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Original scientific article                                      DOI: https://doi.org/10.35469/ak.2021.326
received: 2022-05-25                        UDC: 612.741:796.012

TIME PASSES – HEALTHY HABITS STAY? A LONGITUDINAL 
SMALL SAMPLE COMPARISON OF MUSCLE CONTRACTILE 

PROPERTIES, MOTOR ABILITIES AND LIFESTYLE 
CHARACTERISTICS OF ATHLETES AND NON-ATHLETES

Saša PIŠOT1, Rado PIŠOT1, Boštjan ŠIMUNIČ1

1 Science and Research Centre Koper, Slovenia

Corresponding author:
Saša PIŠOT

Science and Research Centre Koper, Garibaldijeva 1, 6000 Koper, Slovenia
Tel.: +386 5 663 77 00

e-mail: sasa.pisot@zrs-kp.si

ABSTRACT 

Introduction: Because healthy behaviors learned early in life are more likely to be 
maintained during adulthood, we aimed to investigate longitudinal changes of partici-
pants that were regularly involved in extracurricular sport activities (athletes; N = 7; 
4 boys) and those that were not (non-athletes, N = 6; 3 boys)

Methods: Participants of both groups were invited for re-assessment at the age of 
27, in 2019, 12 years after they participated in a 5-year longitudinal study as adoles-
cents (9–14 years of age, in the period 2001‒07). We investigated vastus lateralis (VL) 
and biceps femoris (BF) contractile properties (tensiomyography), maximal running 
speed (photocells), anthropometric measures (bioimpedance), maximal vertical jump-
ing height (squat and countermovement jumps on a ground reaction force plate), and 
lifestyle characteristics (GPAQ and EHIS surveys).

Results: Based on Cohen’s d effect size we found that athletes have lower body 
mass index, higher maximal running speed, better maximal vertical jumping height, 
and shorter BF contraction time, not found in VL, compared to non-athletes. Further-
more, athletes also exhibit healthier lifestyle characteristics such as lower sedentary 
time and higher daily energy expenditure than non-athletes. Athletes follow diet regi-
mens consisting of more regular meals with more protein and indulge less in health-risk 
behavior (smoking and alcohol consumption). However, the self-perception of health 
and quality of life was lower in athletes than in non-athletes. 

Conclusion: EU regulations and the beginning of the COVID-19 pandemics pre-
vented us from conducting a study on a more representative sample. Nevertheless, 
we could confirm that regular sport participation yields better physical performance 



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and a healthier lifestyle but could also have a negative impact on health (injuries) 
and quality of life.

Keywords: motor development, tensiomyography, sport, physical exercise, healthy 
lifestyle

ČAS MINEVA - ZDRAVE NAVADE OSTANEJO? LONGITUDINALNA 
PRIMERJAVA KONTRAKTILNIH LASTNOSTI MIŠIC, GIBALNIH 

SPOSOBNOSTI IN ZNAČILNOSTI ŽIVLJENJSKEGA SLOGA 
ŠPORTNIKOV IN NEŠPORTNIKOV NA MAJHNEM VZORCU

IZVLEČEK

Uvod: Namen raziskave je ugotoviti longitudinalne spremembe v skupini preisko-
vancev, ki so se redno ukvarjali z obšolskimi športnimi dejavnostmi (skupina športni-
kov; N = 7, 4 dečki), in tistih, ki se niso kontinuirano ukvarjali s športom (skupina 
nešportnikov, N = 6, 3 dečki). 

Metode: Udeleženci obeh skupin so bili povabljeni na ponovne meritve pri starosti 
27 let, leta 2019, torej 12 let po tem, ko so bili v starosti od 9-14 let, v obdobju 2001-
2007 vključeni v petletno longitudinalno študijo. Preučevali smo kontraktilne lastnosti 
mišic: vastus lateralis (VL) in biceps femoris (BF) (z metodo tenziomiografije), hitrost 
sprinta z letečim štartom (s fotokamerami), antropometrične podatke (z bioimpedan-
co), dosežek navpičnega skoka (skoki na tenziometrijski plošči s pomočjo in brez pomo-
či rok) ter nekatere značilnosti življenjskega sloga (anketi GPAQ in EHIS). 

Rezultati: Na podlagi velikosti učinka (Cohenove d) smo ugotovili, da imajo špor-
tniki v primerjavi z nešportniki nižji indeks telesne mase, večjo hitrost teka, boljšo zmo-
gljivost vertikalnih skokov in krajši čas krčenja BF, kar pri VL nismo ugotovili. Poleg 
tega imajo športniki tudi bolj zdrave vzorce življenjskega sloga, manj sedijo in več 
gibajo (večja dnevna poraba energije) kot nešportniki. Nadalje prehrano športnikov 
sestavljajo bolj redni obroki z več beljakovinami, poleg tega imajo manj zdravju ne-
varnih praks (kajenje in uživanje alkohola). Kljub temu pa so športniki svoje zdravje in 
kakovost življenja ocenili nižje kot nešportniki. 

Zaključek: Regulativni predpisi EU za področje varovanja osebnih podatkov 
(GDPR) kot tudi začetek pandemije COVID-19 so nas pri izvedbi študije na bolj repre-
zentativnem vzorcu močno ovirali. Kljub temu smo lahko potrdili, da redno ukvarjanje 
s športom ohranja boljšo telesno zmogljivost, bolj zdrav življenjski slog, kar pa lahko 
vpliva na zdravje (poškodbe) in kakovost življenja.

Ključne besede: gibalni razvoj, tenziomiografija, šport, gibalna aktivnost, zdrav 
življenjski slog



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INTRODUCTION

Most research into healthy behavior and predictors of these behaviors generally 
focuses on individuals’ incentives to adopt positive health practices at a single point 
in time and do not provide a framework for how healthy behaviors may change over 
time. Frech (2012) indicates several reasons to investigate how and why engagement 
in healthy behavior changes across pivotal life course stages (Frech, 2012). First, be-
cause healthy behaviors learned early in life are more likely to be maintained during 
adulthood (Lau, Quadrel, & Hartman, 1990; Telama et al., 1997) and because health-
promoting behaviors aid in preventing or delaying chronic or life-threatening disease. 
And secondly, to evaluate whether personal and social resources at one life course 
stage (for example during adolescence) exert an enduring or cumulative impact on 
healthy behaviors at later life course stages. 

Therefore, sport and sport participation are viewed as an effective activity for solv-
ing problems and improving quality of life for individuals and society alike. Not just for 
increasing self-confidence, self-esteem, and positive body image, building our charac-
ter in the form of discipline, teamwork, and responsibility, as well as our importance, 
but also creating motor and sport-specific skills convertible into physical capital and 
improving health, fitness, and an overall sense of physical well-being (Coakley, 2011). 

Researchers have given considerable attention to the athlete development process, 
e.g., positive youth development through sport (Holt, 2008). Utilization of skeletal mus-
cle mass to produce power is of great importance in sport. However, skeletal muscle 
is also indispensable for locomotion, maintenance of body posture, thermoregulation, 
sugar and lipid metabolism and, therefore, for general health. To realize locomotion, the 
muscles must produce power by generating force and shortening velocity at the same 
time. The speed of muscle contraction is largely determined by fiber type composition. 
In children, knowing the fiber type composition may be used to help in formulating 
an informed decision regarding taking up a sport in which the child most likely will 
excel. While there are numerous data on the fiber type composition of various skeletal 
muscle in adults and adolescents, we are aware of only seven cross-sectional studies on 
the fiber type composition of muscle in children between the ages of 2 months and 11 
years (Bell, MacDougall, Billeter, & Howald, 1980; Glenmark, Hedberg, & Jansson, 
1992; Kriketos et al., 1997; Lexell, Sjöström, Nordlund, & Taylor, 1992; Lundberg, 
Eriksson, & Mellgren, 1979; Österlund, Thornell, & Eriksson, 2011; Verdijk et al., 
2014). Furthermore, only one study presented longitudinal data of VL composition 
from adolescence to adulthood (Glenmark et al., 1992) and found lower proportion of 
slow twitch fibers in VL muscle in girls at the age of 16 and the opposite at the age of 
27. Especially where it is very difficult, for ethical reasons, to invasively measure skel-
etal muscle myosin heavy chain proportion or fiber type composition, tensiomyography 
(TMG) provides non-invasive information on changes in functional skeletal properties 
(Valenčič & Knez, 1997). Specifically, it was established that TMG-derived contraction 
time (Tc) could be used to non-invasively estimate skeletal muscle MHC type 1 propor-
tion (Šimunič, Degens, & Rittweger, 2011).



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In the period 2002‒07 we followed with TMG screening >300 children in six yearly 
assessments. Approximately one third (107) were measured on all occasions and ana-
lyzed (Pišot et al., 2004; Šimunič et al., 2017; Završnik et al., 2016; Završnik, Pišot, 
Šimunič, Kokol, & Blažun Vošner, 2017). Briefly, we found that boys in general had 
slower muscles than girls. During early maturation in the VL muscle there is a slow-to-
fast transition that begins between 6 and 10 years of age, which then appears to stabilize 
to adult proportions. Regular participation in sport was associated with a faster biceps 
femoris (BF), but not in VL, for both sexes (Šimunič et al., 2017). We also found cor-
relation between muscle contractile properties and the running speed that was biased 
for both sexes. Specifically, the running speed was less correlated with VL Tc in boys 
than in girls. However, boys’ running speed was more correlated with BF Tc than in 
girls (Završnik et al., 2016, 2017). Our data thus represent a first non-invasive, if indi-
rect, indication of developmental trends in changes in muscle fiber type composition 
in children.

The most interesting finding for us was that regular sport participation in children 
aged between 9 and 14, compared to children without any extracurricular sport par-
ticipation, impacted Tc in non-gravitational BF muscle but not gravitational VL mus-
cle, in both sexes (Šimunič et al., 2017). However, it remains to be seen whether the 
continuation of exercise (sport participation) through adolescence to adulthood could 
further impact Tc in both muscles. Even more, it would be interesting to see the effect 
of continued sport participation on motor abilities, body characteristics and main char-
acteristics of healthy lifestyle (nutrition, PA, habits) in the later adolescence and early 
adulthood of participants. 

After carrying out a 5-year longitudinal monitoring of skeletal muscle contractile 
properties and motor abilities in >300 children within two consecutive research pro-
jects: (i) “The role of biomechanical properties of skeletal muscle in the motor devel-
opment of children”, 2001‒04; and (ii) “Monitoring of changes in skeletal muscle bio-
mechanical characteristics in early childhood and adolescence”, 2004‒07, we invited 
those same subjects for a follow-up assessment in 2019 with the purpose of longitudi-
nally investigate the changes in skeletal muscle contractile properties, physical activity 
level, nutrition, and health related habits.

METHODS

In previous analyses we investigated motor abilities development in 9‒14 year-old 
children as a factor of sex and sport participation. Furthermore, in 2019 we invited the 
subjects to participate again (12 years later), taking the same measurements. Further-
more, at the final assessment we investigated basic lifestyle characteristics which we 
obtained by a questionnaire including demographic data, physical activity, nutrition 
and smoking habits. Additionally, the participants were asked about their participation 
in organized sports, their sport injury history and important life stress events. Results 
of the first six assessments of vastus lateralis (VL) and biceps femoris (BF) contractile 



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properties (tensiomyography), flying running speed (photocells), anthropometric meas-
ures (bioimpedance), and vertical jumping performance (squat and countermovement 
jumps on a ground reaction force plate) were previously reported in four scientific 
publications (Pišot et al., 2004; Šimunič et al., 2017; Završnik et al., 2016, 2017). This 
manuscript presents data from a 12-year follow-up in a subsample of the same partici-
pants.

Participants and recruitment

During the recruitment we focused on inviting the 107 participants who were 
consistently present for all six annual measurements during the 2002‒07 period. The 
participants came from the Slovene towns of Koper, Izola, Piran, Ljubljana, and Mar-
ibor. Due to strict personal data protection legislation (EU 2016/679, General Data 
Protection - GDPR), we had many problems in re-establishing contacts to recruit 
participants for follow-up measurements, as we only had a database with the first 
and last names and their elementary school. The principals of the elementary schools 
could not provide us with the subjects’ contact information, so we had to resort to 
the “snowball” method and social media. We searched for potential acquaintances 
to contact the participants and after they agreed to be contacted, we invited them 
to participate in the follow-up. This was usually done through their social media 
(Facebook and Instagram) or by a phone call. In addition, our research organization 
advertised the invitation to participate in the follow-up through various media (of-
ficial website, Facebook). Despite an enormous amount of time and effort invested, 
we kept receiving responses of interested candidates, but mostly ones who had not 
participated in our previous measurements from 2002‒07 and thus were not eligible 
for the follow-up. Over the course of about six months, we were able to recruit 13 
participants. We divided these participants into two groups: a group of athletes, who 
had regularly participated in organized sport activities during the past 12 years (ath-
letes; N = 7; 4 boys) and non-athletes (N = 6; 3 boys). Specifically, it was evident 
from their questionnaire data that seven of them continuously participated in various 
sports from the age of 9 to 25 and were classified as athletes, while six of them had 
not participated in any sports for at least the past 10 years (non-athletes), although 
five of them had been active during the period of primary school. Table 1 summarizes 
the proportion of those involved in organized sport activities in three age-periods for 
both studied groups.



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Table 1: Proportion of participants’ sport participation in the two studied groups: 
(i) those who continuously practiced sport from 14 to 26 years of age (athletes); and 
(ii) those who did not practice sport at all or only until they were 16 years old (non-
athletes).

Athletes Non-athletes

N 7 6

Sport participation in the age period of 9‒14 5 out of 7 5 out of 6

Sport participation in the age period of 14-16 7 out of 7 2 out of 6

Sport participation in the age period of 16-26 7 out of 7 0 out of 6

Measurement procedures

The follow-up measurements consisted of the following test battery: arm strength 
(dominant hand compression - dynamometer), TMG of two muscles, maximal running 
speed (7-meter sprint with flying start), maximal vertical jump height, body height, 
mass, mass index, and composition measurement (fat mass, muscle mass). After the 
tests, each subject was asked to complete a specific questionnaire consisting of several 
sets of validated questions (GPAQ, EHIS) to provide data on their current PA status, 
injury history, health status, and lifestyle to allow capturing possible factors that might 
influence general fitness (biomechanical muscle characteristics) at each stage of the 
subject’s life (high school, university, and current age).

Tensiomyography (TMG)

The TMG method measured the contractile properties of two skeletal muscles in the 
dominant site (vastus lateralis – VL, and biceps femoris – BF). Each muscle was stimu-
lated with single electric pulses, rectangular in shape, lasting 1 ms. The pulse amplitude 
was gradually increased until the maximum response was obtained. We saved the two 
largest responses for further processing and took the average of both for further analy-
ses. We calculated two contractile parameters for each response: a maximal amplitude 
(Dm, in mm) to be used for the calculation of contraction time (Tc, in ms) between 10 
% to 90 % Dm.



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Measurement of body composition

After body mass and height were measured, body mass index was calculated, and 
composition was measured using a bioimpedance meter (Maltron BioScan 916s, UK). 
We ensured that participants were calm, normally hydrated, and rested for at least 20 
minutes before the measurement. Fat mass was measured via a 4-point measurement.

Maximal running speed

After a standardized 10-minute warm-up, the maximal running speed was measured 
with a flying start over a distance of 7 meters. We assured plenty of room to accelerate 
and decelerate before and after assessment gates (Powertimer 300, Newtest, Finland), 
respectively. Each participant made three attempts and the best result was taken for 
further analysis.

Maximal vertical jumping height

We measured the height of the vertical jump without using hands (hands on the 
hips). Each participant performed three countermovement and three squat jumps on 
a ground reaction force plate (Quattro jump 9290AD, Kistler Ltd., Austria). The best 
result was taken for further analysis.

Questionnaire

The questionnaire designed for this study consisted of several sets of validated 
questions and covered basic socio-demographic data, health status and nutrition, 
physical/sport activity, and aspects of sedentary time. We used an adapted part of 
the European Health Interview survey – EHIS to assess eating habits (regular diet, 
type of diet) and indicators of quality of life. Additionally, physical/sport activity 
was assessed by a self-reported validated questionnaire The Global Physical Activity 
Questionnaire – GPAQ (Armstrong & Bull, 2006).

Statistics

Due to the small sample size, we did not perform classical parametric statistical 
analysis. We performed the non-parametric Mann-Whitney test to compare athletes 
vs. non-athletes only for indicative purposes. Since small samples yield low statisti-
cal power and only large effects will end up significant, we rather calculated effect 
sizes – Cohen’s d values ‒ and interpreted main findings based on the effect size (low 



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< 0.20; moderate 0.20 ≤ Cohen’s d < 0.8 and high ≥ 0.8). For comparing correlations 
between lifestyle characteristics and motor abilities and muscle characteristics, we used 
the Spearman rho coefficient indicating significant correlations at p < 0.05.

RESULTS

We were able to repeat the longitudinal monitoring of the biomechanical character-
istics of skeletal muscle only on a sample of 13 (7 male) participants at the age of 27 
years: 4 participants from Maribor, and 9 from Koper over 7 testing days executed in 
January, February, and October 2019.

In all comparisons there were no statistical differences confirmed by the Mann-
Whitney test; however, due to lower sensitivity of statistical tests in very small samples, 
we interpreted effect sizes. Table 2 shows a progressive trend in basic anthropometric 
data of pooled participants indicating normal growth. When comparing athletes and 
non-athletes (Table 4), we found that athletes had moderately to significantly lower 
body mass index (effect size from 0.42 to 1.07) throughout the whole period and lower 
fat mass (effect size 0.65) at the age of 27 years. This is consistent with lesser sedentary 
time (effect size 0.55) and higher daily energy consumption (effect size 0.47) compared 
to non-athletes. Table 3 presents motor abilities in pooled participants. When compar-
ing athletes and non-athletes (Table 4), we could not confirm higher running speed in 
athletes throughout all periods. However, countermovement and squat jump heights 
were higher in athletes when compared to non-athletes at the age of 27 (effect size 0.81 
and 0.74, respectively). VL Tc was not lower in athletes; it was, however, lower in BF 
with the largest effect size at the age of 27 years (0.64).

Table 2. Basic anthropometric data of pooled participants.

Age 9 years 10 
years 

11 
years 

12 
years

13 
years

14 
years … 27 

years 
N 13 13 13 13 13 13 … 13
Body mass 
index / kg/m2

17.1 
± 2.5

17.9 
± 2.6

18.0 
± 2.9

19.5 
± 3.0

20.0 
± 2.7

20.3 
± 2.4 … 22.8 

± 3.3

Body height / m 1.41 
± .06

1.45 
± .07

1.49 
± .07

1.58 
± .07

1.65 
± .08

1.69 
± .08

1.76 
± .09

Body mass / kg 34.3 
± 7.0

37.9
± 8.2

40.4 
± 9.1

48.9 
± .11

54.8 
± 10.7

58.2 
± 9.7 

71.6 
± 14.4



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Table 3. Pooled data of selected motor abilities.

Age 9 years 10 
years 

11 
years

12 
years

13 
years

14 
years … 27 

years
N 13 13 13 13 13 13 … 13
Running Speed 
/ m/s

5.6 
± 0.5

5.6 
± 0.4

5.7 
± 0.4

6.0 
± 0.4

6.3 
± 0.8

6.2 
± 0.4 … 6.4 

± 0.5
CMJ height / 
cm - - - - - - … 29.8 

± 5.6
Squat jump 
height / cm - - - - - - … 28.3 

± 5.2

CMJ – countermovement jump

Table 4. Comparison between athletes and non-athletes

Group Athletes Non-athletes Effect size*
Number 7 6
Body mass index / kg/m2
9 years 16.7 ± 2.9 17.7 ± 1.9 0.54
10 years 17.3 ± 3.0 18.7 ± 2.2 0.63
11 years 17.4 ± 2.9 18.7 ± 2.9 0.48
12 years 18.7 ± 3.1 21.0 ± 2.4 0.98
13 years 19.4 ± 3.3 20.7 ± 1.8 1.07
14 years 19.9 ± 2.7 20.8 ± 2.0 0.42
… … … …
27 years 21.9 ± 3.7 23.1 ± 2.5 0.65
Fat mass / %
27 years 22.7 ± 6.4 23.7 ± 3.0 0.33
Running speed / m/s
9 years 5.5 ± 0.4 5.7 ± 0.5 -0.39
10 years 5.7 ± 0.4 5.6 ± 0.4 -0.15
11 years 5.6 ± 0.4 5.7 ± 0.4 -0.41
12 years 5.9 ± 0.4 6.0 ± 0.4 -0.02
13 years 6.0 ± 0.3 6.1 ± 0.3 -0.39
14 years 6.1 ± 0.4 6.3 ± 0.3 -0.64
… … … …
27 years 6.4 ± 0.5 6.3 ± 0.5 0.31



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Group Athletes Non-athletes Effect size*
Number 7 6
Countermovement jump height / cm
27 years 31.6 ± 5.7 27.6 ± 5.0 0.81
Squat jump height / cm
27 years 29.9 ± 5.3 26.1 ± 7.2 0.74
Contraction time of vastus lateralis / ms
9 years 20.7 ± 2.5 19.2 ± 2.6 -0.57
10 years 19.0 ± 2.1 17.6 ± 2.1 -0.64
11 years 19.3 ± 4.2 19.2 ± 2.8 -0.01
12 years 21.7 ± 4.4 20.4 ± 2.3 -0.56
13 years 21.6 ± 3.6 22.1 ± 2.4 0.22
14 years 22.7 ± 3.7 23.7 ± 3.3 0.30
… … … …
27 years 21.5 ± 3.0 21.4 ± 2.2 -0.05
Contraction time of biceps femoris / ms
9 years 32.1 ± 3.5 34.2 ± 7.3 0.29
10 years 31.2 ± 2.9 32.8 ± 9.1 0.17
11 years 32.0 ± 3.5 33.9 ± 6.6 0.29
12 years 31.1 ± 4.5 33.3 ± 8.6 0.26
13 years 29.4 ± 3.7 36.9 ± 13.0 0.58
14 years 29.6 ± 1.8 34.9 ± 12.8 0.41
… … … …
27 years 29.1 ± 2.8 33.9 ± 7.6 0.64
Sedentary time / min
27 years 335 ± 157 417 ± 147 0.55
Daily energy consumption / MET
27 years 4663 ± 2823 3346 ± 2780 0.47

MET… metabolic equivalent; *positive values of effect size denote better results for athletes.

Data from GPAQ showed higher daily energy consumption (MET) because of sport 
participation of athletes in parallel to lower daily sedentary time than in non-athletes. 
Even more, sedentary time at age of 27 years correlates negatively with body mass 
index (r = −0.47) and fat mass (r = -0.59), and positively with BF Tc at this same age 
(r = 0.50).



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Additionally, the differences in certain socio-demographic factors and lifestyle 
characteristics between athletes and non-athletes were examined, which are shown 
in Table 5.

Table 5: Comparison of some lifestyle characteristics of athletes and non-athletes 

Group Athletes Non-athletes Effect size*
N 7 6
Self-assessment of…: 
General health status 3.8 ± 0.6 4.2 ± 0.6 0.67
Physical capability 3.3 ±0.7 3.3 ±0.9 0.00
Psychological status 3.8 ±0.6 3.8 ±0.6 0.00
General quality of 
life 3.8 ±0.6 4.2 ±0.3 1.33

Eating habits
Breakfast 5.7 ±0.67 4.0 ±1.9 0.89
Morning snack 3.2 ± 1.0 2.4 ± 1.6 0.50
Lunch 5.5 ±0.7 5.8 ±0.3 1.00
Afternoon snack 3.3 ± 1.6 2.5 ±1.0 0.80
Dinner 5.2 ±1.4 5.5 ±0.7 0.43
Food consumption
Whole-grain bread 4.0 ± 1.2 4.3 ± 2.0 0.15
Olive oil 4.3 ± 1.3 5.8 ± 1.0 1.50
Milk and low-fat 
dairy 3.8 ± 1.2 4.8 ± 2.0 0.50

Cottage cheese, 
yogurt, cheese 4.2 ± 1.5 4.3 ± 2.0 0.05

Dark chocolate 2.3 ± 1.3 2.0 ± 0.9 0.33
Meat products 3.7 ± 1.2 2.0 ± 1.3 1.31
Red meat 2.8 ± 1.2 3.0 ± 1.3 0.15
Poultry 3.8 ± 1.2 2.5 ± 1.4 0.93
Bacon 2.0 ± 0.9 2.3 ± 1.2 0.25
Fish 2.7 ± 1.0 2.3 ± 0.7 0.57
Fizzy and non-fizzy 
non-alcoholic drinks 3.0 ± 1.0 3.8 ± 2.2 0.36

Alcohol beverages 2.3 ± 0.4 3.7 ± 0.9 1.56
Fried dishes 2.8 ± 1.0 3.3 ± 1.2 0.42

* effect sizes are presented as absolute values.



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A noticeable difference between the groups was that athletes reported predominant-
ly standing (physical work) while non-athletes, all but one, reported predominantly 
sedentary work. There were no differences between athletes and non-athletes in terms 
of subjective assessment of physical fitness, mental well-being, and health concerns, 
but interestingly athletes rated their general health (effect size 0.67) and their overall 
quality of life (effect size 1.33) lower than did non-athletes.

Much of the difference between the groups was reflected in eating habits, with ath-
letes eating more regularly and skipping breakfast and afternoon snack less often (ef-
fect sizes 0.89 and from 0.50 to 0.80, respectively) than non-athletes. In terms of food 
choices athletes are more likely to eat meat products (effect size 1.31), especially poul-
try (effect size 0.93). Non-athletes, on the other hand, are more likely to consume olive 
oil (effect size 1.50) and alcoholic beverages (effect size 1.56).

DISCUSSION

Based on effect sizes, due to low number of participants, we could conclude that 
at the age of 27 years athletes had lower body mass index, fat mass, sedentary time, 
BF Tc and higher running speed, countermovement and squat jump heights and daily 
energy consumption when compared to non-athletes. Similar trends as at 27 years were 
also found in the age period from 9 to 14 years, but only for two abovementioned lon-
gitudinal variables: body mass index and BF Tc. Interestingly, only running speed was 
at 9‒14 years lower in athletes than in non-athletes, the opposite as at 27 years. At the 
age of 27, the largest effect size was recorded for countermovement jump height, squat 
jump height, body mass index, and BF Tc (effect sizes > 0.60).

This is the first study to collect 18 years of longitudinal data on TMG parameters 
from childhood to adulthood. We have previously reported that children’s regular par-
ticipation in sports was associated with shorter BF Tc, but not with VL Tc, as found 
here. More specifically, BF Tc differences were observed between sedentary and ath-
letic groups in boys and girls and were significant at 12 years of age (Šimunič et al., 
2017). As this sample was a sub-sample of the previous study a similar result could be 
confirmed for the period of 9-14 years. However, this trend was maintained or even 
slightly increased with regular sport exercise until the age of 27 years. Although short 
Tc was confirmed in BF it was not the case for VL Tc, which seems to be independent 
from regular exercise. A similar situation was observed in adult track and field sprint-
ers where sport participation resulted in a higher proportion of type IIc fibers in the 
BF, which was also associated with a lower BF Tc (19.5 vs. 30.2 ms in sprinters vs. 
sedentary subjects, respectively) (Dahmane, Djordjevič, & Smerdu, 2006). It could be 
that the habitual loading of weight-bearing muscles (as for VL) through normal daily 
physical activity is already relatively high in non-athletes and that the non-weight bear-
ing muscles are more heavily loaded during exercise (Šimunič et al., 2017). If so, this 
may explain the grater adaptation to regular exercise in BF than in VL. Our group of 
athletes consisted of three handball players, two volleyball players, one dancer and one 



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multisport athlete. When we compare their average BF Tc’s at the age of 27 years (29.6 
ms) with the averages of other groups of adult athletes, e.g., male sprinters at 19 ms 
(Šimunič et al., 2017), beach volleyball players at 25 ms (Rodríguez Ruiz et al., 2012), 
gymnasts at 27 ms (Šimunič et al., 2017), and football players at 28 ms (Rey, Lago-
Peñas, Lago-Ballesteros, & Casáis, 2012) they have a longer BF Tc, but a shorter BF 
Tc than non-athletes with 32 ms (Šimunič, 2012; Šimunič, Pišot, Rittweger, & Degens, 
2018). Thus it appears that participation in sports during childhood may lead to a faster 
profile of BF Tc, an important muscle for fast explosive sports such as football, vol-
leyball, sprinting, and gymnastics as well as for overall knee health (Biscarini, Botti, & 
Pettorossi, 2013; Guelich, Xu, Koh, Nuber, & Zhang, 2016). To support this, we have 
previously reported that children who regularly participate in sports also have higher 
running speed (Volmut, Pišot, & Šimunič, 2016) and that this was negatively correlated 
with BF Tc (Pišot et al., 2004; Završnik et al., 2016) - but only in boys beyond the age 
of 13 years. And indeed, we were able to confirm higher sprinting velocity in athletes 
only at the age of 27 years, and not before, as five of the six participants in the non-
athlete group also practiced sports during the age period of 9‒14 years.

For all other non-longitudinal variables of body composition and muscle perfor-
mance, which were not assessed until age 27, differences between the two groups were 
in favor of the athletes. Specifically, athletes had lower fat mass, which is indicative of 
a lower body mass index, and had higher jumping performance, an indicator of over-
all body strength. Regarding lifestyle variables, athletes had lower sedentary time and 
higher daily energy consumption. Although we have found moderately lower sedentary 
time in athletes, when compared to non-athletes, it is not always so, as previous studies 
reported that athletes can be highly active and have high sedentary time, because of an 
independent relationship between moderate-to-vigorous physical activity time and sit-
ting time (Swartzendruber, Croteau, & Maine, 2020; Weiler, Aggio, Hamer, Taylor, & 
Kumar, 2015). The relationship among high sedentary time for athletes’ health, risk of 
cardiovascular and metabolic diseases, despite high activity level, remains to be seen.

In their self-reporting, athletes rated their health and quality of life worse than did 
non-athletes. Although their health status did not reflect any serious medical condition 
or show any chronic diseases, we can speculate that athletes report lower QoL due to 
their recent injury experience as these lower scores primarily affect social and global 
functioning, suggesting that they feel that their injuries limit their ability to participate 
in sports and social life (McGuine, Winterstein, Carr, Hetzel, & Scott, 2012; Valovich 
McLeod, Bay, Parsons, Sauers, & Snyder, 2009). It is also evident that athletes eat 
more regularly, place more emphasis on breakfast, eat healthy snacks with higher in-
take of animal-sourced protein, and consume less fats and less alcohol. Our findings are 
consistent with adolescent athletes eating healthier and having more varied diets (von 
Rosen, Olofsson, Väsbom, & Heijne, 2019).

The major limitation of the study was the small sample number and low response 
rate of participants for re-testing (11 %). This is a major shortcoming of all longitudinal 
studies due to the EU regulation of personal data protection, disinterest of participants 
in attending the later measurements, and certainly in this case the COVID-19 pandem-



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ANNALES KINESIOLOGIAE • 12 • 2021 • 2

ics. Furthermore, we had both sexes in both groups but of similar distribution. There-
fore, we have done only effect size estimation without statistical hypothesis testing.

CONCLUSION

Despite the small sample we can identify a trend which confirms that many years 
of sports participation and regular exercise have positive effects on physical fitness 
and motor abilities, and especially on the contractile properties of the skeletal muscles. 
Athletes have lower body mass index, exhibit greater running speed, better jumping 
performance and shorter contraction time of the posterior thigh muscle. Athletes also 
spent less time sitting and have higher daily energy expenditure than non-athletes, but 
this was not reflected on fat mass. The contraction time of the anterior thigh muscles, 
which did not differ between the groups, confirms our previous findings that the daily 
stimulus of the postural muscles is large enough to maintain contractile properties, 
which is not the case for the posterior thigh muscles, which are less used during daily 
tasks. We also found healthier lifestyle habits in athletes. They practice healthier diet 
regimes with more regular meals containing more protein (meat products, poultry) as 
well as fewer health-risk practices (smoking and alcohol consumption); regarding the 
self-assessed quality of life, athletes reported lower health status and general quality of 
life, which can be explained by the reported injuries and strict sport regimes of athletes.

ACKNOWLEDGEMENTS

We would like to thank the participants, the research teams from the Institute for 
Kinesiology Research of the Science and Research Centre Koper and the Faculty of 
Education University of Maribor, and everyone who helped with recruitment of sub-
jects for repeated measurement study. Special thanks to the companies who provided 
additional incentives with practical rewards. The research study was co-funded by the 
Slovenian Research Agency (research program: P5-0381 Kinesiology for Quality of 
Life) and Foundation for Financing Sports Organisations in the Republic of Slovenia 
(Fundacija za financiranje športnih organizacij v Republiki Sloveniji).

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