A study to examine whether fluctuations in daily training load lead to changes in heart-rate variability in elite rugby union players.
Principle Researcher: Richard Beck
Project Supervisors: Dr Mark Waldron and Dr Stephen Patterson
This Research Project is submitted as partial fulfilment of the requirements for the degree of Master of Science, St Mary’s University
Table of Contents List of figures 3 List of tables 4 Acknowledgements 5 Abstract 6 Chapter 1 Introduction 7 Chapter 2 Methods 12 Chapter 3 Results 18 Chapter 4 Discussion 23
Appendix 1: Ethics application approval 32
Appendix 2: Participant information sheet 33
Appendix 3: Consent form 36
List of figures
Figure 1 A line graph representing the daily group mean fatigue rating on the wellness
questionnaire and the daily group mean heart rate variability (HRV) score across the length of the study
Figure 2 A line graph representing the daily group mean muscle soreness score on the wellness questionnaire and the group mean of the daily training load across the length of the study
Figure 3 Scree plot for principal-component analysis, displaying the presence of 3 principal components with an eigenvalue >1
List of tables
Table 1 Example structure of a typical training week for elite rugby union players
Table 2 Results of the Principal-Component Analysis showing the Eigenvalue, Percentage of Variance explained, cumulative percentage of variance explained by each principal
component (PC) for the wellness and training load measures, in addition to the rotated component loadings for each PC extracted
Acknowledgements
I would like to thank my supervisor Dr Mark Waldron for his support, guidance and insight throughout this research project.
I would also like to thank Yorkshire Carnegie for allowing me to use their players as subjects for this project.
Finally, and most importantly I would like to thank my wife, Nicole, who has supported me through my Masters and this research project.
Abstract
This study investigated whether fluctuations in daily training load would lead to changes in heart rate variability (HRV) within elite rugby union players. Sixteen professional rugby union players (age 27 ± 2 years; height 187.8 ± 6.6 cm; weight 102.7 ± 11.0 kg) recorded their resting HRV and wellness scores daily for 28 days. Session rating of perceived exertion (sRPE) was quantified for all training sessions. One principal component was extracted from the wellness measures, explaining 52% of the variance of all the wellness measures and two principal components were extracted for the training load measures, explaining 71% and 21% of the variance of all the training load measures. Three variables (fatigue, daily load and 3-day load exponentially weighted moving average (EWMA)) were selected to run in a linear mixed model. Changes in daily load (effect size correlations; ES = 0.00; ± 0.01), 3-day EWMA (ES = 0.01; ± 0.01) and fatigue (ES = 1.53; ± 0.65) were found to have an almost certainly trivial change in HRV. In conclusion, this study questions the usefulness of HRV as a monitoring tool for rugby union players as changes in daily training load were not associated with changes in HRV.
Keywords: monitoring; team-sport; heart rate variability; training load; principal component analysis
Chapter 1
Introduction
The physical demands of rugby union training and match play are well known, with players experiencing immediate and prolonged fatigue (Coutts, Reaburn, Piva & Rowsell, 2007; Johnston, Gabbett, Seibold & Jenkins, 2013; Twist & Highton, 2013). Monitoring this fatigue is essential as a prolonged period of imbalance between training and recovery may lead to overreaching or more seriously, overtraining and a reduction in performance (Hedelin, Kentta, Wiklund, Bierle & Henriksson-Larsen, 2000). More attention is now being paid to the
monitoring of athletes and making sure the right tools are selected to ensure fatigue is closely monitored. Heart rate variability (HRV) has recently become a popular monitoring tool to assess cardiovascular recovery and monitor the autonomic nervous system (ANS) (Manzi et al., 2009; Plews, Laursen, Kilding & Bucheit, 2012; Plews, Laursen, Stanley, Kilding & Bucheit, 2013). Heart rate and rhythm are mainly under the control of the ANS which is divided into the sympathetic nervous system and parasympathetic nervous system (Bucheit, 2014). Resting HRV is associated with parasympathetic and sympathetic activity, and is thought to be a marker of homeostasis. One of the purposes of monitoring HRV is to see if there are any changes in cardiac ANS status which may give an indication about overtraining (Stanley, Peake & Bucheit, 2013). For example, in athletes who are well rested and not over trained, their parasympathetic and sympathetic nervous system interact cooperatively to ensure proper regulation of the heart by the nervous system (Bucheit, 2014). On the other hand, a power or speed athlete who is close to overtraining may display low HRV with the sympathetic system driving the heart rate response. However, it is important to note that HRV is rarely this predictable and can alter depending on how it is collected. It is also worth
pointing out that these trends do not always apply in the research (Bucheit et al., 2013a). When combined with other monitoring tools such as wellbeing, coaches can get a more accurate overview of an athlete’s training status. The arrival of smartphone technology applications, which are capable of measuring HRV with minimal cost and in a short period of time, have made measuring HRV even more accessible to coaches (Esco & Flatt, 2014; Flatt & Esco, 2015). Traditional HRV monitoring, requiring up to a 10-min recording period of resting heart rate, takes too long for frequent monitoring (Aubert, Seps & Becker, 2003). Research has shown that 60 second recordings can accurately assess HRV in team-sport athletes and are more practical than traditional methods (Esco & Flatt, 2014). However, with an increase in the number of monitoring tools available, it is important to determine the usefulness of these tools in monitoring athlete recovery.
One of the reasons for using monitoring tools is to avoid non-functional overreaching or overtraining syndrome within athletes. Early and definitive assessment of whether an athlete is suffering from either of these conditions can be difficult to identify and only becomes obvious when an athlete suffers from a fall in performance (Meeusen et al., 2013). Unfortunately, at the moment there is no simple test for overtraining syndrome however, HRV monitoring could be a useful diagnostic aid for early identification of overtraining syndrome (Meeusen et al., 2006). Some research has shown that following an intensified training period, HRV was significantly higher than normal values and so it shows potential as a useful monitoring tool however further research is needed (Halston, 2003). For a monitoring tool, such as HRV, to be valuable to a coach and a useful marker for detecting the onset of overtraining, it must be sensitive to changes in training load. Research into HRV has mainly focussed around endurance athletes over long periods of time (weeks and months) (Manzi et al., 2009; Plews et al., 2012). However, rugby union players take part in matches on a weekly
basis so the use of monitoring tools that are responsive to daily fluctuations in training load may be more useful for assessing fatigue. Research considering the value of HRV as a monitoring tool in team sports players has been limited (Bucheit et al., 2013b; Gastin, Meyer & Robinson, 2013). However, studies involving elite soccer (Thorpe et al., 2015) and
Australian Rules Football (Bucheit et al., 2013b) players reported statistically significant correlations between changes in training load and vagal- related heart variability indices. The training demands on rugby union players are different to the previous samples (Bucheit et al., 2013b; Nakamura et al., 2016; Thorpe et al., 2015), whereby in rugby union there is a much higher collision element and more static isometric activity (Austin, Gabbett & Jenkins, 2011; Fuller, Brooks, Cancea, Hall & Kemp, 2007). As such, further research is needed to assess whether HRV may be a useful tool for monitoring fatigue in rugby union players.
While there are a number of different vagal indices which can be monitored, the natural log of the root mean square of successive RR interval differences (lnRMSSD) has been commonly used to monitor fatigue and training adaptations in athletes (Flatt & Esco, 2015; Nakamura et al., 2015). Acute or accumulated fatigue within an athlete is normally characterised by a reduced lnRMSSD (Nakamura et al., 2016). No study has yet investigated ultra-short-term HRV obtained daily by a team of rugby union players. These ultra-short-term recordings can be taken on smartphones and could be particularly useful for coaches who have a large squad of players to monitor. Previous research has suggested that HRV recordings should take place over several days rather than isolated recordings as cardiac-autonomic activity is sensitive to physical and psychological stressors which may lead to changes in lnRMSSD (Tian et al., 2012). This is thought to be especially true in elite athletes who have multiple training sessions a day and are exposed to other sources of stress (Cipryan & Litschmannova, 2013).
Dose-response relationships between match and training loads with relative internal physiological stressors have been well documented. Indeed, reductions in neuromuscular function (Twist, Waldron, Highton, Burt & Daniels, 2012), biochemical disturbances (West et al., 2014) and a reduction in perceived wellbeing (Twist et al., 2012) following rugby matches and training have been well reported. Less however is known about HRV and its response to daily rugby training and matches. A previous investigation assessing the relationship between training load and HRV during pre-season in female soccer players found that changes in HRV related to changes in perceived training load (Flatt & Esco, 2015). Research using rugby players as participants have been short term studies taking place across only 4-5 days
(Edmonds, Sinclair & Leicht, 2013; Nakamura et al., 2017). However, there has yet to be any research considering the effect of daily training and matches on day-to-day variations in HRV within elite rugby players over a longer period of time. Understanding how HRV fluctuates in response to daily training load could give coaches useful physiological information which could be combined with other monitoring tools to guide player recovery.
Research would suggest HRV is influenced by training load (Bucheit et al., 2013b; Thorpe et al., 2015), however HRV is multifactorial in nature, meaning it could be influenced by a multitude of variables. For example, there could be changes in lnRMSSD without a preceding increase in training load, which consequently could require interventions that lower non-training related stressors rather than lowering non-training loads (Tian et al., 2012). It is therefore likely that multiple measures are needed to explain fluctuations in HRV, such as sleep and wellness measures (e.g. muscle soreness, mood, stress and fatigue) and these should also be taken into consideration to assist with understanding changes in lnRMSSD (Bucheit et al., 2013b). However, if there are too many influences on HRV then it can become hard to interpret which influences are the most important. Dimension reduction techniques, such as
principal component analysis (PCA) can combine common information from multiple measures whilst simultaneously separating unique information that is provided (Federolf, Reid, Gilgien, Haugen & Smith, 2014). Also, if in a statistical model, many of the variables are highly correlated with one another this can lead to statistical problems such as
multicollinearity (Hair, Black, Babin & Anderson, 2009). The PCA was run prior to the linear mixed model, which was used to examine the association between training load and HRV.
Therefore, the aim of this study was to examine whether fluctuations in daily training load led to changes in ultra-short, Smartphone-derived lnRMSSD in elite rugby union players during the regular season. It was hypothesised that an increase in daily training load would result in a reduction in HRV the following day.
Chapter 2
Methods
Participants
Sixteen male professional rugby union players (age 27 ± 2 years; height 187.8 ± 6.6 cm; weight 102.7 ± 11.0 kg) volunteered to participate in this study. All athletes were briefed about the study procedures and signed an informed consent form. The study was granted ethical approval by the St Mary’s Twickenham London ethics committee.
Study design
The athletes were monitored over a 28-day period during the regular season. Resting HRV was self-measured by the athletes every morning upon awakening. The athletes were shown how to fit the chest strap transmitter correctly and use the smartphone application prior to the start of the study. There was also a 3-day trial before the start of the study to familiarise participants with the collection process. A schematic representation of a typical training week is presented in Table 1. The participants took part in approximately seven training sessions per week. Three of these sessions were rugby practice which lasted 60-90 min and contained conditioning games, tactical and technical training and game-based training. The unit sessions lasted 45-60 min and involved practising scrums and line-outs for the forwards and kicking and passing for the backs. The participants also completed two to three resistance training sessions which contained whole-body strength-power workouts consisting of deadlifts, squats, chins, bench press and power cleans.
Table 1. Example structure of a typical training week for elite rugby union players.
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Week 1
am Units, RT Off Units, RT Off Pr Match Off
pm Pr Pr
Am = morning; pm = evening, RT = resistance training; Pr = rugby practice.
Session rating of perceived exertion
The training load was calculated using the sRPE method (Foster et al., 2001). The participants were required to record their rating of perceived exertion (RPE) within 15-30 min following each training session. The RPE of a session was quantified using a modified Borg Category Ratio-10 RPE scale (Foster et al., 2001). The RPE was then multiplied by session duration (min) to determine sRPE-TL. The sRPE-TL from each session was then totalled to give a total training load for that day. This method has been shown before to be a valid tool for estimating exercise intensity in soccer (Impellizzeri, Rampinini, Coutts, Sassi & Marcora, 2004) and rugby league players reported positive correlations of 0.89 and 0.86 with training heart rate and blood lactate concentrations respectively (Gabbett & Domrow, 2007). More recently, there have been concerns with the RPE method with some research suggesting poor reliability for quantifying internal training load especially for short bouts of intermittent exercise (Scott, Black, Quinn & Coutts, 2013). The training mode has also been shown to influence the validity of sRPE when quantifying training load in rugby with different training modes, such as wrestling and skills conditioning, altering the strength of relationship between sRPE and external-load measures (Lovell, Sirotic, Impellizzeri & Coutts, 2013). However, the sRPE method still has strong validity and is a simple non-invasive method of collection, so it was
still considered a valid way to measure internal training (Impellizzeri et al., 2004; Scott et al., 2013). Ideally, a combination of internal and external training load measures would be used for the skills training however there was no access to GPS data as part of this study (Weaving, Marshall, Earle, Nevill & Abt, 2014). Training load was then arranged as daily, three and five-day exponentially- weighted moving averages (EWMA) to consider daily and cumulative loads on subsequent HRV. EWMA were used for the calculation of three and five-day loads, where a stated decreasing weighting for each previous load is applied (Williams et al., 2016). This method was preferred to rolling averages because rolling averages fail to take into account the decaying nature of fatigue effects over time (Hawley, 2002). The EWMA for a day is provided by:
EWMAtoday = Loadtoday * la + ((1- la) * EWMAyesterday)
Where la is value between 0 and 1 that denotes the amount of decay, with higher values discounting old observations at a quicker rate. The la is provided by:
la = 2 / (N+1)
The chosen time decay constant is N, which in this study was three and five. Previous studies have used this rationale to examine training load in relation to sleep (Thornton, Delaney, Duthie & Dascombe, 2017).
The HRV recording was collected daily by the participants immediately upon waking. Participants were required to moisten and fit the chest-strap transmitter (T-31 Non-Coded; Polar, Kemple, Finland) around their chest at the level of the xiphoid process and insert the electrocardiograph (ECG) receiver (HRV fit Ltd, Southampton, UK) into the headphone slot of their phone. Research has suggested this equipment showed nearly perfect agreement (correlation coefficient of 0.99) between the field and a five-minute ECG recording for lnRMSSD determination (Flatt & Esco, 2013). The participants would then open the ithleteä HRV application (HRV fit Ltd, Southampton, UK) on their smartphone and while lying motionless in a supine position and breathing naturally, record their HRV data. This 55 seconds recording was preceded by a one min stabilisation period. The participants were encouraged to keep the chest strap transmitter next to their bed to minimise moving around in the morning before the recording. Once complete, the participants would use the data-export feature of the application and email the HRV results to the researcher. The application provides the lnRMSSD which is then multiplied by twenty for easier interpretation on a 100-point scale (Flatt & Esco, 2013). Previous research using rugby players as participants has reported both intraday lnRMSSD assessments (intraclass correlation coefficient (ICC) =0.96; coefficient of variation (CV) = 3.99%) and interday lnRMSSD assessments (ICC= 0.90; CV = 7.65%) measures were highly reliable (Nakamura et al., 2017). It is not possible to extract the R-R intervals from the device for manual inspection. However, within the application is an irregular- beat detection and correction algorithm that excludes intervals which are
excessively short, <500 milliseconds, or long, >1800 milliseconds. The participants were given a 3-day trial period with the ithleteä system to familiarise themselves with the collection procedure.
After the participants finished their HRV recording, they completed a wellness questionnaire on the ithlete ä application as it has been suggested that combining both can help with interpretation of HRV data (Bucheit, 2014). The participants were asked to complete the questionnaire immediately after the HRV recording to ensure it was finished at approximately the same time of day to avoid daily variation (Meeusen et al., 2006). The questionnaire was made up of questions about the participant’s sleep quality, muscle soreness, mood, fatigue, diet and stress. The participants rated each variable on a nine-point scale with a rating of 5 representing feeling “normal” and 1.0 increments going higher or lower subject to better or worse perceptions of the variable. A higher number would indicate better wellness. The scoring scale was explained to the participants prior to the start of the study. Previous research has reported that subjective questionnaires are responsive to the recovery of rugby players following games (Twist et al., 2012) and fluctuations in training stress (Coutts et al., 2007).
Statistical analysis
PCA, which reduces the dimensionality of a dataset that consists of a number of highly correlated variables, while still maintaining as much of the variation in the data as possible, was undertaken (Federolf et al., 2014). Recent studies examining training-load measures have used a similar data reduction process (Williams et al., 2017; Weaving et al., 2014; Weaving et al., 2018). The PCA combined information provided by the original daily wellness measures (sleep quality, muscle soreness, mood, fatigue, diet and stress) and training load measures (daily load, 3-day cumulative load, 5-day cumulative load, 3 and 5-day EWMA) into a new set of linear variables called principal components (PC). All data were centred and scaled prior to conducting the PCA. Orthogonal varimax rotation was then performed to improve the
identification and interpretation of factors (Hair et al., 2009). A multi-faceted approach was used which involved inspection of the scree plot, eigenvalues (>1) and the accumulation of the variance explained by each PC to establish the optimal number of PCs to extract for the group (Hair et al., 2009; Williams et al., 2017). Variables within a given principal component are correlated with each other, however the principal components themselves are orthogonal (zero correlation) and so explain distinct information. The PCA was performed using IBM SPSS (v.24; IBM, Armonk, New York, USA). Once the PCA procedure had been completed, linear mixed models were used to assess the effect of training load on HRV. The linear mixed model was chosen for its ability to account for repeated measurements within the data and was performed using IBM SPSS (v.24; IBM, Armonk, New York, USA). Variables deemed important by the PCA were treated as fixed effects within the mixed model, while individual participants were treated as random effects.
Chapter 3
Results
A total of 64 individual training weeks which consisted of 223 individual training days were completed by the players during the study period. Figure 1 illustrates how HRV and Fatigue fluctuated across the length of the study. While Figure 2 illustrates how daily training load and muscle soreness fluctuated over the 28-day study. An increase in training load was typically followed by participants reporting increased muscle soreness the following day.
Figure 1. A line graph representing the daily group mean fatigue rating on the wellness questionnaire and the daily group mean heart rate variability (HRV) score across the length of the study. The black line represents daily group mean HRV values and the dashed line
represents the daily group mean fatigue score on the wellness questionnaire (n = 16). 75.0 80.0 85.0 90.0 95.0 100.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 H ea rt R at e V ar ia bi li ty (I nR M S S D x20) F at igue ( au) Time (Days)
Figure 2. A line graph representing the daily group mean muscle soreness score on the wellness questionnaire and the group mean of the daily training load across the length of the study. The black line represents the mean of the groups daily training load and the dashed line represents daily group mean muscle soreness score on the wellness questionnaire (n =16).
In total three principal components were identified. One principal component was identified for the daily wellness measures (grey line) and two principal components for the training load measures (solid black line) (Figure 3). As the scree plot shows these PCs had eigenvalues >1 which is part of the selection criteria for deciding the optimal number of PCs to extract (Hair et al., 2009). The wellness and training load variables were separated as they are theoretically separate constructs. 0 100 200 300 400 500 600 700 0 1 2 3 4 5 6 7 1 3 5 7 9 11 13 15 17 19 21 23 25 27 D ai ly tr ai ni ng loa d (s R P E ) M us lc e sor ene ss ( au) Time (Days)
Figure 3. Scree plot for principal-component analysis, displaying the presence of 3 principal components with an eigenvalue >1. The black line represents training load measures and the dashed grey line represents the daily wellness measures (n=16).
Table 2 shows the PCA, including eigenvalues for each principal component for the daily wellness measures and training load measures and the total variance explained by each principal component for each wellness measure and training load measure. For the wellness measures, fatigue was selected as the representative measure and for the training load measures, 3-day EWMA and daily load were selected.
Table 2. Results of the Principal-Component Analysis showing the Eigenvalue, Percentage of Variance explained, cumulative percentage of variance explained by each principal
component (PC) for the wellness and training load measures, in addition to the rotated component loadings for each PC extracted (n = 16)
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 1 2 3 4 5 6 E ige nva lue Principal component
Component 1 2 3 4 5 6 Wellness measures Eigenvalue 1.76 0.93 0.84 0.81 0.62 0.54 % of variance 51.52 14.46 11.79 11.02 6.40 4.81 Cumulative variance % 51.52 65.98 77.77 88.79 95.19 100.00 Rotated component loadings
sleep quality -0.38 ⎯ ⎯ ⎯ ⎯ ⎯ fatigue -0.47 ⎯ ⎯ ⎯ ⎯ ⎯ muscle soreness -0.42 ⎯ ⎯ ⎯ ⎯ ⎯ stress 0.39 ⎯ ⎯ ⎯ ⎯ ⎯ mood -0.45 ⎯ ⎯ ⎯ ⎯ ⎯ diet -0.31 ⎯ ⎯ ⎯ ⎯ ⎯
Training load measures
Eigenvalue 1.89 1.02 0.49 0.36 0.10 ⎯
% of variance 71.48 20.91 4.79 2.64 0.18 ⎯
Cumulative variance % 71.48 92.39 97.18 99.82 100.00 ⎯ Rotated component loadings
daily load -0.42 -0.53 ⎯ ⎯ ⎯ ⎯
3-day cumulative load -0.42 0.51 ⎯ ⎯ ⎯ ⎯
5-day cumulative load -0.38 0.50 ⎯ ⎯ ⎯ ⎯
3-day Exponential weighted
5-day EWMA -0.50 -0.15 ⎯ ⎯ ⎯ ⎯
The results of the linear mixed model describing the relationship between 3-day EWMA training loads, daily load and fatigue on HRV are displayed in Table 3. An increase in 3-day EWMA, daily load and fatigue were all associated with an almost certainly trivial change in HRV.
Table 3. Effects of wellness and training load measures on Heart Rate Variability (90%CI) (n=16)
Effect (%) 90% CI Effect inference
Intercept (AU)* 81.97 76.01 to 87.85
3-day exponential weighted moving average
-0.01 -0.02 to 0.00 almost certainly trivial
Daily load 0.00 -0.01 to 0.01 almost certainly trivial Fatigue 1.53 0.88 to 2.18 almost certainly trivial CI, confidence interval
Chapter 4
Discussion
The aim of the current study was to investigate whether changes in daily training load led to changes in HRV. Contrary to the hypothesis, there was no dose-response relationship between changes in training load and HRV. As part of the statistical analysis, a PCA was undertaken. The current study is the first to undertake this type of analysis to identify wellness variables that provide similar information for professional rugby union players. The PCA characterised 3 fundamental dimensions with one principal component for the wellness measures and two principal components for the training load measures. For the wellness measures, component 1 explained the largest proportion of variance (51%) and fatigue was selected as the
representative measure for that principal component.
The variables in this study may be unique to the current data set; however, this approach to monitoring and selecting a reduced number of wellness variables is a novel approach. This implies that coaches would not be missing out on information by only asking a player about one wellness variable rather than all six variables. This could save time for athletes and coaches as they would only have to report and interpret one variable rather than six. For the training load measures, 3-day EWMA and daily load were selected as the representative measures for the two principal components. Component 1, which explained the largest proportion of variance (71%), was most associated with the 3 and 5-day EWMA. However, component 2 captured unique additional variance (21%) and so between them they explained 92% of total variance. A similar statistical approach has been used in previous research to look at measures of external and internal training load metrics which capture similar or unique
information (Weaving et al., 2018, Williams et al., 2017). Similarly, to this paper, previous research has found that multiple training load metrics can be reduced without the loss of information, which can make monitoring and reporting of training load more time-efficient for practitioners (Coutts, 2014; Weaving et al., 2018). Also, like other research that has looked at reducing training load measures, this paper found more than one measure is needed to identify a meaningful proportion of all the information supplied by the multiple training load variables (Weaving et al., 2018). 3 and 5-day EWMA capture the greatest amount of information however, daily load and 3 and 5-day cumulative load offer unique additional information. In agreement with previous research this would suggest multiple measures of training load are required to give the best representation of the load imposed by professional rugby players.
In recent years, HRV has been used as a popular monitoring tool to assess changes in cardiac ANS in order to understand an athlete’s level of fatigue and recovery status. A further key finding from this study was that a change in training load led to no change in HRV and that HRV explained different information to the other wellness and training load variables. This is in contrast to previous research which reported a significant correlation between changes in training load and changes in vagal-related heart variability indices (Bucheit et al., 2013b; Thorpe et al, 2015). As has been mention previously, resting HRV is linked with sympathetic and parasympathetic activity and the balance between these two is very important. It could be the case that day-to-day variations in HRV in response to training load is only achievable with a balanced and responsive ANS status and that in this study the participants lacked the
necessary ANS responsiveness from each day’s training. For example, if participants had sympathetic over activity, a day of rest may not have been sufficient for parasympathetic activity to revert back to baseline and so day-to-day variations in HRV would be difficult to
see (Plews et al., 2012). In other words, sympathetic over activity in reaction to a previous days training could still influence future results. This makes it hard to examine the effect daily training load had on the next day’s HRV score. However contrary to this, in a recent meta-analysis it was reported that athletes with a higher training status demonstrated faster
parasympathetic reversibility after exercise than those with a lower training status (Stanley et al., 2013). As the participants in this study were elite rugby players, it could be suggested that they had the necessary ANS flexibility and so this is not the reason for the lack of relationship between HRV and daily training load in this study.
A further reason for the contrast in findings could be due to the different participants used in the studies. The physiological demands of rugby union are very different to soccer and Australian Rules Football and so participants are likely to respond in a different way and also different methodologies were used in the studies. For example, Bucheit et al, (2013) used a different vagal-related HRV index than the current study and different collection methods. One of the closest studies to take into account rugby training demands is a study which used junior rugby league players as its participants which found HRV to fall the day following a match (Edmonds et al., 2013). However, there are some methodological concerns with this study in that there were only nine participants across 4-5 monitoring days and HRV
recordings were taken in the afternoon prior to a training session. Nakamura et al. (2017) also conducted their study using rugby players who were participating in an intensive training camp and found lnRMSSD to fall in response to training load. The intensive nature of the 4-day training camp could be a reason why the authors found a relationship between HRV and training load. Again, the recordings were taken across only 4 days and were not taken immediately upon waking. In the current study, HRV was recorded immediately upon
and temperature, which the autonomic nervous system can be sensitive to while also being sensitive to training effects (Achten & Jeukendrup, 2003; Flatt & Esco, 2015). By taking the recording first thing in the morning it is thought to offer some consistency as the participant will be in the same bed, at the same time, in a quiet environment (Bucheit, 2014). Also by taking the reading in a supine position it is thought to be more sensitive to training load variation than the seated position (Flatt & Esco, 2015). To control for some of these factors, participants were encouraged to keep their phone and chest strap transmitter next to their bed and fit it immediately upon waking to limit activity in the morning before the recording.
As has been mentioned previously, assessing HRV is thought to be a useful marker of cardiac ANS. However, it can be measured in a number of different ways for example, during
exercise, post-exercise and also at rest. One of the issues with measuring HRV during exercise is that it can be difficult to ensure any changes are exclusively ANS related (Bucheit, Papelier, Laursen & Ahmaidi, 2007). To use this method effectively would require exercise intensity to be individualised which would be too difficult in a team setting (Bucheit et al., 2007). With post-exercise HRV recordings it has been reported that it is influenced by a number of factors such as blood pressure regulation and baroreflex activity and so again it is too difficult to assess the ANS influence on HRV (Bucheit et al., 2007). It was therefore considered that morning readings were the best option for athletes and would offer the best chance of
monitoring ANS. In addition to HRV recordings being taken at various times, there are also a number of different indices which can be used to measure HRV. The two methods used most frequently are time domain and spectral analyses. In this study, lnRMSSD, a time domain index was used however different indices reflect different aspects of the ANS with some reflecting cardiac parasympathetic activity and others sympathetic activity (Bucheit, 2014). lnRMSSD, which reflects parasympathetic modulation, was chosen as it can be collected over
a short period of time, it is not as sensitive to breathing patterns compared to spectral indices and the day to day variations of time domain indices is lower than spectral indices (Hamilton, McKechnie & Macfarlane, 2004; Penttila et al., 2001). It has also been shown to be sensitive to fatigue and useful in measuring training adaptations in female soccer players (Flatt & Esco, 2015; Flatt & Esco, 2016).
If training load is not causing HRV to fluctuate then this raises the question about what other factors may have a relationship with HRV. Previous research has suggested that HRV is responsive to environmental conditions and hydration status so they must also be considered (Bucheit et al., 2013b). It has been reported that following high training loads in excessive heat where there has been a decrease in perceived wellness, HRV indices have actually risen rather than fallen in the following 24 hours (Bucheit et al., 2013b). This is due to increases in plasma volume as a response to intense aerobic activity and heat acclimatisation which, can increase HRV independently of changes in fatigue (Bucheit et al., 2013b; Green et al., 1984). The current study took place in January in the UK so excessive heat and dehydration were not thought to be an issue that could affect the results. Participants were also asked not to
consume alcohol during the study as alcohol could have affected hydration levels and HRV.
Training load may not have led to any change in HRV in this study because the loads are considered moderate for the participants. Much of the previous research assessing the relationship between HRV and training load has collected data during pre-season or in endurance sports where training load tends to be higher (Bucheit et al., 2013b; Nakamura et al., 2016; Plews et al., 2012). It may be the case that the training loads in this study (weekly mean of 2113 ± 346 a.u.) were too small to meaningfully change cardiac autonomic function. A study assessing futsal players had similar findings in that they found no differences in
weekly HRV because the training load across the weeks was considered low for the participants (Nakamura et al., 2016). Interestingly, average weekly training load for
professional rugby union players at a similar time of season has previously been reported to be 1522 ± 203 a.u. (Cross, Williams, Trewartha, Kemp & Stokes, 2016). This suggests training load during the time of the study was high in comparison to previously reported weekly training loads in professional rugby union. HRV is an indicator of cardiac ANS recovery and it could be the case that the training demands placed on rugby union players do not cause enough stress on the cardiorespiratory system to lead to any changes in HRV.
Due to the nature of the game, as was mentioned previously, rugby involves a high collision element (Austin et al., 2011; Fuller et al., 2007). For example, it has been reported that forwards are involved in 30 ± 5 rucks per match (Quarrie, Hopkins, Anthony & Gill, 2013). These collisions can lead to tissue trauma which in turn leads to an elevation in inflammatory markers (Cunniffe et al., 2010). This increase in inflammatory markers has been strongly associated with a decrease in HRV (Aeschbacher et al., 2017; Cooper et al., 2015). By monitoring HRV it is possible to identify when vagus nerve activity is inadequate and
inflammation is excessive (Huston & Tracey, 2011). Excessive inflammation can be a sign of overtraining and so the ability to monitor this would be useful for coaches.
Rugby union training can be made up of different elements, for example technical contact drills which involve low movement but a high amount of collisions. These sessions may be rated as ‘very hard’ on the Borg scale and yet place low stress on the cardiovascular system. On the other hand, a ‘very hard’ running-based interval session is given the same score and yet induces very different metabolic, cardiovascular and neuromuscular responses (McLaren, Smith, Spears & Weston, 2016). What may be useful for future research is to use differential
ratings of perceived exertion (dRPE), where players give a rating for breathlessness, leg muscle exertion and upper-body muscle exertion and see if these more specific ratings have a relationship with HRV (McLaren et al., 2016). The fluctuation of HRV over the course of the study may well be due to the contents of the training session from the previous day (Figure 1). For example, a fall in HRV may be due to a more intense running session completed the previous day. It could be the case that the use of HRV monitoring is more useful during pre-season training when training load is higher however further research is required in this area (Cross et al., 2016). If it is the case that HRV is only an indicator of cardiac ANS recovery, then it must be used in conjunction with other monitoring tools such as the countermovement jump and wellness questionnaires (Bucheit, 2014).
The time needed to restore ANS activity level back to pre-exercise level has been reported to depend on not just the intensity of the exercise performed but also athletes’ cardiorespiratory fitness levels with a higher maximal oxygen uptake linked to a quicker ANS recovery (Seiler, Haugen & Kuffel, 2007). Due to the study taking place in the middle of the season, it could be the case that the participants had a good level of fitness which meant the training load
performed did not cause substantial stress to the ANS and so HRV was not significantly affected.
In terms of practical applications for practitioners, it is worthwhile investigating whether multiple wellness and training load parameters are sharing a large proportion of information and whether they can be reduced into fewer variables. This is likely to be more time efficient for both the coach and the athlete. The current study has shown PCA as a useful process for practitioners to adapt. For example, practitioners could collect just ‘fatigue’ as their wellness parameter and daily load and 3-day EWMA as their training load variables rather than having
to collect multiple variables. This process could be adapted in other sports settings in order to extract key measures and improve the fatigue monitoring process.
A limitation in the current study was the sample size used. Due to the relatively small sample size used it was not possible to look at each individual separately and assess the relationship between HRV and training load. Individuals had been accounted for anyway through the random intercept in the linear mixed model but there was not enough statistical power to assess each individual separately. It would be of benefit to look at individual data as there can be substantial intraindividual differences in response to the same training stimuli (Hedelin, Bjerle & Henriksson-Larsen, 2001). It may be the case that HRV is responsive to training load in some players and therefore a useful monitoring tool for some athletes but not others.
Further research is required in this area to assess intraindividual data within professional rugby players and to see if HRV is responsive to training load within some individuals. A larger sample size would also have allowed for investigation into how different positional groups may affect the relationship between training load and HRV. It has been well reported that forwards in rugby union are involved in more contact occurrences and spend a larger proportion of match play above 90% of maximum heart rate than backs (Cunniffe, Proctor, Baker & Davies, 2009; McLellan, Coad, Marsh & Lieschke, 2013). It could be the case that due to these differences in training demands their fatigue response in terms of HRV is different to backs. Further research is required in this area to ascertain whether positional demands do play a role in HRV response. Another potential limitation of the study is that participants were relied upon to collect self-measures with their smartphones. While this process may not have the same validity of a laboratory controlled data-collection process, it is thought to be a more practical way for HRV monitoring to take place for sports teams. It also
allowed for recordings to be taken upon wakening which is thought to control for environmental factors (Bucheit, 2014).
In conclusion, the study has established PCA as a valuable process to combine similar information while at the same time separating unique information provided by wellness and training load measures. The study also raises question marks about HRV as an effective monitoring tool in rugby union players as no relationship was found between changes in training load and changes in HRV. Further research is required to investigate HRV within rugby union players to ensure it is an effective monitoring tool and not just adding to the problem of ‘data overload’.
Appendix 1
Ethics application approval
Approval Sheet
Name of applicant: Richard Beck Name of supervisor: Dr Mark Waldron
Programme of study: Sport, Health and Applied Science
Title of project: The effect of fluctuations in training load on heart rate variability in elite rugby union players
Supervisors, please complete section 1 or 2. If approved at level 1, please forward a copy of this Approval Sheet to the School Ethics Representative for their records. SECTION 1
Approved at Level 1
Signature of supervisor (for student applications)... Date... SECTION 2
Refer to School Ethics Representative for consideration at Level 2 or Level 3
Signature of supervisor... Date... SECTION 3
To be completed by School Ethics Representative Approved at Level 2
Signature of School Ethics Representative:
Appendix 2
Participant information sheet
Participant Information Sheet
TITLE OF STUDY
The effect of fluctuations in training load on heart rate variability in elite rugby union players. BACKGROUND OF THE STUDY
As many of you will have experienced, fatigue following a game or training can be both immediate and prolonged. It is important to monitor this fatigue in order to ensure players have recovered sufficiently before the next match or training session. If an imbalance between training stress and recovery exists for too long, it may lead to overtraining. Monitoring
athletes is therefore important to try and avoid this imbalance occurring. Morning heart rate (HR) monitoring is one tool available to coaches for measuring recovery after training or matches.
With advances in technology, smartphone applications can now be used to aid in the collection of HR data. Collecting HR data can now take as little as 55 seconds and be performed by the athlete. While research in this area has focussed on endurance sports, no research has examined whether selected parameters of morning HR is sensitive to daily changes in training load in elite rugby players. This research is necessary to evaluate whether home-based morning HR monitoring relates to training load in rugby union players.
PURPOSE OF THE STUDY
The aim of this study is to monitor the relationship between daily changes in training load and ultra-short, smartphone-derived HR in elite rugby union players during the regular season. WHO IS ORGANISING THE RESEARCH?
The study will be organised by myself, Richard Beck, as part of my Master’s Dissertation.
It is the intention to recruit to 20 participants from the elite rugby union club Yorkshire Carnegie. In order to be eligible for selection, you must meet the criteria:
1. You must have been medically cleared and screened when you joined the club by the club doctor.
2. You must be training and playing full time with Yorkshire Carnegie. 3. You must be free from injury
WHAT DOES THE STUDY ENTAIL?
The study will involve completing rugby training with Yorkshire Carnegie as usual.
Additionally, HR data collection will involve attaching a heart rate monitor around your chest as soon as you wake up. You will then open up an application on your smartphone and start recording. The recording will take a minute and you will have to lie still in bed and breath normally. Once the data has been collected then it can easily be exported to the researcher. As well as collecting the HRV data you will be asked to complete a wellness questionnaire, involving 5 questions relating to sleep quality, muscle soreness, mood, fatigue and stress. The data collection procedure will be explained more clearly once participation has been
confirmed. The study will run for 28 days and data will be collected on a daily basis. DO YOU HAVE TO TAKE PART?
It is up to you whether you decide to take part in the study. If you are happy to participate then please complete and return the attached consent form. If you don’t want to participate then you will not be contacted again. It is also worth noting that as a participant in this study you have the right to withdraw at any point and all information collected up to that point will be destroyed.
WHAT ARE THE POTENTIAL BENEFITS OF TAKING PART IN THE STUDY? As a participant, you will be provided with some interesting mean data regarding the groups HRV in response to fluctuating training load.
WHAT ARE THE POTENTIAL RISKS OF TAKING PART IN THE STUDY?
There is a risk of musculoskeletal injury by taking part in rugby training. However, this is something which you would be completing anyway. There are no risks with collecting HRV data however, it is asked that no alcohol is consumed within 24 hours of testing sessions. WHAT HAPPENS IF SOMETHING GOES WRONG?
The procedure for this study has been thoroughly tested to guarantee that they meet ethical standards. If there are any problems caused by this study, it is important you contact myself immediately. Details will be provided below.
WHAT WILL WE DO WITH THE RESULTS?
The data I collect will be analysed and published in my Masters dissertation project. As a participant in the study you will have access to that data collected. All data collected will be confidential so participants won’t have access to each other’s data.
HOW WILL WE KEEP ALL YOUR INFORMATION CONFIDENTIAL?
All electronic data will be kept safe on a password- secure computer. Personal data collected from participants will be coded and only the lead researcher will have access to the
WHO DO YOU CONTACT IF YOU WANT TO KNOW MORE? Researcher: Richard Beck – [email protected]
Supervisor : Dr. Mark Waldron - St Mary’s University, Twickenham, Waldegrave Road, TW1 4SX.
Appendix 3 Consent form
Name of Participant: _________________________________________
Title of the project: The effect of fluctuations in training load on heart rate variability in elite rugby union players
Main investigator and contact details: Richard Beck [email protected] Members of the research team: Mark Waldron (Supervisor)
1. I agree to take part in the above research. I have read the Participant Information Sheet which is attached to this form. I understand what my role will be in this research, and all my questions have been answered to my satisfaction.
2. I understand that I am free to withdraw from the research at any time, for any reason and without prejudice.
3. I have been informed that the confidentiality of the information I provide will be safeguarded.
4. I am free to ask any questions at any time before and during the study.
5. I have been provided with a copy of this form and the Participant Information Sheet. Data Protection: I agree to the University processing personal data which I have supplied. I agree to the processing of such data for any purposes connected with the Research Project as outlined to me. Name of participant (print)……….. Signed………..……… Date………... ---
If you wish to withdraw from the research, please complete the form below and return to the main investigator named above.
Title of Project: ______________________________________________________________
I WISH TO WITHDRAW FROM THIS STUDY
Name: _________________________________________
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