B.Sc. Dissertation - The Power Output of a 24-hour Ultra-endurance Cross Country Mountain Bike Event

Abstract

My Undergraduate Dissertation Project - This was a case study project (N=1)
Explorative piece of working, looking to understand the physiological demands of an ultra endurance mountain bike event at the 2016 WEMBO Race

Full text

Manchester Metropolitan University
Department of Exercise and Sport Science
Research Project
By
Alexander James Welburn
12073577
The Power Output of a 24-hour Ultra-endurance Cross Country
Mountain Bike Event
Keywords: Intensity, Cycling, Carbohydrate, Performance, Endurance, Race.

Acknowledgements
I would first like to thank my personal tutor Clare Pheasey, for her help alongside my work,
especially when time was of the essence during the testing right at the start of academic year and
helping me with my bumpy journey in my final undergraduate year.
Three individuals who I would dearly like to thank, whom have played a huge influence in
triggering my curiosity into this research area, Julian rider, Peter Nadin and Nick Glassey who
over the last three years, I have had the pleasure of coaching in which they attained 2 gold’s and 3
silver medals at the 24-hour world championships, a gold and silver and the British 24 hour
championships.

Abstract:
Introduction: Despite an increase in popularity in participation within ultra-endurance events,
research currently remains limited, especially within the understanding of power output during 24-
hour cycling event. Resulting in only as small insight into the intensity of such events, thus this
study aims to describe the power output of an ultra-endurance cross country cycling.
Method: A single, 48 year old, trained endurance athlete participated in this study, V
̇O2max 54.06
mLkgmin-1; 14.9%BodyFat; 1.85 m; 93.5 kg. Two incremental exercise test were performed 5 days
prior to the event to establish physiological zones, through power and blood lactate levels. Field
testing took place at a 24-hour ultra-endurance event, power was recorded though a Stage XT
power meter, lap time, heart rate and carbohydrate intake through energy products were recorded
throughout the race.
Results: The individual completed 28 laps; 00:51 ± 0:06:38; average power was 175 watts 1.87
Wkg-1; 14535 kJ; 310.8 km. Power distribution was, Zone ‘no pedalling’ (08:00:36); Zone 1 LT
(05:18:19); Zone 2 LT-LTP (06:37:54 ); Zone 3 LTP-PMAX (03:40:39) and Zone 4 PMAX+
(00:45:16). Average heart rate, 123 bmin-1. Total fluid intake mL 10350, 369 mLlap-1 ± 138. 621
g of carbohydrates from fluid and 1156 g from gels. Total carbohydrate intake 1777 g through gels
and fluid; 77.9 gh-1.
Conclusion: Alongside a positive pacing strategy, power was distribution across aerobic zones;
average power of 175 W (1.87 Wkg-1) was needed for this success in this event, also characterised
by high oscillation of power within laps.

Table of Contents
1. Introduction...................................................................................................................... 1
2. Methods ............................................................................................................................ 6
2.1 Participants ................................................................................................................. 6
2.2 Laboratory Testing ................................................ ERROR! BOOKMARK NOT DEFINED.
2.3 Field Testing ............................................................................................................... 9
2.3.1 The Course ......................................................................................................... 10
2.4 Data Analysis ............................................................................................................ 10
3. Results ............................................................................................................................ 10
3.1 Lap Times .................................................................................................................. 11
3.2 Power Distribution .................................................................................................... 12
3.3 Carbohydrate Intake .................................................................................................. 14
3.4 Race Variables .......................................................................................................... 15
3.5 Fluid Intake ............................................................................................................... 16
3.6 Raw Race Power ....................................................................................................... 17
3.7 Lap Average Variables .............................................................................................. 18
4. Discussion ...................................................................................................................... 19
4.1 Power Output ............................................................................................................ 20
4.2 Heart Rate ............................................................. ERROR! BOOKMARK NOT DEFINED.
4.3 Pacing .................................................................. ERROR! BOOKMARK NOT DEFINED.
4.4 Factors Affecting Performance .............................. ERROR! BOOKMARK NOT DEFINED.
4.5 Recommendations for Athletes ................................................................................... 24
4.6 Recommendations for Coaches .............................. ERROR! BOOKMARK NOT DEFINED.
4.7 Research Limitations ............................................. ERROR! BOOKMARK NOT DEFINED.
4.8 Future Research .................................................... ERROR! BOOKMARK NOT DEFINED.
4.9 Key Findinds ......................................................... ERROR! BOOKMARK NOT DEFINED.
5. Conclusion ..................................................................................................................... 26
6. References ...................................................................................................................... 27

1
1. Introduction
The first official world mountain bike championships, (MTB) were held in Durango, USA in 1990,
with the sport being first introduced, in to the Olympics in the 1996 Atlanta games. This form of
cycling is known as Olympic cross-country. During the early 90’s, multiple disciplines of cycling
have emerged, such as 24-hour cross country MTB racing. More recently, due to no organiser
being found for the 2011, 24-hour world MTB championships this consequently resulted in an
absence of the event that year. As a consequence, the world endurance mountain bike organization
(WEMBO) was formed. Since then, WEMBO, have organized the 24-hour solo MTB world
championships each year since 2011. Venues being featured all across the globe; 2014 (Fort
William, Scotland); 2015 (Weaverville, California) 2016; (Rotorua, New Zealand) with 2017 in
Finale Ligure, Italy.
Ultra-endurance as defined by Zaryski and Smith (2005) is an event of ≥ 6 hours in duration, with
an increase in popularity (Cejka et al., 2013; Shoak et al., 2013). Event, popularity has increased
within the United Kingdom, such as, Mountain Mayhem; Bontrager 24/12 and Sleepless in the
saddle.
However, despite the growth within ultra-endurance participation, it is currently within its infancy
amongst published research, within this area of cycling; likely a result of its non-Olympic status.
Only recently has the utilisation of power been used to describe 24-hour power output during a
solo road cycling event and not a MTB event (Knechtle et al., 2015). Development within
technological aspects, such as portable cycling power meters SRM (Schoberer Rad Mettechnik,
Jülich Germany); Stage™ (Colorado); Powertap (Cycleops, Madison, USA); Quarq (SRAM,

2
Illinois, U.S.A) have allowed the combining of both laboratory and field based testing. Allowing
for the relationship between laboratory and field based variables, such as race performance and its
relationship to V
̇O2max, or to anaerobic power performance and race performance (Martin et al.,
2001); (Gregory et al., 2007); (Impellzzeri et al., 2005) and (Smekal et al., 2015).
Physiological characteristics amongst different types of cycling and the level of abilities, has been
extensively investigated; Tanaka et al. (1993) observed the aerobic and anaerobic power profiles
of the United States federation road cyclist across different categories. Fernhall and Sewal, (1995)
reported the characteristics of off-road cyclist, amongst varying abilities (expert/professional and
intermediates) noting MTB riders are generally lighter than road cyclists. The application of these
studies give an insight into the physiological difference between different cycling disciplines,
research has also leaned support towards understanding the relationship, between physiological
characteristics and race performance. (Coyle et al., 1998); (Bishop et al., 2000) and (Baron, 2001).
Off-road cycling, specifically Olympic cross country, (XCO-MTB) has been the main area of
interest within the mountain biking discipline. This is not surprising in any manner, as out of the
off-road disciplines XCO-MTB is the only one, to feature in the Olympics. Work by Impellizzeri
et al. (2002); Stapelfeldt et al. (2004) and Impellizzeri et al. (2007) set out to quantify the
physiological demands and/or race intensity of XCO-MTB events, reporting that this form of
racing, has great oscillation amongst power output; values above 500 W on uphill sections with
short bouts up to 700 W are also needed. Concluding that as a sport XCO-MTB racing requires
both highly developed aerobic and anaerobic systems.

3
Yet, insight into similar aspects of race demands or intensity of a 24-hour ultra-endurance currently
is currently unknown through the use of power. The available literature currently available, mainly
comprises of the understanding of energy cost. White et al. (1984) showed an energy expenditure
of 83,680 kJ from a 24-hour cycling time trial, using a heart rate/oxygen consumption relationship;
based from laboratory and field based data collection. Consequently, when using heart rate for
such a prolonged period of time, physiological phenomenon such as ‘cardiac drift’ can occur:
which may over estimate these findings (Jeukendrup and VanDiemen, 1998). Later work by
Bescós et al, (2012) notes an energy expenditure, estimated at 15353 Kcal; again using heart, from
a 24-hour solo MTB. 1102 g of carbohydrate was ingested and 20.7 L of fluid, with an average
intensity of 69 % HRMAX. Acknowledging the limitations associated with heart rate, future research
should utilise power meter as a measurement for energy expenditure, (Bescós et al., 2012).
Chilbkova, (2014) gleaned the habits of 24-hour participants with limited attention on intensity,
thus limits that applicability of this work, when measuring performance.
Nutrition can pose a great challenged for ultra-endurance athletes (Zaryski and Smith 2005). Thus
understanding how feeding patterns may change, throughout an event can allow for a greater
consideration on how an individual’s feeding habits may change over time, thus intervention can
be applied for an individual if needed based, on findings from a competitive event. Carbohydrates
play an invaluable role within endurance sports, (Jeukendrup, 2008) and (Burke et al., 2011). Our
understanding of carbohydrate intake has greatly improved from the work conducted by Wallis et
al. (2005); Jeukendrup (2010) and Burke et al. (2011), noting that that we can ingest up 90 gh-1,
this value should be aimed for when duration exceeds 120 minutes. This can be delivered through
carbohydrate sources that fall under the umbrella of ‘energy products’ (bars; gels and drink)

4
Pfeiffer et al. (2010) reported that oxidation rates did not differ between these. Zhang et al. (2015)
notes extreme variation in osmolality between energy gels, which can cause potential
gastrointestinal issues, in endurance athletes, (Jeukendrup, 2010); (Pfeiffer et al., 2012) and
(Oliveira et al., 2014).
Work by Neumayer et al. (2004) starts to develop our understanding of the ultra-endurance
intensity, through the use of heart rate, during a 20:51:00 cycling event, with an average of 71%
HRMA X being reported. With the suggestion that ultra-endurance threshold lies at about 70% of
HRMAX. Later work by Bescós et al. (2012) despite focusing on the nutritional intake, did report a
similar findings, also reporting intensity was 69% HRMax. These studies despite limited in number,
starts to construction the idea where this threshold may me position as a % of HRMAX.
Alongside the physiological characterised being used to describe different athlete types within
cycling, the use of anthropometrical relationships with performance, have also been explored with
Knechtle (2014) elicits that previous experience is also an important variable to consider, when
predicting performance, within ultra-endurance events. Hoffman (2008) concludes lower BMI and
race times of ultramarathon runners were associated with faster times. Heidenfelder et al. (2015)
and Knechtle et al. (2015) showed that positive pacing during ultra-cycling events, in which speed
decreases throughout the race, appeared to be the adequate strategy, also understanding that
environmental factors will impact on speed such as wind speed. Work by Gregory et al. (2007)
suggest that mountain bike cyclist should focus on improving relative measures, rather than
absolute. This in turn may benefit ultra-endurance cyclists through means of reducing mass allows

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these measures to increase, more specifically Wkg-1at lactate threshold, as a reduction in mass
would increase the Wkg-1.
With much of the current research on understanding the intensities of off-road cycling
predominantly within XC-MTB and not ultra-endurance. (Impellizzeri et al., 2002); (Stapelfeldt
et al., 2004) and (Impellizzeri et al., 2007) with much of the literature focusing on nutritional intake
and energy expenditure (Bescós et al., (2012) and (Chilbkova, (2014). Only a limited amount of
published work is available that focuses on describing the intensity of ultra-endurance; findings
start to indicate the potential location of this ultra-endurance threshold (Neumayer et al., 2003)
(Neumayer et al., 2004) and (Bescós et al., 2012). Therefore a clear gap, remains within the current
literature to explore all these issues with describing the intensity; nutritional intake and
performance during a 24-hour cross country mountain bike event.
Therefor this study aims to (I) describe the power output and heart rate, from a single participant,
during a 24-hour cross country mountain bike competitive event. (II) Describe the physiological
characteristics, thus allowing a comparison to be made, between laboratory and field based data.
(III) To describe the nutritional intake, through energy products, during the 24-hour event.

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2. Methods
2.1 Participant
The Department of Exercise and Sport Science Ethics Committee (Manchester Metropolitan
University, UK) provided ethical approval for this study. The participant gave informed written
consent to participate and was read through the protocol of the study. The athlete was contacted
through a personal coach-athlete relationship. A minimum of 1 year’s previous training and 24-
hour competition experience, was also strictly needed. This was to ensure the participant was fully
aware of nature of 24-hour events in addition, that this individual was targeting this events with a
performance goal in mind.
A single, trained endurance athlete, with 3 years of previous experience at competing in 24-hour
solo cross country mountain bike events was used for this study, physiological characteristics can
be seen in table 1.

7
Table 1: Participants physiological characteristics
Variable
Measure
Age (y)
48
Height (cm)
1.85
Mass (kg)
93.5
Body Fat (%)
14.9
HRMAX (bmin-1)
187
V
̇O2max mLkg min-1
54.06
PMAX (W)
450
Rel. PMAX (Wkg-1)
4.8
PLT (W)
175
Rel. PLT (Wkg-1)
1.9
PLTP (W)
275
Rel. PLTP (Wkg-1)
2.9
2.2 Laboratory Testing
Testing was completed 5 days prior to the 24-hour cycling event. 48-hours prior to testing the
participant was asked to refrain from any form on intense exercise and to abstain from caffeine
intake 12 hours, prior to testing.
Upon arrival to the laboratory, a total body scan using dual energy x-ray absorptiometry (DEXA
– Discovery W S/N845757, software version 13.3) was used to determine whole body fat %.
An electronically braked ergometer (Ergometrics, 800- S ergoline, GmbH, Bitz, Germany) was
used for the incremental exercise test (IET). The ergometer was setup under the instruction of the
participant, to best replicate their preferred position and saddle height. The ramp protocol began
at 50 W with an increase of 25 W every 3 minutes, was the adopted protocol. Heart rate was
recorded using a Polar, (Polar electro Kempele, Finland) H7 Bluetooth monitor, with average heart
Note: V
̇O2max = maximal oxygen consumption; PMAX = maximal power output at
V
̇O2max; PLT = Power at lactate threshold; PLTP = power at lactate turn point: HRMAX =
maximal heart rate. Rel. PLTP = Power relative to body mass at lactate turn point. Rel.
PLP W= Power relative to body mass at lactate turn point

8
rate been taken during the last 30 seconds of each stage, 25 L of blood was taken during the last
30 seconds of each stage, for blood lactate analysis (Bla) through a (Biosen C-line Clinic, England,
Penarth). The test was terminated upon identification of the second incremental Bla increase. This
IET enabled power (P) to be established at lactate threshold (PLT) and at lactate turn point PLTP.
The second IET was a maximal oxygen consumption test (V
̇O2max) used on the same electronically
braked ergometer to identify V
̇O2max and max power (PMAX).
Starting at 200 W with an increase of 25 watts every minute. Breath-by-breath analysis was taken
through a (Cortex, Metalyzer-3B, Leipzig, Germany) Vogler et al. (2010) deemed it a valid form
of measurement. As endurance cyclist prefer higher a higher cadence (Takaishi et al., 1996) the
participant was instructed to maintain 90 rpm. Testing was terminated upon voluntary violation
exhaustion; maximal power was attained using Kuipers et al. (1985) if the individual did not
complete the stage. WCOM was the last fully completed stage, (t) was the amount of time the
participant managed to complete during the last stage, 60 was the duration of each stage, and 25
was the ramp rate of the protocol with WMAX being the resultant value from the IET.
WMAX= WCOM + (t/6025)
(Kuipers et al., 1985)

9
The physiological parameters gathered through the two incremental exercise test, established four
zones. Zone 1; (1-175 W) 1 W to lactate threshold (LT); Zone 2 (175-275 W) LT to lactate turn
point (LTP); Zone 3 (275-450 W) LTP to maximal power (PMAX) and Zone 4 power above PMAX.
In addition to these four, another zone entitled ‘no pedalling’ will be used when no power is being
produced. Refer to table 1 for participants, physiological characteristics.
2.3 Field Testing
The participants had two bicycles, set up under his own accordance, a Trek (USA, Waterloo,
Superfly XL) a 29inch wheeled, full suspension mountain bike. Power was measured though the
means of a Stage Shimano XT (STG, Boulder, USA) (Shimano, Ltd., Oskaka, Japan) unit which
was fitted to both the race and spare bike. Front and rear suspension was set up in accordance with
the manufacturers guidelines.
A global-position-system (GPS) (Garmin, Edge, 810, USA) was positioned on the stem of the
bicycle, in clear view for the participant to see throughout the duration of the event. The screen
display which was of the participant’s preference, included the following; heart rate (HR); 3-
second average power (W); average power (W) distance (km) and time (hh:mm:ss). The device
was recording at 1-second intervals (1 Hz) for all variable. A Garmin, premium heart rate soft-
strap) was used to for recording HR, cadence was capture through the in-built accelerometer in the
stage unit which calculates this value. Prior to the start of the event a zero-off set calibration was
performed through the Garmin on-screen instructions.
To ensure sufficient power for the duration of the event, a (Garmin, Powermonkey-eXplorer, USA)
external battery pack was attached at the half way point; mounted underneath the stem.

10
Electronic lap timing was provided at the event, (SPORTident, Event Timing, Solutions) a chip
corresponding to the participant to the participants number was allocated to every rider, this was
placed into a device at the end of each lap.
There was a designate area in which the participant was allowed to take on mechanical assistance,
helpers were also allowed to provide support within this area, such as providing food and drink
items to the participant. Nutritional intake through energy products was noted every single lap,
based on what the individual had consumed, through the amount of fluid that was left in either the
bottle or gel container.
2.3.1 The Course
A multi-lap, mass start event, with a lap distance of 11.1 Km containing 350 meters of elevation
gain. Starting at midday 12:00:00 pm. The course featured varying sections, consisting of open
forest track; narrow forest track which can in place only be 30 cm in width, inclined and declined
technical aspects, featuring obstacles in which the rider had to negotiate. Individuals have to
complete as many laps within 24-hours. As by the set rules of the event organiser; an individual
can complete another lap providing they start it before 23:59:59.
2.4 Data Analysis
Data recorded from the laboratory was calculated in (Microsoft Excel version 15.19.1).
Immediately after the event completion, data was downloaded off the Garmin device which was
then imported to (Training Peaks,WKO version 4.0, Boulder, USA) for further analysis, also
analysed in Microsoft Excel. Laps were split in accordance with official lap timing. Power zone

11
totals were established as a % of race time. Carbohydrate and fluid intake was added alongside lap
times and power. Descriptive statistics expressed as means  SD, time is expressed as (h:mm:ss).
Figures are all expressed to 1 decimal place. Some figure have colour to illustrated the
physioloigcal zones, blue (Zone 1); Green (Zone 2); Yellow (Zone 3) and Red (Zone 4).
3. Results
3.1 lap Times
Figure 1: Lap times during 24-hour event; 00:51:55 ± 0:06:38
During the first 18 laps, lap times are relatively consistent, only showing a slow increase in lap
times, as lap 3-13 there is only a 00:07:53 increase, after 09:42:39 of racing. We gradual increase
in lap time at 24-28. As shown in figure 1, it can be observed that 19 was the slowest of the 28
laps, this was due to the participant encountering a mechanical, through a puncture in the rear tyre
resulting in the lost time (between fitting a new wheel and having to complete the remainder of
0:00
0:11
0:23
0:34
0:46
0:57
1:09
1:20
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Time (h:mm)
Lap (n)

12
the lap). Laps 1-18 laps had an average lap time of 00:48:48 ± 0:04:52 for the first 14:41:00 hours
of racing. It is only 25-28 we see lap times get consistently longer with an average lap time of
01:00:00 an average time increase by 0:03:27 each lap. The second slowest was the last lap, by
this point in the lead the participant had a large lead over his nearest race rival.
3.2 Power Distribution
Figure 2: Power zone distribution as % of total race time (24:13:33), Zone 1 (1-175 W); Zone 2 (175-275 W); Zone
3 (275-450 W); Zone 4 (450+ W); No pedalling (0 W)
Figure 2 represents the established zones calculated form the laboratory based data, and the % of
total race duration that was spent within them. Zone 1 (03:53:10); zone 2 (06:35:35) zone 3;
(05:08:59); zone 4 (00:45:16) and no pedalling (8:00:05). 33% of total race was actually spent not
with no power being produced. Out of zones 1-4, % race time is mostly spread between zone 1
and zone 3 in comparison to zone 4 where only 3% of race time was spent; power above maximal
aerobic power PMAX. Zone 2, between PLT and PLTP the participant spent the 27 % of total race
time. However as a whole, 0:45:16 was spent above PMAX
0%
5%
10%
15%
20%
25%
30%
35%
Zone 1 Zone 2 Zone 3 Zone 4 No pedaling
(%) of total race time
Zone

13
Figure 3: Power distribution as time within each 25 W bracket. 0-600 W. Average power was 175 watts, * indicates
the bracket containing average power.
The power throughout the race was characterised by a spread within power output left and right of
zone 2. As shown in figure 2, zone 2 had the most amount of time spent within it, despite having
a small range of 100 W compared to zone 1, 3 and 4. Figure 3 further illustrates by the distribution
on 25 W intervals. 225-250 W totalled the time spend at 01:43:33 followed by 250-275 W with
(01:40:52); 275-300 W (1:27:03) and 200-225 W (1:34:27) Time spent above 600 W 0:10:20. Less
than 1 % of total race time.
0:00:00
1:12:00
2:24:00
3:36:00
4:48:00
6:00:00
7:12:00
8:24:00
9:36:00
0-25
25-50
50-75
75-100
100-125
125-150
150-175
175-200
200-225
225-250
250-275
275-300
300-325
325-350
350-375
375-400
400-425
425-450
450-475
475-500
500-525
525-550
550-575
575-600
Duration (h:mm:ss)
Power (W)
Zone 1
Zone 2
Zone 3
Zone 4
*

14
3.3 Carbohydrate Intake
Figure 3: Combined carbohydrate ingestion between gels and fluid. 63.5 glap-1 ± 23.6. Equating to 73.7 ghr-1 *
indicates where caffeine gels were consumed.
The main carbohydrate intake was through the form of energy gels and energy drink, specifically
TORQ (Unitited Kingdom, Shropshire) Vanilla drink mixed at a 6% carbohydrate soloution,
containing 30 g per 500 mL and energy gels (rasberry ripple) containing 28.8 g per 45 g gel. figure
3 ilsutrates that the paticpent was feeding as soon as the race had began , peak intake was at 87.8
glap-1 on laps 11; 13; 15; 25; 26 and 27. As we can also see, caffeine gels were introduced at lap
16; 18 and continually from lap 21 to 27, totalling 890 mg of caffeine. Nothing was consumed on
lap 28, Total intake CHO intake from energy products was 1777 g; 621 g from fluid and 1156 g
from gels.
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Carbohydrate Ingestion (gLap-1)
Lap Number
*
*
*
*
*
*
*
*
*

15
Note: Rel. average power = relative average power against mass; HRMAX = maximal heart rate; PAVG %
of PMAX % = average power as % of maximal power from IET Peak power was calculated as the highest
power output for the set period of time. Race HRMAX = maximal heart rate attained during event.
3.4 Race Variables
Table 2: Race data for the total event duration.
Variable
Unit
Measure
Race HRmax
bmin-1
162
Race Avg HR
bmin-1
123
Avg % of Lab HRMAX
%
65
Avg % of race HRMAX
%
76
Average Power
W
175
Rel. Average power
Wkg-1
1.87
Power % of Lactate Threshold
%
100
Power % of Lactate Turn point
%
64
P% of Max Power
%
38.0
Peak 30 Minute Power
W
271
Peak 20 Minute Power
W
293
Peak 10 Minute Power
W
307
Peak 5 Minute power
W
349
Average Cadence
Rmin-1
65
Total Distance
km
310.8
Energy Expenditure
kJ
14535
Total Duration
(hh:mm:ss)
24:13:13
Physiological characteristics are shown in table 1, which identifies a difference of 25 bmin-1
between Race HRMAX and Lab HRMAX. As shown in table 2. Power data has also been expressed
as a % value against the physioloigcal parameters recorded from the laboratory testing, prior to the
event which can be found in table 1. Energy expenditure was attained as a direct measurement of

16
power output. Cadence, was calculated from accelerometer within the Stage power meter. Average
relative power output was 1.87 Wkg-1 average of 175 W at 93.5 kg for the duration of the event.
3.5 Fluid Intake
Figure 4: The fluid intake per each lap, 369.6 mLlap-1 ± 138.3 mL; 429.5 mLhr-1 (total intake = 10350 mL)
Peak fluid intake was at 500 mLlap-1; as shown in figure 4 this was attained on laps 11; 13; 15;
24; 25; 26; and 27.It was only on lap 19 and 28 where no fluid was consumed. Up until 15 with
the exception of lap 12, fluid intake was consistent. Conversely between laps 16 -22 a reduction
in fluid intake can be seen despite no fluid being. From lap 20 fluid intake started to increase fluid
intake which after lap 25 it remained consistent until lap 27, the last lap the participant did not take
on board any fluids total fluid intake was 10350 mL.
0
100
200
300
400
500
600
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Fluid Intake (mLlap-1)
Lap (n)

17
3.6 Raw Race Power
Figure 5: Rolling 15 second average on power over lap 4.colours represents the four zones. Blue Z1;
Green Z2; Yellow Z3 and Red Z4.
In conjunction to the use of figure 3 and figure 2 illustrating % race time and distribution of power
figure 5 shows how power output varied within lap 4 of the event. Short periods where no pedalling
occured will not show due to the rolling average, despite this, it still shows periods in which the
participants is not pedalling. Additionally, at this type of event power output is of an oscillating,
stochastic nature, further illustrated with colour coordinated zones as shown in figure 3 we do not
limited time was spent in zone 4, only three spike in this lap were found with the rolling average,
there may be more however but data present would consist of a lot of noise.
0
100
200
300
400
500
600
Power (W)
Lap time (s)

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3.7 Lap Average Variables
Table 3: Lap times with lap average power, heart rate and cadence.
Lap Time
Power
HR
Cadence
(h:mm:ss)
(W)
(b·min-1)
(r·min-1)
0:38:01
251
-
83
0:42:17
251
-
81
0:44:47
213
140
77
0:44:20
216
141
77
0:44:57
214
140
78
0:46:23
198
137
75
0:47:59
197
138
77
0:50:00
176
132
73
0:49:51
182
131
72
0:49:21
193
132
73
0:49:11
190
130
72
0:51:15
177
128
72
0:51:55
171
126
69
0:52:40
166
123
70
0:56:36
154
118
69
0:56:29
155
118
70
0:51:38
167
120
69
0:53:44
159
117
68
1:07:22
153
115
71
0:55:25
165
117
69
0:51:55
166
120
71
0:49:37
184
124
68
0:52:29
175
122
65
0:52:06
171
121
66
0:56:27
155
118
66
0:59:51
142
114
66
1:00:10
140
113
65
1:06:47
107
109
64
Note: (-) indicates where no data was collected.
As shown in Table 3, lap times illustrate that the participant adopted a positive pacing strategy for
this event. heart rate; cadence and power all show a slow reduction in values, however after the

19
participant faced a mechanical on lap 19, resulting in a much slower lap time, the proceeding 5
laps were quicker after however with an increase in physiological responses which figure 1 does
not show.
4. Discussion
The aim of this study was to describe the power output of a 24-hour ultra-endurance MTB event,
relate these data to physiological parameters, collected prior to the event through laboratory
testing, in addition to taking note of carbohydrate intake through means of energy products. As
only a limited about of research has investigated ultra-endurance intensity cycling (Neumayer et
al., 2003); (Neumayer et al., 2004), and (Knechtle et al., 2015) with much of the focus on other
areas of cycling, (Impellizzeri et al., 2002) and (Stapelfeldt et al., 2004) .Heart rate represents the
physiological response to exercise power is a better quantification of exercise intensity
(Jeukendrup and VanDiemen, 1988) supporting the approach of using both heart rate and power,
with power, taking dominance.
In this event, the participant finished as the overall leader for the elite category and his age
category, 40 minutes ahead of his nearest race rival. Despite these findings being linked with a
competitive performance resulting in a win, these were the necessary parameters, for this exact
individual, at this specific event, against a particular field of competitors, any findings taken must

20
be used with caution, and cannot be used to generalised for larger population groups, as such these
findings provide suggestions and considerations.
4.1 Power Output.
A surprising finding as shown in figure 1 and 2, was the amount of the time the participant spent
not producing power, 33 % of total race time (08:00:05). This would equate to around 0:17:00 per
lap, based on the average lap time of 0:51:55. This is likely due to a result of environmental factors
such as the nature of this course, such as declines where pedalling is not required to maintain speed,
or additionally the participant was slowing down before sections, to reduce speed in combination
with braking to avoid crashing during certain sections. This area should be further explored to
investigate to what magnitude this occurs during other 24-hour MTB events. The values shown in
figure 5 illustrate how variable power was throughout a lap considering a 15 second rolling average
had been placed on. Unsurprising however the little time is spent in zone 4, above PMAX.
Considering the duration of this event, producing power above this value likely to have a greater
metabolic cost, thus sustaining this for large periods of time would likely result in an increased
fatigue rate. These data start in addition to describing that nature of 24-hour racing, start to
distinguish the differences between different forms of mountain bike racing, with Olympic cross
country being characterised by limitation to performance with anaerobic pathways, with surges
above 700 W (Stapelfeldt et al., 2004).
To date, the only other study using power was during a 24-hour world record solo attempt, showed
similar findings from the data we collected, within pattern of power reduction over time (Knechtle
et al., 2015). Our participant averaged 175 W; 1.87 Wkg-1, findings form Knechtle et al. (2015)

21
reported an average power of 250 W 3.28 Wkg-1, however we must take into consideration, the
difference within the two events, and in fact the type of bicycle used. As we used a mountain bike
not a road bike. Environmental factors such as the circuit, the 24 hour solo world record attempt
was a road circuit, thus larger corners where pedalling is consistent throughout, due to the limited
need to slow down; quite the opposite this instance as we found that 33 % of total race time was
spent not pedalling, which may infer why there are differences within average power output
resulting in a difference a 1.41 Wkg-1.
The highest average power output was for lap 1 and 2 at 251 W; 2.68 Wkg-1. For lap’s, 3-28
average lap power, started to drop alongside, an increase in laps times, which can be seen on figure
1 and table 2 showing that during no lap was average power above PMAX. Maximum 30-minute
power was 271 W; maximum 20-minute power 293; maximum 10-minute power was 307 with 5
minute being 349 W. This shows that this event through the analysis of power output, by this
particular individual is characterised by a larger period of race time where no pedalling occurs,
with a large distribution of between PLT until PMAX.
4.2 Heart Rate
The underpinning focus of this investigation, was to describe the intensity through power output
and its relationship between the laboratory parameters. In addition, heart rate was also used to
provide another quantification of intensity. Average HR for the participant 64 % of HRMAX,
somewhat a little lower than the suggested ultra-endurance threshold (Neumayer et al., 2004).
During the first two laps no data was recorded due to a malfunction of the heart rate strap, this

22
could potentially influence the overall value to some slight degree. Considering that average lap
power was greatest, during the first two laps it would be reasonable to infer a similar finding would
have been shown with heart rate. Nevertheless 22:00:00 worth of data had still been collected.
With little previous work into the ultra-endurance threshold, it starts to construct the suggestion
that it is situated, between 64-70 % of HRMAX. Nevertheless, future work should investigate the
intensity level this threshold is to be found, either leaning support to the current notion or provide
other suggestions.
4.3 Pacing
Figure 1 demonstrates that the individual adopted a positive pacing strategy, where by over the
course of an event, the length in which it takes to complete a lap or section become longer. Noting
that the last lap the participant was approximately 0:06 slower than his previous; by this point the
participant had large lead as such he could afford to lose this time, so decided to take a steadier lap
to ensure they completed the final lap without crashing, so the suggestion that the last few lap
times were a result of fatigue, could be misinterpreted without an understanding of how the race
unfolded, it is important to appreciate the nature of racing and how these sort of tactics or
approaches my influence lap times.
Positive pacing appears to be the suggest method strategy by endurance athletes (Abbiss and
Laursen, 2008). During a recent 24-hour solo, road world record attempt, Knechtle et al. (2015)
also notes a positive pacing strategy was adopted with similar findings in the race across America
would also showed a decrease in power throughout the duration (Hedienfelder et al., 2015). Table
3 shows that lap length increase and power decreasing following a similar patter similar to that of

23
(Neumayer et al., 2004); (Hedienfelder et al., 2015) and (Knechtle et al., 2015). The literature does
start to suggest that ultra-endurance athletes, do adopt a positive pacing strategy, consideration of
the race environment must be taken into account, whether or not, it is a mass start event or solo as
this factor could influence pacing strategy.
4.4 Factors Affecting Performance:
This nature of event entails a prolonged sustained period of power output as shown in figure 2 and
3. For a duration of 24:18:00 as shown in table 2, Lahart et al. (2013) suggests that ultra-endurance
athletes can experience intense, unwanted emotions when in an energy deficient state, under sleep
deprived conditions, which supports the 24-hour mountain bike racing also leads to an energy
deficient state (Bescós et al., 2012). However, as no measure of moods states were taken, no cause-
effect relationships can be established. But consideration that these intense, unwanted emotions
may still prevail. With a mechanical on lap 19 resulted in an increase in lap time, shown in figure
1. It leans support to Lahart et al. (2013) in the need to psychologically prepare an individual for
unwanted stressors, also emphasising that the support crew, should be prepared to deal with such
situations. Energy intake may be a limiting factor as during extreme ultra-endurance events
expenditure cannot be met with intake (Bircher et al., 2006) and (Bescós et al., 2012). Findings
within this study, could not be conclusive within this matter, only carbohydrate intake from energy
products was measured, total carbohydrate intake was 1777 g with Bescós et al. (2012) reporting
only a 1156 g intake. However through means of power, total energy expenditure was large, hourly
carbohydrate intake close to the recommended amount for durations  2.5 h of 90 gh-1 (Burke et
al., 2011) and (Jeukendrup 2011).

24
4.5 Recommendations for Athletes
Bike choice is a factor to consider, due to variety of bike manufactures with different design
technologies, choosing the certain types can be superior over others such as one with 29 Inch wheel
size versus 26 Inch Steiner et al. (2015) noting 29 inch bicycles have superior performance for
elite mountain bikers over 26 inch bicycles, thus it would therefore be advisable that this should
influence cycle choice. In this particular event that participant was on a 29inch bicycle. Findings
gleaned from Seifert et al. (1997); Nishii et al. (2004); Titlestad et al (2006) and Faiss et al. (2007)
lean support to the notion that a bicycle with front and a rear suspension may help improve cycling
performance
Tyre choice selection that best represent a reduction in rolling resistance while maintain adequate
grip, should be chose (Macdermid et al., 2015) however a balance between rolling resistance and
strength of the tyres as such to limit tyre failure or puncturing which as shown in the case leads to
a reduction in lost time, if this does occur.
As shown in figure 3 a large amount of carbohydrates through energy produces were consumed at
73.7 gh-1 close to the suggest figure of 90 gh-1 (Burke et al., 2011). More could be consumed to
attain a closer value, however issue could arise with gastrointestinal issues, if the athlete is not
used to a large intake of carbohydrates , (Jeukendrup, 2010); (Pfeiffer et al., 2012) and (Oliveira
et al., 2014).

25
This leads to the suggestion that during training, in the lead time towards the event. It would be
advisable that during training, session’s carbohydrate intake should be similar to the intake during
the event, a concept of training the gut (Jeukendrup, 2014).
4.6 Recommendations for Coaches
Taking note of the provided athlete’s recommendations, specifically the carbohydrate intake
matching for training, in the lead up towards the event, training sessions should tailor for the nature
of this type of event with large periods of training focusing on areas up to PMAX. Despite only 3%
of total race time being spend above this measure it still equates to approximately 45 minutes.
Training sessions should cater for this find, however these suggestions must be noted with caution
as this was during this particular event by one individual. Coaches should ensure athletes are happy
with all aspects of their positioning on the bicycle, due to the amount of time spent on a saddle.
4.7 Research Limitations
Acknowledging the limitations associated with this study design, in that no cause-effect
relationship can established, thus these findings cannot be generalised for the general populations.
Due to the underpowered sample size. Noting however, despite the increase in technological
developments within the availability and range of power meters currently available, no study has
yet asses the validity of such measurement, only reliability (Hurst et al., 2015). Future research
should hopefully validate such method in assessing power meter through the use stages power

26
meters. Ecological validity may however my come into question between the two environments,
(laboratory and the 24-hour event). First discrepancies between power measure from the cycle
ergometer and stage power unit due to the nature, in which these devices measure power, a simple
solution to reduce validity issues would be to utilise a MTB SRM power meter and a SRM
Ergometer. In addition to the same technologies being incorporated, the SRM ergometer would
allow for a more accurate replication of an individual’s bike position due to the increase in point
of contact adjustment, nonetheless it is accepted there may be difference between power outputs,
in the two environment.
4.8 Future Research
Taking into account the associated limitations in the nature of the study design, through an
unpowered sample size; utilising a larger group of ultra-endurance athletes would allow for the
possible identification and correlation, between physiological parameters and race performance
suggesting if any of this values may be limiting factors to performance. This would also provide
more evidence to support the whereabouts of the ultra-endurance threshold with heart rate.
4.9 Key Findings
An intriguing finding of this study was the amount of time and individual spent not producing
power, 33% of total race time. Average power coincided with PLT, with a large distribution of
power between PLT - PMAX, suggesting a large demand on aerobic pathways. As average power
coincided with power at lactate threshold, this may present as a limiting factor for this participant
and may suggest a possibility of a limiting factor within ultra-endurance, as limited work has
investigated intensity, with the addition of this studies finding there is now a range from 64% -

27
70% of HRMax as the ultra-endurance threshold. Only short a short amount of race time was spent
above PMAX, but still approximately 45 minutes. Carbohydrate intake through energy products was
close to the suggested value of 90 gh-1 (Burke et al., 2011). With over half the total carbohydrate
intake coming from energy gels.
5. Conclusion
To date, no research has described power output, during a 24-hour ultra-endurance cross country
event, only one previous study, has described power output; but this was during a road event
(Knechtle et al., 2015). The findings in this study, show that this event was characterised by large
aerobic pathways with a large degree of oscillation in power output. The participant won the elite
and their respective age category, conceding that this was the power output needed by this
individual to win, under these particular race conditions. This study to some extent, disagrees with
Neumayer et al. (2004), regarding the suggested ultra-endurance threshold of 70 % HRMAX, as we
recorded an average heart rate of 64 % of HRMAX. Therefore it may be of worth for athletes/coaches
seeking to prepare for such events, take taking into consideration the pacing strategy adopted,
recommendations for both athletes and coaches taking into account the noted factors which may
limit performance. Acknowledging the limitations with this study design, in that no cause-effect
relationships can be established, it does however, bring the first insight into the power output of a
24-hour cross country mountain bike event.
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