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CHO periodisation in cycling Case Study: The application of daily carbohydrate periodisation throughout a cycling Grand Tour

CHO periodisation in cycling
Case Study: The application of daily carbohydrate
periodisation throughout a cycling Grand Tour
Nicki Strobel 1 2 ,Marc Quod 3,J. Marc Fell 4,Dominic Valerio 4,David Dunne 4 5 ,Samuel G Impey 4 6
1Uno-X pro cycling team Nutrition Department
2Department of Nutrition and Public Health, Faculty of Health and Sports Sciences, University of Agder, Kristiansand, Norway
3GreenEdge Cycling, Lugano, Switzerland
4Applied Behaviour Systems Ltd. London UK, N1 7UG
5School of Sport and Exercise Sciences. Liverpool John Moores University, UK
6Centre for Exercise and Sports Science Research, School of Medical and Health Sciences, Edith Cowen University, Jo ondalup, WA 6027, Australia
Carbohydrates |Periodisation |cycling
Professional road cycling is recognised as one of the most
energetically demanding competitive sports. A Grand
Tour is composed of 21 stages of almost consecutive daily rac-
ing that varies in exercise intensity, duration and terrain (1,2).
Within a Grand Tour there are large variations in on-bike ex-
ercise energy expenditure (EEE), from 1000 kcal during a
time trial to>4500 kcal in mountain stages. To balance ade-
quate fuelling for performance while maintaining or improving
a rider’s power to weight ratio across 3-weeks of racing requires
matching CHO intake to the demands of different stage types.
The aim of this case report was to describe in detail the quan-
tification of daily on-bike EEE and the delivery of a daily pe-
riodised CHO feeding strategy for a male professional cyclist
during the 2021 Vuelta a Espa˜na.
Athlete and overview of sporting history
The male athlete was 26 years old with six years’ experience
as a professional cyclist, the last three years at UCI World
Tour level. The athlete’s role within the team at the 2021
Vuelta a Espa˜na (his 6th Grand Tour) was as a “domestique”,
supporting the team’s general classification leader during hilly
and mountain stages. The athlete provided informed written
consent for the publication of these data.
Vuelta a Espa˜na 2021
Between August 19 – September 20, the race covered 3,417
km, across 21 racing stages and two rest days. The race in-
cluded two individual time trials (Total 40.9 km), six “flat”
stages (Total 1074.0 km), four “hillly” stages (Total = 688.8
km) and nine “mountain” stages (Total 1614.9 km) as classi-
fied by the official Vuelta a Espa˜na website (Table 1). No data
were collected on stage 21 (34 km time trial).
Body mass
The athlete measured body mass each morning using cali-
brated SECA 875 Class III scales (SECA, Hamburg, Germany)
in voided and fasted state with minimal clothing.
On bike exercise energy expenditure
Daily on-bike EEE was recorded using a power meter (R9100P,
Shimano, Sakai City, Japan) and Garmin 810 bike computer
(Garmin, Olathe, Kansas, USA). The data was stored and ac-
cessed via Training Peaks (Training Peaks, Colorado, USA).
A gross efficiency of 21.7% was used to calculate on-bike kcal
from power data.
Carbohydrate periodisation structure
The rider completed a weighed food diary for all foods at
breakfast, snacks pre-race, and recorded the number of race
foods (rice cakes, small sandwiches etc.) and sports prod-
ucts (gels, bars, CHO drinks) consumed during each stage.
This was done using the remote food photographic method
(RFPM) (4) and reporting any additions/changes immediately
post stage. The recovery meal was weighed and pre-packed by
the chef and the rider weighed any unconsumed food and/or
weighed and reported additional foods eaten. This informa-
tion was shared via a mobile app (Whatsapp, California, USA)
with the nutrition team. The nutrition team then calculated
the CHO intake during the stage and provided within stage
fuelling feedback and subsequent recommendations for the
amounts of CHO containing foods the rider should consume
during the rest of the day. The rider recorded food weights
at dinner and any additional snacks and shared the informa-
tion to complete the day’s food record (Figure 1). All meals
and recovery food were prepared by the team chef according
to pre-planned menus. Alterations to menus due to ingredient
availability were documented and added to the food database.
Race foods were prepared according to specific recipes provid-
ing known quantities of CHO per unit of food. All food was
weighed on digital calibrated scales (Terraillon 14253 Kitchen
Scales, Paris, France) with a precision of 1 g increments up to
5 kg.
Analysis of nutrient intake and energy expenditure
Food labels were used to create a database of the macronu-
trient content of all foods. Where foods were cooked in wa-
ter (i.e., pasta, rice, polenta etc.) a dry weight to cooked
weight conversion was used to calculate the CHO content in
the cooked food.
Daily carbohydrate intake
The riders’ daily CHO intake from Stage 1 to Stage 20 within
the Grand Tour is presented in Figure 2. Mean absolute daily 1 SPSR - 2022 |Mar |158 |v1
CHO periodisation in cycling
CHO intake and relative CHO intake to body mass was 812
±215 g (range: 340 - 1118 g) and 12.2 ±3.2 g·kg1(range:
5.1 - 17.7 g·kg1), respectively. The largest mean absolute
and relative CHO intake occurred on mountain stages, fol-
lowed by hilly stages, flat stages, and then the individual time
trial (ITT) (Figure 2). Additionally, the mean rest day abso-
lute and relative CHO intake was considerably lower than all
racing days.
Carbohydrate distribution across meals
The rider consumed similar amounts of CHO at dinner (197
±76 g) and during post-stage recovery (189 ±43 g), and less
at breakfast (124 ±24 g) (Figure 3). There was less variation
in absolute and relative CHO intakes at breakfast (71 - 152 g;
1.1 - 2.3 g·kg1) compared to both post stage (70 - 267 g; 1.1
- 4.0 g·kg1) and dinner (80 - 326 g; 1.2 - 4.9 g·kg1).
Carbohydrate intake and type during exercise
The mean intake of CHO, and the form of delivery during
each stage is presented in Table 2. Total CHO intake during
exercise ranged from 185 - 508 g which equated to an hourly
CHO intake range of 41 - 106 g·h-1 (Figure 4a and 4b). The
greatest contribution of CHO on-bike came from whole foods
(37 ±10%) then bars (21 ±10%), gels (14 ±6%) and high
concentration CHO drinks (15 ±17%).
On-bike energy expenditure and body mass
On-bike EEE for each stage is presented in Table 1, includ-
ing mean values between different stage types. Body mass for
stages 1 – 20 is displayed in Table 1. From Stage 1 to 20
the rider had a 1 kg increase in body mass (66.8 to 67.8 kg),
varying between 65.0 – 69.0kg throughout the race.
This is the first report to detail the distribution of CHO intake
on a meal-by-meal and stage-by stage basis during a Grand
Tour. The present data provides a unique insight into the
amounts, and the day-to-day variation of CHO required to
fuel a professional cyclist and highlights the application of a
periodised approach to CHO intake to match the highly vari-
able event demands.
Previous research has reported similar mean daily CHO in-
take during Grand Tours (12.6 g·kg1)(3,5,6), however our
data indicates that the daily CHO intake can range from
5.1 – 17.7 g·kg1(Figure 2.). Contemporary sport nutrition
guidelines recommend 8 to 12 g·kg1to support 4 - 5 h of
moderate-to-high intensity exercise (7). While these recom-
mendations are in line with the mean daily CHO intakes re-
ported here, they fail to capture the substantial day-by-day
variation in CHO intake employed by athletes during these
multi-day events.
CHO consumption was manipulated in this case study by
reducing CHO intake on stages where on-bike EEE was lower,
such as flat stages (3022 ±381 kcal) and rest days (563 ±
4 kcal) and increasing intake on more physically demanding
stages such as hilly (4040 ±788 kcal) and mountain (4602 ±
985 kcal) stages (Figure 2). The highest daily CHO intake
was reported on Stage 18 (Figure 2a, b) which corresponded
with the second most physically demanding (5592 kcal) stage
of the race. On this day the athlete consumed 17.7 g·kg1of
CHO, 150% greater than the current recommendations. This
amount of CHO consumption was achieved with 1.9 g·kg1
CHO (123 g) at breakfast, 6.8 g·kg1CHO (462 g, 87 g·h1)
on the bike, 4.0 g·kg1CHO (267 g) in the immediate post
stage recovery period and 4.9 g·kg1CHO (323 g) at dinner.
Although the CHO intake was greater than the total daily rec-
ommendations, it matched the maximal recommendations for
CHO consumption per hour during competition (90 g·h1)
(8, 9, 10) and recovery (8.9 g·kg1CHO over a 5 - 6-hour
period) (7, 11).
In addition to total consumption, this novel data outlines
the composition of in-race CHO intake (Table 2). Our data
show most of the on-bike CHO intake came from whole foods,
followed by Bars, then Gels, and Drinks. During strategic
sections of the race, CHO delivery was increased via concen-
trated multi-source CHO drinks 90g CHO per 500ml (Table
2). This strategy delivered additional CHO during parts of
the race where energy expenditure was high and the athlete’s
opportunity/ability to consume solid foods were limited. Ex-
cluding in-race CHO consumption, the majority of CHO intake
was observed during the post-race recovery period and dinner
(Figure 3). This contrasts with Muros et al. (2019)(3) who
found riders consumed most of their daily CHO at breakfast
(199 ±43 g) and different to Garc´ıa-Rov´es et al. (1998)(5)
who found similar intakes of CHO to be at breakfast (298 ±53
g; 4.5 ±0.7 g·kg1) and dinner (311 ±29 g; 4.7 ±0.6 g·kg1)
with lower intakes at post-stage recovery (134 ±29 g; 2.0 ±
0.5 g·kg1). These observations potentially relate to individ-
ual rider and cultural preferences surrounding meal provision
with additional research required to determine which feeding
pattern is optimal for Grand Tour performance.
Similar to on-bike CHO consumption, the strategic peri-
odised approach to matching CHO intake and EEE resulted
in large variations in meal size across stages. CHO intake var-
ied considerably at breakfast (71 - 152 g; 1.1 - 2.3 g·kg1),
post-stage (70 - 267 g; 1.1 - 4.0 g·kg1) and dinner (80 - 326
g; 1.2 - 4.9 g·kg1) and this variation was dependent on the
physical demands of the current stage in combination with the
anticipated demands of the next stage.
A novel function of this intervention was the ability to de-
liver bespoke nutrition recommendations in a time sensitive
and dynamic racing environment. This was achieved by lever-
aging digital technologies to enhance communication efficiency
and removing the dependency on a single practitioner. Hav-
ing onsite support remains an essential component to capture
any nuance and details that can be missed with only digital
communication; but the addition of a distributed support sys-
tem enables the team to support multiple athletes in different
locations simultaneously. Here the integration of digital sys-
tems facilitated the real time delivery of personalised nutrition
recommendations and highlights the scope for development in
this area (12).
This case report highlights the large variation in CHO in-
takes between different stages of a Grand Tour, that range
well beyond current best practice recommendations. Despite
the availability of specialised sports nutrition products, this
athlete predominantly chose whole foods to provide sufficient
CHO with the strategic addition of concentrated dual source
CHO drinks at key moments of the race. Furthermore, we
demonstrate the application of a distributed nutrition sup-
port network utilising collaborative cloud-based technologies
to enable actionable feedback loops to support riders. 2 SPSR - 2022 |Mar |158 |v1
CHO periodisation in cycling
Table 1. Overview of the physiological demands and stage characteristics during the period of data collection.
On-bike exercise energy expenditure is inclusive of any race-day reconnaissance, warm-ups, racing and cool-
down were appropriate.
Stage Stage type Body Mass
(km) Elevation (m) On-bike EEE
1 Individual TT 66.8 7.1 93 1701
2 Flat 66.3 166.7 1019 2681
3 Mountain 66.8 202.8 2771 4150
4 Flat 65.0 163.9 1532 2780
5 Flat 67.3 184.4 671 2715
6 Mountain 66.8 158.3 1002 3293
7 Mountain 66.9 152 3567 4800
8 Flat 66.4 173.7 897 3083
9 Mountain 66.8 188 4349 5086
Rest day 68.4 561 566
10 Hilly 66.6 189 2181 4672
11 Hilly 66.9 133.6 2517 3411
12 Hilly 66.4 175 2096 3307
13 Flat 66.9 203.7 1639 3202
14 Mountain 68.0 165.7 3301 4343
15 Mountain 66.9 197.5 3694 3014
Rest day 69.0 284 560
16 Flat 67.5 180 2041 3671
17 Mountain 68.3 185.8 2730 5306
18 Mountain 68.4 162.6 4412 5592
19 Hilly 68.2 191.2 3296 4769
20 Mountain 67.8 202.2 4207 5830
Total Mean ±SD 67.2 ±0.9 169 ±42 2522 ±1212 3569 ±1451
Rest day mean ±SD 68.7 ±0.4 - 423 ±196 563 ±4
Individual TT 66.8 7 - 1701
Flat stages mean ±SD 66.6 ±0.9 179 ±15 1300 ±521 3022 ±381
Hilly stages mean ±SD 67.0 ±0.8 172 ±27 2523 ±547 4040 ±788
Mountain stages mean
±SD 67.4 ±0.7 179 ±20 3337 ±1076 4602 ±985 3 SPSR - 2022 |Mar |158 |v1
CHO periodisation in cycling
Table 2. Overview of daily in race carbohydrate intake, carbohydrate per hour of racing and forms of carbo-
hydrate consumed.
Stage Stage type
Carbohydrate Intake (g) g/h
Gels Bars Whole
Foods Drinks Concen-
trated Drinks Total
TT -------
2 Flat 66 75 96 30 0 267 63
3 Mountain 88 75 86 60 0 309 52
4 Flat 22 50 58 60 0 190 48
5 Flat 44 75 118 30 0 267 57
6 Mountain 22 75 72 30 0 199 50
7 Mountain 88 150 96 0 80 414 92
8 Flat 22 75 120 30 0 247 58
9 Mountain 66 75 120 30 160 451 82
Rest day - - - - - - -
10 Hilly 44 50 120 30 160 404 82
11 Hilly 44 0 120 0 80 244 65
12 Hilly 66 50 120 60 0 296 70
13 Flat 44 100 142 0 0 286 54
14 Mountain 22 50 152 0 160 384 81
15 Mountain 22 37 96 30 0 185 41
Rest day - - - - - - -
16 Flat 44 50 154 90 0 338 79
17 Mountain 22 50 152 0 80 304 55
18 Mountain 22 100 120 60 160 462 87
19 Hilly 44 75 118 90 80 407 86
20 Mountain 88 50 120 90 160 508 106
Mean ±SD 46 ±24 66 ±31 115 ±26 38 ±31 59 ±70 324 ±97 69 ±18
Flat stages
mean ±SD 40 ±17 71 ±19 115 ±34 40 ±31 0 ±0 266 + 48 60 ±11
Hilly stages
mean ±SD 55 ±11 44 ±31 120 ±1 45 ±39 80 ±65 338 ±81 76 ±10
stages mean
49 ±33 74 ±35 113 ±28 33 ±32 89 ±74 357 ±115 72 ±23 4 SPSR - 2022 |Mar |158 |v1
CHO periodisation in cycling
Fig. 1. Schematic demonstrating the time course of feedback coming from the rider to the support team. The nutrition
strategy was delivered via a distributed nutrition support network consisting of the performance nutritionist (remote)
and performance chef (at race), with additional support from the doctor (at race) and riders coach (remote) as required. 5 SPSR - 2022 |Mar |158 |v1
CHO periodisation in cycling 6 SPSR - 2022 |Mar |158 |v1
CHO periodisation in cycling
Fig. 2. Total daily carbohydrate (CHO) intake (bars, left axis) and daily on-bike exercise energy expenditure (line,
right axis) during individual stages (a) absolute and (b) relative intake of CHO. Mean and standard deviation of total
daily carbohydrate intake by stage types (c) absolute and (d) relative intake. Symbols denote stage type. Stage 20’s
dinner was not recorded thus total intake is representative of available data for this stage. 7 SPSR - 2022 |Mar |158 |v1
CHO periodisation in cycling 8 SPSR - 2022 |Mar |158 |v1
CHO periodisation in cycling
Fig. 3. Relative carbohydrate intakes at (a) breakfast, (b) recovery and (c) dinner across each stage. Mean and
standard deviation of (d) relative carbohydrate intakes at each meal. Symbols denote stage type. Stage 20’s dinner was
not recorded thus total intake is representative of available data for this stage. 9 SPSR - 2022 |Mar |158 |v1
CHO periodisation in cycling 10 SPSR - 2022 |Mar |158 |v1
CHO periodisation in cycling
Fig. 4. On-bike carbohydrate intake across each stage (a) grams of CHO per hour of racing and (b) absolute carbohy-
drate intake. Mean and standard deviation of on-bike carbohydrate intake by stage types (c) grams of CHO per hour
of racing and (d) absolute carbohydrate intake.
Practical Applications
Generic recommendations of CHO in g·kg1per day do not
reflect the dynamic requirements of Grand Tour racing
Integration of athlete data allows day-to-day periodisation
of nutrition
Collaborative digital platforms can facilitate a distributed
nutrition support system
The analysis of an athlete’s diet can be susceptible to errors in
data collection through conscious or unconscious mechanisms
(13) and recording itself can change eating behaviours (14).
We attempted to mitigate these factors through extensive fa-
miliarisation with this protocol at multiple training camps and
races. Given the duration of the intervention we choose to use
mobile technology to improve compliance by reducing the bur-
den of recording dietary intakes (15).
The authors wish to thank the supporting race staff at
GreenEdge cycling who helped facilitate the intervention.
Conflicts of Interest
JMF, NS, MQ and DV have no competing interests to declare.
DD and SI are shareholders in Applied Behaviour Systems
Data Availability
The data set corresponding to this case study is available from
the corresponding author on reasonable request.
Twitter: Niki Stroble (@nicki_strobel), Marc Fell (@mar-
cfell1), David Dunne (@david_m_dunne), Sam Impey
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Dietary assessment methods that are recognized as appropriate for the general population are usually applied in a similar manner to athletes, despite the knowledge that sport-specific factors can complicate assessment and impact accuracy in unique ways. As dietary assessment methods are used extensively within the field of sports nutrition, there is concern the validity of methodologies have not undergone more rigorous evaluation in this unique population sub-group. The purpose of this systematic review was to compare two or more methods of dietary assessment, including dietary intake measured against biomarkers or reference measures of energy expenditure, in athletes. Six electronic databases were searched for English-language, full-text articles published from January 1980 until June 2016. The search strategy combined the following keywords: diet, nutrition assessment, athlete, and validity; where the following outcomes are reported but not limited to: energy intake, macro and/or micronutrient intake, food intake, nutritional adequacy, diet quality, or nutritional status. Meta-analysis was performed on studies with sufficient methodological similarity, with between-group standardized mean differences (or effect size) and 95% confidence intervals (CI) being calculated. Of the 1624 studies identified, 18 were eligible for inclusion. Studies comparing self-reported energy intake (EI) to energy expenditure assessed via doubly labelled water were grouped for comparison (n = 11) and demonstrated mean EI was under-estimated by 19% (−2793 ± 1134 kJ/day). Meta-analysis revealed a large pooled effect size of −1.006 (95% CI: −1.3 to −0.7; p < 0.001). The remaining studies (n = 7) compared a new dietary tool or instrument to a reference method(s) (e.g., food record, 24-h dietary recall, biomarker) as part of a validation study. This systematic review revealed there are limited robust studies evaluating dietary assessment methods in athletes. Existing literature demonstrates the substantial variability between methods, with under- and misreporting of intake being frequently observed. There is a clear need for careful validation of dietary assessment methods, including emerging technical innovations, among athlete populations.
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In order to quantify the nutritional status and the feeding pattern of professional cyclists during continuous competition, food intake was accurately measured and recorded using the weighed inventory of food (over three 24 h periods) during all meals in ten top professional cyclists during a real 3 weeks' competition. A 24 h period was defined as the time between the start of one stage and the next start. The 24 h period intake of energy, protein, fat, and carbohydrate was used to discover whether these intakes met requirements for endurance exercise. The average intake of energy and macronutrients was: energy = 23.5 +/- 1.8 MJ/24 h period, carbohydrate = 841.4 +/- 66.2 g/ 24h period; protein = 201.8 +/- 17.7g/24 h period; and fat = 158.6 +/- 16.3 g/24 h period. The carbohydrate, protein and fat contribution to energy was 60.0%, 14.5%, and 25.5% respectively. Fluid intake per 24h period was 3.29 +/- 0.94l (1.26 +/- 0.55 l during the race). Our study shows a similar energy intake in comparison with the only previous study in 1989 but there is a change in the feeding pattern of top level cyclists. A more important role is given to the intake of carbohydrate just after competitions together with an increase in protein intake. Both changes could have a positive effect on performance.
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An athlete's carbohydrate intake can be judged by whether total daily intake and the timing of consumption in relation to exercise maintain adequate carbohydrate substrate for the muscle and central nervous system ("high carbohydrate availability") or whether carbohydrate fuel sources are limiting for the daily exercise programme ("low carbohydrate availability"). Carbohydrate availability is increased by consuming carbohydrate in the hours or days prior to the session, intake during exercise, and refuelling during recovery between sessions. This is important for the competition setting or for high-intensity training where optimal performance is desired. Carbohydrate intake during exercise should be scaled according to the characteristics of the event. During sustained high-intensity sports lasting ~1 h, small amounts of carbohydrate, including even mouth-rinsing, enhance performance via central nervous system effects. While 30-60 g · h(-1) is an appropriate target for sports of longer duration, events >2.5 h may benefit from higher intakes of up to 90 g · h(-1). Products containing special blends of different carbohydrates may maximize absorption of carbohydrate at such high rates. In real life, athletes undertake training sessions with varying carbohydrate availability. Whether implementing additional "train-low" strategies to increase the training adaptation leads to enhanced performance in well-trained individuals is unclear.
Key points: Muscle glycogen and intramuscular triglycerides (IMTG, stored in lipid droplets) are important energy substrates during prolonged exercise. Exercise-induced changes in lipid droplet (LD) morphology (i.e., LD size and number) has not yet been studied under nutritional conditions typically adopted by elite endurance athletes, that is, after carbohydrate (CHO) loading and CHO feeding during exercise. We report for the first time that exercise reduces IMTG content in both central and peripheral regions of type I and IIa fibres, reflective of decreased LD number in both fibre types whereas reductions in LD size was exclusive to type I fibres. Additionally, CHO feeding does not alter subcellular IMTG utilisation, LD morphology or muscle glycogen utilisation in type I or IIa/II fibres. In the absence of alterations to muscle fuel selection, CHO feeding does not attenuate cell signalling with regulatory roles in mitochondrial biogenesis. Abstract: We examined the effects of carbohydrate (CHO) feeding on lipid droplet (LD) morphology, muscle glycogen utilisation and exercise-induced skeletal muscle cell signalling. After a 36 h CHO loading protocol and pre-exercise meal (12 and 2 g·kg-1 , respectively), eight trained males ingested 0, 45 or 90 g CHO·h-1 during 180 min cycling at lactate threshold followed by an exercise capacity test (150% lactate threshold). Muscle biopsies were obtained pre- and post-completion of submaximal exercise. Exercise decreased (P<0.01) glycogen concentration to comparable levels (∼700 to 250 mmol·kg-1 dw), though utilisation was greater in type I (∼40%) versus type II fibres (∼10%) (P<0.01). LD content decreased in type I (∼50%) and type IIa fibres (∼30%) (P<0.01) with greater utilisation in type I fibres (P<0.01). CHO feeding did not affect glycogen or IMTG utilisation in type I or II fibres (all P>0.05). Exercise decreased LD number within central and peripheral regions of both type I and IIa fibres, though reduced LD size was exclusive to type I fibres. Exercise induced (all P<0.05) comparable AMPKThr172 (∼4 fold), p53Ser15 (∼2 fold) and CaMKIIThr268 phosphorylation (∼2 fold) with no effects of CHO feeding (all P>0.05). CHO increased exercise capacity where 90 g·h-1 (233 ± 133 s) > 45 g·h-1 (156 ± 66 s; P = 0.06) > 0 g·h-1 (108 ± 54 s; P = 0.03). In conditions of high pre-exercise CHO availability, we conclude CHO feeding does not influence exercise-induced changes in LD morphology, glycogen utilisation or cell signalling pathways with regulatory roles in mitochondrial biogenesis. This article is protected by copyright. All rights reserved.
This systematic review examines the efficacy of carbohydrate (CHO) supplementation on exercise performance of varying durations. Included studies utilized an all-out or endurance-based exercise protocol (no team-based performance studies) and featured randomized interventions and placebo (water-only) trial for comparison against exclusively CHO trials (no other ingredients). Of the 61 included published performance studies (n = 679 subjects), 82% showed statistically significant performance benefits (n = 50 studies), with 18% showing no change compared with placebo. There was a significant (p = 0.0036) correlative relationship between increasing total exercise time and the subsequent percent increase in performance with CHO intake versus placebo. While not mutually exclusive, the primary mechanism(s) for performance enhancement likely differs depending on the duration of the exercise. In short duration exercise situations (∼1 h), oral receptor exposure to CHO, via either mouthwash or oral consumption (with enough oral contact time), which then stimulates the pleasure and reward centers of the brain, provide a central nervous system-based mechanism for enhanced performance. Thus, the type and (or) amount of CHO and its ability to be absorbed and oxidized appear completely irrelevant to enhancing performance in short duration exercise situations. For longer duration exercise (>2 h), where muscle glycogen stores are stressed, the primary mechanism by which carbohydrate supplementation enhances performance is via high rates of CHO delivery (>90 g/h), resulting in high rates of CHO oxidation. Use of multiple transportable carbohydrates (glucose:fructose) are beneficial in prolonged exercise, although individual recommendations for athletes should be tailored according to each athlete's individual tolerance.
Carbohydrate and fat are the two primary fuel sources oxidized by skeletal muscle tissue during prolonged (endurance-type) exercise. The relative contribution of these fuel sources largely depends on the exercise intensity and duration, with a greater contribution from carbohydrate as exercise intensity is increased. Consequently, endurance performance and endurance capacity are largely dictated by endogenous carbohydrate availability. As such, improving carbohydrate availability during prolonged exercise through carbohydrate ingestion has dominated the field of sports nutrition research. As a result, it has been well-established that carbohydrate ingestion during prolonged (>2 h) moderate-to-high intensity exercise can significantly improve endurance performance. Although the precise mechanism(s) responsible for the ergogenic effects are still unclear, they are likely related to the sparing of skeletal muscle glycogen, prevention of liver glycogen depletion and subsequent development of hypoglycemia, and/or allowing high rates of carbohydrate oxidation. Currently, for prolonged exercise lasting 2-3 h, athletes are advised to ingest carbohydrates at a rate of 60 g·h(-1) (~1.0-1.1 g·min(-1)) to allow for maximal exogenous glucose oxidation rates. However, well-trained endurance athletes competing longer than 2.5 h can metabolize carbohydrate up to 90 g·h(-1) (~1.5-1.8 g·min(-1)) provided that multiple transportable carbohydrates are ingested (e.g. 1.2 g·min(-1) glucose plus 0.6 g·min(-1) of fructose). Surprisingly, small amounts of carbohydrate ingestion during exercise may also enhance the performance of shorter (45-60 min), more intense (>75 % peak oxygen uptake; VO2peak) exercise bouts, despite the fact that endogenous carbohydrate stores are unlikely to be limiting. The mechanism(s) responsible for such ergogenic properties of carbohydrate ingestion during short, more intense exercise bouts has been suggested to reside in the central nervous system. Carbohydrate ingestion during exercise also benefits athletes involved in intermittent/team sports. These athletes are advised to follow similar carbohydrate feeding strategies as the endurance athletes, but need to modify exogenous carbohydrate intake based upon the intensity and duration of the game and the available endogenous carbohydrate stores. Ample carbohydrate intake is also important for those athletes who need to compete twice within 24 h, when rapid repletion of endogenous glycogen stores is required to prevent a decline in performance. To support rapid post-exercise glycogen repletion, large amounts of exogenous carbohydrate (1.2 g·kg(-1)·h(-1)) should be provided during the acute recovery phase from exhaustive exercise. For those athletes with a lower gastrointestinal threshold for carbohydrate ingestion immediately post-exercise, and/or to support muscle re-conditioning, co-ingesting a small amount of protein (0.2-0.4 g·kg(-1)·h(-1)) with less carbohydrate (0.8 g·kg(-1)·h(-1)) may provide a feasible option to achieve similar muscle glycogen repletion rates.