On Your Mark, Get Set, Choose! A Randomized Cross-Over Study Comparing Fixed and Self-Selected Rest Periods in Interval Running Among Professional Female Soccer Players.

On Your Mark, Get Set, Choose! 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A Randomized Cross-Over Study Comparing Fixed and Self-Selected Rest Periods in Interval Running Among Professional Female Soccer Players. Asaf Ben-Ari, Yedidya Silverman, Uri Obolski, Israel Halperin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4528664/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jan, 2025 Read the published version in Sports Medicine-Open → Version 1 posted 5 You are reading this latest preprint version Abstract Background Studies on rest durations during high-intensity interval training (HIIT) often compare fixed and self-selected (SS) rest allocation approaches. Frequently, the rest duration under SS conditions is unlimited, leading to inconsistent total rest durations compared to fixed rest conditions. To address this limitation, we recently compared fixed and SS rest conditions during cycling HIIT sessions, while keeping the total rest time equivalent. However, our protocol required athletes to divide a long total rest time (12 minutes) across nine intervals, which may have been overly cognitively demanding. In the current study, we simplified the athletes’ rest allocation task by reducing the number of rest periods available. Methods Following a familiarization session, 24 professional female soccer players completed two running HIIT sessions on a non-motorized treadmill. Each session consisted of twelve 15-second intervals, divided into three blocks, with the goal of maximizing the distance covered. In both conditions, the between-interval rest duration per block amounted to 270 seconds. In the fixed condition, the rest was uniformly allocated to 90 seconds between each interval, whereas in the SS condition, the athletes chose how to allocate the entirety of the 270 seconds of rest. We compared the following outcomes: distance, heart-rate, perception of fatigue, effort, autonomy, enjoyment, boredom, and athletes’ preferences. Outcomes were compared using aggregated measures via paired univariate tests, and across the intervals via mixed-effects models. Results We observed comparable results in most outcomes with the exception of higher autonomy in the SS condition (mean difference = 2.1, 95%CI (0.9, 3.3)) and a negligibly higher heart-rate when comparing the observations across intervals (estimate = 2.5, 95%CI (0.9, 4.2)). Additionally, participants chose to rest for longer durations as the block progressed. Finally, most participants (65%) favored the SS condition. Conclusion This study further solidifies that SS and fixed approaches with matched total rest durations result in similar performance, physiological, and psychological responses. This effect persists even when the total rest duration required to be allocated is relatively short. Therefore, coaches and trainees can choose either approach based on their preferences and training goals. Autonomy self-selected rest HIIT soccer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Key Points Allowing athletes unlimited self-selected rests in HIIT impairs control over training stimuli, whereas controlling the total rest duration available to select from can retain such control. Here, we compared fixed-rest to self-selected-rest HIIT sessions with matched total rest duration using a non-motorized treadmill. Outcomes in both conditions were similar, implying either method can be adopted depending on training objectives and athletes’ preferences. Background Soccer players require a highly developed aerobic capacity [ 1 , 2 ]. Various training methods have been developed to help players build and maintain this capacity, with high-intensity interval training (HIIT) being particularly effective and time-efficient [ 3 – 5 ]. Numerous training types fit the HIIT definition, all sharing a similar structure: high-intensity work bouts interspersed with rest periods [ 6 , 7 ]. In HIIT, the ratio between work and rest determines the overall training effect [ 8 – 10 ]. While rest periods alleviate fatigue and enable subsequent efforts, they simultaneously decrease the aerobic stimulus of the session [ 9 ]. Therefore, selecting the appropriate rest periods duration in HIIT sessions is crucial for personalizing training and optimizing training adaptations. The most common approach to prescribing rest periods in HIIT involves fixed, predetermined durations with work-to-rest ratios ranging from 1:0.5 to 1:20 [ 7 , 11 ]. While this method is convenient and efficient [ 3 , 12 ], it does not account for individual physiological and psychological differences. For example, differences in fatigue accumulation, which in turn likely result in different durations required to recover [ 13 ]. An alternative is the self-selected (SS) approach, where athletes determine the duration of their rest periods. The SS approach offers several unique benefits. First, the mere act of choosing can enhance motivation [ 14 ], enjoyment [ 15 ], and perception of autonomy [ 16 ]. Second, the SS method may better accommodate individual differences by allowing athletes to tailor rest durations to their current physiological and psychological readiness and anticipated performance [ 17 ]. Finally, the SS approach challenges and may develop athletes' decision-making processes. Studies comparing fixed and SS rest durations during HIIT sessions have presented mixed results [ 17 – 24 ]. Some have reported enhanced performance and psychological outcomes with SS rest periods [ 18 – 20 ], while others have shown the opposite [ 22 , 23 ]. The significant variability in prescribed HIIT protocols (e.g., 4x4 minutes [ 20 ] vs. 12x30 meters [ 18 ]) and the length of rest periods in the fixed condition (e.g., 3 minutes [ 24 ] vs. 30 seconds [ 19 ]) may have contributed to these inconsistent results. However, these studies share a major limitation – the total rest duration between fixed and SS conditions was not matched. In the SS condition, athletes selected their rest periods with no lower or upper limit, resulting in different total rest durations than those in the fixed conditions and, consequently, different training stimuli. To overcome the latter limitation, we recently conducted a study employing a novel approach [ 16 ]. In that study, 24 male amateur cyclists performed two HIIT sessions in a crossover design consisting of nine 30-second cycling intervals. Under the fixed condition, cyclists rested for 90 seconds between intervals, thus accumulating 720 seconds of rest ( 8 × 90 sec = 720 sec ). Under the SS condition, cyclists self-selected their rest durations out of the 720 seconds provided to them, which had to be fully utilized by the final rest period. All performance, physiological, and psychological outcomes were similar except for greater perceived autonomy in the SS condition. Furthermore, cyclists in the SS condition chose shorter rest durations in the first half of the session and considerably longer durations in the second half. We speculated that participants found it challenging to effectively distribute the 720-second total rest duration across a relatively high number of intervals. The present study aimed to extend this line of inquiry with three modifications. First, we simplified the task of rest time allocation to streamline the decision-making process for participants. Second, we tested the SS approach among female soccer players to expand our understanding of its applicability across additional populations. Lastly, we implemented the SS approach in a running HIIT protocol to align with training modalities commonly used in soccer. We hypothesized different performance and psychological outcomes between conditions. Methods Participants We recruited 24 professional female soccer players from the Israeli women's soccer first league. Inclusion criteria included healthy participants, aged between 16 and 45, with at least one year of professional-level soccer playing experience. Exclusion criteria included acute injury in the past two weeks, pregnancy, or being fewer than six months after childbirth. Table 1 summarizes the participants’ characteristics, including training history and weekly training volume. Recruitment was done through advertisements on various social media channels and by contacting teams from the Israeli women's soccer first league. Table 1 Participants characteristics. Characteristic N = 24 1 Age [yrs] 22.0 (3.8) Height [cm] 163.4 (6.5) Weight [kg] 59.5 (5.9) Fat [%] 22.2 (4.8) Experience [yrs] 4.9 (2.9) Soccer training [sessions per week] 4.9 (1.1) 1 All values are presented as mean (SD) Procedures We implemented a within-participant, randomly assigned crossover design. All participants attended three laboratory sessions: a familiarization session and two experimental sessions. Given that menstruation may influence perceived performance [ 25 ], to minimize potential confounding factors, we asked participants about their menstrual phase (“When was the first day of your last menstruation?”) and avoided scheduling any of the sessions during their menstruation phase. In addition, given the variability in how different menstrual cycle phases affect athletes’ performance and perceived performance [ 25 , 26 ], we rescheduled sessions if a participant reported any menstrual cycle-related symptoms. The HIIT protocol in the two experimental sessions consisted of twelve 15-second intervals performed on a non-motorized treadmill (Woodway© Curve 3.0 Treadmill, Waukesha, United States). The twelve intervals were divided into three blocks of four intervals, with two minutes of rest between blocks, to simplify the task of time allocation under the SS condition. The two sessions only differed in the rest durations between intervals. Under the fixed condition, participants rested for 90 seconds between intervals, totaling 270 sec of rest per block. Under the SS condition, participants selected how long they would rest between intervals. However, we matched the total rest duration between conditions, meaning that participants had to fully utilize 270 sec of rest over the three rest periods of each block (Fig. 1 shows a diagram of the session’s protocol). At the beginning of each session, we told participants their goal was to cover as much distance as possible across all intervals. Under the SS condition, we added that they should allocate their rest durations with this goal in mind. In both sessions, participants were allowed to choose whether to stand or walk during the rest periods except for the five seconds before the subsequent interval in which they were asked to stand still. We provided participants with feedback regarding the remaining rest time using a screen with a timer next to the treadmill. In the fixed-rest condition, the timer counted down from 90 seconds for each between-interval rest period. In the SS condition, the timer counted down from 270 sec for each block (indicating the total rest time still available to them). Once an interval was completed, the countdown started. When participants announced they were ready to start the next interval, the researcher began a 5-second verbal countdown, after which the interval would commence, and the timer was paused. Figure 2 shows the experimental setup. All sessions began with the same general ~ 8-minute warm-up used in our laboratory [ 27 ]. After this warm-up, participants performed a baseline countermovement jump (CMJ) test. Next, participants performed an exercise-specific warm-up consisting of three minutes of easy steady-state running followed by five 10-second intervals at a gradually increasing speed. The intervals' speed corresponded to 40%, 60%, 80%, and two 100% of the participants' perceived maximal speed, interspersed with rest periods of 1–2 minutes. Two minutes after completing the exercise-specific warm-up, the first interval of the protocol began. Throughout the sessions, we recorded participants’ heart rate (HR). Two minutes after completing the HIIT protocol, participants completed another CMJ test to assess protocol-induced neuromuscular fatigue. Participants also reported their rating of perceived fatigue (ROF) at the beginning of the session and following the second CMJ test completion to assess protocol-induced perceived fatigue. In addition, participants reported their ratings of perceived effort (RPE) after each interval, as well as perceived autonomy and enjoyment after each session. Finally, 24 hours after the final session, we asked participants about their condition preferences. Familiarization (session 1) The session aimed to familiarize participants with running on the curved non-motorized treadmill, the HIIT protocol, experimental conditions, and outcomes. We told participants that the study aimed to assess a new running HIIT protocol. Following the explanations, anthropometric measurements, and warm-up, participants completed a partial protocol composed of four intervals (one block) per condition. Specifically, participants performed four intervals under the fixed-rest condition with 90 seconds of rest between each interval, rested for two minutes, and performed four intervals under the SS rest condition, in which they selected how long to rest between intervals (provided with 270 sec that they were required to utilize fully). Experimental sessions (sessions 2–3) We briefly reviewed the protocol’s goals, and how to rate effort and fatigue using the different questionnaires. Following the warm-up, participants completed the entire protocol composed of twelve 15-second intervals, divided into three blocks of 4 intervals. The procedure was comparable to the familiarization session with two differences: participants only completed one of the conditions at that time (randomly assigned to begin with either SS or fixed), and the protocol consisted of three 4-intervals blocks (twelve intervals in total). Outcome Measures Anthropometric Measurements In the familiarization session, we measured participants' weight, height, and fat-free mass (SECA, Hamburg, Germany). Participants were requested to refrain from meal and caffeine consumption at least 4 hours before each session and to relieve themselves in the bathroom before the measurement. Performance Measures Distance: We measured the distance covered while running each interval as recorded by the treadmill’s proprietary software at a sample rate of 200 Hz (Curve 3.0 Pacer Performance System, version 2013.1.1). The treadmill's display screen was covered throughout all sessions, leaving participants blinded to the running velocity, distance covered, and HR data. Participants were not allowed to hold the treadmill's handrails except when they finished an interval, during which they used them to jump to the sides, straddling the running surface. When processing the treadmill’s data output, we differentiated intervals from rest periods in the following manner: The beginning of an interval was identified by the point where velocity rose from 0 (representing when participants stood still five seconds before each interval); the end of an interval was identified by the point where vertical forces dropped to 0 (representing when participants jumped off the running surface at the end of each interval). CMJ: We measured CMJ performance using a pair of portable force plates (Deltas, Kinvent, Montpellier, France). Participants stood on the force plate, squatted down to a self-selected depth (countermovement), and jumped as high as possible while keeping their hands on their waist. Participants performed three jumps with 45 seconds of passive rest in between. The average height (determined from take-off velocity), net impulse, and flight time of the three jumps were used to analyze and assess protocol-induced neuromuscular fatigue as recommended by others [ 28 , 29 ]. Physiological Measures We measured participants’ HR throughout each experimental session using a chest strap monitor (Polar Electro H10, Kempele, Finland). To fully capture an interval’s effect on HR, given the intervals’ relatively short duration and HR’s delayed response to a change in exercise intensity, we defined an HR interval from when participants started running an interval up to the start point of the subsequent interval. For each HR interval, we recorded the peak HR. Psychological Measures RPE: Immediately after each interval, we asked participants to report their RPE (“How much effort did you exert?”) using a 0 (‘no effort’) to 10 (‘maximal effort’) scale. A printed scale version was hung on the wall in front of the treadmill (Fig. 2 ). In the familiarization session, we defined effort to participants as the “investment of physical and/or mental resources to perform a task” and perceived effort as “the way you experience the investment of those physical and/or mental resources during the task” [ 30 ]. The lower and upper limits of the scale were anchored to complete rest and to running as fast as possible in a 15-second interval, respectively. ROF: Before warm-up and after completing the second CMJ test, we collected ROF using a 0 (‘not fatigued at all’) to 10 (‘total fatigue and exhaustion—nothing left’) scale following the recommendations by Micklewright et al [ 31 ]. Perception of autonomy: We collected perception of autonomy after each session using a modified version of the Intrinsic Motivation Inventory questionnaire [ 32 ], consisting of three 1–5 Likert scale questions: (1) “The way I exercised today is aligned with my choices and preference”; (2) “I feel the way I exercised today is the way I want to exercise”; (3) “I feel like I could make decisions regarding how I exercised today.” to which the answers ranged from 1 (“I totally disagree”) to 5 (“I totally agree”). Boredom: We collected level of boredom after each session using two questions based on the Bored of Sports Scale [ 33 ]: (1) “Were you bored throughout the session?” (personal boredom level) and (2) “Did you find the session boring?” (session boredom level), to which the answers ranged from 0 (“not at all”) to 100 (“very much”). Preferences: Twenty-four hours after completing the last session, we asked participants about their condition preference using an open-ended question (“Out of the two HIIT sessions that you performed, one under the fixed and the other under the SS approach, which one did you prefer?”). Supplementary File 1 includes a detailed account of verbal instructions, scale anchors, and protocols. Statistical Analysis Single-Measurement Comparisons We used paired t-tests to derive confidence intervals (CI) and p-values for the differences between the conditions in total distance, peak HR, RPE, enjoyment, autonomy, and boredom. Given our relatively modest sample size, we took precautions to validate our results further by executing a more conservative non-parametric Wilcoxon signed-rank test. Note that we summed and averaged multiple measurements over the intervals to obtain a single number for distance and RPE. Finally, in addressing participants’ preferences, we performed a single-proportion, exact binomial test. Multiple-Measurement Analysis We employed mixed-effect regression to estimate the effect of the different conditions on distance, HR, and RPE, as the outcome variables. Condition, interval number, and block number were set as categorical fixed effects, while a random intercept was included for each participant. This approach accounts for individual differences in baseline performance levels and adjusts for repeated measurements. Difference-in-Differences We used a difference-in-differences (DID) approach when analyzing the pre-and post-CMJs and ROF levels. For each measurement, we subtracted the participant’s first result (pre-session) from the second result (post-session) in each session and then used t-tests to derive CIs and p-values for the differences between the conditions. Similar to the single-measurement analysis, we validated our DID results further by executing a non-parametric Wilcoxon signed-rank test. For all statistical tests alpha was set at 0.05. Statistical analysis was performed using the R statistical computing environment (R Core Team, Vienna, Austria, version 4.4.0, 2024) via the RStudio integrated development environment for R (Posit Software, PBC, Boston, MA, version 2024.04.0.735). Graphs were made using “ggplot2” R package (version 3.5.1; Wickham 2016). Mixed-effect regression models were employed using “lmerTest” R package (version 3.1.3; Kuznetsova 2017). Results Self-Selected Rest Durations In comparison to the fixed condition’s 90-sec rests, under the SS condition, participants chose a much shorter rest after the first interval (mean ± SD block 1: 67.0 ± 13.1, block 2: 69.9 ± 12.8, block 3: 72.9 ± 15.4), a slightly shorter rest after the second interval (mean ± SD block 1: 84.3 ± 12.8, block 2: 82.8 ± 13.6, block 3: 82.7 ± 9.6) and a much longer rest after the third interval (mean ± SD block 1: 117.7 ± 17.8, block 2: 116.4 ± 18.9, block 3: 113.1 ± 20.7) (Fig. 3 ). Performance Outcomes The total distance covered (meters) in the SS session (mean ± SD: 817.2 ± 62.2) and in the fixed session (mean ± SD: 815.0 ± 56.0) were similar (mean difference (95%CI): -2.97 (-18.9, 12.96), p = 0.703) (Fig. 4 -A). In addition, the mixed effects model of the distance covered in each interval and block showed similar running distances between conditions, with a gradual increase in distance between blocks observed in both (Fig. 5 -A). That is, compared to the first interval (intercept (95%CI) = 65.34 (63.33, 67.36), p < 0.001), under both conditions, a significantly longer distance was covered in the last interval of each block (estimate (95%CI) = 3.55 (2.72, 4.37), p < 0.001) and the last block of each session (estimate (95%CI) = 2.54 (1.83, 3.26), p < 0.001). Full results of the mixed-effects model for distance are available in Supplementary File 1: Table 3. We found significant differences between the pre-post CMJs’ height, net impulse, and flight time in both conditions, indicating neuromuscular fatigue accumulation during the experimental sessions (Supplementary File 1: Table 2 ). However, the DID were small and non-significant in jump height (DID (95%CI): 0.11 (-0.53, 0.75), p = 0.723), net impulse (DID (95%CI): -0.02 (-1.92, 1.88), p = 0.981), and flight time (DID (95%CI): 0.85 (-5.77, 7.47), p = 0.792) between the SS and fixed sessions. Physiological Outcomes The peak HR in the SS session (mean ± SD: 173.3 ± 9.9) and in the fixed session (mean ± SD: 171.9 ± 8.5) were similar (mean difference (95%CI): -1.36 (-4.09, 1.37), p = 0.311) (Fig. 4 -B). However, peak HR was significantly higher in the SS condition across intervals (estimate (95%CI) = 2.55 (0.91, 4.18), p = 0.003) (Fig. 5 -B). To further examine the effect of condition on peak HR, we expanded our model by adding an interaction variable between SS condition and intervals or blocks - for which none of the estimates were statistically significant and did not change the other effects substantially. Finally, peak HR gradually increased over the intervals in both conditions. Full results of the mixed-effects model for peak HR are available in Supplementary File 1: Table 4. Psychological Outcomes The results of five psychological outcomes (RPE, autonomy, personal and session boredom, and enjoyment) are presented in Table 2 . Since RPE was measured for each interval, we also fitted a mixed-effects model, which showed a comparable RPE level between conditions (estimate (95%CI) = 0.105 (-0.02, 0.23), p = 0.01). As might be expected, given the gradually increasing distance and peak HR patterns, RPE also increased with subsequent intervals and blocks (Fig. 5 -C) (see Supplementary File 1: Table 5, for full model results). ROF significantly increased from the beginning to the end of each session. However, the DID between conditions was similar (DID (95%CI): 0.57 (-.024, 1.37), p = 0.159) (Supplementary File 1: Table 2 ). Of the three autonomy questions, the answers to the first (“The way I exercised today is aligned with my choices and preference”) and third (“I feel like I could make decisions regarding how I exercised today”) were statistically higher in the SS session, indicating higher perceived autonomy. The answers to the second question (“I feel the way I exercised today is the way I want to exercise”) were comparable between conditions (Table 2 ). No statistically significant differences were found for enjoyment, personal boredom, and session boredom. Lastly, out of 23 participants asked about their preferred session, 15 selected the SS, and 8 selected the fixed (mean proportion (95%CI): 0.65 (0.46, 1.0). p = 0.105). Table 2 Comparisons of psychological outcomes for SS and fixed conditions. Variable [range] Fixed (Mean (SD)) Self-selected (Mean (SD)) Mean difference (95%CI) 1 P (t-test) 1 P (Wilcox) 2 RPE [0–10] 8.2 (0.6) 8.3 (0.7) 0.1 (-0.1, 0.3) 0.359 0.284 Enjoyment [ 1 – 7 ] 5.0 (1.3) 5.3 (0.9) 0.2 (-0.1, 0.6) 0.110 0.120 Autonomy-Q.1 [ 1 – 5 ] 3.7 (1.3) 4.3 (0.9) 0.6 (0.2, 0.9) 0.002 * 0.006 * Autonomy-Q.2 [ 1 – 5 ] 3.8 (1.0) 3.9 (0.9) 0.1 (-0.2, 0.5) 0.503 0.499 Autonomy-Q.3 [ 1 – 5 ] 3.1 (1.7) 4.5 (0.8) 1.4 (0.6, 2.1) 0.001 * 0.003 * Autonomy-Total [ 1 – 15 ] 10.6 (3.4) 12.7 (2.1) 2.1 (0.9, 3.3) 0.002 * 0.004 * Boredom Personal [0-100] 25.9 (21.3) 20.0 (15.1) -5.9 (-13.6, 1.8) 0.127 0.117 Boredom Session [0-100] 27.7 (22.0) 21.8 (17.4) -5.9 (-13.1, 1.4) 0.107 0.141 Preference 3 8 (0.0) 15 (0.0) 0.7 (0.5, 1) 0.105 - * P-value < 0.05 1 P-values and CIs derived from paired t-tests 2 P-values derived from a non-parametric Wilcoxon signed-rank test 3 Results of single-proportion binomial test with an N = 23 Discussion We compared the effects of fixed and SS rest durations in a running HIIT protocol while matching the total rest time on performance, physiological, and psychological outcomes among 24 professional female soccer players. In the SS condition, most participants chose to gradually increase their rest durations. We found comparable results between conditions in most outcome measures: distance, effort, fatigue, enjoyment, and boredom. The exceptions were peak HR, which was slightly higher in the SS condition, and perception of autonomy, which was higher in the SS condition. Finally, most participants favored the SS condition. To the best of our knowledge, the present study is the second to compare the effects of fixed and SS rest durations in HIIT while matching for total rest duration between conditions. Our results align with the first study, by Colorni et al. [ 16 ], who observed comparable performance and psychological outcomes while perception of autonomy was enhanced in the SS condition. In the present study, we aimed to address a limitation identified by Colorni et al., where athletes were required to manage and allocate 720 seconds of rest across nine intervals. We speculated that this cognitive task might have been overly demanding. Therefore, we modified the protocol to reduce the number and length of the rest periods. Despite this adjustment, and considering the different exercise modalities and cohorts, the overall results of the two studies are highly similar. We observed a slightly higher peak heart rate in the SS condition. However, this difference was minimal and, in our opinion, unlikely to impact training outcomes. Finally, participants chose to rest for a shorter duration at the beginning of each block and for a longer duration towards its end. This pattern is similar to the one observed in Colorni et al., further establishing that participants tend to choose a strategy of initially shorter, and progressively longer, rest durations compared to the fixed condition. The implications of this study, coupled with our previous research by Colorni et al., are as follows: Given the highly similar performance, physiological, and psychological responses, coaches and trainees can choose either approach based on preferences or specific training goals. The SS approach provides athletes with flexibility in training configuration, allowing them to tailor training according to their preferences and perceived abilities. This method also enhances athletes' perception of autonomy, providing psychological benefits [ 34 – 36 ], and challenges their decision-making skills, which are critical in sports like soccer [ 37 , 38 ]. In contrast, the fixed approach provides a predetermined structure, enabling players to focus solely on the task without the cognitive load of decision-making. This method is also logistically simpler, which is beneficial for group sessions or when training space is limited. Additionally, the consistent nature of fixed training allows one to easily track and compare performance between players over time [ 39 , 40 ]. Given the merits of each approach and their comparable effects, coaches can safely expand their HIIT repertoire without any worry about negative effects on performance. This expansion is notable, as coaches understandably tend to rely on the fixed approach, as most HIIT studies and guidelines revolve around this method. By demonstrating the similarities between the two approaches, we hope that the SS approach will become more widely utilized. Several limitations should be considered when interpreting the findings of our study. First, the short duration of each running interval, set at 15 seconds, may have been insufficient to capture differences in the distance covered between the conditions. A longer interval, such as 30 seconds, may have been more suitable to capture such differences, assuming they exist. Second, while necessary for standardization, running in a laboratory setting on a non-motorized treadmill differs from the typical training environment of soccer players, who predominantly train on an outdoor field. Third, our study exclusively recruited female soccer players, which restricts the generalizability of our findings. Finally, the acute nature of our study does not allow us to make any assertions about the long-term effects of the SS training approach. Conclusion Our study adds insights into comparing fixed and SS approaches for prescribing rest periods in a running HIIT protocol. Despite a slight difference in peak HR, overall performance, physiological, and psychological outcomes remained comparable. These findings suggest that coaches and athletes can follow either approach based on training objectives and preferences. Abbreviations HIIT – High-intensity interval training SS – Self-selected CMJ – Countermovement jump HR – Heart rate ROF – Rating of fatigue RPE – Rating of perceived effort CI – Confidence interval DID – Difference in differences Declarations Ethics Approval and Consent to Participate Participants gave their voluntary written informed consent. The study was approved by the Ethics Committee of Tel-Aviv University (approval number: 0005775-2). Consent for Publication Trainee in Figure 1 gave her written consent for her image to be used in the publication of this manuscript. Availability of Data and Materials The datasets used in the current study, together with the R statistical analysis code, are available online at: https://osf.io/3gu9h/ . Competing Interests AB, YS, UO, and IH declare that they have no competing interests. Funding This study was supported by a grant from the Israeli Science Foundation (1249/20). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors’ Contribution AB, YS, UO and IH designed the study; AB collected the data; AB, UO, and IH analyzed the data; AB, YS, UO and IH wrote the manuscript. All authors have read and approved the final version of the manuscript. Acknowledgements Not applicable. References Stølen T, Chamari K, Castagna C, Wisløff U. Physiology of soccer: an update. Sports Med. 2005;35:501–36. https://doi.org/10.2165/00007256-200535060-00004 . Datson N, Hulton A, Andersson H, Lewis T, Weston M, Drust B, et al. Applied Physiology of Female Soccer: An Update. Sports Med. 2014;44:1225–40. https://doi.org/10.1007/s40279-014-0199-1 . Manuel Clemente F, Ramirez-Campillo R, Nakamura FY, Sarmento H. Effects of high-intensity interval training in men soccer player’s physical fitness: A systematic review with meta-analysis of randomized-controlled and non-controlled trials. 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A Randomized Cross-Over Trial Comparing Fixed and Self-Selected Rest Durations in High-Intensity Interval Training Cycling Sessions. Sports Med - Open. 2023;9:52. https://doi.org/10.1186/s40798-023-00601-8 . Edwards AM, Bentley MB, Mann ME, Seaholme TS. Self-pacing in interval training: a teleoanticipatory approach. Psychophysiol. 2011;48:136–41. https://doi.org/10.1111/j.1469-8986.2010.01034.x . McEwan G, Arthur R, Phillips SM, Gibson NV, Easton C. Interval running with self-selected recovery: Physiology, performance, and perception. Eur J Sport Sci. 2018;18:1058–67. https://doi.org/10.1080/17461391.2018.1472811 . Engel FA, Altmann S, Chtourou H, Woll A, Neumann R, Yona T, et al. Repeated Sprint Protocols With Standardized Versus Self-Selected Recovery Periods in Elite Youth Soccer Players: Can They Pace Themselves? A Replication Study. Pediatr Exerc Sci. 2022;34:193–201. https://doi.org/10.1123/pes.2021-0082 . Rodríguez-Barbero S, Rodrigo-Carranza V, Santos-García DJ, Ravé JMG, González-Mohíno F. Acute Effects of Long Interval Training With Varied Recovery Periods in Trained Runners. In Review; 2022. https://doi.org/10.21203/rs.3.rs-1692044/v1 . Seiler S, Hetlelid KJ. The Impact of Rest Duration on Work Intensity and RPE during Interval Training. Med Sci Sports Exerc. 2005;37:1601–7. https://doi.org/10.1249/01.mss.0000177560.18014.d8 . Gibson N, Brownstein C, Ball D, Twist C, Physiological. Perceptual and Performance Responses Associated With Self-Selected Versus Standardized Recovery Periods During a Repeated Sprint Protocol in Elite Youth Football Players: A Preliminary Study. Pediatr Exerc Sci. 2017;29:186–93. https://doi.org/10.1123/pes.2016-0130 . Brownstein CG, Ball D, Micklewright D, Gibson NV. The Effect of Maturation on Performance During Repeated Sprints With Self-Selected Versus Standardized Recovery Intervals in Youth Footballers. Pediatr Exerc Sci. 2018;30:500–5. https://doi.org/10.1123/pes.2017-0240 . Schoenmakers PPJM, Reed KE. The effects of recovery duration on physiological and perceptual responses of trained runners during four self-paced HIIT sessions. J Sci Med Sport. 2019;22:462–6. https://doi.org/10.1016/j.jsams.2018.09.230 . Carmichael MA, Thomson RL, Moran LJ, Wycherley TP. The Impact of Menstrual Cycle Phase on Athletes’ Performance: A Narrative Review. Int J Environ Res Public Health. 2021;18:1667. https://doi.org/10.3390/ijerph18041667 . Meignié A, Duclos M, Carling C, Orhant E, Provost P, Toussaint J-F, et al. The Effects of Menstrual Cycle Phase on Elite Athlete Performance: A Critical and Systematic Review. Front Physiol. 2021;12. https://doi.org/10.3389/fphys.2021.654585 . Malleron T, Har-Nir I, Vigotsky AD, Halperin I. Rating of perceived effort but relative to what? A comparison between imposed and self-selected anchors. Psychol Sport Exerc. 2023;66:102396. https://doi.org/10.1016/j.psychsport.2023.102396 . Bishop C, Jordan M, Torres-Ronda L, Loturco I, Harry J, Virgile A, et al. Selecting Metrics That Matter: Comparing the Use of the Countermovement Jump for Performance Profiling, Neuromuscular Fatigue Monitoring, and Injury Rehabilitation Testing. Strength Cond J. 2023;45:545. https://doi.org/10.1519/SSC.0000000000000772 . Claudino JG, Cronin J, Mezêncio B, McMaster DT, McGuigan M, Tricoli V, et al. The countermovement jump to monitor neuromuscular status: A meta-analysis. J Sci Med Sport. 2017;20:397–402. https://doi.org/10.1016/j.jsams.2016.08.011 . Halperin I, Emanuel A. Rating of Perceived Effort: Methodological Concerns and Future Directions. Sports Med. 2020;50:679–87. https://doi.org/10.1007/s40279-019-01229-z . Micklewright D, St Clair Gibson A, Gladwell V, Al Salman A. Development and Validity of the Rating-of-Fatigue Scale. Sports Med. 2017;47:2375–93. https://doi.org/10.1007/s40279-017-0711-5 . Ostrow KS, Heffernan NT. Testing the Validity and Reliability of Intrinsic Motivation Inventory Subscales Within ASSISTments. In: Penstein Rosé C, Martínez-Maldonado R, Hoppe HU, Luckin R, Mavrikis M, Porayska-Pomsta K, et al. editors. Artificial Intelligence in Education. Cham: Springer International Publishing; 2018. pp. 381–94. https://doi.org/10.1007/978-3-319-93843-1_28 . Wolff W, Bieleke M, Stähler J, Schüler J. Too bored for sports? Adaptive and less-adaptive latent personality profiles for exercise behavior. Psychol Sport Exerc. 2021;53:101851. https://doi.org/10.1016/j.psychsport.2020.101851 . Yu S, Levesque-Bristol C, Maeda Y. General Need for Autonomy and Subjective Well-Being: A Meta-Analysis of Studies in the US and East Asia. J Happiness Stud. 2018;19:1863–82. https://doi.org/10.1007/s10902-017-9898-2 . Mossman LH, Slemp GR, Lewis KJ, Colla RH, O’Halloran P. Autonomy support in sport and exercise settings: a systematic review and meta-analysis. Int Rev Sport Exerc Psychol. 2024;17:540–63. https://doi.org/10.1080/1750984X.2022.2031252 . Halperin I, Wulf G, Vigotsky AD, Schoenfeld BJ, Behm DG. Autonomy: A Missing Ingredient of a Successful Program? Strength Cond J. 2018;40:18–25. https://doi.org/10.1519/SSC.0000000000000383 . Vítor de Assis J, González-Víllora S, Clemente FM, Cardoso F, Teoldo I. Do youth soccer players with different tactical behaviour also perform differently in decision-making and visual search strategies? Int J Perform Anal Sport. 2020;20:1143–56. https://doi.org/10.1080/24748668.2020.1838784 . Lex H, Essig K, Knoblauch A, Schack T. Cognitive Representations and Cognitive Processing of Team-Specific Tactics in Soccer. PLoS ONE. 2015;10:e0118219. https://doi.org/10.1371/journal.pone.0118219 . Shushan T, McLaren SJ, Buchheit M, Scott TJ, Barrett S, Lovell R. Submaximal Fitness Tests in Team Sports: A Theoretical Framework for Evaluating Physiological State. Sports Med. 2022;52:2605–26. https://doi.org/10.1007/s40279-022-01712-0 . Buchheit M. Monitoring training status with HR measures: do all roads lead to Rome? Front Physiol. 2014;5. https://doi.org/10.3389/fphys.2014.00073 . Supplementary Files OnYourMarkGetSetChooseSupplementaryFile1.docx Cite Share Download PDF Status: Published Journal Publication published 14 Jan, 2025 Read the published version in Sports Medicine-Open → Version 1 posted Reviewers agreed at journal 17 Jun, 2024 Reviewers invited by journal 13 Jun, 2024 Editor invited by journal 12 Jun, 2024 Editor assigned by journal 06 Jun, 2024 First submitted to journal 05 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4528664","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":314220690,"identity":"5f1b96bc-e929-4e80-827b-5ceb806582b3","order_by":0,"name":"Asaf Ben-Ari","email":"","orcid":"","institution":"Tel Aviv University","correspondingAuthor":false,"prefix":"","firstName":"Asaf","middleName":"","lastName":"Ben-Ari","suffix":""},{"id":314220691,"identity":"9e06f99b-26f4-4c40-807d-7f8003acc7e8","order_by":1,"name":"Yedidya Silverman","email":"","orcid":"","institution":"Tel Aviv University","correspondingAuthor":false,"prefix":"","firstName":"Yedidya","middleName":"","lastName":"Silverman","suffix":""},{"id":314220692,"identity":"1e2bcad4-0ba3-474d-8ec4-536e294538f4","order_by":2,"name":"Uri Obolski","email":"","orcid":"","institution":"Tel Aviv University","correspondingAuthor":false,"prefix":"","firstName":"Uri","middleName":"","lastName":"Obolski","suffix":""},{"id":314220693,"identity":"9f6f03d8-bd48-40c2-a342-d1831df068df","order_by":3,"name":"Israel Halperin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBACNgkGxgNAmoeBvQFVhocPtxYGiBaew1ClMC1suKyBagEyklG1MODSwifd/OBwQcU9GXPJ9wc/81QcZrBnP2PA8KOGQQaXFjaZYwaHZ5wp5rGcncwszXMmjYGHJ8eAsecYboexSSQYHOZtS+AxuJ3MIM3bZgN0WI4BA28DPi3pHw7z/gNquXmY+TfvPwkGHv43Box/8WrJAdrSANRyg5lNmrcBaAtQhBm/LTkFh2ccA2o5k2xmOedYGg/PjWcFh2WOSeDUIj8jfePjgpoEe4PjBx/feFNzWI69P3njwzc1Nvb8OLSAADMyBxwtB8DxRayWUTAKRsEoGAUYAAC6dElHvUYrnwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2254-2698","institution":"Tel Aviv University","correspondingAuthor":true,"prefix":"","firstName":"Israel","middleName":"","lastName":"Halperin","suffix":""}],"badges":[],"createdAt":"2024-06-04 14:09:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4528664/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4528664/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40798-024-00803-8","type":"published","date":"2025-01-14T15:57:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60186052,"identity":"59d3b012-1313-43bf-9cdd-20258d9b6dba","added_by":"auto","created_at":"2024-07-12 18:45:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":285178,"visible":true,"origin":"","legend":"\u003cp\u003eA diagram of the session’s protocol. Each black rectangle symbolizes an interval. The duration of between-interval rests in each condition are presented above (SS) and below (Fixed) the rectangles.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4528664/v1/d934366cb5ba10b92e31cc7b.png"},{"id":60184461,"identity":"c7318112-c11b-4b9a-bb35-56d94d1aae23","added_by":"auto","created_at":"2024-07-12 18:37:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1904858,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental setup. (A) whiteboard with a list of completed intervals; (B) poster with RPE scale; (C) computer screen with a timer.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4528664/v1/371067bd358823bb25410092.png"},{"id":60184460,"identity":"b256e94d-66e8-4557-9734-7ef67e62bafe","added_by":"auto","created_at":"2024-07-12 18:37:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":195371,"visible":true,"origin":"","legend":"\u003cp\u003eSelf-selected rest periods duration between the four intervals in each block. Each rest period is numbered by the interval that preceded it; i.e., #1 is the rest period after the first interval, #2 is the rest period after the second interval, and so on. The rest period following the fourth interval is not shown since it was predetermined (2 minutes). The thin lines represent self-selected rest duration of different participants, whereas the thick lines represent the overall mean rest duration. The red dashed line represents the rest duration in the fixed condition (90 seconds).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4528664/v1/2a3d434af028fd3071076ab9.png"},{"id":60186051,"identity":"b4347641-11c3-432f-987e-ca77e7e7bcc2","added_by":"auto","created_at":"2024-07-12 18:45:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1201534,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of aggregated outcomes between fixed and SS conditions (A) distance, (B) HR, and (C) RPE. Each horizontal line connects the values of a single participant in both conditions, overlaid on boxplots.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4528664/v1/e004aa849b4fc2f3e9ddc8de.png"},{"id":60184462,"identity":"2219310b-7c69-42d9-950d-f53a9faed6ba","added_by":"auto","created_at":"2024-07-12 18:37:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4632450,"visible":true,"origin":"","legend":"\u003cp\u003eMultiple measurement comparisons between the fixed and SS conditions. (A) distance (meters), (B) HR (beats per min), and (C) RPE (0-10 scale). The thin lines represent the individual outcomes of each participant in the fixed (red) and SS (blue) conditions, whereas the thick lines represent the means over each interval, stratified by blocks.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4528664/v1/c3ce1ad6db3efa166de28670.png"},{"id":74284819,"identity":"fd1e04e7-6bf1-4572-99b1-d4cccfeda5ef","added_by":"auto","created_at":"2025-01-20 16:12:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11149076,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4528664/v1/8af74508-a02e-4375-b4fe-ea317ba6c4e2.pdf"},{"id":60184465,"identity":"29786967-e832-4143-9327-4dde8cb44253","added_by":"auto","created_at":"2024-07-12 18:37:21","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":348524,"visible":true,"origin":"","legend":"","description":"","filename":"OnYourMarkGetSetChooseSupplementaryFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4528664/v1/402780110c3e87aa903445a9.docx"}],"financialInterests":"","formattedTitle":"On Your Mark, Get Set, Choose! A Randomized Cross-Over Study Comparing Fixed and Self-Selected Rest Periods in Interval Running Among Professional Female Soccer Players.","fulltext":[{"header":"Key Points","content":"\u003cul\u003e\n \u003cli\u003eAllowing athletes unlimited self-selected rests in HIIT impairs control over training stimuli, whereas controlling the total rest duration available to select from can retain such control.\u003c/li\u003e\n \u003cli\u003eHere, we compared fixed-rest to self-selected-rest HIIT sessions with matched total rest duration using a non-motorized treadmill.\u003c/li\u003e\n \u003cli\u003eOutcomes in both conditions were similar, implying either method can be adopted depending on training objectives and athletes\u0026rsquo; preferences.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Background","content":"\u003cp\u003eSoccer players require a highly developed aerobic capacity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Various training methods have been developed to help players build and maintain this capacity, with high-intensity interval training (HIIT) being particularly effective and time-efficient [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Numerous training types fit the HIIT definition, all sharing a similar structure: high-intensity work bouts interspersed with rest periods [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In HIIT, the ratio between work and rest determines the overall training effect [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While rest periods alleviate fatigue and enable subsequent efforts, they simultaneously decrease the aerobic stimulus of the session [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, selecting the appropriate rest periods duration in HIIT sessions is crucial for personalizing training and optimizing training adaptations.\u003c/p\u003e \u003cp\u003eThe most common approach to prescribing rest periods in HIIT involves fixed, predetermined durations with work-to-rest ratios ranging from 1:0.5 to 1:20 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While this method is convenient and efficient [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], it does not account for individual physiological and psychological differences. For example, differences in fatigue accumulation, which in turn likely result in different durations required to recover [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. An alternative is the self-selected (SS) approach, where athletes determine the duration of their rest periods. The SS approach offers several unique benefits. First, the mere act of choosing can enhance motivation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], enjoyment [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and perception of autonomy [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Second, the SS method may better accommodate individual differences by allowing athletes to tailor rest durations to their current physiological and psychological readiness and anticipated performance [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Finally, the SS approach challenges and may develop athletes' decision-making processes.\u003c/p\u003e \u003cp\u003eStudies comparing fixed and SS rest durations during HIIT sessions have presented mixed results [\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22 CR23\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Some have reported enhanced performance and psychological outcomes with SS rest periods [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], while others have shown the opposite [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The significant variability in prescribed HIIT protocols (e.g., 4x4 minutes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] vs. 12x30 meters [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]) and the length of rest periods in the fixed condition (e.g., 3 minutes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] vs. 30 seconds [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]) may have contributed to these inconsistent results. However, these studies share a major limitation \u0026ndash; the total rest duration between fixed and SS conditions was not matched. In the SS condition, athletes selected their rest periods with no lower or upper limit, resulting in different total rest durations than those in the fixed conditions and, consequently, different training stimuli.\u003c/p\u003e \u003cp\u003eTo overcome the latter limitation, we recently conducted a study employing a novel approach [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In that study, 24 male amateur cyclists performed two HIIT sessions in a crossover design consisting of nine 30-second cycling intervals. Under the fixed condition, cyclists rested for 90 seconds between intervals, thus accumulating 720 seconds of rest (\u003cem\u003e8 \u0026times; 90 sec\u0026thinsp;=\u0026thinsp;720 sec\u003c/em\u003e). Under the SS condition, cyclists self-selected their rest durations out of the 720 seconds provided to them, which had to be fully utilized by the final rest period. All performance, physiological, and psychological outcomes were similar except for greater perceived autonomy in the SS condition. Furthermore, cyclists in the SS condition chose shorter rest durations in the first half of the session and considerably longer durations in the second half. We speculated that participants found it challenging to effectively distribute the 720-second total rest duration across a relatively high number of intervals.\u003c/p\u003e \u003cp\u003eThe present study aimed to extend this line of inquiry with three modifications. First, we simplified the task of rest time allocation to streamline the decision-making process for participants. Second, we tested the SS approach among female soccer players to expand our understanding of its applicability across additional populations. Lastly, we implemented the SS approach in a running HIIT protocol to align with training modalities commonly used in soccer. We hypothesized different performance and psychological outcomes between conditions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eWe recruited 24 professional female soccer players from the Israeli women's soccer first league. Inclusion criteria included healthy participants, aged between 16 and 45, with at least one year of professional-level soccer playing experience. Exclusion criteria included acute injury in the past two weeks, pregnancy, or being fewer than six months after childbirth. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the participants\u0026rsquo; characteristics, including training history and weekly training volume. Recruitment was done through advertisements on various social media channels and by contacting teams from the Israeli women's soccer first league.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipants characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;24\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge [yrs]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.0 (3.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight [cm]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163.4 (6.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight [kg]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.5 (5.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.2 (4.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperience [yrs]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.9 (2.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoccer training [sessions per week]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.9 (1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eAll values are presented as mean (SD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eProcedures\u003c/h2\u003e \u003cp\u003eWe implemented a within-participant, randomly assigned crossover design. All participants attended three laboratory sessions: a familiarization session and two experimental sessions. Given that menstruation may influence perceived performance [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], to minimize potential confounding factors, we asked participants about their menstrual phase (\u0026ldquo;When was the first day of your last menstruation?\u0026rdquo;) and avoided scheduling any of the sessions during their menstruation phase. In addition, given the variability in how different menstrual cycle phases affect athletes\u0026rsquo; performance and perceived performance [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], we rescheduled sessions if a participant reported any menstrual cycle-related symptoms.\u003c/p\u003e \u003cp\u003eThe HIIT protocol in the two experimental sessions consisted of twelve 15-second intervals performed on a non-motorized treadmill (Woodway\u0026copy; Curve 3.0 Treadmill, Waukesha, United States). The twelve intervals were divided into three blocks of four intervals, with two minutes of rest between blocks, to simplify the task of time allocation under the SS condition. The two sessions only differed in the rest durations between intervals. Under the fixed condition, participants rested for 90 seconds between intervals, totaling 270 sec of rest per block. Under the SS condition, participants selected how long they would rest between intervals. However, we matched the total rest duration between conditions, meaning that participants had to fully utilize 270 sec of rest over the three rest periods of each block (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows a diagram of the session\u0026rsquo;s protocol). At the beginning of each session, we told participants their goal was to cover as much distance as possible across all intervals. Under the SS condition, we added that they should allocate their rest durations with this goal in mind. In both sessions, participants were allowed to choose whether to stand or walk during the rest periods except for the five seconds before the subsequent interval in which they were asked to stand still.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe provided participants with feedback regarding the remaining rest time using a screen with a timer next to the treadmill. In the fixed-rest condition, the timer counted down from 90 seconds for each between-interval rest period. In the SS condition, the timer counted down from 270 sec for each block (indicating the total rest time still available to them). Once an interval was completed, the countdown started. When participants announced they were ready to start the next interval, the researcher began a 5-second verbal countdown, after which the interval would commence, and the timer was paused. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the experimental setup.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll sessions began with the same general\u0026thinsp;~\u0026thinsp;8-minute warm-up used in our laboratory [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. After this warm-up, participants performed a baseline countermovement jump (CMJ) test. Next, participants performed an exercise-specific warm-up consisting of three minutes of easy steady-state running followed by five 10-second intervals at a gradually increasing speed. The intervals' speed corresponded to 40%, 60%, 80%, and two 100% of the participants' perceived maximal speed, interspersed with rest periods of 1\u0026ndash;2 minutes. Two minutes after completing the exercise-specific warm-up, the first interval of the protocol began. Throughout the sessions, we recorded participants\u0026rsquo; heart rate (HR). Two minutes after completing the HIIT protocol, participants completed another CMJ test to assess protocol-induced neuromuscular fatigue. Participants also reported their rating of perceived fatigue (ROF) at the beginning of the session and following the second CMJ test completion to assess protocol-induced perceived fatigue. In addition, participants reported their ratings of perceived effort (RPE) after each interval, as well as perceived autonomy and enjoyment after each session. Finally, 24 hours after the final session, we asked participants about their condition preferences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFamiliarization (session 1)\u003c/h2\u003e \u003cp\u003eThe session aimed to familiarize participants with running on the curved non-motorized treadmill, the HIIT protocol, experimental conditions, and outcomes. We told participants that the study aimed to assess a new running HIIT protocol. Following the explanations, anthropometric measurements, and warm-up, participants completed a partial protocol composed of four intervals (one block) per condition. Specifically, participants performed four intervals under the fixed-rest condition with 90 seconds of rest between each interval, rested for two minutes, and performed four intervals under the SS rest condition, in which they selected how long to rest between intervals (provided with 270 sec that they were required to utilize fully).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eExperimental sessions (sessions 2\u0026ndash;3)\u003c/h2\u003e \u003cp\u003eWe briefly reviewed the protocol\u0026rsquo;s goals, and how to rate effort and fatigue using the different questionnaires. Following the warm-up, participants completed the entire protocol composed of twelve 15-second intervals, divided into three blocks of 4 intervals. The procedure was comparable to the familiarization session with two differences: participants only completed one of the conditions at that time (randomly assigned to begin with either SS or fixed), and the protocol consisted of three 4-intervals blocks (twelve intervals in total).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eOutcome Measures\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eAnthropometric Measurements\u003c/h2\u003e \u003cp\u003eIn the familiarization session, we measured participants' weight, height, and fat-free mass (SECA, Hamburg, Germany). Participants were requested to refrain from meal and caffeine consumption at least 4 hours before each session and to relieve themselves in the bathroom before the measurement.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePerformance Measures\u003c/h2\u003e \u003cp\u003eDistance: We measured the distance covered while running each interval as recorded by the treadmill\u0026rsquo;s proprietary software at a sample rate of 200 Hz (Curve 3.0 Pacer Performance System, version 2013.1.1). The treadmill's display screen was covered throughout all sessions, leaving participants blinded to the running velocity, distance covered, and HR data. Participants were not allowed to hold the treadmill's handrails except when they finished an interval, during which they used them to jump to the sides, straddling the running surface. When processing the treadmill\u0026rsquo;s data output, we differentiated intervals from rest periods in the following manner: The beginning of an interval was identified by the point where velocity rose from 0 (representing when participants stood still five seconds before each interval); the end of an interval was identified by the point where vertical forces dropped to 0 (representing when participants jumped off the running surface at the end of each interval).\u003c/p\u003e \u003cp\u003eCMJ: We measured CMJ performance using a pair of portable force plates (Deltas, Kinvent, Montpellier, France). Participants stood on the force plate, squatted down to a self-selected depth (countermovement), and jumped as high as possible while keeping their hands on their waist. Participants performed three jumps with 45 seconds of passive rest in between. The average height (determined from take-off velocity), net impulse, and flight time of the three jumps were used to analyze and assess protocol-induced neuromuscular fatigue as recommended by others [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePhysiological Measures\u003c/h2\u003e \u003cp\u003eWe measured participants\u0026rsquo; HR throughout each experimental session using a chest strap monitor (Polar Electro H10, Kempele, Finland). To fully capture an interval\u0026rsquo;s effect on HR, given the intervals\u0026rsquo; relatively short duration and HR\u0026rsquo;s delayed response to a change in exercise intensity, we defined an HR interval from when participants started running an interval up to the start point of the subsequent interval. For each HR interval, we recorded the peak HR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePsychological Measures\u003c/h2\u003e \u003cp\u003eRPE: Immediately after each interval, we asked participants to report their RPE (\u0026ldquo;How much effort did you exert?\u0026rdquo;) using a 0 (\u0026lsquo;no effort\u0026rsquo;) to 10 (\u0026lsquo;maximal effort\u0026rsquo;) scale. A printed scale version was hung on the wall in front of the treadmill (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the familiarization session, we defined effort to participants as the \u0026ldquo;investment of physical and/or mental resources to perform a task\u0026rdquo; and perceived effort as \u0026ldquo;the way you experience the investment of those physical and/or mental resources during the task\u0026rdquo; [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The lower and upper limits of the scale were anchored to complete rest and to running as fast as possible in a 15-second interval, respectively.\u003c/p\u003e \u003cp\u003eROF: Before warm-up and after completing the second CMJ test, we collected ROF using a 0 (\u0026lsquo;not fatigued at all\u0026rsquo;) to 10 (\u0026lsquo;total fatigue and exhaustion\u0026mdash;nothing left\u0026rsquo;) scale following the recommendations by Micklewright et al [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePerception of autonomy: We collected perception of autonomy after each session using a modified version of the Intrinsic Motivation Inventory questionnaire [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], consisting of three 1\u0026ndash;5 Likert scale questions: (1) \u0026ldquo;The way I exercised today is aligned with my choices and preference\u0026rdquo;; (2) \u0026ldquo;I feel the way I exercised today is the way I want to exercise\u0026rdquo;; (3) \u0026ldquo;I feel like I could make decisions regarding how I exercised today.\u0026rdquo; to which the answers ranged from 1 (\u0026ldquo;I totally disagree\u0026rdquo;) to 5 (\u0026ldquo;I totally agree\u0026rdquo;).\u003c/p\u003e \u003cp\u003eBoredom: We collected level of boredom after each session using two questions based on the Bored of Sports Scale [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]: (1) \u0026ldquo;Were you bored throughout the session?\u0026rdquo; (personal boredom level) and (2) \u0026ldquo;Did you find the session boring?\u0026rdquo; (session boredom level), to which the answers ranged from 0 (\u0026ldquo;not at all\u0026rdquo;) to 100 (\u0026ldquo;very much\u0026rdquo;).\u003c/p\u003e \u003cp\u003ePreferences: Twenty-four hours after completing the last session, we asked participants about their condition preference using an open-ended question (\u0026ldquo;Out of the two HIIT sessions that you performed, one under the fixed and the other under the SS approach, which one did you prefer?\u0026rdquo;).\u003c/p\u003e \u003cp\u003eSupplementary File 1 includes a detailed account of verbal instructions, scale anchors, and protocols.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eSingle-Measurement Comparisons\u003c/h2\u003e \u003cp\u003eWe used paired t-tests to derive confidence intervals (CI) and p-values for the differences between the conditions in total distance, peak HR, RPE, enjoyment, autonomy, and boredom. Given our relatively modest sample size, we took precautions to validate our results further by executing a more conservative non-parametric Wilcoxon signed-rank test. Note that we summed and averaged multiple measurements over the intervals to obtain a single number for distance and RPE. Finally, in addressing participants\u0026rsquo; preferences, we performed a single-proportion, exact binomial test.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMultiple-Measurement Analysis\u003c/h2\u003e \u003cp\u003eWe employed mixed-effect regression to estimate the effect of the different conditions on distance, HR, and RPE, as the outcome variables. Condition, interval number, and block number were set as categorical fixed effects, while a random intercept was included for each participant. This approach accounts for individual differences in baseline performance levels and adjusts for repeated measurements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDifference-in-Differences\u003c/h2\u003e \u003cp\u003eWe used a difference-in-differences (DID) approach when analyzing the pre-and post-CMJs and ROF levels. For each measurement, we subtracted the participant\u0026rsquo;s first result (pre-session) from the second result (post-session) in each session and then used t-tests to derive CIs and p-values for the differences between the conditions. Similar to the single-measurement analysis, we validated our DID results further by executing a non-parametric Wilcoxon signed-rank test.\u003c/p\u003e \u003cp\u003eFor all statistical tests alpha was set at 0.05. Statistical analysis was performed using the R statistical computing environment (R Core Team, Vienna, Austria, version 4.4.0, 2024) via the RStudio integrated development environment for R (Posit Software, PBC, Boston, MA, version 2024.04.0.735). Graphs were made using \u0026ldquo;ggplot2\u0026rdquo; R package (version 3.5.1; Wickham 2016). Mixed-effect regression models were employed using \u0026ldquo;lmerTest\u0026rdquo; R package (version 3.1.3; Kuznetsova 2017).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSelf-Selected Rest Durations\u003c/h2\u003e \u003cp\u003eIn comparison to the fixed condition\u0026rsquo;s 90-sec rests, under the SS condition, participants chose a much shorter rest after the first interval (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD block 1: 67.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1, block 2: 69.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8, block 3: 72.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4), a slightly shorter rest after the second interval (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD block 1: 84.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8, block 2: 82.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6, block 3: 82.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6) and a much longer rest after the third interval (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD block 1: 117.7\u0026thinsp;\u0026plusmn;\u0026thinsp;17.8, block 2: 116.4\u0026thinsp;\u0026plusmn;\u0026thinsp;18.9, block 3: 113.1\u0026thinsp;\u0026plusmn;\u0026thinsp;20.7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePerformance Outcomes\u003c/h2\u003e \u003cp\u003eThe total distance covered (meters) in the SS session (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 817.2\u0026thinsp;\u0026plusmn;\u0026thinsp;62.2) and in the fixed session (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 815.0\u0026thinsp;\u0026plusmn;\u0026thinsp;56.0) were similar (mean difference (95%CI): -2.97 (-18.9, 12.96), p\u0026thinsp;=\u0026thinsp;0.703) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-A). In addition, the mixed effects model of the distance covered in each interval and block showed similar running distances between conditions, with a gradual increase in distance between blocks observed in both (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e-A). That is, compared to the first interval (intercept (95%CI)\u0026thinsp;=\u0026thinsp;65.34 (63.33, 67.36), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), under both conditions, a significantly longer distance was covered in the last interval of each block (estimate (95%CI)\u0026thinsp;=\u0026thinsp;3.55 (2.72, 4.37), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the last block of each session (estimate (95%CI)\u0026thinsp;=\u0026thinsp;2.54 (1.83, 3.26), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Full results of the mixed-effects model for distance are available in Supplementary File 1: Table\u0026nbsp;3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe found significant differences between the pre-post CMJs\u0026rsquo; height, net impulse, and flight time in both conditions, indicating neuromuscular fatigue accumulation during the experimental sessions (Supplementary File 1: Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, the DID were small and non-significant in jump height (DID (95%CI): 0.11 (-0.53, 0.75), p\u0026thinsp;=\u0026thinsp;0.723), net impulse (DID (95%CI): -0.02 (-1.92, 1.88), p\u0026thinsp;=\u0026thinsp;0.981), and flight time (DID (95%CI): 0.85 (-5.77, 7.47), p\u0026thinsp;=\u0026thinsp;0.792) between the SS and fixed sessions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePhysiological Outcomes\u003c/h2\u003e \u003cp\u003eThe peak HR in the SS session (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 173.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9) and in the fixed session (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 171.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5) were similar (mean difference (95%CI): -1.36 (-4.09, 1.37), p\u0026thinsp;=\u0026thinsp;0.311) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-B). However, peak HR was significantly higher in the SS condition across intervals (estimate (95%CI)\u0026thinsp;=\u0026thinsp;2.55 (0.91, 4.18), p\u0026thinsp;=\u0026thinsp;0.003) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e-B). To further examine the effect of condition on peak HR, we expanded our model by adding an interaction variable between SS condition and intervals or blocks - for which none of the estimates were statistically significant and did not change the other effects substantially. Finally, peak HR gradually increased over the intervals in both conditions. Full results of the mixed-effects model for peak HR are available in Supplementary File 1: Table\u0026nbsp;4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePsychological Outcomes\u003c/h2\u003e \u003cp\u003eThe results of five psychological outcomes (RPE, autonomy, personal and session boredom, and enjoyment) are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Since RPE was measured for each interval, we also fitted a mixed-effects model, which showed a comparable RPE level between conditions (estimate (95%CI)\u0026thinsp;=\u0026thinsp;0.105 (-0.02, 0.23), p\u0026thinsp;=\u0026thinsp;0.01). As might be expected, given the gradually increasing distance and peak HR patterns, RPE also increased with subsequent intervals and blocks (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e-C) (see Supplementary File 1: Table\u0026nbsp;5, for full model results). ROF significantly increased from the beginning to the end of each session. However, the DID between conditions was similar (DID (95%CI): 0.57 (-.024, 1.37), p\u0026thinsp;=\u0026thinsp;0.159) (Supplementary File 1: Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Of the three autonomy questions, the answers to the first (\u0026ldquo;The way I exercised today is aligned with my choices and preference\u0026rdquo;) and third (\u0026ldquo;I feel like I could make decisions regarding how I exercised today\u0026rdquo;) were statistically higher in the SS session, indicating higher perceived autonomy. The answers to the second question (\u0026ldquo;I feel the way I exercised today is the way I want to exercise\u0026rdquo;) were comparable between conditions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). No statistically significant differences were found for enjoyment, personal boredom, and session boredom. Lastly, out of 23 participants asked about their preferred session, 15 selected the SS, and 8 selected the fixed (mean proportion (95%CI): 0.65 (0.46, 1.0). p\u0026thinsp;=\u0026thinsp;0.105).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparisons of psychological outcomes for SS and fixed conditions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable [range]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFixed (Mean (SD))\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-selected (Mean (SD))\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean difference (95%CI)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP (t-test)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP (Wilcox)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRPE [0\u0026ndash;10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.2 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 (-0.1, 0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnjoyment [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.3 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2 (-0.1, 0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutonomy-Q.1 [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.7 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6 (0.2, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutonomy-Q.2 [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 (-0.2, 0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutonomy-Q.3 [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.5 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4 (0.6, 2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutonomy-Total [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.6 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.7 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1 (0.9, 3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoredom Personal [0-100]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.9 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.9 (-13.6, 1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoredom Session [0-100]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.7 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.8 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.9 (-13.1, 1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreference\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7 (0.5, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e*\u003c/sup\u003eP-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eP-values and CIs derived from paired t-tests\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eP-values derived from a non-parametric Wilcoxon signed-rank test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e3\u003c/sup\u003eResults of single-proportion binomial test with an N\u0026thinsp;=\u0026thinsp;23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe compared the effects of fixed and SS rest durations in a running HIIT protocol while matching the total rest time on performance, physiological, and psychological outcomes among 24 professional female soccer players. In the SS condition, most participants chose to gradually increase their rest durations. We found comparable results between conditions in most outcome measures: distance, effort, fatigue, enjoyment, and boredom. The exceptions were peak HR, which was slightly higher in the SS condition, and perception of autonomy, which was higher in the SS condition. Finally, most participants favored the SS condition.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, the present study is the second to compare the effects of fixed and SS rest durations in HIIT while matching for total rest duration between conditions. Our results align with the first study, by Colorni et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], who observed comparable performance and psychological outcomes while perception of autonomy was enhanced in the SS condition. In the present study, we aimed to address a limitation identified by Colorni et al., where athletes were required to manage and allocate 720 seconds of rest across nine intervals. We speculated that this cognitive task might have been overly demanding. Therefore, we modified the protocol to reduce the number and length of the rest periods. Despite this adjustment, and considering the different exercise modalities and cohorts, the overall results of the two studies are highly similar. We observed a slightly higher peak heart rate in the SS condition. However, this difference was minimal and, in our opinion, unlikely to impact training outcomes. Finally, participants chose to rest for a shorter duration at the beginning of each block and for a longer duration towards its end. This pattern is similar to the one observed in Colorni et al., further establishing that participants tend to choose a strategy of initially shorter, and progressively longer, rest durations compared to the fixed condition.\u003c/p\u003e \u003cp\u003eThe implications of this study, coupled with our previous research by Colorni et al., are as follows: Given the highly similar performance, physiological, and psychological responses, coaches and trainees can choose either approach based on preferences or specific training goals. The SS approach provides athletes with flexibility in training configuration, allowing them to tailor training according to their preferences and perceived abilities. This method also enhances athletes' perception of autonomy, providing psychological benefits [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and challenges their decision-making skills, which are critical in sports like soccer [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In contrast, the fixed approach provides a predetermined structure, enabling players to focus solely on the task without the cognitive load of decision-making. This method is also logistically simpler, which is beneficial for group sessions or when training space is limited. Additionally, the consistent nature of fixed training allows one to easily track and compare performance between players over time [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Given the merits of each approach and their comparable effects, coaches can safely expand their HIIT repertoire without any worry about negative effects on performance. This expansion is notable, as coaches understandably tend to rely on the fixed approach, as most HIIT studies and guidelines revolve around this method. By demonstrating the similarities between the two approaches, we hope that the SS approach will become more widely utilized.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered when interpreting the findings of our study. First, the short duration of each running interval, set at 15 seconds, may have been insufficient to capture differences in the distance covered between the conditions. A longer interval, such as 30 seconds, may have been more suitable to capture such differences, assuming they exist. Second, while necessary for standardization, running in a laboratory setting on a non-motorized treadmill differs from the typical training environment of soccer players, who predominantly train on an outdoor field. Third, our study exclusively recruited female soccer players, which restricts the generalizability of our findings. Finally, the acute nature of our study does not allow us to make any assertions about the long-term effects of the SS training approach.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study adds insights into comparing fixed and SS approaches for prescribing rest periods in a running HIIT protocol. Despite a slight difference in peak HR, overall performance, physiological, and psychological outcomes remained comparable. These findings suggest that coaches and athletes can follow either approach based on training objectives and preferences.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHIIT \u0026ndash; High-intensity interval training\u003c/p\u003e\n\u003cp\u003eSS \u0026ndash; Self-selected\u003c/p\u003e\n\u003cp\u003eCMJ \u0026ndash; Countermovement jump\u003c/p\u003e\n\u003cp\u003eHR \u0026ndash; Heart rate\u003c/p\u003e\n\u003cp\u003eROF \u0026ndash; Rating of fatigue\u003c/p\u003e\n\u003cp\u003eRPE \u0026ndash; Rating of perceived effort\u003c/p\u003e\n\u003cp\u003eCI \u0026ndash; Confidence interval\u003c/p\u003e\n\u003cp\u003eDID \u0026ndash; Difference in differences\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics Approval and Consent to Participate\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eParticipants gave their voluntary written informed consent. The study was approved by the Ethics Committee of Tel-Aviv University (approval number: 0005775-2).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for Publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTrainee in Figure 1 gave her written consent for her image to be used in the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of Data and Materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in the current study, together with the R statistical analysis code, are available online at: \u003ca href=\"https://osf.io/3gu9h/\"\u003ehttps://osf.io/3gu9h/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAB, YS, UO, and IH declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by a grant from the Israeli Science Foundation (1249/20). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026rsquo; Contribution\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAB, YS, UO and IH designed the study; AB collected the data; AB, UO, and IH analyzed the data; AB, YS, UO and IH wrote the manuscript. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSt\u0026oslash;len T, Chamari K, Castagna C, Wisl\u0026oslash;ff U. Physiology of soccer: an update. 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Front Physiol. 2014;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphys.2014.00073\u003c/span\u003e\u003cspan address=\"10.3389/fphys.2014.00073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"sports-medicine-open","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"smoa","sideBox":"Learn more about [Sports Medicine-Open](http://sportsmedicine-open.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/smoa/default.aspx","title":"Sports Medicine-Open","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Autonomy, self-selected rest, HIIT, soccer","lastPublishedDoi":"10.21203/rs.3.rs-4528664/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4528664/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eStudies on rest durations during high-intensity interval training (HIIT) often compare fixed and self-selected (SS) rest allocation approaches. Frequently, the rest duration under SS conditions is unlimited, leading to inconsistent total rest durations compared to fixed rest conditions. To address this limitation, we recently compared fixed and SS rest conditions during cycling HIIT sessions, while keeping the total rest time equivalent. However, our protocol required athletes to divide a long total rest time (12 minutes) across nine intervals, which may have been overly cognitively demanding. In the current study, we simplified the athletes\u0026rsquo; rest allocation task by reducing the number of rest periods available.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFollowing a familiarization session, 24 professional female soccer players completed two running HIIT sessions on a non-motorized treadmill. Each session consisted of twelve 15-second intervals, divided into three blocks, with the goal of maximizing the distance covered. In both conditions, the between-interval rest duration per block amounted to 270 seconds. In the fixed condition, the rest was uniformly allocated to 90 seconds between each interval, whereas in the SS condition, the athletes chose how to allocate the entirety of the 270 seconds of rest. We compared the following outcomes: distance, heart-rate, perception of fatigue, effort, autonomy, enjoyment, boredom, and athletes\u0026rsquo; preferences. Outcomes were compared using aggregated measures via paired univariate tests, and across the intervals via mixed-effects models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe observed comparable results in most outcomes with the exception of higher autonomy in the SS condition (mean difference\u0026thinsp;=\u0026thinsp;2.1, 95%CI (0.9, 3.3)) and a negligibly higher heart-rate when comparing the observations across intervals (estimate\u0026thinsp;=\u0026thinsp;2.5, 95%CI (0.9, 4.2)). Additionally, participants chose to rest for longer durations as the block progressed. Finally, most participants (65%) favored the SS condition.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study further solidifies that SS and fixed approaches with matched total rest durations result in similar performance, physiological, and psychological responses. This effect persists even when the total rest duration required to be allocated is relatively short. Therefore, coaches and trainees can choose either approach based on their preferences and training goals.\u003c/p\u003e","manuscriptTitle":"On Your Mark, Get Set, Choose! A Randomized Cross-Over Study Comparing Fixed and Self-Selected Rest Periods in Interval Running Among Professional Female Soccer Players.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-12 18:37:16","doi":"10.21203/rs.3.rs-4528664/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-06-17T09:39:05+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-13T23:21:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Sports Medicine-Open","date":"2024-06-12T21:34:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-06T07:10:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Sports Medicine-Open","date":"2024-06-06T02:12:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"sports-medicine-open","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"smoa","sideBox":"Learn more about [Sports Medicine-Open](http://sportsmedicine-open.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/smoa/default.aspx","title":"Sports Medicine-Open","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f80382d-2e3d-40d6-aeac-071a23af20f1","owner":[],"postedDate":"July 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-20T16:05:53+00:00","versionOfRecord":{"articleIdentity":"rs-4528664","link":"https://doi.org/10.1186/s40798-024-00803-8","journal":{"identity":"sports-medicine-open","isVorOnly":false,"title":"Sports Medicine-Open"},"publishedOn":"2025-01-14 15:57:21","publishedOnDateReadable":"January 14th, 2025"},"versionCreatedAt":"2024-07-12 18:37:16","video":"","vorDoi":"10.1186/s40798-024-00803-8","vorDoiUrl":"https://doi.org/10.1186/s40798-024-00803-8","workflowStages":[]},"version":"v1","identity":"rs-4528664","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4528664","identity":"rs-4528664","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}