Buchheit M, Lacome M, Cholley Y & Simpson B.M. Neuromuscular responses to conditioned soccer sessions assessed via GPS-embedded accelerometers: insights into tactical periodization. IJSPP, In press.

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Abstract

Purpose. To 1) examine the reliability of field-based running-specific measures of neuromuscular function assessed via GPS-embedded accelerometers and 2) examine their responses to three typical conditioned sessions (i.e., Strength, Endurance and Speed) in elite soccer players.

 Methods. Before and immediately after each session, vertical jump (CMJ) and adductors squeeze strength (Groin) performances were recorded. Players also performed a 4-min run at 12 km/h followed by 4 ~60-m runs (run =12 s, r =33 s). GPS (15-Hz) and accelerometer (100 Hz) data collected during the four runs + the recovery periods excluding the last recovery period were used to derive vertical stiffness (K), peak loading force (peak force over all the foot-strikes, Fpeak) and propulsion efficiency (i.e., ratio between velocity and force loads, Vl/Fl).

Results. Typical errors were small (CMJ, Groin, K and Vl/Fl) and moderate (Fpeak), with moderate (Fpeak), high (K and Vl/Fl) and very high ICC (CMJ and Groin). After all sessions, there were small decreases in Groin and increases in K, while changes in F were all unclear. In contrast, the CMJ and Vl/Fl ratio responses were session-dependent: small increase in CMJ after Speed and Endurance, but unclear changes after Strength; the Vl/Fl ratio increased largely after Strength, while there was a small and a moderate decrease after the Endurance and Speed, respectively.

 Conclusions. Running-specific measures of neuromuscular function assessed in the field via GPS-embedded accelerometers show acceptable levels of reliability. While the three sessions examined may be associated with limited neuromuscular fatigue, changes in neuromuscular performance and propulsion-efficiency are likely session objective-dependent.

Keywords: specificity; running mechanisms; fatigue; horizontal force application; association football.

Figure 1. Changes in counter movement jump (CMJ) and groin squeeze (Groin) performance, vertical stiffness (K), peak loading force (Fpeak) and velocity load/force load ratio (Vl/Fl) following the three conditioned sessions. *: possible, **: likely, ***: very likely and ****: almost certain change/difference in the change.

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

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Abstract

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.

@robbyt05

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

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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.

@benMsimpson

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 (P_GPS or Pmet in the figure below) differs largely from the true metabolic demands as assessed via indirect calorimetry (VO₂ measures, P_VO2). P_GPS was actually reported to be very largely greater than P_VO2 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 P_GPS won’t appear to be underestimated anymore.”¹⁴
  • Response: we have in fact used net VO₂, as clearly written p1151, 2^nd 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 P_GPS 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 P_GPS.”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 P_GPS and P_VO2 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 P_GPS 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 P_GPS 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:
  • P_GPS is clearly dissociated from actual muscle activation, as exemplified by very large variations in the P_GPS/EMG ratio during accelerated vs. decelerate running.¹⁰

• P_GPS, 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 P_GPS 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 (P_GPS) may not be as powerful as we though to drive Ferraris.

References

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.

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

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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.

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.

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Abstract

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.

@alirezarabbani