Want to see my report, coach?

In this new paper I merged and developed a bit further the 2 IJSPP papers on 1) the stats that changed my life and 2) some personal thoughts on chasing the 0.2 (i., making an impact) in an elite setting.


The value and importance of sport science varies greatly between elite clubs and federations. Among the different components of effective sport science support, the three most important elements are likely the following:

  1. Appropriate understanding and analysis of the data; i.e. using the most important and useful metrics only and using magnitude-based inferences as statistics. In fact, traditional null hypothesis significance testing (P values) is neither appropriate to answer the types of questions that arise from the field (i.e. assess magnitude of effects and examine small sample sizes) nor to assess changes in individual performances.
  2. Attractive and informative reports via improved data presentation/ visualisation (‘simple but powerful’).
  3. Appropriate communication skills and personality traits that help to deliver data and reports to coaches and athletes. Developing such an individual profile requires time, effort and most importantly, humility

Does beetroot juice really improve sprint and high-intensity running performance? – probably not as much as it seems: how (poor) stats can help to tell a nice story


 A few tweets, re-tweets and emails from colleagues have caught my attention within the last 24 hrs, all pointing toward a new study showing improvements in sprinting and high-intensity intermittent running performance after dietary nitrate supplementation (beetroot shots) (1). In the 36 team-sports players (training 5-10h a week) who volunteered for the study, significant “improvements” in 5- (2.3%), 10- (1.6%) and 20-m (1.2%) sprint times and a 3.9% “increase” in high-intensity intermittent performance were reported, after no longer than 5 days of supplementation! (1)


To all practitioners who may read both the article (1) and the present blog post, the topic is obviously highly relevant; we are all looking for various ways to improve our players’ running performance – even better if these improvements can be gained legally (no doping) and without (physical) efforts. If you can convince yourself to commit to drink daily an awful 70-ml beetroot shot for 5 days before an important competition, then you may have found a really cool and lazy way to get faster and fitter!!

However, before I began to tell (again) every player at the club (who would systematically pass on beetroot because of its taste) to finally commit themselves to drink this stuff, because it really works, I wished to make sure it would be worth the effort, both for them and me. After a deeper read of the paper, a closer look at the study design, the data analysis and the stat approach, I realized that in fact, beetroot supplementation, within the context of the present study, may not be as promising as it could be understood while only reading the title of the paper. This for at least two important reasons: 1) the somewhat limited magnitude of the “changes”, although significant and 2) the questionable study design/data analysis that doesn’t allow individual responses to be clearly accounted for and analyzed.

  1. The magnitude of the “improvement” may not be large enough to be meaningful. When considering the magnitude of the smallest worthwhile changes for different sprint distances (SWC, i.e., the minimum improvement likely to have an impact on the field, such as that required to be 20 cm ahead of an opponent to win a ball) (2), the changes reported in the present study are in fact either smaller (5 m: study 2.3% vs SWC ̴ 4%, 10 m: study 1.6% vs SWC ̴2%) or just similar to (20 m: study 1.2% vs SWC ̴1%) (2). Even for 20-m time, which magnitudes equals the SWC, chances for the “improvement” to be substantial may be no more than 50% at the individual level (when considering a typical error of the measurement (TE) of the same magnitude than the SWC – while in fact the TE may actually be twice as large as the SWC for such a distance (2), decreasing further the likelihood of a substantial change) (2). The same reasoning applies to the “increase” in Yo-YoIR1 performance (+3.9%), which SWC is generally twice larger (̴ +8% (3), +7% as 0.2xSD in the present study). In conclusion, the comparison of the reported changes, although significant, to their specific SWC directly questions the practical impact and in turn, the usefullness of beetroot supplementation in the context of the present study. These data illustrate once again that the use of null hypothesis significance testing (NHST) is clearly limited to assess the actual performance benefit of a supplement or an intervention (4, see the blog on the topic) – in the present case the significant P value likely results from the large sample size (n=36) – different conclusions (and probably less misleading in the present case) would be drawn with lower samples (i.e., n<15).
  1. The data analysis doesn’t allow individual responses to be clearly accounted for/analyzed. In fact, the authors simply chose to compare the sprints/YoYoIR1 performances following beetroot supplementation to these following the placebo drink (Post beetroot – Post placebo, via paired-samples t-tests)!? While it is not clear why such a limited approach was chosen, the proper way to analyze these data would be to look first at within-group changes, and more importantly, to compare these within-group changes (i.e., between-group differences in the changes – typical crossover design, as ‘post beetroot – pre beetroot’ compared with ‘post placebo – pre placebo’). This latter approach is way more powerful and allows the understanding of i) the effect of each treatment per se (within-group effect, in relation to the SWC), ii) the variability of the response within each treatment (SD of the change, which has important implications when using supplementation with athletes – some will respond, some not !! – and how many and by which magnitude?), iii) compare the efficacy of the treatments (differences in the magnitude of the changes) and even more importantly, iiii) compare the magnitude of the individual responses between each treatment (i.e., which treatment shows the greater variability in response). Unfortunately, all these relevant information for practitioners are missing in the manuscript.

That being said, I am happy to keep beetroot shots on the supplement table for the moment (for players that can cope with the taste… at least it hasn’t been shown to be detrimental). I may, however, not use the present study to advertise the benefit of beetroot to the players – if we want to keep our legitimacy and maintain the trust that the players put on us, I believe it is important to come to them with the right message – and in that case, applying some appropriate stats surely helps!


  1. Thompson C, Vanhatalo A, Jell H, Fulford J, Carter c, Nyman L, Bailey SJ and Jones AM. Dietary nitrate supplementation improves sprint and high-intensity intermittent running performance. Nitric Oxide 61 (2016) 55-61.
  2. Haugen T, Buchheit M. Sprint running performance monitoring: methodological and practical considerations. Sports Med. 2016;46(5):641.
  3. Bangsbo J, Iaia FM, Krustrup P. The Yo-Yo Intermittent Recovery Test: a useful tool for evaluation of physical performance in intermittent sports. Sports Med. 2008;38:37–51.
  4. Buchheit M. The Numbers Will Love You Back in Return—I Promise. Int J Sports Physiol & Perf, 2016, 11, 551 -554.

The influence of changes in acute training load on daily sensitivity of morning-measured fatigue variables in elite soccer players

Thorpe RT, Strudwick AJ, Buchheit M, Atkinson G, Drust B, Gregson W. The influence of changes in acute training load on daily sensitivity of morning-measured fatigue variables in elite soccer players. IJSPP, In press

Full text here



Purpose To determine the sensitivity of a range of potential fatigue measures to daily training load accumulated over the previous two, three and four days during a short in-season competitive period in elite senior soccer players (n=10).

Methods Total high-speed running distance, perceived ratings of wellness (fatigue, muscle soreness, sleep quality), counter-movement jump height (CMJ), submaximal heart rate (HRex), post-exercise heart rate recovery (HRR) and heart rate variability (HRV: Ln rMSSD) were analysed during an in-season competitive period (17 days). General linear models were used to evaluate the influence of two, three and four day total high-speed running distance accumulation on fatigue measures.

Results Fluctuations in perceived ratings of fatigue were correlated with fluctuations in total high-speed running distance accumulation covered on the previous 2-days (r=-0.31; small), 3-days (r=-0.42; moderate) and 4-days (r=-0.28; small) (p<0.05). Changes in HRex (r=0.28; small; p= 0.02) were correlated with changes in 4-day total high-speed running distance accumulation only. Correlations between variability in muscle soreness, sleep quality, CMJ, HRR% and HRV and total high-speed running distance were negligible and not statistically significant for all accumulation training loads.

Conclusions Perceived ratings of fatigue and HRex were sensitive to fluctuations in acute total high-speed running distance accumulation, although, sensitivity was not systematically influenced by the number of previous days over which the training load was accumulated. The present findings indicate that the sensitivity of morning-measured fatigue variables to changes in training load is generally not improved when compared with training loads beyond the previous days training.



Player tracking technology: half-full or half-empty glass?

Buchheit M & Simpson BM. Player tracking technology: half-full or half-empty glass? IJSPP, In press

This paper summarizes the main points of my talk in Aspire last year (Monitoring Load conference, Doha, Qatar, 2016) where I, among others, highlited the limitations of the metabolic power concept

Full text here

Abstract. With the ongoing development of (micro) technology, player tracking has become one of the most important components of load monitoring in team sports. The three main objectives of player tracking are the following: i) better understanding of practice (provide an objective, a posteriori evaluation of external load and locomotor demands of any given session or match), ii) the optimisation of training load patterns at the team level and iii) decision making on individual players training programs to improve performance and prevent injuries (e.g., top-up training vs. un-loading sequences, return to play progression). This paper, discusses the basics of a simple tracking approach and the need for the integration of multiple systems. The limitations of some of the most used variables in the field (including metabolic power measures) will be debated and innovative and potentially new powerful variables will be presented. The foundations of a successful player monitoring system are probably laid on the pitch first, in the way practitioners collect their own tracking data, given the limitations of each variable, and how they report and utilize all this information, rather than in the technology and the variables per se. Overall, the decision to use any tracking technology or new variable should always be considered with a cost/benefit approach (i.e., cost, ease of use, portability, manpower / ability to impact on the training program).


Figure. Example of Force load symmetry in a players during his return to play period following a right ankle sprain. The symmetry (with errors bars standing for typical error of measurement8) is calculated from the Force load of all foot impacts during all accelerated running phases (>2m.s-2) of each session. The star represents the date of the injury.


Metabolic power: powerful enough to drive Ferraris?

After many requests following my talk in Aspire last year (Monitoring Load conference, Doha, Qatar, 2016) where I takled the metabolic power concept, I have just put together a written version of this specific section (that will also partly be inlcuded in a IJSPP paper written with Ben Simpson now available here)

Since Osgnagh et al. in 2010,13 who showed the potential application of the metabolic power (MP) concept8 for load monitoring in soccer, the interest for this variable has grown exponentially and is now used across many other team sports.6, 7, 12, 16 In fact, most GPS brands offer now the ability to monitor players’ MP, and a majority of practitioners use this variable when reporting.1 While we have been the firsts to be excited about the potential of this monitoring approach, we have since reconsidered our opinion and question now its usefulness in the field to monitor elite players (i.e., “Ferraris”). This is essentially related to i) recent research findings questioning its validity in the context of team sports-specific movements and ii) the fact that it is only an incomplete metabolic measure of internal load and probably too broad a marker of external load.

What do we actually measure?

It has now been shown by four distinct and independent research groups that locomotor-related MP assessed via either GPS or local positioning system (PGPS or Pmet in the figure below) differs largely from the true metabolic demands as assessed via indirect calorimetry (VO2 measures, PVO2). PGPS was actually reported to be very largely greater than PVO2 during walking,3 but very largely lower during shuttle runs at low speed15 and during soccer-,4 rugby-11 or team-sports3 specific circuits.


While some may see the consistency of such conclusions as a kind of consensus, Osgnach et al. wished to write a letter to the editor to defend their approach and criticize our methodology.14 Since we were not given the chance to respond to this letter by the IJSM editor (“you will not be invited to respond to this letter, but I am sure that you will discuss directly this interesting issue with the di Prampero group”), we wished to comment on their main critics in the present post. This should clarify some discussions and confirm the limitations of MP in the context of interest, i.e., monitoring team-sports specific efforts with the available technology on the market.

  • Resting VO2
    • Osgnagh et al.: “Buchheit et al. have included resting VO2 in their calculations; using net VO2 as in the original methodology would reduce the difference observed and PGPS won’t appear to be underestimated anymore.”14
    • Response: we have in fact used net VO2, as clearly written p1151, 2nd paragraph “Average net VO2 and the respiratory exchange ratio (RER) were calculated for each of the three 1-min efforts and the following 30-s recovery periods”4


  • Anaerobic energy contribution
    • Osgnagh et al.: “Anaerobic energy contribution is not appropriately accounted for in the calculation (the intensity of some efforts may be greater than VO2max, so that they are missed in the overall metabolic cost).”14
    • Response: Agree. But in this case, the actual (true) metabolic demands would have been even greater than those measured, which suggests that the PGPS underestimation would have actually been even greater than that reported in our paper!4


  • Impact of non-locomotor actions
    • Osgnagh et al.: “as shown in Buchheit’s Figure 2, VO2 increases markedly at several points, while the simultaneously determined MP remains close to zero.”14 They suggest that something was wrong with our data.
    • Response: Soccer as most other team sports often includes intense but static movements (passing a ball for example, as in our circuit). It is therefore obvious to observe a rise in VO2 that is not associated with locomotor movements and in turn, changes in MP. This is an important limitation of MP – which may only reflect locomotor-related metabolic activity. But if that was the case, what would be the value of such an impartial measure of metabolic load? This is at odds with all attempts to use MP outputs for overall load monitoring or nutritional (post training/matches recovery) guidelines.6


  • Sampling frequency
    • Osgnagh et al.: “The low sampling frequency of the GPS device used is problematic and explains the underestimation of PGPS.”14
    • Response: while we agree that 4 Hz as used in our study can be considered as low, we don’t believe that this may be the cause of the underestimation since the other researchers have all reported the same underestimation using higher sampling frequencies (i.e., 500,15 1011 and 53 Hz). Note also that we have shown that sampling frequency per se was not the most important factors when it comes to precision and validity.5


Added value to load monitoring systems?

Considering that the agreement between PGPS and PVO2 has only been shown to be acceptable during continuous and linear jog and runs (but neither during walking nor intermittent changes of direction runs)3, the metabolic underestimation may be related to the fact that the current equation initially developed for maximal and linear sprint acceleration8 may not be well suited for team-sport specific running patterns (e.g., including rest, irregular step frequency and stride length, turns, upper body muscle activity, static movements).4


This suggests that the value of PGPS per se to monitor training load in team sports may be questionable. Its usefulness may also be limited with respect to practitioners’ expectations in the field. In fact, practitioners are likely seeking for:

  • Overall estimates of internal load, which are satisfactorily assessed through HR and RPE measures1 – information on the metabolic load of exclusively locomotor-related actions as with PGPS may not be comprehensive enough.
  • Precise measures of external load, which directly relate to specific mechanical constraints on players’ anatomy, which, in turn target specific muscle groups. This has direct implications for training, recovery and injury risk. However:
    • PGPS is clearly dissociated from actual muscle activation, as exemplified by very large variations in the PGPS/EMG ratio during accelerated vs. decelerate running.10


  • PGPS, if it was to be used as a global marker of mechanical work, wouldn’t allow deciphering the underlying mechanisms of the load – we rather use distance while accelerating, decelerating and while running at high-speed since those variables may relate directly to the load of specific muscle groups.


  • Injuries are most generally related to inappropriate volumes of accelerations2 or high-speed running;9 there is in contrast little evidence to suggest that spikes in overall energy consumption per se may play a role in injury etiology.


Until new evidences are provided regarding the validity of PGPS as a valid measure of overall energy expenditure during team-sport specific movements, and given the conceptual limitations (difficulties in deciphering the underlying mechanisms of the load), metabolic power assessed via the current technology (PGPS) may not be as powerful as we though to drive Ferraris.


  1. Akenhead R, Nassis GP. Training Load and Player Monitoring in High-Level Football: Current Practice and Perceptions. Int J Sports Physiol Perform. 2016;11(5):587-93.
  2. Bowen L, Gross AS, Gimpel M, Li FX. Accumulated workloads and the acute:chronic workload ratio relate to injury risk in elite youth football players. Br J Sports Med. 2016.
  3. Brown DM, Dwyer DB, Robertson SJ, Gastin PB. Metabolic Power Method Underestimates Energy Expenditure in Field Sport Movements Using a GPS Tracking System. Int J Sports Physiol Perform. 2016.
  4. Buchheit M, Manouvrier C, Cassirame J, Morin JB. Monitoring Locomotor Load in Soccer: Is Metabolic Power, Powerful? Int J Sports Med. 2015;36(14):1149-55.
  5. Buchheit M, Allen A, Poon TK, Modonutti M, Gregson W, Di Salvo V. Integrating different tracking systems in football: multiple camera semi-automatic system, local position measurement and GPS technologies. J Sports Sci. 2014;32(20)(20):1844-57.
  6. Coutts AJ, Kempton T, Sullivan C, Bilsborough J, Cordy J, Rampinini E. Metabolic power and energetic costs of professional Australian Football match-play. J Sci Med Sport. 2015;18(2):219-24.
  7. Cummins C, Gray A, Shorter K, Halaki M, Orr R. Energetic and Metabolic Power Demands of National Rugby League Match-Play. Int J Sports Med. 2016;37(7):552-8.
  8. di Prampero PE, Fusi S, Sepulcri L, Morin JB, Belli A, Antonutto G. Sprint running: a new energetic approach. J Exp Biol. 2005;208(Pt 14):2809-16.
  9. Duhig S, Shield AJ, Opar D, Gabbett TJ, Ferguson C, Williams M. Effect of high-speed running on hamstring strain injury risk. Br J Sports Med. 2016.
  10. Hader K, Mendez-Villanueva A, Palazzi D, Ahmaidi S, Buchheit M. Metabolic Power Requirement of Change of Direction Speed in Young Soccer Players: Not All Is What It Seems. PLoS One. 2016;11(3):e0149839.
  11. Highton J, Mullen T, Norris J, Oxendale C, Twist C. Energy Expenditure Derived From Micro-Technology is Not Suitable for Assessing Internal Load in Collision-Based Activities. Int J Sports Physiol Perform. 2016.
  12. Malone S, Solan B, Collins K, Doran D. The metabolic power and energetic demands of elite Gaelic football match play. J Sports Med Phys Fitness. 2016.
  13. Osgnach C, Poser S, Bernardini R, Rinaldo R, di Prampero PE. Energy cost and metabolic power in elite soccer: a new match analysis approach. Med Sci Sports Exerc. 2010;42(1):170-8.
  14. Osgnach C, Paolini E, Roberti V, Vettor M, di Prampero PE. Metabolic Power and Oxygen Consumption in Team Sports: A Brief Response to Buchheit et al. Int J Sports Med. 2016;37(1):77-81.
  15. Stevens TG, de Ruiter CJ, van Maurik D, van Lierop CJ, Savelsbergh GJ, Beek PJ. Measured and Estimated Energy Cost of Constant and Shuttle Running in Soccer Players. Med Sci Sports Exerc. 2015;47(6):1219-24.
  16. Vescovi JD. Locomotor, Heart-Rate, and Metabolic Power Characteristics of Youth Women’s Field Hockey: Female Athletes in Motion (FAiM) Study. Research quarterly for exercise and sport. 2016;87(1):68-77.

Applying the acute:chronic workload ratio in elite football: worth the effort?

Buchheit M. Applying the acute:chronic workload ratio in elite football: worth the effort? BJSM 2016, In press.

Full text here

The use of the acute:chronic workload ratio (A/C) has received a growing interest in the past two years to monitor injury risk in a variety of team sports.[1 2] This ratio is generally computed over 28 days (i.e., load accumulated during the current week / load accumulated weekly over the past 28 days), using both internal (session-rate of perceive exertion (Session-RPE) x duration) and external (tracking variables, often GPS-related, such as high-speed running and acceleration variables) measures of competitive and training load. While the potential benefit of such a metric is straight forward for practitioners, there remain several limitations to 1) the assessment of relative external load and in turn, injury risk in players differing in locomotor profiles and 2) the effective monitoring of overall load across all training and matches throughout the year. In turn, these limitations likely compromise the usefulness of the A/C ratio in elite football (soccer).


Figure 1. Daily distance (top panel) ran above 19.8 km/h by an international player (French Ligue 1) during a 6-month period (with matches and training data integrated [5]) and associated 28-d chronic and 7-d acute workloads (middle panel) and their ratio (bottom panel). The player was selected with his national team to prepare for the Euro 2016 (21/05/2016 to 08/06/2016) but wasn’t selected to participate to the final tournament. He then took ̴3 weeks of rest before starting the pre-season with his club (04/07/2016). Since the national team staff didn’t use GPS, there are no running data available during his Euro preparation. We then assumed that during his holidays, whatever the sporting activities he practiced, he was very unlikely to reach a running speed >19.8 km/h – high-speed load is therefore set at “0 distance” for these 3 weeks. Note that in-season, national team training and competitive loads have been predicted using players’ historical club data (based on training schedules and match playing times). As a consequence, the predicted running distance of the 4 matches played with the national team (2 per international break) are similar. While this may be seen as a limitation given the usually large (>15-20%) match-to-match variations in high-speed running, this approach allows at least to produce the A/C ratio through these periods while avoiding erroneous spikes/drops. Finally, these data illustrate also nicely the limitation of the A/C ratio during the pre-season period when no off-season data are available. With no off-season data (which differs from “0 distance”), chronic and acute loads are mathematically defined as similar for the first 7 training days, which results in an unrealistic A/C of 1!? The use of predicted off-season data draws fortunately a much more realistic picture, with a ratio >4 at the start of the pre-season, which decrease as training, and in turn fitness progresses.

Ground travel-induced impairment in wellness is associated with fitness and travel distance in young soccer players

Rabbani, Alireza  and Buchheit Martin. Ground travel-induced impairment in wellness is associated with fitness and travel distance in young soccer players. Kinesiology, In press.

Full text here



The aims of this study were to 1) investigate the influence of ground travel on wellness measures, and 2) examine the possible influence of travel distance and fitness on the magnitude of these possible changes. Compared with home matches, wellness measures showed moderate–to-large impairments for away matches the day prior to the match (D-1) (range; +5 to 68%, (90%CL 1-88); standardized difference: range; +0.6 to +1.75 (0.1-2.07)) and small-to-large impairments the day of the match (D0, range; +7 to +68.1(-1.6-87.5); standardized difference, range; +0.24 to 1.78, (-0.06-2.15)), respectively. There were large and very large negative relationships between the increases of fatigue (r = -0.84, 90%CL -0.95; -0.56) or soreness at D-1 (r = -0.80, -0.93; -0.84) and players’ fitness. There were also very large positive correlations between actual wellness measures and traveling distance to away locations (r range; 0.70 to 87). Ground travel-induced impairment in wellness is associated with fitness and distance of away locations in young soccer players. Simple wellness questionnaires could be used to effectively monitor young soccer players’ freshness and readiness to train or compete during away games.

Key words: association football; fatigue; psychometric measures; monitoring; home advantage.


Does short-duration heat exposure at a matched cardiovascular intensity improve intermittent running performance in a cool environment?


Calvin P. Philp, Martin Buchheit, Cecilia M. Kitic, Christopher T. Minson, James W. Fell. Does short-duration heat exposure at a matched cardiovascular intensity improve intermittent running performance in a cool environment? IJSPP, In press.

Full text here


Purpose: To investigate whether a five-day cycling training block in the heat (35°C) in Australian rules footballers was superior to exercising at the same relative intensity in cool conditions (15°C) for improving intermittent running performance in a cool environment (<18°C).

Methods: Using a parallel-group design, 12 semi-professional football players performed five days of cycling exercise [70% heart rate reserve (HRR) for 45 min (5 x 50 min sessions in total)] in a hot (HEAT, 35±1°C, 56±9% RH) or cool environment (COOL, 15±3°C, 81±10% RH). A 30-15 Intermittent Fitness Test to assess intermittent running performance (VIFT) was conducted in a cool environment (17±2°C, 58±5% RH) prior to, one and three days after the intervention.

Results: There was a likely small increase in VIFT within each group [HEAT: 0.5±0.3 km.h-1, 1.5±0.8 x smallest worthwhile change (SWC); COOL 0.4±0.4 km.h-1, 1.6±1.2 x SWC] three days post the intervention, with no difference in change between the groups (0.5±1.9%, 0.4±1.4 x SWC). Cycle power output during the intervention was almost certainly lower in the HEAT group (HEAT 1.8±0.2 W.kg-1 vs. COOL 2.5±0.3 W.kg-1, -21.7±3.2 x SWC, 100/0/0).

Conclusions: This study indicates that when cardiovascular exercise intensity is matched (i.e. 70% HRR) between environmental conditions, there is no additional performance benefit from short-duration moderate-intensity heat exposure (5 x 50 min) for semi-professional footballers exercising in cool conditions. However, the similar positive adaptations may occur in the HEAT with 30% lower mechanical load, which may be of interest for load management during intense training or rehabilitation phases.

Key Words: heat acclimation; football; plasma volume; relative-intensity exercise, VIFT



Day-to-day heart rate variability (HRV) recordings in world champion rowers: appreciating unique athlete characteristics

Plews DJ, Laursen PB and Buchheit M. Day-to-day heart rate variability (HRV) recordings in world champion rowers: appreciating unique athlete characteristics. IJSPP 2016, In press.

Full text here


Purpose: Heart rate variability (HRV) is a popular tool for monitoring autonomic nervous system status and training adaptation in athletes. It is believed that increases in HRV indicate effective training adaptation, but these are not always apparent in elite athletes. Methods: Resting HRV was recorded in 4 elite rowers (Rower A, B, C and D) over the 7-week period prior to 2015 World Rowing Championship success. The natural logarithm of the square root of the mean sum of the squared differences between R–R intervals (Ln rMSSD), Ln rMSSD:R-R ratio trends, and the Ln rMSSD to R-R interval relationship were assessed for each champion-winning rower.  Results: The time course of change in Ln rMSSD was athlete-dependant, with stagnation and decreases apparent. However, there were consistent substantial reductions in the Ln rMSSD:R-R ratio, Rower A: baseline towards week 5 (-2.35 ±1.94); Rower B baseline to week’s 4 and 5 (-0.41 ±0.48; -0.64 ±0.65 respectively); Rower C baseline to week 4 (-0.58 ±0.66); Rower D baseline to week’s 4, 5 and 6 (-1.15 ±0.91; -0.81 ±0.74; -1.43 ±0.69 respectively).  Conclusion: Reductions in Ln rMSSD concurrent with reductions in the Ln rMSSD:R-R ratio are indicative of parasympathetic saturation. As such, 3 of 4 rowers displayed substantial increases in parasympathetic activity despite having decreases in Ln rMSSD. These results confirm that a combination of indices should be used accordingly to monitor cardiac autonomic activity.

Keywords: cardiac parasympathetic function, monitoring, elites

@theplews1 @PaulBLaursen

Assessing overreaching with HRR: what is the minimal exercise intensity required?

Le Meur Y, Buchhei M, Aubry A, Coutts AJ and Hausswirth Ch. Assessing overreaching with HRR: what is the minimal exercise intensity required? IJSPP 2016, In press.

Full text here


Figure 1 – Changes in heart rate recovery (HRR) at all running intensities during the maximal incremental running test during the overload period. f-OR: functional overreaching.


 Purpose: Faster heart rate recovery (HRR) following high-to-maximal exercise (i.e. ≥90% HRmax) has been reported in athletes suspected of functional overreaching (f-OR). This study investigated whether this response would also occur at lower exercise intensity. Methods and Results: Heart rate recovery and rate of perceived exertion (RPE) responses were compared during an incremental intermittent running protocol to exhaustion in twenty experienced male triathletes (8 control and 13 overload subjects led to f-OR) before (Pre), immediately after an overload training period (Mid) and following a 1-week taper (Post). Both groups demonstrated an increase in HRR values at Mid, but this change was very likely to almost certainly larger in the f-OR group at all running intensities (large to very large differences, e.g. +16 ±7 bpm vs. +3 ±5 bpm, in the f-OR and control groups at 11 km×h-1, respectively). The highest between-group differences in changes in HRR were reported at 11 km×h-1 (13 ±4 bpm) and 12 km×h-1 (10 ±6 bpm). A concomitant increase in RPE values at all intensities was reported only in the f-OR group (large-to-extremely large differences, +2.1 ±1.5 to +0.7 ±1.5 AU). Conclusion: These findings confirm that faster HRR does not systematically predict better physical performance. However, when interpreted in the context of the athletes’ fatigue state and training phase, HRR following submaximal exercise may be more discriminant than HRR measures taken following maximal exercise for monitoring f-OR. These findings may be applied in practice by regularly assessing HRR following submaximal exercise (i.e., during warm-up) for monitoring endurance athletes responses to training.

 Keywords: fatigue, overtraining, heart rate, cardiac response, endurance training