Heart Rate Variability in Elite Youth Soccer Players: Correlations with

Training Load, sRPE and Psychometric Measures

John FitzpatrickMSc Strength and Conditioning StudentAcademy Sport Science Intern Newcastle United FC Supervisor: Dr Matthew Weston

Introduction

“A successful training program in soccer requires an appropriate training stimulus, relative to the physical capabilities of the player, coupled with adequate recovery periods. Failure to maintain this equilibrium can increase injury and lead to overtraining”

(Kuipers and Keizer 1988)

Literature: Heart Rate Variability

Research suggests that the cardiac autonomic nervous system, assessed non-invasively via heart rate variability (HRV) may provide useful information regarding the functional adaptations to a given training stimulus.

(Buchheit et al. 2007)

The usefulness of a marker to assess physiological adaptation to training requires it to be easy to administer so that frequent monitoring is possible with little inconvenience to the athlete.

(Borresen and Lambert 2008).

HRV carries information about the responsiveness of the autonomic nervous system and at rest is mainly influenced by fluctuations in the parasympathetic tone.

(Chapleau, 2011; Saul, 1990)

HRV has been shown to reflect autonomic imbalance in overtrained athletes and a meta-analysis showed that it could be considered a valid marker of autonomic balance in short-term fatigue.

(Bosquet, 2009; Pichot, 2000; Portier, 2001)

Literature: HRV Analysis

1. Analysis of HRV is obtaining high quality ECG tracings under stationary, controlled conditions.

Duration of recordings can vary. However, it is generally recommended to have minimum duration recordings of 5 minutes or even better 10 minutes

(Aubert et al. 2003).

2. Is the recognition of the QRS complex.

It is crucial that before processing, ECG signals are corrected for abnormal and missed beats.

(Aubert & Ramaekers, 1999; Pumprla et al. 2002).

QRS Complex

3. A normal-to-normal interval can be obtained (N–N; that is all intervals between adjacent QRS complexes resulting from sinus node depolarization). Often called R-R interval.

ECG signal showing RR Interval

Literature: HRV Analysis

Using this information various time domain indices can be calculated including:

• SDNN (standard deviation of N–N intervals)

• RMSSD (the square root of the mean squared differences of the successive N-N interval)

• pNN50 (proportion derived by dividing the number of interval differences of successive

N–N intervals greater than 50 ms, by the total number of N–N intervals)

The frequency domain methods provide can accurate and more specific analysis. Breaks down the cardiac cycle into various waves of amplitude and frequency, each representing its action

(Task-Force, 1996).

The most common is the Fast Fourier Transform (FFT) which identifies three spectral components (from very low frequency to high frequency) which then reflect the activity of both sympathetic and parasympathetic, giving a more rounded representation of the autonomic nervous system.

Literature: Team SportsThere are few studies that have examined HRV changes for soccer players during training periods.

Buchheit et al. (2010) reported that daily changes in HRV (i.e. lower coefficient of variation, CV) were significantly associated with maximal aerobic speed (MAS) in a group of young soccer players during a 3-week training camp.

Buchheit et al. (2012) showed a moderate relationship among baseline HRV and maximum sprint ability, acceleration, and repeated sprint ability (RSA) in a group of young soccer players evaluated over a 3-4 month interval.

Buchheit et al. (2011) reported a relationship between exercising HR and performance improvements in a group of sub-elite adult players with an increased HRV after an in-season training camp in the heat.

Bricout et al. (2012) found that an increase of physical and psychological constraints that a soccer match represents, the LF/HF ratio rises significantly; reflecting an increased sympathetic stimulation, which beyond certain limits could be relevant to prevent the emergence of a state of fatigue.

They concluded that HRV analysis allowed to highlight any autonomic adjustments according to the physical loads

Literature: Correlations with sRPE & Psychometric Measures

A strong relationship between HRV and training load at 2 days prior to a game, indicating that early monitoring may assist in identifying training workloads throughout a week.

Edmonds et al. (2012)

Another method of quantifying the adaptive responses to training is to use subjective levels of fatigue, often through the use of a wellness questionnaire.

Gastin et al. (2013) used a nine-item wellness questionnaire and concluded that self-reported player ratings of wellness provide a useful tool for coaches and practitioners to monitor player responses to the rigorous demands of training, competition and life as an elite athlete

Smith and Hopkins (2011) monitored performance, HRV and subjective fatigue in elite rowers throughout an intense 4-week training period. Interestingly, the most improved athlete and the only overtrained athlete both had statistically similar levels of perceived fatigue and changes in LF/HF ratio. However, after looking closely at the data, RMSSD showed a noticeable decline in the overtrained athlete compared to the most improved, who had a moderate increase in RMSSD.

Rationale

There appears to be a strong tendency for HRV to reflect perceived training load and fatigue/ wellness measures.

However, current literature suggests an objective measure of ANS status, such as HRV, should still be considered, as subjective measures from athletes are only meaningful if reported honestly.

Previous literature has looked into correlating HRV with previous days sRPE, however to date there are no studies quantifying player load with objective measures such as GPS or accelerometery.

In addition to this, there are no studies looking into these measures and subsequent correlations with sRPE and subjective fatigue in soccer.

The purpose of this study was therefore to:1. Document the daily variations of selected physiological and psychometric variables

during an intense pre-season training period in professional youth soccer players,2. Examine their usefulness for monitoring training responses (i.e. HRV, fatigue

status/Wellness and fitness).

Methodology: Experimental Approach to the Problem

Observational study design

Elite youth soccer players will be followed for 6 weeks

Dependent Variables • HRV• sRPE• Wellness• Battery of performance tests

Independent Variable• Training load (GPS and Accelerometry)

Methodology: Subjects

8-10 professional youth soccer players will participate in this study.

Data will be collected during a pre season training period in July and August.

Prior to inclusion into the study, players will be examined by a club physiotherapist in order to be deemed free from illness and injury.

Group characteristics, age, height, weight and body composition will be collected at beginning and end of the study.

All players will provide written informed consent.

Study will gain ethical approval from Teesside University before it commences.

Methodology: Training Sessions

All players will take part in normal team training as prescribed by the coaches and strength and conditioning staff. Training content will not altered for the purposes of this study.

Training Days:Monday, Tuesday, Thursday, Friday and Saturday

Rest Days:Wednesday and Sunday

Session Content:• Specific Skills Training• Gym Training• On-field Conditioning

Methodology: HRV Collection

All players will be provided with a beat to beat HR monitor (Polar RS400) and familiarized with the use of this equipment and thoroughly instructed on how to conduct this test.

The HRV tests will take place every morning, after the players have emptied their bladders (Kiviniemi et al, 2007).

Players will wear the HR monitor in a sitting position in a quite room without any distractions, for a 6 minute period.

All collection will be supervised by the lead researchers and strength and conditioning staff on training days and athletes will be trained in order to conduct the test at home on rest days.

Methodology: HRV Analysis

After each test the Polar Pro-trainer software will allow for extraction of RR intervals.

Data will be processed using Kubios HRV software (version 2.0, Department of Physics, University of Kuopio, Finland). Of the 6 minutes recorded the first and last 30 seconds were discarded (Sartor et al, 2013)

Time domain analysis:1. Mean heart rate (beats per minute: bpm);2. Mean intervals RR;3. The standard deviation of the normal-to-normal interval (SDNN); 4. The square root of the mean squared differences of the successive N–N interval (RMSSD);

Spectral analysis (or frequency domain): with the Fast Fourier transform, the power spectrum indices will be calculated as recommended by the Task Force of European Society of Cardiology (Task-Force, 1996)5. Very low frequency (VLF, <0.04Hz);6. Low frequency (LF, 0.04Hz–0.15Hz);7. High frequency (HF, 0.15–0.40 Hz).8. LF/HF Ratio (increase can indicate overtraining)

Methodology: Performance Tests

Performance tests will be conducted in the first and last week of the pre season period.

Tests:• Squat Jump• Counter-Movement Jump• 30m Sprint (10m acceleration & 20-30m maximum velocity)• Arrow Head Agility Left & Right• Yo-Yo Intermittent Recovery Test Level 2• Upper Body Strength Endurance• Movement Screen

Methodology: Training Load, sRPE and Psychometric Measures

Training Load:Global Positioning System (GPS):

PlayerLoad (derived from GPS triaxial accelerometer)

Session RPE Load (Impellizzeri et al, 2004): Total training session duration (min) × session RPE (rate of perceived exertion using a 0 to 10 Borg

scale).

Psychometric Measures:Questionnaire comprised of 5 questions scored on a five-point scale (1 to 5)• Perceived fatigue,• Sleep quality,• General muscle soreness,• Stress levels,• Mood.

• Total Distance Covered (TD)• Distance covered in each of the established speed zones:

• Stationary/walking (0–6.9 km/h),• Jogging (7.0–12.9 km/h), • Running (13.0– 17.9 km/h), • High-intensity running (18.0–20.9 km/h), and • Sprinting (>21 km/h).

Wellness Questionnaire (McLean et al, 2010)

Wellness questionnaire developed by McLean et al (2010) on the recommendations of Hooper & Mackinnon (1995).

Statistical Analysis

Data will be presented as means (±SD) and correlations as means (90% confidence limits, CL)

An ANOVA for repeated measures will be used to assess the time-course of the changes in Training Load, HRV, sRPE and Wellness throughout the training period.

Pearson’s correlation analysis will be used to assess the associations between within-player daily changes in HRV and previous days Training Load, sRPE and Wellness.

The following criteria will be adopted to interpret the magnitude of the correlation (r) between the different measures: ≤0.1, trivial; >0.1–0.3, small; >0.3–0.5, moderate; >0.5–0.7, large; >0.7–0.9, very large; and >0.9–1.0, almost perfect.

(Hopkins et al, 2009)

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Borresen J, Lambert MI (2008) Autonomic control of heart rate during and after exercise: measurements and implications for monitoring training status. Sports Medicine 38:633–646

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Buchheit M, Papelier Y, Laursen PB, Ahmaidi S (2007) Noninvasive assessment of cardiac parasympathetic function: post-exercise heart rate recovery or heart rate variability? American Journal of Physiology: Heart and Circulatory Physiology 293:H8–H10

Buchheit M, Mendez-Villanueva A, Quod MJ, Poulos N, Bourdon P. (2010) Determinants of the variability of heart rate measures during a competitive period in young soccer players. European Journal of Applied Physiology. 109:869-878. Buchheit M, Voss SC, Nybo L, Mohr M, Racinais S. (2011) Physiological and performance adaptations to an in-season soccer camp in the heat: associations with heart rate and heart rate variability. Scandinavian Journal of Medicine & Science in Sports; 21(6):e477-e485. Buchheit M, Simpson MB, Al Haddad H, Bourdon PC, Mendez-Villanueva A. (2012) Monitoring changes in physical performance with heart rate measures in young soccer players. European Journal of Applied Physiology;112:711-723.

Chapleau MW and Sabharwal R. Methods of assessing vagus nerve activity and reflexes (2011). Heart Failure Review 16: 109-127

Edmonds, RC., Sinclair, WH., and Leicht, AS. (2012) The effect of weekly training and a game on heart rate variability in elite youth Rugby League players. Proceedings of the 5th Exercise & Sports Science Australia Conference and 7th Sports Dietitians Australia Update. Research to Practice

Gastin, Paul B.; Meyer, Denny; Robinson, Dean (2013) Perceptions of wellness to monitor adaptive responses to training and competition in elite Australian football. Journal of Strength & Conditioning Research, Published ahead of print.

Hooper SL, Mackinnon LT. Monitoring overtraining in athletes. Recommendations. Sports Med 1995; 20(5):321–327.

Hopkins WG,Marshall SW, Batterham A Metal. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc 2009; 41(1):3–13.

Impellizzeri, F. M., E. Rampinini, A. J. Coutts, A. Sassi, and S. M. Marcora. (2004) Use of RPE-Based Training Load in Soccer. Medicine & Science in Sports & Exercise., Vol. 36, No. 6, pp. 1042–1047.

Kuipers H, Keizer HA (1988) Overtraining in elite athletes. Review and directions for the future. Sports Medicine 6:79–92

McLean BD, Coutts AJ, Kelly V et al. Neuromuscular, endocrine, and perceptual fatigue responses during different length between-match microcycles in professional rugby league players. Int J Sports Physiol Perform 2010; 5(3):367–383.

Pichot V, Roche F, Gaspoz JM, Enjolras F, Antoniadis A, Minini P, Costes F, Busso T, Lacour JR, and Barthelemy JC (2000). Relation between heart rate variability and training load in middle-distance runners. Medicine & Science in Sports & Exercise 32: 1729-1736

Portier H, Louisy F, Laude D, Berthelot M, and Guezennec CY (2001). Intense endurance training on heart rate and blood pressure variability in runners Medicine & Science in Sports & Exercise 33: 1120-1125

Pumprla J, Howorka K, Groves D, et al. Functional assessment of heart rate variability: physiological basis and practical applications. International Journal of Cardiology 2002; 84: 1-14

Task-Force, 1996. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Eur. Heart J. 17, 354–381.

Sartor, F. et al. (2013) Heart rate variability reflects training load and psychophysiological status in young elite gymnasts. Journal of Strength & Conditioning Research, Published ahead of print.

Saul JP. Beat-to-Beat Variations of Heart Rate Reflect Modulation of Cardiac Autonomic Outflow (1990) American Journal of Physiology 5: 32-37

Smith, T.B., & Hopkins, WG. (2011) Heart rate variability and psychological stress in an elite female rower who developed over-training syndrome. New Zealand Journal of Sports Medicine, 38(1): 18-20.

Reference ListChapleau MW and Sabharwal R. Methods of assessing vagus nerve activity and reflexes (2011). Heart Failure Review 16: 109-127

Edmonds, RC., Sinclair, WH., and Leicht, AS. (2012) The effect of weekly training and a game on heart rate variability in elite youth Rugby League players. Proceedings of the 5th Exercise & Sports Science Australia Conference and 7th Sports Dietitians Australia Update. Research to Practice

Gastin, Paul B.; Meyer, Denny; Robinson, Dean (2013) Perceptions of wellness to monitor adaptive responses to training and competition in elite Australian football. Journal of Strength & Conditioning Research, Published ahead of print.

Hooper SL, Mackinnon LT. Monitoring overtraining in athletes. Recommendations. Sports Med 1995; 20(5):321–327.

Hopkins WG,Marshall SW, Batterham A Metal. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc 2009; 41(1):3–13.

Impellizzeri, F. M., E. Rampinini, A. J. Coutts, A. Sassi, and S. M. Marcora. (2004) Use of RPE-Based Training Load in Soccer. Medicine & Science in Sports & Exercise., Vol. 36, No. 6, pp. 1042–1047.

Kuipers H, Keizer HA (1988) Overtraining in elite athletes. Review and directions for the future. Sports Medicine 6:79–92

McLean BD, Coutts AJ, Kelly V et al. Neuromuscular, endocrine, and perceptual fatigue responses during different length between-match microcycles in professional rugby league players. Int J Sports Physiol Perform 2010; 5(3):367–383.

Pichot V, Roche F, Gaspoz JM, Enjolras F, Antoniadis A, Minini P, Costes F, Busso T, Lacour JR, and Barthelemy JC (2000). Relation between heart rate variability and training load in middle-distance runners. Medicine & Science in Sports & Exercise 32: 1729-1736

Portier H, Louisy F, Laude D, Berthelot M, and Guezennec CY (2001). Intense endurance training on heart rate and blood pressure variability in runners Medicine & Science in Sports & Exercise 33: 1120-1125

Pumprla J, Howorka K, Groves D, et al. Functional assessment of heart rate variability: physiological basis and practical applications. International Journal of Cardiology 2002; 84: 1-14

Task-Force, 1996. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Eur. Heart J. 17, 354–381.

Sartor, F. et al. (2013) Heart rate variability reflects training load and psychophysiological status in young elite gymnasts. Journal of Strength & Conditioning Research, Published ahead of print.

Saul JP. Beat-to-Beat Variations of Heart Rate Reflect Modulation of Cardiac Autonomic Outflow (1990) American Journal of Physiology 5: 32-37

Smith, T.B., & Hopkins, WG. (2011) Heart rate variability and psychological stress in an elite female rower who developed over-training syndrome. New Zealand Journal of Sports Medicine, 38(1): 18-20.

Reference ListPumprla J, Howorka K, Groves D, et al. Functional assessment of heart rate variability: physiological basis and practical applications. International Journal of Cardiology 2002; 84: 1-14

Task-Force, 1996. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Eur. Heart J. 17, 354–381.

Sartor, F. et al. (2013) Heart rate variability reflects training load and psychophysiological status in young elite gymnasts. Journal of Strength & Conditioning Research, Published ahead of print.

Saul JP. Beat-to-Beat Variations of Heart Rate Reflect Modulation of Cardiac Autonomic Outflow (1990) American Journal of Physiology 5: 32-37

Smith, T.B., & Hopkins, WG. (2011) Heart rate variability and psychological stress in an elite female rower who developed over-training syndrome. New Zealand Journal of Sports Medicine, 38(1): 18-20.