*Article* **The Effects of Short-Term Visual Feedback Training on the Stability of the Roundhouse Kicking Technique in Young Karatekas**

**Stefano Vando 1,† , Stefano Longo 2,† , Luca Cavaggioni 2 , Lucio Maurino 3 , Alin Larion 4 , Pietro Luigi Invernizzi <sup>2</sup> and Johnny Padulo 2, \***


**Abstract:** The aim of this study was to assess the efficacy of using real-time visual feedback (VF) during a one-week balance training intervention on postural sway parameters in young karatekas. Twenty-six young male karatekas (age = 14.0 ± 2.3 years) were randomly divided into two groups: real-time VF training (VFT; *n* = 14) and control (CTRL; *n* = 12). Their center of pressure (COP) displacement (path length, COPpl; distance from origin, COPod) was assessed pre- and post-training on a Wii Balance Board platform in two positions (Flex: knee of the supporting leg slightly bent, maximum hip and leg flexion of the other leg; Kick: knee of the supporting leg slightly bent, mawashigeri posture for the kicking leg). Both groups trained twice a day for seven days, performing a one-legged stance on the non-dominant limb in the Kick position. During the training, VFT received real-time VF of COP displacement, while CTRL looked at a fixed point. No interaction effect was found (*p* > 0.05). VFT exhibited greater changes pre- and post-training in Flex COPpl (−25.2%, *g* = 1.5), Kick COPpl (−24.1%, *g* = 1.3), and Kick COPod (−44.1%, *g* = 1.0) compared to CTRL (−0.9–−13.0%, *g*-range: 0.1–0.7). It is possible that superimposing real-time VF to a week-long balance training intervention could induce a greater sport-specific balance-training effect in young karatekas.

**Keywords:** intensive training; proprioception; postural sway; testing

#### **1. Introduction**

Karate is a martial art where postural stability is of great importance for performance [1]. Indeed, many actions are performed on a single leg stance at maximum speed using different postures (e.g., kicking and transitions between postures). One of the main kicking techniques employed during kumite competitions (free sparring against an opponent) is a roundhouse kick named mawashi-geri [2]. Its execution requires a coordinated body segment sequence performed at the highest possible speed:


iii. Extending the knee to reach the target with the foot without a damaging impact; and

iv. Flexing the knee of the kicking leg [3]. Given this complex interaction between the supporting leg, speed, trunk stability, and circular trajectory, training to achieve correct postural control is crucial for mawashi-geri efficacy.

To optimize and develop an athlete's fundamental motor skills, static (i.e., stationary body) and dynamic (i.e., while moving) balancing ability is of great importance [4]. It has

**Citation:** Vando, S.; Longo, S.; Cavaggioni, L.; Maurino, L.; Larion, A.; Invernizzi, P.L.; Padulo, J. The Effects of Short-Term Visual Feedback Training on the Stability of the Roundhouse Kicking Technique in Young Karatekas. *Int. J. Environ. Res. Public Health* **2021**, *18*, 1961. https:// doi.org/10.3390/ijerph18041961

Academic Editors: Paul B. Tchounwou, Ewan Thomas, Ivan Chulvi-Medrano and Elvira Padua Received: 30 December 2020 Accepted: 11 February 2021 Published: 18 February 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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/).

also been reported that sport disciplines requiring skilled, fast actions improve postural control [5–8]. In this context, karate has been demonstrated to represent an effective stimulus for balance control improvement [9], most likely due to a combination of practicing intense complex motor tasks and a substantial load being placed on the ankle joint. However, several studies concerning karate and postural control have been conducted in adults [9]. Therefore, there is great interest in finding training methods that aim to improve the postural stability of young karate athletes, bearing in mind that maturity and biological age may have a direct influence on balance system organization. Indeed, the somatosensory afference seems to be developed at 3–4 years of age and the visual system, as well as the vestibular component, reaches complete maturation at 15–16 years of age [10].

In sports where motor control is essential, such as karate [11,12], improvements in performance can be achieved by receiving external feedback about the movement features so-called augmented feedback [13]. This method can be effective in both individual and team sports, and allows athletes to adjust for possible movement errors through instructions given about technical ability [13]. One particular type of augmented feedback involves vision (real-time visual feedback), which can provide this sensory information to the central nervous system, helping to reduce motor output variability [14]. Visual feedback training (VFT) has been successfully employed in different sports; for example, to help athletes improve the mechanical work against gravity in runners [15], the mechanical effectiveness of pedaling during steady-state cycling [16], or the explosive leg press maneuver [17]. VFT has been employed for improving stance stability [18]. The positive effects of VFT seem to refer to enhanced motor guidance, better focus on the task, and motivation as a result of task accomplishment (e.g., training at a higher intensity) [13]. In the context of karate, it has been shown that one session of VFT could acutely improve postural sway in young karatekas [19]. However, it is unclear whether this effect could be reflected in better performance during a more complex task, such as standing on one leg and performing a kicking action.

Recently, the Wii Balance Board (WBB, Nintendo, Kyoto, Japan) has been employed in different fields as a simple, accessible, and reliable device for assessments of bipedal balance [20,21], single-leg stance postural control [22], and muscle asymmetries [23], as well as a tool for balance training in clinical and exercise fields, with and without visual feedback [21,24,25]. The low cost and reliability of this equipment are the main advantages of its broad application in both clinical and performance-related settings.

Therefore, the aim of this study was to assess if superimposing real-time visual feedback on a one-week balance training program performed by young karatekas on the WBB could improve postural stability during a sport-specific kicking action.

#### **2. Materials and Methods**

#### *2.1. Participants*

A total of 38 young male karatekas (mean ± SD: age 13.4 ± 2.4 years; stature 156.5 ± 12.8 cm; body mass 53.0 ± 15.1 kg) participating in a one-week intensive karate training camp volunteered to take part in this study. Participants were randomly divided into two groups: real-time visual feedback training (VFT; *n* = 19, age 14.1 ± 1.8 years, stature 161.4 ± 11.2 cm, body mass 58.4 ± 11.3 kg), and a control condition, in which participants stood in front of a wall looking at a fixed point (CTRL; *n* = 19, age 13.2 ± 2.6 years, stature 154.4 ± 12.2 cm, body mass 54.1 ± 17.0 kg). Members of both groups had at least four years of karate training experience with at least two karate training sessions per week (~3 h per week), and were familiar with exercises involving a single-leg stance. During the training camp, all the athletes lived together and followed a diet provided by a sports nutritionist [26]. None of the participants underwent any specific balance training, strenuous endurance activity, or resistance training outside of their normal training program. The study conformed to the Declaration of Helsinki and subsequent updates, and it was conducted after the approval of the Ethics Committee of Ovidius University of Constanta (23/2020). The procedures, risks, and goals were explained to the participants' tutor. In addition to receiving consent from the subjects, written parental consent was also obtained prior to subjects' participation.

#### *2.2. Experimental Setup*

Participants reported to the experimental room (average temperature: 23 ◦C, min: 22 ◦C, max: 24 ◦C; relative humidity: 55 ± 2.3%) three times for testing procedures in the afternoon (2–4 p.m.) to avoid any circadian effects [27]. For the reliability of measurements and familiarization with procedures, all participants were assessed on a stabilometric platform on the first and second days of testing, with two days in between. The second testing day was used as a baseline measure (Pre). The stabilometric test was performed on the third testing day using the same procedure as for Pre (i.e., post-training—Post). The postural stability tests of the single-legged stance lasted 20 s on a NintendoTM WBB, with the non-dominant leg (supporting leg) and open eyes; trials were performed in random order (Latin square design) with 1 min of recovery in between [28]. The CoreMeterTM software was employed to analyze the center of pressure (COP) from the point of origin of the Cartesian plane.

Two positions were assessed before and after training:


**Figure 1.** Testing position for single-leg balance. (**a**) Flex: knee of the supporting leg slightly bent, maximum hip and leg flexion of the other leg; (**b**) Kick: knee of the supporting leg slightly bent, mawashi-geri posture for the kicking leg. Note: training was performed as in (**b**).

#### *2.3. Data Collection and Analysis*

The WBB, validated by Clark et al. [20], contains four micro foil-type strain-gauge transducers (sampling rate = 100 Hz) located in each of the four corners of the board. The WBB was interfaced with a laptop computer via Bluetooth® using custom software (CoreMeterTM 0.9, Latina, Italy) and calibrated by placing a variety of known loads at different positions on the platform. Once paired successfully, the device can be accessed through the standard Bluetooth® stack. The device can be interrogated at any time to read the current settings from the four strain-gauge sensors on the board, which are delivered as 16-bit integers. By taking into account the position of the sensors and the recorded values, the position of the COP can be easily calculated [26]. The WBB sensors have an internal fixed sampling rate, which we determined to be 100 Hz. Raw calibration data and raw

sensor values were stored in a relational database on the local machine. This allows for flexible post-test data processing. A report-generation tool analyzed the collected data from the database and produced summary reports. The outcome measure used in this study was total COP displacement. Therefore, total COP displacement was chosen as the primary outcome measure because it is known to be a reliable and valid measure of standing balance [20].

The COP coordinates (*X*, *Y*) were calculated using the data from the four sensors on the WBB using the following equation:

$$
\mathbf{W} \begin{pmatrix} X \\ Y \end{pmatrix} = \frac{\sum\_{i=1}^{4} \text{Wght}\_{i} \cdot \begin{pmatrix} x\_{i} \\ y\_{i} \end{pmatrix}}{\sum\_{i=1}^{4} \text{Wght}\_{i}} \tag{1}
$$

where (*x<sup>i</sup>* , *y<sup>i</sup>* ) = coordinates of each pressure sensor (*i*) in the Wii Balance Board's reference frame; Wght<sup>i</sup> = weight recorded on each sensor (*i*); and (*X*, *Y*) = coordinates of the COP [25]. After determination of the COP, its path length (COPpl) and distance from origin (COPod) were calculated automatically by the CoreMeterTM software.

#### *2.4. Training Protocol*

The training protocol was composed of two sessions per day for seven days. The first session was performed in the morning and alternated between 1 min of balance training and 1 min of passive recovery, for a total of 5 min. The second session was held in the afternoon using the same procedure. Both groups performed a one-legged stance on the non-dominant limb while keeping the kicking leg in mawashi-geri posture on the WBB (as in Figure 1b). The VFT group could see the COP displacement in real-time and their goal was to keep it as centered as possible [19,26]. CTRL performed the same protocol as VFT without receiving any COP displacement feedback while staring at a fixed point, trying to stay as steady as possible.

#### *2.5. Statistical Analysis*

Data were tested for normality using the Shapiro–Wilk test. The Student's *t*-test for independent samples was used to detect any initial differences between groups pretest. The reliability of COPpl and COPod measurements was assessed in a randomly selected sub-sample of 15 participants by intra-class correlation coefficient (ICC) with 95% confidence interval (95% CI:), and classified as follows: very high if >0.90, high if between 0.70 and 0.89, and moderate if between 0.50 and 0.69 [29]. Moreover, the standard error of measurement as percentage (SEM%) was calculated for each variable as a measure of absolute reliability [30,31]. The between-group differences in COPpl and COPod changes over time were analyzed using a two-way analysis of variance (ANOVA) with time as a repeated-measure factor (two levels: Pre- and Post-training) and group as a betweenfactor (two levels: VFT and CTRL). The ANOVA effect size was evaluated with partial eta squared (η<sup>p</sup> 2 ) and classified as follows: small, <0.06; medium, 0.06–0.14; and large, >0.14 [32]. The Hedge's *g* effect size with 95% CI: was also calculated and interpreted as follows: trivial, 0.00–0.19; small, 0.20–0.59; moderate, 0.60–1.19; large, 1.20–1.99; and very large, >2.00 [33]. The level of statistical significance was set at *p* ≤ 0.05 in all comparisons. Data were analyzed using XLSTAT 12.3.01 (Addinsoft, SARL, Long Island City, NY, USA) and SPSS (IBM SPSS Statistics v. 19, Armonk, NY, USA) statistical software packages. Descriptive statistics were expressed as mean ± standard deviation (SD). Percentage differences were shown with 95% CI: of the change.

#### **3. Results**

There were no baseline differences between the groups in age, stature, body mass, training experience, and all other variables studied (*p* > 0.05). During the experimental week, eight participants withdrew from the study (three from VFT and five from CTRL) due to personal reasons not linked to the experimental procedures. Four participants (two from

VFT and two from CTRL) were deemed as outliers and removed from the study. Therefore, the sample size was reduced to *n* = 14 in VFT (age 14.4 ± 2.5 years, stature 159.2 ± 10.4 cm, body mass 54.2 ± 11.7 kg) and *n* = 12 in CTRL (age 13.5 ± 1.9 years, stature 156.9 ± 9.2 cm, body mass 52.2 ± 16.0 kg).

#### *3.1. Reliability*

In Flex, reliability was high for both COPpl (ICC = 0.86, 95% CI: = 0.60 to 0.95; SEM% = 5.4%) and COPod (ICC = 0.96, 95% CI: = 0.88 to 0.99; SEM% = 7.9%). Likewise, reliability of the Kick position was high for both COPpl (ICC = 0.91, 95% CI: = 0.74 to 0.97; SEM% = 6.1%) and COPod (ICC = 0.97, 95% CI: = 0.90 to 0.99; SEM% = 7.7%).

#### *3.2. VFT-Induced Effects*

The training-induced effects in VFT and CTRL are reported in Table 1 for both COPpl and COPod. The ANOVA did not reveal any interaction effects in the parameters analyzed (*p* range: 0.07 to 0.49, η<sup>p</sup> 2 range: 0.02 to 0.15, small to medium). There was a significant effect of time in all analyzed variables (*p* range: 0.02 to <0.001, η<sup>p</sup> 2 range: 0.24 to 0.56, large).

**Table 1.** Comparison of stabilometric data between the Pre- and Post-training in the two groups by two-way analysis of variance (ANOVA).


Values are expressed as mean ± SD for the visual feedback training (VFT) and control group (CTRL). Flex: knee (~155◦ ) of the supporting leg slightly bent, flexed hip and knee of the kicking leg; Kick: knee (~155◦ ) of the supporting leg slightly bent, mawashi-geri posture for the kicking leg; COPpl: center of pressure path length; COPod: center of pressure distance from origin; ∆%: percentage difference between Preand Post-training; 95% CI: (%): 95% confidence interval of the Pre–Post percentage difference; η<sup>p</sup> 2 : partial eta-squared.

> In VFT, COPpl changed by −25.2% in Flex (*g* = 1.5, 95% CI: = 0.6 to 2.3, large) and by −24.1% in Kick (*g* = 1.3, 95% CI: = 0.5 to 2.2, large) after intervention. COPod changed by −35.2% in Flex (*g* = 1.0, 95% CI: = 0.2 to 1.8, moderate) and by −44.2% in Kick (*g* = 1.0, 95% CI: = 0.3 to 1.8, moderate) compared to Pre values.

> In CTRL, COPpl changed by −0.9% in Flex (*g* = 0.1, 95% CI: = −0.7 to 0.9, trivial), and by −13.0% in Kick (*g* = 0.6, 95% CI: = −0.2 to 1.5, moderate) after intervention. COPod changed by −11.9% in Flex (*g* = 0.7, 95% CI: = −0.1 to 1.6, moderate), and by −5.6% in Kick (*g* = 0.5, 95% CI: = −0.33 to 1.3, small) positions.

#### **4. Discussion**

This study was designed to investigate the efficacy of a one-week balance training program combined with visual feedback in improving stability during a sport-specific kicking action in young karate athletes. The main results showed that all together, COPpl and COPod changed between Pre- and Post-intervention in all tests. Despite no group × time interaction, the effect size analysis evidenced a greater impact of real-time visual feedback training compared to the control condition in COPpl and in COPod in Kick. These findings indicate that a short-time balance intervention was effective in improving specific balance in young karatekas. It is possible that this type of visual feedback could influence the magnitude of the results within such a short-time intervention.

#### *4.1. Preliminary Considerations*

It is worth mentioning that the WBB (combined with CoreMeterTM software) could be an easy, accessible, and feasible device to assess standing balance in different environments than a laboratory setting. It could be advantageous to study different populations, as previously shown [20,26]. However, force in the horizontal axes cannot be assessed on the WBB, thus representing an inherent limitation of the device. Nonetheless, Clark et al. [20] highlighted that the force levels in the horizontal axes were quite low (rarely exceeding 5 N). Moreover, excellent concurrent validity of the WBB compared to the gold standard has been demonstrated [20,28], suggesting that, despite the inherent limitations, this device can be used effectively to assess standing balance.

COPpl and COPod provide indirect information about the balance control process and strategy. The reduction found in these values indicate an improvement in postural control during a single-leg specific-task balance test. These results suggest that young karatekas can adapt quickly to balance stimuli even within a one-week training performed twice a day, as previously demonstrated in a younger group (~10 years of age) [26].

#### *4.2. Effects of VFT*

The main finding of this investigation was that short-term, sport-specific balance training was able reduce COP displacement (COPpl and COPod). Despite the lack of an interaction effect, the effect size analysis evidenced a greater impact of real-time visual feedback in almost all variables. According to the present results, previous findings showed that VFT was effective in enhancing postural control [18,34,35]. Furthermore, a recent study showed significant improvements in postural sway following one session of real-time visual feedback practice in ~16 year old young karate athletes [19]. Moreover, Shin et al. [36] showed that visual feedback can be crucial in optimizing postural control in young people, which improves until 15–16 years of age due to growth and maturation per se [10].

We tested our participants in two single-leg stance positions, which are crucial for the correct execution of the mawashi-geri kick: Flex (i.e., the "loading" of the kick, with both hip and knee in a flexed position of the kicking leg) and Kick (i.e., the actual mawashigeri posture, with the knee in an extended position). The VFT group obtained a marked decrease in both COPpl and COPod compared to CTRL. Therefore, it can be speculated that training with real-time visual feedback can be more effective than simply staring at a fixed point. We can hypothesize that in a simple task, such as being in the Flex position, the attentional demand for keeping the posture required in the CTRL group was not as high as when receiving a real-time visual feedback of COP displacement. Indeed, it has been demonstrated that the presence of an external cue (e.g., real-time COP displacement) could act as a constant reminder to keep the focus on the task during the training [37]. Interestingly, in the Kick position (i.e., complex task), both groups reduced COPpl values with a similar effect size. Since this testing position reflects the training position performed by both groups, this finding can be interpreted as training–testing specificity (i.e., equal training and testing positions).

The ANOVA did not evidence an interaction effect in any of the variables. It is likely that data variability accounted for this result. As a future perspective, in light of the different Pre–Post effect sizes, it would be interesting to assess whether, with a longer training period, the VFT and CTRL groups would exhibit similar results, or if VFT would superimpose a greater training stimulus compared to balance training alone.

For young karatekas, the improvement of basic and specific motor abilities are key features for the achievement of top fighting results [1]. Training balance with visual feedback, either real-time or at a fixed point, seems to be effective in managing better postural control during a mawashi-geri kick. Therefore, it can be suggested that incorporating these types of exercises during daily practice, particularly when the time for balance training is limited, would be beneficial. As mentioned previously, vision certainly played a great role in the training-induced adaptations seen in the present study. However, it is difficult to differentiate the effective contribution of each sensory system (visual, vestibular, somatosensory) to the final outcomes, and whether or not these systems adapted to the training stimuli. Nevertheless, the consistency between the improvements in postural sway parameters as a consequence of this short-time intervention is encouraging. Future studies may explore the effects of different balance training conditions, conducted over a long time period, and analyzing their effects on both performance parameters and on a competitive karate level.

This study has several possible limitations to be pointed out; firstly, the small sample size of participants. Nonetheless, we tried to recruit as many participants as possible during the one-week training camp, and we observed a significant reduction in the parameters measured. This point highlights that the sample size was probably sufficient for this kind of study design. Secondly, the training period was short in duration. Therefore, longer and more detailed intervention studies are needed to clarify the mechanisms responsible for the training-induced adaptations. Thirdly, the WBB is not a platform created for data collection. Nonetheless, its validity against a gold-standard reference has been demonstrated, as previously mentioned. Finally, no potential asymmetries between legs in the training-induced response were examined. It would be interesting to assess if balance training while standing on the preferred kicking leg would have led to different results.

#### **5. Conclusions**

Superimposing real-time visual feedback to a one-week balance training intervention improved sport-specific balance performance in young karatekas, with a greater effect size compared to balance training alone. These results highlight the potential of using the VFT method in this population of athletes.

**Author Contributions:** Conceptualization, S.V. and J.P.; methodology, S.V., J.P., and L.M.; software, S.V.; formal analysis, S.V., L.C., P.L.I., and S.L.; investigation, S.V., J.P., L.M., and A.L.; resources, S.V., L.C., P.L.I., and S.L.; data curation, S.V.; writing—original draft preparation, critically revising the original draft, S.V., J.P., L.C., L.M., A.L., P.L.I., and S.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board (Ovidius University of Constanta (Prot. 23/2020), data approval 15 January 2020.

**Informed Consent Statement:** Informed consent was obtained from all subjects' parents involved in the study.

**Acknowledgments:** We would like to thank the participants of the study.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Article* **Summated Hazard Score as a Powerful Predictor of Fatigue in Relation to Pacing Strategy**

**Sylvia Binkley 1 , Carl Foster 1, \*, Cristina Cortis 2 , Jos J. de Koning 3 , Christopher Dodge 1 , Scott T. Doberstein 1 , Andrea Fusco 2 , Salvador J. Jaime <sup>1</sup> and John P. Porcari 1**


**Abstract:** During competitive events, the pacing strategy depends upon how an athlete feels at a specific moment and the distance remaining. It may be expressed as the Hazard Score (HS) with momentary HS being shown to provide a measure of the likelihood of changing power output (PO) within an event and summated HS as a marker of how difficult an event is likely to be perceived to be. This study aimed to manipulate time trial (TT) starting strategies to establish whether the summated HS, as opposed to momentary HS, will improve understanding of performance during a simulated cycling competition. Seven subjects (peak PO: 286 ± 49.7 W) performed two practice 10-km cycling TTs followed by three 10-km TTs with imposed PO (±5% of mean PO achieved during second practice TT and a self-paced TT). PO, rating of perceived exertion (RPE), lactate, heart rate (HR), HS, summated HS, session RPE (sRPE) were collected. Finishing time and mean PO for self-paced (time: 17.51 ± 1.41 min; PO: 234 ± 62.6 W), fast-start (time: 17.72 ± 1.87 min; PO: 230 ± 62.0 W), and slow-start (time: 17.77 ± 1.74 min; PO: 230 ± 62.7) TT were not different. There was a significant interaction between each secondary outcome variable (PO, RPE, lactate, HR, HS, and summated HS) for starting strategy and distance. The evolution of HS reflected the imposed starting strategy, with a reduction in PO following a fast-start, an increased PO following a slow-start with similar HS during the last part of all TTs. The summated HS was strongly correlated with the sRPE of the TTs (r = 0.88). The summated HS was higher with a fast start, indicating greater effort, with limited time advantage. Thus, the HS appears to regulate both PO within a TT, but also the overall impression of the difficulty of a TT.

**Keywords:** pacing; cycling; time trial; RPE; performance

#### **1. Introduction**

Pacing is most simply defined as the distribution of energy expenditure over time intended to accomplish a desired goal without excessive fatigue or negative health effects [1,2]. A variety of evidence suggests that appropriate pacing contributes to optimizing performance in time-based athletic events [3–6]. In head-to-head competition, less successful athletes often follow the pacing pattern of the eventual winner, until they are compelled to change to more individually realistic pacing patterns [7,8]. In events where athletes, either elite or recreational, are improving their own best performances, the same pacing pattern is often adopted [9]. In non-athletic individuals, health complications are associated with unaccustomed heavy exercise [10]. Additionally, training sessions that start out too hard, are often associated with poor adherence in persons training for health and

**Citation:** Binkley, S.; Foster, C.; Cortis, C.; de Koning, J.J.; Dodge, C.; Doberstein, S.T.; Fusco, A.; Jaime, S.J.; Porcari, J.P. Summated Hazard Score as a Powerful Predictor of Fatigue in Relation to Pacing Strategy. *Int. J. Environ. Res. Public Health* **2021**, *18*, 1984. https://doi.org/10.3390/ ijerph18041984

Academic Editors: Ewan Thomas, Ivan Chulvi-Medrano and Elvira Padua

Received: 31 January 2021 Accepted: 16 February 2021 Published: 18 February 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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/).

fitness [11]. Therefore, for optimizing performance, preventing health complications and improving adherence with training, proper pacing of exercise bouts is critical.

The basis of pacing reaches back to a hunter–gatherer society, where hunters had to make effort/reward decisions when pursuing game [12]. This problem was shared by migrant groups and armies, with the challenge of achieving goals while avoiding exhaustion. For example, Roman legionnaires were trained to march over twenty miles in a "full step", while carrying up to 27 kg (~50% body weight) [13]. Since inability to sustain the march pace was punishable by death, managing energy expenditure was critical. Even athletes, performing very challenging tasks such as the grand tours in cycling [14] and systematic training for competition, distribute the relative effort during training such that only 10–20% of training is performed at high intensities [15–17]. Similarly, a normal practice for older industrial workers is to "pace" tasks in order to make the workload acceptable [18].

The concept of pacing highlights the importance of controlling intensity throughout an exercise bout in order to avoid unacceptably large homeostatic disturbances [1–6,19–21]. Further, pacing in athletics may represent the difference between a first-place win and an early-race burnout or between a pleasant [22] exercise session and one that is likely to be perceived as too difficult and is unlikely to be repeated [23]. Robinson et al. [24] performed the first controlled studies of pacing in relation to exercise performance as early as 1958, although there was not widespread interest in pacing until the 1990s. They studied homeostatic disturbances during differently paced middle-distance races with the intent of understanding optimal pacing. This early study laid the groundwork for future pacing strategies, suggesting that for middle distance events it is important to follow a relatively even pace and conclude the event with an "end spurt" in order to optimally utilize energetic reserves [1–3].

Contemporary studies have extended this concept by looking at changes in energy expenditure relative to the details of specific athletic competitions. During shorter events, particularly when the primary retarding factor is air resistance (cycling/skating), it appears best to utilize anaerobic energy quickly to compensate for the short race duration, as velocity at the end of a race can be viewed as wasted kinetic energy [25]. The opposite appears to be true in middle and longer distance events, particularly where gravity or water provide the retarding factor (running/swimming), where it is possible to have a large slowdown that negatively impacts performance [4]. Similar results were found by Tucker et al. [26] when analyzing world record performances in 800 m, 5000 m, and 10,000 m running. In the 800 m, greater running speeds were reached during the first lap with a typical slowdown in the second lap. In the 5000 m and 10,000 m, an end spurt was possible because of the maintenance of energy reserves during the middle portion of the race. Similar results were noted in the 2008 Beijing Olympic track races [7]. Noakes et al. [27] noted that in 1-mile running world records, there was a distinct pacing pattern of starting fast, slowing through the middle of the race, and then running faster during the last lap. However, Foster et al. [28] noted that 1-mile running world records had evolved to become much more "even paced" during the last 25 years. Further, Foster et al. noted that when an individual athlete, whether elite or recreational level, bettered their own best performance, they typically used the same relative pacing pattern [9]. Abbiss and Laursen [1] noted the importance of an all-out strategy in shorter races, a positive or gradual decrease in pace after reaching maximum velocity in middle distance events, and an even pacing strategy in longer distance events. Similar evidence was presented by Foster et al. [29] showing that events of different durations had unique pacing profiles. Joseph et al. [30] and Faulkner et al. [31] showed that when time trials (TTs) were normalized to relative distance, all events had a similar structure. This is further supported by the observation that depletion of anaerobically attributable energetic reserves, represented by W', is responsible for failures to maintain power output (PO) during fatiguing tasks with complete depletion of W' at exhaustion [32].

The process through which athletes spontaneously select their pacing strategy is called teleoanticipation [20]. Teleoanticipation can be characterized as the internal "negotiation" an athlete conducts with themselves, based on the presence of a pre-determined and wellpracticed pacing template, their current level of fatigue, and the anticipated distance or time remaining [19]. This internal negotiation is an almost entirely subconscious "risk analysis" that allows for PO regulation throughout a competition [1–3].

While objective physiological measures can be used to measure homeostatic disturbance, exercise intensity can also be appreciated through the rating of perceived exertion (RPE) [33]. RPE has been used in various settings as a subjective measure of exercise intensity at any given moment throughout an exercise bout. A higher RPE usually reflects a higher level of homeostatic disturbance (either from intensity or progressive fatigue related to the duration of an event) [2,21,22,30,31,34,35], while a lower RPE reflects a relative maintenance of homeostasis. When RPE is compared to distance of an event there is a scalar, linear growth pattern despite the occurrence of various modifiers (muscle glycogen depletion, distance, hypoxia-hyperoxia, temperature, mode of exercise) [2,8,9,19–21,29–31,34–38]. The association between RPE versus modulation of PO demonstrates a reciprocal relationship between transiently above-normal PO and RPE [36–38], supported by studies where the length of a TT was deceptively changed [37,38]. Following working at an intensity greater than normal (such as during a break away effort during a race), there is usually a reciprocal decrease in PO in order to counteract dramatic changes in homeostatic disturbance [36,38]. Similarly, if the momentary RPE is lower than expected for that point during a competition, it is likely that PO will increase.

This reciprocal relationship between RPE and changes in PO, and the abrupt decrease in PO after the starting segment of track cycling races [36,38] led to the concept of the Hazard Score (HS) which describes the likelihood that athletes will change their PO during competition, with the twin goals of avoiding catastrophic collapse during an athletic competition while optimizing performance [39,40]. The HS combines the momentary RPE and the percent distance of the race remaining as a predictor of change in PO (e.g., velocity). When an individual begins a race too quickly, they will reduce the speed in order to sustain a rate of growth of RPE [30] that will allow to finish the race without "collapsing". The HS can also be used to calculate a potentially more powerful predictor, the summated HS, throughout an event in order to better understand the effect of accumulated fatigue on pacing during competition. Accordingly, the intent of this study was to evaluate how the summated HS grows during a simulated competitive event in relation to the starting strategy. The hypothesis was that PO would be regulated after an enforced starting strategy in a way designed to control the final value of the summated HS toward a common final value.

#### **2. Materials and Methods**

The subjects for this study were seven well-trained (7–10 h per week), recreational level, cyclists, age 25–61 years. The subjects were mostly long distance "tourists", and performed limited competitive cycling, but regularly participated in "tours" of up to 160 km. Within the classification scheme of De Pauw et al. [41] and Delcroix et al. [42], they were in categories 2–3. The Physical Activity Readiness Questionnaire was completed by each subject to identify contraindications (e.g., exclusion criteria) to exercise testing. Written informed consent was provided by each subject prior to testing and the protocol was approved by the Institutional Review Board for the Protection of Human Subjects at the University of Wisconsin-La Crosse (Protocol 20.SB.080).

For subject characterization, each subject performed maximal incremental exercise on an electronically braked cycle ergometer (Lode, Groningen, Netherlands). Tests were conducted to provide peak PO, maximal oxygen uptake, ventilatory threshold, maximal heart rate, and maximal RPE. After a warm-up stage of 3-min at 25 W, PO was increased of 25 W/min until pedaling cadence could not be maintained within the range of 70–90 rpm.

Following the maximal test each subject performed a total of five 10-km (km) cycling TTs on a Velotron cycle ergometer (Velotron Electronic Bicycle Ergometer, Elite Model, Racer Mate, Seattle, WA, USA). Prior to all TTs, there was a self-selected warm-up of 15–30 min, which included 2–3 bursts of 30–60 s at the anticipated starting velocity. The first two TTs were practice 10-km TTs to allow the athletes to become habituated to the 10-km cycling TT [6]. The subsequent, randomly ordered, three TTs, were conducted in a manner in which the initial PO (3-km) was manipulated, based on the average PO of the first 3-km of the 2nd practice TT (POinit). During the self-paced TT, the subject was only instructed to finish the TT as quickly as possible. During the fast-start TT, the PO during the initial 3-km was 5% greater than POinit. During the slow-start TT, the PO during the initial 3-km was 5% less than POinit. This was reinforced by a visual display visible to the rider and verbal feedback from the investigator. The remaining 7-km were finished as rapidly as possible. A small monetary reward (\$10), based on improving final TT performance versus the 2nd practice TT, was offered to provide a "competitive incentive" during TTs 3–5. PO was measured continuously by the ergometer, and integrated every 0.5-km. The RPE was measured every 1-km using the Category Ratio (0–10) RPE scale [33]. Blood lactate was measured every 2-km in fingertip blood using dry chemistry (Lactate Pro, Arkray, Japan). Heart rate (HR) was measured using radio telemetry with data averaging every 5 s (T31, Polar Electro Oy, Kempele, Finland). Session RPE (sRPE) was measured ~30 min after the cool-down [43].

Descriptive characteristics of subjects were calculated as mean ± standard deviation. Time and average PO of the three experimental TTs (self-paced, fast-start and slow-start) were compared using a one-way Analysis of Variance (ANOVA) with repeated measures. The HS was calculated by multiplying momentary RPE by the remaining fraction of the race [39]. Summated HS was calculated by adding the HS values from each km. Two-way ANOVA with repeated measures was used to analyze differences in lactate, RPE, PO, and HR between the three experimental TTs. Pairwise comparisons were made using Tukey's post-hoc tests. Significance was set at *p* < 0.05 to achieve statistical significance.

#### **3. Results**

Descriptive data from the maximal tests are presented in Table 1. Table 2 shows differences in time, average PO, and sRPE between the three TTs. There were no statistically significant differences in finish time, average PO, and sRPE between the three experimental TTs (*p* > 0.05). On average, the self-paced TT was 15.6 s faster than the slow-start TT and 12.6 s faster than the fast-start TT. sRPE, which included the warm-up, the TT, and cool-down, was the greatest for the fast-start TT and least for the slow-start TT.

**Table 1.** Means ± standard deviations of the descriptive characteristics of men and women during maximal incremental exercise testing.


VO2max: maximal oxygen uptake; PO: power output; VT: ventilatory threshold; HRmax: maximal heart rate.


**Table 2.** Means ± standard deviations of time, average power output (PO), and session rating of perceived exertion (sRPE) of self-paced, fast-start, and slow-start trials. ‐ ‐ ‐ **‐ ‐ ‐**

The pattern of PO, RPE, blood lactate concentration, and HR within the three TTs is shown in Figure 1.

**Figure 1.** *Cont*.

**Figure 1.** Power output (**a**), heart rate (**b**), blood lactate (**c**), and rating of perceived exertion (**d**) responses in relation to starting strategy.

A significant interaction between the starting strategy and the distance covered was shown for PO (*p* = 0.034), RPE (*p* = 0.027), blood lactate concentration (*p* = 0.043), and HR (*p* = 0.046).

‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ As per design of the study, the PO for the fast-start TT was significantly greater than the slow-start TT for the first 3-km. PO for the self-paced TT was significantly greater than the slow start trial at the 500-m mark (Table 3). RPE for the fast-start TT was significantly greater than the slow-start TT at kilometers 1, 2, and 3 (Table 4.). Blood lactate for the fast-start TT was significantly greater than the slow-start TT at the 4 km time point (Table 5). HR for the fast-start TT was significantly greater than the slow-start TT at kilometers 2, 2.5, and 3 (Table 6).


**Table 3.** Means ± standard deviations of power output (W) during self-paced, fast-start, and slowstart time trials.

\* Significantly greater than slow-start trial.

**Table 4.** Means ± standard deviations of rating of perceived exertion (RPE) during self-paced, fast-start, and slow-start time trials.


\* Significantly greater than slow-start trial.

**Table 5.** Means ± standard deviations of blood lactate concentration (mmol·L −1 ) during self-paced, fast-start, and slow-start time trials.


\* Significantly greater than slow-start trial.


**Table 6.** Means ± standard deviations of heart rate (bpm) during self-paced, fast-start, and slow-start time trials.

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\* Significantly greater than slow-start trial. ‐

The pattern of changes in the HS and summated HS within the three TTs is shown in Figure 2.

‐ ‐ ‐ ‐ **Figure 2.** Growth of Hazard Score (**a**) and summated Hazard Score (**b**) in relation to distance during self-paced, fast-start, and slow-start time trials.

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There was a significant interaction between the starting strategy and the distance covered for HS (*p* = 0.022) and summated HS (*p* = 0.031). HS during the fast-start TT was significantly greater than the self-paced TT at kilometers 1 and 3. HS during the fast-start TT was significantly greater than the slow-start TT at kilometers 1 and 2. HS during the self-paced TT was significantly greater than the slow-start TT at kilometers 1, 2, and 3 (Table 7). Summated HS during the fast-start TT was significantly greater than the selfpaced TT from kilometers 3–10. Summated HS during the fast-start TT was significantly greater than the slow-start trial for kilometers 1–10. Summated HS during the self-paced TT was significantly greater than the slow-start trial for kilometers 2–10 (Table 8).

**Table 7.** Means ± standard deviations of the Hazard Score during self-paced, fast-start, and slow-start time trials.


\* Significantly less than fast-start trial; # Significantly greater than slow-start trial.

**Table 8.** Means ± standard deviations of the summated Hazard Score during self-paced, fast-start, and slow-start time trials.


\* Significantly greater than slow-start trial; # Significantly greater than self-paced trial.

The relationship between the sRPE and the summated HS is presented in Figure 3. There was a strong correlation (r = 0.88), suggesting that the perceived net effort of a TT was dependent on the pattern of effort within the TT. In particular, the fast-start TT, which was slower for overall performance than the self-selected TT, produced a higher summated HS and a higher sRPE.

‐ ‐ ‐ **Figure 3.** Session rating of perceived exertion (sRPE) to summated Hazard Score (HS) relationship during self-paced, fast-start, and slow-start time trials.

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#### **4. Discussion**

‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ The main purpose of this study was to determine whether manipulating starting strategy would affect TT performance, the summated HS, or whether the subject would change their PO so that a common value for summated HS was achieved during a simulated competition. Contrary to the hypothesis, it was found that although there was a reduction in PO following the fast-start, the summated HS remained higher compared to the self-start and slow-start strategy. This occurred despite a meaningfully slower performance time in both the fast-start and slow-start TT. This coincides with Robinson et al. [24] who concluded that it is vital to follow a relatively even pace (e.g., self-selected starting strategy) in order to avoid large homeostatic disturbances early during an event. This was supported in earlier studies performed in our laboratory [2,7,9,28–30,36,39]. The results are consistent with the evolution of pacing strategy to a more even pattern during contemporary 1-mile world records [28], and in events where individuals bettered their own best performance [9].

‐ ‐ ‐ ‐ The importance of these data is reflected in the 2008 Olympic pace data of Thiel et al. [7] who showed that some runners in Olympic finals would run with the leaders for part of the race before suddenly dropping off the leading pace and finding a relatively constant individual pace which allowed them to finish the race, usually with an end-spurt. This mirrors the PO pattern observed in the fast-start TT in the current data and the large reduction in PO after a "break away" effort [28]. In light of the present findings, these datacan be interpreted as suggesting that once a critical summated HS is achieved, the PO will require reduction, but that the reduction in PO will not be adequate to force the summated HS toward a common terminal value.

In the present study, the behavior of the summated HS was reflected by the strong correlation between the summated HS and sRPE (r = 0.88). This corresponds with Cohen et al. [36] who showed if RPE is above that usually observed at a specific point during an event, such as after a break away effort, PO will decrease in order to accommodate to, and recover from, large changes in homeostasis. When the RPE comes back into the usually observed scalar pattern of growth, PO returns to the normal profile. Similar results were observed by Schallig et al. [38] in trials where subjects were deceived regarding the duration of the trial. Immediately after being told that a trial was going to be longer than anticipated, subjects reduced PO until the rate of increase of RPE returned to what it normally would have been in the longer trail. Although previous research has shown that athletes tend to follow a predetermined template where the rate of increase of RPE is adjusted to the distance of the race remaining in order to avoid disturbances in homeostasis [30,31], it may be that PO is the variable that is manipulated while RPE continues to increase in a linear fashion. This was also shown in the report of Joseph et al. [30] and Henslin-Harris et al. [44]

where the blinded administration of an inhaled hypoxic gas mixture lead to a reduction in PO without changing the rate of growth of RPE. Other studies have shown that there is a linear relationship between RPE and relative duration of exercise despite exercise conditions [2]. Similar results were shown by Baldassare et al. [40] who found that despite differences in pacing strategies (positive pacing versus even pacing), RPE increased or only slightly decreased similarly with each strategy. Athletes apparently change their pace to match RPE to an anticipated growth pattern, so although HS may be the same between trials at a specific time point, the summated HS would be higher with a faster start.

In the present study, while summated HS was different between TTs, the finish time was not significantly different (although 15 s is a time difference of large practical magnitude) reflecting that since knowledge of the endpoint was present and distance remaining was not relatively large, the athletes were able to generate an end spurt despite the large summated HS. RPE has been shown to increase when athletes are aware of the distance to the endpoint [30,31]. This is also reflected by Foster et al. [6] who showed a significant difference between finishing a race quickly and the ability to maintain a constant, high PO for an extended period of time.

Optimal race performance is not always about who has the highest PO, but who can maintain optimal PO in order to perform a successful end spurt. de Koning et al. [45] compared the effect of various pacing strategies on performance in 1000 and 4000 m track cycling, showing that even small changes in pacing strategy led to changes in performance. This highlighted the importance of pacing strategy in the pursuit of competitive success. While the best time for the 1000 m TT was obtained by the cyclist with the highest peak PO (all-out strategy), the fastest time for the 4000 m was attained with a faster start followed by a constant PO after ~12 s (even pace). Time can also be augmented by the athlete's interaction with the environment. Konings et al. [8] utilized virtual opponents starting either +3% or −1% compared to a familiarization trial. Results showed that even in a lab setting, the use of virtual opponents led to faster performances, showing that the self-selected pacing trial has to be slightly faster than previously attempted in order for the athletes to improve performance. This can be applied to athletes who begin a race too fast leading to an accumulation of fatigue posing potentially, detrimental effects to their performance. More generally, pacing in a way that does not use an unrealistically high PO early within an event can aid in successful athletic competition. It is evident that many high-level athletes may begin a race quickly in order to match the pace of their competitors [7]. This information will also assist in determining the ideal PO for starting in order to optimize performance. However, it must be recognized that to improve performance, an athlete must take a "calculated risk", which often involves a faster start than normal. In many cases, they may develop too much discomfort (e.g., high summated HS) and fail to improve their time. In other cases, this may lead to small improvements in performance which are athletically important.

In this particular study, the specific application was to a maximal effort TT. However, an equivalent argument may be made toward training bouts. Very high early PO can lead to increases in RPE [46] and lead to reduced enjoyment during the training bout [23], which carries the risk that adherence to an exercise program is likely reduced.

One limitation to this study included a limited sample size and task habituated tourist type cyclists rather than experienced TT athletes. Although we have shown that task habituation leads to stable performances [6], more accomplished athletes might deliver somewhat different, and more specifically relevant, results. Future studies should also evaluate the difference of the summated HS in shorter and longer distance events to see how the relationship between RPE and PO differ from a middle-distance event. Further, the effect of PO sequencing within normal training bouts should be considered, relative to adherence to the exercise prescription.

#### **5. Conclusions**

The results of this study indicate that despite a reduction in PO following a faststart TT, the summated HS remains higher with a fast-start strategy. This indicates that summated HS is a powerful predictor to better understand accumulated fatigue on pacing pattern during simulated competition. The sum of all HS from the beginning of the race to the present point have a cumulative effect on the outcome of the event, the physiological state, and the sRPE experienced by the exerciser.

**Author Contributions:** Conceptualization, C.F., S.B., J.P.P., and J.J.d.K.; formal analysis, S.B. and J.P.P.; investigation, S.B. and C.D.; data curation, S.B., C.F., and J.P.P.; writing—original draft preparation, S.B. and C.F.; writing—review and editing, C.F., C.C., J.J.d.K., and A.F.; supervision, C.F., S.T.D., S.J.J., and J.P.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board for the Protection of Human Subjects at the University of Wisconsin-La Crosse (Protocol 20.SB.080).

**Informed Consent Statement:** Written informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.

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