Relationships between physiological characteristics and match demands in elite-level male field hockey players

Abstract

This study investigated the physical characteristics, match demands and their relationships in elite-level male field hockey players (n = 23; 24 ± 3 years). Testing data were collected to investigate the physiological profiles of the players, while match demands were quantified with GPS data over 26 matches. There were significant differences (p = <0.05) between positional groups for pull ups, relative and absolute lower body strength, and body composition. Average total match distance for all the players was (5420 ± 1518 m). There were significant differences between positional groups for defenders (5770 ± 1419 m) vs strikers (4739 ± 1409 m) and midfielders (5549 ± 1545 m) vs strikers. The most high-speed-running (>4.5 ms⁻¹) distance was covered by midfielders (950 ± 275 m) and strikers (908 ± 284 m). Midfielders (116 ± 15 m min) and strikers (120 ± 20 m min) achieved higher intensities during matches compared to defenders (105 ± 13 m min). There were small differences between positional groups for physiological variables, but large variation between positional groups for match demands. There was a significant negative relationship between 2.4 km time trial vs high-speed running (p = <0.05), player load.min and match speed (p = <0.001). Also, there was a negative association between repeat sprint ability total time and high-speed running distance (p = <0.02) and match speed (p = <0.02). The countermovement jump height was associated with match speed (p = <0.05) and there was a negative relationship between body fat % and match speed (p = <0.02). When considering key performance indicators while using GPS devices, match speed (m.min) provides the most valuable information in field hockey players.

Keywords

Anthropometry,change of direction speed,global positioning system (GPS),maximal aerobic speed,performance analysis,repeat sprint ability,strength endurance

Introduction

The use of preseason fitness testing by strength and conditioning specialists has become a familiar process in elite level sports. Field hockey (henceforth referred to as hockey) is an intermittent high intensity sport that requires an array of physical characteristics for performance at the elite level.^1–3 The results from fitness testing allows sport scientists and strength and conditioning specialists to gain an understanding of the current fitness profiles of their players. This information can be used to design a training programme accommodating the specific needs of each player including the demands of their playing position.^4,5 Some key performance indicators of elite level competition include a high aerobic capacity and the ability to perform repeated bouts of high intensity efforts⁶ while executing complex stick and ball skills with precision.^7,8 For example, Spencer et al. reported that international level male field hockey players can perform up to 17 repeat sprint efforts in a match.⁶ Furthermore, players need to be able to execute multiple accelerations, decelerations and changes of direction during the course of a match. Therefore, training programmes designed to improve the overall neuromuscular performance of players associated with these movements is advantageous.¹ However, studies on the physiological characteristics of male hockey players, as measured through specific fitness tests, are sparse.^3,9,10

The majority of the research has been conducted on the 2×35 min half format of the sport and not on the 4×15 min format introduced by the Fédération Internationale de Hockey (FIH) in 2015.¹¹ Physical demands of hockey match play under the new rules have been documented.³ For example, elite level players can cover anywhere from 5000–8000 m in a match.^12–14 Further to this, match intensity data shows players average between 120–138 m . min⁻¹ with an average high speed running (>5 m.s⁻¹) distance of between 635–958 m.¹² Lastly, to perform at the elite level players need to be able to cover a relatively high percentage (range 10%–20%) of their total distance at high speeds (>4.5 m.s⁻¹).^12,15 To the authors’ knowledge, no study has examined the relationship between match demands of the modern game and physiological characteristics of elite-level male players in different playing positions. A better understanding of these relationships is important for identifying key performance priorities of training for the players in different positions.

Therefore, the primary aim of this study was to describe the physiological profiles of elite-level male hockey players and the physical demands placed on them during match play after the implementation of the new rules. A second aim was to determine whether these measurements were different between grouped playing positions. A third aim was to examine the relationships between physical match outputs and performance in the specific fitness tests.

Methods

Subjects

All data were collected from elite-level Chinese male field hockey players (n = 23; 24 ± 3 years). Of the 23 players, 7 were Chinese national team players with approximately 200 combined international caps. All goalkeeper data were excluded for this study.

Design

The study was an observational descriptive study designed to examine all the physiological testing that was performed on two occasions during a 24-week training and competition period leading up to various Chinese National League tournaments. The testing protocol consisted of the evaluation of the player’s anthropometry (stature, body mass, body fat), 10 and 30 m sprint times, change of direction speed, repeat sprint ability (RSA) and aerobic capacity, local muscular endurance, upper and lower body strength and lower body neuromuscular performance. Match demands were assessed using the data from Global Positioning System (GPS) devices throughout each tournament. The average data over the two testing occasions was used to describe the physiological profiles. Ethical clearance was obtained through the Ethics Committee of Scientific Research of Faculty of Physical Education, Ningbo University, China.

Fitness testing procedures

Testing data were collected on two separate occasions over a period of 24 weeks. GPS (GPS) data were collected for 26 matches over the same period. Testing was conducted on week 1 and week 13 of the 24-week cycle. The testing protocol was conducted over four consecutive days to allow for sufficient rest between different tests to avoid fatigue negatively affecting the subsequent test.

Anthropometry

All anthropometric data were collected 30 min before breakfast on the day of testing. The player stood upright wearing only training shorts when the measurements were recorded. Skinfolds were collected and body fat percentage calculated as previously described in the literature.¹⁶

Muscular strength and neuromuscular performance testing

For all strength and neuromuscular performance tests, a specific dynamic warm up was conducted under the supervision of the team strength and conditioning specialist. The 3RM bench press and squat tests were conducted in accordance with the guidelines set out by the National Strength and Conditioning Associations 3RM testing guidelines.¹⁷

Lower body neuromuscular performance was assessed by a counter movement jump (CMJ). Jump height was measured with the My-jump iPhone or iPad app (My Jump 3.2.2 Pacolabs, Spain), where jump height was determined using flight time.¹⁸ Counter-movement jumps (CMJ) jump were performed according to a previously described protocol.¹⁹ Each jump was recorded on an iPad Air (Apple Inc, Cupertino, CA) and analysed using the My-jump application.

Muscular strength endurance

Wide grip pull-ups

Wide grip pull-ups were used to assess the players’ upper body local muscular endurance. The test was conducted as has been previously described.²⁰

Core stability

Core stability was assessed using the standard plank exercise.²¹ Players were instructed to hold this position until volitional fatigue and the time to this point was recorded in minutes to the nearest second. The test was ended if the players lost any form in their hips, torso or shoulders or moved after receiving one warning.

Field based testing

All field-based tests, except the 2.4 km time trial, were conducted on a water based FIH standard synthetic hockey AstroTurf. Prior to any sprint or change of direction tests all players followed a rigorous supervised dynamic warm-up routine. Players were then given 5-min to do any additional stretching of their choice. Photoelectric sensors (Brower Speed trap II wireless sprint system; Brower Timing Systems, Draper, UT, USA) were used to determine the times for each sprint, change of direction (COD) and the 6 × 30 m repeat sprint ability (RSA) test.^22,23

10 and 30 m sprint times

The purpose of the sprint tests was to determine the players’ 10 and 30 m sprint times. Each player performed 2 × 10 m and 2 × 30 m maximal effort sprints from a 3-point start. The fastest sprint for each distance being recorded.

Change of direction speed (COD L-drill)

The player’s ability to quickly change direction over a short, predetermined course was assessed using the 3- Cone–L - drill.²⁴ The fastest time of 2 trials was recorded as the final score.

Repeat sprint ability (RSA)

Repeated sprint ability was assessed using the 6 × 30 m repeat sprint test conducted in accordance to the testing described by Rechichi et al.⁴

Maximal aerobic speed (MAS)

Aerobic capacity as measured by maximal aerobic speed (MAS) was assessed using a 2.4 km time trial run. A predefined route of 1.2 km was identified for each run. The players were instructed to run from the starting line and back as fast as they could. Time to completion was recorded as the players crossed the finish line. MAS (m.s⁻¹) was calculated by dividing the distance completed (e.g. 2400 m) by the time to completion in seconds.²⁵

GPS match demands

All on-field match demands were captured using GPS technology. The locomotive activities during the match for each player were recorded using a single GPS (OptimEye S5, Catapult Sports, Melbourne, Australia) device positioned between the shoulder blades of each player. Data were than processed using the Catapult Openfield software. The unit was placed inside a custom-made pouch on a sports vest that was worn underneath the playing jersey. To minimize inter-unit error, players used the same GPS device throughout the study.¹³ Each unit has a GPS sampling rate of 10 Hz and accelerometer sampling rate of 100 Hz. Speed thresholds were set in accordance with previous research at either low speed (<4.5 m.s⁻¹) or high speed (>4.5 ms⁻¹).^3,8,15 Furthermore, data were processed for individual playing time. However, we excluded all major breaks in play such as penalty corners, video referrals, player substitutions, green or yellow card time outs and goals scored.

Statistical analysis

Data are reported as mean ± standard deviation per position and for the whole group. An analysis of variance was used to determine differences between playing positions. The size of these differences was represented as the standardized effect size (d) with 95% confidence intervals (95% CI). Effect sizes were defined as trivial (<0.2), small (0.2 to <0.5), medium (0.5 to <0.8) and large (≥0.8).²⁶ The effect size data are expressed as; d (95% CI); size of d, ANOVA significance). A Pearson’s correlation and its corresponding 95% confidence interval was computed between match demands and fitness tests. Data are expressed as r (95% CI).

Results

Physiological fitness testing

The physiological characteristics and testing data grouped into playing position are shown in Table 1, with the between playing position comparisons shown in Figure 1. The mean body mass for the group was 76.7 ± 5.4 kg with the defenders being heavier than both the midfielders and strikers. There was no significant difference in player height between groups. Defenders also had the highest mean body fat % when compared to midfielders (d = 1.02 (0.20–1.78); Large, p = 0.014) and strikers (d = 0.48 (−0.26–1.19); Medium, p = 0.37).

Table 1.

Physiological characteristics and test performance data per grouped playing position.

Variable	Defender (n = 8)	Midfielder (n = 7)	Striker (n = 8)	Team (n = 23)
Anthropometric Data
Body mass (kg)	80.0^a ± 7.0	76.3 ± 3.4	73.8 ± 2.9	76.7 ± 5.4
Height (cm)	177.9 ± 4.7	177.4 ± 5.0	177.8 ± 3.2	177.7 ± 4.2
Sum of skinfolds (mm)	59.6^b ± 20.8	43.8 ± 11.8	55.8 ± 13.2	53.5 ± 16.9
Body Fat (%)	13.5^b ± 3.2	10.5 ± 2.5	12.2 ± 2.1	12.2 ± 2.9
Field Based Tests
10m Sprint time (s)	1.81 ± 0.10	1.73 ± 0.07	1.73 ± 0.14	1.76 ± 0.11
30m Sprint time (s)	4.32 ± 0.20	4.20 ± 0.16	4.17 ± 0.16	4.23 ± 0.18
COD L-drill (s)	7.34 ± 0.23	7.17 ± 0.20	7.18 ± 0.26	7.23 ± 0.24
RSA total time (s)	26.77 ± 0.96	26.18 ± 0.80	26.12 ± 1.29	26.36 ± 1.06
2.4 km time trial (min)	8.96 ± 0.52	8.58 ± 0.44	8.56 ± 0.44	8.71 ± 0.50
MAS (m.s⁻¹)	4.51 ± 0.26	4.64 ± 0.25	4.68 ± 0.24	4.61 ± 0.26
Strength and Power Tests
3RM Bench press (kg)	91 ± 8	90 ± 10	85 ± 7	89 ± 9
Relative Bench press (kg.bw)	1.3 ± 0.1	1.3 ± 0.1	1.3 ± 0.1	1.3 ± 0.1
3RM Squat (kg)	163^a ± 22	164^c ± 20	142 ± 16	155 ± 21
Relative Squat (kg.bw)	2.3 ± 0.2	2.3^c ± 0.3	2.1 ± 0.3	2.2 ± 0.3
CMJ (cm)	46.5 ± 4.2	48.9 ± 4.4	49.8 ± 7.8	48.4 ± 5.8
Pull-ups (reps)	9^b ± 5	15 ± 5	13 ± 5	12 ± 5
Plank (min)	3.5 ± 1.0	3.1 ± 0.9	3.7 ± 1.7	3.5 ± 1.3
P = <0.05

Data are represented as mean ± SD.

^aDefenders v Strikers.

^bDefenders v Midfielders.

^cMidfielders v Strikers.

Low Speed Threshold (<4.5 ms^–1)

High Speed Threshold (>4.5 ms^–1)

Figure 1.

Comparison of the differences between playing position for physical characteristics and physical performance in testing data. Differences represented as effect size ± 95% CI. The shaded regions represent the 0.2 (small), 0.5 (medium) and 0.8 (large) effect sizes.

Although not significantly different, defenders recorded the slowest mean times over both 10 m (1.81 ± 0.10 s) and 30 m (4.32 ± 0.20 s) sprint distances, whereas the strikers were on average the fastest over 30 m (4.17 ± 0.16 s) (Table 1). Defenders had the slowest mean 3-cone l-drill times when compared to both midfielders and strikers, however, once again, these were not significantly different.

There was a small difference in the players’ anaerobic capacity between midfielders and strikers (d = 0.04 (–0.69–0.79); Small, p = 0.90). However, there were medium differences between groups for defender’s vs midfielders (d = 0.67 (–0.11–1.41); Medium, p = 0.33) and defenders vs strikers (d = 0.57 (–0.17–1.29); Medium, p = 0.26). None of these differences were statistically significant. Strikers recorded the lowest mean total time (26.14 ± 1.29 s) out of all three groups. For 2.4 km time trial again the defenders recorded the slowest mean times to completion (8.96 ± 0.52 min). There were no significant differences between midfielders (8.58 ± 0.44 min) and strikers (8.56 ± 0.44 min). There were no significant differences between playing position groups for relative strength, however midfielders had a marginally higher relative strength for both upper body (1.3 ± 0.1 bench press kg.bw) and lower body (2.4 ± 0.3 squat kg.bw). For lower body neuromuscular performance, as measured by the CMJ test, strikers had the highest mean jump height, 49.8 ± 7.8 cm, however there were no significant differences between groups. Lastly, for local and global muscular endurance tests, midfielders recorded the highest mean pull ups score (15 ± 5 reps) and the strikers recording the highest mean plank score (3.7 ± 1.7 min), with only a significant difference shown between defenders and midfielders for pull-ups.

Physical match outputs

Match physical outputs as determined by GPS devices are shown in Table 2. Whereas between playing position group comparisons are shown in Figure 2. Total distance was significantly different between defenders and strikers (d = 0.73 (0.48–0.98) Medium, p = 0.0001) and midfielders and strikers (d = 0.54 (0.29–0.79) Medium, p = 0.0001). There were significant differences between all groups for both absolute high-speed distance and percentage high speed distance. Defenders on average performed less high-speed running than both midfielders (p = <0.0001) and strikers (p = <0.0001), whereas midfielders (950 m ± 275 m) on average performed more absolute high-speed distance than defenders (745 m ± 226 m) and strikers (908 m ± 284 m). However, when normalised to percentage of total distance, strikers (19 ± 4%) on average covered a greater percentage of their total distance at high speed, when compared to both defenders (13 ± 3%) and midfielders (17 ± 3%). There was a significant difference in player load between all playing position groups. Defenders had a significantly higher average player load when compared to both midfielders (p = 0.0097) and strikers (p = <0.0001).

Table 2.

Physical outputs during match per playing position.

Variable	Defender (n = 7)	Midfielder (n = 9)	Striker (n = 7)	Team (n = 23)
Match workload data
Playing Time (min)	55 ± 14	48 ± 5	40 ± 14	49 ± 16
Total distance (m)	5770^a ± 1419	5549^c ± 1545	4739 ± 1409	5420 ± 1518
Low speed distance (%)	87.0^a^,b ± 2.7	82.6^c ± 3.4	80.8 ± 3.8	83.7 ± 4.2
Low speed distance (m)	5025^a^,b ± 1259	4599^c ± 1346	3831 ± 1180	4558 ± 1353
High speed distance (%)	13.0^a^,b ± 2.7	17.4^c ± 3.4	19.2 ± 3.8	16.3 ± 4.2
High speed distance (m)	744^a^,b ± 226	950 ± 275	908 ± 284	862 ± 275
Player load (AU)	529^a^,b ± 133	485^c ± 137	423 ± 120	485 ± 138
Match intensity data
Speed (m.min)	105^a^,b ± 13	116 ± 15	120 ± 20	113 ± 17
Player load (load.min⁻¹)	9.74^a^,b ± 1.79	10.2^c ± 1.47	10.71 ± 1.89	10.16 ± 1.75

P = <0.05

Data are presented as mean ± SD.

^aDefenders v Midfielders.

^bDefenders v Strikers.

^cMidfielders v Strikers.

Figure 2.

Comparison of the differences between playing position for match physical performance data. Differences represented as effect size ± 95% CI. The shaded regions represent the 0.2 (small), 0.5 (medium) and 0.8 (large) effect sizes.

When normalising for playing time, strikers covered the most distance per minute of playing time. There were significant differences between defenders and midfielders (p = <0.0001) and between defenders and strikers (p = < 0.0001). There was no significant difference between midfielders and strikers for meters per minute. When player load per minute was examined, there was a significant difference between all groups with strikers having a higher mean player load per minute when compared to both midfielders and defenders.

When considering the relationships between physiological fitness tests and match demands (Figure 3), four tests showed significant relationships with match physical outputs. The 2.4 km time trial was significantly correlated with high speed running (r = –0.47; –0.77 to –0.01); player load (load.min⁻¹) (r = –0.74; –0.90 to –0.42); and match speed (m.min⁻¹) (r = –0.78; –0.91 to –0.49). The RSA total time was significantly correlated with high speed running (r = –0.63; –0.85 to –0.25) and match speed (m.min⁻¹) (r = –0.61; –0.91 to –0.49). Lastly, both CMJ jump height (r = 0.48; 0.03 to 0.76) and body fat % (r = –0.66; –0.86 to –0.29) were correlated with match speed (m.min⁻¹).

Figure 3.

Comparison of the differences between physiological characteristics and match physical performance data. Relationships were presented as r (95% CI).

Discussion

The main finding of the study was that although there were only small differences between positional groups in the majority of the physiological characteristics, the on-field physical demands were significantly different for each playing position. Furthermore, only 4 of the physiological fitness tests showed significant relationships with match physical outputs.

Physiological testing profiles

Players’ height (177.0 ± 4.2 cm) and body mass (76.0 ± 5.4 kg) were within the range that has been reported in previous studies of elite national level players (height: 178 ± 6 cm and body mass: 75 ± 9 kg).^8,15,27,28 Furthermore, the sum of skinfolds (53 ± 17 mm) were similar to those reported on Australian men’s hockey players (Range of 32–74 mm).⁴ Thus, it can be assumed that the anthropometrical profiles of the players in the current study are similar to those published previously.

The 10 m sprint times (1.76 ± 0.11 s) for the whole group were faster than reported in previous studies on South African (1.80 ± 0.10 s) and Australian (1.81 ± 0.07 s) national players.^4,27 A key finding of the study was that both midfielders and strikers have a marginally superior speed and change of direction profile when compared to defenders, indicating that these two characteristics maybe be important in identifying players for these positions. Furthermore, the above profiles seem to match the flow and speed of play for each of the respective positions. For example, strikers are seen as the “play makers” of the team, having to produce high sprint effort and quick changes of direction to beat the defenders. While midfielders are the link between the defenders and strikers and have to accumulate a high degree of high-speed running and total distance as the game transitions between attack and defence. Finally, defenders, although cover more total distance and have more game time, generally spend less time at high speeds, and are not exposed to high sprint efforts as frequently as the other two positions. Creating a testing profile for each position, may aid in the identification of deficiencies of specific physiological qualities in players.

When comparing aerobic fitness, there is a vast discrepancy between studies because of the variety of ways in which aerobic fitness was measured. In this study maximal aerobic speed (MAS) (4.51 ± 0.26 ms⁻¹) has been used as a proxy measure for aerobic fitness. Another study on-field hockey players also used MAS, as determined by the running speed (4.58 ± 0.19 ms⁻¹) of the final completed staged of the multi-stage fitness test. Despite the different methods of measuring the MAS, the data between the two studies were similar.⁸ Some of the primary benefits of using the 2.4 km time trial, were that the players were familiar with the test, the test was easily conducted and showed a high correlation to on field performance. Anaerobic capacity for the present study as measured by the 6 × 30 m repeat sprint (total time = 26.36 ± 1.06 s) was similar to that reported by Spencer et al. (range 25.3–27.91 s) on Australian men’s players.⁴ There were no significant differences between grouped playing positions for the anaerobic capacity test, which was surprising because strikers and midfielders have been reported to do a greater amount of high speed running, repeat sprints efforts as well as sprint counts in a match compared to defenders.^8,15,29

Midfielders and defenders had the highest absolute and relative strength profiles. The upper body strength of the players in this study (1RM bench press (97 ± 10 kg)) was higher than South Africa national players (82 ± 16 kg). Furthermore, with the change in rules and the demanding schedules of hockey tournaments, players are required to be able to withstand exposures to multiple matches in a short period. Therefore, being well conditioning and strong may be a key attribute for withstanding the demands of hockey match play. To the authors knowledge this is the first study to look at the relationship between physiological testing scores and physical match outputs in field hockey players. We have shown the importance of a well-developed aerobic capacity in field hockey players as there was a significant relationship between the 2.4 km time trial test and high-speed running, player load and player load per minute. Moreover, when considering a player’s repeat sprint ability, we showed that there is a significant relationship between high-speed running, match speed (m.min⁻¹) and the RSA test.

Match demands

Although there are many papers on the physical demands of field hockey^6,15,30–33 but few can be related to the present study due to the change in rules in 2015. Total distance is often used as a proxy for match load. In the present study players averaged 5420 ± 1518 m over the four quarters. These values were similar to those reported by Polglaze et al.³³ (6095 ± 938) in matches using the new rules. However, these values were lower than previously reported average distance of 8130 ± 360 m,⁸ 9776 ± 720 m,¹⁵ 7334 ± 877 m,⁹ in matches played under the old rules.²⁸ Ishan et al., reported an average total distance of 8387 ± 578 m in international players playing under the new rules.²⁸ However, in this study total distance was extrapolated and reported for total playing time, and not actual distance covered per player, therefore the results may be overestimated. This method should be used with caution as it is not a true reflection of individual player match demands of the sport.

When considering different velocity zones, high-speed running for the present study was set at 4.5 m.s⁻¹ or above. There was a significant difference between midfielders and defenders and strikers and defenders. Strikers covered the highest percentage (19%) of total distance above 4.5 m.s⁻¹. The group mean high speed distance (862 ± 275 m) and percentage high speed distance (16 ± 4%) were similar to data reported in previous studies.^8,12,15,34 However, comparisons are again difficult to make due to the changes in rules post 2015.

When looking at intensity, as measured by speed (m . min) and player load (load.min), both midfielders and strikers had higher values than defenders. However, strikers had the highest intensity (m . min and load.min) profile overall. The data in this study for the whole group for both intensity variables were lower (–11% and –30% respectively) than has been reported on Australian men’s national players.³³ These differences may be indicative of the level of competition and standard of play that has been used in the present study (i.e. national level vs international level hockey).^15,35

Limitations

A limitation of the study was that we did not record the GNSS satellite and HDOP results for each match. However, using retrospective data retrieved from gnssplanning.com, we were able to estimate the mean projected HDOP and GNSS satellite numbers for the period studied were 0.5–1.5 and 10–18 respectively.

Practical applications

This study assists strength and conditioning specialists in designing the training programmes of the players participating in elite level field hockey. Their training can be customised according to the physical demands of the playing positions and the physiological profile of the players. For example, it must be noted that both strikers and midfielders require superior speed and change of direction profiles. It is plausible to suggest that tests such as sprint times over 10 and 30 m as well as specific change of direction tests may be useful during talent identification or position classification. Furthermore, strikers and midfielders are required to cover a greater distance of high-speed running, therefore this should be considered when designing conditioning sessions for players in these positions. Sports scientists and strength and conditioning specialists working with field hockey players should incorporate specific physiological fitness tests that describe the aerobic fitness (e.g. 2.4 km time trial) as well as the repeat sprint ability of players, as performance in these tests has the best relationship with on-field performance. Furthermore, lower body neuromuscular performance and a low body fat percentage should be tested and monitored over time as they both show some association with match speed (m . min). Lastly, when considering key performance indicators while using GPS devices, high-speed running distance and match speed (m . min) provide the most valuable information in field hockey players, which can be related back to off-field physiological fitness testing.

Footnotes

Acknowledgements

Our thanks go to the coaching staff and players of Liaoning Men’s Field Hockey Team.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was also supported by the National Social Science Fund of China (13BTY049), the Basic Fund of China Institute of Sport Science (Basic 17-30) and Shanghai Science & Technology Committee (15490503200).

ORCID iD

Wayne P Lombard

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