Modelling the HRV Response to Training Loads in Elite Rugby Sevens Players

New paper in collaboration with my colleagues Sean Williams, Dan Howells et al. Full-text link below.

Modelling the HRV Response to Training Loads in Elite Rugby Sevens Players

Key Points

  • A systems theory approach can be used to describe the variation in chronic HRV responses to training within elite Rugby Sevens players.
  • For the majority of athletes, model parameters can be used to accurately predict future responses to training stimuli.
  • Responses that diverge from the predicted values may serve as a useful flag for the investigation of changes in lifestyle factors.
  • Internal training load measures (sRPE) markedly outperformed external load measures (HSD) in predicting future HRV responses to training stimuli.


A systems modelling approach can be used to describe and optimise responses to training stimuli within individuals. However, the requirement for regular maximal performance testing has precluded the widespread implementation of such modelling approaches in team-sport settings. Heart rate variability (HRV) can be used to measure an athlete’s adaptation to training load, without disrupting the training process. As such, the aim of the current study was to assess whether chronic HRV responses, as a representative marker of training adaptation, could be predicted from the training loads undertaken by elite Rugby Sevens players. Eight international male players were followed prospectively throughout an eight-week pre-season period, with HRV and training loads (session-RPE [sRPE] and high-speed distance [HSD]) recorded daily. The Banister model was used to estimate vagallymediated chronic HRV responses to training loads over the first four weeks (tuning dataset); these estimates were then used to predict chronic HRV responses in the subsequent four-week period (validation dataset). Across the tuning dataset, high correlations were observed between modelled and recorded HRV for both sRPE (r = 0.66 ± 0.32) and HSD measures (r = 0.69 ± 0.12). Across the sRPE validation dataset, seven of the eight athletes met the criterion for validity (typical error <3% and Pearson r >0.30), compared to one athlete in the HSD validation dataset. The sRPE validation data produced likely lower mean bias values, and most likely higher Pearson correlations, compared to the HSD validation dataset. These data suggest that a systems theory approach can be used to accurately model chronic HRV responses to internal training loads within elite Rugby Sevens players, which may be useful for optimising the training process on an individual basis.

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

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 & Load


HRV & sRPEHR & LoadHR & Load






– 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.


– 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.

Trend 9 to 12_2013

– 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.

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

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.


230×3 RPE @9 = 4RM

Tuchsherer’s Formula: 1RM = 271.4 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 and sRPE

Daily HRV & 1RM

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;

  1. In this athlete, weekly average changes in 1RM Bench Press strength were related to weekly average changes in HRV (in all but week 5)
  2. On many instances, low HRV days corresponded to higher ratings of perceived exertion, however this didn’t necessarily affect strength performance.
  3. 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.
  4. 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.