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Differences in race history 
by distance of recreational 
endurance runners from The 
NURMI Study (Step 2)
Beat Knechtle 1,2*, Derrick Tanous 3,4, Mabliny Thuany 5, Mohamad Motevalli 3,4, 
Gerold Wirnitzer 6, Claus Leitzmann 7, Katja Weiss 1, Thomas Rosemann 1 & 
Katharina Wirnitzer 3,4,8
Few studies were developed to understand the relationship between running characteristics and 
motivation. The purpose of this study was to assess the relationship between running event history, 
running experience, and best race performances in recreational distance runners. We used a web 
survey to obtain information regarding running experience, racing history, and periodization training 
routines/exercise habits, including weekly volumes and daily mileage and duration across periods 
and conditions. Associations between variables were conducted with the Chi-square test (χ2; nominal 
scale) and Wilcoxon test. Multiple linear regression analysis and multivariate linear regression 
were performed. Concerning the participants’ motive for exercising, a significant difference was 
identified between the race distance subgroups (p < 0.001), where 58% of M/UM runners exercised 
for performance (n = 38) and 64% of HM runners (n = 57) and 57% of 10 km runners (n = 52) exercised 
for recreational purposes. A significant difference was found in the number of years of running 
completed without taking a break (p = 0.004), with marathoners/ultramarathoners reporting the most 
years. Runners competing in different race distances such as 10 km, half-marathon, marathon, and 
ultra-marathon presented differences in training background and habits according to the distance of 
preference.
Running is a global market, with an increase in the participation of athletes in running events and the number 
of events over the last year  worldwide1–3. In the European context, a range of 5% to 31% rate of participation was 
shown between different  countries4. In a scientific context, this growth was associated with a higher interest for 
understanding runners’ profiles, behaviors, and training  habits5,6. The runner’s profile was previously studied in 
different contexts, including differences in economic  level7,8, the profiles consumption and use of sports  watches9, 
training  characteristics5,10, nutritional  behaviors11–13, and health  outcomes14.
As a social phenomenon, and with the potential to improve general physical (i.e., lower risks of all-cause 
and cardiovascular mortality)15 and mental health (i.e., well-being, self-confidence), running is also related 
to social  cohesion16 and used as a potential strategy to improve physical activity levels in an epidemiological 
 context17. In this way, the reasons to start running and to be engaged in running training were also investigated 
 previously18. For non-professional runners, motivational differences were shown in athletes competing in differ-
ent race  distances19–22. For runners in 5 km, fun and health were the most important factors for  training23, while 
ultra-marathoners had higher scores in affiliation, life meaning, and lower body weight  concerns24.
Based on previous studies, a body of evidence is available regarding motivational characteristics and run-
ners’  profiles19,25,26. However, few studies were developed to understand the relationship between running back-
ground and  motivation27. Understanding the motives and habits considering training and competing that enable 
OPEN
1Institute of Primary Care, University of Zurich, 8000 Zurich, Switzerland. 2Medbase St. Gallen Am Vadianplatz, 
Vadianstrasse 26, 9000 St. Gallen, Switzerland. 3Department of Sport Science, University of Innsbruck, 
6020 Innsbruck, Austria. 4Department of Research and Development in Teacher Education, University 
College of Teacher Education, Tyrol, 6020 Innsbruck, Austria. 5Faculty of Sports, University of Porto, Porto, 
Portugal. 6AdventureV & change2V, 6135 Stans, Austria. 7Institute of Nutrition, University of Gießen, 
35390 Gießen, Germany. 8Research Center Medical Humanities, Leopold-Franzens University of Innsbruck, 
6020 Innsbruck, Austria. *email: beat.knechtle@hispeed.ch
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non-professional runners to be engaged in physical exercise is an important feature to provide support and to 
understand why people are or not engaged in running, as well as to develop strategies to maintain the training 
commitment.
Therefore, this is the first exploratory investigation to assess the aspects of motivation, education, training, 
previous experience, and performance in different running groups such as 10 km, half-marathon (HM), and 
marathon (M)/ultramarathon (UM) recreational distance runners. Based on previous  studies28–30, it is assumed 
that there would be differences in these aspects in recreational endurance runners of different distances (10 km, 
HM, M/UM)10.
Materials and methods
Please see the subsequent description of the methodology for the complete profile for this investigation (Part A 
of the arrangement)31, as well as previous  publications10,32–34. Following a  protocol35, the Nutrition and Running 
High Mileage (NURMI) Study has been approved by the ethics board of St. Gallen, Switzerland on the  6th of May 
in 2015 (EKSG 14/145) with a retrospective trial registration (number: ISRCTN73074080). It was required that 
the participants provided informed consent before taking part in the NURMI Study. For the participants’ recruit-
ment and study procedures, the responsive reader is kindly referred to Part A of the arrangement  publication31. 
Figure 1 shows the enrollment and categorization of participants, and their characteristics are shown in Table 1.
Measures
Race performances, training routines, and exercise habits of active distance runners were expressed using the 
following parameters: running experience (total number of years of running fully completed without taking a 
break); racing history (overall number of completed races, ratio of HM/M events to total races, age at time of the 
first running event, the first race distance completed: 10 km, HM, M, best HM/M times, the number of planned 
races completed in the previous two years: HM/M/UM); periodization training routines/exercise habits, includ-
ing weekly volumes (number of running sessions, and breadth of training in km and hours) and daily mileage 
and duration across periods and conditions. Running performance was related to best finishing HM and M time 
based on a normalized aggregate mean transformed to an index (ranging 0–100). The latent variable of run-
ning history was derived by both factors: (1) “running-experience” (by pooled items: “age.first.running event”, 
“age.run”, “age.first.half-marathon”, “age.first.marathon”) and (2) “racing-experience” (by pooled items: “years.
running”, “completed.half-marathon.number”, “completed.marathon.number”), which were defined by specific 
items that were based on manifest variables.
As running experience (e.g., years of running fully completed, age at the first race event, total number of races 
completed) is dependent upon age, the respective items were operationalized with age (e.g., age-related years 
of running, age-related number of completed races over half-marathon distance). Based on this, the respective 
items (e.g., age-related beginning of running, first marathon race completed) were centered by median values, 
and were z-transformed creating a new scale through summarizing the respective items (e.g., years of running 
fully completed, completed races over specified distances). From this the values were categorized with the latent 
factors “running-experience” and “racing-experience” into low (values below − 1), medium (values ranging 
Figure 1.  Enrollment and Categorization of Participants by Race Distance.
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from − 1 to + 1), and high (values higher + 1). A principal component analysis (PCA as heuristic approach) was 
performed to identify the respective factors. The PCA was justified by sufficient high correlations (0.79 by the 
Kaiser–Meyer–Olkin-Kriterium, and p < 0.001 by the Bartlett-Test as highly significant) to derive the extraction 
of two factors. The “Eigen”-Wert > 1 (declaration of 73.4% of total variance of both the latent factors) was defined 
to justify to model two latent factors: “running-experience” (from items: “age.first.running event”, “age.run”, “age.
first.half-marathon”, “age.first.marathon”) and “racing-experience” (from items: “years.running”, “completed.half-
marathon.number”, “completed.marathon.number”).
Statistical analysis
The statistical analyses were all performed with R software (version 3.6.2 Core Team 2019; R Foundation for 
Statistical Computing; Vienna, Austria). The exploratory analysis was performed with descriptive statistics, 
including median with interquartile range (IQR) and mean with standard deviation (SD). PCA was used for 
identifying the latent factors.Significant differences in running and racing activity (experience, training, rac-
ing, etc.) between race distance subgroups were calculated with a non-parametric test. Associations between 
variables were conducted with Chi-square test (χ2; nominal scale) and Wilcoxon test (ordinal and metric scale) 
have been approximated by using F distributions and ordinary least squares. Multiple linear regression analysis 
and multivariate linear regression were performed to test the differences in performance, health, and leisure 
Table 1.  Runner characteristics, including motive to race, experience, and history displayed race distance. 
Note Results are presented as percentage (%), total numbers, and median (IQR). χ2 statistic calculated by 
Pearson’s Chi-squared test and F statistic calculated by Kruskal–Wallis test. 10 km 10 km. HM half-marathon. 
M/UM marathon/ultra-marathon.
Total 100% (245)
10 km37% (91)
HM 36% (89)
M/UM 27% (65)
Statistics
Age (Years)
39 (IQR 17)
37 (IQR 18)
37 (IQR 18)
44 (IQR 17)
F(2,242) = 4.87
p = 0.008
BMI (kg/m2)
21.7 (IQR 3.5)
21.3 (IQR 3.94)
22 (IQR 3.28)
22.2 (IQR 3.25)
F(2,242) = 1.22
p = 0.296
Civil status
 Single
27% (66)
26% (24)
31% (28)
22% (14)
χ2
(4) = 1.95
p = 0.744
 With spouse/married
67% (164)
67% (61)
63% (56)
72% (47)
 Separated/divorce
6% (15)
7% (6)
6% (5)
6% (4)
Motive to race
 Leisure
46% (106)
41% (36)
47% (41)
51% (29)
χ2
(2) = 1.34
p = 0.512
 Performance
54% (125)
59% (51)
53% (46)
49% (28)
Favorite season of racing
 Winter
 < 1% (2)
1% (1)
1% (1)
/
χ2
(6) = 9.04
p = 0.171
 Spring
46% (106)
36% (31)
55% (48)
47% (27)
 Summer
23% (52)
28% (24)
15% (13)
26% (15)
 Autumn
31% (71)
36% (31)
29% (25)
26% (15)
Running experience (years)
7 (IQR 7)
5 (IQR 8)
7 (IQR 6)
8 (IQR 9)
F(2, 241) = 5.77
p = 0.004
First event age (years)
 10 km
30 (IQR 16)
30 (IQR 17)
28 (IQR 15)
33 (IQR 17)
F(2, 151) = 0.69
p = 0.502
F(2, 216) = 1.17
p = 0.313
F(2, 135) = 0.18
p = 0.836
F(2, 239) = 1.77
p = 0.172
 HM
32 (IQR 16)
33 (IQR 15)
30 (IQR 18)
35 (IQR 13)
 M
35 (IQR 13)
33 (IQR 15)
34 (IQR 17)
35 (IQR 12)
 Total
30 (IQR 16)
30 (IQR 17)
28 (IQR 18)
34 (IQR 13)
First event
 10 km
65% (157)
81% (74)
59% (52)
48% (31)
χ2
(4) = 46.24
p < 0.001
 HM
27% (65)
18% (16)
38% (33)
25% (16)
 M
9% (21)
1% (1)
3% (3)
27% (17)
Total races completed
8 (IQR 11)
7 (IQR 11)
6 (IQR 11)
10 (IQR 11)
F(2, 242) = 2.90
p = 0.057
Ratio of HM/M to total races
40 (IQR 50)
20 (IQR 35)
48 (IQR 43)
53 (IQR 49)
F(2, 242) = 18.44
p < 0.001
Completion of planned events (previous 2 years)
 HM
2 (IQR 3)
1 (IQR 2)
3 (IQR 4)
2 (IQR 3)
F(2, 242) = 7.04
p = 0.001
F(2, 242) = 75.19
p < 0.001
F(2, 242) = 28.84
p < 0.001
 M
1 (IQR 2)
0 (IQR 1)
0 (IQR 1)
2 (IQR 2)
 UM
0 (IQR 0)
0 (IQR 0)
0 (IQR 0)
0 (IQR 1)
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motivations based on race distance subgroups. The regression results are displayed as effect plots with a 95% 
confidence interval (95%-CI). The level of statistical significance was set at p ≤ 0.05.
Institutional review board
The study protocol is available online via https:// sprin gerpl us. sprin gerop en. com/ artic les/ 10. 1186/ s40064- 016- 
2126-4 and was approved by the ethics board of St. Gallen, Switzerland on May 6, 2015 (EKSG 14/145). The study 
was conduct-ed in accordance with the ethical standards of the institutional review board, medical professional 
codex, and with the 1964 Helsinki declaration and its later amendments as of 1996, the Data Security Laws, and 
good clinical practice guidelines. Study participation was voluntary and could be canceled at any time without the 
provision of reasons or negative consequences. In-formed consent was obtained from all individual participants 
included in the study considering the data collected, used, and analyzed exclusively and only in the context of 
the NURMI Study for scientific publication.
Results
The total sample included 317 runners of various long distances who finished and submitted the questionnaire. 
A sum of 72 participants were excluded due to failing to meet the inclusion criteria following data clearance. 
The final sample was comprised of 245 runners (10 km: n = 91; NURMI runners: HM: n = 89; M/UM: n = 65), 
including 104 males and 141 females. Together the participants had a BMI of 21.7 kg/m2 (body weight of 65 
kg, height of 1.7 m) and were aged 39 years. Regarding the participants’ nationalities, 72% came from Germany 
(n = 177), 18% were from Austria (n = 44), and 9% were from Switzerland (n = 13) or another country (n = 11).
Significant differences were observed across the race distance subgroups for height (p = 0.007), body weight 
(p = 0.007), and age (p = 0.008) with the M/UM participants being taller (1.8 m, IQR 0.1), heavier (67.5 kg, IQR 
17.5), and older (44 years, IQR 17). No significant difference was observed across the race distance subgroups for 
BMI (p = 0.296) or for civil status (p = 0.744), most participants were married or living with their spouse (67%; 
n = 144) or single (27%; n = 66). No significant differences were found for race distance subgroups regarding the 
participants’ educational background (p = 0.177): 1 (< 1%) had no qualification, 53 (22%) held an A-Levels (or 
similar degree), 83 (34%) held an upper secondary school/technical education degree, 83 (34%) held a university 
degree (or possibly higher), and 25 (10%) did not answer. Concerning the participants’ motive for exercising, 
a significant difference was identified between the race distance subgroups (p < 0.001), where 58% of M/UM 
runners exercised for performance (n = 38) and 64% of HM runners (n = 57) and 57% of 10 km runners (n = 52) 
exercised for recreational purposes. The participants’ characteristics, including their motive to race and running 
experiences are shown in Table 1 based on their self-reported race distances. In Part A, additional details on the 
total sample’s profile and the race distance-specific subgroups are  provided31.
No significant differences were found across the race distance subgroups for the motive to race (p = 0.512) or 
the current motive to run (p = 0.583); performance was the most frequently reported racing motive (54%; n = 125) 
among the whole sample. No significant difference was observed for the favorite race season (p = 0.171); spring-
time was the most favored season for racing for all participants (46%; n = 106), while winter was the least favored 
(< 1%; n = 2). A significant difference was found in the number of years of running completed (consecutively or 
inconsecutively) without taking a break (p = 0.004), with M/UM runners reporting the most years (8; IQR 9) 
and 10 km runners reporting the least (7 IQR 11). Regarding racing history, significant differences between race 
distance subgroups were found in (i) the ratio of completed HM/M events to the total races, where M/UM run-
ners had the highest reports (53; IQR 49; p < 0.001); (ii) the first race distance, where most 10 km (81%; n = 74) 
and HM (59%; n = 52) runners first completed a 10 km race (p < 0.001); (iii) the best time for a HM race, where 
M/UM runners were the fastest on average (99 min ± 13; p < 0.001); (iv) the best time for a M race, where M/UM 
runners were the fastest on average (218 min ± 34; p = 0.029); (v) the completion of HM (p = 0.001), M (p < 0.001), 
and UM (p < 0.001) races in the previous two years, where HM runners completed the most HM races (3; IQR 
4) and M/UM runners completed the most M (2; IQR 2) and UM (0; IQR 1) races. No significant differences in 
racing history between race distance subgroups were identified in overall completed races (p = 0.057), first event 
age in total (p = 0.172), or regardless of 10 km (p = 0.502), HM (p = 0.313), or M distance (p = 0.836).
Non-significant relationships were identified in multivariate linear regression, as seen in Fig. 2, between (i) 
the motives of performance, the 10 km subgroup, and the HM subgroup (b = − 4.21; 95% CI [− 15.2 to 6.81]; 
p > 0.05) or the M/UM subgroup (b = 2.5; 95% CI [− 9.89 to 14.9]; p > 0.05); (ii) the motives of health, the 10 km 
subgroup, and the HM subgroup (b = − 3.07; 95% CI [− 11.5 to 5.39]; p > 0.05) or the M/UM subgroup (b = − 7.9; 
95% CI [− 17.4 to 1.6]; p > 0.05); (iii) the motives of leisure, the 10 km subgroup, and the HM subgroup (b = 4.98; 
95% CI [− 3.42 to 13.4]; p > 0.05) or the M/UM subgroup (b = 4.86; 95% CI [− 4.59 to 14.3]; p > 0.05).
Multivariate linear regression was performed and the following confounders were included within different 
models to predict the best HM and M race time between 10 km and HM or M/UM race distance subgroups: (a) 
years of running history and age at the first running event, which determined 21% of variance (adjusted  R2 = 0.21) 
and a significant difference was identified for M/UM runners (b = 10.9; 95% CI [1.74–20]; p < 0.05) but not for 
HM runners (b = − 5.72; 95% CI [− 14.1 to 2.65]; p > 0.05); (b) training routines and exercise habits (including 
preparation condition 3, preparation condition 4, weekly kilometers of preparation condition 1, professional 
support, and the training extent for main race in months), which determined 22% of variance (adjusted  R2 = 0.22) 
and no significant difference for HM (b = − 6; 95% CI [− 14.6 to 2.65]; p > 0.05) or M/UM (b = 0.679; 95% CI 
[− 9.09 to 10.5]; p > 0.05) race distance groups; (c) racing history (total races completed, the ratio of HM/M 
events to total events, HM races completed, and M races completed), which determined 16% variance (adjusted 
 R2 = 0.16) and no significant difference for HM (b = − 6.26; 95% CI [− 15 to 2.45]; p > 0.05) or M/UM (b = 7.6; 95% 
CI [− 3.47 to 18.7]; p > 0.05) race distance subgroups. In Table 2, multiple linear regression analyses are provided.
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Discussion
This study was the first exploratory investigation aiming to analyze running event history, running experience, 
and best race performance between 10 km, HM, and M/UM recreational runners. The most important findings 
were (i) the runners had a similar BMI regardless of race distance subgroup even though M/UM runners were 
the tallest participants and weighed the most; (ii) no difference was found across race distance subgroups in the 
motive to race or for the linked motives (i.e. exercise motive, original motive to run, present motive to run, motive 
to race); (iii) M/UM runners tallied significantly more years of fully active running experience and completed 
significantly more of their planned marathon and ultra-marathon races in the previous two years; (iv) significant 
differences between the race distance subgroups in best time performances, where M/UM runners were fastest 
on average to complete HM and M events, however, when analyzing best time performances with an index and 
applying confounders (training routines and exercise habits; racing history) in multivariate linear regression 
analyses, no significant differences in performance were found between subgroups; (v) M/UM runners remained 
fastest on average to complete HM and M events when considering the confounders of running experience 
Figure 2.  Effect plots displaying 95%-CI average between 10 km, HM, and M/UM subgroups in exercise/
running/racing motives (n = 231). Note 95%-CIs were computed using the multivariate regression analyses 
(Wald approximation).
Table 2.  Multiple linear regression analyses on running experience, training routines and exercise habits, and 
racing history. Note b = estimate (marginal effects), CI confidence interval, HM half-marathon, M marathon, 
UM ultra-marathon.
Adjusted  r2
Model 1
0.21
Model 2
0.22
Model 3
0.16
b
95%-CI
p
b
95%-CI
P
b
95%-CI
p
Intercept
68.8
56.2–81.5
 < 0.001
 Years of running experi-
ence
0.765
− 0.28–1.25
 < 0.01
 First event age
− 0.97
− 1.3 to − 0.64
 < 0.001
 HM Subgroup
− 5.72
− 14.1 to 2.65
 > 0.05
 M/UM Subgroup
10.9
1.74–20
 < 0.05
Intercept
32
19.4–44.7
 < 0.001
 Preparation condition 3
5.92
1.85–9.98
 < 0.01
 Preparation condition 4
− 2.14
− 5.98 to 1.7
 > 0.05
Prep Condition 1: Weekly 
km
0.274
0.1–0.45
 < 0.01
 Professional support
10.6
0.17–21.1
 < 0.05
 Training extent for main 
race
− 1.2
− 3.17 to 0.77
 > 0.05
 HM subgroup
− 6
− 14.6 to 2.65
 > 0.05
 M/UM subgroup
0.679
− 9.09 to 10.5
 > 0.05
Intercept
39.4
28.7–50.1
 < 0.001
 Races completed in total
0.558
0.05–1.07
 < 0.05
 Ratio of HM/M to total 
races
− 0.0746
− 0.22 to 0.08
 > 0.05
 HM races completed
0.962
− 0.94 to 2.86
 < 0.05
 M races completed
0.634
− 1.7 to 2.97
 > 0.05
 HM subgroup
−6 .26
− 15 to 2.45
 > 0.05
 M/UM subgroup
7.6
− 3.47 to 18.7
 > 0.05
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in years fully active in running without break and the participants’ age at their first running event. Thus, this 
exploratory investigation upholds the assumption that there is a difference in the best race performances con-
sidering time to finish between recreational endurance runners of different distances (10 km, HM, M/UM).
Differences in anthropometry and age across groups
We found that the runners had a similar BMI regardless of race distance subgroup, even though M/UM run-
ners had the highest body height and the heaviest body mass. Furthermore, M/UM runners were older. A study 
investigating master half-marathoners, master marathoners, and master ultra-marathoners found, however, no 
differences regarding their age, body mass, body height, and body mass  index36. A study comparing recreational 
marathoners and recreational ultra-marathoners found differences in anthropometry where marathoners had a 
lower calf circumference but thicker skinfold thicknesses at pectoral, axilla, and suprailiacal sites compared to 
the ultra-marathoners37. Also, a study comparing recreational half-marathoners and marathoners reported that 
half-marathoners had a higher body mass, longer legs, a larger circumference of the upper arm, thicker thigh 
skinfolds, a higher sum of skinfold thicknesses, a higher body fat percentage, and a higher skeletal muscle mass 
than  marathoners29. These disparate findings might be due to different sample sizes and performance levels of 
the subjects.
Differences in motivation across groups
We found no difference across race distance subgroups regarding the motivation to compete or the associated 
motives (i.e., exercise motive, original motive to run, present motive to run, motive to race). Interestingly, this 
finding disagrees with previous  findings19,21,26, and different aspects might explain the discrepancy. Methodo-
logical differences, including analysis stratification by sex and age  groups22,38, training  habits39,40, and country of 
 residence41, can be related to the differences in the findings. Differences between the sexes were shown for mara-
thoners, where women were more motivated about their weight, affiliation, psychological coping, life meaning, 
and self-esteem but were less driven by  competition38. Ultra-marathoners presented higher scores on affiliation 
and life meaning and lower values for body weight concerns, personal goal achievement, and self-esteem38,42. 
The second running boom (1990s) increased the number of runners that are not aiming to become professional 
athletes but their engagement in competitions as a leisure/social  activity16, which people used as a strategy to be 
involved in social groups as well as to know different places around the  world43.
Furthermore, no significant difference was observed for the favorite race season. In elite marathoners, 
however, the seasonal distribution for marathon running has two peaks, spring (weeks 14 to 17) and autumn 
(weeks 41 to 44). During these two periods, the expected temperature is close to the optimal value for marathon 
 running44. It is well-described that interrelationships between marathon results and weather factors such as air 
temperature, wet bulb temperature, and human biometeorological indices  exist45. Most probably, recreational 
runners do not focus on environmental conditions but rather on a specific event they want to compete in.
Differences in running experience across groups
We found a significant difference in the number of years of running completed (consecutively or inconsecutively) 
without taking a break, with M/UM runners reporting the highest number of years and 10 km runners reporting 
the lowest number. M/UM runners reported more years of fully active running experience and completed more 
of their planned marathon and ultra-marathon races in the previous two years compared to the 10 km runners. 
The higher time of experience for M/UM runners and more completed marathon and ultra-marathon races in 
the previous two years highlight the profile of this subgroup. Similar findings showed that long-distance runners 
were older than short-distance runners (i.e., 5 km, 10 km)46,47. These characteristics are also related to the age 
of peak performance since a positive relationship has been reported between the age of peak performance and 
the length of the race  distance48–50. In this way, differences between the race distance subgroups regarding the 
best time performances can also be related to training background and running experience. Besides the genetic 
 component51, the main physiological parameters associated with long-distance performance (i.e., maximal oxy-
gen consumption  (VO2max), running economy, lactate threshold, and velocity associated with  VO2max) are 
developed during training through the increases in the mitochondrial content and skeletal muscle capillary 
 density32,52. Besides that, marathon and ultra-marathon performance are strongly related to sex, morphological, 
and psychological  variables53,54, which can act as confounders in the present study.
Differences in previous performance across groups
We found significant differences between the race distance subgroups regarding the best time performances. 
On average, M/UM runners were faster to complete HM and M events. This finding is not in line with previ-
ous findings. Data covering 107.9 million race results, including 70,000 events held from 1986 to 2018, showed 
that non-professional marathoners were 18% and 17% slower compared to female and male half-marathoners, 
 respectively55. In addition, the best performances can be related to the sex distribution among the subgroups 
since men are overrepresented in M/UM (62%). A body of evidence is available regarding running performance 
differences between  sex56,57, where men tended to perform 10% better compared to  women56. Data from previ-
ous research from the NURMI study confirms sex differences for years of active running, the number of races 
completed, and best time performance, with men being faster on average at HM and M distances compared to 
 women33. However, these differences tended to be null when training routines, exercise habits, and racing history 
was considered confounders. These results indicate that regardless of the subgroup distance, training background 
is important for the best finish time, as shown  previously28. In addition, when considering the confounders of 
running experience in years fully active in running without a break and the participants’ age at their first running 
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event, M/UM runners remained the fastest on average to complete HM and M. These results highlight the mul-
tifactorial and complex nature of the cause of achieved results or successes in sports  disciplines58.
Differences in race performance across groups
We found that M/UM runners remained the fastest on average to complete HM and M events when considering 
the confounders of running experience in years fully active in running without a break and the participants’ age 
at their first running event. Thus, this exploratory investigation upholds the assumption that there is a difference 
in the best race performances considering time to finish between recreational endurance runners of different 
distances (10 km, HM, M/UM). A study comparing 10 km, half-marathon, and marathon showed differences 
regarding age and running speed between the  groups59.
Limitations
Considering the limitation of the cross-sectional design, this study’s findings have some limitations that should 
be addressed, including that no underlying causation can be acquired from the present results. The primary limi-
tation for vital consideration is the self-report feature of the survey methodological approach, which is known 
to result in misrepresented answers due to social  expectations60. In addition, study participation was voluntary, 
which may have led to a non-randomized study population, although the participants were highly motivated. 
For the present study, the distance groups were not stratified by sex, which limits the comparisons, and suggest 
that different sub-groups need to be studied among runners to better understand motives, routines, and physi-
cal exercise engagement. To limit the misreporting effect, the survey included control questions throughout the 
different parts. Additionally, highly motivated distance runners made up the study sample, which likely added 
to the reliability of their responses and enhanced the dataset. Moreover, the sample included 245 endurance run-
ners, which was relatively small considering the commonality of running as a sport. Moreover, other individual 
(nutritional status or the nutritional type maintained by the participants) and environmental characteristics (the 
racing environment, and specific weather conditions) that affect training commitment and performance was not 
considered in the present study (but of the NURMI Study Step 3, not published so far). Despite this limitation, 
the present study presents some advances for the events organizations, coaches, and sports scientists to better 
understand amateur runners of different characteristics. In addition, the race distance subgroups were unequally 
distributed per se, considering that 37% of the total sample were 10 km runners, 36% were HM runners, and 
27% were M/UM runners. Another limitation is that multiple aspects of running competitions were not con-
trolled for, essentially the racing environment itself and the specific weather conditions (poor or good running 
weather, temperature, and humidity), the time of the event, the season, and competition region. Regardless, the 
best time performances were retrospectively verified under random selection. Lastly, the current investigation 
did not include nutritional status or the nutritional type maintained by the participants, as personal nutrition 
is well-known to affect performance. Even though this investigation did not include nutritional results, the 
NURMI study has obtained the runners nutritional evidence that was or will be published in other articles due 
to scientific journal publication demands.
Conclusions
Runners competing in different race distances such as 10 km, half-marathon, marathon, and ultra-marathon 
presented differences in training background and habits according to the distance of preference. Marathoners and 
ultra-marathoners were older, taller, and heavier, were running for more years, and had faster personal best times 
than 10 km runners. Further studies need to consider the second level of information, considering the role of 
competition in runners’ training commitment as well as environmental features related to training commitment.
Data availability
The data sets generated during and/or analyzed during the current study and presented in this article are not 
publicly available. Requests to access the datasets should be directed to info@nurmi-study.com. Subjects will 
receive a brief summary of the results of the NURMI Study if desired.
Received: 27 December 2022; Accepted: 15 October 2023
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Acknowledgements
There are no professional relationships with companies or manufacturers who will benefit from the results of 
the present study. Moreover, this research did not receive any specific grant or funding from funding agencies 
in the public, commercial, or non-profit sectors.
Author contributions
K.W. conceptualized and designed the study together with B.K. and C.L. K.W. conducted data analysis and M.M. 
and D.T. provided statistical expertise. M.M., T.R., K.W., and D.T. drafted the manuscript. T.R., C.L., B.K., M.T., 
and K.W. critically reviewed it. G.W. provided technical support through data acquisition and data management. 
All authors have read and agreed to the published version of the manuscript.
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) 2023

Differences in race history by distance of recreational endurance runners from The NURMI Study (Step 2).
Knechtle, Beat,Tanous, Derrick,Thuany, Mabliny,Motevalli, Mohamad,Wirnitzer, Gerold,Leitzmann, Claus,Weiss, Katja,Rosemann, Thomas,Wirnitzer, Katharina