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Abstract and Figures

Although the sport of triathlon provides an opportunity to research the effect of multi-disciplinary exercise on health across the lifespan, much remains to be done. The literature has failed to consistently or adequately report subject age group, sex, ability level, and/or event-distance specialization. The demands of training and racing are relatively unquantified. Multiple definitions and reporting methods for injury and illness have been implemented. In general, risk factors for maladaptation have not been well-described. The data thus far collected indicate that the sport of triathlon is relatively safe for the well-prepared, well-supplied athlete. Most injuries ‘causing cessation or reduction of training or seeking of medical aid’ are not serious. However, as the extent to which they recur may be high and is undocumented, injury outcome is unclear. The sudden death rate for competition is 1.5 (0.9–2.5) [mostly swim-related] occurrences for every 100,000 participations. The sudden death rate is unknown for training, although stroke risk may be increased, in the long-term, in genetically susceptible athletes. During heavy training and up to 5 days post-competition, host protection against pathogens may also be compromised. The incidence of illness seems low, but its outcome is unclear. More prospective investigation of the immunological, oxidative stress-related and cardiovascular effects of triathlon training and competition is warranted. Training diaries may prove to be a promising method of monitoring negative adaptation and its potential risk factors. More longitudinal, medical-tent-based studies of the aetiology and treatment demands of race-related injury and illness are needed.
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The Impact of Triathlon Training and Racing on Athletes’
General Health
Veronica Vleck Gregoire P. Millet
Francisco Bessone Alves
ÓSpringer International Publishing Switzerland 2014
Abstract Although the sport of triathlon provides an
opportunity to research the effect of multi-disciplinary
exercise on health across the lifespan, much remains to be
done. The literature has failed to consistently or adequately
report subject age group, sex, ability level, and/or event-
distance specialization. The demands of training and racing
are relatively unquantified. Multiple definitions and
reporting methods for injury and illness have been imple-
mented. In general, risk factors for maladaptation have not
been well-described. The data thus far collected indicate
that the sport of triathlon is relatively safe for the well-
prepared, well-supplied athlete. Most injuries ‘causing
cessation or reduction of training or seeking of medical aid’
are not serious. However, as the extent to which they recur
may be high and is undocumented, injury outcome is
unclear. The sudden death rate for competition is 1.5
(0.9–2.5) [mostly swim-related] occurrences for every
100,000 participations. The sudden death rate is unknown
for training, although stroke risk may be increased, in the
long-term, in genetically susceptible athletes. During heavy
training and up to 5 days post-competition, host protection
against pathogens may also be compromised. The inci-
dence of illness seems low, but its outcome is unclear.
More prospective investigation of the immunological,
oxidative stress-related and cardiovascular effects of tri-
athlon training and competition is warranted. Training
diaries may prove to be a promising method of monitoring
negative adaptation and its potential risk factors. More
longitudinal, medical-tent-based studies of the aetiology
and treatment demands of race-related injury and illness
are needed.
Key Points
The sport of triathlon appears to be relatively safe for
the majority of well-trained, well-prepared athletes.
The demands of triathlon training and racing, and
their influence on injury and illness, are not well-
More prospective investigation of the health-related
effects of triathlon participation, with a view to
producing better training and racing guidelines, is
1 Introduction
The sport of triathlon involves a sequential swim, cycle and
run over a variety of distances and formats [1]. At any
given life-stage, the triathlete is likely to be focusing his or
her training on preparation for the shorter-distance sprint or
Olympic-distance races, or for longer-distance half-Iron-
man to Ironman events. Athletes in the 35–39 years and
40–44 years age groups form the majority of participants
Electronic supplementary material The online version of this
article (doi:10.1007/s40279-014-0244-0) contains supplementary
material, which is available to authorized users.
V. Vleck (&)F. B. Alves
CIPER, Faculty of Human Kinetics, University of Lisbon,
Estrada da Costa, Cruz Quebrada-Dafundo 1499-002, Portugal
G. P. Millet
ISSUL, UNIL, Lausanne, Switzerland
Sports Med
DOI 10.1007/s40279-014-0244-0
Non-elite athletes who compete against other athletes
within the same 5-year age range (hereafter referred to as
‘age-groupers’), and particularly those who are less expe-
rienced [3], are less likely to be coached than elite athletes.
According to a study by the USA Triathlon organization,
although only 26 % of athletes did not ‘want or need a
coach’, 47 % did not have a precise training plan [4]. The
sport of triathlon has been shown not to be ‘the sum of its
component sports’ (because the neuromuscular adaptations
to cycling training, for example, interfere with those elic-
ited by running [5,6]). Little research that can help the
triathlete train in an optimal, sport-specific, manner has
been published, however. The training that is involved in
preparation for competition for the various triathlon event
formats and distances [7,8] has been insufficiently quan-
tified [9]. Few detailed longitudinal investigations [1012]
of how changes in training factors may be reflected by
changes in injury and illness status are available. The risk
profile of the athlete as he or she goes into competition, and
the extent to which this is mirrored by race-related prob-
lems, has not been investigated. Although training diaries
have been cited as a crucial diagnostic aid in the man-
agement of ‘tired’ triathletes [10] and are reportedly the
triathletes’ most commonly used method of feedback on
training efficacy [3,12], minimal examination of the extent
to which such logs may be used to minimize maladaptation
has occurred.
This article reviews the literature regarding triathlon
training and racing loads and their effects on the immune
system, oxidative stress and cardiovascular status. The
extent of and putative risk factors for illness and injury
in able-bodied athletes participating in road-based tri-
athlons are described. We report how the development of
specific illnesses or injuries may be influenced by the
environmental conditions and/or cross-training that is
involved [1]. The triathlon-specific research that has thus
far been conducted into potential indicators of maladap-
tation is discussed. Issues that will have to be addressed
if the results of future studies are to lead to practical
improvements in training and racing practice are
2 Triathlon Training
Only one calculation of mean weekly training duration data
from the literature for each discipline, comparing Olympic-
distance and Ironman-distance specialists, has been pub-
lished [9]. These mean values broadly agree with retro-
spective data that were obtained 10 years earlier for age-
groupers [13,14]. Weekly training volumes for world-
ranked elite triathletes have not been well-documented but
are clearly higher [15]. No examination of the extent to
which training practice has changed over time has been
published. However, several differences between sex,
ability and event-distance groups that were noted in 1993
(Table 1) may still hold. Olympic-distance athletes may
spend less overall time per week than Ironman athletes
doing longer, low intensity, ‘long run’ (p\0.05 for both
sexes) and ‘long bike’ sessions (p\0.05, for females
only). The length of such individual sessions is likely less
for Olympic-distance than for Ironman-distance athletes
(p\0.05). Superior Olympic-distance athletes also do
more speed work cycle and fewer long-run sessions per
week (both p\0.05), and inferior Olympic-distance ath-
letes do more back-to-back cycle–run transition training
than Ironman athletes (p\0.05) [12].
In addition, nor are many detailed prospective longitu-
dinal training studies [8,12,16] available. Neal et al. [16]
analyzed the training-intensity distribution of ten recrea-
tional-level athletes (mean ±standard deviation [SD] age
43 ±3 years) over the 6 months leading up to an Ironman
race. Three training periods (January–February, March–
April, and May–June) and 4 testing weeks, were involved.
The athletes spent (mean ±SD) 69 ±9, 25 ±8, and
6±2 % of the total training time for the three training
periods combined doing low-, mid- and high-intensity
exercise, respectively.
Prospective data for ten Olympic-distance athletes who
finished within the top 50 at their non-drafting national
championships 21 weeks later, in 1994, have also been
reported [12]. The athletes were members of a national
squad but given that their data pre-date the inception of the
drafting rule for elite racing, the increased professionalism
of the sport since it gained Olympic status, and that they
were focusing on domestic races rather than on the inter-
national circuit, they are only likely to be representative of
well-trained age-groupers. Approximately 25, 56 and 19 %
of training time was spent swimming, cycling and running,
respectively. Nearly 70 % of training time in each disci-
pline was spent below racing intensity. The changes in
training volume and intensity that occurred in the squad
which included the latter athletes are illustrated in Figs. 1
and 2. It is important to note that the relative proportion of
training time that was spent at higher intensity levels and
the overall weekly rate of overall change in training stress
became increasingly greater as the athletes progressed
towards the competitive period.
Only conference abstracts exist to support the premise
that elite athletes [17] with a current world ranking also
spend approximately 70 % of their exercise time below
racing intensity. Little is known about the training of such
athletes other than it can vary widely, even between ath-
letes with the same coach [8], that international travel may
be involved, and that altitude training is widely practiced in
the lead-up to competition.
V. Vleck et al.
Table 1 Selected potential intrinsic and extrinsic factors for maladaptation that have been found to vary with sex, distance specialization and
ability in triathletes (reproduced from Vleck [12], with permission)
Variable Ability Event distance Sex
M vs.
M vs.
F vs.
M vs.
F vs.
Swim * – * * ** – – – –
Cycle *** – – – – – – – – –
Run *** – – – – * – – – –
Triathlon *** – – – *** – – – – –
Sad or
––– –*
Stressed – ** – – ** – **
––– –*
Worried – – – – – – – ** – –
––– –
Cannot cope * – – – – – ** – ***
Need to get
––– –**
––– ***
Level reached
in cycling
––– –*
Best distance – – *** – – – – –
––– –*
training time
Running ** – – – – – – – – –
Long runs *** * – – – ** – – – –
Overall – – – – * – – – *
Overall *** – *** – * – – – – ***
Swimming – – – – – – – – –
Cycling *** – – – – – – –
Running *** – *** – – – – ** – –
Number of
sessions per
––– –
– – – ––– ––**––
––– –
––– –
Speed work
––– –**
– – – ––– ––**––
Triathlon Training and Health
3 Triathlon Competition
The length of the competitive season, and the number
and type of competitions that it involves, may differ
markedly both between elite athletes and age-groupers
[12], and with event-distance specialization. The relative
intensity at which competition is performed has been
insufficiently quantified, but also differs [1825]
(Table 2). The extent to which it does so is unclear given
that most studies have used different physiological
Table 1 continued
Variable Ability Event distance Sex
M vs.
M vs.
F vs.
M vs.
M vs.
F vs.
cycle run
*–– –––
– – –––– ––**––
Long runs – * – – – – – –
Other types
of run
– – –––– ––**––
Length of
Each long
––– –*––
Each long
––– –*––
Warm up/
Pre-swim – ** – – – – – **
Post-swim – – – – – ** – –
Pre-cycle * – – – * – –
Post-cycle – * * – – – – –
Stretching Pre-swim – * – – – – – *
Post-cycle – – * – * – * *
––– –––
Swim – – – – – –
Run – – – – – – – –
Transition – – – – – –
Train with
Swim – – – – – –
Cycle * *** * – * – – –
Run – – – – – – – –
Type of
Cycle – – – – – *** * – –
Run – – – – – *** * – –
*** – – – – – –
indicates no information, E1994 elite (most likely corresponding to higher ability, well-trained recreational athletes of today), IM Ironman
distance (i.e. 3.8-km swim, 180-km cycle, 42.2-km run), Ffemale, Mmale, NE non-elite (recreational) athletes, OD Olympic distance (i.e. 1.5-
km swim, 40-km cycle, 10-km run), SE 1994 sub-elite (most likely corresponding to good, well-trained, recreational athletes of today
*p\0.05, ** p\0.02, *** p\0.01 from the group marked with the same symbol and in the same row of the table
No differences were observed between the various groups in the use of clipless pedals, use of different types of cycle handlebars or gearshift
For the entire year as opposed to from race to race
V. Vleck et al.
markers for competition intensity. Few studies [18,19,
26] have obtained data relating to the physiological and
other demands of triathlon swimming. This is despite
potentially hazardous interactions between environmental
temperature, water temperature, currents, marine life,
other athletes, exercise intensity and duration, as well as
Fig. 1 Changes in distribution
of training intensity of Olympic-
distance triathletes over a two-
peak competitive season:
(a) swim, (b) bike, (c) run
(reproduced from Vleck [12],
with permission.) EB endurance
base, Pre-comp pre-
competition, Comp competition,
Sswim, Bbike, Rrun,
Lintensity level (rated as 1–5,
with 1 being the lowest
Triathlon Training and Health
‘feed-forward’ fatigue effects from one discipline to the
next [27].
As the intensity and duration of competition changes, so
may the thermal stress that is experienced by the athlete.
Hypoglycaemia, dehydration [28], changes in blood elec-
trolyte concentration and muscle damage [29] may all
occur. The relative extent to which they occur in short-
distance races is unknown. Muscle damage [30,31] seems
to be the most significant of these issues in half-Ironman-
distance events [29]. The extent to which the triathlete may
be at risk for hypo/hyperthermia and other heat-related
illness in sprint distance events is related to environmental
temperatures, humidity and degree of prior heat acclima-
tization [32]. Water temperatures at International Triathlon
Union-sanctioned events start at 13 °C (for 1,500 m) or
14 °C (for 3,000–4,000 m) [33]. The upper allowable
limits are 20–24 °C depending on athlete ability and race
distance/format. They may be adjusted down according to
water–air temperature differences and the weather. Maxi-
mum allowable time spent in the water also varies with
event distance and athlete ability group. Total body water
turnover with Ironman competition can be around 16 L or
1.33 L.h
[25]. Dehydration is usually estimated via
measurements of body mass loss. With Ironman competi-
tion, this may be 3–8 % of the pre-start value (i.e.almost
double that of half-Ironman [23,29]) in males [25,34,35].
It was not reported to be significant in female age-groupers
[36]. Body weight may also increase with competition in
athletes with exercise-associated hyponatremia [34,37
42]. Both hyponatraemia—which is rare in races lasting
less than 4 h, but common in those lasting over 8 h [39],
and heat illness [32] are discussed elsewhere [35,37,40,
41,4346]. However, normally (but not always [25])
plasma volume decreases with short-distance competition
[47], and is either maintained or increased (by 8.1–10.8 %)
after Ironman competition [4850].
4 Immune, Oxidative and Cardiovascular Responses
to Triathlon Training and Competition
Although the demands of training and competition are not
well-described, it has been suggested [51] that triathletes
do ‘extreme amounts of exercise’. Some empirical as well
as epidemiological data suggest that such excess may be
associated with DNA modulation, increased risk of car-
diovascular or pulmonary events [5258], and/or impaired
immune status. Cumulative oxidative stress [54], increased
oxidation of plasma lipoproteins and a subsequent potential
contribution to atherosclerosis may potentially offset the
positive effects of endurance training. Indeed, it has been
postulated that U- and S-shaped relationships between
exercise (load) and health exist in age-groupers [51] and
elite athletes, respectively [59].
4.1 The Immune Response
Longitudinal studies of the response of white blood cell
(WBC) counts or other immune system markers to triathlon
Fig. 2 Changes in weekly rates of total training stress (arbitrary
units) across consecutive macro-cycles of a two-peak competitive
season in Olympic-distance triathletes: (a) swim, (b) bike, (c) run
(reproduced from Vleck [12], with permission). EB endurance base,
Ttransition, PC pre-competition, Ccompetition, Sswim, Bbike,
Rrun, Lintensity level (rated as 1–5 with 1 being the lowest intensity)
V. Vleck et al.
training are scarce. According to a 10-year retrospective
study of Australian Institute of Sport (i.e.elite) athletes
[60] who presented without illness, triathletes had lower
resting total WBC and neutrophil counts than athletes from
other sports (Table 3)[6188]. The authors concluded that
this probably reflected a training-induced adaptive anti-
inflammatory response operating within broader homeo-
static limits rather than any underlying pathology. They
also found that the aerobic component of the sports that
they surveyed exhibited a large positive correlation with
monocyte counts in males (r=0.51) and a moderate
positive correlation in females (r=0.34). Their group
probably involved mostly or all Olympic-distance spe-
cialists. Rietjens et al. [65] also observed many elite
(probably Olympic distance) triathletes to exhibit haema-
tological values near or below the lower limit of the normal
range. However, a 4-year prospective study of Spanish elite
triathletes [71] showed WBC counts to lie within the nor-
mal limits within both the pre-competitive and competitive
periods. However, 16 % of the triathletes in the Australian
Institute of Sport study displayed neutropenia and 5 %
displayed monocytopenia, respectively. This observation
(which was supported by Philip and Bermon [89]) is of
clinical interest. Neutropenic individuals are generally
more susceptible to bacterial infection, such as might occur
after inadequate treatment of a seemingly trivial skin
abrasion. The reason for neutropenia, in particular, is
unclear. It may be due to exercise-induced neutrophil
apoptosis and consequent lower neutrophil lifespan. When
the running section of normal triathlon training is intensi-
fied [90] (as illustrated in Figs. 1and 2), infection risk (as
measured by symptoms of upper respiratory tract infection
[URTI] and increased congestion) may rise. Whether this
means that short- and long-distance specialists, who likely
differ in the proportion of their training that is spent at
higher intensities, may differ in immune status is unknown.
A 6-month prospective study of competitive-level athletes
preparing for Ironman competition [62] demonstrated
accumulation of differentiated and transition T cells, at the
expense of naı
¨ve T cells. This accumulation could com-
promise host protection to novel pathogens during periods
of heavy training [63] (especially when the athlete is at
altitude [91,92] and/or during excessive international tra-
vel [93]). Certainly, Southern Hemisphere athletes were
reported to have a lower infection risk in their ‘off-season’
[64]. The opposite has also been reported, however [94].
Competition has been reported not to pose any acute
health risks to healthy athletes who come well-prepared
and well-supplied [95], but immune suppression can occur
within the post-race recovery period (electronic supple-
mentary material [ESM] Table S1) [24,25,2931,35,36,
40,44,47,48,50,53,5558,67,69,70,95155]. The
observed decreases in WBC, for example, are unlikely to
be wholly explainable by plasma volume expansion as the
magnitude of cell count differences is larger than the typ-
ical race-related change in plasma volume. The suggestion
[104] that completing an Olympic-distance triathlon may
decrease the level of immunoglobulin A (IgA)-mediated
Table 2 Physiological demands of (actual or simulated) triathlon competition (values expressed as mean ±SD)
Study Distance Percentage of maximal
oxygen uptake
Percentage of maximal heart rate Percentage of maximal aerobic
speed/maximal aerobic power/
peak running speed
Cycle Run Swim Cycle Run Swim Cycle Run
Taylor et al. [18] Simulated sprint (lab)
82.1 ±6.0 89.7 ±4.9 89.6 ±3.5 91.9 ±1.9 68.2 ±7.2 87.5 ±3.0
Binnie et al. [19] Simulated sprint (lab)
––––––– –
et al. [20]
Simulated OD swim-
cycle (lab)
82.8 – – 92 – 98 ±277±10 –
Bernard et al. [21] OD (field)
–––91±4– – 60±8–
Le Meur et al. [22] OD (field)
63.4 ±6.5
Gillum et al. [23]IM
68 70 – –
Laursen et al. [24]IM
indicates no information, Ffemale, IM half-Ironman (i.e. 1.9-km swim, 90-km cycle, 21-km run), IM Ironman distance (i.e. 3.8-km swim,
180-km cycle, 42.2-km run), lab laboratory, OD Olympic distance (1.5-km swim, 40-km cycle, 10-km run), SD standard deviation
All values in the table refer to males unless otherwise specified
No swim-related data are available
In both cases, the cycle section involved a 500 kJ (approximately 20 km) task
Draft-legal (i.e. in which slip-streaming behind another cycle(s) is allowed within the cycle section)
Triathlon Training and Health
Table 3 Immunological, oxidative and cardiovascular responses to triathlon training
Study Athlete level Marker type Marker Measure Result
Diaz et al.
17 elite White blood cell
Season start, pre-
competition, start and end
of race period for four
consecutive seasons
Non-significant effect of
period, season or season
period. Neutropenia in 8,
monocytopenia in 9, and
lymphopenia in 1 at some
Horn et al.
48 healthy
rested elites
Overnight ‘at rest’ sample.
Comparison across
multiple sports.
Neutropenia (\2910
/L) in
16 %, monocytopenia
(\0.2 910
et al. [62]
10 recreational
Changes in peripheral
differentiated and
senescent T cells
27, 21, 15, 9 and 3 weeks
(June) prior to and 2 weeks
1%:of differentiated
(KLRG1?/CD57-) CD8?
T cells and ‘transitional’
CD4?and CD8?T cells
with training. Two weeks
post-race: differentiated
CD8?T cells at T0 level, :
senescent CD4?T cells, ;
¨ve (CD45RA?/
CD45RO) cells
Pool et al.
13 M tri, 8 M
Immune function Endotoxin induced IL-6
release in whole blood
24 h post-exercise [Tri-plasma IL-6] and in vitro
[basal IL-6] and [endotoxin
activated IL-6][that of
controls. Post-endotoxin:
[newly induced IL-6] in tri
\in controls
15 IM, 12 UT
transferrin receptor
(CD71) expression,
intracellular iron
and URTI
Every 4 weeks for 1 year Tri \control values for Hb
(10 months), MCHC
(9 months), platelet
(11 months) and
(1 month). Tri \controls
for CD4(?)CD71(?)
[3 months]; Fe(3?)
[1 month]. Less URTI in tri
Rietjens et al.
7 M, 4 F elite Haematology Hb, haematocrit, erythrocyte
count, mean corpuscular
Hb content, mean
corpuscular volume and
plasma ferritin
102 samples over 3 years Erythrocyte count ;in race
compared with training
season. Hematological
values \lower limit of
normal range in off-,
training- and race-season in
46, 55 and 72 %,
et al. [66]
9 UT, 10 tri Hormonal parameters Salivary cortisol response to
waking, overnight urinary
cortisol, cortisone and
catecholamine excretion
10-month season Overnight urinary cortisone
excretion for tri [UT
Knez et al.
16 IM, 29 M
Oxidative stress and
antioxidant status
[MDA]; GPX, CAT and
superoxide dismutase
IM resting GPX[controls.
IM resting plasma [MDA]
\controls, IM GPX and
CAT [controls
Medina et al.
5 F, 10 M Oxidative stress
markers and
Pattern of isoprostane and
prostaglandin metabolites
in urine post-training
;[Tetranor-PGEM and
11beta-PGF(2alpha)] and
[IsoP 8-iso-PGF(2alpha)]. :
(vascular PGI
Variation possibly linked to
Banfi et al.
7 elite, 5
Growth factors and
VEGF, EGF, MCP-1, IL-8 T0: 1-day pre-race season
Tri EGF and IL-8 [control
T1: 30-min post-tri Tri VEGF, EGF, MCP-1 and
IL-8 [control VEGF,
EGF, and MCP-1
Konig et al.
42 M Homocysteine levels Plasma [total Hcy], [vitamin
B(12)], and [folic acid]
Pre- and post 30 days
training, pre- and post-
sprint tri
No change in Hcy post-
training. [Folate][in high-
training group post-training
V. Vleck et al.
Table 3 continued
Study Athlete level Marker type Marker Measure Result
Diaz et al.
5 elite M Overtraining
parameters 5 weeks
up to major race vs.
values at season
Total testosterone, CK, urea,
total cortisol
Wednesday and Thursday of
1-week microcycles with
high loads on Monday,
Tuesday, Friday and
Urea and CK over 4/5
loading weeks [T0 values
Spence et al.
32 elite, 31 AG
tri and
cyclists, 20
UT controls
Respiratory health URTI Nasopharyngeal and throat
swabs for subjects with two
or more URTI symptoms
over 5 months
37 URTI episodes in 28
subjects. Infectious agents
seen in 11 (2 control, 3 AG
and 6 elite). Incidence rate
ratios for illness in controls
and elites [AG
Knopfli et al.
7 elite FEV
extrapolation of
decrease in FEV
to BH
8-min track run at intensities
equal to anaerobic
threshold. Tests at
4.4 ±2.8 °C,
-8.8 ±2.4 °C and
3.6 ±1.5 °C, and humidity
of 52 ±16, 83 ±13 and
93 ±2%
BH :within 2 years. Three
athletes with BH. After
extrapolation of the
decrease in FEV
, it was
determined that 21–57 %
of athletes had newly
developed BH per year
et al. [74
52 tri, 22
Structural and
functional cardiac
Ventricular premature beat
Number of VPB within last
2 min of maximal exercise
tests on treadmill and
bidirectional two-
dimensional echo-doppler
exam for five consecutive
Tri [controls for VPB and
late passive diastolic filling
period amplitude of
excursion of the
interventricular septal
endocardium at the end of
the LV diastole just after
atrial contraction values.
Tri \controls for (P top-
onset systolic septal
contraction) interval and P
top-LV posterior wall
systolic contraction
interval. Tri had more
incomplete right bundle
block. Tri: concentric and
eccentric hypertrophy and
evidence of supernormal
diastolic LV function. Tri
max diastolic LV and RV
internal diameter, diastolic
interventricular septum
thickness and diastolic LV
posterior wall thickness [
controls. It was not always
the best tri who had the
most significant structural
cardiac adaptations
et al. [78,
26 tri, 17
M-mode LV echograms and
doppler recordings of LV
inflow velocity
Tri [controls for LV wall
thickness, relative wall
thickness, LV mass and
doppler-derived ratio of
early-to-late LV inflow
velocities. No difference in
resting systolic function,
diastolic LV fractional
shortening or end systolic
Knez et al.
44 tri, 44 active
Brachial BP, central
haemodynamics (:aortic
BP, wave reflection,
augmentation index,
ejection duration, timing of
reflected wave
No significant difference for
augmentation index, timing
or reflected wave, brachial
or central pulse pressure.
Tri [controls for sub-
endocardial perfusion
capacity, sub-endocardial
perfusion and ejection
Triathlon Training and Health
Table 3 continued
Study Athlete level Marker type Marker Measure Result
Scharf et al.
26 elite M, 27
M controls
Indexed LV and RV
myocardial mass, end-
diastolic and end-systolic
volumes, stroke volume,
ejection fraction, and
cardiac index at rest;
ventricular remodelling
index and maximum LA
Combination of eccentric and
concentric remodeling with
regulative :of atrial and
ventricular chambers. Tri
atrial and ventricular
volume and mass indexes[
controls. Tri LV and RV
end-diastolic volumes [
normal range in 25/26)
Findings different from
other types of elite
Platen et al.
18 tri, 69 UT/
Bone health BMD Athletes vs.controls,
screening questionnaire
Lumbar spine, femoral neck,
trochanter major and
intertrochanteric BMD \
trained controls. Femoral
neck and Ward’s triangle
values [UT
et al. [82]
20 M, 9 F Knee cartilage abnormalities Abnormal MRI findings no
greater than age-related
changes for other athletic
populations and UT
Smith and
8 tri, 13 UT Regional bone density No difference in spine and
total BMD between tri and
controls. Serum
testosterone \in tri
et al. [314]
9 M, 12 F Total body, arms and leg
Just before and immediately
after 24-week competitive
No adverse changes in BMD
et al. [84]
9 tri, 9 inactive
Knee joint cartilage thickness Via nuclear MRI No significant difference
between groups in patella,
femoral trochlea, lateral
femoral condyle, medial
femoral condyle, medial
and lateral tibial plateau
cartilage thickness
et al. [85]
7 M Bone metabolism,
bone turnover;
sexual, calciotropic
and somatotropic
Total and regional BMD,
bone-specific alkaline
phosphatase, osteocalcin,
and urinary type I collagen
Start of training and
32 weeks later
:BMD for lumbar
spine and skull but not
total body or proximal
femur, :1alpha,25-
dihydroxyvitamin D3,
insulin-like growth factor-1
and bioavailability of
insulin-like growth factor-1
index. ;Bone-specific
alkaline phosphatase. No
change in parathyroid
hormone, [testosterone],
[insulin-like growth factor-
binding protein-3] and
West et al.
17–23 years
Tibial morphology Medial, anterior and lateral
cortex thickness. Oedema/
stress fracture on nuclear
Comparison of stress fracture
and non-stress fracture
Significantly different medial
cortex thickness between
groups. Those with oedema
within the cancellous bone
or a stress fracture on MRI
took time off within
2 years due to stress
V. Vleck et al.
immune protection at the mucosal surface has been sup-
ported by data obtained over repeated short-distance races
[108]. As triathletes may be exposed to waterborne
microorganisms during the swim discipline, such a
decreased IgA-mediated immunity may increase the risk of
post-race URTI [156,157]. Neutrophil death [107] has
been seen immediately after half-Ironman-distance com-
petition in males. Significant alterations in oxidative stress
and immunological markers have also been recorded
20 min after Ironman-distance competition [113].
Nonetheless, such immune system alterations, as well as
the muscle damage and metabolic changes that are induced
by Olympic-distance competition, decline rapidly [103,
109]. Five days after Ironman competition, all the oxidative
stress markers that were assayed by Neubauer et al. [55
58] and Wagner et al. [122]—the changes in which may
have partly been due to muscle damage [123]—had
returned to baseline levels [129]. The extent to which any
postulated ‘infection window’ may exist or persist once the
athlete has finished competing appears to be affected by the
existence of positive adaptive mechanisms. Such mecha-
nisms, which may include upregulation of repair mecha-
nisms and increased activity of the endogenous
antioxidative system, are likely to be highly related to the
individual’s training and performance status.
4.2 Oxidative Stress
It is possible that significant differences in the magnitude
of oxidative stress markers [68] may be obtained when
poorly trained vs.well-trained athletes, athletes with lower
vs. higher antioxidant status, or even different periods of
the training year [158] are compared. Even minor differ-
ences in training status among the same athletes can result
in different alterations in markers of lipid peroxidation
[5558]. Data obtained from half-Ironman- and Ironman-
distance athletes, as well as controls [67], also suggest the
existence of a dose-response relationship between oxida-
tive enzyme adaptation and the response to ultra-endurance
exercise. Although it is unclear exactly how triathlon
training or race duration, intensity and/or frequency may
affect the propensity for DNA damage [122], better train-
ing levels may enhance protection against oxidative stress
4.3 Cardiovascular Responses
The other effects of triathlon training and/or competition
with potential health-related repercussions include platelet
and coagulation activation [64,68,119,130,138,159] and
other cardiovascular system-related changes [7478,80,
132,135,143,145,150152,160163]. Platelet activation
(which may increase the risk of thromboembolytic events)
and markedly increased plasmin formation may occur
during competitions lasting over 2 h [130,138,164]. Both
appear to be triggered by run-induced mechanical stress on
thrombocytes and/or inflammation [130]. However,
Olympic-distance triathlon was found to have no signifi-
cant negative effects on either left ventricular function or
myocardial tissue in adult males [151]; nor was Olympic-
distance competition found to affect blood B-natriuretic
peptide concentration—a marker of cardiac failure—in
regularly-trained triathletes [149]. Elevated levels of tro-
ponin and B-type natriuretic peptide were noted 45 min
after both half-Ironman- and Ironman-distance races, and
both markers correlated with decreased right ventricular
ejection fractions [136,144]. Although the levels of these
indicators of myocardial injury were back to normal within
Table 3 continued
Study Athlete level Marker type Marker Measure Result
Lucia et al.
9 Elite Reproductive health Percentage body fat,
hormonal profile (resting
levels of follicle-
stimulating hormone,
luteinizing hormone, total
and free testosterone, and
cortisol), and seminograms
Three times within season
(winter, competitive, and
rest period)
Triathlon training does not
adversely affect
testis axis
et al. [88]
45 including tri Sperm parameters (volume,
liquefaction time, pH,
viscosity, sperm count,
motility, and morphology)
Morphology reaching clinical
relevance for tri.
Parameters tended to ;as
training :
indicates no information, ;indicates decrease, :indicates increase, [] concentration, AG age-groupers, i.e. non-elite athletes who compete against other
athletes within the same 5-year age range, BH bronchial hypereactivity, BMD bone mineral density, BP blood pressure, CAT catalase, CK creatine kinase,
EGF extracellular growth factor,Ffemale, FEV
forced expiratory volume in 1 s, GPX glutathione peroxidase, Hb haemoglobin, Hcy haemocyanin, IsoP
isoproterenol, IL interleukin, IM Ironman (i.e. 3.8-km swim, 180-km cycle, 42.2-km run), IM half-Ironman (i.e. 1.9-km swim, 90-km cycle, 21-km run),
LA left atrial, LV left ventricular, Mmale, MCHC mean corpuscular hemoglobin content, MCP-1 monocyte chemoatractant protein-1, MRI magnetic
resonance imaging, PGEM prostaglandin E2 metabolite, PGF prostaglandin F, PGI
prostaglandin I2, RV right ventricle, T0 baseline, tri triathlete, URTI
upper respiratory tract infection, UT untrained, VEGF vascular endothelial growth factor, VPB ventricular premature beats
Triathlon Training and Health
1 week, Ironman competition was reported [141] to often
result in persistently raised cardiac troponin T (cTnT)
levels (agreeing with Rifai et al. [140]). This increase in
CTnT was associated with echocardiographic evidence of
abnormal left ventricular function. Therefore, abnormal left
ventricular function [144] may increase with race distance
[135,143]. Although such abnormal left ventricular func-
tion generally disappears within 24 h [135], it may be
linked to the occurrence of pulmonary oedema [165167].
However, even when short-term right ventricular
recovery appears complete, long-term training and com-
petition may lead to myocardial fibrosis and remodeling in
a small, genetically susceptible, percentage of athletes [74
77,168]. This theoretically might provide a foundation for
atrial and ventricular arrhythmias and increase cardiovas-
cular risk, particularly in older athletes. La Gerche et al.
[144] found increased right ventricular remodeling in well-
trained endurance athletes with a longer competitive his-
tory. Their results suggest a cumulative effect of repetitive
ultra-endurance exercise on right ventricular change and
possibly myocardial fibrosis. The long-term sequelae of the
structural or other alterations that occur to the adult tri-
athlete heart with training and competition [7477] warrant
further investigation. The long-term consequences of the
transient functional abnormalities that have also been
observed post-triathlon in children [134] are also unknown.
More ventricular premature beats at the end of a maximal
exercise test have been noted in well-trained adult triath-
letes than in controls [75]. However, it was not the triath-
letes with the best competition results who had the most
characteristics of eccentric and concentric left ventricular
hypertrophy; nor did the athletes who exhibited the greatest
training volumes exhibit the most extensive heart adapta-
tions. Nonetheless, the triathlete who displays the first
indications of evolution to a pathological hypertrophic and
dilated cardiac myopathy, i.e.ventricular premature beats
and other specific electrocardiographic and echocardio-
graphic findings, is a candidate for ‘sudden cardiac death’
[75]. Acute changes in baseline hemodynamics and auto-
nomic regulation (characterized by a decrease in stroke
index, blood pressure, total peripheral resistance index,
baroreceptor sensitivity, vagal modulation of the sinus
node, and increased heart rate, cardiac index, and sympa-
thetically-mediated vasomotor tone) that occur with com-
petition may also make Ironman-distance athletes
vulnerable to orthostatic challenge post-race [145,169].
4.4 Other Responses with Potential Health
The other responses to triathlon training and racing that
have potential health consequences include changes in
bone mineral density. One study involving adolescent
females [170] concluded that the generalised anatomical
distribution of triathlon training load does not significantly
enhance total bone mineral density. Junior males, on the
other hand, exhibited lower bone mineral density than
athletes from other sports [81]. They had significantly
elevated levels in most femoral regions, but exhibited no
differences from untrained controls at L2 and L3 of the
lumbar spine. The authors concluded that training regimes
with high volume but low intensities do not, or only
slightly, induce osteogenic effects, while a variable training
protocol with short-lived but high-intensity forces will
have the highest positive stimulatory effects on bone for-
mation. The implications for fracture risk (e.g. in the
Wards triangle, as a result of cycle falls) are unknown.
Thinner anterior tibiae and the presence of oedema on
magnetic resonance imaging (MRI) appears to be a pre-
cursor to stress fracture development, however [86]. In
Ironman triathletes, the spectrum of abnormal MRI find-
ings of the knee and shoulder was no greater than age-
related changes previously reported for other athletic
populations and non-athletes [82,171]. Little else is known
regarding the extent to which the susceptibility to skeletal
problems of triathletes [81,83,170,172,173] is affected
by training-induced modulation of circulating hormone
levels [85,87] and/or relative energy deficiency in sport
[174]. Some triathletes exhibit disordered eating [175,176]
and may suffer from anorexia nervosa [177], bulimia
nervosa [178], or other nutritional disorders [172,173,
179], all of which may influence susceptibility to injury
and/or illness.
5 Illness
Our knowledge of the degree to which the immunological/
oxidative stress of training and racing is reflected by the
occurrence of illness is limited. Only six groups [1012,
64,71,94] have prospectively investigated triathlon illness.
Vleck collected 25.1 ±5.6 weeks (mean ±SD) of
Olympic-distance national squad athlete daily training
diary data in 1994. The eight athletes concerned trained
8:10 ±2:06 hh:mm (mean ±SD) per week, and raced
(mean ±SD) 20.3 ±10.9 times. They rated (mean ±SD)
6.4 ±3.4 of such events as ‘best performances’. Training
and injuries were recorded. The athletes also logged the
occurrence of each one of the highest cited symptoms
within each of Fry et al.’s 12 classes of putative over-
training symptoms [180182]. These symptoms [12] were
interpreted as symptoms of ‘illness’. The athletes logged
247 such separate incidents. Delayed-onset muscle sore-
ness (DOMS) was the most commonly reported symptom,
followed by ‘heavy legs’, loss of appetite and then virus-
related symptoms. Such symptoms coincided with self-
V. Vleck et al.
diagnosed performance decrement on 15 % of occasions.
Performance [135] also declined on 34.7 and 21.5 % of the
occasions that DOMS and headaches were reported,
respectively. It declined on less than 7 % each of the times
that the athlete recorded heavy legs, a sore throat, gastric
problems, or reported viral infection. In 66.7 % of DOMS
cases and 76.9 % of cases of heavy legs, the performance
decrement could have been due to another illness-related
symptom, or even to an injury. Interestingly, the athletes
never reported a drop in performance on the same day that
they reported a ‘stuffy nose’, ‘loss of appetite’, ‘chest
cold’, ‘head cold’, ‘sleeplessness’ or ‘nausea’. The athletes
neither explicitly stated the criteria that they used to decide
whether a drop in performance had occurred or not, nor
how training was interrupted or modified because of
Andersen et al. [94] implemented a slightly different
illness definition, with Ironman athletes. They defined ill-
ness as any health problem that was not related to the
musculoskeletal system (e.g. respiratory tract infections,
influenza or gastrointestinal infections, and not DOMS).
Over 26 weeks, 156 cases affecting 104 athletes (i.e.60 %)
were reported, equating to 5.3 illnesses per 1,000 athlete
days. Nine percent of cases did not lead to any time loss,
34 % led to 1–3 days off, 36 % led to 4–7 days off, 19 %
led to 8–28 days off and 3 % led to more than 28 days off.
Medical diagnosis of illness can itself be problematic
[183]. It is certainly unclear to what extent upper respira-
tory symptoms in triathletes may be due to infection or to
other non-infectious inflammatory symptoms that mimic a
URTI [183]. Of 25 cases of URTI symptoms that were
reported for 63 triathletes and cyclists [72], 28 % each
were due to rhinovirus and influenzae (A and B), 16 % to
parainfluenzae, 8 % each to Streptococcus pneumoniae and
coronavirus, and 4 % each to Epstein–Barr virus reacti-
vation and metapneumovirus. Four percent of URTI
symptoms were unaccounted for and could have been due
to local drying out of the mucosal surfaces and increased
exposure to airborne pathogens [184], to bronchial hyper-
reactivity (the rate of development of which has been
reported to be 195–286 faster in elite athletes than is nor-
mal for asthma development [72,73,185187]) or to
muscle damage-induced migration of inflammatory cyto-
kines [183]. The incidence of URTI in both the triathletes
and the untrained controls who were assessed by a year-
long study [64] was lower than the international average of
two per year.
Thus, the extent to which the immune changes that
occur as a result of the stress of triathlon training and/or
racing alter overall disease susceptibility [156,157] is not
usually likely to be major, but is unclear. However, the
conditions that are involved in open-water swimming may
increase the risk for specific conditions [187] such as
Acanthamoeba keratitis [188], and for uncommon diseases
such as schistosomiasis [189,190] and leptospirosis. Lep-
tospirosis has been incurred by triathletes training [191]
and competing [192197] in contaminated surface water.
Crucially, the affected athletes were only diagnosed as
having been infected after awareness of a leptospirosis
outbreak [198] was independently established for the race
locality. The clinical presentation of leptospirosis varies
and may present similar symptoms to common febrile ill-
nesses. Thus, there is also a potential problem in triathletes
of illness being misdiagnosed [199,200]. The fact that an
inappropriate management strategy (with potentially neg-
ative repercussions for rehabilitation time) may then be
implemented was recently highlighted [199]. However, the
extent to which such issues occur is unknown. At present,
the overall outcomes of triathlete illness in terms of eco-
nomic cost, training time loss and/or even performance
decrement [201] are unquantified. Only (potentially) indi-
rect evidence of the extent to which illness may lead to
changes in training load exists [12,202]. The national
squad triathletes who were examined by Vleck in 1994 [12,
202] logged lower average weekly training durations than
were expected of top-level athletes of that time [9].
Unfortunately, as illness and injury can overlap, it can
prove difficult to ascertain the real outcome of either in
isolation [12].
6 Injury
Injury ‘causing cessation of training for at least one day,
reduction of training, or seeking of medical aid’ has been
reported to affect 29 % [13]to91%[202,203] of adult
triathletes at any one time (ESM Table S2) [12,14,32,39,
42,94,204226,228232,238]. The wide range of
reported values is likely due to a failure to standardize
methodology or surveillance between studies as the Inter-
national Olympic Committee (IOC) guidelines recommend
[233]. The other methodological difficulties with the tri-
athlon injury literature have been reviewed [234237] and
are not repeated here. Only one retrospective study has
compared the prevalence of training-related injury between
different sex, ability-level and event-distance specialization
groups, using the same definition and reporting methods in
each case [12,13], with no difference being found. No one
has yet conducted a similar comparative study across all
the triathlon age groups. Nor does the proportion of athletes
who report for medical aid at sprint distance events [32]
appear to be influenced by age, sex or competitive expe-
rience. Whether the same consistently applies to all the
other triathlon distances and formats [238] is unknown.
Obtaining meaningful injury incidence values for tri-
athletes is a challenging task. This is partly because of
Triathlon Training and Health
difficulties in quantifying and weighting overall training
stress across (at least) swimming, cycling, running and
weight-training [239]. The typical presentation and char-
acteristics of overuse injuries also makes them difficult to
record in epidemiological studies when time-loss defini-
tions are used [240]. No sudden death rates for training
exist and there is no long-term international registry system
for this within races. The sudden death rate for USA Tri-
athlon-sanctioned events over 2006–2008, involving
959,214 participants, was estimated by Harris et al. [241]at
1.5 (0.9–2.5) deaths per 100,000 participations, with an
average age at death of (mean ±SD) 42.8 ±10.1 years. It
was (but not significantly) greater in males and in races
with more participants. When data from 2003 to 2011 (for
triathlon, duathlon, aquathlon, and off-road triathlon
events) were examined [242], an approximate figure of one
death per 76,000 participants per year was obtained. The
absolute fatality rate increased with participation rates.
Most were rated as sudden cardiac death events, yielding a
higher rate than reported for half marathons and marathons
between 2000 and 2010 [243] (i.e.0.28 and 0.52 per
100,000, respectively). According to Harris et al. [241],
sudden death during swimming accounted for 1.4 (0.8–2.3)
deaths per 100,000 participations per year. The equivalent
values for triathlon cycling and running were 0.1
(0.01–0.07) and 0.0 (0.0–0.3). Slightly, but not signifi-
cantly, higher death rates were recorded for the races with
short (\750 m) or longer ([1,500 m) swims than for those
with 750–1,500 m swims. It is not known why.
Self-assessed overuse injury incidence rates of
0.74–76.7 per 100 athletes, and of 10.0–23.8 per 1,000
training and racing hours, respectively (depending on the
month of the year), have been obtained prospectively for
small (n=11–43) samples of Olympic-distance triathletes
[12]. Values of 1.39 and 18.45 incidences per 1,000
training and racing hours over various distances, respec-
tively, have also been obtained [232]. The injuries were not
confirmed by medical diagnosis. A total of 20.1 presenta-
tions for medical assistance per 1,000 h of sprint-distance,
Olympic-distance and fun-distance (i.e.0.15–0.3 km swim,
7–10 km cycle, 1–3 km run) competition has been recor-
ded [238]. Although few directly comparable data exist,
injury rates are usually thought to be higher within com-
petition [94,221,222,232]. The incidence of (traumatic)
crowding-, hydration- and/or heat-related injuries in par-
ticular is also thought to be higher (ESM Table S3) [39,
203,206209,212214,222,226,229,230,238], although
no training-related studies appear to have assessed these
issues. The lack of detail of assessment that has been
involved in most larger-scale studies also makes it difficult
to assess how widespread the problems that have only or
mostly been reported by case studies (e.g. ESM Table S4)
and that may to some extent be ‘triathlon specific’, actually
No prospective intergroup (age, sex, ability or event
distance) comparisons of injury incidence rates exist for the
endurance base, pre-competitive and competitive periods.
Only one study [230] has investigated the effect of race
distance and athlete ability level on the temporal occur-
rence of race injuries—a topic with clear implications for
the depth and timing of provision of medical support. Wind
speed, humidity, and dry-bulb temperatures in the study in
question varied widely, but the extent to which this was
over each race or between events is unclear. Injury (defined
in this case as a presentation for medical assistance)
affected 10.8 % of half-Ironman- and 37.7 % of Ironman-
distance age-group starters, respectively. Previously, it was
reported to affect 15–25 % of elite Ironman-distance
competitors [275,276]. Most athletes took 5–9 h to finish.
A total of 72.2 % of half-Ironman injuries occurred
between hours 6 and 7, during which time medical per-
sonnel needed to be prepared for 78 presentations for
assistance per 1,000 race starters. No equivalent rates exist
for shorter-distance events. The proportion of injuries that
were severe was higher during the Ironman event than for
the half-Ironman, and was calculated to be (mean ±95 %
confidence interval) 38.2 ±6.0 % of those receiving
treatment at any given time. Treatment duration increased
with finishing time. The highest proportion of severe
injuries occurred in the half-Ironman athletes who took
longer to finish, or the Ironman athletes who were faster,
than the rest of their cohort.
Contusions, abrasions/grazes and blisters are the most
commonly reported short-distance race injuries [238]. At
half-Ironman events, dehydration (50.8 %) and muscle
cramps (36.1 %) are the primary medical diagnoses. Both
have been reported in almost equal proportions (38.9 vs.
37.7 %) at an Ironman-distance event [230]. The percent-
age of so-called race injuries that are actually existing,
training-related injuries that have been exacerbated by
competition is unknown. Injury outcome after a race has
finished (e.g. death from complications arising from chest
infection) is also not described (ESM Table S5) [1214,32,
206,208,211,212,214218,220,222,226]. Gradual-
onset overuse injuries are the most commonly reported
training injuries. They have been reported to occur in
approximately three times as many athletes as do acute
injuries [209,215,232,277] (ESM Table S6) [12,14,94,
232,237,238]. The true value may be higher given the fact
that retrospective recall is generally poorer for overuse
injuries than for traumatic injuries [232].
Most athletes rate their training-related injuries as
‘minor’ to ‘moderate’ (i.e.incurring up to 21 days off) when
a time-loss definition is used. However, according to Finch
V. Vleck et al.
‘it is often the medically less severe injuries that are con-
sidered to be more severe by the athlete, although they do not
require medical treatment, as they have the potential to
severely limit an athlete’s performance’’ [278]. Many
injured triathletes may continue training [12,217,226,277].
Running, cycling and swimming training is modified in
17–21, 26.2–75 and 42–78 % of injury cases [202],
respectively, and injury recurrence is probably a major issue
We highlighted the fact that the influence of certain
injury risk factors may differ with sex, format and event-
distance specialization (Table 1and Sect. 2)[12,280].
Minimal examination into which putative risk factors are
most highly linked to injury in each group has taken place
(Table 4and ESM Tables S4 and S5) [1214,32,41,54,
238,244274,281,315,317,318,321324]. Although
various potential (and even triathlon cross-training-spe-
cific) mechanisms of injury have been speculated upon
[217,236,237,282284], they are largely unverifiable. For
example, drowning was the reported cause of death for the
swim fatalities recorded by Harris et al. [241], but
drowning lacks the accurate methods of risk exposure that
are needed to establish aetiology [285]. The actual cause
could be something else (e.g. autonomic conflict [286,
287], deterioration in performance [288] in cold water,
swim-induced pulmonary oedema [249], or hyperthermia).
It is noteworthy that all the swim deaths occurred in open
water, raising the question as to whether there is something
about mass participation competition that is significant
[286]. Periodic health screening (such as the IOC Periodic
Health Evaluation [289]) is not routinely implemented in
the sport of triathlon to screen for risk factors for sudden
cardiac death [290]. With only one abstract on the topic
published thus far, the extent to which triathletes enter
races with pre-existing medical conditions is unknown.
Importantly, of the sudden deaths reported by Harris et al.
[241], seven of nine athletes were found on autopsy to have
had cardiovascular abnormalities. Six had mild left ven-
tricular hypertrophy. Two years later, the USA triathlon
fatality incidents study [242] concluded, despite incom-
plete access to relevant medical data, that most non-trau-
matic deaths were likely due to sudden cardiac death.
However, injuries are usually attributed to ‘‘a result of
failure to adjust pace within safe limits for specific envi-
ronmental conditions’’ [209,237], or to ‘‘inadequate
implementation of (race) safety precautions’’[247].
7 Training and Performance Status Indicators
It has been said that ‘‘a fine line exists between the level of
training that is required for optimal performance, and that
which induces problems’’ [291]. Laboratory-based (physi-
ological, immunological, haematological, cardiorespiratory
and biochemical) testing may therefore sometimes be
conducted to ascertain the individual’s health status. Only
some markers have been shown to be related to triathlon
performance and thereby possess criterion validity
(Table 5)[1012,29,30,65,68,90,145,223,292305].
Whether they are sensitive enough to detect a drop in
performance before it becomes competitively meaningful
[306] is unclear. To date, peak power output and blood
pressure variability appear to be the only variables that are
correlated with triathlon performance that have been used
[300] in prospective investigations of the links between
training and health in triathletes [1012,16,304]. Peak
power output appeared not sensitive enough to detect the
early signs of overreaching in well-trained males [300].
Whether it may react later to more extended exhaustive
training is unknown.
In any case, by the time an underlying problem has been
confirmed in the laboratory, it may be too late. Ideally the
individual’s distress markers should be monitored far more
regularly, in conjunction with his/her training, and on an
ongoing basis. Indeed, it has been observed that as regards
heart rate variability (HRV) related data [305], for exam-
ple, attempting to diagnose the athletes’ physical status
from records obtained on a single isolated day may be a
somewhat meaningless exercise. Weekly averages and
rolling averages of RR-interval (the interval from the peak
of one QRS complex to another on an electrocardiogram)-
related values and the coefficient of variation of HRV, on
the other hand, were shown to differ between an athlete
who developed non-functional overreaching and a control.
These results complemented data obtained in swimmers—
in which a shift in autonomic balance towards sympathetic
predominance 1 week earlier was linked to increased risk
for URTI and muscular problems [307].
Although HRV holds promise as an indicator of mal-
adaptation, as do baroreflex sensitivity and blood pressure
variability [145], monitoring it may only prove realistic for
some. Dolan et al. [3] reported that only 20.9 % of triath-
letes used a heart rate monitor. In contrast, 45.5 % kept a
training diary [12]. Training diary compliance is therefore
likely to be higher. The question arises as to whether the
right things are being monitored in the diary, as well as
how the data are being analyzed. Scores on questionnaires
such as the Daily Analysis of Life Demands for Athletes
(DALDA) [90], the Recovery-Stress Questionnaire for
Athletes (RESTQ-Sport) [303], the Perceived Stress Scale
(PSS), the Training Distress Scale (TDS), the Athlete
Burnout Questionnaire (ABQ) and the Multi-component
Training Distress Scale (MTDS) [10,11], as well as on a
combination of shortened versions of the Profile of Mood
States (such as the Brunel Mood Scale [BRUMS] and the
Triathlon Training and Health
Table 4 Risk factors for injury that have been directly assessed in the triathlon literature (modified and updated from Vleck [202], with
Possible risk factor Injury variable Significant relationship (at the 95 % confidence level or higher) observed between risk
factor and injury variable
Yes No
Sex Overuse injury
Vleck [12] (Retros: anatomical
Collins et al. [211], Villavicencio et al. [226] (BP),
Williams et al. [210], Manninen and Kallinen
[216], Egermann et al. [222] (LB), Zwingenberger
et al. [232], Korkia et al. [214], Burns et al. [221],
Gosling et al. [238]
Number of injuries Vleck [12] (Retros: OD, IM of E, SE or rec level)
Age Injury occurrence Egermann et al. [222], Gosling
et al. [238]
Collins et al. [211], Zwingenberger et al. [232]
Height Injury occurrence Vleck and Garbutt [14], Vleck [12] (Retros: OD F
and IM of both sexes), Korkia et al. [214]
Body mass index Injury occurrence Collins et al. [211], Vleck and Garbutt [14], Korkia
et al. [214], Villavicencio et al. [226], Vleck [12]
(Retros: OD F and IM of both sexes)
COL5A1 CC1 genotype Exercise-associated
muscle cramping
O’Connell et al. [315]–
Foot type, orthopaedic
Injury occurrence Burns et al. [225] Vleck [12] (Retros: F), Vleck and Garbutt [14]
Orthopaedic problems Overuse injury
Vleck and Garbutt [14]
Previous injury Injury incidence Korkia et al. [214]***, O’ Toole
et al. [203], Burns et al. [221],
Migliorini [213], Villavicencio
et al. [226] (BP, NP)
Manninen and Kallinen [216] (lower limb, LB)
Achilles tendon, hamstring,
knee and lower-back injury
Calf injury
Vleck and Garbutt [14]–
Diet Injury occurrence Vleck and Garbutt [14]
Use of NSAIDs Hyponatremia Wharam et al. [281]–
Restless sleeper, restless sleep,
health worries
Overuse injury
Vleck and Garbutt [14]
Psychological state/total mood
disturbance (basic analysis)/
daily or weekly hassles
Overuse injury
Fawkner et al. [219] (daily
Vleck and Garbutt [14]
Position on cycle/degree of
trunk flexion on cycle/use of
Overuse injury
Vleck and Garbutt [14], Manninen and Kallinen
[216] (LB)
Cycle gear ratio/crank length Cycle injury Massimino et al. [209], Vleck and Garbutt [14]
Use of and type of clipless
Overuse injury
Vleck and Garbutt [14]
Cycle cadence Overuse injury
Massimino et al. [209], Vleck and Garbutt [14]
Cycling cadence trained at Overuse injury
Massimino et al. [209], Vleck and Garbutt [14]
Faulty running shoe
Plantar fasciitis Wilk et al. [220]
Training in other sports Overuse injury
Collins et al. [211]* Manninen and Kallinen [216]
Sporting background Injury occurrence Williams et al. [210] (B) Vleck and Garbutt [14], Korkia et al. [214]
Initial sporting background Overuse injury
Williams et al. [210] (B) Collins et al. [211]
Level reached in single sport Injury incidence Vleck [12] (Retros)
V. Vleck et al.
Table 4 continued
Possible risk factor Injury variable Significant relationship (at the 95 % confidence level or higher) observed between risk
factor and injury variable
Yes No
Years of competitive
Injury occurrence Burns et al. [221] (R), Williams
et al. [210] (T, r=0.17***)
Vleck [12] (Retros: S, B, R, elite OD M),
Villavicencio et al. [226] (NP)
Injury incidence Korkia et al. [214], Williams
et al. [210], Egermann et al.
[222], Villavicencio et al.
[226] (NP)
Vleck and Garbutt [14]
Years of competitive
swimming or cycling
Vleck [12] (Retros: E, SE or age-group OD M or F)
Years of competitive running
Number of running
Vleck [12] (Retros: IM M,
Vleck [12] (Retros: OD M)
Number of triathlons
participated in/years of
triathlon experience
Low BP or neck
Villavicencio et al. [226] (NP),
Korkia et al. [214]
Collins et al. [211]
Athletic status Overuse injury
Collins et al. [211], Villavicencio et al. [226] (BP)
Athlete ability level Injury incidence Shaw et al. [224], Egermann
et al. [222]
Korkia et al. [214], Vleck and Garbutt [14] (for
anatomical location)
Performance level Injury incidence Egermann et al. [222] (muscle
tendon injury)
Zwingenberger et al. [232] (top 50 or bottom 50 %)
Personal best time Injury to specific
anatomical site
Vleck and Garbutt [14] (OD: T, S, B, R)
Main competitive distance Injury occurrence Williams et al. [210] (F) Korkia et al. [214], Williams et al. [210] (M)
Race distance trained for Overuse injury
(For anatomical location) Vleck
[13] (for IM vs. OD)
Vleck 2010 [12] (Retros, F)
Race distance done Medical assistance Gosling et al. [238] (B and R) Gosling et al. [238] (S)
Race distance (IM vs.triple
Rust et al. [317]–
Degree and specificity of
coaching and feedback
Overuse injury
Collins et al. [211], Vleck and Garbutt [14] (not
detailed), Egermann et al. [222] (yes/no),
Zwingenberger et al. [232] (yes/no)
Back-to-back cycle run
transition training (yes/no)
Overuse injury
Vleck and Garbutt [14]
Presence of medical care (yes/
Injury Egermann et al. [222]
Presenting for medical aid in
race (yes/no)
Injury incidence Gosling et al. [32]–
Stretching practice/flexibility Injury incidence
Negative, Massimino et al. [209]
(Ank, Ach [before bike and
after swim])
Ireland and Micheli [206], O’Toole et al. [203],
Manninen and Kallinen [216]
Warm-up/cool-down practice Number of injuries Burns et al. [221] Ireland and Micheli [206], Vleck and Garbutt [14],
Korkia et al. [214]
Warm-down/stretching after
Overuse injury
Vleck and Garbutt [14]
Cool-down practiced (yes/no) Number of injuries Korkia et al. [214]
Altered blood flow Gastrointestinal
Wright et al. [318]
Number of races per season/
participation in competition/
time spent competing
Overuse injury
Zwingenberger et al. [232] Villavicencio et al. [226] (CP)
Triathlon Training and Health
Table 4 continued
Possible risk factor Injury variable Significant relationship (at the 95 % confidence level or higher) observed between risk
factor and injury variable
Yes No
Training time Injury incidence
Egermann et al. [222] (T), Shaw
et al. [224] (T, B, R)
Villavicencio et al. [226] (CP), Ireland and Micheli
[206] (SBR); Korkia et al. [214] (SBR), Shaw
et al. [224] (S), Murphy [207], Zwingenberger
et al. [232](\10 h or C10 h), Manninen and
Kallinen [216] (B time and LB)
Time spent cycling Number of cycling
Vleck and Garbutt [14]
Time spent running Number of running
Vleck [12] (Retros: [E and SE
OD and IM] F, r=0.63**)
Number of cycling
Vleck and Garbutt [14] (Retros,
Time spent running Occurrence of
Achilles tendon
Vleck [12] (Retros: OD M,
Time spent doing long runs Number of running
Vleck [12] (Retros: SE M,
r=-0.76***, E OD F,
Vleck [12] (Retros: SE F)
Amount or percentage of
training time spent in each
Injury incidence
Ireland and Micheli [206]
Average time doing intervals,
hard, moderate, easy and hill
training in all disciplines
Injury incidence
Korkia et al. [214]
Total time spent doing speed
cycle work during a race
week without taper
Number of injuries Vleck [12] (Retros, r=
0.29–0.60) * to *** depending
on group
Lower-back injury
Vleck [12] (Retros: OD M,
Percentage of (cycle) training
spent doing interval work
Number of overuse
Vleck [12] (B) (SE OD M,
O’Toole et al. [203] (B, R)
Percentage of time spent or
number of sessions spent
doing cycle hill repetitions
Number of injuries Vleck [12] (Retros OD M,
r=-0.44* and -0.39*
Massimino et al. [209], Vleck [12] (Retros IM M),
O’Toole et al. [203] (B, R)
Time out of seat during
training sessions
Number of injuries Massimino et al. [209]
Percentage of time spent or
number of sessions spent
doing run hill repetitions
Number of injuries Vleck [12] (Retros, M SE OD
and E OD M)
Increased percentage of time
spent doing quality or track
run work
Number of injuries Vleck [12] (Retros, r=0.66 for
M and 0.91 for F***)
Massimimo et al. [209] (R)
Training distance Number of
(cycling) injuries
Ireland and Micheli [207], Collins et al. [211],
Egermann et al. [222]
Number of run
overuse injuries
Vleck [12] (Retros: [E, SE and
NE] M, r=0.23*)
Injury incidence
Burns et al. [221] Massimino et al. [209] (KI), Korkia et al. [214],
O’Toole et al. [203] (S, B, R), Manninen and
Kallinen [216] (LB)
Swimming distance Number of run
Vleck and Garbutt [14]
Overdistance swim work,
fartlek, hypoxic, kick, pull in
Incidence of
Massimino et al. [209]
V. Vleck et al.
Table 4 continued
Possible risk factor Injury variable Significant relationship (at the 95 % confidence level or higher) observed between risk
factor and injury variable
Yes No
Weekly cycling distance Number of injuries Williams et al. [211]
Number of run
Vleck and Garbutt [14]
Cycling overdistance, pace,
Number of cycling
Massimino et al. [209]
Increased cycle overdistance
Number of cycling
Massimino et al. [209]
Distance covered during run
hill repetitions
Occurence of
Achilles tendon
Vleck [12] (Retros: OD M,
Higher pre-season running
Number of injuries Burns et al. [221]–
Mileage for week before event KI incidence
Massimino et al. [209]
Number of triathlon workouts
per week
Injury incidence
Vleck and Garbutt [14] (RI,
Korkia et al. [214]
Number of ‘other’ (than speed,
long or hill repetition) cycle
sessions per week
Number of overuse
Vleck[12] (Retros: OD M,
Vleck [12] (Retros: IM M)
Number of other types of cycle
session, increased percentage
of time sent doing cycle
interval work
Number of injuries Vleck [12] (Retros, r=0.92 for
Number of run sessions per
Number of run
Vleck and Garbutt [14]
Number of run speed sessions Injury incidence
Vleck [12] (Retros, r=0.56 for
Number of hill repetition run
sessions per week
Number of overuse
Vleck [12] (SE OD M,
Number of other (i.e.not
speed, hill repetition or long)
run sessions
Injury incidence
Vleck [12] (Retros, r=0.63 for
OD F*)
Long-run session time Injury incidence
Vleck [12] (Retros, r=0.86 for
Number of running
Vleck [12] (Retros: SE OD M,
Vleck [12] (Retros: SE OD F, r=0.76**)
Duration of speed run sessions Vleck [12] (Retros: IM M) Vleck [12] (Retros: IM F)
Training sequence Injury/KI
Massimino et al. [209], Korkia et al. [214]
Strength training (yes/no) Overuse injury
Korkia et al. [214], Collins et al. [211], Manninen
and Kallinen [216] (LB)
Combined intensity work for
all three disciplines
Injury incidence
Korkia et al. [214]
Pace/intensity (not in detail) Injury incidence
Vleck [12] (Retros, cycle
work)*, Massimino [206]
(Ank, Ach)
Massimino et al. [209] (K), Korkia et al. [214],
O’Toole et al. [203]
Increase in training load Injury incidence
Vleck [12] (Pros) Korkia et al. [214] (Pros)
Cycled faster Foot, ankle,
Achilles tendon
Massimino et al. [209]
Increased other (i.e. not long,
hill repetition or speed) cycle
Foot, ankle,
Achilles tendon
Vleck [12] (Retros, r=0.35)* –
Triathlon Training and Health
Profile of Mood States for Children [POMS-C]) [10,12,
300] and various signs and symptoms of illness and injury
[12], assess mood disturbance, perceived stress and training
or other distress symptoms to various degrees. They may
all potentially be incorporated into such logs.
Main et al. [11] found, using linear mixed modeling, that
both various combinations of training factors and psycho-
logical stressors (as monitored on a weekly basis via the
PSS, BRUMS, TDS and ABQ) were linked with signs and
symptoms of both illness and injury in age-group triath-
letes. The number of training sessions and the number of
completed run sessions per week, as well as perceived
programme difficulty (see Tables 1and 4), had significant
effects on signs and symptoms of URTI, injuries or minor
aches and pains, although less so than did individual athlete
scores on the PSS [308]. We note that the TDS itself
(which was developed from the list of distress symptoms
that Fry et al. [309] identified from interviews with fit
individuals who were exposed to repeated intense training)
was later validated against performance in a laboratory
time-to-fatigue trial. TDS responses were also compared
across a high-intensity training group and a control group
of triathletes, and decreases in running performance in the
training but not the control group were reflected by the
athletes’ TDS scores [310]. However, neither actual nor
self-assessed performance was assessed in the study by
Main et al. [11].
Certainly, potential indicators of the fitness fatigue
response, or of performance (as indeed may training-
related risk factors for injury and illness), are likely to
function better if they have been tailored to the individual
athlete. Vleck [12] retrospectively calculated individual
specific peak performance norms for various indicators on
Fry et al.’s (longer) 1991 list [180,181] of potential
overtraining symptoms, for each of eight national squad
triathletes. The fact that these norms were only obtainable
over an average of six ‘best performance’ occasions rather
than the recommended eight [311], even though the study
lasted approximately 6 months, underlines the difficulties
in producing such norms. The extent that the weekly
values for each distress indicator diverged from the indi-
vidual athlete’s peak performance norm were then mod-
elled together with composite training load scores and
self-reports of performance decrement, using binary
logistic regression. The combination of the heavy legs and
DOMS scores for the same week, the composite appetite
score for the previous week, the POMS-C confusion factor
score for both 2 and 3 weeks before, and the POMS-C
anger factor score for the previous week increased the
predictive power of the model for performance decrement.
New overuse injury had previously been shown to be
associated with an increase in combined weighted cycle
and run training at higher intensity levels 2 weeks prior to
onset. Interestingly, prediction was not improved by
incorporation of any derived training:stress recovery
variables for each of the athletes into the model. This may
have been due to the difficulty in producing valid, indi-
vidual-specific indices that account for relative rather than
absolute changes over time in the training stress to which
each athlete is exposed.
Table 4 continued
Possible risk factor Injury variable Significant relationship (at the 95 % confidence level or higher) observed between risk
factor and injury variable
Yes No
Weighted combined cycle and
run training in intensity
levels 3–5 of 5 (with level 5
being the highest intensity)
Injury incidence
Vleck [12] (Pros)*
indicates no information, Ach Achilles tendon, Ank ankle, Bbicycling, BP back pain, CP cervical pain, E1994 elite (probably most similar to
very-well-trained recreational athletes), Ffemale, IM Ironman (i.e. 3.8-km swim, 180-km cycle, 42.2-km run), Kknee, KI knee injury, LB lower
back, Mmales, NE non-elite, NP neck pain, NSAIDs non-steroidal anti-inflammatory drugs, OD Olympic distance (i.e.1.5-km swim, 40-km
cycle, 10-km run), Pros prospective study, Rrunning, Rec recreational, Retros retrospective study, RI running injuries, Swimming, SE 1994 sub-
elite (probably most similar to good age-groupers, i.e. athletes competing within their 5-year age-group band, of today), SBR swim, bike and run,
*p\0.05, ** p\0.02, *** p\0.001
Very limited data. Potential links between diet/disordered eating/occurrence of female athlete triad and triathlon injury have not yet been
Value correlation coefficient not given because it was calculated for a three-sport sample
Previous lower-limb pain was not linked to the onset of lower-back pain
Unless a prospective study, most incidence data actually refer to incidence proportions
Lumbar pain linked with prior foot, ankle or knee injury
Some indication of a sex, age, event distance and or athlete ability/experience effect seen in this study
V. Vleck et al.