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Edited by: Bruno Travassos, University of Beira Interior, Portugal

Reviewed by: Júlio Alejandro Costa, Portuguese Football Federation, Portugal; Matteo Bonato, University of Milan, Italy

This article was submitted to Movement Science and Sport Psychology, a section of the journal Frontiers in Psychology

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

The present study aimed to investigate the effects of two high-intensity interval training (HIIT) shuttle-run-based models, over 10 weeks on aerobic, anaerobic, and neuromuscular parameters, and the association of the training load and heart rate variability (HRV) with the change in the measures in young futsal players.

Eleven young male futsal players (age: 18.5 ± 1.1 years; body mass: 70.5 ± 5.7 kg) participated in this study. This pre-post study design was performed during a typical 10 weeks training period. HIIT sessions were conducted at 86% (HIIT_{86}; _{100};

The HIIT_{86} model showed clear improvements for the peak oxygen uptake (VO_{2}peak), peak speed in the treadmill incremental test, first and second ventilatory thresholds, RSA best and mean times, CMJ, and SJ. The HIIT_{100} model presented distinct advances in VO_{2}peak, peak speed in the treadmill incremental test, RSA mean time, and CMJ. Between HIIT models comparisons showed more favorable probabilities of improvement for HIIT_{86} than HIIT_{100} model in all parameters. TL data and HIIT models strongly explained the changes in the RSA mean and best times (^{2} = 0.71 and 0.87, respectively), as well as HRV changes, and HIIT models explained positively VO_{2}peak changes (^{2} = 0.72). All other changes in the parameters were low to moderately explained.

The HIIT_{86} proved to be more effective for improving aerobic, RSA, and neuromuscular parameters than HIIT_{100} during a typical 10-week futsal training period. So, strength and conditioning specialists prescribing shuttle-run intermittent exercises at submaximal intensities can manage the individual acceleration load imposed on athlete increasing or decreasing either the set duration or the frequency of change of direction during HIIT programming.

Futsal is a team sport involving a complex range of high-intensity locomotor activities, requiring both aerobic and anaerobic fitness to cope with the multiple requirements of the match (

The training intensity is among the first training variables to be manipulated in most physical conditioning programs for athletes (_{IFT}) in professional handball players. This finding is in line with other research reporting no effect of training intensity on RSA performance outcomes following HIIT models (

Considering the multidirectional running pattern during futsal matches, HIIT strategies are usually composed of shuttle-runs in order to increase the specificity of these HIIT drills (

The dose-response relationship between the accumulated training load (TL) and performance adaptations is another relevant topic in the field of team sports, which deserves attention from coaches and sport scientists. The majority of studies examining the dose-response relationship have been primarily conducted with soccer and rugby players (

The current study aimed to compare the effects of two shuttle run HIIT models performed at 86% (HIIT_{86}) and 100% (HIIT_{100}) of peak speed derived from the Futsal Intermittent Endurance Test (FIET, PS_{FIET}) with a total work duration of 16 and 8 min, respectively, implemented over a period of 10 weeks, on the HRV, aerobic fitness, RSA, and neuromuscular performance of young male futsal players. A second aim of this study was to examine the dose-response relationships between accumulated TL and changes in physical and physiological measures. Based on previous studies (_{86}) would induce superior improvements on the selected physiological and physical measures than the model with less COD and shorter set duration performed at a higher intensity (HIIT_{100}).

The inclusion criteria for the study were regular participation in, at least, 75% of the training sessions during the period of investigation, not suffering from injuries during the same period, and not taking any medication that could alter the outcome of this study. Eleven young male futsal players (mean ± standard deviation; age: 18.5 ± 1.1 years; body mass: 70.5 ± 5.7 kg; height: 1.78 ± 0.07 m) from the U-20 professional futsal team of the first division of Paraná state – Brazil took part in this study. None of the players suffered any injury during the study period and all of them attended more than 75% of the training sessions during the 10 weeks of training. All players and their guardians were informed about the procedures of the study and signed an informed consent form. This study was approved by the local research ethics committee (n° 93777318.0.0000.0121) in accordance with current national and international laws and regulations governing the use of human subjects (Declaration of Helsinki II).

A parallel 2-group longitudinal experimental study design was performed during 10 weeks from February to April of 2019 (5 weeks of pre-season and 5 weeks during the early in-season). During the study period, players were monitored over 105 training sessions, which were distributed into 16 sessions devoted to HIIT models (8 sessions for each group: HIIT_{100} and HIIT_{86}) experimentally implemented for the purposes of this study, 23 sessions to develop strength-power characteristics, 56 sessions dedicated to futsal-specific technical-tactical skills, and 10 matches (3 friendly and 7 official matches;

Experimental design with the number of each training/match session type in each week

During the intervention period, two different shuttle-run HIIT models were applied based on the individual PS_{FIET} of each player, and both training models were performed once a week. Due to the team’s training schedule, the HIIT sessions started only in the 2nd week and ended in the 9th week of training (last week before the 10th congested week: 2 matches within a 7-day period). All HIIT sessions were carried out before the tactical-technical sessions in the morning period. The HIIT_{86} model consisted of 4 sets of 4-min bouts performed at 86% of PS_{FIET} with 3 min of passive recovery between the sets, whereas the HIIT_{100%} model was composed of 8 sets of 60 s bouts at 100% of PS_{FIET} with 45 s of passive recovery between the sets. Each bout was characterized by 15 s of running effort followed by 15 s of passive rest. Thus, players performed 8 and 2 repetitions of 15 s shuttle runs (with a COD every 3.75 s) during each set of the HIIT_{86%} and HIIT_{100%} models, respectively (_{86%} and HIIT_{100%} models, respectively. The average running pace performed by the athletes between the start and return lines for each training model was dictated by a prerecorded audio cue, emitting beeps every 3.75 s (Speaker, Satellite, Taiwan). The distance covered by each athlete during the training sessions was individualized according to their respective PS_{FIET}.

A progressive incremental exercise test was performed on a motorized treadmill (Imbramed ATL, Porto Alegre, Brazil). During the test the treadmill inclination was set at a 1% gradient with an initial speed of 9.0 km/h and then the treadmill speed was increased by 1.0 km/h every minute until volitional exhaustion (_{TREADMILL}) was calculated according to procedures described elsewhere (_{2}peak), and first and second ventilatory threshold (VT_{1} and VT_{2}, respectively) determination, all gas exchange data were filtered using K5 software (Omnia; COSMED, Rome, Italy) to discard outlier points. Subsequently, the data were reduced to means of 15 s for further analysis. The highest 15 s value of oxygen uptake (VO_{2}) was considered as VO_{2}peak. For VT_{1} and VT_{2} determination the ventilation/oxygen uptake (VE/VO_{2}) and ventilation/carbon dioxide production (VE/VCO_{2}) equivalents were used. The first abrupt increase in VE/VO2 without a concomitant increase in VE/VCO2 was considered the VT_{1} (_{2} (

The FIET consisted of shuttle-run bouts of 45 m (i.e., 3 × 15 m) performed at progressive speeds until voluntary exhaustion (_{FIET}) reached at the end of the test by the athletes was reported as the performance criterion for the FIET.

Vertical jump height (cm) was determined using the counter movement jump (CMJ) and the squat jump (SJ). In the CMJ, the participants were instructed to execute a downward movement followed by a complete extension of the legs and were free to determine the countermovement amplitude to avoid changes in jumping coordination. In the SJ, the participants were required to remain in a static position with a 90° knee flexion angle for 3 s before jumping, without any preparatory movements. The CMJ and SJ were executed with the hands fixed on the hips. All jumps were performed on a contact platform (CEFISE, Brazil). A total of 3 attempts were allowed for each jump with a 45 s rest interval between attempts. The best CMJ and SJ attempts were used for further analysis (

All players performed three maximal 15 m sprints with at least 2 min of passive rest between the three trials (

The 40-m RSA test consisted of 8 × 40 m sprints separated by 20 s of passive recovery (_{BEST}) and mean sprint times (RSA_{MEAN}) were recorded as the performance indices.

The resting HRV was obtained by time elapsed between two successive R-waves of the QRS signal of the heart rate (R-R intervals) using an RS800cx (Polar Electro, Finland) heart rate monitor. The resting HRV was recorded on Monday mornings at 7:00 a.m., before and after the 10-week period (

The internal TL was measured using the s-RPE method (

The analysis was performed using established Bayesian inference methods. The physiological and performance data were analyzed as percentage deltas of pre-measure (Δ% = ((_{100} and HIIT_{86}), and the baseline measures centered to the mean of all study subjects included as fixed effects. Additionally, a dose-response analysis was used to verify the relationship of responses to training with the TL and HRV measures. Thus, the training models, delta in the HRV or training load measure, and interaction between training models × HRV/training load were inserted in the model as fixed effects. The Bayesian R^{2} was calculated as an estimate of the proportion of variance explained for new data (

Inferences about the effects were made by interpreting the 90% CI in relation to the region of practical equivalence (ROPE). We specified our ROPE as 0.2 × between-subjects SD (_{2}peak, PS_{FIET}, PS_{TREADMILL}, VT_{2}, VT_{1}, RSA_{BEST}, RSA_{MEAN}, sprint 15-m, CMJ, SJ, and HRV are ± 1.8%, ± 0.9%, ± 1.0%, ± 2.0%, ± 2.3%, ± 0.5%, ± 0.7%, ± 0.9%, ± 1.7%, ± 2.1%, and ± 6.1 ms, respectively. Therefore, an effect was deemed “trivial” when the two bounds of the 90% CI were within the ROPE. Conversely, when the CI overlapped the ROPE the effects were interpreted as “undecided” (_{MEAN}, RSA_{BEST}, and Sprint 15-m where negative and positive effects were “beneficial” and “harmful”, respectively. Additionally, based on the posterior distributions, we calculated the probability (%) of the effect to be harmful/trivial/beneficial. Statistical analyses were performed using statistical software R (v4.0;

Descriptive statistics of observed data (mean ± standard deviation [range]) for aerobic, RSA, sprint, and vertical jump performances before (pre-) and after (post-training) the training period (10 weeks) in each HIIT model are presented in

Observed means ± SD (minimum – maximum) of physiological, and performance parameters of futsal players in each HIIT model pre and post ten weeks of training and the changes.

_{100} ( |
_{86} ( |
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VO_{2}peak (mL/kg/min) |
56.5 ± 5.2 (50.7–62.2) | 59.9 ± 4.1 (54.9–64.4) | 3.5 ± 1.4 (1.6–4.6) | 63.4 ± 3.5 (57.3–66.7) | 66.7 ± 2.6 (63.2–70.0) | 3.3 ± 1.4 (2.0–5.9) |

PS_{FIET} (km/h) |
15.7 ± 0.5 (14.8–16.2) | 16.4 ± 0.8 (15.4–17.2) | 0.8 ± 0.6 (0.2–1.6) | 16.5 ± 0.7 (15.6–17.4) | 17.0 ± 0.6 (16.0–17.8) | 0.5 ± 0.7 (0.00–1.8) |

PS_{TREADMILL} (km/h) |
16.8 ± 1.4 (15.2–18.0) | 17.8 ± 1.0 (16.6–19.1) | 1.0 ± 0.7 (0.1–1.8) | 17.3 ± 1.5 (15.6–19.1) | 18.9 ± 0.3 (18.5–19.3) | 1.6 ± 1.2 (0.0–3.1) |

VT_{2} (km/h) |
14.8 ± 1.6 (13.0–16.0) | 15.0 ± 1.7 (13.0–16.0) | 0.2 ± 0.8 (−1.0–1.0) | 15.4 ± 1.7 (14.0–18.0) | 16.8 ± 0.8 (13.0–16.0) | 1.3 ± 0.8 (0.0–2.0) |

VT_{1} (km/h) |
11.5 ± 1.1 (10.0–13.0) | 11.7 ± 1.6 (10.0–13.0) | 0.2 ± 1.3 (−2.0–1.0) | 11.8 ± 1.7 (11.0–15.0) | 13.3 ± 1.1 (12.0–15.0) | 1.5 ± 1.0 (0.0–3.0) |

RSA_{BEST} (s) |
8.12 ± 0.20 (7.98–8.47) | 8.13 ± 0.22 (7.94–8.50) | 0.01 ± 0.07 (−0.11–0.05) | 8.28 ± 0.24 (7.86–8.56) | 8.06 ± 0.37 (7.47–8.56) | −0.22 ± 0.14 (−0.39–0.00) |

RSA_{MEAN} (s) |
8.69 ± 0.36 (8.33–9.24) | 8.43 ± 0.31 (8.13–8.87) | −0.26 ± 0.10 (−0.37–−0.12) | 8.50 ± 0.18 (8.20–8.71) | 8.25 ± 0.31 (7.81–8.64) | −0.26 ± 0.20 (−0.44–0.09) |

Sprint 15-m (s) | 2.50 ± 0.13 (2.35–2.69) | 2.42 ± 0.06 (2.32–2.47) | −0.08 ± 0.11 (−0.27–0.00) | 2.43 ± 0.08 (2.33–2.53) | 2.37 ± 0.09 (2.29–2.54) | −0.05 ± 0.08 (−0.17–0.05) |

CMJ (cm) | 32.7 ± 1.5 (31.3–34.3) | 35.5 ± 1.5 (34.2–37.8) | 2.8 ± 1.9 (0.0–4.9) | 33.4 ± 3.7 (27.1–37.1) | 38.0 ± 4.7 (28.6–41.0) | 4.6 ± 2.5 (1.5–8.6) |

SJ (cm) | 31.2 ± 2.5 (27.9–33.8) | 32.4 ± 1.8 (30.5–34.6) | 1.2 ± 2.2 (−2.7–2.7) | 31.6 ± 4.2 (25.6–36.3) | 36.6 ± 4.1 (29.3–40.1) | 5.0 ± 4.3 (−1.2–11.7) |

HRV (ms) | 61.0 ± 21.4 (34.4–80.6) | 104.4 ± 20.8 (87.1–137.7) | 43.4 ± 40.1 (4.4–103.3) | 70.5 ± 38.2 (17.2–120.3) | 109.2 ± 32.9 (54.6–150.0) | 38.7 ± 25.4 (11.2–86.1) |

_{2}peak, peak oxygen uptake; PS

_{FIET}, peak speed of FIET test; PS

_{TREADMILL}, peak speed of treadmill incremental test; VT

_{2}, second ventilatory threshold; VT

_{1}, first ventilatory threshold; RSA

_{BEST}, best sprint time in the RSA test; RSA

_{MEAN}, mean sprint time in the RSA test; Sprint 15-m, best time in 15 meters; CMJ, counter movement jump; SJ, squat jump; HRV, heart rate variability; Δ, change pre to post.

Changes for the parameters determined during the FIET and treadmill incremental tests are displayed in _{100} model showed clear beneficial effects (i.e., the full 90% CI boundaries out of ROPE) for VO_{2}peak and PS_{TREADMILL} measurements. For the HIIT_{86} model, clear beneficial changes occurred in the VO_{2}peak, PS_{TREADMILL}, VT_{2}, and VT_{1}. For the HIIT_{100} model, VT_{2} and VT_{1} changes were well supported within the ROPE, nevertheless, these effects are inconclusive because they overlapped the lower and upper ROPE boundaries. For PS_{FIET}, both HIIT models presented no clear improvements, although the probabilities were high for improvement (> 89%), low for trivial (< 8%), and negligible for impairment (< 2.6%). Between HIIT models comparisons showed more favorable probabilities in favor of HIIT_{86} than HIIT_{100} in all parameters; however, clearly more favorable changes for the HIIT_{86} model compared to the HIIT_{100} model were observed only for PS_{TREADMILL}, VT_{2}, and VT_{1} (probabilities > 96%).

Posterior density distributions and the respective means with 90% credible intervals of the pre to post changes in VO_{2}peak, PS_{FIET}, PS_{TREADMILL}, VT_{2}, and VT_{1} (upper to lower panels, respectively) in each HIIT model, and the comparison of changes between models. The effects are adjusted to baseline mean of all study subjects. Black points and error bars are the posterior mean change and 90% credible intervals, respectively. Vertical dashed lines are the lower and upper boundaries of the ROPE (i.e., 0.2× between subjects SD). The texts in each graph are the means (90% CI) and probabilities against ROPE.

Pre to post changes for RSA_{MEAN}, RSA_{BEST}, 15-m sprint, CMJ, and SJ measures are summarized in _{100} model showed clear beneficial effects for RSA_{MEAN} and CMJ measurements, considerable probability of improvement for 15-m sprint time (84%), and inconclusive effects for RSA_{BEST} and SJ. The HIIT_{86} model showed clear beneficial effects for all anaerobic running measurements, except the 15-m sprint time, where a high probability of improvement (92.1%), small/moderate of being trivial (7.1%), and negligible of being harmful (0.8%) were observed. Between HIIT models comparisons showed clearly more favorable changes for the HIIT_{86} than HIIT_{100} model in the RSA_{BEST} and SJ measures (probabilities > 96%). All other effects between model were deemed inconclusive.

Posterior density distributions and the respective means with 90% credible intervals of the pre to post changes in RSA_{BEST}, RSA_{MEAN}, Sprint 15-m, CMJ, and SJ (upper to lower panels, respectively) in each HIIT model, and the comparison of changes between models. The effects are adjusted to baseline mean of all study subjects. Black points and error bars are the posterior mean change and 90% credible intervals, respectively. Vertical dashed lines are the lower and upper boundaries of the ROPE (i.e., 0.2× between subjects SD). The texts in each graph are the means (90% CI) and probabilities against ROPE.

Pre to post change in the HRV revealed clear beneficial changes in both training models, with probabilities > 98.1% (

Posterior density distributions and the respective means with 90% credible intervals of the pre to post changes in HRV in each HIIT model, and the comparison of changes between models. Black points and error bars are the posterior mean change and 90% credible intervals, respectively. Vertical dashed lines are the lower and upper boundaries of the ROPE (i.e., 0.2× between subjects SD). The texts in each graph are the means (90% CI) and probabilities against ROPE.

The total weekly TLs for both HIIT_{86} (black bars) and HIIT_{100} (gray bars) models during each training week are presented in ^{∗}) are presented in _{86} and HIIT_{100} models, with a probability of 55.9% of the TL being lesser in the HIIT_{86} than HIIT_{100}. The total accumulated TL of all training sessions and matches without HIIT was lesser in the HIIT_{86} than HIIT_{100} (−2671 [90% CI; −4137, 9004] a.u.), with a probability of TL being lesser in the HIIT_{86} of 76.5%. Contrarily, as expected, the total accumulated TL over the 8 HIIT sessions was two-fold higher in the HIIT_{86} than the HIIT_{100} model (difference: 778 [90% CI; 609, 941] a.u.), with 100% probability of being higher in the HIIT_{86} model.

Observed means ± SD of training load sum in each week

Since the players followed the same training routine, with the exception of the HIIT sessions, the dose-response relationship between RPE-based TL and Δ rMSSD with changes in aerobic, RSA, 15-m sprint, and jump performances was carried out, adding HIIT models as a covariate in the final model. The regression outputs between total weekly TL (10-week average) and ΔrMSSD in addition to HIIT type with changes in performance measures are displayed in ^{2}) in aerobic fitness, RSA, and power-speed-related performance changes. The explained variance derived from regression models using ΔrMSSD and HIIT type as covariates ranged from 26 to 72%.

Posterior regression medians with 90% credible intervals between pre-to-post changes in VO_{2}peak _{FIET} _{2} _{1} _{TREADMILL} _{BEST} _{MEAN} ^{2}, Bayesian variance explained. Horizontal dashed lines are the lower and upper boundaries of the ROPE (i.e., 0.2× between subjects SD).

Posterior regression medians with 90% credible intervals between pre-to-post changes in VO_{2}peak _{FIET} _{2} _{1} _{TREADMILL} _{BEST} _{MEAN} ^{2}, Bayesian variance explained. Horizontal dashed lines are the lower and upper boundaries of the ROPE (i.e., 0.2× between subjects SD).

The current study aimed to compare the effects of two shuttle run-based HIIT models of varying intensity and total work duration (HITT_{86}: 16 min vs. HITT_{100}: 8 min) on aerobic, HRV, RSA, and neuromuscular performance outcomes in junior male futsal players. The dose-response relationship between RPE-based TL and changes in performance was also examined. The main findings of this study showed that after 10-weeks of futsal training: (i) the HIIT_{86} model was clearly more effective at improving the PS_{TREADMILL} (Δ = 5.4%), VT_{2} (Δ = 10.0%), VT_{1} (Δ = 13.0%), RSA_{BEST} time (Δ = −3.5%), and SJ height (Δ = 13.7%) than the HIIT_{100}; (ii) RPE-based TL in association with HIIT type explained 71% to 87% of the inter-individual variation in RSA performance changes, while the explained variance for the other parameters was smaller (25–59%); and (iii) changes in HRV along with HIIT type accounted for 72% of inter-individual variance in VO_{2}peak changes following the training period.

Comparative studies examining the effectiveness of different training models are increasingly needed and recommended to help guide decision-making of strength and conditioning coaches during the planning of training programs with futsal players (_{86} model clearly improved (i.e., full 90% CI out of ROPE) the majority of the physical and physiological parameters measured (9 out of 11 parameters; _{100} model (5 out of 11 parameters; _{86} model induced larger improvements in aerobic, RSA, and neuromuscular performance outcomes than the HIIT_{100} model. The results presented herein suggest that the HIIT_{86} training, comprising longer sets at a lower intensity, was more effective to enhance performance than the HITT_{100} composed of shorter sets and more intense running efforts. Similarly, _{IFT}) in male adolescent handball players.

It is well known and accepted that aerobic fitness and RSA performance are two discriminant physical qualities of the competitive level in futsal (_{86} over HIIT_{100} at improving aerobic fitness, RSA, and vertical jump performance suggest that this training type (submaximal runs at 86% PS_{FIET} and longer sets) should be preferentially used with young futsal players. The specific adaptations in physical performance following the HIIT_{86} and HITT_{100} models could potentially be related to differences in total work duration between the models (_{86} implies a greater number of COD performed in a single session (96 vs. 48 turns), increasing the total time that athletes spent accelerating per running bout compared to the HIIT_{100} model. ^{–2}) are linearly related during shuttle run drills. Prior research has indicated that accumulated individual acceleration load is positively associated with changes in aerobic fitness and neuromuscular measures in professional soccer players (_{2}peak > 55 mL/kg/min at baseline). Previous studies published on this research topic did not show any further performance gain after different shuttle run training models varying the number of COD required per running bout in male soccer and basketball players (_{86} model used here was also applied in a sample of female futsal players (VO_{2}peak: 47–49 mL/kg/min at baseline) (_{TREADMILL} and RSA performance after HIIT_{86} model. This demonstrates the consistency and effectiveness of this training model (HIIT_{86}) to improve these physical qualities in age-matched male and female futsal players. At the same time, male and female futsal athletes of similar ages can display distinct neuromuscular performance adaptations (i.e., changes in SJ and CMJ height) following HIIT_{86}, with male athletes in the current study being more responsiveness (Δ = 13–17%) than female athletes (Δ = 8–9%) in the study of _{86} model in these other sports scenarios.

From the present results on RPE-based TL and changes in physical performance, it is possible to make inferences about the dose-response relationship during the training process. The present study suggests that TL in addition to training type (HIIT_{86} and HIIT_{100}) accounted for a large portion of the inter-individual variance in RSA (71–87%), 15-m Sprint (46%), and vertical jump (40–47%) performance changes. A positive linear dose-response relationship between TL and changes in these performance measures was found for the HIIT_{86} model, while no (RSA performance) or negative (sprint and vertical jump) relationships were identified for the HIIT_{100} model. These findings are of practical relevance for practitioners and coaches. First, players in both HIIT_{86} and HIIT_{100} models with a similar total TL displayed a distinct adaptive response (especially for RSA_{BEST} and SJ performance), highlighting that the quality/specificity of the training stimuli is the most relevant component of the training process (_{86} model (longer sets) may have been decisive to induce superior gains in performance. Second, players who accumulated higher training loads in the HIIT_{86} model demonstrated the largest improvements in RSA, 15-m Sprint, and vertical jump performance, while the opposite was observed for the HIIT_{100} model. Although these results cannot be easily explained from the data analysis employed in our study, it is possible to suggest that the training loads derived from other training strategies (technical-tactical, strength-power, friendly and official matches) may have influenced the players’ adaptive response to training. For instance, players in the HIIT_{100} model tended to accumulate a higher (with a 73% probability) TL derived from these other training contents (i.e., not involving HIIT sessions) than the HIIT_{86} model (

The explained variances derived from regression models (TL and HIIT type inserted as covariates) for the changes in maximal (VO_{2}peak, PS_{FIET}, PS_{TREADMILL}) and submaximal (VT_{2} and VT_{1}) aerobic performance measures were considered low (25–28%) and moderate (45–59%), respectively. Contrary to what was observed in the anaerobic and jump performance measures, the dose-response relationship for aerobic performance outcomes did not show a distinct pattern between HIIT models (i.e., regression slopes in contrary directions). Of note, positive and negative/null relationships were observed between accumulated TL and changes in maximal (VO_{2}peak and PS_{TREADMILL}) and submaximal (VT_{2} and VT_{1}) aerobic indices, respectively. Several studies have demonstrated a linear dose-response relationship between RPE-based TL and changes in aerobic performance indicators in team sports athletes (_{TREADMILL} changes (Bayesian ^{2} values consider both the relationship of the response to the TL and the HIIT model. Therefore, comparisons with the ^{2} from other studies should be performed with caution to avoid misinterpretations, since they only used the TL as a covariate.

An interesting finding from our study to be highlighted was that similar improvements in the resting HRV were noticed after both HIIT models (HIIT_{86} and HIIT_{100}) outlined here using PS_{FIET} as the reference speed to calibrate running distance. Although improved resting HRV after a period of futsal training has been previously documented in the literature (_{2}peak after the training period, suggesting that players in both HIIT models with greater increases in resting rMSSD demonstrated the largest increments in VO_{2}peak. Of interest, changes in resting HRV and HIIT types accounted for 72% of the inter-individual variance in VO_{2}peak changes in our sample. Another two studies performed with futsal players also showed that an enhanced vagal modulation (inferred by an increase in resting rMSSD value) was largely positively correlated (_{FIET} (39%) and ΔPS_{TREADMILL} (47%) (

One of the strengths of this study was to show how different HIIT models associated with other training components can influence the adaptive responses of players from the same futsal team. In this scenario, where few traditional HIIT sessions are planned by the team technical staff due to the matches schedule and the importance given to technical-tactical and strength/injury prevention sessions, the selection of training stimuli in HIIT sessions is key to maximizing subsequent performance adaptations. From a practical perspective, strength and conditioning specialists should consider spending more time in less intense shuttle run HIIT sessions with more COD than in more intense and shorter sessions with fewer directional changes.

The main limitation of the present study was the small sample size (

This study showed that those players who underwent 8 shuttle run HIIT sessions at 86% PS_{FIET} had superior gains in aerobic, RSA, and neuromuscular performance measures than those who trained at 100% PS_{FIET} during a typical 10-week training period. In addition, the variance explained by the TL along with the HIIT type was clearly larger for the changes in RSA performance outcomes than that observed for aerobic and neuromuscular performance changes. Finally, monitoring resting HRV could be a suitable tool to track changes in VO_{2}peak, since temporal alterations in HRV are strongly related to VO_{2}peak changes.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by this study was approved by the local research ethics committee (n° 93777318.0.0000.0121). Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

FC participated in the design of the study, data collection, data organization, and drafted the manuscript. FB participated in the data organization, and performed the statistical analysis, interpretation, and discussion of results. LF and LB contributed with support of materials and data collection. AT participated in the design of the study and interpretation and discussion of results. RHN contributed to the design of the study and interpretation of results. LG contributed to the design of the study, interpretation and discussion of results, and coordination of project. All authors contributed to the writing and approved the final version.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

We would like to express our gratitude to all colleagues who contributed and to the subjects in this study and the team staff.