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Article

Relationships Between Loaded Countermovement Jumps and 1-RM Back Squat: A Discrete Metrics and Waveform Analysis

by
Mary Claire Geneau
1,2,*,
David L. Carey
1,
Paul B. Gastin
1,
Sam J. Robertson
3 and
Lachlan P. James
1
1
Sport, Performance, and Nutrition Research Group, School of Allied Health, Human Services, and Sport, La Trobe University, Melbourne 3086, Australia
2
Canadian Sport Institute Pacific, Victoria, BC V9E 2C5, Canada
3
Institute for Health and Sport (IHES), Victoria University, Melbourne 8001, Australia
*
Author to whom correspondence should be addressed.
Biomechanics 2026, 6(1), 3; https://doi.org/10.3390/biomechanics6010003
Submission received: 20 September 2025 / Revised: 24 November 2025 / Accepted: 10 December 2025 / Published: 1 January 2026
(This article belongs to the Section Sports Biomechanics)
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Abstract

Background/Objectives: This study evaluated the differences in force–time characteristics of different incrementally loaded countermovement jumps (CMJs) and assessed their relationship to one-repetition maximum (1-RM) back squat performance. Methods: Nineteen resistance-trained males participated in this cross-sectional study, performing CMJs under six conditions (0%, 20%, 40%, 60%, 80%, and 100% body mass) followed by a 1-RM back squat. Multiple regression models were used to evaluate the relationship between discrete CMJ metrics (net concentric impulse, net concentric mean force, eccentric duration) with 1-RM values. Additionally, one-dimensional statistical parametric mapping (SPM) was used to evaluate the intact force–time curve between jump conditions. Results: The multiple regression models explained 53–66% of the variance in 1-RM squat performance, which was greatest under the 80% body mass condition. One-dimensional SPM analysis revealed significant differences in force–time curves across all loading conditions. Conclusions: These findings demonstrate that metrics from a loaded CMJ explained up to 66% of variance in the 1-RM back squat, suggesting the two tests are independent measures of strength. Further, each loaded jump condition elicited unique force-time curves, suggesting that each load requires a different neuromuscular technique.

1. Introduction

Evaluating lower-body strength qualities is essential for improving athletic performance. Heavy dynamic strength, characterized by the ability to dynamically exert force against heavy external loads, stands as a unique and crucial strength characteristic in athletes [1,2,3]. Traditionally, this quality has been measured by evaluating an athlete’s one to five-repetition maximum (i.e., 1-, 3-, 5-RM) of a key exercise like the back squat [4]. This method of assessing heavy dynamic strength is well-supported by research and practice as a valid and reliable means of assessing this property [4]. Although 1-RM testing is widely regarded as the standard method for assessing maximal dynamic strength [4,5], its use in high-performance sport is often limited. Challenges include the substantial time required to complete the test, the high external loads imposed on athletes [6], and potential misalignment with training objectives (e.g., prioritizing maximal strength rather than speed-strength) [7,8]. Consequently, coaches in certain environments may elect to assess heavy dynamic strength less frequently or use velocity-based methods to estimate it [9,10].
Another common method of quantifying lower-body dynamic strength is through measuring single or incrementally loaded squat jumps or CMJs [11]. The velocity at which an individual moves under varied external loads can provide insight on dynamic neuromuscular capacity [12]. Loaded jumps are therefore widely used to assess “fast dynamic strength” [8,13] and to generate load– or force–velocity profiles [14].
However, despite the growing popularity of loaded jump testing, few studies have considered the range of metrics available from this test when performed on force platforms. Specifically, studies often involve only a single outcome metric, such as jump height, peak power or velocity [11,15], despite the potential to measure dozens of force–time metrics using force platform technology [16]. Recent evidence indicates that timing (i.e., phase duration), outcome (i.e., net impulse, jump height), and force (e.g., peak and mean force) metrics each contain unique and valuable information about CMJ performance [17,18]. Therefore, it could be beneficial to assess each of these qualities in loaded jumps to capture the extent of force expression that can be captured in these tasks.
Although 1-RM and loaded jumps have both been utilized to assess dynamic strength in various athletic populations [19,20], few studies have explored the shared variance between these tasks when considering multiple force–time metrics from a loaded CMJ. Due to the high force requirement in both moving a maximal external load (i.e., 1-RM) and a sub-maximal external load for maximal velocity and displacement (i.e., loaded CMJs), these actions involve similar neuromuscular properties [21].
To date, however, the ballistic nature of CMJs and non-ballistic nature of 1-RM back squats have supported evaluating these tests as independent measures of strength [22]. Therefore, many athletes perform both a loaded jump and 1-RM test in their strength assessment batteries [19,20]. Although a distinction is observed between 1-RM squat and vertical jump performance in resistance-trained individuals [1], the extent of this distinction has not been extensively evaluated with heavier loads, particularly when considering timing, force, and outcome metrics available from a CMJ performed on force platforms [8,16]. Furthermore, it is unclear how the commonality between metrics of the CMJ and 1-RM change as a product of load. Since different metrics can represent different qualities of strength in a single task [3,23], it is possible that the commonality between a loaded CMJ and 1-RM differs depending on the metric used to represent each test. It is therefore pertinent to explore the relationships between 1-RM and the range of metrics available from a loaded jump. This information is important for practitioners wishing to direct and refine test and metric selection within strength assessment protocols.
When a task is performed on force platforms, an athlete’s movement can be quantified by evaluating the entire (i.e., intact) force–time curve of the task [8,24]. This approach provides a unique representation of movement strategy that may not be entirely captured by discrete metrics alone [8,24]. Prior work has evaluated the area under the intact force–time curve of key CMJ phases performed under a range of loads (0–80 kg) [8]. The normalized force–time curves across each of the loaded conditions aligned with the changes in jump height and other outcome metrics [8]. While area under the curve analyses can quantify general movement differences throughout an entire movement or within pre-defined phases (e.g., propulsive phase), this approach has marked limitations. For instance, this analysis lacks the sensitivity of detecting patterns of force production not aligned with the pre-determined phases [25,26].
To overcome the limitations of traditional area under the curve analyses, recent investigations have employed one-dimensional statistical parametric mapping (SPM) on unloaded vertical jump data [24]. One-dimensional SPM enables visual examination of movement patterns under normalized time-series data, identifying points of statistical difference that may go undetected when analyzing discrete metrics alone [24]. Therefore, SPM may offer a unique means of evaluating movement strategy in CMJs performed under different loads.
It is possible that the addition of external load will change the way one produces force and/or performs a movement [27], which can in turn influence the neuromuscular stimulus imparted on the system [8]. Such changes are particularly relevant for practitioners wishing to target a specific training stimulus that aligns with session objectives or replicates sport-specific demands. Therefore, understanding how movement behavior varies across different loading conditions can help practitioners select the CMJ condition that best matches the desired neuromuscular stimulus or sport requirement.
Overall, it is advantageous for practitioners to understand the relationships between the range of information available from unloaded and loaded CMJ and 1-RM back squat. This information will help isolate and contextualize the physical characteristics measured in these strength tests, and in turn, support parsimonious and directed strength testing protocols. It is also prudent to understand how movement strategies change under different loaded conditions to help select tests based on their relevance to a population. Therefore, the purpose of this study was two-fold: (1) to explore the relationships between force–time metrics from incrementally loaded CMJs and a 1-RM back squat and (2) evaluate the differences in the temporal force production characteristics between unloaded and loaded CMJs ranging from 20% to 100% BM using SPM. We hypothesized that (1) as the CMJ load became heavier, relationships between CMJ metrics and 1-RM back squat would become stronger and (2) the force characteristics between unloaded and loaded CMJs would become greater as load is added to the CMJ.

2. Materials and Methods

2.1. Participants

Nineteen resistance-trained males between the ages of 19 and 40 years were recruited for this study (Age = 26.1 [6.0] years; BM = 82.26 [10.90] kg, Height = 1.79 [0.09] m). All participants regularly took part in resistance training ≥ 2 times per week for >1 year (ranged from 2 to >10 years) and were free from injury at the time of testing. Ethical approval was obtained from the Human Research Committee at La Trobe University (HEC22070). Written informed consent was obtained prior to the commencement of testing and data were handled in line with the Declaration of Helsinki.

2.2. Data Collection

All participants attended a familiarization session one week prior to the test session where their ability to perform a loaded CMJ and 1-RM back squat to parallel (high bar position, femur parallel to the ground) was evaluated and body mass was recorded. In this session, participants performed two sub-maximal CMJs with loads of 20, 40, 60, 80, and 100% body mass and five sets of two back squats increasing to ~80% 1-RM to ensure both exercises could be performed with proper technique and without pain or discomfort. On the testing day, participants completed a general dynamic warm up including low intensity aerobic activity and light dynamic mobility, followed by a specific warm up before each test. Prior to each jump condition, three to five half squats were performed at the testing load followed by two sub-maximal jumps. Two minutes of rest was given to participants between the specific warm up and recorded test. The following jump conditions were performed with 15 to 20 s rest in between each repetition and two to three minutes of rest in between each condition: 0%, 20%, 40%, 40%, 60%, 80%, and 100% BM. The order of the tests was maintained throughout all testing sessions to gradually expose participants to the load [5,23]. This is common practice in the strength and conditioning training and testing environment to reduce risk of injury from increasing the intensity of the load too abruptly. A total of three repetitions were performed in the unloaded (0% BM) condition, while two repetitions were performed at each load to limit the influence of fatigue. The unloaded condition was performed with a 0.5 kg wooden dowelling, while the 20–100% BM conditions were performed with a 5.0 kg training bar or 20.0 kg Olympic lifting bar (Eleiko Group, Halmstad, Sweden) and IWF Weightlifting Training plates (Eleiko Group, Halmstad, Sweden). All CMJs were performed on two portable force platforms (ForceDecks Lite, VALD Performance, Brisbane, Australia). For each condition, the force platforms were zeroed, the participant was weighted with hands akimbo, and the load for the condition was inputted into the software for analysis, which included 0.5 kg (weight of the dowelling) for the 0% BM condition. Participants were then asked to place the dowelling or loaded barbell securely on their shoulders and step onto the force platforms. They were instructed to stand tall and as still as possible for the countdown “3, 2, 1, Jump” where they were instructed to dip down to a self-selected depth [28] and jump as high as possible keeping the bar snug on their shoulders. To identify optimal performance, the repetition with the highest jump height was selected from each condition for the analysis [29].
Following the jumps, participants rested for 10 min and then completed a specific warm up to find a 1-RM back squat. An estimated 1-RM, determined from current training loads and/or prior 1-RM results, was used to inform the specific warm-up of 60% × 5, 70% × 3, 80% × 2, 90% × 1, 95% × 1, 100% × 1, etc., with between two and five minutes of rest in between sets. This protocol is adapted from the guidelines outlined by the National Strength and Conditioning Association [30]. Loads were adjusted based on participant feedback and expert practitioner knowledge. A single load (rounded to the closest 1 kg) was obtained for analysis.

2.3. Data Processing

Raw ground reaction force–time data were collected using VALD software (ForceDecks, VALD Performance, Brisbane, Australia) and downloaded, processed, and analyzed using custom offline scripts. The unfiltered 1000 Hz data from each force platform was combined to represent a total ground reaction force. All force–time data were filtered using a fourth-order, low pass, bidirectional, Butterworth filter with a 50 Hz cut-off frequency [31]. Each jump was identified using logical feature detection and saved as a separate event. Quiet standing (i.e., system) weight before each jump (during the “3, 2, 1” instruction) was calculated as the mean force between 2.50 s and 2.00 s prior to the peak landing force. Movement onset was defined as the first instance where force dropped below 20 N from quiet standing and toe-off was defined as the first instance that force passed 20 N after maximal force. Each of the jump data was trimmed from onset to toe-off, quiet system weight was subtracted, and data were resampled through an interpolating cubic spline (“pracma” package in R) resulting in force curves with a common duration of 1000 samples, representing time-normalized curves.
Three discrete metrics were obtained from each jump: net concentric impulse [Ns], net concentric mean force [N], and eccentric duration [ms]. These metrics were chosen to represent an outcome, force, and timing metric of a CMJ [17,23] and were extracted from the VALD ForceDecks software. This software defines the start of the jump as <20 N change in system weight, the start of the concentric phase as the moment of peak negative velocity, and take-off as the moment force drops < 20 N. All default manufacturer settings were used for data collection and analysis.

2.4. Statistical Analysis

A series of multiple linear regression models were used to assess the relationships between jump metrics and 1-RM performance. Each of the discrete metrics were assessed for collinearity by calculating the variance inflation factors (VIF). The VIFs were 5.76 for net concentric impulse, 5.48 for net concentric mean force, and 3.07 for eccentric duration. The first method (models M0, M20, M40, M60, M80, and M100) included net impulse, net concentric mean force, and eccentric duration corresponding to each load (0%, 20%, 40%, 60%, 80%, and 100% BM) as the independent variables (i.e., each metric from a single load in each model), and 1-RM set as the dependent variable. The second method (models M20.0, M40.0, M60.0, M80.0, M100.0) included net impulse measured in the unloaded CMJ with each of the loaded conditions (0 + 20%, 0 + 40%, 0 + 60%, 0 + 80%, 0 + 100%) set as the independent variables and 1-RM set as the dependent variable (Table 1). It is typical to include an unloaded CMJ in physical testing batteries, therefore the second series of regression models involved both an unloaded CMJ and loaded CMJ. All discrete metric statistical analyses were performed in RStudio (R version 4.3.1).
To assess movement strategy across loads, one-dimensional SPM involving a repeated-measures, within-subjects analysis of variance (ANOVA) was performed on the time-normalized force–time curves using the ‘spm1d’ package in Python (Python version 3.12.0; (http://www.spm1d.org/ (accessed on 1 May 2023)) [24]. In this analysis, load (% BM) was set as the condition and two normalized force–time curves per load were analyzed for each participant. Post hoc pairwise t-tests were performed to assess the differences in time-normalized force production across each load respective of the critical t-statistic. The p-value was adjusted for multiple comparisons using a Bonferroni correction.

3. Results

Participants’ 1-RM back squat ranged from 100 to 200 kg (mean = 138.3; standard deviation = 24.0). The mean metric values from each CMJ condition are presented in Table 2. These are presented with CMJ height, through this value was not used in the models due to high multicollinearity with net concentric impulse.
Models M0–M100 explained 53–66% variance in 1-RM squat performance (Table 3). The models M20.0–M100.0 explained 57–65% of variance in 1-RM squat performance (Table 4).
The SPM ANOVA displayed a main effect of condition (i.e., load), represented as suprathreshold clusters exceeding the critical threshold (F = 91.60; p < 0.001) during four main portions of the jump trace (Figure 1). The largest difference was observed just before toe-off (Figure 1), followed by three other main differences at ~50, ~400, and ~800 indexes. These differences are represented as red peaks in Figure 1b.
The post hoc SPM t-tests revealed significant differences in the normalized force–time trace spatial patterning between each of the compared conditions (t = 6.3 to 23.6; p < 0.01) (Figure 2). The force curves are spatially visualized relative to t, whereby suprathreshold clusters represent significant differences in the normalized force curve [24,25,32]. Positive clusters represent a reduction in force, and a negative cluster represents an increase in force compared to the other condition (0 or 100%). The most consistent suprathreshold clusters were observed close to toe-off, with significant differences observed between 0% and 20–100% BM, and between 0–60% BM and 100%. Additional suprathreshold clusters were observed at index ~50 and ~400 of the normalized curve when the 0% condition was compared to the 60% condition, and when the 0% and 20% conditions were compared to the 100% condition (Figure 2).

4. Discussion

In the strength and conditioning environment, the time devoted to strength assessment is often limited, and coaches are required to choose one strength test over another. To help inform the test selection process, this study included two aims: First, to evaluate the relationships between force–time metrics of incrementally loaded CMJs and back squat 1-RM and, second, to evaluate the differences in intact force–time characteristics across the CMJ conditions. The regression models involving CMJ force–time metrics explained 53–66% of variance in 1-RM performance, with the greatest shared variance observed in the 80% BM condition. Net impulse was the predominate contributor across models, with little contribution from net concentric mean force or eccentric duration. Furthermore, one-dimensional SPM analyses revealed that each of the CMJ conditions elicited a significantly different force–time curve.
Of the two multiple regression model approaches, the one incorporating three different CMJ metrics at each load explained 53–66% of variance in back squat 1-RM, while the one including net concentric impulse from both an unloaded and loaded CMJ, explained 57–65% of variance in the 1-RM. Notably, the highest shared variance was between net concentric impulse measured in the 80% BM condition, with slightly lower shared variance between the 1-RM and 100% BM CMJ (65–66% versus 58–61%). This difference may reflect a shift in movement strategy at higher external loads (i.e., 100% BM) where the increased task demands change neuromuscular coordination and technique required to perform the task [33,34]. This may in turn constrain the ability to express force ballistically.
Taken together, these findings demonstrate that a substantial proportion of variance in 1RM back squat performance remains unexplained by metrics from incrementally loaded CMJs. This may largely be attributed to the fundamental biomechanical differences between the tasks. Specifically, the CMJ is a ballistic task whereas the back squat is non-ballistic, alter the neuromuscular demand of the movement. Furthermore, the back squat used in this study required participants to achieve a standard depth, which may involve technical proficiency and neuromuscular control that is different from that of the incrementally loaded CMJs. Consequently, although loaded CMJs provide valuable insight into lower-body force production, they do not appear to fully capture the strength qualities assessed by the 1RM back squat.
The results also demonstrate that combining the information from a loaded and unloaded jump does not improve model performance within the constraints of this study, as the models including unloaded CMJ net impulse are comparable to the models with a single load. Since the shared variance was similar across CMJ conditions (57–66%) and the sample size used in this study did not permit the selection or refusal of any of these conditions with statistical certainty, the present results cannot directly inform the selection of one load over another for measuring lower-body strength using a CMJ. Instead, these results offer practitioners information on the amount of shared variance between testing conditions to help them select a task and task constraint best suited for their environment. Future research should aim to replicate this study design using fewer loads, and in different or larger cohorts to better contribute to these findings.
Despite explaining up to 66% of the variance in 1-RM performance, the relationships between CMJ metrics and 1-RM squat are lower than that observed in non-ballistic load-velocity predictions, such as those regularly performed with sub-maximal back squats [10]. A main reason for this difference could be the absolute loads used in the present study. External loads used in load-velocity prediction studies are typically much closer to the actual 1-RM value (e.g., 70–90% 1-RM [35]), whereas the loads used in this study were lower, averaging 62.5% 1-RM (51–91%). The differences may also be explained by the fundamental differences between ballistic (i.e., CMJ) and non-ballistic (i.e., back squat) multi-joint movements [2,36], since ballistic tasks permit a longer acceleration leading to higher peak velocities [36]. Furthermore, the depth of the CMJs were not standardized in the present study to allow athletes to have an unrestricted movement strategy. Since squat depths often differ between a CMJ and back squat to parallel [37,38], performance differences may be partially attributed to the range at which each of these tasks are performed. Overall, these data provide the reader with a novel understanding of the relationships between incrementally loaded CMJs and a back squat 1-RM in resistance-trained individuals.
Another important consideration is the relationships between the back squat 1-RM and each of the jump characteristics. In this analysis, the regression coefficients between 1-RM and both net concentric mean force and eccentric duration were near zero (−0.12–0.01) (Table 3), meaning that neither of these force–time metrics can effectively account for changes in 1-RM strength, and vice-versa. This is important information as both timing and force variables of a jump have been considered valuable measures of dynamic performance [17,18,23]. It is therefore recommended that a 1-RM be considered in addition to a ballistic assessment (i.e., CMJ) to fully capture these qualities.
Apart from shared variance, practitioners should consider the external demand of different CMJ loading conditions when selecting the best testing condition. Participants’ intact force production pattern under each load was assessed using SPM, demonstrating a substantial difference in movement strategy at ~900–1000 indexes in the CMJ normalized force curves. This suggests that any addition of load (e.g., 20% BM) can significantly impact force production patterning immediately before toe-off. These findings are consistent with other research demonstrating that adding a light (10% BM) load affected hip, knee, and ankle work in the concentric but not the eccentric phase of a CMJ [39]. This may be due to the mechanical disadvantage of the lower limbs at the end of this phase, and therefore, the relative load of a given external load prior to toe-off. As one moves through the concentric phase to takeoff, ground reaction force decreases [40], and muscle contributions change due to shifts in center of mass, joint angles, and muscle lengths [41,42]. These changes reduce force production capacity and time to produce force, which makes it more difficult to increase impulse required to perform a jump. Collectively, these disadvantages of this phase may result in a disproportionate change in force production pattering at a given external load. This could in part explain the movement differences at toe-off, but not in other phases of the jump, when performing a jump under lighter conditions.
Additional suprathreshold clusters were observed in the early (~50 index) and middle (~400 indexes) portions of the normalized curve between the unloaded jump and the 60 and 100% BM conditions. Based on the location of the clusters, these differences likely reflect changes in force production during the unloading and braking phases (components of the eccentric phase) of 60 and 100% BM conditions when compared to the unloaded condition. Similar findings were observed in a group of trained males, where adding as little as 20 kg to a squat jump significantly reduced the jump height and increased eccentric force [8], yet significant differences in eccentric rate of force development were only evident when the load was increased to 60 and 80 kg [8]. These variations in eccentric force production rates may reflect changes in movement strategy [43], similar to those identified in the normalized curve analysis of this study. Interestingly, there were no such changes to the 80% condition. It is possible that potentiation [44], warm up, or a learned movement strategy contributed to this finding. Future research should consider using a randomized order of loaded jumps to address these possible factors. Overall, these movement pattern observations can be leveraged to match a desired loaded CMJ condition for the purpose of training and testing.
This study was not without limitations and delimitations. As mentioned, the standardized as opposed to randomized sequence of jump conditions may have introduced order effects such as potentiation or fatigue, potentially skewing the results. A set order was chosen to gradually expose participants to a more demanding tasks, similar to how training is performed, to reduce the risk of injury. Additionally, performing the CMJs with a self-selected depth, intended to promote a natural jump strategy, introduced a variable that could affect outcome metrics. Exploring the relationships between jump metrics form CMJs performed to a standardized depth could supplement the present finings. Although it is common practice to assess movement strategy using force platform technology, the present study did not involve more precise measures of movement such as joint angle changes or muscle activation. The study also did not involve multiple sessions, limiting the reliability to only within-session values. Furthermore, the moderate levels of collinearity between some of the CMJ variables (VIF > 5) may have influenced the regression model outputs. Lastly, this study involved 19 resistance-trained males, which represent only a small portion of the populations that these results may be relevant to. It will be prudent to perform similar analyses with a greater number of participants, females, and those who present with varying training histories and biological characteristics.
This study reports on the relationships between incrementally loaded CMJ metrics and 1-RM back squat and the differences in force production patterning across each jump condition. The main findings suggest that CMJs share 53–66% of variance with back squat 1-RM performance, with net impulse contributing the most to this variance. This study also demonstrates that loaded jumps differ in movement strategy across loads, potentially influencing the neuromuscular requirements and stimulus from this exercise. Overall, these results suggest that CMJs cannot adequately explain 1-RM back squat performance, thus, these findings suggest that it is valuable to assess a CMJ, irrespective of load, in addition to a 1-RM if the goal is to capture a range heavy and light dynamic force expressions.

5. Conclusions

This study provides novel insights for practitioners aiming to assess heavy dynamic strength using a loaded CMJ or a 1-RM back squat. The findings demonstrate the range of information available from CMJs loaded with 0–100% BM and back squat 1-RM, while highlighting force patterning changes as load is added to a CMJ, potentially altering stimulus of the task. Since metrics from the CMJ only explained 53–66% of variance in the 1-RM back squat, it is recommended to include both a CMJ of any load and 1-RM back squat to account for the range of strength information from these tests. Practitioners can then use the intact waveform results from this study to select a CMJ condition that best aligns with the desired training stimuli or reflect sport-specific demands. Overall, this study provides practitioners with valuable information concerning task and metric selection to effectively measure dynamic strength qualities in athletes.

Author Contributions

Conceptualization, M.C.G., P.B.G., S.J.R. and L.P.J.; methodology, M.C.G. and D.L.C.; formal analysis, M.C.G.; investigation, M.C.G.; writing—original draft preparation, M.C.G., D.L.C., P.B.G. and L.P.J.; writing—review and editing, M.C.G., P.B.G., S.J.R. and L.P.J.; visualization, M.C.G.; supervision, D.L.C. and L.P.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was a part of a PhD project that was supported by a La Trobe University (Melbourne, Australia) Full-Fee Research Scholarship that was co-funded by the University and VALD Performance (Brisbane, Australia).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of La Trobe University (HEC22070, December 2021).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to confidentiality purposes.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
BMBody mass
CMJCountermovement jump
1-RMOne-repetition maximum
ANOVAAnalysis of variance
SPMStatistical parametric mapping
SDStandard deviation

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Biomechanics 06 00003 g001
Figure 1. (a) Mean curves of each jump condition. (b) One-dimensional SPM repeated-measures ANOVA results presented as inference curve (red line) with suprathreshold clusters (red shaded area) and critical thresholds (dotted red line). SPM = statistical parametric mapping; SPM{F} = F statistic value. Significance is set at p < 0.05.
Figure 1. (a) Mean curves of each jump condition. (b) One-dimensional SPM repeated-measures ANOVA results presented as inference curve (red line) with suprathreshold clusters (red shaded area) and critical thresholds (dotted red line). SPM = statistical parametric mapping; SPM{F} = F statistic value. Significance is set at p < 0.05.
Biomechanics 06 00003 g001
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Figure 2. One-dimensional SPM t-test comparisons between the 0% BM condition and the 20, 40, 60, 80, and 100% BM conditions (as labeled), and between the 100% BM condition and the 20, 40, 60, and 80% conditions (as labeled), presented a critical threshold (dotted line) with suprathreshold clusters (shaded). Positive clusters represent a reduction in force, and a negative cluster represents an increase in force compared to the other condition (0 or 100%). SPM = statistical parametric mapping; SPM{t} = t statistic value. Significance is set at p < 0.01.
Figure 2. One-dimensional SPM t-test comparisons between the 0% BM condition and the 20, 40, 60, 80, and 100% BM conditions (as labeled), and between the 100% BM condition and the 20, 40, 60, and 80% conditions (as labeled), presented a critical threshold (dotted line) with suprathreshold clusters (shaded). Positive clusters represent a reduction in force, and a negative cluster represents an increase in force compared to the other condition (0 or 100%). SPM = statistical parametric mapping; SPM{t} = t statistic value. Significance is set at p < 0.01.
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Table 1. Multiple regression models used in this study. Impulse = CMJ net concentric impulse; Force = CMJ net concentric mean force; Duration = CMJ eccentric phase duration; RM = repetition maximum. Variable suffixes indicate the loading condition (e.g., Impulse 20% = Net impulse under the 20% BM CMJ condition).
Table 1. Multiple regression models used in this study. Impulse = CMJ net concentric impulse; Force = CMJ net concentric mean force; Duration = CMJ eccentric phase duration; RM = repetition maximum. Variable suffixes indicate the loading condition (e.g., Impulse 20% = Net impulse under the 20% BM CMJ condition).
ModelDependent VariableIndependent Variables
M01-RM Back SquatForce 0% + Duration 0% + Impulse 0%
M201-RM Back SquatForce 20% + Duration 20% + Impulse 20%
M401-RM Back SquatForce 40% + Duration 40% + Impulse 40%
M601-RM Back SquatForce 60% + Duration 60% + Impulse 60%
M801-RM Back SquatForce 80% + Duration 80% + Impulse 80%
M1001-RM Back SquatForce 100% + Duration 100% + Impulse 100%
M20.01-RM Back SquatImpulse 0% + Impulse 20%
M40.01-RM Back SquatImpulse 0% + Impulse 40%
M60.01-RM Back SquatImpulse 0% + Impulse 60%
M80.01-RM Back SquatImpulse 0% + Impulse 80%
M100.01-RM Back SquatImpulse 0% + Impulse 100%
Table 2. All performance metrics from the incremental loaded CMJs and 1-RM back squat. All values are presented as mean (standard deviation). BM = body mass.
Table 2. All performance metrics from the incremental loaded CMJs and 1-RM back squat. All values are presented as mean (standard deviation). BM = body mass.
Condition [% BM]Jump Height [cm]Net Concentric Mean Force [N]Net Concentric
Impulse [Ns]		Eccentric
Duration [ms]
035.25 (6.11)828.48 (213.47)221.04 (40.12)501.52 (95.13)
2027.05 (5.32)744.86 (202.10)230.99 (43.92)545.91 (66.62)
4021.25 (4.85)673.13 (190.17)237.35 (44.84)597.35 (70.85)
6016.42 (3.84)638.35 (22.38)237.97 (48.51)645.04 (131.13)
8013.22 (2.94)612.15 (211.53)239.77 (44.39)648.36 (139.31)
10010.60 (5.16)546.53 (297.02)207.21 (81.57)602.37 (235.82)
Table 3. Multiple regression model outputs using multiple metrics from each jump condition to explain performance in 1-RM back squat. Force = net concentric force; Impulse = net concentric impulse; Duration = eccentric duration; CI = confidence interval.
Table 3. Multiple regression model outputs using multiple metrics from each jump condition to explain performance in 1-RM back squat. Force = net concentric force; Impulse = net concentric impulse; Duration = eccentric duration; CI = confidence interval.
ModelPredictorCoefficient95% CIStandardized
Coefficient		r2Adjusted r2AIC
M0CMJ 0% Force [N]0.010.10–0.120.100.530.44169.23
CMJ 0% Duration [ms]−0.070.29–0.15−0.21
CMJ 0% Impulse [Ns]0.40.13–0.940.68
M20CMJ 20% Force [N]−0.01−0.14–0.120.130.590.51166.75
CMJ 20% Duration [ms]−0.12−0.34–0.11−0.22
CMJ 20% Impulse [Ns]0.49−0.20–1.180.67
M40CMJ 40% Force [N]−0.01−0.09–0.070.020.620.54165.51
CMJ 40% Duration [ms]−0.10−0.27–0.06−0.25
CMJ 40% Impulse [Ns]0.450.04–0.360.77
M60CMJ 60% Force [N]0.00−0.08–0.080.220.610.54165.87
CMJ 60% Duration [ms]−0.04−0.13–0.05−0.13
CMJ 60% Impulse [Ns]0.39−0.07–0.850.62
M80CMJ 80% Force [N]−0.02−0.09–0.050.020.660.58163.11
CMJ 80% Duration [ms]−0.05−0.12–0.03−0.15
CMJ 80% Impulse [Ns]0.520.12–0.930.77
M100CMJ 100% Force [N]−0.06−0.12–0.00−0.970.590.48166.75
CMJ 100% Duration [ms]−0.09−0.14–0.03−1.15
CMJ 100% Impulse [Ns]0.650.20–1.102.11
Table 4. Multiple regression model outputs using multiple metrics from each jump condition to explain performance in 1-RM back squat. Force = net concentric force; Impulse = net concentric impulse; Duration = eccentric duration.
Table 4. Multiple regression model outputs using multiple metrics from each jump condition to explain performance in 1-RM back squat. Force = net concentric force; Impulse = net concentric impulse; Duration = eccentric duration.
ModelPredictorsEstimate95% CIStandardized
Coefficient		r2Adjusted
r2		AIC
M20.0CMJ 0% Impulse [Ns]−1.00−2.54–0.54−1.680.570.51165.73
CMJ 20% Impulse [Ns]1.31−0.11–2.722.39
M40.0CMJ 0% Impulse−0.43−1.31–0.45−0.720.580.53165.31
CMJ 40% Impulse [Ns]0.77−0.02–1.561.45
M60.0CMJ 0% Impulse−0.34−1.12–0.44−0.560.580.53165.22
CMJ 60% Impulse [Ns]0.65−0.01–1.311.29
M80.0CMJ 0% Impulse−0.21−0.71–0.29−0.350.650.60162.04
CMJ 80% Impulse [Ns]0.580.15–1.021.12
M100.0CMJ 0% Impulse0.360.15–0.560.600.610.56163.91
CMJ 100% Impulse [Ns]0.100.01–0.200.39
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Geneau, M.C.; Carey, D.L.; Gastin, P.B.; Robertson, S.J.; James, L.P. Relationships Between Loaded Countermovement Jumps and 1-RM Back Squat: A Discrete Metrics and Waveform Analysis. Biomechanics 2026, 6, 3. https://doi.org/10.3390/biomechanics6010003

AMA Style

Geneau MC, Carey DL, Gastin PB, Robertson SJ, James LP. Relationships Between Loaded Countermovement Jumps and 1-RM Back Squat: A Discrete Metrics and Waveform Analysis. Biomechanics. 2026; 6(1):3. https://doi.org/10.3390/biomechanics6010003

Chicago/Turabian Style

Geneau, Mary Claire, David L. Carey, Paul B. Gastin, Sam J. Robertson, and Lachlan P. James. 2026. "Relationships Between Loaded Countermovement Jumps and 1-RM Back Squat: A Discrete Metrics and Waveform Analysis" Biomechanics 6, no. 1: 3. https://doi.org/10.3390/biomechanics6010003

APA Style

Geneau, M. C., Carey, D. L., Gastin, P. B., Robertson, S. J., & James, L. P. (2026). Relationships Between Loaded Countermovement Jumps and 1-RM Back Squat: A Discrete Metrics and Waveform Analysis. Biomechanics, 6(1), 3. https://doi.org/10.3390/biomechanics6010003

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