Heart Rate Variability in College Football Players throughout Preseason Camp in the Heat

Here’s a quick look at our latest study examining cardiac-autonomic responses to preseason camp in the heat among college football players. The free full text can be accessed here: Heart rate variability in college football players throughout preseason camp in the heat IJSM

Intensive training periods tend to increase RHR and decrease HRV, reflecting stress and fatigue. However, adaptations to heat exposure (e.g., plasma volume expansion) tend to have the opposite effects. So we wanted to see what happens when players were exposed to both intense training and intense heat stress during preseason camp.

Despite increases in perceived fatigue throughout the 2-week period, RHR and HRV reflected responses consistent with heat acclimation.

HRV initially decreased in linemen, then peaked after a day of rest. Non-linemen faired a little better with smaller decrements in perceived fatigue and more frequent day-to-day improvements in RHR and HRV.

These results indicate that heart rate parameters and perceived fatigue are independent markers of training status, and that desirable cardiovascular adaptations can occur in the presence of soreness and fatigue.

This is especially important for tech companies who try to infer recovery status from HRV alone. As HRV improved throughout camp, an app’s algorithm would report to coaches that players are well-recovered. Given that no player feels well-recovered during preseason camp in the heat, the technology suddenly loses credibility for being wrong and will likely be dismissed.

This is unfortunate because the heart rate parameters are likely reflecting important adaptations that may indicate better tolerance to training in the heat, a reduced exercising heart rate, and improved fitness. Thus, I encourage users to ignore “recovery” scores and interpret the data in appropriate context.


We aimed to characterize cardiac-autonomic responses to a 13-day preseason camp in the heat among an American college football team. Players were categorized as linemen (n=10) and non-linemen (n=18). RHR, natural logarithm of the root-mean square of successive differences multiplied by twenty (LnRMSSD), and subjective wellbeing (LnWellness) were acquired daily. Effect sizes±90% confidence interval showed that for linemen, LnRMSSD decreased (moderate) on day 2 (71.2±10.4) and increased (moderate) on day 12 (87.1±11.2) relative to day 1 (77.9±11.2) while RHR decreased (small–moderate) on days 6, 7, and 12 (67.7±9.3–70.4±5.5 b∙min-1) relative to day 1 (77.1±10.1 b∙min-1). For non-linemen, LnRMSSD increased (small–large) on days 3–5, 7, 12, and 13 (83.4±6.8–87.6±8.5) relative to day 1 (80.0±6.5) while RHR decreased (small–large) on days 3–9, 12, and 13 (62.1±5.2–67.9±8.1 b∙min-1) relative to day 1 (70.8±6.2 b∙min-1). Decrements in LnWellness were observed on days 4–10 and 13 for linemen (moderate) and on days 6–9, 12, and 13 for non-linemen (small–moderate). Despite reductions in LnWellness, cardiac-autonomic parameters demonstrated responses consistent with heat-acclimation, which possibly attenuated fatigue-related decrements.

Modelling the HRV Response to Training Loads in Elite Rugby Sevens Players

New paper in collaboration with my colleagues Sean Williams, Dan Howells et al. Full-text link below.

Modelling the HRV Response to Training Loads in Elite Rugby Sevens Players

Key Points

  • A systems theory approach can be used to describe the variation in chronic HRV responses to training within elite Rugby Sevens players.
  • For the majority of athletes, model parameters can be used to accurately predict future responses to training stimuli.
  • Responses that diverge from the predicted values may serve as a useful flag for the investigation of changes in lifestyle factors.
  • Internal training load measures (sRPE) markedly outperformed external load measures (HSD) in predicting future HRV responses to training stimuli.


A systems modelling approach can be used to describe and optimise responses to training stimuli within individuals. However, the requirement for regular maximal performance testing has precluded the widespread implementation of such modelling approaches in team-sport settings. Heart rate variability (HRV) can be used to measure an athlete’s adaptation to training load, without disrupting the training process. As such, the aim of the current study was to assess whether chronic HRV responses, as a representative marker of training adaptation, could be predicted from the training loads undertaken by elite Rugby Sevens players. Eight international male players were followed prospectively throughout an eight-week pre-season period, with HRV and training loads (session-RPE [sRPE] and high-speed distance [HSD]) recorded daily. The Banister model was used to estimate vagallymediated chronic HRV responses to training loads over the first four weeks (tuning dataset); these estimates were then used to predict chronic HRV responses in the subsequent four-week period (validation dataset). Across the tuning dataset, high correlations were observed between modelled and recorded HRV for both sRPE (r = 0.66 ± 0.32) and HSD measures (r = 0.69 ± 0.12). Across the sRPE validation dataset, seven of the eight athletes met the criterion for validity (typical error <3% and Pearson r >0.30), compared to one athlete in the HSD validation dataset. The sRPE validation data produced likely lower mean bias values, and most likely higher Pearson correlations, compared to the HSD validation dataset. These data suggest that a systems theory approach can be used to accurately model chronic HRV responses to internal training loads within elite Rugby Sevens players, which may be useful for optimising the training process on an individual basis.

Podcast Interview: HRV in football and rugby

I recently had the pleasure of discussing HRV in football and rugby on the Rugby Renegade Podcast. Soundcloud and iTunes links below.


iTunes link: https://itunes.apple.com/zw/podcast/rugby-renegade-podcast/id1102026866?mt=2


HRV in a Team Setting: Follow-up Thoughts

Today I am going to share some thoughts on why I think HRV is useful for monitoring athletes individually or in team settings. However, I will state upfront that among the research, HRV analysis varies a great deal in important variables such as; position, time of day, duration of measurement, frequency of measurements, analysis (time domain, frequency domain) and populations (type of athlete, gender, age, etc.).  Therefore, the monitoring of a specific variable (performance, recovery, stress, etc.) requires careful consideration of methodology. I would also like to make it abundantly clear that HRV monitoring is most effective when considered with other variables (RPE, POMS, etc.).

For the purposes of safe, effective and efficient improvement in sport performance, HRV monitoring can be extremely valuable. As a non-invasive measure of autonomic status, HRV provides an objective measure of the collective stress load (emotional, physical, and physiologic) that an athlete carries.

HRV and Training Load

I’d like to preface this section by saying that although HRV reflects training load reasonably well, we need to be careful of not being too presumptuous. The ANS is an incredibly complex system that is impacted by damn near everything we do and experience (training, nutrition, emotion, etc.). Therefore, being cognizant of our athletes stresses (or of our own if we use HRV for our own training) outside of sport is critical. Looking for a perfect correlation between training load and HRV score is not looking at the big picture.  I was guilty of this in the past.

In a team setting, athletes are often subjected to the same workloads, be it in the weight room or on the field. Monitoring individual responsiveness to training provides coaches with a handle on training program efficiency and quality of effect.

  • What does HRV alone tell us?

My understanding of HRV is that once baseline is established (though baseline is not static) HRV reflects autonomic balance. An imbalance indicates stress. For example; If parasympathetic tone is unusually low or high compared to baseline values, something is going on; a red flag.


  • Analysis:

Through monitoring of training volumes/load, self reported stress, etc. we can try and assume the cause of the imbalance.  What can the change in HRV be attributed to? It is likely a combination of the physical and mental stress of training/sport however this may require further investigation. Tracking performance markers and other variables in conjunction with HRV will allow for an appropriate mode of action for a red flagged or “at-risk” athlete.

From my understanding of the research we can associate certain changes in HRV patterns with a specific interpretation.

  • Small but consistent decrements in HRV over a training period: training loads are appropriate as fatigue is expected to accumulate but not to an unreasonable level.
  • Large changes in HRV over a training period: This is a red flag. There is a marked imbalance indicating high stress. Reduce loads until HRV approaches baseline values.
  • No change or small increases in HRV over a training period: Training stress levels are below the athletes capacity and therefore increased loads will likely be well tolerated.

I must reiterate that other measures of performance and training status should obviously be considered before trying to infer any meaning from an HRV score.

For those using ithlete or bioforce keeping tabs on the weekly and monthly changes can be extremely valuable as this gives a better idea of overall patterns. I picked this tip up from Joel Jamieson at the CVSPS last spring.

In a perfect world, our athletes report to pre-season camp in shape. A high level of fitness is typically associated with high HRV, lower resting heart rates and a high tolerance to physical stress (fast recovery) – I realize I’m preaching to the choir here.

Furthermore, it appears that better conditioned athletes handle inflammation better (Martin-Sanchez et al. 2011) or is this a genetic thing? We know elite athletes typically have good genetics (for their craft); does this include more favorable inflammatory responses and is this common among most elite athletes? Perhaps someone can chime in on this? Regardless, this is important because there is a strong link between inflammation and HRV (Kylosov et al. 2009, Thayer 2009, Soares-Miranda 2012).

Continuing with our hypothetical situation; these conditioned athletes should respond well to the high volumes of training. In contrast, less conditioned athletes will become easily overloaded from the commencement of pre-season training and may be at a greater risk of injury, premature overreaching, emotional distress, etc.

This was what I was trying to get at when I was thinking about the possibility of trying to create favourable autonomic profiles of athletes prior to intensive training. The concept isn’t new as we are insistent that our athletes train over the summers and prepare for camp. Monitoring HRV throughout this time may be a good indicator of physical condition. We want to see HRV profiles that indicate a high level of fitness and tolerance for stress.

This leads me to the next topic…


Referring back to the CVSPS last spring I distinctly remember Landon Evans say during his presentation something to the effect of; “Come on guys, HRV in the collegiate setting? Really?”. He’s absolutely right. Monitoring HRV in the collegiate setting is not easy! It’s especially difficult if you are responsible for multiple teams. Football players may in fact be the hardest group of athletes to get HRV data from.

Having said this, I believe that it can be done if it’s important enough to you and the coaching staff. This doesn’t mean you’ll get all 100 guys on the roster taking measurements, but perhaps starting with key players, starters, etc. is more realistic. Dealing with smaller teams (basketball, volley ball, tennis, etc.) is a bit more doable but a challenge nonetheless.

If using HRV as a monitoring tool is important to you, start small and progress from there. Every coach will have different resources and circumstances and therefore it’s hard to generalize. I look forward to the release of the ithlete Team HRV system as this will make this process much easier. From my understanding of the product, athletes will be able to take their measurement at home on their smart phone and the data will automatically be uploaded to a web based interface that will allow the coaches/sports medicine staff to see all players’ trends on the same chart. Red flagging at-risk athletes will be much easier and quicker with this system.

Wrap UP

Is HRV an effective monitoring tool? I think it is. Based on personal experience and the research, there is quite a bit of evidence to support its efficacy. Taking into consideration other measures of performance and training status HRV gives you an important and objective measure of the athletes overall stress levels.

However, I urge all readers to keep in mind that I am not an expert on this stuff. I theorize a lot so take my articles with a grain of salt. For all I know, there are several researchers over in Europe cursing my name for completely misinterpreting their research. Hopefully this isn’t the case.

I will write on this topic again in the future as I still have plenty of thoughts on the issue. Only so much can be discussed in one article. Thoughts for another time: HRV and performance prediction, HRV and injury, HRV as an early warning sign and more.

I decided not to discuss and cite a ton of research today since I’ve done this in plenty of previous posts. Instead I will just provide references as recommended reading so I don’t come off as baseless in my thoughts and allows readers to investigate this topic for themselves.

Further Reading/References in Team Settings:

Baumert, M. et al. (2006) Changes in heart rate variability of athletes during a training camp. Biomed Tech, 51(4): 201-4.

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


Further Reading/References in athletes thought not necessarily in team settings:

Atlaoui, D. et al. (2007) Heart rate variability training variation and performance in elite swimmers. International Journal of Sports Medicine, 28(5): 394-400

Bosquet, L., Merkari, S., Arvisais, D., Aubert, A.E. (2008) Is heart rate a convenient tool to monitor over-reaching? A systematic review of the literature. British Journal of Sports Medicine, 42(9): 709-714.

Botek, M. et al. (2012) Return to play after health complications associated with infection mononucleosis guided on ANS activity in elite athlete: a case  study. Gymnica, 42(2)

Buchheit, M. et al (2009) Monitoring endurance running performance using cardiac parasympathetic function. European Journal of Applied Physiology, DOI 10.1007/s00421-009-1317-x

Chen, J., Yeh, D.,  Lee, J., Chen, C.,  Huang, C.,  Lee, S., Chen, C.,  Kuo, T., Kao, C., & Kuo, C. (2011) Parasympathetic nervous activity mirrors recovery status in weightlifting performance after training. Journal of Strength and Conditioning Research, 25(6):  1546-1552

Hedelin, R., Bjerle, P., & Henriksson-Larsen, K. (2001) Heart Rate Variability in athletes: relationship with central and peripheral performance. Medicine & Science in Sports & Exercise, 33(8), 1394-1398.

Hellard, P., et al. (2011) Modeling the Association between HR Variability and Illness in Elite Swimmers. Medicine & Science in Sports & Exercise, 43(6): 1063-1070

Huovinen, J. et al. (2009) Relationship between heart rate variability and the serum testosterone-to-cortisol ratio during military service. European Journal of Sports Science,9(5): 277-284

Kiviniemi, A.M., Hautala, A., Kinnumen, H., & Tulppo, M. (2007) Endurance training guided by daily heart rate variability measurements. European Journal of Applied Physiology, 101: 743-751.

Kiviniemi, A.M., Hautala A.J., Kinnunen, H., Nissila, J., Virtanen, P., Karjalainen, J., & Tulppo, M.P. (2010) Daily exercise prescription on the basis of HR variability among men and women. Medicine & Science in Sport & Exercise, 42(7): 1355-1363.

Kylosov, AA. et al. (2009) Changes in inflammatory activity, heart rate variability, and biochemical indices in young athletes during the regular training cycle. Human Physiology, 35(4): 465-478.

Manzi, V. et al (2009) Dose-response relationship of autonomic nervous system responses to individualized training impulse in marathon runners. American Journal of Physiology, 296(6): 1733-40

Mourot, L. et al (2004) Decrease in heart rate variability with overtraining: assessment by the Poincare plot analysis. Clinical Physiology & Functional Imaging, 24(1):10-8.

Nigam, A.K. (2010) Resting heart rate and overtraining in athletes. International Referred Research Journal, 2(21): 38-40.

Pichot, V., Busso, T., Roche, F., Gartet, M., Costes, F., Duverney, D., Lacour, J., & Barthelemy, J. (2002) Autonomic adaptations to intensive overload training periods: a laboratory study. Medicine & Science in Sports & Exercise, 34(10), 1660-1666.

Soares-Miranda, L. et al. (2012) High levels of C-reactive protein are associated with reduced vagal modulation and low physical activity in young adults. Scandinavian Journal of Medicine and Science in Sports, 22(2): 278-84

Thayer, J. (2009) Vagal tone and the inflammatory reflex. Cleveland Clinic Journal of Medicine, 76(2): 523-526

Tian, Ye. et al. (2012) HRV threshold values for early warning non-functional overreaching in elite women wrestlers. Journal of Strength and Conditioning Research, Published ahead of print

Uusitalo, A.L.T., et al (2000) Heart rate and blood pressure variability during heavy training and overtraining in the female athlete. International Journal of Sports Medicine, 21(1): 45-53