Effect of Water Ingestion on HRV: Implications for daily measures

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.

water tachogram

Tachogram including pre and post water ingestion

pre water consumption


Post water results


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.


  1. Routledge, H.C., Chowdhary, S., Coote, J. H., & Townend, J. N. (2002). Cardiac vagal response to water ingestion in normal human subjects. Clinical Science103, 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 Physiology289(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 Research20(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

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.

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

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

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

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

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.

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.

New Study: Smartphone-derived HRV and Training Load in a Female Soccer Team

About a week ago our latest study was published ahead of print in the International Journal of Sports Physiology and Performance.

Smartphone-derived Heart Rate Variability and Training Load in a Female Soccer Team.

This study was 4 years in the making and is without a doubt the biggest project we’ve done to date. Since my Masters in 2011/2012, it’s been my number one priority to study the usefulness of smartphone-derived HRV in a team of athletes. Every project we’ve done leading up to this was simply to enable us to conduct this study. This includes:

  • Validation of the smartphone app (link)
  • Evaluating the agreement between standard HRV recordings (5-min) and ultra-short recordings utilized by the app (60-s) (link)
  • Evaluating the time-course of HRV stabilization in athletes to determine the most convenient and valid recording methodology (link)
  • And some case study work (link)

Finally, in 2014 we implemented smartphone-HRV monitoring with a collegiate female soccer team throughout their spring season. The icing on the cake was having this paper accepted in IJSPP, a journal that I’ve been reading for years and that has published some very important papers that have advanced the practical application of HRV monitoring in field settings. The following will serve as a brief overview of the study.


  • Up until recently, HRV data has been traditionally recorded via ECG in the laboratory or with heart rate monitors in the field. The cost and time consuming nature of data collection and analysis procedures with these systems make them prohibitive in team-sport settings. Smartphone HRV technology is an affordable, user-friendly and new alternative that has yet to be studied in the field.
  • Smartphone apps utilize ultra-short recording procedures for HRV data acquisition (brief stabilization period followed by ~1 min recording). These modified recording procedures have not been studied in field settings and therefore it is unclear if meaningful training status information can be acquired with such short R-R interval recordings.
  • It is unclear which position is more preferable for HRV recording. Parasympathetic saturation has been observed in highly fit athletes in the supine position. This is when HRV is low despite very low resting heart rates. Therefore, HRV measures following an orthostatic stimulus (upright posture) have been proposed for use in highly fit athletes to counteract saturation effects. More research to determine which position is most suitable for team-sport athletes is required.
  • The weekly HRV mean and CV have been proposed to be more meaningful than isolated (once per week) measures. No previous research has assessed the evolution of mean and CV values in response to varying weekly training load in collegiate female team-sport athletes. Particularly from ultra-short, smartphone-derived measures.
  • Lastly, previous work has demonstrated that HRV measured between 3 and 5 days per week was sufficient for reflecting weekly mean values in endurance athletes. It is unclear if this applies to team-sport athletes engaged in regular strength and conditioning and soccer training. Reducing HRV measurement requirements to between 3 and 5 days per week would make HRV monitoring much more practical for coaches and athletes by reducing compliance demands.


HRV data was recorded daily by the athletes after waking with the ithlete smartphone app over 3 weeks of moderate, high and low training load. As this study took place before the Wellness feature was added to the application, Wellness measures (fatigue, sleep, soreness, mood and stress) were acquired on M-W-F of each week via SurveyMonkey (see guide here).

photo 2

Training load was quantified via sRPE which was acquired between 15-30-min following all resistance training, conditioning and soccer practice sessions via email (SurveyMonkey) delivered to each athletes smartphone.


The weekly mean and CV for HRV in both standing and supine measures was determined first intra-individually and then averaged as a group. This was also done for sRPE and Wellness values.

The supine and standing HRV mean and CV were then determined for M-W-F of each week for 3-day values and again for M-T-W-R-F for 5-day values. These were then compared to the 7-day values (the criterion).


The 5 and 3-day measures within each week provided very good to near perfect intraclass correlations (ICCs ranging from 0.74 – 0.99) with typical errors ranging from 0.64 – 5.65 when compared with the 7-day criteria. The supine values demonstrated a smaller CV compared to standing. Therefore the supine measures over 3 and 5 days agreed strongly with the 7-day measures. The standing measures, particularly when measured across 3-days showed the lowest agreement.

HRV mean values demonstrated small effects in response to varying TL where the lowest HRV mean occurred during the high load week and highest HRV mean occurred during the low load week. The CV values were highest during the high load week and lowest during the low load week. The CV was more sensitive to changes in TL than the mean values (moderate effects). Wellness values were lowest during the high load week (moderate effects) and similar between moderate and low load weeks (trivial effects).

ijspp fig

Brief Discussion and Practical Applications

This study demonstrated that the HRV CV showed greater sensitivity to the changes in TL over the 3-week training period. Essentially, during high load training, the athletes experienced greater fluctuation in their scores. Greater training stress caused greater homeostatic perturbation, reflected in their Wellness and HRV scores in both standing and supine positions. In contrast, during the low load week, there was less day-to-day fluctuation in HRV because there was less fatigue from training stress. Therefore, monitoring CV changes throughout training may provide insight regarding training adaptation. Athletes experiencing greater fatigue will likely show greater CV values. More experienced athletes and those with higher fitness will likely demonstrate lower CV values. When these athletes show increases in the CV, it may be due to non-training related stressors. Comparing individual values to the group average will help identify athletes who may require further follow-up to determine if training of lifestyle modification is necessary.

Quoting from the paper:

Smart-phone derived, ultra-short HRV is a potentially useful, objective internal training status marker to monitor the effects of training in female team-sport athletes as part of a comprehensive monitoring protocol. Coaches and physiologists are encouraged to evaluate the weekly CV in addition to the weekly mean when interpreting HRV trends throughout training as this marker was more sensitive to TL adjustment in the short-term (i.e, 3 weeks). An increase in lnRMSSDmean and decrease in lnRMSSDcv were observed when TL was reduced following moderate and high TL weeks and interpreted as a positive response. Both supine and standing CV measures related well to TL in this study but only supine CV values acceptably maintained this relationship when assessed in 5 and 3 days. Therefore, caution should be used when evaluating standing HRV when only 5 or 3-day measures are used. Seated measures may provide a lower CV relative to standing while still providing an upright posture to counteract possible saturation effects. This may make seated measures preferable to standing as a lower CV is more likely to be captured in fewer than 7 days as demonstrated with the supine values. Reducing HRV data collection to 5 days per week may alleviate compliance demands of athletes and thus may make HRV implementation a more practical monitoring tool among sports teams.

HRV stabilization in athletes: towards more convenient data acquisition

Our “stability” paper has recently been published in Clinical Physiology and Functional Imaging.



Resting heart rate variability (HRV) is a potentially useful marker to consider for monitoring training status in athletes. However, traditional HRV data collection methodology requires a 5-min recording period preceded by a 5-min stabilization period. This lengthy process may limit HRV monitoring in the field due to time constraints and high compliance demands of athletes. Investigation into more practical methodology for HRV data acquisition is required. The aim of this study was to determine the time course for stabilization of ECG-derived lnRMSSD from traditional HRV recordings. Ten-minute supine ECG measures were obtained in ten male and ten female collegiate cross-country athletes. The first 5 min for each ECG was separately analysed in successive 1-min intervals as follows: minutes 0–1 (lnRMSSD0–1), 1–2 (lnRMSSD1–2), 2–3 (lnRMSSD2–3), 3–4 (lnRMSSD3–4) and 4–5 (lnRMSSD4–5). Each 1-min lnRMSSD segment was then sequentially compared to lnRMSSD of the 5- to 10-min ECG segment, which was considered the criterion (lnRMSSDCriterion). There were no significant differences between each 1-min lnRMSSD segment and lnRMSSDCriterion, and the effect sizes were considered trivial (ES ranged from 0·07 to 0·12). In addition, the ICC for each 1-min segment compared to the criterion was near perfect (ICC values ranged from 0·92 to 0·97). The limits of agreement between the prerecording values and lnRMSSDCriterion ranged from ±0·28 to ±0·45 ms. These results lend support to shorter, more convenient ECG recording procedures for lnRMSSD assessment in athletes by reducing the prerecording stabilization period to 1 min.

CPFI figure

In collaboration with Dr. Fabio Nakamura, we have a new paper currently in review that assesses the suitability of ultra-short (60-s) measures with minimal stabilization in elite team-sport athletes using the Polar system. We will also be assessing if these modified HRV recording procedures sufficiently reflect changes in fitness after a training program. Overall, shortened lnRMSSD recording procedures appear very promising. This will hopefully enhance the practicality of HRV monitoring among sports teams.

Weekly endurance performance and HRV: A case study of a collegiate cross-country athlete

During the fall season of 2013, we collected daily HRV, perceived wellness, training load and performance (race times) from a collegiate cross country runner. We wrote this up as a case study and it was published this month in JASC.

Endurance performance relates to resting heart rate and its variability: A case study of a collegiate male cross-country athlete.

This post is a brief summary of our findings.

This athlete competed in 8 km races on Saturday’s throughout the 8 week competitive season for a total of 6 races. HRV data was collected daily with ithlete in a seated position after waking and elimination. Daily wellness questionnaires were delivered to the athletes e-mail via SurveyMonkey (thanks to Carl Valle for this idea) which asked the subject to rate his perceived sleep quality, muscle soreness, fatigue, mood and stress levels out of 5 for a total wellness score /25. Training sessions were quantified with sRPE and a basic TRIMP value. Race times were collected from the host University’s official final results.

Resting heart rate and HRV derived from the smartphone measures were averaged weekly. In addition the coefficient of variation (CV) was calculated for all weekly HR measures.

*Note: lnRMSSD (i.e., HRV) is modified by the application (lnRMSSDx20) which is how the data is presented.

Our initial hypothesis was that the weekly mean of HRV would relate best to performance (8 km races) and that HRV would be a more sensitive measure than basic RHR. However, we found a near perfect correlation between the CV of HRV and performance (r = 0.92). When the CV was high for a given week, performance was worse (slower 8 km race time) whereas when the CV was lower, performance was better. A very similar, but slightly weaker correlation was found with the CV of resting heart rate. The figure below represents the relationship between weekly HRVcv and 8 km race times.

CV 8 km

The weekly mean related less well to performance. Quoting from the paper:

It has been suggested that as a result of tapering and the associated decrease in lnRMSSDmean, the relationship between lnRMSSDmean and performance is reversed (1). This may explain our finding of only a moderate correlation between lnRMSSDmean and 8 km race time and indicates that associations between endurance performance and lnRMSSDmean must be assessed within the context of the training phase and period (1).

The CV reflects the fluctuation in a metric (i.e., HRV/HR in this case) across the week. It’s been suggested that this marker represents the fatigue (decrease in HRV) and recovery process (return toward baseline). However, we also know that non-training related stressors may also effect daily changes. Therefore, a week with higher CV likely represents higher overall stress whereas a decrease in CV might indicate lower stress and/or better recovery, etc. This however should be taken into context with other indications of training status (e.g., TL, wellness, etc.) as a decreased CV has previously been associated with non-functional overreaching in an elite female triathlete in a case study by Daniel Plews and colleagues.

Two races were held at the same course, several weeks apart at the same time of day on Saturday. See excerpt from the paper below:

HRmean and lnRMSSDmean measures were similar on both occasions this season (lnRMSSDmean 73.8, HRmean 70.1 in week 3, lnRMSSDmean 72.6, HRmean 70.5 in week 8), however lnRMSSDcv and HRcv values in week 8 were nearly half of the values in week 3 (lnRMSSDcv 17, HRcv 12.7 in week 3, lnRMSSDcv 9.2, HRcv 6.5 in week 8). Most importantly, race time was 1:49 (min:sec) faster in week 8.  

Obviously, as this was only a case study of a single athlete across a single season, our results need to be interpreted with caution. However, if you’re monitoring HRV in yourself or of athletes, it would likely be best to include the CV in addition to the weekly mean when evaluating training status.

Looking ahead, we have 2 new manuscripts in review right now that evaluate the mean and CV of smartphone-derived HRV in a team of collegiate female soccer players as we make associations with training load changes and performance adaptations. These are very practical papers and the first, to my knowledge, that utilize smartphone applications with ultra-short measures (~1 min) for athlete monitoring.


HRV Monitoring Interview

Here’s a recent interview I had the pleasure of doing with Chris Beardsley of the Strength and Conditioning Research site:


We go over;

  • What HRV is and why we measure it
  • Practical and valid recording methodology as it pertains to shortened measurement duration and stabilization periods
  • HRV data interpretation for training and athlete monitoring
  • Practical recommendations

Trend Analysis: Importance of Context

It’s pretty well documented that regular aerobic exercise can result in an increase in resting HRV. Generally, you can expect no change or even a slight increase in HRV the day following low to moderate intensity aerobic exercise. However, with higher intensity training, HRV can take up to 48-72 hours to return to baseline, depending on intensity, duration, training status, fitness level, age, gender, etc. (Stanley et al. 2014). I’ve seen this numerous times with my own data where HRV decreases significantly 24 hours following interval sessions (particularly when they are not performed regularly) and increase beyond baseline by 48 hours.

Because low-moderate aerobic work tends to have an acute stimulatory effect of parasympathetic activity, it has been suggested that this would be useful as active recovery following high intensity sessions.

“because (at least) autonomic supercompensation following low-intensity training may occur within 24 h and since cardiac parasympathetic reactivation is delayed by the build-up of metabolites, inclusion of low intensity training subsequent to a high-intensity session may accelerate metabolite breakdown [88]. Athletes who train twice daily may also benefit from the accelerated recovery (metabolic recovery, as reflected by autonomic recovery) afforded by a low-intensity training session” (Stanley et el. 2013)

I recently included moderate intensity aerobic work on off days (approx.10 mins each on treadmill, cycle and rower for a total of 30 mins) and following my training sessions (10-12 mins on cycle) over about a three week period. Previous to this, very little aerobic work was being done, at least not consistently. During this time I lifted on Mon-Tue-Thurs-Fri each week. Below is my HRV data (lnRMSSDx20, standing) that includes a few weeks prior to the inclusion of regular aerobic work as well as the few weeks that followed.

daily trend aerobic weeks Next, I’ve included the weekly mean HRV and %CV (coefficient of variation) values.

mean and CV o2 weeks


I started performing the aerobic work midway through week 3 and continued until week 6. The trends both clearly show an increase in HRV during this time. We also see quite a large change in %CV with the regular aerobic work. In the weeks before and following the aerobic work, there are much bigger day to day changes in HRV which is quite typical for me. The inclusion of regular moderate aerobic work attenuated the daily changes I’d typically see following heavy training sessions. Clearly the post-workout aerobic work and active recovery work on off days was effective at promoting recovery. However,it’s important to clarify that HRV parameters are reflective of cardiovascular-autonomic activity, which does not necessarily include neuromuscular ability, CNS potential, etc.

“changes in cardiac parasympathetic activity are useful for monitoring aspects of recovery that are dependent on cardiovascular function. By contrast, changes in cardiac parasympathetic activity are less useful for monitoring other aspects of recovery such as restoration of muscle and liver glycogen, or repair of damaged muscle tissue” (Stanley et al. 2013)

Therefore, the lack of day to day changes in my HRV following heavy resistance training workouts does not imply that I was fully recovered within 24 hours and could repeat performances (e.g., heavy squats), only that that particular system was recovered. Thus, for strength athletes in particular, HRV is only one marker to consider when assessing daily recovery status. More work needs to be done in this area to determine how useful HRV monitoring is in this population and how it can be used effectively.

This data also shows how interpretation of a trend is context dependent, as mean and %CV values are affected by exercise mode and intensity. Thus, if working with team sport athletes, we may expect larger fluctuation and a lower mean when less aerobic exercise is prescribed and vice versa. Even endurance athletes will experience similar HRV changes when preparing for competition as the amount of high intensity/interval training increases and low-moderate intensity/steady state work decreases. This is often characterized with a bell-shaped HRV trend (example below).

bell shaped trend

The above data is taken from a case study we did of a collegiate endurance athlete over his competitive season (will be in a future edition of JASC). There is clearly a progressive increase in HRV up to a peak, at which point there is a progressive decrease. This is likely a result of more high intensity training and lower volumes of moderate/steady state  work as the athletes prepares to peak, further supporting the need to assess HRV changes in context to training phase, goal, structure, etc. (Buchheit,2014).

Changes in HRV are always context dependent. Decreases in the trend are not always associated with fatigue, nor are increases always associated with higher “readiness”. Nothing is ever as black and white as we’d like it to be. Additional reference to training load, psychometrics and performance will help with interpretation and if necessary intervention.


Buchheit, M. (2014). Monitoring training status with HR measures: do all roads lead to Rome?Frontiers in Physiology5.

Stanley, J., Peake, J. M., & Buchheit, M. (2013). Cardiac parasympathetic reactivation following exercise: implications for training prescriptionSports Medicine43(12), 1259-1277.

Our HRV posters and abstracts from NSCA National Conference

Below are the 4 abstracts and screen shots of our posters that we presented last weekend at the NSCA National Conference in Las Vegas. Many of these projects were in progress at the time of submission deadlines so 3/4 of the posters are actually just small parts of bigger projects. Following the title links will take you to the NSCA page where the posters can be viewed with zoom-in feature. There were plenty of good posters being presented all weekend so it would be worth your time to scroll through the website and view some of the other applied S&C research.


Michael R. Esco,  Andrew A. Flatt, Robert L. Herron

PURPOSE:  Heart rate variability (HRV) and heart rate recovery (HRR) are noninvasive indicators of cardiovascular autonomic control and are becoming popular for observing physiological changes associated exercise training and reflecting recovery status.  Previous research suggests there is a relationship between HRV and HRR, though the extent of this link remains unclear.  Additionally, measuring HRV in different body positions (supine vs. stand [SUP vs STA]) could further help explain the variance found in HRR following maximal exercise.  The purpose of this study was to determine the extent of variation in HRR that could be accounted for by HRV measured in SUP and STA in collegiate-female athletes.  METHODS: Twenty-three females (height = 1.65 ± 0.06 m, weight = 60.8 ± 6.3 kg, VO2peak = 44.6 ± 5.2 mL∙kg-1∙min-1) participated in this study.  Each participant rested in the supine position while HRV was recorded during the last 5-min of a 10-min SUP period, followed by an additional 6-min STA period of which the final 5-min was analyzed.  Participants completed a modified Bruce protocol treadmill exercise test to attain VO2peak.  Immediately following the exercise test, each subject actively walked at 0.89 m∙s-1 and 1.5% grade, while recording HRR at the 1-min (HRR1) and 2-min recovery mark (HRR2).  HRV values were expressed as root mean of successive R-R interval differences (RMSSD).  Pearson-product moment correlations were used to investigate the relationships between the HRR and HRV variables.  RESULTS:  The STA and SUP values were as follows; RMSSD = 40.4 ± 26.3 ms and 87.17 ± 38.8 ms, respectively.  Mean values for HRR1 was 28 ± 11 bpm and for HRR2 was 49 ± 11 bpm.  Significant correlations were found for STA and HRR1 (r = 0.54, p = 0.008) and HRR2 (r = 0.48, p = 0.020).  However, no significant relationships were found between SUP and HRR1 (r = 0.25, p = 0.255) or HRR2 (r = 0.38, p = 0.073).  CONCLUSIONS:  These results provide evidence that HRR is related to resting parasympathetic modulation when measured in the standing position within collegiate-female athletes. However, no association was found between HRR and supine HRV.  Therefore, when compared to the resting supine measures, standing RMSSD appears to be more strongly related to post-exercise vagal return.  PRACTICAL APPLICATIONS:  HRV and HRR are two non-invasive markers of cardiovascular autonomic control.  Both markers are becoming popular objective measures to consider when monitoring athletic recovery status and physiological adaptation to training.  Practitioners need to be aware that resting HRV and HRR may be independently associated with cardiac-autonomic control.  Therefore, these measures could reflect different responses to training.  Additional research involving longitudinal investigation is needed.

HRR HRV poster




 Andrew A. Flatt, Michael R. Esco

PURPOSE: The purpose of this study was to determine if the mean value of 5 and 3 day per week heart rate variability (HRV) recordings from both supine and standing positions would accurately reflect the weekly mean value in collegiate female soccer players during spring season training. METHODS: Twelve female soccer players (height = 165.12 ± 5.32 cm; weight = 60.78 ± 6.00 kg; body fat = 27.3 ± 4.98; VO2max = 46.08 ± 3.14 ml.kg.-1min-1) recorded their HRV with a smart phone application, a wireless ECG receiver and a chest-strap transmitter each morning for a one week period during a spring season strength and conditioning cycle. The participants first performed a supine followed by a standing measurement after waking and elimination. Upon completion of their morning HRV recordings, each athlete manually exported their data to a spreadsheet via the smart phone application to the investigator for analysis. The HRV parameter that was evaluated by the application was the natural log transformed root mean square of successive normal-to-normal interval differences multiplied by 20 (lnRMSSDx20) from a 55-sec recording. Weekly (7 day) mean values were calculated for each athlete for the supine and standing lnRMSSDx20 measures. Thereafter, mean values were gathered from Monday through Friday for the 5 day recordings and Monday, Wednesday and Friday for the 3 day recordings.  Agreement between the 5 and 3 day mean lnRMSSDx20 values and the weekly mean values were determined with repeated measures analysis of variance, intraclass correlations (ICC), and the method of Bland-Altman. RESULTS: The mean supine lnRMSSDx20 values were as follows: 89.39 +/- 6.84 for the 7 day; 89.72 +/- 7.00 for 5 day; 89.09 +/- 7.09 for 3 day.  When compared to the 7 day supine measures, the 5 and 3 day values revealed ICC values of 0.99 and 0.96, respectively, with tight limits of agreement (2.53 above to 1.87 below the mean difference of 0.33 for 5 day and 3.50 above to 4.10 below the mean difference of -0.30 for 3 day).  The mean standing lnRMSSDx20 values were as follows: 70.43 +/- 9.36 for 7 day; 70.65 +/- 9.17 for 5 day; 70.31 +/- 9.62 for 3 day.  When compared to the 7 day standing measures, the 5 and 3 day values revealed ICC values of 0.98 and 0.96, respectively, with tight limits of agreement (4.25 above to 4.01 below to mean difference of 0.22 for 5 day and 5.31 above to 5.55 below the mean difference of -0.12 for 3 day). CONCLUSIONS: This study showed that lnRMSSDx20 recordings in supine and standing positions averaged over 5 and 3 days showed good agreement with the 7 day mean in female collegiate soccer players during a spring season microcycle. Future research should aim to determine if 5 and 3 day recordings reflect changes in training status over a chronic period. PRACTICAL APPLICATIONS: lnRMSSDx20 values averaged over a one week period can be used as an objective measure of training status in athletes. However, obtaining data with a daily frequency is challenging in the applied sports setting, limiting the potential usefulness of HRV as a monitoring tool among sports teams. It appears that 5 day or 3 day recordings of ultra-short-term lnRMSSDx20 obtained by athletes on their smart phone device will suitably reflect the 7 day mean. This greatly reduces compliance demands of athletes.  Limiting data acquisition to 5 or 3 weekdays instead of over the entire 7 day period may enhance the practicality and convenience of HRV monitoring in field settings.

comparing poster


*This was taken from one week of what was actually 12 week training study.  However, data collection was still ongoing during the time of submission deadlines for the conference. The full paper will include additional weeks with an overload and unloading period to see if the 3 and 5 days still reflect the 7 day mean. CV will also be included. This paper was inspired by recent work by Plews et al. 


Andrew A. Flatt, Michael R. Esco

PURPOSE: This study aimed to determine if resting heart rate variability (HRV) values reflect previous day training load in a team of collegiate female soccer players after the first day of spring season strength and conditioning (S&C) training. METHODS: A team of female collegiate soccer players (n = 11; height = 165.16 ± 5.82 cm; weight = 60.26 ± 6.30 kg; body fat = 27.07 ± 5.39 %; VO2max = 46.76 ± 2.40 ml.kg.-1min-1) volunteered for this study. Supine and standing HRV values were acquired from each participant with a specialized smart phone application that utilized a wireless ECG receiver and a chest-strap transmitter. Supine and standing measures were obtained following waking and bladder emptying on the first day of spring S&C training (SUPRE and STPRE, respectively) and on the two days that followed (SUPOST1, STPOST1, respectively and SUPOST2, STPOST2, respectively). The natural log transformed root mean square of successive normal-to-normal interval differences multiplied by 20 (lnRMSSDx20) was the HRV parameter evaluated in this study. This value was automatically determined by the smart-phone application following a 55-sec recording which was manually exported to the investigator for analysis via e-mail. A one-way repeated measures analysis of variance (ANOVA) procedure with Tukey Post-hoc follow up tests were used to determine if there was any significant differences across the three days in lnRMSSDx20. RESULTS: The mean supine lnRMSSDx20 values for SUPRE, SUPOST1, SUPOST2 were 92.68 ± 8.19, 90.07 ± 7.58 and 90.62 ± 10.52, respectively. The supine values were not significantly different (p > 0.05). The mean standing lnRMSSDx20 values for STPRE, STPOST1, STPOST2 were 71.73 ± 10.07, 66.85 ± 10.10 and 70.78 ± 11.41, respectively. STPRE and STPOST2 were significantly higher compared to STPOST1 (p < 0.05).   CONCLUSIONS: The results of this study show changes in mean standing lnRMSSDx20 following a heavy training day in collegiate female soccer players.  However, there were no significant mean differences in the supine HRV values across the three days. Therefore, standing HRV measures may better reflect recovery status following a day of heavy training compared to HRV measured in the supine position. Future work should assess whether HRV measures can reflect training load over a longitudinal training program. PRACTICAL APPLICATIONS: Advancements in technology have made for more affordable and convenient tools for acquiring HRV data in the field for the purposes of monitoring fatigue and training status in athletes. Though HRV has been traditionally measured in a supine position, this data suggests that a standing position may be a more sensitive marker in response to heavy training load in female team-sport athletes. It should be noted that while mean HRV values provide the coach with a general indication of recovery status of the team, individual assessment should also be considered.

first day poster

*As with the above abstract, the full paper will include multiple weeks of data looking at the acute responses to training in both standing and supine positions.


Michael R. Esco, Andrew A. Flatt

This paper was recently published and can be accessed here.

ultra short poster


We actually had one more abstract to present in an oral presentation, but we ended up not being able to do it last minute and pulled it. Will save it for the future. The title was: