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Article

Hydraulic Resistance Device for Force, Power, and Force–Velocity–Power Profile Assessment During Resisted Sprints with Different Overloads †

by
Matic Sašek
1,
Oskar Cvjetičanin
1 and
Nejc Šarabon
1,2,3,4,*
1
Faculty of Health Sciences, University of Primorska, 6310 Izola, Slovenia
2
Andrej Marušič Institute, University of Primorska, 6000 Koper, Slovenia
3
Laboratory for Motor Control and Motor Behavior, S2P, Science to Practice, Ltd., 1000 Ljubljana, Slovenia
4
Ludwig Boltzmann Institute for Rehabilitation Research, 3100 St. Pölten, Austria
*
Author to whom correspondence should be addressed.
†
Some of the data material in this article was previously used for a conference presentation at the 29th Annual Congress of the European College of Sport Science in Glasgow, Scotland, as “Sašek, M., Cvjetičanin, O., & Šarabon, N. (2024). The force-velocity profiles of 20-m sprints with hydraulic resistance device: a theoretical background for optimal resistance loads. Book of Abstracts: 29th Annual Congress of the European College of Sport Science, 387. https://www.ecss.mobi/DATA/CONGRESSES/GLASGOW_2024/DOCUMENTS/2024_BOA_Web.pdf”.
Appl. Sci. 2025, 15(3), 1064; https://doi.org/10.3390/app15031064
Submission received: 30 December 2024 / Revised: 16 January 2025 / Accepted: 20 January 2025 / Published: 22 January 2025
(This article belongs to the Special Issue Technologies in Sports and Physical Activity)
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Versions Notes

Abstract

:
This study aimed to evaluate the ability of a hydraulic resistance device (HRD) to assess the force and power output and force–velocity–power profile of short sprints, while examining the effects of hydraulic overload on these outcomes. Twenty-eight amateur athletes performed 20 m sprints under minimal (MiL), moderate (MoL), and high (HiL) overloads. Sprint velocity was measured with the HRD, while resistance force (Fr) was assessed from the pressure via the HRD and from the reaction force via the force plate (FP). Using velocity and Fr during the sprints, maximal velocity (vmax), average horizontal force (Favg), average power (Pavg), and FvP profile variables (F0, v0, and Pmax) were calculated. A two-way ANOVA analysed the effects of overload and calculation method. In addition, a correlation between the HRD and FP measurements was evaluated. For all variables, very high to excellent correlation between the HRD and FP was observed (r ≥ 0.96). However, the Favg, Pavg, F0, and Pmax calculated by the HRD were lower than the FP across all overloads (η2 ≥ 0.51; p < 0.001). Regardless of the method used, Favg, Pavg, and F0 were highest at HiL (η2 ≥ 0.38; p < 0.001), and v0 was highest at MiL (η2 = 0.35; p < 0.001), whereas overload had no significant effect on Pmax (η2 = 0.01; p = 0.770). The HRD is a feasible means for monitoring force and power output during hydraulic resisted sprints but should not be directly compared to other resistance devices. HiL produced the highest Favg, Pavg, and F0 and may be optimal for increasing power output and improving acceleration performance.

1. Introduction

The overload sprint is a unique form of resistance training that has attracted a great deal of research attention over the past decade [1,2], primarily because it has been shown to be a beneficial stimulus for improving acceleration performance, an important component in many sports disciplines [3]. Compared to sprinting without resistance, an athlete generates higher horizontal forces during overload sprints (i.e., resisted sprints) [4]. As different overloads biomechanically replicate different acceleration phases of the sprint [5], resisted sprint training has been utilised in practise with different equipment and overloads [6].
Although it would be beneficial in practise, there is still no consensus on whether lighter or heavier overloads are preferable for resisted sprint training [1,2,5]. Similarly to other fast actions, sprint performance is determined by the ability of the neuromuscular system to generate horizontal force and power [7,8]. Therefore, improving power output during sprinting (i.e., generating higher horizontal forces at a given sprint speed) leads to faster acceleration and also a higher maximal velocity (vmax) [9]. The generally accepted approach in resistance training, whereby the overload which results in maximal power output during exercise is considered optimal [10,11], could also be applied to resisted sprinting [12,13]. However, the power output (determined as the product of velocity and horizontal force) during an overloaded sprint is poorly understood. Thus, it is questionable whether the power output is greater at higher overloads, which increase horizontal force production more but decrease the sprint velocity, or at lower overloads, which increase horizontal force production less and almost maintain a normal sprint velocity.
To calculate these parameters when sprinting with overload, the velocity and the applied resistance force (Fr) must be known. This is a challenge and requires advanced devices that accurately monitor the magnitude of Fr [14]. Previously, such calculations were performed using motorised sprint resistance devices and sleds that allowed monitoring of the resultant horizontal force (Fh), power output, and the so-called force–velocity–power profile (FvP) [13,15,16,17]. This profile is based on the progression of changes in Fh and power over the sprint velocity and reflects the neuromuscular ability of the human body to generate maximal theoretical horizontal force (F0), maximal theoretical velocity (v0), and maximal theoretical power (Pmax) during sprinting [18]. Profiling is a popular method for monitoring the biomechanics of normal sprinting [19,20,21]. In contrast, there are few studies on force output, power output, and FvP profile during overloaded sprints [13,17]. There is a lack of studies in which non-motorised devices are used to apply the overload and acquire force and power metrics. The main reason for this is the difficulty in predicting Fr with such devices and, furthermore, calculating Fh and power output [14].
As more affordable non-motorised devices using hydraulic resistance have not yet been evaluated in the context of the biomechanics of sprinting with overload, a hydraulic resistance device (HRD) was tested in this study. The HRD is a technical solution that generates and measures Fr during overloaded sprints [22]. The device also offers the possibility of measuring sprint velocity. With the HRD, it is possible to obtain measurements during a resisted sprint that allow the calculation of force, power output, and FvP profile, similarly to sprints with sleds [17,23]. The use of the HRD for such purposes is still unexplored, so it remains questionable whether the device can be successfully used for such monitoring. Furthermore, it remains questionable which hydraulic overload provides the most optimal conditions for maximising the force and power output during short sprints.
For these reasons, this study aimed to (i) compare the calculation of force output, power output, and FvP profile between the HRD and a force plate (FP) and (ii) evaluate the differences in the same variables between different hydraulic overloads. This would provide valuable information on the HRD’s ability to test and train resisted sprints. We expected good feasibility and hypothesised that the highest force and power output would be achieved with a higher overload [24].

2. Materials and Methods

2.1. Experimental Design

A cross-sectional study was conducted. During a single visit, participants completed a series of 20 m sprints under minimal (MiL), moderate (MoL), and high HRD (HiL) overload conditions. The 20 m sprints were performed because such distances are often used in practise for resisted sprint training [25,26]. A magnetic encoder and a pressure sensor integrated into the HRD were used to measure the sprint velocity and Fr. In addition, a Fr was simultaneously determined from the reaction forces using a force plate (FP), which was fixed under the HRD. The variables of force output, power output, and FvP were calculated from the velocity and the Fr via the HRD and the FP.

2.2. Participants

The sample size was determined a priori with the G*Power software (version 3.1.9.7) [27], using a statistical method based on a two-way analysis of variance (ANOVA) with a 2 × 3 design. The calculations were based on an effect size value (partial eta squared; η2) of 0.7, which was derived from a study comparing the rate of force development in 30 m sprints between different sled loads [28]. With an alpha level of 0.05 and a statistical power of 0.90, the model showed that a minimum sample size totalling 12 participants was required. For this study, 28 amateur athletes consisting of 11 males (age = 26.9 ± 3.3, body height = 181.5 ± 4.6 cm, body mass = 80.1 ± 6.4 kg, and normal sprint 20 m split time = 3.68 ± 0.16 s) and 17 females (age = 23.4 ± 3.5, body height = 166.6 ± 7.5 cm, body mass = 64.1 ± 4.7, and normal sprint 20 m split time = 3.96 ± 0.19 s) were recruited. The participants were amateur athletes in various team and individual sports who completed at least five sport-specific training sessions per week. All participants had previous experience of resisted sprinting and were healthy individuals with no injuries or illnesses that could affect the results. Prior to this study, they were fully informed about the study procedures and provided their written informed consent. The protocol was approved by the Medical Ethics Committee under number 0120-690/2017/8 and was conducted in accordance with the Declaration of Helsinki.

2.3. Procedures

During the tests, the participants completed a standardised warm-up consisting of four minutes of 50 cm box stepping at 100 bpm, dynamic exercises, strength exercises for the lower limbs (heel raises, hip raises, squats, crunches, and push-ups; 10 repetitions each), running drills (skipping, high knees, and hopping) and three submaximal 20 m sprints. After the warm-up, three sprints under MiL (Fr ~ 15 N), three under MoL (Fr ~ 40 N), and three under HiL (Fr ~ 110 N) were completed in randomised order. To mimic normal sprint conditions, MiL was used in accordance with previous studies utilising motorised devices [15,29]. The starting position is shown in Figure 1. The start was from the two-point stance with instructions to complete an “all-out” 20 m sprint. The HRD was positioned 1 m behind the participant and the height of the rope was individually adjusted to a horizontal position. The cord was attached to the participants using a specially designed belt. An experienced measurement technician ensured that the cord was taut before the start of the sprint. The rest time between sprint trials was at least 5 min to ensure adequate recovery.

2.4. Data Acquisition and Analysis

The non-motorised HRD used to overload the participants in this study is a state-of-the-art device that generates manually adjustable hydraulic resistance. The device is designed for training purposes and monitor sprint performance. A detailed description of its resistance-generating properties can be found elsewhere (see [22] for details). The device measures position and hydraulic pressure, and previous studies have confirmed that the timing of sprints with various overloads is reliable [30].
The device has an integrated magnetic encoder (model AEAT-601B, Broadcom Inc., San Jose, CA, USA), which was used to capture changes in position over time. In addition, an integrated pressure sensor (model NP400, 5 mA, 5 V, NOVUS Inc., Canoas, Brazil) was used to record changes in system pressure over time. The hydraulic pressure and position signals were acquired synchronously with a sampling rate of 1000 Hz using specially developed software (ARS Dynamometry, S2P, Ljubljana, Slovenia; created with LabVIEW 8.1, National Instruments, Austin, TX, USA). The signals were processed in Python using custom-developed code. The start of the sprint was determined when the position signal increased by 0.1 m from standstill. The signals obtained up to 20.1 m were analysed in this way [29]. The instantaneous velocity during the 20 m sprint was calculated as a derivative of the position. A fourth-order Butterworth low-pass filter with a cut-off frequency of 2 Hz was then used to smooth the velocity data [16]. Finally, the velocity–time relationship was calculated, and the vmax was determined for each overload condition. The trials with the highest vmax were used for further analyses.
The Fr during the sprints was determined using the HRD and the FP. The pressure signal obtained from the HRD was calibrated in a laboratory experiment to determine the Fr directly. In addition, a 3D force plate (type 9229A, Kistler Instruments, Winterthur, Switzerland) was firmly mounted under the HRD. The FP reaction forces generated by pulling the HRD were recorded synchronously with pressure and position signals at a frequency of 1000 Hz using MARS software (version 4.9; Kistler Instruments, Winterthur, Switzerland). Since only the antero-posterior force is usually considered in the evaluation of the force output, the power output, and the calculation of the FvP profile of the sprint [17], the horizontal component of the reaction force was considered as Fr calculated via the FP. The Fr obtained from the HRD and FP was filtered using a fourth-order Butterworth low-pass filter with a cut-off frequency of 2 Hz [16]. Finally, an average Fr from both methods was calculated from the Fr generated via a 20 m sprint. To better illustrate the data acquisition process, readers can see step 1 in Supplementary File.
For each overload condition, the force and power outputs were first calculated. Due to the 0.1 m delay between initiation and actual start [31], a monoexponential function was fitted to the vmax and the relative acceleration constant from the original velocity–distance plots to obtain distance-corrected velocity data [18]. Next, Fh was calculated using the methodology from previous studies evaluating the FvP profile of sled sprints (see Step 2 in Supplementary File) (see step 2 in Supplementary File) [13,14,17]. The aerodynamic drag force calculated from the running velocity, anthropometric data, and environmental variables [23], the net horizontal antero-posterior force derived from the velocity [23], and the Fr measured using either the HRD or FP were combined to obtain Fh. An average Fh (Favg) over the 20 m sprint was calculated and normalised to body mass (N/kg) as a representative parameter for the force output. In addition, the power output during the 20 m sprint was calculated as the product of Fh and velocity. The average power (Pavg) was calculated and normalised to body mass (W/kg) as a representative parameter for the power output (see step 4 in Supplementary File). As the maximal Fr was reached by the HRD after approximately one second of sprinting, only the force and velocity data after the peak Fr were used to calculate the profiles (see step 4 in Supplementary File). The procedure involved applying a linear regression to the Fh velocity data of each overload condition [23]. In the end, the v0 and F0 (normalised to body mass; N/kg) were extracted, and the maximal theoretical power was calculated using Equation (1).
Pmax = (F0 × v0)/4

2.5. Statistical Analyses

Statistical analyses were performed in SPSS Statistics version 29 (IBM Corp., Armonk, NY, USA) and R (R Foundation for Statistical Computing, Vienna, Austria). The mean and standard deviation (SD) were given as a measure of the centrality and dispersion of the data. The Shapiro–Wilk test was used to check the assumption of normality. The effect of overload on vmax was assessed with a one-way analysis of variance (ANOVA), while the 3 (overload: MiL vs. MoL vs. HiL) × 2 (method: FP vs. HRD) repeated measures ANOVA was used for the remaining variables. Greenhouse–Geisser adjustments of the p-values were used if the assumptions on sphericity were violated. The effect size was assessed using partial eta squared (η2), with a value from 0.01 to 0.05 representing a small effect, one from 0.06 to 0.14 representing a medium effect, and a value greater than 0.15 representing a large effect. To detect differences between the overloads and the different calculation methods, a post hoc test was performed using the Holm–Bonferroni probability adjustment, with the significance level set at p < 0.05. To further assess the feasibility of the HRD, a correlation between the HRD and the FP was calculated for the outcome variables using Pearson’s product moment correlation coefficient (r). The degree of correlation was interpreted as trivial (0.00–0.09), small (0.10–0.29), moderate (0.30–0.49), large (0.50–0.69), very large (0.70–0.89), nearly perfect (0.90–0.99), or perfect (1.00) [32].

3. Results

The Shapiro–Wilk test confirmed a normal distribution of all variables. A one-way ANOVA showed a significant effect of the overload condition on vmax (F = 508.3; p < 0.001; η2 = 0.95). The post hoc analysis revealed a significant decrease in vmax from MiL to MoL (MD [95CI] = 0.56 [0.47–0.66] m/s; p < 0.001), from MiL to HiL (MD [95CI] = 2.29 [2.04–2.54] m/s; p < 0.001), and from MoL to HiL (MD [95CI] = 1.73 [1.53–2.92] m/s; p < 0.001).
The results of a two-way ANOVA are shown in Table 1. There was no significant interaction effect for Fr, Favg, and Pavg (F ≤ 1.3; p ≥ 0.286); however, the main effects of overload and method were observed for these variables (F ≥ 24.7; p < 0.001). In all overload conditions, the HRD yielded significantly lower Fr, Favg, and Pavg than the FP (Table 2). Regardless of the calculation method, Fr increased significantly with the overload and was highest in HiL (Table 3 and Figure 2). Favg and Pavg were highest at HiL, with no significant difference between MiL and MoL (Table 3 and Figure 2).
The FvP profiles of the sprints calculated from the FP and the HRD under three overload conditions are shown in Figure 3. A significant interaction effect was found for F0, v0, and Pmax (F ≥ 12.9; p < 0.001). Under all overload conditions, the HRD-calculated F0 and Pmax were significantly lower than those calculated by the FP (Table 2). Conversely, the HRD under MoL and HiL conditions showed a significantly higher v0 than the FP (Table 2). Regardless of the method used, the F0 was higher under HiL than under MoL and MiL, and only under the FP were the differences in F0 between the MoL and MiL conditions significant (Table 3). For both methods, MiL v0 was higher than for MoL and HiL, and MoL v0 was higher than HiL (Table 3). There were no differences in Pmax between the overloads.
Fr showed a small-to-moderate correlation, while all other variables showed a nearly perfect-to-perfect correlation between the FP and the HRD (Table 2).

4. Discussion

To our knowledge, this is the first study to investigate force output, power output, and FvP profiles during sprinting under overload conditions with a non-motorised HRD. The results show that the HRD underestimates Fr, Favg, Pavg, F0, and Pmax. Both, FP and HRD showed that the force output, power output, and FvP profiles were significantly affected by the overload conditions. Favg, Pavg, and F0 were the highest under HiL, while v0 and vmax were the highest under MiL. These results, which shed light on the specific biomechanical properties of sprints with the HRD, are consistent with observations from studies on sled sprints.
The HRD is a unique, non-motorised resistance device designed for sprint training and testing [22,30]. While there are numerous options for resisted sprinting, few allow for the simultaneous measurement of resistance force and sprint velocity [14]. A study by Sugisaki et al. [16] compared a motorised device with ground reaction force plates embedded in the ground and showed a very high correlation (r = 0.99) but underestimated the Fh by 5–23% with the motorised device. Similarly, our results showed a nearly perfect-to-perfect correlation between the HRD and the FP, although the HRD significantly underestimated Favg, Pavg, F0, and Pmax. These results support the suitability of the HRD for monitoring the force output, power output, and FvP profiles in sprints with hydraulic overload. Using these parameters to optimise load during resisted sprints could improve the training efficiency [24]. However, due to the underestimation of Fr and the associated fluctuations in other variables, we recommend using the HRD consistently in the training process as its variables should not be used interchangeably with those of other resistance devices.
As expected, vmax decreased significantly under MoL and HiL conditions, similar to the 12–43% reduction reported at 3, 7, and 14 kg overloads using a motorised device [33]. In a review of sled sprints, loading regimens were categorised as low (2.5–10% vmax decrease), moderate (11–30% vmax decrease), heavy (31–50% vmax decrease), and very heavy (> 50% vmax decrease) [4]. In our population, MoL (Fr ~ 35 N) represented a low-to-moderate overload, as the vmax decreased from a minimum of 2.43 to a maximum of 12.35%. HiL (Fr ~ 110 N), on the other hand, corresponded to a moderate-to-heavy overload, as the vmax decreased from a minimum of 12.76 to a maximum of 46.97%. However, as two higher overloads were compared with the MiL (Fr ~ 15 N) in this study, such interpretations should be treated with caution, and even greater decreases compared to the vmax of the normal sprint are to be expected. Nevertheless, the ability of the HRD to produce a significant reduction in vmax emphasises its versatility for achieving various resisted sprint training objectives, including improving the sprint technique, speed–strength, power, or strength–speed capacity [34]. This flexibility allows overloads to be customised to an athlete’s specific needs. By customising the Fr, coaches can tailor the overloads to different sprint acceleration phases or parts of the force–velocity curve to ensure that the training stimulus matches the goals [5].
One of the aims of this study was to determine how HRD overloads alter the force or power output and the FvP profile. To our knowledge, only two studies have investigated the FvP profiles of sled sprinting (with overload) under different conditions [13,17]. In both studies, v0 decreased and F0 increased with greater overloads, while Pmax remained unchanged or changed only minimally. This is probably because Pmax is the product of F0 and v0, which change in an inversely proportional manner with increasing overloads. The FvP profiles calculated from the HRD and FP in our study yielded identical results. F0 increased and v0 decreased significantly from MiL to HiL, while Pmax remained unchanged. These observations confirm that different HRD overload conditions can create biomechanical conditions specific to the sprint acceleration phase over an extended period of time [5]. As the FvP profiles of overload sprints can be directly determined using a non-motorised hydraulic device, studies focusing on the assessment of neuromuscular capacity during resisted sprinting in athletes could benefit from the use of the HRD. In addition, the HRD is user-friendly, affordable compared to motorised devices [22], and able to monitor training loads and adaptations using integrated hardware and external software. The future development of a fully integrated system could improve its practical application.
It has already been suggested that the FvP profile variables, in addition to neuromuscular capacity information, have limited value for training prescription per se [35]. The use of theoretical and instantaneous F0 and Pmax may not represent the force and power output over the entire sprint. The description of this ability is particularly important as overground sprint running is a cyclical task where overall performance is influenced by the ability to generate a high horizontal force [36,37] and maintain a high power output [38] throughout the sprint. Therefore, Favg and Pavg may be more appropriate performance metrics for optimising overload and resisted sprint training as they reflect the force and power output over the entire 20 m of sprinting. An illustrative example comparing the FvP profile and average variables for optimisation from the results of our study is the following. If the goal is to train with the highest power output, the MiL, MoL, and HiL could all be optimal because a similar Pmax was obtained in the 20 m sprint. However, the Pavg suggests that HiL may be more effective because an average power output during the 20 m sprint was the highest. It remains questionable whether the overloads that lead to a higher power output are the optimal training stimuli for sprint acceleration performance. Interestingly, the results of the sled sprint intervention study conducted by Rodriguez-Rossell et al. [39] indicated that the greatest improvements in acceleration performance were achieved when overloads that decreased vmax by 24% and 32% were used. For comparison, a similar decrease in vmax was observed during HiL in our study (15–40% vmax decrease). The Favg and Pavg could also be used within sessions to optimise training volume. Just as strength training performed with a lower power decay between repetitions has been associated with an improved performance [40], tracking the Pavg decay could be a useful metric for improving the economy of resisted sprint training. Notwithstanding the potential of the HRD and its outcome variables, further research is required to test the true effectiveness of resisted sprint training with the HRD.
Notwithstanding the promising results and the potential of HRD for resisted sprinting, some limitations of this study should be considered. Firstly, the sample included only amateur athletes from different sports, which may limit the applicability to highly trained athletes [41]. Therefore, further studies should focus on the evaluation of FvP profiles, force, and power output with the HRD in a population of athletes who typically perform resisted sprints. Secondly, the outcome variables of sprints with an overload were not compared to normal (i.e., unloaded) sprint conditions. Therefore, it is not possible to distinguish whether more “optimal” (i.e., higher force and power output) biomechanical conditions are achieved when simply performing normal 20 m sprints. Finally, the Fr and, consequently, all derived outcome variables from the HRD were compared with the variables obtained with a single FP under the device. To further confirm the HRD’s ability to measure the main resisted sprint kinetics, the results should be compared with the ground reaction forces.

5. Conclusions

The present study has shown that the HRD can be used for monitoring resisted sprints as it allows for the precise measurement of force and power output and the FvP profile under different overload conditions. Regardless, it is not recommended to interchange it with other devices as the result variables may differ. Resisted sprint Favg, Pavg, F0, v0, and Pmax can be used in practise to evaluate resistance sprint performance (i.e., athletes’ neuromuscular capacity) and optimise training and loading. All HRD overload conditions can be used for specific sprint acceleration training [5]. However, the highest Pavg and Favg values in 20 m sprints were obtained under HiL, making such overload conditions desirable if the goal is to improve force and power production during the acceleration phase of the sprint.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/app15031064/s1: Figure S1: The procedure for calculating force output, power output, and FvP profiles from the force plate and hydraulic resistance device data.

Author Contributions

Conceptualisation, M.S. and N.Š.; methodology, M.S., O.C. and N.Š.; software, M.S., O.C. and N.Š.; validation, M.S., O.C. and N.Š.; formal analysis, M.S., O.C. and N.Š.; investigation, M.S. and N.Š.; resources, M.S., O.C. and N.Š.; data curation, M.S., O.C. and N.Š.; writing—original draft preparation, M.S.; writing—review and editing, M.S., O.C. and N.Š.; visualisation, M.S., O.C. and N.Š.; supervision, N.Š.; and project administration, M.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Slovenian Research Agency under grant KINSPO—Kinesiology for the effectiveness and prevention of musculoskeletal injuries in sports (P5-0443).

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the Medical Ethics Committee (approval number 0120-690/2017/8, the date of approval is 20 February 2018).

Informed Consent Statement

Written informed consent has been obtained from the patients to publish this paper.

Data Availability Statement

The data that support the findings of this study are available from the authors (M.S. and N.Š.) upon reasonable request.

Conflicts of Interest

Author N.S. was employed by the company S2P, Science to Practice. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

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Figure 1. Participant in start position connected to a hydraulic resistance device positioned on a force plate.
Figure 1. Participant in start position connected to a hydraulic resistance device positioned on a force plate.
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Figure 2. Differences between minimal overload (white bars), moderate overload (grey bars), and high overload (black bars) determined with the force plate (FP) and the hydraulic resistance device (HRD): (a) resistance force (Fr); (b) average force (Favg); and (c) average power (Pavg). The figure shows significant differences between the variables.
Figure 2. Differences between minimal overload (white bars), moderate overload (grey bars), and high overload (black bars) determined with the force plate (FP) and the hydraulic resistance device (HRD): (a) resistance force (Fr); (b) average force (Favg); and (c) average power (Pavg). The figure shows significant differences between the variables.
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Figure 3. Average force–velocity (solid line) and power–velocity relationship (dashed line), i.e., the force–velocity–power profile (FvP), with standard deviation (grey shading): (a) minimal overload (MiL), calculated with the force plate (FP); (b) MiL, calculated with the hydraulic resistance device (HRD); (c) moderate overload (MoL), calculated with the FP; (d) MoL, calculated with the HRD; (e) high overload (HiL), calculated with the FP; and (f) HiL, calculated with the HRD. The maximal theoretical force (F0), maximal theoretical velocity (v0), and maximal theoretical power (Pmax) are shown in Panel (a).
Figure 3. Average force–velocity (solid line) and power–velocity relationship (dashed line), i.e., the force–velocity–power profile (FvP), with standard deviation (grey shading): (a) minimal overload (MiL), calculated with the force plate (FP); (b) MiL, calculated with the hydraulic resistance device (HRD); (c) moderate overload (MoL), calculated with the FP; (d) MoL, calculated with the HRD; (e) high overload (HiL), calculated with the FP; and (f) HiL, calculated with the HRD. The maximal theoretical force (F0), maximal theoretical velocity (v0), and maximal theoretical power (Pmax) are shown in Panel (a).
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Table 1. Two-way analysis of variance (ANOVA) comparing the variables between the different methods and overloads.
Table 1. Two-way analysis of variance (ANOVA) comparing the variables between the different methods and overloads.
VariableOverloadCalculation MethodTwo-Way ANOVA
FPHRDMain EffectInteraction
Fr
(N)		MiL16.86 (2.64)13.53 (2.28)overload: F = 6167.0; p < 0.001; η2 = 0.99
method: F = 28.2; p < 0.001; η2 = 0.51		overload × method: F = 1.3; p = 0.286; η2 = 0.04
MoL35.55 (3.78)33.11 (3.64)
HiL114.80 (6.60)110.60 (4.13)
Favg
(N/kg)		MiL2.24 (0.30)2.19 (0.28)overload: F = 120.4; p < 0.001; η2 = 0.82
method: F = 30.5; p < 0.001; η2 = 0.53		overload × method: F = 0.5; p = 0.589; η2 = 0.02
MoL2.23 (0.37)2.24 (0.37)
HiL2.72 (0.28)2.67 (0.25)
Pavg
(W/kg)		MiL8.77 (1.72)8.56 (1.60)overload: F = 16.3; p < 0.001; η2 = 0.38
method: F = 24.7; p < 0.001; η2 = 0.48		overload × method: F = 0.5; p = 0.587; η2 = 0.02
MoL8.73 (1.71)8.55 (1.55)
HiL9.72 (1.98)9.47 (1.83)
F0
(N/kg)		MiL7.44 (0.99)7.33 (0.99)overload: F = 49.5; p < 0.001; η2 = 0.65
method: F = 152.6; p < 0.001; η2 = 0.85		overload × method: F = 27.2; p < 0.001; η2 = 0.50
MoL8.05 (1.04)7.59 (0.88)
HiL9.24 (1.19)8.82 (1.10)
v0
(m/s)		MiL7.67 (0.65)7.65 (0.64)overload: F = 14.52; p < 0.001; η2 = 0.35
method: F = 109.0; p < 0.001; η2 = 0.80		overload × method: F = 12.9; p < 0.001; η2 = 0.32
MoL7.29 (0.72)7.36 (0.70)
HiL6.23 (1.08)6.31 (1.06)
Pmax (W/kg)MiL14.32 (2.60)14.08 (2.53)overload: F = 0.24; p = 0.770; η2 = 0.01
method: F = 103.1; p < 0.001; η2 = 0.80		overload × method: F = 14.7; p < 0.001 η2 = 0.13
MoL14.70 (2.63)14.01 (2.34)
HiL14.39 (3.03)13.90 (2.90)
The descriptive values are presented as the mean (standard deviation). F0, maximal theoretical horizontal force; F, Snedecor’s F; Favg, average force; p, p-value; FP, force plate; Fr, resistance force; HiL, high overload; HRD, hydraulic resistance device; MiL, minimal overload; MoL, moderate overload; N, Newtons; Pavg, average power; Pmax, maximal theoretical power; v0, maximal theoretical velocity; W, Watts; and η2, partial eta squared.
Table 2. Mean difference and correlation with 95% confidence intervals in variables between the force plate (FP) and the hydraulic resistance device (HRD).
Table 2. Mean difference and correlation with 95% confidence intervals in variables between the force plate (FP) and the hydraulic resistance device (HRD).
VariableOverloadFP vs. HRD
MD (95% CI)r (95% CI)
Fr (N)MiL3.33 (2.04–4.61) ***0.10 (−0.28–0.46)
MoL2.44 (0.76–4.12) **0.32 (−0.06–0.62)
HiL4.14 (1.86–6.43) ***0.48 (0.12–0.72)
Favg (N/kg)MiL0.05 (0.03–0.06) ***0.99 (0.98–0.99)
MoL0.04 (0.01–0.06) **0.99 (0.97–0.99)
HiL0.05 (0.02–0.08) **0.99 (0.97–0.99)
Pavg (W/kg)MiL0.22 (0.11–0.32) **0.99 (0.97–0.99)
MoL0.18 (0.05–0.30) **0.98 (0.96–0.99)
HiL0.25 (0.11–0.39) **0.98 (0.96–0.99)
F0 (N/kg)MiL0.10 (0.08–0.13) ***1.00 (0.99–1.00)
MoL0.46 (0.36–550) ***0.98 (0.96–0.99)
HiL0.42 (0.32–0.52) ***0.98 (0.95–0.99)
v0 (m/s)MiL0.02 (−0.00–0.04)1.00 (0.99–1.00)
MoL−0.07 (−0.10–0.05) ***0.99 (0.97–0.99)
HiL−0.07 (−0.12–0.03) **0.99 (0.99–1.00)
Pmax (W/kg)MiL0.24 (0.18–0.31) ***1.00 (0.99–1.00)
MoL0.70 (0.51–0.89) ***1.00 (0.99–1.00)
HiL0.49 (0.35–0.62) ***0.99 (0.98–1.00)
F0, maximal theoretical horizontal force; F, FP, force plate; Favg, average force; Fr, resistance force; HiL, high overload; HRD, hydraulic resistance device; MD, mean difference; MiL, minimal overload; MoL, moderate overload; N, Newtons; Pavg, average power; Pmax, maximal theoretical power; r, Pearson’s product–moment correlation coefficient; v0, maximal theoretical velocity; W, Watts; and 95% CI, 95% confidence interval. *** p < 0.001 and ** p < 0.01.
Table 3. The mean difference (MD) in variables calculated from the force plate (FP) and the hydraulic resistance device (HRD) between overloads.
Table 3. The mean difference (MD) in variables calculated from the force plate (FP) and the hydraulic resistance device (HRD) between overloads.
VariablesMethodMD (95% CI)
MiL vs. MoLMiL vs. HiLMoL vs. HiL
Fr (N)FP−18.70 (−20.53–−16.85) ***−97.92 (−101.16–−94.69) ***79.23 (−83.00–−75.46) ***
HRD−19.6 (−21.7–−17.4) ***−97.1 (−99.5–−94.8) ***−77.5 (−80.2–−74.9) ***
Favg (N/kg)FP−0.04 (−0.09–0.02)−0.49 (−0.61–0.37) ***−0.45 (−0.55–−0.35) ***
HRD−0.05 (−0.10–0.01)−0.48 (−0.59–−0.38) ***−0.44 (−0.53–0.35) ***
Pavg (W/kg)FP0.05 (−0.25–0.35)−0.95 (−1.64–−0.26) **−1.00 (−1.53–0.47) ***
HRD0.01 (−0.28–0.29)−0.92 (−1.49–−0.35) **0.93 (−1.41–0.44) ***
F0 (N/kg)FP−0.61 (−1.01–−0.21) **−1.80 (−2.30–−1.30) ***−1.20 (−1.70–−0.70) ***
HRD−0.26 (−0.64–0.12)−1.49 (−1.91–−1.07) ***−1.23 (−1.67–−0.79) ***
v0 (m/s)FP0.38 (0.27–0.50) ***1.43 (1.10–1.77) ***1.05 (0.79–1.32) ***
HRD0.29 (0.18–0.40) ***1.34 (1.03–1.66) ***1.05 (0.79–1.32) ***
Pmax (W/kg)FP−0.39 (−1.15–0.37)−0.07 (−1.05–0.92)0.32 (−0.53–1.17)
HRD0.07 (−0.65–0.79)0.18 (−0.72–1.07)0.11 (−0.66–0.88)
F0, maximal theoretical horizontal force; Favg, average force; Fr, resistance force; HiL, high overload; MD, mean difference; MiL, minimal overload; MoL, moderate overload; N, Newtons; Pavg, average power; Pmax, maximal theoretical power; v0, maximal theoretical velocity; W, Watts; and 95% CI, 95% confidence interval. *** p < 0.001 and ** p < 0.01.
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Sašek, M.; Cvjetičanin, O.; Šarabon, N. Hydraulic Resistance Device for Force, Power, and Force–Velocity–Power Profile Assessment During Resisted Sprints with Different Overloads. Appl. Sci. 2025, 15, 1064. https://doi.org/10.3390/app15031064

AMA Style

Sašek M, Cvjetičanin O, Šarabon N. Hydraulic Resistance Device for Force, Power, and Force–Velocity–Power Profile Assessment During Resisted Sprints with Different Overloads. Applied Sciences. 2025; 15(3):1064. https://doi.org/10.3390/app15031064

Chicago/Turabian Style

Sašek, Matic, Oskar Cvjetičanin, and Nejc Šarabon. 2025. "Hydraulic Resistance Device for Force, Power, and Force–Velocity–Power Profile Assessment During Resisted Sprints with Different Overloads" Applied Sciences 15, no. 3: 1064. https://doi.org/10.3390/app15031064

APA Style

Sašek, M., Cvjetičanin, O., & Šarabon, N. (2025). Hydraulic Resistance Device for Force, Power, and Force–Velocity–Power Profile Assessment During Resisted Sprints with Different Overloads. Applied Sciences, 15(3), 1064. https://doi.org/10.3390/app15031064

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