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
HRV.”

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.

Abstract
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.

Background:

  • 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.

Methods:

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.

srpesm

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).

Results

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.

http://onlinelibrary.wiley.com/doi/10.1111/cpf.12233/abstract

ABSTRACT

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.

3 Month HRV and Wellness trends of two D1 Athletes

Below are the HRV trends of two NCAA D1 athletes from a team we’ve been working with over a 3 month period of virtually the same training schedule.

  • The vertical gray bars represent average perceived wellness (9 point scale)
  • The dotted horizontal black line is daily HRV
  • The thin black horizontal line is the 7-day rolling average
  • The dashed parallel horizontal lines represent the smallest worthwhile change (SWC = 0.5xCV)
  • HRV and wellness was acquired daily by the athletes with the ithlete finger sensor in the seated position.

Interestingly, these two athletes have very similar responses. About 3 weeks into the trend was a very intense training camp that was held out of state before Christmas. One athlete appears to experience more fatigue than the other with nearly the whole week below the SWC and a more pronounced decrease in wellness. HRV and wellness for both athletes improve over Christmas break. Following Christmas there is an intense 2-week training period followed by a reduction in training load. Both athletes frequently fall below the SWC here. Athlete A oscillates up and down while Athlete B remains below the SWC for nearly an entire week along with a decrease in wellness (middle of the trends). Both athletes trend upward after the intense training period and remain steady throughout the last half of the trend.

Athlete A

Athlete B

What makes things interesting is when athletes do not respond as expected. This is when the monitoring becomes invaluable as training intervention becomes extremely important.

Reviewing Survey Monkey as a free tool for Daily Wellness Questionnaires

Working with athletes in a team setting versus one on one or in small groups limits our ability to engage in small talk with each athlete before training or during warm-up. Being able to ask the athlete how they’re feeling, how they slept, how sore they are, what they’ve been eating, etc., provides insight as to the general state of the athlete and may be used to guide training on that particular day. Small modifications based on insight garnered from these conversations can help you determine if you’ll be pushing it a little harder or reducing the volume a little. Essentially, this is the simplest form of monitoring and managing training. However, it’s difficult to have 20 of these conversations before a workout with an entire team.

A popular method for acquiring this information without having to have individual conversations is to have your athletes respond to a “Wellness” questionnaire that surveys the athletes on their perceived quality of sleep, stress levels, soreness, etc. A brand new study from the JSCR by Gastin et al (2013), demonstrate the effectiveness of daily Wellness questionnaires (among many others). A team of Australian Football players were surveyed daily throughout their season with a brief questionnaire asking the athletes to rate levels of sleep quality, soreness, muscular strain, stress and so forth. Results showed that subjective ratings of physical and psychological wellness responded to weekly training adjustments. Scores reflected improved wellness (less strain, better sleep, etc) throughout the week allowing for optimal states for competition followed by significant decreases in overall wellness following competition. Scores also showed improvement during periods of unloading. Perhaps most interestingly, questionnaire scores discriminated individual differences for muscle strain following a competition as players with higher maximum speed reported higher levels of muscle strain. Evidently, simple daily questionnaires can prove to be quite insightful and useful for monitoring athletes.

If you’re part of a well funded program, you can purchase fancy software that will allow for easy data collection, interpretation and visualization of the data. Unfortunately this is not a luxury that most coaches have, particularly those involved in youth and amateur sports. In discussing this topic with Carl Valle a while back, he suggested Survey Monkey as a simple and free tool for collecting this data. So I created an account and have been testing it out over the last little while. Below are screen shots of its features, and some brief descriptions of how it works, pros, cons, etc.

Below is a screen shot of the survey I’ve been using. Thanks to Mladen Jovanovic, John Fitzpatrick, Aiden Oakley, Rhys Morris and Josh Dixon for their insights earlier this summer on survey options and collection methods. To my knowledge, this survey was created by Martin Buchheit.

wellness questionnaire

Creating a free account with Survey Monkey simply requires the user to provide an email address and create a password. However, the free account has restrictions (discussed later) but can be unlocked with upgrading and paying for premium accounts.

Admittedly, I am not the most tech savvy guy, so the number one thing I was hoping for was that it would be user friendly and intuitive… and it was.

Below is the screen for creating a new survey. Once you’ve created a survey you can re-create it easily with “Copy an existing survey”.

SM1

Creating survey questions:SM2

Once you’ve created your survey:SM3

Sending options for your survey:SM4

Add e-mail addresses of your athletes for email collection:SM5

Personalize the email (Default Shown):SM6

Schedule your survey delivery:SM7

Analyze results as a group:SM8

Or analyze results by individual:SM9

And the feature you’re likely wondering about… SM10

Sorry folks, need to upgrade your account to export data. They need to make money somehow, right?

A screenshot of what the athlete see’s in their email on their smart phone (this can be customized):

photo 1

A view of the actual survey after following the link from the email on Smart Phone:

photo 2

Pros:

  • Easy to create and send surveys
  • Nice visualization of the data
  • Can assess results as a team or individual with several options of how you want the data presented
  • Can schedule when the survey is to be delivered
  • All of these features are free
  • This can also be used to collect sRPE info by scheduling the survey to be sent at the appropriate time, provided you know session duration or total reps.

RPE

Cons:

  • As far as I’m aware, you must re-create the survey and schedule to send it every day. This takes about 2 minutes. If any of you are Survey Monkey experts and know how to automatically re-send the same survey with a new collector please tell me.
  • Data can be exported easily to excel but you must pay to upgrade your account for this feature
  • A numeric value is not assigned to a given rating e.g., 1-5 points. This must be done manually in excel so that you can create daily totals (out of 25 possible points). See Mladen’s free spreadsheet for more on this here

Hopefully this was helpful.