REVIEW ARTICLE
Sports Med 2011; 41 (6): 433-448 0112-1642/11/0006-0433/$49.95/0
ª 2011 Adis Data Information BV. All rights reserved.
The ACE Gene and Human Performance 12 Years On Zudin Puthucheary,1,2 James R.A. Skipworth,1 Jai Rawal,1,2 Mike Loosemore,2,3 Ken Van Someren2,3 and Hugh E. Montgomery1,2 1 University College London Institute for Human Health and Performance, London, UK 2 University College London Institute for Sport, Exercise & Health, London, UK 3 English Institute of Sport, Bisham Abbey National Sports Centre, Marlow, UK
Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Renin-Angiotensin Systems (RAS) and Human Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Human RAS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 The Angiotensin I-Converting Enzyme (ACE) Insertion/Deletion Polymorphism and Human Physical Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 ACE Polymorphism and Cardiac Muscle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 ACE Genotype and Skeletal Muscle. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 ACE and Maximal Oxygen Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Abstract
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Some 12 years ago, a polymorphism of the angiotensin I-converting enzyme (ACE) gene became the first genetic element shown to impact substantially on human physical performance. The renin-angiotensin system (RAS) exists not just as an endocrine regulator, but also within local tissue and cells, where it serves a variety of functions. Functional genetic polymorphic variants have been identified for most components of RAS, of which the best known and studied is a polymorphism of the ACE gene. The ACE insertion/deletion (I/D) polymorphism has been associated with improvements in performance and exercise duration in a variety of populations. The I allele has been consistently demonstrated to be associated with enduranceorientated events, notably, in triathlons. Meanwhile, the D allele is associated with strength- and power-orientated performance, and has been found in significant excess among elite swimmers. Exceptions to these associations do exist, and are discussed. In theory, associations with ACE genotype may be due to functional variants in nearby loci, and/or related genetic polymorphism such as the angiotensin receptor, growth hormone and bradykinin genes. Studies of growth hormone gene variants have not shown significant associations with performance in studies involving both triathletes and military recruits. The angiotensin type-1 receptor has two functional polymorphisms that have not been shown to be associated with performance, although studies of hypoxic
Puthucheary et al.
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ascent have yielded conflicting results. ACE genotype influences bradykinin levels, and a common gene variant in the bradykinin 2 receptor exists. The high kinin activity haplotye has been associated with increased endurance performance at an Olympic level, and similar results of metabolic efficiency have been demonstrated in triathletes. Whilst the ACE genotype is associated with overall performance ability, at a single organ level, the ACE genotype and related polymorphism have significant associations. In cardiac muscle, ACE genotype has associations with left ventricular mass changes in response to stimulus, in both the health and diseased states. The D allele is associated with an exaggerated response to training, and the I allele with the lowest cardiac growth response. In light of the I-allele association with endurance performance, it seems likely that other regulatory mechanisms exist. Similarly in skeletal muscle, the D allele is associated with greater strength gains in response to training, in both healthy individuals and chronic disease states. As in overall performance, those genetic polymorphisms related to the ACE genotype, such as the bradykinin 2 gene, also influence skeletal muscle strength. Finally, the ACE genotype may influence metabolic efficiency, and elite mountaineers have demonstrated an excess of I alleles and I/I genotype frequency in comparison to controls. Interestingly, this was not seen in amateur climbers. Corroboratory evidence exists among high-altitude settlements in both South America and India, where the I allele exists in greater frequency in those who migrated from the lowlands. Unfortunately, if the ACE genotype does influence metabolic efficiency, associations with peak maximal oxygen consumption have yet to be rigorously demonstrated. The ACE genotype is an important but single factor in the determinant of sporting phenotype. Much of the mechanisms underlying this remain unexplored despite 12 years of research.
1. Renin-Angiotensin Systems (RAS) and Human Performance Some 12 years ago, a polymorphism of the angiotensin I-converting enzyme (ACE) gene became the first genetic element shown to impact substantially on human physical performance.[1] This article reviews what we have learned in the intervening period. 1.1 Methods
This article does not represent a formal, structured systematic review, but is rather a contextual discussion of the field and of the key relevant papers. As such, we used PubMed, MEDLINE and Google Scholar to identify articles of relevance published between 1 May 1998 (the first published report of the ACE genotype ª 2011 Adis Data Information BV. All rights reserved.
being associated with physical performance) and 1 March 2010. The primary search terms were ‘ACE/angiotensin converting enzyme/angiotensin 1-converting enzyme’, with ‘genotype/polymorphism’. Search results were then narrowed using terms relevant to performance phenotypes, including. ‘performance’, ‘power’, strength’, ‘athlete’, ‘VO2max’, ‘altitude’, ‘hypoxia’ and ‘elite’. Studies were excluded if no English language translation were available. Only human studies were sought. 1.2 Human RAS
The endocrine renin-angiotensin system (RAS) was long considered a key regulator of circulatory homeostasis. Here, renin (a 37 kDa aspartyl protease) cleaves hepatically derived angiotensinogen to yield decapeptide angiotensin I. This, in Sports Med 2011; 41 (6)
ACE Gene and Human Performance
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turn, is acted upon by the peptidyl dipeptidase ACE to generate octapeptide angiotensin II (Ang II). Agonist action of Ang II at the angiotensin type-1 receptor (AT1R) causes an elevation in arterial blood pressure through direct arterial vasoconstriction, and through salt and water retention provoked by adrenal aldosterone release. The vascular role of other receptors for Ang II (such as AT2R) and its degradation products (e.g. the AT4R) are less well characterized.[2] Meanwhile, ACE also cleaves bradykinin, a 9-amino acid peptide member of the kinin-kallikrein system, which is a potent vasodilator.[3] Bradykinin acts on the two receptors, bradykinin type-1 receptor (BK1R) and BK2R.[4] Bradykinin levels are therefore inversely related to ACE activity.[5,6] Increasing, ACE activity therefore drives hypertensive responses (increased AT1R activation) and diminishes hypotensive responses (reduced BK2R activation), thereby playing a crucial role in the regulation of human blood pressure and salt and water balance.[7] In addition to this endocrine RAS, however, local tissue and cellular RAS (paracrine, autocrine and intracrine) also exist in diverse tissues where they serve a variety of functions, many of which are related to the regulation of tissue growth and injury responses.[8-10] Functional genetic polymorphic variants have been identified for most components of the RAS, including renin, angiotensinogen, and the Ang II and bradykinin receptors (table I). By far the best known (and best studied) is a polymorphism in
the human ACE gene. Plasma ACE levels are very stable within individuals, but marked interindividual variations exist.[27] The absence (deletion [D]) rather than the presence (insertion [I]) of a 287base pair (bp) Alu repeat sequence within intron 16 of the ACE gene is associated with elevated plasma[11] and tissue[12,13] ACE activity; those homozygous for the deletion allele demonstrate cardiac and monocyte ACE activity almost >75% than that found in those of I/I or I/D genotypes.[12,13] 1.3 The Angiotensin I-Converting Enzyme (ACE) Insertion/Deletion Polymorphism and Human Physical Performance
The ACE I/D polymorphism was the first specific gene variant to be associated with human physical performance.[1] Maximum duration of a standardized repetitive elbow flexion exercise (using a 15 kg barbell) was recorded in 78 Caucasian military recruits before and after 10 weeks of identical military training. Baseline performance was independent of ACE genotype, unlike improvements in exercise duration with training, which were strongly genotype-dependent; gains of 79.4 – 25.2 and 24.7 – 8.8 seconds were seen in those of I/I and I/D genotype, respectively (p = 0.005 and 0.007), but not in D/D homozygotes (7.1 – 14.9 seconds; p = 0.642). The I/I homozygotes thus showed an 11-fold greater improvement than those of D/D genotype. In addition, the association of the ACE genotype with performance did not seem limited to the
Table I. Selected polymorphisms of the renin-angiotensin system and associated receptors Gene
Polymorphism/alleles
Gene location
Functional effect
References
ACE (17q22-24)
287 bp I/D
Intron 16
Protein levels
11-14
C > T (position 4656)
30 UTR
Protein levels
M > T (position 235)
Exon2 (+704)
Protein levels
A > C (position 20)
50 UTR promotor
Protein levels
A > G (position 6)
50 UTR promotor
Protein levels
C > T (position 532)
50 UTR
Protein levels
Renin (1q32-q32)
rs5707 (T > G)
Intron 4
Protein levels
22
Angiotensin II type-1 receptor (3q21-q25)
A > C (position 1166)
30 UTR
Receptor sensitivity
23-25
T > A (position 810)
Promoter
Unknown
G > A (position 1675)
Intron 1
Protein levels
Angiotensinogen (1q42-q43)
Angiotensin II type-2 receptor (Xq22-q23)
15-21
26
bp = base pair; D = deletion; I = insertion; UTR = untranslated region.
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Sports Med 2011; 41 (6)
436
young Caucasian male. In 83 postmenopausal women randomized to receive hormone replacement therapy rather than placebo,[28] those of I/I genotype showed greater increases in adductor pollicis muscle strength than those of I/D or D/D genotype (mean – standard error 16.0 – 1.53%, 14.3 – 2.67% and 7.76 – 4.13%, respectively; p = 0.017 for gene effect, p = 0.004 for I allele effect). Since then, a wealth of other studies has supported an association of the ACE genotype with sporting performance. In general, the I allele seems associated with endurance-orientated events.[9] Thus, in a study of 91 British Olympic-standard runners (79 Caucasian), I allele frequency increased with competitive distance, from 0.35 to 0.53 and 0.62 for the three distance groups £200 m, 400–3000 m and ‡5000 m, respectively (p = 0.009 for linear trend).[29] This was noted to hold true in the subanalysis of the 79 Caucasians. Meanwhile, in a study of 35 truly elite ultra-distance swimmers, genotype frequencies differed (p < 0.01) for those classified as better at 1- to 10-km distances (6% I/I vs 47% I/D vs 47% D/D) when compared with those who were best at 25-km races (18.8% I/I vs 75% I/D vs 6.2% D/D). I-allele frequency was 0.29 for the shorter distance swimmers and 0.59 for the 25-km group.[30] Similarly, an excess of the I allele was identified amongst the 64 members of the Australian Olympic rowing squad in the 1996 Atlanta games (p < 0.02),[31] amongst longdistance cyclists and Russian endurance athletes,[9,32] and amongst the fastest 100 South African-born finishers (103 I [51.5%] and 97 D [48.5%]), and of the 2000 and 2001 South African Ironman triathlons (140 I [42.2%] and 192 D [57.8%]; p = 0.036).[33] While the I allele seems associated with endurance-orientated events, the D allele seems associated with strength- and power-orientated performance.[9] The majority of swimming events are undertaken in <2 minutes[29] and thus power rather than endurance is likely to be key to success. In Olympic level swimmers, an excess of the D allele was noted compared with controls (0.60 vs 0.51; p = 0.034),[29] a finding replicated amongst elite Caucasian swimmers from European and Commonwealth championships (p = 0.004), where a significant excess of the D allele was noted, especially in those competing over shorter disª 2011 Adis Data Information BV. All rights reserved.
Puthucheary et al.
tances (p = 0.005 for £400 m).[34] Further studies have demonstrated this association, but only in the elite short-distance swimmers (<200 m).[30,35] Conflicting data do exist, but generally seem explained by the study of heterogeneous (often mixed race and sex, and sporting discipline) subject groups.[36-42] While one must be cautious, in that association does not imply causality, the reports have generally maintained consistency. It should be acknowledged that the majority of earlier studies were population studies, with little physiological data, this being more common in more recent studies. Homogeneity is desirable in these genetic performance studies as the ACE gene is only one of many genetic factors affecting performance, and the addition of other (nongenetic factors) such as age, sex and sporting discipline, which may themselves interact with genotype, often results in too great a variance in phenotype to be able to discern the role of genotype. Notable exceptions do exist, and a well designed study in 121 Israeli runners reported a positive association between the D/D genotype and elite endurance performance.[42] One explanation for this atypical finding is that it represents artefact related to the heterogeneous nature of the Israeli Caucasian Jewish population.[43] Alternatively, this may be a true reflection of the Israeli population, though greater numbers would be needed to clarify this. A large Korean-based study showed a decreasing frequency of the D allele with advancing levels of performance in powerorientated athletes (in track and field, and weight lifting).[44] Whilst the subjects were from mixed disciplines and the numbers in each discipline were small, this study (just as those in Israeli runners) is noteworthy in its conflict with the prevailing view. Table II summarizes studies relating the ACE genotype to sporting performance in the last 12 years. In theory, such associations with the ACE genotype might be due to linkage of the ACE I/D polymorphic site with functional variation in an adjacent locus, such as the nearby growth hormone (GH).[76] The T > A variant in intron 4 of the GH1 gene has been associated with lower levels of GH and insulin-like growth factor-I.[77] Sports Med 2011; 41 (6)
Amir et al.[42] [45]
Cam et al.
[46]
Cerit et al.
[47]
Colakoglu et al. [48]
Juffer et al.
[49]
Cohort
No. of subjects and ethnicity
Performancea
Outcome measureb
Association with performance
I/D associations
Runners
121 Israeli
Elite
Performance
Yes
D and endurance
Sprinters
88 Caucasian
Non-elite
Performance
Yes
D and short distance
Army
186 Caucasian
Army
Performance
Yes
D and short duration
Athletes
99 Caucasian
Non-elite
Performance
Yes
D and strength
Footballers
52 mixed
Elite
Prevalence
Yes
D more prevalent
Cyclists
50 Caucasian
Elite
Performance
Yes
D/D and endurance
[50]
Mixed
141 mixed
Elite
Prevalence
Yes
D/D and endurance rowers
Winnicki et al.
[51]
Mixed
233 mixed
Sedentary
Performance
Yes
D/D and sedentary lifestyle
Nazarov et al.
[9]
Mixed
217 Caucasian
Elite
Sport performance
Yes
D/D and short distance
Swimmers
72 Caucasian
Elite
Prevalence and performance
Yes
D/D and short distance
Woods et al.[34]
Swimmers
102 Caucasian
Elite
Prevalence and performance
Yes
D/D and short distance
Giaccaglia et al.[53]
Elderly
213 mixed
Sedentary
Training
Yes
D/D and strength
Army
67 Chinese
Army
Performance
Yes
. D/D and VO2max
Tsianos et al.[55]
Climbers
284 mixed
Elite
Performance
Yes
I and high ascent
Gayagay et al.[31]
Rowers
64 Caucasian
Elite
Prevalence
Yes
I and endurance
Myerson et al.[29]
Runners
91 Caucasian
Elite
Sport performance
Yes
I and endurance
Montgomery et al.[1]
Army
78 Caucasian
Army
Performance
Yes
I and endurance
Collins et al.[33]
Triathletes
166 Caucasian
Elite
Performance
Yes
I and endurance
Hruskovicova et al.[56]
Runners
445 Caucasian
Elite
Performance
Yes
I and endurance
Cieszczyk et al.[57]
Rowers
55 Caucasian
Elite
Prevalence
Yes
I and endurance
Min et al.[58]
Track and field
277 Japanese
Non-elite
Prevalence
Yes
I and endurance
Mixed
192 Caucasian
Elite
Prevalence and performance
Mixed
I and endurance
Lucia et al.
Munisea et al.
Costa et al.
Zhao et al.
[52]
[54]
Rankinen et al.
Continued next page
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[36]
ACE Gene and Human Performance
ª 2011 Adis Data Information BV. All rights reserved.
Table II. Studies in athletes associating the ACE insertion/deletion (I/D) genotype with sporting performance Study
438
ª 2011 Adis Data Information BV. All rights reserved.
Table II. Contd No. of subjects and ethnicity
Performancea
Outcome measureb
Association with performance
I/D associations
Swimmers
35 Caucasian
Elite
Performance
Yes
I and endurance, D and short distance
Runners
55 Caucasian
Non-elite
Performance
Yes
I and endurance, D and short distance
Mixed
1027 Caucasian
Adolescents
Performance
Yes
I and endurance, D and strength
Thompson et al.
Climbers
139 Caucasian
Elite
Prevalence and performance
Yes
I and high ascent
Hurlbut et al.[62]
Sedentary
40 Caucasian
Sedentary
Training
Yes
I and insulin requirements
Dekany et al.[63]
Mixed
50 Caucasian
Elite
Performance
Yes
I and metabolic efficiency
Williams et al.[64]
Army
58 Caucasian
Army
Training
Yes
I and metabolic efficiency
Kim et al.[44]
Power athletes
155 Korean
Elite
Sport performance
Yes
I and power
Kritchevsky et al.[65]
Elderly
3075 Caucasian
Sedentary
Activity
Yes
I/I and mobility limitation
Goh et al.[66]
Rugby players
17 Singaporean
Non-elite
Performance
Yes
. I/I and VO2max
Mixed
60 Caucasian
Elite
Prevalence
Yes
I and elite status
Mixed
160 Turkish
Athletes and sedentary
Prevalence
Yes
I and athletes
Sprinters
230 African-Americans
Elite
Sport performance
No
Army
148 mixed
Army
Training
No
Frederiksen et al.
Elderly
684 Caucasian
Sedentary
Activity
No
Scott et al.[70]
Runners
271 Africans
Elite
Performance
No
Day et al.[71]
Sedentary
62 Caucasian
Sedentary
Performance
No
Oh[72]
Mixed
139 Korean
Elite
Performance
No
Papadimitriou et al.[73]
Track and field
101 Greek
Elite
Prevalence
No
Thomis et al.[74]
Twins
54 Caucasian
Sedentary
Training
No
McCauley et al.[75]
Active
79 Caucasian
Active
Performance
No
Frederiksen et al.[69]
Elderly
203 Caucasian
Sedentary
Performance
No
Tsianos et al.[30] [59]
Cam et al.
Moran et al.
[60]
[61]
Alvarez et al.[32] [67]
Turgut G et al. [68]
Scott et al.
[39]
Sonna et al.
[69]
Sports Med 2011; 41 (6)
a b
Elite performance status refers to athletes involved in competition at an international level.
Training as an outcome measure refers to an alteration in a pre-determined set of training exercises. . D = deletion; I = insertion; VO2max = maximal oxygen consumption.
Puthucheary et al.
Cohort
Study
ACE Gene and Human Performance
This variant was examined in the aforementioned South African Ironman, but no association was seen with performance.[78] There was, however a differential in the post-race temperature, with the AA genotypes having a significantly higher temperature (37.7 – 0.8C vs 37.2 – 0.8C; p = 0.019), consistent with a genetic predisposition to metabolic efficiency (see sections 1.5 and 1.6). A study in 847 British military recruits failed to demonstrate a GH-dependant effect on left ventricular (LV) mass by training, suggesting the effects of ACE I/D on exercise-induced LV growth (see section 1.4) are not mediated through linkage to the GH site.[79] Alternatively, these effects of the ACE genotype may be mediated through changes in Ang II activity. Ang II acts on the AT1R, the gene for which itself has two polymorphisms[23,80] that appear functional.[40,81,82] However, these do not seem to be associated with differences in performance,[32,83] although one small Korean study (17 females) has suggested a possible association with training-related gains in maximal oxygen con. sumption (VO2max).[84] Studies of hypoxic ascent (see section 1.5) have yielded conflicting results.[85,86] Finally, ACE genotype does influence bradykinin levels (see section 1.2). The ACE I allele is associated with higher kinin activity, while a common gene variant in the BK2R exists: the absence (-9) rather than the presence (+9) of a 9-bp fragment is associated with greater receptor response and gene transcription.[87,88] In a study of running distance in 81 Olympic-standard track athletes, the ‘high-kinin receptor activity’ haplotype (ACE I/BK2BR -9) was associated with endurance performance (p = 0.003). This suggests that at least part of the association of ACE with performance phenotypes is mediated through changes in kinin activity at its BK2R.[89] Similarly, in the 2000 and 2001 South African Ironman triathlons, the B2BKR -9/-9 genotype occurred at a significantly higher frequency amongst the triathletes (27.0%) when compared with controls (19.3%; p = 0.035), the -9 allele being associated with shorter finishing times.[90] Interestingly the -9 allele was also associated with reduced weight loss among the triathletes, consistent with an effect on metabolic efficiency (see section 1.5).[91] ª 2011 Adis Data Information BV. All rights reserved.
439
Thus, ACE genotype is associated with differences in athletic performance; in part through modulation of kinin levels (although a role for altered Ang II activity at its receptors cannot be excluded). But through what physiological processes might such biochemical effects be exerting their influence? 1.4 ACE Polymorphism and Cardiac Muscle
Ang II acts as a trophic agent on cardiac muscle[92,93] in both normal and diseased states.[94] The expression of myocardial RAS components rises during LV hypertrophy (LVH),[95] which is likely to be mediated through the increased synthesis of Ang II and subsequent AT1R activation.[92,93,96] ACE inhibition in animal models attenuates LVH and also leads to a greater regression in LV mass than other similarly hypotensive agents.[97-99] The rise in racehorse LV mass with training correlates with athletic performance.[100] Positive correlations have also been seen between LV mass, aerobic performance and race jumping ability.[101,102] In humans, an exaggerated LV growth response to training is associated with the ACE D allelle.[103] Thus, in 140 Caucasian male military recruits, the rise in echocardiographically assessed LV mass associated with 10 weeks of physical training was +2.0, +38.5 and +42.3 g in the I/I, I/D and D/D groups, respectively (p < 0.0001). Furthermore, the rise in prevalence of ECG-defined LVH was also ACE genotype dependent (p < 0.01).[103] These findings were replicated in a cohort of 74 male endurance athletes,[104] in whom athletes of D/D genotype had significantly higher LV mass indices (LVMI) as well as a higher prevalence of LVH (LVMI >131 g/m2). However, the highest LVMI (150 – 23 g/m2) was seen in the 15 athletes with both ACE D/D and AT1R AC/CC genotypes. The association of the ACE D allele with LV mass has since been confirmed in elite wrestlers,[105] elite football players[83] and endurance athletes.[106] Studies that have examined subjects exposed to multiple hypertrophic stimuli at different timepoints, or diverse population groups have, however, (perhaps unsurprisingly) failed to identify this association.[40,107-111] Nonetheless, the ACE D allele does seem associated Sports Med 2011; 41 (6)
Puthucheary et al.
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with LVH in disease states such as non-insulindependent diabetes mellitus,[112] hypertension,[113] hypertrophic cardiomyopathy[114] and calcific aortic stenosis.[115] Rather counter intuitively, however, I/I (compared with D/D) genotype has been associated with a significantly greater impairment in fractional shortening in response to ultra-endurance exercise.[116] The extent of extra-cardiac adaptation (in response to training) has also been associated with the ACE genotype.[117] In a study of 56 athletes, the I/I genotype was associated with increased aortic compliance in response to training. Alterations on aortic compliance affect LV work, thus influencing LV mass indirectly. Such effects, in humans, may be less dependent upon ACE genotype-dependent differences in Ang II activity at the AT1R. The administration of sub-hypotensive doses of the AT1R antagonist losartan to 141 British Army recruits did not alter the LV growth response to exercise.[118] In a study of 90 patients undergoing anti-hypertensive therapy, B2BKR +9/+9 genotype was associated with poor LV mass regression.[119] This was uncorrected for the patients ACE genotype. In a group of 109 military recruits undergoing 10 weeks of basic physical training, both the ACE and B2BKR genotypes interacted biologically in an additive way, with those of a genotype likely to be associated with lowest kinin activity (ACE D/D, B2BKR +9/+9) exhibiting the greatest LV growth. In these, mean LV growth was 15.7 g, compared with -1.37 g in those homozygous for ACE I and B2BKR -9 alleles (p = 0.003 for trend across genotypes).[120] Sheer increases in LV size, however, do not automatically lead to increased performance. Further, the ACE I allele is associated with the lowest cardiac growth response but also with the endurance-exercise phenotype. It thus seems likely that other mechanisms exist. 1.5 ACE Genotype and Skeletal Muscle
The ACE genotype may also influence human skeletal muscle growth. Certainly, Ang II transduces mechanical load to yield growth responses,[121] which might translate to greater strength gains. In keeping with 33 healthy subjects undergoing a ª 2011 Adis Data Information BV. All rights reserved.
9-week training regimen, greater gains in quadriceps strength (both isometric and dynamic) were seen in D-allele carriers.[121] Other work supports the role of the ACE genotype in the regulation of muscle strength.[122] In 103 patients with chronic obstructive pulmonary disease (COPD), the D allele was associated with greater quadriceps strength, using non-volitional testing.[123] These effects may, in part, be mediated by genotype-dependent differences in skeletal muscle growth, but may also be mediated by differences in muscle fibre type. In 41 untrained healthy volunteers, the I allele was associated with a predominance of type-I muscle fibre (fatigue resistant, ‘slow twitch’) when compared with the D allele.[124] Just as in the heart, this association of genotype with muscle performance may be partly mediated through changes in bradykinin activity at the BK2R. Bradykinin modulates the action of insulin on skeletal muscle and fat.[125] Animal models have demonstrated improved insulin-dependent glucose transport with ACE inhibitors.[126,127] Other metabolic influences of bradykinin on muscle substrates and transport include those on glycogen levels, lactate concentration,[128] the availability of glucose/free fatty acid substrates,[129] and the expression of the GLUT4 glucose transporter.[130] In 110 patients with COPD, reduced BK2R activity was associated with reduced quadriceps strength.[131] The ACE genotype may also be associated with differences in the mechanical/metabolic efficiency of skeletal muscle.[132] In a study of Caucasian male military recruits undergoing an 11-week physical training programme, baseline delta efficiency (DE; defined as the change in work performed per minute to the change in energy expended per minute) was independent of genotype.[64] However, recruits of I/I genotype showed a significant increase in DE (an absolute change of +1.87%, representing a proportional increase of 8.62% relative to baseline) not seen in recruits of D/D genotype (absolute change of -0.39%). In keeping with such an effect on ‘metabolic efficiency’, the I allele seemed to be associated with a relative anabolic response (in terms of both muscle and fat mass) amongst military recruits under conditions of high calorie expenditure Sports Med 2011; 41 (6)
ACE Gene and Human Performance
during intensive physical training.[133] As before, subjects of different genotype were phenotypically indistinguishable prior to engaging in training. Such effects may, in part, be mediated through ACE-genotype-dependent modulations in kinin activity. In a study of 115 healthy men and women, DE was strongly associated with BK2R genotype (23.84 – 2.41% vs 24.25 – 2.81% vs 26.05 – 2.26% for those of +9/+9 vs +9/-9 vs -9/-9 genotypes; p = 0.0008).[89] This study also found evidence of interaction with the ACE I/D genotype. Subjects who were of ACE I/I and B2BKR -9/-9 genotype had the highest baseline DE.[89] Further, the D allele was associated with greater rises in core temperature during a standardized heat-exertion test.[134] The ACE genotype may also affect metabolic efficiency via systemic effects, although these are less well documented.[135,136] If ACE genotype influences metabolic efficiency, then one might anticipate a marked association of genotype with performance in hypoxic environments. Elite British male mountaineers who have ascended beyond 7000 m without the use of supplemental oxygen, demonstrate a significant excess in I allele (and I/I genotype) frequency when compared with controls.[137] The same finding was made in 139 mountaineers attempting an ascent to 8000 m, in whom the I allele was associated with maximal altitudes achieved (8079 – 947 m for D/Ds, 8107 – 653 m for I/Ds, and 8559 – 565 m for I/Is; p = 0.007).[61] Whilst a role for genotype-dependent differences in muscle metabolic efficiency may underpin these findings, other mechanisms may also play a role. Whilst not identified in 126 tourist climbers ascending Mount Kilimanjaro (5895 m) or in a high-altitude pulmonary oedema study of 164 climbers at 4559 m,[138,139] others have suggested that part of this association may be mediated through an increased risk of acute mountain sickness being associated with the D allele.[140,141] Meanwhile, the ACE I allele seems to be associated with an enhanced exertional ventilatory response to acute hypoxia,[142] and thus with the preservation of arterial oxygenation at high altitude in rapid ascent.[143] Corroboratory epidemiological evidence exists among the Quechua-speaking native people living above 3000 m in South America, ª 2011 Adis Data Information BV. All rights reserved.
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and those in the Ladakh region of India living above 3600 m.[144,145] The I allele was found in greater frequency in those who had migrated from the lowlands. The relationship is unclear, and negative studies exist. Gonza´lez et al.[146] studied 63 athletes exposed to an altitude of 2200 m, and failed to demonstrate a relationship between ACE genotype and the erythropoietic response to altitude. Finally, ACE genotype may be associated with differences in the muscle injury response. In a study of 70 physically active subjects undergoing eccentric exercise, the strongest independent determinant of peak creatine kinase (CK) levels was ACE genotype.[91] Here, the I/I genotype was associated with a greater CK rise (adjusted odds ratio 1.3; 95% CI 1.03, 1.64; p = 0.02). Others have failed to replicate these findings (peak changes in serum CK levels being non-significantly lower amongst those of I/I genotype).[147] However, the use of a racially heterogeneous cohort might explain this finding, as might the study of ‘loaded squats’ rather than upper-limb loading, as in the previous study.[147] Currently, observational data suggest that the use of ACE inhibitors may modulate muscle metabolism to a indicate where mass (and metabolic efficiency) are altered to a measurable level.[148] High-quality randomized trials in both physiological and pathophysiological states are now warranted to determine the effects of RAS modulation on muscle mass, function and ultimately on global physical performance. 1.6 ACE and Maximal Oxygen Consumption
. VO2max is a physiological characteristic bounded by the parametric limits of the Fick equation: ðLV end-diastolic volume LV endsystolic volumeÞ heart rate arteriovenous oxygen difference . Elite endurance athletes have a high VO2max due primarily to a high cardiac output from a large compliant cardiac chamber (including the myocardium and pericardium), which relaxes [149] quickly . and fills to a large end-diastolic volume. Peak VO2max has been associated with performance Sports Med 2011; 41 (6)
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in competitive endurance-based sport.[150] To date, any putative . association between ACE genotype and peak VO2max remains unproven. Abraham et al.[151] studied 57 patients, stratified by ACE genotype, with impaired LV function of ischaemic origin. No differences in baseline LV function were noted across ACE genotypes, although ACE D/D genotype was associated with a decreased mean . VO2max. Similarly, amongst 47 postmenopausal women, those subjects of I/I genotype demon. strated a 6.3 mL/kg/min higher VO2max than those of . D/D genotype and a 3.3 mL/kg/min higher VO2max (p < 0.05) than the ACE I/D genotype group.[152] However, other studies cast doubt upon these data. Two studies, one in sedentary females and the other in both active and sedentary females, using cycle ergometry and maximal treadmill exercise tests, found no. relationship between ACE genotype and peak VO2max.[71,153] However, both groups have relatively small cohorts (62 and 77, respectively). The contribution . of cardiac contractile performance to VO2max is likely to be limited in those with impaired cardiac contratile function (such as those with cardiac disease per se, or in the elderly in whom systolic and diastolic function may be limited when compared with younger populations). Elderly females, just as those with heart disease, may be receiving a range of medications that might confound reliable observation. A much larger cohort of US army recruits undergoing 8 weeks of basic training, found no significant association between . ACE genotype, peak VO2max or other measures of performance.[39] However, this study was also flawed; the 147 recruits included had varying baseline fitness levels, and were of both sexes, diverse ages and were drawn from a spread of ethnic groups, making reliable interpretation difficult. Further studies in army recruits do, however, suggest that the cardiopulmonary response to training does not seem ACE-genotype dependent.[154] Bouchard et al.[155,156] studied 99 families with 415 pairs of siblings, and found no association of the ACE locus (17q23) with . baseline VO2max or its response to a 20-week standardized endurance training programme. Studies in COPD have also yielded conflicting results.[157,158] A single study in post-myocardial ª 2011 Adis Data Information BV. All rights reserved.
infarction patients. has demonstrated a differential increase in VO2max as a result of training between the I/I and D/D genotypes.[159] Overall, further studies are required of the response to different training regimens (intensity and duration), in populations of homogeneous race, sex and age, and disease state if this issue is to be satisfactorily addressed. Other questions also need to be addressed. Is the discrepancy in the propensity to gain muscle mass in the I/I homozygotes in some studies and D/Ds in others the result of competing effects of RAS on muscle growth and metabolic efficiency? Or is it of changes in substrate use, in which a dietary influence might play a role? Is the association of the ACE I allele with performance of mountaineers mediated through the same mechanisms as those for elite endurance performance at sea level? Can one mimic the training effects of I/I homozygotes in D/D homozygotes with the use of ACE inhibitors? Finally, the relative role of Ang II and bradykinin (and of their specific receptors) needs clarification. 2. Conclusions Human sporting phenotypes result from the interaction of genetic variation with environmental stimuli. The ACE I/D polymorphism is but one such genetic factor – the D allele tending to be associated with power/sprint performance, and the I allele with endurance sports. The mechanisms underlying such observations remain inadequately explored, as does the role for specific RAS antagonists in modulating such performance. The prevalence of both the D and I alleles in populations worldwide suggest that they may both have offered different survival advantages. That of the I allele may relate to improved endurance performance, and enhanced oxygen utilization in times of both exercise and illness. The D allele, being associated with gains in strength with training, may offer separate advantages related directly to strength itself, but also to the acquisition of increased muscle bulk in response to muscle strength training/high loading. In addition, however, ACE genotype influences a variety of other phenotypes, such as haemorrhage response[160] to Sports Med 2011; 41 (6)
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the outcome from infection[161] - all of which may offer separate evolutionary selection pressures beyond those exerted through ‘fitness phenotypes’ alone. Indeed, such issues remind us of the reason for study. Whilst research in the field of sports genetics might raise the spectre of drug doping,[162] it has intrinsic scientific value, and may also suggest possible therapeutic targets. Acknowledgements The authors have no conflicting interests to declare. No funding was received for this article. All contributors have met criteria for authorship.
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111. Rizzo M, Gensini F, Fatini C, et al. ACE I/D polymorphism and cardiac adaptations in adolescent athletes. Med Sci Sports Exerc 2003; 35 (12): 1986-90 112. Estacio RO, Jeffers BW, Havranek EP, et al. Deletion polymorphism of the angiotensin converting enzyme gene is associated with an increase in left ventricular mass in men with type 2 diabetes mellitus. Am J Hypertens 1999; 12 (6): 637-42 113. Kuznetsova T, Staessen JA, Wang JG, et al. Antihypertensive treatment modulates the association between the D/I ACE gene polymorphism and left ventricular hypertrophy: a meta-analysis. J Hum Hypertens 2000; 14 (7): 447-54 114. Yoneya K, Okamoto H, Machida M, et al. Angiotensinconverting enzyme gene polymorphism in Japanese patients with hypertrophic cardiomyopathy. Am Heart J 1995; 130 (5): 1089-93 115. Dellgren G, Eriksson MJ, Blange I, et al. Angiotensinconverting enzyme gene polymorphism influences degree of left ventricular hypertrophy and its regression in patients undergoing operation for aortic stenosis. Am J Cardiol 1999; 84 (8): 909-13 116. Ashley EA, Kardos A, Jack ES, et al. Angiotensin-converting enzyme genotype predicts cardiac and autonomic responses to prolonged exercise. J Am Coll Cardiol 2006; 48 (3): 523-31 117. Tanriverdi H, Evrengul H, Kaftan A, et al. Effects of angiotensin-converting enzyme polymorphism on aortic elastic parameters in athletes. Cardiology 2005; 104 (3): 113-9 118. Myerson SG, Montgomery HE, Whittingham M, et al. Left ventricular hypertrophy with exercise and ACE gene insertion/deletion polymorphism: a randomized controlled trial with losartan. Circulation 2001; 103 (2): 226-30 119. Hallberg P, Lind L, Michaelsson K, et al. B2 bradykinin receptor (B2BKR) polymorphism and change in left ventricular mass in response to antihypertensive treatment: results from the Swedish Irbesartan Left Ventricular Hypertrophy Investigation versus Atenolol (SILVHIA) trial. J Hypertens 2003; 21 (3): 621-4 120. Brull D, Dhamrait S, Myerson S, et al. Bradykinin B2BKR receptor polymorphism and left-ventricular growth response. Lancet 2001; 358 (9288): 1155-6 121. Folland J, Leach B, Little T, et al. Angiotensin-converting enzyme genotype affects the response of human skeletal muscle to functional overload. Exp Physiol 2000; 85 (5): 575-9 122. Williams AG, Day SH, Folland JP, et al. Circulating angiotensin converting enzyme activity is correlated with muscle strength. Med Sci Sports Exerc 2005; 37 (6): 944-8 123. Hopkinson NS, Nickol AH, Payne J, et al. Angiotensin converting enzyme genotype and strength in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2004; 170 (4): 395-9 124. Zhang B, Tanaka H, Shono N, et al. The I allele of the angiotensin-converting enzyme gene is associated with an increased percentage of slow-twitch type I fibers in human skeletal muscle. Clin Genet 2003; 63 (2): 139-44 125. Caldiz CI, de Cingolani GE. Insulin resistance in adipocytes from spontaneously hypertensive rats: effect of long-
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141. Droma Y, Hanaoka M, Basnyat B, et al. Adaptation to high altitude in sherpas: association with the insertion/ deletion polymorphism in the angiotensin-converting enzyme gene. Wilderness Environ Med 2009; 19 (1): 22-9 142. Patel S, Woods DR, Macleod NJ, et al. Angiotensinconverting enzyme genotype and the ventilatory response to exertional hypoxia. Eur Respir J 2003; 22 (5): 755-60 143. Woods DR, Pollard AJ, Collier DJ, et al. Insertion/deletion polymorphism of the angiotensin I-converting enzyme gene and arterial oxygen saturation at high altitude. Am J Respir Crit Care Med 2002; 166 (3): 362-6 144. Bigham AW, Kiyamu M, Leo´n-Velarde F, et al. Angiotensin-converting enzyme genotype and arterial oxygen saturation at high altitude in Peruvian Quechua. High Alt Med Biol 2008; 9 (2): 167-78 145. Qadar Pasha MA, Khan AP, Kumar R, et al. Angiotensin converting enzyme insertion allele in relation to high altitude adaptation. Ann Hum Genet 2001; 65 (6): 531-6 146. Gonzalez AJ, Hernandez D, De Vera A, et al. ACE gene polymorphism and erythropoietin in endurance athletes at moderate altitude. Med Sci Sports Exerc 2006; 38 (4): 688-93 147. Heled Y, Bloom MS, Wu TJ, et al. CK-MM and ACE genotypes and physiological prediction of the creatine kinase response to exercise. J Appl Physiol 2007; 103 (2): 504-10 148. Di Bari M, van de Poll-Franse LV, Onder G, et al. Antihypertensive medications and differences in muscle mass in older persons: the Health, Aging and Body Composition Study. J Am Geriatr Soc 2004; 52 (6): 961-6 . 149. Levine BD. VO2max: what do we know, and what do we still need to know? J Physiol 2008; 586 (1): 25-34 150. Hagerman FC, Connors MC, Gault JA, et al. Energy expenditure during simulated rowing. J Appl Physiol 1978; 45 (1): 87-93 151. Abraham MR, Olson LJ, Joyner MJ, et al. Angiotensinconverting enzyme genotype modulates pulmonary function and exercise capacity in treated patients with congestive stable heart failure. Circulation 2002; 106 (14): 1794-9 . 152. Hagberg JM, Ferrell RE, McCole SD, et al. VO2 max is associated with ACE genotype in postmenopausal women. J Appl Physiol 1998; 85 (5): 1842-6 153. Roltsch MH, Brown MD, Hand BD, et al. No association between ACE I/D polymorphism and cardiovascular hemodynamics during exercise in young women. Int J Sports Med 2005; 26 (8): 638-44 154. Woods DR, World M, Rayson MP, et al. Endurance enhancement related to the human angiotensin I-converting enzyme I-D polymorphism is not due to differences in the cardiorespiratory response to training. Eur J Appl Physiol 2002; 86 (3): 240-4 155. Bouchard C, Rankinen T, Chagnon YC, et al. Genomic scan for maximal oxygen uptake and its response to training in the HERITAGE Family Study. J Appl Physiol 2000; 88 (2): 551-9 156. Bouchard C, Leon AS, Rao DC, et al. The HERITAGE family study: aims, design, and measurement protocol. Med Sci Sports Exerc 1995; 27 (5): 721-9
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157. Zhang X, Wang C, Dai H, et al. Association between angiotensin-converting enzyme gene polymorphisms and exercise performance in patients with COPD. Respirology 2008; 13 (5): 683-8 158. Kanazawa H, Otsuka T, Hirata K, et al. Association between the angiotensin-converting enzyme gene polymorphisms and tissue oxygenation during exercise in patients with COPD. Chest 2002; 121 (3): 697-701 159. Defoor J, Vanhees L, Martens K, et al. The CAREGENE study: ACE gene I/D polymorphism and effect of physical training on aerobic power in coronary artery disease. Heart 2006; 92 (4): 527-8 160. Welsby IJ, Podgoreanu MV, Phillips-Bute B, et al. Genetic factors contribute to bleeding after cardiac surgery. J Thromb Haemost 2005; 3 (6): 1206-12
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161. Harding D, Baines PB, Brull D, et al. Severity of meningococcal disease in children and the angiotensin-converting enzyme insertion/deletion polymorphism. Am J Respir Crit Care Med 2002; 165 (8): 1103-6 162. Wang P, Fedoruk MN, Rupert JL. Keeping pace with ACE: are ACE inhibitors and angiotensin II type 1 receptor antagonists potential doping agents? Sports Med 2008; 38 (12): 1065-79
Correspondence: Dr Zudin Puthucheary, Institute of Human Health and Performance, Archway Campus, University College London, Archway, N19 5LW, London, UK. E-mail:
[email protected]
Sports Med 2011; 41 (6)
Sports Med 2011; 41 (6): 449-461 0112-1642/11/0006-0449/$49.95/0
REVIEW ARTICLE
ª 2011 Adis Data Information BV. All rights reserved.
Effect of Mouth-Rinsing Carbohydrate Solutions on Endurance Performance Ian Rollo and Clyde Williams School of Sport and Exercise and Health Sciences, Loughborough University, Loughborough, UK
Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Evidence: Performance Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Cycle Time Trials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Running Time Trials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Performance Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Ratings of Perceived Exertion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Pre-Exercise Nutritional Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7. Practical Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Abstract
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Ingesting carbohydrate-electrolyte solutions during exercise has been reported to benefit self-paced time-trial performance. The mechanism responsible for this ergogenic effect is unclear. For example, during short duration (£1 hour), intense (>70% maximal oxygen consumption) exercise, euglycaemia is rarely challenged and adequate muscle glycogen remains at the cessation of exercise. The absence of a clear metabolic explanation has led authors to speculate that ingesting carbohydrate solutions during exercise may have a ‘non-metabolic’ or ‘central effect’ on endurance performance. This hypothesis has been explored by studies investigating the performance responses of subjects when carbohydrate solutions are mouth rinsed during exercise. The solution is expectorated before ingestion, thus removing the provision of carbohydrate to the peripheral circulation. Studies using this method have reported that simply having carbohydrate in the mouth is associated with improvements in endurance performance. However, the performance response appears to be dependent upon the pre-exercise nutritional status of the subject. Furthermore, the ability to identify a central effect of a carbohydrate mouth rinse maybe affected by the protocol used to assess its impact on performance. Studies using functional MRI and transcranial stimulation have provided evidence that carbohydrate in the mouth stimulates reward centres in the brain and increases corticomotor excitability, respectively. However, further research is needed to determine whether the central effects of mouthrinsing carbohydrates, which have been seen at rest and during fatiguing exercise, are responsible for improved endurance performance.
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1. Introduction The endogenous stores of carbohydrate are finite. Prolonged fixed-intensity exercise to fatigue is associated with the depletion of muscle glycogen and/or hypoglycaemia. Thus, it is widely accepted that providing carbohydrate during exercise can improve endurance capacity by preventing hypoglycaemia and can provide a ready fuel for the working muscles (for reviews see Coyle[1] and Tsintzas and Williams[2]). A common method of providing carbohydrate during exercise is in the form of carbohydrate-electrolyte solutions. Providing carbohydrate in the form of a carbohydrateelectrolyte solution supplies fuel as well as providing fluid and electrolytes that are lost as a consequence of sweating. Ingesting fluid during exercise has been reported to reduce cardiovascular stress and hyperthermia associated with exerciseinduced dehydration.[3] In addition, fluid ingestion has been reported to have a profound metabolic effect during exercise. For example, ingesting fluid alone has been reported to improve endurance capacity[4] by reducing the utilization of muscle glycogen.[5] Therefore, it is not surprising that most laboratory studies have examined the influences of ingesting carbohydrate-electrolyte solutions on endurance capacity (>1 hour) rather than endurance performance (i.e. time trials). This is because the associated increases in core temperature and heart rate during short-duration exercise are not as pronounced as exercise of greater durations (>1 hour).[3,6] In addition, hypoglycaemia and severe depletion of muscle glycogen have not been reported following short periods (£1 hour) of. intense (>70% maximal oxygen consumption [VO2max]) exercise.[7] The metabolic effect of carbohydrate ingestion appears to differ depending upon the mode of exercise. For example, blood glucose concentrations during prolonged treadmill running do not decrease to the same extent as with prolonged cycling.[2] It is important to note that the majority of studies investigating the impact of carbohydrate ingestion on endurance performance have used cycling rather than running as the mode of exercise. McConell et al.[8] reported that during highintensity cycling, only a small percentage (26%) ª 2011 Adis Data Information BV. All rights reserved.
of the total carbohydrate ingested actually enters the peripheral circulation during exercise. In addition, ingesting glucose has been reported to have no effect on carbohydrate oxidation, muscle metabolism or performance when cycling to fa. tigue at approximately 80% VO2max.[8] Furthermore, when glucose was infused directly into the circulation (60 g/h), the rate of muscle glycogen oxidation was unaffected. Exogenous carbohydrate was reported to contribute to only 9 g of the 54 g of carbohydrate oxidized in the final quarter of a 1-hour cycling time trial.[9] However, in running, the ingestion of 50 g of carbohydrate in a 5.5% solution has been reported to result in a 28% sparing of glycogen in the vastus lateralis muscle during a 60-minute treadmill run. The ingestion of the carbohydrate solution resulted in a 42% sparing of glycogen in the type I muscle fibres, with type II muscle fibres unaffected. The amount of glycogen spared was directly related to the magnitude of serum insulin increase within the first 20 minutes of exercise.[10] Nevertheless, adequate concentrations of glycogen remained in the muscle . following the 60-minute treadmill run at 70% VO2max in both the carbohydrate and placebo trials. For a comprehensive review on muscle glycogen metabolism during both running and cycling exercise, consult Tsintzas and Williams.[2] To our knowledge, no studies have measured muscle glycogen concentrations in response to mouth rinsing with a carbohydrate solution. Despite the absence of a clear metabolic rationale, both fluid and carbohydrate ingestion have been reported to independently improve timetrial performance.[6] Below et al.[6] asked . subjects to cycle at a constant intensity (80% VO2max) for 50 minutes followed by a 10-minute time trial, in which the task was to complete a fixed amount of work as quickly as possible. Providing both fluid and carbohydrate improved time-trial performance by approximately 6%. Furthermore, the beneficial independent effects of fluid and carbohydrate ingestion on performance were reported to be additive. The improvements in performance with fluid ingestion was attributed to maintaining a higher cardiac output and attenuating the increases in core temperature and heart rate, which were observed when no fluid was ingested. However, Sports Med 2011; 41 (6)
Endurance Performance and Mouth-Rinsing Carbohydrate Solutions
there was no evidence that carbohydrate ingestion influenced either core temperature or heart rate. Furthermore, ingesting carbohydrate did not appear to have a significant effect on blood glucose concentrations or carbohydrate oxidation. Thus, an explanation by which carbohydrate improved performance in this study was reported to be ‘unclear’.[6] It is important to note that not all studies have reported a benefit following the ingestion of fluid[11] or carbohydrate[12,13] on time-trial performance. Nevertheless, there is substantial evidence showing that ingesting appropriate carbohydrate-electrolyte solutions during exercise can improve endurance performance of approximately 1 hour in duration. Benefits to performance have been reported in both cycling[14-17] and running.[18,19] However, a mechanism to explain this improvement in performance remains to be established. Intriguingly, the absence of a clear metabolic benefit when subjects ingest carbohydrate has led authors to speculate that carbohydrate may influence ‘central’ or ‘non-metabolic’ pathways during exercise. To this end, this review will include a consideration of those studies that have investigated the potential ‘central’ effect of carbohydrate on performance. Studies testing this hypothesis have removed the provision of glucose or fluid to the peripheral circulation by requiring their subjects to simply mouth rinse the carbohydrate solution without ingestion. Thus, this review will focus on the performance response to simply having carbohydrate in the mouth and potential mechanisms by which this may exert an ergogenic effect. The impact of pre-exercise nutritional status, mode of exercising testing, concentration and type of carbohydrate in the rinsed solution will be discussed separately. The importance of the method and protocols used to detect a possible ergogenic effect of mouthrinsing carbohydrate-electrolyte solutions will also be considered. The literature cited in this review was retrieved using online search databases (i.e. PubMed and SportDiscus). Key search terms used included ‘carbohydrate’, ‘mouth rinse’, ‘performance’, ‘oral’, ‘central’ and ‘exercise (running and cycling)’. ª 2011 Adis Data Information BV. All rights reserved.
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2. Evidence: Performance Studies To our knowledge, only six studies have investigated the influence of mouth rinsing with a carbohydrate solution on endurance performance. The purpose of this section is to review the studies in the order in which they were published. Furthermore, the impact that mouth rinsing with a carbohydrate solution has on performance, has only been investigated using cycling and running. Therefore, the original studies completed in cycling will be reviewed first followed by the studies that used running as a mode of exercise. 2.1 Cycle Time Trials
Carter et al.[20] were the first to provide evidence that mouth rinsing with a carbohydrate solution during exercise could improve cycle time-trial performances of approximately 1 hour in duration. In this study, seven male and two female cyclists completed two experimental trials, where the task was to complete a fixed amount of work (914 – 40 kJ) as quickly as possible. In the two trials, which were separated by 1 week, the subjects mouth rinsed with either a 6.4% maltodextrin solution or water (25 mL) at every 12.5% of the time trial completed. The solutions were rinsed in the mouth for approximately 5 seconds before being expectorated. The trials were completed following a 4-hour postprandial period. However, the exact composition of the pre-exercise meal was not stated. The mean power output was significantly greater when mouth rinsing with carbohydrate than with water (259 – 16 W vs 252 – 16 W, respectively). Of note, an increase in power output was observed during the first three-quarters of the time trial. Eight of the nine cyclists improved their performance during the carbohydrate trial. Thus, time to complete the fixed amount of work was reduced on average by 2.9% when cyclists mouth rinsed with carbohydrate rather than water (59.57 – 1.50 minutes vs 61.37 – 1.56 minutes, respectively). There was no difference in heart rate (172 – 1 beats/min and 171 – 1 beats/min) or ratings of perceived exertion (RPE) [16 – 1] when mouth rinsing with carbohydrate or water, respectively. Unfortunately, the volume of expectorate Sports Med 2011; 41 (6)
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was not measured and there was no blood sampling during exercise because the investigators did not want to disrupt the time-trial performance. Thus, whether or not any carbohydrate was inadvertently ingested could not be established. Pottier et al.[21] investigated performance responses of 12 male cyclists to the same trial conditions reported by Carter et al.[20] In this study, each cyclist was required to complete the time trial (975 – 85 kJ) on four occasions separated by 48 hours. The four experimental conditions involved either mouth rinsing with a placebo or carbohydrate-electrolyte solution, or ingesting a placebo or carbohydrate-electrolyte solution. The total quantity of solution rinsed/ingested was 14 mL/kg body mass. The subjects received 2 mL/kg body mass of the solution before the 5-minute warm up (100 W). The subjects then received 1.5 mL/kg body mass immediately before and at every 12.5% of the time trial completed. During the mouth-rinse trials, subjects mouth rinsed the solution for 5 seconds before the solution was expectorated. The carbohydrate-electrolyte solution was a commercially available sports drink. The placebo solution was identical in formulation except that it contained no carbohydrate. The cyclists completed the time trial significantly faster (3.7%) when mouth rinsing with the carbohydrateelectrolyte solution (61.7 – 5.1 minutes) than with mouth rinsing the placebo (64.1 – 6.5 minutes). However, ingesting the carbohydrate-electrolyte solution was reported not to improve performance (63.2 – 6.9 minutes) over the ingestion of the placebo solution (62.5 – 6.9 minutes). Surprisingly, mouth rinsing with the carbohydrate-electrolyte solution resulted in a greater improvement in performance than when ingesting the same solution. The authors suggest that performance was improved due to the presence of carbohydrate in the oral cavity. However, they speculate that this performance benefit may be lost due to the short oral transit time when the carbohydrate-electrolyte solution is ingested.[21] There were no clear differences in the physiological variables (blood lactate, blood glucose, heart rate) or RPE recorded during exercise. However, an interesting observation is that when ingesting or mouth rinsing with the carbohydrate-electrolyte solution, subjects began ª 2011 Adis Data Information BV. All rights reserved.
Rollo & Williams
their exercise with higher blood glucose concentrations than subjects in either placebo trial. In this study, subjects were requested to consume a carbohydrate-rich meal 3 hours before the test and consume a carbohydrate-rich diet (400 g carbohydrate) the day before the trial. Unfortunately, the actual values and subject compliance to these dietary requests are not reported. It is important to note that different day-to-day dietary preparation for the time trials would have large effects on performance.[17] For example, the effect of mouth rinsing with a carbohydrate solution was investigated in cyclists who had consumed a standardized breakfast 2 hours before completing a time trial.[22] The breakfast provided before exercise contained 2.4 g of carbohydrate per kg of the subjects body mass. Ingesting similar quantities of carbohydrate 3 hours before exercise has been reported to increase muscle glycogen by 11–15%.[23,24] Fourteen male endurance-trained cyclists completed the same time trial as that used in previous cycling performance studies.[20,21] Identical to Carter et al.,[20] cyclists mouth rinsed with a 6.4% maltodextrin solution or water immediately before and every 12.5% of the time trial completed. Eight of 14 cyclists completed the time trial faster when mouth rinsing with the maltodextrin solution than with water. However, in this study, the performance time (68.14 – 1.14 minutes vs 67.52 – 1.00 minutes) and average power output (265 – 5 W vs 266 – 5 W) did not differ between the carbohydrate or water trials, respectively. There were no differences in heart rate reported between trials. Unfortunately, no blood samples or expired air was collected during exercise. Chambers et al.[25] reported the results of two separate cycling time-trial performance studies that used the same protocols as described by Carter et al.[20] In both studies, the time trials were completed following an overnight fast, with each trial separated by at least 3 days. Subjects mouth rinsed with either a carbohydrate or placebo solution immediately before and every 12.5% of the time trial completed. In these studies, the test solutions were mouth rinsed for approximately 10 seconds (double the duration of previous studies) before being expectorated into a bowl. Sports Med 2011; 41 (6)
Endurance Performance and Mouth-Rinsing Carbohydrate Solutions
The placebo solutions used in both studies were water that was artificially sweetened with a noncalorific concentrate, aspartame and saccharin. In the first performance study, eight male cyclists mouth rinsed either a placebo or 6.4% glucose solution. Seven of eight cyclists completed the time trial (914 – 29 kJ) faster when mouth rinsing with the carbohydrate than the placebo solution. The mean times to complete the time trial were 60.4 – 3.7 minutes and 61.6 – 3.8 minutes, respectively. Thus, the average improvement was 2.0 – 1.5% when mouth rinsing with the carbohydrate solution. There were no differences in mean RPE (16.0 – 1.8 vs 16.0 – 1.6) for the carbohydrate and placebo trials. Mean heart rate was elevated on the carbohydrate trial (180 – 3 vs 177 – 4 beats/min); however, there were no significant differences during the two trials. In the second study, six male and two female cyclists mouth rinsed with either a placebo or a 6.4% maltodextrin solution. As in the first study, seven of eight subjects completed the carbohydrate trial faster than the placebo trial. Mean times to complete the time trial (837 – 68 kJ) were 62.6 – 4.7 minutes and 64.6 – 4.9 minutes for the carbohydrate and placebo trials, respectively. The mean increase in power output being 3.1 – 1.7%. Despite values being lower than those in the first study, no differences were reported in the RPE (15 – 2 vs 15 – 2) or heart rate response during either the carbohydrate or placebo trials, respectively. It is important to note that subjects were unable to identify the carbohydrate solution in either study. To interpret the significance of the performance benefits reported in cycling is difficult because investigators commonly fail to set threshold values for ‘worthwhile’ improvements in trial performances. The implications of performance differences within the known day-to-day variation of the testing methods are discussed in section 3. 2.2 Running Time Trials
Whitham and McKinney[26] were the first to examine the influence of mouth rinsing with a carbohydrate solution on running performance. In their study, running performance was assessed using a manually controlled treadmill; the subjects ª 2011 Adis Data Information BV. All rights reserved.
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were seven healthy males. Their previous running experience was . reported to be ‡10 km although the runners’ VO2max values (57.8 – 2.7 mL/kg/min) were below those that are associated with endurance-trained athletes.[27] Following a 4-hour postprandial period, the subjects . completed a 15minute warm-up run at 65% VO2max, followed by a 45-minute time trial. The aim of the time trial was to achieve the greatest distance possible in the set time. At the beginning of exercise and at every 6 minutes during the time trial, runners were issued with a 500 mL bottle containing either 200 mL of 6% carbohydrate (97% polysaccharide, 2% disaccharide, 1% glucose) solution or placebo. Unsweetened lemon juice was added to both the carbohydrate and placebo solutions in an attempt to make the taste of the solutions indistinguishable. The runners were instructed to mouth rinse with a mouthful of the solution for 5 seconds before expectorating into a bowl. No difference was reported in respiratory exchange ratios, oxygen consumption, heart rate or the RPE between the carbohydrate and placebo trials. Further analyses of the results could not be undertaken because the authors did not report numerical values for their study. Blood glucose was reported to increase as a result of the time trial but, again, no values are reported. Overall distance covered in 45 minutes was not significantly different when the runners mouth rinsed with either the 6% carbohydrate (9333 – 988 m) or a flavourmatched placebo solution (9309 – 993 m).[26] With regard to performance, the authors recognize that the running tests requiring a manual control of pace may not be optimal for detecting a potentially sub-conscious ‘central’ effect of carbohydrate mouth rinse. Rollo et al.[28] investigated mouth rinsing with a carbohydrate-electrolyte solution on running performance using an automated treadmill that allowed runners to change their running speed without manual input.[29] The subjects were ten endurance-trained male runners who completed two trials separated by 1 week. After an overnight fast of 13–15 hours, the runners completed each 1-hour time trial during which they were instructed to cover as much distance as possible in the set time. Solutions were supplied to the Sports Med 2011; 41 (6)
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runners in plastic volumetric syringes. In random order, runners either mouth rinsed with 25 mL of 6.4% carbohydrate-electrolyte solution (glucose syrup and maltodextrin) or placebo solution immediately before and at 15, 30 and 45 minutes. The placebo solution was matched in formulation to the carbohydrate-electrolyte solution except that it contained no carbohydrate. The solution was mouth rinsed for 5 seconds before being expectorated into a pre-weighed container. No differences were found in respiratory exchange ratios (~0.90) or carbohydrate oxidation between trials (~3.0 g/min). The total distance covered during the carbohydrate trial (14298 – 685 m) was significantly greater than that achieved when runners mouth rinsed with the placebo (14086 – 732 m), representing 1.5% of the total distance covered. There were no differences in the RPE reported by the runners. Previous mouth-rinse studies did not record the volume of expectorated solution; therefore, it is not known whether any of the rinsed solution was ingested during the mouth-rinse procedure. However, in this study, there was clear evidence that subjects were able to mouth rinse and expectorate the test solution without ingestion during exercise (volume of expectorate equal to or greater than volume rinsed). Albeit, it is important to note that saliva in the mouth would have contributed to the volume of expectorate; thus, small quantities of the solution may have been ingested. Nevertheless, blood glucose concentrations were no different before or after the 1-hour run when mouth rinsing with the carbohydrate-electrolyte or placebo solution (4.3 – 0.3 mmol/L). 3. Performance Protocols Studies investigating the effect of mouth rinsing with carbohydrate solutions have assessed endurance performance (i.e. time trials). To our knowledge, no studies have investigated the effect of mouth rinsing with a carbohydrate solution on endurance capacity (time to fatigue). The main difference between endurance performance and endurance capacity protocols is that exercise intensity is self-selected by the subject during the time trials. It appears that subjects completing ª 2011 Adis Data Information BV. All rights reserved.
time trials have a greater ability to accurately replicate their performance in comparison to competing time-to-exhaustion tests.[30-32] However, this is not the case in all studies, particularly when adequate habituation trials have been completed.[33,34] It is important to consider that a placebo effect may have a large influence, especially when investigating small changes in exercise performance. For example, in a study that asked cyclists to complete a 40-km time trial, simply informing them that they were ingesting carbohydrate was reported to enhance their performance by approximately 4%.[35] In those studies that report a benefit of carbohydrate mouth rinse, Carter et al.[20] reported that four of their nine subjects were able to identify the trial that used carbohydrate, despite the maltodextrin solution being colourless and unsweetened. Pottier et al.[21] examined whether or not subjects could detect the carbohydrate solution by organizing an independent test. In a triangular sensory test, only 8 of 34 subjects were able to identify the carbohydrateelectrolyte solution. Chambers et al.[25] used artificial sweeteners in the test solutions and reported that none of the subjects were able to identify the carbohydrate solution. In the running study that reported a benefit of mouth rinsing with carbohydrate, Rollo et al.[28] reported that only two of ten runners were able to distinguish between the carbohydrate and placebo solution. The solution differed from the maltodextrin solution used in the previous running mouth-rinse study reported by Whitham and McKinney.[26] However, the solutions were suitably indistinguishable without the addition of any potentially undesirable masking agents such as bitter lemon juice, which was added to both test solutions in the study by Whitham and McKinney.[26] All the cycling studies investigating mouth rinsing with carbohydrate have used the same performance test. This test, which requires cyclists to complete a set amount of external mechanical work as quickly as possible (approximately 1 hour in duration), has been reported to have a coefficient of variation (CV) of 3.35%.[31] Of note, Pottier et al.[21] is the only cycling study to report an improvement in performance greater than that Sports Med 2011; 41 (6)
Endurance Performance and Mouth-Rinsing Carbohydrate Solutions
of the known variation of performing the cycling time trial (i.e. 3.7%) [table I]. Asking runners to cover as much distance as possible during 1 hour of treadmill running has a CV of 1.4%.[29] Therefore, in running, the reported benefit of mouth rinsing with a carbohydrate solution (1.5%) was beyond the day-to-day variation of the testing method. The CVs of the performance tests were derived between subjects, which is appropriate for repeated-measures data. Nevertheless, authors of performance studies should be encouraged to report and justify the smallest worthwhile changes for the performance test used. It is important to note that the use of inferential statistics, which categorizes performance differences into graded magnitudes, such as positive, trivial and negative, provides a more comprehensive analysis of performance data.[37] In performance studies, it is interesting to note that different pacing strategies are adopted by subjects in cycling and in running time trials. Observations from running studies show that runners typically maintain their self-selected running speed for the majority of the trial and sprint towards the end of the 1 hour.[26,29] In cycling studies, power output gradually declines during the first three-quarters of the time trial before being increased towards the end of the set amount of external work.[16,20,21,25] These observations suggest that care must be taken when making comparisons between cycling and running studies. The distinct difference being that mouth rinsing with a carbohydrate solution appears to improve running performance by increasing self-selected speed, whereas, in cycling, the benefit to performance appears to be achieved by reducing the decline in power output during the time trial. 4. Ratings of Perceived Exertion Studies that report a performance benefit from mouth rinsing with a carbohydrate solution, commonly report no difference in the subjects’ perception of effort, despite working at higher work loads (table I). A similar phenomenon has also been reported in studies that have investigated the perceptual and performance response to ingesting caffeine.[38] These observations are of ª 2011 Adis Data Information BV. All rights reserved.
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interest because they are consistent with reports that subjects ‘feel better’ and report lower RPE while ingesting a carbohydrate-electrolyte solution during prolonged exercise.[39-43] Lowering the perception of effort during exercise may be a viable mechanism by which mouth rinsing with a carbohydrate solution could improve performance. Speculatively, if mouth rinsing with a carbohydrate solution lowers the perception of effort at a given workload, the subject may increase their self-selected exercise intensity to match their ‘anticipated’ perception of effort. Thus, performance would be improved by the increases in power output or running speed that are self-selected during exercise. Using conventional time-trial performance protocols may not be the optimal method to use in order to test this hypothesis. This is because time-trial tests require the subject to perform at their maximal effort in order to achieve their best performance. Thus, if a subject was feeling ‘bad’, this may not be reflected in their self-selection of exercise intensity due to the overriding motivation to perform well. Therefore, instead of a time trial, Rollo et al.[44] adopted a less demanding procedure to allow more focus on the psychological response to mouth rinsing with a carbohydrate-electrolyte solution. In this study, subjects were simply asked to select their exercise intensity according to ‘how they felt’, whilst mouth rinsing 25 mL of either a 6% carbohydrate-electrolyte (glucose syrup and maltodextrin) or placebo solution. Following an overnight fast (12–13 hours), ten recreational male runners completed a 10-minute warm up, followed by a 30-minute self-selected run. The trials were completed on an automated treadmill that allowed changes in running speed without manual input. The subjects mouth rinsed with the test solution for 5 seconds immediately prior to and then at 3, 6 and 9.5-minutes during the warmup run, and at 5-minute intervals during the 30-minute run. In order for the runners to selfselect the same range of running speeds, they were asked to select a pace that represented a rating of 15 (hard) on the Borg RPE scale.[45] Mouth rinsing the carbohydrate-electrolyte solution significantly altered the self-selection of running speed during the first 5 minutes of the 30-minute run Sports Med 2011; 41 (6)
Carter et al.[9] (2004)
7 M, 2 F
Pottier et al.[21] 12 M (2010)
63.2 – 2.7
61.7 – 3.1
Beelen et al.[22] 14 M (2009)
NR
8M Chambers et al.[25] (2009)
60.8 – 4.1
6 M, 2 F
Cycle
Cycle
Cycle
Cycle
914 – 40 kJ
975 – 85 kJ
4h
3h
1053 – 48 kJ 2 h
914 – 29 kJ
Maltodextrin
6.4
Water
0
Sucrose 6 (5.4 g)/glucose (0.46 g) Placebo
0
Maltodextrin
6.4
Water
0
Overnight Glucose Placebo
57.8 – 3.2
Cycle
837 – 68 kJ
Overnight Maltodextrin Placebo
7M
Rollo et al.[36] (2010)
10 M
a
57.8 – 2.7
63.9 – 4.3
Run
Run
45 min
1h
4h
13–15 h
8 (5)
8 (5)
8 (10)
0 6.4
8 (10)
0
Maltodextrin
6
Placebo
0
Glucose/ maltodextrin
6.4
Placebo
0
10 (5)
4 (5)
% Diff.
172 – 1
16 – 1
59.57 – 1.50 mina 2.9
171 – 1
16 – 1
61.37 – 1.56 min
161 – 12
15.4 – 1.4 61.7 – 5.1 mina
157 – 12
15.5 – 1.7 64.1 – 6.5 min
169 – 2
16.4 – 0.3 68.14 – 1.14 min
168 – 2
16.7 – 0.3 67.52 – 1.00 min
180 – 3
16 – 1.8
60.4 – 3.7 mina
177 – 4
16 – 1.6
61.6 – 3.8 min
181 – 10
15 – 1.8
62.6 – 4.7 mina
180 – 10 (peak HR)
15 – 1.5
64.6 – 4.9 min
NR
NR
9333 – 988 km
~160 – 20
NR
9309 – 993 km
163 – 13
14 – 1
14298 – 685 kma
163 – 12
14 – 1
14086 – 732 km
3.7b
NA
2.0 – 1.5 3.1 – 1.7
NA
1.5b
Indicates reported significant difference between CHO and placebo trials.
% Diff. indicates performances differences beyond day-to-day variation in testing method. . F = female; HR = heart rate; M = male; NA = not applicable; NR = not reported; RPE = ratings of perceived exertion; VO2max = maximal oxygen consumption; % Diff. = percentage performance differences. b
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Whitham and McKinney[26] (2007)
6.4
8 (5)
456
ª 2011 Adis Data Information BV. All rights reserved.
Table I. Summary table of studies completed investigating the influence of mouth rinsing carbohydrate (CHO) solutions on endurance performance . Mode Time trial Fasting Beverage CHO No. of mouth HR RPE Result Study (y) No. of VO2max [mean – SD] duration (%) rinses (duration (beats/min) [mean – [mean – SD] subjects (mL/kg/min) [sec]) [mean – SD] SD] and sex [mean – SD]
Endurance Performance and Mouth-Rinsing Carbohydrate Solutions
compared with mouth rinsing the placebo (12.5 – 0.1 km/h vs 12.1 – 1.1 km/h, respectively). Total distance covered during the 30-minute run was greater during the carbohydrate trial (6584 – 520 m) than the placebo trial (6469 – 515 m). In addition, the increase in speed at the beginning of exercise corresponded with runners experiencing elevated feelings of pleasure as assessed by the ‘feeling scale’.[46] Future studies could benefit from administering psychological scales that have been validated during exercise. This is because psychological scales, such as those used by Backhouse et al.,[39] may offer additional insights into the possible central effects of mouth rinsing with carbohydrate solutions. For example, the RPE scale[45] provides information on the intensity of the perceived exertion but it does not help describe ‘how’ the runners feel during exercise.[46] Furthermore, whether a subject is feeling ‘good or bad’ (pleasure/ displeasure) or feels ‘energized’ (i.e. an activated state) during exercise is also relevant because it is likely that this may have a significant impact on performance.[39,47] Nevertheless, endurance performance and endurance capacity will be determined by an array of physiological and psychological factors that will influence motivation of the individual. Thus, it is important to note that mouth rinsing with a carbohydrate solution is probably just one of many sensory inputs that may affect the perception of exertion during exercise. 5. Pre-Exercise Nutritional Status Many athletes avoid eating immediately before early morning training or competition. However, given the choice, most athletes would prefer to have a meal a few hours prior to competing rather than to fast before exercise. Consuming a preexercise meal in combination with ingesting carbohydrate-electrolyte solutions during exercise has been reported to improve endurance capacity compared with when either of these interventions is adopted alone. These findings have been reported in both cycling[48] and running[49] studies. In contrast, a common variable that exists between time-trial investigations that report a performance benefit with the ingestion of carboª 2011 Adis Data Information BV. All rights reserved.
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hydrate-electrolyte solutions[7,15,17,19,50] and those that do not[13,36,51] is the pre-exercise carbohydrate status of the subject. For example, studies that have attempted to ‘optimize’ endogenous stores of glycogen prior to exercise, often report no further improvements in performance when ingesting carbohydrate-electrolyte solutions during exercise. Interestingly, Chambers et al.[25] recognize that the central response to detecting carbohydrate in the mouth could be altered by the physiological status of the body (i.e. in a fasted or fed state). The effect of hunger and satiety on the central response to taste has recently been investigated by Haase et al.[52] In this study, functional MRI was used to investigate the blood-oxygenlevel dependent signal change to taste stimuli in 18 healthy subjects. The subjects were presented with a variety of taste stimuli, including saccharin, sucrose and sodium chloride solutions, in either a hungry or satiated state. Globally, brain activation in the hunger condition produced more robust activation to pure taste stimuli relative to water. In addition, tasting sucrose was reported to result in the most robust activation compared with all the other taste stimuli.[52] As previously mentioned, Beelen et al.[22] reported that performance benefits associated with mouth rinsing with a carbohydrate solution are not evident following the ingestion of a preexercise meal.[22] Accordingly, as reported by Whitham and McKinney,[26] the 4-hour postprandial period may also account for the absence of an effect in runners; however, the evidence is inconclusive. It is too early to speculate that the potential benefit of mouth rinsing with a carbohydrate solution could be lost following a carbohydrate-rich pre-exercise meal. This is because both Carter et al.[20] and Pottier et al.[21] reported significant performance benefits from mouth rinsing with a carbohydrate solution in subjects who started exercise following a 4- and 3-hour fast, respectively. 6. Mechanisms The first mouth rinse study performed by Carter et al.[20] was devised following two key Sports Med 2011; 41 (6)
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observations from investigations into cycling timetrial performance with carbohydrate supplementation. The time trials, approximately 1 hour in duration, required cyclists to complete an individualized amount of work as fast as possible. First, it was reported that ingesting a carbohydrate-electrolyte drink was associated with improved 1-hour endurance performance (2.3%) in comparison with ingesting a placebo solution.[16] Second, it was found that infusing glucose directly into the peripheral circulation (60 g/h) had no impact on performance in comparison with infusing a non-glucose-containing saline.[9] Thus, despite increasing glucose availability to the working muscle, the rate of muscle glycogen oxidation was unaffected. The obvious difference between the ingestion and infusion study was the route of entry of carbohydrate into the body (i.e. infusing glucose bypassed the gastrointestinal tract). It is well known that the digestion of carbohydrate begins in the mouth where the tongue begins the analysis of food, determining whether it is nutritive (i.e. sugar) and should be ingested, or is potentially harmful and therefore should be expectorated.[53] The ability of the body to recognize incoming energy and also potentially toxic substances would clearly be an evolutionary advantage. Sweet stimuli (e.g. glucose, sucrose, fructose and artificial sweeteners) are detected by taste receptor cells (G-protein-coupled receptor proteins T1R2 and T1R3) on the tongue.[54] These receptor cells release a neurotransmitter (a-gustducin) that is detected by primary afferent nerve fibre terminals, which send information to the brainstem. The central processing of sweet taste activates feeding circuits as well as brain reward systems that promote sweet appetite.[54] Interestingly, studies have reported that flavouring or sweetening solutions can substantially increase the voluntary intake of fluid during both exercise and recovery.[55,56] Receptors on the tongue also extract information about the texture and temperature of food. This processing prepares the gastrointestinal system for compounds in the mouth by causing the organism to salivate, masticate, swallow or expel, as well as to release insulin and other peptides.[53] In humans, simply tasting food in the ª 2011 Adis Data Information BV. All rights reserved.
Rollo & Williams
oral cavity can stimulate the release of insulin from the pancreas, known as cephalic phase insulin release. For example, under fasting conditions, both nutritive (sucrose) and non-nutritive sweetener (saccharin) solutions have been reported to stimulate cephalic phase insulin release, when mouth rinsed for 45 seconds and expectorated without ingestion.[57] However, this study was performed in resting subjects and mouth rinsing with a carbohydrate solution for 45 seconds would be an impractical recommendation during exercise. From studies performed on rodents, it was hypothesized that two separate groups of carbohydrate receptors exist in the mouth. Specifically, one group of receptor has been proposed to respond to ‘sweetness’ and the other to ‘polysaccharide’. This hypothesis is based upon observations that, given a free choice, rodents preferred maltodextrin over sucrose, maltose, glucose and fructose at low concentrations; sucrose was only preferred at high concentrations.[58] Other species have also been reported to possess ‘polysaccharide’ receptors in the mouth.[59] However, whether these receptors are present in humans is unknown. Interestingly, in humans, it has been reported that both glucose (sweet) and maltodextrin (nonsweet) in the mouth activate regions in the brain, such as the insula/frontal operculum, orbitofrontal cortex and striatum, which are associated with reward.[25] Furthermore, no central activation of possible reward centres was reported when subjects mouth rinsed with an artificially sweetened solution. These findings suggest that there are separate receptors that respond independently to ‘sweetness’ and carbohydrate.[25] The regions of the brain that are activated by carbohydrate in the mouth are also associated with reward and are believed to mediate behavioural responses to rewarding stimuli.[60] To this end, a potential rationale for mouth rinsing with carbohydrate solutions maybe that carbohydrate provides a rewarding stimulus. This, in turn, impacts on behaviour; for example, the self-selection of greater exercise intensities during time-trial performances. Unfortunately, however, thus far the concentrations of the glucose and maltodextrin solutions Sports Med 2011; 41 (6)
Endurance Performance and Mouth-Rinsing Carbohydrate Solutions
used in the functional MRI studies have been more concentrated (18%) than those solutions that have been reported to improve exercise performance (6–6.4%). Nevertheless, they provide an important insight into the response of the brain to the presence of carbohydrate in the oral cavity. A recent study has provided evidence for a link between carbohydrate in the mouth and skeletal muscle function.[61] In this study, transcranial magnetic stimulation of the primary motor cortex was used to investigate the effect of mouth rinsing with unsweetened carbohydrate on corticomotor excitability and voluntary force production. The exercise protocol required 17 participants to perform fatiguing isometric elbow flexions for 30 minutes. The authors report that the amplitude of the motor evoked potential from the right first dorsal interosseous increased by 9% whilst mouth rinsing carbohydrate, when the muscle was voluntarily activated.[61] However, it is important to note that these observations were obtained during exercise that targeted a relatively small group of arm muscles that had previously been exercised to fatigue. To date, no studies have investigated whether simply mouth rinsing with a carbohydrate solution can restore or maintain performance following fatiguing ‘whole body’ exercise such as running or cycling. 7. Practical Implications Theoretically, mouth rinsing with a carbohydrate solution without ingestion during exercise could have several practical applications. For example, individuals who experience gastrointestinal complaints while ingesting sports drinks during exercise may use mouth rinsing as a method to seek a performance benefit.[62] However, thus far, it appears that mouth rinsing with a carbohydrate solution does not improve gastrointestinal comfort in comparison with ingestion of appropriate volumes of fluid during exercise.[19,28] Apart from exercise performance, mouth rinsing with a carbohydrate solution could cause individuals to self-select higher intensities during exercise without the ingestion of additional energy. Therefore, individuals who are attempting to lose weight ª 2011 Adis Data Information BV. All rights reserved.
459
may benefit from a greater energy deficit achieved during exercise in comparison with the ingestion of a carbohydrate solution, or even water. Furthermore, it would appear that this benefit could be gained without any increase in the perception of effort during exercise. However, it is important to note that the only study to investigate the self-selected response to exercising at a set RPE whilst mouth rinsing with a carbohydrate solution was performed using recreationally active young men.[44] Further studies are required to investigate if mouth rinsing with a carbohydrate solution could provide an ergogenic stimulus for an obese population embarking on an exercise programme. 8. Conclusions There are relatively few studies that have investigated the effect of mouth rinsing with carbohydrate solutions on endurance performance. The majority of these studies have been conducted in cycling. The available evidence suggests that mouth rinsing with carbohydrate solutions routinely during exercise can have a beneficial effect on endurance performance of approximately 1 hour in duration in fasted subjects. Although not all studies have reported a benefit from mouth rinsing with a carbohydrate solution, it is important to note that, thus far, no studies have reported any adverse or negative effects on performance. The mechanisms to explain the ergogenic effect of mouth rinsing with carbohydrate solutions during exercise include the (i) activation of reward centres in the brain; (ii) lowering of the perception of effort during exercise; and (iii) increase of corticomotor excitability. When mouth rinsing with carbohydrate solutions, these mechanisms could theoretically lead to subjects self-selecting an exercise intensity beyond that selected when mouth rinsing with water or a placebo solution. Future research may extend these observations to include how the nutritional status of the subject, as well as the concentration of the carbohydrate solution along with the frequency of mouth rinses, influences the CNS and exercise performance. Sports Med 2011; 41 (6)
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Acknowledgements No sources of funding were used to assist in the preparation of this review. The authors have no conflicts of interest that are directly relevant to the content of this review.
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17. Neufer PD, Costill DL, Flynn MG, et al. Improvements in exercise performance: effects of carbohydrate feedings and diet. J Appl Physiol 1987 Mar; 62 (3): 983-8 18. Millard-Stafford M, Rosskopf LB, Snow TK, et al. Water versus carbohydrate-electrolyte ingestion before and during a 15-km run in the heat. Int J Sport Nutr 1997; 7 (1): 26-38 19. Rollo I, Williams C. Influence of ingesting a carbohydrateelectrolyte solution before and during a 1-hr running performance test. Int J Sport Nutr Exerc Metab 2009; 19 (6): 645-58 20. Carter JM, Jeukendrup AE, Jones DA. The effect of carbohydrate mouth rinse on 1-h cycle time trial performance. Med Sci Sports Exerc 2004; 36 (12): 2107-11 21. Pottier A, Bouckaert J, Gilis W, et al. Mouth rinse but not ingestion of a carbohydrate solution improves 1-h cycle time trial performance. Scand J Med Sci Sports 2010; 20 (1): 105-11 22. Beelen M, Berghuis J, Bonaparte B, et al. Carbohydrate mouth rinsing in the fed state does not enhance time trial performance. Int J Sports Nutr Exerc Metab 2009; 19 (4): 400-9 23. Chryssanthopoulos C, Williams C, Nowitz A, et al. Skeletal muscle glycogen concentration and metabolic responses following a high glycaemic carbohydrate breakfast. J Sports Sci 2004; 22 (11-12): 1065-71 24. Wee SL, Williams C, Tsintzas K, et al. Ingestion of a highglycemic index meal increases muscle glycogen storage at rest but augments its utilization during subsequent exercise. J Appl Physiol 2005; 99 (2): 707-14 25. Chambers ES, Bridge MW, Jones DA. Carbohydrate sensing in the human mouth: effects on exercise performance and brain activity. J Physiol 2009; 578 (8): 1779-94 26. Whitham M, McKinney J. Effect of a carbohydrate mouthwash on running time-trial performance. J Sports Sci 2007; 25 (12): 1385-92 27. Trappe S. Marathon runners: how do they age? Sports Med 2007; 37 (4-5): 302-5 28. Rollo I, Cole M, Miller R, et al. The influence of mouthrinsing a carbohydrate solution on 1 hour running performance. Med Sci Sports Exerc 2010; 42 (4): 798-804 29. Rollo I, Williams C, Nevill A. Repeatability of scores on a novel test of endurance running performance. J Sports Sci 2008; 26 (13): 1-8 30. Hickey MS, Costill DL, McConell GK, et al. Day to day variation in time trial cycling performance. Int J Sports Med 1992; 13 (6): 467-70 31. Jeukendrup A, Saris WH, Brouns F, et al. A new validated endurance performance test. Med Sci Sports Exerc 1996 Feb; 28 (2): 266-70 32. Laursen PB, Francis GT, Abbiss CR, et al. Reliability of time-to-exhaustion versus time-trial running tests in runners. Med Sci Sports Exerc 2007; 39 (8): 1374-9 33. Maughan RJ, Fenn CE, Leiper JB. Effects of fluid, electrolyte and substrate ingestion on endurance capacity. Eur J Appl Physiol Occupat Physiol 1989; 58 (5): 481-6 34. Fallowfield JL, Williams C. Carbohydrate intake and recovery from prolonged exercise. Int J Sport Nutr 1993 Jun; 3 (2): 150-64 35. Clark VR, Hopkins WG, Hawley JA, et al. Placebo effect of carbohydrate feedings during a 40-km cycling time trial. Med Sci Sports and Exerc 2000; 32 (9): 1642-7
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36. Rollo I, Williams C. Influence of ingesting a carbohydrateelectrolyte solution before and during a 1-hour run in fed endurance-trained runners. J Sports Sci 2010; 28 (6): 593-602 37. Batterham AM, Hopkins WG. Making meaningful inferences about magnitudes. Sportscience 2005; 9: 6-13 38. Doherty M, Smith P, Hughes M, et al. Caffeine lowers perceptual response and increases power output during highintensity cycling. J Sports Sci 2004; 22 (7): 637-43 39. Backhouse SH, Ali A, Biddle SJ, et al. Carbohydrate ingestion during prolonged high-intensity intermittent exercise: impact on affect and perceived exertion. Scand J Med Sci Sports 2007; 17 (5): 605-10 40. Backhouse SH, Bishop NC, Biddle SJ, et al. Effect of carbohydrate and prolonged exercise on affect and perceived exertion. Med Sci Sports and Exerc 2005; 37 (10): 1768-73 41. Utter A, Kang J, Nieman D, et al. Effect of carbohydrate substrate availability on ratings of perceived exertion during prolonged running. Int J Sport Nutr 1997; 7 (4): 274-85 42. Utter AC, Kang J, Nieman DC, et al. Carbohydrate attenuates perceived exertion during intermittent exercise and recovery. Med Sci Sports Exerc 2007; 39 (5): 880-5 43. Utter AC, Kang J, Nieman DC, et al. Carbohydrate supplementation and perceived exertion during prolonged running. Med Sci Sports Exerc 2004; 36 (6): 1036-41 44. Rollo I, Williams C, Gant N, et al. The influence of carbohydrate mouth rinse on self-selected speeds during a 30-min treadmill run. Int J Sports Nutr Exerc Metab 2008; 18 (6): 585-600 45. Borg G. Ratings of perceived exertion and heart rates during short-term cycle exercise and their use in a new cycling strength test. Int J Sports Med 1982; 3 (3): 153-8 46. Hardy CJ, Rejeski W. Not what, but how one feels: the measurement of affect during exercise. J Sport Exerc Psychol 1989; 11: 304-17 47. Acevedo E, Gill D, Goldfarb A, et al. Affect and perceived exertion during a two-hour run. Int J Sport Psychol 1996; 27: 286-92 48. Wright DA, Sherman WM, Dernbach AR. Carbohydrate feedings before, during, or in combination improve cycling endurance performance. J Appl Physiol 1991; 71 (3): 1082-8 49. Chryssanthopoulos C, Williams C. Pre-exercise carbohydrate meal and endurance running capacity when carbohydrates are ingested during exercise. Int J Sports Med 1997; 18 (7): 543-8
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50. Carter J, Jeukendrup AE, Mundel T, et al. Carbohydrate supplementation improves moderate and high-intensity exercise in the heat. Pflugers Arch 2003 May; 446 (2): 211-9 51. Widrick JJ, Costill DL, Fink WJ, et al. Carbohydrate feedings and exercise performance: effect of initial muscle glycogen concentration. J Appl Physiol 1993; 74 (6): 2998-3005 52. Haase L, Cerf-Ducastel B, Murphy C. Cortical activation in response to pure taste stimuli during the physiological states of hunger and satiety. Neuroimage 2009; 44 (3): 1008-21 53. Katz DB, Nicolelis MA, Simon SA. Nutrient tasting and signaling mechanisms in the gut. IV: there is more to taste than meets the tongue. Am J Physiol Gastrointest Liver Physiol 2000; 278 (1): G6-9 54. Berthoud HR. Neural systems controlling food intake and energy balance in the modern world. Curr Opin Clin Nutr Metab Care 2003; 6 (6): 615-20 55. Passe DH, Horn M, Murray R. Impact of beverage acceptability on fluid intake during exercise. Appetite 2000; 35 (3): 219-29 56. Wilmore JH, Morton AR, Gilbey HJ, et al. Role of taste preference on fluid intake during and after 90 min of running at 60% of VO2max in the heat. Med Sci Sports Exerc 1998; 30 (4): 587-95 57. Just T, Pau HW, Engel U, et al. Cephalic phase insulin release in healthy humans after taste stimulation? Appetite 2008; 51 (3): 622-7 58. Sclafani A. Starch and sugar tastes in rodents: an update. Brain Res Bull 1991; 27 (3-4): 383-6 59. Feigin MB, Sclafani A, Sunday SR. Species differences in polysaccharide and sugar taste preferences. Neurosci Biobehav Rev 1987 Summer; 11 (2): 231-40 60. Rolls ET. Sensory processing in the brain related to the control of food intake. Proc Nutr Soc 2007; 66 (1): 96-112 61. Gant N, Stinear CM, Byblow WD. Carbohydrate in the mouth immediately facilitates motor output. Brain Res 2010; 1350: 151-8 62. Brouns F, Beckers E. Is the gut an athletic organ? Digestion, absorption and exercise. Sports Med; 15 (4): 242-57
Correspondence: Dr Ian Rollo, School of Sport, Exercise and Health Sciences, Loughborough University, Ashby Road, Loughborough, Leicestershire, LE11 3TU, UK. E-mail:
[email protected]
Sports Med 2011; 41 (6)
Sports Med 2011; 41 (6): 463-476 0112-1642/11/0006-0463/$49.95/0
REVIEW ARTICLE
ª 2011 Adis Data Information BV. All rights reserved.
Effects of Bicycle Saddle Height on Knee Injury Risk and Cycling Performance Rodrigo Bini,1,2 Patria A. Hume1 and James L. Croft1 1 Sport Performance Research Institute New Zealand, School of Sport and Recreation, Auckland University of Technology, Auckland, New Zealand 2 CAPES Foundation, Ministry of Education of Brazil, Brasilia, Brazil
Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Literature Search Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Methods for Configuring Saddle Height. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Percentage of Lower Leg Length Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Knee Angle Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.3 Comparing Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Effects of Bicycle Saddle Height Configuration on Cycling Performance. . . . . . . . . . . . . . . . . . . 3.2.1 Cycling Performance Time . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Energy Expenditure/Oxygen Uptake (VO2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Power Output . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.4 Cycling Economy (Power Output to VO2 Ratio). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.5 Pedal Force Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Effects of Bicycle Saddle Height Configuration on Knee Injury Risk . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Lower Limb Kinematics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Knee Joint Forces and Moments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.3 Muscle Mechanics and Activation Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Limitations of the Cited Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Practical Implications and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Abstract
463 464 465 465 465 465 469 469 469 470 470 470 470 470 471 471 471 473 474 474 475
Incorrect bicycle configuration may predispose athletes to injury and reduce their cycling performance. There is disagreement within scientific and coaching communities regarding optimal configuration of bicycles for athletes. This review summarizes literature on methods for determining bicycle saddle height and the effects of bicycle saddle height on measures of cycling performance and lower limb injury risk. Peer-reviewed journals, books, theses and conference proceedings published since 1960 were searched using MEDLINE, Scopus, ISI Web of Knowledge, EBSCO and Google Scholar databases, resulting in 62 references being reviewed. Keywords searched included ‘body positioning’, ‘saddle’, ‘posture, ‘cycling’ and ‘injury’. The review revealed that methods for determining optimal saddle height are varied and not well established, and have been based on relationships between saddle
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height and lower limb length (Hamley and Thomas, trochanteric length, length from ischial tuberosity to floor, LeMond, heel methods) or a reference range of knee joint flexion. There is limited information on the effects of saddle height on lower limb injury risk (lower limb kinematics, knee joint forces and moments and muscle mechanics), but more information on the effects of saddle height on cycling performance (performance time, energy expenditure/oxygen uptake, power output, pedal force application). Increasing saddle height can cause increased shortening of the vastii muscle group, but no change in hamstring length. Length and velocity of contraction in the soleus seems to be more affected by saddle height than that in the gastrocnemius. The majority of evidence suggested that a 5% change in saddle height affected knee joint kinematics by 35% and moments by 16%. Patellofemoral compressive force seems to be inversely related to saddle height but the effects on tibiofemoral forces are uncertain. Changes of less than 4% in trochanteric length do not seem to affect injury risk or performance. The main limitations from the reported studies are that different methods have been employed for determining saddle height, small sample sizes have been used, cyclists with low levels of expertise have mostly been evaluated and different outcome variables have been measured. Given that the occurrence of overuse knee joint pain is 50% in cyclists, future studies may focus on how saddle height can be optimized to improve cycling performance and reduce knee joint forces to reduce lower limb injury risk. On the basis of the conflicting evidence on the effects of saddle height changes on performance and lower limb injury risk in cycling, we suggest the saddle height may be set using the knee flexion angle method (25–30) to reduce the risk of knee injuries and to minimize oxygen uptake.
1. Introduction The increased popularity of cycling as a sport and recreational activity has led to a higher incidence of acute[1,2] and overuse[3,4] (90% and 85%, respectively) injuries. Anterior knee pain will occur in 25% of the population sometime during their life[5] and for cyclists, the knee joint is one of the most affected by overuse injuries.[4] Overuse injuries can be a result of poor positioning on the bicycle.[6] However, there is disagreement within scientific and coaching communities regarding optimal configuration of bicycles for athletes.[6] The most controversial aspect of configuration of the bicycle is saddle height and, consequently, this has been the focus of most studies regarding body position on the bicycle.[7-11] Nevertheless, cyclists often select the saddle position relative to the pedals (and therefore crank) by comfort rather than scientific knowledge. There ª 2011 Adis Data Information BV. All rights reserved.
is concern that an improper position could lead to joint overuse injuries,[12] mainly those affecting the knee joint.[3] On the other hand, most of the strategies to prevent knee injuries based on the configuration of bicycle components have not been assessed by scientific research.[13] Wishv-Roth[14] recently indicated that understanding the geometry and research around optimal configuration of the bicycle components is vital to maximize performance and minimize injury for both recreation and elite cyclists. Most guidelines reported in magazines are based on empirical data, without guidance from scientific experimental research. Sports medicine practitioners need to be able to advise their athletes on ways to reduce knee injury risk in cycling whilst maintaining or improving cycling performance. Therefore, an understanding of how saddle height may be configured and the effects it has on knee injury risk and cycling performance, are important Sports Med 2011; 41 (6)
Effects of Bicycle Saddle Height on Injury Risk and Performance
for better prescription by the sports medicine practitioner for bicycle configuration. This review summarizes, for the sports medicine practitioner, the literature on methods for determining bicycle saddle height configuration and the effects of saddle height on cycling performance (measured via performance time, energy . expenditure/oxygen uptake [VO2], power output and pedal force application) and knee injury risk measures (measured via lower limb kinematics, knee joint forces and moments, and muscle mechanics). 2. Literature Search Methodology Peer-reviewed journals, books, theses and conference proceedings published since 1960 were searched using MEDLINE, Scopus, ISI Web of Knowledge, EBSCO and Google Scholar databases. Keywords searched included ‘body positioning’, ‘saddle’, ‘posture, ‘cycling’ and ‘injury’. Results were searched for the keyword ‘knee joint’ to locate studies regarding the effects of saddle position on the knee joint. Articles were excluded if they did not have at least an English abstract, or if they were concerned with the analysis of different bicycle saddles, saddle pressure, and/or the effects on erectile dysfunction, resulting in 62 references being reviewed. 3. Findings Section 3.1 outlines methods for configuring saddle height. Knowledge of the various methods available is needed for interpretation of the two following sections on the effects of bicycle saddle height configuration on cycling performance (section 3.2) and knee injury risk (section 3.3). Sports medicine practitioners, coaches and cyclists need to be aware of how changing seat height for performance may influence injury risk and vice versa. Since initial investigations of saddle height on physiology and performance,[15] sports scientists have been searching for the ‘optimal’ configuration of bicycle components to increase performance and prevent injuries.[8] A variety of methods have been proposed, some of which are based upon scientific studies and others on anecdotal experience. Some ª 2011 Adis Data Information BV. All rights reserved.
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methods, as in the following, are used for determining saddle height are based on lower limb length: (i) Hamley and Thomas;[15] (ii) trochanteric length;[16] (iii) length from ischial tuberosity to floor;[17] (iv) Greg LeMond;[18] and (v) the heel method.[6] A reference range of knee joint flexion has been also used to set saddle height.[18,19] Experimental studies (see table I) and reviews and empirical-based articles (see table II) examining effects of saddle configuration have shown that ‘optimal’ saddle height depends on the outcome variable measured as follows: (i) cycling performance time;[15] (ii) energy expenditure/ . [16,17] VO2; (iii) power output;[22] (iv) lower limb kinematics;[7,11,16,20,28] (v) pedal force application;[27,28] (vi) knee joint forces and moments;[24,35] and (vii) muscle mechanics.[23,26] 3.1 Methods for Configuring Saddle Height
This section outlines the various methods for configuring saddle height. All measurements for lower leg length of the cyclist have been taken in a standing position unless otherwise indicated. For a proper configuration, the saddle height measurement must be completed with the crank in line with the seat tube and the measurement taken from the pedal surface to the top of the saddle. The use of various saddle height methods and the effects on performance or injury risk outcomes are contained in subsequent sections. 3.1.1 Percentage of Lower Leg Length Methods
The inseam leg length, ischial and trochanteric methods are all based on anthropometric length measurements of the lower leg for configuration of saddle height. Hamley and Thomas Method
The Hamley and Thomas[15] method was probably the first research-based method. For a proper set-up using this method (see figures 1 and 2a), the saddle height must be set at 109% of inseam leg length measurement. Trochanteric Length Method
The trochanteric length method (see figure 1) uses the length from the most prominent bony surface of the greater trochanter to the floor.[16] Sports Med 2011; 41 (6)
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ª 2011 Adis Data Information BV. All rights reserved.
Table I. Summary of experimental studies examining effects of saddle configuration Method of setting saddle height
Outcome measures
No. of subjectsa
Main results and notes
Hamley and Thomas[15]
Percentage of inseam leg length
Time to exhaustion during constant load cycling exercise
100
109% of inseam leg minimized time to exhaustion during constant workload cycling exercise. No additional information on how different saddle heights were compared
Desipres[20]
Percentage of inseam leg length
Muscle activity and joint kinematics
3 male junior cyclists
No significant effects of saddle height (95% and 105% of the inseam leg length) on quadriceps and hamstrings activation. Ankle joint kinematics were most affected when raising the saddle height
Shennum and DeVries[17]
Percentage of inseam leg length
5 aged between 16 and 18 y
Between 100% and 103% of inseam leg length . minimized VO2. Between 103% and 104% of inseam leg length could minimize power output
Rugg and Gregor[21]
Percentage of inseam leg length
Muscle estimated length, shortening velocity, moment arm of lower limb muscles
5 male cyclists
102% of the trochanteric length (high saddle height) increased shortening of the vastii group, while the hamstring group was not affected due to its bi-articular attachment
Peveler et al.[9]
Hamley and Thomas[15] method and LeMond methods[18]
Knee angle when pedal was at the bottom dead centre
14 male and 5 female cyclists
No difference between Hamley and Thomas[15] and Greg LeMond methods. Both methods did not ensure that the knee angle was between 25–30 for minimizing knee joint load
Peveler et al.[22]
Degree of knee angle, percentage of inseam leg length
Anaerobic power
9 male trained cyclists, 3 male non-cyclists, 15 female non-cyclists
25 knee angle resulted in significantly higher mean power compared with 109% inseam leg length in those that fell outside the recommended range on the anaerobic test
Peveler[8]
Degree of knee angle, percentage of inseam leg length
Nordeen Snyder[16]
Percentage of trochanteric length
Price and Donne[11]
Percentage of trochanteric length
. VO2
. VO2
. VO2, joint kinematics
. VO2, joint kinematics
5 male cyclists, 2 male non-cyclists, 8 female non-cyclists 10 female non-cyclists aged between 18 and 31 y
14 competitive road cyclists with mean – SD age of 22.9 – 4.1 y
. VO2 was significantly lower at a saddle height set using 25 knee angle compared with 35 knee angle or 109% of inseam leg length . 100% of trochanteric length minimized VO2 compared with 95% and 105%. Major adaptations for knee and ankle joint kinematics when shifting the saddle height Reduced efficiency at 104% of trochanteric length (higher saddle height) compared with 100% and 96%. Optimal range of saddle height for minimal . VO2 was between 96% and 100% of trochanteric height Continued next page
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Sports Med 2011; 41 (6)
Study
Method of setting saddle height
Outcome measures
No. of subjectsa
Main results and notes
Jorge and Hull[23]
Percentage of trochanteric length
Muscle activity
6 cyclists of different training levels
Higher quadriceps and hamstring activation for saddle height at 95% of trochanteric length compared with 100%
Sanderson and Amoroso[7]
Percentage of trochanteric length
Muscle activity, estimated muscle length and joint kinematics
13 female cyclists with mean – SD age of 25.6 – 5.9 y
Increased activation of gastrocnemius medialis with greater saddle height (107%) compared with the preferred (102%) and low (92%) saddle height. Both muscles of triceps surae do not operate on the same length range when the saddle height is modified. Soleus was more affected by saddle height in relation to length and velocity of contraction than gastrocnemius medialis, mainly when the saddle height was raised by 5% of the preferred position. Gastrocnemius medialis length seems affected by the combination of ankle and knee joint kinematics
Gonzalez and Hull[24]
Percentage of trochanteric length
Average absolute hip and knee joint moments
3 male trained cyclists
97% of trochanteric length minimized the average absolute hip and knee moments
McCoy and Gregor[25]
Percentage of trochanteric length
Compressive and anterior-posterior force of the tibiofemoral joint
10 male non-athletes (mean age 29 y)
No effects of saddle height (94%, 100% and 106%) on the compressive force of the tibiofemoral joint for 10 male subjects riding at 200 W of power output and 80 rpm of pedalling cadence
Ericson et al.[26]
Percentage of the ischial tuberosity to the floor
Muscle activity
6 healthy non-cyclists aged between 20 and 31 y
Increased activation of gluteus medius, semimembranosus, soleus and gastrocnemius medialis for 120% ischial tuberosity to the floor (higher saddle height) compared with 102% and 113%)
Ericson and Nisell[27]
Percentage of ischial tuberosity to floor
Pedal force effectivenessb
6 healthy non-cyclists aged between 20 and 31 y
Saddle heights (102%, 113% and 120% of the ischial tuberosity to the floor) did not affect force effectiveness
Diefenthaeler et al.[28]
1 cm relative to preferred saddle height
Pedal force, muscle activity and joint kinematics
3 elite cyclists aged between 23 and 30 y
Saddle height altered pedalling technique and muscle activity with optimal results for preferred saddle height
Rankin and Neptune[29]
Saddle position relative to the bottom bracket
Power output
Computational simulation
Small changes in saddle height (1 cm) affected power output. Ankle joint compensates for most changes in saddle height
Houtz and Fischer[30]
Lowest possible on the ergometerc
Muscle activity
3 healthy female non-cyclists
Reduced muscle activation in high saddle heights associated with less perceived effort
a
Subjects’ characteristics were not always specified in the papers. Where possible the age, sex and cycling level are reported.
b
Ratio of the force perpendicular to the crank (effective force) to the total force applied to the pedal (resultant force).
c Saddle height configuration relative to subject anthropometry was not reported. . rpm = revolutions per minute; VO2 = oxygen uptake.
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Study
Effects of Bicycle Saddle Height on Injury Risk and Performance
ª 2011 Adis Data Information BV. All rights reserved.
Table I. Contd
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Table II. Summary of review- or empirical-based articles examining effects of saddle configuration Study
Method of setting saddle height
Outcome measures
Paper type
Main results and notes
Burke and Pruitt[6,18]
Heel, inseam leg length and LeMond methods, and degree of knee joint angle
Optimize power output and reduce the risk of injuries
Book chapter
Knee joint range method used 25–30. No recommendation for any of the four methods
Silberman et al.[31]
LeMond[18] and Holmes et al.[19] methods
Optimize power output and reduce the risk of injuries
Review
Greg LeMond[18] and Holmes et al.[19] methods as possibilities for saddle height configuration
Mellion[32]
Percentage of inseam leg length
Overview of overuse problems and cycling injuries
Review
109% of inseam leg to fit saddle height. 96% of the sum of shank and thigh length as an alternative set for saddle height. Saddle fore-aft adjust by the knee to pedal axis (see figure 1b)
Wanich et al.[3]
Percentage of inseam leg length
Overview of overuse problems and cycling injuries
Review
109% of inseam leg method for optimal fitting of the saddle height
Holmes et al.[19]
Degree of knee joint angle
Clinical based analysis of the common overuse problems and cycling injuries
Review
Minimal knee joint range 25–30 for minimizing knee joint load
Moore[33]
Degree of knee joint angle
Body positioning for cycling
Magazine article
Borysewicz[34]
Percentage of trochanteric length
Holmes et al.[19] method but with knee joint range 20–30 . Cyclists could minimize VO2 setting saddle height at 96% of trochanteric length
De Vey Mestdagh[10]
Percentage of trochanteric length or inseam leg length
Optimize power output and reduce the risk of injuries
Review
Nordeen-Snyder[16] method optimal to set the saddle height, use 100% of trochanteric length or 107% of the inseam leg
Gregor[12]
Percentage of trochanteric length or inseam leg
Biomechanical variables related to cycling
Review
Saddle height affects knee joint resultant force, muscle activity, joint kinematics and muscle length
. VO2
Book chapter
. VO2 = oxygen uptake.
Settings of 100% of trochanteric length have been reported.[11,16] Length from Ischial Tuberosity to Floor Method
The length from the ischial tuberosity to the floor method (see figure 1) is measured with the cyclist standing and the distance taken between the most prominent bony surface of the ischial tuberosity to the floor.[17] Settings of 113% of ischial tuberosity to floor length have been reported.[35] LeMond Method
The Greg LeMond method[18] involves the measurement of the inseam leg length and the configuration of the saddle height based on 88.3% of the distance between the top of the saddle and the centre of the bottom bracket. This method (see ª 2011 Adis Data Information BV. All rights reserved.
figures 1 and 2b) is based on the empirical experience of three-times Tour de France winner Greg LeMond. It is important to note that this method does not consider differences in the crank length dimensions. Longer crank length (i.e. 5 mm) results in lower pedalling cadence and smaller knee flexion angle.[36] Further research may look at the effects of crank length on performance variables and on variables related to the risk of injuries. Heel Method
The empirical heel method (see figure 3a) is commonly used.[18] When the cyclist is seated on the saddle, the knee must be fully extended when the heel is on the pedal and the crank is in line with the seat tube. Sports Med 2011; 41 (6)
Effects of Bicycle Saddle Height on Injury Risk and Performance
3.1.2 Knee Angle Methods Holmes et al. Method
The Holmes et al.[19] method (see figure 3b) involves measurement of the knee angle flexed when the pedal is at the bottom dead centre and the cyclist is seated on the saddle, for 25 of flexion for chondromalacea and patellar tendinitis, between 25 and 30 of flexion for quadriceps tendinitis and medial plica/medial patellofemoral ligament injury, and between 30 and 35 of flexion for iliotibial band syndrome and biceps tendinitis. Howard Method
A variation of the Holmes et al.[19] method was reported by Burke[18] as the Howard method, for a knee angle of 30 with the pedal at the bottom dead centre and the cyclist seated on the saddle.
469
Similar to the Holmes et al.[19] method, the knee angle measurement depends on the ankle angle. Increasing ankle plantar flexion results in higher knee flexion angle. 3.1.3 Comparing Methods
Peveler et al.[9] compared the knee angle when the pedal was in the bottom dead centre using different methods. They observed that length-based measures (Hamley and Thomas[15] and LeMond[18] methods) did not ensure the same knee joint angle range. Only 13 of 19 cyclists reached the desired knee angle range (25–35) using either method. The reason is possibly because the length-based methods do not take into account individual variations in femur, tibia and foot length.[37] Review papers by De Vey Mestdagh,[10] Silberman et al.[31] and Wanich et al.[3] reported a series of cycling posture adjustments for performance improvement and injury prevention during cycling based on measuring joint angles and segment lengths, in relation to optimal references from experimental research[15-17] and from empirical knowledge. As reported by Peveler,[8] most of the references for posture optimization on the bicycle were based on empirical data and therefore we still do not have enough valid or reliable scientific studies to determine which method is the best. The knee flexion angle method seems more reasonable than the length methods for reducing the risk of injuries and improving performance[8] because it may standardize the kinematics of the knee, which is one of the most affected joints in terms of injuries in cycling,[4] and one of the most important for power production.[24] 3.2 Effects of Bicycle Saddle Height Configuration on Cycling Performance
a
b
c
Fig. 1. Examples of lower leg length measurements: (a) ischial tuberosity; (b) trochanteric length; and (c) inseam leg length.
ª 2011 Adis Data Information BV. All rights reserved.
Since Hamley and Thomas[15] reported that bicycle saddle height affected time to exhaustion during constant workload cycling trials, studies have investigated the effect of saddle height on other parameters. In this section, we review studies that have examined the effects of saddle height on cycling performance measures . (cycling performance time, energy expenditure/VO2, power output and pedal force application). Sports Med 2011; 41 (6)
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a
b
H
H
Fig. 2. Saddle-to-pedal axis distance used for (a) setting the saddle height by Hamley and Thomas,[15] trochanteric length,[16] and length from the ischial tuberosity to the floor[17] methods and saddle to the centre of the bottom bracket distance; and (b) setting the saddle height by the LeMond method.[18]
3.2.1 Cycling Performance Time
There was only one study that investigated the effects of saddle height on cycling performance time. Hamley and Thomas[15] measured time to exhaustion during constant load trials in the laboratory for 100 non-specified performers. A longer time to exhaustion could be achieved when setting the saddle height at 109% of the inseam leg length. . 3.2.2 Energy Expenditure/Oxygen Uptake (VO2)
There seems to be. an optimal range of saddle heights to minimize VO2 but studies differ on the optimal saddle height configuration.[15-17] Shennum and DeVries[17] and Nordeen-Snyder[16] reported that a 5% reduction height resulted in a . . in saddle 5% increase in VO2. VO2 was minimized with saddle height set between 100% and 103% of inseam leg length during steady-state cycling for five healthy subjects.[17] and when set to 100% of trochanteric length (about 107% of inseam leg length) for ten healthy females.[16] Borysewicz[34] . reported lowest VO2 during 45 minutes of steadystate cycling when the saddle height was set at 96% . of trochanteric length. VO2 during steady-state cycling has been reported as significantly lower for 25 knee angle at the bottom dead centre than for 35 knee angle at the bottom dead centre and 109% of inseam leg length conditions.[8] 3.2.3 Power Output
The effects of saddle height on power output and subsequent increased cycling performance ª 2011 Adis Data Information BV. All rights reserved.
have been observed in anaerobic exercises,[22] with suggested increased power output at higher saddle positions, compared with aerobic cycling.[11] Few studies on saddle height changes could be included for this topic because power output was set as an independent variable with focus on the . measurement of physiological variables (i.e. VO2).[15-17] . 3.2.4 Cycling Economy (Power Output to VO2 Ratio)
Cycling economy is an important performance predictor because it. indicates the ratio between power output and VO2.[38] The majority of research on saddle height evaluated economy based on steady state cycling (i.e. fixed power . output) and effects on VO2.[15-17] Peveler and Green[8,37] observed the effects of different saddle height . configuration on cycling economy based on VO2 measurement during steady-state cycling, with optimal results when setting the saddle height as 25 of knee angle. Price and Donne[11] reported that with power output fixed at 200 W, economy was better with seat height at either 96% or 100% of trochanteric length compared with 104%. 3.2.5 Pedal Force Application
Any relationship between maximal performance and saddle height depends on the optimization of pedal force application.[11] Changing saddle height can affect the ankle angle,[16,22,28,29] which works as a link between the force produced Sports Med 2011; 41 (6)
Effects of Bicycle Saddle Height on Injury Risk and Performance
in the hip and knee joints and the crank.[39,40] Ericson and Nisell[27] found no significant effects on the force transferred from the pedal to the crank generating propulsive torque for pedal forces from six recreational cyclists at different saddle heights. Pedalling technique, based on effective pedal force application of trained cyclists, compared with recreational cyclists, may be more sensitive to changes in saddle height.[27,28] In summary, when the saddle height is set at 96–100% of the trochanteric leg length[16,17]. or using the knee flexion angle (25),[8] reduced VO2 and higher economy were observed. Moreover, when the saddle height is set to 109% of the inseam leg length (D102% of the trochanteric leg length), performance time during a time-to-exhaustion test is optimized.[15] On the other hand, no substantial effects in pedal force were found when changing the saddle height.[27] 3.3 Effects of Bicycle Saddle Height Configuration on Knee Injury Risk
One of the main reasons for the prevalence of knee injuries in cyclists is the relationship between knee joint forces and kinematics.[41] In this section are studies that have examined the effects of saddle height on knee injury risk measures (lower limb kinematics, knee joint forces and moments and muscle mechanics).
a
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3.3.1 Lower Limb Kinematics
Most studies regarding cycling lower limb kinematics have focused on sagittal plane movement.[7,16,17] Typical ranges of motion of these joints in the sagittal plane are 45 for hip angle (from the thigh parallel to the horizontal axis), 75 for knee angle (25–100 of knee flexion angle), and 20 for ankle angle (about –10 from the neutral ankle position).[42] Saddle height affects lower limb kinematics of the ankle,[16,20,28,29] the knee[11,12] or both the ankle and knee.[7,17] Hip and ankle joint angles are most affected by the kinematic method of measurement (i.e. 2-dimensional vs 3-dimensional [3-D]).[43] The lower limb also moves inward in the frontal plane and this movement is affected by saddle height.[44] Between 4% and 5% change (increase or decrease) in saddle height resulted in a 25%[7] change in knee range of motion and a 40%[42] reduction in knee joint angle when the pedal was at the bottom dead centre and a 25%[11] to 51%[7] change in the maximal ankle angle. Changes in joint range of motion cause changes in muscle length[7] and in moment arms[21] of the active muscles and force production. 3.3.2 Knee Joint Forces and Moments
During stationary cycling, maximal compressive force on the patellofemoral joint has been estimated to be between 800 N (riding at 75 W
b
α
Fig. 3. Saddle height configuration based on (a) the heel method; and (b) the Holmes et al.[19] and the Howard[18] methods.
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and 70 revolutions per minute [rpm]) and 1500 N (riding at 157 W and 80 rpm).[41,45,46] Assuming a contact area between the patella and the femur of 0.026 m2[47] and a peak force of 1500 N on the patellofemoral joint,[41] we can achieve 30 MPa of pressure at the cartilage, which is above the reported physiological load.[48] Three studies have reported compressive forces on the patellofemoral joint during cycling.[41,46,49] Ericson and Nisell[49] developed a kinetic model that estimated from trigonometric procedures the patellofemoral compressive forces during cycling. Using three saddle heights (102%, 113% and 120% from the ischial tuberosity to the floor), they showed that compressive force was inversely related to saddle height. Bressel[41] showed that backward pedalling resulted in a shift in the location of peak pedal force to a more flexed knee angle, which increased patellofemoral compressive force. Neptune and Kautz[46] described that reverse cycling has been used in rehabilitation. However, Bressel[41] reported that it can increase patellofemoral compressive force by producing higher knee flexion angles when peak force is applied on the pedal. This example highlights the relationship between joint kinematics and patellofemoral compressive load. Neptune and Kautz’s[46] muscle-skeletal model results agreed with Bressel’s[41] results of increased patellofemoral compressive force during backward pedalling. However, for a very similar workload (D150 W), Neptune and Kautz[46] observed lower peak patellofemoral compressive force. This result suggested that a musculo-skeletal model improved the analysis of knee joint forces, compared with the kinetic model, because it included the effects co-contraction of the knee joint muscles. During cycling, the knee joint flexors provide an important contribution to knee extension, which could reduce the compressive patellofemoral force by co-contraction.[50] Tibiofemoral forces are important because compressive forces on the menisci and the shear forces on the anterior and the posterior ligaments of the knee have been linked with injury.[46] Ruby et al.[44] used a 3-D kinetic model of the knee to report compressive tibiofemoral forces and anterior shear forces on the knee throughout the ª 2011 Adis Data Information BV. All rights reserved.
Bini et al.
crank cycle. This was the first study to report medio-lateral forces on the knee and rotational moments around the long axis of the tibia, and their results led to the analysis of cycling as a 3-D movement. However, we could not find studies reporting the effects of different saddle heights on the 3-D forces and moments of the knee joint. Ericson and Nisell[51] reported that saddle height followed an inverse relationship with tibiofemoral compressive force and shear force for six healthy subjects riding in a constant load trial. McCoy and Gregor[25] reported no effects of saddle height on the compressive force of the tibiofemoral joint for ten male subjects riding at 200 W of power output and 80 rpm of pedalling cadence. When in vivo forces on the anterior cruciate ligament were compared at three levels of workload (75, 125 and 175 W) and two pedalling cadences for eight subjects,[52] there were no significant differences in peak anterior cruciate ligament strain in any situation. Therefore, cycling can be useful in rehabilitation exercise programmes because of the low strain imposed on the anterior cruciate ligament. One study found that backward pedalling can increase the shear component and reduce the compressive component at the tibiofemoral joint.[46] Patients with menisci damage may be better off pedalling backwards, while patients with patellofemoral disorders or ligaments (anterior and/or posterior cruciate ligaments) injuries should avoid pedalling backwards. The complex relationship between joints affects changes in lower limb joint moments. Increased extensor moments and reduced flexor moments were observed when saddle height was at a low position (102% of ischial tuberosity to the medial malleolus[35] or 94% of the leg length).[25] The opposite behaviour was observed with a high saddle (120% of the ischial tuberosity) compared with the average position (113% of the ischial tuberosity),[35] and for the 106% of the leg length compared with the average position (100%).[25] For the ankle joint, Sanderson and Amoroso[7] reported increased peak extensor moment with a low saddle and decreased peak extensor moment when the saddle was raised from the reference position. Regardless of some discrepancies between studies, it seems that a 5% change in saddle height Sports Med 2011; 41 (6)
Effects of Bicycle Saddle Height on Injury Risk and Performance
affects force production and joint moments, joint angles and muscle length. Knee joint angle and moment are strongly affected by saddle height but the optimal saddle height is still unclear because different methods have been used to measure angles and moments. Moreover, Umberger and Martin[43] and Sanderson and Amoroso[7] reported that cyclists chose an average of 104% and 102%, respectively, of the trochanteric length as the saddle height, which suggests that cyclists in these studies would have adapted to a different position than one that could minimize joint moments (97% of trochanteric length for Gonzalez and . Hull[24]) or VO2.[11] As previously observed by Herzog et al.[53] and Savelberg and Meijer,[54] longterm adaptations of training can affect the muscle force-length relationship. These adaptations increase the variability of the results and make it difficult to assess the contribution of adapted position. Only Umberger and Martin[43] and Sanderson and Amoroso[7] reported the preferred saddle heights of their cyclists. Few studies have estimated knee joint forces during cycling with changes in saddle height, and some controversial results have emerged from the reviewed research.[25,51] For the patellofemoral joint, an inverse relationship was observed in one study[49] while for the tibiofemoral joint, controversial results have been reported.[25,51] Joint kinematics and moments results have had different outcomes.[11,16,20,28] Joint kinematics and moments also seem to depend on cycling expertise, which compromises comparison between studies.[11,16,20,28] Therefore, we do not have enough evidence to define ‘optimal’ saddle height based on the results of knee joint forces or joint kinematics. If the aim is to minimize the risk of patellofemoral joint injuries, the inverse relationship between saddle height and patellofemoral compressive force may be used as a reference. 3.3.3 Muscle Mechanics and Activation Patterns
The effects of muscle length on force production have been a focus of much sports science research.[55] Direct measurement of muscle length is usually not possible for ethical reasons, but indirect measurements using ultrasound,[56,57] or anthropometric models[58] have been used to esª 2011 Adis Data Information BV. All rights reserved.
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timate fascicle length and its effect on force production in sports.[59] Grieve et al.[58] proposed anthropometric methods based on cadaver measurements of the muscle-tendon unit length while Frigo and Pedotti[60] reported a model to estimate muscle-tendon unit length based on the line of action of lower limb muscles. Both studies reported relationships between predicted muscle length and kinematics, which allow the estimation of muscle length during dynamic situations. Some studies[7,21,59] have proposed measuring kinematics to infer muscle length during cycling. The force production and the magnitude of joint load depend on muscle length. Rugg and Gregor[21] observed in five cyclists pedalling at 90 rpm of cadence that increasing saddle height resulted in increased shortening of the vastii group, but no significant change in the hamstring group, possibly due to its bi-articulate attachment. Sanderson and Amoroso[7] applied the model of Grieve et al.[58] to evaluate the effects of three different saddle heights on gastrocnemius and soleus. Gastrocnemius and soleus muscles operated in different length ranges when saddle height was raised 5% and lowered 10% from the preferred saddle height. Length and velocity of contraction in the soleus was more affected by saddle height than that in the gastrocnemius, with greatest changes occurring when the saddle height was raised 5% from the preferred position. Gastrocnemius length seemed to be affected by the combination of ankle and knee joint kinematics. These results extend previous data with similar experimental design.[12] There is inconclusive information on musclelength behaviour during dynamic situations.[56] Computational models have been used to estimate muscle force production and length of shortening during cycling,[61,62] but these models have not been used to investigate saddle height changes. Future simulations of muscle-length force production during cycling at different saddle heights would add important information regarding the best saddle height for muscle force production. Given the changes in muscle length that occur with changes in saddle height, it is likely that neural drive to control muscle force would also change. Muscle force and joint load also depends on neural drive. The first report of changes in Sports Med 2011; 41 (6)
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muscle activation at different saddle heights was by Houtz and Fischer,[30] who observed increased muscle activation when the saddle was reduced 15% from the reference position. Houtz and Fischer’s study,[30] and later studies,[20,28] have been conducted with a limited numbers of subjects, which does not allow results to be generalized to the population. Jorge and Hull[23] found increased quadricep and hamstring activation with saddle height set at 95% of trochanteric length compared with 100%. For high saddle height based on the ischial tuberosity to floor height, Ericson et al.[26] found that the semi-membranosus and gastrocnemius medialis had increased activation for six healthy subjects, which was subsequently confirmed by Sanderson and Amoroso[7] for 13 trained female cyclists. The differences may be related to the preparation of surface electromyography or to the pedalling skills of the subjects (i.e. trained cyclists or healthy subjects). There was also a report that muscle timing (i.e. onset and offset) would be modified by saddle height;[12] however, there is still no conclusive evidence. Currently, we cannot define an optimal saddle height for improving performance or preventing injuries using evidence from muscle activity studies. 3.4 Limitations of the Cited Studies
There are many limitations in the research studies reviewed. The different approaches for setting saddle height made it difficult to compare results between studies. Only Shennum and . DeVries[17] reported their results of VO2 with comparison to other methods, while Peveler et al.[9] highlighted the differences in the knee joint angle using different methods to configure the saddle height. Sample size ranged from 3[30] to 100.[15] Expertise of the subjects ranged from trained road cyclists[11] to healthy non-cyclists[16,17,26,27] to mixed levels of cycling experience.[8] It is possible that experienced cyclists adapt to a specific position as a result of the time spent training. Such adaptation may be less marked for recreational cyclists or those that ride in multiple positions (e.g. triathletes). However, we could not find any studies that had a focus on training cyclists to ride ª 2011 Adis Data Information BV. All rights reserved.
at different saddle heights and measured the differences in performance. Different outcome variables were analysed to indicate the effects of optimal saddle height for injury prevention and performance optimization. Most studies did not report the sensitivity or variability of these variables to changes in saddle height. It is possible that different positions are optimal for performance versus injury prevention. The magnitude of the differences in some studies[8,37] was too small (effects sizes 0.07–0.20), so it was unclear how substantial the changes were in the studies. If we consider previously reported optimal settings for saddle height (96–100% of trochanteric length) and we use the ‘optimal’ setting of the saddle to the bottom bracket length (0.773 m) and crank length (0.191 m) reported by Gonzalez and Hull,[24] (resulting in a saddle height of 0.964 m) for a subject 177.8 cm tall, cycling at 90 rpm, our ‘optimal range’ for the saddle height will be between D0.925 m and 0.964 m. This difference of D4 cm is more than any experienced cyclist would consider, and a 4% difference. in saddle height would result in ~5% change in VO2.[16] Most methods of setting saddle height resulted in different joint kinematics, which would affect joint forces and increase risk of injury.[9] 3.5 Practical Implications and Recommendations
The configuration of the bicycle saddle height is not standardized in relation to the methods that can be used for this configuration. The optimal reference for each method is not well defined and a wide range (i.e. 96–100% of the trochanteric length to the floor) used for performance optimization has been proposed. Evidence for performance improvements has led to using the knee joint angle method from Holmes et al.[19] rather than the leg length methods.[8] Future studies may focus on the effects of previous training adaptation on the optimal reference for the knee angle for setting the bicycle saddle height. Given the limitations of the research studies reviewed, sports medicine practitioners are encouraged to advise their cycling athletes to conSports Med 2011; 41 (6)
Effects of Bicycle Saddle Height on Injury Risk and Performance
figure their bicycle using the Holmes et al.[19] method, which involves the measurement of the knee angle when the pedal is at the bottom dead centre. For proper configuration of the saddle height using this method, the knee must be flexed between 25 and 30, which has been related to lowering the knee joint load[10] and improving cycling economy.[8] 4. Conclusions Methods for determining optimal saddle height are varied and have not been comprehensively compared using experimental research studies. There is limited information on the effects of saddle height on lower limb injury risk, but more information on the effects of saddle height and cycling performance. The range of 25–30 of knee flexion has been advocated. to reduce the risk of knee injuries and minimize VO2. Given that overuse knee joint injury is common in cyclists, future studies should determine how saddle height can be optimized to improve cycling performance and reduce knee joint forces to reduce lower limb injury risk. Acknowledgements The authors have no conflicts of interest directly relevant to the contents of this article. The International Society of Biomechanics (via a student international travel grant) and the CAPES Foundation PhD scholarship (Brazil) supported Rodrigo Bini to complete this review. The Auckland University of Technology supported Dr James Croft and Professor Patria Hume to complete this review.
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Correspondence: Mr Rodrigo Bini, Sport Performance Research Institute New Zealand, School of Sport and Recreation, Auckland University of Technology, Private Bag 92006, Auckland, New Zealand. E-mail:
[email protected]
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REVIEW ARTICLE
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Influence of Intensity of Physical Activity on Adiposity and Cardiorespiratory Fitness in 5–18 Year Olds Tvisha Parikh1,2 and Gareth Stratton1 1 Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, UK 2 Department of Arthroscopy and Sports Medicine, Sri Ramachandra University and Hospital, Chennai, India
Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Reviewing Method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Measurement of Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Physical Activity (PA) Measurement. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Adiposity Measurement. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.3 Fitness Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Influence of PA Intensity on Adiposity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Observational Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Intervention Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Influence of PA Intensity on Cardiorespiratory Fitness. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Observational Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Intervention Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Abstract
477 478 478 479 479 479 481 481 481 481 483 483 483 483 483 486 486
Physical activity (PA) is being increasingly promoted in children in an attempt to curb the rising epidemic of childhood obesity and its future consequences of obesity and cardiovascular diseases in adulthood. Although many reviews and guidelines have been published regarding PA in children and adolescents, none have specifically focused on the influence of intensity of activity on the crucial health aspects of fatness and cardiorespiratory fitness. Therefore, we conducted an online search for pertinent literature and reviewed 25 studies for this purpose. We found that there were limited studies that assessed the influence of ‘intensity’ of PA on health parameters, and there was considerable inconsistency in defining the thresholds for moderate (MPA) and vigorous (VPA) levels of PA. Collectively, we concluded that VPA is a significant predictor of fatness and significantly correlated to fitness. The association between the intensity of PA and cardiorespiratory fitness is
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more obscure compared with fatness because of limited studies and the varying conclusions made by them. However, decreased adiposity and increased aerobic capacity have been observed with participants who spent more time performing VPA. Further research needs to be undertaken to arrive at uniform thresholds for defining MPA and VPA and to obtain the exact dose of VPA and MPA, individually, to increase aerobic fitness and decrease adiposity.
1. Introduction Physical activity (PA) is widely considered as a primary component of a healthy lifestyle. With the growing epidemic of obesity and cardiovascular diseases worldwide, and the protective benefits conferred by PA, it is being increasingly promoted to combat this problem. In recent years, childhood obesity has become the most prevalent preclinical disorder.[1] Obesity and cardiovascular diseases in adults are thought to have their roots in childhood and adolescence. High levels of adiposity are associated with low levels of cardiovascular fitness[2] and being overweight in adolescence has been found to have an association with cardiovascular mortality;[3] also, obese children become obese adults.[4] This makes the increasing prevalence of overweight and obese children and adolescents a major public health concern[5] and promotion of the modifiable risk factor, i.e. PA, as one of the solutions to it. PA guidelines for the young were first devised in 1988 by the American College of Sports Medicine.[6] Since then, many different guidelines have been devised. A systematic review conducted by Strong et al. in 2005 recommended at least 60 minutes of moderate to vigorous PA (MVPA) per day.[7] An hour of MVPA per day has been deemed to be the minimum requirement to achieve health benefits.[8] However, studies have shown high levels of obesity even though participants have exceeded the recommended amount of MVPA.[9] The probable reason for this may be due to the sporadic nature of children’s PA, which naturally occurs in bouts of short duration and high intensity.[10] Therefore, the principles of dose response, namely moderate (MPA) and vigorous (VPA) levels of PA, need to be considered ª 2011 Adis Data Information BV. All rights reserved.
in relation to fitness and fatness.[11,12] This probably necessitates a more defined dose-response relationship between the type of PA and health benefits rather than advocating a wide range of MVPA. Moreover, recent literature stresses the importance of VPA over MPA.[5,13,14] Although various reviews have been conducted on PA in children, none have focused on the influence of intensity of PA. In order to evaluate and highlight the effect of intensity on fatness and cardiorespiratory fitness, we conducted a systematic review of the available literature. 2. Reviewing Method For the purpose of this review, we conducted an online search for relevant literature. Literature was retrieved in two ways. We searched the databases Web of Science, PubMed, CINAHL Plus and Google Scholar for a combination of the following terms: ‘physical activity’, ‘exercise’, ‘moderate physical activity’, ‘vigorous physical activity’, ‘intensity of physical activity’, ‘volume of activity’, ‘children’, ‘adolescents’, ‘fitness’, ‘fatness’, ‘adiposity’ and ‘cardiovascular’. Terms were combined such that every search included one term related to PA ‘moderate PA’ or ‘vigorous PA’ or ‘intensity’ or ‘volume’; one term related to subjects ‘children’ or ‘adolescents’; and one term related to outcome measure ‘adiposity’ or ‘fatness’ or ‘fitness’ or ‘cardiovascular’. In this way, 48 searches were made using the terms in each of the mentioned databases. We also searched for relevant references and citations linked to the articles obtained during this primary search. Thus, we included those studies that assessed the effect of the intensity of PA on cardiovascular fitness and obesity parameters and were conducted Sports Med 2011; 41 (6)
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. BF = body fat; BMI = body mass index; CVF = cardiovascular fitness; MVPA = moderate to vigorous PA; TPA = total physical activity; VO2peak = peak oxygen consumption; %BF = percentage of BF.
Patrick et al.[17] 878 (2004)
16
11–15
7
5 421 Gutin et al.[2] (2005)
1 min
Among 7 dietary and PA variables, insufficient VPA was the only risk factor for higher BMI
VPA was better correlated with CVF than MPA. Variance in CVF was significantly explained by MPA,VPA and MVPA VPA was the only significant predictor of %BF (b = -4.19; p = 0.001). VPA (p < 0.001) was better correlated with %BF than MPA (p < 0.01) 1 min
TPA, MPA, MVPA and VPA predict CVF Only VPA was a significant predictor of BF (b = -0.081; p = 0.02) 3 9–10 780 Ruiz et al.[16] (2006)
1 min
Only VPA and not TPA is associated with high total fatness. Low levels of VPA had 4· higher odds of being overweight compared with high levels of VPA 1 min 9.5 (children) 3–4 15.6 (adolescents) Ortega et al.[15] 557 children and (2007) 517 adolescents
Results of fitness
Only VPA is an independent predictor of %BF and MVPA and VPA correlated with and . abdominal fat. Pearson’s correlation is stronger for were independently linked to VO2peak VPA than MVPA with %BF and abdominal fat, but not with MPA 10 sec 3–4 8–11
Observational Studies
All the studies we included used accelerometers to measure PA. Accelerometers measured the total activity performed by the participants and the time they spent performing various intensities of activities such as MPA, VPA and MVPA and total PA (TPA). Twelve of the 17 studies measured PA over 3–4 days and 12 studies used 1-minute epochs. The intensity of PA was defined in different ways. When expressed in terms of metabolic equivalents (METs), most studies defined MPA as activity between 3–6 METs and VPA as activity >6 METs. However, when activity was defined as counts/min as measured by the accelerometer, the thresholds for MPA and VPA differed. Rowlands et al.[9] defined MPA and VPA as between 636–1645 and >1646 counts/min respectively, whereas Lohman et al.[22] defined it as >3000 and >5200 counts/min, respectively. Two studies expressed the thresholds in terms of caloric expenditure as mentioned in table II.[13,21] Some studies[5,13,19,24] derived their own thresholds, while others used those that had been validated earlier in other studies.[9,20-23,25]
225
3.1.1 Physical Activity (PA) Measurement
Dencker et al.[14] (2008)
3.1 Measurement of Parameters
No. of days wearing Epoch Results of adiposity accelerometer
We included 25 studies in the review that were published from 1999 to 2009. Seventeen of these studies were observational, while eight were intervention studies. Among the 25 studies, 22 examined the effect of PA on adiposity, while 14 examined the effect on cardiorespiratory fitness. The number of participants in the studies ranged from 34 to 5500 in observational studies and from 15 to 80 in intervention studies. Summaries of the observational studies are presented in tables I and II. Table III presents a summary of the intervention studies.
No. of participants Age group (y)
3. Results
479
Study (y)
in participants aged 5–18 years after 1999, and published in the English language. We excluded studies that measured PA using pedometers and questionnaires and included those studies that used the accelerometer to measure PA.
Table I. Summary of observational studies that used metabolic equivalents (METs) to define intensity of physical activity (PA): moderate PA (MPA) is 3–6 METs and vigorous PA (VPA) is >6 METs
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Table II. Summary of observational studies that used counts/min and kcal/kg/min to define intensity Study (y)
No. of participants
Age group (y)
No. of days wearing accelerometer
Intensity threshold (counts/min) MPA VPA
Steele et al.[5] (2009)
1862
9–10
3–7
2000–3999
Mark and Janssen[11] (2009)
2498
8–17
4–7
Gaya et al.[18] (2009)
163
8–17
7
>2000
>3000
Wittmeier et al.[13] (2008)
251
8–10
1
0.096 kcal/kg/min
0.144 kcal/kg/min
12
3
Ness et al.[19] (2007)
5500
Results of adiposity
>4000
VPA had maximum association with obesity indices; MPA did not. Accumulation of >60 min/d MPA had <34% chance of obesity >3000
MVPA accrued in bouts predicted adiposity status independent of the total volume of MVPA MPA decreases systolic BP. VPA does not. Neither predict diastolic BP Stepwise regression: VPA, but not MPA, was associated with BF and BMI. ANOVA: MPA and VPA significantly influenced BF and BMI. Partial correlation (controlling for sex): VPA and MVPA to BF; only VPA to BMI
>3600
Association of fat mass and difference of 15 min of MVPA: b = -0.25 (boys); b = -0.15 (girls)
152
7–10
4
971–2333
>2334
Significant negative correlation in boys of waist circumference with VPA and MPA, and of BMI with MPA. None in girls
Butte et al.[21] (2007)
897
10.8
3
0.04–0.1 kcal/kg/min
>0.1 kcal/kg/min
Negative association between bouts of MVPA and %BF
76
8–10
4–7
636–1645
>1646
VPA (p < 0.005) and TPA (<0.05) were statistically negatively correlated with %BF. When controlling for VPA, correlation between TPA and %BF was close to 0
12
6
>3000
>5200
VPA had slightly more negative correlations with adiposity indices than MVPA
Sports Med 2011; 41 (6)
Lohman et al.[22] (2006)
1553
. VO2max and VPA are significantly correlated (0.23; p < 0.001) Positive association with number and bouts of MVPA
Continued next page
Parikh & Stratton
Hussey et al.[20] (2007)
Rowlands et al.[9] (2006)
Results of fitness
MVPA
>2000 1000–2000 3–6 8.5 Rowlands et al.[25] (1999)
33
3 9–10 1292 Ekelund et al.[24] (2004)
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BF = body fat; BMI =.body mass index; BP = blood pressure; CVF = cardiovascular fitness; MPA = moderate physical activity; MVPA = moderate to vigorous PA; TPA = total PA; VPA = vigorous PA; VO2max = maximal oxygen consumption; %BF = percentage of BF.
TPA, MPA and VPA correlated with fatness
Time spent in VPA (b = -0.0034; p = 0.015) and MVPA (b = -0.0019; p = 0.032) are independent predictors of BF. TPA, MVPA and VPA correlated with skinfold thickness >2000 >3000
>2000; hard intensity: >3500 1000–1999 4 5–10.5 Abbott and Davies[23] (2004)
40
No. of days wearing accelerometer Study (y)
Table II. Contd
No. of participants
Age group (y)
Intensity threshold (counts/min) MPA VPA
MVPA
Results of adiposity
VPA (r = -0.44; p = 0.004) was statistically negatively correlated with %BF. Time spent in MPA was not associated with BF
Results of fitness
TPA, MPA and VPA correlate with and predict CVF
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481
Intervention Studies
The intervention studies involved training of a group of participants at particular intensities, which were specific for that study. The training aimed at attaining a particular heart . rate (HR) or maximal oxygen consumption (VO2max) or maximal aerobic velocity. The intensities were derived for all participants individually on the basis of their maximal exercise tests. Thus, all participants in a. study trained at a particular percentage of their VO2max or HR, and the absolute training intensities, may have differed between participants. The aim of the studies was to assess change in various adiposity parameters and cardiovascular fitness after a period of systematic training. Table III mentions these definitions in detail. 3.1.2 Adiposity Measurement
The percentage of body fat (%BF), percentage of abdominal fat, waist circumference, sum of skinfold thicknesses and body mass index (BMI) were the commonly used parameters to express adiposity or fat content. The %BF was calculated using either dual energy x-ray absorptiometry (DEXA), sum of skinfolds, adiposity equations or hydrostatic weighing. 3.1.3 Fitness Measurement
To assess the cardiorespiratory fitness, participants performed a maximal exercise protocol until exhaustion. The tests performed were a maximal exercise test on either a treadmill or ergometer, or a field test such as the bleep test. One of the .following was measured as a marker of. fitness: VO2max or peak oxygen consumption (VO2peak) measured using indirect calorimetry, maximal power output on ergometer/kg body mass, time until exhaustion or endurance time, or a change in exercise HR. 3.2 Influence of PA Intensity on Adiposity 3.2.1 Observational Studies
Almost all observational studies tested the influence of both MPA and VPA, individually, on adiposity. However, three studies reported associations with MVPA only.[11,19,21] Higher intensity PA was found to be the most significant predictor of various adiposity indices. Studies that controlled Sports Med 2011; 41 (6)
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Table III. Summary of intervention studies Study (y)
No. of participants
Age group (y)
Intervention
Intensity of training
Results of adiposity Significant decrease in BMI and fat mass. Increase in HDL
54
14
40 min, 2 d/wk for 3 mo
16 min at 90–95% HRmax; recovery 70%
Gamelin et al.[27] (2009)
22
9.6 – 1.2a
30 min, 3 d/wk for 7 wk
100–190% MAV (minimal . velocity eliciting VO2max)
Barbeau et al.[28] (2007)
201
8–12
80 min, 5 d/wk for 10 mo
HR >150
Obert et al.[29] (2009)
50
9–11
25–30 min, 3 d/wk for 2 mo
100–130% of MAV
Watts et al.[30] (2004)
19
14.3 – 1.5a
60 min, 3 d/wk for 8 wk
Ergometer 65–85% HRmax and resistance
Gutin et al.[31] (2002)
80
13–16
5 d/wk for 8 mo
DeStefano et al.[32] (2000)
15
9–12
20–30 min, 2 d/wk for 12 wk
Target HR: HR at anaerobic threshold
Significant decrease in %BF
Owens et al.[33] (1999)
74
7–11
40 min, 5 d/wk for 4 mo
5.3 METs (70–75% HRmax)
Significant decrease in %BF
a
. 55–60% VO2 or 75–80% . VO2
. Significant increase in VO2max
. Large and significant increase in VO2peak and MAV
Decrease in BMI (r = -0.17) and %BF (r = -0.16) compared with control
. Increase in VO2max compared with control
. Increase in VO2max in trained group only. Decrease in BP. Peak systolic and diastolic wall motion velocities did not change. LV thickness, mass and shortening fraction unchanged in all
Significant decrease in BF
No difference between high- or moderateintensity training on BF
. High-intensity group improved VO2 more than moderate-intensity group
. Significant increase in VO2max and resting energy expenditure
Significant decrease in exercise HR
Mean – SD.
BF = body fat; BMI = body mass index; BP lipoprotein; HR = heart rate; HRmax = maximal HR; LV = left ventricular; MAV = maximum aerobic . = blood pressure; HDL = high-density . . velocity; METs = metabolic equivalents; VO2 = oxygen consumption; VO2max = maximal VO2; %BF = percentage of BF.
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Sports Med 2011; 41 (6)
Tjonna et al.[26] (2009)
Results of fitness
Physical Activity Intensity on Adiposity and Cardiorespiratory Fitness
for age and sex also found that VPA remained the most significant predictor.[2,5,15,16,20,21,24] VPA continued to remain an independent predictor of adiposity in studies that controlled for confounding factors such as race, parental BMI, birthweight, maturation age, television viewing, dietary intake, sedentary behaviour and socioeconomic status.[5,15,24] In one study, when the sum of skinfolds was substituted with BMI in the linear models, no component of PA contributed to variance in BMI.[24] Although VPA was highly negatively correlated with adiposity indices in the majority of the studies, negative correlations with TPA, MPA and MVPA were also reported.[13,20,24,25] 3.2.2 Intervention Studies
All studies observed a decrease in body fat after a period of intervention. This intervention period varied from 7 weeks[27] in one study to 10 months in another.[28] Also, all the studies used varying terminologies to describe the intensity at which the intervention groups trained. Intensity was described . either in terms of HR, oxygen consumption (VO2) or maximum aerobic velocity. 3.3 Influence of PA Intensity on Cardiorespiratory Fitness 3.3.1 Observational Studies
All studies concluded that PA influences cardiorespiratory fitness positively. Correlations were found between all intensities of PA and fitness. While certain studies found no statistical differences between correlations of MPA, VPA or MVPA with fitness,[14,16,25] some found VPA to be better correlated than MPA.[2,20] 3.3.2 Intervention Studies
All the studies noted an increase in cardiorespiratory fitness after a period of training. While [26-28,31,32] expressed this as an increase most . studies in VO2max after both moderate- and vigorousintensity training, one study noted a decrease in sub-maximal HR suggesting an improvement in fitness.[33] ª 2011 Adis Data Information BV. All rights reserved.
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4. Discussion The aim of this review was to identify the influence of different intensities of PA on adiposity and fitness in the age group of 5–18 years. Time spent in higher intensity PA emerged to be the most significant predictor of various adiposity indices. The studies that divided the time spent in higher intensity PA into quartiles or tertiles, observed a significant difference (p < 0.001) between those who spent maximum time in higher intensity PA compared with those who spent the least time.[5,15,16,19,24] Odds of being overweight ranged from 4- to 5.21-fold greater in groups who spent the least time in VPA compared with those who spent the maximum time.[13,15] Wittmeier et al.[13] concluded that 15 minutes of VPA was associated with favourable body composition results, whereas 45 minutes of MPA would be required to attain the same benefit. A similar deduction was made by Steele et al.[5] who reported that 6.5 minutes of VPA could decrease waist circumference by ~1.32 cm, whereas 13.6 minutes of MPA would decrease it by ~0.49 cm. Ortega et al.[15] suggested from their results that the negative effect of watching more than 2 hours of television per day on central fatness could be attenuated by an appropriate level of VPA. They suggested that the recommendation of 60 min/day could be enough if sufficient VPA was accumulated during such a period. Rowlands et al.[9] noted that although both VPA and TPA were significantly negatively correlated with %BF when controlling for VPA, the correlation of TPA and %BF was close to zero. This suggests that a higher dose of VPA was more important in maintaining an appropriate level of adiposity in childhood. Our literature review revealed the following two basic methods to test the relationship of intensity and outcome variables: (i) the relationship of free-living activity and outcome variables such as in observational studies; and (ii) the effects of prescribed intensities of physical training on outcome variables such as in intervention studies. Calculating the dose of exercise actually obtained in intervention studies and its relationship to the outcome variables is also a technique that was described in terms of HR achieved and attendance Sports Med 2011; 41 (6)
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at training sessions during interventions.[28,31,33] In the observational studies, the intensity of the free-living activity was measured and activity levels defined in absolute values in contrast to the relative definitions in the intervention studies. The drawback of absolute intensity is that due to differences in bodyweight between children, VPA for one child may be MPA for another. Among the thresholds defining the absolute values of intensity, a variability was seen, probably because they adopted different validation methods and were derived from different populations.[34,35] The impact of the variability was reflected in the expression of the results. For example, in the study by Dencker et al.,[14] which adopted METs based on the Freedson et al.[36] equation, MPA was 3–6 METs, which corresponded to 1002– 3498 counts/min and VPA was >6 METs, which corresponded to >3498 counts/min. Rowlands and colleagues[25,35] defined MPA between 636– 1645 counts/min and VPA in the range of 1646– 2334 counts/min.[9,20,23,25] Whereas Dencker et al.[14] did not show a relationship between adiposity and MPA, Rowlands et al.[25] did. As depicted in table II, among the studies that used these lower thresholds,[9,20,23,25] one[25] reported an association between MPA and adiposity, while two[9,23] did not find any association. Incidentally, those studies[9,20,23,25] that used lower thresholds also recruited low numbers of participants compared with the other observational studies. We can critique that this weakens their results.[5,11,15,19,22] Although none of the studies explicitly mentioned the sample size necessary for a statistical conclusion, studies that recruited high numbers of participants produced more robust results. In a recent study by Steele and colleagues[5] that recruited 1862 children, an accumulation of >60 minutes of MPA per day was associated with a 34% lower chance of being obese. However, they used a higher threshold for MPA (2000–3999) than other studies.[9,25] Their regression coefficients depicted a very strong association with VPA, but also demonstrated an association with other levels of PA. A cross-sectional analysis of 5500 children suggested that a modest increase in PA of 15 minutes of MVPA was associated with 50% lower odds of obesity in boys and nearly 40% in ª 2011 Adis Data Information BV. All rights reserved.
Parikh & Stratton
girls.[19] However, the threshold of MVPA in this study[19] (>3600 counts/min) was similar to the threshold for VPA in the study by Dencker et al.[14] Lohman et al.[22] used a threshold of 5200 counts/min for VPA, which could be the reason why only a slight difference in the magnitude of the relationship of body fat to VPA (r = -0.19) compared with MVPA (>3000 counts/min) [r = -0.16], resulted. Irrespective of whether MPA or VPA was used, higher counts/min correlated more strongly with body fat. When intensity was expressed in energy expenditure, time spent in VPA, but not MPA, was significantly associated with measures of adiposity.[13] The association of cardiorespiratory fitness with different intensities of PA is less defined than the association of adiposity in observational studies.[2,14,16,18,20,21,25] It can be concluded from the available studies that although VPA would influence cardiorespiratory fitness the most, lower intensity can also produce significant improvement. In intervention studies, participants were enrolled in a training programme and their measures of adiposity and cardiorespiratory fitness were compared with their pre- intervention values or with controls that did not experience the intervention (table III). The intervention studies were well designed in that all, except one,[32] were well controlled and randomized. Although there were lower numbers of participants compared with observational studies, statistically significant results were obtained. Five of the studies exercised participants at high intensity.[26,27,29,31,32] These studies noted an improvement in adiposity and cardiorespiratory fitness. Other studies trained participants at moderate levels of intensity and obtained the same effect on fat mass and/or cardiorespiratory fitness, suggesting no influence of intensity of PA on these parameters.[28,30,31,33] Three of the intervention studies measured the dose of exercise achieved in terms of the HR attained and the attendance at the training sessions.[28,31,33] However, only one of them expressed the dose-response relationship.[28] After regression analyses, they noted that the higher the HR and attendance at training sessions, the greater the decreases in body fat.[28] In the study by Gutin et al.,[31] although the high-intensity Sports Med 2011; 41 (6)
Physical Activity Intensity on Adiposity and Cardiorespiratory Fitness
group did not achieve the prescribed HRs, they were still significantly higher than the achieved HR of the moderate-intensity group, with a significant increase in cardiorespiratory fitness in contrast to an insignificant increase in the moderate group. However, there was no difference in fatness between the two groups. The mean HR achieved by the intervention group in the study by Owens et al.[33] was less than the prescribed ‘hard’ intensity of the intervention. The intervention group noted a significant decrease in body fat and increase in fitness compared with the control group. It is worthwhile considering that unlike observational studies, many intervention studies focused on overweight and obese participants.[26,30-33] The difference in results between observational and intervention studies may suggest that the influence of intensity varies depending on the baseline body composition of participants, and that those who were overweight gained adequate, if not equivalent, benefit from moderate-intensity activity as well. In addition, although a significant decrease in adiposity was observed when participants exercised at 70–75%[33] or 90–95% maximal HR (HRmax);[26] since these results are derived from two different studies, their results cannot be compared. Only Gutin et al.[31] compared moderately exercising and vigorously exercising groups. They found no difference in the effects of MPA compared with VPA on adiposity, but observed a greater effect of VPA on cardiorespiratory fitness.[31] This . study defined moderate intensity . as 55–60% VO2 and high intensity as 75–80% VO2. Most of the other studies[26,28,30,32,33] expressed these intensities in terms of HRmax, which makes it difficult to make comparisons between studies, draw definite conclusions regarding them and subsequently derive health recommendations. Since PA of children and adolescents occurs in short bouts and is sporadic, measurement of activity by accelerometers is influenced by the epoch time used. There could be an underestimation of VPA and an overestimation of MPA when a longer duration of epoch is used. Since the majority of the observational studies we reviewed found VPA to be significantly associated with adiposity, despite using 1-minute epochs, it can be ª 2011 Adis Data Information BV. All rights reserved.
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reassured that VPA and adiposity have strong associations. The use of a 10-second epoch by Dencker et al.[14] and a 5-second epoch by Steele et al.[5] increased the validity of their study findings. The relationship between accelerometry counts and energy expenditure is different in children and in adolescents and accelerometers must be calibrated for application in these two populations. The calibration seems to be easier in adolescents due to their ‘continuous’ habitual PA, whereas it is more difficult in children due to their sporadic or recess activity.[10] A study particularly relevant to the sporadic nature of children’s activity was conducted by Mark and Janssen.[11] They showed that, among participants who engaged in high levels of MVPA, the prevalence of being overweight was 25% in those who engaged in a high percentage of bouts compared with 33% in those with a low percentage. They concluded that MVPA accumulated in bouts predicted adiposity independent of the total volume of MVPA. Most studies found VPA to be significantly correlated to fatness even after controlling for age and sex.[5,11,15,19-21,24] However, only a few studies have controlled for genetics, parental BMI and sexual maturation status.[15,19,24] Ness et al.[19] also adjusted for maternal education, smoking in pregnancy, birthweight, gestational age, sleep pattern and television viewing, and did not find any changes in the relationship between PA and adiposity. Highly specialized and crude techniques were employed to measure the adiposity and cardiorespiratory capacity in the studies reviewed. Eleven of the 22 studies that measured adiposity used the DEXA or hydrostatic weighing, which have been found to be very accurate techniques.[2,9,14,19,21,23,26,28,30,31,33,37,38] This strengthens the quality of these studies because they directly measured adiposity. A measure such as BMI is less accurate, since it does not distinguish between fat and fat-free mass. Ekelund et al.[24] suggested that BMI and the sum of skinfold thicknesses are different entities and that BMI may not be the most suitable measure of adiposity in children because there was no association between PA and adiposity when adiposity was Sports Med 2011; 41 (6)
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defined as BMI. However, due to many participants, it would be difficult to perform DEXA in cross-sectional studies and crude measurements have to be used. The study by Ness et al.[19] performed DEXA on a large sample of 5500 children, which enhanced the quality of the study. Although this review included studies that used BMI as one of the outcome measures, all, except one study,[17] also measured body fat or waist circumference.[13,20,26,28] Patrick et al.[17] concluded that among seven dietary and PA variables, VPA was the only significant risk factor. Thus, despite these studies expressing results in terms of BMI, they are included in the review. Cardiorespiratory fitness was measured using gas analysis in most studies. Specialized techniques for measurement of adiposity and fitness were used in all intervention studies strengthening the results. All the studies were methodical with their statistical analyses. They all established correlations between intensities of activity and outcome variables and expressed the level of statistical significance. Various regression analyses were also conducted, with adiposity and fitness being predicted, and many confounding factors were controlled for during these analyses. 4.1 Limitations
There was a paucity of literature on studies specifically measuring and comparing the intensities of PA. Due to this limited availability of literature, we did not check the statistical significance of the studies in terms of participant numbers. Recruitment of participants in the observational studies could be biased because of the possibility that overweight children would not want to participate in such studies. This means that the samples may not be representative of the population. Parental bias also needs to be accounted for. Healthy and fit parents were probably more enthusiastic about participation in such studies than obese parents. Also, the activity patterns of children could be different when they are aware that their activity levels are being monitored. In addition, as measures of PA, accelerometers are not accurate, mainly because of ª 2011 Adis Data Information BV. All rights reserved.
their inability to capture certain types of PA such as swimming and bicycling.[39] Not all intervention studies included measures of maturation to exclude effects of normal growth and maturation on health and fitness. The review included studies that measured parameters using robust laboratory measures, such as DEXA and indirect calorimetry, as opposed to weaker measures such as BMI and field tests of cardiorespiratory fitness and precise epoch times of 5 seconds against 1 minute. However, we did not apply exclusion criteria for studies related to techniques used to generate data before drawing inferences on findings. 5. Conclusions In the majority of observational studies that we reviewed, VPA was an independent or significant predictor of adiposity. MPA predicted adiposity in only a few studies. The influence of intensity on cardiorespiratory fitness was variable with VPA and MPA being significantly correlated to fitness. Among the intervention studies, a decrease in adiposity and an increase in cardiorespiratory fitness were observed in most studies irrespective of moderate or vigorous activity interventions. Thus, although VPA significantly determines adiposity over MPA, this difference of association of intensity with cardiorespiratory fitness is less clear. Despite the inconsistencies in definitions of intensity among many studies, on the basis of a few large studies using robust techniques and statistics, it can be concluded that the accumulation of higher accelerometer counts per minute and the accumulation of more of these minutes are beneficial to reduce adiposity and increase fitness. Definite conclusions can be reached with further research in this field, especially with consistent definitions of the intensities and more intervention studies comparing groups exercising at moderate and vigorous intensities. Intensity levels of PA also need to be well defined before a particular intensity of PA is prescribed to children for specific health benefits. However, considering issues of compliance, especially with the overweight and obese population, it is important to promote regular engagement in at least MPA. MPA can be recommended as the starting Sports Med 2011; 41 (6)
Physical Activity Intensity on Adiposity and Cardiorespiratory Fitness
point for this population before their participation in VPA can be assured. Acknowledgements The authors thank Mr Lawrence Foweather with his help in analysing some of the literature. The authors also thank Sportslinx and the Liverpool Working Neighbourhood Fund for funding this review. The authors have no conflicts of interest.
References 1. Dietz WH. Health consequences of obesity in youth: childhood predictors of adult obesity. Pediatrics 1998; 101 Suppl 3: 518 2. Gutin B, Yin Z, Humphries MC, et al. Relations of moderate and vigorous physical activity to fitness and fatness in adolescents. Am J Clin Nutr 2005; 81: 746-50 3. Must A, Jacques PF, Dallal GE, et al. Long term morbidity and mortality of overweight adolescents. N Engl J Med 1992 Nov; 327: 1350-5 4. Serdula MK, Ivery D, Coates RJ, et al. Do obese children become obese adults? A review of literature. Prev Med 1993; 22: 167-77 5. Steele RM, Sluijs EMF, Cassidy A, et al. Targeting sedentary time or moderate- and vigorous-intensity activity: independent relations with adiposity in a population-based sample of 10-y-old British children. Am J Clin Nutr 2009; 90: 1185-92 6. Andersen LB, Maarike H, Sardinha LB, et al. Physical activity and clustered cardiovascular risk in children: a crosssectional study. Lancet 2006 Jul; 368: 299-304 7. Strong WB, Malina RM, Blimkie CJ, et al. Evidence based physical activity for school-age youth. J Pediatr 2005; 146: 732-7 8. Guinhouya CB, Hubert H, Soubrier S, et al. Moderate-tovigorous physical activity among children: discrepancies in accelerometry-based cut-off points. Obesity (Silver Spring) 2006 May; 14: 774-7 9. Rowlands AV, Eston RG, Powell SM. Total physical activity, activity intensity and body fat in 8- to 10-yr-old boys and girls. J Exerc Sci Fit 2006; 4 (2): 96-102 10. Baquet G, Stratton G, Van Praagh E, et al. Improving physical activity assessment in pre-pubertal children with high frequency accelerometry monitoring: a methodological issue. Prev Med 2007; 44: 143-7 11. Mark AE, Janssen I. Influence of bouts of physical activity on overweight in youth. Am J Prev Med 2009; 36 (5): 416-21 12. Rowlands AV, Eston RG. The measurement and interpretation of children’s physical activity. J Sports Sci Med 2007; 6: 270-6 13. Wittmeier KD, Mollard RC, Kriellaars DJ. Physical activity intensity and risk of overweight and adiposity in children. Obesity 2008; 16: 415-20 14. Dencker M, Thorsson O, Karlsson MK, et al. Daily physical activity related to aerobic fitness and body fat in an urban sample of children. Scand J Med Sci Sports 2008; 18: 728-35
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15. Ortega FB, Ruiz JR, Sjostrom M. Physical activity, overweight and central adiposity in Swedish children and adolescents: the European Youth Heart Study. Int J Behav Nutr Phys Act 2007 Nov; 4: 61 16. Ruiz JR, Rizzo NS, Hurtig-Wennlof A, et al. Relations of total physical activity and intensity to fitness and fatness in children: the European Youth Heart Study. Am J Clin Nutr 2006; 84: 299-303 17. Patrick K, Norman G, Calfas KJ, et al. Diet, physical activity and sedentary behaviours as risk factors for overweight in adolescence. Arch Pediatr Adolesc Med 2004 Apr; 158: 385-90 18. Gaya AR, Alves A, Aires L, et al. Association between time spent in sedentary, moderate to vigorous physical activity, body mass index, cardiorespiratory fitness and blood pressure. Ann Hum Biol 2009 Jul-Aug; 36: 4379-87 19. Ness AR, Leary SD, Mattocks C, et al. Objectively measured physical activity and fat mass in a large cohort of children. PLoS Med 2007 Mar; 4: e97 20. Hussey J, Bell C, Bennett K, et al. Relationship between the intensity of physical activity, inactivity, cardiorespiratory fitness and body composition in 7–10-year-old Dublin children. Br J Sports Med 2007; 41: 311-6 21. Butte NF, Puyau MR, Adolph AL, et al. Physical activity in non-overweight and overweight hispanic children and adolescents. Med Sci Sports Exerc 2007 Aug; 39 (8): 1257-66 22. Lohman TG, Ring K, Schmitz KH, et al. Associations of body size and composition with physical activity in adolescent girls. Med Sci Sports Exerc 2006 Jun; 38: 1175-81 23. Abbott RA, Davies PS. Habitual physical activity and physical activity intensity: their relation to body composition in 5.0–10.5-y-old children. Eur J Clin Nutr 2004; 58: 285-91 24. Ekelund U, Sardinha LB, Anderssen SA, et al. Associations between objectively assessed physical activity and indicators of body fatness in 9- to 10-y-old European children: a population-based study from 4 distinct regions in Europe (the European Youth Heart Study). Am J Clin Nutr 2004; 80: 584-90 25. Rowlands AV, Eston RG, Ingledew DK. Relationship between activity levels, aerobic fitness, and body fat in 8- to 10-yr-old children. J Appl Physiol 1999; 86: 1428-35 26. Tjonna AE, Stolen TO, Bye A, et al. Aerobic interval training reduces cardiovascular risk factors more than a multitreatment approach in overweight adolescents. Clin Sci 2009; 116: 317-26 27. Gamelin FX, Baquet G, Berthoin S, et al. Effect of high intensity intermittent training on heart rate variability in prepubescent children. Eur J Appl Physiol 2009; 105: 731-8 28. Barbeau P, Maribeth HJ, Howe CA, et al. Ten months of exercise improves general and visceral adiposity, bone, and fitness in black girls. Obesity 2007 Aug; 15: 2077-85 29. Obert P, Nottin S, Baquet G, et al. Two months of endurance training does not alter diastolic function evaluated by TDI in 9-11 year old boys and girls. Br J Sports Med 2009; 43: 132-5 30. Watts K, Beye P, Siafarikas A, et al. Exercise training normalizes vascular dysfunction and improved central adiposity in obese adults. J Am Coll Cardiol 2004; 43: 1823-7
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31. Gutin B, Barbeau P, Owens S, et al. Effects of exercise intensity on cardiovascular fitness, total body composition, and visceral adiposity of obese adolescents. Am J Clin Nutr 2002; 75: 818-26 32. DeStefano RA, Caprio S, Fahey JT, et al. Changes in body composition after a 12-wk aerobic exercise program in obese boys. Pediatr Diabetes 2000; 1: 61-5 33. Owens S, Gutin B, Jerry A, et al. Effect of physical training on total and visceral fat in obese children. Med Sci Sports Exerc 1999; 31 (1): 143-8 34. Treuth MS, Ningqi H, Deborah RY, et al. Accelerometry measured activity or sedentary time and overweight in rural boys and girls. Obes Res 2005 Sep; 13: 1606-14 35. Rowlands AV, Thomas PWM, Eston RG, et al. Validation of the RT3 triaxial accelerometer for the assessment of physical activity. Med Sci Sports Exerc 2004 Mar; 36: 518-24 36. Freedson PS, Sirard J, Dehold E, et al. Calibration of the Computer Science and Applications, Inc. accelerometer [abstract]. Med Sci Sports Exerc 1997 May; 29 Suppl.: S45
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37. Lohman TG, Harris M, Teixeira PJ, et al. Assessing body composition and changes in body composition: another look at dual-energy x-ray absorptiometry. Ann N Y Acad Sci 2000; 904: 45-54 38. Glickman SG, Marn CS, Supiano MA, et al. Validity and reliability of dual-energy x-ray absorptiometry for the assessment of abdominal adiposity. J Appl Physiol 2004; 97: 509-14 39. Goran MI. Measurement issues related to studies of childhood obesity: assessment of body composition, body fat distribution, physical activity, and food intake. Pediatrics 1998 Mar; 101 (3): 505-18
Correspondence: Professor Gareth Stratton, Professor of Paediatric Exercise Science, Chair REACH Group, Sport and Exercise Sciences, Liverpool John Moores University, Tom Reilly Building, Byrom St Campus, Liverpool, L3 3AF, UK. E-mail:
[email protected]
Sports Med 2011; 41 (6)
Sports Med 2011; 41 (6): 489-506 0112-1642/11/0006-0489/$49.95/0
REVIEW ARTICLE
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Can Neuromuscular Fatigue Explain Running Strategies and Performance in Ultra-Marathons? The Flush Model Guillaume Y. Millet1,2 1 Universite´ de Lyon, F-42023, Saint-Etienne, France 2 Inserm U1042, Grenoble, F-38000, France
Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Neuromuscular Alterations in Ultra-Marathon Running . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Central Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Alterations at the Muscle Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. The Flush Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Power Output Change in Ultra-Marathon Runners. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Description of the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Feed-Forward and Feedback Mechanisms Influence the Filling Rate . . . . . . . . . . . . . . . . . . . . . . 3.4 Apart from the Filling Rate, Which Factors Influence the Quantity of Water?. . . . . . . . . . . . . . . . 3.5 The Waste Pipe. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 The Security Reserve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Abstract
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While the industrialized world adopts a largely sedentary lifestyle, ultramarathon running races have become increasingly popular in the last few years in many countries. The ability to run long distances is also considered to have played a role in human evolution. This makes the issue of ultra-long distance physiology important. In the ability to run multiples of 10 km (up to 1000 km in one stage), fatigue resistance is critical. Fatigue is generally defined as strength loss (i.e. a decrease in maximal voluntary contraction [MVC]), which is known to be dependent on the type of exercise. Critical task variables include the intensity and duration of the activity, both of which are very specific to ultra-endurance sports. They also include the muscle groups involved and the type of muscle contraction, two variables that depend on the sport under consideration. The first part of this article focuses on the central and peripheral causes of the alterations to neuromuscular function that occur in ultra-marathon running. Neuromuscular function evaluation requires measurements of MVCs and maximal electrical/magnetic stimulations; these provide an insight into the factors in the CNS and the muscles implicated in fatigue. However, such measurements do not necessarily predict how muscle
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function may influence ultra-endurance running and whether this has an effect on speed regulation during a real competition (i.e. when pacing strategies are involved). In other words, the nature of the relationship between fatigue as measured using maximal contractions/stimulation and submaximal performance limitation/regulation is questionable. To investigate this issue, we are suggesting a holistic model in the second part of this article. This model can be applied to all endurance activities, but is specifically adapted to ultra-endurance running: the flush model. This model has the following four components: (i) the ball-cock (or buoy), which can be compared with the rate of perceived exertion, and can increase or decrease based on (ii) the filling rate and (iii) the water evacuated through the waste pipe, and (iv) a security reserve that allows the subject to prevent physiological damage. We are suggesting that central regulation is not only based on afferent signals arising from the muscles and peripheral organs, but is also dependent on peripheral fatigue and spinal/supraspinal inhibition (or disfacilitation) since these alterations imply a higher central drive for a given power output. This holistic model also explains how environmental conditions, sleep deprivation/mental fatigue, pain-killers or psychostimulants, cognitive or nutritional strategies may affect ultra-running performance.
1. Introduction More than 30 000 articles have been published about fatigue. Limiting keywords to ‘muscle’ and ‘fatigue’ still gave us more than 12 000 articles. It is known that the magnitude and aetiology of fatigue depend on the exercise under consideration.[1] Critical task variables include the muscle activation pattern, the type of muscle group involved and, the type of muscle contraction. However, the intensity and duration of activity are probably among the most important factors. This article focuses on ultraendurance running exercises, the so-called ultramarathons. Throughout the world (e.g. in the US, Europe, Japan, Korea, South Africa), ultra-marathons have become increasingly popular in the last few years. For example, Hoffman et al.[2] recently analysed the participation in 161 km ultra-marathons in North America and showed that the number of finishes increased exponentially over the period 1977–2008 through a combination of increases in the number of participants, average annual number of races completed by each individual, and number of races organized every year. It is considered that more than 30 000 runners took part in at least one ultra-marathon in France in ª 2011 Adis Data Information BV. All rights reserved.
2009. There is no consensus about the definition of contemporary ultra-marathons; some authors consider it to be any distance greater than a marathon, while for others, it is any event that exceeds 4 hours[3] or 6 hours[4] in duration. Ultramarathons can last for up to 40 hours or even several days (e.g. 6-day races) and are basically of two types: (i) ultra-marathons performed on a mostly flat road (24 hours, 100 km); and (ii) those run on varying terrains (e.g. 100 miles in the US). Contrary to what is usually claimed, ultra-marathon running is not new; the Six-Day Professional Pedestrian Races in London and New York have existed since the 1880s.[5] Importantly, the ability to run, rather than only walk, over long distances (i.e. without fatigue) may have played a role in human evolution.[6] For example, it has been suggested that endurance running may have helped hominids to exploit protein-rich resources. Thus, while endurance running is now primarily a form of recreation, its roots may be as ancient as the origin of the human genus.[6] This type of extreme event can also be seen as a testbed for ideas on how some people manage to perform physical feats at which others can only marvel.[7] Several models of fatigue have been proposed in the literature. For example, Abbiss and Laursen[8] reviewed the following eight different models that Sports Med 2011; 41 (6)
The Flush Model of Fatigue
may be applied to prolonged cycling: cardiovascular/ anaerobic, energy supply/energy depletion, neuromuscular fatigue, muscle trauma, biomechanical, thermoregulatory, psychological/motivational and the central governor model. All of these are interrelated. For example, changes in biomechanical patterns may be both the cause and consequence of neuromuscular fatigue, which is also influenced by modifications in cardiovascular/ anaerobic metabolism, muscle trauma and thermal conditions, and all potentially associated with the central governor. Similarly, depending on the authors and the scientific field, central fatigue has been presented as a decrease in percentage maximal voluntary activation (%VA),[9] neurobiological modifications in the brain,[10] a modification of motor control[11] or alterations in cognitive function.[12] In exercise physiology, most published articles have defined fatigue as strength loss (i.e. a decrease in maximal voluntary contraction [MVC]). Strength loss in the fatigued state is multifactorial and is generally divided into central (i.e. above the neuromuscular junction) and peripheral (muscular), these two origins being interdependent on the mediation of peripheral afferences. The central/peripheral distinction was already proposed by Bainbridge in 1931.[13] In this context, central fatigue is an altered ability of the CNS to recruit motor units at a higher discharge rate than the frequency of tetanic fusion. In other words, a decrease in maximal voluntary activation (i.e. central fatigue) might be due to a de-recruitment of motor units and/or a decrease of the discharge frequency beyond the frequency of tetanic fusion, both factors leading to force decline. Central fatigue is variably implicated in total fatigue. It has been shown that prolonged exercise is associated with a large decrease in %VA, especially with running.[14] However, the role of central fatigue and its supraspinal and spinal components in the cessation of exercise (if the intensity is fixed) or in performance (if the intensity is self-chosen) is not clear. The problem is further complicated by the fact that (i) environmental conditions such as hypoxia and hyperthermia may exacerbate central fatigue or perceived exertion,[15,16] two different forms of central alterations; and (ii) that mental ª 2011 Adis Data Information BV. All rights reserved.
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fatigue – another type of central alteration – has been demonstrated to play a role in performance limitation.[17] Although central fatigue is of great importance, this does not imply that peripheral fatigue is unimportant. How peripheral changes, central fatigue, environmental conditions and a runner’s strategies (whether cognitive, nutritional or tactical) affect ultra-marathon performance and regulation of speed during a race has never been considered. Thus, the first aim of this article is to review the central and peripheral factors that might influence strength loss during very prolonged running exercise. Describing these central and peripheral factors is essential but it does not predict how they affect submaximal muscle function during ultraendurance running or how they influence speed regulation during a real competition (i.e. including pacing strategies). This poses the question of the relationship between fatigue evidenced by measures taken during maximal contractions/stimulation and performance limitation/regulation. The second aim of this article is then to propose a model that integrates these different parameters, including central and peripheral fatigue, which may help us to understand pacing strategies and performance during ultra-marathons. While the model is particularly well adapted to ultra-marathons, it can be applied to any type of endurance performance. 2. Neuromuscular Alterations in Ultra-Marathon Running The alterations in neuromuscular function after prolonged running, cycling and skiing were reviewed by Millet and Lepers in 2004.[14] They focused on the origin of muscle fatigue after prolonged exercises lasting from 30 minutes to several hours. The authors showed that the knee extensors isometric strength loss increased in a non-linear way with exercise duration when running for longer than 2 hours. Since then, several articles have been published on this topic.[18-23] As shown in figure 1, the tendency toward no further decrease in knee extensor strength with increasing in running duration is confirmed. Less is known about the decrease in peak power after prolonged running. Nevertheless, it Sports Med 2011; 41 (6)
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Knee extensors strength loss (%MVC)
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45 40 35
*
30 25 20 15 0
500
1000
1500
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Duration (min) Fig. 1. Relationship between strength loss in the knee extensors expressed as a percentage of maximal voluntary contraction (%MVC) at rest and duration of running exercise.[18,19,23-30] * Indicates the value is from unpublished observations.
has been reported that the decrease in countermovement jump performance was around 45–60% of the knee extensors isometric MVC decrease after prolonged running.[19,24,25,31] Similarly, Lepers et al.[24] reported that isokinetic strength loss was smaller when measured over a concentric contraction compared with the eccentric or isometric mode. 2.1 Central Contribution
Methods such as the twitch interpolation technique, the ratio of electromyogram (EMG) signal during MVC normalized to the M-wave response (Mmax) or the comparison of the forces achieved with voluntary and electrically evoked contractions, have systematically shown that central fatigue largely contributes to muscle fatigue during long distance running.[20,22,25-27,32] It is known that the decrease in central activation occurring during exercise can be caused by several factors at the spinal (motoneurone properties, afferent input) and/or supraspinal levels.[9,33,34] A few studies have measured the changes in strength of muscles not involved in the exercise to further explore the origin of the lower central drive postexercise.[20,27,32] It was hypothesized that a loss of grip strength after running would be a good indicator of supraspinal fatigue but no consistent changes were observed in grip strength following running exercise. Thus, this measurement did not ª 2011 Adis Data Information BV. All rights reserved.
allow any conclusion of the existence or the absence of supraspinal fatigue after prolonged running because selective supraspinal fatigue may have occurred. Ohta et al.[35] investigated biochemical modifications during a 24-hour run and from indirect measurements such as serum serotonin and free tryptophan levels, they suggested that this type of exercise induces some supraspinal fatigue. It has been suggested for years that the accumulation of serotonin in several brain regions contributes to the development of fatigue during prolonged exercise.[36] This was thought to be due to an increase in the concentration ratio of free tryptophan to branched-chain amino acids because (i) branched-chain amino acids are oxidized; and (ii) higher plasma free fatty acids during prolonged exercise cause a parallel increase in free tryptophan since the free fatty acids displace tryptophan from their usual binding sites on albumin.[36] This in turn increases the concentration of free tryptophan (the serotonin precursor) in the brain. Meeusen et al.[10] went further suggesting that other neurotransmitters such as dopamine probably also play a significant role in supraspinal fatigue. Nevertheless, to the best of our knowledge, no study has clearly shown any evidence of central fatigue (e.g. a depressed %VA) with an increased serotonin/dopamine ratio or other biochemical changes in the CNS. Meeusen et al.[10] acknowledged that fatigue is probably due to a complex interaction between Sports Med 2011; 41 (6)
The Flush Model of Fatigue
central and peripheral mechanisms. It is worth noting that we recently observed a significant correlation between %VA changes during a 24-hour treadmill run for plantar flexor and knee extensor muscles,[26] which could be indicative of a common supraspinal mechanism regulating the neural drive to the working muscle. Another possibility is that hyperventilation lowers the arterial carbon dioxide tension and blunts the increase in cerebral blood flow, which can lead to an inadequate oxygen delivery to the brain and contribute to the development of fatigue.[37] Nevertheless, this is less likely to occur during ultra-marathons, because of the relatively low level of ventilation. While some central activation deficit has been observed for knee extensor muscles in cycling,[38] there is a lower level of central fatigue after activities that result in less muscle damage than do running.[39] When marathon skiing[40] and 30-km running[27] in similar competitive conditions and duration were compared, the decrease in %VA was more pronounced for running than for skiing. Millet and Lepers[14] suggested that this result indicated spinal modulation rather than cortical alteration after the running exercise. Data from reflex measurements, such as the Hoffmann reflex (Hmax), provide interesting insights into the origin of central fatigue. The Hmax/Mmax ratio as been used as an indicator of motoneuronal excitability, and more generally to evidence modulations of neural drive at the spinal level.[41] This index was found to decline during a 24-hour treadmill run and was correlated with decreases in MVC and %VA, especially at the end of the exercise (personal observation). This finding concurs with those of Racinais et al.[20] who reported depressed H-reflexes after a 90-minute run. This could be due to reduced motoneurone excitability or pre-synaptic inhibition. In both cases, inhibitory mechanisms could be limiting muscle force production. Such inhibitory action may result from thin afferent fibre (group III–IV) signalling, which may have been sensitized by the production of pro-inflammatory mediators, produced during prolonged exercise.[42-46] So, while supraspinal fatigue may play a role in reduced neural drive after prolonged exercises, it can be suggested that spinal adaptation, such as inhibiª 2011 Adis Data Information BV. All rights reserved.
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tion from type III and IV group afferents or disfacilitation from muscle spindles contributes to this reduced central drive. Group III–IV afferent fibres may also contribute to the submaximal output from the motor cortex (see Taylor et al.[47]). Taken together, this suggests that high central fatigue due to prolonged running is not due solely to CNS biochemical changes but that afferent fibres are probably involved. The twitch interpolation technique at the peripheral nerve does not allow discrimination between central fatigue originating from a supraspinal site and/or from the spinal level. Researchers at the Prince of Wales Medical Research Institute in Sydney (e.g. Todd et al.[48]) have measured supraspinal deficit by superimposing magnetic stimulations of the motor cortex to voluntary contractions. Recently, this method has been used to demonstrate the existence of supraspinal %VA alteration in the quadriceps after cycling exercise and prolonged MVCs.[49,50] Future studies should use this new method to further investigate central fatigue after ultra-distance running exercises. 2.2 Alterations at the Muscle Level
Central fatigue alone cannot explain the entire strength loss after prolonged running exercises. Alterations of neuromuscular propagation, failure of excitation-contraction coupling and modifications in the intrinsic capability of force production may also be involved. To the best of our knowledge, there has been no measure of change in action potential conduction velocity using high-density EMG after prolonged running exercise. Information about the propagation of the action potentials can then only be deduced from changes in the M-wave characteristics. This is problematic,[51] especially in the case of ultramarathons, since muscle oedema and sweat can complicate interpretation of the Mmax. Limits also exist for mechanical twitch responses after ultramarathons,[14] in particular the fact that fully potentiated twitches were not always used in the past (e.g. Millet et al.[25]). Tetanic responses are slight or not influenced by potentiation. A significant but moderate (~10%) decrease in highfrequency force response has been found for knee Sports Med 2011; 41 (6)
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extensors after prolonged[27] or ultra-long[26] running exercise. Indirect indices of muscle damage (creatine kinase, myoglobin, C-reactive protein, myosin heavy chain fragments, lactate dehydrogenase, aspartate aminotransferase, alanine aminotransferase, cytokines) also suggest the existence of some peripheral alterations[26,44-46,52-54] but from the few results available, it appears that subjects show wide variability in the degree of muscle damage.[26] Potential explanations for this variability in inter-individual response include differences in genes, training (particularly the repeated bout effect,[55] that likely occurs, particularly during downhill running training[56]) flexibility, oxidative stress,[18] muscle fibre type[57] and running technique. No direct evidence exists to preferentially support any one factor and it is probably a combination of them all that explains the large difference in muscle damage among subjects. Nevertheless, for the last two potential factors (i.e. typology and running technique), it is interesting to report that (i) there was no significant relationship between knee extensor peripheral fatigue and percentage type I muscle fibres in the vastus lateralis;[26] and that (ii) ultra-long running (from Paris to Beijing [i.e. 8500 km in 161 days, ~53 km daily]) modified the running patterns towards a ‘smoother’ style in one case study.[58] This latter point was evidenced by (i) a higher stride frequency and duty factor; (ii) a reduced aerial time with no change in contact time; (iii) a lower maximal vertical force and loading rate at impact; and (iv) a decrease in both potential and kinetic energy changes at each step.[58] We also measured a reduced running economy after the trip. Thus, even if it is possible that the running pattern changes could be linked to the decrease in maximal strength also observed, we suggested that running pattern modification was a strategy to reduce the potential deleterious effects of his ultra-long distance run rather than to decrease the energy cost of running. Further studies should also examine the potential influence of running technique on muscle damage during ultra-endurance running, particularly when running on variable terrain (trails). Anecdotal information from qualified coaches suggests that technical ability in ª 2011 Adis Data Information BV. All rights reserved.
downhill sections might be a real determinant of fatigue and performance. Low-frequency fatigue (LFF; also called prolonged low-frequency force depression[59]) [i.e. the preferential loss of force at low frequencies of electrical stimulation] is a prominent characteristic of exercises involving lengthening contractions of the active muscles such as eccentric- and stretch shortening cycle-type exercises,[60] and has been associated with failure of the excitationcontraction coupling.[61] Contrary to what is generally observed after downhill running,[56,62] most studies did not show LFF after prolonged running exercise despite several experiments that have measured this factor during ultra-long running exercises including the recent 24-hour treadmill study.[26-28,32] Only recently have we been able to able to measure LFF after one of the most extreme exercises realized by humans in race conditions: a 166-km mountain ultra-marathon with 9500 m of positive and negative elevation change.[23] It can then be suggested that minimal exercise intensity is required to induce mechanical or metabolic disturbances that can result in developing LFF. However, there is a limitation in that it is theoretically possible that axonal hyperpolarization preferentially depresses the high-frequency response during tetanic muscle stimulation. Thus, an absence of modification to the low- to highfrequency ratio could have resulted from the combined effects of LFF, which preferentially depresses low-frequency response, and hyperpolarization, which preferentially depresses highfrequency response.[26] 3. The Flush Model Measuring central activation changes or force/ EMG responses during MVCs or electrically evoked stimulation after prolonged running gives some insight into the potential factors implicated in fatigue at the CNS and/or muscle level. However, it does not predict how this affects submaximal muscle function during ultra-endurance running or how this influences speed regulation during a real competition when pacing strategies are allowed. This poses the question whether there is any relationship between fatigue evidenced by Sports Med 2011; 41 (6)
The Flush Model of Fatigue
measures taken during maximal contractions/ stimulation and performance limitation/regulation. Before answering this question, the change in power output during ultra-marathons must first be described. 3.1 Power Output Change in Ultra-Marathon Runners
Only one study[31] has examined the changes in efficiency/energy cost to determine whether power output can be connected to speed. Since it is unlikely that a large uncoupling exists between power output and velocity, monitoring the speed change over flat-course ultra-marathons (the typical event being the 24-hour race) gives an excellent idea of the mechanical power produced by the runner. During a self-paced 24-hour treadmill run where the subjects were asked to give their best performance as they would in a normal race, Martin et al.[26] showed a clear decrease in velocity during the first 16 hours before there was a tendency for this to level off. Over a shorter distance (68 km) in a real competition, Utter et al.[63] showed that subjects reached a rating of perceived exertion (RPE) similar to the subjects of Martin et al.[26] (i.e. 15.4 – 0.4) but there was an increase of RPE up to the end of the race. While several studies have investigated 24-hour races,[64-66] we are not aware of any data reporting velocity changes during an official race. Anecdotal evidence and personal data suggest that speed is reduced during ultra-marathons, even in elite athletes. Also, unpublished results showed that the speed of the top five runners in the 2007 French 24-hour championship had a tendency to decrease but that this was less pronounced for the winner. Over a shorter distance (100 km at the 1995 International Association of Ultra Runners World Challenge), Lambert et al.[67] reported that the best runners (i) reduced their initial speed less and later in the race; and (ii) they showed less variation in their speeds than did their lesserperforming counterparts. The general tendency was still that these 107 runners reduced their speed over time.[67] In marathon running, it has been reported[68] that some elite athletes are able to maintain their pace throughout the race but ª 2011 Adis Data Information BV. All rights reserved.
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their slower counterparts (especially young men[69]) slow down over the distance. It is more difficult to document power output and speed change during ultra-trails (i.e. off-road ultra-marathons) since the terrain is usually hilly, even mountainous. Future studies should measure the changes in speed at given slopes using global positioning system tools. Data from Utter et al.[63] nevertheless suggest that heart rate (HR) decreases over a 68-km ultra-marathon. Personal data also show that HR generally decreases over an ultra-endurance race in mountains (see figure 2a and the first 22 hours in figure 2b) and that a correlation exists between change in HR and change in elevation speed (in m/hour) similar to the relationship seen between change in HR and speed variation after the first 10 km of a marathon.[70] When using HR to predict speed variations, one should also consider the HR drift with fatigue (i.e. HR increases at a given speed). This is visible in the first 1–2 hours in figure 2 but the change in HR due to cardiac drift is less than the speed variation. However, any decrease in HR during competitions can also only be due to a decreased power output. 3.2 Description of the Model
Despite the lack of systematic studies on speed change over ultra-marathons, the data presented strongly suggest that power output/speed decreases with time in ultra-marathon running, even in elite athletes. Following the initial question about the relationship between fatigue as evidenced by measures taken during maximal contractions/ stimulations and performance limitation/regulation, one should then ask whether the decreased speed is due to strength loss. Based on the fact that knee extensor and plantar flexor muscle strength decreased by ~30–40% and since the force developed at each step is very low, it does not appear that strength loss can directly explain this speed reduction. Marcora et al.[71,72] suggested that the locomotor muscles’ capacity for force production is always well above the requirements of highintensity cycling, which is about 20% of MVC. In other words, even if this point has been debated,[73] failure to produce the force/power required by Sports Med 2011; 41 (6)
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a 2500
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Race duration (h) Fig. 2. Typical heart rate (HR) changes during two mountain ultra-marathon races: one crossing the island of la Reunion (155 km [a]) and one around the Mont-Blanc (165 km [b]). The global tendency of heart change is indicated by the solid line. The profile of the course is also given (grey shading).
the exercise despite maximal voluntary effort does not seem to limit submaximal performance as commonly assumed. While it is not possible to predict to what extent the fatigue mechanisms identified during maximal isometric contractions would affect performance during an ultra-endurance running event, it may nevertheless be hypothesized that there is an indirect effect. As proposed by the teleª 2011 Adis Data Information BV. All rights reserved.
oanticipatory system [74] or the central governor model,[75,76] the level of muscle activation (and so the speed) is thought to be progressively reduced to keep the RPE during running below a maximum tolerated level[26] (i.e. to maintain the body below a homeostatistically acceptable exercise intensity).[77,78] In relation to the data presented above regarding central and peripheral fatigue, the last part of the present article will discuss Sports Med 2011; 41 (6)
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(i) how RPE is progressively increased with time for a constant (or even lower) running speed; and (ii) how in relation to changes in environmental conditions, sleep deprivation/mental fatigue, drugs, cognitive or nutritional strategies, this may regulate performance in ultra-marathon running. We propose a conceptual model based on the flush toilet (figure 3). The ‘flush model’ is based on the central governor model proposed by Noakes et al.[76] (i.e. agrees with the fact that exercise performance is regulated by the CNS specifically to prevent catastrophic physiological failure). However, the flush model emphasizes the importance of peripheral fatigue that has been described in detail in the first part of the present article. Also, because it has been suggested that the central governor integrate the input from various systems all related to exercise,[79] the flush model was also built to take into account changes not associated with exercise. The present model was mainly designed to explain the role of fatigue on perfor-
mance in ultra-marathon running and consists of four components: the ball [or buoy, (1) in figure 3] represents RPE and can increase or decrease based on the filling rate (2) and the water evacuated through the waste pipe (3). There is also a security reserve (4), also called the emergency reserve,[80] which allows the subject to prevent physiological harm.[81] We believe that the flush model can help us to understand running strategies during an ultra-marathon, but a few questions must first be addressed. 3.3 Feed-Forward and Feedback Mechanisms Influence the Filling Rate
First, what influences the filling rate? At the beginning of an ultra-marathon, running pace is based on a control system which estimates the optimal power output.[82] Depending on the runner, his or her goal may be either simply finishing the race, finishing the race in a certain time Security reserve 4
Death Exercise cessation I feel good (±) RPE
Nociceptive information (muscle, joint, tendon, blister, digestive problems, etc.)
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Fig. 3. The flush model. Rating of perceived exertion (RPE) is assimilated to the volume of water in the tank (i.e. an increase in volume of water signifies a higher RPE and decreasing the level of water in the tank indicates decreasing RPE). The water can get in (filling rate, 2) and out (via waste pipe, 3) and the level of water can be detected by the ball (1). The level of water depends on the filling rate, mainly determined by peripheral changes and central inhibition/disfacilitation (feedback and feed-forward mechanisms), but other factors such as mental fatigue, nutritional strategies, sleep deprivation, environmental conditions and exceptional events during races can affect the level. The size of the security reserve (4) is mainly determined by motivation. Psychostimulants and pain killers can modify the sensitivity of the RPE sensor (i.e. the ball).
ª 2011 Adis Data Information BV. All rights reserved.
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or being well placed at the finish (for instance being in the top ten runners). The estimation is based on several factors such as distance, elevation, environmental conditions, training status and runabilty (i.e. technical difficulty) of the course. This teleoanticipation means that the runner has a template that contains existing data on exercise performance (the so-called ‘experience’ or personal history of running). In fact, Ulmer[82] considers that this template may be inborn but it is generally accepted that optimal pacing strategy is the result of a learning process.[67] The initial pace gives an initial filling rate of the tank and the higher the initial speed for a given running, the faster the filling rate. This is mainly due to both feed-forward and feedback mechanisms. Feedback mechanisms have been widely documented in the literature. For instance, Amann et al.[83] nicely showed that by attenuating the ascending activity of nociceptive and metaboreceptive Ad (group III) and C fibres (group IV), somatosensory feedback from the locomotor muscles influences central motor drive. Even if the this experimental study has been criticized for lack of proper placebo procedures,[84] the authors concluded that locomotor muscle afferent feedback, which also facilitates performance through optimizing muscle oxygen delivery,[85] exerted an inhibitory influence on the determination of central motor drive during high-intensity exercise. While acidosis or inorganic phosphate accumulation is unlikely to occur in ultra-marathon running, other biochemical mediators, such as the accumulation of extracellular potassium[86] or cytokines (especially interleukin-6 and its antagonist IL-ra) due to structural muscle damage[44-46] (see section 2.2 about peripheral fatigue), could trigger group III/IV afferent fibres and mediate the sensation of fatigue.[74] Nociceptive information is much more complicated; there is a potential role for pain arising not only from the muscles but also from other sites such as joints and tendons. Even blisters (cutaneous afferences[87]) or digestive problems[42] could all potentially play a role. For shorter distances (i.e. higher intensity and ventilation rate), dyspnoea may also be involved in nociceptive information[88] but probably not in ultra-marathon running. It should also be noted ª 2011 Adis Data Information BV. All rights reserved.
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that a modest temporary reduction in pressure pain perception was observed after a 100-mile (161-km) trail run, only in the faster runners.[89] Although the subject is debated,[85] feedback from the locomotor muscles probably plays a major role in central motor drive regulation in ultramarathons. As stated in section 2.1, the downregulation of group III/IV afferents at the spinal and supra-spinal levels[90] is a probable explanation for why maximal %VA is lower after running than after cycling or skiing for similar intensity/ distance.[14] While afferent feedback certainly has a key function, the regulation of central motor command is complex and also depends on the environment. For example, altitude and elevated temperature are two conditions frequently encountered by ultra-marathon runners (e.g. races in Nepal or in deserts). Regarding altitude, using a sub-maximal test until exhaustion in hypoxia/normoxia while the muscles were maintained in identical complete ischaemic conditions, we showed that (i) inhibitory mechanisms from working muscles play a major role in the cessation of the exercise in hypoxia and that (ii) a minor but significant direct effect of inspired oxygen fraction on the CNS could potentiate this limiting mechanism and explain why performance was slightly reduced in hypoxia.[15] Similarly, Amann et al.[91] showed that peripheral fatigue measured with femoral nerve magnetic stimulation at task failure was substantially less severe in hypoxia compared with normoxia or moderate hypoxia. This was attributed to brain hypoxic effects on effort perception, leading the subjects to stop earlier. Similar conclusions can be deducted from hypoglycaemia[92] or hyperthermia experiments. Regarding this latter factor, it is worth noting that high temperature does not alter performance over brief contractions but does cause reductions during sustained contractions[16] or prolonged exercises.[93] Thus, sensory feedback contributes to central fatigue and effort perception, presumably through its indirect projection into the anterior cingulate cortex.[85] Other authors have argued that sensory signals from peripheral receptors do not contribute to perception of effort but generate other sensations experienced during exercise (e.g. muscle Sports Med 2011; 41 (6)
The Flush Model of Fatigue
pain and thermal sensation[84,94]). In all cases, the increase in RPE with fatigue is not abolished by spinal blockade of somatosensory feedback from the muscles; there must be other mechanisms. Besides triggering inhibitory mechanisms, peripheral fatigue implies that greater muscle activation is required for a given mechanical power to be produced in the fatigued condition. Indeed, at a given force or power output, the onset of fatigue is usually concomitant with a rise in neuromuscular cost (EMG signal amplitude), which points to the recruitment of additional motor units and/ or a higher discharge rate in order to compensate for peripheral alterations. It is known that RPE changes and the increase in muscle activity during a constant-load exercise are correlated.[95] Marcora et al.[71] showed that the reduced locomotor muscle force after drop jumps resulted in a higher RPE at a given power output and a reduced time to exhaustion during high-intensity constant-power cycling. They suggested that the effects were mediated by the increased central motor command required to exercise with weaker locomotor muscles,[71] which increased the perception of effort probably throughout its corollary discharge to sensory areas of the brain.[84] Similarly, Gagnon et al.[96] tested the effects of pre-induced quadriceps fatigue (using electrostimulation) on endurance performance of healthy individuals and patients with chronic obstructive pulmonary disease. These authors demonstrated that endurance time significantly decreased by 20–25% in the experimental condition in both groups. There has been hot debate about the role of afferent feedback from fatigued locomotor muscles as an important determinant of endurance exercise performance.[84,85] Gagnon et al.[96] suggested that that the enhanced metaboreflex is not the main mechanism through which exercise tolerance was reduced in the fatigued state in both study populations. Moreover, a change in muscle efficiency after ultra-marathon running[31] can affect muscle recruitment to maintain a given task. Spinal inhibition and/or disfacilitation (see section 2.1) after ultramarathon running could also necessitate higher central command (i.e. reinforcing feed-forward mechanisms) from supraspinal sites. Finally, the ª 2011 Adis Data Information BV. All rights reserved.
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gain of motoneurones decreases in fatigued conditions such that additional synaptic drive at a premotoneuronal level is required to maintain a constant firing rate[9] (i.e. a larger descending drive is needed to continue exercise at the same power output). So, as well as the nociceptive signal coming from the peripheral receptors, these feed-forward mechanisms (also called the ‘sense of effort’[1,79]) could also partly explain the RPE drift.[97] Interestingly, RPE for the same exercise could vary with environmental conditions and is not necessarily associated with a decrease in MVC (e.g. at altitude).[98] In summary, the initial pace and the adjustments made in response to these feed-forward and feedback mechanisms[81] directly affect filling rate. RPE is probably affected by both central command output and muscle afferents.[85] In other words, as stated by Smirmaul,[94] an interaction between the sense of effort and the sensations obtained from afferent sensory feedback that is probably the ultimate regulator of exercise performance. We suggest that for ultra-marathons performed over hilly terrain, feed-forward and feedback mechanisms are mainly implicated in uphill/ flat sections and downhill sections, respectively. 3.4 Apart from the Filling Rate, Which Factors Influence the Quantity of Water?
The second question is whether the volume of water (absolute RPE) depends only on the filling rate? The answer is clearly no. It has recently been argued that it is an interaction between the sense of effort and the sensations obtained from afferent sensory feedback that is probably the ultimate regulator of exercise performance.[94] However, while these two factors certainly play a crucial role, it is, for example, possible to start an exercise with more water in the tank than usual (i.e. with a higher RPE at the beginning of exercise than is normally the case). Indeed, it is possible to feel some fatigue without any exhaustive physical load, for example, after a stressful day. More importantly, Marcora et al.[17] reported that the time to exhaustion at 80% of peak power output was significantly reduced by ~15% after 90 minutes of a demanding cognitive task. This was associated with a higher RPE at the beginning of the Sports Med 2011; 41 (6)
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cycling exercise compared with the control condition. Since RPE increased similarly over time in both conditions, mentally fatigued subjects reached their maximal tolerated RPE and disengaged from the cycling exercise earlier than did the controls.[17] Similarly, although one night of sleep deprivation does not usually affect MVC or intense exercise,[99] several studies have demonstrated a deleterious effect on endurance performance.[100,101] Interestingly, a higher RPE for a given load has been observed after sleep deprivation.[99,100,102] Some authors[100,101] showed that, after sleep deprivation, subjects ran a shorter distance during a 30-minute self-paced treadmill exercise than did their controls, yet their perception of effort was similar. The authors suggested that altered perception of effort may account for decreased endurance performance after sleep deprivation. Other data show that RPE is not only dependant on sensory information and cortical output. For example, unknown or unexpected running exercise duration may affect RPE,[103] suggesting that RPE has an affective component. Also, changing the tempo of music that cyclists were listening to influenced their self-chosen power and cadence.[104] In summary, effort perception probably involves the integration of multiple signals from a variety of perceptual cues. Alternatively, as suggested in section 3.6 for the amphetamines or pain killers, it can be argued that perception is a complex neurocognitive process that does not depend only on the intensity of the sensory signal because sensory signals are processed at brain level and interpreted by the subject. It is then possible that mental fatigue, music and sleep deprivation affect the processing of sensory signals rather than provide additional sensory signals. Nevertheless, in that case, the rate of increase in RPE (filling rate) would be changed rather than RPE at the beginning of the exercise, as is the case, for example, for mental fatigue.[17] 3.5 The Waste Pipe
The third question is whether rest is the only way to decrease the level of water in the tank. It is the most obvious but probably not the only one. For example, it may be possible to reduce the ª 2011 Adis Data Information BV. All rights reserved.
water level while running using suitable psychological strategies.[105] Various psychological routines can be used by runners to attenuate the discomfort of intense physical exertion. These strategies, labelled ‘dissociative thoughts’ (i.e. the runner distracts him-/herself by thoughts of a more external nature) are performed to diminish the sensations of pain during a marathon.[105] Nevertheless, it has been reported that the best marathon runners adopt associative cognitive strategies (i.e. are centered on their own sensations),[106] probably with the goal of maintaining optimal running technique/efficiency and so decreasing peripheral fatigue (and the filling rate). Another way to decrease the level of water in the tank might be nutritional strategies. Chambers et al.[107] recently showed that rinsing the mouth with solutions containing glucose and maltodextrin could improve cycling performance. The authors suggested that this could be due to activation of brain regions involved in reward and motor control since functional MRI measurements showed that these regions believed to mediate emotional and behavioural responses to a rewarding sensory stimulus were activated. Thus, in addition to its peripheral action (i.e. slowing the tank filling rate), glucose ingestion could have some central effects (i.e. decreasing the water level). Unidentified oral receptors in the mouth could counteract the increase in RPE, permitting higher central command and power output. Interestingly, the suggestion of downhill cycling through hypnotic manipulation decreased RPE without altering exercise HR or blood pressure responses.[108] One could also suggest that reducing maximal neural drive (i.e. central fatigue, a decrease in %VA) may reduce the size of the tank. In this context, Søgaard et al.[109] acknowledged that central fatigue can only be demonstrated during MVCs, but these authors suggested that a decrease in %VA may have contributed to the increase in RPE during sustained low-intensity contractions (i.e. 15% MVC for 43 minutes). They based this speculation on the fact that central fatigue was among the factors that predicted RPE changes. To our knowledge, no neurophysiogical basis exists regarding the potential role of central fatigue to explain at least in part the RPE changes. Sports Med 2011; 41 (6)
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3.6 The Security Reserve
The fourth question is why exercise stops (in the case of a time to exhaustion) or why the runner decides to adjust his or her speed/power output (in the case of a time trial). Assuming a constant level of motivation, exercise cessation appears to occur at the same RPE whatever the rising slope[110-112] and the starting level.[17,71] Marcora et al.[17] have incorporated Brehm’s theory – a general motivation theory that does not specifically refer to exercise – to propose a psychobiological model of endurance performance. Marcora et al.’s model postulates that subjects decide to withdraw effort (i.e. disengage) when an exercise is perceived to be either too difficult or the effort demanded exceeds the upper limit of what people are willing to invest. Alternatively, it has been proposed that exercise terminates when the feelings of discomfort overwhelm the potential rewards of continuing to exercise.[112] This was reviewed in 2003 by Kayser.[79] It is interesting to report that an opiate antagonist, naloxone, leads to significant reductions in exercise performance when compared with control trial.[113] The authors of the article concluded that working capacity was limited by the individual RPE, which can be attenuated by endogenous opioids rather than by physiological fatigue. Thus, it is not task failure but task disengagement that sets the exercise limit before reaching the security reserve. Even in highly motivated competitors, task disengagement always occurs before there is a threat to life.[79] Humans do not usually exceed their security reserve. There are a few exceptions where dramatic loss in body homeostasis was reached causing collapse such as during marathons or triathlons (e.g. Gabriela Andersen-Schiess in the 1984 Olympics or Julie Moss in the 1982 Hawaii Ironman), especially in (i) hot environments with subjects not always familiar with these environmental conditions (i.e. unadapted template/sensor efficiency); or (ii) under the effect of psychostimulants (e.g. Tom Simpson who died). One could pose the question whether elite athletes finish with a lower security reserve. While the common belief is that better athletes can ‘dig deeper’ and work relatively harder than their less ª 2011 Adis Data Information BV. All rights reserved.
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successful counterparts,[70] we are not aware of any scientific study supporting this concept. One indirect argument has been proposed by EsteveLanao et al.[70] who found that the pattern of percentage maximum heart rate (%HRmax) response during an event was very similar in athletes with large differences in running performance. These authors concluded that better runners are faster due to their underlying physiological capacity rather than to their ability to put greater relative effort into their competition. Studies investigating . the percentage of maximal oxygen uptake (VO2max) sustained during competition in function of the level of performance are contradictory. It was found that the faster running speed of the more trained runners over 10–90 km was . not due a higher %VO2max during competition but was due to their superior running economy.[114] In contrast, performance was significantly related to the specific endurance (i.e. the average speed sustained over. a 24-hour running exercise expressed in %VO2max).[57] Nevertheless, a higher . %VO2max or %HRmax sustained during competition does not necessarily mean that the elite athletes can ‘dig deeper’ since this may be due to physiological differences in terms of endurance. Further studies must examine this interesting question. A very important factor in the flush model is that the sensor may be deregulated (i.e. the interpretation of the incoming signal may be affected).[115] In other words, the processing of sensory signals is affected rather than additional sensory signals provided. This is true in two opposing cases: amphetamines[80] (or more generally when dopaminergic system is manipulated[115]) and pain killers,[83,116] both of which induce higher peripheral fatigue and/or metabolic disruptions. For example, higher lactate/HR[80,116] or lower peak doublet response to a magnetic stimulation at the cessation of exercise has been reported.[83] Interestingly, while more anecdotal, Amann et al.[83] reported that all their subjects needed assistance in disembarking the cycling ergometer after injection of intrathecal fentanyl. In the evening (i.e. several hours after exercise cessation), their subjects reported continuing problems with ambulation and muscle soreness, which had never been Sports Med 2011; 41 (6)
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observed in any of the many other studies requesting exhaustion conducted in that laboratory.[83] It should be noted that psychostimulants (e.g. amphetamines, cocaine) could act by providing a pleasure sensation (i.e. water leak throughout the waste pipe rather than sensor deregulated). Some runners, even elite athletes, use local anaesthetics/ anti-inflammatory drugs (e.g. Tissugel in France), particularly applied to the knee joint. While this is not prohibited by the World Anti-Doping Agency policy, the flush model suggests that this may be beneficial for improving performance in ultra-marathon running. According to the flush model, there is always a reserve for muscle recruitment (the security reserve) that can be used for the so-called ‘end spurt’[117] when the runner is at his or her highest level of peripheral fatigue. In ultra-marathons, this is clearly illustrated in figure 2b representing the HR data of an ultra-marathon runner performing a race around the Mont-Blanc. In this example, very particular race conditions (from 11th to 6th place with opponents regularly announced as potential targets) led the runner to accelerate at the end of the race when his muscle fatigue was higher than at ~22 hours. This further illustrates that central regulation is not totally based on peripheral changes. Since RPE was not recorded, it is not possible to say whether this was related to the enjoyment of overtaking several opponents near the finish line, which counteracted the sensation of fatigue (i.e. the same level of water despite increasing the power output with some water being evacuated through the waste pipe) and/or a decrease in the security reserve due to increased motivation. While mental processes are important in all sports, it is probably particularly true for ultramarathon runners. Weir et al.[118] suggested that the central governor is most applicable to endurance exercise since the decline in muscle performance under intensely fatiguing exercise conditions can be directly attributed to peripheral fatigue. It is further possible that for shorter distances, the organism is equipped with a system to protect its integrity, but at a peripheral level.[79] For example, glycogen depletion may alter excitationcontraction coupling, which in turn limits muscle ª 2011 Adis Data Information BV. All rights reserved.
contraction capacity and so restricts muscle damage. Also, it has been suggested that for exercise of several minutes duration, RPE is increased by sleep deprivation but when it is as short as 30 seconds, sleep deprivation causes only a small change in the perception of exercise intensity[99] and thus no reduction in performance. Marino et al.[119] recently argued that events such as the marathon and, for example, walking 1000 km are both quantitatively and qualitatively different, and not simply because the 1000 km race is longer. They proposed that it could be that the limits to performance in a 1000-km race are predominantly mental, while the limits to performance in the marathon are predominantly physiological. Thus, it can be suggested that ultra-marathon running is an interesting model to study central regulation of exercise. We believe that all interventions designed to manipulate RPE and the sensation of discomfort are of particular interest in this sport. 4. Conclusion As Marino et al.[119] recently pointed out, Mosso concluded in his book La fatica (fatigue) published 120 years ago,[120] that two phenomena categorize fatigue, the diminution of muscular force and the sensation of fatigue: ‘‘That is to say, we have a physical fact which can be measured and compared and a psychic fact which eludes measurement.’’ Thus, the study of fatigue should address both the perception of effort and the decline in force that occurs during sustained exercise.[1] The aim of the present article was to review these two aspects of fatigue and to propose a model that integrates the ‘neuromuscular’ and ‘physiological’ factors of fatigue (responsible for maximal force reduction) in ultra-marathon running to explain the regulation of performance. It has been argued[76,79,121] that fatigue can be understood as a highly regulated strategy conserving cellular integrity, function and, indeed, survival. The flush model, dedicated to integrating the fatigue mechanisms in ultra-marathon performance (and more generally to any type of endurance performance), using a holistic approach, is fully compatible with this statement. Sports Med 2011; 41 (6)
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Acknowledgements The author would like to thank Professor Ken Nosaka for his valuable comments on the manuscript and Wanda Lipski for English language correction. No sources of funding were used to conduct this study or prepare this manuscript. The author has no conflicts of interest that are directly relevant to this article.
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70. Esteve-Lanao J, Lucia A, deKoning JJ, et al. How do humans control physiological strain during strenuous endurance exercise? PloS One 2008; 3 (8): e2943 71. Marcora SM, Bosio A, de Morree HM. Locomotor muscle fatigue increases cardiorespiratory responses and reduces performance during intense cycling exercise independently from metabolic stress. Am J Physiol Regul Integr Comp Physiol 2008 Mar; 294 (3): R874-83 72. Marcora SM, Staiano W. The limit to exercise tolerance in humans: mind over muscle? Eur J Appl Physiol 2010 Mar 11; 763-70 73. Marcora SM, Staiano W. Reply to: What limits exercise during high-intensity aerobic exercise? Eur J Appl Physiol 2010 Jul 2; 110: 663-4 74. Ament W, Verkerke GJ. Exercise and fatigue. Sports Med 2009; 39 (5): 389-422 75. Hampson DB, St Clair Gibson A, Lambert MI, et al. The influence of sensory cues on the perception of exertion during exercise and central regulation of exercise performance. Sports Med 2001; 31 (13): 935-52 76. Noakes TD, St Clair Gibson A, Lambert EV. From catastrophe to complexity: a novel model of integrative central neural regulation of effort and fatigue during exercise in humans. Br J Sports Med 2004 Aug; 38 (4): 511-4 77. St Clair Gibson A, Noakes TD. Evidence for complex system integration and dynamic neural regulation of skeletal muscle recruitment during exercise in humans. Br J Sports Med 2004 Dec; 38 (6): 797-806 78. St-Clair Gibson A, Lambert MI, Noakes TD. Neural control of force output during maximal and submaximal exercise. Sports Med 2001; 31 (9): 637-50 79. Kayser B. Exercise starts and ends in the brain. Eur J Appl Physiol 2003; 90: 411-9 80. Swart J, Lamberts RP, Lambert MI, et al. Exercising with reserve: evidence that the central nervous system regulates prolonged exercise performance. Br J Sports Med 2009 Oct; 43 (10): 782-8 81. Tucker R. The anticipatory regulation of performance: the physiological basis for pacing strategies and the development of a perception-based model for exercise performance. Br J Sports Med 2009 Jun; 43 (6): 392-400 82. Ulmer HV. Concept of an extracellular regulation of muscular metabolic rate during heavy exercise in humans by psychophysiological feedback. Experientia 1996 May 15; 52 (5): 416-20 83. Amann M, Proctor LT, Sebranek JJ, et al. Opioid-mediated muscle afferents inhibit central motor drive and limit peripheral muscle fatigue development in humans. J Physiol 2009 Jan 15; 587 (Pt 1): 271-83 84. Marcora S. Perception of effort during exercise is independent of afferent feedback from skeletal muscles, heart, and lungs. J Appl Physiol 2009 Jun; 106 (6): 2060-2 85. Amann M, Secher NH. Point: Afferent feedback from fatigued locomotor muscles is an important determinant of endurance exercise performance. J Appl Physiol 2010 Feb; 108 (2): 452-4 86. Overgaard K, Lindstrom T, Ingemann-Hansen T, et al. Membrane leakage and increased content of Na+ -K+ pumps and Ca2+ in human muscle after a 100-km run. J Appl Physiol 2002 May; 92 (5): 1891-8
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Correspondence: Professor Guillaume Millet, Laboratoire de Physiologie de l’Exercice (EA 4338), Me´decine du SportMyologie, Hoˆpital Bellevue, 42055 Saint Etienne, Cedex 2, France. E-mail:
[email protected]
Sports Med 2011; 41 (6)
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RESEARCH REVIEW
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The Relationship between Substrate Metabolism, Exercise and Appetite Control Does Glycogen Availability Influence the Motivation to Eat, Energy Intake or Food Choice? Mark Hopkins,1,2 Asker Jeukendrup,3 Neil A. King4 and John E. Blundell1 1 BioPsychology Group, Institute of Psychological Sciences, University of Leeds, Leeds, UK 2 Department of Sport, Health & Nutrition, Leeds Trinity University College, Leeds, UK 3 Exercise Metabolism Research Group, School of Sport and Exercise Sciences, University of Birmingham, Birmingham, UK 4 Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
Abstract
The way in which metabolic fuels are utilized can alter the expression of behaviour in the interests of regulating energy balance and fuel availability. This is consistent with the notion that the regulation of appetite is a psychobiological process, in which physiological mediators act as drivers of behaviour. The glycogenostatic theory suggests that glycogen availability is central in eliciting negative feedback signals to restore energy homeostasis. Due to its limited storage capacity, carbohydrate availability is tightly regulated and its restoration is a high metabolic priority following depletion. It has been proposed that such depletion may act as a biological cue to stimulate compensatory energy intake in an effort to restore availability. Due to the increased energy demand, aerobic exercise may act as a biological cue to trigger compensatory eating as a result of perturbations to muscle and liver glycogen stores. However, studies manipulating glycogen availability over short-term periods (1–3 days) using exercise, diet or both have often produced equivocal findings. There is limited but growing evidence to suggest that carbohydrate balance is involved in the short-term regulation of food intake, with a negative carbohydrate balance having been shown to predict greater ad libitum feeding. Furthermore, a negative carbohydrate balance has been shown to be predictive of weight gain. However, further research is needed to support these findings as the current research in this area is limited. In addition, the specific neural or hormonal signal through which carbohydrate availability could regulate energy intake is at present unknown. Identification of this signal or pathway is imperative if a casual relationship is to be established. Without this, the possibility remains that the associations found between carbohydrate balance and food intake are incidental.
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1. Determinants of Appetite Control in Relation to Energy Balance Classically, attempts have been made to describe the control of appetite and energy intake (EI) using the following three theories: the glucostatic, lipostatic and aminostatic. However, these models are inadequate when describing the complexities of eating behaviour and food preference that characterize the modern study of appetite control. In the 1990s, there was a strong interest in the postabsorptive mechanisms of appetite control and, in particular, the role of fuel utilization in the control of food intake. In the 2000s, this was replaced by a focus on pre-absorptive hormonal control of EI. Current interest has focused particularly on the role of gut peptides such as ghrelin, peptide tyrosine-tyrosine, glucagon-like peptide-1 and cholecystokinin. However, with the exception of ghrelin, gut peptides are more concerned with the post-prandial signalling of satiety rather than with the general control of eating. In addition, they act episodically to influence the pattern of eating behaviour rather than influence eating in the longer term. Although interest in the role of fuel utilization has declined, the issue was never resolved and there is evidence of renewed interest.[1-5] Logic suggests that the way in which metabolic fuels are utilized will confer properties on behaviour in the interests of regulating energy balance. This forms part of a psychobiological approach to appetite control in which physiological mediators act as drivers of behaviour.[6] Exercise becomes particularly important in this regulation for two reasons. First, exercise is a potent metabolic stimulus[7] and thus may act as a strong potential determinant of behaviour. Second, exercise is widely presented as a major public health approach to combat rising obesity levels. However, exercise-induced weight loss responses are variable, and some people lose less weight than theoretically calculated.[8] The prevailing view dating from Jean Mayer 50 years ago[9] is that exercise induces a compensatory increase in EI to restore energy balance. It would be advantageous to understand the underlying mechanisms behind any compensation in order to prescribe exercise more effectively. This is of particular importance as it is ª 2011 Adis Data Information BV. All rights reserved.
becoming clear that behavioural and biological compensation to exercise is variable between individuals.[8,10-12] This article considers the relationship between substrate metabolism and appetite control with reference to research on exercise, glycogen availability and feeding. It is intended that this will contribute to the understanding how substrate metabolism and associated physiological mechanisms of appetite control could affect compensatory eating. This, in turn, has important implications for the efficacy of exercise for weight management. This review explores the theoretical basis for any glycogen-driven regulation of eating behaviour, evaluating existing evidence that supports or refutes such regulation. As research in this area is contradictory, current limitations and areas for future research are also identified. A PubMed database search was conducted using the keywords: ‘exercise’ OR ‘physical activity’ AND ‘energy intake’, OR ‘food intake’ OR ‘appetite’ AND ‘substrate metabolism’ OR ‘carbohydrate oxidation’ OR ‘fat oxidation’. No limits were set for the search period employed. Additional references were identified from the reference lists of articles highlighted through the database search.
2. Metabolic Impairment, Energy Balance and Compensatory Eating Metabolic disturbances that impair the capacity for whole-body and skeletal muscle fat oxidation are commonly cited as causal factors in the development of obesity and the susceptibility to weight regain. A high fasting, non-protein respiratory quotient (RQ) has been found to predict weight gain,[13-16] while formerly obese individuals (who have a higher RQ than never-obese individuals) experience greater weight regain following weight loss.[17-20] Furthermore, obese and formerly obese individuals display a blunted increase in fat oxidation following increased dietary fat intake,[21] potentially contributing to an individual’s susceptibility to overconsumption and weight gain.[22] Recently, Barwell et al.[11] reported that the change in fasting RQ during exercise training was Sports Med 2011; 41 (6)
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associated with the bodyweight response to exercise. Following 7 weeks of aerobic exercise, large variability in the change in fat mass was reported in 55 sedentary women. While the mean fat loss was only -0.97 – 1.5 kg (mean – SD), individual responses ranged from -5.3 to 2.1 kg. This marked variation following chronic exercise is consistent with previous findings (figure 1).[8,10] Changes in fasted RQ explained 7% of the variance in the fat mass response. However, as net exercise-induced energy expenditure (ExEE) only accounted for 36% of the remaining variance, a large proportion remained unaccounted for. As post-intervention RQ was measured 15–24 hours after the final exercise bout, the variability in weight loss may have also reflected differences in post-exercise substrate metabolism, as transient increases in fat oxidation persist for 24–48 hours after exercise.[24] Insight into the determinants of the biological response (and variability) to exercise is of great importance. When performed without dietary control, exercise
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induces moderate weight loss even under conditions of high compliance.[8] While important health benefits are still achieved independent of bodyweight,[23,25] the degree of weight loss can affect motivation and compliance.[26] Powerful biological and behavioural compensatory mechanisms elicited by exercise (or the subsequent energy deficit) have been suggested as a means through which perturbations to energy balance are defended.[27,28] While a single bout of aerobic exercise does not stimulate increased post-exercise EI,[29-32] it is becoming clear that differences in the biological and behavioural compensation to acute[33] and chronic aerobic exercise[8,11] could account for some of the lower than expected weight loss. For example, King et al.[8] reported highly divergent bodyweight and fat mass responses (-14.7 to 1.7 kg and -9.5 to 2.6 kg, respectively) to 12 weeks of supervised aerobic exercise in overweight and obese individuals. Based on the relationship between actual and predicted weight loss, participants
Body mass Fat mass 6 4
Change in body and fat mass (kg)
2 0 −2 −4 −6 −8 −10 −12 −14
Mean change in body mass = −3.3 ± 3.3 kg Mean change in fat mass = −3.8 ± 3.5 kg
−16 Fig. 1. Individual changes in body mass and fat mass following 12 weeks of supervised aerobic exercise (2500 kcal/wk) in 58 overweight and obese individuals (reproduced from King et al.,[23] with permission from BMJ Publishing Group Ltd).
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were retrospectively classified as responders or non-responders. Non-responders lost only -1.5 – 2.5 kg (mean – SD) [approximately half the predicted weight loss], while the responders lost -6.3 – 3.2 kg. Energy intake increased by +268 – 455 kcal/day in the non-responders, while average daily hunger increased by approximately 7%. However, EI decreased by -130 – 485 kcal/day in the responders, while daily hunger remained constant. This diversity indicates that while some are able to tolerate sustained periods of energy deficit without exhibiting compensation, others experience resistance to exercise-induced weight loss (while still experiencing meaningful changes in other health markers).[23] Indeed, as weight loss exceeded that predicted in some individuals, the energy deficit created by the ExEE appears to have been augmented via additional behavioural or biological responses. Understanding the reasons behind this disparity in the ability to tolerate periods of energy deficit remains an important area for future research. Although individuals will differ in the degree and direction of biological and behavioural compensation, the mechanisms behind this variability are poorly understood. The relationship between exercise and any compensation is critical in determining the success of an individual during exercise-induced weight loss. Characterization of the factors that drive compensation is needed if effective weight management programmes are to be developed. Furthermore, although it is apparent that specific biological (e.g. substrate metabolism) and behavioural (e.g. EI) mechanisms can independently shape the response to exercise, how these factors interact is unclear. It is known that substrate metabolism during fasted, postprandial and exercise conditions is influenced by nutritional status.[34] However, the idea that substrate metabolism may stimulate behavioural changes in EI, satiety and food preference has received less attention. Indeed, rather than being simply a response to exercise or EI, substrate metabolism may also act as a biological determinant of eating behaviour. This is consistent with a psychobiological approach to appetite control, in which physiological mediators act as drivers of behaviour.[6] ª 2011 Adis Data Information BV. All rights reserved.
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3. The Metabolic Control of Food Intake: Does Substrate Metabolism Drive Energy Intake? Substrate metabolism has long been implicated in the energostatic control of food intake, in which increased fatty acid oxidation (FAO) is thought to reduce EI via the maintenance of post-meal satiety.[35] While the exact mechanisms are poorly understood, changes in hepatic energy status (hepatocellular adenosine triphosphate/adenosine diphosphate [ATP/ADP] ratio) resulting from altered FAO may influence EI via the stimulation of vagal afferent nerve activity.[36] Although hepatic FAO has been suggested to be pivotal in this control, Langhans[37,38] has recently suggested that FAO in intestinal enterocytes may also be important (although this has been challenged).[39] Scharrer and Langhans[40] initially demonstrated that mercaptoacetate, a coenzyme A (CoA)dehydrogenase inhibitor that suppresses FAO, stimulated EI in rats fed a high-fat diet. Increased EI following the pharmacological inhibition of FAO has been consistently replicated using other substances such as methyl-palmoxirate and etomoxir.[35] Recently, Gatta et al.[41] demonstrated that the administration of (-)-hydroxycitrate (HCA) in time-blinded males enhanced post-meal satiety and delayed subsequent meal requests. The authors attributed this effect to an HCA-induced increase in non-esterified fatty acid (NEFA) oxidation, although as substrate oxidation was not directly measured, the exact mechanisms remain unclear. Furthermore, efforts to suppress EI by the stimulation of FAO have failed to consistently show an effect on eating behaviour.[35,37] This may be because changes in whole-body fat oxidation provides a weak regulatory signal, as the amount of fat ingested or oxidized on a day-to-day basis is very small compared with the total amount of energy stored as adipose tissue.[22] Indeed, it is important to note that the specific signal(s) associated with the neurological detection of changes in energy status is poorly understood (see section 3.3). Peripheral changes in nutrient availability may still influence EI. The control of EI has often been described using a negative feedback model, whereby perturbations to energy balance trigger Sports Med 2011; 41 (6)
Substrate Metabolism, Exercise and Appetite
corrective responses (e.g. EI) to restore energy homeostasis.[42] Due to its limited storage capacity, carbohydrate availability is tightly regulated and its restoration is a high metabolic priority following depletion.[22] Aerobic exercise is known to profoundly alter substrate metabolism during and following exercise. Given that whole-body carbohydrate storage is typically 400–800 g,[43] 90 minutes of high-intensity exercise could induce a total carbohydrate oxidation of a similar order.[44] This may have implications for the peripheral control of short-term EI, as dynamic changes in blood glucose (glucostatic theory)[9] and glycogen availability (glycogenostatic theory)[45] have been linked to satiety, meal initiation and EI. The glycogenostatic theory[45-47] suggests that glycogen availability is critical in determining eating behaviour. Flatt[45-47] suggests that feeding is designed to maintain the body’s glycogen stores (muscle and liver) at a specific set point, with any challenges to availability strongly defended. As such, Flatt[45-47] suggests that reductions in glycogen (via diet or exercise for example) act as an internal biological cue that elicits feeding in order to restore glycogen levels.[45] This is based on evidence from an animal study demonstrating a negative relationship between carbohydrate balance on day 1 and ad libitum EI on day 2.[45] There have been numerous attempts to replicate these findings in humans. These studies have manipulated glycogen availability over short-term periods (1–3 days) using exercise, diet or both. However, attempts have often produced equivocal findings. This may be influenced by the absence of direct measurements of glycogen, with inferences often made from substrate oxidation rates and nutrient balances. While these are distinct physiological processes that do not directly reflect glycogen concentrations, they may provide insight into changes in energy homeostasis that could encourage the storage or oxidation of glycogen. For example, a positive carbohydrate balance would encourage carbohydrate storage as muscle or liver glycogen, as dietary carbohydrate intake exceeds its rate of oxidation.[22] A negative carbohydrate balance would limit the amount of dietary carbohydrate available for storage as the rate of oxidation exceeds intake.[22] However, ª 2011 Adis Data Information BV. All rights reserved.
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assumptions based on short-term carbohydrate balances should be made cautiously. For example, prolonged surfeits in carbohydrate intake may induce compensatory changes in carbohydrate oxidation to re-establish carbohydrate balance at a new level or induce de novo lipogenesis, especially in the event of excessive carbohydrate ingestion.[4] 3.1 Substrate Metabolism, Exercise and Energy Intake: Acute Manipulation of Glycogen Availability
Due to the increased energy demand, a bout of aerobic exercise will attenuate muscle and liver glycogen levels.[44] Based on a simple and balanced depletion-repletion model (as suggested by Flatt[45-47]) acute exercise may act as the stimuli that perturbs glycogen availability, eliciting a compensatory drive to increase EI after exercise to restore availability. Almeras et al.[48] reported that post-exercise compensatory eating was related to substrate metabolism during exercise. In line with similar studies, no mean increase in EI was reported in 11 lean men following 90 minutes of. cycling (60% maximal oxygen consumption [VO2max]). However, when participants were retrospectively divided into ‘high’ or ‘low’ fat oxidizers based on their exercise RQ, post-exercise EI was significantly lower (p < 0.05) in the high-fat oxidizers. Exercise induced a -1.7 MJ net energy deficit in the high-fat oxidizers, but a net positive energy balance of a similar order in the low-fat oxidizers. It was proposed that the post-exercise orexigenic drive was lower in the high-fat oxidizers, as a lower exercise RQ would have attenuated any exercise-induced glycogen depletion. However, as muscle glycogen levels were not measured, this can only be inferred. Indeed, as the mean difference in RQ between the two groups was small (0.02), glycogen usage would have been similar in both groups. Kissileff et al.[49] have also reported a relationship between post-exercise EI and exercise RQ. Using a liquid test meal administered 15 minutes post-exercise, EI was suppressed in nine lean women following cycling (40 minutes; 90 W). Exercise RQ in the last 10 minutes was positively correlated to EI (r = 0.76; p = 0.002), Sports Med 2011; 41 (6)
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again suggesting that a greater reliance upon carbohydrate during exercise may augment EI post-exercise. This hypothesis that post-exercise EI is mediated by glycogen availability is also supported by the proposal that exercise-induced overconsumption when exposed to high-fat food is a biologically driven mechanism to ensure that sufficient carbohydrate is consumed.[50] Adiposity could moderate the peripheral metabolic control of EI. Obesity is associated with impaired whole-body and skeletal muscle fat oxidation under resting and exercise conditions.[51] In the fasted state, lean healthy individuals display a robust preference for fat oxidation for resting energy needs. In addition, they are also able to switch easily between carbohydrate and fat oxidation in response to acute homeostatic challenge (e.g. postprandial[52] or insulin-stimulated conditions).[53] This characteristic metabolic profile has been termed ‘metabolic flexibility’.[54] However, obese individuals are typically characterized by metabolic inflexibility, exhibiting a heavy reliance upon carbohydrate under fasted conditions[53] and an inability to increase carbohydrate oxidation under insulin-stimulated conditions.[55] As a result of the increased energy demand, exercise may also act as a homeostatic challenge that creates an environment in which metabolic impairments become apparent.[56] Indeed, it has been shown in overweight individuals that resting RQ predicts peak fat oxidation rates during exercise.[57] Theoretically, metabolically inflexible individuals who display a blunted ability to upregulate fat oxidation during exercise may be more susceptible to compensatory eating. Enhanced reliance upon carbohydrate oxidation during exercise could augment reductions in stored muscle and liver glycogen, enhancing any drive to restore availability via feeding. However, whether metabolic flexibility influences appetite control has not been addressed, and represents an area for future research. It should be noted that Kissileff et al.[49] failed to show any relationship between exercise RQ and post-exercise EI in a second group of nine obese women. This initially appears to contradict the idea that obese, metabolically inflexible individuals may be more susceptible to metaboliª 2011 Adis Data Information BV. All rights reserved.
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cally driven EI compensation. However, as exercise was prescribed as an absolute (90 W) workload . rather than relative exercise intensity (i.e. VO2max), it is not valid to make direct comparisons between groups. However, Imbeault et al.[58] also failed to find any relationship between exercise RQ and the post-exercise macronutrient composition. Differences in duration, intensity and ExEE, and the timing and composition of the post-exercise test meal may account for the equivocal findings. Consequently, further research is needed to establish the relationship between the exercise RQ and compensatory eating. Indeed, as there is large inter-individual variability in resting[59] and exercise substrate metabolism,[60] simply examining the mean exercise RQ and EI response may mask important information.[8,10,33] As such, it is necessary to establish whether variability in the EI response to exercise can be partly explained by the individual variability in exercise substrate metabolism. Aerobic exercise training has been shown to increase fat oxidation at the same relative absolute and relative exercise intensities.[61-63] Exercise training may act to attenuate acute compensatory eating following an exercise bout, as the dependency on muscle glycogen during exercise will be reduced. While consistent with improved short-term appetite control in physically active individuals,[64-66] whether exercise training mediates the relationship between glycogen availability and the regulation of feeding is unknown. The post-exercise period may represent a period of metabolic vulnerability important for exerciseinduced compensatory eating. While few studies have examined post-exercise substrate partitioning in overweight individuals, in lean individuals there is evidence of enhanced fat oxidation which promotes the restoration of glycogen.[67-71] This transient post-exercise increase is evident for approximately 3 hours but does not necessarily translate into increased 24-hour fat oxidation.[72] In line with resting and exercising substrate metabolism,[59,60] this period of metabolic change is characterized by a high degree of inter-individual variability.[73] As such, metabolically inflexible individuals may also rely more heavily on carbohydrate oxidation during recovery as well as during Sports Med 2011; 41 (6)
Substrate Metabolism, Exercise and Appetite
exercise. Theoretically, this may strengthen or prolong any compensation arising from the need to restore glycogen availability. However, this has yet to be examined and should be addressed in future studies. Interestingly, females demonstrate greater reliance upon fat oxidation during exercise than males,[74] but this situation may be revered post-exercise with a greater reliance on carbohydrate oxidation seen in females.[70] Based on the glycogenostatic theory,[45] this may encourage greater compensatory eating. However, while gender may influence hormonal and appetite responses to exercise,[75] and potentially the degree of weight seen following chronic exercise,[76,77] how it influences any glycogen-driven regulation of EI is unknown. 3.2 Substrate Metabolism, Exercise and Energy Intake: Short- and Medium-Term Manipulation of Glycogen Availability
When examining the relationship between exercise RQ and compensatory eating, it is important to distinguish between acute and chronic exercise. Not only will each present different homeostatic challenges, but appetite is regulated by both short-term, ‘episodic’ signals and more stable, long-term ‘tonic’ signals.[78] It therefore needs to be determined whether glycogen availability acts as a short-term signal, potentially influencing food preference or satiety, and whether repeated episodic signalling of this nature influences the long-term regulation of appetite and energy balance. The processes through which substrate metabolism may influence EI in response to an exercise intervention, and the factors that may mediate this relationship are presented in figure 2. While such episodic signalling may affect the short-term regulation of EI, the repeated exposure to this stimuli, such as would be the case with chronic exercise training, may represent a mechanism for the long-term regulation of appetite and bodyweight. When hunger is measured. immediately after high-intensity exercise (>60% VO2max), a transient suppression has been reported.[29,30,79,80] Consequently, any metabolically driven compensatory eating may be initially counteracted by exerciseª 2011 Adis Data Information BV. All rights reserved.
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[81] induced anorexia. Interestingly, Klausen et . al. examined the effects of high-intensity (60% VO2max) . or low-intensity (30% VO2max) exercise on ad libitum EI over the following day. Due to the higher exercise RQ (0.96 vs 0.91; p < 0.01), perturbations to stored glycogen should have been greater during such exercise. According to Flatt’s theory,[45-47] the metabolic drive to replenish these stores (via compensatory eating) should therefore have been greater. High-intensity exercise stimulated increased fat intake, which was 4.2% higher than habitual intake (p < 0.01) and 3.2% (p < 0.05) higher than following low-intensity exercise but this did not translate into a significant increase in EI. The small exercise-induced perturbation to muscle glycogen stores may have accounted for this, but glycogen levels were not measured. The issue of exercise intensity is worth noting, as it influences the proportion of fat and carbohydrate oxidized during exercise.[82,83] As carbohydrate is the predominant substrate oxidized at higher intensities,[82,83] compensatory eating driven by glycogen availability should be greater following such exercise. However, as studies examining the effect of exercise intensity on EI are contradictory,[58,84] this can only be speculated upon. This is also true for long-term exercise, where repeated exposure to different intensity exercise may alter the effect on appetite and bodyweight regulation. Church et al.[12] have examined the effects of exercise dose (4, 8 and 12 kcal/kg/week) on compensation during 6 months of supervised exercise. A discrepancy between actual and predicted weight loss was only evident at the highest exercise dose, suggesting that a greater ExEE may elicit greater compensation. However, all exercise doses examined were modest (equal to 72, 136, 193 min/week) and the mechanisms behind compensation were not examined. As such, the effect that exercise dose has on biological and/or behavioural compensation during chronic exercise needs to be further examined. The need for prolonged measurement of EI is emphasized when the time-course of compensation to deviations in free-living EI is examined, as a ‘lag time’ in the corrective response elicited by energy depletion or surfeit has been noted. Bray et al.[85] reported that corrective responses in EI
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Moderating factors
Mediating factors
Appetite characteristics
Behavioural endpoint
Exercise factors: • intensity • duration • interval/continuous • acute vs chronic
Individual factors: • habitual diet • obese/lean • aerobic fitness • sex • preceding nutrient intake
Psychometric eating traits: • disinhibition • restraint • binge eating
Substrate availability
Insulin sensitivity ⎧ ⎨ ⎩ Appetite and satiety peptides
⎧ ⎨ ⎩
Exercise intervention
Postexercise period
CNS modulation
• Food preference • Food hedonics
(+) • Meal size • Pattern of eating • Food selection (−)
⎧ ⎪ ⎪ ⎨ ⎪ ⎪ ⎩
Hunger
EI
Satiety control
TDEE
Homeostatic system Hedonic system
Fig. 2. The complex process through which exercise may influence the expression of appetite via changes in substrate metabolism, and central and peripheral systems that ultimately influence energy intake (EI). TDEE = total daily energy expenditure.
were evident only on days 3 and 4 following prior deviations in EI. Consequently, compensatory eating in response to glycogen depletion may not be immediately apparent. Where EI has been measured over longer periods (1–3 days), studies examining the relationship between glycogen availability and EI have again reported mixed findings. In a series of studies, Stubbs et al.[86] manipulated glycogen availability using 1-day isoenergetic depletion (3% carbohydrate) or control (47% carbohydrate) diets, with ad libitum EI being assessed over the subsequent day. Similarly, Shetty et al.[87] manipulated glycogen availability using 2-day isoenergetic diets composed of 79%, 48% or 9% carbohydrate, with ad libitum EI measured over the subsequent 2 days. In both cases, the dietary-induced manipulation of glycogen availability (which was not measured) had no impact on EI. Instead, dietary carbohydrate changes were compensated for by alterations in substrate oxiª 2011 Adis Data Information BV. All rights reserved.
dization that re-established carbohydrate balance. However, in a third study, Stubbs et al.[88] manipulated glycogen levels using ad libitum 20%, 40% and 60% fat diets over a 7-day period. A negative relationship (p = 0.01) between carbohydrate balance on one day and EI on the following day was observed, accounting for 5–10% of the variance in EI. While inferences are made concerning glycogen availability based on shortterm carbohydrate balance, this suggests that glycogen availability provides a modest feedback mechanism through which EI is regulated. However, any increase in orexigenic drive arising from energy availability may take time before it impacts on eating behaviour. This is consistent with the notion that the homeostatic regulatory system is not sensitive to immediate changes in energy balance,[89] with external or non-homeostatic factors influencing whether such perturbations are expressed behaviourally. Sports Med 2011; 41 (6)
Substrate Metabolism, Exercise and Appetite
Snitker et al.[90] used a combination of exercise and dietary restriction to manipulate muscle glycogen stores to examine the relationship between glycogen availability and EI. Exhaustive exercise and 3-day high (75%) or low (10%) carbohydrate diets were used to manipulate glycogen stores (n = 8), with ad libitum EI assessed for two subsequent days. Despite a 46 – 21% difference in muscle glycogen, no treatment differences in EI were observed. However, EI on the second day of feeding was negatively correlated (p = 0.03) with carbohydrate balance on the first day, accounting for 9% of the variance in EI. This may have represented a delayed response in eating behaviour driven by the need to restore glycogen back to a set point. More recent evidence also supports the idea that eating behaviour is in part regulated by the need to maintain or restore glycogen availability.[3-5] Pannacciulli et al.[2] measured 24-hour carbohydrate balance in 67 energy-stable men and 45 women using a respiratory chamber, and subsequently assessed ad libitum EI over a 3-day period. It was found that 24-hour carbohydrate oxidation (r = 0.40; p < 0.01) and 24-hour carbohydrate balance (r = -0.34; p < 0.01) predicted subsequent EI. Furthermore, weight gain over the 3-day period (1.0 – 1.1 kg; range = -1.2 to +4.9 kg) was positively correlated with 24-hour carbohydrate oxidation (r = 0.23; p = 0.01) and negatively correlated with 24-hour carbohydrate balance (r = -0.20; p = 0.03). These findings are supported by Galgani et al.,[3] who reported that carbohydrate balance was an independent predictor of ad libitum EI (r2 = 0.10; p = 0.01) in 47 men and 11 women when they switched from a 1 day high-carbohydrate diet to an isoenergetic 3-day high-fat diet. Similarly, Burton et al.[5] reported that carbohydrate balance at the end of a 6-hour high-energy turnover condition (where individuals exercised but immediately restored the ExEE to maintain energy balance) was correlated with EI (r = -0.49; p < 0.05), with a positive carbohydrate balance associated with lower subsequent ad libitum EI at a buffet lunch. These data are consistent with Flatt’s glycogenostatic theory[45-47] and indicate that a positive carbohydrate balance, which would encourage the maintenance or storage of carbohydrate as glycogen, is associated with a reduced level of feeding. ª 2011 Adis Data Information BV. All rights reserved.
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3.3 Limitations and Areas for Future Research
While these data suggest that glycogen availability could play a role in the homeostatic regulation of EI, it should be noted that the specific pathway through which glycogen availability is signalled to the brain is unknown. The body attempts to maintain energy homeostasis via regulatory feedback processes that control EI, energy expenditure and energy storage.[91] This requires that the brain receives continuous feedback concerning the energy status of the periphery, in order to initiate short-term (e.g. meal initiation/size/ termination) and long-term (e.g. bodyweight regulation) strategies to maintain homeostasis.[92] Information concerning the energy status of the periphery is conveyed to the CNS via afferent hormonal (e.g. leptin, insulin) and neural signalling.[93] The arcuate nucleus of the hypothalamus is commonly regarded as the primary site for the integration of nutrient related signals, and for the initiation of any corrective responses in EI or energy expenditure.[94] While the hormonal control of EI via adipose and gut peptides is becoming clearer,[75] neural energy sensing and the specific intracellular substrate for such ‘fuel sensing’ is at present poorly defined.[92] This represents a major gap in the research as the identification of an accompanying signal from the liver and/or skeletal muscle is needed if a casual relationship between glycogen availability and EI is to be established. At present, while a relationship exists,[1-3] it cannot be ruled out that this is co-incidental with other hormones acting to regulate EI while also mediating substrate metabolism. Indeed, it has been noted that most of the known signalling pathways through which EI is controlled stem from tissues that have little glycogen content.[4] Vagal afferent nerve activity has traditionally been suggested to act as the signal between the liver and the CNS in the energostatic control of EI,[36] with changes in the hepatic ATP/ADP ratio indicating changes in energy status. Acute exercise is known to cause potent hepatic metabolic changes that increase the provision of fuel for muscle contraction.[7] Vagal afferent signals stemming from exerciseinduced changes in liver glycogen could potenSports Med 2011; 41 (6)
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tially initiate a hypothalamic response to eating behaviour. However, the role of hepatic afferent signalling has recently been questioned,[37] in part as vagal afferent fibres in the liver are scarce. While attention has now focused on the role of enterocytes in the intestine for the detection of ‘energy flow’ in the body,[37] this clearly would not represent a plausible mechanism through which skeletal muscle energy status could be monitored. Cellular ‘energy sensors’ such as AMP-activated protein kinase (AMPK) could act as possible signalling pathways through which peripheral changes in glycogen availability are conveyed to the hypothalamus. AMPK is an enzyme involved in ‘sensing’ the energy status of a cell and the regulation of fuel availability and energy homeostasis.[93] It is expressed in peripheral tissues[93] and has emerged as a nutrient sensor in the hypothalamus that influences eating behaviour.[93,95,96] Exercise leads to activation of AMPK in skeletal muscle,[7] with low muscle glycogen content further augmenting its activation at rest and during exercise.[97-99] One effect of increased AMPK activation is elevated skeletal muscle and whole-body fat oxidation during exercise.[7] Importantly, AMPK has been suggested to be a key enzyme in the coordination and integration of peripheral and central energy regulation.[95] Indeed, increased activation of hypothalamic AMPK has been shown to increase EI in animals,[96,100] and is thought to mediate orexigenic or anorexigenic signals arising from substances involved in appetite regulation.[95] Again, further work is needed to determine exactly how AMPK influences food intake,[95] but this may represent a plausible mechanism through which changes in glycogen availability exerts an effect over EI. As such, experiments concerning the identification of potential mechanism(s) by which glycogen availability is signalled to the brain to regulate EI are clearly warranted to further our understanding in this area. It should also be noted that the degree of variance in EI accounted for by carbohydrate balance is typically small (approximately 10%). Given that this modest relationship exists under controlled laboratory conditions, the regulatory potency of this effect under ecologically valid ª 2011 Adis Data Information BV. All rights reserved.
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conditions may be questioned. External or nonhomeostatic factors will play a far greater role in influencing eating behaviour in the ‘real world’, and food-related hedonic processes for example could override this (and other biological signals) in the regulation of food intake.[101] Furthermore, dietary intake prior, during and immediately after can all profoundly influence substrate metabolism during and following exercise.[34] As such, specific nutritional timings or strategies could influence any glycogen-driven regulation of EI. However, how these would affect the regulation of appetite and bodyweight is unknown. Despite this, there is limited evidence to suggest that carbohydrate balance also influences longterm changes in bodyweight. Eckel et al.[1] reported that a negative carbohydrate balance was predictive of long-term weight gain. Energy balance was measured in 39 participants following either a 15-day isocaloric high-fat (50% fat) or highcarbohydrate diet (55% carbohydrate). Body composition was subsequently tracked for 4 years. There was a significant inverse relationship (r = -0.46; p < 0.05) between carbohydrate balance following the high-carbohydrate diet and the change in fat mass over the follow-up period. Individuals who had the highest positive 24-hour carbohydrate balance (indicative of carbohydrate storage) following the high-carbohydrate diet gained significantly less fat mass over the follow-up period. It was suggested that these individuals were less susceptible to weight gain due to their ability to maintain glycogen levels within a set range as a positive carbohydrate balance would have encouraged carbohydrate storage.[1] Based on Flatt’s model,[45-47] this would potentially attenuate any metabolic drive for food intake. However, inferences are again made between carbohydrate balance and glycogen availability, and given the longitudinal nature of the study, it is difficult to determine whether carbohydrate balance represented a biological marker of divergent behaviour or the mechanisms through which susceptibility was conferred. While data from long-term interventions are needed to support this, these data are consistent with studies showing that impaired resting fat oxidation is a causal factor in the development of obesity.[13-15] Sports Med 2011; 41 (6)
Substrate Metabolism, Exercise and Appetite
4. Food Preference and Hedonic Reward Exercise could also influence aspects of eating behaviour such as macronutrient preference or the hedonic response to food. It is intuitive to suggest that the depletion of muscle and liver glycogen would be reflected in an increased preference or palatability for dietary carbohydrate intake (to encourage the replenishment of these stores). However, the role that substrate metabolism may play in determining food choice is difficult to establish, as findings relating to exercise and the motivation to eat are conflicting. Elder and Roberts[102] have noted that there is no consistent evidence to suggest that acute or chronic exercise influences macronutrient selection. Furthermore, it was noted that acute exercise studies fail to demonstrate any consistent changes in preference.[102] Interestingly, it has recently been shown that individual differences in the hedonic response to exercise may be important in determining susceptibility to overconsumption. Finlayson et al.[33] reported a dichotomous response in compensatory eating in healthy women following cycling. A group of susceptible ‘compensators’ were identified who over-consumed relative to the energy cost of exercise when compared with non-compensators who ate less. After exercise, compensators exhibited enhanced implicit wanting for food, especially high-fat sweet foods, and rated their food as more palatable compared with non-compensators. This type of exercise-induced change in food hedonics may act as a powerful non-homeostatic trigger to facilitate compensatory eating in susceptible individuals, and appears to display the same variability that characterizes the biological response to exercise. Elder and Roberts[102] propose a model through which nutrient availability could influence the short- and long-term food preference. The authors suggest that acute exercise reduces liver and muscle glycogen stores, resulting in an immediate increase in hunger and hedonic reward. However, chronic exercise induces adaptations that lead to more stable levels of metabolic fuels (glucose and free fatty acids), acting to suppress ‘overall’ levels of hunger that have an overarching influence on day-to-day eating behaª 2011 Adis Data Information BV. All rights reserved.
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viour. As such, exercise has the capacity to create a negative energy balance via long-term reductions in hunger, despite transient increases in hunger or hedonic reward following acute exercise. However, prolonged exercise-induced weight loss may actually increase hunger. King et al.[10] examined the effects of 12 weeks of supervised aerobic exercise on fasting and average daily hunger in 58 overweight and obese individuals. Large variability in weight loss was again observed, with individuals classified as responders or non-responders based on changes in body composition relative to ExEE.[8] Non-responders (n = 26), who only lost 1.0% of initial bodyweight, exhibited a significant increase in fasting and daily hunger after the intervention. In contrast, the responders, who lost 5.7% of their initial bodyweight, did not show an increase in daily hunger, despite an increase in fasting hunger. The satiety quotient[103] associated with a fixed energy meal was also measured before and after the intervention. This approach revealed two processes that acted concurrently to mediate the effect of exercise on appetite regulation. While exercise increased the orexigenic drive (fasting hunger), post-prandial satiety signalling was improved. This dual process is important as it reflects both changes in homeostatic energy status (orexigenic drive) and the interaction between the homeostatic system and the physiological action of food on satiety signalling.[10] These findings have recently been supported[104] and provide evidence for the important role of hedonic and homeostatic process in the association between exercise and EI. 5. Conclusion In an attempt to maintain energy balance, it has been proposed that challenges to energy availability are met with compensatory responses in EI to maintain homeostasis. Where exercise acts as the stimuli that perturbs energy balance, compensatory response in EI have been shown to be highly variable,[8,10,104] and these interact with other behavioural and biological responses to determine the propensity for weight change.[11] However, the mechanisms that underlie such compensation are poorly understood. ImpairSports Med 2011; 41 (6)
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ments in resting substrate metabolism have previously been linked to the susceptibility to weight gain,[13-15] and changes in resting RQ during exercise training may mediate changes in bodyweight.[11] The glycogenostatic theory, as proposed by Flatt,[45-47] suggests that glycogen availability may be central in eliciting negative feedback signals to restore energy homeostasis. As glycogen availability is tightly regulated, depletion of these stores may act as a biological cue to stimulate EI in an effort to restore glycogen levels to a predetermined set point.[45-47] As exercise can alter substrate metabolism and availability,[34] depletion of such energy stores may act a mechanism through which exercise-induced compensatory eating is driven. Evidence to suggest a direct link between substrate metabolism during acute exercise and compensatory eating is limited,[48,49] and primarily based on retrospective categorization of ‘high’ and ‘low’ fat oxidizers. Indeed, despite the attraction of the glycogenostatic model, short-term manipulations of glycogen stores via exercise or diet in humans has not produced consistent findings.[86-88,90] Despite large intra- and inter-day fluctuations in glycogen stores, there is no consistent evidence to strongly support a direct role of glycogen level in the acute regulation of appetite and EI. The detection of this relationship will depend on the experimental protocol, the magnitude of glycogen depletion and the sensitivity to measure adaptive changes in behaviour (e.g. EI). However, recent evidence suggests that carbohydrate balance plays a role in the short-term regulating EI and bodyweight.[1-3] A negative carbohydrate balance has been shown to be predictive of greater ad libitum feeding.[2,3,88,90] Furthermore, a negative carbohydrate balance has also been shown to be predictive of weight gain.[1] However, the limited number of studies in this area means that several important questions remain unanswered. Fundamental to this is the identification of a specific mechanism through which peripheral changes in glycogen availability are signalled to the brain. Acknowledgements The authors declare no conflicts of interest relevant to this manuscript.
ª 2011 Adis Data Information BV. All rights reserved.
MH has previously received funding from the Biotechnology and Biological Sciences Research Council (BBSRC grant number BBS/B/05079). NK and JB receive funding from the Biotechnology and Biological Sciences Research Council (BBSRC grant numbers BBS/B/05079 and BB/G530141/1).
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Correspondence: Mark Hopkins, Department of Sport, Health & Nutrition, Leeds Trinity University College, Brownberri Lane, Leeds, LS18 5HD, UK. E-mail:
[email protected]
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