HRV: Means and Variation

Posted on April 5, 2014 by hrvtraining

At this point, most of you are aware that a single HRV (lnRMSSD) score taken in isolation does not necessarily imply or reflect an acute change in performance, fatigue, recovery, etc (though it may sometimes).

Here’s why:

Below are two separate HRV trends I pulled from a training cycle I did last year at week 1 and week 8.

If someone were taking once per week recordings, or pre and post training phase recordings on isolated days, you can see how they can get entirely different results based on which day they measured. Suppose measures were taken on Friday’s from the above trends. These values are 84 and 76.7, respectively. However, if we look at the weekly mean values, we would get 73.6 and 78.3. From the isolated readings, one would conclude that HRV decreased nearly 10 points. However, the weekly mean shows an entirely different change (HRV actually increased from 73.6 to 78.3). Therefore, it’s quite clear that when averaged weekly, HRV scores allow for more meaningful interpretation.

	Isolated Measure (Friday)	Weekly Mean
Week 1	84	73.6
Week 8	76.7	78.3

See the following papers for more on weekly mean vs. isolated recordings (Le Meur et al. 2013; Plews et al. 2012; Plews et al. 2013)

One limitation of the weekly mean value is that is does not reflect the fluctuation in scores throughout the 7 day period. A simple way of determining this is to calculate the coefficient of variation (CV) from the 7 day HRV values (see Plews et al. 2012 for more on CV).

The coefficient of variation is calculated as follows;

CV = (Standard Deviation/Mean)x100

Below is 9 weeks worth of data from a training cycle I performed early last year that resulted in some personal records (PR’s) and was discussed in this post. This time, in addition to the weekly mean values I have also calculated the CV for each week.

Without going into too much detail about the training cycle (see the original post for that), I will highlight a few keep observations.

HRV Avg	HRV CV	Brief Notes
73.6	7.5	1^st week after detraining, Good
77.4	5.6	Good
77.5	2.3	Good
76.2	5.7	Stress, poor sleep, deload
79.37	3.0	Good
79.7	4.0	Good
77.9	11.4	Stressful week
77.8	6.8	↑ intensity, ↓ Volume, Good
78.2	4.8	PR(1RMs)
81.1	4.7	Deload, Good

Below are the HRV trends from Week 1 – 4 of the cycle.

Week 1 was my first week training after about 10 days off from lifting (Christmas holidays). Clearly the trend from week 1 reflects the fatigue and recovery as I lifted on M W F that week. On week 2 I performed the same workouts on the same days but with a little more weight for each set. However, it appears (based on CV) that this may have been less stressful. In week 3, I moved to lifting 4 days/week with moderate loads and CV decreases further. Interestingly, the following week (week 4), the weights feel heavy, I feel pretty rough and I take an unplanned deload (CV increases, mean decrease).

Further analysis of the CV and weekly mean can include calculating the smallest worthwhile change (see Buchheit, 2014) to see if a change is practically meaningful. (Will do this in the future once I figure out how to display SWC on a chart).

The point of this post was to introduce the CV concept for those who may not be familiar. I believe that the CV likely provides information regarding stress, fatigue and adaptation that the weekly mean may not reflect. Therefore, the CV and mean values should be considered together.

References:

Buchheit, M. (2014). Monitoring training status with HR measures: do all roads lead to Rome? Frontiers in physiology, 5. http://journal.frontiersin.org/Journal/10.3389/fphys.2014.00073/full

Le Meur, Y., Pichon, A., Schaal, K., Schmitt, L., Louis, J., Gueneron, J., … & Hausswirth, C. (2013). Evidence of parasympathetic hyperactivity in functionally overreached athletes. Medicine and Science in Sports and Exercise, 45(11), 2061-2071.

Plews, D. J., Laursen, P. B., Kilding, A. E., & 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), 3729-3741.

Plews, D. J., Laursen, P. B., Kilding, A. E., & Buchheit, M. (2013a). Evaluating training adaptation with heart-rate measures: a methodological comparison. International Journal of Sports Physiology & Performance, 8(6).

HRV Measurement Frequency: Comparing 3 day to 7 day per week recordings

Posted on January 29, 2014 by hrvtraining

There is very little longitudinal HRV data within the research, particularly in team sport athletes. In many cases, HRV will be assessed pre and post or pre, mid and post of a pre-season camp or what have you. This is very likely due to the inconvenience of acquiring this type of data in athletic populations. For the study to be research quality, validated tools must be used (ECG, Polar, Suunto, Omegawave, etc.). In addition, standard measurement procedures are required to ensure that the data is of sufficient quality. This generally involves a 5 minute rest period followed by a 5 minute recording. However, some researchers have used shorter resting and recording durations. Measurement standards for athlete monitoring in the field need to be developed. This is an area my colleague Dr. Esco and I are working on in our lab. This includes cross-validating field HRV tools, assessing the suitability of shorter recording durations and determining the time course for stabilization of HRV (i.e., how long should the resting condition be prior to recording).

Apart from the issue of valid and reliable tools and measurement protocols, another major issue that prevents HRV from being widely used as a component of a comprehensive athlete monitoring program is compliance. Due to day to day variations it is highly unlikely that a single HRV recording per week is sufficient. A recent paper by Le Meur et al. (2013) found that:

“using mean weekly values obtained from daily HRV recordings, rather than isolated HRV assessments, may improve the diagnostic utility of HRV indices in endurance-trained athletes to assess training-induced adaptations of the autonomic nervous system. The present results suggest that the day-to-day variability of HRV values is too high to allow clear detection of autonomic modulations associated with F-OR using single-day values.”

Understanding that daily HRV recordings can be difficult to obtain from our athletes, Plews et al. (2013) sought to determine what the minimum measurement frequency is that still appropriately reflects the weekly mean value.

“We have previously demonstrated HRV values averaged over 1 week provide a superior representation of training-induced changes than HRV values taken on a single day. In the current study, we have shown that HRV values averaged at random over a minimum number of 3 days will allow for an equivalent representation of training adaptation than values averaged for up to 7 days in trained triathletes. Conversely, recreational athletes will need a slightly greater number of days averaging (~5 days) due to their greater day-to-day variations in Ln rMSSD values.”

Last March, I posted some data that assessed the trends of daily vs. once per week and twice per week HRV recordings here. Today I’d like to revisit this topic. However, in this post I will be comparing weekly mean values to 3 day/week values. In the study mentioned above by Plews et al., the 3 days were randomly selected and were compared with performance changes. I’ve elected to consistently use Mon-Wed-Fri recordings to assess how a more structured and consistent approach would work and compare to the weekly mean trend. Working with a team of athletes, it would be much easier to designate 3 specific week days as HRV recording days. However, which 3 days are selected should likely depend on the training/competition schedule. Selecting days that all fall after intense training/competition may skew the results and not sufficiently reflect the recovery seen on days after lower intensity or rest days.

Below is the first week of every month from the last year (Jan 2013 – Jan2014) of my ithlete data. I figured 1 week per month would sufficiently show trend changes due to changes in fitness, lifestyle etc, without having to spending too much time analyzing data in excel for every week of the year.

Below is HRV data from a Collegiate Cross Country athlete throughout the fall competitive season.

Below is some in-season data from a female Collegiate soccer player.

Lastly, below I’ve posted a randomly selected week from each month over 9 months from a competitive male powerlifter with Cerebral Palsy.

It was specifically stated that non-elite athletes may require more than a 3 day average to sufficiently reflect performance changes. This is due to a higher degree of variation in day to day measures.

“Conversely, recreational athletes will need a slightly greater number of days averaging (~5 days) due to their greater day-to-day variations in Ln rMSSD values.” Plews et al.

None of the above athletes are “elite” (as much as I’d like to think I am). Clearly there are a few weeks that do not match up for each data set, but the 3 day average (drawn from Mon-Wed-Fri in each set) appears to follow the weekly mean trend reasonably well. Having your athletes record HRV on a mobile device 3 days per week would certainly be more manageable than daily recordings. I intend to investigate this more officially in the future.

Refs:

Le Meur, Y., Pichon, A., Schaal, K., Schmitt, L., Louis, J., Gueneron, J., … & Hausswirth, C. (2013). Evidence of Parasympathetic Hyperactivity in Functionally Overreached Athletes. Medicine and Science in Sports and Exercise.

Plews, D. J., Laursen, P. B., Le Meur, Y., Hausswirth, C., Kilding, A. E., & Buchheit, M. (2013). Monitoring Training With Heart Rate Variability: How Much Compliance is Needed for Valid Assessment?. International Journal of Sports Physiology and Performance. Ahead of print.

Training Load and Nutrition Impact on HRV: 10 Week Data Analysis

Posted on December 6, 2013 by hrvtraining

Below is 10 weeks worth of my own training data that includes;

HRV – Collected daily on ithlete in standing position immediately after waking
HR – Taken from the ithlete HRV measures
Load – Sets*Reps*Weight(lbs)
sRPE – Reps*RPE of session(1-10 scale)

All data is presented as weekly mean values.

HRV & Load

HRV & sRPEHR & Load

HR & sRPE

Data

Training

– Training volume in weeks 1-5 involved 3 straight working sets for main lifts alternating between weeks of 5’s, triples and singles. Working weight for each set was predetermined based on previous week but would be adjusted if need be. Training volume progressively decreases as working sets were reduced from 3 top sets to 1 top set. Assistance work was mostly just maintained during the reduced load period. Week 10 was more of a true deload where all working set weights were reduced but only to about 80% while assistance work was reduced slightly as well. Keep in mind that volume for each week would vary based on whether I was performing sets of 5, 3, or 1 for top sets.

Thoughts

– Even prior to week 1 displayed in the data, I had not taken a deload in quite some time (end of August). Performance (strength) had progressively been increasing and I didn’t feel the need so I kept at it. My HRV was consistently averaging in the low 70’s which is quite low compared to my typical average of about 80 (based on several years of data). Once I started having some nagging soft tissue problems accumulate I decided to taper the volume. I was seeing how my body and HRV responded to deloading keeping intensity high but just cutting volume. HRV trended back towards baseline though soft tissue problems weren’t quite resolved.

– Week 10 was Thanksgiving week and I traveled to my folks place. Training was reduced yet HRV decreased. I attribute this entirely to the drastic change in my nutrition during this week. Fruit and Vegetable intake decreased significantly. Processed foods and carb intake increased dramatically. It was an atrocious but delicious week of eating. This is not the first time that I’ve seen HRV change due to similar changes in eating.

– In the chart below you can see HRV decline during the high volume/load period followed by a progressive increase during the taper. This is then disrupted with a progressive drop during Thanksgiving week of binge eating. HRV then trends back up this week as eating improves and regular training resumes.

– HRV and HR need to be taken into context when being used to guide or monitor training. Other stressors always need to be considered. Neither will ever perfectly correlate with training load as this would assume that only training affects the ANS. It also worth mentioning that HR reflected training load better than HRV in this case and simple RHR should certainly not be dismissed or overlooked.

– Acute changes in HRV/HR won’t always “make sense” or correspond to perceptions of soreness, fatigue, mood etc. (though they do quite often). The weekly mean values tend to provide a better reflection of training/life style. I don’t adjust training on a day to day basis basis until I’m approaching my top sets.

HRV in a bit more detail: Part 2

Posted on November 22, 2013 by hrvtraining

Part 1 of this series provided information on heart function, ECG basics, HRV basics and how the Autonomic nervous system influences heart rate. For Part 2, I’ll discuss and display basic HRV analysis concepts to try and enhance your understanding of HRV. I’ll relate as much of this discussion as possible to smart-phone based HRV tools as it is unlikely that most readers of this site have access to an ECG.

Athlete vs. Non-Athlete

Fit individuals generally have lower resting heart rates and greater parasympathetic activity at rest. These adaptations to training may be a result of both intrinsic heart adaptations (SA node remodeling, increase in ventricle capacity) and autonomic adaptations (greater vagal activity).

Below is an ECG segment from a collegiate male endurance athlete. This sample is likely capturing the normal fluctuation in heart rate that occurs in response to respiration (breathing). Heart rate tends to speed up on inspiration and slow down on expiration. The technical term used to describe this phenomenon is “Respiratory Sinus Arrhythmia”.

Endurance Athlete ECG

For comparison, below is a screen shot of a healthy non-athlete ECG. Here, you can clearly see a higher resting heart rate and less variability.

Non-athlete ECG

The Excel snap shot below is what R-R interval data looks like once exported from the ECG software (Acqknowledge in this case) to a workbook. Though specialized HRV software is much more functional, it’s certainly possible to perform some time series (statistical) analysis on the R-R interval data with basic excel functions (i.e. Standard Deviation, Mean R-R Interval, RMSSD, etc.). Today, we’ll focus primarily on RMSSD as this parameter appears to be the preferred HRV index for athlete monitoring (See Plews et al. 2013 and this for more on RMSSD).

R-R Intervals

Tachogram

With HRV software analysis, ECG recordings are converted to a tachogram, which plot the successive R-R intervals on the y-axis and the number of beats within the ECG segment on the x-axis. This provides a nice visual representation of heart rate variability over a given time and makes for easy software analysis.

Below is a 5 minute ECG segment from an endurance athlete converted to a tachogram with our Nevrokard HRV software. Notice how the R-R intervals vary considerably over a broad range. The time domain values follow.

Endurance Athlete Tachogram

In contrast, below is the tachogram and time domain analysis of an age matched non-endurance athlete for comparison. Note how the R-R intervals are relatively stable and within a narrow range, demonstrating less variability.

Non-athlete Tachogram

To put this in perspective for the good folks using ithlete or BioForce, I’ll convert the raw RMSSD values (displayed in the “Summary Statistics” screen shots above) to ithlete/BioForce values. To do this, we simply log transform the raw RMSSD and multiply it by 20 (lnRMSSDx20). Keep in mind that the ithlete uses a 55-sec test and BioForce uses 2.5-min test. The values shown here are from 5-min ECG samples, but you get the idea.

Ectopic Beats and Artifacts

Last post, I discussed “normal” beats originating from the SA node. Any beats originating from outside the SA node disturb cardiac rhythm and can therefore impact HRV. These are called ectopic beats. Electrical interference, or excessive noise or movement can create “artifacts” which can also affect the data. It’s important to manually inspect ECG data for ectopic beats or artifacts and correct them (replace with the adjacent “normal” cycle) or discard the ECG sample entirely if there are excessive disturbances. Most smart-phone HRV tools do not provide R-R interval data and therefore manual inspection for ectopic beats is impossible. Conveniently, devices such as ithlete and BioForce are designed to automatically detect and correct irregular beats. For example, the application will detect and replace R-R intervals that are unlikely to occur in healthy, resting individuals (e.g., R-R intervals below 500ms or above 1800ms). I’d assume OmegaWave Pro, SweetBeat and other devices also have this feature.

Below is an example of an ectopic beat from an ECG which appears to be a premature atrial contraction. You’ll see this again in a moment on the tachogram as well.

Measurement Protocol

For athlete monitoring, HRV data is ideally collected as soon after waking in the morning as possible after bladder emptying. We want as close to resting conditions as possible. It would be wise to avoid checking e-mails, text messages and anything else that can alter mood, excite you, upset you, etc. Even water consumption will have an acute impact on HRV. Consistency of measurement protocol and time of measurement are important for longitudinal monitoring. In addition, being as motionless as possible and undisturbed is equally as important.

In the screen shot below towards the right hand side, we can clearly see when the individual gets restless and moves or adjusts his position. We can also see the ectopic beat that occurs toward the start of the measure (same ectopic beat shown above in the ECG). Slight and subtle movements can clearly impact heart rate so be as still as possible when you perform your measures at home.

In the tachogram below, notice how heart rate changes considerably at around the 10 minute mark. This is a result of a researcher entering the exam room where the subject was resting during an ECG recording. Clearly, the resting condition was disturbed as the subject was excited/stressed from the person entering the room. Thus, do your best to remain as undisturbed as possible when performing a measure at home.

All data shown today were from supine ECG recordings. Next post I’ll discuss and show HRV changes in response to postural change (i.e., from supine to standing).

Reviewing HRV, RPE, 1RM and Grip Strength Data Over 6 Weeks

Posted on October 15, 2013 by hrvtraining

I’ve been continuing to collect data on a competitive powerlifter that trains out of our facilities here at AUM. This athlete has cerebral palsy and therefore only competes in raw bench press. Currently, he can press approximately 2.21x his bodyweight (265lbs at 120lb). I’ve posted his older training cycle data previously here and here. This time around, I’ve been tracking a few different variables that are listed and described below. The purpose of this was to see if any of the monitored variables were able to reflect or predict daily variations in 1RM strength.

1RM – Unlike previous cycles, I calculated his 1RM bench press each session based on reps performed and RPE. For example; on his first working set of the day, if he performed 3 reps at an RPE of 9 (1 rep left in the tank), this was considered a 4RM weight and approximately 85% of 1RM using Mike Tuchscherer’s 1RM formula/table. I’ve chosen this specific formula because it is designed for powerlifters. We pause each bench press rep at the bottom which obviously decreases the total amount of reps that can be performed. After trying a few different formulas I found that most were under-predicting his actual strength.

Example:

230×3 RPE @9 = 4RM

Tuchsherer’s Formula: 1RM = 271.4

ExRx.net Calculator: 1RM = 251

Obviously, since many of these are calculated and not true 1RM’s, there is some give or take with these values.

sRPE – Following his workout, I asked him to rate the entire session on a 10 point scale. I do not multiply this by total reps performed but rather just use the rating as a general indicator of how hard the workout was for him.

Hand Dynamometry – Grip strength for each hand was assessed prior to each session via hand dynamometer (starting after week 2). You’ll note that there is a difference between his right and left grip strength due to his condition.

HRV – The athlete measured HRV each morning after waking on his iPod Touch with ithlete in a seated position.

Details of First Training Cycle (Weeks 1-3):

3 weeks in duration
Trained 3 days/week (M-W-F)
Monday: sets of 3 progressing from approximately 82% in week 1 to 87% by week 3
Wednesday: sets of 5 progressing from approximately 75% in week 1 to 80% in week 3
Friday: Singles progressing from approximately 92% in week 1 to 100% in week 3

Details of Second Training Cycle (Weeks 4-6):

3 weeks in duration
Trained 3 days/week (M-W-F)
Monday: Same as previous cycle
Wednesday: Speed work progressing from 60-70% from week 4 to week 6 (no 1rm calculations on these days)
Friday: Same as previous cycle

Assistance work progressed each week and would consist of rowing/pull ups, dumbbell pressing variations and some lower body exercise.

Data is presented below:

Daily HRV and sRPE

Daily HRV & 1RM

*Regarding the last two 1RM’s on the above chart, 26o is likely lower than his true 1RM that day. He smoked it but I cut him off there. The 277 1RM was based off a 3RM calculation that is probably a little higher than his current ability.

Daily sRPE shows a progressive increase from week 1 -3 which accurately reflects the progressive increase in intensity for his main work. A decrease in HRV in week 3 along with high RPE’s and a slight decline in 1RM suggests some fatigue accumulation.

Week 4-6 is the second training cycle. Day one of week 4 is missed and therefore this cycle doesn’t start until the Wednesday. This missed workout caused us to slightly extend the cycle to fit one more lift in on a Monday of the last week.

Since Wednesday’s are speed focused in the second cycle, intensity is reduced and therefore, RPE was expected to be lower. However, Wednesday of week 6, the workout is rated quite high with an 8 which also happens to be on his lowest HRV day of the entire 6 weeks. The speed emphasis prevents me from collecting a good 1RM estimation and therefore average values are based on only Mon and Fri lifts in contrast to the previous cycle that allowed for 1RMs to be calculated on all three days.

In week 6, HRV peaks which is in complete contrast to the first cycle where HRV bottomed out in week 3. Interestingly, session RPE’s are lower in week 6 vs. week 3. As HRV declined in week 3, RPE increased, whereas in week 6, though intensity increased, HRV continued to climb and RPE did not increase. There are several instances where HRV relates to RPE (high RPE on low HRV days and vice versa).

1RM avg peaked in week 6 along with HRV avg, however I included an extra workout (the last Monday) in this average as this was the day that made up for the missed workout at the beginning of the second cycle. Therefore the average is of 8 days (4 lifts) rather than the typical 7 days (3 lifts).

HRV on a given day doesn’t particularly appear to be a good predictor of the subtle variation in 1RM strength in this athlete, however weekly mean values showed a strong relationship. This of course needs to be taken in context with where one is within a training cycle. You won’t magically set a PR because your HRV is high or your weekly mean is high.

Raw Data Below

Grip strength testing did not start until week 3. In this athlete, it does not seem to provide any insight as to daily performance potential, fatigue etc. Perhaps this assessment is more useful for lifts directly involving grip requirements (e.g. deadlifts, Olympic lifts, etc.).

Though not presented, sleep ratings never really dropped below 4 out of 5 and therefore sleep did not seem to be impacted by nor affect the other variables.

This data set has a laundry list of limitations. The main one being that 1RM’s were mostly calculated based on the athletes reported RPE of a set and not a true RM attempt, thus leaving plenty of room for error.

I attribute the adjustment in cycle 2 to its success compared to cycle 1. Adding in the speed work and removing the sets of 5 resulted in less fatigue and allowed for more recovery.

This data set convinces me of nothing, but simply encourages me to continue to explore the relationship between HRV and strength in athletes. Though no conclusions should be drawn, the main findings of this small case study are as follows;

In this athlete, weekly average changes in 1RM Bench Press strength were related to weekly average changes in HRV (in all but week 5)
On many instances, low HRV days corresponded to higher ratings of perceived exertion, however this didn’t necessarily affect strength performance.
Grip strength assessed via hand dynanometer did not appear to be a useful indicator of anything in particular (other than grip strength of course) in this athlete.
The peak in HRV and Strength in week 6 along with lower than expected sRPE suggests that the second cycle was well tolerated and fatigue was minimal (likely due to the programming adjustment). This is in contrast to week 3 from the end of the first cycle where HRV fell to lowest values, as did strength, while sRPE’s peaked.

Reviewing Survey Monkey as a free tool for Daily Wellness Questionnaires

Posted on September 21, 2013 by hrvtraining

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

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

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

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

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

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

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

Creating survey questions:

Once you’ve created your survey:

Sending options for your survey:

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

Personalize the email (Default Shown):

Schedule your survey delivery:

Analyze results as a group:

Or analyze results by individual:

And the feature you’re likely wondering about…

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

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

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

Pros:

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

Cons:

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

Hopefully this was helpful.

HRV and Training Cycle Review of Powerlifter with CP

Posted on August 18, 2013 by hrvtraining

I’ve been continuing to work with Zarius out of our lab here at AUM. I provided a detailed account of his recent powerlifting meet prep and competition data in this post. Zarius is 22 years old, weighs 120lbs and has Cerebral Palsy. Due to travel/work schedules we have had a hard time completing a solid training cycle since his last competition earlier this summer. We were finally able to get a good 1 month of training in before one of us had to travel again. Here is an overview of the training and HRV data from our most recent training cycle.

The Plan:

Our schedules allow for 3 training days per week. We train full body every Mon-Wed-Fri. Zarius is able to do some lower body exercises and we always finish each session with some walking laps around the track. (As an aside, prior to getting involved with the Human Performance Lab here at AUM about a year ago, Zarius’ main mode of transportation was his wheel chair. Now he walks most of the time. A testament to the great work done by Dr. Esco and the staff here at AUM as well as the effort put forth by Zarius.) Since we’re not currently preparing for a meet, we only performed the competition press once per week and performed incline and narrow-grip press on the other two training days as his main movements. I also started recording RPE for each set of his main lifts. There is definitely a learning curve to using RPE so I take the earlier values with a grain of salt. Volume for the main lifts remained constant while intensity increased each week. For his assistance work we just did some basic progressive overload. Since we wouldn’t be training for at least a week after the training cycle I wanted to overreach him a little. Zarius recorded his HRV each morning after waking with ithlete in a seated position.

The Data and Analysis:

* Training Load (TL) above is simply referring to the average intensity (%1RM) that we used for his main lifts that week. 7.4=74% , etc. For his future cycles, I’ll be collecting sRPE as he gets more comfortable with the rating system.

Week 1:

– Lowest intensity week

– Avg. RPE for main lift this week was 8.4

– Previous week mean HRV was 85.2 to serve as a baseline, though not on chart. Near +2 increase (86.9) in HRV after week 1.

Week 2:

– Intensity increases

– Avg. RPE for main lifts this week increases slightly to 8.6

– +1 increase in week mean HRV. Training is well tolerated and apparently not very stressful.

Week 3:

– Highest intensity week

– Avg. RPE for main lifts this week increases to 9.1. This week he had four RPE@10 compared to week 2 which had 1 RPE@10 and 0 in week 1. (RPE@10 means it was a maximal set, no further reps could be performed)

– As expected and consistent with his previous meet prep cycle, HRV declines this week -2.3 to 85.7. You’ll note that on weekends his HRV tends to climb back up, indicating good recovery even though the week was quite physically stressful.

Week 4:

– Low intensity lift on Monday

– Bench 1RM Test with competition rules (pause and rack command) on Wednesday. Zarius pressed 260lbs cleanly (at 120lbs bodyweight) which is a 10lb Gym personal record and is 5lbs over his competition best of 255lbs.

– – 3.5 change in HRV this week down to 82.2. This is consistent with his competition cycle where HRV reached lowest values the week of competition. However, we deloaded after week 3 of his competition prep which allowed HRV to recover and peak going in to competition. In this case, due to time constraints, we tested before the deload. It’s possible he may have been stronger with a proper deload before testing.

Week 5:

– No training this week due to travel

– HRV week mean returns to baseline at 84.9 (the week before the start of the cycle with no training had a week mean HRV of 85.2).

Overall, this was a successful training cycle based on the result of his 1RM test. Training to at or very near failure with high intensity appeared to have a large effect on his HRV during training evidenced by the downward trend starting in week 3. Though volume was reduced considerably, the 1RM test day appeared to be very taxing as it had the biggest effect on his HRV (consistent with his previous competition). During week 5 where there were no training sessions, HRV returned to baseline values.

Here is a little video interview that the local media did on Zarius. Take a second and check it out. http://www.montgomeryadvertiser.com/apps/pbcs.dll/article?AID=2013130726027

HRV Reflects Travel Stress

Posted on July 22, 2013 by hrvtraining

Travelling is a part of competitive sport and is an issue that most athletes deal with on a regular basis. Non-athletes may not appreciate just how stressful travelling can be, particularly when it is combined with the demands of training, practice and competition schedules. I’ve learned that travel is especially more stressful for individuals who like to maintain a regular routine. This includes athletes who like to eat at regular times, train at regular times, sleep, etc. To make things worse, crossing time zone’s can add further issues by effecting sleep patterns and disturbing circadian rhythms.

I’m sure if your athletes are open and honest enough, they will be able to communicate any issues they’re experiencing as a result of the travel. However, there’s evidence to show that HRV monitoring is capable of reflecting the time course of individual adaptation to travelling via changes in autonomic activity in healthy folks (Tateishi & Fujishiro 2002, Tateishi et al. 2000). Taken with perceived stress and fatigue, an objective measure such as HRV may provide coaches a more complete picture of their athletes response to the stresses associated with travel.

Brief Research Review

Dranitsin (2008) investigated the effects of travel across 5 times zones and acclimatization to a hot and humid environment in 13 elite junior rowers. The athletes were monitored during a training camp in Kiev for 9 days and during training camp in Beijing for 17 days before and during the Junior Rowing World Championship. HRV was collected daily in the mornings after waking and bladder emptying with a Polar S810i (presently the RS800). Data was collected for 5 minutes supine followed by 5 minutes standing.

HRV parameters remained relatively unchanged after relocation to Beijing until the 3rd day. At this point, standing HRV values decreased until day 6 at which point they returned to baseline. Supine HRV did not show any significant changes until the 8th day of acclimatization. Parasympathetic measures (RMSSD, SD1 and HF) trended upwards in the standing position in response to 3 days of competition. The authors looked at correlations between HRV indices and training load, humidity, etc., but I will not discuss them. With all of the factors involved it would be very difficult to attribute a change in HRV to a specific variable. Overall, it was interesting to see a lag in the HRV response to the change in time zone and climate. The increase in parasympathetic HRV parameters in response to competition likely reflect fatigue although this is not discussed.

Botek et al. (2009) had a case study published investigating the effects of travel across time zones in two elite athletes; a male decathlete and a female swimmer. HRV data was collected upto 10 days prior to their flights and for an additional 8 days after relocation. HRV was measured with a VarCor PF 7 system in both supine and standing positions. Training load was prescribed based on recommendations from the VarCor PF7 software analysis (similar to OmegaWave in a way).

In athlete A (male, decathlete), parasympathetic indices of HRV decreased well below baseline on the day of the flight and remained suppressed until the 4th day after relocation. In athlete B (female, swimmer), HRV remained at baseline until the third day, at which point it decreased significantly. HRV was not recorded every day in athlete B but it appears that HRV trended back toward baseline in the subsequent days. The author’s mention that athlete B experienced larger than normal drops in HRV in response to familiar training which is interesting to note. The results from this case study suggest that HRV responses to travel across time zones are individual. Information about training load and manipulation based on HRV is presented, however it would’ve been nice to see discussion on how the athletes performed in competition as this is ultimately what matters most.

Personal HRV Data from Travel

Below I’ve posted my HRV data from the past 4 weeks that involved considerable travel. The comments provide details of what was happening on a day to day basis regarding location, training, etc.

“Routine” implies I’m at home following my regular routine, “Away” obviously implies that I’m out of town, not following my routine.

Toward the end of week 1, I traveled North-East for a little over a week. I returned home only for a few days at the end of week 2 before I headed South-West to Vegas for the NSCA conference.

Week 1 & 2

Week 1 – Best representation of baseline. HRV decreased on day 1 of travel.

Week 2 – Very little training this week. HRV appears most impacted from a night of drinking and again after my birthday which I attribute mainly to my eating.

Week 3 – The scores toward the end of the week are deceiving as the green indication indicated baseline/recovery. However, after very little sleep, I woke up and performed my measurement while I was barely awake. I could have fallen asleep standing if I shut my eyes. Furthermore, with the time difference and changed sleep pattern, my HRV measure would’ve been performed at an unusual time which can impact scores. The next day after returning from Vegas I slept for 10 straight hours and woke up with the highest score I’ve had in a long time. Clearly I was in recovery mode. I opted to go for a long, very low intensity walk (to the tune of “The Very Best of Cat Stevens” every single step of the way) and held off on training til the next day.

Week 4 – Upon returning home and resuming my training, you can really see the effects of the last few weeks. Moderate workouts (low volume with moderate intensity) were tough and caused large drops in HRV with slow recovery. Additionally, and as expected, strength was down. This is due to both the travel (and associated life style) as well as the near cessation of training during that time. It is rare for me to see scores in the high 60’s and low 70’s but it dipped that low several times over the last two weeks.

For more effect I’ve added a weekly mean trend and my ithlete screen shots below.

Weekly Mean HRV

There is a clear downward trend over the 4 week period. The result of which can be attributed to several factors, not just the travel aspect. Obviously, with more preparation and more effort, I could have minimized these effects and maintained my training. It goes without saying that competitive athletes will likely have much more structure to their transitioning from one time zone to another to reduce the negative effects as much as possible.

So, from my experience and based on the limited research on the topic, it appears that; a) response to travel stress is individual and b) when considered with subjective measures of fatigue, HRV may serve as a useful tool for reflecting the effects of travel and the associated period of adaptation.

NSCA Conference 2013

References

Botek, M., Stejskal, P., & Svozil, Z. (2010). Autonomic nervous system activity during acclimatization after rapid air travel across time zones: A case study. Acta Universitatis Palackianae Olomucensis. Gymnica, 39(2), 13-21.

Dranitsin, O. V. (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.

Tateishi, O., & Fujishiro, K. (2002). Changes in circadian rhythms in heart rate parasympathetic nerve activity after an eastward transmeridian flight. Biomedicine & Pharmacotherapy, 56, 309-313.

Tateishi, O. et al. (2000). Autonomic nerve tone after an eastward transmeridian flight as indicated by heart rate variability. Annals of Noninvasive Electrocardiology, 5(1), 53–59.

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

Posted on June 11, 2013 by hrvtraining

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.

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

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.

Weekly Mean HRV Trend

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

Daily HRV Trend

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

Posted on May 21, 2013 by hrvtraining

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:

These tools/metrics are simple, inexpensive and non-invasive
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.
I wanted both objective and subjective markers
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:

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:

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:

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:

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

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

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.

HRVtraining

HRV research and consulting by Andrew Flatt Ph.D.

Category Archives: Monitoring

HRV: Means and Variation

HRV Measurement Frequency: Comparing 3 day to 7 day per week recordings

Training Load and Nutrition Impact on HRV: 10 Week Data Analysis

HRV in a bit more detail: Part 2

Reviewing HRV, RPE, 1RM and Grip Strength Data Over 6 Weeks

Reviewing Survey Monkey as a free tool for Daily Wellness Questionnaires

HRV and Training Cycle Review of Powerlifter with CP

HRV Reflects Travel Stress

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

Monitoring Training in a High School Football Player

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