Physical fitness is one of the major determinants of performance and success in sport (Joyner and Coyle, 2008). Aerobic capacity has been consistently identified as a critical performance indicator, particularly in endurance-based activities (Lucia et al., 2001; Haugen et al., 2022). In rugby, aerobic capacity is strongly associated with performance outcomes such as repeated high-intensity efforts and total running distance (Swaby et al., 2016; Vachon et al., 2021) which become increasingly important at higher levels of competition, especially in professional team sports (Metaxas et al., 2009). Previous studies (Smart et al., 2014; Cunningham et al., 2018) have established a relationship between endurance capacity and game-specific actions in rugby, such as ruck efficiency, tackling success, work rate, and activity rate. Rugby union is inherently intermittent, requiring repeated high-intensity efforts (e.g., accelerations, sprints, and physical collisions) interspersed with lower-intensity activities (e.g., walking and jogging) (Duthie et al., 2003; Cunniffe et al., 2009; Cahill et al., 2013; Lacome et al., 2014). These intermittent demands underscore the importance of aerobic capacity in maintaining high-intensity performance throughout a match (Yuan et al., 2024).

Aerobic capacity is known to improve an athlete’s ability to perform repeated high-intensity efforts (Girard et al., 2011; Vachon et al., 2021). When aiming to improve aerobic performance, increasing maximal oxygen uptake (V̇O_2max) is a primary objective. Two methods are commonly used to increase aerobic qualities: continuous exercise performed as a single bout over a long period with moderate intensity (Steele et al., 2021) and high-intensity interval training (HIIT). HIIT consists of repeated bouts of exercise at intensities between the second lactate threshold and near V̇O_2max, interspersed with periods of lower-intensity recovery (Billat, 2001; Buchheit and Laursen, 2013a; Seiler, 2024), and is a time-efficient method that exposes athletes to higher training intensities, which are favorable for improving cardiorespiratory fitness (Midgley et al., 2007; Buchheit and Laursen, 2013a). Consequently, in team sports such as rugby union, HIIT is suggested as an effective training strategy, as it better replicates the sport’s physiological and mechanical demands while improving aerobic capacity (Buchheit and Laursen, 2013b; Tee et al., 2016; Yuan et al., 2024).

Therefore, accumulating time spent at or above 90% of peak oxygen uptake (T ≥ 90% V̇O_2peak) may lead to greater training adaptations, such as improvements in peak oxygen uptake (V̇O_2peak) and repeated sprint ability while enhancing the overall efficiency of HIIT protocols, thereby supporting the use of a fast-start strategy high-intensity interval training (HIIT-FS) to optimize the training stimulus (Wenger and Bell, 1986; Thevenet et al., 2006; Midgley et al., 2007; Buchheit and Laursen, 2013a; Manuel Clemente et al., 2021; Seiler, 2024). Programming HIIT involves adjusting up to nine different parameters (Buchheit and Laursen, 2013a), including the intensity and duration of the work intervals, the intensity and duration of the rest intervals; the mode of exercise, the number of repetitions, the number of sets, and the duration and intensity of recovery periods between sets.

HIIT protocols can be modified by adjusting the intensity and/or duration parameters within the sets (e.g., fast-start strategy), to better align with specific training objectives and the nonlinear, variable demands of team sports (Wilson, 2016; Harper et al., 2019). In this context, short-interval HIIT formats are commonly used (Buchheit and Laursen, 2013b). Recent studies have found that varying the intensity parameters within a HIIT session, including the HIIT-FS with initial work intervals at higher intensities, can increase T ≥ 90% V̇O_2peak compared to other strategies (e.g., constant-intensity HIIT) in individual endurance sport such as running (De Aguiar et al., 2013), cycling (Lisbôa et al., 2015; Bossi et al., 2020; Miller, Perez and Farrell, 2023), and cross-country skiing (Rønnestad et al., 2020; 2022). However, its effects in team sports remain poorly understood, as no study to date has investigated the physiological responses to a HIIT session with varied parameters in a rugby-specific context. A faster V̇O₂ kinetics, that is, a quicker rise in oxygen uptake, has been observed with fast-start strategies and may improve the ability to repeat high-intensity efforts in rugby, potentially increasing T ≥ 90% V̇O_2peak (Bailey et al., 2011). However, this mechanism remains unclear in team sports, particularly in rugby union.

The purpose of this study was to compare the physiological, perceptual, and external load responses of a HIIT session using a HIIT-FS strategy to a constant-intensity HIIT protocol (HIIT-C) in professional rugby union players. The primary aim was to evaluate differences in T ≥ 90% V̇O_2peak between protocols. The secondary aim was exploratory: to compare external load variables (e.g., total distance, distance ≥ 7 km·h^-1, distance ≥ 16 km·h^-1, mechanical work distance), heart rate responses, and rating of perceived exertion (RPE) between the two conditions.

We hypothesized that (i) HIIT-FS would result in a longer T ≥ 90% V̇O_2peak compared to HIIT-C, and (ii) no significant differences would be observed in perceptual responses or external load due to the matched average intensities. These hypotheses were tested at the whole-session level, with additional sub-analyses performed across fragmented time segments within the protocol.

Fifteen healthy, male academy rugby players (Table 1) volunteered to participate in this study (19.4 ± 1.7 years old; 184.6 ± 7.8 cm; 88.5 ± 12.7 kg). Sample size was estimated based on the primary outcome, T ≥ 90% V̇O_2peak, using prior work comparing Constant-Intensity High-Intensity Interval Training (HIIT-C) and HIIT-FS in running (De Aguiar et al., 2013). With α = 0.05 and β = 0.2, the required sample size was calculated as 7 (G*Power 3.1.9.6, Universität Kiel, Germany) All players were recruited from two clubs competing in the first and second divisions of French professional rugby union during the 2023/24 season. All procedures were in accordance with the Declaration of Helsinki and participants signed informed consent forms. The inclusion criteria were: athletes aged over 18 years, and athletes who had been part of an academy program for more than one year. Exclusion criteria were: (1) pre-study -participation in other sports or injury in the previous two weeks; and (2) mid-study dropout - inability to complete the three sessions due to illness, injury, or breathing discomfort while wearing the gas analysis (required for valid gas analysis) due to breathing difficulties. Due to illness (n = 2), injury (n = 4) and incapacity to sustain the sessions (n = 1), eight participants completed all sessions (19.9 ± 2.2 years old; 184.1 ± 8.8 cm; 89.4 ± 15.7 kg).

Participants completed three sessions (Figure 1) over a ten-day period, with at least one rest day between each. All sessions were scheduled during a three-week in-season window without championship matches. Conditioning sessions replaced regular rugby-specific fitness training to maintain overall training load, while lower-body resistance training was removed during the study period. Participants were instructed to arrive 90 minutes before the sessions, in a rested state and fully hydrated with water provided on arrival. The first session involved the 30-15 Intermittent Fitness Test (30-15 IFT) to assess aerobic fitness and determine the velocity at intermittent fitness test (VIFT) and V̇O_2peak (Buchheit, 2008). This test consisted of 30-second 40-m shuttle runs, interspersed by 15 seconds of passive recovery. Velocity began at 8 km.h^-1 and increased by 0.5 km.h^-1 per stage, with pacing guided by auditory signals every 20 m. The test ended when a participant failed to reach the 3-meter marker three times in one stage. The final completed stage was used to determine VIFT. All tests were supervised by experienced strength and conditioning coaches, with verbal encouragement provided throughout.

In the second and third sessions, participants completed the HIIT-C and HIIT-FS protocols in randomized order. Both protocols were matched for work-to-rest ratio and average intensity to compare physiological responses (e.g., T ≥ 90% V̇O_2peak) with HIIT-FS. The HIIT-C protocol (Figure 1) consisted of 2 sets of 8 repetitions of 30 s intervals at 88% VIFT (Buchheit, 2008), with 15 s of passive recovery between intervals and 3 minutes of passive recovery between sets. The HIIT-FS protocol (Figure 2) matched the average intensity of HIIT-C (88% VIFT) but was structured with varied intensity: intervals 1-4 at 98% VIFT and intervals 5-8 at 78% VIFT, resulting in a weighted mean of 88% VIFT per set. This weighted mean was based on average running velocity, not total distance or energy expenditure.

All sessions took place on the same outdoor synthetic turf with an average temperature of 24.1 ± 3.2 ºC, with a relative humidity of 70.2 ± 3.4% between 4:00pm to 6:30pm. Interval pacing was guided by audio signals, and cones were individually spaced according to each athlete’s target velocity.

A standardized three-phase warm-up preceded both HIIT sessions. Phase 1 included general movements (10 squats, 10 lunges, 10 hip thrusts). Phase 2 involved two sets of two squat jumps and two countermovement jumps. Phase 3 consisted of 10 × 20-m running drills, 5 × 50-m accelerations, and one 30-second run at 90% of VIFT.

The peak heart rate (HR_peak), the peak oxygen uptake (V̇O_2peak) and VIFT were determined by the 30-15 intermittent fitness test (30-15 IFT) (Buchheit, 2008). HR was recorded continuously using a Polar H10 heart rate monitor (Polar Electro Oy, Kempele, Finland) with a sampling frequency of 1 Hz. HR_peak was defined as the highest 1-second HR value recorded during the test. Gas exchanges were measured continuously at 15-second intervals throughout the test using a Metamax 3B-R2 (Cortex Biophysics, Leipzig, Germany) previously validated by Macfarlane and Wong (Macfarlane and Wong, 2012). Participants wore the unit secured with a vest positioned on the thorax, with the system carefully aligned over the clavicle to allow full arm mobility during running. An oronasal face mask (7450 series V2, HansRudolph, Shawnee, KS, United States) was fitted to enable gas flow through a bi-directional digital turbine. Prior to each test, the flow sensor was calibrated with a 3 L syringe, and gas analyzers were calibrated with ambient air and reference gas (15% O₂, 5% CO₂), following the manufacturer's recommendations. HR_peak and V̇O_2peak were defined as the highest HR and oxygen uptake measured during a 30-second period prior to the subject's voluntary exhaustion during the test.

Heart Rate was continuously monitored using a Polar H10 heart rate monitor with a sampling frequency of 1 Hz. The time spent at or above 90% of peak heart rate (T 90%HR_peak) was defined as the cumulative duration during which heart rate values equalled or exceeded 90% of HR_peak, as established during the 30-15 IFT. Mean HR (HR_mean) was defined as the average HR values across all work intervals during the HIIT session. The percentage of HR_peak (%HR_peak) was calculated using the following equation %HR_peak = (HR_mean/HR_peak) x 100.

Gas exchanges were recorded continuously at 5-second intervals using the same metabolic card and calibration procedures as during the 30-15 IFT (Cortex Metamax 3B-R2, Cortex Biophysik GmbH, Leipzig, Germany). T ≥ 90% V̇O_2peak was defined as the cumulative time during which V̇O₂ was equal to or greater than 90% of V̇O_2peak. Mean oxygen uptake (V̇O_2mean) was defined as the average V̇O₂ across all work intervals during the HIIT sessions. The percentage of V̇O_2peak (% V̇O_2peak) was calculated using the equation % V̇O_2peak = (V̇O_2mean/ V̇O_2peak) x 100. Mean ventilation (V̇E_mean) and mean breathing frequency (f_Rmean) were also determined as the average values across all work intervals during the HIIT session.

External loads of each HIIT session were measured using GPS devices sampled at 10Hz (GPS, Vector S7, Catapult Innovations, Melbourne, Australia). Each device was positioned between the scapulae in a sport vest and was turned on 15 min prior to data collection in accordance with the manufacturer’s guidelines to guarantee signal quality. Signal quality was verified by an average horizontal dilution of precision (HDOP) of 0.75 ± 0.07 and an average of 15.2 ± 0.5 connected satellites. To minimize inter-unit variability, the same device was used by each participant across sessions. Total distance was defined as the sum of meters covered during the session, while running distance referred to the distance covered at speeds greater than 7 km.h^-1, a threshold recognized as the transition from walking to running (Varley et al., 2017). High-speed running distance was calculated as the total distance covered at speeds above 16 km.h^-1 (Varley et al., 2017). Mechanical work distance was defined as the total distance covered in meters greater than 2m.s ^-1 during both acceleration and deceleration phases (Buchheit et al., 2014).

Thirty minutes after each HIIT session, participants rated their perceived exertion using the Borg CR10 scale (Foster et al., 2001).

The statistical analysis was structured in two main phases, the first consisting of a global comparison between the two experimental protocols, in which mean values calculated over the entire duration of each protocol were evaluated to provide an overall assessment. The second phase involved a more detailed, time-specific comparison, where the protocols were analysed at distinct time points, both at the level of the complete sets and at the level of half-sets segments.

For the time-specific analysis, comparisons were conducted between equivalent sets of the two protocols (i.e., Set 1 of HIIT-C vs. Set 1 of HIIT-FS; Set 2 of HIIT-C vs. Set 2 of HIIT-FS). Each set was further subdivided into two 3-minute halves (intervals 1-4 and intervals 5-8), enabling a segment-by-segment comparison of protocol effects.

Statistics are presented as means ± standard deviation (SD), along with 95% confidence intervals (CI). Data normality was assessed using the Shapiro–Wilk test. Depending on the distribution and variance, either a paired t-test or a Wilcoxon signed-rank test was used to evaluate differences between protocols.

Analyses were conducted across physiological variables (T ≥ 90% V̇O_2peak, V̇O_2mean, mean %V̇O_2peak, HR_mean, mean %HR_peak, V̇E_mean, and f_Rmean), external load variables (total distance, distance ≥ 7 km·h^-1, distance ≥ 16 km·h^-1, and mechanical work distance), and the perceptual variable rating of perceived exertion (RPE).

The effect size was calculated using Cohen’s effect sizes (d), with interpretations categorized as trivial (< 0.2), small (≥ 0.2), moderate (≥ 0.5), or large (≥ 0.8). Statistical significance was set at p < 0.05. All calculations were performed using JASP software (version 0.19.3; JASP Team, Amsterd am, Netherlands) and Microsoft Excel (Redmond, USA).

Physiological responses are summarized in Table 2 and illustrated in Figure 2 and Figure 3. During the set 1, (Table 2) HIIT-FS protocol elicited significantly greater V̇O_2mean (47.1 ± 3.8 ml·kg^-1·min^-1 vs 44.5 ± 4.0; +2.6%; p < 0.05; d = 1, large), mean %V̇O_2peak (87.2 ± 2.8 % vs 82.5 ± 3.8 %; +4.7 percentage points; d = 1, large), and T ≥ 90% V̇O_2peak (179.4 ± 60.3 s vs 128.1 ± 66.8 s; CI 95% = 128.9-229.9 vs 72.3-184; +40%; p < 0.05; d = 2.07, large; CI 95% = 0.82-4.26) (figure 3) compared to HIIT-C. In set 2, (Table 2) although the HIIT-FS protocol still resulted in significantly higher V̇O_2mean (45.8 ± 3.6 vs. 44.1 ± 4.3 mL·kg^-1·min^-1; +1.6%; p < 0.05; d = 0.8, large) and mean %V̇O_2peak (84.8 ± 3.4% vs. 81.8 ± 3.0%; +3.0 percentage points; p = 0.05; d = 0.8, large), the difference in T ≥ 90% V̇O_2peak was not statistically significant (139.4 ± 82.9 s vs. 102.5 ± 59.6 s; p = 0.28) (Figure 3). Over the total session (Table 2), HIIT-FS protocol resulted in significantly higher V̇O_2mean (46.5 ± 3.7 mL·kg^-1·min^-1 vs 44.3 ± 4.1; +2.2%; d = 1.6, large), mean %V̇O_2peak (86.1 ± 3.0 % vs 82.1 ± 3.2 %; +4 percentage points; d = 1.5, large), and T ≥ 90% V̇O_2peak (318.8 ± 138.9 s vs 230.6 ± 115.6 s; CI 95% = 202.6-434.9 vs 134-327.2; +38.2%; d = 0.88, large; CI 95% = 0.03-1.70) (Figure 2), compared to the HIIT-C protocol (p ≤ 0.05).

Compared to HIIT-C, V̇E_mean during HIIT-FS approached statistical significance in both set 1 (138.6 ± 12.5 vs. 131.4 ± 13.4 L·min^-1; +5.5%; p = 0.052; d = 0.83, large) and set 2 (147.8 ± 16.4 vs. 139.6 ± 14.6 L·min^-1; +5.9%; p = 0.055; d = 0.81, large). Over the entire session, V̇E_mean was significantly higher for HIIT-FS compared to HIIT-C (143.2 ± 14.2 vs. 135.0 ± 13.9 L·min^-1; +6.0%; p = 0.041; d = 0.89, large) (Table 2). In addition, f_Rmean was significantly greater during HIIT-FS in set 1 (54.9 ± 7.5 vs. 52.9 ± 6.9 cycles·min^-1; +3.8%; p = 0.036; d = 0.92, large), in set 2 (61.7 ± 6.2 vs. 57.9 ± 6.5 cycles·min^-1; +6.6%; p < 0.001; d = 1.99, large), and across the total session (58.3 ± 6.7 vs. 55.4 ± 6.6 cycles·min^-1; +5.3%; p < 0.001; d = 2.92, large) (Table 2), when compared to HIIT-C.

However, no significant difference between the two protocols was observed for HR_mean, HR_peak, mean %HR_peak and T 90%HR_peak for set 1, set 2 and the total session (p > 0.05) (Table 2).

When the analysis was carried out by two intervals blocks (1-4 and 5-8), the HIIT-FS protocol elicited greater physiological responses in intervals 1–4. V̇O_2mean was significantly higher for HIIT-FS in both set 1 (45.3 ± 4 mL·kg^-1·min^-1 vs 40.8 ± 3.9; +4.5%; p < 0.001; d = 3, large) and set 2 (44 ± 3.4 mL·kg^-1·min^-1 vs 40.8 ± 4.3; +3.1%; p = 0.002; d = 1.6, large) (Table 2). Similarly, mean %V̇O_2peak was significantly higher in set 1 (83.8 ± 2% vs 75.8 ± 4.4%; +8 percentage points; p < 0.001; d = 3, large) and set 2 (81.6 ± 3.3% vs 75.8 ± 3.8%; +5.8 percentage points; d = 1.6, large) (Table 2). T ≥ 90% V̇O_2peak was also significantly greater for HIIT-FS in set 1 (75 ± 23.5 s vs 31.2 ± 24.2 s; CI 95% = 55.4-94.6 vs 11-51.5; +140%; p < 0.001; d = 4.1, large; CI 95% = 0.18-2.0), and in set 2 (60.6 ± 34.2 s vs 30 ± 18.6 s; CI 95% = 32.1-89.2 vs 14.5-45.5; +77%; p = 0.018; d = 1.09, large; CI 95%= 0.18-2) (Figure 3). V̇E_mean was significantly higher for HIIT-FS in set 1 (129.2 ± 11.5 L·min^-1 vs 114.2 ± 11.3; +13%; p = 0.01; d = 1.2, large) and set 2 (139.3 ± 15.2 L·min^-1 vs 125.2 ± 14.4; +11%, p = 0.012; d = 1.2, large) (Table 2). Finally, f_Rmean was significantly higher in set 1 (53.3 ± 8 cycles·min^-1 vs 48.9 ± 6.6; +9%; p = 0.023; d = 0.89, large), and set 2 (60 ± 6.2 cycles·min^-1 vs 53.9 ± 6.3; +11.3%; p < 0.001; d = 2.6, large) (Table 2) compared to HIIT-C. No significant differences were observed between HIIT-FS and HIIT-C protocols during intervals 5–8 in either set 1 or set 2 for V̇O_2mean, mean %V̇O_2peak, T ≥ 90% V̇O_2peak, V̇E_mean, or f_Rmean (p > 0.05) (Table 2; Figure 3). Similarly, (Table 2), for sets 1–2 across both interval blocks (1-4 and 5-8), HR_mean, HR_peak, and mean %HR_peak did not significantly differ between protocols (p > 0.05). However, T ≥ 90% HR_peak was significantly greater for the HIIT-FS protocol compared to HIIT-C (129.8 ± 24 s vs. 109 ± 34 s; +19.1%; p < 0.05; d = 0.9) (Table 2).

External load as measured by GPS, revealed no significant differences between the HIIT-C and HIIT-FS groups in total distance covered (Table 3). The HIIT-C group covered 2298 ± 142.4 m and the HIIT-FS group covered 2336.5 ± 153.8 m (p = 0.12). Similarly, no significant difference was observed between the two HIIT protocols for distances covered above 7 km.h^-1 and 16 km.h^-1, and for the MW distance. However, the HIIT-FS group reported a significantly higher RPE (Table 3) compared to the HIIT-C group with a large effect size (+15%; p = 0.023; d = 1.8, large; CI 95% = 0.6-2.9).

In this study, we compared the effects of HIIT-FS and HIIT-C protocols on T ≥ 90% V̇O_2peak, V̇O_2mean, RPE and external load in rugby players, hypothesizing that HIIT-FS would elicit greater T ≥ 90% V̇O_2peak without differences in external load or RPE. In line with our hypothesis, HIIT-FS significantly increased T ≥ 90% V̇O_2peak with no differences in external load. However, RPE was significantly higher for HIIT-FS, contrary to our initial assumption. HIIT has been shown to improve V̇O_2peak, a key determinant of aerobic fitness, and various HIIT strategies can be employed to enhance V̇O₂ capacity (Buchheit and Laursen, 2013b). A key physiological rationale for HIIT efficacy lies in its capacity to sustain oxygen uptake near V̇O_2max for extended periods during repeated efforts, thereby maximizing cardiovascular and muscular adaptations. Specifically, time spent above 90% of V̇O_2peak has been proposed as a crucial training stimulus for improving aerobic power and endurance-related capacities (Midgley et al., 2007; Rønnestad et al., 2022). Our study highlights that the HIIT-FS method increased significantly T ≥ 90% V̇O_2peak, compared to HIIT-C in total session and set 1(+38.2%; +40%, respectively; p < 0.05) but not in set 2 (p > 0.05). In addition, higher V̇O_2mean and mean %V̇O_2peak were observed in the HIIT-FS condition. This latter marker is particularly relevant, as longer durations spent at high %V̇O_2peak are associated with greater training-induced adaptations and improvements in endurance performance (Rønnestad et al., 2022; Odden et al., 2024). These differences were not observed during intervals 5-8 in either set, likely due to the lower prescribed intensities in the second halves of each set in the HIIT-FS protocol (78% VIFT vs. 98% VIFT in intervals 1-4), despite equivalent overall intensities (88% VIFT), suggesting that HIIT-FS benefits are front-loaded, optimizing early intervals. The increase in T ≥ 90% V̇O_2peak observed with HIIT-FS in this study is consistent with findings from endurance sports such as cycling (Bossi et al., 2020; Miller et al., 2023), cross-country skiing (Rønnestad et al., 2020; 2022) and running (De Aguiar et al., 2013). Two studies (Rønnestad et al., 2020; Bossi et al., 2020), demonstrated in particular that fast-start strategies resulted in longer durations ≥90% V̇O_2peak compared to constant-intensity protocols, reporting 12.0 vs 10.8 minutes and 6.8 vs 4.8 minutes, respectively. Notably, these studies also implemented HIIT-FS during interval-based efforts, though the designs varied from the present work. In contrast, one previous work (Miller et al., 2023) reported no significant difference in total T ≥ 90% V̇O_2peak between fast-start and constant intensity HIIT (25.2% vs. 26.1%). This discrepancy may stem from differences in anaerobic work capacity (W’) prescriptions, which are based on the depletion of sustainable work above critical speed or power. Such an approach differs fundamentally from the VIFT-based method used in the present study, which may better reflect the intermittent demands of team sports.

Unlike prior research, which primarily focused on individual endurance athletes, the current study targeted a team-sport population, specifically rugby union players, using fast-start strategies with elevated initial intensities. By prescribing intensity based on the 30-15 Intermittent Fitness Test (IFT), this approach enhances ecological validity for team-sport applications, especially during running-based short intervals (Stanković et al., 2021). Only one study (De Aguiar et al., 2013) investigated short-interval HIIT-FS in a manner relevant to team sports. Using a protocol with 30-second running effort and 15-second rest intervals at 125-105% intermittent critical velocity, they observed significantly greater time ≥95% V̇O_2peak compared to standard prescriptions (286 ± 150 s vs 113 ± 40 s and 106 ± 71 s; p < 0.05). This approach overlaps with team-sport demands and aligns with findings from studies focused on sport-specific conditioning (Manuel Clemente et al., 2021; Kumari et al., 2023). However, it is important to note that although the work-to-rest ratio was consistent, De Aguiar et al.’s study used intensity prescriptions based on critical velocity, a method less commonly applied in team sports. In contrast, our study used the VIFT, a more sport-specific and practical tool for prescribing HIIT intensity in team settings.

In the current study, no significant differences were observed between protocols during intervals 5-8 (p > 0.05). This lack of difference reflects the reduced intensity during intervals 5-8 in the HIIT-FS protocol and suggests that the physiological benefits of HIIT-FS are primarily concentrated in the early phase of the session. Specifically, the elevated initial intensity (98% vs. 88% VIFT) in HIIT-FS substantially increased cardiorespiratory demand during intervals 1-4, as evidenced by significantly greater V̇O_2mean, mean %V̇O_2peak, and T ≥ 90% V̇O_2peak compared to HIIT-C. These findings support the notion that manipulating work-rate distribution, specifically through the HIIT-FS format within a session, can meaningfully influence V̇O₂ demand and ventilatory load (Buchheit and Laursen, 2013a) while also replicating the specific physiological demands of rugby union activity.

Fast-start strategies have been associated with accelerated V̇O₂ responses and improved V̇O₂ kinetics due to their influence on pacing during high-intensity efforts (Bailey et al., 2011). In our study, higher V̇O₂ demands in HIIT-FS intervals 1-4 (98% VIFT) likely reflect priming effects on V̇O₂ (Goulding et al., 2023) as demonstrated by significant increases in V̇O₂mean (+4.5% for set 1; +3.1% for set 2; p < 0.05). Higher exercise intensities appear to enhance the homogeneity of quadriceps femoris muscle recruitment and motor unit activation patterns (Hodson-Tole and Wakeling, 2009; Heinonen et al., 2012). This may involve greater type II fiber recruitment, which, due to its lower mechanical efficiency and higher O₂ demands, contributes to elevated V̇O₂ (Vanhatalo et al., 2011). The resultant physiological stress likely includes phosphocreatine depletion (Gaitanos et al., 1993) and metabolite accumulation (e.g., hydrogen ions) (Girard et al., 2011), which may ultimately support improvements in repeat high-intensity effort capacity, an essential quality in rugby. The higher running speeds in the HIIT-FS protocol likely imposed altered running kinematics, as increased speed in HIIT has been associated with greater biomechanical stress (García-Pinillos et al., 2019).

Breathing frequency is a sensitive and immediate marker of internal load, and a key feature that differentiates f_Rmean from other variables is its very fast response at the onset (e.g., first 30 s of intervals) of exercise (Nicolò et al., 2017). In this study, as shown in Table 2, HIIT-FS increased f_Rmean significantly (+3.8% for set 1; +6.6% for set 2, p < 0.05), especially in intervals 1-4 (+9% for set 1;+11.3% for set 2, p < 0.05), reflecting higher starting intensities (98% vs. 88% VIFT) encountered in the HIIT-FS protocol. Similar ventilatory responses have been observed during high-intensity efforts with varying intensity (Nicolò et al., 2017). These findings highlight the elevated ventilatory demand triggered by the initial bouts of HIIT-FS, underscoring the distinct physiological loading profile of this format. In line with this, V̇E_mean was also significantly higher in HIIT-FS across the full session (+9%; p < 0.05), indicating greater metabolic and ventilatory stress.

Importantly, despite these physiological differences, HR measurements (HR_mean, HR_peak, mean %HR_peak, T ≥ 90% HR_peak) only showed a significant difference for T ≥ 90% HR_peak during set 2, intervals 1-4. This aligns with known physiological dynamics, as HR typically lags behind V̇O₂ in its response to exercise onset (Buchheit and Laursen, 2013a), and may not accurately reflect short-term fluctuations in effort. Similar patterns have been reported in previous fast-start HIIT studies, where HR responses showed no significant differences between protocols (Rønnestad et al., 2022). It can be suggested that a more accumulated fatigue during the second set in HIIT-FS protocol may have contributed to the reduced physiological responses observed, particularly during intervals 1-4. From an external load perspective, GPS data (Table 3) revealed no significant differences between protocols for total distance (2297.9 ± 142.4 m vs. 2336.5 ± 153.8 m, p = 0.12), running distance ≥ 7 km·h^-1 (2117.5 ± 140.2 m vs. 2131.8 ± 145.1 m, p = 0.47), and high-speed running distance ≥ 16 km·h^-1 (1482.7 ± 451.3 m vs. 1345.4 ± 380.9 m, p = 0.2). This is likely attributable to the matched average intensities between the two protocols and the limitations of GPS technology in detecting rapid velocity changes during short intervals (Torres-Ronda et al., 2022). However, subjective effort, as measured by RPE, was significantly higher in the HIIT-FS condition (p < 0.05). This contrasts with earlier findings reporting either lower RPE or no difference between interval protocols (Rønnestad et al., 2020; 2022; Bossi et al., 2020).This discrepancy may be explained by the higher starting intensities in our HIIT-FS protocol (98% vs. 88% VIFT), which aligns with previous observations (Bok et al., 2023) regarding the perceived exertion associated with higher intensities. Notably, our study is the first to specifically address a team sport context, employing running-based intensity prescription via the 30-15 IFT and incorporating a short-interval training protocol.

From a practical standpoint, the HIIT-FS strategy appears to be an effective method for increasing both the absolute time and relative percentage spent at ≥ 90% V̇O_2peak in young elite male rugby players, compared to traditional HIIT. By front-loading the effort (e.g., 30-second initial intervals at 98% VIFT) within a 30-15-second interval format, rugby strength coaches can better exploit early interval phases to stimulate greater aerobic stress (e.g., T ≥ 90% V̇O_2peak), especially useful when working within the tight time constraints typical of rugby training schedules.

However, due to the higher perceived exertion and potentially greater mechanical load associated with HIIT-FS, careful consideration is required before implementing it. When only a small number of sets can be completed (e.g., 2 sets), HIIT-FS may be the more efficient option, as it induces greater V̇O₂ demands early in the session. Conversely, if the session allows for higher volumes (e.g., 4-5 sets), the HIIT-C format may be more effective in accumulating overall T ≥ 90% V̇O_2peak across the entire session.

Because HIIT-FS elicits high running speeds and accelerations, it is especially well suited for intermittent team sports like rugby, where repeated high-intensity efforts closely reflect match demands. This format may be particularly useful during the pre-season (to build an aerobic base), late tapering (to maintain aerobic fitness with reduced volume), and even in-season congested schedules or rehabilitation phases, where minimizing total load while preserving intensity is important. Importantly, the frequency and placement of HIIT-FS sessions should be tailored to individual tolerance, fatigue status, and injury risk.

While the findings offer valuable insights, their applicability to other populations (e.g., female athletes, amateur athletes, sedentary individuals) should be interpreted with caution due to differences in subject characteristics.

In young elite male rugby players, the HIIT-FS protocol resulted in a longer time spent ≥ 90% V̇O_2peak and higher % V̇O_2peak compared to the HIIT-C protocol, despite having a similar exercise duration. These findings highlight the potential of the fast-start strategy to enhance the intensity and physiological impact of interval training without increasing external load or session time. While the HIIT-FS protocol was associated with a higher perceived effort (RPE), no significant differences were observed in HR responses or external workload as measured by GPS.

This study provides novel insight into the acute physiological responses to fast-start interval training in a team-sport context, using a running-based prescription aligned with the demands of rugby. However, given the short-term nature of this investigation, further research is warranted to assess the chronic adaptations to fast-start HIIT protocols over longer training periods. Future studies should also consider their application across different team sports, training phases, and athlete populations, particularly within the constraints of time-limited, high-performance environments.