Effect of Competitive Status and Experience on Heart Rate Variability Profiles in Collegiate Sprint-Swimmers

Here’s a new paper from my time at Bama. A practical summary follows the link and abstract below.

Link to free full text:


When first getting started with tracking HRV in athletes, the inter-individual variation in trend characteristics can be confusing. Some athletes will display very high values and others will show lower values. Likewise, some will show quite stable values while others display substantial day-to-day variation. Naturally, the following question arises: why do some athletes have higher and more stable values than others?

Collegiate swim rosters typically include a mixed roster of athletes (males and females with a broad range of experience and skill). In this investigation we compared HRV trend characteristics between the national-level (including 6 Olympians) and conference-level sprint-swimmers throughout 4 weeks of standardized preparatory training. We also obtained details of individual training history.

The main findings were that national-level swimmers had higher and more stable HRV (higher mean LnRMSSD, lower LnRMSSD coefficient of variation) than their conference-level teammates. Differences in trend characteristics were attributable to a greater history of training and competing among the national-level swimmers (i.e., greater training age).

Whether these findings can be explained by greater aerobic fitness (we don’t think so), greater familiarity with training (possibly), or chronic physiological adaptations (possibly) among the higher-level swimmers is unclear.

The findings may be of some practical use for coaches when interpreted with previous work (see links below). For example, preliminary expectations with HRV monitoring should be that higher-level swimmers will display higher and more stable values throughout training and vice-versa for lower-level athletes. This may be interpreted to mean that the higher-level athletes could tolerate greater loads or that the lower-level athletes may need reduced loads. However, it is unclear if these training modifications would offer any performance/adaptation advantage. In addition, a higher-level athlete showing lower and less-stable values may be cause for concern (fatigue, stress, detraining, etc. depending on context). Whereas a lower-level athlete displaying higher and more stable values is likely adapting well to the training.

We’ve previously assessed how overload and tapering impact HRV in sprint-swimmers here.

We’ve previously assessed associations between subjective indicators of recovery and daily HRV in sprint-swimmers here.

Heart rate-based indices to detect parasympathetic hyperactivity in functionally overreached athletes. A meta-analysis

Our new meta-analysis determined that parasympathetic hyperactivity in overreached endurance athletes is best detected using weekly averaged versus isolated HRV values and in the standing versus supine position.

Thanks to Agustín Manresa-Rocamora, Antonio Casanova-Lizón, Juan A. Ballester-Ferrer, José M. Sarabia, Francisco J. Vera-Garcia, and Manuel Moya-Ramón for inviting my collaboration.

The full text can be accessed at the link below:


Ultrashort Versus Criterion Heart Rate Variability Among International-Level Girls’ Field Hockey Players

Here’s our latest study comparing 1 min vs 5 min HRV throughout a 4-week camp in international-level girls field hockey players. Values were highly correlated, showed similar responses to load, & similar associations with fitness. Practically same insight, 80% less time. Thanks to Drs. Gonzalez-Fimbres and Hernandez-Cruz for the collaboration.

Link to full free text below:


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

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

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

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

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

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

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

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


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

Revisiting 60-s HRV recordings vs. Criterion in athletes

I’ve recently had the pleasure of peer-reviewing a few very well-written and carried out studies investigating duration requirements for stabilization preceding HRV recordings by different research groups. I look forward to seeing the published versions as the quality of the papers was very high.

In reviewing these papers it prompted me to reconsider what we all have been using as the criterion period. My colleagues and I have published 5 papers using a 5-min R-R sample preceded by a 5-min ‘stabilization’ period (10 min total duration) as the criterion (as has other groups), which is in line with traditional procedures. But I think we failed to address an important limitation of these procedures…

The issue is that the ‘traditional procedures’ were not devised for the purposes of establishing LnRMSSD specifically (rather, they needed to accommodate spectral analysis), nor were they devised for reflecting fatigue and adaptation to training programs. Therefore, for these specific purposes, it can be argued that the traditional procedures may not be as relevant, or at the very least, calls into question whether the 5-10 min period following the 0-5 min stabilization is in fact a criterion within this context.

Some things to consider:

  • 10 min is a long time to lay or sit still, especially for athletes who struggle to go 30-sec without checking their iPhone (I don’t think anyone disputes this). Are they more relaxed and stable in this situation or are they growing impatient and restless?
  • Are ANS responses and adaptation to training best measured in a completely relaxed state, or perhaps in response to a mild stimulus such as orthostasis (sitting or standing) (previous thoughts on this here)?
  • Should we be as skeptical with the ‘criterion’ recordings as much as as we are with 60-s recordings? How do we know if one is better than the other in the context of monitoring athletes? There’s now numerous studies by different groups showing the usefulness of 60-s measures for reflecting training responses, associating with fitness, etc.
  • Perhaps the question shouldn’t be regarding the optimal duration of the recording but rather, what is the shortest, most convenient procedure possible that still provides meaningful training status information? I don’t think an athlete or coach cares if their 60 sec HRV isn’t the same as the criterion if it’s still providing useful information.
  • I’m doubtful we would have completed any longitudinal training studies where HRV recordings were >60 sec on a near-daily basis. In my experience, >60 sec measures are not feasible with teams. Therefore, it’s ~60 s or we don’t bother.
  • Should future research instead try to determine what are the best ways to perform a ~60 sec HRV measure to limit noise from confounding factors? How can we improve the validity and reliability of 60-sec measures? How long from food/fluid ingestion should we wait? Can we obtain this with PPG rather than HR straps? What is the best position to measure in? etc.

To be clear, I still think that research evaluating stabilization requirements and comparing to the ‘criterion’ is absolutely meaningful and an important starting point. This was not intended to be critical, but rather to open discussion on future research directions.



The effect of training status on HRV in D-1 collegiate swimmers

When implementing HRV monitoring with a new team, the coach will be quick to point out the inter-individual variability in the athletes’ trends. Some athletes are showing high scores and some are low. Some are showing considerable daily fluctuation while others show very consistent numbers. Or, some show substantial fluctuation during this period but minimal fluctuation during that period. This can be confusing and difficult to interpret, but with some context, the trends (and changes therein) can usually be explained.

Greater fitness levels are associated with higher resting HRV and faster parasympathetic reactivation following exercise. This likely contributes to the smaller coefficient of variation  (CV) we (and others) have observed in athletes with higher VO2max and intermittent running performance. So if we were to categorize athletes of the same sport based on competitive level (i.e., training status), we should see group differences between their average lnRMSSD and CV. What makes our approach different from previous work is the longer observation period (1 month), the use of a finger sensor (PPG) and smartphone application using ultra-short HRV recordings for daily data acquisition and inclusion of the CV in the analysis. This was presented at the NSCA National Conference in New Orleans this July. Full manuscript in production soon.


Andrew A. Flatt, Bjoern Hornikel, Michael R. Esco

University of Alabama, Tuscaloosa, AL

Resting heart rate variability (HRV) fluctuates on a daily basis in response to physical and psychological stressors and may provide useful information pertaining to fatigue and adaptation. However, there is limited research comparing HRV profiles between athletes of the same sport who differ by training status. PURPOSE: The purpose of this study was to compare resting heart rate (RHR) parameters between national and conference level Division-1 Collegiate swimmers and to determine if any differences were related to psychometric indices. METHODS: Twenty-four subjects were categorized as national (NAT, n = 12, 4 female) or conference level competitors (CONF, n=12, 5 female). Over 4 weeks, daily HRV was measured in the seated position by the subjects after waking and elimination with a validated smartphone application and pulse-wave finger sensor (app)  utilizing a 55-second recording period. Subjects then completed a questionnaire on the app where they rated perceived levels of sleep quality, muscle soreness, mood, stress and fatigue on a 9-point scale. The HR parameters evaluated by the app include RHR and the log-transformed root-mean square of successive RR interval differences multiplied by 20 (lnRMSSD). The 4-week mean for RHR (RHRm) and lnRMSSD (lnRMSSDm) in addition to the coefficient of variation (CV) for RHR (RHRcv) and lnRMSSD (lnRMSSDcv) were determined for comparison. In addition, psychometric parameters were also averaged between groups and compared. Independent t-tests and effect sizes ± 90% confidence limits (ES± 90% CL) were used to compare the HR and psychometric parameters. RESULTS: NAT was moderately taller (184.9 ± 10.0 vs. 175.5 ± 12.5 cm; p = 0.06, ES ± 90% CL = 0.83 ± 0.70) and heavier (80.4 ± 9.7 vs. 75.2 ± 11.9 kg; p = 0.26, ES ± 90% CL = 0.48 ± 0.67) than CONF, though not statistically significant. The results comparing HR and psychometrics are displayed in Table 1. lnRMSSDm and lnRMSSDcv was moderately higher and lower, respectively, in NAT compared to CONF (p<0.05). CONCLUSION: Higher training status is associated with moderately higher lnRMSSDm and lower lnRMSSDcv compared to those of lower training status. This was observed despite no significant difference in perceived stressors that may affect HR parameters. PRACTICAL APPLICATION: Training status appears to be a determinant of daily HRV and its fluctuation. This may be because higher level athletes are more fit and recover faster from training, resulting in a more stable HRV pattern. This information can be useful to practitioners when interpreting HRV trends in athletes. For example, an increase in HRV with reduced daily fluctuation may indicate improvements in an athletes training status. Alternatively, an athlete with high training status demonstrating reduced HRV and greater daily fluctuation may be showing signs of fatigue or loss of fitness depending on the context of the current training phase and program.

table swim HRV comarison

This figure shows a year of data from two athletes (Olympic level on top vs. Conference level on bottom) to provide a nice visual representation of their trend differences. HRV trend swim comparison

New Study: Intra- and inter-day reliability of ultra-short-term HRV in elite rugby union players

Here’s a look at our latest study in collaboration with Fabio Nakamura and colleagues, now in press with JSCR (Abstract below). In this study, HRV was recorded as a team at the training facility, not immediately after waking. This is the approach that many coaches are interested in using given the issue with compliance when trying to get athletes to perform HRV measures on their own at home after waking. Controlled and supervised measures at the facility appear promising, at least in these high level athletes.

It’s important to understand that autonomic activity is constantly making adjustments to physical, chemical and perceived psychological stimuli. Thus, HRV is inherently not the most reliable metric. However, training status/fitness appear to have a strong affect on day to day variation in HRV. More fit athletes recover faster/tolerate training better and thus tend to show less deviation from baseline compared to less fit athletes, of which will experience much greater homeostatic disruption from training and greater day to day variation. I strongly believe that the amount of daily fluctuation (i.e., lnRMSSDcv) is a very useful indication of fitness, stress and training adaptation.

We currently have a paper in production looking at the effect of training status on HRV. In the mean time, compare the trends below of an Olympic level and a conference level athlete, both short-distance swimmers (similar age and physical characteristics) across 4 consecutive weeks of training.

lnrmssd compareIntra- and inter-day reliability of ultra-short-term heart rate variability in rugby union players.

The aim of this study was to examine the intra-day and inter-day reliability of ultra-short-term vagal-related heart rate variability (HRV) in elite rugby union players. Forty players from the Brazilian National Rugby Team volunteered to participate in this study. The natural log of the root mean square of successive RR interval differences (lnRMSSD) assessments were performed on four different days. HRV was assessed twice (intra-day reliability) on the first day and once per day on the following three days (inter-day reliability). The RR interval recordings were obtained from 2-min recordings using a portable heart rate monitor. The relative reliability of intra- and inter-day lnRMSSD measures were analyzed using the intraclass correlation coefficient (ICC). The typical error of measurement (absolute reliability) of intra- and inter-day lnRMSSD assessments were analyzed using the coefficient of variation (CV). Both intra-day (ICC = 0.96; CV = 3.99%) and inter-day (ICC = 0.90; CV = 7.65%) measures were highly reliable. The ultra-short-term lnRMSSD is a consistent measure for evaluating elite rugby union players, in both intra- and inter-day settings. This study provides further validity to using this shortened method in practical field conditions with highly trained team sports athletes.

Full text on Research Gate

New Study: Individual HRV responses to preseason training in D-1 women’s soccer players

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.

Individual HRV responses to 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.


Full Text on Research Gate

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

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

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

HRV futsal Fig 1

Full Text on Research Gate

New Study: Interpreting daily HRV changes in female soccer players

Here’s a quick look at our latest study published ahead of print in the Journal of Sports Medicine and Physical Fitness. The full text is available here. Below is the abstract and some brief comments about the findings.

Interpreting daily heart rate variability changes in collegiate female soccer players

BACKGROUND: Heart rate variability (HRV) is an objective physiological marker that may be useful for monitoring training status in athletes. However, research aiming to interpret daily HRV changes in female athletes is limited. The objectives of this study were (1) to assess daily HRV (i.e., log-transformed root mean square of successive R-R interval differences, lnRMSSD) trends both as a team and intra-individually in response to varying training load (TL) and (2) to determine relationships between lnRMSSD fluctuation (coefficient of variation, lnRMSSDcv) and psychometric and fitness parameters in collegiate female soccer players (n=10).

METHODS: Ultra-short, Smartphone-derived lnRMSSD and psychometrics were evaluated daily throughout 2 consecutive weeks of high and low TL. After the training period, fitness parameters were assessed.

RESULTS: When compared to baseline, reductions in lnRMSSD ranged from unclear to very likely moderate during the high TL week (effect size ± 90% confidence limits [ES ± 90% CL] = -0.21 ± 0.74 to -0.64 ± 0.78, respectively) while lnRMSSD reductions were unclear during the low TL week (ES ± 90% CL = -0.03 ± 0.73 to -0.35 ± 0.75, respectively). A large difference in TL between weeks was observed (ES ± 90% CL = 1.37 ± 0.80). Higher lnRMSSDcv was associated with greater perceived fatigue and lower fitness (r [upper and lower 90% CL] = -0.55 [-0.84, -0.003] large, -0.65 [-0.89, -0.15] large).

CONCLUSIONS: Athletes with lower fitness or higher perceived fatigue demonstrated greater reductions in lnRMSSD throughout training. This information can be useful when interpreting individual lnRMSSD responses throughout training for managing player fatigue.

The idea of evaluating relationships between the coefficient of variation of lnRMSSD  (lnRMSSDcv) with fitness parameters was inspired by a 2010 paper by Martin Buchheit et al. In that study,  greater lnRMSSDcv derived from post-submaximal exercise recordings negatively correlated with maximum aerobic speed in youth soccer players. We had similar findings in our current paper where we observed large negative relationships between lnRMSSDcv (derived from waking, ultra-short smartphone  recordings) and VO2max and Yo-Yo IRT-1.

Another objective of this study was to focus on individual HRV responses in addition to group responses (see figure below). An interesting observation we made was that greater lnRMSSDcv was also associated with higher perceived fatigue. This finding is in contrast to a recent case comparison study by Plews et al. that found a decreased lnRMSSDcv to be associated with non-functional overreaching in an elite triathlete. However, this can possibly be explained by the severity of fatigue. For example, the decreased lnRMSSDcv observed in the triathlete was accompanied with a chronically suppressed lnRMSSDmean. Thus, lnRMSSD decreased and did not periodically return to baseline.

In our current study, large decreases in lnRMSSD typically returned to baseline after 24-72 hours. Thus, loads were not so high that the athletes were unable to return to baseline. Therefore, it is possible that there may be a progression in one’s HRV trend leading from moderately fatigued to severely fatigued that is characterized first by a greater lnRMSSDcv (reflecting fatigue and recovery process) followed by chronic suppression of lnRMSSD with no rebounding to baseline (reduced lnRMSSDmean and reduced lnRMSSDcv). More on this to come.


Figure interpreting daily HRV