Daily Heart Rate Variability before and after Concussion in an American College Football Player

Our latest paper is a case report demonstrating substantial changes in HRV following concussion in a college football player. The full text can be accessed here. The main findings were:

1: The post-concussion HRV trend appeared similar to trends commonly associated with training fatigue. Therefore, staff should investigate the possibility of an unreported concussion when similar trends are observed in athletes. 

2. Alterations in HRV persisted well beyond return to play clearance. This may have implications for clinical treatment and return to play considerations.

3. Since HRV demonstrated greater daily fluctuation post-concussion, isolated (i.e., single time-point) HRV recordings are likely inadequate for assessing persisting effects on the autonomic nervous system. Thus, near-daily HRV assessment may be required.

4. The convenient methodology used to monitor HRV (60-second finger-pulse plethysmography on a mobile application) can feasibly be implemented with an entire roster of athletes.


This case report demonstrates the effects of sport-related concussion (SRC) on heart rate variability (HRV) in an American college football player. Daily measures of resting, ultra-short natural logarithm of the root mean square of successive differences (LnRMSSD), subjective wellbeing, and Player Load were obtained each training day throughout a 4-week spring camp and 4 weeks of preseason training. SRC occurred within the first 2 weeks of the preseason. During spring camp and preseason pre-SRC, the athlete demonstrated minimal day-to-day fluctuations in LnRMSSD, which increased post-SRC (LnRMSSD coefficient of variation pre-SRC ≤ 3.1%, post-SRC = 5.8%). Moderate decrements in daily-averaged LnRMSSD were observed post-SRC relative to pre-SRC (Effect Size ± 90% Confidence Interval = −1.12 ± 0.80), and the 7-day rolling average fell below the smallest worthwhile change for the remainder of the preseason. LnRMSSD responses to SRC appeared similar to trends associated with stress and training fatigue. Therefore, performance and sports medicine staff should maintain regular communication regarding player injury and fatigue status so that HRV can be interpreted in the appropriate context. Detection and monitoring of autonomic dysregulation post-SRC may require near-daily assessment, as LnRMSSD showed greater daily fluctuations rather than chronic suppression following the head injury.


HRV-guided vs. pre-planned training at altitude in an elite wheelchair marathoner

This new paper is in collaboration with Santi Sanz-Quinto and colleagues from his dissertation work. The case study compares HRV-guided vs. pre-planned training at altitude in an elite wheelchair marathoner with CMT.

Influence of Training Models at 3,900-m Altitude on the Physiological Response and Performance of a Professional Wheelchair Athlete: A Case Study.


This case study compared the effects of two training camps using flexible planning (FP) vs. inflexible planning (IP) at 3,860-m altitude on physiological and performance responses of an elite marathon wheelchair athlete with Charcot-Marie-Tooth disease (CMT). During IP, the athlete completed preplanned training sessions. During FP, training was adjusted based on vagally mediated heart rate variability (HRV) with specific sessions being performed when a reference HRV value was attained. The camp phases were baseline in normoxia (BN), baseline in hypoxia (BH), specific training weeks 1-4 (W1, W2, W3, W4), and Post-camp (Post). Outcome measures included the root mean square of successive R-R interval differences (rMSSD), resting heart rate (HRrest), oxygen saturation (SO2), diastolic blood pressure and systolic blood pressure, power output and a 3,000-m test. A greater impairment of normalized rMSSD (BN) was shown in IP during BH (57.30 ± 2.38% vs. 72.94 ± 11.59%, p = 0.004), W2 (63.99 ± 10.32% vs. 81.65 ± 8.87%, p = 0.005), and W4 (46.11 ± 8.61% vs. 59.35 ± 6.81%, p = 0.008). At Post, only in FP was rMSSD restored (104.47 ± 35.80%). Relative changes were shown in power output (+3 W in IP vs. +6 W in FP) and 3,000-m test (-7s in IP vs. -16s in FP). This case study demonstrated that FP resulted in less suppression and faster restoration of rMSSD and more positive changes in performance than IP in an elite wheelchair marathoner with CMT

3 Month HRV and Wellness trends of two D1 Athletes

Below are the HRV trends of two NCAA D1 athletes from a team we’ve been working with over a 3 month period of virtually the same training schedule.

  • The vertical gray bars represent average perceived wellness (9 point scale)
  • The dotted horizontal black line is daily HRV
  • The thin black horizontal line is the 7-day rolling average
  • The dashed parallel horizontal lines represent the smallest worthwhile change (SWC = 0.5xCV)
  • HRV and wellness was acquired daily by the athletes with the ithlete finger sensor in the seated position.

Interestingly, these two athletes have very similar responses. About 3 weeks into the trend was a very intense training camp that was held out of state before Christmas. One athlete appears to experience more fatigue than the other with nearly the whole week below the SWC and a more pronounced decrease in wellness. HRV and wellness for both athletes improve over Christmas break. Following Christmas there is an intense 2-week training period followed by a reduction in training load. Both athletes frequently fall below the SWC here. Athlete A oscillates up and down while Athlete B remains below the SWC for nearly an entire week along with a decrease in wellness (middle of the trends). Both athletes trend upward after the intense training period and remain steady throughout the last half of the trend.

Athlete A

Athlete B

What makes things interesting is when athletes do not respond as expected. This is when the monitoring becomes invaluable as training intervention becomes extremely important.

Weekly endurance performance and HRV: A case study of a collegiate cross-country athlete

During the fall season of 2013, we collected daily HRV, perceived wellness, training load and performance (race times) from a collegiate cross country runner. We wrote this up as a case study and it was published this month in JASC.

Endurance performance relates to resting heart rate and its variability: A case study of a collegiate male cross-country athlete.

This post is a brief summary of our findings.

This athlete competed in 8 km races on Saturday’s throughout the 8 week competitive season for a total of 6 races. HRV data was collected daily with ithlete in a seated position after waking and elimination. Daily wellness questionnaires were delivered to the athletes e-mail via SurveyMonkey (thanks to Carl Valle for this idea) which asked the subject to rate his perceived sleep quality, muscle soreness, fatigue, mood and stress levels out of 5 for a total wellness score /25. Training sessions were quantified with sRPE and a basic TRIMP value. Race times were collected from the host University’s official final results.

Resting heart rate and HRV derived from the smartphone measures were averaged weekly. In addition the coefficient of variation (CV) was calculated for all weekly HR measures.

*Note: lnRMSSD (i.e., HRV) is modified by the application (lnRMSSDx20) which is how the data is presented.

Our initial hypothesis was that the weekly mean of HRV would relate best to performance (8 km races) and that HRV would be a more sensitive measure than basic RHR. However, we found a near perfect correlation between the CV of HRV and performance (r = 0.92). When the CV was high for a given week, performance was worse (slower 8 km race time) whereas when the CV was lower, performance was better. A very similar, but slightly weaker correlation was found with the CV of resting heart rate. The figure below represents the relationship between weekly HRVcv and 8 km race times.

CV 8 km

The weekly mean related less well to performance. Quoting from the paper:

It has been suggested that as a result of tapering and the associated decrease in lnRMSSDmean, the relationship between lnRMSSDmean and performance is reversed (1). This may explain our finding of only a moderate correlation between lnRMSSDmean and 8 km race time and indicates that associations between endurance performance and lnRMSSDmean must be assessed within the context of the training phase and period (1).

The CV reflects the fluctuation in a metric (i.e., HRV/HR in this case) across the week. It’s been suggested that this marker represents the fatigue (decrease in HRV) and recovery process (return toward baseline). However, we also know that non-training related stressors may also effect daily changes. Therefore, a week with higher CV likely represents higher overall stress whereas a decrease in CV might indicate lower stress and/or better recovery, etc. This however should be taken into context with other indications of training status (e.g., TL, wellness, etc.) as a decreased CV has previously been associated with non-functional overreaching in an elite female triathlete in a case study by Daniel Plews and colleagues.

Two races were held at the same course, several weeks apart at the same time of day on Saturday. See excerpt from the paper below:

HRmean and lnRMSSDmean measures were similar on both occasions this season (lnRMSSDmean 73.8, HRmean 70.1 in week 3, lnRMSSDmean 72.6, HRmean 70.5 in week 8), however lnRMSSDcv and HRcv values in week 8 were nearly half of the values in week 3 (lnRMSSDcv 17, HRcv 12.7 in week 3, lnRMSSDcv 9.2, HRcv 6.5 in week 8). Most importantly, race time was 1:49 (min:sec) faster in week 8.  

Obviously, as this was only a case study of a single athlete across a single season, our results need to be interpreted with caution. However, if you’re monitoring HRV in yourself or of athletes, it would likely be best to include the CV in addition to the weekly mean when evaluating training status.

Looking ahead, we have 2 new manuscripts in review right now that evaluate the mean and CV of smartphone-derived HRV in a team of collegiate female soccer players as we make associations with training load changes and performance adaptations. These are very practical papers and the first, to my knowledge, that utilize smartphone applications with ultra-short measures (~1 min) for athlete monitoring.


HRV and Training Cycle Review of Powerlifter with CP

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

Monitoring Training in a High School Football Player

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

Basic Descriptors

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

Monitoring Variables

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

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

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

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

My rationale for the selected variables is quite simple:

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

4 Weeks of Data and Analysis

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

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

Week 1

Week 1

Week 1:

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

Week 2

Week 2:

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

Week 3

Week 3:

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

Week 4

Week 4:

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

4 Week Trend

Further Analysis 

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

Mean Values, Correlations

Mean Values, Correlations

Brief Thoughts

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

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

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

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

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

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

HRV Case Study of a Powerlifter with Cerebral Palsy Preparing for Competition

Shortly after my relocation to Alabama, I was given the opportunity  to oversee the competition preparation of a young powerlifter who had been training here at the AUM Human Performance Lab under the care of Dr. Mike Esco and his staff. He was about 5 weeks out from competition at the time of my arrival. Below is a detailed account of the training program with HRV data, training load and sleep score.

The athlete is a 22 year old male with Cerebral Palsy and can therefore only compete in the Bench Press. He competes in the 123lb weight class (actual weight is 121). His best competition lift was 200lbs recorded this past February at his first competition.

After observing a couple of workouts, I could see that Zarius was missing out on some poundage due to technical flaws. The focus of the program was therefore to improve his bench press technique and get him more accustomed to the competition commands. We trained 3x/week and used a full body, undulating approach that enabled us to Bench Press each session to further develop technique.

The original program is below and was followed with only minor adjustments here and there. The chosen sets/reps and percentages were inspired by those outlined Tri-Phasic Training. This allowed for the completion of only quality reps; avoiding failure and saving the grinding for competition. You’ll notice the corresponding rep ranges for each percentage are well below typical capabilities. (i.e. 85%x2 rather than 85%x5-6). Assistance work progressed in weight or reps each week based on performance.


Beginning on day one of week one, the athlete recorded HRV each morning with ithlete on his iPod Touch in a seated position. Sleep was rated on a scale of 1-5 on the app. Training load was manually entered based on training intensity to make interpretation easier from the trend in relation to his HRV. Perceived values are not included.

Below is all of the raw data as it appears when exported from the app into Excel followed by a recreation of his 4 week trend. I’ve highlighted high and low HRV days in the respective colors used by ithlete. You’ll note that measurements are missing on two occasions; 4/18 and 5/12.



Below are images of his weekly averages of HRV and training load. Training load in this context is simply intended to represent a progressive increase in intensity followed by a deload and then competition.



There’s a clear progressive increase in his HRV trend right up until the start of week 3. Week 3 was the highest intensity training week with a slight reduction in volume. It appears that intensity rather than volume created more fatigue. His HRV peaks during the deload week. The deload week included 2 workouts. On Monday we worked up to his opener of 240lb for a single and on Wednesday we worked up to 70% for a few singles with emphasis on the competition commands and pausing.

You can see that the morning of the competition (5/11) there is a small drop in HRV. I attribute this to pre-competition anxiety based on feedback of mood, perception, etc. He appears to have slept well leading up to the meet. HRV remains suppressed until the 3rd day after the competition where it starts to trend back up, however still remains below average. This clearly shows the additional psychological/emotional stress that competing places on the body.


1st Attempt – 240 Good
2nd Attempt – 250 Good
3rd Attempt – 255 Miss at lockout (very debatable)

He added 50lbs to his competition best since February. His next meet will be in October where he’ll be looking to shorten the gap he has to close to fulfill his dreams of qualifying for the Paralympics.


Individual HRV Responses In Professional Soccer Players During A Competitive Season

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

Materials and Methods

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

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

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

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


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


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

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

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

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


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