Repeated Sprint training for endurance athletes...refer to this as the art of periodization...

Repeated Sprint training for endurance athletes: Signalling pathways and effect on performance

Wouter Timmerman

VTDL Clinic, Wuustwezel 2017

Sprint training for endurance athletes?!

McArdle et al., 1986

History of endurance training •  Joe Banks, 1902, 1600m in 4’16”8

o  1x/wk 30’ o  5x100m max – 200m fast

•  Finland, 1910-1930, WR 1500-marathon o  Long walks (4h) o  Set of short 150m max o  5k easy, 2k fast, 5 sprints

Steve Magness, Scienceofrunning.com

Coaching

Rio Olympics – Ben Kanute (USA)

https://www.trainingpeaks.com/blog

•  Athletes do sprint training for decades

•  You are implementing sprint training

•  Sprints are necassary for OD triatlon

•  AND….

Simplified signalling pathways

(Baar, 2006). The result is an increased capacity togenerate ATP aerobically. Thus, at the molecularlevel, it may be the blend of signals induced fromcombined high-volume training and high-intensitytraining that elicits either a stronger or more frequentpromotion of the aerobic muscle phenotype throughPGC-1a mRNA transcription (Fig. 2). As well, thelower intensity higher volume training sessions arelikely to promote the development of the aerobicphenotype without disturbing autonomic balancethat could lead to overtraining (Seiler et al., 2007).These speculative comments highlight an importantarea for future research.

How do we optimally structure training programs forhigh-performing endurance athletes?

While this manuscript offers a unique discoursedescribing a binary model by which training isorganized into periods characterized by either hightraining intensities, or high training volumes, thereality of the matter is that athletes often performsessions where there are mixed amounts of both (e.g.a 6-h group bike training session over hilly terrain).Thus, characterizing all training sessions as beingeither a prolonged low-intensity, moderate-intensityor high-intensity session can be problematic. Never-theless, the synthesis of this information reveals apattern highlighting the importance of applyingperiods of both high-intensity training and high-volume training at the appropriate time in a training

program, in order to elicit an optimal intense exerciseperformance. Experts in training program designrefer to this as the art of periodization (Issurin,2008). While the high-intensity training stimulusover the lead up period to intense exercise perfor-mance appears critical (Londeree, 1997), the sub-maximal or prolonged training durations (volume ofrepeated muscular contractions) cannot be down-played (Fiskerstrand & Seiler, 2004). These high-volume training periods may elicit the molecularsignals needed to stimulate mitochondrial proteinsynthesis without creating undue autonomic distur-bance that could lead to overtraining (Seiler et al.,2007). Over time, the progressive result is likely to bean improved efficiency of skeletal muscle and adevelopment of the fatigue-resistant aerobic musclephenotype. Indeed, development of the successfulintense exercise athlete tends to require a numberof years exposure to high training volumes andintensities (Schumacher et al., 2006). The art ofsuccessful intense exercise coaching, therefore, ap-pears to involve the manipulation of training sessionsthat combine long duration low-intensity periodswith phases of very high-intensity work, appropriaterecovery and tapering (Mujika et al., 2000; Issurin,2008; Pyne et al., 2009).The paper will finish with two practical examples

that demonstrate the effectiveness of this model. Thefirst example is New Zealand’s Olympic 800-m run-ning legend, Sir Peter Snell. Snell was a protégé of thelate New Zealand athletics coach Arthur Lydiard,who was renowned for organizing the training of

Fig. 2. Simplified model of the adenosine monophosphate kinase (AMPK) and calcium–calmodulin kinase (CaMK) signalingpathways, as well as their similar downstream target, the peroxisome proliferator-activated receptor-g coactivator-1a (PGC-1a). This ‘‘master switch’’ is thought to be involved in promoting the development of the aerobic muscle phenotype. High-intensity training appears more likely to signal via the AMPK pathway, while high-volume training appears more likely tooperate through the CaMK pathway. ATP, adenosine triphosphate; AMP, adenosine monophosphate; GLUT4, glucosetransporter 4; [Ca21], intramuscular calcium concentration.

High-intensity and high-volume training

7

Laursen et al. 2010

Performance

Sprint interval training (SIT) Determining factors

SIT Short SIT Example SIT

Repetitions 1-12x 4-10x 6x Time 30” 6-10” 30” Intensity All out All out All out Rest R:3-5’ R:1-4’ R:3’

6x30” max R:3’

SIT vs endurance training Determining factors SIT ET Repetitions 4-6x 1x Time 30” 40-60’ Intensity All out 65% VO2max Rest 4’30” / Duration 6wk, 3x/wk (18x training)

Burgomaster et al. 2008

SIT vs endurance

156 K. A. Burgomaster and others J Physiol 586.1

Table 5). Lactate accumulation was reduced after training;however, the relative exercise-induced changes weremodest and overall there were no significant effects(Table 5).

Figure 2. Maximal activity or total protein content ofmitochondrial enzymes citrate synthase (CS; A), 3-hydroxyacylCoA dehydrogenase (β-HAD; B) and pyruvate dehydrogenase(PDH; C) measured in biopsy samples obtained before (PRE) andafter (POST) 6 weeks of sprint interval training (SIT) or 6 weeksof endurance training (ET)Values are means ± S.E.M. (n = 10 per group); WW, wet weight. ∗Maineffect for condition (P < 0.05), such that post-training (POST) >pretraining (PRE).

Figure 3. Total protein content of PGC-1α measured in biopsysamples obtained before (PRE) and after (POST) 6 weeks ofsprint interval training (SIT) or 6 weeks of endurance training(ET)Values are means ± S.E.M. (n = 10 per group); WW, wet weight. ∗Maineffect for condition (P < 0.05), such that post-training (POST) >pretraining (PRE).

Discussion

The major novel finding from the present study was that6 weeks of SIT elicited adaptations in selected markersof skeletal muscle CHO and lipid metabolism andmetabolic control during exercise that were comparableto those elicited by ET, despite a much lower trainingvolume and time commitment. By design, weekly trainingvolume was ∼90% lower in the SIT group (∼225 versus∼2250 kJ week−1 for ET) and necessitated a training time

Figure 4. Muscle glycogen concentration measured at rest andduring cycling exercise that consisted of 60 min at 65% V̇O2peakbefore (PRE) and after (POST) 6 weeks of sprint interval training(SIT) or 6 weeks of endurance training (ET)Values are means ± S.E.M. (n = 10 per group); DW, dry weight. ∗Maineffect for condition (P < 0.05), such that post-training (POST) >pretraining (PRE). †Condition (PRE and POST) × time (0 and 60 min)interaction (P < 0.05), such that POST 60 min > PRE 60 min in bothgroups.

C⃝ 2008 The Authors. Journal compilation C⃝ 2008 The Physiological Society

Burgomaster et al. 2008

Short SIT vs control Determining factors Short SIT control Repetitions 10x

Continue normal training

regime (4h/wk)

Time 6” Intensity All-out Rest 1’ Duration 2wk, 3x/wk (6x training)

Jakeman et al. 2012

Results - performance

10k TT High-intensity interval trainingThe high-intensity sprint training protocol was similar to

that used previously by Gaitanos et al. (1993) but with an in-creased recovery between sprints. Six sessions of sprint-inter-val exercise were spread over 14 days, with 1 or 2 days ofrest between each session. Each training session consisted of10 repeated 6-s all-out cycling efforts against 7.5% of bodyweight (Monark peak bike Model 894E, Monark ExerciseAB), which was added to the bike once the participant wascycling at 100 r·min–1 with 1 min of passive recovery be-tween sprints.

Peak powerPeak power was measured for each sprint in the 6 training

sessions and was automatically calculated by computer(Monark Anaerobic Test Software version 2.24.2, MonarkExercise AB). The largest peak power generated during eachtraining session was taken as the maximal power output forthat session.

Post-training assessmentParticipants in the training group underwent a second time

to exhaustion test 3 days after completion of the last trainingsession, followed 1 day later by the self-paced time trial. Par-ticipants in the control group performed their second time toexhaustion and self-paced time trial approximately 2 weeksafter completion of baseline testing.

Data and statistical analysesData are expressed as means ± standard deviation. Data

was checked for skewness and kurtosis and these values didnot exceed twice the standard error; therefore, the data wasdeemed to be normally distributed. Results were comparedusing the methodology proposed by Hopkins for controlledtrials (Hopkins 2003) and peak power was analysed using a1-way repeated measures analysis of variance (ANOVA)with Student Newman Keuls post hoc testing. The signifi-cance level was set at 0.05 (p < 0.05) and the Cohen’s effectsize was defined as follows: d < 0.2, trivial effect; 0.2–0.5,small effect; 0.6–1.1, moderate effect; and 1.2–1.9, large ef-fect (Cohen 1988).

Results

Time trialAt baseline, time to complete 10-km was not significantly

different between groups (HIT group 851 ± 100 s; controlgroup 778 ± 90 s; p = 0.2, Fig. 1) and remained unchangedin the control group when repeated after 2 weeks (session 1:778 ± 90 s, session 2: 780 ± 81 s; p = 0.97, d = 0.02;Fig. 1). Following 2 weeks of HIT training, the time takento complete the 10-km time trial was significantly reducedby ∼10% (pretraining 851 ± 100 s, post-training 768 ± 95 s;p = 0.03, d = –0.86; Fig. 1).

Time to exhaustionAt baseline, time to exhaustion was not significantly differ-

ent between groups (HIT group 718 ± 74 s; control group649 ± 70 s; p = 0.13, d = 0.82; Fig. 2) and was unchangedin the control group when repeated after 2 weeks (session 1:649 ± 70 s, session 2: 650 ± 71 s; p = 0.98, d = 0.01;

Fig. 2). Following 2 weeks of HIT training, the time to ex-haustion had increased by 4%, although this was not signifi-cant (pretraining 718 ± 74 s, post-training 746 ± 91 s; p =0.19, d = 0.35; Fig. 2).

OBLAAt baseline, time to OBLA was similar between groups

(HIT group 464 ± 80 s; control group 474 ± 74 s; p = 0.87,d = –0.14; Fig. 3A) and was unchanged in the control groupwhen repeated after 2 weeks (session 1: 464 ± 80 s, ses-sion 2: 458 ± 99 s; p = 0.76, d = –0.19; Figs. 3A and 3B).Following 2 weeks of HIT training, the time to OBLA hadsignificantly increased by 17% (pretraining 464 ± 80 s, post-training 541 ± 101 s; p = 0.01, d = 1.28; Figs. 3A and 3C).However, the time to lactate threshold (the point where bloodlactate starts to rise) remained the same following training(Fig. 3C). There was a significant positive relationship be-tween change in OBLA and change in time trial performance(p = 0.001; R2 = 0.63) but not time to exhaustion (p =0.123; R2 = 0.21).

Fig. 1. Time taken to complete 2 self-paced 10-km time trials2 weeks apart in the control and high-intensity training (HIT)groups. *, p = 0.004 post-HIT compared with baseline HIT.

Fig. 2. Time to volitional exhaustion in 2 tests carried out 2 weeksapart in the control and high-intensity training groups (p = 0.19).

978 Appl. Physiol. Nutr. Metab. Vol. 37, 2012

Published by NRC Research Press

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PPO sprints

Peak powerDuring the HIT training programme peak power generated

increased gradually over the 6 sessions, with session 6 beingsignificantly higher than session 1 (session 1: 675 ± 89 W,session 6: 718 ± 104 W; p < 0.05; Fig. 4).

DiscussionWhile endurance training is a powerful strategy to improve

aerobic performance it is not necessarily time efficient. In thepresent study we demonstrate for the first time that extremelyshort bouts of high-intensity exercise repeated over 2 weeksis required to improve aerobic performance and produce anattenuation of blood lactate accumulation normally seen witha longer duration of sprints (Burgomaster et al. 2005) or lon-ger training intervention (Linossier et al. 1993). Following2 weeks of HIT, we demonstrated a significant 11% reduc-tion in the time required to complete a 10-km self-pacedtime trial, with time trial performance being strongly associ-ated with maintaining low blood lactate concentrations (Ja-cobs et al. 2011) in trained athletes.Following 2 weeks of HIT, we reported a significant shift

in blood lactate accumulation during the incremental time toexhaustion test (Fig. 3), relating to a higher work rate (of atleast 30 W) required to raise blood lactate levels to4 mmol·L–1. This is similar to the shifts reported previouslyfollowing a 12-week endurance and interval training program(Hurley et al. 1984). There are a number of potential explan-ations for the improvement in blood lactate accumulation.Following 2 weeks of 4–6 repetitions of 30-s sprint HIT, ithas been shown that skeletal muscle lactate accumulation isreduced during a 2-stage submaximal cycle test (Burgomasteret al. 2006) and during a 30-s maximal sprint (Rodas et al.2000). The decrease in lactate accumulation in skeletalmuscle could be due to a decreased rate of glycogenolysispost-HIT (Burgomaster et al. 2005) or to increased activityof pyruvate dehydrogenese, allowing an increased use of pyr-uvate in oxidative metabolism (Burgomaster et al. 2008).Further, there is an increase in skeletal muscle MCT1 andMCT4 content after 1 and 6 weeks of 30-s sprint HIT (Bur-gomaster et al. 2007), and in animals it has been shown thatskeletal muscle lactate uptake increases with increasingMCT1 content (Baker et al. 1998). Therefore, it could poten-tially take longer to reach OBLA post-HIT because of in-creased skeletal muscle uptake of lactate from the blood.Further investigation is required to determine the mechanismsregulating lactate metabolism following HIT.

Fig. 3. Change in blood lactate profile. (A) Time to onset of bloodlactate accumulation (OBLA) in 2 incremental exercise tests 2 weeksapart in the control and high-intensity training (HIT) groups (*, p =0.004 post-HIT compared with baseline HIT); (B) blood lactate re-sponse in controls (p > 0.05 pre- vs. post-training); (C) blood lactateresponse following HIT (p > 0.05 pre- vs. post-training).

Fig. 4. Peak power during the 6 training sessions. *, p < 0.05 ses-sion 1 vs. session 6.

Jakeman et al. 979


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Jakeman et al. 2012

Results - physiology

Incremental test

High-intensity interval trainingThe high-intensity sprint training protocol was similar to

that used previously by Gaitanos et al. (1993) but with an in-creased recovery between sprints. Six sessions of sprint-inter-val exercise were spread over 14 days, with 1 or 2 days ofrest between each session. Each training session consisted of10 repeated 6-s all-out cycling efforts against 7.5% of bodyweight (Monark peak bike Model 894E, Monark ExerciseAB), which was added to the bike once the participant wascycling at 100 r·min–1 with 1 min of passive recovery be-tween sprints.

Peak powerPeak power was measured for each sprint in the 6 training

sessions and was automatically calculated by computer(Monark Anaerobic Test Software version 2.24.2, MonarkExercise AB). The largest peak power generated during eachtraining session was taken as the maximal power output forthat session.

Post-training assessmentParticipants in the training group underwent a second time

to exhaustion test 3 days after completion of the last trainingsession, followed 1 day later by the self-paced time trial. Par-ticipants in the control group performed their second time toexhaustion and self-paced time trial approximately 2 weeksafter completion of baseline testing.

Data and statistical analysesData are expressed as means ± standard deviation. Data

was checked for skewness and kurtosis and these values didnot exceed twice the standard error; therefore, the data wasdeemed to be normally distributed. Results were comparedusing the methodology proposed by Hopkins for controlledtrials (Hopkins 2003) and peak power was analysed using a1-way repeated measures analysis of variance (ANOVA)with Student Newman Keuls post hoc testing. The signifi-cance level was set at 0.05 (p < 0.05) and the Cohen’s effectsize was defined as follows: d < 0.2, trivial effect; 0.2–0.5,small effect; 0.6–1.1, moderate effect; and 1.2–1.9, large ef-fect (Cohen 1988).

Results

Time trialAt baseline, time to complete 10-km was not significantly

different between groups (HIT group 851 ± 100 s; controlgroup 778 ± 90 s; p = 0.2, Fig. 1) and remained unchangedin the control group when repeated after 2 weeks (session 1:778 ± 90 s, session 2: 780 ± 81 s; p = 0.97, d = 0.02;Fig. 1). Following 2 weeks of HIT training, the time takento complete the 10-km time trial was significantly reducedby ∼10% (pretraining 851 ± 100 s, post-training 768 ± 95 s;p = 0.03, d = –0.86; Fig. 1).

Time to exhaustionAt baseline, time to exhaustion was not significantly differ-

ent between groups (HIT group 718 ± 74 s; control group649 ± 70 s; p = 0.13, d = 0.82; Fig. 2) and was unchangedin the control group when repeated after 2 weeks (session 1:649 ± 70 s, session 2: 650 ± 71 s; p = 0.98, d = 0.01;

Fig. 2). Following 2 weeks of HIT training, the time to ex-haustion had increased by 4%, although this was not signifi-cant (pretraining 718 ± 74 s, post-training 746 ± 91 s; p =0.19, d = 0.35; Fig. 2).

OBLAAt baseline, time to OBLA was similar between groups

(HIT group 464 ± 80 s; control group 474 ± 74 s; p = 0.87,d = –0.14; Fig. 3A) and was unchanged in the control groupwhen repeated after 2 weeks (session 1: 464 ± 80 s, ses-sion 2: 458 ± 99 s; p = 0.76, d = –0.19; Figs. 3A and 3B).Following 2 weeks of HIT training, the time to OBLA hadsignificantly increased by 17% (pretraining 464 ± 80 s, post-training 541 ± 101 s; p = 0.01, d = 1.28; Figs. 3A and 3C).However, the time to lactate threshold (the point where bloodlactate starts to rise) remained the same following training(Fig. 3C). There was a significant positive relationship be-tween change in OBLA and change in time trial performance(p = 0.001; R2 = 0.63) but not time to exhaustion (p =0.123; R2 = 0.21).

Fig. 1. Time taken to complete 2 self-paced 10-km time trials2 weeks apart in the control and high-intensity training (HIT)groups. *, p = 0.004 post-HIT compared with baseline HIT.

Fig. 2. Time to volitional exhaustion in 2 tests carried out 2 weeksapart in the control and high-intensity training groups (p = 0.19).

978 Appl. Physiol. Nutr. Metab. Vol. 37, 2012


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OBLA from incremental test

Peak powerDuring the HIT training programme peak power generated

increased gradually over the 6 sessions, with session 6 beingsignificantly higher than session 1 (session 1: 675 ± 89 W,session 6: 718 ± 104 W; p < 0.05; Fig. 4).

DiscussionWhile endurance training is a powerful strategy to improve

aerobic performance it is not necessarily time efficient. In thepresent study we demonstrate for the first time that extremelyshort bouts of high-intensity exercise repeated over 2 weeksis required to improve aerobic performance and produce anattenuation of blood lactate accumulation normally seen witha longer duration of sprints (Burgomaster et al. 2005) or lon-ger training intervention (Linossier et al. 1993). Following2 weeks of HIT, we demonstrated a significant 11% reduc-tion in the time required to complete a 10-km self-pacedtime trial, with time trial performance being strongly associ-ated with maintaining low blood lactate concentrations (Ja-cobs et al. 2011) in trained athletes.Following 2 weeks of HIT, we reported a significant shift

in blood lactate accumulation during the incremental time toexhaustion test (Fig. 3), relating to a higher work rate (of atleast 30 W) required to raise blood lactate levels to4 mmol·L–1. This is similar to the shifts reported previouslyfollowing a 12-week endurance and interval training program(Hurley et al. 1984). There are a number of potential explan-ations for the improvement in blood lactate accumulation.Following 2 weeks of 4–6 repetitions of 30-s sprint HIT, ithas been shown that skeletal muscle lactate accumulation isreduced during a 2-stage submaximal cycle test (Burgomasteret al. 2006) and during a 30-s maximal sprint (Rodas et al.2000). The decrease in lactate accumulation in skeletalmuscle could be due to a decreased rate of glycogenolysispost-HIT (Burgomaster et al. 2005) or to increased activityof pyruvate dehydrogenese, allowing an increased use of pyr-uvate in oxidative metabolism (Burgomaster et al. 2008).Further, there is an increase in skeletal muscle MCT1 andMCT4 content after 1 and 6 weeks of 30-s sprint HIT (Bur-gomaster et al. 2007), and in animals it has been shown thatskeletal muscle lactate uptake increases with increasingMCT1 content (Baker et al. 1998). Therefore, it could poten-tially take longer to reach OBLA post-HIT because of in-creased skeletal muscle uptake of lactate from the blood.Further investigation is required to determine the mechanismsregulating lactate metabolism following HIT.

Fig. 3. Change in blood lactate profile. (A) Time to onset of bloodlactate accumulation (OBLA) in 2 incremental exercise tests 2 weeksapart in the control and high-intensity training (HIT) groups (*, p =0.004 post-HIT compared with baseline HIT); (B) blood lactate re-sponse in controls (p > 0.05 pre- vs. post-training); (C) blood lactateresponse following HIT (p > 0.05 pre- vs. post-training).

Fig. 4. Peak power during the 6 training sessions. *, p < 0.05 ses-sion 1 vs. session 6.

Jakeman et al. 979


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Jakeman et al. 2012

Short SIT vs SIT Determining factors

Short SIT Short SIT SIT

Repetitions 4-6x 4-6x 4-6x Time 10” 10” 30” Intensity All out Rest 2’ 4’ 4’ Duration 3x/wk, 2wk (6x training)

Hazell et al. 2010

Results – performance and capacity

(3.8%; 1.8 ml kg-1 min-1; P = 0.06). There were no sig-nificant differences among training groups. The controlgroup showed no change in VO2max (from 45.2 ± 4.6 to45.3 ± 3.5 ml kg-1 min-1; P = 0.899).

Relative peak power output

There was no significant difference in baseline relative peakpower output among groups (P = 0.437), but there was asignificant interaction with time (training group vs. time;

P = 0.017; Fig. 3). The 30 s:4 min group increased 9.5%(1.3 W kg-1; P \ 0.001), the 10 s:4 min group increased8.5% (1.3 W kg-1; P \ 0.001) while the 10 s:2 min groupimproved 4.2% (0.6 W kg-1; P = 0.029). None of theseobserved group gains were significantly different from each

other. The control group showed no change (14.2 ± 1.9 to

14.2 ± 1.6 W kg-1; P = 0.959).

Relative average power output

Baseline relative average power output values were similar

among groups (P = 0.383) and there was a significantinteraction with time (training group vs. time; P = 0.002;Fig. 4). The 30 s:4 min group improved 12.1% (1.0 W kg-1;

P \ 0.001) and the 10 s:4 min group increased 6.5%(0.6 W kg-1; P = 0.003). The observed increase in the10 s:2 min group (2.9%; 0.3 W kg-1) failed to reach

statistical significance (P = 0.136). None of these changeswere significantly different from each other. Again, thecontrol group showed no change (9.5 ± 1.2 to 9.3 ±

1.1 W kg-1; P = 0.510).

Reproducibility of power outputs during training

sessions

There was a significant difference in the ability of the

training groups to maintain peak power output during the

training sessions (P \ 0.001; Fig. 5). The 10 s:4 mingroup maintained 96% of their peak power output and the

10 s:2 min group 95% which were both significantlygreater (P \ 0.001) than the 89% observed for the30 s:4 min group.

5 km Time Trial Performance (sec)

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30:4 10:4 10:2 CONTROL

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025

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Fig. 2 VO2max (ml kg-1 min-1) before and after 2 weeks of training.

Thin lines represent individual data and the thick lines are means.Both the 30:4 and 10:4 training groups improved significantly whilethe 10:2 approached significance. The control group did not change.**P \ 0.001, !P = 0.06

Relative Peak Power Output (W·kg-1)

0

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30:4 10:4 10:2 CONTROL

Fig. 3 Wingate relative peak power output (W kg-1) before and after2 weeks of training. Thin lines are individual data and the thick linesare means. All training groups improved significantly while thecontrol group did not change. **P \ 0.001, *P \ 0.03

156 Eur J Appl Physiol (2010) 110:153–160

123






14.2 ± 1.6 W kg-1; P = 0.959).






1.1 W kg-1; P = 0.510).


sessions






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Hazell et al. 2010

Results – Wingate test

Similarly, there was a significant difference in the abilityof the training groups to maintain average power output

during training sessions (P \ 0.001; Fig. 5). The10 s:4 min group and 10 s:2 min group maintained 84 and82% of their peak power output, respectively, which were

both significantly greater (P \ 0.001) than the 58%observed for the 30 s:4 min group.

As with both peak and average power output, there was

a significant difference in the minimum power output

observed during the training sessions (P \ 0.001; Fig. 5).

Minimum power output for the 10 s:4 min group and

10 s:2 min group was 73 and 69% of their peak poweroutput, respectively. Both were significantly greater

(P \ 0.001) than the 40% for the 30 s:4 min group.Total work performed also differed among training

groups (P = 0.005). As expected, the 30 s:4 min group(83–124 kJ) performed significantly more total work

(P \ 0.009) during the 4–6 bouts per training session thaneither the 10 s:4 min group (38–58 kJ) or the 10 s:2 min

group (39–59 kJ). There was no difference in total workperformed between the 10 s:4 min and the 10 s:2 min

groups (P = 0.999).

Body composition

Baseline values for body composition (body mass, leanmass, fat mass, body fat percentage) were similar among

groups (P [ 0.430). Training did not cause changes inbody composition (P [ 0.490) and, as expected, there wereno body composition changes in the control group

(P C 0.626).

Discussion

Recent studies utilizing SIT (repeated maximal 30-s efforts

with 4 min recovery) have reported significant increases in

both anaerobic and aerobic power (Gibala and McGee2008). Increases in glycolytic (MacDougall et al. 1998) and

oxidative enzymes activity (Burgomaster et al. 2005, 2006,

2007; MacDougall et al. 1998), muscle buffering capacity(Burgomaster et al. 2007; Gibala et al. 2006), and/or ionic

regulation (Harmer et al. 2000) have been implicated in

these responses. Moreover, it has been demonstratedthat SIT up-regulates peroxisome proliferator-activated

receptor-co-activator-1a (PGC-1a), a potent regulator ofmitochondrial biogenesis (Burgomaster et al. 2008; Gibalaet al. 2009), which could be the underlying mechanism

responsible for the observed aerobic adaptation. This

information is exciting because it means that such adap-tations can be obtained with a substantial reduction

in exercise training time. Consequently, many types of

athletes and perhaps even non-athletes interested in beingphysically active for health reasons can benefit from this

novel type of training. However, not much is known

regarding which particular aspect of SIT provides thispowerful stimulus. The purpose of the current study was to

determine whether the observed improvements in anaero-

bic and aerobic performance are due to the generation ofpeak power output, the total work completed over 30 s,

and/or the brief recovery interval. If the peak power output

generation is most important we would expect that thetwo-10 s treatments would produce similar or greater

Relative Average Power Output (W·kg-1)

05

6

7

8

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10

11

12

** *


30:4 10:4 10:2 CONTROL

Fig. 4 Wingate relative average power output (W kg-1) before andafter 2 weeks of training. Thin lines are individual data and the thicklines are means. Both the 30:4 and 10:4 training groups improvedsignificantly while the 10:2 and the control group did not change.**P \ 0.001, *P \ 0.03

40

60

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100

40

60

80

100

Training SessionPeak Power Output (%)

Training Session Average Power Output (%)

Training SessionMinumum Power Output (%)

**

**

**

30 :4 10:4 10:2 30:4 10:4 10 :2 30:4 10:4 10 :2

Fig. 5 Reproducibility of peak, average, and minimum power duringthe training sessions. The 30 s:4 min maintained significantly lesspeak, average, and minimum power than the 10 s:4 min and10 s:2 min training groups. Data are mean ± SD. **P \ 0.001

Eur J Appl Physiol (2010) 110:153–160 157

123






14.2 ± 1.6 W kg-1; P = 0.959).






1.1 W kg-1; P = 0.510).


sessions






0450

500

550

600

650

700

750

800

850

900

***

*


30:4 10:4 10:2 CONTROL


025

30

35

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70


**** †


30:4 10:4 10:2 CONTROL




0

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30:4 10:4 10:2 CONTROL



123

Hazell et al. 2010

SIT review on competitive athletes •  Sprint performance

•  Endurance performance o  0-30% volume

•  2.3 - 4.4% 40k bike •  3.1% 10k run

o  65% volume •  = 10k run

Iaia et al. 2010

Determining factors

SIT

Repetitions 8-12 Time 30” Intensity All out Rest 3’-4’30 Duration 9wk, 2-4x/wk

Conclusion short SIT (6-10”) •  Rest needs to be long enough for

better maximal 30s en peak PO

•  Only 6 training sessions for statistical improvement in short SIT

•  Improvement isn’t very clear on incremental test

•  Improvement can be (partly) due to neuromuscular adaptation

Conclusion SIT (30”) •  30s SIT is superior to 10s SIT for

aerobic adaptions •  Improves sprint performance

•  Can replace endurance training in competitive athletes o  At least for short term (10wk)

•  Improves endurance performance o  If trainingvolume is maintained

SIT vs HIIT vs STCT Determining factors

SIT HIIT STCT

Repetitions 4-10 4-7x 1x Time 30” 4’ 20-36’ Intensity 200% PPO 90% PPO 65% PPO Rest 4’ 2’ / Duration 4wk, 3x/wk (12x training)

Granata et al. 2015

Results – PGC-1alpha

STCT groups (89 and 66% ofWPeak, respectively) did notimprove mass-specific mitochondrial respiration, and theresults are in agreement with those for the continuoustraining group (71% ofWPeak) in Daussin et al. (6).

It has been suggested that workload and oxygen-uptakefluctuations, rather than training volume or intensity, arekey determinants of training-induced improvements inmitochondrial respiration(6).However, althoughworkloadandoxygenuptakefluctuations arehallmarks of both SITand HIIT, only SIT, which elicits a much higher trainingintensity, increased mass-specific mitochondrial respira-tion in our study. This finding further strengthens the

notion that training intensity is an important factor fortraining-induced improvements in mitochondrial respira-tion. Similar toDaussin et al. (6), our research suggests thattraining volume is less important in determining changesin mitochondrial respiration, as neither STCT nor HIITimproved mass-specific mitochondrial respiration after4 wk of training, despite a training volume almost 3 timesgreater than SIT. This result may be explained by findingsfrom previous reports that showed muscle fiber activationincreases with exercise intensity (28) and that SIT elicitsgreater reliance on oxidative phosphorylation within eachbout and with each subsequent bout (29). Therefore,

Figure 3. Fold change compared to baseline of the protein content of PGC-1a (A), p53 (B), PHF20 (C), and TFAM (D) in whole-muscle lysates prepared from skeletal muscle biopsy samples (vastus lateralis). Biopsies were obtained at rest at baseline (dottedline) and after 4 wk of training at each of the 3 training intensities: STCT, HIIT, and SIT. Representative immunoblots (from leftto right: pre-STCT, post-STCT, pre-HIIT, post-HIIT, pre-SIT, post-SIT) are presented above the graph of each target protein.All values are means 6 SD. †P , 0.05, main effect of time; *P , 0.05 vs. pre; #P , 0.05 vs. post-HIIT.

8 Vol. 30 February 2016 GRANATA ET AL.The FASEB Journal x www.fasebj.org

Granata et al. 2015

Results 20k TT

with p53 inside the mitochondria (21), rather than theabundance of TFAM itself.

Similar to p53 protein content and mass-specificrespiration, PHF20 protein content increased only af-ter SIT. PHF20 is a transcription factor that bindsdirectly to the p53 promoter and transcriptionallyupregulates (22) and stabilizes (23) p53. Therefore, atraining-intensity–dependent increase in PHF20 pro-tein may help to explain the increase in p53 proteinonly after SIT and suggests that 4 wk of SIT improvemitochondrial respiration via a mechanism involvingthe coordinated up-regulation of the PHF20–p53pathway. Future investigations of these hypotheses arewarranted.

Mitochondrial respiration andendurance performance

Mitochondrial respiration has been reported to be thestrongest determinant of TT performance in highly-trained athletes (4). Consistent with these findings, ourresults revealed a significant linear correlation betweenpretraining CI+IIP and 20k TT performance across the29 participants who completed the study. However,

despite being the only training intervention to increasemitochondrial respiration, SIT did not improve 20k TTperformance after training. This discrepancy betweenchanges in mitochondrial respiration and enduranceperformance is further supported by the lack of corre-lation between changes in CI+IIP and 20k TT time, andthe subsequent loss of correlation between these 2 pa-rameters after training. This finding indicates that mi-tochondrial respiration was not the key mediator oftraining-induced changes in endurance performancefor the moderately-trained population in our study.Nonetheless, SIT may represent an effective way toboost the oxidative capacity of moderately-trained men,which, when coupled with other training at lowerintensity, could contribute to improved enduranceperformance.

CONCLUSIONS

Our results demonstrate that, in youngmoderately-trainedmen who complete a 4-wk training protocol, training in-tensity is an important factor that determines changes inmitochondrial respiration. We provide evidence that aslittle as 4 wk of SIT was sufficient to induce improvementsinmitochondrial quality, such as increasedmitochondrial-specific respiration. These results suggest that SIT pro-motes greater and faster mitochondrial adaptations inskeletal muscle of young moderately-trained men thandoes HIIT and STCT; however, our results and the litera-ture suggest that physiological and mitochondrial adapta-tions can occur with longer STCT- or HIIT-like protocols.We also showed dissociation between changes in mito-chondrial content and mitochondrial respiration. Ourresults indicate that changes in the protein content of PGC-1a, p53, and PHF20 may be more strongly associated withtraining-induced changes in mitochondrial respirationrather than mitochondrial content, and suggest that thesechanges may be mediated by different molecular pathwaysor time courses. The present study also highlights the po-tential role of PHF20 in the regulation of mitochondrialrespiration in humans via the coordinated upregulation ofthe PHF20–p53 pathway.

The authors thank Ms. E. Brentnall and Mr. A. Ronacher forvaluable help in data collection. This study was funded by agrant from the ANZ-MASON foundation (to D.J.B.) and NaturalSciences and Engineering Research Council of Canada Discov-ery Grant RGPIN 435807-13 (to J.P.L.). The authors declareno conflicts of interest.

REFERENCES

1. Luft, R. (1994) The development of mitochondrial medicine. Proc.Natl. Acad. Sci. USA 91, 8731–8738

2. Wang, C. H., Wang, C. C., and Wei, Y. H. (2010) Mitochondrialdysfunction in insulin insensitivity: Implicationofmitochondrial rolein type 2 diabetes. Ann. N. Y. Acad. Sci. 1201, 157–165

3. Larsen, S., Nielsen, J., Hansen, C. N., Nielsen, L. B., Wibrand, F.,Stride, N., Schroder, H. D., Boushel, R., Helge, J. W., Dela, F., andHey-Mogensen, M. (2012) Biomarkers of mitochondrial content inskeletal muscle of healthy young human subjects. J. Physiol. 590,3349–3360

4. Jacobs, R. A., Rasmussen, P., Siebenmann, C., Dı́az, V., Gassmann,M., Pesta, D., Gnaiger, E., Nordsborg, N. B., Robach, P., and

Figure 6. Correlation between 20k TT time and maximumADP-stimulated mass-specific mitochondrial respiration (CI+IIP),before (A) and after (B) training. Data include participants fromall 3 groups.

TRAINING-INDUCED MITOCHONDRIAL ADAPTATIONS 11

Granata et al. 2015

Results – Performance overview

participant’s time point value was normalized to baseline;therefore, immunoblot data are presented as fold change vs.baseline.

CS activity assay

CS activity was determined in triplicate on amicrotiter plate byadding 5 ml of a 2 mg/ml muscle homogenate, 40 ml of 3 mM

acetyl CoA, and 25 ml of1 mM 5,59-dithiobis(2-nitrobenzoicacid) (DTNB) in Tris buffer to 165 ml 100 mM Tris buffer(pH 8.3) kept at 30°C. After addition of 15 ml of 10 mM oxa-loacetic acid, the plate was immediately placed in an xMark-Microplate spectrophotometer (Bio-Rad) at 30°C, and after30 s of linear agitation, absorbance at 412 nm was recordedevery 15 s for 3 min. CS activity is reported as moles per hourper kilogram protein.

TABLE 3. Respiration values

Measurement Time pointSTCT(n = 9)

HIIT(n = 11)

SIT(n = 9)

Mass-specific (pmol O2 · s21 · mg21)CIL Pre 9.0 6 2.8 4.9 6 2.2@ 8.1 6 1.5

Post 8.3 6 3.1 5.5 6 1.6 10.0 6 1.8CIP Pre 63.2 6 15.3 45.2 6 8.4@ 59.1 6 8.0

Post 60.1 6 10.6 45.1 6 6.2 70.8 6 13.8CI+IIP Pre 88.6 6 19.3 68.1 6 11.6@ 85.6 6 12.1

Post† 89.5 6 15.5# 65.6 6 7.7 106.9 6 19.0*,#,‡

CI+IIE Pre 111.4 6 24.1 87.4 6 16.2 107.4 6 12.0Post$ 110.7 6 21.1 87.9 6 15.9 139.6 6 36.7*,#,‡

CIIE Pre 44.2 6 11.9 41.7 6 7.7 47.0 6 6.3Post† 46.9 6 13.7 41.1 6 4.5 60.9 6 17.5*,#,‡

CIVE Pre 132.6 6 30.9 124.0 6 17.9 132.3 6 22.3Post 128.3 6 36.6 125.1 6 14.0 160.9 6 49.2

Mitochondrial-specific (pmol O2 · s21 · CS21)CIL/CS activity Pre 1.0 6 0.4 0.6 6 0.3 0.9 6 0.3

Post 0.9 6 0.3 0.6 6 0.2 1.1 6 0.3CIP/CS activity Pre 7.0 6 2.2 5.8 6 1.0 6.5 6 1.5

Post 6.1 6 1.0 5.4 6 1.3 7.5 6 1.3CI+IIP/CS activity Pre 9.8 6 2.8 8.8 6 1.5 9.4 6 2.1

Post 9.1 6 1.5 7.9 6 1.8 11.3 6 1.4*,#

CI+IIE/CS activity Pre 12.4 6 3.8 10.7 6 1.7 11.7 6 2.1Post 11.3 6 1.8 11.0 6 2.5 14.5 6 2.0*,#,‡

CIIE/CS activity Pre 4.9 6 1.7 5.3 6 0.8 5.3 6 0.9Post 4.9 6 1.3 4.9 6 0.9 6.6 6 0.9*,#,‡

CIVE/CS activity Pre 14.6 6 4.3 16.1 6 3.1 14.4 6 2.8Post 13.0 6 3.2 15.2 6 3.9 16.7 6 3.4

CIL, leak respiration through CI; CIP, maximum coupled mitochondrial respiration through CI; CI+IIP, maximum coupled mitochondrialrespiration through CI+II; CI+IIE, maximum noncoupled mitochondrial respiration through CI+II; CIIE, maximum noncoupled mitochondrialrespiration through CII; and CIVE, maximum noncoupled mitochondrial respiration through CIV. Pre, before training; post, after training. Allvalues are means 6 SD. †P , 0.05, main effect of time; $0.1 , P . 0.05, trend toward a significant main effect of time; @P , 0.05 vs. pre-STCTand pre-SIT; *P , 0.05 vs. pre of the same group; #P , 0.05 vs. post-HIIT; ‡P , 0.05 vs. post-STCT.

TABLE 2. Participants’ parameters before and after 4 wk of training

Measurement Time pointSTCT(n = 9)

HIIT(n = 11)

SIT(n = 9)

BM (kg) Pre 77.4 6 10.6 80.2 6 13.8 84.5 6 19.4Post 76.7 6 10.8 80.0 6 13.3 84.8 6 18.1

WLT (W) Pre 194.9 6 46.1 198.1 6 27.4 204.4 6 39.7Post† 208.9 6 50.0 214.7 6 29.7 222.3 6 45.4

WPeak (W) Pre 275.6 6 54.6 264.1 6 37.4 280.8 6 48.2Post† 284.4 6 62.5 293.2 6 34.3* 293.3 6 51.5*

20k TT time (s) Pre 2216.7 6 183.8 2247.7 6 147.5 2162.3 6 143.1Post† 2130.9 6 176.0* 2138.1 6 90.7* 2131.9 6 165.1

CS activity Pre 9.3 6 1.6 8.0 6 2.2 9.4 6 1.8(mol · h21 · kg protein21) Post 9.8 6 0.8 8.6 6 1.8 9.6 6 1.9

Pre, before training; post, after training. All values are means6 SD. †P, 0.05, main effect of time; *P,0.05 vs. values before training (pre) of the same group.

TRAINING-INDUCED MITOCHONDRIAL ADAPTATIONS 5

Granata et al. 2015

Conclusion •  HIIT en STCT are superior to SIT

for improving 20k TT performance

•  Specificity of training

•  Metabolic/biochemical improvements are not necessary performance improvements

Implementation of SIT in a training

program

Sprint interval training (SIT) Determining factors

SIT Short SIT Example SIT

Repetitions 1-12x 4-10x 6x Time 30” 6-10” 30” Intensity All out All out All out Rest R:3-5’ R:1-4’ R:3’

6x30” max R:3’

Relative contribution metabolic pathways

KU Leuven, OPO L09H3A

PCr resynthesis

KU Leuven, OPO L09H3A

SIT sources of ATP generation

MUSCLE PYRUVATE DEHYDROGENASE ACTIVITY IN HUMANS E467

that determined PDH, transformation also determined PDH, flux. Lactate was probably used as a metabolic fuel during recovery from exercise to replenish the PCr pool (16) and support intracellular ATP requirements during this period.

Contribution of PDH, flux to ATPproduction. During intense muscular contractions of the duration employed in the present study, the major sources of ATP production have traditionally been thought to come from PCr breakdown and anaerobic glycolysis (20). However, it is not clear what proportion of ATP production is supported by aerobic metabolism during 30 s of maximal sprint activity. Thus it was of interest in the present study to determine the proportions of the various fuels utilized to generate ATP and determine what proportion was supplied by flux through PDH,. ATP production from both anaerobic metabolism (PCr and glycolysis) and aerobic glucose oxidation are summarized in Table 2.

In bouts 1 and 2, - 67-71% of the ATP was generated from anaerobic sources: 16% was from PCr and 51-55% was from anaerobic glycolysis. Meanwhile, aerobic glycolysis contributed 29-33% of the ATP. Because there were no apparent differences between the amount or proportions of fuels utilized to supply ATP, the drop in power output from 19.3 kJ in bout 1 to 16.3 in bout 2 was probably related to the development of muscle fatigue (24). In contrast, during bout 3 total ATP production decreased, contributing to a further decrease in power output to 14.2 kJ. In bout 3, only 37% of the ATP was generated from anaerobic sources while generation from aerobic glycolysis increased to 63%. The difference in total ATP generation and the proportion of fuels utilized between bout 3 and bouts 1 and 2 were primarily attributed to a 3-fold decrease in ATP production from anaerobic glycolysis and a 1.4-fold increase in aerobic glycolysis. Flux through PDH, provided an increasing amount of energy with each bout (220 mmol ATP in bout 1, 255 mmol ATP in bout 2, and 357 mmol ATP in bout 3) and was the result of greater PDH, transformation during each recovery period.

Summary of PDH, regulation. During intense cycling exercise PDH, was rapidly transformed to PDH,. The

Table 2. Sources of ATP generation during three consecutive 30-s bouts of maximal isokinetic exercise separated by 4 min of rest-recovery

Anaerobic Aerobic

Bout PCr Glycolysis Total Glycolysis Total

1 ATP, mmol 118 410 528 220 748 % of total 16 55 71 29 100

2 ATP, mmol 126 390 516 255 771 % of total 16’ 51 67 33 100

3 ATP, mmol 91 122 212 357 569 % of total 16 21 37 63 100

Data are expressed as mmol of ATP and are also summarized as % of total ATP generated. ATP production from PCr and anaerobic glycolysis were calculated from the breakdown of PCr and the accumulation of lactate, respectively. Production of ATP from aerobic glycolysis was calculated from total acetyl-CoA production as the area under the PDH, curves for each bout; 1 mmol of acetyl-CoA from glycogenolysis was equal to 19.5 mmol of ATP. Contribution of fat fuels was assumed to be negligible (20).

primary stimulus for this transformation was probably Ca2+, which activates PDH-phosphatase and inhibits PDH-kinase (37). During exercise, greater PDHa may have been further supported by falls in NADH/NAD and ATP/ADP, increases in pyruvate and H+ concentrations, and attenuation of the rise in acetyl-CoA/CoASH. However, the physiological relevance of these changes on PDHa regulation may not be realized until recovery when intracellular Ca2+ decreases and the alterations in these regulatory factors persist. During exercise PDH, flux matched measured values of PDHa. It is suggested that the role of PDH, is to supply oxidizable substrate to the TCA cycle for generation of ATP and that this source of energy becomes increasingly important with each successive bout of maximal isokinetic cycling because anaerobic sources become increasingly inhibited.

During recovery, the influence of Ca2+ was removed but PDH, remained elevated due to lower NADH/NAD and ATP/ADP, greater pyruvate and H+ concentrations, and recovery of acetyl-CoA/CoASH. Measured PDH, was also similar to PDHa flux during recovery, allowing a significant portion of the accumulated lactate to be oxidized. Correlation and multilinear regression analysis were able to explain 76.5% of the variation in PDH,, suggesting that all of the regulatory factors examined may have been important determinants of PDHa transformation and PDH, flux during rest and recovery from exercise.

Summary

The present study examined PDH, regulation and lactate metabolism in human muscle during repeated bouts of maximal isokinetic cycling. Transformation of PDH, was rapid and complete after each of three 30-s maximal sprints. Lactate accumulation during exercise was attributed to differences in the maximal fluxes of glycolysis and PDH,, but no evidence was found to indicate that lactate production was dependent on O2 availability. Thus the results of the present study support our hypothesis that lactate accumulation in maxi- mally contracting human skeletal muscle results from a greater rate of glycolytic pyruvate production than pyruvate oxidation by PDH,. During exercise, glycolytic flux was much greater than PDHa flux, resulting in muscle lactate accumulation. Conversely, during recovery from exercise, maintenance of muscle PDHa flux while glycolytic flux was slowed appears to be responsible for net lactate oxidation.

The primary determinant of PDH, during exercise appears to have been Ca2+, whereas the other regulatory factors examined were secondary. Conversely, during rest and recovery from exercise, the important determinants of PDH, were found to be ATP/ADP and NADH/ NAD as well as the concentrations of pyruvate, PCr and H+. The contribution of PDHa flux to total ATP production during repeated 30-s bouts of maximal sprint activity became increasingly important with successive bouts, accounting for 29, 33, and 63% of total energy production, respectively. The progressive increase in intramuscular [H+] during the recovery periods served to simulta- neously inhibit glycolysis and maintain greater PDH,

Putman et al. 1995

Training sets •  5x50m Free max R:2’30”/2’00”/1’30”/1’00”

•  8x30” max R:4’

•  10x10” max R:1’

•  10” max every 20’ after a corner

•  ….

Common mistakes •  SIT must hurt

o  Not so with 5-10”

•  Rest is too short

•  Too often too many repetitions

•  No SIT in the winter because of interference with aerobic training

•  Think it can replace endurance training

D’OH

How to implement SIT •  Year round implementation

o  Change the determining factors of SIT

•  Trial and error o  Science can only give you

guidelines

•  Endurance sports requires endurance training o  SIT can be an extra stimulus

Thank you!

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Repeated Sprint training for endurance athletes...refer to this as the art of periodization...

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