Tag Archives: monitoring

New Study: Intra- and inter-day reliability of ultra-short-term HRV in elite rugby union players

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Here’s a look at our latest study in collaboration with Fabio Nakamura and colleagues, now in press with JSCR (Abstract below). In this study, HRV was recorded as a team at the training facility, not immediately after waking. This is the approach that many coaches are interested in using given the issue with compliance when trying to get athletes to perform HRV measures on their own at home after waking. Controlled and supervised measures at the facility appear promising, at least in these high level athletes.

It’s important to understand that autonomic activity is constantly making adjustments to physical, chemical and perceived psychological stimuli. Thus, HRV is inherently not the most reliable metric. However, training status/fitness appear to have a strong affect on day to day variation in HRV. More fit athletes recover faster/tolerate training better and thus tend to show less deviation from baseline compared to less fit athletes, of which will experience much greater homeostatic disruption from training and greater day to day variation. I strongly believe that the amount of daily fluctuation (i.e., lnRMSSDcv) is a very useful indication of fitness, stress and training adaptation.

We currently have a paper in production looking at the effect of training status on HRV. In the mean time, compare the trends below of an Olympic level and a conference level athlete, both short-distance swimmers (similar age and physical characteristics) across 4 consecutive weeks of training.

lnrmssd compareIntra- and inter-day reliability of ultra-short-term heart rate variability in rugby union players.

The aim of this study was to examine the intra-day and inter-day reliability of ultra-short-term vagal-related heart rate variability (HRV) in elite rugby union players. Forty players from the Brazilian National Rugby Team volunteered to participate in this study. The natural log of the root mean square of successive RR interval differences (lnRMSSD) assessments were performed on four different days. HRV was assessed twice (intra-day reliability) on the first day and once per day on the following three days (inter-day reliability). The RR interval recordings were obtained from 2-min recordings using a portable heart rate monitor. The relative reliability of intra- and inter-day lnRMSSD measures were analyzed using the intraclass correlation coefficient (ICC). The typical error of measurement (absolute reliability) of intra- and inter-day lnRMSSD assessments were analyzed using the coefficient of variation (CV). Both intra-day (ICC = 0.96; CV = 3.99%) and inter-day (ICC = 0.90; CV = 7.65%) measures were highly reliable. The ultra-short-term lnRMSSD is a consistent measure for evaluating elite rugby union players, in both intra- and inter-day settings. This study provides further validity to using this shortened method in practical field conditions with highly trained team sports athletes.

Full text on Research Gate

New Study: Individual HRV responses to preseason training in D-1 women’s soccer players

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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.

Full text link:

Individual HRV responses to preseason training in D-1 women’s soccer players

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

New Study: Monitoring weekly HRV in futsal players during the preseason

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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

New Study: Interpreting daily HRV changes in female soccer players

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Here’s a quick look at our latest study published ahead of print in the Journal of Sports Medicine and Physical Fitness. Below is the abstract and some brief comments about the findings.

Full text link:

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

HRV monitoring for strength and power athletes

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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.

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

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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.

 

Exam Stress Impact on HRV and the Immune System: Implications for Student Athletes

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The importance of academic examination periods for student athletes at both the high school and collegiate level cannot be overstated. In most cases, academic performance can affect scholarship money (in the collegiate setting); playing time; academic standing (probation) and so forth. Any former athlete can probably attest that exam weeks involve a lot of cramming, plenty of caffeine, suboptimal eating habits and sleep deprivation. Needless to say, the added stress load from exams and exam preparation can have consequences on physical health and possibly performance. In this discussion I will review the available literature pertaining to exam periods and HRV in students to determine its potential usefulness at reflecting this stress. In addition, I will present and discuss some data I’ve collected from a couple of students during their recent exam weeks.

*Note: Very little of the research specifically involves student-athletes.

Perhaps one of the more interesting studies involving college students over exam periods was performed by Dimitriev et al. (2008). Female students (n=70, Age 20-25) were evaluated twice; once during the semester and once on the morning of examinations. Data on HRV, blood pressure, state anxiety and self-reported test performance (how they expected to perform on the exam) was collected. Essentially, students who performed the best on exams were the most pessimistic and showed the largest decrease in parasympathetic indices of HRV (HF and RMSSD). Students who performed best on the exams tended to have a higher state anxiety compared to during the semester while poor performers showed less state anxiety and less change in HRV.

Srinivasan et al. (2006) evaluated perceived stress and HRV in 36 male and female first year med students. A strong correlation was found between the high stress group and LF and LF/HF HRV parameters. Though not statistically significant, there was also a tendency for lower HF values among the high stress group. Kumar et al. (2013) also reported reductions in HRV in med students over exams compared to periods of lower stress before and after exams.

In a study by Tharion et al. (2009) a group of 18 undergraduates (9 male, age 18.7) had HRV and blood pressure assessed on the morning of examination (high stress) and one month later during holidays (low stress). As expected, during the exam period students showed lower HRV (total variability of both time and frequency domain). Interestingly changes in LF, HF, LF/HF and RMSSD were not statistically significant, though there were differences. These changes are likely still meaningful.

An investigation involving 20 female undergrads (age 18-19) showed that compared to the non-exam period, HRV parameters gathered during exams decreased (RMSSD, HF, with increases in LF and LF/HF) indicating a withdrawal of parasympathetic modulation and increase in sympathetic tone (Zaripov & Barinova, 2008). This was despite no changes in perceived stress during exams. Simic and colleagues (2006) reported a linear increase in perceived exam apprehension leading up to exams while HRV reached lowest values during exams and  increased thereafter.

This next study did not include HRV analysis however I felt it was worth including. Kang et al. (1997) collected blood samples from 87 high-school students at mid-semester, final exams and post-exams to assess immune responses to the academic stressor. Results showed that during the exam week there were significant immunological alterations; “Natural killer cell activity was significantly lower, whereas lymphocyte proliferation and neutrophil superoxide release were significantly higher. These immune changes tended to return toward baseline during the postexam period, but the enhanced neutrophil reactivity continued to rise.” Another study involving first year med students also found a strong link between stress related immunosuppression and health based on blood sample assay.

Athletes may be at greater risk of immune suppression compared to non-athletes because of the physical demands (strain, monotony) of their sport participation (Putlur et al. 2004). Enforcing responsible exam preparation will increase likelihood of exam performance and reduce the need for procrastination which is a major contributing factor to stress among students (Tice & Baumeister, 1997).

Data from a High School Football Player Over Exams

  • Exam week occurs in the last week of the ithlete trend below.
  • Following Monday, HRV remains well below baseline until Saturday
  • Poor sleep due to late nights studying
  • Though the athlete is currently involved in various Track & Field events as well as summer football, he had no competitions during exam week.

VL_Exam

Below is the weekly mean HRV in the 3 weeks prior to and the exam week.

  • HRV weekly mean does not reflect the acute stress experienced by this athlete day to day (a limitation of only assessing weekly mean)
  • Overall the exams appeared to be only a moderate stressor for this athlete
  • I included reaction test data because it was available; interestingly it was also effected (slower) during exam week
  • See here for more details and info on the Reaction Test

V_Exam_Big_Trend

I have included solo charts of the HRV and Reaction trend because I continue to see a consistent negative correlation between  HRV weekly mean and Reaction weekly mean. These charts reflect this relationship better than the above chart.

V_Exam_HRV_AVG

Weekly Mean HRV Trend

V_Exam_Reaction_AVG

Weekly Mean Reaction Time Trend

Data from a Grad Student Strength Coach in the Final Weeks of School

Below is the ithlete HRV data from a colleague who had a very stressful last month of grad school.

  • Week 1 represents baseline
  • Week 2 is the week before major presentations and a research paper due date. He reports this week to be highly stressful
  • Week 3 -5 include presentations, final exams, research paper deadlines, etc.
  • Week 6-7 represent the ascent in his HRV trend back to baseline after completing his school work.
  • Both the weekly mean trend and the daily trend provide a good reflection of his perceived stress
J_dailyHRV_exams

Daily HRV Trend

J_weeklyHRV_exams

Weekly Mean HRV Trend

Closing Thoughts 

Not surprisingly, exam periods are stressful for students. HRV appears to do a reasonably good job of reflecting this. However, responses tend to be individual with some students being more effected than others. If competitive seasons conflict with examination schedules, coaches may want to consider reducing training loads or at the very least, keep a closer eye on fatigue and stress in their athletes. Using a proactive approach by enforcing responsible exam preparation far enough in advance (study hall, tutoring, etc.) should reduce potential stress related issues by discouraging procrastination and associated changes in life style. Of course this is much easier said than done.

References

Dimitriev, D. A., Dimitriev, A. D., Karpenko, Y. D., & Saperova, E. V. (2008). Influence of examination stress and psychoemotional characteristics on the blood pressure and heart rate regulation in female students. Human Physiology, 34(5), 617-624.

Glaser, R., Rice, J., Sheridan, J., Fertel, R., Stout, J., Speicher, C., … & Kiecolt-Glaser, J. (1987). Stress-related immune suppression: Health implications. Brain, behavior, & immunity, 1(1), 7-20.

Kang, D. H., Coe, C. L., McCarthy, D. O., & Ershler, W. B. (1997). Immune responses to final exams in healthy and asthmatic adolescents. Nursing research, 46(1), 12-19.

Kumar, Y., Agarwal, V., & Gautam, S. (2013). Heart Rate Variability During Examination Stress in Medical Students. International Journal of Physiology, 1(1), 83-86.

Putur, P. et al. (2004) Alteration of immune function in women collegiate soccer players and college students. Journal of Sports Science & Medicine, 3: 234-243.

Shrinivasan, K., Vaz, M., & Sucharita, S. (2006). A study of stress and autonomic nervous function in first year undergraduate medical students. Indian Journal of Physiology & Pharmacology, 50(3), 257.

Simić, N. (2006). Evaluation of examination stress based on the changes of sinus arrhythmia parameters. Acta Medica Croatica 60(1): 27.

Tharion, E., Parthasarathy, S., & Neelakantan, N. (2009). Short-term heart rate variability measures in students during examinations. Natl Med J India, 22(2), 63-66.

Tice, D. M., & Baumeister, R. F. (1997). Longitudinal study of procrastination, performance, stress, and health: The costs and benefits of dawdling. Psychological Science, 454-458.

Zaripov, V. N., & Barinova, M. O. (2008). Changes in parameters of tachography and heart rate variability in students differing in the level of psychoemotional stress and type of temperament during an academic test week. Human Physiology, 34(4), 454-460.

Monitoring Training in a High School Football Player

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Though I’m currently a solid 17 hour drive away from home, I still correspond with several athletes I formerly worked with prior to my relocation. I’ve got a few athletes sending me their ithlete data every week. I finally had time to sit down and analyze some of it and so today I’ll present and discuss the past four weeks worth of data from a high school football player.

Basic Descriptors

This athlete is currently a high school sophomore and will be the starting Quarterback for his high school Varsity Football Team. In addition to high school football, this athlete is also competing in track and field (Javelin, Shot Put and Triple Jump) and summer football.

Monitoring Variables

HRV: The athlete measures HRV with ithlete in a standing position  every morning after waking and bladder emptying.

Subjective Sleep Score: Following his HRV measurement, sleep was rated (1-5 scale) and comments were entered regarding the previous days events on the ithlete app.

sRPE: I also asked the athlete to provide a rating of perceived exertion score after each training session, practice or competition (1-10 scale) and input this into the ithlete training load feature. This is not multiplied by session duration.

Reaction Test: Lastly, the athlete performed a simple reaction test with this application after ithlete to assess psycho-motor speed.

My rationale for the selected variables is quite simple:

  1. These tools/metrics are simple, inexpensive and non-invasive
  2. The total time required to complete these is between 3-5 minutes each day. Keeping them easy and quick helps with compliance which as you’ll see, was a non-issue for this athlete.
  3. I wanted both objective and subjective markers
  4. The Reaction test often gets talked about but rarely do I see any data. After having some personal success with it I decided to test it out with him.

4 Weeks of Data and Analysis

The following data is from the last 4 weeks where the athletes Track&Field  and Football schedules overlapped, resulting in a significant increase in physical stress. I have no influence on his current training, schedule, etc. and therefore this analysis is entirely retrospective. Furthermore, I always recommend that training and life style remain unchanged when people start using ithlete. After a few months of training we then analyze the data and determine what course of action to take from there. By making training/life style manipulations right from the start it will be hard to determine how effective they may be. With that said, the data is presented below, broken down into each constituent week.

*Note: Click images to enlarge. Reaction test results fall under “Tap” in the tables starting in week 2.

Week 1

Week 1

Week 1:

  •  No Reaction Test data this week, commences in week 2.
  • Training appears to be well tolerated all week with a spike in HRV after a rest day followed by a track meet on Saturday 4/28. The track meet appears to be more stressful than is perceived by the athlete based on the 9 point drop.
  • Training load weekly sum is 31
  • HRV weekly mean is 92.4
Week 2

Week 2

Week 2:

  • He appears to be insufficiently recovered from the track meet and persists with intense training. HRV remains below 90 all week while the previous week stayed above 90.
  • With some fatigue accumulated he has a track meet on Friday followed by a Football game on Saturday. The trend this week indicates high fatigue compared to the previous week.
  • Training load weekly sum increases by 16%.
  • HRV weekly  mean drops by 8 points; Sleep total drops slightly, First Reaction weekly mean is 262.1
Week 3

Week 3

Week 3:

  • Poor sleep and high soreness is reported early this week after the very stressful previous week. On 5/7 he stays home from school with cold/flu symptoms.
  • He recovers quickly and the rest of the week looks pretty good as his HRV trends back  up over 90.
  • Football game on Saturday causes a decent drop in HRV. Sunday is a rest day.
  • HRV weekly mean improves to 86.6; Training load decreased; Reaction speed decreased (faster).
Week 4

Week 4

Week 4:

  • HRV peaks at 96 after a much needed day off on Sunday
  • 2 Track meets this week with a new personal best throw; perceived training load decreases slightly and HRV started trending up approaching 90.
  • HRV weekly mean increases slightly, Sleep quality increases, Reaction Time is similar to previous week (slight increase).
4 Week Trend

4 Week Trend

Further Analysis 

In the screen shot below, I’ve included a table and chart of the weekly mean of HRV and Reaction Time, as well as the weekly sums of Training Load and Sleep score. In the table to the right I’ve calculated some correlations.

Mean Values, Correlations

Mean Values, Correlations

Brief Thoughts

This data set supports the theory of monitoring not just the daily, but also the weekly trend changes in HRV. However, keeping tabs on the day to day changes, particularly after intense workouts or competition, can allow for more appropriate training load manipulations to try and influence the weekly changes. This is particularly important during a competitive season where overreaching is not desired. Clearly in this case, the athlete experienced some overreaching after the abrupt increase in physical stress evidenced by his illness, disturbed sleep etc. However, the overreaching was short-term and the consequences short-lived as he quickly recovers. When HRV peaks in week 4 we also see an increase in performance (Track PR). Of course the overreaching easily could have been avoided had he not been trying to train for and compete in two different sports at the same time. However, this is the reality of many high school athletes who try and juggle multiple sports in the same season.

Similar to my experience discussed here, his Reaction test essentially mirrored HRV when the weekly means were calculated. Perceived training load clearly had the biggest effect on these two variables. Unfortunately we didn’t incorporate the Reaction Test until week 2 so keep that in mind when looking at the correlation values as week 1 was not included with Reaction Time.

In this case, I do not believe that the RPE of the competitions provided a good reflection of actual competition stress. In many cases when he had a competition, HRV would decline quite a bit yet the RPE would be moderate. Competing adds another element of stress unaccounted for in these situations which should be considered by coaches.

I believe that this athletes short term overreaching and subsequent illness and sleep disturbances could easily have been avoided. Reacting to the decrease in HRV, increase in Reaction time, increased soreness, poor sleep ,etc. by allowing for more recovery time likely would’ve averted this. However, how this would effect his performance in the following weeks when HRV peaks and he see’s an increase in performance is unknown. After several days of a decreasing trend in HRV, rest should be strongly considered, particularly during competition periods.

The comments section of ithlete was valuable in communicating to me brief details about what in particular may be causing stress. This is an undervalued and underrated feature in my opinion.

HRV and Reaction test weekly mean and perceived training load weekly sum each appear to be sensitive markers of the physical stress load experienced by this athlete. Adjusting training loads appropriately in response to these variables may have prevented the unintentional overreaching and illness experienced by this athlete. From this set of data we can conclude that HRV, Reaction test and perceived exertion ratings were effective markers of training status with this athlete.

Individual HRV Responses In Professional Soccer Players During A Competitive Season

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In a team setting environment, athletes are often exposed to similar training loads during practices, training and competition. Monitoring of only the external training load provides coaches with an incomplete picture of how individual athletes may be responding and adapting to the training schedule. Two athletes can in fact respond entirely differently to the same program. A recently published case study by Bara-Filho et al. (2013) demonstrates how HRV, when measured periodically throughout training, can help distinguish these individual differences in professional soccer players exposed to the same training schedule. The following is a brief summary and review of this case study.

Materials and Methods

Subject 1 was a 26 year old Mid-Fielder with 7 years of professional playing experience. Subject 2 was a 19 year old Right Back with only 1 year of professional playing experience.

Over a 3 week period during a competitive season, both subjects participated in training that consisted of small-sided games, simulated matches, strength training, sprint training, and low-intensity aerobic recovery work. Training took place 1-2 times per day, 5 day’s/week culminating in a competition on the 6th day and rest on the 7th. Both subjects were starters in the 3 matches that occurred over the observation period.

HRV was measured on 5 occasions throughout the 3 week period on each Saturday and Monday morning (excluding the last Monday). This allowed for HRV indices to be evaluated both after the weekly training load was accumulated (Saturday) and after recovery (Monday). This is precisely the protocol that I discussed in a recent post entitled Making HRV More Practical for Athletes: Measurement Frequency.

HRV data was collected in the morning with a Polar RS800 watch while the athletes rested in a supine position.

Results

Total weekly TRIMP values were similar in both athletes. After the first measurement (M1) Subject 1 showed an increasing trend in several HRV values (RMSSD, HF, SDNN, SD1) indicating good adaptation to training and quality recovery from competition. Subject 2 showed a progressively decreasing trend in these same HRV values indicating an accumulation of fatigue and insufficient recovery.

Discussion

The authors suggest that subject 2, who saw a decreasing trend in his HRV values, may have been experiencing stressors unrelated to sport that may have contributed to his insufficient recovery. Though subjective measure (questionnaires) were not included, the physical training coach reported that athlete 2 would inform him that he was experiencing disturbed sleep, fatigue during training, and poor recovery.

A lower level of playing experience in subject 2 was reported as another possible explanation for his descending HRV trend. The psychological stressors and anxiety experienced by this younger athlete may have also contributed.

The authors briefly discuss the limitations of a supine measurement only when using HRV to monitor training load in athletes. Essentially, individuals with low resting heart rates appear to be subject to “parasympathetic saturation” in the supine position, possibly skewing the data. Therefore, including measurement performed in the standing position may serve as a resolution to this issue. I discussed this topic in a previous post entitled Supine vs. Standing HRV Measurement.

Finally, the authors conclude that HRV values were useful in monitoring the effects of a competitive training schedule in athletes as these values appear to be sensitive to individual characteristics as well as stress and recovery. A stable or increasing HRV trend appears to be favorable as it indicates quality recovery and adaptation to training. In contrast, a decreasing trend in HRV indicates higher stress and impaired recovery which may necessitate recovery interventions and reductions in training load.

Reference

Bara-Filho, M.G., et al. (2013) Heart rate variability and soccer training: a case study. Motriz: rev. educ. fis. 19(1): 171-77. Free Full-Text

Reaction Test for Athlete Monitoring: Research and Considerations

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Distinguishing functional over-reaching (FOR) from non-function over-reaching (NFOR)can be difficult to do during overload periods; particularly when laboratory measures are inaccessible to the coach or athlete. A common criteria used to determine FOR from NFOR is to assess performance before and after overload training. The fatigue accumulated from the increased training loads will result in expected performance decrements. After an unloading period of 1-2 weeks, performance should return to or exceed pre-overload performance values. An athlete can be considered NFOR if performance remains suppressed after this 2 week period.

Coaches can be proactive in their efforts to avoid NFOR with their athletes by maintaining various monitoring strategies. Keeping tabs on certain variables throughout overload periods allows the coach to detect early warning signs that may indicate excessive fatigue in an athlete(s). Such a metric often discussed is the reaction test. Today I will review some of the available research that investigates the efficacy of the reaction test as a method of potentially determining or indicating NFOR in athletes.

Why The Reaction Test?

The theory behind why the reaction test may serve as a good indicator of overreaching and/or the overtraining syndrome has been postulated by Nederhof et al (2006). Essentially, the overtraining syndrome has several signs and symptoms also seen in chronic fatigue syndrome and major depression. Both chronic fatigue and major depression are associated with slower psychomotor ability. Thus, it is hypothesized that psychomotor speed may be slower in athletes with OTS.

Reaction Test and Overreaching

Nederhof and colleagues (2007) put their theory to the test and evaluated performance, perceived fatigue/mood (RESTQ-sport and POMS) and psychomotor speed (reaction tests) in trained cyclists (n=14) and a control group (n=14). Training load was monitored via sRPE (RPE x session length). Testing was performed at baseline, following a 2 week overload period and once more following a 2 week taper. Of the 14 cyclists, 5 were considered FOR (they fulfilled at least 2 out of the three objective criteria in combination with at least 1 subjective criterion during the second but not during the third exercise test) and 7 were considered well trained (WT) while the remaining 2 were excluded.

Two reaction tests were used. The first described test was the “Finger Pre-Cuing Task” that required the individual to react to a prompt by pressing the correct keys on a computer. The other test was the “Determination Test” that required either manual of pedal reaction in response to visual or auditory stimuli also on a computer. Full descriptions of these tests can be read in the full text here.

The control group and the WT group improved their reaction time at each test. The FOR group however showed increased (slower) reaction time after the overload period but improved reaction time beyond baseline values after the taper. Regarding statistical significance the authors stated; “After high load training the FO group was 20% slower than the control group and 8% slower than theWT group. For comparison, patients with major depression are 20 to 26% slower than healthy controls [21,32] and patients with chronic fatigue syndrome are 15% slower than healthy controls [21]. Thus, although not statistically significant, differences in the present study are meaningful“.

Rietjans et al (2005) aimed to determine if a combination of test parameters could help detect overreaching in 7 well trained male cyclists. Over a 2 week period, training load was doubled while intensity was increased by 15%. Values for the following tests/assessments were collected pre and post training period: Maximal incremental cycle ergometer test with continuous ventilatory measurements and blood lactate values, time trial, basal blood parameter tests, hormones (GH, IGF-1, ACTH, neuro-endocrine stress test, shortened POMS, RPE and a cognitive reaction time test.

The results: “A novel finding was that reaction times increased significantly, indicating that overreaching might adversely affect speed of information processing by the brain, especially for the most difficult conditions. After the intensified training period, neither changes in exercise-induced plasma hormone values, nor SITT values were observed. During the CAPT only cortisol showed a significant decrease after the intensified training period. Hemoglobin showed a significant decrease after the intensified training period whereas hematocrit, red blood cell count (RBC) and MCV tended to decrease. The intensified training had no effect on physical performance (Wmax or time trial), maximal blood lactate, maximal heart rate and white blood cell profile. The most sensitive parameters for detecting overreaching are reaction time performance (indicative for cognitive brain functioning), RPE and to a lesser extend the shortened POMS. This strongly suggests that central fatigue precedes peripheral fatigue. All other systems, including the neuro-endocrine, are more robust and react most likely at a later stage in exhaustive training periods.”

Reaction Test and Perceived Performance 

Nederhof and colleagues (2008) set out to determine if reaction tests are related to perceived performance in rowers. On 5 occasions over the course of a season, reaction tests were performed along with perceived performance measures (“Reduced Sense of Accomplishment” scale from the Athlete Burnout Questionaire) in varsity rowers. The same two reaction tests (Finger Pre-Cueing and the Determination Test) described above were used. The results showed that a significant relationship between the Determination Test and perceived performance. The authors stated; “…rowers who scored higher on the ‘‘Reduced Sense of Accomplishment’’ scale of the Athlete Burnout Questionnaire had longer reaction times on the determination test. For every point the rowers scored higher, their reaction times were 18 ms longer on the action mode and 12 ms on the reaction mode of the determination test. This effect was not found for the finger pre-cueing task.”

Though their hypothesis was supported, the authors affirm that several practical issues require resolution.

My Reaction Test Data Compared to HRV over 4 Different Training Periods

For a much more elaborate discussion on this experiment you can see the original post here. Essentially what I found was that Reaction test average and HRV average mirrored each other at each training period. HRV decreased and Reaction time increased (slower) during High Intensity and again during High Volume training reflecting fatigue. During reduced training loads HRV increased and Reaction time decreased (faster).

Reaction average trend

HRV Avg Trend Reaction Blog

Considerations and Limitations

The reaction test appears to be a test worthy of consideration for coaches looking to incorporate monitoring variables into their training regime. The following is a list of factors to keep in mind regarding this test:

• Caffeine has a well-established effect on reaction time and should therefore be controlled for when implementing reaction testing

• Psychological factors can impact the effectiveness and reliability of the test. Though this is an objective test, the effort put forth by the athlete may not be consistent. Since this test is sensitive to small changes in reaction time, this can obscure data and thus interpretation.

• As with HRV, it is probably best to experiment with a reaction test with a small sample of athletes to determine its usefulness before trying to implement with an entire team.

• Just like any other monitoring variable, reaction time should be considered with other factors when attempting to draw meaningful interpretations from the results.

Reaction time test results appear to respond early to fatigue during overload training. Reaction times (test dependent) may correlate with perceived performance. The simplicity, practicality, affordability and non-invasiveness of a reaction test make it appealing to coaches as a field test.

References

Nederhof, E., et al. (2006) Psychomotor speed: possibly a new marker for overtraining syndrome. Sports Medicine, 36(10): 817-28.

Nederhof, E., Lemmink, K., Zwerver., J. & Mulder, T. (2007) The effect of high load training on psychomotor speed. International Journal of Sports Medicine, 28: 595-601.

Nederhof, E., Visscher, C. & Lemmink, K. (2008) Psychomotor speed is related to perceived performance in rowers. European Journal of Sport Science, 8(5): 259-265

Rietjans, GJ., et al. (2005) Physiological, biochemical and psychological markers of strenuous training induced fatigue. International Journal of Sports Medicine, 26(1): 16-26.