Force-Time Waveform Shape Reveals Countermovement Jump Strategies of Collegiate Athletes

Absorption function loss due to the history of previous ankle sprain explored by unsupervised machine learning

BACKGROUND

Ankle sprains are common and cause persistent ankle function reduction. To biomechanically evaluate the ankle function after ankle sprains, the ground reaction force (GRF) measurement during the single-legged landing had been used. However, previous studies focused on discrete features of vertical GRF (vGRF), which largely ignored vGRF waveform features that could better identify the ankle function.

PURPOSE

To identify how the history of ankle sprain affect the vGRF waveform during the single-legged landing with unsupervised machine learning considering the time-series information of vGRF.

METHODS

Eighty-seven currently healthy basketball athletes (12 athletes without ankle sprain, 49 athletes with bilateral, and 26 athletes with unilateral ankle sprain more than 6 months before the test day) performed single-legged landings from a 20 centimeters (cm) high box onto the force platform. Totally 518 trials vGRF data were collected from 87 athletes of 174 ankles, including 259 ankle sprain trials (from previous sprain ankles) and 259 non-ankle sprain trials (from without sprain ankles). The first 100 milliseconds (ms) vGRF waveforms after landing were extracted. Principal component analysis (PCA) was applied to the vGRF data, selecting 8 principal components (PCs) representing 96% of the information. Based on these 8 PCs, k-means method (k = 3) clustered the 518 trials into three clusters. Chi-square test assessed significant differences (p < 0.01) in the distribution of ankle sprain and non-ankle sprain trials among clusters.

FINDINGS

The ankle sprain trials accounted for a significantly larger percentage (63.9%) in Cluster 3, which exhibited rapidly increased impulse vGRF waveforms with larger peaks in a short time.

SIGNIFICANCE

PCA and k-means method for vGRF waveforms during single-legged landing identified that the history of previous ankle sprains caused a loss of ankle absorption ability lasting at least 6 months from an ankle sprain.

Identifying special operative trainees at-risk for musculoskeletal injury using full body kinematics

Introduction: Non-combat musculoskeletal injuries (MSKIs) during military training significantly impede the US military's functionality, with an annual cost exceeding $3.7 billion. This study aimed to investigate the effectiveness of a markerless motion capture system and full-body biomechanical movement pattern assessments to predict MSKI risk among military trainees. Methods: A total of 156 male United States Air Force (USAF) airmen were screened using a validated markerless biomechanics system. Trainees performed multiple functional movements, and the resultant data underwent Principal Component Analysis and Uniform Manifold And Projection to reduce the dimensionality of the time-dependent data. Two approaches, semi-supervised and supervised, were then used to identify at-risk trainees. Results: The semi-supervised analysis highlighted two major clusters with trainees in the high-risk cluster having a nearly five times greater risk of MSKI compared to those in the low-risk cluster. In the supervised approach, an AUC of 0.74 was produced when predicting MSKI in a leave-one-out analysis. Discussion: The application of markerless motion capture systems to measure an individual's kinematic profile shows potential in identifying MSKI risk. This approach offers a novel way to proactively address one of the largest non-combat burdens on the US military. Further refinement and wider-scale implementation of these techniques could bring about substantial reductions in MSKI occurrence and the associated economic costs.

Countermovement, Hurdle, and Box Jumps: Data-Driven Exercise Selection

Apart from squat jumps, countermovement jumps (CMJ), and drop jumps, differences among other jump variations are not as well researched, making data-driven exercise selection difficult. To address this gap, this study compared selected concentric and eccentric jump parameters of maximal effort CMJ, hurdle jumps over 50 cm hurdle (HJ), and box jumps onto a 50 cm box (BJ). Twenty recreationally trained men (25.2 ± 3.5 years) performed 3 repetitions of CMJs, HJs, and BJs, each on separate days. The data were collected using force platforms and a linear position transducer. The mean of 3 trials of each jump variation was analyzed using repeated measures ANOVA and Cohen's d. Countermovement depth was significantly greater (p ≤ 0.05) and peak horizontal force significantly lower during CMJ compared to HJ and BJ. However, there were no differences in peak velocity, peak vertical and resultant force, and total impulsion time. Finally, BJ significantly decreased peak impact force by ~51% compared to CMJ and HJ. Therefore, the propulsive parameters of HJ and BJ seem to be similar to CMJ, despite CMJ having a greater countermovement depth. Furthermore, overall training load can be decreased dramatically by using BJ, which reduced peak impact force by approximately half.

Predicting Time to Take-Off in a Countermovement Jump for Maximal Quickness From Upright and Squat Starting Positions

A countermovement jump (CMJ) is common in sport and often time-constrained. Little is known about contributors to quickness in jumps. This study examined effective predictors of time to take-off and effects of the starting position on reaction time and take-off time in a countermovement jump performed for quickness from upright and squat positions. Forty-nine collegiate athletes performed CMJs for quickness from upright and squatting starting positions to 75% of their maximal jump height. Several variables were calculated from the kinetic data related to jump performance. Correlation and multiple regression were used to determine variables related and predictive of time to take-off under both conditions. Paired t-tests evaluated differences in reaction and take-off times between conditions. In the upright condition, an increasing rate of force development and force, and decreasing time variables, impulses, and countermovement depth were associated with shorter time to take-off. The time to take-off prediction included rates of force development, force, time, and impulse. In the squat condition, shorter time to take-off was associated with lesser time variables, eccentric impulse, force at the end of the eccentric phase, and countermovement depth, and a greater rate of force development, concentric impulse, peak power, peak force, and reaction time. The time to take-off prediction equation included time to the bottom of the countermovement, time to peak force, and peak power. Reaction and take-off times were longer in the upright condition. Quick jump efficiency may be improved by strategies to increase maximum strength and the eccentric rate of force development while decreasing countermovement depth and time to bottom.

Kinetic time-curves can classify individuals in distinct levels of drop jump performance

This study examined whether analysing kinetic features of drop jumps (DJ) as one-dimensional biomechanical curves can reveal specific patterns that are consistent and can cluster DJ performance. Hierarchical clustering analysis on DJ from 40 cm data performed by 128 physically active male participants (23.0 ± 4.5 yrs, 1.84 ± 0.07 m, 79.1 ± 10.8 kg) was performed on the derived time-normalised force, power and vertical stiffness curves to unmask the underlying patterns and to explore the dissimilarities identified from the subgroup (cluster) analysis. Results revealed poor, average and top DJ performers. Top performers exhibited larger peak force, power and vertical stiffness compared to the other two groups, and the poor performers had lower values compared to the average performers (p < .05). The time curves of force, power and vertical stiffness exhibited between cluster dissimilarities from ~25% to ~70%, and ~20% to 40% plus ~55% to 70% from the beginning of the ground contact, respectively. The force and power time-curves distinguished DJ ability similarly since they shared 69% of the cases in the top performers' cluster. The content of cases (membership) for vertical stiffness was different from the membership for the force and power time-curve clusters. In conclusion, stiffness should be considered during plyometric training, but does not distinctly define DJ performance.

Comparisons of Countermovement Jump Force-Time Characteristics Among National Collegiate Athletic Association Division I American Football Athletes: Use of Principal Component Analysis

ABSTRACT

Merrigan, JJ, Rentz, LE, Hornsby, WG, Wagle, JP, Stone, JD, Smith, HT, Galster, SM, Joseph, M, and Hagen, JA. Comparisons of countermovement jump force-time characteristics among NCAA Division I American football athletes: use of principal component analysis. J Strength Cond Res 36(2): 411-419, 2022-This study aimed to reduce the dimensionality of countermovement jump (CMJ) force-time characteristics and evaluate differences among positional groups (skills, hybrid, linemen, and specialists) within National Collegiate Athletic Association (NCAA) division I American football. Eighty-two football athletes performed 2 maximal effort, no arm-swing, CMJs on force plates. The average absolute and relative (e.g., power/body mass) metrics were analyzed using analysis of variance and principal component analysis procedures (p < 0.05). Linemen had the heaviest body mass and produced greater absolute forces than hybrid and skills but had lower propulsive abilities demonstrated by longer propulsive phase durations and greater eccentric to concentric mean force ratios. Skills and hybrid produced the most relative concentric and eccentric forces and power, as well as modified reactive strength indexes (RSIMOD). Skills (46.7 ± 4.6 cm) achieved the highest jump height compared with hybrid (42.8 ± 5.5 cm), specialists (38.7 ± 4.0 cm), and linemen (34.1 ± 5.3 cm). Four principal components explained 89.5% of the variance in force-time metrics. Dimensions were described as the (a) explosive transferability to concentric power (RSIMOD, concentric power, and eccentric to concentric forces) (b) powerful eccentric loading (eccentric power and velocity), (c) countermovement strategy (depth and duration), and (d) jump height and power. The many positional differences in CMJ force-time characteristics may inform strength and conditioning program designs tailored to each position and identify important explanatory metrics to routinely monitor by position. The overwhelming number of force-time metrics to select from may be reduced using principal component analysis methods, although practitioners should still consider the various metric's applicability and reliability.

Is the Shape of the Force-Time Curve Related to Performance in Countermovement Jump? A Review

Countermovement jump (CMJ) is frequently used to assess the neuromuscular capacity in athletes and track adaptations to training, typically through outcome variables such as jump height, peak/mean force, power or velocity, and rate of force development. Recently, there has been an increasing interest to analyze the shape of the force-time curve of the CMJ and its relationship to CMJ performance. This aim of the present review was to collect and analyze the available literature pertaining to this topic. One approach to analyze CMJ curve shape is to classify it as "unimodal" or "bimodal," based on the number of force peaks. The difference between athletes showing unimodal and bimodal curves is negligible in terms of jump height, while unimodal curves are associated with higher reactive strength index. Rather than the number of peaks, the most important characteristics that maximizes CMJ height seems to be the temporal alignment of peak force with the instant of the lowest center-of-mass position (i.e., when the jumper transitions from the braking to the propulsive phase). Other than bimodal/unimodal classification, the "shape factor" (the value of force impulse, divided by the area of the rectangular shape drawn around) has been emerging as another approach to assess CMJ curve shape; however, the studies exploring its relationship with performance are few and inconclusive.

Objective classification of countermovement jump force-time curve modality: within athlete-consistency and associations with jump performance

Force-time curves produced during a countermovement jump (CMJ) have traditionally been classified by visual observation as either unimodal (one concentric phase peak) or bimodal (two peaks). The association between CMJ modality and jump performance remains unclear and future studies may benefit from standardising and expanding modality classification. This study described a numerical method based on the timing and relative magnitude of concentric force-time curve prominences. Adult male elite rugby union players (n = 214) performed six CMJs on a force-instrumented treadmill and an algorithm using turning-point logic was applied to categorise jumps and define modality sub-groups. A sensitivity analysis demonstrated that the minimum prominence threshold (MPT) affected categorisation, as the proportion of bimodal jumps decreased with each 1% increase in MPT. Within-athlete consistency was also affected; between 43% and 63% of participants were consistently categorised as bimodal or unimodal depending on the selected MPT. Modified reactive strength index (RSImod), but not jump height or take-off momentum, was greater in unimodal jumps. Take-off momentum and RSImod were greater in subcategories where maximum force occurred early in the concentric phase. Future research should implement objective classification methods to enhance transparency and comparability and consider subcategories to investigate CMJ force production strategies.