Monthly Archives: February 2013

HRV and Adaptation: Insights from a hockey player

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One of the first things I learned from using ithlete was that my HRV trend reflected my progressive adaptation to exercise. After my first conditioning session in two years I saw an immense drop in my HRV. Each session thereafter resulted in smaller fluctuations in my HRV score until eventually my trend was virtually unchanged from it. In the screenshot below you can see that in early October of 2012, I experienced a huge drop in HRV with a red indication. That was the morning after my first conditioning session (stair intervals). These workouts were performed each Wednesday thereafter with a progressive increase in sets and duration however you’ll see that HRV isn’t nearly as effected as it initially was. The next major drop in my HRV occurred in late November but wasn’t the result of a conditioning session.

HRV Chart

I also learned that HRV reflects illness, as well as the time it takes to fully recover from it. In the screen shot below you can see when I get sick and how long it took for my HRV to return to baseline levels (discussed in much more depth here).

073112_1517_Illnessreco3.png

Based on the sRPE levels, you can see that moderate workouts were causing large drops in HRV; a clear indication that my body was still fighting the infection even though I was symptom free.  I was able to adjust my training accordingly and not push it too hard until my body was ready to handle it.

See also a case study by Botek et al (2012) who used HRV to guide an elite athlete back to competition after having infectious mononucleosis.

HRV has also been shown to reflect acclimatization to heat (Dranitsin, 2008; Epstein et al. 2010).

Hockey Player

About 8 weeks ago I posted the HRV trend of a hockey player that I’m working with. Below in his screen shot you can see that the first two weeks of workouts were very taxing. However, we only had a short period of time to train and therefore waiting for optimal recovery wasn’t an option.

A.E.Trend

Keeping tabs on HRV, subjective measures of fatigue and performance markers I would make small changes in his training. For example I would maintain intensity but reduce volume when his HRV was low and he felt fatigued. I’ve found volume to affect HRV much more than intensity.

At this point his HRV baseline has improved 8 points and his HRV is not nearly as affected after intense training sessions. He’s put on about 10lbs of lean body mass and has increased both strength and fitness considerably; clear indications of positive adaptation. I’ve found that with this particular athlete HRV is generally in line with his perceived levels of fatigue. For example, he’ll report some soreness but that he feels great and highly motivated to train. This will typically corresponds with a good HRV score. However, on days where he reports feeling fatigued, unmotivated etc, HRV is almost always below baseline. Sleep duration and quality will also correlate with his HRV and perceived fatigue.

AETrendFeb

In each of the above examples, HRV has been a valuable tool in reflecting adaptation. Acute changes (daily change) in HRV are valuable in that they reflect a transient response to a significant stressor (response to a workout, high emotional stress, awful sleep, extreme deviation in nutrition, etc.). Monitoring the weekly and monthly trends provides insight as to how training and global stress is being tolerated over time (cycle to cycle). At no point did this athletes strength performance drop during our training. Even on days with low HRV he was able to hit or match a PR in strength exercises. However, strength levels weren’t too high to begin with so I wouldn’t read too much into that.

I’ve had some compliance issues with this athlete and taking daily measurements so getting him to fill in the comments section or use the training load function was out of the question. Therefore that data is not posted.

In closing, taken with subjective measures of fatigue, performance and global stress, HRV can potentially reveal meaningful information about adaptation and training response.

References

Botek, M. et al. (2012) Return to play after health complications associated with infection mononucleosis guided on ANS activity in elite athlete: a case  study. Gymnica, 42(2)

Dranitsin, O. (2008) The effect on heart rate variability of acclimatization to a humid, hot environment after a transition across five time zones in elite junior rowers. European Journal of Sport Science, 8(5): 251-258 Abstract

Epstein, Y. et al. (2010) Acclimation to heat interpreted from the analysis of heart rate variability by the Multipole Method. Journal of Basic & Clinical Physiology Pharmacology, 21(4): 315-23 Abstract

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).

4 Months of HRV, sRPE, Tap Test and Sleep score: Charts, Tables and Analysis

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Since about mid-September of 2012 I started using a CNS Tap Test to see if it provided any indication of training fatigue or if it correlated with my HRV. In addition to tracking my tap test and HRV, I’ve also documented  sRPE and sleep score.

Descriptions

Tap Test – On the tap test app,  perform as many taps as possible in 10 seconds with right index finger and left index finger. I charted these values both separately as Right and Left as well as there total (sum). Tap test was performed immediately following morning HRV test.

HRV – Standard ithlete HRV measurement performed immediately after waking and bladder emptying. The measurements were all performed in the standing position. The ithlete uses the following formula for the HRV value:  20 x Ln (RMSSD). RMSSD is a time domain measure that reflects parasympathetic tone and has been shown to correlate reliably with the high frequency component of frequency domain measures (Sinnreich et al. 1998).

sRPE – Following a workout session I would rate perceived exertion on a scale of 1-10. Generally, active recovery/aerboic work would fall between 1-5 while resistance sessions fell between 6-10.

Sleep Score – I used the ithlete sleep rating score to track sleep quality. On a scale of 1-5 I would rate sleep quality after HRV measurement. Generally, an uninterrupted 7-8 hour sleep was rated as 5. One disturbance/wake was given a 4, etc.

Not Discussed – Today I will not be including discussion on strength performance in relation to HRV or Tap test as I did not really keep track of this. However, in the future I will do this once I determine the best way to quantify this.

Below are the charts with brief comments regarding training/stress for that month.

OCTOBER

Oct_data

– High stress and lack of training in early October due to work related trip over 3-4 days.

NOVEMBER

Nov_data

– Most consistent training month, most sessions completed, most stable HRV, highest HRV Avg, highest Tap Test Avg, highest sleep score. (more on averages and sleep at the end)

DECEMBER

Dec_data

– Highest strength demonstrated in this month out of the 4. Training interruption over the Christmas holiday.

JANUARY

Jan_data

– HRV effected by NYE party but Tap Test appears unaffected (alcohol, late night, etc.). Detrained slightly from lack of training of holidays. Training resumes, transitioning to lifting 4 days/week. Lowest HRV avg, lowest tap sum avg, fewest aerobic sessions.

Comparison of HRV, Sleep, and Tap Test Averages 

Avg_data1

data table avg

– HRV and Tap Test both peak during November which also has the highest average sleep rating. However from the table above you can see that these are by very small percentages.

– In the table and chart below you can see that peak HRV and peak Tap average also occur during the month of most consistent training, most aerobic sessions and most overall training sessions.

– HRV, Tap Test Left, Right and Sum all reach lowest averages in January. January also has the fewest aerobic sessions and comes after a period of detraining (discussed in depth here) in late December.

Comparison of HRV, # of Aerobic Sessions, # of Resistance Sessions & Sum of all sessions

lift_vs_hrv

lift_vs_hrv_table

Main Findings

Highest HRV avg, highest sleep avg, highest tap sum avg, highest left tap avg all occur in November. This corresponds with most total and most aerobic training sessions.

Conversely, lowest HRV avg, lowest tap left, right and sum average occur during January which also corresponds with fewest aerobic sessions but not with lowest sleep avg.

As you can clearly see, there is very little variation in month to month values  and therefore no significant or meaningful conclusions can really be made. However, my HRV data does fall inline with the overwhelming amount of research that shows HRV increases in response to aerobic exercise.

In a future experiment I will track performance ratings in addition to all of the other variables to see if there is any correlation. I will also plan some overload training to see how these markers respond. My training was relatively static during these 4 months.

Correlation between HRV, sRPE and subjective fatigue in athletes

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Today I will review the research I’ve read that investigates the relationship between perceived exertion ratings of a workout session (sRPE), subjective levels of fatigue and HRV in effort to examine the usefulness of HRV in reflecting training load in athletic populations. Like all of my articles, this report is based on my interpretation of the research and perspectives from personal experience.

The Research

In a brand new study from the JSCR, Sartor and colleagues (2013) followed elite male gymnasts (n=6, age 16) over 10 weeks of training. HRV was monitored daily every other week while sRPE was collected immediately following each workout. HRV strongly correlated to previous day sRPE in both supine (HF%, HF%/LF%) and supine to seated measurements (mean RR, mean HR, HF%, SD1). Relationships were also seen between HRV, and perceived wellness (foster’s index). HRV correlated with training load (sRPE) and psychophysiological status.

Though sRPE wasn’t used in this next study, KeTien (2012) monitored HRV, blood-urine nitrogen (BUN) and profile of mood states (POMS) in 24 national level rugby players over an 8 week conditioning program. The program progressed from more aerobic based work to more anaerobic/interval based work. Spectral measures of HRV correlated with both POMS and BUN at each time point throughout the training period.

During the 2006 World Cup, Parrado and colleagues (2010) set out to determine if perceived tiredness could predict cardiac autonomic response to overload in elite field hockey players (n=8).  A strong correlation was found between per­ceived tiredness scores and HRV. Higher levels of perceived tiredness (acquired from questionnaire) were related to lower values of parasympathetic tone (RMSSD), pNN50 and higher LF/HF ratio. In order to discern changes in HRV brought on by fatigue from changes in HRV caused by pre-competitive anxiety, the researchers had the athletes complete anxiety questionnaires.

“Results show that cognitive anxiety and self-confidence sub­scales of the CSAI–2 were not related to perceived tiredness nor to heart rate variability. In the absence of a relation between cognitive anxiety and heart rate variability, it can be assumed that the relationship established between heart rate variability indexes and perceived tiredness scores are attributable to the fatigue state.”

Accounting for pre-game anxiety is very important as previous research has shown this to affect HRV (Edmonds et al. 2012, Mateo et al. 2012, Murray et al. 2008), thus making it difficult to distinguish fatigue from acute anxiety on the morning of a competition.

Edmonds et al. (2012) found that HRV (HF) correlated with sRPE in youth rugby players (n=9) during a one week microcycle of practices and a game. However, game day HRV values were lower which was attributed to the aforementioned pre-game anxiety since training loads were reduced before the competition.

Smith and Hopkins (2011) monitored performance, HRV, sRPE and subjective fatigue in elite rowers (n=10) throughout an intense 4 week training period. Interestingly, the most improved athlete and the only overtrained athlete both had statistically similar levels of perceived fatigue and changes in LF/HF ratio. However, after looking closely at the data, RMSSD showed a noticeable decline in the OT athlete compared to the most improved who had a moderate increase in RMSSD. The determining factor however in this case was performance changes.

Thiel at al. (2012) found that in 3 elite male tennis players, HRV, serum urea and psycho-physical state (assessed by EBF-52 questionnaire) each responded to overload training. As training load increased, HRV (RMSSD) decreased, perceived fatigue increased and serum urea increased. However, performance increased (V02 max, Single Leg CMJ, DJ index) and therefore performance metrics should always be considered when trying to discern functional overreaching (FOR) from non-functional overreaching (NFOR). HRV changes act as an early warning sign while performance decrements may represent the initial transition from FOR to NFOR.

Cipryan et al (2007) found that HRV correlated to performance in hockey players (age 17, n=4) but did not correlate to self-reported health status. Therefore, coaches should use caution when using perceived stress to predict ANS status and thus an objective measure (like HRV) is still recommended.

In elite female wrestlers, perceived stress (in the form of; excessive competition schedule, social, education, occupational, economical, travel, nutritional, etc) contributed to NFOR when HRV parameters were significantly increased (Tian et al. 2012). There was no mention of perceived stress/recovery in the NFOR group with significant decreases in HRV parameters. Regardless, subjective measures of stress including non-training related events require consideration when planning training. Monitoring the global stress of an athlete is more meaningful then simply training load.

Plews et al. (2012) monitored HRV and perceived measures of recovery (sleep, soreness, etc.) in two elite triathletes over a 77 day period leading up to competition. One athlete was considered NFOR. Perceived levels of recovery were not associated with HRV. However, the NFOR athlete admitted that she felt deterred from  reporting  low scores as anything below a certain score would be automatically sent to the coach. Therefore, when relying on perceptual measures from athletes, coaches must be prudent in ensuring honest reports. HRV was a better indicator of fatigue in this study.

The last study I’d like to mention only appears to be available in German at the moment. I translated the paper with google, however it was very rough to say the least. Therefore I will simply quote the pertinent information from the abstract:

“6 endurance athletes measured morning heart rate, heart rate variability (HRV) and mood state during a normal training period, a 17 day ultrarace (Deutschlandlauf) and following a recovery period. 4 out of 6 runners could not finish the race due to injury or exhaustion. 3 of them showed diagnostically relevant criteria of overreaching. All runners who quit the race showed increased morning heart rate, decreased HRV and a decreased mood state during competition. The studied parameters showed individually different adaptations but there were early changes that preceded the abortion of the run that gave diagnostically relevant information.” (Bossmann 2012)

Thoughts

Though there appears to be a strong tendency for HRV to reflect perceived training load and subjective fatigue, an objective measure of ANS status should still be considered. Subjective measures from athletes are only meaningful if honestly reported.

I’ve personally seen a strong correlation between morning HRV score and session rating of perceived exertion (sRPE) of the previous day’s workout. However, I’ve learned that this relationship isn’t perfect. I’ve experienced situations where;

–          Perceived exertion may be high but HRV response may be minimal if the workout is familiar (exercise selection, order, intensity, etc.).

–          In direct contrast to the above, perceived exertion may be moderate but HRV response may be significant if the workout is unfamiliar.

–          Non-training related factors affect HRV. Sleep, aerobic fitness, mental stress, nutrition, etc. can all impact ANS activity, possibly obscuring the relationship between training load and HRV.

–          Stress from travel, illness, occupation, etc. may have a larger impact on ANS than is perceived and reported.

–          More on other factors effecting HRV here.

In conclusion, obtaining both objective and subjective measures of fatigue along with performance indicators will provide a more accurate indication of training status. Monitoring of these variables regularly should enable the coach to better manipulate training loads to ensure progression and avoid unintentional overreaching.

References

Bossman, T. (2012) Effects of ultra-long-distance running on selected physiological and psychological parameters as a possible marker of overloading. Swiss Journal of Sports Medicine, 60(1): 21-5. Full Text

Cipryan, L., Stejskal, P., Bartakova, O., Botek, M., Cipryanova, H., Jakubec, A., Petr, M., & Řehova, I. (2007)  Autonomic nervous system observation through the use of spectral analysis of heart rate variability in ice hockey players.  Acta Universitatis Palackianae Olomucensis. Gymnica, 37(4): 17-21. Free Full-Text

Edmonds, RC., Sinclair, WH., and Leicht, AS. (2012) The effect of weekly training and a game on heart rate variability in elite youth Rugby League players. Proceedings of the 5th Exercise & Sports Science Australia Conference and 7th Sports Dietitians Australia Update. Research to Practice  Abstract

Ke-Tien, Y.(2012) Effects of Cardiovascular Endurance Training Periodization on Aerobic performance and Stress Modulation in Rugby Athletes. Life Science Journal, 9(2): 1218-25. Full-Text

Mateo, M. et al. (2012) Heart rate variability and pre-competitive anxiety in BMX discipline. European Journal of Applied Physiology, 112(1): 113-23.

Murray, N. P. et al. (2008) Heart rate variability as an indicator of pre-competitive arousal. International Journal of Sport Psychology, 39: 346-355.

Plews, DJ., Laursen, PB., Kilding & Buchheit, M. (2012) Heart rate variability in elite triathletes, is variation in variability the key to effective training? A case comparison. European Journal of Applied Physiology, 112(11): 3729-41.

Parrado, E.  et al. (2010)Perceived tiredness and HRV in relation to overload during a field hockey world cup. Perceptual and Motor Skills, 110(3): 699-713 Abstract

Sartor, F. et al. (2013) Heart rate variability reflects training load and psychophysiological status in young elite gymnasts. Journal of Strength & Conditioning Research, Published ahead of print.

Smith, T.B., & Hopkins, WG. (2011) Heart rate variability and psychological stress in an elite female rower who developed over-training syndrome. New Zealand Journal of Sports Medicine, 38(1): 18-20.

Thiel, C. et al. (2012) Functional overreaching in preparation training of elite tennis professionals. Journal of Human Kinetics, DOI: 10.2478/v10078-011-0025-x

Tian, Y., He, ZH., Zhao, JX., Tao, DL., Xu, KY., Earnest, CP. & McNaughton, LR. (2012) Heart rate variability threshold values for early-warning non-functional overreaching in elite women wrestlers. Journal of Strength & Conditioning Research, Published ahead of print

 

New HRV Research Vol: 2

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Here are 5 new studies pertaining to HRV and training.

Previous Edition: Vol: 1       

1.

Sartor, F. et al. (2013) Heart rate variability reflects training load and psychophysiological status in young elite gymnasts. Journal of Strength & Conditioning Research, Published ahead of print. 

Abstract

In gymnastics monitoring of the training load and assessment of the psychophysiological status of elite athletes is important for training planning and to avoid overtraining, consequently reducing the risk of injures. The aim of this study was to examine whether heart rate variability (HRV) is a valuable tool to determine training load and psychophysiological status in young elite gymnasts. Six young male elite gymnasts took part in a 10 week observational study. During this period, beat to beat heart rate intervals were measured every training day in week 1, 3, 5, 7 and 9. Balance, agility, upper limb maximal strength, lower limb explosive and elastic power were monitored during weeks 2, 4, 6, 8 and 10. Training load of each training session of all 10 weeks was assessed by session-RPE and psychophysiological status by Foster’s index. Morning supine HRV (HF% and LF%/ HF%) correlated with the training load of the previous day (r=0.232, r=-0.279, p<0.05 ). Morning supine to sitting HRV difference (mean RR, mean HR, HF%, SD1) correlated with session-RPE of the previous day (r=-0.320, r=0.301 p<0.01, r=0.265, r=-0.270, p<0.05) but not with Foster’s index. Training day/reference day HRV difference (mean RR, SD1) showed the best correlations with session-RPE of the previous day (r=-0.384, r=-0.332, p<0.01) and Foster’s index (r=-0.227, r=-0.260, p<0.05). In conclusion, HRV, and in particular training day/reference day mean RR difference or SD1 difference, could be useful in monitoring training load and psychophysiological status in young male elite gymnasts.

2.

Boutcher, S.H. et al. (2013) The relationship between cardiac autonomic function and maximal oxygen uptake response to high-intensity intermittent exercise training. Journal of Sports Sciences, Published ahead of print.

Abstract

Major individual differences in the maximal oxygen uptake response to aerobic training have been documented. Vagal influence on the heart has been shown to contribute to changes in aerobic fitness. Whether vagal influence on the heart also predicts maximal oxygen uptake response to interval-sprinting training, however, is undetermined. Thus, the relationship between baseline vagal activity and the maximal oxygen uptake response to interval-sprinting training was examined. Exercisers (n = 16) exercised three times a week for 12 weeks, whereas controls did no exercise (n = 16). Interval-sprinting consisted of 20 min of intermittent sprinting on a cycle ergometer (8 s sprint, 12 s recovery). Maximal oxygen uptake was assessed using open-circuit spirometry. Vagal influence was assessed through frequency analysis of heart rate variability. Participants were aged 22 ± 4.5 years and had a body mass of 72.7 ± 18.9 kg, a body mass index of 26.9 ± 3.9 kg · m−2, and a maximal oxygen uptake of 28 ± 7.4 ml · kg−1 · min−1. Overall increase in maximal oxygen uptake after the training programme, despite being anaerobic in nature, was 19 ± 1.2%. Change in maximal oxygen uptake was correlated with initial baseline heart rate variability high-frequency power in normalised units (r = 0.58; P < 0.05). Thus, cardiac vagal modulation of heart rate was associated with the aerobic training response after 12 weeks of high-intensity intermittent-exercise. The mechanisms underlying the relationship between the aerobic training response and resting heart rate variability need to be established before practical implications can be identified.

 

3.      

James, DVC. Et al (2012) Heart Rate Variability: Effect of Exercise Intensity of Post-Exercise Response.  Research Quarterly for Exercise & Sport. 83(4)

Abstract:

The purpose of the present study was to investigate the influence of two exercise intensities (moderate and severe) on heart rate variability (HRV) response in 16 runners 1 hr prior to (-1 hr) and at +1 hr, +24 hr, +48 hr, and +72 hr following each exercise session. Time domain indexes and a high frequency component showed a significant decrease (p < .001) between -1 hr and +1 hr for severe intensity. The low frequency component in normalized units significantly increased (p < .01) for severe intensity at +1 hr. Only severe exercise elicited a change in HRV outcomes postexercise, resulting in a reduction in the parasympathetic influence on the heart at +1 hr; however, values returned to baseline levels by +24 hr.

 

4.

Gravitis, U. et al (2012) Correlation of basketball players physical condition and competition activity indicators. Lase Journal of Sports Science, 3(2): 39-46

Abstract

We failed to find any research about whether physical condition affects the indicators of a basketball player’s competition activity, and if yes, then to what extent; whether there is direct correlation between the indicators of a basketball player’s physical condition and his shooting accuracy in a game, as well as the number of obtained and lost balls by him.

Aim of the research: to investigate correlation of basketball players’ indicators of physical condition and competition activity.

Male basketball players aged 21-25 years participated in the research. On the pre-game day all basketball players were tested. Players’ heart rate was interpreted with the scientific device Omega M. A computer gave conclusion about a player’s degree of tension, as well as the degree of adaptation to physical loads, the readiness of the body energy provision system, the degree of the body training and the psycho-emotional condition, as well asthe total integral level of sports condition at the given moment. On the next day of the competition calendar game the content analysis of the competition technical recording was made to compare the player’s whose physical indicators were lower performance with his average performance in the whole tournament. Altogether 80 cases have been analysed when a player having lower physical condition indicators participated in a game.

All in all the players having lower indicators of physical condition in 80% of cases competition activity results were lower than their average performance in the tournament. The Pearson’s rank correlation coefficient also shows a close connection between the indicators of physical condition and competition activity (r=0.687; p<0.01), in comparison to a player’s average performance during the whole tournament. Basketball players’ indicators of physical condition have close correlation (r=0.687; p<0.01) with the indicators of competition activity.The results of physical condition test obtained with the help of the device Omega M can be used to anticipate basketball players’ performance of their competition activity.

 

5.

Chalencon S, Busso T, Lacour J-R, Garet M, Pichot V, et al. (2012) A Model for the Training Effects in Swimming Demonstrates a Strong Relationship between Parasympathetic Activity, Performance and Index of Fatigue.  PLoS ONE 7(12): e52636. doi:10.1371/journal.pone.0052636

Abstract

Competitive swimming as a physical activity results in changes to the activity level of the autonomic nervous system (ANS). However, the precise relationship between ANS activity, fatigue and sports performance remains contentious. To address this problem and build a model to support a consistent relationship, data were gathered from national and regional swimmers during two 30 consecutive-week training periods. Nocturnal ANS activity was measured weekly and quantified through wavelet transform analysis of the recorded heart rate variability. Performance was then measured through a subsequent morning 400 meters freestyle time-trial. A model was proposed where indices of fatigue were computed using Banister’s two antagonistic component model of fatigue and adaptation applied to both the ANS activity and the performance. This demonstrated that a logarithmic relationship existed between performance and ANS activity for each subject. There was a high degree of model fit between the measured and calculated performance (R2 = 0.84±0.14,p<0.01) and the measured and calculated High Frequency (HF) power of the ANS activity (R2 = 0.79±0.07, p<0.01). During the taper periods, improvements in measured performance and measured HF were strongly related. In the model, variations in performance were related to significant reductions in the level of ‘Negative Influences’ rather than increases in ‘Positive Influences’. Furthermore, the delay needed to return to the initial performance level was highly correlated to the delay required to return to the initial HF power level (p<0.01). The delay required to reach peak performance was highly correlated to the delay required to reach the maximal level of HF power (p = 0.02). Building the ANS/performance identity of a subject, including the time to peak HF, may help predict the maximal performance that could be obtained at a given time.