Kutac et al.
Journal of Physiological Anthropology(2023) 42:28
https://doi.org/10.1186/s40101-023-00348-x

Journal of
Physiological Anthropology

Open Access

ORIGINAL ARTICLE

The effect of regular running on bodyweight and fat tissue of individuals aged 18
to 65
Petr Kutac1* , Václav Bunc2, Marek Buzga1, Miroslav Krajcigr1 and Martin Sigmund3

Abstract
Background Age and reduction in performed physical activity cause physiological changes that include an increasein body fat (BF) and visceral fat (VF) during aging. These parameters, together with increased body mass (BM), aresome of the risk factors of several noninfectious diseases. However, changes in body composition can be influenced
by regular physical activity. Running is a suitable, accessible, and the most effective physical activity cultivating people. The objective of this study is to investigate the effects of long-term, regular PA, specifically recreational running,
on changes in body composition among recreational adult runners covering a weekly distance of at least 10 km,
compared with inactive adult individuals within the same age bracket.
Methods The study included 1296 runners and inactive individuals (691 male and 605 female), divided into 5 age
groups: 18–25, 26–35, 36–45, 46–55, and 56–65 years. Runners are as follows: ran ≥ 10 km/week, and inactive is as follows: did not follow the WHO 2020 physical activity recommendations. The measured parameters included BM, BF,
and VF. To check statistical significance, the Mann–Whitney U-test was used. Practical significance was assessed using
the effect of size.
Results All age groups of runners were selected to include individuals who run at least 10 km per week. In fact, theyran, on average, from 21.6 to 31.4 km per week in relation to age and showed significantly lower values of BM, BMI,
BF, and VF (p < 0.05) than inactive individuals. Exceptions included insignificant differences (p > 0.05) in BM and BMI
in males in the age category of 18–25 and in females in the age category of 18–25 and 26–35.
Conclusion The selected runners had to run at least 10 km per week. Their actual average volume was significantly
higher (from 21.6 to 31.4 km/week), and the results showed that it could lead to significantly better body composition values. It may lead to significant changes in body mass, body fat, and visceral fat. It may meet the contemporarysocietal expectations for physical activities that are both achievable and effective at the lowest possible volume.
Keywords Running, Inactive individuals, Weight status, Body fat, Visceral fat*Correspondence:
Petr Kutac
petr.kutac@osu.cz
1
Department of Human Movement Studies, University of Ostrava,
Ostrava 701 03, Czech Republic
2
Faculty of Education, Charles University, Praha 6, Praha 162 52, Czech
Republic
3
Application Centre BALUO, Faculty of Physical Culture, Palacky
University, Olomouc 779 00, Czech Republic

Introduction
Movement is a fundamental biological need, and theability to realize it is one of the basic prerequisites forhealthy aging. Long-term active individuals are able tomaintain their independence even in old age. The question that arises pertains to how regular physical activity (PA) impacts various body composition parameters.
Functional and physiological changes occur during aging
in dependence on the individual’s behavior [1]. Such

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Kutac et al. Journal of Physiological Anthropology(2023) 42:28

changes also include changes in body composition,
characterized by increased adiposity and loss of fatfree mass (FFM), and thus, muscle mass, which, among
other things, is the result of insufficient PA volume [2,3]. Such changes can lead to the occurrence of sarcopenia, characterized by low levels of three parameters:muscle strength, muscle quantity/quality, and physicalperformance [4]. Sarcopenia occurs more frequently inindividuals 60 years and older [5]. Sarcopenia limits everyday function and contributes to invalidity, decreasing
one’s independence and self-reliance [6, 7]. However, theresults of a study by Bautmans et al. [8] showed that aregular loss of FFM in inactive participants, ranging from
0.34 to 1.28%, starts at the age of 30 years. Changes occur
in both men and women [9–12]. Changes in womenare uneven due to menopause [13]. The results basedon data from the Study of Women’s Health Across the
Nation (SWAN) [13] showed a considerable increase inbody fat (BF) in women, accompanied by a decrease in
fat-free mass (FFM) about 2 years prior to the final menstrual period. After that, the FFM decreases, and the BF
increases evenly.
Assessing body composition is a critical aspect of anindividual’s health diagnosis. Merely evaluating BM and
BMI falls short in providing insights into the prerequisites for PA. This requires an assessment of individual
BM fractions and their distribution, primarily focusing
on adipose tissue and muscle mass. Obesity is characterized by an increased amount of body fat (BF), which isrecognized as a risk factor for a range of diseases (diabetes, cardiovascular diseases, musculoskeletal disorders) [14]. Currently, increased attention is being paid
to visceral fat (VF). Elevated levels of VF are considereda more substantial risk factor for severe metabolic andcardiovascular diseases compared to BF or BMI [15–17].
VF is more metabolically active and secretes cytokines
and hormones that exert metabolic disturbances such as
insulin resistance and chronic low-grade inflammation ata higher rate [18].
Regular PA is a variable and manageable parameter,
making it a fundamental requirement for maintaining
ideal body composition and optimal values for body fat
and muscle mass parameters. An adequate volume andquality of muscle mass empower individuals to engage
in physical activities even at an older age. Therefore,
regular PA is considered a preventive factor of many
chronic, noninfectious diseases such as cardiovasculardiseases, cancer, type 2 diabetes, bone health degradation, and increased disability [19–21]. Many of these
diseases are major factors in the mortality rate [22, 23].
A regular PA creates conditions for a better health condition which may lead to lower mortality. The resultsof a study by Reimers and colleagues [24] indicate that

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mortality decreases by 30 to 35% in physically active individuals compared to inactive individuals. Regular PA not
only improves quality of life but also extends life expectancy without chronic diseases. The results of the healthanalysis of adults aged 50 to 75 years with a variety of PA
levels showed that strongly active male and female individuals lived, on average, 6.3 years longer in good healthcondition and 2.9 years longer without chronic diseases
than inactive individuals [25]. According to the study
by Alvero-Cruz et al. [26], physically active individuals—master athletes (athletes at the age of 40 and above
who regularly participate in competitions), are considered a model of healthy aging as they can reduce theage-related decline of physiological abilities compared tosedentary individuals [26]. Regular PA is also importantin the prevention of obesity. This is confirmed by including regular PA in the pillar management of people withobesity [27]. It is suitable to include a PA that requires
the participation of large muscle groups, such as running,in people who are not obese (according to the WHO:BMI < 30.0 kg/m2). Compared with other PAs, running iseasily accessible, and its implementation does not require
any special equipment or special conditions. Running isthus a very popular type of PA. The participation in running activities has considerably increased recently. For
example, in the USA, about 19 million runners finishedrunning races in 2013; 17.1 million people competed in2015 in the USA, and the number of road races increased
by 2300 between 2014 and 2015 [28]. The participationof male and female individuals in recreational running in
the Czech Republic can be analyzed based on the result
lists from a series of running races for amateur runners“Běhej lesy” [29] that take place all over the country. The
average ratio of running women in the entire group was
about 69%. As age increases, the ratio of female participation tends to decrease. The female ratio was only 35.8%
in the oldest category of 56 + . The results of a study by
Lee et al. [28] showed that regular running has a positive effect on the cardiovascular, metabolic, musculoskeletal, and neuropsychiatric system, reduces the body
fat to body mass ratio, and increases the fat-free mass
ratio, as well as muscle mass. Running thus secondarily
reduces premature mortality rate by 25–40% and extends
life expectancy by up to 3 years compared to nonrunners[30].
The escalating proportion of inactive individuals represents a significant concern within the current adult population. The most recent available European data revealthat the prevalence of physical inactivity in adults in theEuropean Union (EU) exceeds 30% (36.2%, 95% CI: 35.1–37.3) [31]. These results come from a cross-sectional survey involving 19,645 people aged 18 to 64. In the Czech
Republic, the prevalence of physical inactivity stands at

Kutac et al. Journal of Physiological Anthropology(2023) 42:28

35.9% (95% CI: 32.4–39.5) [31]. One of the contributing
factors to physical inactivity is the notable rise in overweight and obesity within the population [3]. According
to the World Health Organization (WHO), 39% of adultmen and 40% of adult women are overweight, while 11%of men and 15% of women are classified as obese among
those aged 18 and older [32].
As running affects the ratio of muscle and fat mass inreal time, the question arises whether regular leisure running will have a positive effect on body composition (BC)of runners at an older age and whether the changes in BC
caused by aging will be different in runners than in nonactive individuals [33, 34].
The primary objective of this study was to investigate the impact of a long-term, regular PA in the formof leisure-time running with the minimal volume of
10 km/week on changes in body mass, body fat, and visceral fat in recreational adult runners both sexes compared to inactive adult individuals of the same age range
18–65 years. The second objective of the study was todetermine whether the changes induced by the applied
PA regime depend on age and gender.
To explain the above, we formulated a working hypothesis that long-term physically active adults who regularly
engage in running for a minimum of 10 km/week havebetter prospects for PA and significantly better bodycomposition parameters than inactive individuals in all
age categories (18–25, 26–35, 36–45, 46–55, 56–65). Thechanges in body composition parameters depend on age
and gender.

Materials and methods
The study was implemented within an interdisciplinary
study with several outcomes that examined mutual relations: Healthy Aging in Industrial Environment StudyProgram 4 (4HAIE) (CZ.02.1.01/0.0/0.0/16_019/000079
8). The main objective of the 4HAIE study was to assess
the effect of air pollution on health, physical activity, andaging. The study focused on the adult population in two
regions of the Czech Republic. The Moravian-SilesianRegion is an industrial region with increased air pollution, and the South Bohemian Region is a control region
where agriculture prevails, with relatively low levels of
air pollution. Participants were selected deliberately. The
number of participants was also determined with respect
to project funding and the measurement capacity of thelaboratory.
Recruitment strategy 4HAIE

For the 4HAIE study, specifically, the recruitment ofstudy participants was commissioned to a professionalsocial science research and marketing company selected
through a publicly advertised tender. The sample wasPage 3 of 13

collected using quota sampling based on location, age,gender, and PA status (active runner vs. inactive). A
detailed list is presented in the article by Elavsky et al.
[35]. The study aimed to enroll a total of 1500 participants aged 18 to 65, with 60% being physically active runners and 40% inactive controls. These participants were
evenly distributed between two regions: an industrial
area with high pollution levels, serving as the experimental group (Moravian-Silesian Region—excluding the
district of Bruntál and specific counties in the district ofFrýdek-Místek), and a low-pollution region designated as
the control group (South Bohemian Region). The selected
sample size was determined to be the largest feasible sizethat would accommodate the cohort’s stratification by
age groups and enable the analysis of outcomes targetedby the interdisciplinary teams involved. No formal unifying power analysis was however conducted. Participants were being recruited from the community throughvarious approaches, including in-person recruitment
through agency workers and online (using social media,
job portals and other websites) and off-line advertisements (billboards, newspaper ads, flyers). Specialized
recruitment activities tailored to runners were also carried out, such as presentations and information booths
at running races, through running clubs, and at otherrelevant community events. The final number of participants measured was 1311 due to restrictions imposedin the Czech Republic as part of combating COVID-19.
Recruitment efforts commenced in February 2019, withmeasurements being completed in August 2021). Inclusion and exclusion criteria are presented in Table 1.
Procedures and protocol 4 HAIE

Participation in the study was voluntary, and all participants signed a written informed consent agreement thatthey received during the initial measurements in the laboratory. To safeguard the anonymity of each participant,a unique code label was assigned to them. This code label
was used as the reference in all measurement protocols
and databases. The laboratory measurement protocol ispresented in Fig. 1. The measurement protocol did notinclude the participants’ dietary analysis. Further information regarding the 4HAIE study’s design, methods,
and measurement protocol can be found in previously
published works. Physiological and somatic measurements were presented in the publication by Cipryan et al.
[37]; the behavioral, psychological, and neuroimaging
protocol (MRI structural sequences) was presented in the
publication by Elavsky et al. [35]. The study was approvedby the Ethics Committee of the University of Ostrava(protocol code OU-87674/90–2018 and date of approval
29 November 2018) and followed the principles of theHelsinki Declaration.

Kutac et al. Journal of Physiological Anthropology(2023) 42:28

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Table 1 Inclusion and exclusion criteria
Common criteria
Inclusion criteria

Aged between 18 and 65 years; non-smoking; able to perform normal
physical activity including running (i.e., no medical restrictions on physical activity mandated by a physician); residing in the region for the past
5 years, without any plans to move in the following year; having
a smartphone and access to the Internet (Wi-Fi or data)

Exclusion criteria

Acute (in the past 6 weeks) medical issues (pain, injury, surgery)preventing normal physical activity, other acute illnesses; pregnancy;
radiological examination in the past 7 days; factors that would exclude
a graded exercise test or magnetic resonance examination (suchas a pacemaker, radioactive or surgical devices/implants, insulin pump)

Inclusion criteria for runners
Running as a main exercise activity, > 150 min of moderate or > 75 min of strenuous physical activity per week (or an equivalent combinationof moderate and strenuous physical activity) [36], ≥ 10 km of running per week for at least 6 weeks prior to the tests, intending to continue runningfor next 12 months, permanent (≥ 5 years) whole-year residency in the determined areas, not planning to move away from the determined areas during the next 12 months, with Internet access, and using a smartphone (with iOS or Android 5.0 or higher)
Inclusion criteria for “inactive” individuals< 150 min of moderate or < 75 min of strenuous exercise per week (i.e., not meeting current public physical activity guidelines [36], capable of running, but not running or running irregularly and/or less than 6 weeks prior to the tests, no contraindications to exercise, permanent (≥ 5 years) wholeyear residency in the determined areas, not planning to move away from the determined areas during the next 12 months, with Internet access,and using a smartphone (with iOS or Android 5.0 or higher)

Somatic measurements

Fig. 1 Measurement protocol

Study participants

Out of the initial cohort of 1311 individuals (698males, 613 females) from the 4HAIE study, the current study focuses on 1296 individuals (605 females
and 691 males). Fifteen participants (8 females and7 males) were excluded due to missing data from the
DXA body composition measurement. These individuals had implants or metal parts in their bodies, and sotheir parameter values would be distorted. A detailed
description of the participants is presented in Table 2.
Table 2 also states the average kilometers and running
duration per week for the group of active runners. We
obtained the values from the Aerobic Centers Longitudinal Study (ACLS) questionnaire [38]. We analyzed
the runners’ episodes per week, number of kilometers,
and duration of each episode.

To ensure standard measurement conditions, the participants were admitted to the research center 15 h
before measurement (they slept in the sleep laboratory), which allowed us to monitor their behavior andhydration prior to the measurement. Measurements
started at 8:30 a.m. the next morning. Participants were
measured barefoot, in their underwear. All measurements were taken in the following order: body height(BH), body mass (BM), and body composition. BH andBM, which are the input parameters for the bone densitometer software, were measured using the InBody
BSM 370 stadiometer (Biospace, South Korea), whichis regularly calibrated. Table 3 presents the BH values
without any significant differences between runners
and inactive individuals (p ˃ 0.05). Additionally, the BH
and BM values were used to calculate the body massindex (BMI). The weight status was assessed according
to WHO: P1: ≤ 18.49 (kg/m2)—underweight, P2: 18.50–
24.99 (kg/m2)—normal weight, P3: 25.00–29.99 (kg/m2)—overweight (pre-obesity), P4: ≥ 30.00 (kg/m2)—
obesity class. Body composition included body fat (BF)
and visceral fat (VF) expressed by area ­(cm2). To measure body composition, the DXA method was used, witha Hologic QDR Horizon A bone densitometer (Hologic,
Waltham, MA, USA), which is regularly calibrated andundergoes technical safety control (TSC). The reliability and typical error of measurement were also checkedin the densitometer [39]. The positions during measurement and the measured segments were presented in
the protocol article [37].

Kutac et al. Journal of Physiological Anthropology(2023) 42:28

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Table 2 Numbers and basic characteristics of all participants in monitored individuals divided into five groups by age (mean ± SD)
Age categories (years) RunnersnInactive
Age (years)
M ± SD

km/week
M ± SD

Min/week
M ± SDn

Age (years)
M ± SD

Male
18–25

90

21.2 ± 2.4

22.7 ± 14.6

136.5 ± 118.9

52

21.2 ± 2.3

26–35

111

31.1 ± 2.7

29.6 ± 20.5

170.1 ± 136.1

56

30.1 ± 2.7

36–45

125

40.7 ± 2.5

26.7 ± 18.2

151.3 ± 95.2

57

41.1 ± 2.5

46–55

87

49.5 ± 2.8

31.4 ± 21.4

181.8 ± 115.6

51

50.1 ± 2.9

56–65

24

60.4 ± 2.9

28.3 ± 17.6

189.5 ± 108.2

38

60.6 ± 3.0

Female18–25

74

21.2 ± 2.3

22.7 ± 15.0

159.4 ± 137.1

66

21.2 ± 2.3

26–35

55

31.0 ± 2.8

25.9 ± 14.8

152.9 ± 81.7

58

30.0 ± 3.0

36–45

102

41.1 ± 2.9

25.9 ± 14.5

163.3 ± 91.0

59

41.0 ± 2.7

46–55

58

49.5 ± 2.9

21.6 ± 14.0

144.6 ± 110.4

65

49.8 ± 2.8

56–65

13

58.0 ± 2.4

25.4 ± 15.2

175.5 ± 108.7

55

59.5 ± 2.8

n Frequency, M Mean, SD Standard deviation

We evaluated the aging stages of reproduction in
women. Women classified themselves into the following categories according to a self-assessment: 1—
premenopausal, 2—early perimenopausal, 3—late
perimenopausal, 4—early postmenopausal, and 5—latepostmenopausal [40]. No significant differences were
found between the runners and inactive individuals.
Physical activity assessment

To analyze physical activity, we used the standardized,
internationally recognized Aerobic Centers Longitudinal
Study (ACLS) questionnaire [38]. The ACLS questionnaire was administered online via the Qualtrics platform.
The participants completed a questionnaire prior to thecommencement of laboratory measurements; the averagetime to complete the questionnaire was 30 min.
We analyzed the number of running episodes per week
and the duration of each episode in the runners. Basedon the values, we calculated the duration of running per
week. To quantify running, we also used the records ofkilometers run and the duration of each running episodein the week. The values of body composition of runners
in terms of the implemented PAs were used as an indirectcontrollable indicator of questionnaire validity.
Statistics analysis

The normality of the distribution was verified by the
Shapiro–Wilk test. The data did not have a normal distribution; therefore, we used the nonparametric Mann–
Whitney U-test to assess the differences between thevalues of runners and inactive individuals and to assess
the effect of age. The level of statistical significance for all
the used tests was set at α = 0.05. Practical significancewas assessed using the effect of size d. The d-value at thelevel of 0.3 indicates a small effect, 0.3–0.5 a medium
effect, and ˃ 0.5 large effect [41]. The value of d ˃ 0.3 isconsidered practically significant. To compare the representation in the individual BMI categories (male,
female) and in the categories of the reproduction age
stage (female) between runners and inactive individuals,
we used the chi-square test for the contingency tables.
The internal consequences of all data assessed by thechi-square test were tested using the Bonferroni post
hoc test [42]. The statistical processing of the results was
performed using IBM SPSS Statistics (Version 24; IBM,
Armonk, NY, USA).

Results
The somatic parameters monitored by men and womenin the groups of runners and inactive individuals are presented in Fig. 2. Analyses of monitored somatic parameters and their differences in individuals of different
ages between runners and inactive individuals, as wellas within individual groups, are presented in Fig. 2 andTable 3. Figure 3 shows the assessment of the weight status of the individual monitored groups. When assessing the body (BF) ratio, we only used the relative value
expression (percentage) due to the different body mass(BM) of runners and inactive individuals.
Figure 2 presents the development of somatic parameters in runners and inactive individuals of different
ages, with significant changes highlighted in each group.
Only changes that were statistically significant (p < 0.05)
and practically significant (d > 0.3) are labelled significant. Natural development prevails until 35 years of age;
implemented physical training prevails at a later age.

Kutac et al. Journal of Physiological Anthropology(2023) 42:28

Fig. 2 Development of somatic parameters in runners and inactive individuals of different ages

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Kutac et al. Journal of Physiological Anthropology(2023) 42:28

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Table 3 Statistical analysis results—somatic parameters of male and female runners vs inactive participants
Parameters

18–25 years
Diff (d)26–35 years
Diff (d)36–45 years
Diff (d)46–55 years
Diff (d)56–65 years
Diff (d)BH (cm)− 0.8NS− 0.1NS− 0.9NS− 0.4+ 1.7NSBM (kg)NS− 3.5− 5.7* (0.17)− 7.2*** (0.31)− 9.0*** (0.38)− 7.2* (0.30)

BMI (kg/m2)− 0.9NS− 1.6* (0.20)− 2.0*** (0.31)− 2.8*** (0.37)− 2.8** (0.33)

BF (%)− 6.3*** (0.55)− 5.2*** (0.50)− 5.0*** (0.50)− 5.5*** (0.55)− 5.3*** (0.51)

VF ­(cm2)− 9.0* (0.22)− 18.4*** (0.32)− 39.2*** (0.48)− 50.5*** (0.50)− 56.7** (0.44)

BH (cm)− 0.3NS+ 0.7NS+ 1.0NS+ 1.1NS+ 2.1NS

NS

NS

Male

FemaleBM (kg)

− 2.5

− 4.4

− 9.3*** (0.29)− 4.4* (0.21)− 11.2** (0.34)

BMI (kg/m2)− 1.0NS− 1.7NS− 3.6*** (0.36)− 2.0** (0.28)− 4.7** (0.38)

BF (%)− 4.1*** (0.33)− 5.9*** (0.53)− 6.7*** (0.50)− 6.3*** (0.53)− 9.0*** (0.54)

VF ­(cm2)− 8.1* (0.18)− 24.2*** (0.38)− 34.7*** (0.38)− 29.0*** (0.38)− 51.9** (0.40)

BH Body height, BM Body mass, BMI Body mass index, BF Body fat, VF Visceral fat, Diff Difference, (d) effect size, NS Non-significant*

p < 0.05, **p < 0.01, ***p < 0.001

Our analysis revealed a positive relationship between
age and somatic parameters (body mass, BMI, body fat,and visceral fat) in both male runners and inactive individuals. With increasing age, these parameters increased
gradually. However, only visceral fat showed a significant increase. There was an increase in runners between
the age groups of 18–25 and 26–35 (p < 0.001, d = 0.37).
There was a significant increase in inactive individuals
between the age groups of 18–25 and 26–35 (p < 0.001,
d = 0.41), as well as between 26–35 and 36–45 (p < 0.001,
d = 0.44).
Our study also found a positive relationship between
age and somatic parameters in females. With increasing
age, body mass gradually increased, with the exceptionof the age group 56–65 in runners and 46–55 in inactive individuals. The changes between age categories
were consistent for both body mass and body mass index(BMI). The percentage of body fat (BF) also increased
continuously with increasing age, with the exception ofa decrease observed in runners aged 56 to 65 years. In
addition, we observed a continuous increase in visceralfat (VF) in both female groups. However, the changes in
BF and VF across age categories were continuous but notstatistically significant.
Table 3 presents the differences between the values ofthe monitored somatic parameters of the runners andinactive individuals in the individual age groups. In themonitored parameters, age dependencies differ between
men and women.
When comparing male runners and inactive participants, the results showed that runners in all age categories had a considerably lower body mass (BM), BMI andpercentage body fat (BF) ratio. The applied running load

has a considerable effect on the visceral fat (VF) value.
Differences were statistically and practically significant(p < 0.05, d > 0.3), except for the values of BM and BMI
in the age category of 18–25 (p ˃ 0.05). Practical significance was not demonstrated in VF in the age categoryof 18–25 and in the BM and BMI in the age category of
26–35 years (d < 0.3).
When comparing female runners and inactive participants, as well as male participants, no significant differences in body height were observed between runners andinactive participants (p > 0.05). Female runners had lower
values of body mass (BM), body mass index (BMI), bodyfat (BF), and visceral fat (VF) than inactive participants inall age categories. However, statistical and practical significance for BM was only observed in the age categoryof 56–65 years (p < 0.05, d > 0.3). In BMI, statistical andpractical significance was confirmed in the age categories 36–45 and 56–65 (p < 0.05, d > 0.3). The percentage
of BF ratio showed statistical and practical significance inall age categories (p < 0.05, d > 0.3). Statistical significancewas also confirmed in all age categories for VF (p < 0.05),
but practical significance was not confirmed in the age
category of 18–25 years (d < 0.3). Physical training isessential in higher age categories as it can considerably
slow down physiological aging.
Figure 3 presents the percentage representation ofparticipants in the individual BMI categories and theresults of a comparison between the runners and inactive
participants.
The underweight category (P1) in the monitored group
included both runners (male in the age categories of
18–25 and 26–35 years, female 18–25 to 36–45 years)
and inactive individuals (male in the age categories ofKutac et al. Journal of Physiological Anthropology(2023) 42:28

Fig. 3 Weight status assessment of the individual monitored groups

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Kutac et al. Journal of Physiological Anthropology(2023) 42:28

18–25 years and female 18–25 to 36–45 years). In all age
categories, both female and male runners had a higher
percentage representation in the normal weight category(P2) compared to inactive individuals, with a significant
difference found in female runners in the age categoriesof 36–45 and 56–65. In the overweight category (P3),
inactive male individuals have a higher percentage sharethan runners in the age categories of 18–25, 36–45, and
56–65 years. The difference in the age category of 56–65
is significant. Inactive female individuals have a higher
percentage share in the P3 category than runners in theage categories of 26–35 to 46–55.
In the obesity category (P4), inactive male and female
individuals had a higher share in all age categories thanrunners. The difference was significant in the female age
category of 56–65.

Discussion
Comparison of somatic parameters of runners
and non‑runners

The selected runners were required to complete at least
10 km or running per week. The actual average volume
was from 21.6 to 31.4 km/week, and it showed a significant effect on body mass, body fat, and visceral fat. The
running volume was implemented by the runners in thelong term, and it was a part of their daily routine and thus
was not altered as a result of participating in the study.
When compared with inactive participants, the runners
showed significantly lower values of BF and VF in all age
categories. In terms of body mass, running is an effective
activity to maintain optimal weight. In all age categories,
active runners (both male and female) showed a better
weight status compared to inactive individuals. Moreover, a more detailed assessment of weight status revealed
a higher proportion of runners of normal weight (P2) anda lower proportion of obese runners (P4) in all age categories compared to inactive individuals (Fig. 3). Diet anddietary habits were not monitored in the study. We operated under the assumption that active individuals adapt
their dietary habits to support their PA. Dietary habits
remain unchanged [43].
When assessing male runners in all age categories combined, 36.2% were classified as overweight (P3) and 2.1%
as obese (P4), indicating considerable differences from
population data reported by the World Health Organization (WHO) in 2021, which states that 39% of men
are overweight and 11% are obese [32, 44]. Overweightin runners is most likely caused by the higher volumeof their muscle mass due to the implemented running,which is documented by the lower values of their BF.
In inactive male individuals in all age categories,
there were 41.7% overweight (P3) individuals and 21.7%
obese (P4) individuals. The values are higher than the

Page 9 of 13

population data [32]. When assessing female runners
in all age categories, 16.6% individuals were overweight(P3), and 2.0% were obese (P4). These values are also
considerably lower than the population data. The WHO
states 40% of overweight women and 15% of obese
women. In inactive female individuals in all age categories, 27.0% were overweight (P3) individuals, and 16.5%
were obese (P4). The overweight values compared to the
population data [32] were considerably lower, and theobesity numbers were higher. Compared to Eurostat,which states 21.4% obese men and 17.7% women in the
Czech Republic, the obesity values (P4) in runners (maleand female) are considerably lower, and the values of
the inactive individuals (male and female) are within theintentions of the values presented [44].
The assessment of PA according to BMI indicates thatinactive individuals are more likely to be overweight orobese. However, many authors do not consider using the
BMI for obesity assessment to be suitable as it does notreflect the values of visceral fat, body fat, and its distribution [10, 45, 46]. Therefore, it is suitable to use percentile graphs when assessing the adequacy of body mass(especially for children) or to use the more precise bodycomposition evaluation. Other anthropometric measurements can be used for more precise diagnostics (waist
circumference or waist-to-hip ratio) to assess of the bodyfat distribution [47]. However, it is better to use modern
methods (BIA, DXA, CT, MRI) that allow quantificationof the volume and mass of body tissues, including visceral fat [48–50].
The assessment of the risk of obesity evaluated by BMI
was confirmed in the assessment of body fat (BF) and visceral fat (VF). Compared to inactive individuals, runners
had significantly lower BF and VF values in all age categories, except for unconfirmed practical significance in VF
values in the female age categories of 18–25 years. The
oldest age category of male runners (56–65) had mean
VF values within the mean values of the inactive male age
category of 36–45. The oldest age category of female runners (56–65) had mean VF values corresponding with theinactive female age category of 26–35. In particular, thesignificantly lower VF values in the monitored runners
are significant as VF is metabolically more active andsecretes cytokines and hormones that exert metabolic
disturbances such as insulin resistance and chronic lowgrade inflammation at a higher rate [18]. Therefore, VF
is also considered to be a more important risk factor in
cardiovascular diseases and obesity than increased BM,
BMI, or BF [15–17].
Our results correspond to the conclusions of case studies that deal with the effect of regular aerobic physicalactivity (PA) on the reduction of adipose tissue and visceral fat. A regular and long-term PA stimulates the blood

Kutac et al. Journal of Physiological Anthropology(2023) 42:28

supply in the fat tissue and mobilizes fat that results inthe supply of fatty acids into the skeletal muscles at a rate
corresponding to the metabolic requirements [51, 52].
Regular PA increases fat mobilization. Therefore, physically active individuals have relatively low-fat mass andlower VF values that can “slow down” aging. The durationand intensity of the load are fundamental for the actuallevel of BF and VF [51, 52].
The results of the weight status and body compositionassessment of the monitored individuals indicate thata regular PA in the form of recreational running has aneffect on the reduction of the prevalence of overweight
and obesity and the related secondary medical risks.
Obesity is a risk factor in many serious diseases, such as
musculoskeletal disability affecting bones; joints and soft
tissue [53, 54]; metabolic diseases: diabetes mellitus type2, metabolic syndrome, and nonalcoholic fatty liver disease [55–61]; and cardiovascular diseases: ischemic heartdisease, stroke, heart failure, and hypertension [62–66].
Changes in somatic parameters in individuals of differentages

All monitored somatic parameters increase with theincreasing age of the participants in male runners andinactive individuals. The increase in BM, BMI, and BF
is sharper by the age category of 36–45 let (Fig. 2B–D).
However, no step differences were found between adjacent age categories. The changes found could thus belabelled as continuous. The slight decrease in the values of BM, BMI, and BF in runners in the age categoryof 46–55 was probably caused by the higher volume ofrunning load. This age category has the highest runningvolume (Table 2). A detailed analysis of the differences
between the age categories showed the largest difference in the mean values in runners between the age categories of 18–25 and 56–65 (BM = 7.6 kg, BMI = 2.5 kg/
m2, BF = 4.5%). The largest difference in mean values
in inactive individuals was in mean values between age
categories of 18–25 and 56–65 years (BM = 11.3 kg,
BMI = 4.4 kg/m2, BF% = 3.5%). Visceral fat (VF) increased
continuously without any significant changes between
the age categories starting at 26–35 in runners and 36–45
allowed in inactive individuals (Fig. 2E). The analysis of
VF development curve (Fig. 2E) shows a step changethat signals an elevated increase in the VF in the age category of 56–65 (runners and inactive). The largest difference in the mean VF values of runners and inactive wasin the age categories of 18–25 and 55–65 (runners: 64.6
­cm2, inactive: 112.7 ­cm2). The described differences in
the monitored parameters (BM, BMI, BF, and VF) were
not significant, except for changes in VF between therunners’ age categories of 18–25 vs 26–35, and between
the inactive individuals’ age categories of 18–25 vsPage 10 of 13

26–35 years and 26–35 vs 36–45 years. The results show
an obvious confrontation between a natural development
regression and training in the form of regular leisuretime running with increasing age.
Like in the male category, the monitored somaticparameters increase in both female runners and inactive individuals with increasing age of the participants.
However, the BM, BMI, and BF values of runners slightly
decrease in the age category of 56–65 (Fig. 2G–I). This
could be caused by the higher volume of run kilometers
per week, which is the highest of all age groups (Table 2).
No significance was confirmed in differences between
adjacent age categories; therefore, changes are continuous. The greatest changes in the mean values of BM,
BMI, and BF in the runners were found between theage categories of 18–25 and 46–55 (BM: 7.2 kg, BMI:
2.3 kg/m2, and BF: 1.7%). The highest differences in themonitored parameters in the inactive were found in thefollowing groups: 18–25 and 55–65 (BM: 12.1 kg, BMI:
5.2 kg/m2, and BF: 5.6%). Visceral fat (VF) also increased
continuously in both runners and inactive individuals
from the age category of 18–25 until the age category of
56–65 years. The difference found between age categories
also was not significant. The highest difference in VF wasfound between 18–25 and 56–65. The difference was 34­cm2 in runners and 77.8 ­cm2 in inactive individuals. Our
results did not confirm the results of the SWAN study[13], according to which there should be a considerable
increase in BF in females approximately 2 years prior to
the final menstrual period. In terms of average age andmenstrual status, our age category of 46–55 falls into
this group. When we compared the BF values of the age
category (36–45 years), the increase in BF was not significant in runners or in inactive women (Fig. 2I). Thisdifference may be caused by a different type of data. The
SWAN study used longitudinal data, and our study usedcross-sectional data. Also, individual differences in women’s reproductive profiles, which change from puberty tomenopause, must be considered.
Based on the obtained results, the hypothesis was
accepted. The measured sample size is sufficiently large,
allowing us to generalize the results. An exception is thenumber of subjects in the oldest age group, 56–65. It may
be caused by a lower overall number of people running
regularly at that age. This assumption is confirmed by the
numbers of runners in this age group taking part in therunning competition “Běhej lesy” [29]. The percentage
ratio of men aged 56–65 in the total number of men was
6.9% in 2023; there were 2.4% women.
The acquired data align with the WHO’s recommendations for weekly physical activity. This PA took the formof running, with a volume ranging from 21.6 to 31.4,
depending on the age category. The individuals engaged

Kutac et al. Journal of Physiological Anthropology(2023) 42:28

in running as a long-term part of their lifestyle, and they
were accustomed to it. Therefore, they did not need tomake any significant changes to their daily routines during their participation in the study.
Limitations

This study has several limitations. The first limitation isthat this was a cross-sectional study and not a longitudinal study. This limitation is based on the setting of the
4HAIE study, with one of the goals being to establish acohort of active and inactive individuals, allowing for
future analysis of changes in the observed parameters as
they age.
The second limitation is that this was a deliberate selection, as designed by the 4HAIE project. The individuals
had to meet the selection criteria of the 4HAIE study:
location, age, gender, and PA status.
The third limitation involves not having assessed theinfluence of diet or caloric intake. We are aware that
diet is an important factor that may influence bodycomposition, namely, its fat component. However, the4HAIE project protocol did not include a validated dietassessment.
The fourth limitation is the analysis of running in thegroup of runners through self-reporting that is exposed
to subjective evaluation. However, a standardized instrument was used.
Another limitation includes the number of participantsin the oldest age category (56–65), namely in the group of
runners. We did not manage to involve more participantsin that age category in the 4HAIE project.
Strengths of the study

A strong point of the study is the fact that a comprehensive sample of both physically active and inactive individuals was measured under precisely defined
standard conditions. The participants were admitted
to the research center 15 h before the measurements
began, allowing us to monitor their behavior prior to themeasurements.
Despite the mentioned limitations, it was indeed
demonstrated that an achievable volume of PA in theform of recreational running led to significant changes
in the observed parameters among recreational runners, thereby improving their prospects for health andphysical fitness compared to inactive individuals of thesame age group. Although a minimum running volumeof 10 km/week was required, the actual average values
were higher (ranging from 21.6 to 31.4 km/week). Thisis likely related to the motivation of the runners. When
individuals adhere to a specific type of PA, running in
our case, they dedicate a significant portion of theirPage 11 of 13

leisure time to this activity. The obtained data will also
be utilized in a planned follow-up longitudinal study.

Conclusions
The study results have shown that recreational running,with a minimum volume of 10 km/week (in reality, theaverage volume ranged from 21.6 to 31.4 km/week), led
to significantly improved body composition values. It can
induce significant changes in body mass, body fat, andvisceral fat. However, there are differences between men
and women in the relationship between changes in bodycomposition parameters. These changes can provide suitable conditions for engaging in PA at an older age, whichis crucial for maintaining independence in old age.
An important conclusion is that the observed significant differences could be attributed to the volume of running, which is feasible without altering one’s daily workroutine. Running also offers the advantage of being easily
accessible compared to other forms of PA, and its implementation does not require special equipment, partners,
or the acquisition of specific skills. Recommended levelsof PA are significantly exceeded by recreational runners
who have chosen to engage in regular PA, often participating in amateur running races for which they prepare.
The results also showed that the body composition
evaluation can be used for the evaluation of the effectiveness of the physical routine with a sufficient level ofaccuracy.
Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40101-​023-​00348-x.
Additional file 1.
Acknowledgements
Not applicable.
Authors’ contributions
PK, conceptualized and designed this study, designed the data collection
instruments, collected data, carried out the initial analysis, drafted the initialmanuscript, reviewed and revised the manuscript, and carried out the initial
analyses. VB, conceptualized and designed this study, reviewed and revised
the manuscript, and critically reviewed the manuscript for important intellectual content. MB, conceptualized and designed this study, reviewed andrevised the manuscript, and critically reviewed the manuscript for important
intellectual content. MK, analyzed the data, prepared tables, critically reviewed
the manuscript for important intellectual content, and approved the final
draft. MS, participated in editing the manuscript according to the reviewers’
instructions and reviewed and revised the edited manuscript.
Funding
This research was funded by Program 4 HAIE—Healthy Aging in Industrial
Environment (grant number: CZ.02.1.01/0.0/0.0/16_019/0000798).
Availability of data and materials
All data generated or analyzed during this study are included in this published article.

Kutac et al. Journal of Physiological Anthropology(2023) 42:28

Declarations
Ethics approval and consent to participate
The study was approved by the Ethics Committee of the University of Ostrava(protocol code OU-87674/90–2018 and date of approval 29 November 2018)
and followed the principles of the Helsinki Declaration.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Received: 7 June 2023 Accepted: 23 November 2023

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