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About hrvtraining

Researcher and Professor. Former athlete and coach.

New Podcast Interview: HRV in Soccer

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Last week I had pleasure of being interviewed on the Just Kickin’ It Podcast. In the interview we discuss HRV basics, implementation and interpretation with soccer teams, our recent research findings and future directions.

Thank you to Brian and Josh for having me on. I also encourage you to check out the podcast archives as there are some great interviews with other researchers and coaches (i.e., Dr. Mike Young, Dr. Tim Gabbett and Dr. Shawn Arent to name a few I’ve listened to), in addition to plenty of others that are on my list.

Enjoy and Merry Christmas.

https://soundcloud.com/just-kickin-it-pod/episode-35-andrew-flatt

 

 

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

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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.

 

Trend Changes versus Daily Changes

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HRV fluctuates to a certain extent on a daily basis. I’ve seen athletes with coefficient of variations (CV, a marker of deviation from the weekly mean) as low as 2% to >15%. An athlete’s CV changes over time, which itself serves as what I believe to be, an important indication of training adaptation.  The CV is related to individual fitness level and training stress and possibly even performance potential. Measurement position will also affect the CV with lower CV’s observed in the supine position compared to standing.

Here’s an important lesson I’ve learned about interpreting HRV in athletes. A daily change in HRV can occur for a number of reasons, and may or may not have any meaningful impact on acute performance or “readiness”. Putting too much focus on an acute change in HRV without stepping back and observing the overall trend is a bit myopic. This isn’t to say that daily changes aren’t useful, just that a full appreciation of the training process, including the evolution of the trend in response to training will enable better analysis and therefore decision-making. This is because longitudinal changes in an athlete’s HRV trend do not occur for no reason. Increases, decreases, greater fluctuation, less fluctuation, when assessed over time, are all very meaningful.

Observe the screenshot below which details the last 6 months of a high level collegiate sprint-swimmers trend. The data pretty much interprets itself when you compare the changes in the trend to changes in training and life events.

6motrend

What can we observe from this?

  • Greater fluctuation and a decreasing trend during heavy training stress
  • Less fluctuation and an increasing trend during reduced training stress
  • Greater fluctuation and a decreasing trend during normal training with increased academic stress (preparing for and writing exams). Thanks to recent work from Bryan Mann, we know that this increase in non-training related stress may put athletes at greater risk of injury or illness.

I strongly believe that to use HRV effectively, you need to consider the changes in the trend, and not just the day to day stuff. When asked what HRV products are worthwhile or what do I think of App X or product Z, I always suggest that they invest in one that provides the best visualization of the data over time and includes other markers of training status (i.e., load, wellness, etc.). This enables more meaningful interpretation of the data and can therefore be more insightful and useful when determining the appropriate action to take with regards to training program adjustment.

When do we intervene?

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At what point should the coach or trainer implement a training or lifestyle intervention when an athlete is showing warning signs of excess fatigue?

This is easy to determine when looking back on the data retrospectively, but in real-time this can be a challenging question to answer. Especially when performance remains relatively stable during the early stages. There’s a sometimes blurry line between being too soft (changing the plan at every red flag) and being too hard (ignoring too many red flags).

In observing this athletes trend, it appears that the situation could’ve been easily avoided had some type of intervention been made early enough. The trend for HRV, and perceived measures of sleep quality, fatigue, soreness and stress all indicate that this athlete is heading for trouble.

downward trend

With poor sleep and high levels of training/non-training related stress the immune system is compromised and the athlete gets sick.

At what point do we intervene? Intervention starts with a conversation. The conversation acknowledges a red flag and helps determine what means of action to take (if any at all). In this situation, the first uncharacteristically low sleep rating should’ve started the conversation.

Effect of Water Ingestion on HRV: Implications for daily measures

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One of the more challenging aspects of implementing HRV monitoring with athletes is ensuring that daily measures are performed reliably. Unreliable or inconsistent measurement procedures can lead to invalid data (false positives or false negatives) and therefore a misinterpretation of training and recovery status. With ultra-short HRV recordings (i.e., ~60 s) it is even more important that measures be strictly standardized to improve the quality of the data.

Waking measures are preferred to capture one’s HRV in a truly rested condition, before any external stimuli can confound the measure. A potential confounding variable that users should be aware of is the effect that water ingestion has on various physiological processes that stimulate autonomic activity and thus alter one’s HRV. This was brought to my attention several years ago by my colleague, Dr. James Heathers.

Immediate changes in HRV take place following water consumption that can last for up to 45 minutes or longer. For example, Routledge and colleagues1 tested the effects of 500 ml water ingestion on HRV in 10 healthy individuals between the ages of 24 and 34 years. On two separate occasions, the subjects reported to the lab in a randomized order for 500 ml water ingestion or 20 ml water ingestion (control). The experiments took place at 8 am before the subjects had anything to eat or drink and after bladder emptying. For a 30 minute period, subjects rested in a semi-supine position before water ingestion. HRV was determined from 5-min ECG windows immediately before and at 5, 20 and 35-min post water ingestion.

Resting HR on average was between 2 – 7 bpm lower than control throughout the post-consumption 45-min period. RMSSD increased between 8 -13 ms during this period compared to control which increased between 2 – 8.8 ms.

Experiment

Out of curiosity I conducted a similar but much smaller experiment (n=1) to see how HRV responded to 500 ml water ingestion. The data is analyzed in 5-min segments before and after drinking in the seated position with a 1-min period excluded from analysis during which the water was ingested. The tachogram and results are posted below.

water tachogram

Tachogram including pre and post water ingestion

pre water consumption

Pre

Post water results

Post

In Martin Buchheit’s, recent review paper, a 3% smallest worthwhile change for lnRMSSD is suggested. In this situation water consumption resulted in an increase in lnRMSSD nearly 2x the smallest worthwhile change.

results table water hrv

*Note that lnRMSSDx20 represents the modified HRV value provided by HRV app’s like ithlete. This has been highlighted for those who are only familiar with these values.

Why does water consumption increase HRV?

The autonomic responses to water ingestion appear to initially be due to the stimulation of osmoreceptors within the gut which causes vasoconstriction (a sympathetic response) and a slight increase in total peripheral resistance.2 Increased baroreceptor sensitivity and increased cardiac-vagal stimulation are thought to occur to counteract the pressor effect (increases in blood pressure) which is why we see a slowdown in resting HR and increase in HRV.2 Effects from the Renin-Angiotensin-Aldosterone system can also not be ruled out given their role in mediating body fluid levels that can effect cardiovascular responses. Water temperature may also have an effect as 250 ml of ice water appears to increase HRV to a greater extent than room-temperature water.3 This may be due to stimulation of thermal vagal receptors in the esophagus.3 Additionally, water ingestion following exercise has been shown to increase parasympathetic reactivation.4

Implications for Daily Monitoring

Tell your athletes to wait until after measuring their HRV to drink fluids and to do so consistently. Otherwise, values may be obscured with a false positive when they drink fluids before the measure.

References:

  1. Routledge, H.C., Chowdhary, S., Coote, J. H., & Townend, J. N. (2002). Cardiac vagal response to water ingestion in normal human subjects. Clinical Science, 103, 157-162.
  2. Brown, C. M., Barberini, L., Dulloo, A. G., & Montani, J. P. (2005). Cardiovascular responses to water drinking: does osmolality play a role?.American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 289(6), R1687-R1692.
  3. Chiang, C. T., Chiu, T. W., Jong, Y. S., Chen, G. Y., & Kuo, C. D. (2010). The effect of ice water ingestion on autonomic modulation in healthy subjects.Clinical Autonomic Research, 20(6), 375-380.
  4. Oliveira, T. P., Ferreira, R. B., Mattos, R. A., Silva, J. P., & Lima, J. R. P. (2011). Influence of water intake on post-exercise heart rate variability recovery.Journal of Exercise Physiology Online.

New Study: Ultra-Short HRV is Sensitive to Training Effects in Team Sports Players

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Here’s a quick look at a recent study of ours in press with the Journal of Sports Science and Medicine. We’ve shown previously that lnRMSSD can be validly assessed in 60-s from an isolated measure in a variety of athletes, but in this paper we demonstrate that 60-s measures are capable of tracking lnRMSSD changes in elite athletes. The full text is open access on the JSSM site.

Abstract
The aim of this study was to test the possibility of the ultra-short-term lnRMSSD (measured in 1-min post-1-min stabilization period) to detect training induced adaptations in futsal players. Twenty-four elite futsal players underwent HRV assessments pre- and post-three or four weeks preseason training. From the 10-min HRV recording period, lnRMSSD was analyzed in the following time segments: 1) from 0-5 min (i.e., stabilization period); 2) from 0-1 min; 1-2 min; 2-3 min; 3-4 min; 4-5 min and; 3) from 5-10 min (i.e., criterion period). The lnRMSSD was almost certainly higher (100/00/00) using the magnitude-based inference in all periods at the post- moment. The correlation between changes in ultra-short-term lnRMSSD (i.e., 0-1 min; 1-2 min; 2-3 min; 3-4 min; 4-5 min) and lnRMSSDCriterion ranged between 0.45 – 0.75, with the highest value (p = 0.75; 90% CI: 0.55 – 0.85) found between ultra-short-term lnRMDSSD at 1-2 min and lnRMSSDCriterion. In conclusion, lnRMSSD determined in a short period of 1-min is sensitive to training induced changes in futsal players (based on the very large correlation to the criterion measure), and can be used to track cardiac autonomic adaptations.

ultra short HRV sensitive to training effects

Thanks to Dr. Fabio Nakamura and his research group out of Brazil for inviting me to collaborate with them on this one. We have several more in production looking at daily HRV changes in response to training  in different teams and how Wellness and Fitness markers influence HRV responses.

Interpreting HRV Trends in Athletes: High Isn’t Always Good and Low Isn’t Always Bad

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This article was written for the FreelapUSA site. The intro is posted below. You can follow the link for the full article. Thanks to Christopher Glaeser from Freelap for inviting my contribution as I’ve found this site to be a great resource.

Interpreting HRV Trends in Athletes: High Isn’t Always Good and Low Isn’t Always Bad

Heart rate variability (HRV) monitoring has become increasingly popular in both competitive and recreational sports and training environments due to the development of smartphone apps and other affordable field tools. Though the concept of HRV is relatively simple, its interpretation can be quite complex. As a result, considerable confusion surrounds HRV data interpretation. I believe much of this confusion can be attributed to the overly simplistic guidelines that have been promoted for the casual-end, non-expert user.

In the context of monitoring fatigue or training status in athletes, a common belief is that high HRV is good and low HRV is bad. Or, in terms of observing the overall trend, increasing HRV trends are good, indicative of positive adaptation or increases in fitness while decreasing trends are bad, indicative of fatigue accumulation or “overtraining” and performance decrements. In this article I address the common notions of both acute and longitudinal trend interpretation, and discuss why and when these interpretations may or may not be appropriate. We will briefly explore where these common interpretations or “rules” have come from within the literature, and then discuss some exceptions to these rules.

Continue reading article on FreelapUSA

Early changes in HRV relate to eventual fitness changes in collegiate soccer players

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Numerous studies have shown that increases in fitness (e.g., VO2max, MAS, Yo-Yo, etc.) are associated with increased cardiac-parasympathetic activity among healthy, athletic and clinical populations. This is one of the reasons why aerobic exercise is considered to be cardio-protective, due to enhanced resting vagal-modulation.

However, there is considerable inter-individual variation in how a given individual responds to an exercise program. Following a standardized endurance training program, some individuals will show significant improvements in aerobic fitness while others will show only small improvements. Some may even regress. Why this occurs is likely due to a variety of potential variables including genetic factors, appropriateness of training stimulus and life style factors (i.e., sufficient recovery, sleep quality, nutrition, stress, etc.). Given the association between fitness changes and HRV changes, monitoring HRV throughout training may be useful in evaluating individual adaptation to a training program.

In our latest study (in press with JSCR), we wanted to determine if changes in HRV mid-way through a training program related to eventual changes in intermittent running performance in a collegiate female soccer team. It would be useful for coaches to be able to identify athletes who may not be coping well with training earlier on rather than waiting until post-testing to realize some athletes didn’t improve much. Coaches can then investigate the potential cause (i.e., fatigue, insufficient sleep, etc.) and intervene accordingly with modifications to training load or life style factors to get athletes back on track.

Before and after a 5-week conditioning program, we tested the team’s intermittent running capacity with the Yo-Yo IRT1. The conditioning program was designed based on the individuals max aerobic speed (MAS) adapted from Dan Baker’s MAS guide (link). Below is a screen shot of the conditioning program (unofficial).

MAS prog. Flatt

During week 1 and week 3, the athletes recorded their resting HRV each morning after waking with their smartphone using the ithlete HRV application which we validated previously (link). The weekly mean and weekly coefficient of variation (CV) for HRV and HR values were calculated. Change variables from week 1 to week 3 of HRV and HR (mean and CV) were correlated with the changes in Yo-Yo IRT1 performance from week 0 to week 5.

We found a very large correlation between the change in HRV CV at week 3 and Yo-Yo IRT1 changes at week 5 (r = -0.74). A large correlation was also found between the change in HRV mean and Yo-Yo IRT1 (r = 0.50). The HR measures showed only moderate correlations with the eventual changes in fitness.

Based on these results, it appears that monitoring HRV throughout training may be useful for evaluating how individual athletes are adapting to training. Specifically, we’re looking for two possible trend changes:

  1. A decrease in day-to-day fluctuation in HRV scores (i.e., decreased HRV CV)
  2. An increase in the weekly mean

Athletes demonstrating the opposite (increased CV and/or decreased weekly mean) may require a little closer attention from coaching personnel  to ensure that the training load is appropriate or that the athlete’s are taking care of the non-training factors that can be effecting their recovery.

Another novel finding of this study was that ultra-short HRV recordings (~1 min) derived from a smartphone app used by the athletes provided meaningful training status information. This indicates that HRV monitoring can be much more affordable and convenient than traditional approaches (i.e., longer recording periods with more expensive HRV tools).

I have plans for a much more elaborate post in the near future on the HRV CV. I’ll cover previous research, post some data and discuss how to interpret changes in the CV with appropriate context.

Link to current study: Evaluating individual training adaptation with Smartphone-derived heart rate variability in a collegiate female soccer team

New study: Assessing shortened field-based HRV data acquisition in team-sport athletes

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Our latest study “Assessing shortened field-based heart rate variability data acquisition in team-sport athletes” is now available ahead of print in IJSPP.

This project was a collaboration with Dr. Fabio Nakamura, Lucas Pereira, Dr. Irineu Loturco and Dr. Rodrigo Ramirez-Campillo. We have several more papers in production, in review and in press, so stay tuned for those.

This study expands on previous work of ours (link, link) that  assessed the agreement between ultra-short (60 s) HRV measures with standard 5 minute measures (following a 5 min stabilization period). This study differs from our previous work in a few key areas:

1. We assessed HRV (LnRMSSD) in the seated position here versus the supine position previously. Having to accommodate to the seated position may take longer than the supine position due to the vertical positioning and extra stress on the heart. Additionally, the seated position has been suggested in recent review papers to be the preferred position for athlete monitoring. Therefore, investigation into the time-course for HRV stabilization (i.e., how long must we wait to achieve a stable R-R signal), in addition to the agreement between ultra-short measures and the criterion (5 min post 5 min stabilization) segment from seated measures is required.

2. This study also evaluated the ratio between LnRMSSD and the R-R interval (LnRMSSD:R-R). Previous work has shown that highly fit athletes can demonstrate “parasympathetic saturation” which is characterized by a decrease in HRV despite very low resting heart rates. Daniel Plews et al. describe this phenomenon in their recent review paper: “The underlying mechanism is likely the saturation of acetylcholine receptors at the myocyte level: a heightened vagal tone may give rise to sustained parasympathetic control of the sinus node, which may eliminate respiratory heart modulation and reduce
HRV.”

3. In our previous studies we used an ECG for HRV analysis which is considered the gold standard, though not very practical for routine monitoring. In this study we used the Polar RS800,  a field tool heart rate monitor system that is more commonly used in practical settings.

4. Lastly, this study included elite level athletes whereas our previous work included collegiate athletes.

Our results show that the first 5 min LnRMSSD value (stabilization period) was not different than the criterion segment (mins 5-10). Additionally, we found that each isolated minute from the stabilization period (i.e., min 0-1, 1-2, 2-3, etc.) showed good agree with the criterion. Therefore, when 5 minute measures cannot be obtained due to time constraints or for compliance reasons, 60 s measures appear suitable for valid assessment, in agreement with our previous investigations.

In our next paper (in press) we assess if ultra-short LnRMSSD measures are sensitive to training effects in elite athletes.

Abstract
Purpose: The aims of this study was to compare the LnRMSSD and the LnRMSSD:RR values obtained during a 5-min stabilization period with the subsequent 5-min criterion period and to determine the time course for LnRMSSD and LnRMSSD:RR stabilization at 1-min analysis in elite team-sport athletes. Methods: Thirty-five elite futsal players (23.9 ± 4.5 years; 174.2 ± 4.0 cm; 74.0 ± 7.5 kg; 1576.2 ± 396.3 m in the YoYo test level 1), took part in this study. The RR interval recordings were obtained using a portable heart rate monitor continuously for 10-min in the seated position. The two dependent variables analyzed were the LnRMSSD and the LnRMSSD:RR. To calculate the magnitude of the differences between time periods, the effect size (ES) analysis was conducted. To assess the levels of agreement the intra-class correlation coefficient (ICC) and the Bland-Altman plots were used. Results: TheLnRMSSD and LnRMSSD:RR values obtained during the stabilization period (i.e., 0-5-min) presented very large to near perfect ICCs with the values obtained during the criterion period (i.e., 5-10-min), with trivial ES. In the ultra-short-term analysis (i.e., 1-min segments) the data showed slightly less accurate results, but only trivial to small differences with very large to near perfect ICCs were found. Conclusion: To conclude, LnRMSSD and LnRMSSD:RR can be recorded in 5-min without traditional stabilization periods under resting conditions in team-sport athletes. The ultra-short-term analysis (i.e., 1-min) also revealed acceptable levels of agreement with the criterion.

HRV, Stress and Training Adaptation

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Below is my HRV trend following my first year of working on my PhD. Training was held pretty much constant throughout this time period (lift 4x/week, moderate aerobic work 2-3x/week). Both my first and second semester were equally as busy, however my perception of stress and my HRV trend was much lower and higher, respectively,  in semester 2. The first few weeks of the first semester were so bad I was sick for over  a week with the flu, which rarely happens to me (occurring at the lowest dip, early in the trend).

SWCtrendphdyr1

From the trend above, we can see that I spent a lot of time below the smallest worthwhile change (SWC=0.5*CV) during the first semester. Over Christmas I didn’t fall below the SWC at all. In the second semester I experienced considerably less scores below the SWC. Once summer started, I experienced an immediate increase in scores.

Here’s the trend with perceived stress (purple columns, lower score = higher perceived stress). Clearly a big difference between first and second semester.

phdyr1hrvtrend

In reviewing this data, I’m reminded of a study that evaluated the relationship between perceived stress and fitness adaptations in healthy subjects (thanks to Fabio Nakamura for sharing this one with me a while back). Perceived stress levels measured before a training program showed moderate, negative correlations with improvements in fitness. See abstract below:

Ruuska, Pirita S., et al. “Self-rated mental stress and exercise training response in healthy subjects.” Frontiers in Physiology 3 (2012): 51.

Abstract

Purpose: Individual responses to aerobic training vary from almost none to a 40% increase in aerobic fitness in healthy subjects. We hypothesized that the baseline self-rated mental stress may influence to the training response.Methods: The study population included 44 healthy sedentary subjects (22 women) and 14 controls. The laboratory controlled training period was 2 weeks, including five sessions a week at an intensity of 75% of the maximum heart rate for 40 min/session. Self-rated mental stress was assessed by inquiry prior to the training period from 1 (low psychological resources and a lot of stressors in my life) to 10 (high psychological resources and no stressors in my life), respectively. Results: Mean peak oxygen uptake (VO2peak) increased from 34 ± 7 to 37 ± 7 ml kg−1 min−1 in training group (p < 0.001) and did not change in control group (from 34 ± 7 to 34 ± 7 ml kg−1 min−1). Among the training group, the self-rated stress at the baseline condition correlated with the change in fitness after training intervention, e.g., with the change in maximal power (r = 0.45, p = 0.002, W/kg) and with the change in VO2peak (r = 0.32, p = 0.039, ml kg−1 min−1). The self-rated stress at the baseline correlated with the change in fitness in both female and male, e.g., r = 0.44, p = 0.039 and r = 0.43, p = 0.045 for ΔW/kg in female and male, respectively. Conclusion: As a novel finding the baseline self-rated mental stress is associated with the individual training response among healthy females and males after highly controlled aerobic training intervention. The changes in fitness were very low or absent in the subjects who experience their psychological resources low and a lot of stressors in their life at the beginning of aerobic training intervention.

There’s also ample research demonstrating strong relationships between HRV and fitness adaptation in a variety of populations. This includes research showing that baseline HRV relates to fitness changes, in addition to the change in HRV from pre to post training relating well with changes in fitness.

We have 2 paper’s in review at the moment that show how early changes in HRV showed very large relationships with changes in fitness markers (lab and field measures) in team-sport athletes.

~50% (or more) of the variance in fitness adaptation is explained by genetics, so there’s not too much we can do about that. But the other things that explain smaller %variance in training adaptation should be managed. This includes the obvious: stress, sleep quality, nutrition, etc. which all tend to influence HRV as well.