Impact of High-Intensity Interval Training on Different Slopes on Aerobic Performance: A Randomized Controlled Trial

Abstract

This study investigated the impact of six high-intensity interval training (HIIT) running sessions on 1% or 10% slopes on various physiological and performance parameters in 25 men. The participants underwent assessments of VO_2max, time to exhaustion on 1% slope (TLim1%), and time to exhaustion on 10% slope (TLim10%) in the initial three visits. They were then randomly assigned to control (CON), HIIT on 1% slope (GT1%), or HIIT on 10% slope (GT10%) groups. Over three weeks, participants performed six HIIT sessions with equalized workload based on their individual maximal oxygen uptake (vVO_2max). The sessions comprised 50% of TLim, with a 1:1 ratio of exercise to recovery at 50% vVO_2max. The results indicated significant improvements in VO_2max and peak velocity (VPeak) after HIIT on both slopes. Heart rate (HR) differed between sessions for GT1%, while no significant differences were observed for GT10%. Ratings of perceived exertion (RPE) were significantly reduced for GT1% after the third session, with a similar trend for GT10%. In summary, six HIIT sessions on a 1% or 10% slope effectively enhanced VO_2max and VPeak, but there was no improvement in TLim performance, suggesting no adaptive transfer between training groups.

1. Introduction

Significant adaptations are observed in maximal oxygen uptake (VO_2max) and aerobic performance after a few short sessions of high-intensity interval training (HIIT) performed at speeds close to VO_2max (vVO_2max) [1,2] and/or above maximum speed [3,4]. There is substantial evidence supporting the benefits of HIIT in competitive runners [5], highlighting that this high-intensity protocol, executed in brief periods, elicits physiological responses comparable to those achieved by continuous programs of prolonged exercise [6,7,8].

These adaptations establish a favorable relationship between training time and effectiveness [9,10,11] for the HIIT training model, regardless of the sport in focus, athlete’s experience, and fitness level [4]. In addition to the intensification proposed by HIIT, the variation of training stimuli emerges as a potential strategy for metabolic and neuromuscular enhancements [12], beyond adaptations related to running performance.

In a competitive running scenario, physiological and mechanical changes resulting from alterations in terrain (horizontal vs. inclined vs. declined) influence the dynamics of the stretching–shortening cycle and, consequently, the energy cost [13,14]. In this sense, uphill running, for example, demonstrates an increase in neuromuscular [15] and metabolic [16] overload compared to horizontal running, resulting in a higher perception of effort. Studies indicate an increase in muscle activation and a predominance of concentric overload during uphill running [17], possibly explaining the increase in energy cost and the reduction in acute performance [18].

Currently, there is substantial evidence supporting the effectiveness of different training programs for runners, aiming to minimize disparities caused by variations in inclination and optimize results [19]. Traditional approaches, such as continuous training at different intensities and resistance training regimens, have been widely studied and associated with improvements in endurance, metabolic efficiency, and aerobic performance in runners [20]. However, there is a current gap in specific evidence regarding the effects of HIIT on runners, especially concerning different inclinations. While HIIT is recognized for its general benefits, the specific application of these principles in varied uphill contexts in running still seems to lack investigations to establish clear and direct evidence on acute responses in variables of interest of aerobic performance [21].

Although previous studies have addressed the effects of HIIT in different sports contexts, there is a scarcity of research specifically focused on runners and their response to HIIT at different slope percentages [22,23]. In addition to methodological limitations, available studies have not adequately explored the complexity of the relationship between HIIT and uphill running. Furthermore, the lack of experimental research hinders a comprehensive understanding of adaptations over time [24]. This study aimed to investigate the impact of six sessions of HIIT running on 1% and 10% slopes on key physiological parameters, including VO_2max, peak velocity (VPeak), heart rate (HR), rate of perceived exertion (RPE), and time to exhaustion (TLim). Given the higher neuromuscular demand associated with a 10% slope, our hypotheses were as follows: (a) running on a 10% slope will result in more substantial adaptive improvements compared to a 1% slope (H1); (b) HR and RPE will show equal adaptation patterns over time; and (c) training on a 10% slope will lead to significant performance gains for both TLim1% and TLim10% (H3—adaptive transfer hypotheses), while training on a 1% slope will generate specific improvements, primarily in flat terrain performance.

2. Materials and Methods

2.1. Study Design and Registration

This study followed all the items proposed in the guidelines of CONSORT for reporting parallel group randomized trials. All procedures were performed in accordance with the Declaration of Helsinki and included in the clinical trial registration of the U.S. National Institutes of Health (ClinicalTrials.gov; NCT02511964). This research analyzed the effect of HIIT with different slope programs (1% slope or uphill running on 10% slope) on aerobic performance in healthy people, using a randomized, between-group design (experimental group [EG] and control group [CG], respectively). The primary outcomes of this study involve the dependent variables: VO_2max, total time of protocol (TTotal), peak of velocity (VPeak), metabolic demands, and absolute time values (min) of time to exhaustion for the 1% slope and uphill running on the 10% slope (TLim1% and TLim10%). As a secondary outcome, HR and RPE were observed, as well as whether there was a possible transfer of adaptations between the training group, measured based on TLim performance. Scheme 1 presents the entry and exclusion of participants and the primary and secondary outcomes.

2.2. Subjects

Twenty-five male college students, physically active and familiar with aerobic activities on a treadmill, participated in this study. Individuals were invited through announcements made at the university and in a fitness center where the study was conducted over the course of six months. As inclusion criteria, participants needed to have a minimum of 2 years of aerobic training experience and perform high-intensity exercises a minimum of twice a week, with at least 150 min of moderate or vigorous aerobic activities executed. This study excluded those with a recent history of injury due to the potential interference on running performance, low adherence to the exercise program (i.e., an interval of more than three days between sessions), or the use of any ergogenic substance that could potentially interfere with the study results.

All individuals were invited to have their questions clarified after signing the consent form. This study was approved by the ethics committee of the university (#045.2010). Using the statistical package G-Power (Free Version 3.0.5) for analysis, “ANOVA Repeated Measures Between Factors”, with two measures and three groups, the sample size considered an error of 5%, statistic power of 80%, and an effect size of 0.60 (moderate) (19), resulting in 24 participants. Considering sample loss and significant abandonment when conducting chronic experimental models, a greater number of participants were recruited, expecting a loss of around 20% to 30%.

Table 1. Average (SD) of the subjects’ characteristics, training sessions, physiological parameters, and performance.

Variables	GT1%	GT10%	CON
	(n = 9)	(n = 8)	(n = 8)
Age years (SD)	26 (5)	28 (3)	26 (3)
Anthropometry and Body Composition
Body mass kg (SD)	79.5 (9.8)	84.0 (13.6)	82.2 (10.5)
Height cm (SD)	178.0 (7.4)	175.1 (5.9)	177.3 (6.5)
BF % (SD)	13.5 (4.3)	14.7 (3.6)	12.5 (4.2)
Physiologic variables
VO2max mL∙kg−1∙min−1 (SD)	52.6 (4.4)	54.1 (4.1)	54.7 (5.6)
HRmax bpm (SD)	193 (7)	191 (10)	193 (10)

Subtitle: GT_1%—running group on 1% slope; GT_10%—running group on 10% slope; CON—control group; SD—standard deviation; HR_max—maximum heart rate; BF—body fat.

contains the anthropometry, body composition, physiological parameters, and performance of the groups investigated.

2.3. Withdrawal Criteria

Participants were automatically removed from this study if they did not fully comply with the evaluation processes within a period of two weeks or did not complete the six training sessions exactly twice a week. Participants were instructed in such cases to abandon the experiment and were free to withdraw, without harm, at any time.

2.4. Experimental Approach

The comparison between running programs was carried out using a randomized clinical trial composed of three groups (i.e., HIIT on 1% slope (GT1%) [n = 9], HIIT on 10% slope (GT10%) [n = 8], and control (CON) [n = 8]). The allocation was given a random strategy conducted by a researcher not involved in the experiment (described in specific sessions). The different groups (GT1%, GT10%, and CON) before the start of the experimental sessions, that is, pre-intervention, did not present significant differences for the TLim1% variable (p = 0.632). The GT1% and GT10% groups underwent a training program with six sessions, while the control group did not train. The pre- and post-training sessions were performed in three visits, as described in Figure 1. All training sessions were carried out at the same time of day with the temperature controlled between 21 and 23 degrees and a relative humidity of ~60%. All participants were instructed not to eat in the three hours preceding the tests and not to perform physical activity in the 24 h before the evaluations. The protocol to evaluate VO_2max and the time to exhaustion evaluation were performed on different days, with 48 h in between.

2.5. Procedures

2.5.1. Anthropometry and Body Composition

Standard measurements established by the International Society for the Advancement of Kinanthropometry (ISAK) were used, consisting of the following indicators: body weight, height, and skinfold. Height was measured with the volunteer in a standing position and barefoot, with the ankles, calves, buttocks, scapula, and head leaning on a wall. The position of the head followed Frankfurt’s plan, and stature was measured at the moment of inhaling air. Body mass was measured while the participants wore light clothes (Mechanic, Filizola, Brazil). The relative body fat was estimated using the skinfold technique, in which body density is calculated using the seven-fold protocol proposed by Jackson and Pollock [25] where values are collected at each point in a rotational sequence on the right side of the body and the average value of three measures is recorded. The measurements were performed by a single investigator using a skinfold compass (Slim Guide, Rosscraft, Canada). After calculating the body density, it was converted to a percentage of body fat using the equation proposed by Siri [26].

2.5.2. Estimation of VO_2max and vVO_2max

After a 6 to 10 min rest in the supine position and a subsequent assessment of HR and blood pressure, participants commenced walking on a treadmill at 5 km/h with a 1% inclination. The speed was incrementally increased by 1.0 km/h per minute until reaching 65% of heart rate reserve (HRRes), at which point, the speed was maintained for 6 min. HR and RPE were recorded every minute. Subsequently, additional increments of 1.0 km/h were introduced every minute until participants could no longer sustain running, at which point, the actual maximum HR (HR_maxR) and VPeak were recorded.

Verbal encouragement was given to participants to achieve maximum performance. The VO_2max and vVO_2max (velocity associated with VO_2max) were predicted from the equations for running proposed by ACSM and the reserve method proposed by Swain et al. [27]. The reliability of the method using running provided a typical measurement error of 2.4 mL⁻¹·kg⁻¹·min⁻¹ (4.9%) and an intraclass correlation coefficient of 0.864 [28].

2.5.3. Time to Exhaustion (TLim)

After a 6 min warm-up at 50% of vVO_2max, participants underwent a time to exhaustion test on a treadmill with inclinations of either 1% or 10%, randomly determined. The test aimed to determine the maximum duration achievable at vVO_2max. HR and RPE were closely monitored at 15 s intervals. In the post-training test, a new vVO_2max was established based on the achieved VO_2max. Given the variations in speed between pre- and post-training, we utilized the metabolic demand (mL·kg⁻¹·min⁻¹) estimated at the training speed (pre and post) for each gradient, multiplied by the duration in minutes achieved in the time to exhaustion test (TLim), as the primary outcome under investigation.

2.5.4. Training Program

Participants completed a total of six training sessions over three weeks (two sessions per week), with an interval of exactly three days between each session. The running sessions began with a 6 min warm-up to 60% vVO_2max. The stimuli were administered in vVO_2max, with equalized workloads calculated by the ACSM running equation for the two slope percentages investigated (1% or 10%). The stimulus volume was represented by 50% of TLim, with an equal percentage of recovery at 50% vVO_2max (i.e., a 1:1 ratio). Participants began each stimulus after the speed of the treadmill was reached. Likewise, the end of each stimulus occurred with a sudden movement to the side of the treadmill, with the speed quickly reduced to the stipulated recovery intensity. Two investigators, positioned on each side of the treadmill, carried out process safety. Both HR (Polar FT1 cardiac monitor—USA) and RPE were monitored continuously throughout the training session. All individuals in the experimental groups performed interval training sessions until maximum voluntary exhaustion or until they reached six stimuli. In other words, if there was no exhaustion, the subjects could perform a maximum of six stimuli on the slope gradient to which they were allocated. The impulse training (TRIMP) was calculated based on the product of total time multiplied by HR.

2.5.5. Analysis of Rate of Perceived Exertion (RPE)

The assessment of perceived exertion was performed for both experimental groups using the modified Borg scale CR10, where 0 means no exertion and 10 means maximum exertion. A dimension scale of 60 × 30 cm was positioned on the mirror in front of the treadmill, and each participant was encouraged to verbally indicate an effort score every 30 s and at the end of the stimulus.

2.5.6. Randomization

After initial screening, participants were randomly distributed for TLim assessments on a 1% or 10% slope. After this, participants were again randomized for blind allocation within the three different experimental groups. A new randomization was performed to determine the TLim retest on the 1% or 10% slope. A simple randomization was applied. The randomization process was made by lot using paper stored in sealed opaque envelopes. The allocation of participants was concealed from the blinded assessor. Participants were reminded not to disclose their group allocation during the follow-up assessment.

2.5.7. Blinding and Data Analysis and Treatment

To avoid possible analysis biases, the data were collected by two different researchers associated with the project and the research group (A.S. and E.P.) and analyzed by a third researcher (group leader M.S). The researcher responsible for data analysis remained blind throughout the data collection process. The names of all participants remained confidential, being excluded from the data sheet and replaced with numbers. Participants did not have access to the results until the study ended. Afterwards, everyone received a report of their performances via email.

2.6. Statistical Analysis

The results are shown as the average and standard deviation (SD). After assumptions testing, the comparison of the TTotal between GT1% and GT10% was performed by an independent t-test. A two-way variance analysis (ANOVA) (group × time) with repeated measures was used to test differences between the VO_2max and VPeak averages for the three groups investigated (CON, GT1%, GT10%). The differences between the metabolic demands and absolute time values (min) of TLim1% and TLim10% and the effect of training specificity were determined by a three-way ANOVA (group vs. test vs. time), with repeated measurements only for the factors test and time. The post hoc Tukey test was applied to identify differences between groups. The effect size (ES) was calculated using Cohen’s “d” index with the following threshold values: <0.2: trivial; 0.2–0.6: small; 0.6–1.2: moderate; >1.2: large. The analysis was performed in the SPSS program (v. 17, SPSS Inc., Chicago, IL, USA), considering a significance level of p ≤ 0.05.

3. Results

TLim1% showed a longer time compared to TLim10% (as shown in

Table 2. Mean and standard deviation (SD) of training effect on the dependent variables.

Characteristics	Pre-Training			Post-Training
	GT1% (n = 9)	GT10% (n = 8)	CON (n = 8)	GT1% (n = 9)	GT10% (n = 8)	CON (n = 8)	(Time/Interaction)
VO2max (mL·kg−1·min−1)	52.6 (4.4)	54.1 (4.1)	54.7 (5.6)	56.0 (4.6) *	57.1 (5.5) *	54.0 (4.2)	p = 0.000; p = 0.002
VPeak (km·h−1)	15.8 (1.5)	16.3 (1.9)	16.9 (1.3)	16.8 (1.6)	17.7 (1.9) *	16.9 (1.1)	p = 0.000; p = 0.001
TLim1%
Time (min)	6.1 (1.2)	6.2 (2.1)	6.6 (2.6)	5.8 (1.6)	6.0 (3.2)	6.0 (2.3)	no difference
Velocity (km·h−1)	14.1 (1.3)	14.5 (1.2)	14.7 (1.6)	15.1 (1.3) *	15.4 (1.6) *	14.5 (1.5)	p = 0.001; p = 0.000
VO2 demand (mL·kg−1)	324 (75)	329 (122)	368 (172)	329 (109)	333 (176)	324 (121)	p = 0.884; p = 0.680
TLim10%
Time (min)	3.6 (0.5)	4.5 (1.4)	4.3 (1.2)	3.6 (0.7)	4.0 (1.9)	3.7 (1.6)	no difference
Velocity (km·h−1)	9.9 (1.0)	10.0 (1.0)	10.1 (1.2)	10.9 (1.0) *	11.1 (1.1) *	10.4 (1.1)	p = 0.001; p = 0.000
VO2 demand (mL·kg−1)	192 (40)	235 (82)	237 (65)	200 (50)	223 (106)	198 (57)	p = 0.884; p = 0.680

Subtitle: GT_1%—running group on 1% slope; GT_10%—running group on 10% slope; CON—control group; SD—standard deviation; V_Peak—peak velocity in maximum incremental test; VO₂ demand (mL·kg⁻¹)—product of metabolic demand by the total exercise time on 1% and 10% slopes. There were no differences between the pre-intervention conditions. * Differences between experimental groups and control (p < 0.05).

), which resulted in different TRIMP values (1186.0 ± 367.0 and 767.1 ± 234.5, respectively, for 1% and 10%; p = 0.001). TTotal performed in GT1% was significantly longer compared to GT10% (30.4 ± 9.4 min vs. 22.3 ± 5.1 min, respectively; p = 0.035), despite the same number of stimuli received by GT1% and GT10% (4.7 ± 1.3 and 4.8 ± 1.3 stimuli, respectively; p = 0.920). However, the TRIMP values observed between GT1% and GT10% showed significant differences (5506.6 ± 1684.8 vs. 3986.2 vs. 903.1, respectively). Despite this, significant increases were found after six interval running sessions for VO_2max (p = 0.002) and VPeak (p = 0.001) in both the GT1% and GT10% groups, with no significant interaction for the groups. The CON group did not differ after the training period for all dependent variables. Regarding the effect size (ES) calculations between the pre- and post-measurements, the groups that underwent training demonstrated ESs of 0.67 (GT1%) and 0.70 (GT10%), while the CON group demonstrated only 0.03 for VO_2max. Similarly, for VPeak, which demonstrated effect sizes of 0.67 and 0.70 for GT1% and GT10%, respectively, the CON group demonstrated only 0.01. The results are presented in Table 2.

Significant differences were observed when comparing the metabolic demands of TLim1% vs. TLim10% and the absolute values for both conditions. Although there was significant improvement in VPeak after training, there were no changes in metabolic demand after training for TLim1% and TLim10% as well as for the control group, which was not subjected to any type of intervention. Consequently, there was no influence of specific running tests observed on any of the groups (interaction vs. test vs. time, p = 0.680). TLim performance with two inclinations showed trivial ESs.

HR differed significantly between session 1 (S1) and session 6 (S6) for the group that trained on a 1% slope (p < 0.001), presenting lower HR values for the same workload. However, there were no significant differences between S1 and S6 for the GT10% group (p > 0.05). The results are presented in Figure 2A. RPE suffered significant reductions from the third training session (S3) for the GT1% group (p < 0.001) compared to S1, suggesting an adaptive response for effort modulation. A similar pattern was observed for the GT10% group, although of a smaller magnitude. The adaptive RPE responses can be observed in Figure 2B. Figure 3A,B illustrate the intrasession HR and RPE, comparing S1 vs. S6.

Unintentional Harm

Risks to health and physical integrity are inherent in physical exercise. On the other hand, the benefits are disproportionately greater than the risks. Therefore, due to this disproportionality, the benefits are worth the possible risks. In any case, we were concerned with monitoring the effects of the training program on the health and safety of the subjects who made up the experimental groups. To this end, two investigators experienced in physical training followed the procedures appropriately, in accordance with ethical and safety precepts. However, delayed muscle soreness was frequently reported by at least 50% of participants in the GT10% group after the first session (adapting later), since such a performance format was not routine. Furthermore, one of the participants was forced to abandon the study due to a muscle strain in the gastrocnemius region. Adequate guidance was provided to the participant and contact was maintained for an appropriate follow-up period.

4. Discussion

This study aimed to determine the effect of six HIIT running sessions on a 1% or 10% slope on VO_2max, VPeak, HR, RPE, and TLim performance, as well as the influence of the specific training on performance on the different slopes. The main effects observed were significant increases in VO_2max and Vpeak, without any effects of training on TLim1% and TLim10% performance, regardless of the slope used. To the best of our knowledge, this was the first study to compare the effects of interval training on different slopes in a short training period and observe the increase in VO_2max and VPeak.

The significant difference between the TLim1% and TLim10% tests was also an important finding, as this directly impacted the impulse training (i.e., TRIMP, volume × intensity) between the groups (1186.0 ± 367.0 and 767.1 ± 234.5, respectively, for 1% and 10%; p = 0.001). This condition reflected the TRIMP values developed over the sessions, but the ESs of interventions on 1% and 10% slopes showed moderate clinical significance, similar to that previously observed for VO_2max and vVO_2max [29]. Additionally, it is worth highlighting that the six sessions were sufficient to generate chronotropic adaptations, at least for GT1%. The RPE responses were also significantly modified for both experimental protocols, suggesting that the different modulation of effort observed was the result of interoceptive (metabolic) or cognitive changes (there is no way to distinguish).

Several studies have shown the effects of HIIT on physiological and biochemical markers and performance in six sessions of cycling [3,4] or running on a horizontal plane [29,30]. In contrast, little chronic evidence has been published on uphill running [15]. This study supports the evidence that only a small number of sessions with reduced exercise duration leads to physiological and performance adaptations from the administration of stimulus in vVO_2max. Considering the TLim1% and TLim10% performances, even after metabolic equalization between running protocols (377 ± 119 s and 246 ± 68 s for 1% and 10% slopes, respectively), the lowest Ttotal was developed by the protocol using a 10% slope. This was an important finding and demonstrated that with less training time, the results after six interval running sessions were similar to VO_2max and VPeak between GT1% and GT10%. Moderate ESs for the GT1% and GT10% groups for these variables support these findings and further suggest that uphill running may be established as a strategy for greater efficiency and time savings compared to the TTotal observed in GT1%.

The improvement in VO_2max seemed to be primarily affected by the power developed during running (intensity-dependent), together with the time spent in VO_2max [31]. The application of only 50% of TLim as the duration of the training protocol, although this did not lead the participants to maximum effort and still produced a lower TTotal of running on the 10% incline, did not appear to have been a determining factor for the adaptive responses of VO_2max. The reduction in HR throughout the sessions is an attribute that could explain the intrinsic changes in VO_2max since they are collinear variables. Therefore, we can infer that the reduced HR response for the same workload came from an adaptation related to the volume of blood ejected, which, in turn, altered the capacity to perform work. However, this theoretical rationale would be valid exclusively for the GT1% group and would not explain the effects of training on GT10%, suggesting that other adaptive factors may have been linked to these effects for the 10% slope.

The TTotal administered to each stimulus was probably sufficient to achieve VO_2max (~2 min) in both training protocols (GT1% and GT10%). Although we did not directly address this topic due to the indirect instruments used, the HR of both groups reflected this in the first three sessions. So, it is speculated that permanence in VO_2max seems to be crucial for the improvement of this variable [31]. In cases where the intensity exceeds the severe exercise domain (approximately 136% of vVO_2max), fatigue may be established before VO_2max is reached [32].

Despite the significant training effect of vVO_2max on aerobic performance demonstrated in the literature regarding running on a horizontal plane [29,33] and maximum sprint running [15], TLim was not sensitive enough to demonstrate these differences after six interval sessions, regardless of the difference in TRIMP. The increase in intervention time may have enabled effective improvements in TLim1% and TLim10% performance, as noted in the studies of Paradisis et al. [15]. In addition, the great inter-subject variability of the TLim test (CV = 25–30%) may also have contributed to reducing the statistical power and the ability to observe significant effects of training [34].

Finally, HIIT with six training sessions performed on 1% and 10% inclines was effective in producing a significant increase in VO_2max and VPeak. The smallest impulse training with the uphill running intervention suggests the importance of this strategy for greater time saving and even adaptive effects on VO_2max and its associated speed. In addition, intervention time is not enough to improve the performance of TLim and, therefore, no crossed effect was developed.

Limitations

The lack of an instrument for the direct analysis of gas exchange is the main limitation of our study. Despite this, the indirect measure used presents significant validity, as well as appropriate predictive validity, and reliability (typical measurement error of 2.4 mL⁻¹·kg⁻¹·min, suggesting consistency in the results). Furthermore, the lack of a biochemical, such as lactate, could have helped us respond to certain events that occurred in this study.

5. Conclusions

The study results suggest the use of stimulus HIIT on two different slopes (1% or 10%) for the diversification of training and the improvement of VO_2max and VPeak. However, the exercise volume on the 10% slope was 36% lower than that when running on the 1% slope. The uphill running strategy is defined as time efficient to generate significant improvements in VO_2max and VPeak, the latter being a determinant predictor of human performance.

The development of a training protocol for TLim response allows for the individualization of time and workloads. When different inclination percentages are used in training, specific tests can be suggested for the development of these protocols because of the differences in TLim1% and TLim10%. Further studies using different types of training (e.g., continuous aerobic exercise) with inclination should be tested to assess the possible advantages of different training strategies.

The adaptive HR response on the 1% slope was superior to the 10% running protocol. HIIT on a 1% slope produced greater impacts on RPE after the six exercise sessions. However, both running protocols significantly benefited.