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Effects of Increased Load of Low- Versus High-Intensity Endurance Training on Performance and Physiological Adaptations in Endurance Athletes

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Purpose: To compare the effects of increased load of low- versus high-intensity endurance training on performance and physiological adaptations in well-trained endurance athletes. Methods: Following an 8-week preintervention period, 51 (36 men and 15 women) junior cross-country skiers and biathletes were randomly allocated into a low-intensity (LIG, n = 26) or high-intensity training group (HIG, n = 25) for an 8-week intervention period, load balanced using the overall training impulse score. Both groups performed an uphill running time trial and were assessed for laboratory performance and physiological profiling in treadmill running and roller-ski skating preintervention and postintervention. Results: Preintervention to postintervention changes in running time trial did not differ between groups (P = .44), with significant improvements in HIG (-2.3% [3.2%], P = .01) but not in LIG (-1.5% [2.9%], P = .20). There were no differences between groups in peak speed changes when incremental running and roller-ski skating to exhaustion (P = .30 and P = .20, respectively), with both modes being significantly improved in HIG (2.2% [3.1%] and 2.5% [3.4%], both P < .01) and in roller-ski skating for LIG (1.5% [2.4%], P < .01). There was a between-group difference in running maximal oxygen uptake changes (P = .04), tending to improve in HIG (3.0% [6.4%], P = .09) but not in LIG (-0.7% [4.6%], P = .25). Changes in roller-ski skating peak oxygen uptake differed between groups (P = .02), with significant improvements in HIG (3.6% [5.4%], P = .01) but not in LIG (-0.1% [0.17%], P = .62). Conclusion: There was no significant difference in performance adaptations between increased load of low- versus high-intensity training in well-trained endurance athletes, although both methods improved performance. However, increased load of high-intensity training elicited better maximal oxygen uptake adaptations compared to increased load of low-intensity training.
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Effects of increased load of low- vs. high-intensity endurance training on
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performance and physiological adaptations in endurance athletes
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Original investigation
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Rune Kjøsen Talsnes1,2, Roland van den Tillaar2 and Øyvind Sandbakk3
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1Meråker High School, Trøndelag County Council, Steinkjer, Norway.
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2Department of Sports Science and Physical Education, Nord University, Bodø, Norway.
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3Centre for Elite Sports Research, Department of Neuromedicine and Movement Science,
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Norwegian University of Science and Technology, Trondheim, Norway.
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Corresponding Author:
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Rune Kjøsen Talsnes
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Department of Sports Science and Physical Education
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Nord University
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8026 Bodø, Norway
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E-mail: rune.k.talsnes@nord.no
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Phone: +47 99430935
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Running head
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Endurance training intensity
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Abstract Word Count
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250
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Text-Only Word Count
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3358
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Number of Figures and Tables
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Figures: 4 Tables: 4
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Abstract
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Purpose: To compare the effects of increased load of low- vs. high-intensity endurance training
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on performance and physiological adaptations in well-trained endurance athletes.
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Methods: Following an 8-week pre-intervention period, fifty-one (36 men and 15 women)
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junior cross-country skiers and biathletes were randomly allocated into a low-intensity (LIG,
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n=26) or high-intensity training group (HIG, n=25) for an 8-week intervention period, load-
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balanced using the overall training impulse (TRIMP)-score. Both groups performed an uphill
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running time-trial and were assessed for laboratory performance and physiological profiling in
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treadmill running and roller-ski skating pre- and post-intervention.
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Results: Pre- to post-intervention changes in running time-trial did not differ between groups
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(p=0.44), with significant improvements in HIG (-2.3±3.2%, p=0.01) but not in LIG (-
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1.5±2.9%, p=0.20). There were no differences between groups in peak speed changes when
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incremental running and roller-ski skating to exhaustion (p=0.30 and p=0.20, respectively),
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with both modes being significantly improved in HIG (2.2±3.1% and 2.5±3.4%, both p<0.01)
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and in roller-ski skating for LIG (1.5±2.4%, p<0.01). There was a between-group difference in
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running VO2max changes (p=0.04), tending to improve in HIG (3.0±6.4%, p=0.09) but not in
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LIG (-0.7±4.6%, p=0.25). Changes in roller-ski skating VO2peak differed between groups
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(p=0.02), with significant improvements in HIG (3.6±5.4%, p=0.01) but not in LIG (-
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0.1±0.17%, p=0.62).
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Conclusion: There were no significant difference in performance adaptations between
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increased load of low- vs. high-intensity training in well-trained endurance athletes although
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both methods improved performance. However, increased load of high-intensity training
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elicited better VO2max adaptations compared to increased load of low-intensity training.
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Keywords: biathlon, endurance performance, maximal oxygen uptake, training intensity
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distribution, training volume, XC skiing
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Introduction
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Endurance training involves the manipulation of training intensity, duration, frequency and
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mode, with the goal of maximizing physiological adaptations and performance.1,2 Accordingly,
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the organization and optimization of endurance training, and in particular training volume and
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intensity distribution, is widely debated among both sports scientists and practitioners.1-3 Most
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elite endurance athletes adopt a training model consisting of high volumes of low-intensity
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training (LIT) combined with low-to-moderate amounts of moderate- (MIT) and high-intensity
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training (HIT).1-3 However, the exact volume and training intensity distribution depends on the
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demands of the given endurance sport, individual requirements, as well as the phase of the
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annual training cycle.1,3,4
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Endurance athletes progress their overall training stimulus throughout the annual cycle, which
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might be achieved through increased load of LIT or by performing a larger load of MIT and/or
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HIT.1 While LIT is seen as an important stimulus for inducing peripheral adaptations such as
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increased mitochondrial biogenesis and capillary density of the skeletal muscle,5,6 central
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adaptations such as increased stroke volume of the heart, leading to improved maximal oxygen
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uptake (VO2max), are regarded as more responsive to HIT.5-7 However, LIT and HIT have many
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similarities (e.g., upregulating PGC-1α) and both intensities seem to elicit complex and
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integrated adaptations.1,5
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To better understand how progression in endurance training load by different intensity
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distributions influence performance and physiological adaptations in endurance athletes, valid
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methods for the matching of training load is required. The majority of previous intervention
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studies where training load has been matched for total work or oxygen consumption (iso-
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energetic method) emphasizes the superiority of HIT for maximizing physiological
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adaptations.7-9 However, such studies are not realistic from the perspective of how endurance
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athletes train and perceive stress,3 since endurance athletes can perform far more work, both
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energetically and in terms of total work at a lower autonomic disturbance, with LIT compared
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to HIT.10 Accordingly, progressing the overall training stimulus with increased load of LIT may
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be advantageous for optimizing adaptative responses at a tolerable level of stress, although most
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experimental evidence suggests superior adaptations while adopting a more polarized intensity
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distribution11 with greater training intensification.12
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Therefore, the present study compared the effects of increased load of LIT vs. HIT during an
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8-week intervention period on performance and physiological adaptations in well-trained
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endurance athletes. This was done by matching the increase of LIT and HIT for overall load by
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the training impulse method (TRIMP), in which we hypothesized that more HIT would elicit
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superior VO2max adaptations and thereby greater performance improvements compared to more
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LIT over 8 weeks.
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Methods
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Participants
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Fifty-one (36 men and 15 women; Table 1) national-level junior cross-country skiers and
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biathletes volunteered to participate in the study. All athletes were students at a Norwegian high
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school with a specialized study program for cross-country skiing (n=41) and biathlon (n=10).
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The Regional Committee for Medical and Health Research Ethics waived the requirement for
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ethical approval for this study. Therefore, the ethics of the study are in accordance with the
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institutional requirements, and approval for data security and handling obtained from the
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Norwegian Centre for Research Data (NSD). All athletes were fully informed of the nature of
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the study and its experimental risks before providing written consent. Several athletes (n=21)
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were <18 years, and therefore, the parents were asked to provide parental consent. Some
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athletes dropped out of the study (low-intensity training group [LIG]=2; high-intensity training
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group [HIG]=5) due to sickness (n=3), injury (n=2), or other reasons (n=2). In addition, two
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athletes from LIG were excluded from the final analyses due to lack of 85% compliance with
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the prescribed training.
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**Table 1 around here**
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Design
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Following an 8-week pre-intervention period, the athletes were randomly allocated into either
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a group with increased load of LIT (LIG, n=26) or a group with increased load of HIT (HIG,
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n=25) for an 8-week intervention during their late preparation period (SeptemberNovember).
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The training was balanced for overall load using a TRIMP score, and groups were matched for
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sport, age, sex, physiological indices, and pre-intervention training characteristics. Both groups
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performed an uphill running time-trial (TT) in the field and were assessed for laboratory
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performance and physiological profiling in treadmill running and roller-ski skating before (pre)
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and after (post) the intervention.
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Methodology
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Pre-intervention period
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Prior to the intervention, all athletes followed an 8-week baseline period consisting of the same
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training guidelines. The athletes were instructed to focus on high-volume LIT interspersed with,
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on average, one weekly MIT and one weekly HIT session. In addition, 23 weekly strength or
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speed sessions were integrated into LIT sessions or performed as a single session. Based on
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this, individualized training programs were developed together with the athletes personal
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coaches to ensure optimal adjustments of load. The athletes were familiarized with the different
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test protocols before performing all pre-tests during the last week of the pre-intervention period.
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Intervention period
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Training plans during the 8-week intervention period were based on a theoretical framework
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developed by the researchers and adopted to each athlete in close collaboration with coaches.
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The groups increased their overall training load in the intervention period by adopting two
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different training regimes. LIG continued with the same focus as during the pre-intervention
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period, but with increased volume of LIT, whereas HIG changed towards increased frequency
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and volume of HIT with reduced volume of LIT. Weekly mesocycle load was designed with
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three different load structures (high, moderate, and low) for both groups, where the coaches,
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individually adjusted and optimized load for each athlete. Based on previous research 13,14 and
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pilot testing of selected athletes, the use of the training impulse (TRIMP) method was
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incorporated as the most valid method for the matching of training load between groups.
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Accordingly, all within-group mesocycle loads were balanced for overall load (TRIMP)
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between-groups. TRIMP was calculated by multiplying the duration in three intensity zones
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with a weighting factor (i.e., LIT, MIT, and HIT are given a score of 1, 2, and 3, respectively).
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Total TRIMP was then obtained by adding the different intensity zone scores. Distribution of
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MIT and HIT sessions per week together with weekly mesocycle loads for both groups are
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displayed in Figure 1. All athletes were instructed to maintain the same diet and training plans
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were designed to maintain similar volume of strength and speed training during the intervention
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period.
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**Figure 1 around here**
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Training monitoring
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All athletes recorded their own training using an online training diary developed by the
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Norwegian Top Sport Centre (Olympiatoppen) by applying the modified session-goal approach
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(SG/TZ).15 Training intensity distribution was recorded using a five-zone intensity scale but
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reported using a three-zone scale (LIT, MIT, and HIT), which better corresponds with relevant
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literature and underlying physiological mechanisms.16 For MIT and HIT sessions performed as
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intervals, time in the intensity zone of the session was registered from the beginning of the first
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interval to the end of the last interval, including recovery periods. Moreover, strength and speed
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training were registered from the start to the finish of that separate part (e.g., strength, speed,
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plyometrics) during the session, including recovery periods. Training mode is reported as
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specific (classical and skating roller-skiing) and non-specific (running and cycling) endurance
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training. In addition, intensity control was achieved by regular use of heart rate (HR) monitoring
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and [La-] measurements throughout the intervention period.
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Test protocols and measurements
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Training plans were designed to include standardized training load in the last two days prior to
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the first day of testing. The athletes were instructed to follow self-selected preparation
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procedures and not to consume any large meals or caffeinated beverages within the last 2 hours
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before the test. There were always >24 hours between all tests for each athlete. The TT in
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combination with laboratory tests were chosen to obtain a comprehensive understanding of
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performance both in practical and laboratory conditions, as well as the underlying physiological
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mechanisms.
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Uphill running TT (test day 1)
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Prior to the TT, athletes performed a 30-min LIT self-selected warm-up procedure.
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Performance times were recorded using two synchronized watches and the Racesplitter
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timekeeping system (Makalu Logistics Inc, Fontana, USA). The TT was performed on asphalt
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with a total distance of 6.4 km (elevation: 270 m) and 4.5 km (elevation: 160 m) for men and
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women, respectively. Weather conditions were stable during each test day, being partly cloudy
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with low and stable wind, but differed in ambient temperature and humidity between pre and
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post (15 vs. 2 C and 70 vs. 90%, respectively). Due to different reasons, six athletes in LIG
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and one athlete in HIG were not able to perform the TT at both pre and post. Hence, 35 athletes
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were included in the final TT analysis (LIG, 10 men and 5 women; HIG, 14 men and 5 women).
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Laboratory treadmill running test (test day 2)
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Following a 10-min individual running warm-up (6072% of maximal HR [HRmax]), all athletes
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performed one 5-min submaximal stage running at 10.5% incline and at the same absolute speed
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(8 km·h-1 for men and 7 km·h-1 for women). After a 2-min recovery period, the athletes
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performed an incremental test to exhaustion in order to determine VO2max and performance
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measured as peak treadmill speed ([Vpeak] calculated according to Sandbakk et al .,17). The test
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was performed at 10.5% incline with a 1-km·h-1 increase in speed every minute until voluntary
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exhaustion. Starting speed was set to 9 km·h-1and 8 km·h-1 for men and women, respectively.
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Laboratory treadmill roller-ski skating test (test day 3)
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After a 10-min individual running warm-up (6072% of HRmax) as on test day 2, the athletes
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completed two 5-min submaximal stages at 5% incline while treadmill roller-ski skating. The
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two stages were performed at the same absolute speed for men (12 and 14 km·h-1) and women
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(10 and 12 km·h-1), with 1-min recovery in between. Following a 5-min recovery period, peak
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oxygen uptake (VO2peak) and performance measured as Vpeak were determined.17 The test was
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performed at 5% incline with a starting speed of 14 and 12 km·h-1 for men and women,
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respectively. The incline was kept constant, while the speed was subsequently increased by 2
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km·h-1 every minute up to 20 km·h-1 for men and 18 km·h-1 for women, and thereafter by 1
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km·h-1 until voluntary exhaustion. The athletes were instructed to use the skating G3 sub-
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technique during the entire test.
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Treadmill running was performed on a 2.5 x 0.7-m motor-driven treadmill and treadmill roller-
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ski skating on a 3.5 x 2.5-m treadmill (RL 2500 and RL 3500E, Rodby, Vänge, Sweden). For
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all submaximal testing, respiratory recordings were collected between the third and fourth
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minute of each 5-min stage and HR defined as the average over the last 30 s. Respiratory
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variables were measured using open-circuit indirect calorimetry with mixing chamber (Oxycon
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Pro, Jaeger GmbH, Hoechberg, Germany) and HR by a Garmin Forerunner 935 (Garmin Ltd.,
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Olathe, KS, USA). Rate of perceived exertion (RPE) using the 620-point Borg scale and [La]
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were taken from the fingertip directly after completing each stage. [La-] was measured using
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the stationary Biosen C-Line lactate analyzer (Biosen, EKF Industrial Electronics, Magdeburg,
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Germany). In addition, gross efficiency was measured for the submaximal roller-ski stages and
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defined as the ratio of work and metabolic rate.18 For the incremental test to exhaustion,
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respiratory variables and HR were measured continuously, and VO2max/peak defined as the
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highest 1-min average. HRmax was defined as the highest 5-sec HR measurement, whereas RPE
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was determined directly after, and [La-] approximately 1 min after.
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Statistical analysis
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All data are reported as means ± standard deviations (SD). Assumption of normality was tested
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with a ShapiroWilk test in combination with visual inspection of histograms. Adopted from
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previous literature,19,20 individual response magnitudes were summarized in three different
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categories: nonresponse defined as <0% change, moderate response as 0% to 5% change, and
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large response as >5% change. An adaptation index for each athlete was also calculated as the
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mean of the percentage change in treadmill running VO2max and Vpeak, treadmill roller-ski
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skating VO2peak and Vpeak from pre- to post.20 To test for differences between groups, a
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univariate general linear model (GLM) analysis of covariance (ANCOVA) was used, with the
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percentage change from pre- to post as the dependent variable, and baseline values as a
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covariate to adjust for possible between-group differences pre-intervention. Pre- to post
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changes within groups were assessed using a paired-samples t-test. Between-group differences
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in baseline and training characteristics were tested using an independent-samples t-test. Effect
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size (ES) was calculated as Cohen’s d by using the mean pre- to post change between groups,
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divided by the pooled pre-test SD (interpreted as follows: 0.00.24 trivial, 0.250.49 small,
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0.51.0 moderate, >1.0 large).21 For all comparisons, statistical significance was set at an alpha
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level of p<0.05, and p=0.050.1 indicated trends. All data analyses were conducted using SPSS
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26.0 (SPSS Inc, Chicago, IL, United States).
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Results
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Training characteristics
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Comparisons of training characteristics between groups are shown in Table 2. Weekly TRIMP
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during the pre-intervention and intervention periods did not differ between groups (p=0.60 and
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p=0.93, respectively), whereas the training intensity distribution shifted from having a similar
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pattern across groups during the pre-intervention to clearly differing during the intervention.
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During the intervention period, LIG performed 16% more endurance training hours compared
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to HIG (p<0.01), due to 25% more hours of LIT (p<0.01). HIG performed 118% more hours of
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HIT compared to LIG (p<0.01), whereas hours of MIT did not differ between groups (p=0.35).
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The volume of strength and speed training performed during the intervention period did not
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differ between groups (p=0.67 and 0.23, respectively).
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**Table 2 around here**
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Baseline characteristics and body mass
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There were no differences between groups in age, anthropometrics, or any performance or
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physiological indices before the intervention. There were no between-group differences in body
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mass changes (p=0.12), although an increase was observed in HIG (1.9±2.2%, p<0.01) but not
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in LIG (0.5±2.1%, p=0.19).
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Performance adaptations
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There were no between-group differences in running TT performance changes (p=0.44), but
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HIG improved by -2.3±3.2% (p=0.01), with no change in LIG (-1.5±2.9%, p=0.20). The
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individual response magnitudes for TT performance changes are shown in Figure 2. The
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changes in treadmill running Vpeak did not differ between groups (p=0.30) but were improved
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in HIG (2.2±3.1%, p<0.01), with a corresponding non-change in LIG (1.4±4.2%, p=0.18, Table
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3). Treadmill roller-ski skating Vpeak changes did not differ between groups (p=0.20) but were
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improved within both LIG and HIG (1.5±2.4% and 2.5±3.4%, respectively, both p<0.01).
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**Figure 2 around here**
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**Table 3 around here**
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Physiological adaptations
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There was a between-group difference in treadmill running VO2max changes (p=0.04, Table 3),
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tending to improve in HIG (3.0±6.4%, p=0.09), with a corresponding non-change in LIG (-
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0.7±4.6%, p=0.25). There were no between-group differences in submaximal adaptations
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running at absolute speeds, although trivial to small effects of reduced RER, HR, %HRmax, and
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RPE in HIG vs. LIG were found (see Table 3 for all details).
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The change in treadmill roller-ski skating VO2peak was different between groups (p=0.02), with
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improvements in HIG (3.6±5.4%, p=0.01) and a corresponding non-change in LIG (-0.1±4.0%,
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p=0.62). Overall, positive submaximal adaptations (i.e., %VO2max, RER, %HRmax, and RPE) in
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roller-ski skating at absolute speeds were found in HIG and not in LIG, although gross
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efficiency was improved in both groups (see Table 4 for all details). Individual response
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magnitudes for Vpeak and VO2max/peak in treadmill running and roller-ski skating are presented in
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Figure 3, while Figure 4 shows the adaptation index for each athlete in LIG and HIG.
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**Table 4 around here**
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**Figure 3 around here**
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**Figure 4 around here**
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Discussion
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The present study compared the effects of increased load of LIT vs. HIT on performance and
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physiological adaptations in well-trained endurance athletes. The main findings were that
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performance adaptations, including uphill running TT performance and peak speed when
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incremental running and roller-ski skating to exhaustion in the laboratory, did not differ
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significantly between the two groups progressing their training with different endurance
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training intensities. However, while both groups improved their performance, increased load of
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HIT elicited 34% greater changes in running VO2max and roller-ski skating VO2peak compared
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to increased load of LIT.
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In contrast to most previous intervention studies where endurance training load is matched for
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total work or oxygen consumption,7-9 the present approach induced a similar increase in TRIMP
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load when progressing the overall training stimulus for both groups.22,23 Accordingly, a
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significant between-group difference in LIT and HIT load was achieved while obtaining similar
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training loads. Although the intervention per se was regarded as successful because most
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athletes improved their performance, there are potential limitations with this approach caused
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by, e.g., between-athlete variations in adaptive signaling and stress tolerance to LIT and HIT
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training. In addition, this approach does not consider variations in metabolic vs. neuromuscular
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load between different training modalities (e.g., running vs. XC skiing). Although there was a
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change towards more specific training in the intervention period compared to baseline training,
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these changes were non-significant and similar between-groups. Accordingly, the design could
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be regarded valid for the purpose of the study.
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With such matching of training load progression, the present study found little or no effects on
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performance adaptations in running or roller-ski skating when increasing the load of LIT vs.
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HIT in well-trained endurance athletes. Although the individual response magnitudes indicated
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more positive performance adaptations in HIG, the present statistical findings are in contrast to
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those of Stöggl and Sperlich11 and Vesterinen et al.,24 who demonstrated superior performance
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adaptations of a more polarized intensity distribution with greater HIT load compared to high-
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volume LIT regimes. However, Ingham et al.25 and Nuuttila et al.26 found similar performance
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adaptations of high-volume LIT and HIT regimes, which is in line with the present findings and
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implies that similar performance progression can be achieved both by increased load of LIT
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and HIT during the preparation period in endurance athletes.
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In accordance with the hypothesis, increased load of HIT led to 34% better VO2max adaptations
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in running and roller-ski skating compared to increased load of LIT. These findings were
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strengthened by the greater individual response magnitudes and adaptation index as well as
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better submaximal adaptations (e.g., reduced HR) at absolute speeds in HIG. Better VO2max
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adaptations in HIG are likely explained by increased O2 delivery capacity,5,6,12 supported by
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other short-term training intensification studies.7-9 This argues that even when matching
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training load with a more ecologically valid method as employed here, a high HIT stimulus
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seems needed to stress the cardiovascular system sufficiently and will thereby increase VO2max
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more than when compensating with increased load of LIT.5,12 Still, only trivial to small effects
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in the differences in physiological adaptations were found, which is likely explained by the
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relatively high training status and the short intervention period.27-29 Altogether, progressing the
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overall training stimulus by intensification seems favorable if the goal is to elicit VO2max
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adaptations over a relatively short training period in well-trained endurance athletes. To what
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extent these adaptations can be transferred also to performance benefits over a longer timescale
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requires further examination.
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The individual response magnitudes revealed that some athletes in LIG also improved their
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VO2max to the same extent as HIG, indicating individual variations in how athletes respond to
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different endurance training in eliciting VO2max.24,30 The present sample of athletes, including
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both sexes and different initial levels, could in part have contributed to the subsequent variations
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in training response. However, the groups were matched for sex and physiological indices pre-
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intervention, and baseline values were adjusted for as a covariate in the statistical analysis. In
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this context, no significant sex-differences in any performance or physiological adaptations
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were found. Accordingly, the present group comparisons are likely valid, although future
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studies should further investigate individual responses to changes in training volume and
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intensity distribution, as well as overall load adjustments in endurance athletes.
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It seems obvious that improved VO2max had a positive effect on performance adaptations in
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HIG. However, the reasons for improved performance in LIG without improving VO2max could
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be explained by increased fractional utilization of VO2max (i.e., anaerobic threshold). In this
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context, an interesting feature is that the number of LIT sessions above 2.5 hours in LIG might
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have provided a different stimulus for adaptive signaling than shorter LIT sessions.
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Accordingly, the hypothesis was that LIT and HIT induce complementary adaptations, which
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is partly induced through different molecular pathways.1,5 However, this remains speculative
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as muscle biopsies or other measures to examine underlying mechanisms were not included in
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the present design.
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Practical applications
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The data presented in this study provide novel information with relevance for optimizing the
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training volume and intensity distribution in periods when the overall training stimulus is
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progressed in endurance athletes. The present data indicate that performance progression can
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be achieved both by increased load of LIT and HIT, although a sufficient HIT stimulus seems
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to be beneficial for eliciting maximal energy delivery capacities in 8 weeks. However, the more
425
long-term effects and the effect of different periodization models of LIT and HIT focus prior to
426
the competition period require further attention in future studies.
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Conclusions
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The present study found no significant difference in performance adaptations in running or
431
roller-ski skating during 8 weeks of increased load of LIT vs. HIT in well-trained endurance
432
athletes, although both methods improved performance. However, increased load of HIT
433
elicited better VO2max adaptations compared to increased load of LIT.
434
435
436
Acknowledgements
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The authors would like to thank the athletes and their coaches for their enthusiastic cooperation
438
and participation in the study. Particular gratitude is directed to Lars Jonatan Engdahl, Johan
439
Persson, and Henek Tomson for their help with collecting laboratory data. Moreover, the
440
authors would like to thank Knut Skovereng and Guro Strøm Solli for valuable comments on
441
the manuscript. The study is funded by Meråker High School and the Research Council of
442
Norway (RCN) (project no. 298645).
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Figure legends
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Figure 1 Training program for 8 weeks of (A) low-intensity training group and (B) high-
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intensity training group, including weekly distribution of moderate- (MIT) and high-intensity
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training (HIT) sessions and overall training load (TRIMP) within three different mesocycle
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loads (low, moderate, and high)
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Figure 2 Individual response magnitude for pre- to post changes in uphill running time trial
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performance summarized in three different categories: nonresponse (white), <0% change;
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moderate response (grey), 05% change; and large response (black) >5% change
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Figure 3 Individual response magnitude for pre- to post changes summarized in three
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different categories: nonresponse (white), <0% change; moderate response (grey), 05%
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change; and large response (black) >5% change. (A) Maximal oxygen uptake in treadmill
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running, (B) peak speed in treadmill running, (C) peak oxygen uptake in treadmill roller-ski
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skating, (D) peak speed in treadmill roller-ski skating
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Figure 4 Adaptation index for each individual athlete in (A) low-intensity training group and
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(B) high-intensity training group, calculated as the mean of the percentage change in maximal
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oxygen uptake and peak speed in treadmill running and peak oxygen uptake and peak speed in
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treadmill roller-ski skating from pre- to post
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Table 1. Baseline characteristics of the 51 well-trained endurance athletes participating in the
study (mean ± SD)
Variables
Men (n = 36)
Women (n = 15)
Age (y)
17 ± 1
17 ± 0
Body height (cm)
181.3 ± 0.7
167.2 ± 3.6
Body mass (kg)
72.7 ± 7.1
62.0 ± 5.4
Body mass index (kg·m-2)
22.1 ± 1.6
22.2 ± 2.2
RUN-VO2max (L·min-1)
5.08 ± 0.56
3.48 ± 0.35
RUN-VO2max (mL·min-1·kg-1)
70.3 ± 4.5
56.0 ± 3.4
SKATE-VO2peak (L·min-1)
4.86 ± 0.55
3.32 ± 0.36
SKATE-VO2peak (mL·min-1·kg-1)
66.8 ± 4.9
53.7 ± 3.9
Annual training volume (h y-1)
529 ± 95
493 ± 103
RUN-VO2max, maximal oxygen uptake in running; SKATE-VO2peak, peak oxygen uptake in
roller-ski skating.
14
599
600
601
602
Table 2. Training characteristics during an 8-week baseline and 8-week intervention period among 42 well-
trained endurance athletes, randomized into either LIG or HIG (mean ± SD)
8-week baseline period
8-week intervention period
LIG (n=22)
HIG (n=20)
LIG (n=22)
HIG (n=20)
Training forms
Training volume (h)
97.0 ± 14.2
96.3 ± 18.1
108.7 ± 10.7*
94.8 ± 11.6#
Sessions (n)
60.7 ± 8.1
61.2 ± 9.9
67.0 ± 5.6*
67.0 ± 7.1*
Sickness/injury (d)
1.3 ± 2.6
0.6 ± 1.6
1.8 ± 2.8
1.7 ± 2.7
Training forms
Endurance (h)
87.0 ± 12.9
84.7 ± 19.1
95.6 ± 9.3*
82.5 ± 10.4#
Strength (h)
7.7 ± 3.3
8.4 ± 1.8
9.0 ± 2.2
8.8 ± 2.0
Speed (h)
2.3 ± 1.1
3.2 ± 0.9#
4.1 ± 2.1
3.5 ± 1.0
Training mode
Specific (h)a
40.5 ± 13.4
41.3 ± 9.6
52.6 ± 8.6*
43.7 ± 9.4
Non-specific (h)b
45.1 ± 9.2
43.2 ± 9.5
43.0 ± 7.9
38.8 ± 9.0
Specific/non-specific (%)
47/53
49/51
55/45
53/47
Endurance training volume
Compliance (%TRIMP)
NaN
NaN
98 ± 9
100 ± 7
Load (TRIMP/wk)
729 ± 98
725 ± 157
781 ± 80*
779 ± 87
Load (TRIMP)
5831 ± 781
5804 ± 1257
6249 ± 640*
6230 ± 696
LIT load (TRIMP)
4649 ± 630
4586 ± 1121
5092 ± 587*
4303 ± 665#
MIT load (TRIMP)
489 ± 214
258 ± 237
434 ± 69
403 ± 122
HIT load (TRIMP)
703 ± 269
760 ± 204
723 ± 133
1523 ± 193*#
LIT (h)
78.8 ± 11.7
76.3 ± 18.8
88.0 ± 9.1*
70.4 ± 10.0#
MIT (h)
4.2 ± 1.8
3.8 ± 2.0
3.6 ± 0.6
3.4 ± 1.0
HIT (h)
4.0 ± 1.5
3.8 ± 1.3
4.0 ± 0.7
8.7 ± 1.0*#
LIT/MIT/HIT (%)
90/5/5
90/5/5
92/4/4
85/4/11
Endurance training sessions
LIT (n)
39.9 ± 4.8
37.9 ± 7.0
44.9 ± 4.1*
37.1 ± 5.6#
LIT sessions ≥150 min (n)
7.1 ± 2.2
6.7 ± 2.3
10.3 ± 2.2*
2.3 ± 1.4*#
MIT (n)
5.6 ± 2.2
6.1 ± 2.4
4.9 ± 0.8
4.1 ± 1.1*#
HIT (n)
7.1 ± 2.2
8.6 ± 1.7
6.8 ± 1.0
15.6 ± 1.7*#
LIT/MIT/HIT (%)
76/11/13
72/11/16
80/9/11
65/7/28
LIG, low-intensity training group; HIG, high-intensity training group; LIT, low-intensity training; MIT,
moderate-intensity training; HIT, high-intensity training. Compliance is calculated as percent of total TRIMP
in relation to total TRIMP prescribed. a classical and skating roller skiing; b running and cycling.
*Significantly different from baseline period (*p<0.05) #Significantly different from LIG (#p<0.05).
15
603
604
605
Table 3. Anthropometrics and TT performance as well as performance and physiological indices during treadmill running at pre- and
post-intervention in 42 well-trained endurance athletes, randomized into either LIG or HIG (mean ± SD)
LIG (n=22)
HIG (n=20)
LIG vs. HIG
Pre
Post
Pre
ES
Anthropometrics
Body mass (kg)
70.8 ± 7.5
71.2 ± 8.0
67.5 ± 7.9
0.10
Body mass index (kg·m-2)
22.5 ± 1.6
22.6 ± 1.6
21.4 ± 1.6
0.19
TT performance (4.5/6.4-km)
Mean finishing time (s)
27:14
26:49
28:06
0.06
RUN submaximal (7/8-km·h-1)
VO2 (L·min-1)
3.28 ± 0.46
3.20 ± 0.45
3.13 ± 0.43
0.22
VO2 in % VO2max
70.9 ± 6.2
69.9 ± 6.2
69.7 ± 5.5
0.07
RER
0.91 ± 0.04
0.91 ± 0.03
0.92 ± 0.05
0.75
HR (beats·min-1)
167 ± 12
165 ± 11
164 ± 10
0.27
HR in %HRmax
83.2 ± 4.8
82.2 ± 4.7
82.9 ± 4.2
0.29
Borg (6-20)
12.7 ± 1.3
12.4 ± 1.6
12.8 ± 1.4
0.21
[La-] (mmol·L-1)
2.12 ± 0.84
1.90 ± 0.58
2.27 ± 0.90
0.03
RUN TTE
VO2max (L·min-1)
4.68 ± 0.92
4.63 ± 0.83
4.54 ± 0.80
0.18
VO2max (mL·min-1·kg-1)
65.7 ± 7.6
64.7 ± 6.3
66.7 ± 7.1
0.22
RER
1.13 ± 0.04
1.15 ± 0.04
1.14 ± 0.05
0.30
HRmax (beats·min-1)
199 ± 6
199 ± 7
197 ± 9
0.02
[La-] (mmol·L-1)
11.02 ± 1.49
11.57 ± 1.91
11.48 ± 1.78
0.06
TTE (s)
350 ± 63
360 ± 57
359 ± 55
0.36
Vpeak (km·h-1)
14.5 ± 1.4
14.7 ± 1.3
14.8 ± 1.2
0.10
TT, time trial; LIG, low-intensity training group; HIG, high-intensity training group; ES, effect size; RUN, laboratory test running; VO2,
oxygen uptake; VO2max, maximal oxygen uptake; HR, heart rate; HRmax, maximal heart rate; [La-], blood lactate; RER, respiratory
exchange ratio; TTE, time to exhaustion; Vpeak, peak velocity. *Significantly different from pre (*p< 0.05). #Significantly different from
pre- to post change in LIG (#p<0.05).
16
Table 4. Performance and physiological indices obtained during treadmill roller-ski skating at pre and post-intervention in 42 well-trained
endurance athletes, randomized into either LIG or HIG (mean ± SD)
LIG (n=22)
HIG (n=20)
LIG vs. HIG
Pre
Post
Pre
ES
SKATE submaximal (10/12-km·h-1)
VO2 (L·min-1)
3.19 ± 0.51
3.12 ± 0.49*
3.05 ± 0.42
0.10
VO2 in % VO2peak
71.8 ± 5.3
70.3 ± 4.4*
71.6 ± 5.9
0.29
RER
0.93 ± 0.03
0.91 ± 0.03
0.95 ± 0.05
0.13
HR (beats·min-1)
173 ± 10
173 ± 9
170 ± 10
0.32
HR in %HRmax
86.4 ± 4.2
86.5 ± 3.3
86.2 ± 3.8
0.40
Borg (6-20)
11.2 ± 1.9
11.6 ± 1.8
11.9 ± 1.2
0.44
[La-] (mmol·L-1)
2.72 ± 0.91
2.79 ± 0.77
3.06 ± 1.21
0.27
GE (%)
13.8 ± 0.6
14.2 ± 0.6*
13.9 ± 0.8
0.08
SKATE submaximal (12/14-km·h-1)
VO2 (L·min-1)
3.57 ± 0.55
3.52 ± 0.52
3.44 ± 0.47
0.08
VO2 in % VO2peak
80.6 ± 5.6
79.5 ± 4.5
80.7 ± 4.8
0.41
RER
0.96 ± 0.04
0.95 ± 0.03
0.97 ± 0.03
0.15
HR (beats·min-1)
184 ± 9
183 ± 7
180 ± 11
0.12
HR in %HRmax
92.0 ± 3.2
91.5 ± 2.2
91.4 ± 3.7
0.17
Borg (6-20)
14.4 ± 1.3
14.1 ± 1.4
14.6 ± 1.2
0.31
[La-] (mmol·L-1)
4.11 ± 1.37
4.09 ± 1.11
4.28 ± 2.01
0.05
GE (%)
14.3 ± 0.6
14.6 ± 0.3*
14.4 ± 0.7
0.01
SKATE TTE
VO2peak (L·min-1)
4.48 ± 0.89
4.46 ± 0.84
4.30 ± 0.72
0.18
VO2peak (mL·min-1·kg-1)
62.8 ± 7.0
62.5 ± 6.5
63.4 ± 6.7
0.18
RER
1.11 ± 0.05
1.11 ± 0.04
1.11 ± 0.05
0.01
HRpeak (beats·min-1)
198 ± 7
199 ± 7
196 ± 8
0.10
[La-] (mmol·L-1)
10.84 ± 1.66
11.16 ± 2.17
10.78 ± 1.60
0.12
TTE (s)
281 ± 56
299 ± 56*
292 ± 71
0.18
Vpeak (km·h-1)
21.0 ± 1.6
21.3 ± 1.6*
21.4 ± 1.8
0.11
LIG, low-intensity training group; HIG, high-intensity training group; ES, effect size; SKATE, laboratory test roller-ski skating; VO2,
oxygen uptake; VO2peak, peak oxygen uptake; HR, heart rate; HRpeak, peak heart rate; [La-], blood lactate; GE, gross efficiency; RER,
respiratory exchange ratio; TTE, time to exhaustion; Vpeak, peak velocity; *Significantly different from pre (*p< 0.05). #Significantly
different from pre- to post change in LIG (#p<0.05).
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Figure 1.
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... This systematic review and meta-analysis synthesized data from 11 studies (Androulakis- Korakakis et al., 2018;Devereux et al., 2022;Fiorenza et al., 2019;Gantois et al., 2019;Kelly et al., 2021;Kon et al., 2019;Liu et al., 2024;Mallol et al., 2019;Talsnes et al., 2022a;Talsnes et al., 2022b;Wen et al., 2024) (10 RCTs, 1 randomized crossover) examining MRT in trained athletes ( Table 1). The studies encompassed diverse athletic populations including cyclists and triathletes (Fiorenza et al., 2019;Mallol et al., 2019), powerlifting (Androulakis-Korakakis et al., 2018), soccer players (Liu et al., 2024;Wen et al., 2024), basketball players (Gantois et al., 2019), cross-country skiers (Talsnes et al., 2022a;Talsnes et al., 2022b), and mixed-sport athletes (Devereux et al., 2022) while (Kelly et al., 2021) and (Kon et al., 2019) involved Gaelic football players and canoe athletes, respectively. ...
... This systematic review and meta-analysis synthesized data from 11 studies (Androulakis- Korakakis et al., 2018;Devereux et al., 2022;Fiorenza et al., 2019;Gantois et al., 2019;Kelly et al., 2021;Kon et al., 2019;Liu et al., 2024;Mallol et al., 2019;Talsnes et al., 2022a;Talsnes et al., 2022b;Wen et al., 2024) (10 RCTs, 1 randomized crossover) examining MRT in trained athletes ( Table 1). The studies encompassed diverse athletic populations including cyclists and triathletes (Fiorenza et al., 2019;Mallol et al., 2019), powerlifting (Androulakis-Korakakis et al., 2018), soccer players (Liu et al., 2024;Wen et al., 2024), basketball players (Gantois et al., 2019), cross-country skiers (Talsnes et al., 2022a;Talsnes et al., 2022b), and mixed-sport athletes (Devereux et al., 2022) while (Kelly et al., 2021) and (Kon et al., 2019) involved Gaelic football players and canoe athletes, respectively. ...
... Some studies included elite athletes with extensive training backgrounds, such as national-level juniors training over 500 h annually (Talsnes et al., 2022a). Sample sizes ranged from 11 participants (Fiorenza et al., 2019) to 51 participants (Talsnes et al., 2022b), with a total of 276 participants across all studies. Mean participant ages spanned from 17.1 years (Wen et al., 2024) to 42.9 years (Mallol et al., 2019), with the majority of participants (78%) being male athletes. ...
Article
Full-text available
Introduction The “no pain, no gain” philosophy has long influenced athletic training approaches, particularly in high-intensity workouts like metabolic resistance training (MRT). However, the necessity of discomfort-inducing training for optimal athletic performance remains debatable. This systematic review and meta-analysis examined whether MRT provided comparable or better results than traditional training methods in trained athletes. Methods A systematic search of PubMed/MEDLINE, Web of Science, Scopus, and SPORTDiscus (January 2004 - December 2024) identified RCTs comparing MRT with traditional training in athletes. Two reviewers screened studies and assessed bias risk using Cochrane RoB 2. Random - effects meta - analyses were conducted for outcomes like VO2max, peak power, sprint performance, blood lactate, time to exhaustion, and jump height. GRADE was used to evaluate evidence certainty. Results Eleven studies (n = 276 participants) met inclusion criteria. MRT demonstrated a statistically significant improvement in sprint performance (SMD = 1.18, 95% CI: 0.00 to 2.36, p < 0.0001) and countermovement jump height (SMD = 0.80, 95% CI: −0.04 to 1.64, p = 0.0007), indicating notable gains in explosive power. VO2max improvements were observed (SMD = 0.30, 95% CI: −0.19 to 0.79, p = 0.10) but did not reach statistical significance. Peak power output showed a moderate but non-significant positive effect (SMD = 0.54, 95% CI: −2.05 to 3.13, p = 0.55), while blood lactate changes varied widely (SMD = −1.68, 95% CI: −8.58 to 5.22, p = 0.29), reflecting high heterogeneity across studies. Time to exhaustion presented a small positive effect (SMD = 0.23, 95% CI: 0.00 to 0.46, p = 0.18), but without statistical significance. Subgroup analyses revealed that younger adults (19–25 years) and experienced athletes benefited the most from MRT, with low-frequency training (≤2 sessions/week) yielding the most favorable adaptations. Moderator analysis confirmed that sprint performance had the strongest response to MRT, while aerobic measures exhibited more variability. Conclusion The evidence demonstrates the capacity of MRT to enhance athletic performance comparable to or exceeding traditional training methods while requiring reduced time commitment. These findings suggest that optimal performance adaptations can be achieved through well-designed MRT protocols without necessitating excessive training volumes. Systematic Review Registration https://inplasy.com/inplasy-2024-11-0024, identifier: 36 INPLASY2024110024.
... Studies have shown that the effect of HIIT on VO2 max enhancement may vary depending on the sport's characteristics. In intermittent sports such as ice hockey, athletes must perform frequent short sprints, skates, stops, and turns (Talsnes et al., 2021), which places higher demands on the anaerobic system and fast-twitch muscle fibres. HIIT, however, is highly compatible with these exercise demands through short bursts of high-intensity stimulation. ...
... HIIT is more compatible with ice hockey games' high-intensity, short-interval characteristics than traditional, long-duration, low-to-moderate-intensity endurance training. For example, Talsnes et al. (2021), suggested that conventional training tends to build base endurance. In contrast, HIIT's interval training simulates the high-intensity work demands of the game, which may positively impact athletes' game performance. ...
... For example, Treff, et al. [39] did not find any changes in VO 2max (+0.6%, p = 0.67), possibly due to the relatively low volume of HIT (6%). Moreover, this also underpins the findings in Talsnes, et al. [43], where an increased load of HIT elicited better VO 2max adaptations. Based on the findings in this review, a HIT volume (18-26%) combined with a substantial volume of LIT (70-80%) may be the most beneficial for improvement in VO 2max and VO 2peak . ...
... A training intensity distribution consisting predominantly of LIT combined with HIT sessions allows athletes to repeatedly practice the movement patterns specific to their sport. The high volume of LIT in polarized training enables athletes to practice the movement patterns for long durations without excessive fatigue [43], allowing for more repetitions of the specific skill. Meanwhile, the HIT sessions provide opportunities for athletes to practice more repetitions at speeds that occur in competitions, leading to more economical movement patterns [45]. ...
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High-intensity training (HIT) has commonly been the most effective training method for improvement in maximal oxygen uptake (VO2max) and work economy, alongside a substantial volume of low-intensity training (LIT). The polarized training model combines both low- and high-intensity training into a specific training intensity distribution and has gained attention as a comprehensive approach. The objective of this review was to systematically search the literature in order to identify the effects of polarized training intensity distribution on VO2max, peak oxygen uptake (VO2peak), and work economy among endurance athletes. A literature search was performed using PubMed and SPORTDiscus. A total of 1836 articles were identified, and, after the selection process, 14 relevant studies were included in this review. The findings indicate that a polarized training approach seems to be effective for enhancing VO2max, VO2peak, and work economy over a short-term period for endurance athletes. Specifically, a training intensity distribution involving a moderate to high volume of HIT (15–20%) combined with a substantial volume of LIT (75–80%) appears to be the most beneficial for these improvements. It was concluded that polarized training is a beneficial approach for enhancing VO2max, VO2peak, and work economy in endurance athletes. However, the limited number of studies restricts the generalizability of these findings.
... Thus, sports activities are possible only due to the development of adaptation processes in the body. Depending on the duration of physical exertion, a person has various adaptive reactions of an immediate and long-term nature (Lyzohub, 2001;Talsnes et al., 2022). ...
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Purpose. The article considers the possibility of using quantitative and qualitative indicators, which characterize in general the adaptive capacity of the children’s body under the condition of systematic specific physical load. Material & Methods. The methodology consisted in the formation of so-called matrices of pairwise comparison of indicators, arranged in separate blocks. The comparison was carried out in the form of an estimated ratio of importance in pairs of indicators. Results. Our studies coincide with the results of M. Antomonov and confirm the fact that the characteristics of the integral assessment of adaptive capacity of the body reflect a few indicators that are grouped in separate blocks and include all factors that affect adaptation processes and have a simple and adequate form of analysis Conclusions: According to the totality and significance of the experts’ answers, it has been established that the block of factors “lifestyle” has the greatest influence on adaptive capacity of the children’s body under the condition of systematic specific physical load. The following blocks share the second place: “athlete efficiency” and “functional state of the cardiovascular and respiratory systems”. The third and fourth positions in terms of weight coefficients were occupied by the following blocks of indicators: “neurodynamic properties of a person” and “psycho-emotional state”.
... It is well established that the duration of exercise influences the physiological adaptations that occur in the body. Therefore, training-related physiological and performance adaptations are the results of ideal manipulations of internal load throughout a sufficient period of time (Talsnes et al. 2022). One of the important components of this manipulation of TLs is controlling exercise intensity during one or multiple sessions. ...
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In sports, session rating of perceived exertion (sRPE) is used to reflect internal training load (TL). TL reflects the body physiological strains during exercise sessions. The extent to which exercise intensity and duration affect sRPE during successive football training sessions is unclear. The study investigated the impact of exercise intensity and duration on the session rating of perceived exertion (sRPE) in relation to physiological variables during football training sessions. A sample of 47 youth male football players underwent two high-intensity exercise trainings with a 15-minute break in between. The first training consisted of three 15-minute "walk-sprint-jog" sessions, followed by three minutes of recovery. The second training continued until exhaustion. The levels of sRPE, physiological data, blood and urine analysis were assessed pre-exercise, after each session, and after exhaustion. Results showed a progressively significant increase in sRPE, physiological, blood and urine parameters from the first session until exhaustion. The impact of cumulative duration on the holistic perception of workload showed a linear increment during consecutive exercise sessions. The study concludes that sRPE demonstrates sensitivity to the accumulation of perceived fatigue resulting from exercise duration during football training sessions, even with consistently maintained exercise intensity.
... In recent years, numerous studies have sought to address these limitations by enhancing sports performance monitoring and analysis through advanced technologies and methodologies. For instance, researchers have analyzed key factors influencing athletic performance by monitoring various parameters during training sessions [1][2][3]. Kocakulak et al. [4] utilized nanobiosensors to collect biological data from athletes, exploring their applications in sports medicine and doping detection. Rana et al. [5] systematically reviewed wearable sensor technologies, focusing on their roles in communication, data fusion, and analysis across various sports disciplines. ...
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This study explores the application of Internet of Things (IoT) devices and biochemical sensors in sports performance monitoring, focusing on the biomechanical force characteristics of athletes to address limitations in traditional methods, such as limited data types, poor real-time accuracy, and insufficient visualization. Emphasizing mechanobiological principles, the analysis targets key force-producing regions of the body—such as the feet, legs, and torso—to optimize energy efficiency, motion precision, and overall athletic performance. Biochemical sensors were employed to monitor real-time biomechanical and physiological data, while IoT devices ensured accurate data transmission, visualization, and feedback. Data accuracy was enhanced through methods such as zero correction, timestamp synchronization, and Kalman filtering, while data transmission efficiency was optimized using a lossless compression algorithm, hierarchical structuring, the MQTT protocol, and encryption via the AES algorithm. Data organization utilized a star-structured MySQL database with composite indexing for swift access. Analytical tools such as the Apriori algorithm for data correlation, linear discriminant analysis for feature extraction, and multi-source data fusion enabled detailed visualization of performance metrics. Experimental applications in football and sprinting demonstrated the effectiveness of IoT-based monitoring. Football experiments captured multi-dimensional data on technical characteristics, while sprint tests recorded precise performance metrics, including real-time speed profiling and timing accuracy. For instance, in a 100-meter sprint test, an IoT system measured an athlete's performance at 12.54 seconds with 100% accuracy, surpassing manual timing methods. These findings highlight the transformative potential of IoT devices and biochemical sensors in sports analytics, offering enhanced accuracy, real-time tracking, and actionable insights to refine athletic performance and decision-making.
... Typically, this approach does not include the potential effect of training progression (increased training load or intensity over time), even though load progression is one of the key principles of physical training, i.e., an important factor to optimize training adaptations (20). Furthermore, previous studies for male and female football players have only investigated the associations between development and accumulated training load (10, [16][17][18], while the role of intensity on physical performance development has not been studied, although generally training adaptations are dependent on training intensity (21). To the authors' knowledge, there are no previous studies that have investigated the associations between football training intensity or progression of training and performance development, even though they are general principles of physical training. ...
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Introduction This observational study investigated: (1) potential changes in female football players’ in-season training load, intensity and physical performance, and (2) if in-season accumulated training load, intensity, or their progression are associated to changes in physical performance. Methods Thirty-five national level female players (∼21 years, n = 35) from three top-teams of the Finnish national league participated. Players performed tests at the beginning and at the end of the 27-week in-season. Tests were: 30-m sprint, countermovement jump (CMJ) and 1,200-m shuttle run, used to calculate maximal aerobic speed (MAS). Players’ external and internal training load and intensity were monitored in all on-field training sessions and official matches (3,941 data samples) using Polar Team Pro system. Results Training load decreased towards the end of the in-season (p < 0.05), but intensity remained stable. No changes in physical performance test results occurred from before to after in-season tests at a group level. Change of CMJ correlated negatively with accumulated training load, intensity and progression of total distance (TD) and low-intensity running distance (LIRD) (r = −0.398 to −0.599, p < 0.05). Instead, development of MAS correlated positively with progression of TD and LIRD intensities (r = 0.594 and 0.503, p < 0.05). Development of both CMJ and MAS correlated positively with intensity progression of very-high-intensity running distance (VHIRD) and number of accelerations and decelerations (r = 0.454–0.588, p < 0.05). Discussion Reduced training load over the in-season is not detrimental for players’ physical performance when training intensity progressively increases. Intensity progression of VHIRD, moderate- and high-intensity accelerations and decelerations are indicators of both MAS and CMJ development, respectively.
... Studie found through a study of 15 professional basketball players that different training load variables were significantly related to players' game performance, especially the pressure stability during the game had an important impact on players' performance [4]. In addition, a systematic review conducted pointed out that monitoring training load is essential for understanding the physical demands of basketball and developing appropriate training plans [5]. They emphasized that there are significant differences in the application of internal and external load monitoring methods in different studies, and unified standards are needed to better compare and analyze the results. ...
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This study analyzed the exercise load and exercise intensity of the China U24 3x3 women team in multiple games, combined with the performance data of the players in the game, and explored the impact of these factors on exercise performance. The results showed that there was a significant positive correlation between load intensity and average heart rate, and high load intensity usually led to a higher average heart rate. In addition, performance data such as scoring and rebounding were also significantly correlated with load intensity and load volume. Players with higher scores often underwent higher load volume and load intensity in the game. This shows that in high-intensity games, players' physical fitness and performance are closely related, and reasonable monitoring and management of load intensity are crucial to improving player performance and reducing the risk of injury. The study also showed that there were significant differences in load volume and load intensity between the U24 Blue Team and the U24 Red Team in different games, further emphasizing the importance of load management in game strategy and physical training. This study provides empirical evidence for basketball teams to formulate scientific training plans and game strategies, and emphasizes the importance of exercise load and intensity management to improve the overall performance of the team and the individual performance of the players.
... However, the additional intensity of exercise (40-55% vs 65-80% VO2max) prescribed in the LVMI and LVVI groups with the same volume of exercise did result in a significant increase in VO2max. It suggests that the intensity of exercise is more important than the volume of exercise in terms of increasing the level of VO2max Talsnes (Talsnes et al., 2022) argues that even though both low and high intensity improve maximal oxygen uptake capacity of exercise, high intensity training is more sufficient to elicit better maximal oxygen uptake adaptations, which is consistent with our view. Although it is often suggested that the genetic component of physical fitness undermines its prognostic power, genetic differences explain only 25-47% of the individual variation in VO2max (Bouchard et al., 1999). ...
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Background: To examine the mechanisms of adaptations in cardiorespiratory fitness with different dose of amount and intensity exercise training in middle-aged men. Methods: A total of 67 sedentary subjects aged 40-49 yr were assigned to participate for 12 weeks in a control group or in one of three exercise groups: 1) low volume/moderate intensity 2) low volume/vigorous intensity and 3) high volume/vigorous intensity. They were tested for VO2max, cardiac output (Q) and stroke volume (SV) before and after training and maximal arterial-venous oxygen difference (a-vO2diff) calculated by the Fick Equation. Results: Contrasted to control group, VO2max increased similar in both LVVI and HVVI groups after 12 weeks; It indicated that the intensity of exercise appears to make a greater benefit than the amount of exercise on VO2max. However, Maximal cardiac output (Qmax) and a-vO2diff contributed to increase VO2max were differences in both of vigorous intensity groups. In LVVI group, Qmax together with maximal a-vO2diff contributed to the greater VO2max; in HVVI group, the majority of the increment in VO2max was relied on larger Qmax whereas a widened a-vO2diff. Conclusion: It is appropriate to recommend vigorous intensity exercise to improve cardiorespiratory fitness and encourage higher amount to confer additional benefit for Qmax.
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The purpose of the study was to examine the effects of progressively increased training intensity or volume on the nocturnal heart rate (HR) and heart rate variability (HRV), countermovement jump, perceived recovery, and heart rate-running speed index (HR-RS index). Another aim was to analyze how observed patterns during the training period in these monitoring variables were associated with the changes in endurance performance. Thirty recreationally trained participants performed a 10-week control period of regular training and a 10-week training period of either increased training intensity (INT, n = 13) or volume (VOL, n = 17). Changes in endurance performance were assessed by an incremental treadmill test. Both groups improved their maximal speed on the treadmill (INT 3.4 ± 3.2%, p < 0.001; VOL 2.1 ± 1.8%, p = 0.006). In the monitoring variables, only between-group difference (p = 0.013) was found in nocturnal HR, which decreased in INT (p = 0.016). In addition, perceived recovery decreased in VOL (p = 0.021) and tended to decrease in INT (p = 0.056). When all participants were divided into low-responders and responders in maximal running performance, the increase in the HR-RS index at the end of the training period was greater in responders (p = 0.005). In conclusion, current training periods of increased intensity or volume improved endurance performance to a similar extent. Countermovement jump and HRV remained unaffected, despite a slight decrease in perceived recovery. Long-term monitoring of the HR-RS index may help to predict positive adaptations, while interpretation of other recovery-related markers may need a more individualized approach.
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Purpose: To provide novel insight regarding the influence of exercise modality on training load management by: 1) providing a theoretical framework for the impact of physiological and biomechanical mechanisms associated with different exercise modalities on training load management in endurance exercise, and 2) comparing effort-matched low-intensity training sessions performed by top level athletes in endurance sports with similar energy demands. Practical Applications and Conclusions: The ability to perform endurance training with manageable muscular loads and low injury risks in different exercise modalities are influenced both by mechanical factors, as well as muscular state and coordination which interrelate in optimizing power production while reducing friction and/or drag. Consequently, the choice of exercise modality in endurance training influence effort beyond commonly used external and internal load measurements and should be considered alongside duration, frequency and intensity when managing training load. By comparing effort-matched low-to-moderate intensity sessions performed by top level athletes in endurance sports, this study exemplifies how endurance exercise with varying modalities leads to different tolerable volumes. For example, the weight-bearing exercise and high impact forces in long-distance running puts high loads on muscles and tendons, leading to relatively low training volume tolerance. In speed skating, flexed knee and hip position required for effective speed skating leads to occlusion of thighs and low volume tolerance. In contrast, the non-weight-bearing, low-contraction exercises in cycling or swimming allows for large volumes in the specific exercise modalities. Overall, these differences have major implications on training load management in sports.
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In short-term studies, block periodization of high-intensity training (HIT) has been shown to be an effective strategy that enhances performance and related physiological factors. However, long-term studies and detailed investigations of macro, meso, and micro-periodization of HIT blocks in world-class endurance athletes are currently lacking. In a recent study, we showed that the world's most successful crosscountry (XC) skier used two different periodization models with success throughout her career. One including extensive use of HIT blocks, namely BP, and one using a traditional method namely TRAD. In this study, we compare BP with TRAD in two comparable successful seasons and provide a detailed description of the annual use of HIT blocks in BP. The participant is the most-decorated winter Olympian, with 8 Olympic gold medals, 18 world championship titles, and 114 world cup victories. Training data was categorized by training form (endurance, strength, and speed), intensity [low (LIT), moderate (MIT), and HIT], and mode (running, cycling, and skiing/roller skiing). No significant difference was found in the total endurance training load between BP and TRAD. However, training volume in BP was lower compared to TRAD (15 ± 6 vs. 18 ± 7 h/wk, P = 0.001), mainly explained by less LIT (13 ± 5 vs. 15 ± 5 h/wk, P = 0.004). Lower volume of MIT was also performed in BP compared to TRAD (13 vs. 38 sessions/year), whereas the amount of HIT was higher in BP (157 vs. 77 sessions/year). While BP included high amounts of HIT already from the first preparation period, followed by a reduction toward the competition period, TRAD had a progressive increase in HIT toward the competition period. In BP, the athlete performed seven HIT blocks, varying from 7 to 11 days, each including 8-13 HIT sessions. This study provides novel insights into successful utilization of two different periodization models in the worlds best XC skier, and illustrates the macro, meso and micro-periodization of HIT blocks to increase the overall amount of HIT.
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The capacity for human exercise performance can be enhanced with prolonged exercise training, whether it is endurance- or strength-based. The ability to adapt through exercise training allows individuals to perform at the height of their sporting event and/or maintain peak physical condition throughout the life span. Our continued drive to understand how to prescribe exercise to maximize health and/or performance outcomes means that our knowledge of the adaptations that occur as a result of exercise continues to evolve. This review will focus on current and new insights into endurance and strength-training adaptations and will highlight important questions that remain as far as how we adapt to training.
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Purpose: Investigate development of specific performance adaptions and hormonal responses every 4 week during a 12-week HIT period in groups with different interval-training prescriptions. Methods: Sixty-three well-trained cyclists performing a 12-week intervention consisting of 2-3 HIT sessionsweek in addition to ad libitum low intensity training. Groups were matched for total training load, but increasing HIT (INC) group (n=23) performed interval-sessions as 4x16 min in week 1-4, 4x8 min in week 5-8 and 4x4 min in week 9-12. Decreasing HIT (DEC) group (n=20) performed interval-sessions in the opposite order as INC, and mixed HIT (MIX) group (n=20) performed all interval-sessions in a mixed distribution during 12 weeks. Cycling-tests and measures of resting blood-hormones were conducted pre, week 4, 8 and 12. Results: INC and MIX achieved >70% of total change in workload eliciting 4 mMolL [la] (Power4mM) and V˙ O2peak during week 1-4, versus only 34-38% in DEC. INC induced larger improvement vs. DEC during week 1-4 in Power4mM (ES: 0.7) and V˙ O2peak (ES: 0.8). All groups increased similarly in peak power output (PPO) during week 1-4 (64-89% of total change). All groups' pooled, total- and free-testosterone and free-testosterone/cortisol-ratio decreased by 22±15%, 13±23% and 14±31% (all P<0.05), and insulin-like growth factor-1 increased by 10±14% (P<0.05) during week 1-4. Conclusions: Most of progression in Power4mM, V˙ O2peak and PPO was achieved during weeks 1-4 in INC and MIX, and accompanied by changes in resting blood-hormones consistent with increased but compensable stress load. In these well-trained subjects, accumulating 2-3 hweek performing 4x16 min work bouts at best effort induces greater adaptions in Power4mM and V˙ O2peak than accumulating ~1 hweek performing best effort intervals as 4×4 min.
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Purpose: This study aimed to compare the effects of three different high-intensity training (HIT) models, balanced for total load but differing in training plan progression, on endurance adaptations. Methods: Sixty-three cyclists (peak oxygen uptake (V˙O2peak) 61.3 ± 5.8 mL·kg·min) were randomized to three training groups and instructed to follow a 12-wk training program consisting of 24 interval sessions, a high volume of low-intensity training, and laboratory testing. The increasing HIT group (n = 23) performed interval training as 4 × 16 min in weeks 1-4, 4 × 8 min in weeks 5-8, and 4 × 4 min in weeks 9-12. The decreasing HIT group (n = 20) performed interval sessions in the opposite mesocycle order as the increasing HIT group, and the mixed HIT group (n = 20) performed the interval prescriptions in a mixed distribution in all mesocycles. Interval sessions were prescribed as maximal session efforts and executed at mean values 4.7, 9.2, and 12.7 mmol·L blood lactate in 4 × 16-, 4 × 8-, and 4 × 4-min sessions, respectively (P < 0.001). Pre- and postintervention, cyclists were tested for mean power during a 40-min all-out trial, peak power output during incremental testing to exhaustion, V˙O2peak, and power at 4 mmol·L lactate. Results: All groups improved 5%-10% in mean power during a 40-min all-out trial, peak power output, and V˙O2peak postintervention (P < 0.05), but no adaptation differences emerged among the three training groups (P > 0.05). Further, an individual response analysis indicated similar likelihood of large, moderate, or nonresponses, respectively, in response to each training group (P > 0.05). Conclusions: This study suggests that organizing different interval sessions in a specific periodized mesocycle order or in a mixed distribution during a 12-wk training period has little or no effect on training adaptation when the overall training load is the same.
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Researchers have retrospectively analyzed the training intensity distribution (TID) of nationally and internationally competitive athletes in different endurance disciplines to determine the optimal volume and intensity for maximal adaptation. The majority of studies present a “pyramidal” TID with a high proportion of high volume, low intensity training (HVLIT). Some world-class athletes appear to adopt a so-called “polarized” TID (i.e., significant % of HVLIT and high-intensity training) during certain phases of the season. However, emerging prospective randomized controlled studies have demonstrated superior responses of variables related to endurance when applying a polarized TID in well-trained and recreational individuals when compared with a TID that emphasizes HVLIT or threshold training. The aims of the present review are to: (1) summarize the main responses of retrospective and prospective studies exploring TID; (2) provide a systematic overview on TIDs during preparation, pre-competition, and competition phases in different endurance disciplines and performance levels; (3) address whether one TID has demonstrated greater efficacy than another; and (4) highlight research gaps in an effort to direct future scientific studies.
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Researchers have retrospectively analyzed the training intensity distribution (TID) of nationally and internationally competitive athletes in different endurance disciplines to determine the optimal volume and intensity for maximal adaptation. The majority of studies present a “pyramidal” TID with a high proportion of high volume, low intensity training (HVLIT). Some world-class athletes appear to adopt a so-called “polarized” TID (i.e., significant % of HVLIT and high-intensity training) during certain phases of the season. However, emerging prospective randomized controlled studies have demonstrated superior responses of variables related to endurance when applying a polarized TID in well-trained and recreational individuals when compared with a TID that emphasizes HVLIT or threshold training. The aims of the present review are to: (1) summarize the main responses of retrospective and prospective studies exploring TID; (2) provide a systematic overview on TIDs during preparation, pre-competition, and competition phases in different endurance disciplines and performance levels; (3) address whether one TID has demonstrated greater efficacy than another; and (4) highlight research gaps in an effort to direct future scientific studies.
Article
Purpose: The aim was to compare mesocycles with progressively increasing workloads and varied training intensity distribution (TID), i.e. high-intensity (HIGH, > 4 mmol·L blood lactate), low-intensity (LOW, < 2 mmol·L blood lactate) or a combination of HIGH and LOW (referred to as "polarized", POL) on 5000-m running time and key components of endurance performance in recreational runners. Methods: Forty-two runners (peak oxygen uptake (VO2peak): 45.2 ± 5.8 mL·min·kg) were systematically parallelized to one of three groups performing a 4-wk mesocycle with equal TID (2-4 training sessions) followed by a 3-wk mesocycle with increased weekly TRIMP (i.e. 50% increase compared to the first 4-wk mesocycle) of either HIGH, LOW or POL and one week tapering. VO2peak, velocity at lactate threshold and running economy were assessed at baseline (T0), after four (T1), seven (T2) and eight weeks (T3). Results: The 5000-m time decreased in all groups from T0 to T2 and T3. VO2peak increased from T0 to T2 and T3 (p < 0.03) with HIGH and from T0 to T2 (p = 0.02) in LOW and from T0 to T3 (p = 0.006) with POL. Running economy improved only from T1 to T3 and from T2 to T3 (p < 0.04) with LOW. An individual mean response analysis indicated a high number of responders (n=13 of 16) in LOW, with less in HIGH (n=6/13) and POL (n=8/16). Conclusion: On a group level, HIGH, LOW and POL improve 5000-m time and VO2peak. Changes in running economy occurred only with LOW. Based on the individual response of recreational runners the relative risk of non-responding is greater with HIGH and POL compared to LOW.
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The aim of this study was to investigate factors that can predict individual adaptation to high-volume or high-intensity endurance training. After the first 8-week preparation period, 37 recreational endurance runners were matched into the high-volume training group (HVT) and high-intensity training group (HIT). During the next 8-week training period, HVT increased their running training volume and HIT increased training intensity. Endurance performance characteristics, heart rate variability (HRV), and serum hormone concentrations were measured before and after the training periods. While HIT improved peak treadmill running speed (RSpeak ) 3.1 ± 2.8% (P < 0.001), no significant changes occurred in HVT (RSpeak : 0.5 ± 1.9%). However, large individual variation was found in the changes of RSpeak in both groups (HVT: -2.8 to 4.1%; HIT: 0-10.2%). A negative relationship was observed between baseline high-frequency power of HRV (HFPnight ) and the individual changes of RSpeak (r = -0.74, P = 0.006) in HVT and a positive relationship (r = 0.63, P = 0.039) in HIT. Individuals with lower HFP showed greater change of RSpeak in HVT, while individuals with higher HFP responded well in HIT. It is concluded that nocturnal HRV can be used to individualize endurance training in recreational runners. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.