Context: we previously resorted to standardized HRV measures performed in the athletic training room with college football players to overcome non-compliance with post-waking tests.
Problem: pre-training hydration practices confound HRV measures. Players typically opt for cold bottles of water or Gatorade. Thus, we needed to determine how much and for how long these drinks impacted HRV.
Findings: Gatorade had small effects that lasted about 45 min. Effects of water were larger and persisted for 60 min.
If measuring HRV in a lab/clinic/training facility, be mindful of recent fluid ingestion. HRV measures obtained within 60 min of 591 ml water or 45 min of an equal volume of Gatorade will be capturing their physiology effects and result in falsely elevated values. This would result in misinterpretation of autonomic status.
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.
Heart rate variability (HRV) is a physiological marker of training adaptation among athletes. However, HRV interpretation is challenging when assessed in isolation due to its sensitivity to various training and non-training-related factors. The purpose of this study was to determine the association between athlete-self report measures of recovery (ASRM) and HRV throughout a preparatory training period. Ultra-short natural logarithm of the root mean square of successive differences (LnRMSSD) and subjective ratings of sleep quality, fatigue, muscle soreness, stress and mood were acquired daily for 4 weeks among Division-1 sprint-swimmers (n = 17 males). ASRM were converted to z-scores and classified as average (z-score −0.5–0.5), better than average (z-score > 0.5) or worse than average (z-score < −0.5). Linear mixed models were used to evaluate differences in LnRMSSD based on ASRM classifications. LnRMSSD was higher (p < 0.05) when perceived sleep quality, fatigue, stress and mood were better than average versus worse than average. Within-subject correlations revealed that 15 of 17 subjects demonstrated at least one relationship (p < 0.05) between LnRMSSD and ASRM variables. Changes in HRV may be the result of non-training related factors and thus practitioners are encouraged to include subjective measures to facilitate targeted interventions to support training adaptations.
Effect sizes ± 90% confidence interval for resting heart rate parameters relative to subjective categorization.
Here’s a brief look at a new paper of ours in press with JSCR. This is a very small study that we submitted as “Research Note” that looked at changes in HRV (via finger pulse sensor) and training load (via Polar Team2) across preseason training in D-1 women’s soccer players.
The purpose of this study was to track changes in training load (TL) and recovery status indicators throughout a 2-week preseason and to interpret the meaning of these changes on an individual basis among 8 Division-1 female soccer players. Weekly averages for heart ratevariability (lnRMSSD), TL and psychometrics were compared with effect sizes (ES) and magnitude based inferences. Relationships were determined with Pearson correlations. Group analysis showed a very likely moderate decrease for total training load (TTL) (TTL week 1 = 1203 ± 198, TTL week 2 = 977 ± 288; proportion = 1/2/97, ES = -0.93) and a likely small increase in lnRMSSD (week 1 = 74.2 ± 11.1, week 2 = 78.1 ± 10.5; proportion = 81/14/5, ES = 0.35). Fatigue demonstrated a very likely small improvement (week 1 = 5.03 ± 1.09, week 2 = 5.51 ± 1.00; proportion = 95/4/1; ES = 0.45) while the other psychometrics did not substantially change. A very large correlation was found between changes in TL and lnRMSSD (r = -0.85) while large correlations were found between lnRMSSD and perceived fatigue (r = 0.56) and soreness (r = 0.54). Individual analysis suggests that 2 subjects may benefit from decreased TL, 2 subjects may benefit from increased TL and 4 subjects may require no intervention based on their psychometric and lnRMSSD responses to the TL. Individual weekly changes in lnRMSSD varied among subjects and related strongly with individual changes in TL. Training intervention based on lnRMSSD and wellness responses may be useful for preventing the accumulation of fatigue in female soccer players.
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.
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 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.
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.
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.
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.
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.
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.
Tachogram including pre and post water ingestion
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.
*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.
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.
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.
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.
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.
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.
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.
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.
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.
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.