Abstract
ackground: The aim of the study was to investigate the effects of a novel two-week dynamic high-intensity interval training (HIIT) protocol, characterized by fixed-load and variable-time intervals (“two times up to ten minutes”), on performance and metabolic adaptations in well-trained cross-country skiers.Methods: Ten qualified skiers (six males, four females) completed six interval training sessions over two weeks. Pre- and post- intervention tests were performed to assess maximal oxygen consumption (VO2max) and ski speed reached, blood lactate concentration, respiratory gas exchange data, and body weight.Results: Maximal speed at VO2maxincreased significantly from 13.5±2.16 to 14.8±1.7 km/h (p= 0.0196; Cohen’s d = 1.06). VO2maxitself was retained (p> 0.05), equivalence testing confirmed stable values within a±2.8 mL/kg/min margin. Time to reach RER = 1.0 improved significantly across sessions (p= 0.021), indicating enhanced metabolic efficiency. Body weight decreased modestly but statistically significantly by 0.54 kg (p= 0.016). Conclusions: The dynamic HIIT protocol improved maximal perfor- mance (speed at VO2maxby 32.9%) and metabolic efficiency in trained skiers without alter- ing VO2max. These findings support the usefulness of flexible, individualized HIIT models to enhance aerobic endurance, especially for athletes at risk of performance plateaus. Keywords:dynamic high-intensity interval training; maximal
decreased modestly but statistically significantly by 0.54 kg (p= 0.016). Conclusions: The dynamic HIIT protocol improved maximal perfor- mance (speed at VO2maxby 32.9%) and metabolic efficiency in trained skiers without alter- ing VO2max. These findings support the usefulness of flexible, individualized HIIT models to enhance aerobic endurance, especially for athletes at risk of performance plateaus. Keywords:dynamic high-intensity interval training; maximal oxygen uptake;cross-country skiing; skiing speed; tapering 1. Introduction Maximal oxygen consumption plays a significant role in modern sports science and medicine as a functional biomarker for assessing the cardiovascular system’s functions [1,2]. It is a widely described marker to evaluate the risk of cardiovascular events [3]. The same is true in sports science. In cross-country skiers, the result of competitions significantly correlates with peak oxygen consumption as discovered by Sanbakk et al. While analyzing the differences between national and international level skiers it became clear that the efficiency and technique of both groups of athletes were at a similar level; however, a measurable difference was detected in aerobic capacity and peak oxygen consumption, where international-level athletes showed better results (p< 0.05) [4]. In investigations where this correlation was not visible, it has been shown that technical aspects such as efficiency compensated for it, as studied in cyclists by Luciano et al. [5] and in runners by Moses et al. [6]. J. Funct. Morphol. Kinesiol.2025,10, 407
J. Funct. Morphol. Kinesiol.2025,10, 407 2 of 15 By studying the characteristics of cross-country skiing and the functioning of energy systems when engaged in this sport, Losnegard et al. have investigated a direct connection between the ability of athletes to perform short, intense repetitions, in which excess post- exercise oxygen consumption (EPOC) is observed, the rapid recovery of metabolic and oxygen uptake kinetics after such efforts, and results of competitions in cross-country skiing [7]. In the current literature, researchers have recognized that high-intensity, maximal oxygen consumption interval training is an effective way to improve VO2max. This is con- firmed by Bacon et al. in their meta-analysis [8] and emphasized even more by Storoschuk and colleagues in their recent work on Zone 2 training vs. HIIT [9]. The studies of Norwe- gian author Helgerud J. determined that the 4×4 high-intensity interval training method was more effective than moderate-intensity training [10]. Sloth et al. and Gist have also confirmed the effectiveness of sprint interval training on VO2maxenhancement in their meta-analyses [11,12]. Gibala and McGee showed that even very short high-intensity interval training (HIIT) protocols, involving only about 15 min of intense work over two weeks, can induce metabolic adaptations comparable to more traditional endurance train- ing [13]. More data on mitochondrial and cardiovascular adaptations with low-volume HIIT supports that high intensity can “replace” some of the volume of longer moderate- intensity work [14]. Seiler over the years has argued that HIIT should not be considered in isolation but in the context of the total training load and intensity balance across the season. The polarized load distribution model is widely described in research and used by practitioners as it shows importance of complementary low-intensity loads in effective HIIT usage [15–17]. However, the most recent research still indicates the need to continue studying high-intensity interval training to find the most effective model as not every model has been tested. Within the wide range of HIIT types training outcomes can be highly variable across individuals as Wen described in her meta-analysis [18]. This invites researchers to continue working and improve their understanding of this training
recent research still indicates the need to continue studying high-intensity interval training to find the most effective model as not every model has been tested. Within the wide range of HIIT types training outcomes can be highly variable across individuals as Wen described in her meta-analysis [18]. This invites researchers to continue working and improve their understanding of this training process. According to J. B. Kreher et al., it is known that up to 60% of professional athletes have experienced overreaching at some point in their careers, whereas true overtraining syn- drome is far less common, affecting fewer than 5% of athletes. Therefore, the optimization of training load dosing is considered a significant and topical problem in modern sports and physical activity science [19]. There is little question that HIIT is an essential component of a comprehensive training program for aerobic endurance athletes. Nevertheless, the distribution of the specific intensity of training and optimal types of interval training sessions to enhance performance are still unclear [20]. High-intensity interval training interventions of short duration have gained attention for their efficiency and physiological impact, especially in athletic populations. Recent stud- ies have demonstrated that even a brief training cycle—commonly referred to as a “shock cycle”—can elicit significant improvements in endurance performance and cardiovascular fitness. For example, the Salzburg 10/7 HIIT Shock Cycle Study validated a protocol in- volving 10 HIIT sessions over 7 consecutive days, leading to measurable gains in VO2max and time-trial performance among trained individuals [21]. Similarly, previous short-term interventions with as few as six sessions across two weeks have shown notable effects on mitochondrial adaptations, aerobic capacity, and insulin sensitivity in both recreational and trained populations [22,23]. These findings support the use of condensed, high-intensity training blocks as a viable approach for studying acute physiological adaptations and performance responses. The dynamic HIIT model proposed in this study differs from traditional protocols, where interval durations are fixed, and athletes adjust intensity to meet predetermined time targets.
viable approach for studying acute physiological adaptations and performance responses. The dynamic HIIT model proposed in this study differs from traditional protocols, where interval durations are fixed, and athletes adjust intensity to meet predetermined time targets.
J. Funct. Morphol. Kinesiol.2025,10, 407 3 of 15 Such fixed-time approaches can lead to inconsistencies in training load—some intervals may be too easy, others excessively demanding—potentially limiting adaptive responses or increasing fatigue risk. In contrast, our dynamic protocol keeps exercise intensity constant while allowing interval duration to be the variable based on individual tolerance, aiming to minimize load mismatches and reduce the risk of non-functional overreaching. We hypothesized that the novel dynamic HIIT protocol, combining fixed-load with variable-time intervals, would minimize the risk of non-functional overreaching while elicit- ing significant improvements in aerobic performance characteristics inwell-trained skiers. The study aimed to investigate the effects of a novel two-week dynamic high-intensity interval training (HIIT) protocol, characterized by fixed-load and variable-time intervals (two times up to ten minutes), on performance and metabolic adaptations in well-trained cross-country skiers. 2. Materials and Methods 2.1. Participants Four female (age 18±2 years; VO2max51.2±2.2 mL/kg/min; body mass 63.18±3.58 kg) and six male (age 17±1.67 years; VO2max58.57±2.81 mL/kg/min; body mass 74.76±3.63 kg) skiers participated in this study. The investigation was con- ducted by the ethical standards established by the Helsinki Declaration and approved by the LASE Ethical Committee (3/51813). Before any data collection sessions, all participants received an explanation of the study procedures. They had the opportunity to ask questions and sign the study participant’s consent form. For minors, written consent from parents was also obtained. The inclusion criteria for the study were as follows: •Consent to participate in the study. •Age 12–45 years. •The training volume of the participants was 7 h or more a week for the last year. •Sufficient skills to perform the predicted load on the treadmill ergometer. •VO2maxfor male participants > 50 mL/kg/min, and for females > 40 mL/kg/min. •Not taking any medication. •Passed pre-participation medical screening in the last 12 months. Exclusion criteria: •Uncontrolled arrhythmia causing symptoms or hemodynamic compromise. •Syncope. •Acute respiratory virus infection in the last 2 weeks. •Uncontrolled asthma. •Chest pain typical of ischemia. •Confusion. 2.2. Procedures The current study was designed to examine the effect of a novel, dynamic “two times up to ten minutes” HIIT protocol on
•Passed pre-participation medical screening in the last 12 months. Exclusion criteria: •Uncontrolled arrhythmia causing symptoms or hemodynamic compromise. •Syncope. •Acute respiratory virus infection in the last 2 weeks. •Uncontrolled asthma. •Chest pain typical of ischemia. •Confusion. 2.2. Procedures The current study was designed to examine the effect of a novel, dynamic “two times up to ten minutes” HIIT protocol on VO2max, body mass, maximal aerobic power, anerobic power, and aerobic capacity. The model for this experiment is represented in Figure. Training loads outside of studied intervals were partially regulated, coaches still prescribed the total volume as per the athlete’s plan, but no Zone 4 or higher (more than 80% of HRmax, determined during initial testing) work was allowed during the two-week period. This study used a prospective, longitudinal paired study design. All exercise tests and interval training sessions were performed in a laboratory setting. Before the start of the experiment, anthropometric data were collected: the body mass of
J. Funct. Morphol. Kinesiol.2025,10, 407 4 of 15 the athletes in underwear was determined in kilograms (kg) using scales (“Tanita” Model: MPB 300K100, Seoul, Republic of Korea), which was later needed to calculate the relative maximum oxygen consumption. Caloric or macronutrient intake or changes in body composition was not measured. Next, the research participants were invited to familiarize themselves with the treadmill ergometer (“Lode” Model: Valiant Ultra 250, Groningen, The Netherlands) and roller skis (“Ski-go” Model: skate Nr 3, Kiruna, Sweden). At the end of every test and interval, immediately post-exercise, blood lactate level was measured using a spectrometer (“EKF diagnostic” Model: Biosen C-line, Barleben, Germany). Breath by breath gas analysis was performed in exercise tests and every interval session to determine respiratory and indirect calorimetry values (“Vyaire” Model: Vyntus CPX, Houten, The Netherlands). Before the start of any tests, a calibration of the gas analyzer was performed according to the manufacturer’s guidelines to ensure the most precise measurements. Post-intervention VO 2max stress test Dynamic HIIT training ʹʹ2 x up to 10 minʹʹ Two weeks, 3 sessions per week, 6 sessions total, 2 intervals per session Each interval performed at same VO2max level intensity up to 10min with work:rest ratio of 1:2 between intervals Continuous monitoring in laboratory envirorment: HR, gas exchange, blood lactate levels Pre-intervention VO 2max stress test Figure 1.The model for testing and interval training used in the study. 2.2.1. Warm-Up Before exercise tests and interval sessions participants performed a standardized warm-up on a bicycle ergometer (“Lode” Model: Corvial, Groningen, The Netherlands) for 20 min, wearing a heart rate monitor (“Polar” Model: H10, Kempele, Finland) with a heart rate of 120±5 bpm. A 5 min treadmill warm-up was performed to familiarize participants with the conditions. 2.2.2. Exercise Testing During the warm-up part on the treadmill the test starting load was determined using live respiratory gas exchange data, particularly RER, with an aim to reach 1.0, which corresponds to ventilatory threshold 2 (VT2), a vigorous intensity according to Macintosh and colleagues [24]. This physiological phenomenon is increasingly used in the context of metabolic flexibility and metabolic reaction to
Testing During the warm-up part on the treadmill the test starting load was determined using live respiratory gas exchange data, particularly RER, with an aim to reach 1.0, which corresponds to ventilatory threshold 2 (VT2), a vigorous intensity according to Macintosh and colleagues [24]. This physiological phenomenon is increasingly used in the context of metabolic flexibility and metabolic reaction to different loads [25–27]. After determining this intensity, the load was gradually reduced to a complete break, and the participant was informed that the maximal oxygen consumption test would start imminently. This protocol tries to implement recent ideas by Davic C. Poole and Andrew M. Jones, where the idea of performing a high-intensity bout above critical power (CP), which corresponds to VT2, is suggested as optimal to determine VO2max [28]. The test was performed at determined incline of 6% and the only requirement for the participant was to cover as much distance as possible within ten minutes. A self-paced maximal effort to volitional fatigue at the intensity eliciting VO2max was performed. Race conditions were simulated in the laboratory environment. According to a previously
J. Funct. Morphol. Kinesiol.2025,10, 407 5 of 15 arranged scheme, the participant communicated with the treadmill operator, who could change the speed (increase or reduce it), but not the incline, which at all times stayed at 6%. After the test, a capillary blood sample is taken from the finger to determine the blood lactate concentration. As a result, the lowest load—speed (km/h)—at a 6% incline that induced peak oxygen consumption was sought. At this stage, the participant was evaluated to determine whether the cross-country skier achieved the previously set criteria (VO2max for males > 50 mL/kg/min, and females > 40 mL/kg/min). To measure physiological and performance changes after two weeks of dynamic high-intensity interval training, the same test protocol was used. 2.2.3. Interval Training A dynamic high-intensity interval training protocol of “two times up to 10 min” was used. A total of six training sessions were performed during the two weeks of training. In- tervals were performed at the lowest load intensity that reached peak oxygen consumption as determined during initial testing. The participant was instructed to follow subjective feelings and to perform at the intended load until exhaustion. The time spent during the first interval load is registered. In the following rest period before the next (second) interval, the time spent in the first interval is doubled, thus determining the rest period, obtaining a load:rest ratio of 1:2. During the rest period, the participant could choose their actions freely; they were allowed to sit, walk, have a drink, or perform other actions that the participant considered necessary. Next, the participant was instructed to perform a second interval at the same load intensity until exhaustion. After the second interval, the day’s work was completed, and the participant was invited to come to the next training session. See the model of a training session in Figure. Each interval training session is followed by at least one day off. Warm-up 20 min on cycling ergometer HR at 120 bpm 1st interval Roller skiing on the treadmill At 6 % incline Up to 10 min at VO2max speed Rest Double the length of
come to the next training session. See the model of a training session in Figure. Each interval training session is followed by at least one day off. Warm-up 20 min on cycling ergometer HR at 120 bpm 1st interval Roller skiing on the treadmill At 6 % incline Up to 10 min at VO2max speed Rest Double the length of first interval Work:rest ratio of 1:2 2nd interval Roller skiing on the treadmill At 6 % incline Up to 10 min at VO2max speed Blood La level measured immediately post-exercise Figure 2.The model for interval training session used in the study. 2.3. Statistical Analysis “R stats” R studio (Version 2021.09.2 Build 382, Auckland, New Zealand) computer software was used for data and statistical analysis. Data are presented as mean±standard deviation (SD). Normality was assessed for each variable using the Shapiro–Wilk test (α= 0.05).If the normality assumption and homogeneity of variances (F-test) were satisfied, parametric tests were used; otherwise, non-parametric equivalents were applied. For repeated measures across sessions, the Friedman test was used when data violated parametric assumptions; otherwise, repeated-measures ANOVA was considered. Post hoc comparisons between individual sessions were conducted using Wilcoxon signed-rank tests with Bonferroni correction (α= 0.05) when data were non-normal. Effect sizes for parametric tests were expressed as Cohen’s d, whereas rank-biserial correlation (raβ) was used for non-parametric tests. Correlations between baseline perfor- mance and training response were analyzed using Pearson’s r when both variables were normally distributed and Spearman’sρotherwise. Stability of VO2max post-intervention was evaluated using the two one-sided tests (TOST) equivalence procedure. Finally, a post
J. Funct. Morphol. Kinesiol.2025,10, 407 6 of 15 hoc power analysis for the observed Pearson correlation was conducted using GPower (version 3.1.9.7). 3. Results 3.1. Performance at VO2max In pre- and post-exercise tests, roller skiing speed at VO2maxin km/h was measured. In the pre-test, the speed reached by participants was 13.5±2.16 km/h; in the post-test, it was 14.8±1.7 km/h. The mean increase in speed was 1.4±1.29 km/h after two weeks of training (p= 0.0196), as displayed in Figure, where each dot represents an individual value. The boxplot shows the interquartile range (IQR): lower edge (Q1): 25th percentile; upper edge (Q3): 75th percentile; middle line: median (50th percentile). The largest gain for one subject was 4.4 km/h: from 10.6 to 15 km/h; re-running the pairedt-test without this value yieldedp= 0.0014, confirming that the reported effect is robust to the exclusion of this extreme value. The speed of some participants increased by 0.5 km/h. No one in the post-test reached a lower speed than in the pre-test. The effect size was large with Cohen’s d = 1.06 and Hedge’s g = 0.93. The gain scores violated normality (Shapiro–Wilk p= 0.0019).A Wilcoxon signed-rank test confirmed a significant increase in maximum speed (p= 0.0078). Gain scores were negatively correlated with baseline speed (Pearson’s r=−0.75, p= 0.035; Spearmanρ=−0.71,p= 0.049), indicating that participants with lower initial speeds experienced grater improvements. Post hoc power analysis showed power of 0.85, which exceeds the conventional threshold of 0.80. Figure 3.The mean roller skiing speed of the participants on the treadmill before and after two weeks of interval training. There were no statistically significant changes observed in VO2maxitself(p> 0.05). the pre-test showed VO2maxof 55.3±5.2 mL/kg/min with post-test levels reaching 57.2±5.5 mL/kg/min. The largest gain was 6.9 mL/kg/min: from 47.9 to54.8 mL/kg/min. Some participants reached lower VO2maxlevels in the post-test. The largest loss was 3.4 mL/kg/min: from 53.6 to 50.2 mL/kg/min. A TOST equivalence test (∆=±2.8 mL/kg/min, 5% of baseline) confirmed that VO2maxdid not meaningfully change after the dynamic HIIT (TOSTp= 0.03). 3.2. Metabolic Adaptations Lactate concentration in the capillary blood was measured after every interval during the training session and
Description
Dynamic HIIT improved roller skiing speed without altering VO2max.