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

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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.

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Abstract

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?

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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.

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figure-1

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.

ABSTRACT

 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

Quantification of training load during return to play following upper and lower body injury in Australian Rules Football

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Ritchie D, Hopkins WG, Buchheit M, Cordy J, & Bartlett JD. Quantification of training load following upper and lower body injury in Australian Rules Football. IJSPP, in Press.

Ritchie IJSPP 2016b.JPG

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Abstract:

Training volume, intensity and distribution are important factors in training design during periods of return to play. The aim of this study was to quantify the effect of injury on training load (TL) before and after return to play in professional Australian Rules Football.

Weekly perceived training load (RPE-TL) for 44 players was obtained for all indoor & outdoor training sessions, while pitch-based training was monitored via GPS (total distance, high-speed running, and mean speed). When a player sustained a competition time-loss injury, weekly TL was quantified for 3 weeks before and after return to play. General linear mixed models, where inferences about magnitudes standardized with between-player SD, were used to quantify effects of lower and upper body injury on TL compared to the team.

While total RPE-TL was similar to the team within 2 weeks of return to play, distribution of training was different, whereby skills RPE-TL was likely and most likely lower for upper and lower body injury, respectively, and most likely replaced with small to very large increases in running and other conditioning load. Weekly total distance and high-speed running was most likely moderately-largely reduced for lower and upper body injury until after return to play, at which point, total RPE-TL, training distribution, total distance and high-speed running were similar to the team. Mean speed of pitch-based training was similar before and after return to play compared to the team.

Despite injured athletes obtaining comparable training loads to injured players, the distribution of the training is different until after return to play, indicating the importance of monitoring all types of training athletes complete.

Shooting performance and fly time in highly-trained wing handball players: not everything is as it seems

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Karcher C & Buchheit M. Shooting performance and fly time in highly-trained wing handball players: not everything is as it seems. IJSPP 2016

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Abstract

Purpose: The aims of this investigation were to 1) assess the usefulness of counter movement jump (CMJ) testing to predict handball-specific jumping ability and 2) examine the acute effect of transiently- modified jumping ability (i.e., flight time) on shooting efficiency in wing players. Methods: Eleven young highly-trained wing players performed 3 counter movement jumps and 10 typical wing jump shots with 3 different modalities: without any constraint (CONTROL), while stepping on a 14-cm step (STEP) and wearing a weighted vest (VEST, 5% of body mass). Flight time and the associated scoring efficiency during the jump shots were recorded. Results:  There was no clear correlation between jump shot and CMJ flight time, irrespective of the condition (r=0.04-0.18). During jump shots, flight time was most likely longer (ES=1.42-1.97) with VEST (635.4±31 ms) and STEP (615.3±32.9 ms) than CONTROL (566±30.5 ms) and very likely longer with VEST than with STEP (ES=0.6). The correlation between scoring efficiency and jump shot flight time was not substantial both within each modality and for all shots pooled. The difference in scoring efficiency between the 3 jumps with the longest vs. shortest flight times were either small (VEST, 48% vs. 42%) or non-substantial (two other conditions). Conclusions: The use of CMJ as a predictor of handball-specific jumping ability is questioned given the dissociation between CMJ and jump shot flying time. These results also show that transiently-affected flight time may not affect scoring efficiency, which questions the importance of jumping ability for success in wing players.

 Key Words: shooting efficiency; strength training; transfer.

Chasing the 0.2

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Buchheit M. Chasing the 0.2. IJSPP 2016, 11, 417-418. Original publication here

Having-an-impact_0001

credits

“A life is not important except in the impact it has on other lives” once said Jackie Robinson, the 6-time US All-Star baseball player. Making an impact is probably what drives most of us working as academics, applied sport scientists, or both. While academics may leave their footprint in the research community via their ground-breaking publication records, they can also indirectly help practitioners and athletes in the field through relevant research findings. Sport scientists, on the other hand, have direct contact with coaches and athletes and the responsibility to act daily to help them succeed.1 But making an impact—reaching a 0.2 as we are now used to saying in reference to the smallest important (standardized) effect in statistics2—is everything but easy. I heard Chris Carling (British sport scientist) once say that researchers and sport scientists often answer questions that are not asked.

The publication path is a long and winding road. From the first study protocol draft to an ahead-of-print manuscript, there are often months or even years of work, blood, sweat, and tears. Considering that sport science overall is not a major research field compared with medicine, for example, and that most peer-reviewed journals are not open-access, what is the actual audience for our manuscripts? Often it is apparent when dealing with practitioners that they couldn’t be bothered reading even the abstract of a paper and would rather ask directly, “Ok, but in the end what does the paper say?” The great success of Yann Le Meur’s infographics,3 Jacquie Tran’s sketchnotes,4 and the rise of personal blogs using advanced data-visualization techniques5 is evidence of the disconnect between journal manuscripts’ focus, format, and accessibility in comparison with practitioners’ needs and science-information literacy. I am not saying that researchers should give up publishing, but the choice of their research questions could sometimes be wiser and more relevant to the field. The informative nature and clarity of manuscript figures may also deserve more attention to allow nonscientists to better extract the pertinent information.

For academics, the benefit of better research questions is multiple. It may not only translate into greater impact on the field but also directly increase their paper citations and, in turn, their holy grail: the H factor. As exemplified with my personal publication records available on Google Scholar,6 a study in which I was involved during my PhD studies—using heart-rate arousals to detect changes in sleep stages, published in 2004 in a prestigious journal7—has only been cited 7 times since (8 times now!), while our recent review on high-intensity training8 has reached 164 citations in less than 3 years. Heart-rate patterns during sleep are probably interesting, but how to program interval training in athletes seems much more important to other researchers. Note, however, that in addition to generating large numbers of citations, some papers can also seduce a very large audience in terms of readership, exemplified by high altmetric9 scores, for example (eg, reads, downloads, shares on social media). Nonetheless, over the last 15 years, I have certainly published too many interesting-only papers that lacked clear practical applications. Who, in the field, has the time and the resources to detect ventilatory thresholds using electromyographic signals?10 Mea culpa! For sport scientists working in the field, continuing to publish high-quality research may also ensure their professional stability—if a club they work for ends their employment, having maintained minimal research activity likely increases their ability to “fall back” into academia.

So, how do researchers come to ask questions that make their working hours relevant and impactful? How do sport scientists select the area in which to put their efforts at their club? The first steps toward a 0.2 progress may be as simple as focusing on the “big rocks” (the rest being just pebbles and sand). Practically, this means targeting the 3 to 5 most important areas clearly identified as having a meaningful impact on the athletes’ programs and performance. In an extreme case, I would say that in our field, research studies that can’t help guide or change practice are not far from useless. Forget the unessential, forget big data strategies. Save time, energy, and resources to focus on what is known to matter to practitioners and athletes. Do simple but powerful. Ideally, academic researchers should always be aligned with practitioners’ (eg, sport scientists, strength and conditioning coaches, nutritionists) needs, who should, in the best-case scenario, be the researchers themselves, or at least those initiating the research questions. However, since the majority of coaches, support staff, and athletes often don’t know what to expect from applied research and scientific support at the club, it is only by sitting right next to them during training sessions and team debriefs, by sharing meals and cups of coffee, living daily with them in “the trenches,” that we can appreciate what they may find useful and which information they rely on to make their decisions. Whether a given coach or athlete better understands visual data (eg, printed reports, e-mails) versus verbal and informal feedback or relies more on quantitative versus qualitative information cannot be predicted. Understanding the specific codes of a sport or a very specific community of athletes takes many years. Having the respect and trust of high-profile athletes is often more a matter of personality and behavior than scientific knowledge and skills. While this sounds obvious for people already in the “industry,” master’s degrees and PhD qualifications often are of little benefit in the quest to create such a collaborative and productive environment. As described by the fantastic David T. Martin, we sport scientists (monkeys) and coaches and athletes (felines and big cats) don’t belong to the same species. We have different expectations, behave differently, and tend to make our decisions based on evidence and facts, while they rely on feelings and experience. Creating these links, building these bridges, connecting rather than collecting the dots, requires time and effort. Having a strong character is often compulsory to survive in most places, but open-mindedness, humility, and a form of kindness are probably some of the most important personality traits to develop to make a 0.2 in this world.

With these engaging personal and social skills in mind, it is not surprising that the majority of the most renowned researchers, sports scientists, and performance managers to date have, in parallel to their academic journeys, exposed themselves deeply to the elite sport culture, either directly (as coaches) or indirectly (as athletes). Joint positions (university and elite clubs) as typically offered in the United Kingdom, Australia, and New-Zealand or self-created similar environments (ie, working and/or playing in an elite club during one’s undergraduate program or PhD) represent in my eyes the optimal training options for a new generation of sport scientists to emerge. Only they may manage to ask the right questions, publish worthy papers for our journal, and have, in turn, >0.2 impacts on elite performance.

Martin Buchheit
Associate Editor, IJSPP

References

1. Pyne DB. Working with the coach. Int J Sports Physiol Perform. 2016;11(2):153. http://dx.doi.org/10.1123/IJSPP.2016-0034
2. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc. 2009;41(1):3–13. doi:10.1249/MSS.0b013e31818cb278
3. Le Meur Y. Infographics. http://ylmsportscience.blogspot.fr/. Accessed April 5, 2016.
4. Tran J. Sketchnotes. https://phdblog.jacquietran.com/category/sketchnotes/.
5. Prestidge B. https://public.tableau.com/profile/brian.prestidge#!/vizhome/EPLInjuries_4/Intro. Accessed April 5, 2016.
6. Buchheit M. https://scholar.google.com/citations?user=JwnPRl4AAAAJ&hl=en. Accessed April 5, 2016.
7. Viola AU, Brandenberger G, Buchheit M, et al. Sleep as a tool for evaluating autonomic drive to the heart in cardiac transplant patients. Sleep. 2004;27(4):641–647. PubMed
8. Buchheit M, Laursen PB. High-intensity interval training, solutions to the programming puzzle: part I: cardiopulmonary emphasis. Sports Med, 2013;43(5):313–338.
9. Altmetric. https://frontiers.altmetric.com/details/2124380. Accessed April 5, 2016.
10. Racinais S, Buchheit M, Girard, O. Breakpoints in ventilation, cerebral and muscle oxygenation, and muscle activity during an incremental cycling exercise. Front Physiol. 2014;5:142

The numbers will love you back in return – I promise

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Buchheit M. The numbers will love you back in return – I promise. IJSPP 2016, 11, 551 – 554

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Abstract: The first sport science-oriented and comprehensive paper on magnitude-based inferences (MBI) was published 10 years ago in the first issue of this journal. While debate continues, MBI is today well-established in sports science and in other fields, particularly clinical medicine where practical/clinical significance often takes priority over statistical significance. In this commentary, some reasons why both academics and sport scientists should abandon null hypothesis significance testing (NHST) and embrace MBI are reviewed. Apparent limitations and future areas of research are also discussed. The following arguments are presented: P values and in turn, study conclusions, are sample-size dependent, irrespective of the size of the effect; significance doesn’t inform on magnitude of effects, yet magnitude is what matters the most; MBI allows authors to be honest with their sample size and better acknowledge trivial effects; the examination of magnitudes per se helps provide better research questions; MBI can be applied to assess changes in individuals; MBI improves data visualisation; and lastly, MBI is supported by spreadsheets freely available on the internet. Finally, recommendations to define the smallest important effect and improve the presentation of standardized effects are presented.

Keywords: magnitude-based inferences; null hypothesis significance testing; sample size; trivial effect; smallest important effect.

 

Figure 3 MBI

Figure 3. Differences in various anthropometric, physiological and performance measures between two groups of young soccer players differing by their maturity status (0.9 ± 0.3 vs. -0.2 ± 0.4 years from predicted peak height velocity)30 when expressed in percentages (A), using Cohen’s effect size principle (B) and as a factor of variable-specific smallest worthwhile differences (SWD) (C):28 0.2 x between-athletes SD for height, MAS and matches tracking data; performance-related changes for HRR and MSS (723 and 222%, respectively). The numbers of * indicate the likelihood for the between-group differences to be substantial, with 1 symbols referring to possible difference, 2 to likely, 3 to very likely and 4 to almost certain differences. Note that that magnitude of the between-group differences and their likelihood varies between the panels. My suggestion is to use the method used in panel C (with a variable-specific SWD). MSS: maximal sprinting speed, MAS: maximal aerobic speed, HRR: heart rate recovery after submaximal exercise, D>16 km/h: distance ran above 16 km/h during matches, #HIR: number of high intensity runs during matches.

 

 

Psychometric and physiological responses to a pre-season competitive camp in the heat with a 6-hr time difference in elite soccer players

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Buchheit, M., Cholley Y. and Lambert P. Psychometric and physiological responses to a pre-season competitive camp in the heat with a 6-hr time difference in elite soccer players. IJSPP, In press.

 

Purpose. The aim of the present study was to examine in elite soccer players some psychometric and physiological responses to a competitive camp in the heat, after travelling across 6 time-zones. Methods. Data from 12 elite professional players (24.6±5.3 yr) were analyzed. They participated in an 8-day pre-season summer training camp in Asia (heat index 34.9±2.4 ⁰C). Players’ activity was collected during all training sessions and the friendly game using 15-Hz GPS. Perceived training/playing load was estimated using session rate of perceived exertion (RPE) and training/match duration. Psychometric measures of wellness were collected upon awakening before, during and after the camp using simple questionnaires. HR response to a submaximal 4-min run (12 km/h) and the ratio between velocity and force load (accelerometer-derived measure, a marker of neuromuscular efficiency) response to 4 ~60-m runs (22-24 km/h) were collected before, at the end and after the camp. Results. After a large increase, the RPE/m.min-1 ratio decreased substantially throughout the camp. There were possible small increases in perceived fatigue and small decreases in subjective sleep quality on the 6th day. There were also likely moderate (~3%) decreases in HR response to the submaximal run, both at the end and after the camp, which were contemporary to possible small (~8%) and most-likely moderate (~19%) improvements in neuromuscular efficiency, respectively. Conclusions. Despite transient increases in fatigue and reduced subjective sleep quality by the end of the camp, these elite players showed clear signs of heat acclimatization, which were associated with improved cardiovascular fitness and neuromuscular running efficiency.

Fig 1 final color2

Figure 1. Upper panel: change in locomotor load (measured via GPS) and heat index before, during and after the Asian camp. The flights represent the different flying trips, with their specific duration indicated into brackets. As wearing GPS was not allowed during the official match, the total distance covered was extrapolated from historical club data (Team A) against Team B for illustration. The timing of the monitoring sessions is also indicated, with Run standing for the submaximal run and the 4 60-m runs, and Wellness for the psychometric questionnaires. Lower panel: changes in perceived training load (rate of perceived exertion, RPE, method) and the RPE/distance per min ratio. The gray area represents the Hong Kong (HK) camp, while the grey and shaded area represents the time spent in Beijing. ****: very likely different vs. pre camp.

Keywords: heart rate monitoring, wellness, neuromuscular efficiency, association football, heat training.

Metabolic power requirement of change of direction speed in young soccer players: not all is what it seems

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Hader K, A Mendez-Villanueva , D Palazzi, S Ahmaidi and M Buchheit. Metabolic power requirement of change of direction speed in young soccer players: not all is what it seems. PlosOne, In press. Full text here

Abstract

Purpose. The aims of this study were to 1) compare the metabolic power demand of straight-line and change of direction (COD) sprints including 45° or 90°-turns, and 2) examine the relation between estimated metabolic demands and muscular activity throughout the 3 phases of COD-sprints.

Methods. Twelve highly-trained soccer players performed one 25-m and three 20-m sprints, either in straight-line or with one 45º- or 90º-COD. Sprints were monitored with 2 synchronized 100-Hz laser guns to assess players’ velocities before, during and after the COD. Acceleration and deceleration were derived from changes in speed over time (Figure 1). Metabolic power was estimated based on di Prampero’s approach (2005). Electromyography amplitude (RMS) of 2 lower limb muscles was measured. The expected energy expenditure during time-adjusted straight-line sprints (matching COD sprints time) was also calculated.

Results. As shown in Figure 2, locomotor-dependant metabolic demand was largely lower with COD (90°, 142.1±15.0 J.kg-1) compared with time-adjusted (effect size, ES = -3.0; 193.2±18.7 J.kg-1) and non-adjusted straight-line sprints (ES = -1.7; 168.4±18.2 J.kg-1). Metabolic power requirement was angle-dependent, moderately lower for 90º-COD vs. 45º-COD sprint (ES = -1.0; 149.5±12.9 J.kg-1). Conversely, the RMS was slightly– (45°, ES = +0.5; +2.1%, 90% confidence limits (±3.6) for vastus lateralis muscle (VL)) to-largely (90°, ES = +1.6; +6.1 (3.3%) for VL) greater for COD-sprints. Metabolic power/RMS ratio was 2 to 4 times lower during deceleration than acceleration phases (Figure 7).

Conclusion. Present results show that COD-sprints are largely less metabolically demanding than linear sprints. This may be related to the very low metabolic demand associated with the deceleration phase during COD-sprints that may not be compensated by the increased requirement of the reacceleration phase. These results also highlight the dissociation between metabolic and muscle activity demands during COD-sprints, which questions the use of metabolic power as a single measure of running load in soccer.

Key words: Energy demand, muscular activity, electromyography amplitude, acceleration, deceleration, sprint, braking forces, running load.

 

Fig 1w

Fig. 1: Electromyography amplitude (RMS) of vastus lateralis and biceps femoris muscles and speed profiles during sprints with (45° or 90°) or without (i.e., straight-line, SL) one change of direction (COD). 90°25: 25-m sprint with one 90°-COD. The medial panel represents the standardized difference (Std Diff) of RMS between COD- and SL- sprints. The number of ‘*’ and ‘†’ refers to possible, likely, very likely and almost certain difference versus straight-line and 45°-COD sprints, respectively.

 

Fig 2

Fig. 2: Estimated energy expenditure of sprints with (45° or 90°) or without (i.e., straight-line, SL) one change of direction (COD); 90°25: 25-m sprint with one 90°-COD. The upper panel represents the standardized difference (Std Diff) between COD- and SL sprints. Since 90°25 vs. 20-m SL sprints could not be properly compared (i.e., differences in both running time and distance), their standardized difference (black circle) was not provided. The number of ‘*’ and ‘†’ refers to possible, likely, very likely and almost certain between-sprints differences versus the 45°-COD sprint trial, and within-sprint differences vs. the acceleration phase, respectively. The associated number refers to the magnitude of the difference, with 1 standing for small, 2 for moderate, 3 for large and 4 for very large magnitude.

 

Fig7 Ratio phases

Fig. 7: Metabolic power/electromyography amplitude (RMS) ratio during the different phases of sprints with (45° or 90°) or without (i.e., straight-line (SL)) one change of direction (COD). 90°25: 25-m sprint with one 90°-COD; BF: biceps femoris; VL: vastus lateralis. The number of ‘*’ and ‘†’ refers to possible, likely, very likely and almost certain difference versus straight-line and 45°-COD sprints, respectively. The associated number refers to the magnitude of the difference, with 1 standing for small, 2 for moderate, 3 for large and 4 for very large magnitude

 

The tracking of morning fatigue status across in-season training weeks in elite soccer players

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Thorpe RT, Strudwick AJ, Buchheit M, Atkinson G, Drust B and Gregson W. The tracking of morning fatigue status across in-season training weeks in elite soccer players. IJSPP, In Press. Full text here

Thorpe IJSPP 2016

Purpose: To quantify the mean daily changes in training and match load and any parallel changes in indicators of morning-measured fatigue across in-season training weeks in elite soccer players.
Methods/ Following each training session and match, ratings of perceived exertion (RPE) were recorded to calculate overall session load (RPE-TL) in 29 English Premier League players. Morning ratings of fatigue, sleep quality, muscle soreness, as well as sub-maximal exercise heart rate (HRex), post-exercise heart rate recovery (HRR%) and variability (HRV) were also recorded. Data were collected for a median duration of 3 weeks (range: 1-13) and reduced to a typical weekly cycle including a weekend match day. Data were analysed using within-subjects linear mixed models.
Results: RPE-TL was approximately 600 AU (95%CI: 546-600) higher on match day vs the following day (P<0.001). RPE-TL progressively decreased by ˜ 60 AU per day over the 3 days prior to a match (P<0.05).  Morning-measured fatigue, sleep quality and soreness tracked the changes in RPE-TL, being 35-40% worse on post-match day vs pre-match day (P<0.001).  Perceived fatigue, sleep quality and soreness improved by 17-26 % from post-match day 1 to day 3 with further smaller (7-14%) improvements occurring between post-match day 4 and pre-match day (P<0.01). There were no substantial or statistically significant changes in HRex, HRR% and HRV over the weekly cycle (P>0.05).
Conclusions: Morning-measured ratings of fatigue, sleep quality and muscle soreness but not HR-derived indices are sensitive to the daily fluctuations in session load experienced by elite soccer players within a standard in-season week.
Key Words-Training, Performance, Wellness, Recovery