Citation: Tomovic, M.; Toliopoulos,
A.; Koutlianos, N.; Dalkiranis, A.;
Bubanj, S.; Deligiannis, A.; Kouidi, E.
Correlation between
Cardiopulmonary Indices and
Running Performance in a 14.5 km
Endurance Running Event. Int. J.
Environ. Res. Public Health 2022, 19,
12289. https://doi.org/10.3390/
ijerph191912289
Academic Editors: Pantelis
T. Nikolaidis and Masatoshi
Nakamura
Received: 3 August 2022
Accepted: 23 September 2022
Published: 27 September 2022
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International  Journal  of
Environmental Research
and Public Health
Article
Correlation between Cardiopulmonary Indices and Running
Performance in a 14.5 km Endurance Running Event
Milena Tomovic 1
, Alexandros Toliopoulos 1
, Nikolaos Koutlianos 1, Anastasios Dalkiranis 1, Sasa Bubanj 2
,
Asterios Deligiannis 1
and Evangelia Kouidi 1,*
1
Sports Medicine Laboratory, School of Physical Education and Sports Science, Aristotle University,
Thermi PC, 57001 Thessaloniki, Greece
2
Faculty of Sport and Physical Education, University of Nis, 18000 Nis, Serbia
*
Correspondence: kouidi@phed.auth.gr
Abstract: Background: Running is a common recreational activity, and the number of long-distance-
race participants is continuously growing. It is well-established that regular physical activity can
prevent and manage non-communicable diseases and beneﬁt public health. Training for a long-
distance race requires development of speciﬁc aerobic abilities and should generate the desired
race performance. The purpose of this study was to support the training design and motivation of
recreational endurance runners, by investigating whether a 14.5 km race performance of long-distance
runners correlates with their cardiopulmonary indices measured in the laboratory. Methods: To
examine the relationships of a 14.5 km running performance with the cardiopulmonary parameters of
amateur runners, a cross-sectional study design was applied. Fifteen (eleven men and four women)
recreational long-distance runners (aged 41.3 ± 9.2 years) from Northern Greece were included in the
study and were evaluated in the laboratory within one week before an endurance running race—the
14.5 km Philip Road race, in Greece. The laboratory-based examinations of the athletes consisted of a
comprehensive medical pre-participation screening and maximal cardiopulmonary exercise testing.
Results: The results showed that the 14.5 km race performance time (73.8 ± 9.7 min) signiﬁcantly
correlated with the cardiopulmonary-exercise-testing speed-related indices at speciﬁc submaximal
and maximal workloads (p < 0.01, p < 0.05), while the cardiopulmonary indices of oxygen uptake
did not reliably predict race running time (p > 0.05). Conclusions: There is a better correlation of
the 14.5 km running performance of recreational long-distance runners with the cardiopulmonary-
exercise-testing speed-related indices at speciﬁc workloads than with the indices of oxygen uptake,
running economy or respiratory economy. When preparing a training strategy, amateur long-distance
runners should mostly rely on speciﬁc running-speed-related laboratory data rather than on oxygen-
uptake values.
Keywords: running; maximal oxygen uptake; running economy; sports performance; cardiopulmonary
exercise test
1. Introduction
Running is a common recreational activity, and the number of long-distance-race
participants is continuously growing [1–5]. In England alone, more than 3 million adults
participate in recreational running each month, and the USA and Australia show similar
trends [6–8]. According to the Physical Activity Council (the USA’s deﬁnitive source for
sports, ﬁtness and recreational activity participation), running is one of the top 10 recre-
ational activities that inactive Americans would choose, if about to commence regular
exercise [8]. Regular physical activity is one of the cornerstones of public health, as it is
proven to help prevent and manage non-communicable diseases such as hypertension,
obesity and several cancers. An active lifestyle respects the environment and induces
behaviours that can preserve and improve environmental health [7,9]. Regular running
Int. J. Environ. Res. Public Health 2022, 19, 12289. https://doi.org/10.3390/ijerph191912289
https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2022, 19, 12289
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has conﬁrmed health beneﬁts [10–14], and a recent systematic review and meta-analysis
indicates that running reduces the risk of all-cause, cardiovascular and cancer mortality by
27%, 30% and 23%, respectively [9]. The same research identiﬁes a literature gap in studies
that would include sustained running participation, reproducible assessment of running
habits and accurate evaluation of running performance.
Training for a long-distance race requires development of speciﬁc aerobic abilities and
should generate the desired race performance. Amateur runners often use elite runners’
training methodologies, risking external overload, with consequential higher incidence
of overuse injuries [15–17]. Furthermore, the studied relationship between recreational
runners’ motivation and the incidence of injury emphasises the importance of adequate
training methodology for injury prevention, especially among novice runners [17–19]. Thus,
it is important to understand and objectively evaluate physiological and other parame-
ters that, according to the existing literature, might inﬂuence or predict the long-distance
running performance of amateur runners [20–22]. The available scientiﬁc evidence does
not offer a reliable equation for performance prediction, and relevant studies are char-
acterised with high data heterogeneity and often controversial ﬁndings. It is not clear if
anthropometric [22], cardiorespiratory [21,23] or training load [21,24] indicators form valid
performance-prediction models for popular long-distance amateur running events [20].
Additionally, performance prediction models do not identify the physiological parameters
necessary for adequate training prescription. Data on anthropometric parameters are in-
complete [22], while cardiopulmonary indices and training load indicators such as maximal
oxygen uptake and kilometres of running per week, although studied repeatedly and with
an immense amount of data, still do not provide the best solution for training design and
follow up [22,23].
The purpose of this study was to support the training design and motivation of
recreational endurance runners, by investigating whether a 14.5 km race performance
of long-distance runners correlates with their cardiopulmonary indices measured in the
laboratory. Hopefully, our ﬁndings will help recreational runners, not ready for or capable
of a whole marathon race, reach the desired performance level.
2. Materials and Methods
To examine the relationships of a 14.5 km running performance with cardiopulmonary
parameters of amateur runners, a cross-sectional study design was applied. Fifteen (eleven
men and four women) recreational long-distance runners from Northern Greece were
included in the study and were evaluated in the laboratory within one week before an
endurance running race—the 14.5 km Philip Road race, Greece. The laboratory-based
examinations of the athletes consisted of a comprehensive medical pre-participation screen-
ing and maximal cardiopulmonary exercise testing (CPET) performed on a treadmill. All
amateur runners included were healthy and had been training regularly. They gave an
informed consent and completed a questionnaire with detailed medical and training history.
Participants’ training history will be presented in the results section.
Anthropometric parameters were measured prior to CPET (height—SECA Leices-
ter resuscitation meter; weight, body fat and muscle mass percentage—Omron Karada
Scan BF511, HBF-511T-E/HBF-511B-E). Maximal treadmill (Montara Trackmaster 428, KS,
USA) CPET, via a breath-by-breath gas-analyzing system (Geratherm Respiratory GmbH’s
BlueCherry, Bad Kissingen, Germany), followed the clinical exam in an appropriate labora-
tory environment (room temperature 20 ◦C and relative humidity between 25–50%). The
gas-analyzing system was validated prior to each testing. Heart rate (HR) was monitored
by Polar Receiver Pulstik system (Geratherm Respiratory GmbH, Bad Kissingen, Germany).
Participants did not train nor consume any nutritional supplements or caffeinated bev-
erages 24 h before testing. The maximal ramp exercise protocol [25] included: warm-up:
1 min at speed of 2 km/h, slope 0% and 3 min at speed of 5 km/h, slope 0%; test: speed
5 km/h and slope 1%. The workload was enhanced by a gradual increase in the speed, by
1.2 km/h per min, until exhaustion. Moreover, the slope increased to 2%, when participants
Int. J. Environ. Res. Public Health 2022, 19, 12289
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reached a speed of 13 km/h, and did not increase further with speed increment beyond
13 km/h. Recovery was 5 min.
During the CPET, minute ventilation (VE, L/min), oxygen consumption (VO2, relative
(mL/kg/min) and absolute values (mL/min)), respiratory exchange ratio (RER), VCO2
(carbon dioxide production, mL/kg/min), ventilatory equivalents for oxygen and carbon
dioxide (VE/VO2, VE/VCO2), PETO2 (end tidal oxygen volume), PETCO2 (end tidal carbon
dioxide volume), HR, oxygen pulse (O2 pulse, mL/beat), time to voluntary exhaustion
(4 min of warm up and 5 min of recovery were excluded) and the 2nd ventilatory threshold
(VT) were measured [26]. Additionally, the same parameters as well as the running speeds
(“v” expressed in km/h) were noted at the VT and RER1 (RER value of 1, VO2 = VCO2)
points of the CPET: VO2VT, VO2RER1, vVT, vRER1, HRVT, HRRER1 and maximal parameters:
VO2max, vVO2max, vpeak, HRmax, VEmax, max O2 pulse, RERmax, tidal volume max,
VO2max/VO2ref% (maximal VO2 expressed as a percentage of a VO2max value predicted
according to participant’s age, sex and training habits) and VT/VO2max% (percentage of
achieved maximal VO2 at VT CPET’s point).
Running economy (RE)8, RE10, RE12, VO2/WR and VO2/WR (7.9–13.1 km/h) values
were used as running economy indicators. The RE8, RE10 and RE12 indicators were derived
from VO2 of each athlete at speeds of 8 km/h, 10 km/h and 12 km/h, respectively, and were
expressed in mL/min/kg. The VO2/WR and VO2/WR (7.9–13.1) indicators were calculated
directly from the ergospirometer, and they represent VO2 achieved at these speeds and
converted to a work rate (WR) expressed in watts (mL/min/watt). The VO2/WR index
represents the average value through the whole test load, while the VO2/WR (7.9–13.1)
index represents the average oxygen uptake per work rate between speeds of 7.9 km/h
and 13.1 km/h. These speciﬁc values of speeds were marked because they were reached by
all participants; thus, a direct comparison between them was possible. Furthermore, the
speed of 7.9 km/h is the lowest intensity that forced running over walking.
Two weeks after the lab evaluation, the athletes participated in the Philip Road race—
14.5 km route from Vergina to Veria, in Greece. The competition was entirely on asphalt,
and it started at 10.30 am. The athletes were offered water at 5.4 km and water and isotonic
ﬂuid at 10.4 km. The weather on the race day was clear and with optimal conditions. The
race performance times of the runners were collected from the ofﬁcial results, and these
time records were net, i.e., the time from the moment the athletes crossed the starting line
to the time they arrived at the ﬁnish line.
Descriptive statistics were used to describe categorical variables. Continuous variables
were expressed as mean ± SD and Shapiro–Wilk test was used for testing the normality
of all data. The differences between values were evaluated using the paired sample t-test.
Relationships between categorical variables were tested using the chi-squared statistic. The
Pearson linear correlation coefﬁcient was used for quantitative values, and, for the non-
linear data, we used Spearman non-parametric correlation. Statistical analysis was carried
out with the IBM SPSS statistical program (Social Package for Social Sciences, Chicago, IL,
USA, version 25.0). A two-tailed p < 0.05 was accepted as statistically signiﬁcant.
3. Results
All ﬁfteen recruited recreational runners (aged 41.3 ± 9.2 years) completed the 14.5 km
race and had no injuries or any health disorders during the race. They had been practicing
running for the past 5.6 (±5.6) years, with a frequency of 5 (±1.4) days, 7.2 (±3.1) hours
and 52.7 (±19.5) km per week. Their demographic, anthropometric and race performance
data, as shown in Tables 1 and 2, contain the overview of the participants’ CPET results.
Int. J. Environ. Res. Public Health 2022, 19, 12289
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Table 1. Demographic, anthropometric and race performance characteristics of participants.
Participants
Race Performance
(min)
Average Race
Speed (km/h)
Age
(Years)
Height
(cm)
Weight
(kg)
BMI b
(kg/m2)
Body Fat
(%)
Muscle
Mass (%)
1
57.93
15.02
37
189
83
23.24
19
38.3
2
59.38
14.65
37
180.5
76
23.33
21.6
37
3
64.18
13.56
48
181
77
23.50
18.7
37.7
4
66.7
13.04
51
169
68
23.81
18.1
38.4
5
67.83
12.83
52
178
74
23.36
18.6
37.4
6
71.23
12.21
38
171
73
24.96
23
37.1
7
71.42
12.18
34
176
72
23.24
17.2
40.7
8
71.85
12.11
39
184
92
27.17
28.1
33.4
9
74.38
11.70
32
156.5
57
23.27
32.9
28.4
10
78.13
11.14
39
173.5
78
25.91
26
35
11
79.83
10.90
39
169.5
78
27.15
25.7
35.8
12
81.23
10.71
66
173
79
26.40
20.7
35.4
13
86.63
10.04
33
177
66
21.70
20.3
35.5
14
86.65
10.04
40
168.5
68
23.95
30.8
30.5
15
89.03
9.77
34
171
60
20.52
22.6
33.6
Mean Value
73.8
12.0
41.3
174.5
73.4
24.1
22.9
35.6
SD a
9.7
1.6
9.2
7.8
8.8
2.0
4.8
3.2
Median
71.85
12.11
39
173.5
74.0
23.50
21.6
35.8
a standard deviation; b body mass index.
Table 2. Cardiopulmonary exercise testing results of participants.
Participants
Test Time
(min)
VE b (L/min)
Running
Speed at VT c
Point (km/h)
Maximal
Running
Speed
VO2max d
(L)
VO2max d
(mL/min/kg)
Oxygen
Pulse
(mL/Beat)
Maximal
Heart Rate
(Beat/min)
1
11.17
123
15.4
16.1
4.24
51.1
24
170
2
12.28
144
17.1
17.4
3.96
52.1
22
177
3
10.08
103
11.6
15.1
3.21
41.7
22
160
4
11.75
132
16.6
16.6
3.8
55.9
21
181
5
9.97
146
13.6
14.9
3.3
44.6
23
141
6
10.15
118
15.1
15.1
3.21
44.0
25
169
7
11.17
123
16.1
16.1
3.76
52.2
22
175
8
11.32
135
15.6
16.5
4.08
44.3
29
163
9
10.10
95
14.4
15.1
2.66
46.7
14
185
10
11.25
143
15.1
16.1
3.45
44.2
24
171
11
9.63
131
14.6
14.6
3.48
44.6
20
176
12
8.97
116
13.4
13.9
3.46
43.8
21
171
13
8.82
108
13.1
13.9
3.14
47.6
17
182
14
9.10
113
13.6
14.1
3.01
44.3
17
178
15
8.03
91
12.6
13.1
2.18
36.3
12
179
Mean Value
10.3
121.4
14.5
15.2
3.4
46.2
20.9
171.9
SD a
1.2
17.4
1.5
1.2
0.5
4.9
4.4
11.0
Median
10.10
123.00
14.60
15.10
3.45
44.59
22.00
175.00
a standard deviation; b minute ventilation; c ventilatory threshold; d maximal oxygen consumption.
Int. J. Environ. Res. Public Health 2022, 19, 12289
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Correlation analysis of the anthropometric parameters showed, for the race perfor-
mance times, signiﬁcant coefﬁcients only for the height (174.5 ± 7.8 cm, r = −0.534, p < 0.01)
and the muscle mass percentage (35.6 ± 3.2%, r = −0.696, p < 0.01) of the participants.
From the participants’ training history data, only the number of kms that participants
achieved during their weekly training showed a statistically signiﬁcant negative correlation
(52.7 ± 19.5 km, r = −0.640, p < 0.05) with the race performance time.
Data obtained during the participants’ CPET, which had a statistically signiﬁcant
negative correlation with the athletes’ race performance, measured as race end time, were:
fatigue time (10.3 ± 1.2 min, r = −0.718, p < 0.01), vVO2max (14.5 ± 1.5 km/h, r = −0.531,
p < 0.05, Figure 1), vpeak (15.2 ± 1.2 km/h, r = −0.754, p < 0.01, Figure 2), absolute VO2max
(3.4 ± 0.5 L/min, r = −0.617, p < 0.05), max O2 pulse (20.9 ± 4.4 mL/beat, r = −0.607,
p < 0.05, Figure 3) and tidal volume max (2.4 ± 0.4, r = −0.550, p < 0.05). The speed values
(vVT, vRER) achieved by runners at the VT and RER1 points of their CPET had a signiﬁ-
cant (p < 0.01) negative correlation (r = −0.733, r = −0.671, respectively, Figures 4 and 5)
with the runners’ race performance times. Relative VO2max (46.2 ± 4.9 mL/min/kg,
r = −0.422, p > 0.05), VO2VT (42.5 ± 4.9 mL/min/kg, r = −0.390, p > 0.05), VT/VO2max%
(92.1 ± 7.1%, r = −0.468, p > 0.05), VE (121.4 ± 17.4, r = −0.468, p > 0.05), VO2max/VO2ref%
(130.2 ± 17.9%, r = 0.252, p > 0.0.5) and HRVT (162.3 ± 14.9/min, r = 0.354, p > 0.05) did not
show any statistically signiﬁcant positive or negative correlation.
Running economy indicators were not signiﬁcantly correlated with the race perfor-
mance times of the studied runners: RE8 (27.2 ± 2.1 mL/min/kg, r = −0.036, p > 0.05),
RE10 (33.2 ± 2.3 mL//min/kg), r = 0.232, p > 0.05), RE12 (38.5 ± 3.3 mL/min/kg), r = 0.079,
p > 0.05), VO2/WR (7.1 ± 1.6 mL/min/watt, r = 0.164, p > 0.05) and VO2/WR (7.9–13.1)
(5.2 ± 6.7 mL/min/watt, r = −0.181, p > 0.05).
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 
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Data obtained during the participants’ CPET, which had a statistically significant 
negative correlation with the athletes’ race performance, measured as race end time, were: 
fatigue time (10.3 ± 1.2 min, r = −0.718, p < 0.01), vVO2max (14.5 ± 1.5 km/h, r = −0.531, p < 0.05, 
Figure 1), vpeak (15.2 ± 1.2 km/h, r = −0.754, p < 0.01, Figure 2), absolute VO2max (3.4 ± 0.5 
L/min, r = −0.617, p < 0.05), max O2 pulse (20.9 ± 4.4 mL/beat, r = −0.607, p < 0.05, Figure 3) 
and  tidal  volume  max  (2.4  ±  0.4,  r  =  −0.550,  p  <  0.05).  The  speed  values  (vVT,  vRER) 
achieved by runners at the VT and RER1 points of their CPET had a significant (p < 0.01) 
negative correlation (r = −0.733, r = −0.671, respectively, Figures 4 and 5) with the runners’ 
race performance times. Relative VO2max (46.2 ± 4.9 mL/min/kg, r = −0.422, p > 0.05), VO2VT 
(42.5 ± 4.9 mL/min/kg, r = −0.390, p > 0.05), VT/VO2max% (92.1 ± 7.1%, r = −0.468, p > 0.05), 
VE (121.4 ± 17.4, r = −0.468, p > 0.05), VO2max/VO2ref% (130.2 ± 17.9%, r = 0.252, p > 0.0.5) 
and HRVT (162.3 ± 14.9/min, r = 0.354, p > 0.05) did not show any statistically significant 
positive or negative correlation. 
 
Figure 1. Correlation between participants’ speed at maximal oxygen consumption point during 
cardiopulmonary exercise testing and race performance time (r = −0.531, p < 0.05). VO2max: maximal 
oxygen consumption; CPET: cardiopulmonary exercise testing. 
Figure 1. Correlation between participants’ speed at maximal oxygen consumption point during
cardiopulmonary exercise testing and race performance time (r = −0.531, p < 0.05). VO2max: maximal
oxygen consumption; CPET: cardiopulmonary exercise testing.
Int. J. Environ. Res. Public Health 2022, 19, 12289
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Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 
6  of  11 
 
 
 
Figure 2. Correlation between participants’ maximal speed during cardiopulmonary exercise test‐
ing and race performance time (r = −0.754, p < 0.01). CPET: cardiopulmonary exercise testing. 
 
Figure 3. Correlation between participants’ maximal oxygen pulse during cardiopulmonary exer‐
cise testing and race performance time (r = −0.607, p < 0.05). CPET: cardiopulmonary exercise testing; 
max O2 pulse: maximal oxygen pulse. 
Figure 2. Correlation between participants’ maximal speed during cardiopulmonary exercise testing
and race performance time (r = −0.754, p < 0.01). CPET: cardiopulmonary exercise testing.
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 
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Figure 2. Correlation between participants’ maximal speed during cardiopulmonary exercise test‐
ing and race performance time (r = −0.754, p < 0.01). CPET: cardiopulmonary exercise testing. 
 
Figure 3. Correlation between participants’ maximal oxygen pulse during cardiopulmonary exer‐
cise testing and race performance time (r = −0.607, p < 0.05). CPET: cardiopulmonary exercise testing; 
max O2 pulse: maximal oxygen pulse. 
Figure 3. Correlation between participants’ maximal oxygen pulse during cardiopulmonary exercise
testing and race performance time (r = −0.607, p < 0.05). CPET: cardiopulmonary exercise testing;
max O2 pulse: maximal oxygen pulse.
Int. J. Environ. Res. Public Health 2022, 19, 12289
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Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 
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Figure 4. Correlation between participants’ speed at ventilatory threshold during cardiopulmonary 
exercise testing and race performance time (r = −0.733, p < 0.01). VT: ventilatory threshold; CPET: 
cardiopulmonary exercise testing. 
 
Figure 5. Correlation between participants’ speed at respiratory exchange ratio value (RER = 1) dur‐
ing cardiopulmonary exercise testing and race performance time (r = −0.671, p < 0.01). CPET: cardi‐
opulmonary exercise testing; RER1: respiratory exchange ratio equal to one (RER = 1). 
Figure 4. Correlation between participants’ speed at ventilatory threshold during cardiopulmonary
exercise testing and race performance time (r = −0.733, p < 0.01). VT: ventilatory threshold; CPET:
cardiopulmonary exercise testing.
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 
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Figure 4. Correlation between participants’ speed at ventilatory threshold during cardiopulmonary 
exercise testing and race performance time (r = −0.733, p < 0.01). VT: ventilatory threshold; CPET: 
cardiopulmonary exercise testing. 
 
Figure 5. Correlation between participants’ speed at respiratory exchange ratio value (RER = 1) dur‐
ing cardiopulmonary exercise testing and race performance time (r = −0.671, p < 0.01). CPET: cardi‐
opulmonary exercise testing; RER1: respiratory exchange ratio equal to one (RER = 1). 
Figure 5. Correlation between participants’ speed at respiratory exchange ratio value (RER = 1)
during cardiopulmonary exercise testing and race performance time (r = −0.671, p < 0.01). CPET:
cardiopulmonary exercise testing; RER1: respiratory exchange ratio equal to one (RER = 1).
Int. J. Environ. Res. Public Health 2022, 19, 12289
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4. Discussion
The results showed that the 14.5 km race performance time of recreational runners
was signiﬁcantly correlated with the running speeds achieved during CPET at VT, RER1,
VO2max and the test peak point. The good predictive potential of speciﬁc running speeds
(peak velocity, vVO2max, vVT) achieved during the exercise testing of recreational en-
durance runners was previously documented by a recent review [22] and other stud-
ies [21,27–29]. Running speeds associated with anaerobic threshold, maximal oxygen
uptake and CPET peak values include, as determinants, both the aerobic and anaerobic
attributes of the selected runners [22,27,30]. This can explain the great predictive poten-
tial of these parameters for a long-distance running event, which, in fact, requires both
well-developed aerobic and anaerobic power [30]. Additionally, as already conﬁrmed in
the literature, the speed at the anaerobic threshold depends on both the maximal oxygen
uptake and running economy [29–31]. Thus, the signiﬁcant correlation of this speciﬁc
speed with running performance, as noted in our study, is fully and favourably justiﬁed
for separating the evaluation of the maximal oxygen uptake and running economy [29–31].
We can reasonably assume that the cumulative effect of the previous parameters shows
a better predictive potential and responds to the complexity of long-distance-running’s
physiological requirements.
The CPET indices of oxygen uptake (relative VO2max, VO2VT, VT/VO2max%,
VO2max/VO2ref%) were not valid for predicting the performance times in our amateur
long-distance runners. Only the absolute value of VO2max showed a signiﬁcant correlation
with race time. This ﬁnding should be carefully interpreted, since the VO2, in L/min, as
a measure of aerobic capacity, highly depends on individual anthropometric characteris-
tics [26]. Thus, the use of this value should not be considered as a primary index to predict
the performance times in amateur long-distance runners.
The scientiﬁc literature has acknowledged the importance of oxygen uptake and aero-
bic power for endurance running activities, but amateur running performance prediction
models remain dependent on running-speed indices and, according to some studies, anaer-
obic threshold and RE values [31–34]. Our study did not prove the utility of the calculated
RE indicators, and studies with similar results concluded that RE is not useful in deﬁning
mid-distance running performance [28,35]. Furthermore, a recent systematic review and
meta-analysis found that the existing evidence is not clear on the relationship of RE and mid-
and long-distance running performance, as studies on this relationship have a high level of
endogenous selection bias and an absence of allocation methodologies. Additional research
is needed to evaluate the relationship between RE and running performance [36–38].
The participants’ CPET data for max O2 pulse demonstrated an interesting relationship
with the race performance time. This parameter is traditionally used as a predictor of good
cardiovascular health and for the evaluation and prognosis of ischemic cardiomyopathies.
However, the existing scientiﬁc literature did not use max O2 pulse for performance
prediction until now. O2 pulse depends on HR and oxygen uptake, and its maximal value
is mainly determined by aerobic capacity—HRmax is age-related and does not depend on
other individual physiological and pathological characteristics [26]. Future evaluation of
max O2 pulse might be interesting for performance and health-related prognosis, especially
in recreational athletic and patient populations. When the cardiovascular and respiratory
ﬁtness of physically active subjects is evaluated, it can be helpful from motivational and
ﬁnancial perspectives to produce, besides health indices, data that can predict or proclaim
individual performance determinants [39,40]. Furthermore, physical ﬁtness as a health
determinant can help in deﬁning tools for public health improvement, as it has been proven
that regular physical activity can prevent and manage non-communicable diseases [7,11].
The available scientiﬁc evidence is heterogeneous regarding the utility of the an-
thropometric parameters of amateur long-distance runners for performance-prediction
models [21,22]. Our study results on runners’ anthropometry should be evaluated in regard
to that heterogeneity. Recreational runners differ in body composition, and their training
and race participation does not require professional runners’ discipline and consequential
Int. J. Environ. Res. Public Health 2022, 19, 12289
9 of 11
body image. Thus, the study of anthropometric indices in amateur runners represents a
great challenge for future research, since its utility may not be limited to only performance-
prediction models. Anthropometric features of amateur mid- and long-distance runners
can help deﬁne healthy and injury-free running guidelines.
The outcomes of this study add valuable insight on amateur long-distance running
performance and answer the literature’s need for a better understanding of the relationship
between recreational runners’ cardiopulmonary indices and their training design. Our
research highlights the cumulative effect of maximal oxygen uptake and running economy,
expressed through running speeds, as better tools of training design for long-distance
amateur running events. We deﬁne the cardiopulmonary indices suitable for the laboratory
control of the training adaptations that are important for safe, motivated and injury-free
recreational long-distance running. When designing the training strategy of amateur long-
distance runners, one should use the running speeds achieved during CPET at VT, RER1,
VO2max and the test peak point for progress control and periodization plan. The main
limitations of the study were the small sample size and the absence of gender segregation.
5. Conclusions
There is a better correlation of the 14.5 km running performance of recreational long-
distance runners with CPET speed-related indices at speciﬁc workloads than with the
indices of oxygen uptake, running economy or respiratory economy. When preparing a
training strategy, amateur long-distance runners should mostly rely on speciﬁc running-
speed-related laboratory data rather than on oxygen-uptake values.
Author Contributions: M.T., A.T., N.K., A.D. (Anastasios Dalkiranis), S.B., A.D. (Asterios Deligiannis)
and E.K. have made substantial contributions to the conception and design of the work as well as the
acquisition, analysis and interpretation of data. 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 University Ethics Committee approved the study proto-
col in accordance with the Helsinki Declaration for human research (approval number EC-16/2020,
Thessaloniki, 12 May 2020, Vassilis Mougios).
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. The data are not publicly available due to the privacy of the included subjects.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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Correlation between Cardiopulmonary Indices and Running Performance in a 14.5 km Endurance Running Event.
Tomovic, Milena,Toliopoulos, Alexandros,Koutlianos, Nikolaos,Dalkiranis, Anastasios,Bubanj, Sasa,Deligiannis, Asterios,Kouidi, Evangelia