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

New Podcast Interview: HRV in Soccer

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.



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

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.

Some Soccer Team HRV Data

One observation I’ve made from monitoring my own HRV is that I will typically see major acute decreases in my trend following new training stimuli. However, after a few weeks of consistent training with the new program, I will see much smaller fluctuation in response to workouts despite high RPE. Essentially, with familiarity of the training stimulus, the body may experience less of an “alarm” stage. This enables higher training frequencies and volumes with less soreness and so forth.

Below is a small sample of some team data I’ve collected in a collegiate soccer team I worked with this past year. What your viewing is the first 3 days (Mon-Tues-Wed) of a new training cycle (Figure 1) and then the same training cycle performed a few weeks later with typical incremental progressions in resistance (for strength training) and distance (for conditioning) (Figure 2). On Monday’s we lifted in the morning and practiced and conditioned in the afternoon. Tuesday’s were off entirely. Therefore Monday’s HRV scores follow a weekend of rest representing “baseline”, Tuesday scores reflect Monday’s workload, and Wednesday marks 48 hours post workout (training resumed Wednesday afternoon).


Figure 1.

Figure 1.

Figure 2.

Figure 2.

In the first week of the new training program and structure (figure 1), 9 of 11 players showed a decrease in HRV following Monday’s workout (some more than others). A few weeks later, only 5 of 11 players showed an acute decrease.

Further discussion and analysis with much more data (complete weeks, periods of overload and deload, sRPE, psychometrics, etc.) and from measures obtained in supine and standing positions will be left for the manuscript.

Individual HRV Responses In Professional Soccer Players During A Competitive Season

In a team setting environment, athletes are often exposed to similar training loads during practices, training and competition. Monitoring of only the external training load provides coaches with an incomplete picture of how individual athletes may be responding and adapting to the training schedule. Two athletes can in fact respond entirely differently to the same program. A recently published case study by Bara-Filho et al. (2013) demonstrates how HRV, when measured periodically throughout training, can help distinguish these individual differences in professional soccer players exposed to the same training schedule. The following is a brief summary and review of this case study.

Materials and Methods

Subject 1 was a 26 year old Mid-Fielder with 7 years of professional playing experience. Subject 2 was a 19 year old Right Back with only 1 year of professional playing experience.

Over a 3 week period during a competitive season, both subjects participated in training that consisted of small-sided games, simulated matches, strength training, sprint training, and low-intensity aerobic recovery work. Training took place 1-2 times per day, 5 day’s/week culminating in a competition on the 6th day and rest on the 7th. Both subjects were starters in the 3 matches that occurred over the observation period.

HRV was measured on 5 occasions throughout the 3 week period on each Saturday and Monday morning (excluding the last Monday). This allowed for HRV indices to be evaluated both after the weekly training load was accumulated (Saturday) and after recovery (Monday). This is precisely the protocol that I discussed in a recent post entitled Making HRV More Practical for Athletes: Measurement Frequency.

HRV data was collected in the morning with a Polar RS800 watch while the athletes rested in a supine position.


Total weekly TRIMP values were similar in both athletes. After the first measurement (M1) Subject 1 showed an increasing trend in several HRV values (RMSSD, HF, SDNN, SD1) indicating good adaptation to training and quality recovery from competition. Subject 2 showed a progressively decreasing trend in these same HRV values indicating an accumulation of fatigue and insufficient recovery.


The authors suggest that subject 2, who saw a decreasing trend in his HRV values, may have been experiencing stressors unrelated to sport that may have contributed to his insufficient recovery. Though subjective measure (questionnaires) were not included, the physical training coach reported that athlete 2 would inform him that he was experiencing disturbed sleep, fatigue during training, and poor recovery.

A lower level of playing experience in subject 2 was reported as another possible explanation for his descending HRV trend. The psychological stressors and anxiety experienced by this younger athlete may have also contributed.

The authors briefly discuss the limitations of a supine measurement only when using HRV to monitor training load in athletes. Essentially, individuals with low resting heart rates appear to be subject to “parasympathetic saturation” in the supine position, possibly skewing the data. Therefore, including measurement performed in the standing position may serve as a resolution to this issue. I discussed this topic in a previous post entitled Supine vs. Standing HRV Measurement.

Finally, the authors conclude that HRV values were useful in monitoring the effects of a competitive training schedule in athletes as these values appear to be sensitive to individual characteristics as well as stress and recovery. A stable or increasing HRV trend appears to be favorable as it indicates quality recovery and adaptation to training. In contrast, a decreasing trend in HRV indicates higher stress and impaired recovery which may necessitate recovery interventions and reductions in training load.


Bara-Filho, M.G., et al. (2013) Heart rate variability and soccer training: a case study. Motriz: rev. educ. fis. 19(1): 171-77. Free Full-Text