Comparison of blood lactate levels between treadmill running and over-ground running during incremental tests

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Comparison of blood lactate levels between treadmill running

and over-ground running during incremental tests

A study on elite male runners Oa Blom

Examensarbete, 30 hp

Magisterprogrammet i Idrottsmedicin, 60 hp Vt 2020

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Abstract

Tests to evaluate runners' aerobic capacity aiming to design training programs are often

performed on a treadmill, while the training mainly takes place on over-ground surfaces. Studies have shown that different degrees of inclination on treadmills can compensate for the

differences in heart rate (HR) response between running on treadmills and over-ground surfaces. The purpose of the study was to test whether the blood lactate concentration (BLC) differs between over-ground running and treadmill running at matched HR, and if so; can inclination of the treadmill be adjusted to generate equal BLC at matched HR? Eight male elite runners performed three incremental running tests where HR and BLC were measured; on a flat treadmill at six running velocities, on a running track at six velocities equal to the HR at test one, and on a treadmill at a fixed HR on different inclines (0°, 0.3°, 0.6°, 0.9°, 1.1° and 1.5°). The results revealed a non-significant trend indicating that over-ground running yields higher BLC at matched HR than treadmill running, and that 0.3° incline on treadmill correlated best with over- ground running. This study demonstrates a clear tendency of higher BLC at a given HR when running on an over-ground surface in comparison to running on a treadmill. Furthermore, a 0.3°

incline on a treadmill is suggested to compensate for the difference in BLC at matched HR, between running on a treadmill and on an over-ground surface. However, more research with a larger sample size is needed to conclude and generalize the results.

Keywords: Lactate threshold, Anaerobic, Running track, Laboratory, Testing

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Introduction

The research of lactate metabolism goes back more than 200 years, when the Swedish

researcher Jöns Jacob Berzelius found lactate in the muscles of hunted stags (Gladden, 2008). In the last 50 years, identifying the blood lactate (BL) threshold during exercise testing has been a frequently used method to test and predict performance in endurance athletes (Faude,

Kindermann & Meyer, 2009; McMorries, Joubert, Jones & Faries, 2019).

When running at an increased intensity, an imbalance between the appearance and removal of the blood lactate develops, resulting in an exponential increase in blood lactate. This partly reflects an aerobic-anaerobic transition, due to increased activation of glycolytic fast twitch muscle fibres, described in literature as the lactate threshold (LaT). There are two typical breaking points of the LaT. The first LaT is usually defined as the aerobic threshold (AeT), the point where the first increase in BL occurs compared to resting or a stable baseline of BL. This is usually set at a blood lactate concentration (BLC) of 2 mmol/L. The second breaking point is the anaerobic threshold (AT), which is described as “the highest intensity at which lactate

production and elimination are in equilibrium (maximal lactate steady state [MLSS])”. (Faude, Kindermann & Meyer, 2009)

Lactate threshold concepts

The values at which the LaT occurs differ between individuals but have generally been defined with different fixed standardized values across the literature, ranging from 2 mmol/L to 4.0 mmol/L (Åstrand, Rodahl, Dahl & Strømme, 2003). However, there are several different concepts when it comes to measuring LaT in athletes. Faude, Kindermann & Meyer (2009) located, in a review, 25 different concepts of identifying the LaT. Some of the concepts were based on fixed LaT, and used 2, 2.5, 3 and 4 mmol/L, with 4 mmol/L being the most commonly used method. This may be due to its applicability to endurance-trained athletes. When testing endurance-trained athletes it was noticed that, when testing intensities that resulted in the BL being below 4 mmol/L, the athletes could endure that intensity for a long period of time with a steady BL value. When the intensity was set so that the BL was above 4 mmol/L, there was a continual and exponential increase in BL. Therefore, the onset of blood lactate accumulation (OBLA) was set to 4 mmol/L (Yoshida, Chida, Ichioka & Suda, 1987). In other words, OBLA is a

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fixed LaT that represents mean MLSS at group level. OBLA is widely used as a marker of LaT in athletes, even though there are individual differences in lactate concentration when LaT occurs (Coyle, 1995; Stallknecht, Vissing, & Galbo, 1998). However, since there are individual

differences at what lactate concentration LaT occurs, some researchers use individual LaT when estimating LaT in athletes. Usually, individual AeT is set to a blood lactate concentration (BLC) of 0.2-1.0 mmol/L above the lowest exercise lactate value, while the individual AT (IAT) is set to a BLC of 1,5 mmol/L above the first LaT (Faude Kindermann & Meyer, 2009).

Importance of knowing athletes’ individual LaT

To perform at the top level in sports, it is important to determine and develop the factors that affect performance. In endurance sports such as distance running, the LaT is highly correlated with performance (Faude, Kindermann & Meyer, 2009), even more than VO2max (Ghosh, 2004;

Goodwin, Harris, Hernández & Gladden, 2007). Also, training at the intensity of the AT increases VO2max, aerobic capacity and AT, which all are highly correlated with distance running

performance (Ghosh, 2004). Therefore, it is of high interest to investigate athletes' IAT-intensity for their sport-specific training so it can be implemented in their individual training schedule. If the athletes know what HR their IAT is estimated to, they can in a valid way intensity control their training with HR monitoring. For example, this is relevant for runners to identify when running on a treadmill and over-ground running.

Testing the LaT in athletes

When testing individuals’ LaT, incremental tests with increasing intensity every one-four minutes till exhaustion are often used (Goodwin, Harris, Hernández & Gladden, 2007). There is little research on optimal time for testing the LaT in incremental tests, but a practical time that has been shown to be valid and reliable for each incremental stage is three minutes, when measuring endurance performance and LaT (Bentley, McNaughton & Batterham, 2001;

Goodwin, Harris, Hernández & Gladden, 2007; Weltman et. al., 1990).

Since LaT is dependent on specific muscle adaptations, muscle fiber composition and the motor unit recruitment, LaT is sport specific and highly individual (Ghosh, 2004). Runners achieve higher submaximal LaT results on a treadmill while cyclists get higher results on a cycle

ergometer. Triathletes, however, who train in both cycling and running, seem to achieve a more

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similar LaT when comparing cycle ergometer LaT to treadmill running LaT (Millet, Vleck &

Bentley, 2009). This supports the theory that lactate threshold is very muscle specific and dependent on how adapted the individual is on the particular exercise.

When testing runners’ endurance performance to design training programs and determine LaT, testing is mainly done on a treadmill (Löllgen & Leyk, 2018). However, endurance runners such as marathon runners and track and field athletes compete on running tracks or over-ground surfaces. There are significant differences in muscle activation when running on over-ground surfaces and treadmills, even when running indoors (Wang, Hong & Li, 2014; Oliveira, Gizzi, Ketabi, Farina & Kersting, 2016; Barnes & Kilding, 2015), which makes it logical to assume that there might be a difference in LaT on a treadmill and over-ground running. Jones and Doust (1996) argue that running on a treadmill with 1% grade reflects the extra energy cost (based on oxygen consumption) of running over-ground outdoors, due to the difference in air resistance.

However, Sperlich et. al. (2011) showed no correlation between energy cost of running (based on oxygen consumption) and LaT at matched velocity, which shows that these findings are not applicable when it comes to testing the LaT. Another study suggests that a 0.4% grade on a treadmill reflects running over-ground outdoors even better and that using this grade on the treadmill is strongly correlated with the IAT-running velocity during over-ground running

(Mugele et. al., 2018). However, Mugele et. al. (2018) only found a modest correlation with LaT- running heart rate (HR) between the different test conditions. This may be because external factors that affect HR were not controlled for, such as intake of stimulants such as caffeine and nicotine (Smits, Temme & Thien, 1993). The LaT-running velocity relationship is less practically applicable than the LaT-HR relationship, due to the difference in wind resistance depending on environmental factors which might affect velocity but not HR. Therefore, the purpose of the study was to investigate whether the BLC differs between over-ground running and treadmill running by performing two LaT tests: one on a treadmill and one on a running track with matched HR. Furthermore, the purpose was also to investigate whether it is possible to duplicate over-ground IAT on a treadmill, by adjusting the incline of the treadmill. This may contribute to increased knowledge and understanding of how to estimate over-ground IAT-HR on a treadmill in a valid way and thus improve the design of training programs and evaluation of aerobic capacity in athletes.

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The hypothesis of present study is: running on an over-ground surface will result in higher BLC at matched HR as compared with running on a treadmill. If the hypothesis is true, the question remains as to whether over-ground running BLC at IAT-HR can be reproduced on a treadmill by using a particular inclination on the treadmill at matched HR.

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Method

Experimental Approach to the Problem

To investigate whether running on an over-ground surface gives higher BLC than running on a treadmill at matched HR, two running tests with gradually increasing speed were used. The first two test sessions consisted of running tests with incremental velocity every three minutes, with a one-minute break between the intervals: the first session on a treadmill and the second on a running track. Furthermore, to investigate whether over-ground running BLC at IAT-HR can be reproduced on a treadmill, a third running test was also performed. The third session used a similar procedure as the first two tests, but instead of using incremental velocity, incremental incline was used (0°, 0.3°, 0.6°, 0.9°, 1.1° and 1.5°) at a set HR (IAT-HR from the running track test), to see which incline corresponded best, by testing the BLC correlation at each incline with.

All treadmill testing was done on the same treadmill, in the Umeå Movement and EXercise Laboratory (UMEX) at Umeå university. The over-ground-testing was done on the indoor running track at Nolia in Umeå. Incremental running tests with three-minute stages have been shown to have high test-retest reliability, when testing LaT and HR at different BLC levels in male subjects (Weltman et. al., 1990). The tests were performed in three sessions on three different days, with at least one day between the sessions. They were told to abstain from vigorous physical exercise the day before the tests. All tests were performed at the same time of the day (± 90 minutes). The participants were able to choose what day of the week and what time of day suited them to increase the feasibility for the participants to complete all tests. They were told to prepare for the tests as they were demanding training sessions. Some participants performed their tests in the morning and others in the afternoon. Capillary blood samples were taken by the experimenter. Blood samples were analyzed immediately after collected at each interval. Before the first test session, weight and height were measured.

Participants

Ten competitive track and field running athletes, competing at middle and/or long distances, from IFK Umeå track and field athlete team were invited to participate in the study. The invitations were emailed to the participants in April, 2020. Out of the ten invited participants, nine were males and one was female. All met the inclusion criteria, which was: 1. Elite runner at national or regional level; 2. Healthy and willing to perform three running test sessions within

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two weeks. Two athletes dropped out before the test started. The eight remaining participants were all male. Seven of these completed all three tests while one only completed the first and second test session. They all received an information letter about the study before they decided if they wanted to participate. All participants signed an informed consent (appendix 1) and a health declaration (appendix 2). The participants were covered by Kammarkollegiet’s insurance.

The data was treated confidentially. All data in the current study is presented in such a way that no individual data can be linked to that person. All participants had access to their individual results. The structure of the study has been ethically tested by the department's research council.

Table 1. Group anthropometric and descriptive running data presented in means ± standard deviation (1SD).

Participants (N) Age Weight (kg) Length (cm) BMI 10 KM time (min)

8 31.1 ± 6.9 70.7 ± 5.5 181.9 ± 5.5 21.4 ± 1.5 32.87 ± 1.74

Equipment

All treadmill testing was done on a 2.5 × 0.7 m motor-driven treadmill (RL 2500E, Rodby, Sweden). Blood samples were analyzed with a Biosen C-line monitor (EKF Diagnostics, Germany), which was calibrated before each test session. Biosen C-line has high reliability at measuring lactate (CV ≤ 3%) (Burfeind & Heuwieser, 2012). HR was recorded with a chest strapped Polar H10 HR sensor (Polar Electro Oy, Finland), connected to a Garmin Forerunner 235 (Garmin Ltd., United States).

Procedure Warm-up

A standardized warm-up routine was used where the subjects ran 10 minutes at a set pace of 1.5-2 km/h below their starting velocity during the first session, followed by five minutes of non- standardized active rest and dynamic stretching. Similar standardized warm-up routines have been previously used in studies of LaT testing (Dantas & Doria, 2015; Forsyth, Burt, Ridley &

Mann, 2017; Menzies et al., 2010).

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Test 1

The first sessions consisted of a LaT test done on a flat treadmill. The test started at an

individual chosen running velocity of about 12 km/h that was increased by about 2 km/h every three minutes (see Table 2 for group mean running velocity (± 1SD) at each interval). At the end of every three minutes, during the one-minute break, a capillary blood sample was taken to measure blood lactate concentration (mmol/L); perceived exertion was measured with the rate of perceived exertion (RPE) scale (Borg, 1998) and HR was recorded. HR was measured at

exactly 3:00, while RPE was measured a few seconds after (3:00-3:10). While RPE was measured by one experimenter, the blood sample was collected by another experimenter. The velocity was adjusted on the treadmill monitor, in the one-minute break between each interval.

Test 2

The second session consisted of a running test performed on an indoor track at Nolia in Umeå. A similar procedure was used as in the first test and the same test blood sampling procedures were included at the end of every three-minute stage. This time, however, the running velocity for each three-minute interval was matched to the individual HR recorded at the same interval on Test 1, with the goal of reaching a steady state-HR at the end of each interval. To facilitate and ensure that the experimenter was close to the runner when each interval was completed, running distance was estimated based on the velocity from Test 1. This was to make sure that blood samples, HR and RPE were taken immediately after each interval during all tests. The same test procedures as in Test 1 were included at the end of every three minutes, during the one-minute break.

Test 3

The third session consisted of running on a treadmill with 0°. 0.3°, 0.6°, 0.9°, 1.1° and 1,5°

incline, one interval on every incline. The intensity of this run was set at an IAT (±2 beats/min) estimated from Test 2. The same test procedures as in Test 1 were included at the end of every three minutes, during the one-minute break. The incline was adjusted on the treadmill monitor, in the one-minute break between each interval. The estimation was a visual estimation from looking at the data points of the BLC and the HR and looking at what heart rate the BL exponentially rapidly increased, which would correspond to be at the MLSS.

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Test 1: Treadmill running Test 2: Running on track Test 3: Treadmill running

Participant running on a treadmill with 0° incline.

Experimenter is writing down heart rate values in the background.

Capillary blood sample is taken from one of the participants by the experimenter during the one-minute break.

Participant running on a treadmill with an incline of 0- 1.5°.

Statistical analysis

Normality was confirmed using the Ryan-Joiner test. The data was presented in mean ± SD and analyzed with paired t-test. Statistical significance was set to α=0.05. Correlations were analyzed with Pearson’s correlation coefficient. To interpret the correlation coefficient (R), the following guidelines, presented by Schober, Boer & Schwarte (2018), were applied: 0-0.1 negligible correlation; 0.1-0.39 weak correlation; 0.4-0.69 moderate correlation; 0.7-0.89 strong correlation; 0.9-1 very strong correlation. Statistical analyses were done with Minitab 19 software. Microsoft Excel 2006 (Microsoft Corporation, United States) was used for descriptive statistics analyses.

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Results

A paired t-test revealed that there was a tendency to difference (P=0.053) in BLC between running on a treadmill (Test 1) and running on the track (Test 2) at matched HR. This tendency is displayed in Figure 2. There was no statistical significant difference in BLC at matched HR at any specific interval, but as seen in Figure 1, the mean BLC is apparent higher in Test 2 than in Test 1. The mean BLC exponentially increased at interval 4-6 and approached statistical significance at interval 6.

To normalize the intensity for Test 1 (treadmill) interval 1-6, the running velocity was set to a velocity based on the individual runner’s running capacity. Mean running velocity for all participants is presented in Table 2.

Table 2. Mean ± 1SD running velocity (km/h) at interval 1-6 at Test 1.

Interval 1 Interval 2 Interval 3 Interval 4 Interval 5 Interval 6

Running velocity

11.6 ± 0.5 13.5 ± 0.7 15.2 ± 1.0 16.7 ± 1.2 18.1 ± 1.3 19.3 ± 1.3

Table 3. Mean values ± 1SD for all participants at all intervals. (HR=Heart rate; BLC=Blood lactate concentration; RPE=Rate of perceived exertion).

HR BLC RPE

Test 1: Treadmill 150 ± 17.6 1.8 ± 0.9 12.7 ± 3.1

Test 2: Track 149.4 ± 17.4 2.2 ± 1,8 12.5 ± 3.2

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Figure 1. Mean ± 1SD BLC on a treadmill (grey) and running track (black), at individual running velocity; interval 1-6, with matched HR at each interval during the different tests. A paired T-test revealed no significant difference in BLC between Test 1 (treadmill) and Test 2 (running track) at any interval (Interval 1: P=0.457; Interval 2: P=0.542; Interval 3: P=0.955; Interval 4: P=0.833;

Interval 5: P=0.241; Interval 6: P=0.059). (BLC=Blood lactate concentration)

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Figure 2. Scatter plot with line of identity, revealing BLC distribution at matched HR during track and treadmill (Test 1 and Test 2). (BLC=Blood lactate concentration)

Pearson’s correlations coefficient was used to see which incline on the treadmill (Test 3) correlated best with running on the running track (Test 2), at the IAT-HR, see Figure 3-8. The correlation between the different inclines was: 0°: R=0.73; 0.3°: R=0.88; 0.6°: R=0.8; 0.9°: 0.73;

1.1°: R=0.65; 1.5°: R=0.43. Figure 9 shows how running on a flat treadmill (Test 1) correlated with running on a flat treadmill again (test 3, interval 1). Pearson’s correlations coefficient revealed a strong correlation (R=0.77) between Test 3 (interval 1) and Test 1 at the IAT-HR.

Figure 3. Figure 4.

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Figure 3-8. Scatter plot showing the correlation (R²), the regression line and line of identity, between BLC at matched HR on running track (X) and treadmill (Y) (0°-1.5° incline). The HR used was the IAT-HR estimated from Test 2 (running track). (BLC=Blood lactate concentration)

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Figure 9.

Figure 9. Scatter plot with regression line and line of identity, revealing correlation (R²) between group mean BLC at IAT at test 1 and 0° incline at test 3.

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Discussion

The hypothesis of this study was that running on an over-ground surface will result in higher BLC at matched HR as compared with running on a treadmill. The primary outcome revealed there was no statistically significant difference in BLC at matched HR between running on a treadmill and running at an over-ground indoor surface at any interval. However, the results at the highest running velocity displayed a clear tendency of higher BLC during running on track as compared to treadmill at matched HR. Previous research on trained runners has demonstrated that running on an over-ground surface yields significantly higher BLC than when running on a flat treadmill when running velocity is matched (Heck et al., 1985). It could therefore be argued that outdoor running inflicted more wind resistance than indoor running and therefore revealed more work demand and higher BLC when intensity is matched. However, one recent study on competitive distance runners demonstrates that the energy cost (estimated by dividing steady state oxygen uptake with running velocity) of running on a treadmill with 1% incline is higher than running on an over-ground surface (Mooses, Tippi, Mooses, Durussel & Mäestu, 2015).

This could be explained by the important link between running technique and running economy (Folland et al., 2017); running on a treadmill yields different muscle activation than running on an over-ground surface (Saunders, 2004, Wang, Hong & Li, 2014; Oliveira, Gizzi, Ketabi, Farina &

Kersting, 2016; Barnes & Kilding, 2015). However, running economy and LaT at given velocities have shown to have no correlation (Sperlich et. al., 2011). This shows the importance of comparing relevant parameters. In the present study, the reason why the blood lactate levels revealed clear tendencies to be higher when running on a running track than on a treadmill may be several. A reasonable explanation for this may have to do with the AT. Running on a treadmill versus running over-ground differs in muscle activation (Saunders, 2004; Wang, Hong & Li, 2014; Oliveira, Gizzi, Ketabi, Farina & Kersting, 2016; Barnes & Kilding, 2015). It is reasonable that it generally differed in which HR the AT was achieved, between the treadmill and the running track. When the AT is reached, the BLC increases exponentially (Goodwin, Harris,

Hernández & Gladden, 2007). This means that if the runners generally reached the AT at a lower HR on the running track, which may explain the faster increasing rise in BLC. However, if the AT is identical, but the running technique and biomechanical differences makes the ability to recruit a higher proportion of fast-twitch muscle fibers is better when running on an over-

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ground surface, this could be another explanation. The muscle fiber composition, as well as the muscular adaptations affect the ability to produce lactate. Fast-twitch muscle fibers or glycolytic muscle fibers have a higher anaerobic capacity than slow-twitch muscle fibres, ie. can more quickly produce energy through glycolytic metabolism (Bogdanis, 2012; Rogatzki, Ferguson, Goodwin, & Gladden, 2015). Since the end product of the glycolytic metabolism is lactate, which may explain the differences.

The next question was to see if BLC at matched HR (over-ground running IAT-HR) could be reproduced on a treadmill, by using different degrees of inclination on the treadmill. The findings demonstrated that BLC at the IAT-HR extracted from over-ground running had the highest correlation at 0.3° incline on a treadmill. Previously, 1% incline on a treadmill seems to be an accepted incline to use in research when testing athletes on treadmills (Da Silva, et al., 2011; Legaz-Arrese, et al., 2011; Sandbakk, et al., 2020), since Jones and Doust (1996) first demonstrated it. Even though more recent research suggests that 0.4% incline is a better incline to duplicate over-ground running (Mugele et. al., 2018), the 1% incline is still being used for testing in research (Sandbakk, et al., 2020). However, Mugele et. al. (2018) only found a

“modest” correlation (R=0.746) between HR at the IAT at running track and an inclined treadmill (0.4% incline). To the best of my knowledge, the current study is the first to describe such a strong correlation (R=0.88) between BLC at IAT-HR on an over-ground running surface and on an inclined treadmill. An advantage of the current study in comparison to the study by Mugele et.

al. (2018) and Jones and Doust (1996) is that the present study tested several different inclines with small increases in incline for every interval. It should be considered, however, that the current study did not test 0.4% incline. Furthermore, Mugele et. al. (2018) did two stepwise incremental tests with which they estimated the IAT; one at the 0.4% inclined treadmill and one on the running track. They then tested the correlation of HR and BLC at each different IAT, which makes it different from the current study’s protocol, which tested the correlation of the BLC at a matched HR. Since there are several differences between this study and Mugele’s et. al.

(2018) (i.e. test procedure, participants gender, running experience and age), the results are not comparable and no generalizable conclusion can be drawn that 0.3° incline in treadmill running correlates better with over-ground running when comparing the BLC at the IAT-HR.

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The correlation between running on a treadmill at Test 1 and Test 3 (first interval, 0° incline) was surprisingly lower (R=0.77) than Test 2 and Test 3 at 0.3° and 0.6° incline. The reason why the correlation between two identical running conditions could be explained by the differences in the test protocol between Test 1 and Test 3. First, IAT at Test 1 was extracted by

interpolation, which means that the participants never actually ran at this intensity, but that the intensity and BLC were calculated by an equation. Second, the IAT was on average set out between test 4 and test 5 on Test 1, while it was the first interval after the warm-up during Test 3. This could affect the HR and performance due to an increased body temperature of the prolonged exercise time in the laboratory environment in Test 1 in contrast to Test 3.

Methodological Reflection

The participants in the current study consisted of a heterogeneous mixture of well-trained individuals. Due to the dropout, the group consisted only of men, which makes the group more homogeneous, as women and men physiologically differ in running performance (Cheuvront, 2005). This thus decreased the generalizability of the study but increased the intrinsic validity of the study. On the other hand, there were scattered levels on the runners: running times at 10 km differed with up to over five minutes within the group. Furthermore, some participants competed in long-distance running as a marathon, while others competed in shorter distances, such as 800 meters. There was also a large spread regarding age, with the youngest participant being a 19-year-old and the oldest a 41-year-old. It is known that blood lactate levels in physical performance decrease with increasing age (Mattern, 2003). These variations in age and

competition level were necessary from a recruitment perspective, to bring together enough participants to complete the study with some statistical power.

Diet and fluid intake are important parameters that affect physical performance (Murray, 2007;

Ormsbee, 2014), so it may seem arbitrary not to control these factors. These factors are also important when it comes to heart rate at work (Casa et al., 2010; Murray, 2007), which is of great importance in this study. So even if the participants were well-trained athletes performing at high levels, it could be of great value to check diet and fluid intake before each test. It might be enough to note this to see what differs between the different tests and between the tests against training sessions. An advantage in the current study, however, is that the time of the day when the tests were performed differed no more than a maximum of 90 minutes, which for a

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person with regular fluid and food intake would mean to use equal intake before any test. A sometimes-overlooked consideration in exercise testing is the intra-individual differences (day- to-day variation) in physiological parameters. However, although there are intra-individual differences in BL levels between different days, LaT has been shown to be highly reproducible if diet is normal and heavy exercise is avoided one to two days prior to testing (Hauser, Bartsch, Baumgärtel & Schulz, 2013; Hering, Hennig, Riehle & Stepan, 2018). This indicates that daily variation in BLC can be traced to factors such as dietary intake and recovery status. Further research should control for dietary and fluid intake to increase the reliability of the tests.

Another aspect of test timing is time-of-day variation. To make the tests more feasible for the participants, they had to choose what time of the day they would perform their tests. This resulted in some participants performing the tests in the morning while others performed the tests in the evening. However, previous research has failed to show any significant time-of-day variation in LaT and BLC testing (Rowland et al., 2011; Şekir, Özyener & Gür, 2002).

One consideration regarding Test 3 is that it tested differences in BLC at the IAT-HR exclusively, at different inclines. A protocol that would have given more data about each incline would have been to perform one LaT test (six intervals, as done in Test 1 and Test 2) for each incline.

However, this would dramatically increase the risk of dropouts and would have been less

feasible due to the need to complete five more tests for each participant. Another consideration is that one should test more inclines, to see if i.e. 0.2° incline would correlate higher than 0.3°.

This should be considered as a limitation of the treadmill used in the current study, since the inclines used were based on which inclines that could be selected on the treadmill. With 0° as a starting point, 0.3°, 0.6°, 0.9°, 1.1° and 1.5° were the smallest increases in incline that could be made. Which also meant that it was not possible to use the same slope as the otherwise most comparable study, which used 0.4% incline (Mugele et. al., 2018).

Treadmill running experience was not registered. However, experienced runners who are unaccustomed to running on a treadmill can familiarize themselves with the treadmill in less than 10 minutes (Van Hooren et. al., 2020), which corresponds to the warm-up time before each test in this study. On the other hand, there can be significant differences between whether a runner is used to running on a treadmill or not, as there is support for the fact that there is a

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difference in both movement patterns and muscle activation between running on an over- ground surface and a treadmill (Van Hooren et. al., 2020). This may have affected the results and should be considered in further studies.

Another draw-back that might have affected the results is that the room temperature was not normalized between the treadmill testing (Test 1 and Test 3) and the over-ground running testing (Test 2). Test 1 and Test 3 were done in the same laboratory with a stable room

temperature, while Test 2 was done in a track and field arena, in which room temperature was not accounted for. It has been shown that environmental temperature affects BL accumulation and HR (No & Kwak, 2016). This could have been easily adjusted by measuring the air

temperature in the arena and then setting the laboratory temperature to match. Furthermore, the natural air-resistance in over-ground running may affect the heart rate by aiding the body’s cooling effect during running (Bongers, Hopman & Eijsvogels, 2017). This could be compensated for by using cooling techniques such as using an electric fan directed towards the runner.

Another environmental factor that might differ between the laboratory and over-ground testing is air ventilation, which affects BL accumulation and HR during exercise testing (Van

Schuylenbergh, Eynde & Hespel, 2004). This is a factor that might be harder to control and measure in a non-laboratory setting.

Whether the estimation of IAT by visually interpreting the BLC curve was the best way to estimate IAT is debatable. There are several ways to determine IAT, as described in the

literature. For example, some have been based on a certain slope on the BLC curve, while others have a certain mmol increase from baseline or from when BLC rises by 0.5 mmol/L two intervals in a row in a stepwise increasing load test on the treadmill. In this study, IAT was estimated by visually inspecting the BLC curve from Test 2 and from there estimating MLSS. However, all methods presented so far have been found to have a certain margin of error or have been criticized for not being generalizable, which could lead to MLSS being higher or lower than the estimated IAT. This in turn could affect the results. Above all, an overestimated value could affect the results on Test 3, by letting the runners run at an intensity over MLSS and thus exhaust them until the last intervals. However, the only gold standard test to accurately determine the IAT, is to do several tests on different days, one day for an intensity and then evaluate where MLSS is achieved (Carter & Newhouse, 2019; Faude, Kindermann & Meyer,

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2009). This was practically not feasible for the current study. The aspect of running on a level above the IAT could also explain the trend towards an exponentially increasing difference in BL accumulation during higher intensities (interval 4-6) between Test 1 and Test 2 (Figure 1). This should be taken into consideration when interpreting these results.

Limitations

There were some limiting factors in the current study. Two major limitations were low statistical power and the limited amount of time for conducting the research. This affected all parts of the study. It affected the recruitment, as it was difficult to gather a large number of participants who met the inclusion requirements. It also influenced the testing, as it was not possible to have more participants or to do more tests. Another limiting factor was the small group sample of eight individuals, which in this context is also called a heterogeneous group. The risk of type II errors increases. In the current study, there was a clear non-significant trend, which should encourage further studies with more participants. A last limitation is that the participants were only men. Further research should include more women to increase the generalizability of the findings.

Ethical and Social Reflection

Firstly, the participants might have experienced some unease from the capillary blood tests.

However, participants received information about the potential unease from the capillary blood tests before participating in the study. Secondly, the participants might have experienced pressure to participate in all three test occasions. To eliminate the risk for this to happen, the participants received written information and signed an informed consent about the free will of both participating and cancelling the participation in the study at any time, without any

explanation. The third potential unpleasant feeling the participants might experience is muscle soreness from the tests, even though this is not very likely due to their physical level of fitness.

However, elite runners at this level of competition are used to participating in similar tests and are generally interested in the results.

As there are considerations and risks with this study, it is important to consider what benefit they may have for society and whether the benefit is considered to outweigh the risks. This study focuses on elite male runners and thus probably also adds the most for elite male runners.

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What this can hopefully bring to society is to be part of the research on how to facilitate and improve LaT testing on treadmills, in order to contribute to designing more reliable tests and thus more reliable evaluations of runners. This is of great value and benefit to runners, but also other types of athletes. Since this would make the training programs more accurate and

individualized, this could help athletes maximize their fitness and performance.

Practical implications and further research

The approaching significant trend that BLC is higher when running on an over-ground surface than when running on a treadmill, when HR is matched, invites further research to examine this theory. A larger sample of participants and controlled external factors should be implemented, such as room temperature, air ventilation, nutrition intake and hydration. The strong correlation between running on an over-ground surface and running on a treadmill with 0.3° incline should also be further investigated with bigger samples. If 0.3° is the best incline to use on a treadmill for duplicating over-ground running, this could be very useful in laboratory testing when designing training programs and evaluating physical fitness in athletes.

Conclusion

The current study with elite runners demonstrated a clear tendency of a higher BLC level at the highest running velocity at running track as compared to treadmill at matched HR. Furthermore, the results revealed that a 0.3° incline on the treadmill corresponded best to the running track, when comparing BLC at the over-ground running IAT-HR in these athletes. Since testing and evaluating runners’ aerobic fitness mainly is performed on treadmills, while training is mainly done on an over-ground surface, valid protocols for testing IAT at treadmills are important for accurate control and guidance of training intensity via HR monitoring when running on an over- ground surface. More research with larger sample size is warranted to evaluate the most optimal treadmill incline at IAT-testing that matches the HR-BLC-relationship to over-ground running.

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Appendices

APPENDIX 1.

Informationsbrev till testdeltagare i studien “The relationship between the individual lactate threshold and heart rate at treadmill and over-ground running”.

Detta är ett informationsbrev till dig som valt att medverka i studien och tillika examensarbetet The relationship between the individual lactate threshold (iLaT) and heart rate in treadmill and over-ground running. Mitt namn är Oa Blom (BSc) och jag är student på Magisterprogrammet i Idrottsmedicin. Mitt examensarbete skrivs under handledning av Michael Svensson (Med. Dr., lektor i idrottsmedicin) och handlar om tillförlitlighet i iLaT-testning på löpband. Nedan följer information som är viktig för dig som deltar att känna till:

När man springer med en viss intensitet uppstår en obalans av ackumulering och avlägsnande av laktat från musklerna till blodet. Detta kallas för laktattröskeln eller, som i folkmun,

“mjölksyratröskeln”. Laktattröskeln högerförskjuts generellt med uthållighetsbaserad träning vilket innebär att man som löpare kan uppnå högre löphastighet innan laktatnivå ökar påtagligt i blodet. Den individuella laktattröskeln vid löpning har visats starkt korrelera till prestation, till och med av större betydelse än VO2max och ger värdefull information om löparens aeroba status. Bestämning av iLaT kan vidare vara värdefullt för bestämning av intensitetszonerna i träningsprogram för uthållighetsidrottare. Idag testas oftast laktattröskeln på löpband och det finns forskning som tyder på att laktattröskeln skiljer sig löpning på löpband och löpning på fast underlag. Tidigare studier vid avdelningen för Idrottsmedicin vid Umeå universitet tyder på att laktattröskeln infinner sig på en högre puls vid löpning på löpband jämfört med löpning på fast underlag. Syftet med aktuellt uppsatsarbete är att undersöka om puls-laktatförhållandet skiljer sig åt mellan löpning på löparband och fast underlag (= primär hypotes). Om en skillnad visar sig är ett sekundärt syfte att testa om en specifik lutning på löparbandet kan kompensera

skillnaden så att löpning på löparband med lutning korrelerar med löpning på fast underlag gällande lika puls-laktatförhållande (= sekundär hypotes).

Vem söker vi?

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Vi söker dig som är: elitlöpare på nationell eller regional elitnivå, är frisk och vill genomföra tre löptester inom loppet av två veckor.

Detta händer under studien

Studien utförs vid avdelningen för Idrottsmedicin vid Umeå universitet. Blodprover kommer att tas via stick i finger och frågor om kön, hälsotillstånd och handdominans kommer att ställas.

Testerna kommer att utföras vid tre tillfällen och innebär löpning med submaximal, delvis kraftigt ansträngande intensitet (laktattröskeltest). Vid varje tillfälle kommer kapillär blodprovstagning (stick i fingret) genomföras för insamling av kapillärblod. Varje blodprov motsvarar ca 10 mikroliter blod och 5 provtagningar genomförs per tillfälle, sammanlagt 15 blodprov. Blodprovstagningen innebär ett litet nålstick i fingret som vanligtvis ger snabbt

övergående smärta. Blodprovstagningen genomförs enligt Good laboratory practice och innebär mycket små risker.

I blodproverna mätas laktatkoncentrationen i blodet. Dessa kommer att användas för att identifiera din individuella laktattröskel. Efter blodets analyserats slängs blodet. Vi kommer alltså inte att spara eller använda din DNA.

Konfidentialitet och sekretess

Det är av yttersta vikt för oss att datan och personlig informations hanteras med yttersta konfidentialitet. Resultaten kommer att behandlas på så sätt att inga obehöriga kan ta del av dem. All data hanteras enligt GDPR och du har rätt att begära dina data raderad när som helst.

Du kommer att få tillgång till dina individuella resultat.

Frivillighet

Ditt medverkande i studien är frivilligt och du kan när som helst avbryta ditt medverkande i studien utan att motivera varför.

Försäkring

Du som deltagare själv för eventuella förluster av inkomst eller andra personliga utgifter som kan tillkomma.

Alla deltagare i forskningsprojektet är försäkrade av Kammarkollegiets personskadeförsäkring.

Ansvariga för studien:

Forskningshuvudman: Umeå universitet Student: Oa Blom

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Handledare Michael Svensson (Med. Dr., lektor i idrottsmedicin) Personuppgiftsansvarig: Umeå universitet

Dataskyddsombud: Marit Juselius, pulo@umu.se Kontakt: Oa Blom, 0702130392, oablom@gmail.com Informerat samtycke till deltagande i studien.

Studie: The relationship between the individual lactate threshold and heart rate at treadmill and over-ground running.

Jag har tagit del av ovanstående skriftlig information om studien.

Jag har haft möjlighet att diskutera frågor gällande mitt deltagande i studien.

Jag samtycker om hur min data hanteras i den här studien.

Jag är medveten om att mitt deltagande i studien är helt frivilligt, och att jag när som helst kan avbryta mitt deltagande utan att motivera varför.

Jag samtycker genom min underskrift till att deltaga i studien.

_____________________________ _______________________

Deltagarens underskrift Ort och datum

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APPENDIX 2.

Comparison of blood lactate levels between treadmill running and over-ground running during incremental tests