduce at T2 (10.27 ± 13.08, p = 0.014). No changes in respiratory rate are evident (from the T0 value of 0.250 ± 0.021 Hz, p = 0.169).
Sports and exercise play a basic part in everybody’s life either because practice is related to life expectancy and the health burden of several important conditions (from cardiometabolic to cancer). Accordingly individual monitoring of sport and exercise activity could provide a convenient method to promote an individualized approach to optimization of life style. Numerically, about one on four individuals in Italy practices sports regularly (https://www.coni.it/it/coni/i-numeri-dello-sport.html
), thereby contributing to the overall section of people that practice physical activity at any level (about 34 million), which implies an important contribution to annual economy (≈3% GPD).
The present study suggests that modern assessment of cardiac autonomic adjustments to various levels of elite sports training, at least in the specific case of basket, may be achieved employing simple metrics, such as computer analysis of HRV combined with sound underlying physiology and bioengineering. The approach is simple, can be utilized also with actual individual personal electronics and outside of institutional settings and is capable of detecting relatively small changes in ANS regulation, under the assumptions that HRV properties provide information on sports training and individual health. Our study, however, suffers among the limitations, of employing a small population.
HRV has moved also to internet through commercial applications, aimed at millions of practitioners/clients that will require some form of quality control, comprised of health certification. This approach could be as well useful in world health crisis, like the present pandemic, in order to monitor the possible untoward effects of lockdown and activity limitation together with increased personal stress [53
We conclude with the consideration that in addition to the large interest to HRV indices as markers of training in elite sports, cardiac autonomic evaluation furnishes also a convenient biomarker of individual health. The future is most likely to bring some radical change in relation also to the rapid growth in wearables and the availability of internet approaches that will free users from space and time constraints. HRV, as an easy and simplified metrics of health, intended as capacity of adaptation to the dynamics of own environment, may thus become useful to furnish a personalized window to optimize the sustainability of sport and exercise as contributor to national health.
In the context of elite player performance, ANSI represents a simple, economical and easy to appreciate method to define individually the (%rank) level integrating all linear indicators of CAR. Accordingly it may also be used to derive the effectiveness of sports training programs, whereby the higher % rank ANSI would mean a better state of physical fitness and preparedness for the athletes ahead of a competition.