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

Abstract

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.

FS_JSCR

Full Text on Research Gate

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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.
ABSTRACT

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

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New Podcast: Discussing Smartphone HRV Apps

I recently had a chance to sit down and discuss all things HRV monitoring with James Darley of the Historic Performance Podcast. There’s also a number of great interviews in the podcast archives worth checking out.

Topics discussed:

  • Background
  • Physiological basis for HRV as a recovery status metric
  • Preferred HRV parameter for athletes
  • HRV recording methodology (position, conditions, time of day, etc.)
  • Considerations for chosing the right HRV app for your situation
  • Recent research
  • Interpreting HRV data

Link to Podcast with show notes 

Show in Overcast App

 

 

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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

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HRV monitoring for strength and power athletes

This article is a guest post for my colleague, Dr. Marco Altini’s website. Marco is the creator of the HRV4training app that enables HRV measures to be performed with no external hardware (e.g., HR strap), just the camera/flash of your smartphone and your finger tip. He has several archived articles pertaining to HRV measurement procedures and data analysis from compiled user data that are well worth checking out.

The intro is posted below. Follow the link to read the full article.

Intro

​A definitive training program or manual on how to improve a given physical performance quality in highly trained individuals of any sport does not exist. Nor will it ever. This is because of (at least) two important facts:

  1. High inter-individual variability exists in how individuals respond to a given program.
  2. The performance outcome of a training program is not solely dependent on the X’s and O’s of training (i.e., sets, reps, volume, intensity, work:rest, frequency, etc.) but also largely on non-training related factors that directly affect recovery and adaptation.

The closest we’ll get to such a definitive training approach, (in my opinion) may be autoregulatory training. This concept accepts the 2 facts listed above and attempts to vary training accordingly in attempt to optimize the acute training stimulus to match the individual’s current performance and coping ability.

Improvements in physical performance are the result of adhering to sound training principles rather than strict, standardized training templates. A thorough understanding of sound training principles enables good coaches and smart lifters to make necessary adjustments to a program when necessary to maintain continued progress. In other words, good coaches can adapt the training program to the athlete rather than making the athlete to try and adapt to the program. This is the not so subtle difference between facilitating adaptation and trying to force it.

The theme of this article is not about traditional training principles, but rather about recovery and adaptation concepts that when applied to the process of training, can help avoid set-backs and facilitate better decision-making with regards to managing your program. Given that this site is about HRV, naturally we’re going to focus on how daily, waking measures of HRV with your Smartphone may be useful in this context. For simplicity, we will focus on one HRV parameter called lnRMSSD which reflects cardiac-parasympathetic activity and is commonly used by most Smartphone applications. Drawing from research and real-life examples of how HRV responds to training and life-style factors, I hope to demonstrate how HRV can be used by individuals involved in resistance training-based sports/activities to help guide training.

 

Continue reading on the HRV4training site.

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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.

 

 

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

It’s been a good start to the Thanksgiving break with the  acceptance of our latest study entitled “Initial weekly HRV response is related to the prospective change in VO2max in female soccer players” in IJSM (Abstract below).

We’re currently working on supporting these findings with a much larger sample size in the new year.

ABSTRACT

The aim of this study was to determine if the early response in weekly measures of HRV, when derived from a smart-phone application, were related to the eventual change in VO2max following an off-season training program in female soccer athletes. Nine female collegiate soccer players participated in an 11-week off-season conditioning program. In the week immediately before and after the training program, each participant performed a test on a treadmill to determine maximal oxygen consumption (VO2max). Daily measures of the log-transformed root mean square of successive R-R intervals (lnRMSSD) were performed by the participants throughout week 1 and week 3 of the conditioning program. The mean and coefficient of variation (CV) lnRMSSD values of week 1 showed small (r = -0.13, p= 0.74) and moderate (r = 0.57, p = 0.11), respectively, non-significant correlations to the change in VO2max at the end of the conditioning program (∆VO2max). A significant and near-perfect correlation was found between the change in the weekly mean lnRMSSD values from weeks 1 and 3 (∆lnRMSSDM) and ∆VO2max (r = 0.90, p = 0.002). The current results have identified that the initial change in weekly mean lnRMSSD from weeks 1 to 3 of a conditioning protocol was strongly associated with the eventual adaptation of VO2max.

 

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