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Health status of recreational 
runners over 10‑km 
up to ultra‑marathon distance 
based on data of the NURMI Study 
Step 2
Katharina Wirnitzer1,2,3, Patrick Boldt4, Gerold Wirnitzer5, Claus Leitzmann6, 
Derrick Tanous1,2, Mohamad Motevalli1,2, Thomas Rosemann7 & Beat Knechtle7,8*
Endurance running is well‑documented to affect health beneficially. However, data are still conflicting 
in terms of which race distance is associated with the maximum health effects to be obtained. 
Therefore, the aim of this study was to compare the health status of endurance runners over different 
race distances. A total of 245 recreational runners (141 females, 104 males) completed an online 
survey. Health status was assessed by measuring eight dimensions in two clusters of health‑related 
indicators (e.g., body weight, mental health, chronic diseases and hypersensitivity reactions, 
medication intake) and health‑related behaviors (e.g., smoking habits, supplement intake, food 
choice, healthcare utilization). Each dimension consisted of analytical parameters derived to a general 
domain score between 0 and 1. Data analysis was performed by using non‑parametric ANOVA and 
MANOVA. There were 89 half‑marathon (HM), 65 marathon/ultra‑marathon (M/UM), and 91 10‑km 
runners. 10‑km runners were leaner than both the HM and M/UM runners (p ≤ 0.05). HM runners 
had higher health scores for six dimensions (body weight, mental health, chronic diseases and 
hypersensitivity reactions, medication intake, smoking habits, and health care utilization), which 
contributed to an average score of 77.1% (score range 62–88%) for their overall state of health. 
Whereas 10‑km and M/UM runners had lesser but similar average scores in the overall state of health 
(71.7% and 72%, respectively). Race distance had a significant association with the dimension 
“chronic diseases and hypersensitivity reactions” (p ≤ 0.05). Despite the null significant associations 
between race distance and seven (out of eight) multi‑item health dimensions, a tendency towards 
better health status (assessed by domain scores of health) among HM runners was found compared to 
other distance runners. However, the optimal state of health across all race distances supported the 
notion that endurance running contributed to overall health and well‑being.
Trial registration number: ISRCTN73074080. Retrospectively registered 12th June 2015.
As the basic form of human movement, running is the most popular leisure-time physical  activity1. This low-cost 
and convenient activity can be practiced at any age with little effort and a lower level of expertise and  mastery2. 
Over the past decades, the number of recreational and professional runners has increased across various dis-
tances, marathons in  particular3, and various reasons for actively following a running routine have been reported 
by runners. While health-oriented purposes have been shown to be the most significant motive for  running1,4, 
literature indicates that several motives including but not limited to leisure, hobby, weight control, winning, 
OPEN
1Department of Research and Development in Teacher Education, University College of Teacher Education Tyrol, 
Innsbruck, Austria. 2Department of Sport Science, University of Innsbruck, Innsbruck, Austria. 3Research Center 
Medical Humanities, Leopold-Franzens University of Innsbruck, Innsbruck, Austria. 4Department of Child and 
Adolescent Psychiatry and Psychotherapy, LVR-Klinik Viersen, Viersen, Germany. 5adventureV & change2V, Stans, 
Austria. 6Institute of Nutrition, University of Gießen, Gießen, Germany. 7Institute of Primary Care, University of 
Zurich, Zurich, Switzerland. 8Medbase St. Gallen Am Vadianplatz, St. Gallen, Switzerland. *email: beat.knechtle@
hispeed.ch
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and social reasons encourage runners to engage in running activities/events3,5. Motivations for running could 
potentially influence the intensity, duration, and frequency of training routines as well as lifestyle behaviors 
in endurance runners, which together might affect short- and long-term health  status4,5. Despite the fact that 
distance runners are depicted as the healthiest fraction of the general population, it has been reported that a 
“faster and further” dosage  fails6.
Research indicated that among 26 different kinds of sport, endurance running provides the most favorable 
health  implications9. Regular participation in recreational running was found to positively affect body weight 
(BW), body fat, blood pressure, blood glucose levels, insulin sensitivity, blood-lipid profile, and musculoskeletal 
 health10–12. Additionally, running could favorably influence mood, well-being, and mental  status13,14. Other 
mental feelings, including fear, depression, worries, anxiety, and anger within the context of an adjustment 
disorder, might be positively affected following regular endurance  running14. Distance running contributes to 
the prevention of chronic diseases by lowering the risks, such as cardiovascular disease (e.g., coronary artery 
disease, stroke)15,16 and different types of  cancer17,18. As a potential link between running and overall mortality, 
cardiorespiratory fitness is a strong predictor for morbidity and mortality, and further reduces total mortality 
from cardiovascular disease, cancer, infections, and other  causes15,19. Substantial health-related advantages fol-
lowing endurance running are correlated with running exposure in a dose–response association, as the larger 
effects on health are achieved with increased loads of  running8. Moreover, evidence supports more beneficial 
health effects of regular endurance running on cardiovascular risk factors, particularly artery carotid diameter 
 thickness20 and low-grade  inflammation21 compared to irregular endurance running. In addition to the well-
established fact that endurance running is an effective tool to improve individual  health7, regular and long-term 
involvement in running activities could be a powerful tool to affect public health positively and thus tackle global 
health  problems8,9.
It has been shown that marathon runners benefit from a greater metabolic fitness (e.g., insulin response, fast-
ing lipids, fasting insulin), aerobic performance (e.g., velocity at  VO2max, running economy), exercise metabo-
lism (e.g., lactate threshold), and skeletal muscle levels of mitochondrial proteins compared to sedentary subjects 
with matched cardiovascular fitness, age, gender, and body mass index (BMI)22. Marathon running was found to 
significantly diminish the risk of coronary plaque prevalence as a result of reducing the relevant risk factors (e.g., 
hypertension and hyperlipidemia)23. In addition to a low incidence of cardiovascular disease, marathoners are 
shown to have an extended longevity compared to the general  population24. The favorable health consequences 
of distance running are not limited to marathoners, as distance runners in lower and higher mileages were also 
shown to have comparable outcomes. Evidence indicates that ultra-marathoners were healthier and less often 
sick compared to the general  population25. Half marathon running was found to positively affect immune cell 
proportions, pro-inflammatory cytokine levels, and recovery behavior on a short-term basis as a midterm anti-
inflammatory  effect26. Research on 10-km running demonstrates a positive relationship between running and 
cardio-metabolic health, independent of exercise volume and cardiorespiratory  fitness27. Furthermore, compared 
to longer-distance runners, 10-km runners also appear to be at a lower risk of injuries; however, weekly mileage 
and race distance were identified as risk factors for injuries in endurance  runners7,28.
Despite the aforementioned advantageous influences, there have been reports of some adverse effects of 
distance running on health status (e.g., musculoskeletal injuries, unintended weight reduction, cardiovascular 
abnormalities) that potentially increase with  age28–30. The increased exercise-induced stress during an ultra-
marathon run leads to several pathophysiological changes, such as an increase in acute phase proteins, a decrease 
in testosterone, an increase in liver values, hemolysis, skeletal muscle cell damage, micro-hematuria, and a loss of 
bone  mass31. Ultra-marathoners also tend to suffer from more knee pain, stress fractures, allergies, and asthma 
than the general  population25. In addition, intensive and long-lasting endurance running was found to lead 
arterial changes toward constricting the coronary, cerebral, and peripheral  arteries1,32,33, which not only affects 
performance but could also be associated with an increased risk for acute cardiac disorders (e.g., cardiac death, 
clinical arrhythmias, angina, myocardial infarcts)34,35. While older individuals are at a higher  risk36, the incidence 
of race-related cardiac arrest was found to be significantly higher in males than female marathoners—although 
the overall risk is  low34. It was found that a half marathon running could also significantly increase post-exercise 
levels of biomarkers related to cardiovascular damage and  dysfunction37, which is associated with an increased 
risk for race-related cardiac  arrest34. Moreover, activation of the inflammatory response and the detoxification 
process was shown by proteomic profile changes after a half marathon race, and additional pathways associated 
with immune response, lipid transport, and coagulation were  involved38. Distance running is also associated 
with a high risk of running-induced injuries, as approximately half of the active runners reported having more 
than one injury per year, with excess BW, the weekly mileage, and the race distance recognized as relevant 
risk  factors7,28. Furthermore, gastrointestinal complaints (due to decreased exercise-induced mesenteric blood 
flow)39, symptomatic  hyponatremia35, and exercise-induced asthma, as well as hay fever, are reported in distance 
 runners39,40.
In spite of the well-recognized effects of endurance running on different health parameters, there is a paucity 
of research comparing the health status among different groups of endurance runners. The available health-
associated reports did not distinguish different race distances and instead have focused on 10-km  runners41, 
half  marathoners26,37,38,  marathoners12,22,24, or ultra-marathoners31,32. Therefore, the aim of the present study was 
to investigate the health-related indicators and behaviors of recreational endurance runners and compare their 
health status across different race distances. It was hypothesized that the health status differs between endurance 
runners over 10-km, half-marathon, and marathon/ultra-marathon race distances.
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Methods
Study design and ethical approval. 
The present study is a part of the NURMI (Nutrition and Run-
ning High Mileage) Study and has been conducted following a cross-sectional  design42. The NURMI study was 
designed by an interdisciplinary team of scientists and aims to assess and compare recreational endurance run-
ners by sex, race distance, diet type, etc. Data collection was conducted via a series of self-reported online sur-
veys in three separate but subsequent steps. The NURMI Study Step 1 will therefore examine epidemiological 
aspects (e.g., age, sex, and prevalence of diet type at running events), Step 2 focuses on behaviors considering 
running training and racing, nutrition, health, etc., and Step 3 investigates running performance linked to diet 
and sports-psychological parameters.
The subsequent method was introduced in detail  elsewhere10,42,43, to which the interested readers are kindly 
referred. The study protocol was approved by the ethics board of St. Gallen, Switzerland, on May 6, 2015 (EKSG 
14/145). The trial registration number is ISRCTN73074080.
Experimental approach and inclusion criteria. 
Endurance runners in the NURMI study were mostly 
engaged from German-speaking countries, including Germany, Austria, and Switzerland. Runners were con-
tacted and recruited mainly via social media, websites of the organizers of marathon events, online running 
communities, email-lists and runners’ magazines, as well as via magazines for health, nutrition and lifestyle, 
trade fairs on sports, plant-based nutrition and lifestyle, as well as through personal contacts.
Participants completed an online survey within the NURMI Study Step 2, which was available in German and 
English at www. nurmi- study. com. Prior to completion of the questionnaire, participants were provided a writ-
ten description of the procedures and gave their informed consent to take part in the study. In parallel, physical 
and psychological information—including the assignment to one of three basic areas of sports (as participants 
are mainly active in running due to either health, leisure, or performance foci)—motivation and aim of run-
ning activities, and details regarding other sports activities to balance for running were obtained to differentiate 
between a health, leisure, or predominantly performance-orientated approach. For successful participation in 
the study, the following inclusion criteria were determined initially: (1) written informed consent; (2) at least 
18 years of age; (3) questionnaire Step 2 completed; (4) having a BMI < 30 kg/m2; and (5) successful participation 
in a running event of at least a half-marathon distance in the past two years. However, to avoid an irreversible 
loss of valuable data sets, those who met the inclusion criteria 1–4 but stated being 10-km runners were included 
as additional participants and were assigned to a further race distance group.
To control for a minimal status of health linked to a minimum level of fitness and to further enhance the 
reliability of data sets, BMI-associated criteria were implemented in the present study. With a BMI ≥ 30 kg/m2, 
however, other health-protective and/or weight loss strategies other than running are necessary to reduce body 
weight safely, and could thus potentially affect health-related data. Therefore, participants with a BMI ≥ 30 kg/
m2 (n = 3) were excluded from data analysis.
Data clearance and classification of participants. 
Control questions were included throughout differ-
ent sections of the survey to control for self-reported information of running-related variables (history, training, 
racing, etc.), and consequently, to identify inconsistent or conflicting data. In general, from the initial number 
of 317 endurance runners, 72 participants who did not meet the inclusion criteria or did not provide consistent 
or complete answers to essential questions (e.g., sex, age, race distance, health-related questions) were excluded 
from the study. As a result, a total of 245 runners with complete data sets were included for descriptive statistical 
analysis after data clearance (Fig. 1).
Participants were initially categorized according to race distance: half-marathon and marathon/ultra-mara-
thon (data were pooled since the marathon distance is included in an ultra-marathon). The shortest distance for 
ultra-marathon was 50 km, and the longest distance was 160 km in the present study. In addition, a total of 91 
highly-motivated 10-km runners provided accurate and complete answers; however, they had not successfully 
participated in either a half-marathon or marathon. In general, the most frequently stated race distance was 
considered the main criterion to assign runners to the respective study groups.
It is well-established that the BMI of active runners is lower than the general  population44, and people with a 
higher BMI might have a different health status, as their main goal to engage in running activities is to achieve 
and maintain a healthy BW. The World Health  Organization45,46 recommends maintaining a BMI in the range of 
18.5–24.9 kg/m2  (BMINORM) for individuals, while at the same time pointing to an increased risk of co-morbidities 
for a BMI 25.0–29.9 kg/m2 and moderate to severe risk of co-morbidities for a BMI > 30 kg/m2. Therefore, calcu-
lated BMI was classified into three categories, under 18.49,  BMINORM, and over 25, to differentiate health-related 
findings based on BMI subgroups. In addition, given the importance of diet types in endurance runners’ health 
 status10,20, participants were assigned into three dietary subgroups of omnivores, vegetarians, and  vegans47.
Health‑related dimensions. 
As a latent variable, health status was derived by using both the two clusters 
of health-related indicators and health-related  behaviors10,48. Each cluster pooled four dimensions defined by 
specific items based on manifest measures. The following dimensions described health-related indicators: (1) 
BW and BMI; (2) mental health (stress perception); (3) chronic diseases and hypersensitivity reactions: preva-
lence of chronic diseases (incl. heart disease, state after heart attack, cancer), prevalence of metabolic diseases 
(incl. diabetes mellitus 1, diabetes mellitus 2, hyperthyroidism, hypothyroidism), prevalence of hypersensitiv-
ity reactions (incl. allergies, intolerances); and (4) medication intake (for thyroid disease, for hypertension, for 
cholesterol level, for contraception). The following dimensions described health-related behaviors: (1) smoking 
habits (current and history of smoking); (2) supplement intake (supplements prescribed by a doctor, supple-
ments for performance enhancement, supplements to cope with stress); (3) food choice (motivation, desired 
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ingredients, avoided ingredients); and (4) healthcare utilization and regular check-ups. Together, these eight 
dimensions described health outcomes. Resulting from this, eight domain scores were derived, which generated 
scores between 0 and 1. Low scores indicate detrimental health associations, while higher scores indicate benefi-
cial health associations [given as mean scores plus standard deviation and percentage (%)].
Statistical analysis. 
The statistical software R version 3.5.0 Core Team 2018 (R Foundation for Statistical 
Computing, Vienna, Austria) was used to perform all statistical analyses. Exploratory analysis was performed 
by descriptive statistics (median and interquartile range (IQR)). Significant differences between race distance 
subgroups and domain scores to describe health status were calculated by using a non-parametric ANOVA. 
Chi-square test and Kruskal–Wallis test were used to examine the association between race distance subgroups 
and domain scores with nominal scale variables, and Wilcoxon test and Kruskal–Wallis test (ordinal and metric 
scale) approximated by using the F distributions. State of health was statistically modeled as a latent variable and 
was derived by manifest variables (e.g., BW, cancer, smoking). In order to scale the state of health described by 
the respective dimensions of health, a heuristic index between 0 and 1 was defined (equivalence in all items). 
In order to test the statistical hypothesis considering significant differences between subgroups of race distance, 
sex, age, academic qualification, and weekly mileage of running for each dimension, a MANOVA was performed 
Figure 1.  Enrollment and categorization of participants.
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to define health status. The assumptions of the ANOVA were verified by residual analysis. The level of statistical 
significance was set at p < 0.05 (statistical trend: 0.05 ≥ p < 0.10).
Ethics approval. 
The study protocol was approved by the ethics board of St. Gallen, Switzerland on May 
6, 2015 (EKSG 14/145). The study was conducted in accordance with the ethical standards of the institutional 
review board, medical professional codex and the with the 1964 Helsinki declaration and its later amendments 
as of 1996 as well as Data Security Laws and good clinical practice guidelines.
Consent to participate. 
All participants gave written informed consent prior to the testing procedure.
Results
Sociodemographic data. 
A total of 245 endurance runners (141 women and 104 men) with a mean age of 
39 (IQR 17) years and a BMI of 21.72 (IQR 3.50) kg/m2 were included for final data analysis. Germany (n = 177), 
Austria (n = 44), and Switzerland (n = 13) had the majority of endurance runners, but 4.5% of participants 
(n = 11) were from other countries, including Belgium, Brazil, Canada, Italy, Luxemburg, Netherlands, Poland, 
Spain, and the UK. There were 154 NURMI-Runners (89 half-marathoners, 65 marathoners/ultra-marathoners) 
and 91 runners over the 10-km distance. The participants reported following an omnivorous diet (44%), vegetar-
ian diet (18%), or vegan diet (37%). Moreover, with regard to the level of academic qualification, 34% of endur-
ance runners (n = 83) had upper secondary/technical education or a university (or higher) degree. In addition, 
67% of endurance runners were married or living with partner (Table 1). The characteristics of the subjects are 
presented in Tables 1 and 2.
The basic assignment of endurance runners to sports areas was 54% for leisure activity, 36% for sports 
achievement, and 10% for health concerns. The main motivation of endurance runners to start running was for 
hobby (35%), health (19%), or BW loss (18%). The major goal for participation in running events reported was 
to achieve a specific runtime (51%) followed by the pleasure of running (39%). As a supplementary physical 
activity, summer sports (53% cycling, 31% respectively swimming, hiking/rambling and trail/uphill running) 
were reported to be more prevalent than winter sports.
Table 1.  Anthropometric and sociodemographic characteristics of the endurance runners. Data are presented 
as “percentage of prevalence (n)” or “median (IQR)”. BMI body mass index, BW body weight, HM half-
marathon, IQR interquartile range, km kilometers, M/UM marathon/ultra-marathon.
Total
HM
M/UM
10 km
Number of Subjects
100% (245)
36% (89)
27% (65)
37% (91)
Sex
 Female
58% (141)
55% (49)
38% (25)
74% (67)
 Male
42% (104)
45% (40)
62% (40)
26% (24)
Age (years) (median)
39 (IQR 17)
37 (IQR 18)
44 (IQR 17)
37 (IQR 18)
BW (kg) (median)
65.0 (IQR 14.2)
65.0 (IQR 13.0)
67.5 (IQR 17.5)
62 (IQR 11.0)
BMI (kg/m2) (median)
21.72 (IQR 3.50)
21.97 (IQR 3.28)
22.15 (IQR 3.25)
21.30 (IQR 3.94)
Diet
 Omnivorous
44% (109)
44% (39)
51% (33)
41% (37)
 Vegetarian
18% (45)
22% (20)
15% (10)
16% (15)
 Vegan
37% (91)
34% (30)
34% (22)
43% (39)
Academic qualification
 No Qualification
 < 1% (1)
1% (1)
–
–
 Upper Secondary Education/Technical Qualification/GCSE 
or Equivalent
34% (83)
37% (33)
40% (26)
26% (24)
 A Levels or Equivalent
22% (53)
17% (15)
23% (15)
25% (23)
 University Degree/Higher Degree (i.e., doctorate)
34% (83)
30% (27)
28% (18)
42% (38)
 No Answer
10% (25)
15% (13)
9% (6)
7% (6)
Marital status
 Divorced/Separated
6% (15)
6% (5)
6% (4)
7% (6)
 Married/Living with Partner
67% (164)
63% (56)
72% (47)
67% (61)
 Single
27% (66)
31% (28)
22% (14)
26% (24)
Country of residence
 Austria
18% (44)
17% (15)
20% (13)
18% (16)
 Germany
72% (177)
73% (65)
69% (45)
74% (67)
 Switzerland
5% (13)
7% (6)
8% (5)
2% (2)
 Other
4% (11)
3% (3)
3% (2)
7% (6)
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The median number of events completed in our sample was eight races, and the marathoners/ultra-maratho-
ners finished the highest number of races. Depending on the stage of preparation for the main event and/or sea-
son within the course of the year, 70% of runners reported their weekly mileage at a medium volume (19–36 km), 
while 17% and 13% of runners reported low (< 19 km) and high (> 36 km) volumes, respectively (Table 2).
Health‑related indicators. 
Dimension of BW and BMI. There was a significant difference in BW between 
race distance subgroups  (F(2, 242) = 5.05, p = 0.007), with 10-km runners weighing less than half-marathoners and 
Table 2.  Characteristics of running activity of the subjects. Data are presented as “percentage of prevalence 
(n)” or “median (IQR)”. HM half-marathon, IQR interquartile range, km kilometers, M/UM marathon/ultra-
marathon. a Sport for health: Those who take part in sports for health reasons and train 2–3 times a week 
for 30–60 min at a low to moderate intensity with the aim of maintaining or improving their health. b Sport 
for leisure: Those who take part for leisure reasons and train 2–5 times a week for 60–90 min at a moderate 
intensity with the aim of enjoying their free time actively. c Sport for performance: Performance athletes train 
3–6 times a week, at moderate to high intensities and assiduously follow a long-term training plan, including 
assessing their performance, with the aim of ascertaining and improving it and measuring it against that of 
other athletes in competitions.
Total
HM
M/UM
10 km
Number of subjects
100% (245)
36% (89)
27% (65)
37% (91)
Basic assignment to areas of sport
Sport for  Healtha
10% (23)
8% (7)
5% (3)
14% (13)
Sport for  Leisureb
54% (133)
64% (57)
37% (24)
57% (52)
Sport for  Performancec
36% (89)
28% (25)
58% (38)
29% (26)
Motive for running
Initial Motivation for Running
 Counteraction to Job
9% (22)
10% (9)
11% (7)
7% (6)
 Leisure Activity
4% (11)
7% (6)
5% (3)
2% (2)
 Hobby
35% (85)
33% (29)
38% (25)
34% (31)
 Weight Maintenance
7% (17)
9% (8)
6% (4)
5% (5)
 Weight Loss
18% (45)
17% (15)
15% (10)
22% (20)
 Health
19% (46)
19% (17)
18% (12)
19% (17)
 Other
8% (19)
6% (5)
6% (4)
11% (10)
Aim for running events
 For the Pleasure of Running
39% (90)
40% (35)
47% (27)
32% (28)
 Specific Placing
3% (8)
2% (2)
5% (3)
3% (3)
 Specific Time
51% (117)
51% (44)
44% (25)
55% (48)
 Taking Part is All that Matters
7% (16)
7% (6)
4% (2)
9% (8)
Completion of running events
Total Races Completed Before the NURMI Study (median)
8 (IQR 11)
7 (IQR 11)
10 (IQR 10)
7 (IQR 12)
Races Completed in the Past 2 Years Over Distances (median)
8 (IQR 11)
6 (IQR 11)
10 (IQR 11)
7 (IQR 11)
Half-Marathon
2 (IQR 3)
3 (IQR 4)
2 (IQR 3)
1 (IQR 2)
Marathon/Ultra-Marathon
1 (IQR 2)
0 (IQR 1)
2 (IQR 3)
0 (IQR 1)
Running Training per week (Mean mileage, km)
Low Mileage (≤ 1 km)
17% (41)
26% (23)
5% (3)
16% (5)
Medium Mileage (> 19–36 km)
70% (172)
65% (58)
63% (41)
80% (73)
High Mileage (> 36–100 km)
13% (32)
9% (8)
32% (21)
3% (3)
Other sports to balance for running
Summer Sports
 Cycling
53% (130)
55% (49)
57% (36)
49% (45)
 Swimming
31% (75)
35% (31)
22% (14)
33% (30)
 Hiking/Rambling
31% (75)
33% (29)
32% (20)
29% (26)
 Trail/Uphill Running
31% (75)
33% (29)
46% (29)
19% (17)
 Triathlon
19% (46)
21% (19)
17% (11)
18% (16)
Winter Sports
 Skiing (alpine)
14% (34)
15% (13)
16% (10)
12% (11)
 Cross Country Skiing
11% (26)
12% (11)
13% (8)
8% (7)
 Snowboarding
7% (16)
9% (8)
5% (3)
5% (5)
 Ski Touring
4% (9)
7% (6)
5% (3)
–
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marathoners/ultra-marathoners. However, there was no difference in the health-related item BMI between the 
subgroups (χ2
(4) = 1.35, p = 0.854) (Table 3). In addition, 10-km runners showed the lowest calculated BMI, while 
half-marathoners contributed the largest fraction of  BMINORM (85%). Although no significant between-group 
difference was observed in the dimension of “BW and BMI”  (F(2, 242) = 0.84, p = 0.433), comparative data showed 
that half-marathoners had the highest score for the health-related indicator “BW and BMI” (0.69 ± 0.39), and 
were followed closely by marathon/ultra-marathon runners (0.67 ± 0.39) (Table 4).
Dimension of mental health. There was no significant association between race distance and mental health 
(χ2
(2) = 5.83, p = 0.054) (Table 3). However, half-marathoners reported least often to suffer from perceived stress 
(27%, n = 23). Although no significant between-group difference was observed in the dimension of “mental 
health”  (F(2, 219) = 2.95, p = 0.054), comparative data showed that half-marathoners had the highest score with 
regard to mental health (0.73 ± 0.45) (Table 4).
Dimension of chronic diseases and hypersensitivity reactions. There was no significant association between 
race distance and the prevalence of (1) cardiovascular diseases and cancer (χ2
(4) = 4.76, p = 0.313), (2) metabolic 
diseases (χ2
(10) = 13.25, p = 0.210), and (3) hypersensitivity reactions (χ2
(4) = 8.90, p = 0.064). However, none of 
the half-marathoners reported having chronic diseases, and half-marathoners most often reported having no 
metabolic diseases (92%, n = 78) and no hypersensitivity reactions (73%, n = 62) while having allergies the least 
often (22%, n = 19), (Table 3). Overall, half-marathoners scored highest significantly with regard to the health-
related indicator chronic diseases and hypersensitivity reactions, and it was the only dimension with significant 
between-group differences (0.88 ± 0.18,  F(2, 219) = 3.31, p = 0.038) (Table 4).
Dimension of medication intake. There was no significant association between medication intake and race dis-
tance (χ2
(6) = 2.64, p = 0.852). Furthermore, there was no significant association between race distance and the 
intake of contraceptives (χ2
(2) = 5.93, p = 0.051) (Table 3). However, half-marathoners most often reported hav-
ing no regular medication intake (87%, n = 74). Although no significant between-group difference was observed 
in the dimension of “medication intake”  (F(2, 219) = 0.20, p = 0.817), comparative data showed that half-maratho-
ners had the highest score with regard to medication intake (0.87 ± 0.34) but were closely followed by two other 
groups (Table 4).
Health‑related behaviors. 
Dimension of smoking habits. Race distance and current or former smoking 
were not significantly associated (χ2
(4) = 4.00, p = 0.406) (Table 3). In addition, half-marathoners showed the 
highest fraction of non-smokers (67%, n = 57). Although no significant between-group difference was observed 
in the dimension of “smoking habits”  (F(2, 219) = 2.00, p = 0.138), comparative data showed that half-marathoners 
showed the best health-related behaviors with regard to smoking habits (0.83 ± 0.25) (Table 4).
Dimension of supplement intake. There was no significant association between race distance and (1) supple-
ment intake prescribed by a doctor (χ2
(2) = 0.07, p = 0.968), (2) the consumption of performance-enhancing 
substances (χ2
(4) = 3.52, p = 0.476), or (3) the intake of substances to cope with stress (χ2
(4) = 6.66, p = 0.155) 
(Table 3). Although no significant between-group difference was observed in the dimension of “supplement 
intake”  (F(2, 219) = 0.92, p = 0.400), comparative data showed that 10-km runners had the highest health scores 
with regard to supplement intake (0.92 ± 0.17) but were closely followed by two other groups (Table 4).
Dimension of food choice. There was no significant association between race distance and motives for food 
choice (1) because it is healthy (χ2
(2) = 0.74, p = 0.690), health-promoting (χ2
(2) = 1.00, p = 0.607), and good for 
maintaining health (χ2
(2) = 2.15, p = 0.341); (2) in order to obtain vitamins (χ2
(2) = 0.15, p = 0.928), minerals/trace 
elements (χ2
(2) = 0.10, p = 0.953), antioxidants (χ2
(2) = 1.06, p = 0.587), phytochemicals (χ2
(2) = 2.92, p = 0.232), 
and fiber (χ2
(2) = 2.58, p = 0.276); or (3) with regard to the avoidance of the following ingredients (Table 3): 
refined sugar (χ2
(2) = 1.89, p = 0.390), sweeteners (χ2
(2) = 5.63, p = 0.060), fat in general (χ2
(2) = 3.13, p = 0.210), 
saturated fats (χ2
(2) = 0.21, p = 0.899), cholesterol (χ2
(2) = 0.46, p = 0.794), alcohol (χ2
(2) = 1.22, p = 0.542), and caf-
feine (χ2
(2) = 3.04, p = 0.219). However, there was a significant association between race distance and food choice 
with regard to the avoidance of the following ingredients (Table 3): white flour (χ2
(2) = 8.70, p = 0.013), sweets 
(χ2
(2) = 6.29, p = 0.043), and nibbles (χ2
(2) = 6.11, p = 0.047), with 10-km runners reporting doing so more often 
(all three food items) than the other distance runners. Although no significant between-group difference was 
observed in the dimension of “food choice”  (F(2, 219) = 1.32, p = 0.270), comparative data showed that 10-km run-
ners had the best health-related behaviors with regard to food choice (0.72 ± 0.20) (Table 4).
Dimension of healthcare utilization. There was no significant association between the use of regular health 
check-ups and race distance (χ2
(2) = 2.64, p = 0.268) (Table 3). Although no significant between-group difference 
was observed in the dimension of “healthcare utilization”  (F(2, 219) = 1.32, p = 0.270), comparative data showed 
that half-marathoners had the highest scores with regard to healthcare utilization (0.62 ± 0.49) while maratho-
ners/ultra-marathoners scored lowest (0.49 ± 0.50) (Table 4).
Results of the MANOVA. 
The findings of the MANOVA considering the health status of endurance run-
ners are presented in Table 5, indicating significant differences for the following results: (1) education (academic 
qualification) had an association with BW and BMI (p = 0.004), smoking habits (p = 0.005), and supplement 
intake (p = 0.022); (2) race distance had a significant association with the dimension “chronic diseases and hyper-
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Cluster and respective Dimensions
HM
M/UM
10 km
Statistics
‘Health-Related Indicators’
BW and BMI
 BW (kg) (median)
65.0 (IQR 13.0)
67.5 (IQR 17.5)
62 (IQR 11.0)
F(2, 242) = 5.05, p = 0.007
 BMI (median)
21.97 (IQR 3.28)
22.15 (IQR 3.25)
21.30 (IQR 3.94)
χ2
(4) = 1.35, p = 0.854
   ≤ 18.49
4% (4)
6% (4)
8% (7)
  18.50–24.99
85% (76)
82% (53)
79% (72)
   ≥ 25–29.99
10% (9)
12% (8)
13% (12)
Mental health
χ2
(2) = 5.83, p = 0.054
 Stress Perception
  Yes
27% (23)
42% (23)
44% (36)
  No
73% (62)
58% (32)
56% (46)
Chronic diseases/hypersensitivity reactions
 Prevalence of Chronic Diseases
χ2
(4) = 4.76, p = 0.313
  Heart Disease
–
2% (1)
–
  Heart Attack
–
–
–
  Cancer
–
–
1% (1)
  No Diseases
100% (85)
98% (54)
99% (81)
 Prevalence of Metabolic Diseases
χ2
(10) = 13.25, p = 0.210
  Diabetes Mellitus 1
–
4% (2)
–
  Diabetes Mellitus 2
1% (1)
–
1% (1)
  Hyperthyroidism
–
2% (1)
2% (2)
  Hypothyroidism
7% (6)
7% (4)
4% (3)
  Other Diseases
–
–
2% (2)
  No Diseases
92% (78)
87% (48)
90% (74)
 Prevalence of Hypersensitivity Reactions
χ2
(4) = 8.90,  p = 0.064
  Allergies
22% (19)
25% (14)
35% (29)
  Intolerances
5% (4)
4% (2)
11% (9)
  No Reactions
73% (62)
71% (39)
54% (44)
Medication intake (regularly)
χ2
(6) = 2.64,  p = 0.852
 Thyroid Disease
7% (6)
11% (6)
7% (6)
 Hypertension
4% (3)
2% (1)
2% (2)
 Cholesterol Level
–
–
–
 Other Medication
2% (2)
4% (2)
6% (5)
 No Medication
87% (74)
84% (46)
84% (69)
 Contraceptives (females only)
12% (10)
5% (3)
20% (16)
χ2
(2) = 5.93,  p = 0.051
‘Health-Related Behaviors’
Smoking habits
χ2
(4) = 4.00,  p = 0.406
 Non-Smoker
67% (57)
56% (31)
52% (43)
 Ex-Smoker
32% (27)
42% (23)
45% (37)
 Smoker
1% (1)
2% (1)
2% (2)
Supplement intake
 Prescribed by doctor
8% (7)
7% (4)
7% (6)
χ2
(2) = 0.07,  p = 0.968
 To boost your performance
χ2
(4) = 3.52, p = 0.476
  Occasionally
16% (14)
11% (6)
9% (7)
  Regularly/every day
2% (2)
4% (2)
1% (1)
 To cope wit stress
χ2
(4) = 6.66, p = 0.155
  Occasionally
6% (5)
7% (4)
6% (5)
  Regularly/every day
5% (4)
–
–
Food Choice
 Motivation
  Because it is healthy
74% (63)
73% (40)
68% (56)
χ2
(2) = 0.74, p = 0.690
  Because it is health-promoting
81% (69)
82% (45)
87% (71)
χ2(2) = 1.00, p = 0.607
  Because it is good for maintaining health
88% (75)
87% (48)
94% (77)
χ2(2) = 2.15, p = 0.341
 Avoided ingredients
  Refined Sugar
66% (56)
58% (32)
70% (57)
χ2(2) = 1.89, p = 0.390
  Sweetener
82% (73)
64% (35)
82% (67)
χ2(2) = 5.63, p = 0.060
Continued
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sensitivity reactions” (p = 0.038); (3) there was an association between sex and smoking habits (p = 0.048); (4) 
training (weekly mileage) had an association with food choice (p = 0.003); and (5) there was an association 
between age and healthcare utilization (p = 0.002). However, no significant associations were found considering 
the dimensions of mental health and medication intake.
Discussion
This study aimed to investigate the potential differences in the health status of recreational half-marathoners, 
marathoners/ultra-marathoners, and 10-km runners. Mental health, BW and BMI, the prevalence of chronic 
diseases and hypersensitivity reactions, medication and supplement intake, smoking habits, food choice from 
ingredients to be avoided or desired, and regular or routine health checkups were measured and compared 
between the study groups. The main findings were (1) that while no association between race distance and seven 
health dimensions were found, “chronic diseases and hypersensitivity reactions” had a significant association 
with race distance, and (2) compared to 10-km and marathon/ultra-marathon runners, half-marathoners showed 
a tendency towards better scores in six out of eight dimensions of health (BW/BMI, mental health, chronic 
diseases and hypersensitivity reactions, medication intake, smoking habits, and health care utilization) with 
an average score of 77.1%; the half-marathon distance was found to contribute best to the overall health status 
among endurance runners.
Interestingly, only 8% of half-marathon runners and 10% of the overall sample reported “sport for health” as 
the basic assignment to a sports area, while “sport for leisure” (54% of total participants, 64% of half-marathoners) 
and “sport for performance” (36% of total participants, 28% of half-marathon runners) were ranked higher. 
Cluster and respective Dimensions
HM
M/UM
10 km
Statistics
  Fat in General
38% (32)
44% (24)
51% (42)
χ2(2) = 3.13, p = 0.210
  Saturated Fats
58% (49)
58% (32)
61% (50)
χ2(2) = 0.21, p = 0.899
  Cholesterol
42% (36)
45% (25)
48% (39)
χ2(2) = 0.46, p = 0.794
  White Flour
60% (51)
60% (33)
79% (65)
χ2
(2) = 8.70, p = 0.013
  Sweets
62% (53)
51% (28)
72% (59)
χ2
(2) = 6.29, p = 0.043
  Nibbles
58% (59)
53% (29)
72% (59)
χ2
(2) = 6.11, p = 0.047
  Alcohol
52% (44)
53% (29)
60% (49)
χ2
(2) = 1.22, p = 0.542
  Caffeine
38% (32)
25% (14)
39% (32)
χ2
(2) = 3.04, p = 0.219
 Desired ingredients
  Vitamins
82% (70)
80% (44)
80% (66)
χ2
(2) = 0.15, p = 0.928
  Minerals/trace elements
73% (62)
71% (39)
73% (60)
χ2
(2) = 0.10, p = 0.953
  Antioxidants
54% (46)
45% (25)
52% (43)
χ2
(2) = 1.06, p = 0.587
  Phytochemicals
44% (37)
40% (22)
54% (44)
χ2
(2) = 2.92, p = 0.232
  Fiber
71% (60)
69% (38)
70% (57)
χ2
(2) = 0.04, p = 0.980
Health care utilization
 Regular check-ups or routine health checks
62% (53)
49% (27)
54% (44)
χ2
(2) = 2.64, p = 0.268
Table 3.  Descriptive and ANOVA results for the eight dimensions of health status displayed by race distance. 
Data are presented as “percentage of prevalence (n)” or “median (IQR)”. BMI body mass index, BW body 
weight, HM half-marathon, IQR interquartile range, km kilometers, M/UM marathon/ultra-marathon.
Table 4.  Domain scores of ‘health-related indicators’ and ‘health-related behaviors’ of endurance runners, 
displayed by race distance groups. Data are presented as Domain Scores and (SD): Low scores indicate 
detrimental health effects; high scores indicate beneficial health effects (scales: 0–1). BMI body mass index, BW 
body weight, HM half-marathon, km kilometers, M/UM marathon/ultra-marathon.
Total
HM
M/UM
10 km
Statistics
Health-Related Indicators
 BW and BMI
0.65 (0.40)
0.69 (0.39)
0.67 (0.41)
0.60 (0.42)
F(2, 242) = 0.84,  p = 0.433
 Mental Health
0.63 (0.48)
0.73 (0.45)
0.58 (0.50)
0.56 (0.50)
F(2, 219) = 2.95,  p = 0.054
 Chronic Diseases/Hypersensitivity Reactions
0.85 (0.19)
0.88 (0.18)
0.85 (0.19)
0.81 (0.20)
F(2, 219) = 3.31,  p = 0.038
 Medication Intake
0.85 (0.36)
0.87 (0.34)
0.84 (0.37)
0.84 (0.37)
F(2, 219) = 0.20,  p = 0.817
Health-Related Behaviors
 Smoking
0.79 (0.27)
0.83 (0.25)
0.77 (0.27)
0.75 (0.27)
F(2, 219) = 2.00,  p = 0.138
 Supplement Intake
0.90 (0.20)
0.88 (0.23)
0.91 (0.21)
0.92 (0.17)
F(2, 219) = 0.92,  p = 0.400
 Food Choice
0.68 (0.22)
0.67 (0.21)
0.65 (0.26)
0.72 (0.20)
F(2, 219) = 1.32,  p = 0.270
 Healthcare Utilization
0.56 (0.50)
0.62 (0.49)
0.49 (0.50)
0.54 (0.50)
F(2, 219) = 1.32,  p = 0.270
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“Hobby” and “health” with 34% and 19% of total participants, respectively, were ranked highest among other 
initial motives for running, with no considerable difference between the study groups. The number of completed 
races shows that endurance athletes in the present study are not novices but rather active in recreational (not 
professional) running. It has been shown that recreational participation in running activities could affect some 
health-related  findings49, which could be linked to the participants’ slight emphasis on specific personal achieve-
ments versus the joy of running (53% vs. 47%) as the main goal to participate in running events. Consistent with 
the present findings, it has been reported that “the joy of running races” was a top reason, and “winning” was 
identified as an unimportant reason to participate in running  events4. Although “health” was the second-highest 
ranked reason among the seven motivations for running, it could be considered as the  1st rank (by 44%) when 
pooled with two other health-related motivations (BW loss and maintenance). This finding is consistent with the 
literature available, with the main underlying intention probably being to achieve the advantageous effects and 
pronounced benefits associated with  health1,4, especially for long-term adherence to running  activity4,50. Run-
ning is expected to be a powerful strategy in the prevention of diseases, promotion of health, and maintenance 
of a good state of health, especially in elderly populations with an age of ≥ 50  years50.
Table 5.  MANOVA results for the eight dimensions of health status. BMI body mass index, BW body weight, 
df degrees of freedom. F F-value, η2 partial effect (small: 0.01; medium: 0.059; large: 0.138), p p value for 
between-group differences.
Cluster
Dimension
Subgroup
F
df
η2
p
Health-Related Indicators
BW and BMI
Race Distance
0.39
2
0.00
0.677
Sex
1.17
1
0.01
0.281
Age
0.00
1
0.00
0.999
Education (academic qualification)
5.66
2
0.05
0.004
Training (weekly mileage)
0.23
2
0.00
0.797
Mental health
Race Distance
2.97
2
0.03
0.053
Sex
3.43
1
0.02
0.065
Age
1.04
1
0.00
0.310
Education (academic qualification)
0.48
2
0.00
0.619
Training (weekly mileage)
0.95
2
0.01
0.390
Chronic diseases/hypersensi-
tivity reactions
Race Distance
3.04
2
0.03
0.050
Sex
0.61
1
0.00
0.435
Age
0.24
1
0.00
0.623
Education (academic qualification)
0.65
2
0.01
0.525
Training (weekly mileage)
0.71
2
0.01
0.492
Medication Intake
Race Distance
0.20
2
0.00
0.815
Sex
0.92
1
0.00
0.340
Age
3.05
1
0.01
0.082
Education (academic qualification)
1.43
2
0.01
0.241
Training (weekly mileage)
0.56
2
0.01
0.573
Health-Related Behaviors
Smoking habits
Race Distance
2.08
2
0.02
0.128
Sex
3.96
1
0.02
0.048
Age
1.97
1
0.01
0.161
Education (academic qualification)
5.35
2
0.05
0.005
Training (weekly mileage)
0.25
2
0.00
0.776
Supplement intake
Race Distance
1.04
2
0.01
0.356
Sex
1.74
1
0.01
0.189
Age
3.05
1
0.01
0.082
Education (academic qualification)
3.88
2
0.04
0.022
Training (weekly mileage)
0.37
2
0.00
0.686
Food choice
Race Distance
1.62
2
0.02
0.200
Sex
0.20
1
0.00
0.655
Age
0.55
1
0.00
0.459
Education (academic qualification)
0.29
2
0.00
0.749
Training (weekly mileage)
6.06
2
0.06
0.003
Healthcare utilization
Race Distance
1.37
2
0.01
0.256
Sex
2.86
1
0.01
0.092
Age
9.62
1
0.05
0.002
Education (academic qualification)
1.40
2
0.01
0.249
Training (weekly mileage)
0.11
2
0.00
0.899
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BW and BMI. 
Four out of five endurance runners in this study were found to have a BW that corresponds 
to a healthy  BMINORM. Half-marathoners most often matched the  BMINORM and consequently had higher health 
scores compared to marathoners/ultra-marathoners and 10-km runners. However, 10-km runners were found 
to have lower BW than half- to ultra-marathoners, nicely matching their reports where BW loss was ranked 2nd 
highest motivation to start running. In addition, the higher score of 10-km runners in food choices compared 
to runners over longer distances could be partially associated with the existing findings regarding their trend 
toward having a lower BW. Another justification could be the higher number of vegan runners in 10-km com-
pared to half-marathon and marathon/ultra-marathon groups in the present study.
About 25% of runners in the present study stated BW management (loss: 18%, and maintenance: 7%) as the 
reason to start running. However, the half-marathoners seem to established a good balance between running-
induced energy required and dietary intake, as they reported least often a decrease in BW due to a change in their 
diet. These findings emphasize the significance of BW control strategies for endurance runners as dietary changes 
potentially cause unintended BW  loss29,51, and adherence to appropriate nutrition strategies for sustainable BW 
management is highly advised to endurance  runners29. Although the lower BMI and being leaner were found 
to be associated with increased endurance running  performance52, and training/competing in longer race dis-
tances correlates with a decrease in BW and body  fat53, evidence excludes marathon runners or ultra-endurance 
athletes from this  fact54,55. This is consistent with the present findings where marathon/ultra-marathon runners 
had a slight but non-significant higher BMI. The higher BMI of ultra-marathon runners compared to shorter 
distance endurance runners might be due to the lower importance of running speed in long-distance compared 
to shorter distance runs. In general, however, reports from the successful runners over 10-km and marathon 
distance indicate that an optimal BMI for health and performance was found to be between 19 and 20 kg/m256. 
The vegan diet was shown to effectively reduce BW and particularly body  fat57,58, with favorable effects on run-
ning performance, if planed  appropriately59. Consistently, previous data from our laboratory show that vegan 
endurance runners are significantly leaner than omnivores (64 kg vs. 68 kg), contributing to their overall state 
of health with the highest health score (69%)10.
Mental health. 
While most participants were not suffering from mental stress, half-marathoners reported 
lower perception of pressure and stress compared to 10-km runners and marathoners/ultra-marathoners. In line 
with the present findings, it has been found that endurance running leads to stress reduction, a better mood, and 
higher resilience to psychological pressure and  anxiety43,60. However, data in terms of the appropriate amount 
of physical activity in order to maximize these positive effects while avoiding negative effects is sparse. Too little 
exercise does not evoke beneficial effects, but too much exercise (defined as overtraining) can cause the percep-
tion of  stress60. Half-marathon allows performance to increase within a short period of time, which provides the 
feeling of  success38. These characteristics are supposed to lead to a certain degree of life satisfaction and thus a 
resilience to stress and pressure  perception43.
Chronic disease and hypersensitivity reactions. 
The present study revealed a significant difference 
between the race distance groups and the dimension, “chronic diseases and hypersensitive reactions”, most ben-
eficially contributing to the half-marathoners’ state of health. Recreational endurance running is well accepted, 
having various health effects with robust evidence for regular running to add benefits in aerobic, metabolic, 
and cardiovascular function at rest. Consistent with the study findings, running has beneficial influences on the 
prevention of chronic and cardio-metabolic diseases, including but not limited to coronary heart disease, stroke, 
hypertension, diabetes mellitus type 2, and hypercholesterolemia, mainly via increasing cardiorespiratory fitness 
as a strong predictor for morbidity and  mortality8,9,12,15. This is in line with another finding from the present 
study, where race distance was found to have a significant association with chronic diseases and hypersensitivity 
reactions. These exercise-induced advantageous effects are based on various mechanisms, such as adaptations to 
the cardiorespiratory and cardio-metabolic system (e.g., changes in the musculoskeletal system and heart muscle 
cells, increased maximal oxygen uptake), modifications in hormonal response and enzymatic activity, the acti-
vation of both inflammatory response and detoxification processes, the involvement of pathways associated to 
immune response, lipid transport and coagulation, and further genetic  adaptions38,61.
The present findings could be influenced by the distribution of diet types, particularly vegetarians and vegans, 
among the endurance runners. It has been reported that appropriately planned vegetarian and vegan diets are 
healthful and nutritionally adequate even for athletes and provide health benefits for the prevention and treat-
ment of cardio-metabolic disorders and certain diseases such as ischemic heart disease, type 2 diabetes, hyper-
tension, inflammatory problems, and some types of  cancer47,62. More specifically, the higher prevalence of plant 
diets together with the null association between race distance and the incidence of allergies in the present study 
is in line with the available data on the protective effects of fruits and vegetables on the incidence of food aller-
gies, including allergic  asthma18 as well as the lower prevalence of allergies in vegan endurance runners (20%) 
compared to omnivores (32%) and vegetarians (36%)10. Despite the null association between the occurrence of 
food intolerances and race distance in the present study, gastrointestinal complaints due to food intolerances 
are common among endurance  runners63, probably caused by subclinical food sensitivities that occur during 
vigorous  exercise64.
Medication intake. 
Medication intake in the form of contraceptives was lower with a statistical trend 
(p = 0.051) in marathoners/ultra-marathoners compared to half-marathoners and 10-km runners. This finding, 
however, could be explained by a sex-based bias as there were fewer females (38%) among marathoners/ultra-
marathoners than in half-marathoners (55%) and 10-km runners (74%). Indeed, 85% of those who reported an 
intake of thyroid hormones were women, and 100% of those who reported an intake of other hormones than 
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thyroid medication were women who reported the intake of contraceptives. However, there was no associa-
tion between sex and the dimension “medication intake” when runners were pooled for the MANOVA. As a 
well-established fact associated with the present findings, women suffer more often from hypothyroidism than 
 men65, and importantly, more than 100 million women worldwide use contraceptive pills to avoid undesired 
 pregnancies66. Although there were no associations between race distance and the intake of any medication, race 
distance had a considerable association (score range 0.82–0.86) with medication intake. However, as the major-
ity of distance runners (84–87%) reported no medication intake, caution must be considered when interpreting 
the present limited data concerning the intake of non-contraceptives medications across different subgroups of 
distance runners.
Smoking habits. 
A low rate of smoking (< 2%) was found in endurance runners across all race distances. 
Consistently, data indicate that smoking prevalence is usually quite low among endurance  runners67. This can 
be justified by undesired performance limitations due to  smoking68 and the health-consciousness of athletes in 
 general69. On the other hand, adhering to regular physical exercise, particularly endurance running, can be an 
effective way to prevent people from smoking or even help in smoking cessation by reducing cessation-related 
mood symptoms, cigarette cravings, and withdrawal symptoms among temporarily abstinent  smokers68. In the 
present study, there was no association between smoking habits and race distance, but half-marathoners showed 
a better score in this dimension. While no comparable data are available in the literature, evidence has found a 
positive association between smoking quitters and running activity in terms of weekly training  mileage67.
Supplement intake and performance‑enhancing substances. 
The most commonly reported sup-
plement by the runners was vitamin D. Several studies have detected a huge difference between required and real 
vitamin D intake in athletes  worldwide70,71. In addition to dietary intake, athletes’ vitamin D level depends on 
skin color, training day-time, indoor/outdoor training, and geographic  location71. Although supplement intake 
was not associated with race distance, it was found to have high scores (score range 0.88–0.92) among race dis-
tance groups, with a slight predominance in 10-km runners. However, the prevalence of intake was generally 
low, reflected by high health scores across all race distance subgroups. Compared with the highest rate of supple-
ment intake reported by half-marathoners (16%), a recent study reported that 30% of female and 40.2% of male 
endurance runners consume supplements in order to enhance  performance72. Although few studies have yet 
compared different groups of endurance runners regarding the patterns of supplement  intake73, it has been well-
documented that endurance athletes use supplements to a greater extent than non-endurance  athletes74, proba-
bly due to the higher exercise-induced nutritional requirements associated with long-time training, competition, 
and  recovery75. Reports from a recent study on elite track and field athletes indicated that distance runners have 
a significantly higher prevalence in supplemental micronutrient but not macronutrient intake when compared 
to runners in other track and field  disciplines76. Moreover, there is some evidence for an increasing problem of 
doping among elite endurance  runners77. However, as the participants in the present study were mostly recrea-
tional runners, they may have different choices of dietary supplements, which could be associated with their 
different goals for engaging in training and competition compared to elite  athletes49. In addition, findings from 
the present study regarding the participants’ attitudes towards food choices characterize them as being health-
conscious, so they might have been aware of potential detrimental effects of risky performance-enhancing sub-
stances. In general, despite the fact that the beneficial effects of many supplements on the promotion of health, 
prevention of chronic disease, and enhancement of athletic performance remain  unclear78, it is well-established 
that these products significantly contribute to the nutrient requirements of  athletes78–80.
Food choice. 
The present study showed that food choice was not associated with race distance, but the run-
ners over the 10-km distance reported choosing food in order to avoid white flour, sweets, and nibbles more 
often than half to ultra-marathoners. This is even reflected by their higher score for food choice (72% vs. 67% 
and 65%) along with their motivation for choosing food based on health-promoting and health-maintaining 
reasons. However, caution must be warranted while interpreting the findings, as the higher score of 10-km run-
ners in food choice could be potentially associated with their lower BMI among the study groups. Although the 
majority of the runners in this study reported following a mixed diet, 59% of 10-km and 56% of half-marathon 
runners reported following vegetarian/vegan diets, which were recently found to add most advantageous ben-
efits to the runners’ state of health mainly due to maximizing favorable food choice behaviors in endurance 
 runners10. The imbalanced distribution of vegans in the 10-km group (compared to the overall groups) might 
explain, in part, the highest scores for both supplement intake and food choice, as vegans are known to be more 
health-conscious and thus take special care and compensate for potential deficiencies considering critical nutri-
ents such as vitamin  B12
10,59,81. Considering a health-related food choice to get desired ingredients by a specific 
choice of healthy and health-maintaining items, most participants reported health-conscious behavior across all 
race distance subgroups. This finding was in line with available  literature2,69, where athletes were characterized 
as being health-conscious, particularly with regard to food  choice10.
Healthcare utilization. 
Overall, most athletes reported seeing a doctor at least once a year and making 
use of regular health checkups. These findings were consistent with the previous  literature82 and emphasize the 
fact that regular and sustainable physical activity can diminish morbidity rates and thus the necessity for doctor 
 consultations83. The endurance runners of the present study were found to have a good balance between healthy 
physical activity and vigorous exercise, which could be advantageous for gaining the desired health  effects2, and 
importantly for the avoidance of the detrimental consequences of overtraining following excessive running or 
training activities. In the present study, there was a statistically significant association between race distance and 
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age. Interestingly, and although being older than runners over other distances, marathoners/ultra-marathoners 
had a low score for regular and routine health checkups, indicating disadvantageous contribution to overall 
health from weak healthcare utilization.
Limitations, strengths, and future perspectives. 
There are limitations worth mentioning. The pre-
sent study shares with others the limitations of the cross-sectional design. The fact that the findings relied on 
self-reported records should be considered as the primary limitation since under- and over-reporting are poten-
tially prevalent in self-reported data. However, this effect was compensated by using control questions. Also, the 
high intrinsic motivation of the participants could be consequential to increase the accuracy of their answers to 
provide a high quality of the data set. The operationalization of state of health as a latent variable (domain scores) 
should also be considered as a statistical limitation. Nonetheless, the health score was identified as a meaningful 
tool to assess the health status. In this regard, however, retrospective rating of the cross-sectional design might 
raise misunderstandings about the associations between health-related variables and race distance, and thus, 
caution must be warranted in the representativeness of the present findings. Moreover, the sex-based imbalance 
in the study groups (particularly the higher number of males in the marathon/ultra-marathon group and females 
in the 10-km group) could be influential on the health-related findings, as females are well-known to be more 
health-conscious than males considering favorable habits and healthy lifestyles (e.g., physical activity, alcohol/
nicotine, plant-based diets). Nevertheless, the data contribute to the growing scientific interest and knowledge in 
health-related consequences of endurance exercise for distance running in particular, and can be taken as a step 
towards broadening the body of evidence in the field.
Although it is well-established that endurance running offers various health benefits, the body of science is 
still contradictory considering both quantity and quality of running activity that enables obtaining the maximum 
beneficial health effects and preventing the minimum undesired or adverse effects. Therefore, specific knowledge 
about the interconnectedness of running distance (in training and racing) and health can provide a better basis 
for athletes, coaches, physicians, and specialists to optimize health-related training and racing strategies. Thus, the 
results might be useful for different populations by providing such knowledge to aid the decision of an active and 
healthy lifestyle, with regular involvement in running training, and also to advise individuals to run for sustain-
able health outcomes. Even at community and public health levels, health authorities can use this information 
to support policies towards investing in running programs that promote sustainable running training strategies.
Conclusions
Regardless of the race distance, endurance runners in the present study showed an optimal state of health. This 
finding supports the notion that endurance running contributes beneficially to an increased level of health. Half-
marathon running was found to contribute to 62–88% of their overall state of health; in addition, the higher 
score of half-marathon runners in overall state of health (77.1% vs. 72.0% in marathon/ultra-marathon runners 
and 71.7% in 10-km runners), along with the predominance of half-marathoners in six out of eight dimensions, 
might suggest that recreational runners over the half-marathon distance have a tendency toward a better health 
status compared to runners over shorter and longer distances. However, among eight health-related dimensions 
investigated in the present study, only the “chronic diseases and hypersensitivity reactions” dimension was found 
to have a significant association with race distance, with a significantly better status for half-marathon runners 
compared to marathoners/ultra-marathoners and 10-km runners.
Data availability
The datasets generated during and/or analysed during the current study are not publicly available but may be 
made available upon reasonable request.
Received: 10 September 2021; Accepted: 16 May 2022
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Acknowledgements
There are no professional relationships with companies or manufacturers who will benefit from the results of 
the present study.
Author contributions
K.W. conceptualized, designed and developed the study design and the questionnaires together with B.K. and 
C.L. K.W. performed data analysis. P.B. and K.W. drafted the manuscript, M.M., D.T. and T.R. helped in drafting 
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Scientific Reports |        (2022) 12:10295  | 
https://doi.org/10.1038/s41598-022-13844-4
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the manuscript, and B.K. and K.W. critically reviewed it. Technical support was provided by G.W. All authors 
read and approved the final manuscript.
Funding
This research did not receive any specific grant or funding from funding agencies in the public, commercial, or 
not-for-profit sectors.
Competing interests 
The authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to B.K.
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© The Author(s) 2022

Health status of recreational runners over 10-km up to ultra-marathon distance based on data of the NURMI Study Step 2.
Wirnitzer, Katharina,Boldt, Patrick,Wirnitzer, Gerold,Leitzmann, Claus,Tanous, Derrick,Motevalli, Mohamad,Rosemann, Thomas,Knechtle, Beat