Individual Adaptation in Cross-Country Skiing Based on Tracking during Training Conditions

arch on heart rate (HR), mean arterial pressure (MAP) and blood pressure (BP) during speci c training stages is less common in endurance athletes, whereas resting BP and HR are less studied in relationship to HRmax. In the current study, the objective was to conduct a medium-term HR, BP and MAP analysis while tracking individual training outcomes. The study was conducted during the 2017–2018 season, over 43 days and 1033 km of training volume, on 12 competitive male cross-country ski athletes. One VO2maxtest was performed 10 days before the start of the training program. After the test, training volume and intensity was preset for each subject, according to the general training methodology. Early morning HR, MAP and BP measurements were taken as part of the basic functional analysis. Training volume was correlated to both distance (p=0.01, r=0.85, CI95%=0.80 to 0.88) and training HR%, namely the percentage of HRmax(p=0.01, r= 0.47, CI95%= 0.58 to 0.34). Both the supine (sHR) and orthostatic HR (oHR) values were signi cantly correlated with the training intensity. We obtained a signi cant correlation between sHR and oHR values and the training objective (p=0.01). An increased oHR was correlated to high intensity training activity (HIT) during the second training session (p=0.01). Heart rate and blood pressure measurements represent predictive functional adaptation parameters over di erent training phases. We highlight a link between sHR, oHR, MAP data, and the athletes' ability to perform in lower e ort zones during physical exertion. However, we failed to validate MAP as a cardiovascular stress indicator following high intensity training. Keywords:elite athlete; training; sport performance 1. Introduction Planning the training intensity is of particular importance in sports performance,

di erent training phases. We highlight a link between sHR, oHR, MAP data, and the athletes' ability to perform in lower e ort zones during physical exertion. However, we failed to validate MAP as a cardiovascular stress indicator following high intensity training. Keywords:elite athlete; training; sport performance 1. Introduction Planning the training intensity is of particular importance in sports performance, while monitoring the athlete can o er speci c data regarding the adaptation process, the recovery status or the main physiological changes that occur. Some of the most common measurements used for training monitoring are the heart rate (HR), blood pressure (BP) and mean arterial pressure (MAP), along with blood lactate and sleep quality. Day by day training activity may signi cantly di er from laboratory research conditions [1–3]. As a result, eld-based research can be more relevant to daily training activity, even if common methods are used in the study methodology. Over the training stages, cardiovascular and neural adaptations seem to be related to various training characteristics, such as e ort intensity, e ort volume and environmental conditions, during either short or long training periods. Of the main changes, physical e ort in uences vascular remodeling [4]. Yet, aerobic physical training favors angiogenesis, positively in uencing the number of capillaries and therefore the gas exchange area, while improving Sports2019,7, 211; doi:10.3390 /sports7090211 /journal/sports

Sports2019,7, 211 2 of 13 oxygen di usion and vagal tonus [5,6]. Without proper adaptation, the heart rate will increase due to reduced parasympathetic and increased sympathetic activity [7]. Increased long-term sympathetic activity induces cardiovascular stress, a drop in muscular strength, agility and an increased reaction time, in uencing both resting and e ort cardiovascular parameters. Despite the use of HR as a predictor of autonomic cardiovascular activity, few data are available regarding heart rate and blood pressure during rest-to-e ort and e ort-to-rest transitions [8]. During the resting or physical e ort period, neural adaptation will strongly in uence the HR measurement result and therefore the muscle contraction frequency changes the hearts sympathetic stimulation. During high intensity training (HIT), performances can change due to a drop in both cardiac output and HRmax, of which values fail to exceed a 90% peak. Both power and muscle contraction frequency can describe the e ort intensity better than the HR value, which can easily be a ected by other factors [9,10]. During physical exertion, both systemic vasoconstriction and local vasodilatation fail to bring a signi cant change in diastolic blood pressure, whereas systolic blood pressure reaches a plateau near maximal exercise intensity [11,12]. The main outcome is an increased MAP, which can be maintained during the post e ort period [13]. Contrary to HRmaxuse during training, several authors have utilized HR measurements during recovery periods [14–16]. Research papers on resting HR and BP tend to be less speci c in elite cross-country skiing during medium-term periods, while long-term MAP and HR are less studied in relation to HRmax. A small number of research papers that investigate long-term training performance following HIT activity are available [13,17,18]. Yet, HR use during short-term periods is well known. Several groups are using HR data for individual training and performance tracking. However, less data regarding the use of resting BP and MAP in training is being published. Performance drop is related much more to training-induced fatigue and reduced muscle ber recruitment. Starting from this point, our objective was to conduct a medium term HR, BP and MAP

well known. Several groups are using HR data for individual training and performance tracking. However, less data regarding the use of resting BP and MAP in training is being published. Performance drop is related much more to training-induced fatigue and reduced muscle ber recruitment. Starting from this point, our objective was to conduct a medium term HR, BP and MAP analysis while tracking individual training performances. Based on our hypothesis, speci c physiological HR, BP and MAP changes can be associated with individual adaption according to the training characteristics. By testing such a hypothesis, daily activity during both general and speci c training conditions can be signi cantly improved by using simple, cost e cient and noninvasive methods. 2. Materials and Methods We conducted an observational study between October and December 2017, during part of the 2017–2018 cross-country ski training season. To conduct the study and publish the current results, written informed consent was received from: (1) the participants, (2) federal management, and (3) the University Ethical Committee. 2.1. Participants The study group consisted of 12 international competitive male cross-country skiers with a mean age of 23 (18–28). To be included in the study group, the subjects had to ful l the following criteria: (1) male cross-country ski athlete, (2) general medical acceptance, (3)>18 years old, (4) currently competing at professional national or international level. Exclusion was pre-set by using the following criteria: (1) medical incompatibility with the pre-determined training program, (2) health condition that would inhibit the study activity, (3) age less than 18 or (4) lack of international or national competitive activity. Two individuals (n=2) were excluded at the start of the study due to age (<18 years old).

Sports2019,7, 211 3 of 13 2.2. Procedures The study was conducted during the 2017–2018 season, over 43 days and 1033 km of training volume, on a sample of 12 competitive male cross-country ski athletes. Each athlete undertook one VO2maxtest 10 days before the start of the training program, which was preset for each subject according to the general training methodology. Five (n=5) training zones were determined for each athlete (45–100% of VO2max), namely: training zone 1: zone 1 (Z1, 45–65% of VO2max), training zone 2: zone 2 (Z2, 66–80% of VO2max), training zone 3: zone 3 (Z3, 81–87% of VO2max), training zone 4: zone 4 (Z4, 88–93% of VO2max) and training zone 5: zone 5 (Z5, 94–100% of VO2max) [19]. Morning heart rate (HR) and blood pressure (BP) were measured daily as part of the basic functional analysis over the pre-determined training period. 2.3. Maximum Rate of Oxygen Consumption during the Incremental Exercise Test (VO2max) Each athlete performed the VO2maxtest by undertaking the Bruce Maximal Testing Protocol [20]. The test was applied using Cosmed Quark CPET equipment (Rome, Italy) and a Cosmos T150 running ergometer over seven e ort stages (1 to 7), each consisting of 3 min lengths. The VO2maxtest was conducted after calibrating the Cosmed unit with known O2(16%) and CO2(4%) concentrations. The ow meter was calibrated at the start of each test using the Cosmed Syringe (3 L). The VO2max measurement was validated by applying the following criteria: respiratory exchange ratio (RER)>1.10, 10 b/min of the predicted HRmaxand/or 150 mL O2/min changes. From the VO2maxtest data, the following parameters were of particular importance: maximum oxygen consumption (VO2max), maximum reached heart rate (HRmax), the ventilatory threshold 1 (VT1, b/min) and the ventilatory threshold 2 (VT2, b/min). The ventilator thresholds were determined by applying theV Slopemethod [21]. 2.4. Training Monitoring The training analysis was completed using the global positioning systems (GPS): Polar V800 (+/–2% error) (Kempele, Finland) and a Polar H7 Bluetooth HR monitor (Kempele, Finland). The following parameters were monitored during training: (1) heart rate (b/min, %), (2) distance (km), (3) movement speed (km/h), (4) time (min), (5)

thresholds were determined by applying theV Slopemethod [21]. 2.4. Training Monitoring The training analysis was completed using the global positioning systems (GPS): Polar V800 (+/–2% error) (Kempele, Finland) and a Polar H7 Bluetooth HR monitor (Kempele, Finland). The following parameters were monitored during training: (1) heart rate (b/min, %), (2) distance (km), (3) movement speed (km/h), (4) time (min), (5) positive (Dif+) and (6) negative altitude gain (Dif ). 2.5. Training Periodization Two training sessions (n=2) were run each day during 33 of the 43 study days (76.74%). On each day that had two training sessions (T1and T2), T1was programed in the morning, whereas T2was programed after a 6 to 8 h recovery period. Against the mean training volume (1033 km), a di erence between 5 and 10% (51.6–103.3 km) was monitored between the study subjects. Fifty four (n=54) training sessions were conducted on skis, eleven sessions (n=11) involved trail running, and ve sessions (n=5) were conducted on roller skis. 2.6. Basic Functional Analysis Heart rate (HR) and blood pressure (BP) measurements were included in the study methodology as the basic functional analysis. Each measurement took place in the early morning. Heart rate (HR, b/min) and blood pressure (BP, mmHg) analyses were performed by applying an orthostatic test using the following methodology: 3 min supine positioning followed by HR measurement (sHR, b/min); 3 min orthostatic positioning followed by HR measurement (oHR, b/min). In addition to HR analysis, blood pressure (BP) was measured using an aneroid sphygmomanometer (mmHg) under the following protocol: 3 min supine positioning, followed by BP measurement (sSBP, mmHg, systolic blood pressure; sDBP, mmHg, diastolic blood pressure); 3 min orthostatic positioning followed by orthostatic BP measurement (systolic blood pressure, oSBP mmHg; diastolic blood pressure, oSBP, mmHg). At least two (n=2) measurements were taken each time, resulting in a mean value if the rst

Sports2019,7, 211 4 of 13 measurement did not exceed 5 mmHg. If such a di erence was obtained, a third measurement was conducted. Each measurement was repeated if the sounds were not clearly de ned [22]. 2.7. Secondary Measurements By usingDTA (di erence between systolic blood pressure and mmHg) andDP (di erence between heart rate values, b/min), the Crampton Index (CI) was calculated for each data set. Later, MAP (mean arterial pressure) was obtained by applying the following formula: Equation (1). Formula used to calculate (a) the Crampton Index and (b) MAP: a. Crampton Index=25 3.15+ DTA 10 DTP 20 b. MAP = (2 DBP)+SBP 3 (1) The following criteria were used to interpret the Crampton Index:<50 (insu cient adaptation), 50–75 (poor adaptation), 75–100 (good adaptation),>100 (very good adaptation). The MAP normal range was considered to be between 70 and 110 mmHg [23,24]. 2.8. Analysis GraphPad Prism 5.0 software (GraphPad Software Inc., Sand Diego, CA, USA) was used for the statistical analysis. The main statistical indicators used to describe the sample were the mean and median values. The D'Agostino and Pearson normality test was applied for data normalization. The Wilcoxon matched pairs test was used to compare the evolution of one parameter, while through the Spearman test we calculated the correlation between two parameters. A two-tailed Mann–Whitney test was applied to identify the di erence between two items. The signi cance level was set at =0.05, with a pre-determined con dence interval of 95% (CI95%). 3. Results 3.1. E ort Capacity Individual e ort capacity was assessed by performing incremental exercise testing. VO2peak reached a mean value of 78.86 mL/min/kg (74.6–82.3). VT1was determined at 75.52% (73.2–78.9%), while VT2was determined at 86.5% of HRmax(84.4–88.1%). 3.2. Training E ort Analysis The physical training activity consisted of 1.033 km and 4.7432 min, respecting the general training guidance described in Table 1and T2training seasons. Of the training volume, 81.7% of the activity (843.961 km) was performed between 45–80% of VO2max, equivalent to the low aerobic e ort training zone. Di erences were monitored between T1and T2regarding the volume, speci cally through e ort time

training activity consisted of 1.033 km and 4.7432 min, respecting the general training guidance described in Table 1and T2training seasons. Of the training volume, 81.7% of the activity (843.961 km) was performed between 45–80% of VO2max, equivalent to the low aerobic e ort training zone. Di erences were monitored between T1and T2regarding the volume, speci cally through e ort time (minutes of e ort), and distance (kilometers), along with altitude gain (meters) and mean e ort HR (b/min) (Table).

Sports2019,7, 211 5 of 13 Table 1.General data indicating training (T 1–T 2) outcome. Information Related to One Individual Training Session Mean Value (Min to Max) Statistical Data between T 1and T2 T1 T2 p r Distance, km 28.23 10.83 0.51 0.05 (9.22–52.26) (2.09–22.55) Time, minute 129.3 63.66 0.01 0.31 (24.17–208) (15.52–130.5) Positive altitude gain, m 430.5 232.5 0.007 0.26 (35–1115) (10–965) Negative altitude gain, m 390 162.7 0.01 0.24 (47.78–94) (0–650) HR % 69.75 65.56 0.0014 0.19 (59–100) (55–92.13) Z1, % 4.9 (0–78) 2.2 (0–85) 0.06 0.15 Z2, % 6.1 (0–88) 6.2 (0–86) 0.01 0.37 Z3, % 4.1 (0–77) 5.9 (0–76) 0.01 –0.31 Z4, % 26.4 (0–87) 18.6 (0–91) 0.02 –0.30 Z5, % 58.5 (0–100) 67.1 (0–100) 0.046 0.16 Note: T1— rst training of the day; T2—second training of the day;p—probability level; r—Pearson product-moment correlation coe cient; HR—heart rate; VO2=maximum rate of oxygen consumption; VT1=ventilator threshold 1, VT2=ventilator threshold 2, Z5=anaerobic power training zone, Z4=anaerobic training zone, Z3=high aerobic training zone, Z2=aerobic training zone, Z1=low aerobic training zone. 3.3. Basic Functional Evaluation Data The mean sHR value was determined at 54.2 b/min, while oHR reached 81.3 b/min (p=0.01). The Crampton Index was calculated at 101.5 of the mean value, while both the sSBP and sDBP values (112.8 and 67.2 mmHg, respectively) were lower compared to the oSBP and oDBP measurements (117.6 and 79.6 mmHg, respectively) (p=0.0017). Training volume was correlated to both distance (p=0.01, r=0.85, CI95%=0.80 to 0.88) and training HR%, namely the % from HRmax(p=0.01, r= 0.47, CI95%= 0.58 to 0.34). Both the sHR and oHR values were signi cantly correlated with the training performed 12 h before the basic functional analysis. As a result, we obtained a statistically signi cant correlation between the analyzed sHR and oHR values and the training objective (p=0.01). Based on the data, the athletes' capacity to perform and maintain lower e ort HR ranges was signi cantly correlated with both their sHR and oHR values (p=0.01, r= 0.38, CI95%= 0.50 to 0.24), whereas T1and T2volume was signi cantly correlated with the Crampton Index (p=0.001) (Figure).Sports 2019, 7, x FOR PEER REVIEW 5

values and the training objective (p=0.01). Based on the data, the athletes' capacity to perform and maintain lower e ort HR ranges was signi cantly correlated with both their sHR and oHR values (p=0.01, r= 0.38, CI95%= 0.50 to 0.24), whereas T1and T2volume was signi cantly correlated with the Crampton Index (p=0.001) (Figure).Sports 2019, 7, x FOR PEER REVIEW 5 of 13 Note: T 1–first training of the day; T2–second training of the day; p–probability level; r–Pearson product-moment correlation coefficient; HR–heart rate; VO 2 = maximum rate of oxygen consumption; VT 1 = ventilator threshold 1, VT2 = ventilator threshold 2, Z5 = anaerobic power training zone, Z4 = anaerobic training zone, Z3 = high aerobic training zone, Z2 = aerobic training zone, Z1 = low aerobic training zone. 3.3. Basic Functional Evaluation Data The mean sHR value was determined at 54.2 b/min, while oHR reached 81.3 b/min (p = 0.01). The Crampton Index was calculated at 101.5 of the mean value, while both the sSBP and sDBP values (112.8 and 67.2 mmHg, respectively) were lower compared to the oSBP and oDBP measurements (117.6 and 79.6 mmHg, respectively) (p = 0.0017). Training volume was correlated to both distance (p = 0.01, r = 0.85, CI95% = 0.80 to 0.88) and training HR%, namely the % from HR max (p = 0.01, r = −0.47, CI95% = −0.58 to −0.34). Both the sHR and oHR values were significantly correlated with the training performed 12 h before the basic functional analysis. As a result, we obtained a statistically significant correlation between the analyzed sHR and oHR values and the training objective (p = 0.01). Based on the data, the athletes’ capacity to perform and maintain lower effort HR ranges was significantly correlated with both their sHR and oHR values (p = 0.01, r = −0.38, CI95% = −0.50 to −0.24), whereas T 1 and T2 volume was significantly correlated with the Crampton Index (p = 0.001) (Figure 1). Figure 1. Correlation between effort time and the Crampton Index (p = 0.001). The T1 results were correlated with the Crampton Index, unlike the

with both their sHR and oHR values (p = 0.01, r = −0.38, CI95% = −0.50 to −0.24), whereas T 1 and T2 volume was significantly correlated with the Crampton Index (p = 0.001) (Figure 1). Figure 1. Correlation between effort time and the Crampton Index (p = 0.001). The T1 results were correlated with the Crampton Index, unlike the T2 training data. The measurements are illustrated in Table 2 for both training activities. The individual ability to perform over a predetermined HR range was correlated to training intensity, as shown through the specific training zones illustrated in Table 3. An inappropriate HR% range (>10%), contrary to the training objective, was correlated with increased sHR–oHR and sBP– oBP values (p = 0.0116, r = −0.21, CI95% = −0.38 to −0.04) during basic functional measurements. Table 2. Comparative data for the T1 and T2 training activities and the Crampton Index. Crampton Index Mean Value Training Data Mean Value Statistical Result p r CI95% Upper Lower 101.5 T 1 training data Distance, km 28.23 (9.22–52.26) 0.008 −0.19 −0.33 −0.04 Time, minute 129.3 (24.17–208) 0.07 −0.13 −0.27 0.01 Pace, km/h 14.15 (8.5–25.2) 0.33 −0.07 −0.22 0.07 Positive altitude gain, m 430.5 (35–1115) 0.012 −0.20 −0.35 −0.03 Negative altitude gain, m 390 (47.78–94) 0.02 −0.18 −0.34 −0.02 HR % 69.75 (59–100) 0.0007 0.30 0.15 0.43 Z1, % 4.9 (0–78) 0.6687 −0.03 −0.18 0.11 Z2, % 6.1 (0–88) 0.6863 −0.03 −0.18 0.12 Z3, % 4.1 (0–77) 0.0626 0.13 −0.01 0.2 Z4, % 26.4 (0–87) 0.0014 0.23 0.08 0.37 Figure 1.Correlation between e ort time and the Crampton Index (p=0.001). The T1results were correlated with the Crampton Index, unlike the T2training data. The measurements are illustrated in Table

Sports2019,7, 211 6 of 13 Table 2.Comparative data for the T1 and T2 training activities and the Crampton Index. Crampton Index Mean Value Training Data Mean Value Statistical Result p r CI95% Upper Lower 101.5 T 1training data Distance, km 28.23 (9.22–52.26) 0.008 0.19 0.33 0.04 Time, minute 129.3 (24.17–208) 0.07 0.13 0.27 0.01 Pace, km/h 14.15 (8.5–25.2) 0.33 0.07 0.22 0.07 Positive altitude gain, m 430.5 (35–1115) 0.012 0.20 0.35 0.03 Negative altitude gain, m 390 (47.78–94) 0.02 0.18 0.34 0.02 HR % 69.75 (59–100) 0.0007 0.30 0.15 0.43 Z1, % 4.9 (0–78) 0.6687 0.03 0.18 0.11 Z2, % 6.1 (0–88) 0.6863 0.03 0.18 0.12 Z3, % 4.1 (0–77) 0.0626 0.13 0.01 0.2 Z4, % 26.4 (0–87) 0.0014 0.23 0.08 0.37 Z5, % 58.5 (0–100) 0.0009 0.24 0.38 0.09 T 2training data Distance, km 10.83 (2.09–22.55) 0.9654 0.00 0.16 0.16 Time, minute 63.66 (15.52–130.5)0.6278 0.03 0.20 0.12 Pace, km/h 11.28 (7.5–51.5) 0.0667 0.16 0.33 0.01 Positive altitude gain, m 232.5 (10–965) 0.1819 0.13 0.31 0.06 Negative altitude gain, m 162.7 (0–650) 0.4968 –0.06 –0.25 0.13 HR, % 65.56 (55–92.13) 0.0438 0.16 –0.00 0.32 Z1, % 2.2 (0–85) 0.8572 0.01 –0.15 0.18 Z2, % 6.2 (0–86) 0.7330 –0.02 –0.19 0.13 Z3, % 5.9 (0–76) 0.2283 –0.09 –0.26 0.06 Z4, % 18.6 (0–91) 0.7851 –0.02 –0.18 0.14 Z5, % 67.1 (0–100) 0.5423 –0.05 –0.21 0.11 Note: T1— rst training of the day; T2—second training of the day;p—probability level; r—Pearson product-moment correlation coe cient; HR—heart rate; VO2=maximum rate of oxygen consumption; VT1=ventilator threshold 1, VT2=ventilator threshold 2, Z5=anaerobic power training zone, Z4=anaerobic training zone, Z3=high aerobic training zone, Z2=aerobic training zone, Z1=low aerobic training zone. The individual ability to perform over a predetermined HR range was correlated to training intensity, as shown through the speci c training zones illustrated in Table. An inappropriate HR% range (>10%), contrary to the training objective, was correlated with increased sHR–oHR and sBP–oBP values (p=0.0116, r= 0.21, CI95%= 0.38 to 0.04) during basic functional measurements. Resting sHR and oHr were correlated with both sBP values (p=0.0026, r=0.21, CI95%=0.07 to 0.35) and exercise HR (p=0.0001, r=0.293, CI95%=0.149 to

The study analyzes heart rate and blood pressure in cross-country skiers during training.