Heart Rate Variability in College Football Players throughout Preseason Camp in the Heat

Here’s a quick look at our latest study examining cardiac-autonomic responses to preseason camp in the heat among college football players. The free full text can be accessed here: Heart rate variability in college football players throughout preseason camp in the heat IJSM

Intensive training periods tend to increase RHR and decrease HRV, reflecting stress and fatigue. However, adaptations to heat exposure (e.g., plasma volume expansion) tend to have the opposite effects. So we wanted to see what happens when players were exposed to both intense training and intense heat stress during preseason camp.

Despite increases in perceived fatigue throughout the 2-week period, RHR and HRV reflected responses consistent with heat acclimation.

HRV initially decreased in linemen, then peaked after a day of rest. Non-linemen faired a little better with smaller decrements in perceived fatigue and more frequent day-to-day improvements in RHR and HRV.

These results indicate that heart rate parameters and perceived fatigue are independent markers of training status, and that desirable cardiovascular adaptations can occur in the presence of soreness and fatigue.

This is especially important for tech companies who try to infer recovery status from HRV alone. As HRV improved throughout camp, an app’s algorithm would report to coaches that players are well-recovered. Given that no player feels well-recovered during preseason camp in the heat, the technology suddenly loses credibility for being wrong and will likely be dismissed.

This is unfortunate because the heart rate parameters are likely reflecting important adaptations that may indicate better tolerance to training in the heat, a reduced exercising heart rate, and improved fitness. Thus, I encourage users to ignore “recovery” scores and interpret the data in appropriate context.

ABSTRACT 

We aimed to characterize cardiac-autonomic responses to a 13-day preseason camp in the heat among an American college football team. Players were categorized as linemen (n=10) and non-linemen (n=18). RHR, natural logarithm of the root-mean square of successive differences multiplied by twenty (LnRMSSD), and subjective wellbeing (LnWellness) were acquired daily. Effect sizes±90% confidence interval showed that for linemen, LnRMSSD decreased (moderate) on day 2 (71.2±10.4) and increased (moderate) on day 12 (87.1±11.2) relative to day 1 (77.9±11.2) while RHR decreased (small–moderate) on days 6, 7, and 12 (67.7±9.3–70.4±5.5 b∙min-1) relative to day 1 (77.1±10.1 b∙min-1). For non-linemen, LnRMSSD increased (small–large) on days 3–5, 7, 12, and 13 (83.4±6.8–87.6±8.5) relative to day 1 (80.0±6.5) while RHR decreased (small–large) on days 3–9, 12, and 13 (62.1±5.2–67.9±8.1 b∙min-1) relative to day 1 (70.8±6.2 b∙min-1). Decrements in LnWellness were observed on days 4–10 and 13 for linemen (moderate) and on days 6–9, 12, and 13 for non-linemen (small–moderate). Despite reductions in LnWellness, cardiac-autonomic parameters demonstrated responses consistent with heat-acclimation, which possibly attenuated fatigue-related decrements.

New study: Association between Subjective Indicators of Recovery Status and Heart Rate Variability among Divison-1 Sprint-Swimmers

Our latest study investigates the relationship between subjective indicators of recovery status and HRV among NCAA Division 1 sprint-swimmers. The main findings were:

1) Perceived sleep quality showed the strongest relationship with post-waking LnRMSSD.

2) LnRMSSD demonstrated stronger associations with subjective parameters than resting heart rate.

We report both group and individual relationships. The full text is available here.

Association between Subjective Indicators of Recovery Status and Heart Rate Variability among Divison-1 Sprint-Swimmers

Abstract

Heart rate variability (HRV) is a physiological marker of training adaptation among athletes. However, HRV interpretation is challenging when assessed in isolation due to its sensitivity to various training and non-training-related factors. The purpose of this study was to determine the association between athlete-self report measures of recovery (ASRM) and HRV throughout a preparatory training period. Ultra-short natural logarithm of the root mean square of successive differences (LnRMSSD) and subjective ratings of sleep quality, fatigue, muscle soreness, stress and mood were acquired daily for 4 weeks among Division-1 sprint-swimmers (n = 17 males). ASRM were converted to z-scores and classified as average (z-score −0.5–0.5), better than average (z-score > 0.5) or worse than average (z-score < −0.5). Linear mixed models were used to evaluate differences in LnRMSSD based on ASRM classifications. LnRMSSD was higher (p < 0.05) when perceived sleep quality, fatigue, stress and mood were better than average versus worse than average. Within-subject correlations revealed that 15 of 17 subjects demonstrated at least one relationship (p < 0.05) between LnRMSSD and ASRM variables. Changes in HRV may be the result of non-training related factors and thus practitioners are encouraged to include subjective measures to facilitate targeted interventions to support training adaptations.

Figure 1 Effect Size SPORTS jpeg

Figure 1

Effect sizes ± 90% confidence interval for resting heart rate parameters relative to subjective categorization.

Revisiting 60-s HRV recordings vs. Criterion in athletes

I’ve recently had the pleasure of peer-reviewing a few very well-written and carried out studies investigating duration requirements for stabilization preceding HRV recordings by different research groups. I look forward to seeing the published versions as the quality of the papers was very high.

In reviewing these papers it prompted me to reconsider what we all have been using as the criterion period. My colleagues and I have published 5 papers using a 5-min R-R sample preceded by a 5-min ‘stabilization’ period (10 min total duration) as the criterion (as has other groups), which is in line with traditional procedures. But I think we failed to address an important limitation of these procedures…

The issue is that the ‘traditional procedures’ were not devised for the purposes of establishing LnRMSSD specifically (rather, they needed to accommodate spectral analysis), nor were they devised for reflecting fatigue and adaptation to training programs. Therefore, for these specific purposes, it can be argued that the traditional procedures may not be as relevant, or at the very least, calls into question whether the 5-10 min period following the 0-5 min stabilization is in fact a criterion within this context.

Some things to consider:

  • 10 min is a long time to lay or sit still, especially for athletes who struggle to go 30-sec without checking their iPhone (I don’t think anyone disputes this). Are they more relaxed and stable in this situation or are they growing impatient and restless?
  • Are ANS responses and adaptation to training best measured in a completely relaxed state, or perhaps in response to a mild stimulus such as orthostasis (sitting or standing) (previous thoughts on this here)?
  • Should we be as skeptical with the ‘criterion’ recordings as much as as we are with 60-s recordings? How do we know if one is better than the other in the context of monitoring athletes? There’s now numerous studies by different groups showing the usefulness of 60-s measures for reflecting training responses, associating with fitness, etc.
  • Perhaps the question shouldn’t be regarding the optimal duration of the recording but rather, what is the shortest, most convenient procedure possible that still provides meaningful training status information? I don’t think an athlete or coach cares if their 60 sec HRV isn’t the same as the criterion if it’s still providing useful information.
  • I’m doubtful we would have completed any longitudinal training studies where HRV recordings were >60 sec on a near-daily basis. In my experience, >60 sec measures are not feasible with teams. Therefore, it’s ~60 s or we don’t bother.
  • Should future research instead try to determine what are the best ways to perform a ~60 sec HRV measure to limit noise from confounding factors? How can we improve the validity and reliability of 60-sec measures? How long from food/fluid ingestion should we wait? Can we obtain this with PPG rather than HR straps? What is the best position to measure in? etc.

To be clear, I still think that research evaluating stabilization requirements and comparing to the ‘criterion’ is absolutely meaningful and an important starting point. This was not intended to be critical, but rather to open discussion on future research directions.

 

 

The effect of training status on HRV in D-1 collegiate swimmers

When implementing HRV monitoring with a new team, the coach will be quick to point out the inter-individual variability in the athletes’ trends. Some athletes are showing high scores and some are low. Some are showing considerable daily fluctuation while others show very consistent numbers. Or, some show substantial fluctuation during this period but minimal fluctuation during that period. This can be confusing and difficult to interpret, but with some context, the trends (and changes therein) can usually be explained.

Greater fitness levels are associated with higher resting HRV and faster parasympathetic reactivation following exercise. This likely contributes to the smaller coefficient of variation  (CV) we (and others) have observed in athletes with higher VO2max and intermittent running performance. So if we were to categorize athletes of the same sport based on competitive level (i.e., training status), we should see group differences between their average lnRMSSD and CV. What makes our approach different from previous work is the longer observation period (1 month), the use of a finger sensor (PPG) and smartphone application using ultra-short HRV recordings for daily data acquisition and inclusion of the CV in the analysis. This was presented at the NSCA National Conference in New Orleans this July. Full manuscript in production soon.

THE EFFECT OF TRAINING STATUS ON HEART RATE VARIABILITY IN DIVISION-1 COLLEGIATE SWIMMERS

Andrew A. Flatt, Bjoern Hornikel, Michael R. Esco

University of Alabama, Tuscaloosa, AL

Resting heart rate variability (HRV) fluctuates on a daily basis in response to physical and psychological stressors and may provide useful information pertaining to fatigue and adaptation. However, there is limited research comparing HRV profiles between athletes of the same sport who differ by training status. PURPOSE: The purpose of this study was to compare resting heart rate (RHR) parameters between national and conference level Division-1 Collegiate swimmers and to determine if any differences were related to psychometric indices. METHODS: Twenty-four subjects were categorized as national (NAT, n = 12, 4 female) or conference level competitors (CONF, n=12, 5 female). Over 4 weeks, daily HRV was measured in the seated position by the subjects after waking and elimination with a validated smartphone application and pulse-wave finger sensor (app)  utilizing a 55-second recording period. Subjects then completed a questionnaire on the app where they rated perceived levels of sleep quality, muscle soreness, mood, stress and fatigue on a 9-point scale. The HR parameters evaluated by the app include RHR and the log-transformed root-mean square of successive RR interval differences multiplied by 20 (lnRMSSD). The 4-week mean for RHR (RHRm) and lnRMSSD (lnRMSSDm) in addition to the coefficient of variation (CV) for RHR (RHRcv) and lnRMSSD (lnRMSSDcv) were determined for comparison. In addition, psychometric parameters were also averaged between groups and compared. Independent t-tests and effect sizes ± 90% confidence limits (ES± 90% CL) were used to compare the HR and psychometric parameters. RESULTS: NAT was moderately taller (184.9 ± 10.0 vs. 175.5 ± 12.5 cm; p = 0.06, ES ± 90% CL = 0.83 ± 0.70) and heavier (80.4 ± 9.7 vs. 75.2 ± 11.9 kg; p = 0.26, ES ± 90% CL = 0.48 ± 0.67) than CONF, though not statistically significant. The results comparing HR and psychometrics are displayed in Table 1. lnRMSSDm and lnRMSSDcv was moderately higher and lower, respectively, in NAT compared to CONF (p<0.05). CONCLUSION: Higher training status is associated with moderately higher lnRMSSDm and lower lnRMSSDcv compared to those of lower training status. This was observed despite no significant difference in perceived stressors that may affect HR parameters. PRACTICAL APPLICATION: Training status appears to be a determinant of daily HRV and its fluctuation. This may be because higher level athletes are more fit and recover faster from training, resulting in a more stable HRV pattern. This information can be useful to practitioners when interpreting HRV trends in athletes. For example, an increase in HRV with reduced daily fluctuation may indicate improvements in an athletes training status. Alternatively, an athlete with high training status demonstrating reduced HRV and greater daily fluctuation may be showing signs of fatigue or loss of fitness depending on the context of the current training phase and program.

table swim HRV comarison

This figure shows a year of data from two athletes (Olympic level on top vs. Conference level on bottom) to provide a nice visual representation of their trend differences. HRV trend swim comparison

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

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: Monitoring weekly HRV in futsal players during the preseason

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 Podcast: Discussing Smartphone HRV Apps

I recently had a chance to sit down and discuss all things HRV monitoring with James Darley of the Historic Performance Podcast. There’s also a number of great interviews in the podcast archives worth checking out.

Topics discussed:

  • Background
  • Physiological basis for HRV as a recovery status metric
  • Preferred HRV parameter for athletes
  • HRV recording methodology (position, conditions, time of day, etc.)
  • Considerations for chosing the right HRV app for your situation
  • Recent research
  • Interpreting HRV data

Link to Podcast with show notes 

Show in Overcast App

 

 

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

It’s been a good start to the Thanksgiving break with the  acceptance of our latest study entitled “Initial weekly HRV response is related to the prospective change in VO2max in female soccer players” in IJSM (Abstract below).

We’re currently working on supporting these findings with a much larger sample size in the new year.

ABSTRACT

The aim of this study was to determine if the early response in weekly measures of HRV, when derived from a smart-phone application, were related to the eventual change in VO2max following an off-season training program in female soccer athletes. Nine female collegiate soccer players participated in an 11-week off-season conditioning program. In the week immediately before and after the training program, each participant performed a test on a treadmill to determine maximal oxygen consumption (VO2max). Daily measures of the log-transformed root mean square of successive R-R intervals (lnRMSSD) were performed by the participants throughout week 1 and week 3 of the conditioning program. The mean and coefficient of variation (CV) lnRMSSD values of week 1 showed small (r = -0.13, p= 0.74) and moderate (r = 0.57, p = 0.11), respectively, non-significant correlations to the change in VO2max at the end of the conditioning program (∆VO2max). A significant and near-perfect correlation was found between the change in the weekly mean lnRMSSD values from weeks 1 and 3 (∆lnRMSSDM) and ∆VO2max (r = 0.90, p = 0.002). The current results have identified that the initial change in weekly mean lnRMSSD from weeks 1 to 3 of a conditioning protocol was strongly associated with the eventual adaptation of VO2max.

 

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.

Making HRV More Practical For Athletes: Measurement Frequency?

Perhaps the biggest limitation with HRV monitoring in a team setting is obtaining and maintaining compliance from athletes. Daily HRV measurements can become monotonous, particularly for athletes who may not fully understand the value of the data. One question I’ve had in mind for a while now is; what is the minimal frequency of HRV measurements we can acquire that can still offer meaningful information regarding training status in athletes?

If you’ve read any of the research on HRV and athletes, you’d note that HRV is rarely measured daily. This is likely because having each subject report to the lab everyday to have their HRV measured on an ECG is impractical. However, with the advent of valid and reliable devices such as the Polar RS800, R-R intervals can be collected in the field making more frequent measurements a little more practical in the research setting. However, for the practitioner in the field, an even more practical, economical and user friendly device is desired. Thankfully smart phone app’s such as ithlete were created to accommodate this.

So now we have very affordable, very user friendly smart phone applications that can provide us with HRV data. The trick is getting the athletes to use them often enough so that we can use the data for monitoring purposes. Is it more of a reasonable expectation of our athletes to collect only one or two HRV measurements per week as opposed to every day? Will this provide us with enough information to draw meaningful interpretations from?

After giving it some thought, I decided to review some data over a 3 month period. With my own HRV data, I recreated trends in excel with; once per week, twice per week and daily measurements. The purpose of this is to see what these varying frequencies of measurement reveal in the trend. I’ve also included sleep score data which is graded 1-5 based on perceived quality and quantity after waking.

Daily Measurement 3 Month Trend

 daily3monthtrend

 

–          There is a period of time between the 4th week of December to the 2nd week of January that my HRV trend declines and I rarely see scores over 80. During this time (the Christmas Holidays)I was not lifting regularly and experienced some detraining.

–          Daily measurements allow for sRPE to be recorded providing the coach with a good indication of how the athlete is perceiving and responding to the workouts. Conversely, the sRPE allows the coach to see when the athlete is experiencing high stress in the absence of a high load training day.

–          Daily measurements allow the coach to see acute changes in HRV which can be important in planning or manipulating training.

–          It’s worth mentioning that my highest levels of strength were displayed over the last week of February and early March (early March not included). This is expected as I am nearing the end of my training cycle which has transitioned from moderate intensity/high volume to high intensity/low volume. Coincidentally, my HRV is reaching peak heights. I’m not entirely sure what to attribute these high scores to as I have been doing less aerobic work than normal. This may or may not have any meaning. Some vid’s are posted below from this “realization” phase. 

 

 

 Once Per Week HRV Measurement 3 Month Trend

 onceweekhrv

–          I chose Monday as the reference day because it is the day of the week furthest from training stress that can influence HRV. My goal was to find a day that gives me the best indication of baseline HRV. Since I train Mon-Fri and rest on weekends this left Sunday or Monday as the best options. I selected Monday over Sunday because Saturday nights can be social, late, etc. and therefore affect Sunday morning results.

–          This trend clearly shows my detraining period over the holidays.

–          Given that I adjust my training when necessary to avoid excessive fatigue accumulation, my baseline HRV is relatively consistent apart from the detraining period. Training is being well tolerated because I’m intentionally adjusting my training for that purpose. However, in a more pre-planned setting such as a collegiate weight room the result/trend would likely differ; particularly in a preparation phase (pre-season, early off-season, etc)

–          Coaches should be cautious when using weekly measurements due to potentially low scores caused by non training related stressors that may obscure interpretation. For example, if an athlete has a rough sleep Sunday night, HRV may be lower than usual Monday morning. This does not mean the athlete is fatigued or should have training loads reduced. Therefore, coaches need to keep tabs on performance and feedback from the comments section.  Clearly, weekly measurements have its limitations however it still may offer some value.

Twice Per Week HRV Measurements 3 Month Trend

twiceweek3monthhrv

–          I chose Monday and Saturday as my reference days because Monday represents HRV at rest while Saturday represents HRV after fatigue has been accumulated all week from training. This may provide some insight as to how stressful the training was based on Mon-Fri change in HRV. I am a bad example for this as I try and allow for HRV to reach baseline at least once during the week using Wednesday as an active recovery day. Data from an athlete involved in training, practices, class, etc. would have a different trend.

–          This trend allows for comparison of Sleep quality pre and post microcycle. In my trend, Mondays sleep scores never fall below 4 while there are 2’s and a 3 from Friday night’s sleep.

– As with the weekly measurement, this trend fails to capture major acute changes (highs and low’s).

Final Thoughts

Weekly measurements performed after a day or two of rest to allow for a true measure of baseline HRV can be useful in determining how an individual is coping with training on a week to week basis. However, I would urge you to be very cautious when interpreting trends as a low score caused by poor sleep or something other than training fatigue can provide a false sense of training response. This is where subjective measures, performance indications and regular communication is important.

Twice per week measurements might be the frequency which provides us with the most meaningful information from the least amount of data and therefore demand from the athlete. Seeing how HRV changes from pre to post training over a one week period likely provides much more meaningful information about training status verses weekly measures. It goes without saying that this needs to be manipulated according to the team’s training/practice/competition schedule. I used myself as the example today but most teams will not have Saturday and Sunday completely off from training.

Perhaps starting with weekly or twice weekly measurements is sufficient for getting athletes started and comfortable with the device. The goal should be to eventually get them to take daily measurements as this will provide more complete information including sRPE, daily sleep score and comments. The comments section is highly underrated and I intend to elaborate more on it’s value in a future post.