Correlation between Cardiopulmonary Indices and Running Performance in a 14.5 km Endurance Running Event

ackground: 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 bene t public health. Training for a long- distance race requires development of speci c 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

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) signi cantly correlated with the cardiopulmonary-exercise-testing speed-related indices at speci c 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 speci c 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 speci c 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 de nitive source for sports, tness 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 Health2022,19, 12289.

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 Health2022,19, 12289.

Int. J. Environ. Res. Public Health2022,19, 12289 2 of 11 has con rmed health bene ts [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 identi es 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 speci c 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 in uence or predict the long-distance running performance of amateur runners [20–22]. The available scienti c evidence does not offer a reliable equation for performance prediction, and relevant studies are char- acterised with high data heterogeneity and often controversial ndings. 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 ndings will help recreational runners, not ready for or capable of a whole marathon race, reach the desired performance level. 2. Materials and Methods

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 ndings 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

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 Health2022,19, 12289 3 of 11 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 (O2pulse, 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, HRRER1and maximal parameters: VO2max, vVO2max, vpeak, HRmax, VEmax, max O2pulse, RERmax, tidal volume max, VO2max/VO2ref% (maximal VO2expressed as a percentage of a VO2max value predicted according to participant's age, sex and training habits) and VT/VO2max% (percentage of achieved maximal VO2at 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 VO2of 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 VO2achieved 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 speci c 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,

km/h. These speci c 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 uid 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 of cial 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 nish 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 coef cient 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-tailedp< 0.05 was accepted as statistically signi cant. 3. Results All fteen 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, contain the overview of the participants' CPET results.

7.2 ( 3.1) hours and 52.7 ( 19.5) km per week. Their demographic, anthropometric and race performance data, as shown in Tables, contain the overview of the participants' CPET results.

Int. J. Environ. Res. Public Health2022,19, 12289 4 of 11 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/m 2 ) 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

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 Health2022,19, 12289 5 of 11 Correlation analysis of the anthropometric parameters showed, for the race perfor- mance times, signi cant coef cients 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 signi cant 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 signi cant 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), vpeak (15.2 1.2 km/h, r = 0.754,p< 0.01, Figure), absolute VO 2max (3.4 0.5 L/min, r = 0.617,p< 0.05), max O2pulse (20.9 4.4 mL/beat, r = 0.607, p< 0.05, Figure) 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 signi - cant (p< 0.01) negative correlation (r = 0.733, r = 0.671, respectively,Figures ) 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 signi cant positive or negative correlation. Running economy indicators were not signi cantly 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 5 of 11 Data obtained during

This research explores how cardiopulmonary metrics relate to running performance in amateur athletes.