High Training Volumes are Associated with a Low Number of Self-Reported Sick Days in Elite Endurance Athletes

Abstract

It has been proposed that high exercise loads increase the risk of infection, most frequently reported as upper respiratory tract infections, by suppressing the immune system. Most athletes will not train when experiencing sickness due to the fear of health complications. However, high training volumes are incompatible with high rates of non-training days, regardless of the cause. The purpose of this observational study was to examine the relationship between self-reported, exercise-constraining days of sickness (days when the athlete decided not to train due to symptoms of disease, either self-reported or by a physician) and the volumes of exercise training in elite endurance athletes by analyzing data from training logs kept for several years. The subjects included 11 elite endurance athletes (8 male, 3 female) competing at national and international levels in cross-country skiing, biathlon and long-distance running. Training logs available from these 11 subjects added to a total of 61 training years. The number of training hours per year (462, 79-856; median, range) was significantly and negatively correlated to the reported number of days not training due to sickness (15, 0-164) by a 3(rd) degree polynomial regression (R(2) = 0.48, F ratio = 18, p < 0.0001). We conclude that elite endurance athletes can achieve high training volumes only if they also experience few sick-days. Key pointsTop level performance demands high training volumes and intensities, which may compromise immune function.Elite athletes must have an immune system capable of intact function also when under sever physiological and psychological stress.Elite performance, especially in endurance sports, is therefore incompatible with a high rate of infections.A negative correlation between infections and exercise training load among elite athletes is consequently observed - the less sick you are the more you can train.

Full text

©Journal of Sports Science and Medicine (2014) 13, 929-933
http://www.jssm.org
Received: 25 June 2014 / Accepted: 02 September 2014 / Published (online): 01 December 2014
High Training Volumes are Associated with a Low Number of Self-Reported
Sick Days in Elite Endurance Athletes
Sandra Mårtensson, Kristina Nordebo and Christer Malm

Sports Medicine Unit and School of Sports Sciences, Umeå University, Sweden
Abstract
It has been proposed that high exercise loads increase the risk of
infection, most frequently reported as upper respiratory tract
infections, by suppressing the immune system. Most athletes
will not train when experiencing sickness due to the fear of
health complications. However, high training volumes are in-
compatible with high rates of non-training days, regardless of
the cause. The purpose of this observational study was to exam-
ine the relationship between self-reported, exercise-constraining
days of sickness (days when the athlete decided not to train due
to symptoms of disease, either self-reported or by a physician)
and the volumes of exercise training in elite endurance athletes
by analyzing data from training logs kept for several years. The
subjects included 11 elite endurance athletes (8 male, 3 female)
competing at national and international levels in cross-country
skiing, biathlon and long-distance running. Training logs availa-
ble from these 11 subjects added to a total of 61 training years.
The number of training hours per year (462, 79-856; median,
range) was significantly and negatively correlated to the report-
ed number of days not training due to sickness (15, 0-164) by a
3rd degree polynomial regression (R2 = 0.48, F ratio = 18, p <
0.0001). We conclude that elite endurance athletes can achieve
high training volumes only if they also experience few sick-
days.
Key words: Upper respiratory tract symptoms, infection, high
volume training, immunosuppression.
Introduction
High training intensities and volumes increase the risk of
infection by impairing the immune function (Nieman,
2003; Nieman et al., 1989; 1990; Pedersen and Ullum,
1994; Peters and Bateman, 1983). Thus, elite athletes with
high training intensities have a higher number of infec-
tious episodes than recreational athletes and sedentary
people (Gleeson and Walsh, 2012; Spence et al., 2007).
However, in order to become an elite athlete absence of
infections are important, and such findings may appear
illogical (Malm, 2006). One issue in earlier studies has
been that some conclusions regarding elite exercise train-
ing are based on non-elite athletes (Heath et al., 1991;
Nieman et al., 1989; 1990) or on comparing athletes to
non-athletes (Nieman et al., 2000). In addition, few stud-
ies have confirmed that pathogenic infections are present,
but such studies have made statements that elite exercise
training increases the odds ratio for an infection (Spence
et al., 2007). Most athletes do not have the facilities re-
quired to carry out on-demand virus titers or establish
bacterial cultures, and thus refrain from training based
exclusively on self-diagnosis. The experience of these
athletes thus makes the term Exercise Constrained Sick
Days more appropriate. This can be defined as any day
when the athlete chose not to train due to experienced
symptoms of infections, self-reported or by a physician.
The relationship between the long term risk of
URTI, immune function and exercise load is commonly
modelled as a J-shaped curve with URTI on the y-axis
and exercise load on the x-axis (Nieman, 1994a). This
model suggests that sedentary individuals have a fixed
risk of URTI, and that moderate exercise training de-
creases, while intense exercise training increases, the rate
of URTI by a mechanism in which the immune system is
modulated. Recently, support for this model was present-
ed (Spence et al., 2007). The acute, repeated occurrence
of impairment of immunity, the “open window” for in-
fections to enter the body (Brines et al., 1996; Nieman,
1997; Pedersen and Bruunsgaard, 1995; Pedersen and
Ullum, 1994) may result in a long-term increase in infec-
tion rates (the J-shaped curve).
We have previously shown that infection rates af-
ter a marathon increased only in subjects who had report-
ed a pre-race infection (Ekblom et al., 2006). Consequent-
ly, we formed the hypothesis that the relationship between
training and infection may form an S-curve when true
elite athletes are included (Malm, 2006). Most studies
have not differentiated between athletes with “high” and
“elite” exercise loads, with the consequence that an incor-
rect definition of “elite” athletes has been used. We pre-
sented a pilot study data from one elite runner’s training
log that covered 16 years (Malm, 2006), and concluded
that an increased rate of infection is incompatible with a
high training volume. These findings are in line with the
recently published review article by Dhabhar (Dhabhar,
2014), where short term physical stress such as exercise,
which is perceived as a positive event by the athlete, be-
longs to the immunoprotective response, enhancing sur-
vival and promoting enhanced physical performance.
The aim of this study was to test the hypothesis
that exercise training loads are negatively correlated to the
self-reported number of Exercise Constrained Sick Days.
Methods
Subjects
Male (N = 4) and female (N = 3) cross-country (XC)
skiers, male biathletes (N = 2) and male long-distance
runners (N = 2) completed the study. The inclusion crite-
rion was that the athlete should have demonstrated “top
national or international level performance in an endur-
ance sports”. This does introduce a certain amount of
Research article

Infections in elite athletes
930
subjectivity into whether the criterion was met, but all
subjects were accepted as elite athletes to the School of
Sports Science at Umeå University based on their compet-
itive results from past years. Subjects were aged 17-24
years at the first year of reporting, and they reported a
training period of 3-16 years. The number of observations
of training years was 61 in all statistical calculations. A
retrospective Power calculation was done (using the soft-
ware G*power, version 3.1.3, www.psycho.uni-
duesseldorf.de/abteilungen/aap/gpower3/) for Chi2 tests
(goodness-of-fit for contingency tables) using an effect
size of 0.3, p = 0.05, N = 61 and 2 degrees of freedom
(between three levels of training volume) results in a
Power of 0.54, while increasing the effect size to 0.5
gives a power of 0.95. Ethical permission (Ref. No. 2011-
236-31M) was granted by the Regional Ethics Committee
for northern Sweden, located at Umeå University. All
subjects signed an informed consent form and the study
was conducted in accordance with the WMA Declaration
of Helsinki – Ethical Principles for Medical Research
Involving Human Subjects 2008. The study met also the
ethical standards of IJSM (Harriss and Atkinson, 2011).
Procedure
Endurance athletes, who often keep rigorous records of
their training, were asked to summarize their written
training logbooks. Training volume (km or hours), the
number of sick days and the number of days injured had
to have been recorded for participation. Eleven endurance
athletes’ training logs from the past 3-16 years (pending
number of complete years recorded) met these criteria,
and were summarized. Training logs are based on a daily
subjective recordings, thus the definitions of “sick” and
“injured” do not necessarily include examination by a
physician, but always resulted in a no-training day. Con-
sequently, the term self-reported Exercise Constrained
Sick Days is used. Raw data from the training logs were
summarized by the researchers in 12 month intervals as
the sum of hours or kilometres of training, number of
Exercise Constrained Sick Days due to sickness, and
number of days injured (defined as trauma or over-use
injuries) each year. Four subjects did not report the num-
ber of days injured in their training logs, without stating a
reason for not reporting. A minimum of three years of
reported training was required for inclusion. Two athletes
(runners) summarized training in kilometres, which were
transformed into training hours using an assumed average
speed of 4 min 20 sec·km-1, for an elite distance runner or
cross country skier, in which distance-training, interval-
training and recovery running are averaged. Other varia-
bles, such as modes of training, diet, sleeping habits,
travel schedules, medication, hygienic habits or other
potential confounding factors were not taken into consid-
eration. Dietary records and performance variables were
not included. Subjects participated in the elite athletic
program at the School of Sports Science, Umeå Universi-
ty, demanding at least a top national performance level.
The authors have no conflicts of interest.
Statistical analysis
All statistical calculations were carried out in JMP 7.0.1
(SAS Institute Inc. Cary, NC, USA). We investigated
correlations using third-degree polynomial regression
fitting. The Exercise Constrained Sick Days data was not
normally distributed, and thus the data was log-
transformed before partition analysis. The 11 athletes had
trained for a total of 61 years, with a span of 3-16 years.
In order to investigate the correlation between training
volume and the number of Exercise Constrained Sick
Days, these 61 years were divided into three groups based
on the number of Exercise Constrained Sick Days report-
ed, using partition statistics (JMP 7.0.1). Partition was
made to give 3 groups for which the differences between
the groups was most significant (lowest p-value) when
comparing number of training hours. We analysed the
difference in number of training hours between the three
groups using the non-parametric Kruskal-Wallis test. No
adjustment for age was made. Due to the low number of
subjects in each sport, and each sex, no further division of
data, such as split between sports or sex, was done.
Results
The results demonstrate that the number of training days
missed by an athlete due to self-reported sickness is nega-
tively correlated to the volume of training in a mixed
population of elite cross-country skiers, biathletes and
long-distance runners.
Subjects reported a total of 61 training years, had
trained an average of 462 (79-856) hours per year (medi-
an and range), were sick on 15 (0-164) days and injured
on 21 (0-164) days. The number of training years reported
was not significantly correlated to the number of self-
reported Exercise Constrained Sick Days reported (R2 =
0.33, p = 0.16) (Table 1).
Table 1. Group data on training volume and Exercise Con-
strained Sick Days in elite endurance athletes
Training Group
#TY
MECSD
10th and 90th percentile
< 266 h/year
12
54
18 and 160
266 - 538 h/year
27
17
6 and 57
> 538 h/year
22
8
0 and 27
#TY: Number of training years included. MECSD: Median Exercise
Constrained Sick Days
Training years are ranked from 1-61 according to
the number of training hours per year in Figure 1 (x-axis)
and plotted against the number of Exercise Constrained
Sick Days and the number of training hours in a dual y-
axis graph. Figure 1 shows that the number of training
hours per year is inversely correlated with the number and
variation in Exercise Constrained Sick Days.
A 3rd degree polynomial equation was derived (R2
= 0.48, p < 0.0001):
S = 37.5 - 0.048 * T + 0.00027*(T-453)2 - 5.6*10-7*(T-
453)3
where S is the number of sick days and T is the amount of train-
ing in hours.
This function is plotted in Figure 2 as a solid line,
with the area between the 95% Confidence Intervals (CI)

Mårtensson et al.
931
coloured grey and the 95% CI for individual data points
shown as dashed lines (Figure 2).
Figure 1. Sick days per year on left y-axis (solid line) and
Training hours per year on right y-axis (dashed line) plotted
against Individual training years on the x-axis. The variation
in the number of sick days falls at approximately 400 train-
ing hours, indicating that, the number of sick days per year
must be below 50 in order to train more than 400 hours per
year.
Figure 2. A third-degree polynomial bivariate fit (solid line)
of Sick days per year (y-axis) and Training hours per year
(x-axis) shows a significant (R2 = 0.48, F Ratio = 18, p <
0.0001, N = 61 training years from 11 subjects) decrease in
the number of sick days reported as the number of hours of
training increases. The shaded area indicates the 95% CI of
the model and the dashed lines indicate the 95% CI for the
individual data points.
The dataset of 61 individual training years was di-
vided into three groups based on the number of completed
hours of training using partition statistics to obtain the
maximum significance of the differences in the number of
ECDs between groups (Table 1). The skewness was 2.65,
and thus it was necessary to carry out log-transformation
of the ECD data. Loge (ECD) was then plotted along the
y-axis for the three groups (plotted as x-variable). Parti-
tion using Log transformed sick-days data resulted in
three groups; training less than 266 h/year (N = 12), train-
ing 266-538 h/year (N = 27) and training more than 538
h/year (N = 22). The non-parametric Kruskal-Wallis test
on non-transformed data gave Chi2 = 27, p < 0.0001 when
comparing number of sick days between the three training
groups, with median ECDs at 54 (10th percentile = 18,
90th percentile = 160), 17 (6, 57) and 8 (0, 27), respective-
Our results demonstrate that a high training load is ac-
companied by a low incidence of days of training lost due
to self-reported days of sickness, and is compatible with
our previous suggestion (Malm, 2006).
Our findings agree with those of Moreira et al.
(Moreira, Delgado, Moreira and Haahtela, 2009) in which
a combination of the classic J-curve proposed by Nieman
(1994a) and the S-curve proposed by Malm (2006) was
presented. The combined model suggests that the risk of
sickness in less-fit individuals can be displayed as a J-
curve, while the curve tends to flatten as fitness increases.
Moderate physical activity lowers the risk for infection in
non-athletic adults from that of inactive adults, which is
reflected in the J-curve nature of the results for such sub-
jects (Nieman, 1994b; Nieman et al., 2011). It may be
necessary to separate less-fit individuals from well-trained
individuals when interpreting the effects of exercise on
infection (Matthews et al., 2002), because of the demon-
strated increased self-reported upper respiratory tract
infection in elite, compared to recreational athletes
(Gleeson et al., 2013)
Many studies have examined endurance running
events such as marathons, and report that there is an in-
creased risk of acquiring an infection in the weeks follow-
ing such an event. However, Ilback et al. (1991) (in rats)
and Ekblom et al. (2006) (in humans) have shown that
pre-effort infection is the probable cause of the increase in
post-effort infection rates, not the post-effort sensitivity to
infections.
The present study did not investigate immune
function, but others have reported a different immune
response in elite compared to sedentary individuals
(Walsh et al., 2011). Different inclusion criteria could
therefore partly explain the difference in results between
the present and previous (Nieman, 1994a; 1994b) studies.
It can be argued that an immune system capable of
fighting infection also during and after repetitive, strenu-
ous exercise is necessary in order to become a successful
elite athlete. Thus, measurements of infection rates in elite
athletes are biased due to positive selection, and this could
explain the S-curve/flattened J-curve (Malm, 2006). Elite
athletes, such as the participants in this study, have the
ambition to train every healthy day, and thus more train-
ing hours can be completed in years with fewer infections.
These arguments are not in contradiction to such findings
as by Spence et al. (Spence et al., 2007), showing that
elite athletes have higher reported episodes of infections
than non-elite subjects. All athletes participating in the
present study performed at an elite level in their sport, and
had the ambition to reach high training volumes every
year in order to compete on a national and international
elite level; they were all accepted to the School of Sports
Science at Umeå University. However, because of infec-
tions, they were not able to reach their goals every season,
ly.
Discussion

Infections in elite athletes
932
manifested as the data points at the low end of Training
hours per year in Figure 2. Thus, the argument that infec-
tions caused low training volumes, may be equally valid
as the opposite; that high training volumes will cause
infections. The latter being practically and statistically
impossible, as 500-800 training hours per year will de-
mand very few sick days, regardless cause and pathology.
Consequently, a large variation in the number of sick days
will be present in any study including elite athletes, unless
a biased selection of only health elite subjects is done.
The results in the present study do not by any
means explain the cause, but are in line with our pilot
study (Malm, 2006) as well as our conclusion regarding
infection rates in marathon runners (Ekblom et al., 2006);
In elite athletes, exercise load is negatively correlated
with self-diagnosed Exercise Constrained Sick Days. A
recent review by Moreira et al. (2009) also conclude that
“among elite athletes, the relationship between exercise
load and immune dysfunction tends to flat”. The demands
associated with elite sports require an immune system that
is capable of fighting off infections also in situations with
extreme physical and mental challenges.
Because this study is based on retrospectively
summarize training logs, self-reported sickness, and a
relatively few athletes, future studies should include a
much larger number of subjects, as well as pathogen iden-
tification to investigate the mechanisms that govern the
immunological response and clinical outcome of exercise
training and competition. Of interested would also be to
correlate sick days and training volumes to diet, perfor-
mance and other co-founding factors in a prospective
approach. This could benefit our understanding not only
of the mechanisms behind the function of the immune
system, and its adaptation in elite athletes, but also the
clinical application of exercise to optimize both immune
function and performance.
Perspective
Elite athletes are individuals in pursuit of reaching their
genetic limits of physical performance. They encounter
numerous obstacles on this pathway, injuries and disease
being two of the most common. This study makes a sim-
ple, but not scientifically published, observation that min-
imizing the number of Exercise Constrained Sick Days is
a key issue for high training volumes in elite athletes,
whose key to winning is maintained health.
Conclusion
In elite athletes, high training volumes are incompatible
with a high number of non-training days, regardless of
cause. Consequently, the correlation between the number
of sick days and training hours was found to be negative.
References
Brines, R., Hoffman-Goetz, L. and Pedersen, B.K. (1996) Can you
exercise to make your immune system fitter? Immunology To-
day 17(6), 252-254
Dhabhar, F.S. (2014) Effects of stress on immune function: the good, the
bad, and the beautiful. Immunology Research 58(2-3), 193-210.
Ekblom, B., Ekblom, O. and Malm, C. (2006) Infectious episodes before
and after a marathon race. Scandinavian Journal of Medicine
and Science in Sports 16(4), 287-293.
Gleeson, M. (2007) Immune function in sport and exercise. Journal of
Applied Physiology 103(2), 693-699.
Gleeson, M., Bishop, N., Oliveira, M. and Tauler, P. (2013) Influence of
training load on upper respiratory tract infection incidence and
antigen-stimulated cytokine production. Scandinavian Journal
of Medicine and Science in Sports 23(4), 451-457.
Gleeson, M. and Walsh, N.P. (2012) The BASES expert statement on
exercise, immunity, and infection. Journal of Sports Science
30(3), 321-324.
Harriss, D.J. and Atkinson, G. (2011) Update - ethical standards in sport
and exercise science research. International Journal of Sports
Medicine 32(11), 819-821.
Heath, G.W., Ford, E.S., Craven, T.E., Macera, C.A., Jackson, K.L. and
Pate, R.R. (1991) Exercise and the incidence of upper respirato-
ry tract infections. Medicine and Science in Sports and Exercise
23(2), 152-157.
Ilback, N.G., Crawford, D.J., Neufeld, H.A. and Friman, G. (1991) Does
exercise stress alter susceptibility to bacterial infections? Upp-
sala Journal of Medicine and Science 96(1), 63-68
Malm, C. (2006) Susceptibility to infections in elite athletes: the S-
curve. Scandinavian Journal of Medicine and Science in Sports
16(1), 4-6.
Matthews, C.E., Ockene, I.S., Freedson, P.S., Rosal, M.C., Merriam,
P.A. and Hebert, J.R. (2002) Moderate to vigorous physical ac-
tivity and risk of upper-respiratory tract infection. Medicine and
Science in Sports and Exercise 34(8), 1242-1248.
Moreira, A., Delgado, L., Moreira, P. and Haahtela, T. (2009) Does
exercise increase the risk of upper respiratory tract infections?
British Medical Bulletin 90, 111-131.
Nieman, D.C. (1994a) Exercise, infection, and immunity. International
Journal of Sports Medicine 15 (Suppl 3), S131-141.
Nieman, D.C. (1994b) Exercise, upper respiratory tract infection, and
the immune system. Medicine and Science in Sports and Exer-
cise 26(2), 128-139.
Nieman, D.C. (1997) Risk of upper respiratory tract infection in athletes:
An epidemiologic and immunologic perspective. Journal of
Athletic Training 32(4), 344-349.
Nieman, D.C. (2003) Current perspective on exercise immunology.
Current Sports Medicine Reports 2(5), 239-242.
Nieman, D.C., Henson, D.A., Austin, M.D. and Sha, W. (2011) Upper
respiratory tract infection is reduced in physically fit and active
adults. British Journal of Sports Medicine 45(12), 987-992.
Nieman, D.C., Johanssen, L.M. and Lee, J.W. (1989) Infectious epi-
sodes in runners before and after a roadrace. Journal of Sports
Medicine and Physical Fitness 29(3), 289-296.
Nieman, D.C., Johanssen, L.M., Lee, J.W. and Arabatzis, K. (1990)
Infectious episodes in runners before and after the Los Angeles
Marathon. Journal of Sports Medicine and Physical Fitness
30(3), 316-328.
Nieman, D.C., Nehlsen-Cannarella, S.L., Fagoaga, O.R., Henson, D.A.,
Shannon, M., Hjertman, J.M., Schmitt, R.L., Bolton, M.R:,
Austin, M.D., Schling, B.K. and Thorpe, R. (2000) Immune
function in female elite rowers and non-athletes. British Journal
of Sports Medicine 34(3), 181-187.
Pedersen, B.K. and Bruunsgaard, H. (1995) How physical exercise
influences the establishment of infections. Sports Medicine
19(6), 393-400.
Pedersen, B.K. and Ullum, H. (1994) NK cell response to physical
activity: possible mechanisms of action. Medicine and Science
in Sports and Exercise 26(2), 140-146.
Peters, E.M. and Bateman, E.D. (1983) Ultramarathon running and
upper respiratory tract infections. An epidemiological survey.
South African Medical Journal 64(15), 582-584.
Spence, L., Brown, W.J., Pyne, D.B., Nissen, M.D., Sloots, T.P.,
McCormack, J.G., Locke, A.S. and Fricker, P.A. (2007) Inci-
dence, etiology, and symptomatology of upper respiratory ill-
ness in elite athletes. Medicine and Science in Sports and Exer-
cise 39(4), 577-586.
Walsh, N. P., Gleeson, M., Pyne, D. B., Nieman, D. C., Dhabhar, F. S.,
Shephard, R. J., Oliver, S.J., Bermon, S. and Kajeniene, A.
(2011) Position statement. Part two: Maintaining immune
health. Exercise Immunology Review 17, 64-103.

Mårtensson et al.
933
Key points
• Top level performance demands high training vol-
umes and intensities, which may compromise im-
mune function.
• Elite athletes must have an immune system capable
of intact function also when under sever physiologi-
cal and psychological stress.
• Elite performance, especially in endurance sports, is
therefore incompatible with a high rate of infections.
• A negative correlation between infections and exer-
cise training load among elite athletes is consequent-
ly observed – the less sick you are the more you can
train.
AUTHORS BIOGRAPHY
Sandra MÅRTENSSON
Employement
Sports Med
icine Unit, Umeå University,
Sweden
Degree
BSc
Research interest
Infection risk and elite athletes. Currently
working with health maintenance, exercise
training and diet.
E-mail: sandravm88@hotmail.com
Kristina NORDEBO
Employement
Sports Medicine Unit, Umeå University,
Sweden
Degree
MS
Research interest
Infections and adaptations to exercise.
E-mail: k_nordebo@hotmail.com
Christer MALM
Employement
Assoc. Prof., Sports Medicine Unit, Umeå
University, Sweden
Degree
PhD
Research interest
Muscle adaptation to exercise, immune
function, physical performance and testing.
E-mail: christer.malm@umu.se

Christer Malm
Sports Medicine Unit and School of Sports Sciences, Umeå
University, Sweden