Citation: Motevalli, M.; Wagner,
K.-H.; Leitzmann, C.; Tanous, D.;
Wirnitzer, G.; Knechtle, B.; Wirnitzer,
K. Female Endurance Runners Have
a Healthier Diet than Males—Results
from the NURMI Study (Step 2).
Nutrients 2022, 14, 2590. https://
doi.org/10.3390/nu14132590
Academic Editor: Paul J. Arciero
Received: 19 May 2022
Accepted: 21 June 2022
Published: 22 June 2022
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nutrients
Article
Female Endurance Runners Have a Healthier Diet than
Males—Results from the NURMI Study (Step 2)
Mohamad Motevalli 1,2
, Karl-Heinz Wagner 3
, Claus Leitzmann 4, Derrick Tanous 1,2
, Gerold Wirnitzer 5,
Beat Knechtle 6,7
and Katharina Wirnitzer 1,2,8,*
1
Department of Sport Science, Leopold-Franzens University of Innsbruck, 6020 Innsbruck, Austria;
seyed.motevalli-anbarani@student.uibk.ac.at (M.M.); derrick.tanous@student.uibk.ac.at (D.T.)
2
Department of Subject Didactics and Educational Research and Development, University College of Teacher
Education Tyrol, 6010 Innsbruck, Austria
3
Department of Nutritional Sciences, University of Vienna, 1090 Vienna, Austria;
karl-heinz.wagner@univie.ac.at
4
Institute of Nutrition, University of Gießen, 35390 Gießen, Germany; claus@leitzmann-giessen.de
5
AdventureV & Change2V, 6135 Stans, Austria; gerold@wirnitzer.at
6
Institute of Primary Care, University of Zurich, 8091 Zurich, Switzerland; beat.knechtle@hispeed.ch
7
Medbase St. Gallen Am Vadianplatz, 9001 St. Gallen, Switzerland
8
Research Center Medical Humanities, Leopold-Franzens University of Innsbruck, 6020 Innsbruck, Austria
*
Correspondence: info@nurmi-study.com; Tel.: +43-(650)-5901794
Abstract: Sex has been recognized to be an important indicator of physiological, psychological, and
nutritional characteristics among endurance athletes. However, there are limited data addressing
sex-based differences in dietary behaviors of distance runners. The aim of the present study is to
explore the sex-speciﬁc differences in dietary intake of female and male distance runners competing
at >10-km distances. From the initial number of 317 participants, 211 endurance runners (121 fe-
males and 90 males) were selected as the ﬁnal sample after a multi-level data clearance. Participants
were classiﬁed to race distance (10-km, half-marathon, marathon/ultra-marathon) and type of diet
(omnivorous, vegetarian, vegan) subgroups. An online survey was conducted to collect data on
sociodemographic information and dietary intake (using a comprehensive food frequency question-
naire with 53 food groups categorized in 14 basic and three umbrella food clusters). Compared to
male runners, female runners had a signiﬁcantly greater intake in four food clusters, including “beans
and seeds”, “fruit and vegetables”, “dairy alternatives”, and “water”. Males reported higher intakes
of seven food clusters, including “meat”, “ﬁsh”, “eggs”, “oils”, “grains”, “alcohol”, and “processed
foods”. Generally, it can be suggested that female runners have a tendency to consume healthier foods
than males. The predominance of females with healthy dietary behavior can be potentially linked to
the well-known differences between females and males in health attitudes and lifestyle patterns.
Keywords: sex; gender; nutrition; dietary assessment; food frequency; protein; fruit; vegetables;
distance running; half-marathon; marathon
1. Introduction
The importance of sex-related comparison in sports nutrition topics has been widely
discussed over the past decade [1]. It is well-established that the nutritional requirements
of athletes are potentially affected by physical and physiological differences between
males and females [2,3]. These sex-based differences seem to be more predominant in
ultra-endurance athletes who are recommended to pay superior attention to their speciﬁc
nutritional needs due to the prolonged training/racing activities [4,5].
Sex differences in endurance performance are not limited to the menstrual cycle that
causes unfavorable effects on training procedures in female athletes (mainly due to the asso-
ciated challenges and anemia rather than hormonal ﬂuctuations) [6,7]. Evidence shows that
Nutrients 2022, 14, 2590. https://doi.org/10.3390/nu14132590
https://www.mdpi.com/journal/nutrients
Nutrients 2022, 14, 2590
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females have a lower oxygen-carrying capacity (due to fewer erythrocytes and hemoglobin
levels) than males, which can affect their endurance performance negatively [8]. In addition,
females are more susceptible to developing thyroid disorders compared to males [9] result-
ing in performance-limiting outcomes, including fatigue [10]. However, males seem to be
more prone to cardiovascular abnormalities as it has been shown that cardiac death and
coronary heart disease are more prevalent in males than females [11,12], which increases
the likelihood of unfavorable health- and performance-related consequences. Considering
the fact that male athletes are characterized as being more inﬂuenced by risky behaviors
such as performance-enhancing substance abuse [13,14], their cardiovascular health is of
greater concern. Research indicates that in muscle metabolism pathways during endurance
activities, females have a higher capacity to utilize muscle lipids as fuel, and males rely
more on muscle and liver glycogen resources [15,16]. To achieve an optimal level of en-
durance performance, however, females may need further training adaptations compared
to males [17,18] due to the basic sex-speciﬁc physical differences (e.g., body mass, muscle
mass, and fat mass) [6,19].
Nutritional requirements and patterns may also be affected by sex, whether dependent
or independent of the mentioned physical and physiological differences between males
and females. It has been shown that female athletes have a greater prevalence of uninten-
tional caloric imbalance than males in order to reach and maintain the appropriate body
composition required for an optimized level of endurance performance [6,18,19]. Females
have also been reported to be generally more health conscious than males, which also
can be associated with their attitudes towards food choice, including a greater intake of
fruits, vegetables, and whole foods [20]. In contrast, it has been shown that males are more
motivated to increase physical activity in their daily routines rather than modifying their
nutritional habits [21]. Generally, the various health- and lifestyle-related beliefs between
females and males have been predicted to be responsible for up to 50% of sex-speciﬁc
dietary choices [20].
Dietary assessment is a crucial part of sports nutrition practice, which helps identify
nutritional inadequacy (that commonly occurs following restrictive diets) and optimize
dietary strategies for improving performance and health. Nutritional concerns, particu-
larly energy deﬁciency, are more critical in both male and female long-distance runners
compared to those who run in shorter races [18,22]. Likewise, nutritional requirements are
positively associated with increasing intensity, duration, and frequency of running/training
sessions [18,23]. Data show that typical daily foods may not fulﬁll the nutritional needs
of endurance runners to support their physiological requirements [22,24]. This concern
is more serious for endurance athletes who follow unbalanced and/or inappropriately-
planned diets, which has been shown to occur in all diet types (e.g., omnivorous or plant-
based diets) [25–27]. It has been reported that even ultra-endurance events can be com-
pleted successfully without any health-related consequences by athletes who consume only
plant-based foods [28,29]. This ﬁnding supports that by following the well-recognized
dietary guidelines, appropriately planned plant-based diets can maintain the health of
long-distance runners [28,29].
Regardless of the well-established sex differences in physical, physiological, and
nutritional characteristics of general populations [30], there is limited evidence comparing
dietary intake between male and female endurance athletes, particularly distance runners.
Despite the advancement of knowledge in illustrating sex-based differences, the majority
of sports nutrition topics have a paucity of female-speciﬁc examinations, resulting in the
misapplication of many scientiﬁc conclusions for female athletes [31]. Available studies
regarding the nutrient requirements of endurance athletes [32–34] are not consistent in
covering all sex-based differences, or they did not distinguish race distance and diet type
of female and male endurance runners [35,36]. Therefore, the present study was conducted
to investigate and compare the dietary intake of female and male distance runners across
different subgroups of diet type and race distance. It was hypothesized that female runners
have a dietary intake more advantageous to health.
Nutrients 2022, 14, 2590
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2. Materials and Methods
2.1. Study Design and Ethical Approval
The present study is a part of the Nutrition and Running High Mileage (NURMI) Study
Step 2. The study protocol [37] was approved by the ethics board of St. Gallen, Switzerland
(EKSG 14/145; 6 May 2015) with the trial registration number ISRCTN73074080. The
methods of the “NURMI Study Step 2” have been previously described in detail [38,39].
2.2. Participants and Experimental Approach
Endurance runners were mainly recruited from Austria, Germany, and Switzerland
and were contacted via social media, websites of organizers of marathon events, online
running communities, email lists, and runners’ magazines, as well as via additional/other
multi-channel recruitments and through personal contacts. Participants were asked to com-
plete an online survey within the “NURMI Study Step 2”, which was available in German
and English (https://www.nurmi-study.com/en (accessed on 10 May 2022)). Participants
were provided with a written description of the procedures and gave their informed consent
before completing the questionnaire. The following inclusion criteria were initially required
for successful participation in the “NURMI Study Step 2”: (1) written informed consent;
(2) at least 18 years of age; (3) questionnaire Step 2 completed; (4) successful participation
in a running event of at least half-marathon distance in the past two years.
Female and male participants were further categorized according to race distance and
kind of diet. Race distance subgroups were half-marathon and (ultra-)marathon (data were
pooled since the marathon distance is included in an ultra-marathon); the shortest and
longest ultra-marathon distances reported were 50 km and 160 km, respectively. However,
a total number of 74 runners who completed the 10-km distance, but had not successfully
participated in either a half-marathon or a marathon, also provided accurate and useable
answers similar to runners competing over half-marathon or higher. In order to avoid an
irreversible loss of these valuable data sets, those who met the inclusion criteria (1) to (3)
were kept as additional race distance subgroup. Dietary subgroups were omnivorous (or
Western diet, with no restriction on any food items), vegetarian (devoid of all ﬂesh foods,
including ﬁsh and shellﬁsh, but including eggs and/or dairy products), and vegan diet
(devoid of all foods from animal sources, including honey) [40,41] with a minimum of
6-month adherence to the self-reported diet types.
2.3. Data Clearance
From the initial number of 317 endurance runners, a total of 106 participants were
excluded from the data analysis. Of these, 46 participants did not meet the basic inclusion
criteria. In order to control for a minimal status of health linked to a minimum level
of ﬁtness and to further enhance the reliability of data sets, the Body Mass Index (BMI)
approach following the World Health Organization (WHO) standards [42,43] was applied.
On this basis, one participant with a BMI ≥ 30 kg/m2 was excluded from the data analysis
since ﬁrst other health-protective and/or weight loss strategies other than running are
necessary to safely reduce body weight. Further, as a result of the speciﬁc exclusion criteria
for the present study, an additional number of 25 runners were identiﬁed and excluded
for consuming ≤50% carbohydrates of their total dietary intake (which is lower than the
minimum level recommended for maintaining a health-performance association [25,44,45]).
Moreover, 34 participants with conﬂicting statements on water intake (e.g., stated never
drinking water) were excluded from the analysis to avoid conﬂicting data on dietary
intake [44]. In addition, a total of 24 runners (11%) had to be shifted to other dietary
subgroups: 4 vegan runners: respectively 2 to omnivores and 2 to vegetarian samples;
and 20 (9%) vegetarian runners had to be shifted to the omnivores subsample. However,
89% (n = 187) of the recreational runners correctly assessed their kind of diet. As the ﬁnal
sample, 211 runners (121 women and 90 men) with complete data sets were included for
statistical analysis. Figure 1 shows the participants’ enrollment and classiﬁcations within
the present study.
Nutrients 2022, 14, 2590
4 of 17
 
subgroups: 4 vegan runners: respectively 2 to omnivores and 2 to vegetarian samples; and 
20 (9%) vegetarian runners had to be shifted to the omnivores subsample. However, 89% 
(n = 187) of the recreational runners correctly assessed their kind of diet. As the final sam-
ple, 211 runners (121 women and 90 men) with complete data sets were included for sta-
tistical analysis. Figure 1 shows the participants’ enrollment and classifications within the 
present study. 
 
Figure 1. Participants’ enrollment and classifications by sex. 
2.4. Measures and Statistical Modelling 
Based on the food frequency questionnaire (FFQ) of the “German Health Interview 
and Examination Survey for Adults (DEGS)” (DEGS-FFQ; with friendly permission of the 
Robert Koch Institute, Berlin, Germany) [46,47], participants were asked to report their 
regular food intake based on the consumption frequency (single-choice out of 11 options 
ranging from “never” to “5 times a day”) and quantity of a broad variety of specific die-
tary items (single-choice from various options depending on the food group) particularly 
in the past four weeks, including meals eaten while out, i.e., in restaurants, canteens, at 
friends’ houses, etc. 
Based on the 53 food groups of the DEGS-FFQ and following the Nova classification 
system of the Food and Agriculture Organization (FAO, Rome, Italy) [48–51], subgroups 
of foods were categorized with the corresponding questions pooled for a total of 17 food 
clusters in order to perform quantitative and qualitative data analyses (Table 1). Self-re-
ported data, including sociodemographic information, motive(s) for diet type adherence, 
and pooled food frequency, were linked to sex-based groups. 
 
 
Figure 1. Participants’ enrollment and classiﬁcations by sex.
2.4. Measures and Statistical Modelling
Based on the food frequency questionnaire (FFQ) of the “German Health Interview
and Examination Survey for Adults (DEGS)” (DEGS-FFQ; with friendly permission of the
Robert Koch Institute, Berlin, Germany) [46,47], participants were asked to report their
regular food intake based on the consumption frequency (single-choice out of 11 options
ranging from “never” to “5 times a day”) and quantity of a broad variety of speciﬁc dietary
items (single-choice from various options depending on the food group) particularly in the
past four weeks, including meals eaten while out, i.e., in restaurants, canteens, at friends’
houses, etc.
Based on the 53 food groups of the DEGS-FFQ and following the Nova classiﬁcation
system of the Food and Agriculture Organization (FAO, Rome, Italy) [48–51], subgroups
of foods were categorized with the corresponding questions pooled for a total of 17 food
clusters in order to perform quantitative and qualitative data analyses (Table 1). Self-
reported data, including sociodemographic information, motive(s) for diet type adherence,
and pooled food frequency, were linked to sex-based groups.
Nutrients 2022, 14, 2590
5 of 17
Table 1. Modeling of the Clusters for Food Frequency (Basic Nutrition and Consumption Cluster 1 to
14; Umbrella Cluster for Preparation Cluster 15 to 17).
Basic Food Clusters
Cluster 1
Grains
a—grains
b—whole grains
cornﬂakes; white bread; white pasta
muesli; wholegrain; mixed bread; wholegrain pasta; wholegrain
rice; other grains
Cluster 2
Legumes, nuts, and pulses
pulses; nuts and seeds; legumes
Cluster 3
Fruit and vegetables
vegetable juice; fruit; vegetables
Cluster 4
Dairy products
milk; cheese; yoghurt
Cluster 5
Dairy alternatives
milk alternatives
Cluster 6
Meat
a—meat
b—processed meat
chicken; beef; pork; deer
fried nuggets; hamburger; sausage; kebab; pork; processed meat
Cluster 7
Meat alternatives
tofu; seitan; tempeh; etc.
Cluster 8
Fish, shellﬁsh, and seafood
Cluster 9
Eggs
Cluster 10
Oils and spreads
butter; margarine; oils
Cluster 11
Sweets and snacks
sweets; snacks; salty snacks
Cluster 12
Water and unsweetened tea
Cluster 13
Beverages
Cluster 14
Alcohol
Preparation/Umbrella Clusters
Cluster 15
Protein
a—plant protein
b—animal protein
legumes and beans; vegetables; grains (couscous, quinoa); dairy
alternatives (e.g., soy products); meat alternatives
dairy products; eggs; meat and processed meat products; ﬁsh,
seafood, and shellﬁsh
Cluster 16
(Ultra-)processed foods
and free/added sugar
sugary carbonated drinks; kcal reduced/artiﬁcially sweetened
drinks; fruit juice; free sugar in tea; free sugar in coffee; cereals;
sweet and savory spreads; margarine; pasta; sweets, cakes, and
biscuits; salty snacks, butter; processed meat; processed
plant products
Cluster 17
Free/added sugar
Sweet spread; sugary carbonated drinks; fruit juice; free sugar
in tea; free sugar in coffee; cereals; sweets, cakes, and biscuits
2.5. Statistical Analysis
The statistical software R version 4.1.1 (10 August 2021) Core Team 2018 (R Foundation
for Statistical Computing, Vienna, Austria) was used to perform all statistical analyses.
Exploratory analysis was done by descriptive statistics: mean values and standard deviation
(SD), median and interquartile range (IQR). Chi-square tests (χ2, nominal scale) were
conducted to examine the association of sex with nationality, marital status, academic
qualiﬁcation, diet type, race distance, and dietary motives. Kruskal–Wallis tests (ordinal
and metric scale) were approximated by using the t or F distributions or using ordinary
least squares and standard errors (SE) with R2 to test the association of sex with age, body
weight, height, and BMI. Food cluster as the latent variable was derived by 53 manifest
parameters (assessing how often and how much consumption of speciﬁc dietary items). In
order to scale the food consumption displayed by measures, items, and clusters, a heuristic
index (as a new composite variable) ranging from 0 to 100 was deﬁned (equivalence in all
items; FFQ was calculated by multiplying the reports of both questions, and dividing by
Nutrients 2022, 14, 2590
6 of 17
the maximum then). A linear regression model was used to examine signiﬁcant differences
in the intake of speciﬁc food clusters by sex and age. The assumptions of the regression
analysis have been veriﬁed by inspection of graphs of residuals. Differences in respective
food clusters between females and males are displayed as effect plots (95% conﬁdence
interval). The level of statistical signiﬁcance was set at p ≤ 0.05.
3. Results
From a total of 211 runners (including 121 females and 90 males) with a median
age of 38 (IQR 18) years, there were 74 runners of 10-km, 83 half marathoners, and
54 marathoners/ultramarathoners based on race distance, and 95 omnivores, 40 vege-
tarians, and 76 vegans based on kind of diet. The majority of endurance runners (96%)
were from German-speaking countries (i.e., Germany, Austria, and Switzerland), while 4%
of participants were from other countries worldwide.
Descriptive analysis showed signiﬁcant differences between females and males in
age (p = 0.023), where males with a median age of 42 (IQR 17) years were older than
females with a median age of 37 (IQR 15) years, and BMI (p < 0.001), where males had
a higher BMI (22.91 kg/m2, IQR 2.86) compared to females (20.94 kg/m2, IQR 3.05). No
signiﬁcant difference (p > 0.05) was found between male and female runners in academic
qualiﬁcation or marital status. There was a signiﬁcant sex-based difference in race distance
(p < 0.001), where the majority of 10-km runners and half marathoners were female, and
most marathon/ultramarathon runners were male. A signiﬁcant sex-based difference was
detected in diet type (p = 0.013), as vegetarian and vegan diets were more common in
females and omnivorous were more prevalent in male runners. While endurance runners
reported mostly “health & wellbeing” (by 85%) as the main reason/motive to adhere
to their self-reported diet types, “social aspects” was the only motive with a signiﬁcant
difference between females and males (41% vs. 65%, respectively; p = 0.010). Table 2 shows
the sex-based differences in sociodemographic characteristics of the participants.
Table 2. Sociodemographic characteristics of female and male runners.
Total
n = 211
Females
n = 121
Males
n = 90
Statistics
Age (years)
38 (IQR 18)
37 (IQR 15)
42 (IQR 17)
F(1, 209) = 5.26; p = 0.023
Body Weight (kg)
65.0 (IQR 14.1)
59.8 (IQR 10.6)
73.6 (IQR 12.3)
F(1, 209) = 189.68; p < 0.001
Height (m)
1.7 (IQR 0.1)
1.7 (IQR 0.1)
1.8 (IQR 0.1)
F(1, 209) = 191.83; p < 0.001
BMI (kg/m2)
21.72 (IQR 3.40)
20.94 (IQR 3.05)
22.91 (IQR 2.86)
F(1, 209) = 33.21; p < 0.001
Academic
Qualiﬁcation
Upper Secondary/Technical
A Levels or Equivalent
University/Higher Degree
No Answer
33% (69)
23% (49)
34% (72)
9% (21)
30% (36)
23% (28)
36% (43)
12% (14)
37% (33)
23% (21)
32% (29)
8% (7)
χ2(3) = 2.14; p = 0.709
Marital Status
Divorced/Separated
Married/Partner
Single
5% (11)
68% (143)
27% (57)
7% (8)
61% (74)
32% (39)
3% (3)
77% (69)
20% (18)
χ2(2) = 5.75; p = 0.056
Country of Residence
Austria
Germany
Switzerland
Other Countries
17% (36)
74% (156)
5% (11)
4% (8)
10% (12)
80% (97)
5% (6)
5% (6)
27% (24)
66% (59)
6% (5)
2% (2)
χ2(3) = 11.03; p = 0.012
Race Distance
10-km
HM
M/UM
35% (74)
39% (83)
26% (54)
45% (55)
38% (46)
17% (20)
21% (19)
41% (37)
38% (34)
χ2(2) = 17.95; p < 0.001
Diet Type
Omnivorous
Vegetarian
Vegan
45% (95)
19% (40)
36% (76)
36% (44)
21% (26)
42% (51)
57% (51)
16% (14)
28% (25)
χ2(2) = 8.64; p = 0.013
Note. IQR—Interquartile range. BMI—body mass index. km—kilometers. HM—half-marathon.
M/UM—marathon/ultra-marathon. Statistical methods: Kruskal–Wallis tests (represented by median and
IQR data) and Chi-square tests (represented by prevalence data).
Nutrients 2022, 14, 2590
7 of 17
Signiﬁcant differences between female and male participants were found in the con-
sumption of 11 out of 17 food clusters (p < 0.05). Compared to males, female runners
reported a greater intake of four food clusters including beans and seeds (p = 0.008), fruit
and vegetables (p < 0.001), dairy alternatives (p = 0.012), and water (p = 0.002). In con-
trast, males had a higher intake of seven food clusters including grains (p < 0.001), meat
(p < 0.001), ﬁsh (p = 0.033), eggs (p = 0.041), oils (p = 0.033), alcohol (p < 0.001), and processed
foods (p = 0.001). There was no signiﬁcant difference between female and male runners
in the consumption of six food clusters, including dairy (p = 0.159), meat alternatives
(p = 0.488), snacks (p = 0.086), beverages (p = 0.350), protein (p = 0.599), and free/added
sugar (p = 0.212). Table 3 displays the sex-based differences in intake of 17 food clusters
and the subset items.
Table 3. Differences between female and male runners in food clusters and items.
Females
Males
Statistics
n = 121
n = 90
Part A—Basic clusters
FC—1 (Total of grains)
15.43 ± 7.86
21.90 ± 8.16
F(1, 209) = 36.40; p < 0.001
FC—1a (Total of reﬁned grains)
9.99 ± 8.14
15.54 ± 9.57
F(1, 209) = 19.64; p < 0.001
Cornﬂakes
1.60 ± 3.57
1.44 ± 4.99
F(1, 209) = 2.34; p = 0.127
White bread
6.07 ± 6.35
10.36 ± 9.18
F(1, 209) = 12.03; p = 0.001
White pasta
8.81 ± 8.48
13.84 ± 9.42
F(1, 209) = 16.03; p < 0.001
FC—1b (Total of whole grains)
17.12 ± 8.48
22.95 ± 9.17
F(1, 209) = 22.12; p < 0.001
Muesli
14.89 ± 12.32
18.80 ± 14.00
F(1, 207) = 3.91; p = 0.049
Whole grain bread
14.45 ± 8.54
18.99 ± 9.40
F(1, 209) = 16.23; p < 0.001
Whole grain pasta
9.37 ± 8.11
11.22 ± 9.36
F(1, 209) = 1.65; p = 0.201
Whole grain rice
5.87 ± 6.57
8.96 ± 8.26
F(1, 209) = 7.17; p = 0.008
Other whole grains
6.07 ± 6.35
10.36 ± 9.18
F(1, 209) = 12.03; p = 0.001
FC—2 (Total of beans and seeds)
28.47 ± 13.89
23.70 ± 13.74
F(1, 209) = 7.12; p = 0.008
Nuts & seeds
22.25 ± 13.21
16.11 ± 12.67
F(1, 209) = 13.04; p < 0.001
Legumes
15.98 ± 10.65
15.71 ± 10.74
F(1, 209) = 0.23; p = 0.630
FC—3 (Total of fruit and vegetables)
34.09 ± 13.03
26.84 ± 11.77
F(1, 209) = 19.30; p < 0.001
Vegetable juice
5.48 ± 9.74
5.70 ± 11.58
F(1, 209) = 1.01; p = 0.315
Fruit
19.93 ± 9.30
18.16 ± 8.73
F(1, 209) = 2.92; p = 0.089
Vegetables
34.73 ± 12.56
27.08 ± 10.50
F(1, 209) = 22.01; p < 0.001
FC—4 (Total of dairy)
9.70 ± 12.11
10.77 ± 9.67
F(1, 209) = 2.00; p = 0.159
Milk
7.57 ± 11.31
9.67 ± 11.71
F(1, 209) = 3.00; p = 0.085
Cheese
7.10 ± 8.89
8.12 ± 8.05
F(1, 209) = 1.76; p = 0.187
Yogurt
7.81 ± 11.00
7.17 ± 9.09
F(1, 209) = 0.04; p = 0.833
FC—5: Dairy alternatives
18.08 ± 15.04
13.69 ± 15.51
F(1, 209) = 6.44; p = 0.012
FC—6 (Total of meat)
4.95 ± 9.81
12.46 ± 13.70
F(1, 209) = 19.26; p < 0.001
FC—6a (Total of unprocessed meat)
5.43 ± 10.68
13.04 ± 14.47
F(1, 209) = 17.24; p < 0.001
Chicken
2.42 ± 5.16
4.98 ± 6.35
F(1, 209) = 12.75; p < 0.001
Beef and pork and deer
4.34 ± 8.90
11.25 ± 13.20
F(1, 209) = 18.29; p < 0.001
FC—6b (Total of processed meat)
3.93 ± 8.40
10.52 ± 12.67
F(1, 209) = 19.72; p < 0.001
Fried nuggets
1.32 ± 3.19
2.62 ± 3.64
F(1, 209) = 11.67; p = 0.001
Hamburger
0.43 ± 1.44
1.67 ± 3.10
F(1, 209) = 12.15; p = 0.001
Sausage
0.25 ± 1.20
1.47 ± 3.14
F(1, 209) = 14.23; p < 0.001
Kebab
0.34 ± 1.01
1.57 ± 2.78
F(1, 209) = 15.49; p < 0.001
Other processed meat
4.05 ± 9.51
9.78 ± 13.02
F(1, 209) = 14.40; p < 0.001
FC—7: Meat alternatives
5.99 ± 6.02
6.16 ± 7.44
F(1, 209) = 0.48; p = 0.488
FC—8: Fish
3.80 ± 5.70
5.57 ± 6.90
F(1, 209) = 4.60; p = 0.033
FC—9: Eggs
6.91 ± 8.65
9.16 ± 8.86
F(1, 209) = 4.22; p = 0.041
Nutrients 2022, 14, 2590
8 of 17
Table 3. Cont.
Females
Males
Statistics
n = 121
n = 90
FC—10 (Total of oils)
10.24 ± 10.66
15.49 ± 14.99
F(1, 209) = 4.60; p = 0.033
Butter
4.50 ± 8.76
8.00 ± 13.53
F(1, 209) = 0.88; p = 0.348
Margarine
5.92 ± 8.73
7.49 ± 11.36
F(1, 209) = 0.13; p = 0.717
Other oils
4.95 ± 5.36
7.74 ± 7.50
F(1, 209) = 5.71; p = 0.018
FC—11 (Total of snacks)
9.83 ± 6.67
11.81 ± 7.63
F(1, 209) = 2.98; p = 0.086
Sweet snacks
9.77 ± 6.43
10.51 ± 6.78
F(1, 209) = 0.43; p = 0.511
Salty snacks
5.22 ± 6.66
7.66 ± 7.67
F(1, 207) = 6.13; p = 0.014
FC—12 (Total of water)
39.28 ± 22.17
29.92 ± 18.09
F(1, 209) = 9.77; p = 0.002
Water
61.92 ± 28.33
56.16 ± 26.33
F(1, 209) = 2.24; p = 0.136
Unsweetened tea
25.36 ± 17.63
16.52 ± 14.25
F(1, 209) = 17.48; p < 0.001
FC—13: Beverages
14.19 ± 5.22
13.40 ± 4.57
F(1, 209) = 0.88; p = 0.350
FC—14: Alcohol
2.75 ± 3.77
5.06 ± 5.64
F(1, 209) = 13.04; p < 0.001
Part B—Umbrella clusters
FC—15 (Total of protein)
39.60 ± 14.30
38.64 ± 13.81
F(1, 209) = 0.28; p = 0.599
FC—15a (Total of plant protein)
35.23 ± 14.88
30.12 ± 13.94
F(1, 209) = 6.40; p = 0.012
FC—15b (Total of animal protein)
12.80 ± 14.72
18.73 ± 14.98
F(1, 209) = 9.04; p = 0.003
FC—16: Processed foods & free/added sugar
23.27 ± 12.62
30.25 ± 15.62
F(1, 209) = 10.81; p = 0.001
FC—17: Free/added sugar
13.62 ± 8.60
16.19 ± 11.21
F(1, 209) = 1.57; p = 0.212
Note. Data are presented as mean ± standard deviation. The values are based on a calculated index ranging from
0 to 100 (points; %), representing an integrated scale from the frequency of food consumption within the past four
weeks and the amount of food intake. FC—food clusters. Statistical methods: Kruskal–Wallis tests (F-values).
Figure 2 displays the 95% conﬁdence interval to show sex-related differences in food
clusters in runners. The food clusters with more than 5% difference between males and
females include “grains” (both subclusters: reﬁned and whole grains), “meat” (both subclus-
ters: unprocessed and processed meat), “animal protein”, “processed foods & free/added
sugar”, “fruit and vegetable”, and “water and unsweetened tea”, where males had a higher
consumption compared to the opposite sex in the ﬁrst four clusters and female in the two
latter clusters.
Further details regarding the regression results, including p-values, are presented in
Table 4. Age was a signiﬁcant predictor for consumption of the cluster “fruit and vegetables”
(p = 0.010), with a marginal (but not signiﬁcant) association with the two clusters “eggs”
(p = 0.058) and “plant protein” (p = 0.056).
Table 4. Regression results for age- and sex-based interactions in food clusters.
Age
Sex *
β
95%-CI
p
β
95%-CI
p
FC—1a (Total of reﬁned grains)
−0.07
[1.08, −1.21]
0.908
5.58
[8.03, 3.13]
<0.001
FC—1b (Total of whole grains)
−0.48
[0.66, −1.62]
0.407
6.01
[8.46, 3.56]
<0.001
FC—2 (Total of beans and seeds)
−0.39
[1.41, −2.19]
0.673
−4.63
[−0.77, −8.49]
0.019
FC—3 (Total of fruit and vegetables)
−2.11
[−0.51, −3.72]
0.010
−6.45
[−3.01, −9.89]
<0.001
FC—4 (Total of dairy)
0.43
[1.88, −1.02]
0.558
0.91
[4.02, −2.20]
0.565
FC—5 (Dairy alternatives)
−0.04
[1.95, −2.02]
0.971
−4.38
[−0.12, −8.64]
0.044
FC—6a (Total of unprocessed meat)
1.29
[2.90, −0.32]
0.115
7.13
[10.58, 3.67]
<0.001
FC—6b (Total of processed meat)
1.10
[2.45, −0.25]
0.110
6.18
[9.08, 3.28]
<0.001
FC—7 (Meat alternatives)
0.32
[1.19, −0.55]
0.470
0.05
[1.91, −1.81]
0.001
Nutrients 2022, 14, 2590
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Table 4. Cont.
Age
Sex *
β
95%-CI
p
β
95%-CI
p
FC—8 (Fish)
0.49
[1.30, −0.32]
0.239
1.58
[3.32, −0.16]
0.074
FC—9 (Eggs)
1.09
[2.22, −0.04]
0.058
1.84
[4.26, −0.58]
0.136
FC—10 (Total of oils)
0.87
[2.52, −0.78]
0.299
4.92
[8.45, 1.38]
0.007
FC—11 (Total of snacks)
−0.11
[0.81, −1.04]
0.808
2.02
[4.00, 0.04]
0.046
FC—12 (Total of water)
−1.26
[1.41, −3.93]
0.355
−8.88
[−3.16, −14.61]
0.003
FC—13 (Beverages)
0.30
[0.94, −0.35]
0.366
−0.91
[0.48, −2.29]
0.198
FC—14 (Alcohol)
0.12
[0.72, −0.49]
0.703
2.27
[3.57, 0.97]
0.001
FC—15a (Plant protein)
−1.83
[0.05, −3.70]
0.056
−4.43
[−0.41, −8.44]
0.031
FC—15b (Animal protein)
1.48
[3.40, −0.44]
0.130
5.38
[9.50, 1.26]
0.011
FC—16 (Processed foods & free/added sugar)
−0.15
[1.67, −1.97]
0.872
7.03
[10.94, 3.13]
<0.001
FC—17 (Free/added sugar)
−0.14
[1.13, −1.42]
0.826
2.62
[5.36, −0.12]
0.061
Note. * The female sample is considered the reference. β—regression coefﬁcient. CI—conﬁdence interval.
p—p-value. FC—food clusters. Statistical methods: Kruskal–Wallis tests (F-values).
nts 2022, 14, x FOR PEER REVIEW 
9 of 18 
 
Figure 2. Forest plots with 95% confidence interval to display sex-based differences in basic (the left 
column) and umbrella (the right column) food clusters. Females are considered the reference, and 
the differences are shown based on the variations of males from females. FC—food clusters. 
Further details regarding the regression results, including p-values, are presented in 
Table 4. Age was a significant predictor for consumption of the cluster “fruit and vegeta-
bles” (p = 0.010), with a marginal (but not significant) association with the two clusters 
“eggs” (p = 0.058) and “plant protein” (p = 0.056). 
Figure 2. Forest plots with 95% conﬁdence interval to display sex-based differences in basic (the left
column) and umbrella (the right column) food clusters. Females are considered the reference, and the
differences are shown based on the variations of males from females. FC—food clusters.
Nutrients 2022, 14, 2590
10 of 17
4. Discussion
The present study investigated and compared female and male endurance runners in
dietary intake (differentiated by 14 basic clusters and 3 umbrella clusters of food frequency).
The most important ﬁndings were that (1) females had a signiﬁcantly higher intake of four
food clusters (i.e., “beans and seeds”, “fruit and vegetables”, “dairy alternatives”, and
“water”) than males; (2) males had a signiﬁcantly greater intake of seven food clusters (i.e.,
“grains”, “meat”, “ﬁsh”, “eggs”, “oils”, “alcohol”, and “processed foods”) than females;
(3) no signiﬁcant sex-based difference was observed in the consumption of six food clusters
(i.e., “dairy”, “meat alternatives”, “snacks”, “beverages”, “protein”, “free/added sugar”);
(4) sex has been found to be a signiﬁcant predictor for consumption of the majority of
food groups; (5) except for “fruit and vegetables” age failed to be a signiﬁcant predictor
of the food groups. As another main outcome, the hypothesis of the present study i.e.,
“female runners having a more advantageous dietary intake regarding a healthy lifestyle”,
was veriﬁed.
The purpose of the dietary assessment was to identify nutritional inadequacy in order
to optimize health-related approaches in general populations and develop individualized
dietary strategies for improving the health and performance of athletes [52,53]. Overall,
the most common dietary assessment methods include a dietary record, 24-h dietary re-
calls, in-depth interviews, and the food frequency questionnaire [52–54]. Evidence has
shown that food records, dietary recalls, and detailed interviews are time-consuming and
challenging to conduct precisely in athletes [55,56]. On the other hand, food frequency
questionnaires have been reported to be a simple, fast, and low-cost method with less bur-
den on participants compared to other methods [57]. Hence, food frequency questionnaires
can be the most appropriate survey method to assess the dietary intake of athletes [57,58].
Athletes in general–but particularly those who follow restrictive and unbalanced diets–are
at higher risk for low energy intake than sedentary people if their diet is not planned
appropriately [25,59]. Considering the importance of diet for health status and athletic
performance, it is crucial that the ﬁrst and most important step in any sports nutrition
practice is to assess and monitor the dietary intake/status of athletes [56].
In line with the ﬁndings from the present study, it has been reported that sex is an
important predictor of dietary choices, which mainly originates from different health and
lifestyle beliefs between males and females [20]. According to the literature, the inﬂuence
of sex on dietary intake is not limited only to runners [60,61] but has also been documented
in the general population [62,63]. Reports from national dietary investigations on general
populations of D-A-CH countries (including Germany, Austria, and Switzerland; home
of the majority of participants) also show that sex is a remarkable contributor to dietary
intake/patterns [64–67]. Dietary-related sex differences in endurance runners, however,
cannot only be attributed to the patterns of supplement intake, as previously reported by
the “NURMI Study” [68].
4.1. Fluid and Alcohol
In the present study, data on hydration habits revealed that sex seems to be an inﬂu-
encing variable in the consumption of water (with a predominance of females) but not
beverages. Comparable results from an investigation of recreational runners showed a
signiﬁcant sex-based difference in the type of ﬂuid intake, where female runners consumed
more water, coffee, and tea, and males more sweet beverages or alcoholic drinks [60].
National dietary reports for the German population also indicated a greater consumption
of water, coffee, and tea in females than males [67]. Consistently, male runners in the
present study reported nearly a two-fold consumption of alcohol compared to females.
While data from the Austrian general population show that males had a 3-times greater
intake of alcohol than females [65], this ratio was 2:1 in a similar investigation in Switzer-
land [66]. According to the dietary recommendations of D-A-CH nutrition organizations,
the sex-based differences in the maximum tolerable alcohol intake is also two-fold (i.e.,
Nutrients 2022, 14, 2590
11 of 17
max. 10 g/day for healthy females and max. 20 g/day for healthy males) [69]. Generally,
male athletes are at a higher risk of binge drinking than females [60,70].
4.2. Carbohydrate Foods
The consumption of grains (both reﬁned and whole grains) was higher in males than
females in the present study. This ﬁnding is inconsistent with the results from the national
German report, where females had a higher intake of grains and cereals [67]. Assuming
an equal ability of females and males to store and utilize carbohydrates [71], the present
ﬁnding might be associated with the increased portion of females in 10-km and males in
M/UM subgroups. In this regard, it has been reported that sex difference in carbohydrate
intake is likely to disappear when the data is adjusted to training volume [61]. Consistent
with the present ﬁndings, results from a comparable investigation show that female distance
athletes tend to consume fewer carbohydrates than males [72]. Grains are not the only
source of carbohydrates since other food clusters (e.g., “fruit and vegetables” and “beans
and seeds”) also contribute to the carbohydrate supply. Female runners in the present study
reported a higher intake of both the clusters “fruit and vegetables” and “beans and seeds”
than males. Consistently, it has been documented that females are more eager than males
to consume fruits and vegetables [20], and this food cluster showed the highest contrast
between the dietary patterns of females and males [73]. The signiﬁcant predominance of
females in the consumption of fruit and vegetables has also been shown by German [64,67],
Swiss [66], and Austrian [65] studies on general populations. However, it was unanimously
found that the majority of both males and females do not reach the recommendation of
ﬁve portions of fruits and vegetables per day. Regarding dietary attitudes, while females
more frequently than males indicated that vegetables are the major component of a healthy
diet, they expressed that the consumption of carbohydrates should be decreased [68]. This
ﬁnding may be linked to the heightened concerns about body image among females in
general populations, and especially female athletes [62,74].
4.3. Protein and Fat-Based Foods
Research has shown that male athletes have a generally higher protein intake than
recommended [4,75]. This outcome, however, is not consistent with the nutritional recom-
mendations indicating the greater need for baseline protein intake for female endurance
athletes due to their higher rate of protein oxidation than males [76,77]. Male runners in the
present study reported a generally greater intake of animal protein foods (meat, ﬁsh, eggs)
than females; however, no sex-based difference was observed in the consumption of dairy
products and meat alternatives. The predominance of males in the consumption of meat
has been periodically shown in national studies on general populations of Germany [67],
Austria [65], and Switzerland [66]. Although animal sources derive approximately 75% of
the general protein supply in athletes [78], it has been reported that both male and female
marathoners consume a higher portion of plant-based proteins than other athletes and the
general population [75,79]. Dietary shifts toward a lower intake of animal sources and
more plant foods can result in a lower intake of processed meat (including fast foods) and
high-fat foods [80] and consequently improve health and performance [25]. The present
ﬁndings also indicate a greater consumption of oils and processed foods by male runners.
While similar investigations on athletes [3] and general populations [61] support the present
ﬁndings, the most reasonable justiﬁcation that has been reported is the lower ability and
time of males in preparation of meals, which leads them to consume convenient/fast food
and restaurant meals [3].
4.4. Health Insights in Food Intake
In general, the present ﬁndings show that female runners have a tendency towards
a healthier dietary intake pattern than their male counterparts. It has been reported that
female athletes mainly prefer to consume dietary sources containing more micronutrient
density to fulﬁll their health-related concerns [20,30,81], whereas male athletes seem more
Nutrients 2022, 14, 2590
12 of 17
interested in consuming macronutrients, especially from protein sources, aiming to main-
tain and improve muscle mass and strength. It has also been found that the prevalence of
consuming high-ﬁber meals (as an indicator of a healthy diet) is considerably higher in
females than males [20].
The general higher intake of healthier food clusters by female runners appears to
be linked to the higher level of females’ health consciousness compared to males [20,82].
Regardless of sex, previous ﬁndings from the NURMI project show that runners who
follow a vegan diet had a higher level of health consciousness, mainly due to their more
beneﬁcial choice of dietary items compared to non-vegan runners [38]. Such sex-based
differences in health consciousness and dietary behaviors can also be associated with the
well-documented fact that females are generally more interested in diet and health, while
males consider physical activity as the main part of a healthy lifestyle [30]. However, it is
necessary to consider that regular physical activity, independent of sex, alters the attempts
toward a healthier dietary pattern in order to gain further outcomes [83]. As a general fact
in sport science, different nutritional requirements of athletes competing in different types
of sports should also be considered a potential factor to justify dietary contradictions [36].
Educational level and, more importantly, speciﬁc knowledge about nutrition and sport
sciences may also be associated with health behaviors, particularly adhering to a healthier
diet [84]. In terms of academic qualiﬁcation, however, there was no signiﬁcant difference
between female and male participants in the present study. The unbalanced distribution
of race distance and diet type subgroups across male and female groups may partially
contribute to the ﬁnding on sex-based dietary differences.
Unlike sex, age was not a signiﬁcant predictor for consumption of the majority of food
groups except for one food cluster (i.e., fruit and vegetables). While the null effect of age
on general dietary intake can be linked to the fact that male runners were signiﬁcantly
older than female runners, data from dietary studies on general populations indicate that
age can be a moderate indicator of dietary patterns [30,73]. It should be considered that
most participants in the present study were recreational runners. Evidence indicates that
performance level, deﬁned as the term professionalism, can be a key indicator of precise
and personally tailored dietary intake and strategies for training and racing independent
of age [85,86]. In this regard, the literature reports that the major motives of recreational
athletes to take part in sport events are health and/or hobby [87,88], while professional
athletes are mainly motivated by performance and competition-related aspects [89].
4.5. Limitations and Strengths
Some limitations in the present investigation should be mentioned. The study was
conducted following a cross-sectional design producing self-reported ﬁndings; therefore,
caution should be taken by interpreting the results. However, several control questions
were implemented in different parts of the survey to minimize validity bias and control for
contradictory data, and accordingly, participants’ statements were checked for congruency
and meaningfulness. The unbalanced distribution of diet type and race distance subgroups
among male and female groups (Figure 1) may also be considered as another limitation
affecting the sex-based ﬁndings and interpretations. Moreover, as a potential selection
bias, about half of the endurance runners in the present study stated adhering to a vegan
or vegetarian diet, which is markedly higher than the prevalence in general populations.
Finally, despite the well-approved validity of a FFQ as a practical method to assess dietary
intake and patterns [56,57], especially for athletic populations [57,58], this method seems
unsuited to provide details about the macro- and micro-nutrient status of the athletes (on
which a considerable number of nutritional recommendations are based on).
However, the ﬁndings contribute valuable and novel data to current scientiﬁc knowl-
edge regarding the sex-related patterns of dietary intake among recreational endurance
runners categorized across different subgroups of diet types and race distance. Although
the present study opens a direction for future interventional studies on athletic populations,
future research with larger and more differentiated samples of distance runners will assist
Nutrients 2022, 14, 2590
13 of 17
in providing comparable data for a better understanding of the dietary patterns of female
and male runners.
Finally, the results from the present study will also provide a window into the targeted
sex-speciﬁc approaches to precisely tailor and personalize the dietary needs and nutritional
requirements of male and female distance runners. Endurance runners, their coaches,
and sports nutrition specialists can beneﬁt from the results when designing and applying
nutritional strategies for long-term adherence to training and competition.
5. Conclusions
The sex-based comparison of endurance runners showed that there are remarkable
differences between females and males in their dietary intake (assessed by a food frequency
questionnaire), supporting the fact that female runners tend to consume healthier foods.
While physiological differences between females and males can play a key role in many
sex-based nutritional and behavioral variances, it seems that health-oriented attitudes
and lifestyle of females can be considered the most reasonable justiﬁcation for the present
ﬁndings. However, there is an obvious necessity to design more detailed interventions using
further analyses of interacting factors to improve the knowledge of sex differences in dietary
choices of endurance athletes and, consequently, to support sports dietitians, nutritionists,
and coaches to provide more precise and personalized recommendations. In general,
nutrition education, training opportunities, and sports nutrition counseling to expand a
runner’s personalized knowledge about health and sports discipline-speciﬁc behaviors can
be recommended practically to improve the healthy runner lifestyle, including nutritional
competencies (e.g., healthy ingredients, nutrients as well as requirements, and foods) in
matching the higher exercise-induced demands for active males and females alike.
Author Contributions: K.W. conceptualized, designed and developed the study design and the
questionnaires together with C.L. and B.K. K.W. performed data analysis together with K.-H.W. and
M.M. drafted the manuscript. K.-H.W., C.L. and K.W., helped in drafting the manuscript. K.-H.W.,
C.L., D.T. and K.W. critically reviewed it. Technical support was provided by G.W. All authors have
read and agreed to the published version of the manuscript.
Funding: This study has no ﬁnancial support or funding.
Institutional Review Board Statement: The study protocol (available online via https://springerplus.
springeropen.com/articles/10.1186/s40064-016-2126-4 (accessed on 10 May 2022)) was approved by
the ethics board of St. Gallen, Switzerland on 6 May 2015 (EKSG 14/145). The study is 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.
Informed Consent Statement: Study participation was voluntary and could be cancelled at any time
without provision of reasons and without negative consequences. Informed consent was obtained
from all participants included in the study considering the data collected and analyzed exclusively
and only in the context of the “NURMI Study”.
Data Availability Statement: The datasets generated during and/or analyzed during the current
study are not publicly available, but may be made available upon reasonable request. Participants
will receive a brief summary of the results of the “NURMI Study”, if desired.
Acknowledgments: There were no professional relationships with companies or manufacturers who
will beneﬁt from the ﬁndings 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.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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Female Endurance Runners Have a Healthier Diet than Males-Results from the NURMI Study (Step 2).
Motevalli, Mohamad,Wagner, Karl-Heinz,Leitzmann, Claus,Tanous, Derrick,Wirnitzer, Gerold,Knechtle, Beat,Wirnitzer, Katharina