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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Trend Changes versus Daily Changes

HRV fluctuates to a certain extent on a daily basis. I’ve seen athletes with coefficient of variations (CV, a marker of deviation from the weekly mean) as low as 2% to >15%. An athlete’s CV changes over time, which itself serves as what I believe to be, an important indication of training adaptation.  The CV is related to individual fitness level and training stress and possibly even performance potential. Measurement position will also affect the CV with lower CV’s observed in the supine position compared to standing.

Here’s an important lesson I’ve learned about interpreting HRV in athletes. A daily change in HRV can occur for a number of reasons, and may or may not have any meaningful impact on acute performance or “readiness”. Putting too much focus on an acute change in HRV without stepping back and observing the overall trend is a bit myopic. This isn’t to say that daily changes aren’t useful, just that a full appreciation of the training process, including the evolution of the trend in response to training will enable better analysis and therefore decision-making. This is because longitudinal changes in an athlete’s HRV trend do not occur for no reason. Increases, decreases, greater fluctuation, less fluctuation, when assessed over time, are all very meaningful.

Observe the screenshot below which details the last 6 months of a high level collegiate sprint-swimmers trend. The data pretty much interprets itself when you compare the changes in the trend to changes in training and life events.

6motrend

What can we observe from this?

  • Greater fluctuation and a decreasing trend during heavy training stress
  • Less fluctuation and an increasing trend during reduced training stress
  • Greater fluctuation and a decreasing trend during normal training with increased academic stress (preparing for and writing exams). Thanks to recent work from Bryan Mann, we know that this increase in non-training related stress may put athletes at greater risk of injury or illness.

I strongly believe that to use HRV effectively, you need to consider the changes in the trend, and not just the day to day stuff. When asked what HRV products are worthwhile or what do I think of App X or product Z, I always suggest that they invest in one that provides the best visualization of the data over time and includes other markers of training status (i.e., load, wellness, etc.). This enables more meaningful interpretation of the data and can therefore be more insightful and useful when determining the appropriate action to take with regards to training program adjustment.

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When do we intervene?

At what point should the coach or trainer implement a training or lifestyle intervention when an athlete is showing warning signs of excess fatigue?

This is easy to determine when looking back on the data retrospectively, but in real-time this can be a challenging question to answer. Especially when performance remains relatively stable during the early stages. There’s a sometimes blurry line between being too soft (changing the plan at every red flag) and being too hard (ignoring too many red flags).

In observing this athletes trend, it appears that the situation could’ve been easily avoided had some type of intervention been made early enough. The trend for HRV, and perceived measures of sleep quality, fatigue, soreness and stress all indicate that this athlete is heading for trouble.

downward trend

With poor sleep and high levels of training/non-training related stress the immune system is compromised and the athlete gets sick.

At what point do we intervene? Intervention starts with a conversation. The conversation acknowledges a red flag and helps determine what means of action to take (if any at all). In this situation, the first uncharacteristically low sleep rating should’ve started the conversation.

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