Citation: Sanchez-Trigo, H.; Zange, J.;
Sies, W.; Böcker, J.; Sañudo, B.;
Rittweger, J. Effects of Aging and
Fitness on Hopping Biomechanics.
Int. J. Environ. Res. Public Health 2022,
19, 13696. https://doi.org/10.3390/
ijerph192013696
Academic Editors: Yufei Cui and
Dariusz Mosler
Received: 22 September 2022
Accepted: 18 October 2022
Published: 21 October 2022
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International  Journal  of
Environmental Research
and Public Health
Article
Effects of Aging and Fitness on Hopping Biomechanics
Horacio Sanchez-Trigo 1,*
, Jochen Zange 2
, Wolfram Sies 2, Jonas Böcker 2, Borja Sañudo 1
and Jörn Rittweger 2,3
1
Department of Physical Education and Sports, University of Seville, 41013 Sevilla, Spain
2
German Aerospace Center (DLR), Institute of Aerospace Medicine, 51147 Cologne, Germany
3
Department of Pediatrics and Adolescent Medicine, University Hospital Cologne, 50931 Cologne, Germany
*
Correspondence: fstrigo@us.es
Abstract: Physical exercise promotes healthy aging and is associated with greater functionality and
quality of life. Muscle strength and power are established factors in the ability to perform daily tasks
and live independently. Stiffness, for mechanical reasons, is another important constituent of running
performance and locomotion. This study aims to analyze the impact of age and training status on
one-legged hopping biomechanics and to evaluate whether age-related power decline can be reduced
with regular physical exercise. Forty-three male subjects were recruited according to their suitability
for one of four groups (young athletes, senior athletes, young controls and senior controls) according
to their age (young between 21 and 35, vs. older between 59 and 75) and training status (competing
athletes vs. non-physically active). The impact of age and training status on one-legged hopping
biomechanics were evaluated using the two-way analysis of variance (ANOVA) method. Signiﬁcant
differences among groups were found for hopping height (p < 0.05), ground contact time (p < 0.05),
peak ground reaction force (p < 0.05) and peak power (p < 0.01). No differences among groups were
found in ground-phase vertical displacement and vertical stiffness (p > 0.05). Young athletes and older
non-physically active people achieved the best and worst performance, respectively. Interestingly,
there were not any differences found between young non-physically active people and senior athletes,
suggesting that chronic training can contribute to partly offset effects that are normally associated
with aging.
Keywords: physical ﬁtness; sedentary behavior; aging; biomechanics; stiffness; muscle power
1. Introduction
Current demographic data show a signiﬁcant population aging in developed countries,
leading to increased health care costs [1]. Preventing mobility limitations and maintaining
independent functioning in the aging population is of major public health importance [2].
One of the factors associated to age-associated decline in mobility is the decline in muscle
force and power, which is aggravated by a sedentary lifestyle [3]. Both sedentarism and
aging cause a decline in muscle performance and functionality. Thus, it is of interest to
assess separately the respective contributions of sedentarism and aging to better understand
this process, and to evaluate to which degree can a physically active life compensate age-
associated decline in muscle power. Master athletes are particularly interesting to study
these processes. These are individuals who train to compete in athletic events at a high
level beyond a typical sports retirement age [4]. Master athletes can be considered as rare
examples of aging without the common confounder of increased sedentarism at older
age [4]. Previous research on master athletes has shown a clear effect of age and athletic
specialization on muscle power since, while aging is associated with a 40% reduction
in jumping power from the 3rd to the 7th decade of life, sprint-trained master athletes
have greater jumping power than endurance master athletes [4–6]. To a large extent, the
age-related decline in jumping power is explicable by age-effects on body composition [7].
However, to the best of our knowledge, no previous study has yet compared age-related
Int. J. Environ. Res. Public Health 2022, 19, 13696. https://doi.org/10.3390/ijerph192013696
https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2022, 19, 13696
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effects on muscle power between master athletes and a cohort matched in age, height and
weight that is non-physically active. In this study, we compare the jumping performance
and biomechanics of athletes and ordinary active non-athletes control subjects, both at
a young age and in elder subjects typically affected by aging processes. Our aim is to
quantify how far the plyometric performance of elite master athletes is superior above age
matched non-athletes, and how far the jumping biomechanics of athletes with respect to
non-athletes is affected by aging and ﬁtness. To answer this research question, the effects of
age and training status on jumping biomechanical parameters will be assessed. It is our
hypothesis that a prolonged engagement in exercise is related to a reduced age-related
decline in muscle power and performance.
2. Materials and Methods
2.1. Participants
This study was conducted within the MALICoT project, which was designed to
compare intramuscular connective tissue between young athletes, young non-physically
active people, senior athletes and senior non-physically active people. Speciﬁcally, the study
targeted power athletes (jumpers and sprinters) for the athlete groups. For the recruitment
process, the study was advertised via social media and on the DLR website (www.dlr.de/
me/en/desktopdefault.aspx/tabid-15377/ accessed on 20 September 2022). An online
questionnaire was ﬁlled out by interested subjects. Activity levels were quantiﬁed using the
Freiburger questionnaire for physical activity [8,9] and the subjects’ energy expenditure was
estimated, in terms of metabolic equivalents of task (METs). The questionnaire included
questions to check criteria for inclusion and exclusion. Inclusion criteria were (a) age either
between 20 and 35 for the young groups and age between 60 and 75 for the senior groups;
(b) ≥4 h per week training and regular competition in sprint running or jumping events
for the athletic groups, and ≤25 METs per week spent in exercise for the non-physically
active group; (c) male sex; and (d) ability to provide informed consent (all groups). People
were excluded when diagnosed with diabetes, when they had contraindications against
magnetic resonance imaging or against muscle biopsy, or when they had experienced
injuries or musculoskeletal disorders likely to interfere with the testing protocol.
All participants provided written informed consent prior to participating in this study.
The experimental protocol was approved by the Ethical Committee of the Ärztekammer
Nordrhein in Düsseldorf, Germany (ref. no. 2018269). The study was prospectively
registered on the German register of clinical trials (www.drks.de accessed on 20 September
2022) with registration number DRKS00015764.
2.2. Sample Size
As mentioned above, this study is part of the MALICoT project, whose main goal is to
investigate endomysium and perimysium content as a function of age and training state in
humans. The lack of information on age- and training-state dependency of muscle tissue’s
elastic modulus makes a statistically-motivated sample size deﬁnition difﬁcult. However,
a sample size of 12 subjects per each of the four sub-groups (young athletes, young non-
physically active people, senior athletes and senior non-physically active people), and,
thus, a total of 48 seemed a feasible goal, based on previous experience. On the other hand,
preliminary data suggest a variation coefﬁcient of 1.76% for reproducibility. Thus, with such
a good reproducibility, this study aimed to allow the estimation of group means and their
standard deviation, and to discover effects of age and training state. As stated above, there
has been no previous quantitative human study on endomysium thickness. For sample size
calculation, we therefore rely on previous research reporting a group difference between 6-
and 18-week-old chickens of 1.03 µm with standard deviation of 1.24 µm in endomysium
thickness [10]. Using a t-test to test the primary hypothesis, and setting α = 0.05 and β = 0.2,
we arrive at an estimated sample size of 24 subjects per group. The study aimed, therefore,
at including 12 subjects per group, and, thus, a total of 48 participants.
Int. J. Environ. Res. Public Health 2022, 19, 13696
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2.3. Testing Procedures
Jumping mechanography was employed to assess motor performance. This technique
relies on plyometric tests performed on a force platform to evaluate dynamic muscle
function and has been found to be a reliable and sensitive measure of mobility performance
in elite athletes as well as in frail patients employing both two-legged and single-legged
jumps [3,11]. In the present study, a multiple one-leg hopping (M1LH) test using the
dominant leg was carried out [12–14]. Subjects were instructed to start with shallow hops,
increase height to maximum followed by 4 to 5 maximum hops and ﬁnally reduce hop
height again.
The M1LH test was performed on a force plate (Leonardo Mechanography GRFP,
Novotec Medical Inc., Pforzheim, Germany) continuously measuring the vertical ground
reaction forces (GRF) at a sampling rate of 800 Hz.
2.4. Data Processing
The GRF signals recorded during the M1LH test were analyzed using the module
‘signal’ within the software package R (R Core Team, 2020, Vienna, Austria). Each individual
hop was identiﬁed by the detecting the ﬂying phases (absence of GRF) and the phases of
ground contact (positive GRF). The following variables were calculated for each hop:
•
Flight time (FT): duration of the ﬂying phase of the hop, that is, time interval in which
the subject has no contact with the ground;
•
Ground contact time (GCT): interval of time in which the subject’s leg is contact with
the ground after the FT;
•
Maximum GRF: peak GRF registered during the GCT after landing, and prior to the
next hop;
•
Hopping height (HH). Calculated from ﬂight time using the equation of uniformly
accelerated motions [15]:
HH = g·FT2/8
where:
g—gravity acceleration constant (9.81 m/s2)
FT—ﬂight time
•
Vertical acceleration (AV) of the center of mass (COM) over time. Calculated from GRF
and subject’s body mass [16]:
AV(t) = F(t) − m·g
m
where:
F(t)—GRF over time
m—body mass
g—gravity acceleration constant (9.81 m/s2)
•
Vertical velocity (VV) of the COM over time. Calculated from the integration in the
time domain of the acceleration-time data [16]:
VV(t) =
Z
AV(t)dt + c =
Z F(t) − m·g
m
dt + c
where:
AV(t)—Vertical acceleration over time
F(t)—GRF over time
m—body mass
g—gravity acceleration constant (9.81 m/s2)
c—integration constant
The integration constant (c) was based upon the assumption that vertical velocity of
the COM was zero at the middle of the GCT; in other words, assuming that COM peak
Int. J. Environ. Res. Public Health 2022, 19, 13696
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downward displacement is reached at the middle of the GCT between hops [16,17]. Since
GRF data is discrete, the previous integration was implemented by summation of the
GRF samples.
•
Vertical displacement (DV) of the COM during ground contact. Calculated from
numerical double integration in the time domain of the acceleration-time data, or
equivalently, from the numerical integration in the time domain of the vertical velocity-
time data [16,18]:
DV(t) =
Z
VV(t)dt + c =
x F(t) − m·g
m
dt dt + c
where:
VV(t)—Vertical velocity over time
F(t)—GRF over time
m—body mass
g—gravity acceleration constant (9.81 m/s2)
c—integration constant
Since our goal was to determine COM displacement, the integration constant (k) was
set to zero at the initial instant [19]. Given that velocity data are discrete, the previous
integration was implemented by summation of the velocity samples.
•
Max DV(t): Maximum vertical downward displacement of the COM during ground
contact (also known as countermovement depth);
•
Power output, normalized to subject’s body weight [20]:
P(t) = F(t)·VV(t)
m
where:
VV(t)—Vertical velocity over time
F(t)—GRF over time
m—body mass
•
Vertical stiffness (K), calculated for each hop as the ratio between the peak GRF and
maximum COM displacement, according to the spring–mass model [18,21,22]. Since
body size inﬂuences stiffness [23], K was normalized by body mass for each subject
and expressed as kN/m/kg [19,24]:
K =
maxF(t)
maxDV(t)·m−1
where:
max F(t)—Maximum GRF;
m—body mass.
According to the spring–mass model, max F(t) and max DV(t) coincide in the middle
of the ground-contact phase during hopping [18]. In fact, the vertical stiffness parameter, K,
is only valid if the lower extremity behaves like a simple spring–mass system [19,25]. To
evaluate that assumption, the linear correlation between DV(t) and F(t) was also calculated.
Only those hops for which this correlation is r > 0.80 comply with the assumption of
spring-like behavior [19,25]. Hops that were unable to meet this criterion were not used for
data analysis.
Finally, for each subject the three highest hops (the three hops with the highest HH)
were selected, and the averages for all the previously described parameters were computed
using these three hops.
Int. J. Environ. Res. Public Health 2022, 19, 13696
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2.5. Statistical Analysis
The impact of age and training status on the M1LH test was evaluated by comparing
all previously described biomechanical parameters using the two-way analysis of variance
(ANOVA) with two factors (age × training status). To assess assumptions of homoscedas-
ticity, Levene’s test was performed. Normality was evaluated using Shapiro–Wilk’s test.
Group means were compared performing Tukey’s post-hoc test, if a signiﬁcant main ef-
fect was observed. Statistical signiﬁcance was set at p < 0.05. Pearson correlation (r) was
employed to measure linear correlation and evaluate the assumption of spring–mass-like
behavior. These statistical analyses were performed using Jamovi (The Jamovi project, 2019,
Version 1.0).
3. Results
3.1. Participants Characteristics
Forty-three male subjects completed the study. Twenty-two young subjects (21–35
years old) and twenty-one senior subjects (59–75 years old) were recruited. Among them,
ten young subjects and ten senior subjects regularly trained and competed as athletes in
sprint or jumping events, while the remaining subjects (twelve young and eleven senior)
were only ordinary physically active without performing intensive and speciﬁc training
like the subjects in the two athletes’ groups do. Thus, four groups were established: young
athletes (YA), young controls (YC), senior athletes (SA) and senior controls (SC). Their
characteristics are summarized in Table 1.
Table 1. Participants characteristics.
Young Athletes
Young Controls
Senior Athletes
Senior Controls
N
10
12
10
11
Height [cm]
178.9 ± 7.7
180.8 ± 6.7
177.6 ± 7.6
176.9 ± 5.8
Weight [kg]
76.2 ± 13.7
75.4 ± 13.0
74.8 ± 8.4
79.8 ± 8.8
Age [years]
23.9 ± 2.3
28.9 ± 4.5
65.1 ± 4.1
66.1 ± 4.8
Activity Level [METs/week]
55.4 ± 22.8
20.4 ± 42.9
94.3 ± 39.5
23.9 ± 13.2
N: number of subjects. METs: metabolic equivalents of task.
3.2. Biomechanical Parameters
Table 2 shows the descriptive statistics (average ± standard deviation) of the biome-
chanical parameters calculated for the M1LH test. As described in Section 2.4, these
parameters included HH, GCT, maximum GRF, maximum DV, K and maximum power.
Table 2. Biomechanical parameters of the multiple one-legged hopping test.
Young Athletes
Young Controls
Senior Athletes
Senior Controls
Hopping Height [cm]
16.6 ± 3.3
11.8 ± 2.5
10.7 ± 3.4
6.9 ± 2.3
Ground Contact Time [ms]
275 ± 48
320 ± 50
303 ± 53
348 ± 48
Max GRF [kN]
2.87 ± 0.52
2.32 ± 0.57
2.31 ± 0.31
2.26 ± 0.30
Max DV [%]
9.3 ± 1.8
9.8 ± 1.7
9.7 ± 2.3
9.0 ± 1.8
Vertical Stiffness [N/m/kg]
230 ± 86
165 ± 49
180 ± 59
191 ± 55
Max Power [W/kg]
32.9 ± 6.5
25.5 ± 4.8
22.7 ± 4.9
18.1 ± 3.2
Data reported as average ± standard deviation. GRF: ground reaction forces. DV: Vertical displacement of the
center of mass during ground contact.
3.3. Hopping Height
A two-way ANOVA was performed to analyze the effect of age and training status on
hopping height revealing that there was not a statistically signiﬁcant interaction between
the effects of age and training status (F-value = 0.264, p = 0.610). Simple main effects
analysis showed that age did have a statistically signiﬁcant effect on hopping height (F-
value = 34.995, p < 0.001), and that training status also had a statistically signiﬁcant effect
Int. J. Environ. Res. Public Health 2022, 19, 13696
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on hopping height (F-value = 21.823, p < 0.001). Tukey’s post-hoc test results for multiple
comparisons are shown in Table 3.
Table 3. ANOVA results for hopping height.
Comparison
Mean
Difference
SE
df
t
p-Value
Cohen’s d
95% C.I.
Lower
Upper
Young − Seniors
5.40
0.913
36.0
5.92
<0.001
1.88
1.09
2.66
Athletes − Controls
4.26
0.913
36.0
4.67
<0.001
1.48
0.747
2.21
Young athletes − Young controls
4.73
1.29
36.0
3.658
0.004
1.644
0.651
2.637
Young athletes − Senior athletes
5.87
1.32
36.0
4.436
<0.001
2.038
0.987
3.090
Young athletes − Senior controls
9.66
1.32
36.0
7.305
<0.001
3.356
2.127
4.586
Young controls − Senior athletes
1.14
1.26
36.0
0.903
0.803
−0.394
−1.286
0.497
Young controls − Senior controls
4.93
1.26
36.0
3.919
0.002
1.712
0.736
2.689
Senior athletes − Senior controls
3.79
1.29
36.0
2.947
0.027
1.318
0.358
2.278
3.4. Ground Contact Time
A two-way ANOVA was performed to analyze the effect of age and training status on
GCT revealing that there was not a statistically signiﬁcant interaction between the effects
of age and training status (F-value = 6.21 × 10 −7, p = 0.999). Simple main effects analysis
showed that age did not have a statistically signiﬁcant effect on GCT (F-value = 3.16, p
= 0.084), although simple main effects analysis showed that training status did have a
statistically signiﬁcant effect on GCT (F-value = 8.30, p = 0.007). Tukey’s post-hoc test
results for multiple comparisons are shown in Table 4.
Table 4. ANOVA results for ground contact time.
Comparison
Mean
Difference
SE
df
t
p-Value
Cohen’s d
95% C.I.
Lower
Upper
Young − Seniors
−28.0
15.8
36.0
−1.78
0.084
−0.564
−1.22
0.0932
Athletes − Controls
−45.4
15.8
36.0
−2.88
0.007
−0.913
−1.59
−0.234
Young athletes − Young controls
−45.4
22.3
36.0
−2.032
0.195
−0.913
−1.85
0.0240
Young athletes − Senior athletes
−28.0
22.8
36.0
−1.227
0.614
−0.564
−1.51
0.3776
Young athletes − Senior controls
−73.4
22.8
36.0
−3.214
0.014
−1.477
−2.47
−0.4804
Young controls − Senior athletes
17.4
21.7
36.0
0.800
0.854
−0.349
−1.24
0.5407
Young controls − Senior controls
−28.0
21.7
36.0
−1.290
0.575
−0.563
−1.46
0.3329
Senior athletes − Senior controls
−45.4
22.2
36.0
−2.041
0.192
−0.913
−1.85
0.0200
3.5. Maximum Ground Reaction Forces
A two-way ANOVA was performed to analyze the effect of age and training status
on max GRF revealing that there was not a statistically signiﬁcant interaction between the
effects of age and training status (F-value = 3.35, p = 0.075). Simple main effects analysis
showed that age did have a statistically signiﬁcant effect on max GRF (F-value = 4.97,
p = 0.032), and that training status also had a statistically signiﬁcant effect on max GRF
(F-value = 4.56, p = 0.040). Tukey’s post-hoc test results for multiple comparisons are shown
in Table 5.
Int. J. Environ. Res. Public Health 2022, 19, 13696
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Table 5. ANOVA results for maximum ground reaction forces.
Comparison
Mean
Difference
SE
df
t
p-Value
Cohen’s d
95% C.I.
Lower
Upper
Young − Seniors
0.312
0.140
36.0
2.23
0.032
0.707
0.0421
1.37
Athletes − Controls
0.299
0.140
36.0
2.14
0.040
0.677
0.0141
1.34
Young athletes − Young controls
0.5546
0.198
36.0
2.7979
0.039
1.2576
0.298
2.217
Young athletes − Senior athletes
0.5677
0.203
36.0
2.8018
0.039
1.2873
0.306
2.269
Young athletes − Senior controls
0.6103
0.203
36.0
3.0121
0.023
1.3840
0.395
2.373
Young controls − Senior athletes
0.0131
0.193
36.0
0.0681
1.000
−0.0298
−0.916
0.856
Young controls − Senior controls
0.0557
0.193
36.0
0.2893
0.991
0.1264
−0.760
1.013
Senior athletes − Senior controls
0.0426
0.197
36.0
0.2161
0.996
0.0966
−0.811
1.004
3.6. Maximum DV
A two-way ANOVA was performed to analyze the effect of age and training status on
maximum DV revealing that there was not a statistically signiﬁcant interaction between the
effects of age and training status (F-value = 0.9687, p = 0.332). Simple main effects analysis
showed that age did not have a statistically signiﬁcant effect on maximum DV (F-value
= 0.0956, p = 0.759), and that training status did not have a statistically signiﬁcant effect
on maximum DV (F-value = 0.0609, p = 0.806). Tukey’s post-hoc test results for multiple
comparisons are shown in Table 6.
Table 6. ANOVA results for maximum DV.
Comparison
Mean
Difference
SE
df
t
p-Value
Cohen’s d
95% C.I.
Lower
Upper
Young − Seniors
0.00186
0.00601
36.0
0.309
0.759
0.0980
−0.545
0.741
Athletes − Controls
0.00148
0.00601
36.0
0.247
0.806
0.0783
−0.565
0.721
Young athletes − Young controls
−0.00443
0.00852
36.0
−0.5201
0.954
−0.2338
−1.147
0.680
Young athletes − Senior athletes
−0.00406
0.00871
36.0
−0.4658
0.966
−0.2140
−1.147
0.719
Young athletes − Senior controls
0.00334
0.00871
36.0
0.3836
0.980
0.1763
−0.757
1.109
Young controls − Senior athletes
0.000375
0.00828
36.0
0.0452
1.000
−0.0198
−0.906
0.866
Young controls − Senior controls
0.00777
0.00828
36.0
0.9384
0.784
0.4100
−0.482
1.302
Senior athletes − Senior controls
0.00740
0.00848
36.0
0.8727
0.819
0.3903
−0.521
1.302
3.7. Vertical Stiffness
A two-way ANOVA was performed to analyze the effect of age and training status on
K revealing that there was not a statistically signiﬁcant interaction between the effects of
age and training status (F-value = 3.658, p = 0.064). Simple main effects analysis showed
that age did not have a statistically signiﬁcant effect on K (F-value = 0.385, p = 0.539), and
that training status did not have a statistically signiﬁcant effect on K (F-value = 1.852, p =
0.182). Tukey’s post-hoc test results for multiple comparisons are shown in Table 7.
Table 7. ANOVA results for vertical stiffness.
Comparison
Mean
Difference
SE
df
t
p-Value
Cohen’s d
95% C.I.
Lower
Upper
Young − Seniors
12.3
19.8
36.0
0.621
0.539
0.197
−0.448
0.841
Athletes − Controls
27.0
19.8
36.0
1.36
0.182
0.431
−0.220
1.08
Young athletes − Young controls
64.9
28.1
36.0
2.309
0.115
1.038
0.0931
1.982
Young athletes − Senior athletes
50.2
28.7
36.0
1.748
0.315
0.803
−0.1484
1.754
Young athletes − Senior controls
39.3
28.7
36.0
1.367
0.528
0.628
−0.3157
1.572
Young controls − Senior athletes
−14.7
27.3
36.0
−0.537
0.949
0.235
−0.6532
1.123
Young controls − Senior controls
−25.6
27.3
36.0
−0.937
0.785
−0.410
−1.3011
0.482
Senior athletes − Senior controls
−10.9
28.0
36.0
−0.391
0.979
−0.175
−1.0828
0.733
Int. J. Environ. Res. Public Health 2022, 19, 13696
8 of 11
3.8. Maximum Power
A two-way ANOVA was performed to analyze the effect of age and training status on
maximum power revealing that there was not a statistically signiﬁcant interaction between
the effects of age and training status (F-value = 0.848, p = 0.363). Simple main effects
analysis showed that age did have a statistically signiﬁcant effect on maximum power
(F-value = 31.105, p < 0 .001), and that training status also had a statistically signiﬁcant
effect on maximum power (F-value = 14.452, p < 0 .001). Tukey’s post-hoc test results for
multiple comparisons are shown in Table 8.
Table 8. ANOVA results for maximum power.
Comparison
Mean
Difference
SE
df
t
p-Value
Cohen’s d
95% C.I.
Lower
Upper
Young − Seniors
0.890
0.160
36.0
5.58
<0.001
1.77
0.999
2.54
Athletes − Controls
0.607
0.160
36.0
3.80
<0.001
1.21
0.501
1.91
Young athletes − Young controls
0.754
0.226
36.0
3.33
0.010
1.497
0.5178
2.476
Young athletes − Senior athletes
1.037
0.231
36.0
4.48
<0.001
2.060
1.0061
3.114
Young athletes − Senior controls
1.497
0.231
36.0
6.47
<0.001
2.973
1.8014
4.145
Young controls − Senior athletes
0.283
0.220
36.0
1.29
0.576
−0.563
−1.4592
0.333
Young controls − Senior controls
0.743
0.220
36.0
3.38
0.009
1.476
0.5224
2.430
Senior athletes − Senior controls
0.460
0.225
36.0
2.04
0.192
0.913
−0.0196
1.846
4. Discussion
The goal of this study was to assess the effects of aging and ﬁtness on jumping
performance and biomechanical parameters. To do so, four groups (i.e., YA, SA, YC and
SC) were established according to their age (young, between 21 and 35, vs. older, between
59 and 75) and ﬁtness status (competing athletes vs. non-physically active).
YA and SC showed the highest (16.6 ± 3.3 cm) and lowest (6.9 ± 2.3 cm) HH, re-
spectively, which differed signiﬁcantly from the other two groups (YC: 11.8 ± 2.5 cm, SA:
10.7 ± 3.4 cm; all p < 0.05). GCT was signiﬁcantly shorter for YA (275 ± 48 ms) compared
to SC (348 ± 48 ms; p = 0.014), with no statistical differences between the other groups
(YC: 320 ± 50 ms, SA: 303 ± 53 ms; all p > 0.05). Maximum GRF was signiﬁcantly higher
for YA (2.87 ± 0.52 kN) compared with the rest of the groups (YC: 2.32 ± 0.57 kN, SA:
2.31 ± 0.31 kN, SC: 2.26 ± 0.30 kN; all p < 0.05). Peak power was signiﬁcantly higher
for YA (32.9 ± 6.5 W/kg) compared with the rest of the groups (YC: 25.5 ± 4.8 W/kg,
SA: 22.7 ± 4.9 W/kg, SC: 18.1 ± 3.2 W/kg; all p < 0.01), and for YC compared to SC
(p < 0.01). No statistically signiﬁcant differences among groups were found in maximum
DV, expressed as a percentage of subject’s height (YA: 9.32 ± 1.8%, YC: 9.77 ± 1.7%, SA:
9.73 ± 2.3%, SC: 8.99 ± 1.8%; all p > 0.05). No statistically signiﬁcant differences among
groups were found in vertical stiffness, normalized to body mass (YA: 230 ± 86 N/m/kg,
YC: 165 ± 49 N/m/kg, SA: 180 ± 59 N/m/kg, SC: 191 ± 55 N/m/kg; all p > 0.05).
As expected, the best performance was observed in YA, and the worst performance was
registered in SC in the described M1LH test. Interestingly, there were not any differences
found between YC and SA, so these results suggest that chronic training could be associated
to a counterbalance of effects that are normally associated with aging. Within young
participants, YA showed signiﬁcantly higher GRF and power than YC, while there were
no differences in ground contact time, vertical displacement (during countermovement)
and stiffness, so it could be hypothesized that higher ﬁtness improves performance by
increasing force application and muscle power, but it doesn’t affect the other biomechanical
parameters. Within older participants, SA showed a signiﬁcantly higher performance
than SC, although there were no statistically signiﬁcant differences between the analyzed
biomechanical parameters, probably due to the reduced number of participants. Within
trained individuals of different age, YA showed signiﬁcantly higher GRF and power than
SA, while there were no differences in ground contact time, vertical displacement (during
Int. J. Environ. Res. Public Health 2022, 19, 13696
9 of 11
countermovement) and stiffness, suggesting, therefore, that aging negatively affects force
application and muscle power, but it doesn’t affect the remaining biomechanical parameters.
Age-related changes in muscle power have been previously reported in the literature [26].
Within sedentary individuals, YC had a better performance than SC probably attributable to
a signiﬁcantly higher muscle power, suggesting again that aging negatively affects muscle
power [27]. In conclusion, both aging and sedentarism result in a decreased muscle power
in the M1LH test, but lifelong training could be associated to a counterbalance of the effects
of aging [28–32].
There are several limitations to the study. First, the number of participants is reduced,
which could be limiting the signiﬁcance of our ﬁndings. Second, there were male par-
ticipants only. Including females might have unveiled other results, as there are major
differences between female and male skeletal muscles, including differences in energy
metabolism, ﬁber type composition, and contractile speed [33]. Finally, only sports with a
high implication of muscle power (sprinting and jumping) were considered in the partici-
pants’ selection. It would be of interest to include other athletic modalities and sports.
Future research directions might include studying differences in muscle architecture
and the connective tissue of the muscles to better understand the underlying causes of age-
related decline in power and how to optimize physical training to counteract such processes.
In the elder athletes, the superior performance may result from both, an intensive training
and a genetically determined slower aging process. The number of athletes performing
sprint or jumping disciplines in high age is extremely small, and much smaller compared
with the more frequent elder endurance runners. The small number of cases could suggest
that the conservation of plyometric performance in senior sprinters and jumpers might not
only result from adaptation on training but may a have genetical component affecting aging
as well. Future studies should analyze genetical characteristics of master athletes to clarify
this question. More importantly, further research and action are required to propagate
master athletics as a role model and therefore contribute to improve life quality in our
aging society.
5. Conclusions
Lifelong athletic training can contribute to partly offsetting age-related muscle power
decline.
Author Contributions: Conceptualization, J.R. and J.Z.; methodology, J.R. and J.Z.; formal analysis,
H.S.-T.; investigation, J.Z., J.B. and W.S.; data curation, J.Z., J.B. and H.S.-T.; writing—original draft
preparation, H.S.-T.; writing—review and editing, J.R. and B.S.; visualization, H.S.-T.; supervision,
B.S: project administration, J.R.; funding acquisition, J.R. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: The study was conducted according to the guidelines of the
Declaration of Helsinki, and approved by the Ethical Committee of the Ärztekammer Nordrhein
in Düsseldorf, Germany (ref. no. 2018269). The study was prospectively registered on the German
register of clinical trials (www.drks.de accessed on 20 September 2022) with registration number
DRKS00015764.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data presented in this study are available on request from the
corresponding author.
Acknowledgments: Not applicable.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
Int. J. Environ. Res. Public Health 2022, 19, 13696
10 of 11
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Effects of Aging and Fitness on Hopping Biomechanics.
Sanchez-Trigo, Horacio,Zange, Jochen,Sies, Wolfram,Böcker, Jonas,Sañudo, Borja,Rittweger, Jörn