Tag Archives: Sprinter

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

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

Link to free full text:

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

Context:

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.

HRV and Wellness responses to overload and taper in sprint-swimmers

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Here’s a brief overview of our latest study capturing daily HRV and wellness ratings throughout overload and tapering in collegiate sprint swimmers preceding a championships competition.

The majority of research in the area has primarily focused on endurance athletes. It’s been a goal of mine for a while now to examine HRV responses in athletes participating in anaerobic events such as short-distance swimming.

The athletes completed wellness questionnaires and recorded HRV daily via smartphone and validated pulse-wave finger sensor (seated position) after waking. The observation period lasted 6-weeks which included 1 week of baseline, 2 weeks of overload and a progressive 3-week taper. The overload was characterized by a substantial increase in training intensity while overall volume varied by up to only 20%. Of the group, 2 athletes went on to compete in the 2016 Olympic summer games.

Full text link:

HRV and psychometric responses to overload and taper in collegiate sprint-swimmers 

OBJECTIVES:

The purpose of this study was to evaluate cardiac-parasympathetic and psychometric responses to competition preparation in collegiate sprint-swimmers. Additionally, we aimed to determine the relationship between average vagal activity and its daily fluctuation during each training phase.

DESIGN:

Observational.

METHODS:

Ten Division-1 collegiate sprint-swimmers performed heart rate variability recordings (i.e., log transformed root mean square of successive RR intervals, lnRMSSD) and completed a brief wellness questionnaire with a smartphone application daily after waking. Mean values for psychometrics and lnRMSSD (lnRMSSDmean) as well as the coefficient of variation (lnRMSSDcv) were calculated from 1 week of baseline (BL) followed by 2 weeks of overload (OL) and 2 weeks of tapering (TP) leading up to a championship competition.

RESULTS:

Competition preparation resulted in improved race times (p<0.01). Moderate decreases in lnRMSSDmean, and Large to Very Large increases in lnRMSSDcv, perceived fatigue and soreness were observed during the OL and returned to BL levels or peaked during TP (p<0.05). Inverse correlations between lnRMSSDmean and lnRMSSDcv were Very Large at BL and OL (p<0.05) but only Moderate at TP (p>0.05).

CONCLUSIONS:

OL training is associated with a reduction and greater daily fluctuation in vagal activity compared with BL, concurrent with decrements in perceived fatigue and muscle soreness. These effects are reversed during TP where these values returned to baseline or peaked leading into successful competition. The strong inverse relationship between average vagal activity and its daily fluctuation weakened during TP.

While group responses are certainly meaningful, the individual responses provide more meaningful information to practitioners. The figure below shows the individual trends from 3 athletes that exemplify 3 common training responses I’ve observed in a variety of athletes.

swim-trend-hrvtraining-blog

Subject B (middle) has the smallest CV at baseline and subsequently handles the overload very well, with minimal reductions in lnRMSSD. This indicates that Subject B is in great shape and could probably handle greater loads.

Subject C (bottom) displays what I would consider to be a very expected response to overloading. There is a considerable increase in daily lnRMSSD fluctuation (i.e., increased CV) and progressive but small decrease in the trend. I interpret this type of response to indicate that the loads are sufficient enough to provoke the fatigue/recovery process but not so high that HRV becomes suppressed. This is possibly indicative of a load/dose of training that is high but within the overall recovery capacity of the athlete.

Subject A (top) has the highest CV of the group at baseline and subsequently responds the least favorably to the overload. lnRMSSD pretty much crashes almost immediately and remains suppressed for several days (boxed data points). The coach pulled back on subject A due to high fatigue, reduced performance, decrements in pulse-rate recovery between sets, etc. The trend immediately improves until about 1-week out from competition at which point loads again were further reduced. Ultimately, this athlete improved upon previous best times at competition from that year, suggesting that the interventions were effective.

The main take-home would be that the typical response to intensified training includes a reduction and greater daily fluctuation in HRV, along with decrements in wellness scores. Athletes demonstrating different responses (i.e., minimal change in HRV trend or conversely chronic suppression of HRV) may be coping better or worse than expected. Coaches should then investigate and address factors contributing to the poor response.

HRV Data from a High School Sprinter

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Here is some more data and analysis from a nationally ranked high school sprinter (Junior) that I have using ithlete. Please note that the sprinter trains primarily with his sprint coach. I work with him roughly 3 days/week on mobility, restoration, etc.  He was an ideal candidate for monitoring HRV as he is an extremely motivated and dedicated athlete and there was no doubt in my mind that he could handle the daily measurements. The data stops in early January because he somehow broke the HRV receiver I gave him. A new one has been ordered recently I’ve been told. This data collection is primarily for observational purposes since I do not control or manipulate his training as mentioned above.

November

ZWNovTable

ZWNovtrend

  •  After 1 week of using ithlete, I had him start using the comments section and sleep score.
  • His resting heart rate was higher than I expected. I had him perform his measurements standing but in hindsight I should’ve had him do them seated based on his RHR.
  • HRV average is mid 70’s which is what I expect from an anaerobic athlete. Still would expect his HR to be at least in the high 60’s in standing position.
  • Clearly he stays up super late on weekends and sleeps in late. Been on his case about this. 

First Half of December

ZWDec1Table

 ZWDec1Trend1

  • HR/HRV average remains consistent. Coping with training well.
  • Race day on 12/7, hit a PB in his part of the relay. Not a hard race, treated as practice.
  • Reports of back soreness that persisted long enough for him to seek treatment (documented in next table).

Christmas Break – Second Half of December & Early January 

ZWXmasTable

ZWXmasTrend

  •  This last section of data is from his Christmas break. Interestingly his HRV average drops and his RHR increases. I attribute this to the change in routine (off of school), staying up late regularly, etc. I also notice changes in my HRV when my routine is interrupted. The body likes consistency.
  • Things appear to be going well though as he seldom gets below baseline scores (amber).
  • Race day on 1/6 and hits a PB on 60m.

Given that this athlete is still young and taking advantage of “newbie” strength gains, I would expect him to hit PB’s relatively consistently on the track. Based on his trend, fatigue was never really an issue. More training may have been well tolerated.

I’d like to get him to start using the training load feature too now to get a better idea of how hard his workouts are (perceptively).