1. Heart Rate Variability in Elite Youth
Soccer Players: Correlations with
Training Load, sRPE and Psychometric
Measures
John Fitzpatrick
MSc Strength and Conditioning Student
Academy Sport Science Intern Newcastle United FC Supervisor: Dr Matthew Weston

2. 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)

3. 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)

4. 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

5. 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.

6. Literature: Team Sports
There 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

7. 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.

8. 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).

9. 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)

10. 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.

11. 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

12. 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.

13. 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)
1. Very low frequency (VLF, <0.04Hz);
2. Low frequency (LF, 0.04Hz–0.15Hz);
3. High frequency (HF, 0.15–0.40 Hz).
4. LF/HF Ratio (increase can indicate overtraining)

14. 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

15. 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).

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

17. Statistical Analysis
Data will be presented as means (±SD) and correlations as means (90%
confidence limits, CL)
AnANOVA 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 inHRV 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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