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.

Reaction Test for Athlete Monitoring: Research and Considerations

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

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

Why The Reaction Test?

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

Reaction Test and Overreaching

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

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

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

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

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

Reaction Test and Perceived Performance 

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

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

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

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

Reaction average trend

HRV Avg Trend Reaction Blog

Considerations and Limitations

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

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

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

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

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

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


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

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

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

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

HRV and Reaction Test Data and some updates on our HRV research

I posted some data a couple of months ago comparing my HRV to my tap test results to see if there was any correlation between the two. You can see that post here if you missed it. It was around that time that I also started using a Reaction Test app. Today I’ll be posting and reviewing my Reaction Test data with my HRV data to see what it might reveal. At the end of the post I’ll provide some brief updates on what’s been happening since I started working in the Human Performance Lab here at Auburn (Montgomery).

HRV: I continue to use ithlete as my main HRV metric. Daily measurements are performed each morning after waking and bladder emptying. All measurements are performed in the standing position with paced breathing. The HRV value provided by ithlete is Ln RMSSD x 20; a time domain measure of parasympathetic tone.

Reaction Test: The reaction test is performed after my HRV test and my Tap test (I’m still doing these but will not include them today). All reaction tests were performed using right index finger. The app functions as follows;

  1. initiate app
  2. Tap target area to start the test
  3. React to stimuli (color change) as fast as possible by tapping the screen
  4. Repeat for a total of 5 reactions (variable time intervals between)


I used excel to calculate daily average with the reaction test data (plotted on the charts below).

Keep in mind that for a correlation between high HRV and good Reaction Test, we want to see an inverse relationship in the trends. We’re looking for a fast Reaction time (trending down) with a higher HRV score (trending up).

Chart 1 – HRV, Reaction Test Average and Session RPE (secondary axis)  


For more clarity I’ve also included excel screen shots of the raw data. I’ve sectioned off 4 different areas and noted the goal/purpose of that particular time of training. It works out so that there is a High Intensity section, a Deload section, a High Volume Section, and a Semi-Deload section. The “Semi-Deload” period occurs over the past week that I’ve moved to Alabama. I figured it would be wise to scale intensity and volume back very slightly while I settle in to a new place and new work environment. To give an example, I essentially removed a main working set and stuck with familiar weights. Assistance work was relatively unchanged.


* I must have forgotten to perform a reaction test or forgot to save it on 03/16 which was a Saturday and therefore it is not included.

I’ve highlighted any score that was +/- 10% from the total average. So for exampme; if HRV was 10% higher than the average of all HRV scores, I would shade that day green. Likewise for Reaction Test. Red shading denotes 10% or greater reduction.

After examining the acute relationship between Reaction Test and HRV I decided to examine the averages for each training block. I’ve shifted my focus lately a little bit more on weekly trend changes vs. daily trend changes. As you can see in the charts below, there is a very strong relationship between HRV AVG and Reaction Test AVG during each training section.


–          Intensity Section – This section was the last 2 weeks of my 9 week training cycle that I performed after the Christmas break (discussed here). Volume was low but intensity was Maximal. HRV is at it’s lowest average while Reaction Test is at its highest (slowest reaction time) average.

–          Deload – During the deload week HRV average rebounds to peak levels while reaction time improves to near peak levels.

–          High Volume – This marks the start of a new training cycle. HRV drops quite a bit and Reaction Time average increases (slower reaction).

–          Semi-Deload – HRV returns to near peak values while Reaction Test peaks (quickest reaction time average).

From this data set, intensity appeared to have the biggest effect on Reaction Test average and HRV average. High volume work with moderate intensity also had a significant impact on these averages. It should be kept in mind that the Intensity period followed several weeks of training and therefore some fatigue had already been accumulated. I didn’t start using the reaction test until late February.  HRV and Reaction averages improve over periods of reduced training load.

Given that I was able to hit some PR’s in the gym during the Intensity section (under high fatigue), I’m inclined to say at this point, based on this data set, that these tests are not necessarily indicators of performance potential (strength), but rather markers of fatigue. In the future I would like to see how these tests match up with “finer” motor skills in other athletes.

Quick Updates

I made it safely to Montgomery, AL after a nice visit with some family at my folks place in Cincinnati over Easter. Total travel time was about 17.5 hours. We wasted no time in getting to work in the lab. We’ve got 3 projects going on right now (the first two being more health related  as opposed to sports/performance).

  1. I’m helping Dr. Esco complete a study comparing post-exercise HRV recovery after two different modes of exercise (cycling vs. treadmill at same intensity/duration).
  2. We are starting a new study comparing post-exercise HRV in middle aged men after 3 modes or resistance training; Eccentric only; Concentric Only; Traditional Resistance Training
  3. We have put the wheels in motion for a cross-validation study comparing ithlete to EKG. We did some pilot work with about 6 subjects so far and have IRB Forms and Consent Forms about ready for submission. We’ll measure ithlete and EKG simultaneously in about 20 males and 20 females then run the data. This is a very important study to me. In order to improve what we know about HRV and performance, we need more data. Using EKG’s in the field is not practical. What we need to start seeing is data from athletes that are performing measurements at home when they wake up. The device needs to be extremely easy to use and the data needs to be immediately available to the coach. At this time, smart phone app’s are the best way to do this. There are plenty of limitations with this but at the end of the day, if we’re going to apply this stuff in a team setting we need easy to use, affordable tools.
  4. This last project doesn’t exist yet. But I’m hoping to collect data on either the men’s tennis team or the women’s soccer team. I’ll provide more info on this if and when it starts to take shape.

Let me be clear right from the start in saying that Dr. Esco is running the show here. I’ve learned a ton from him already about the research process and anything that I accomplish over the next little while will be because of him.

Lastly, I attended my first Roller Derby which was quite the experience.