Rugby Union is a dynamic and combative team sport participated in globally (1). Two teams of 15 players, broadly categorized as forwards (n = 8) and backs (n = 7) contest a match for 80 min (1). The sport is comprised of intermittent, high-intensity activities incorporating high-speed running, sprinting, accelerations, and decelerations (2–4). Rugby Union also involves collision-based activities at the tackle area (tackle and breakdown contest) and the set piece (scrum and maul). Time motion analysis and global positioning systems (GPS) studies report that forwards experience approximately 60% more high level impacts during contact situations than backs (4). However, there are significant limitations of using GPS technology to determine contact occurrence and quantitative measurement of force, rendering it unreliable to determine the physical strain placed on the players (5).
Although the technology to accurately quantify physical collisions in rugby is currently lacking, the recognition of their impact made upon the athlete is not (6). The forces and mechanical stress in rugby can cause exercise-induced muscle damage and impact-induced muscle damage which may be distinct in their symptomology and recovery time course (7). These physical collisions have been shown to increase indirect markers of muscle damage (8,9), reduce neuromuscular function (10,11), and increase perception of muscle soreness (11). Sport scientists have examined a wide array of modalities to enhance recovery from the damaging collisions of rugby match play, some of which may mildly alleviate symptoms (12). However, despite multiple interventions being implemented, we have reported that elite rugby players are in pain every day throughout a competitive rugby season (13). It is, therefore, crucial that accurate and quantitative markers are developed to assess the extent of the impact-induced muscle damage to allow more targeted interventions to be developed. One potential candidate is assessing the energy expenditure of players given that the total energy expenditure (TEE) of young rugby league players was 5% higher when training weeks involved collisions (14).
Resting metabolic rate (RMR) is the primary component of TEE and is the energy expended to maintain homeostasis at rest. Indirect calorimetry (IC) requiring both oxygen (V˙O2) and carbon dioxide (V˙CO2) to be measured is the most accurate method of assessing RMR (15). Large variations in the estimation of RMR using prediction equations have been noted in a variety of sports (16), especially athletes with a high fat-free mass (17) such as rugby (18–20). It is, therefore, imperative that RMR is accurately measured rather than predicted using equations. Importantly, much of the existing understanding around effectively calculating an athlete’s energy requirements are based on studies which primarily utilize recreational or youth athletes and are thus limited by lower training ages and exposures to lower absolute intensities of work. To our knowledge, there are no data on the daily variations in RMR across an entire competitive match week in any sport, including positional differences. It is therefore crucial that potential changes in RMR are explored in highly trained professional athletes with IC performed before and the days after a competitive fixture.
To facilitate recovery, it is essential that rugby players are provided with the correct nutrition in terms of both the total energy intake and the provision of recovery promoting foods. The majority of nutrition research in rugby has focused on preparation for match play, ensuring muscle glycogen concentrations are optimal for performance (21). It appears elite players now have a good understanding of this (22); however, the nutritional intakes in the days after a match are much more variable (22) with many players decreasing total energy intake the day after a game. If muscle damage arising from match play causes an increase in energy requirements in recovery, current guidelines could be underestimating player’s needs postcompetition.
To this end, the objectives of the present study were twofold. 1) To assess, for the first-time, changes in RMR in an elite group of professional rugby union players measured throughout a competitive week, including the days before and after a professional game using IC. 2) To explore the relationship between game day (GD) factors (e.g., the number of physical collisions), and changes in RMR. These data would provide more accurate information into the energy requirements of players in the days after a game, which could help recovery strategy, as well as providing a novel noninvasive assessment of the effects of the physical collisions upon the players.
METHODS
Participants
A convenience sample of 22 healthy elite rugby union players, all members of an English Premiership squad, was recruited for this study. The participants included six internationals, and many established Premiership or Super 15 players (mean ± SD, age; 25.7 ± 4.1 yr, body mass; 104.6 ± 12.6 kg). Five participants were excluded from the analysis having sustained an injury during games which prevented them from completing all aspects of the study. All playing positions were covered in the remaining 17 players who were eligible for the full study analysis. All participants gave written informed consent before commencing the study. Ethical approval (18/SPS/004) was granted by the university research ethics committee at Liverpool John Moores University, UK.
Research design
The study was designed to allow RMR to be measured within the training schedules of elite rugby players during a complete microcycle. Timepoints throughout the study are described relative to GD using +/− symbols for days before (−) and days after (+) GD. Owing to the timing of team selection defining when recruitment could occur, the first measurement was taken at GD − 2. Measurements were then repeated every day, apart from the GD itself, as this was deemed too disruptive to the players’ habitual routine. Table 1 details the training schedule for the match week. Seven microcycles were used to attain the total data set, with all games played on the Saturday afternoon (GD). This ensured that the training schedules throughout the microcycle were the same and there were no conflicting kick-off times, which would alter the time relative to match play of the subsequent measures. Internal and external loads for training and match play were recorded throughout the week. The weeks chosen were throughout the middle of the season (weeks 13–30) so the players were accustomed to the training load and rigors of match play.
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TABLE 1: The training sessions throughout the competitive microcycle.
Resting metabolic rate
The RMR of participants was assessed six times in total. All measures were completed at the same time between 7:00 and 9:00 am and players arrived after an overnight fast, with their last meal at least 8 h before measurement. Players awoke and came straight to the training ground as per reliable outpatient protocol (23). To ensure best practice, a private room was established at the training facility away from the main building where temperature was maintained at 21°C to 23°C, the room was dimly lit, and quiet (15). Players lay in a comfortable supine position and were reminded to stay awake. A 20-min resting period was prescribed, as the minimum sufficient time to achieve rest (24). A ventilated hood was used rather than mouth piece and nose clip to reduce day-to-day variance (25). The coefficient of variance for our protocol was measured at 1.13% for RMR and 1.62% for RER. The ventilated hood was placed over the head of the athlete, and expired gas was analyzed using the dilution canopy method (Vyntus CPX canopy, CareFusion, Hoechberg, Germany). The gas analyzer was calibrated every day using the manufacturer’s automated flow and digital volume transducer calibration (15.92% O2 and 5.03% CO2). The first 5 min of measurements were discarded following best practice guidelines (15). Measurements were subsequently recorded for 15 min continuously at 10-s intervals for V˙O2 and V˙CO2. Data were exported into Microsoft Excel (2018, Seattle, USA), and mean RER across the measurement period generated, with the calorific value, carbohydrate, and fat oxidation rates determined according to the table of Zuntz (26).
Measurement of lean body mass
Lean body mass was measured using a dual-energy X-ray absorptiometry fan beam scanner (Hologic Horizon W; Hologic, Bedford, MA), with scanning and analysis performed by the same trained individual using Apex software version 13.5.3.1 (Hologic). Players were scanned twice during the period of data collection for this study and the scan corresponding closest to their week of participation used, which was no longer than 4 wk. Measurements were taken first thing in the morning before eating, drinking, or exercise and protocols implemented to maximize reliability of positioning (27).
Training and match loads
Internal loads for each training day and the GD were assessed by the session RPE (sRPE) using a modified Borg scale (28). This RPE of the training session was multiplied by the training duration to calculate a player load in arbitrary units (sRPE; AU) (28). External demands of all rugby training sessions and match play were recorded using micro-technological units worn by players containing GPS (10 Hz) and accelerometer (100 Hz) (Catapult Innovations, Melbourne, Australia). Data were downloaded and analyzed using Catapult Sprint software (Catapult Innovations, Melbourne, Australia). The total distance covered, number of high-speed efforts (>60% positional average) and the number of very high-speed efforts (>80% individual average) were recorded (29,30). The GPS sampling frequency of 10 Hz is the most reliable in team sports measuring high speed running activities (31).
Contacts were analyzed in match play by a professional rugby union analyst with over 5-yr experience working in English domestic and European rugby using NacSport (Analysis Pro, UK). The potential collisions were then further reviewed by an expert ex-professional player with 15 yr and over 250 matches played in English domestic, European, and International Rugby Union. The games were reviewed to ensure contacts recorded involved an actual collision. For example, a scrum may be analyzed as a single contact but there may have been more than one engagement process involving a full collision before the match restarted. Some players may also be analyzed as having been involved at a breakdown to keep possession, but they may not necessarily have endured a collision as part of this. The nature of these were then also accounted for as either set piece based (scrum or maul), or general phase play (breakdown and tackle area).
Data analysis
All data are presented as mean (± SD). All statistical analyses were completed using SPSS (Version 24 for Windows, SPSS Inc., Chicago, IL). A one-way repeated-measures ANOVA was used to compare all gas exchange measures and the work completed by players throughout training days and during the competitive GD. The tests of within subjects’ effects provided values for Mauchly’s test for sphericity. If this was violated, then a Greenhouse–Geisser correction was used. The difference between means was tested at a significance level of P < 0.05. The least significant difference was used post hoc to compare specific timepoints when the ANOVA revealed a significant difference between measures over the week. This was examined in the whole group (n = 17), subgroups forwards (n = 11) and backs (n = 6). A Spearman’s correlation was run to assess any associations between changes in RMR throughout the microcycle, with the metrics of physical load and collision data gathered from the competitive match play (n = 17). A Spearman’s rank-order correlation coefficient value (rs) was generated, and this was tested at P < 0.05 to test the significance of any relationships found (32).
RESULTS
Training and Match Demands
The training schedule and structure of sessions can be seen in Table 1 with the internal and external demands of the week in Table 2. It should be noted that data are presented as n = 14 for these analyses due to faults with GPS data collection, resulting in lost running metrics for some training sessions in three of the participants.
TABLE 2: Comparison of metrics recorded for training and match play throughout the competitive micro cycle.
Player load
There was no significant difference in player load on GD + 3 compared with GD. This was also true for the subgroups of forwards and backs. The player load on all other days of the training week were significantly lower than the GD in the whole group and when subdivided into forwards and backs.
High-speed running distance
In the whole group, there was no significant difference in high speed running distance covered on GD + 3 compared with GD. In the forwards subgroup, there was only significantly less HSR distance covered on GD – 1 (P = 0.001) and GD + 2 (P = 0.013) compared with GD. In the backs subgroup, there was significantly less HSR distance covered on GD − 2 (P = 0.005), GD − 1 (P < 0.0005), GD + 2 (P < 0.0005), and GD + 3 (P = 0.019) compared with GD.
Number of high speed running efforts
In the whole group, there were significantly fewer HSR efforts on GD − 2 (P = 0.002), GD − 1 (P < 0.0005), GD + 2 (P < 0.0005), and GD + 3 (0.031) compared with GD. In the forwards subgroup, significantly fewer HSR efforts were completed on GD − 1 (P = 0.001) and GD + 2 (P = 0.014) compared with GD. In the backs subgroup, significantly fewer HSR efforts were completed on GD − 2 (P = 0.003), GD − 1 (P = <0.0005), GD + 2 (P = 0.001), and GD + 3 (P = 0.001) compared with GD.
Very high speed running distance
In the whole group, very high speed running (VHSR) distance was only significantly lower on GD − 1 (P = 0.002) and GD + 2 (P = 0.002) compared with GD. Within the forwards subgroup, there was no significant difference in VHSR distances covered on any day compared with GD. The backs covered significantly fewer VHSR meters on GD − 1 (P = 0.005) and GD + 2 (P = 0.006).
Very high-speed running efforts
In the whole group, the number of VHSR efforts completed was only significantly lower on GD − 1 (P = 0.003), and GD + 2 (P = 0.013) compared with GD. In the forwards subgroup, there was no significant difference in VHSR efforts on all training days compared with GD. In the backs subgroup, there were only significantly less VHSR efforts on GD − 1 (P = 0.001), and GD + 2 (P = 0.013) compared with GD.
Changes in RMR
Changes in RMR adjusted for lean body mass across the microcycle can be seen in Figure 1A, whereas the absolute (kcal·d−1) and relative (kcal·kg−1·d−1) RMR measures are displayed in Table 3. Lean body mass (measured by dual-energy X-ray absorptiometry) was 74.8 ± 7.4 kg for the whole group, 78.2 ± 5.6 kg for the forwards, and 68.6 ± 6.0 kg for the backs. In the whole group, there was a significant increase in RMR from GD − 1 to GD + 1 (P = 0.005) and GD − 1 to GD + 3 (P = 0.04). In the forwards subgroup, there was a significant increase in RMR between GD − 1 to GD + 1 (P = 0.017) and GD − 1 to GD + 3 (P = 0.045). However, in the backs subgroup, there was no significant difference in RMR at any timepoint across the week.
FIGURE 1: Gas exchange measurements across the microcycle. A, RMR (kcal·kg·day−1). B, RER. C, V˙O2 (L·min−1). D, V˙CO2 (L·min−1). E, Carbohydrate (CHO) oxidation (g·min−1). F, Fat oxidation (g·min−1). Measurements displayed as mean ± SD. with individual data points for all participants. Forwards (filled black triangle), backs (empty circle). *Significant difference (P < 0.05) for the whole group when compared with GD − 1. +Significant difference (P < 0.05) for the forwards group when compared with GD − 1.
TABLE 3: Absolute and adjusted measurements of RMR across the competitive microcycle for all players (n = 17).
Changes in RER
Changes in RER across the microcycle can be seen in Figure 1B. In the whole group, there were significant increases at GD + 2 (P = 0.030) and GD + 3 (P = 0.006) compared with GD − 1. In the positional subgroups, there were no significant differences across the microcycle (P = 0.065 and P = 0.177) for forwards and backs, respectively.
Changes in V˙O2 and V˙CO2
Figures 1C and 1D show the measures of V˙O2 and V˙CO2. There were significant increases in V˙O2 in the whole group at GD + 1 (P = 0.008) and GD + 3 (P = 0.041) compared with GD − 1. These significant increases were also observed in the forwards at GD + 1 (P = 0.025) and GD + 3 (P = 0.027) compared with GD − 1. There were no significant differences for V˙O2 in the backs subgroup across the week. There were significant increases in V˙CO2 in the whole group at GD + 1 (P = 0.008), GD + 2 (P = 0.01), and GD + 3 (P = 0.001) compared with GD − 1. These significant increases were also observed in the forwards at GD + 1 (P = 0.037) and GD + 3 (P < 0.001) compared with GD − 1. There were no significant differences across the week in measures of V˙CO2 in the backs.
Changes in Carbohydrate and Fat Oxidation
Measures of carbohydrate and fat oxidation are displayed in Figures 1E and 1F. Carbohydrate oxidation significantly increased at GD + 2 (P = 0.044) and GD + 3 (P = 0.003) compared with GD − 1 in the whole group. In the forwards, a significant increase was measured at GD + 3 (P = 0.003) compared with GD − 1, whereas there were no significant differences across the microcycle in the backs for carbohydrate oxidation. Fat oxidation decreased significantly at GD + 3 (P = 0.029) in the whole group and at the same timepoint in the forwards (P = 0.028) compared with GD − 1. There were no significant differences measured for fat oxidation across the microcycle in the backs.
Associations of Match Demands with Changes in Metabolic Measurements
Table 4 displays the Spearman’s coefficient associations between the physical match demands, and changes in RMR. In the whole group, there were no significant associations found between phase contacts, total contacts, player load, HSR meters, HSR efforts, VHSR meters, VHSR efforts, and the change in RMR observed between GD−1 and GD + 1. This was also true when the positional subgroups of forwards and backs were analyzed.
TABLE 4: Spearman’s coefficient (r s) associations derived from changes in RMR between GD − 1 and GD + 1.
DISCUSSION
The aim of the present study was to assess changes in RMR in an elite group of professional RU players measured throughout a competitive week and explore the impact of GD factors on changes in RMR. To this end, we monitored RMR using IC alongside GD and training demands in 22 Premiership RU players throughout a game week. We report, for the first-time, that RMR increased significantly after elite rugby union match play, a change that was not observed after intense training with the same training loads. These data, therefore, illustrate that changes in RMR after match days exist, reflecting a yet unreported increased energy demand in the days after a game of elite rugby and allows the development of individualized nutritional strategies to help facilitate recovery. Furthermore, increased RMR may also represent the physical collisions of match play and indeed could suggest that RMR may be used as a noninvasive marker of muscle damage.
We have reported a mean increase in RMR after match play of ~231 kcal·d−1 at GD + 1, a 10% increase from GD − 1. We are confident this represents a truly significant increase given that it is greater than the suggested 6% required as meaningful change using the canopy method (25). The rigor in our protocol also resulted in a lower coefficient of variance than reported previously (25). Importantly, these increases in RMR were due to significant increases in V˙O2 and V˙CO2 and are not merely EPOC being measured as increased V˙O2. The range of increased RMR was large, with individual responses between 240 and 1000 kcal. The greatest increases in RMR were seen in the forwards, who underwent more physical collisions during a game at the scrum, maul, and tackle area versus backs (4). The whole group, and forwards positional group, also experienced increased RMR which remained elevated 3 d postgame. This sustained increase at GD + 3 may be a result of the lower limb resistance training session on GD + 2 given that resistance training, especially with an eccentric component, has been shown to increase RMR (33). It is possible that this sustained increase in RMR, as a result of the resistance training session, negatively affected the recovery from match play, therefore extending the period during which RMR remained elevated, although this suggestion remains speculative and requires further investigation.
Along with changes in RMR in the days after the game we also report significant changes in RER. The increased RER at GD + 2 and GD + 3 corresponds with significant increases in resting carbohydrate oxidation coupled with a significant reduction in fat oxidation at GD + 3. These significant changes in carbohydrate oxidation are occurring at a time where markers of muscle damage and soreness typically peak after match play (8,9,11). Muscle damage-induced reductions in glucose transport may result in a decreased whole-body glucose tolerance which has been reported after a laboratory based muscle damage protocol (34). It should also be recognized that the inflammatory cytokine activity associated with muscle damaging exercise, together with the presence of various cell types, such as neutrophils and macrophages (35), may alter substrate oxidation in the recovery period (36). Taken together, we have demonstrated increased RMR and altered carbohydrate oxidation, after match play, which suggests that postexercise nutrition should be specifically tailored to the unique metabolic demands of this period. Moreover, we have shown highly individual responses with some players increasing their RMR by 1000 kcal. It is crucial to identify such players and tailor their dietary plans and recovery strategies accordingly.
Given that the participants in the present study were full-time professional players, in the middle of a competitive playing season, it was not possible to either control or record dietary intake. It is possible that some of the differences in RMR and RER seen between the forwards and backs could have been a result of differing diets of the two subgroups. However, although there is evidence that the thermic effect of food and the total energy content of a meal may alter RMR measures (15) we do not believe that the player to player variations in diet would have any meaningful effects on RMR or RER in the present study. Previous research has reported that a large meal containing 1300 kcal had negligible effects upon measuring RMR and RER when measured 7 h later, and in lean male subjects both measures had returned to baseline at 8 h after this meal (37). Given that both the forwards and backs in the present study had undergone a minimum of an 8-h fast before having their RMR and RER assessed, it is unlikely that differences in diet would be a primary contributor to the observed changes. Moreover, we believe that this group of players consumed a more than adequate energy availability as indicated by no major changes in body mass over the testing periods. This group is unlikely to be in low-energy availability; however, future studies should attempt to measure or control dietary intake to fully explore this hypothesis.
We propose that the muscle damage as a result of elite rugby union match play could be a key factor in accounting for the changes in metabolism we have witnessed. By carefully monitoring the internal and external demands of the competitive week, we have shown that when contact sport athletes are exposed to comparable player load (including HSR and VHSR metrics) to that of a match day but without the physical collisions, there is no change in RMR in the following days. We, therefore, speculate that the collisions encountered on a GD could be responsible for the significant changes in RMR reported at GD + 1. This may account for the increases in TEE previously observed in youth players when a training session contained collisions similar to that of match play (14).
When we investigated the positional groups of forwards and backs, there were differences in how they reacted to match play. The backs subgroup did not show any significant changes in RMR or RER postmatch, albeit they did show a similar pattern across the week as seen in the forwards subgroup. The backs did not experience as many contact incidents as the forwards as has previously been shown (4), and they were not involved in the static exertions of the scrum and maul which are potentially damaging. These positional differences may further substantiate our hypothesis that the contact-based activities are responsible for the metabolic changes reported here.
The total number of contacts was rigorously evaluated; however, the Spearman’s correlations did not show any significant correlations of changes in RMR with the match demands or collisions experienced. There was one back who exhibited a large increase of ≈796 kcal in RMR. Although the actual number of contacts performed by this player was not significantly different to the mean of the backs group, subjective analysis of these collisions (by experienced rugby staff) classified the magnitude and intensity of these as being much greater than typical. Examples like this, coupled with the current inability to accurately quantify collision activities, emphasizes the need for a practical measure of the impact contact sports have upon these athletes to be developed.
Practical Implications
From an applied perspective the periodization of nutrition throughout microcycles to optimize adaptation and ultimately performance is well established under the “fuel for the work required” paradigm (38). The novel data presented here could enhance the application of this in team sports, especially those involving muscle damage due to collision-based activities. Even using a modest physical activity level of 1.3 to 1.4 for a GD + 1 rest day, would translate these findings into a required increase in energy intake of >300 kcal. This, on a day where the continued restoration of muscle glycogen is a primary concern, in a population who habitually appear to consume lower than the recommended carbohydrate intakes, may require a conscious intervention (22). Carbohydrate intake as part of an in-season week in elite rugby union players appears to be 3 g·kg−1 on GD + 1 (22), therefore an extra ~70 g carbohydrate could be an increase of ~20% required on that day. We speculate the timing of carbohydrate feeding may also require further investigation though, if indeed substrate oxidation is altered until the muscle damage due to match play is resolved (34,39).
Given that the true definition of RMR involves “strict and steady resting conditions” it could be argued that the present study did not actually measure RMR at any timepoint where in fact morning metabolic rate was actually measured. Indeed, it could be argued that rugby players (and indeed many athletes) during a competitive season are never truly at “rest” bringing about methodological questions over when during a training period RMR should be measured to accurately predict energy requirements. A protocol according to best practice and adhering strictly to a minimum rest time, fasted measurement and proper outpatient protocols as per RMR are crucial for reliability but this measure may need to be categorized differently (15,23). In the applied world, the term “morning metabolic rate” may be a more accurate description of what is actually being measured, and future studies may choose to adopt this terminology.
CONCLUSIONS
In conclusion, the present study has for the first time assessed the RMR of elite rugby union players across a competitive match week using IC. We report a significant increase in the RMR of these contact sport athletes in the days after match play. There were also significant shifts in RER at 2 and 3 d after competition. We propose these changes could be attributed to the collisions experienced in match play rather than the internal and external loads the athletes are exposed to throughout the microcycle. The metabolites and markers of these muscle damaging actions need to be researched further to help guide athletes as how best to feed their recovery after competition. This research is the first step in working toward a novel noninvasive marker of muscle damage. Further studies need to control factors of energy availability and exercise modalities responsible for the forms of muscle damage. Protocols more readily useable in the “real world” of applied performance science then need to be designed to truly shift the paradigm of athlete monitoring and optimize recovery from contact sports.
The research was funded by the rugby club as part of the postgraduate work of J. H. Thank you to M. C. for providing equipment support for the IC. All authors contributed to the study design and preparation of the manuscript. The authors would like to thank all the players for the time taken to perform the RMR analysis and the rugby club for their support.
The authors reported no potential conflict of interest. The results of the current study do not constitute endorsement by ACSM. All results presented here are done so clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.
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