HRV Monitoring During Strength and High-Intensity Interval Training Overload Microcycles

Thanks to Christoph Schneider for inviting my collaboration on this new study from his PhD work. The full-text can be viewed here.

Heart Rate Variability Monitoring During Strength and High-Intensity Interval Training Overload Microcycles


Objective: In two independent study arms, we determine the effects of strength training (ST) and high-intensity interval training (HIIT) overload on cardiac autonomic modulation by measuring heart rate (HR) and vagal heart rate variability (HRV).

Methods: In the study, 37 well-trained athletes (ST: 7 female, 12 male; HIIT: 9 female, 9 male) were subjected to orthostatic tests (HR and HRV recordings) each day during a 4-day baseline period, a 6-day overload microcycle, and a 4-day recovery period. Discipline-specific performance was assessed before and 1 and 4 days after training.

Results: Following ST overload, supine HR, and vagal HRV (Ln RMSSD) were clearly increased and decreased (small effects), respectively, and the standing recordings remained unchanged. In contrast, HIIT overload resulted in decreased HR and increased Ln RMSSD in the standing position (small effects), whereas supine recordings remained unaltered. During the recovery period, these responses were reversed (ST: small effects, HIIT: trivial to small effects). The correlations between changes in HR, vagal HRV measures, and performance were weak or inconsistent. At the group and individual levels, moderate to strong negative correlations were found between HR and Ln RMSSD when analyzing changes between testing days (ST: supine and standing position, HIIT: standing position) and individual time series, respectively. Use of rolling 2–4-day averages enabled more precise estimation of mean changes with smaller confidence intervals compared to single-day values of HR or Ln RMSSD. However, the use of averaged values displayed unclear effects for evaluating associations between HR, vagal HRV measures, and performance changes, and have the potential to be detrimental for classification of individual short-term responses.

Conclusion: Measures of HR and Ln RMSSD during an orthostatic test could reveal different autonomic responses following ST or HIIT which may not be discovered by supine or standing measures alone. However, these autonomic changes were not consistently related to short-term changes in performance and the use of rolling averages may alter these relationships differently on group and individual level.


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 Deload Periods

Before I review my own data from my overload and deload period, I first wanted to discuss some of the available research that I have pertaining to HRV response to overload training and following recovery.

Some Research Pertaining to HRV and Taper/Deload Periods

Pichot et al. (2000) monitored HRV in middle distance runners over 3 weeks of intensive training followed by a 1 week recovery week consisting primarily of moderate aerobic work. RMSSD decreased progressively over weeks 1-3 and rebounded to peak values during the recovery week.

Pichot et al. (2002) found that RMSSD increased after an aerobic training period in sedentary subjects. After transitioning to a 4 week overload period, RMSSD decreased significantly followed by an abrupt rebound reaching peak values during a 2 week recovery period.

In a study by Baumert et al. (2006), baseline HRV values were established prior to training camp in track and field athletes. After week 1 of a 2 week training camp, RMSSD declined significantly. At 3-4 day’s post-training camp, RMSSD started to return toward pre-camp basal values.

In elite rowers, Iellamo et al (2004) reported that HRV indices decreased as training load increased from 50% to 100%. However, during a taper for the World Championships, HRV values returned to baseline. “Reduction in training load during the World Championship resulted in a return of autonomic indices to the level observed at 50% training load”

Though not a comparison for pre and post HRV values following overload, Buchheit et al. (2004) showed that moderate training loads are better than no training or intensive training for the purposes of increasing vagal-related HRV indexes. Their data revealed that moderately trained individuals had higher basal HRV values compared to sedentary and highly trained individuals.

Reviewing my data

In older posts I discussed my experimentation with not taking planned deload weeks but rather reducing training loads on days when HRV was low. This method of managing training loads worked very well during times of consistent, albeit, relatively unchanging training. However, due to work/travel schedules and other set-backs I really didn’t plan any overload training. I was mostly doing my best at not losing strength. A feat much easier to accomplish than gaining strength. At the present time, I believe that one can get away without doing week long deloads at fixed intervals (every 4th week or so) if training is managed on a daily basis. However, by design, this set-up really doesn’t allow for overreaching as you would back off as soon as your trend declined for too long. 

It was my goal in my latest training cycle to not focus on daily HRV changes but instead evaluate weekly changes. My training, though still manipulated slightly on a day to day basis (particularly in week 4 of the cycle) was much more pre-planned than I had been doing previously. My training set-up was designed so that HRV would return to above baseline after each weekend.. which it did. The purpose of this was to be fresh for the beginning of each week and to avoid premature overreaching. A deload was planned following the last week of the cycle. 

Below is a screen shot of my HRV trend that includes interesting trend changes in response to different events/training. See my previous post here for a more extensive review of my 9 week training cycle. This post will focus primarily on the last 3 weeks of the trend (weeks 8, 9 and 10 of the cycle)


During week 6 and 7 of my trend HRV baseline reached peak values since the holidays. However, during weeks 8-9 HRV steadily decreases. In fact, in week 9, HRV remains below baseline until the weekend. Typically my HRV will come back up after a recovery day on Wednesdays. The difference between weeks 6-7, 8-9, and 10 are volume and intensity related.

During weeks 6-7 my training volume reduced and my intensity increased only slightly. In week 6, it is reasonable to say that I reduced more stress than I added based on volume and intensity change and sRPE. In week 7 however, there are 2 sRPEs of 9 which marks the initial decent in the trend. During weeks 8-9, volume reduced only slightly but intensity increased to near maximal in week 8 and as close to maximal as I could get in week 9. In that 14 day period I performed 8 workouts of near maximal or at maximal intensity on my main barbell lifts.

It was also during these last two weeks of the training cycle that I experienced nagging pains, high levels of soreness etc.

Though weeks 8 and 9 are the most taxing, my sRPE doesn’t change all that much (primarily 8’s with a rare 9). This does not do a good job of reflecting the change in volume/intensity. Perhaps I need to re-evaluate my current method of rating workouts and tracking training load.

Week 10 is a deload week and HRV returns to peak levels. Soft tissue problems progressively resolve and I’m anxious to start a new cycle.

Wrap Up

HRV will likely decline during intensive training and return to baseline following a recovery period of reduced training loads. Perhaps focusing more on weekly changes in HRV as opposed to daily acute changes is more meaningful during overload periods; permitting a more controllable approach to overreaching.


Baumert, M. et al. (2006) Changes in heart rate variability of athletes during a training camp. Biomed Tech, 51(4): 201-4.

Buchheit, M., et al. (2004) Effects of increased training load on vagal-related indexes of heart rate variability: a novel sleep approach. American Journal of Physiology – Heart & Circulatory Physiology, doi:10.1152/ajpheart.00490.2004.

Iellamo, F., Pigozzi, F., Spataro, A., Lucini, D., & Pagani, M. (2004) T-wave and heart rate variability changes to assess training in world class athletes. Medicine & Science in Sports and Exercise, 36(8): 1342-1346.

Pichot, V., Busso, T., Roche, F., Gartet, M., Costes, F., Duverney, D., Lacour, J., & Barthelemy, J. (2002) Autonomic adaptations to intensive overload training periods: a laboratory study. Medicine & Science in Sports & Exercise, 34(10), 1660-1666.

Pichot, V., et al. (2000) Relation between heart rate variability and training load in middle-distance runners. Medicine & Science in Sport & Exercise, 32(10): 1729-36.