Discussion
One of the main factors to improve endurance in high-level athletes is to reach high intensities. HR, lactate and Borg Scale data will show us if the specific tennis exercises that were chosen achieve adequate intensity. The analysis of shots and errors supposes an interesting information that will allow us to know the speed of game during the tests and the effect of the fatigue on players and their technical actions.
Heart rate
Achieving the correct level of intensity is one of the essential requirements for ensuring significant aerobic adaptations. The intense activation of fast-twitch muscle fibres with close-to-maximum intensity loads can only be achieved for short periods of time with high-intensity activities.19 Intermittent sports such as tennis prioritise a considerable ability for repeating effort at high levels of intensity.20 It therefore stands to reason to think that IT would best adjust to the characteristics of tennis.
In our study, we have found high HR and high lactate levels in both kinds of specific IT (figure 1). These results are in line with other publications discussing tennis21 as well as some team sports.22–24
Figure 1Distribution of average individual heart rate (HR) for high-intensity interval training and intermittent interval training in the first, second and third series (see online supplementary table S1 S2 S3) and mean. The box shows how the sample is scattered, defined by percentiles 25 (lower limit) and 75 (upper limit); the red line shows the median and the black lines the lower and upper limits, outside of which values are considered as outliers. These are marked with red crosses. High average HR values are observed while there are no significant differences between the two exercises.
The two specific kinds of IT therefore allow for a sufficient level of intensity to promote considerable aerobic and mixed adaptations.
Lactate
In the IIT work, we have obtained more stable lactate values throughout the different series, which may be down to clearance during the recovery stages.25 The end values did not show major differences, but a final increase in HIIT was observed (figure 2), which may be down to increased fatigue, while lactate levels for IIT remained high. This may be explained by the increased participation of fast-twitch muscle fibres in these kinds of efforts, which feature greater accelerations, decelerations and changes of direction.23
Figure 2Distribution of individuals’ lactate levels in high-intensity interval training (HIIT) and intermittent interval training (IIT), in the first, second and third series (see online supplementary table S1 S2 S3). The box shows how the sample is scattered, defined by percentiles 25 (lower limit) and 75 (upper limit); the red line shows the median and the black lines the lower and upper limits, outside of which values are considered as outliers. These are marked with red crosses. An increase in the last HIIT series is shown. There are significant differences between the first and second series and the third in HIIT.
The final increase in HIIT comes from the long periods of work without rests leading to increased fatigue and high lactate production.
Borg scale
We have used the subjective value of fatigue to monitor the intensity of work, due to a relationship having been established with the physiological intensity of exercise.26–28 It was also chosen because it is a non-invasive, convenient system much used in recent years in team sports for training and in competition.26 29 30
There are no significant differences between the two types of training and, in both, the load has been slightly underestimated compared with HR taking into account the established connection between career efforts and Borg Scale,27 with average values at 16 (figure 3). We should bear in mind when using perceived exertion systems that the results may be altered by different factors, such as motivation, skill or how regularly a subject undertakes different types of training. We believe that when using the Borg Scale, we should familiarise the player with this kind of tool31 and adjust the analysis to the type of exercise and the individual nature of each subject’s response.
Figure 3Individuals’ distribution on the Borg Scale for high-intensity interval training and intermittent interval training, in the first, second and third series (online supplementary table S1 S2 S3). The box shows how the sample is scattered, defined by percentiles 25 (lower limit) and 75 (upper limit); the red line shows the median and the black lines the lower and upper limits, outside of which values are considered as outliers. These are marked with red crosses. There are no differences between the two types of training.
Monitoring intensity and fatigue: number of shots
The characteristics of play entail manifestations of specific strength with eccentric and stretch-shortening cycle actions will determine the kind of exercises as well as the most convenient systems for ensuring adaptations are significant.24 32–35
The study of the number of shots and their variation throughout the exercise may be a good way of monitoring intensity and fatigue levels, along with their relationship with the loss in motor skill. When training with specific exercises, optimum motor action must be controlled, as must the possibility of maintaining suitable levels of intensity and effectiveness.
In figure 4, intensity in absolute values is higher in HIIT, but when the real rally time is considered, that is to say without rests between shot time, intensity or shot frequency is higher in IIT. Systems with very short work and recovery periods may allow for increasing the activation of fast-twitch muscle fibres.36 This difference in absolute terms was significant in the first two series but not in the third.
Figure 4Distribution of average shot numbers for high-intensity interval training (HIIT) and intermittent interval training (IIT), in the first, second and third series (see online supplementary table S1 S2 S3). The box shows how the sample is scattered, defined by percentiles 25 (lower limit) and 75 (upper limit); the red line shows the median and the black lines the lower and upper limits, outside of which values are considered as outliers. These are marked with red crosses. A continuous fall in the number of shots carried out in HIIT can be observed, while in IIT this figure remains stable.
Intensity levels are better maintained in IIT. This higher level of relative intensity and its less significant loss is doubtless down to the introduction of rest periods, which allow for greater speeds for longer periods of time.
In our study, greater intensity of play is identified in IIT, along with better capacity for maintaining these high-speed actions over longer periods of training.
Monitoring fatigue: number of errors
The increase in the time spent playing at high speeds leads to fatigue that includes a reduction in the player’s ability to control distance to the ball and the placing of the body for the shot. This leads to a drop in shot control.6 7
Périard et al37 found peripheral and central fatigue, reflected in the difficulty of undertaking intense contractions during the final stage of games. Similarly, losses in precision and the ability to apply strength after hours of play have also been registered.7 8 38 39 Local and sensory fatigue may be reflected in terms of shot precision.32 40
When undertaking endurance training, we must also take into account the role that fatigue plays in decision-making and the overall development of play. Ferrauti et al13 suggest that when tennis players begin to perceive fatigue, the way shots are prepared and played are modified with the resulting negative effect on technique, a reduction in speed, alteration in decision-making, rashness in terms of play and the consequent increase in errors.
In our study, an increase in the number of errors can be seen continuously in HIIT, becoming significantly higher in each series, and these values are significantly higher than the ones produced in IIT from the first series onward (figure 5). This reduced ability to maintain shot effectiveness can be seen when comparing figures 4 and 5, showing an obvious reduction in the number of shots while the number of errors increased inversely. On the other hand, with IIT, the number of shots and errors committed remains stable.
Figure 5Distribution of average error numbers for high-intensity interval training (HIIT) and intermittent interval training (IIT), in the first, second and third series (see online supplementary table S1 S2 S3). The box shows how the sample is scattered, defined by percentiles 25 (lower limit) and 75 (upper limit); the red line shows the median and the black lines the lower and upper limits, outside of which values are considered as outliers. These are marked with red crosses. A continuous increase in the number of errors in HIIT can be observed, while in IIT this figure remains stable.
Efforts carried out in short intervals accompanied by periods of recovery allow for greater effectiveness in terms of shot execution.
Our findings obtained are relevant both for coaches and for physical trainers, allowing them to make good use of specific resistance exercises for tennis players. Training sessions made up of specific exercises with short intervals are effective in improving maximum aerobic capacity and maintaining suitable precision of movement. In this regard, improvements in VO2 max were observed, while an improvement in quantity and precision in specific actions with football players was also achieved.9
While HR, lactate or the speed of movement are highly important factors in these kinds of intermittent sports, factors such as deceleration capacity and technical execution must also be taken into account.