Study design and ethical approval
The present study is a part of the NURMI (Nutrition and Running 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 runners by sex, race distance, diet type, etc. Data collection was conducted via a series of self-reported online surveys 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 contacted 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 written description of the procedures and gave their informed consent to take part in the study. In parallel, physical and psychological informationâ€”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)â€”motivation and aim of running 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 18Â years of age; (3) questionnaire Step 2 completed; (4) having a BMIâ€‰<â€‰30Â 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 1â€“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 BMIâ€‰â‰¥â€‰30Â 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 BMIâ€‰â‰¥â€‰30Â kg/m2 (nâ€‰=â€‰3) were excluded from data analysis.
Data clearance and classification of participants
Control questions were included throughout different 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.Â 1).
Participants were initially categorized according to race distance: half-marathon and marathon/ultra-marathon (data were pooled since the marathon distance is included in an ultra-marathon). The shortest distance for ultra-marathon was 50Â km, and the longest distance was 160Â 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 18.5â€“24.9Â kg/m2 (BMINORM) for individuals, while at the same time pointing to an increased risk of co-morbidities for a BMI 25.0â€“29.9Â kg/m2 and moderate to severe risk of co-morbidities for a BMIâ€‰>â€‰30Â kg/m2. Therefore, calculated 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 runnersâ€™ health status10,20, participants were assigned into three dietary subgroups of omnivores, vegetarians, and vegans47.
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: prevalence 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 hypersensitivity 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, supplements for performance enhancement, supplements to cope with stress); (3) food choice (motivation, desired 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 beneficial health associations [given as mean scores plus standard deviation and percentage (%)].
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 Kruskalâ€“Wallis test were used to examine the association between race distance subgroups and domain scores with nominal scale variables, and Wilcoxon test and Kruskalâ€“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 to define health status. The assumptions of the ANOVA were verified by residual analysis. The level of statistical significance was set at pâ€‰<â€‰0.05 (statistical trend: 0.05â€‰â‰¥â€‰pâ€‰<â€‰0.10).
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.
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 (nâ€‰=â€‰177), Austria (nâ€‰=â€‰44), and Switzerland (nâ€‰=â€‰13) had the majority of endurance runners, but 4.5% of participants (nâ€‰=â€‰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%), vegetarian diet (18%), or vegan diet (37%). Moreover, with regard to the level of academic qualification, 34% of endurance runners (nâ€‰=â€‰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.
The median number of events completed in our sample was eight races, and the marathoners/ultra-marathoners finished the highest number of races. Depending on the stage of preparation for the main event and/or season within the course of the year, 70% of runners reported their weekly mileage at a medium volume (19â€“36Â km), while 17% and 13% of runners reported low (<â€‰19Â km) and high (>â€‰36Â km) volumes, respectively (Table 2).
There was a significant difference in BW between race distance subgroups (F(2, 242)â€‰=â€‰5.05, pâ€‰=â€‰0.007), with 10-km runners weighing less than half-marathoners and marathoners/ultra-marathoners. However, there was no difference in the health-related item BMI between the subgroups (Ï‡2(4)â€‰=â€‰1.35, pâ€‰=â€‰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 â€œBW and BMIâ€ (F(2, 242)â€‰=â€‰0.84, pâ€‰=â€‰0.433), comparative data showed that half-marathoners had the highest score for the health-related indicator â€œBW and BMIâ€ (0.69â€‰Â±â€‰0.39), and were followed closely by marathon/ultra-marathon runners (0.67â€‰Â±â€‰0.39) (Table 4).
There was no significant association between race distance and mental health (Ï‡2(2)â€‰=â€‰5.83, pâ€‰=â€‰0.054) (Table 3). However, half-marathoners reported least often to suffer from perceived stress (27%, nâ€‰=â€‰23). Although no significant between-group difference was observed in the dimension of â€œmental healthâ€ (F(2, 219)â€‰=â€‰2.95, pâ€‰=â€‰0.054), comparative data showed that half-marathoners had the highest score with regard to mental health (0.73â€‰Â±â€‰0.45) (Table 4).
There was no significant association between race distance and the prevalence of (1) cardiovascular diseases and cancer (Ï‡2(4)â€‰=â€‰4.76, pâ€‰=â€‰0.313), (2) metabolic diseases (Ï‡2(10)â€‰=â€‰13.25, pâ€‰=â€‰0.210), and (3) hypersensitivity reactions (Ï‡2(4)â€‰=â€‰8.90, pâ€‰=â€‰0.064). However, none of the half-marathoners reported having chronic diseases, and half-marathoners most often reported having no metabolic diseases (92%, nâ€‰=â€‰78) and no hypersensitivity reactions (73%, nâ€‰=â€‰62) while having allergies the least often (22%, nâ€‰=â€‰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 (0.88â€‰Â±â€‰0.18, F(2, 219)â€‰=â€‰3.31, pâ€‰=â€‰0.038) (Table 4).
There was no significant association between medication intake and race distance (Ï‡2(6)â€‰=â€‰2.64, pâ€‰=â€‰0.852). Furthermore, there was no significant association between race distance and the intake of contraceptives (Ï‡2(2)â€‰=â€‰5.93, pâ€‰=â€‰0.051) (Table 3). However, half-marathoners most often reported having no regular medication intake (87%, nâ€‰=â€‰74). Although no significant between-group difference was observed in the dimension of â€œmedication intakeâ€ (F(2, 219)â€‰=â€‰0.20, pâ€‰=â€‰0.817), comparative data showed that half-marathoners had the highest score with regard to medication intake (0.87â€‰Â±â€‰0.34) but were closely followed by two other groups (Table 4).
Race distance and current or former smoking were not significantly associated (Ï‡2(4)â€‰=â€‰4.00, pâ€‰=â€‰0.406) (Table 3). In addition, half-marathoners showed the highest fraction of non-smokers (67%, nâ€‰=â€‰57). Although no significant between-group difference was observed in the dimension of â€œsmoking habitsâ€ (F(2, 219)â€‰=â€‰2.00, pâ€‰=â€‰0.138), comparative data showed that half-marathoners showed the best health-related behaviors with regard to smoking habits (0.83â€‰Â±â€‰0.25) (Table 4).
There was no significant association between race distance and (1) supplement intake prescribed by a doctor (Ï‡2(2)â€‰=â€‰0.07, pâ€‰=â€‰0.968), (2) the consumption of performance-enhancing substances (Ï‡2(4)â€‰=â€‰3.52, pâ€‰=â€‰0.476), or (3) the intake of substances to cope with stress (Ï‡2(4)â€‰=â€‰6.66, pâ€‰=â€‰0.155) (Table 3). Although no significant between-group difference was observed in the dimension of â€œsupplement intakeâ€ (F(2, 219)â€‰=â€‰0.92, pâ€‰=â€‰0.400), comparative data showed that 10-km runners had the highest health scores with regard to supplement intake (0.92â€‰Â±â€‰0.17) but were closely followed by two other groups (Table 4).
There was no significant association between race distance and motives for food choice (1) because it is healthy (Ï‡2(2)â€‰=â€‰0.74, pâ€‰=â€‰0.690), health-promoting (Ï‡2(2)â€‰=â€‰1.00, pâ€‰=â€‰0.607), and good for maintaining health (Ï‡2(2)â€‰=â€‰2.15,Â pâ€‰=â€‰0.341); (2) in order to obtain vitamins (Ï‡2(2)â€‰=â€‰0.15, pâ€‰=â€‰0.928), minerals/trace elements (Ï‡2(2)â€‰=â€‰0.10, pâ€‰=â€‰0.953), antioxidants (Ï‡2(2)â€‰=â€‰1.06, pâ€‰=â€‰0.587), phytochemicals (Ï‡2(2)â€‰=â€‰2.92, pâ€‰=â€‰0.232), and fiber (Ï‡2(2)â€‰=â€‰2.58, pâ€‰=â€‰0.276); or (3) with regard to the avoidance of the following ingredients (Table 3): refined sugar (Ï‡2(2)â€‰=â€‰1.89, pâ€‰=â€‰0.390), sweeteners (Ï‡2(2)â€‰=â€‰5.63, pâ€‰=â€‰0.060), fat in general (Ï‡2(2)â€‰=â€‰3.13, pâ€‰=â€‰0.210), saturated fats (Ï‡2(2)â€‰=â€‰0.21, pâ€‰=â€‰0.899), cholesterol (Ï‡2(2)â€‰=â€‰0.46, pâ€‰=â€‰0.794), alcohol (Ï‡2(2)â€‰=â€‰1.22, pâ€‰=â€‰0.542), and caffeine (Ï‡2(2)â€‰=â€‰3.04, pâ€‰=â€‰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 (Ï‡2(2)â€‰=â€‰8.70, pâ€‰=â€‰0.013), sweets (Ï‡2(2)â€‰=â€‰6.29, pâ€‰=â€‰0.043), and nibbles (Ï‡2(2)â€‰=â€‰6.11, pâ€‰=â€‰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 â€œfood choiceâ€ (F(2, 219)â€‰=â€‰1.32, pâ€‰=â€‰0.270), comparative data showed that 10-km runners had the best health-related behaviors with regard to food choice (0.72â€‰Â±â€‰0.20) (Table 4).
There was no significant association between the use of regular health check-ups and race distance (Ï‡2(2)â€‰=â€‰2.64,Â pâ€‰=â€‰0.268) (Table 3). Although no significant between-group difference was observed in the dimension of â€œhealthcare utilizationâ€ (F(2, 219)â€‰=â€‰1.32, pâ€‰=â€‰0.270), comparative data showed that half-marathoners had the highest scores with regard to healthcare utilization (0.62â€‰Â±â€‰0.49) while marathoners/ultra-marathoners scored lowest (0.49â€‰Â±â€‰0.50) (Table 4).
Results of the MANOVA
The findings of the MANOVA considering the health status of endurance runners are presented in Table 5, indicating significant differences for the following results: (1) education (academic qualification) had an association with BW and BMI (pâ€‰=â€‰0.004), smoking habits (pâ€‰=â€‰0.005), and supplement intake (pâ€‰=â€‰0.022); (2) race distance had a significant association with the dimension â€œchronic diseases and hypersensitivity reactionsâ€ (pâ€‰=â€‰0.038); (3) there was an association between sex and smoking habits (pâ€‰=â€‰0.048); (4) training (weekly mileage) had an association with food choice (pâ€‰=â€‰0.003); and (5) there was an association between age and healthcare utilization (pâ€‰=â€‰0.002). However, no significant associations were found considering the dimensions of mental health and medication intake.
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, â€œchronic diseases and hypersensitivity reactionsâ€ 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 â€œsport for healthâ€ as the basic assignment to a sports area, while â€œsport for leisureâ€ (54% of total participants, 64% of half-marathoners) and â€œsport for performanceâ€ (36% of total participants, 28% of half-marathon runners) were ranked higher. â€œHobbyâ€ and â€œhealthâ€ 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 participantsâ€™ slight emphasis on specific personal achievements 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 â€œthe joy of running racesâ€ was a top reason, and â€œwinningâ€ was identified as an unimportant reason to participate in running events4. Although â€œhealthâ€ 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. Running 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 ofâ€‰â‰¥â€‰50Â years50.
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 compared 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 distances 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 20Â kg/m256. The vegan diet was shown to effectively reduce BW and particularly body fat57,58, with favorable effects on running performance, if planed appropriately59. Consistently, previous data from our laboratory show that vegan endurance runners are significantly leaner than omnivores (64Â kg vs. 68Â kg), contributing to their overall state of health with the highest health score (69%)10.
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 perception 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, â€œchronic diseases and hypersensitive reactionsâ€, most beneficially contributing to the half-marathonersâ€™ 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 activation 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 treatment of cardio-metabolic disorders and certain diseases such as ischemic heart disease, type 2 diabetes, hypertension, 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 allergies, 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 in the form of contraceptives was lower with a statistical trend (pâ€‰=â€‰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 thyroid medication were women who reported the intake of contraceptives. However, there was no association between sex and the dimension â€œmedication intakeâ€ 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 0.82â€“0.86) with medication intake. However, as the majority of distance runners (84â€“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.
A low rate of smoking (<â€‰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 supplement 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, athletesâ€™ 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 0.88â€“0.92) among race distance 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 supplement 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, probably 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 recreational 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 participantsâ€™ attitudes towards food choices characterize them as being health-conscious, so they might have been aware of potential detrimental effects of risky performance-enhancing substances. 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 athletes78,79,80.
The present study showed that food choice was not associated with race distance, but the runners 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 runners 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 benefits to the runnersâ€™ 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 nutrients such as vitamin B1210,59,81. 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.
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 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 present 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 potentially 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 sustainable 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.