HRV responses to in-season training among D-1 college football players

During spring training camp, we found that Linemen demonstrate the greatest reductions in LnRMSSD at ~20 h post-training, followed by Mid-Skill and Skill, possibly reflecting inadequate cardiovascular recovery between consecutive-day sessions for the larger players, despite lower PlayerLoad values. (Full-text available here)

Our first follow-up study during the early  part of the competitive season found the same position-based trend, where Linemen demonstrated the greatest reductions in LnRMSSD at ~20 h post-training, followed by Mid-Skill and Skill. However, the magnitude of the reductions in LnRMSSD during the in-season were smaller relative to spring camp. We speculate that both reduced PlayerLoad values (15-22% lower than spring camp) and adaptation to intense preseason training in the heat and humidity during the preceding weeks account for the smaller LnRMSSD reductions observed during the early part of the competitive season. (Full-text available here)

Cardiac-Autonomic Responses to In-Season Training Among Division-1 College Football Players.

Despite having to endure a rigorous in-season training schedule, research evaluating daily physiological recovery status markers among American football players is limited. The purpose of this study was to determine if recovery of cardiac-autonomic activity to resting values occurs between consecutive-day, in-season training sessions among college football players. Subjects (n = 29) were divided into groups based on position: receivers and defensive backs (SKILL, n = 10); running backs, linebackers and tight-ends (MID-SKILL, n = 11) and linemen (LINEMEN, n = 8). Resting heart rate (RHR) and the natural logarithm of the root-mean square of successive differences multiplied by twenty (LnRMSSD) were acquired at rest in the seated position prior to Tuesday and Wednesday training sessions and repeated over three weeks during the first month of the competitive season. A position × time interaction was observed for LnRMSSD (p = 0.04), but not for RHR (p = 0.33). No differences in LnRMSSD between days was observed for SKILL (Tuesday = 82.8 ± 9.3, Wednesday = 81.9 ± 8.7, p > 0.05). Small reductions in LnRMSSD were observed for MID-SKILL (Tuesday = 79.2 ± 9.4, Wednesday = 76.2 ± 9.5, p < 0.05) and LINEMEN (Tuesday = 79.4 ± 10.5, Wednesday = 74.5 ± 11.5, p < 0.05). The individually averaged changes in LnRMSSD from Tuesday to Wednesday were related to PlayerLoad (r = 0.46, p = 0.02) and body mass (r = -0.39, p = 0.04). Cardiac-parasympathetic activity did not return to resting values for LINEMEN or MID-SKILL prior to the next training session. Larger reductions in LnRMSSD tended to occur in players with greater body mass despite having performed lower workloads, though some individual variability was observed. These findings may have implications for how coaches and support staff address training and recovery interventions for players demonstrating inadequate cardiovascular recovery between sessions.

Figure 1

 

Our next paper, currently in production, will feature HRV responses among positions throughout the entire preparatory and competitive season.

New Study: Monitoring weekly HRV in futsal players during the preseason

Here’s a quick look at our latest collaboration with Dr. Fabio Nakamura and colleagues, published in J Sport Sci: Sci Med Football. This paper nicely demonstrates the inter-individual variation in HRV responses to training in team sports. An interesting finding was the large negative relationship between the weekly mean of lnRMSSD and the weekly CV of lnRMSSD. Essentially, the athletes with higher HRV tended to show smaller daily fluctuations in HRV and vice versa. This is likely an effect of higher fitness, which we (and others) have touched on in previous studies.
ABSTRACT

This study aimed to compare the weekly natural log of the root-mean-square difference of successive normal inter-beat (RR) intervals (ln RMSSDWeekly) and its coefficient of variation (ln RMSSDCV) in response to 5 weeks of preseason training in professional male futsal players. A secondary aim was to assess the relationship between ln RMSSDWeekly and ln RMSSDCV. The ln RMSSD is a measure of cardiac–vagal activity, and ln RMSSDCV represents the perturbations of cardiac autonomic homeostasis, which may be useful for assessing how athletes are coping with training. Ten futsal players had their resting ln RMSSD recorded prior to the first daily training session on four out of approximately five regular training days·week−1. Session rating of perceived exertion (sRPE) was quantified for all training sessions. Despite weekly sRPE varying between 3455 ± 300 and 5243 ± 463 arbitrary units (a.u.), the group changes in ln RMSSDWeekly were rated as unclear (using magnitude-based inference), although large inter-individual variability in ln RMSSD responses was observed. The ln RMSSDCV in weeks 4 and 5 were likely lower than the previous weeks. A large and significant negative correlation (r = −0.53; CI 90%: −0.36; −0.67) was found between ln RMSSD and ln RMSSDCV. Therefore, monitoring individual ln RMSSD responses is suggested since large inter-individual variations may exist in response to futsal training. In addition, higher values of ln RMSSD are associated with lower oscillations of cardiac autonomic activity.

HRV futsal Fig 1

Full Text on Research Gate

Early HRV changes relate to the prospective change in VO2max in female soccer players

It’s been a good start to the Thanksgiving break with the  acceptance of our latest study entitled “Initial weekly HRV response is related to the prospective change in VO2max in female soccer players” in IJSM (Abstract below).

We’re currently working on supporting these findings with a much larger sample size in the new year.

ABSTRACT

The aim of this study was to determine if the early response in weekly measures of HRV, when derived from a smart-phone application, were related to the eventual change in VO2max following an off-season training program in female soccer athletes. Nine female collegiate soccer players participated in an 11-week off-season conditioning program. In the week immediately before and after the training program, each participant performed a test on a treadmill to determine maximal oxygen consumption (VO2max). Daily measures of the log-transformed root mean square of successive R-R intervals (lnRMSSD) were performed by the participants throughout week 1 and week 3 of the conditioning program. The mean and coefficient of variation (CV) lnRMSSD values of week 1 showed small (r = -0.13, p= 0.74) and moderate (r = 0.57, p = 0.11), respectively, non-significant correlations to the change in VO2max at the end of the conditioning program (∆VO2max). A significant and near-perfect correlation was found between the change in the weekly mean lnRMSSD values from weeks 1 and 3 (∆lnRMSSDM) and ∆VO2max (r = 0.90, p = 0.002). The current results have identified that the initial change in weekly mean lnRMSSD from weeks 1 to 3 of a conditioning protocol was strongly associated with the eventual adaptation of VO2max.

 

Some Soccer Team HRV Data

One observation I’ve made from monitoring my own HRV is that I will typically see major acute decreases in my trend following new training stimuli. However, after a few weeks of consistent training with the new program, I will see much smaller fluctuation in response to workouts despite high RPE. Essentially, with familiarity of the training stimulus, the body may experience less of an “alarm” stage. This enables higher training frequencies and volumes with less soreness and so forth.

Below is a small sample of some team data I’ve collected in a collegiate soccer team I worked with this past year. What your viewing is the first 3 days (Mon-Tues-Wed) of a new training cycle (Figure 1) and then the same training cycle performed a few weeks later with typical incremental progressions in resistance (for strength training) and distance (for conditioning) (Figure 2). On Monday’s we lifted in the morning and practiced and conditioned in the afternoon. Tuesday’s were off entirely. Therefore Monday’s HRV scores follow a weekend of rest representing “baseline”, Tuesday scores reflect Monday’s workload, and Wednesday marks 48 hours post workout (training resumed Wednesday afternoon).

 

Figure 1.

Figure 1.

Figure 2.

Figure 2.

In the first week of the new training program and structure (figure 1), 9 of 11 players showed a decrease in HRV following Monday’s workout (some more than others). A few weeks later, only 5 of 11 players showed an acute decrease.

Further discussion and analysis with much more data (complete weeks, periods of overload and deload, sRPE, psychometrics, etc.) and from measures obtained in supine and standing positions will be left for the manuscript.

HRV Monitoring in a Team Setting: The Research

Though my original interest in HRV monitoring was for personal usage with my powerlifting training (and still is), I have become much more interested in its application with my athletes. In July, I wrote a post discussing some of the research and my thoughts on HRV in a team setting. I’ve come across some more great research that pertains to HRV monitoring in team settings and would like to share some thoughts on the topic.

Below is a  list of questions I’d like to address:

  • How effective is HRV monitoring in a team setting really?
  • What difference is there, if any, when monitoring HRV in elite vs. sub-elite athletes?
  • How practical is HRV monitoring in a team setting?
  • Can we create favorable autonomic profiles in athletes prior to intensive training blocks to improve global (all players) responsiveness to training? (to avoid injury, overtraining, etc)
  • How can we apply research that used frequency domain measures (HF, LF, HF/LF) with mobile apps/devices like ithlete and Bioforce that use RMSSD, a time domain measure of parasympathetic tone?

Keep in mind that I do not train elite athletes and therefore much of what I discuss is based on my interpretations of the research, discussions I’ve had with others and some theory. I certainly am not capable of providing answers to any of the above question.

First, I’d like to present brief summaries of the research I’ve read on the topic. I’ve only included studies that used HRV to monitor fatigue, training load, etc. At this time I’m not including studies using HRV during exercise, or post-exercise.

In some cases I could not get access to the full-text which you will see noted in the respective tables. Please enlighten me of any research on this topic I may have not included. I apologize for the poor presentation of the table’s below. I originally had all of this in a more reader friendly format in Word but for some reason it does not transfer over to wordpress very well.

Author Ke-Tien (2012)
Sport Male, National Level Rugby (n=24)
Aim To verify biological and psychological stress markers during strenuous cardiovascular endurance training periodization, using Profile of Mood States questionnaires, HRV & blood urine nitrogen as the criteria measurements.
Main Findings HRV correlated to profile of mood states survey and blood-urnine nitrogen in elite male national rugby players (n=24).
HRV Analysis Non-daily, Frequency Domain
Author Edmonds et al. (2012)
Sport Male, Elite Youth Rugby (n=9)
Aim To investigate the influence of weekly training & a competitive game on HRV in elite youth rugby league players, & to identify the importance of HRV as a monitoring tool for Rugby League player preparation.
Main Findings Prior to a match, elite youth, players exhibited a significant reduction in HRV that was sustained for at least 24 hours post-game. This withdrawal of parasympathetic &/or increased sympathetic control of HR possibly may result from pre-match anxiety as well as the physical demands of the game. Strong relationships between HRV and training load at Pre-2 indicate that early monitoring may assist in identifying training workloads for the upcoming week.
HRV Analysis Daily, Time & Frequency Domain
Author Oliveira et al. (2012a)  – Abstract Only
Sport Male, Elite Futsal (n=11)
Aim The aim of this study was to determine the changes in physical performance and resting heart rate variability (HRV) in professional futsal players during the pre-season and in-season training periods.
Main Findings Players improved their RSA & Yo-Yo IR1 performance with concomitant improvements in HRV. These indices were maintained during the in-season period while RSAbest was improved & RSAdecrement impaired. Frequent monitoring of these performances and HRV indices may assist with identification of individual training adaptations and/or early signs of maladaption.
HRV Analysis Non-daily, Unknown
Author Vantinnen et al. (2007)
Sport Male, Elite Soccer (n=24)
Aim To introduce a method commonly used in Finnish sport to monitor the exercise intensity & changes in recovery state of players in team sports by examining their heart rate (HR/HRV) responses to training & relaxation stimulus.
Main Findings Individual differences do exist in practices & games. This would imply that coaches need to quantify each game or practice exercise intensity & recovery for each individual, in order to organize & optimally prepare an individual training plan for each athlete.
HRV Analysis Various over 3 weeks (daily, nocturnal, 24 hr), Time and Frequency Domain
Author Oliveira et al. (2012b) – Abstract Only
Sport Male, Caliber Unknown, Soccer (n=10
Aim The aim of this study was to analyze whether the heart rate variability (HRV), assessed at the beginning of a soccer preseason, reveals a correlation with the improvement of physical performance over this training period.
Main Findings There were significant improvements in Yo-Yo IR1 performance & in the 30-m sprint time. The qualitative analysis revealed that the differences in Yo-Yo IR1 performance were very likely positive, were almost certainly positive for the sprint, but were inconclusive for the vertical jump. There was a strong correlation between one parasympathetic index and the change in performance. The study showed a strong correlation between parasympathetic indices of HRV with the performance improvement in Yo-Yo IR1 in the athletes during pre-season.
HRV Analysis Non-daily, Unknown
Author Rodas, G. et al. (2011) – Abstract Only
Sport Elite, Field Hockey (n=? entire team)
Aim To determine the changes in HRV during the 2006 World Cup
Main Findings HRV decreases progressively & the values of the parameters related to parasympathetic system activity (RMSSD & HF) reduce, which are indicative of good psychic-physical adaptability to the workload. At the same time, the value of the parameters related to sympathetic system activity (LF and LF/HF) increases, suggesting an increase in fatigue, tiredness and poor adaptability in general. Consequently, the analysis of HRV may be a good marker for monitoring the psychic-physical state, cardiovascular adaptability during exercise & a possible state of physical overload in athletes participating in competitions.
HRV Analysis Day of competitions only – Time and Frequency Domain
Author Martin-Sanchez et al. (2011)
Sport Male Pro Soccer (n=12) & Age/Sex matched Amateur Soccer (n=9)
Aim To determine if an intensive preseason training program modifies the inflammatory status in professional soccer players and if this inflammatory profile may be associated with the physical state.
Main Findings A negative association between cardiac low frequency & the plasma content of alpha-1 antichymotrypsin isotype 4, & a positive association between cardiac low frequency & fibrinogen gamma-chain isotype 3 was found. Our results suggest that the cardiac functional state of soccer players may be correlated with these proteins. Pro soccer players showed a decreased content of circulating proteins associated with inflammation compared with those in recreational soccer players.
HRV Analysis Morning of analysis – Time and Frequency Domain
Author Cipryan et al. (2010)
Sport Male, Hockey Junior Level (n=8), Adult (N=10)
Aim To present inter-individual differences in the reaction of autonomic nervous system (ANS) activity to the same training program, and to thereby support the importance of individual training in team sports during the conditioning period.
Main Findings The SA HRV monitoring mostly revealed significant differences in the level of the ANS activity among the players. A number of junior & adult players were characterized by almost permanently high ANS activity whereas other players occurred below the ANS activity level of healthy individuals.  The training efficiency (overreaching and injury reduction) can be positively influenced by creating training groups of players with similar ANS activity.
HRV Analysis Non-daily – Frequency Domain
Author Cipryan et al. (2007)
Sport Male, U-18 National Level Hockey (n=4)
Aim To investigate the influence of regular sport training on the activity of the autonomicnervous system (ANS) and to disclose patterns of interrelations between them.
Main Findings The results demonstrated that the player with the highest average TS (total score)& the highest average PT(total power) also showed the most consistent results & objectively the best performance in sport. On the other hand, the player with the lowest average TS and the lowest average PT also obtained the lowest average mark in the coach’s evaluation of his sports performance. The tendency to progression of the ANS  activity was different for each subject. The self-reports health status survey, which was given before measurements were taken, did not correspond with the results of the SA HRV measurement.
HRV Analysis Non-daily, Frequency Domain
Author Hap et al. (2010)
Sport Male, High Level Volleyball (n=8)
Aim The goal of the work was to verify the possibility of volleyball playersʼ training load optimization during a one week training microcycle based on the longitudinal observation of dynamics of SA HRV complex indices.
Main Findings 2 Players had above average levels ANS activity indicating higher training loads could be tolerated.4 Players had low ANS activity (but not below average) showing evidence of some fatigue and adaptation. Training loads are appropriate.

2 Players had below average ANS activity and their training adaptability was reduced.

HRV Analysis Daily – Frequency Domain
Author Parrado et al. (2010) – Abstract Only
Sport Elite, Field Hockey (n=? entire team)
Aim The aim of the study was to examine the utility of perceived tiredness to predict cardiac autonomic response to overload among feld hockey players during the 2006 World Cup.
Main Findings Results showed a negative correlation between perceived tiredness scores & time domain indexes, & a positive correlation of perceived tiredness scores and the high frequency component ratio (LF/HF ratio) of heart rate variability. Anxiety did not influence the precompetitive cardiac response despite somatic anxiety’s correlation with sympathetic response (LF/HF ratio) & tiredness scores. Perceived tiredness predicted the autonomic cardiac response to competitive overload. Thus, the perceived tiredness assessment would be a good early marker of fatigue & overload states during competition
HRV Analysis Day of analysis, Frequency Domain
Author Mazon et al. (2011)
Sport Male, Volleyball (n=32)
Aim To investigate the effects of selective loads of periodization model (SLPM) on autonomic modulation of HRV and endogenous stress markers before and after a competition period in volleyball players.
Main Findings SLPM did not change the cardiac autonomic modulation of HRV, but promoted beneficial adaptations in athletes, including positive changes in the plasma concentration of the endogenous stress markers. The absence of changes in HRV indicates that there is no direct relationship between cardiac autonomic modulation & endogenous stress markers in the present study.
HRV Analysis Pre & Post Training Cycle, Frequency Domain
Author Di Fronso et al. (2012)  – Abstract On
Sport Male, Amateur Basketball (n=7)
Aim To investigate the relationship between Heart Rate Variability (HRV) and performance in players of a basketball team during playoffs.
Main Findings Findings of this study suggest that vagal activity, expressed by HF index of HRV, can be positively related to the athletes’ performance. In particular, higher values of HF index during the morning of the match were associated with higher levels of athletes’ performance during the game.
HRV Analysis Morning of Competitions – Frequency Domain
Author Dranitsin (2008)
Sport Elite Male (n=12) and Female (n=1) Rowers
Aim The aim of this study was to examine the simultaneous effect on HRV of acclimatization to a hot, humid environment and a transition of five time zones in elite junior rowers.
Main Findings Major physiological adaptation of HRV indices in the standing position during acclimatization to a humid, hot environment, with a transition across five time zones, occurs within the first 5 days in elite athletes before returning to baseline. Indices of heart rate variability in the supine position correlate with the length of high-intensity training sessions on the previous day.
HRV Analysis Daily, Time Domain
Author Iellamo et al. (2004)
Sport Elite Male Rowers (n=8)
Aim To test the hypothesis that training-induced variations in T-wave amplitude at higher training loads are paralleled by changes in HR spectral profile.
Main Findings From 50% to 100% of training load, there was a significant decrease in HRV and increase in sympathetic tone. As training reduced to 50% during the World Championships, HRV returned to base line and a return of autonomic indices to previous levels was seen. 
HRV Analysis Non-Daily – Frequency Domain

I’ll discuss my thoughts on the questions I listed above in my next post.

Please share any studies pertaining to HRV usage in a team setting that I may have missed in the comments below or e-mail me andrew_flatt@hotmail.com

I joined twitter recently too @andrew_flatt

References:

Cipryan, L. & Stejskal, P. (2010) Individual training in team sports based on ANS activity assessments. Medicina Sportiva, 14(2):  56-62 Free Full-Text

Cipryan, L., Stejskal, P., Bartakova, O., Botek, M., Cipryanova, H., Jakubec, A., Petr, M., & Řehova, I. (2007)  Autonomic nervous system observation through the use of spectral analysis of heart rate variability in ice hockey players.  Acta Universitatis Palackianae Olomucensis. Gymnica, 37(4): 17-21. Free Full-Text

Di Fronso, S. et al. (2012) Relationship between performance and heart rate variability in amateur basketball players during playoffs. Journal for Sports Sciences & Health, 8 (Suppl 1):S1–S70 45

Dranitsin, O. (2008) The effect on heart rate variability of acclimatization to a humid, hot environment after a transition across five time zones in elite junior rowers. European Journal of Sport Science, 8(5): 251-258 Abstract

Edmonds, RC., Sinclair, WH., and Leicht, AS. (2012) Theeffect 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. 5th Exercise & Sports Science Australia Conference and 7th Sports Dietitians Australia Update Research to Practice , 19-21 April 2012, Gold Coast, QLD, Australia , p. 183. Abstract

Hap, P., Stejskal, P. & Jakubec, A. (2010) Volleyball players training intensity monitoring through the use of spectral analysis of HRV during a training microcycle. Acta Universitatis Palackianae Olomucensis. Gymnica, 41(3): 33-38 Free Full-Text

Iellamo, F., Pigozzi, F., Spataro, A., Lucini, D., & Pagani, M. (2004) T-wave and heart rate variability changes to assess training in world class athletes. Medicine & Science in Sports and Exercise, 36(8): 1342-1346. Abstract

Ke-Tien, Y.(2012) Effects of Cardiovascular Endurance Training Periodization on Aerobic performance and Stress Modulation in Rugby Athletes. Life Science Journal, 9(2): 1218-25. Full-Text

Martin-Sanchez, F. (2011) Functional status and inflammation after preseason training program in professional and recreational soccer players: a proteomic approach. Journal of Sports Science & Medicine, 10: 45-51 Free Full-Text

Mazon, J. et al. (2011) Effects of training periodization on cardiac autonomic modulation and endogenous stress markers in volleyball players. Scandinavian Journal of Medicine & Science in Sports, doi: 10.1111/j.1600-0838.2011.01357.x Free Full-Text

Oliveira, RS. et al. (2012a) Seasonal changes in physical performance and HRV in high level futsal players. International Journal of Sports Medicine. DOI: 10.1055/s-0032-1323720 Abstract

Oliveira, RS. et al. (2012b) The correlation between heart rate variability and improvement in soccer player’s physical performance. Brazilian Journal of Kinanthropometry, 14(6) Abstract

Parrado, E.  et al. (2010)Percieved tiredness and HRV in relation to overload during a field hockey world cup. Perceptual and Motor Skills, 110(3): 699-713 Abstract

Rodas, G. et al. (2011) Changes in HRV in field hockey players during the 2006 World Cup. Apunts Medicina de l’Esport, (46): 117-123 Abstract

Vantinnen, T. et al. (2007) Practical experiences from measuring exercise intensity and recovery state with HR monitoring in team sport. Symposium Proceedings 6th IACSS Calgary, Alberta. Full-Text

 

HRV in a Team Setting

Monitoring athletes throughout training provides coaches with extremely valuable information regarding each athlete’s responsiveness to imposed training loads. Most would agree that the main objective for any coach (at competitive levels) is to win. If you fail to do this you will likely be fired.

I think we can also agree that bringing our athletes to peak physical condition (as it applies to their sport) will increase our chances of winning. To do this effectively, physical preparation in both team practice and S&C must be balanced. The right balance of training loads will yield optimal adaptation.

Adaptation is Key

Training (technical and physical) is a stressor our athletes must recover from. If the stress is too great, adaptation will be compromised. If the stress is insufficient, improvements will not take place. Therefore, the training stimulus must be within our athlete’s ability to adapt, allowing for performance improvements. This is pretty well understood by most coaches. However, the ability to balance loads effectively is much less understood. Too often coaches rely on pre-planned training regime’s that fail to take into account each athletes individual adaptive capacity. It is the coach’s responsibility to critically evaluate several issues that arise throughout the year such as;

  • Why did an athlete get hurt?
  • Why did an athlete fall ill?
  • Why is the team seeing decrements in performance?
  • Why are we not performing to our abilities throughout the entire match?
  • Why are certain athletes improving while others are regressing?

I’m sure you can think of more questions to consider.

Monitoring HRV in Sports Teams

Hap, Stejskal & Jakubec (2010) set out to monitor the HRV of 8 competitive male volley ball players (approximately 18-25 years old) over a 7 day microcycle during training camp. The 7 day camp had the athletes partake in 11-13 volleyball practices and 14-16 conditioning sessions. The training was entirely pre-planned and HRV scores were not shared with players or coaches. HRV was measured once each day for a total of 7 times (6 measurements were performed in the morning immediately after waking and 1 measurement was performed under controlled conditions in the afternoon).

The results showed 2 athletes demonstrated above average ANS activity (high HRV) throughout the entire week. In these athletes, the load was below training capacity and higher training levels could have been tolerated to further increase performance. In 4 athletes, HRV scores decreased to the lower end of average. This indicates a moderate level of fatigue and that training load corresponded to their training capacities. In the last 2 athletes, HRV scores were negative (below average). Training stress was too high in these individuals and reduced loads and recovery/regeneration modalities would’ve increased the quality of their training.

In this instance, the pre-planned training program was appropriate for 50% of the team. 25% were overtrained and 25% were undertrained.

In another study, Cipryan & Stejskal (2010) decided to monitor the HRV of competitive hockey players. There were 18 subjects, 8 were junior level players (18 years old) and 10 were from the adult team (mid-20’s). Both teams underwent their own training and practice programs. HRV was measured twice per week in the morning (Mon and Fri) throughout the 2 month training program.

The results show that from the junior team, 2 players showed above average adaptation capacity. 1 player showed decreased HRV scores indicating high fatigue. Training was appropriate for 5/8 players. In the adult men’s team, 3 players showed higher HRV suggesting that more (volume or intensity) training would’ve been tolerated. 1 player showed decreased HRV. This player could not see an increase in HRV back to baseline levels because the training did not conform to his adaptive abilities. This player was at risk of more frequent health complications. This training program was appropriate for 60% of the team. 30% was undertrained and 10% was overtrained.

In the discussion, the authors proposed that athletes be separated into groups during training with 3 separate programs available. One program for athletes with low HRV (decreased loads) one program from athletes responding appropriately (moderate loads) and one program for athletes with high HRV (increased loads).

The last study that I’ll discuss has been mentioned before in previous articles that I’ve written. Cipryan et al. (2007) measured HRV in Czech U-17 male hockey players once per week in the morning over a 3-5 month period. In addition, the coaches were asked to rate each players performance on a scale of 1-10. The researchers found that as HRV increased, performance was rated better and correlated to more playing time. When HRV was low performance was rated lower. Performance correlated with HRV score.

Thoughts

What I found interesting was that in 2 of the above studies, HRV was monitored only once or twice per week and was still able to provide important data regarding training status. This makes the application of HRV in a team setting much more realistic. Daily measurements can certainly be done and would likely provide more accurate data but can prove to be difficult. The ability to perform HRV measurements are limited by; having access to valid and reliable measuring devices; having a qualified individual(s) to record and analyze data; having athletes capable of following measurement instructions. HRV applications on smart phones certainly would make this process much easier. These are much more cost effective and convenient.

It appears that pre-planned training certainly isn’t optimal for realizing athletic potential in athletes. Though this is very inconvenient for the coach, having the ability to adjust training prescription for certain athletes based on HRV can increase the quality of training and adaptation while decreasing health complications (illness, injury, overtraining).

How often do coaches punish players for poor performance with intense conditioning in practice sessions following a previous competition? How many coaches punish teams with physical conditioning due to team rule infractions? How often are ill or injured players returning to training and competition before they’re ready? Clearly these strategies require some re-evaluation. It is quite possible your training program, no matter how good it looks on paper, is only appropriate for 50-60% of your players.


References

Cipryan, L. & Stejskal, P. (2010) Individual training in team sports based on ANS activity assessments. Medicina Sportiva, 14(2):  56-62

Cipryan, L., Stejskal, P., Bartakova, O., Botek, M., Cipryanova, H., Jakubec, A., Petr, M., & Řehova, I. (2007)  Autonomic nervous system observation through the use of spectral analysis of heart rate variability in ice hockey players.  Acta Universitatis Palackianae Olomucensis. Gymnica, 37(4): 17-21.

Hap, P., Stejskal, P. & Jakubec, A. (2010) Volleyball players training intensity monitoring through the use of spectral analysis of HRV during a training microcycle. Acta Universitatis Palackianae Olomucensis. Gymnica, 41(3): 33-38