Dimensionality Reduction Differentiates Sensitive Force-Time Characteristics from Loaded and Unloaded Conditions throughout Competitive Military Training

Classification of Force-Time Metrics Into Lower-Body Strength Domains

ABSTRACT

Geneau, MC, Carey, DL, Gastin, PB, Robertson, S, and James, LP. Classification of force-time metrics into lower-body strength domains. J Strength Cond Res 38(9): 1561-1567, 2024-The purpose of this study was to classify force-time metrics into distinct lower-body strength domains using a systematic data reduction analysis. A cross-sectional design was used, whereby competitive field sport athletes ( F = 39, M = 96) completed a series of drop jumps, squat jumps, countermovement jumps (CMJs), loaded CMJs, and 2 isometric tasks on portable force platforms, resulting in a total of 285 force-time performance metrics. The metrics were split into 4 test "families" and each was entered into a sparse principal component analysis (sPCA) model. A single metric from each component of each family-specific sPCA were selected based on the loading, reliability, and simplicity of the metric and entered into a second sPCA that included metrics across all tests. The final sPCA revealed 7 principal components each containing 2 metrics and explained a total of 53% variance of the dataset. The final principal components were interpreted as 7 lower-body strength domains: (a) dynamic force, (b) dynamic timing, (c) early isometric, (d) maximal isometric, (e) countermovement velocity, (f) reactive output, and (g) reactive timing. The findings demonstrate that a total of 7 metrics from a drop jump, CMJ, and isometric test can be used to represent ∼50% of variance in lower-body strength performance of field sport athletes. These results can help guide and simplify the lower-body strength diagnosis process in field sport athletes.

A framework for test measurement selection in athlete physical preparation

Preparing athletes for competition requires the diagnosis and monitoring of relevant physical qualities (e.g., strength, power, speed, endurance characteristics). Decisions regarding test selection that attempt to measure these physical attributes are fundamental to the training process yet are complicated by the myriad of tests and measurements available. This article presents an evidenced based process to inform test measurement selection for the physical preparation of athletes. We describe a method for incorporating multiple layers of validity to link test measurement to competition outcome. This is followed by a framework by which to evaluate the suitability of test measurements based on contemporary validity theory that considers technical, decision-making, and organisational factors. Example applications of the framework are described to demonstrate its utility in different settings. The systems presented here will assist in distilling the range of measurements available into those most likely to have the greatest impact on competition performance.

Countermovement Jump Force-Time Curve Analyses: Reliability and Comparability Across Force Plate Systems

ABSTRACT

Merrigan, JJ, Strang, A, Eckerle, J, Mackowski, N, Hierholzer, K, Ray, NT, Smith, R, Hagen, JA, and Briggs, RA. Countermovement jump force-time curve analyses: reliability and comparability across force plate systems. J Strength Cond Res 38(1): 30-37, 2024-Considering the growing prevalence of commercial force plates providing automated force-time analyses, understanding levels of agreement across force plate systems is warranted. Countermovement jump (CMJ) metrics across Vald ForceDecks (FD), Hawkin Dynamics (HD), and Sparta Science (SS) force plate systems were compared. Twenty-two subjects completed CMJ testing (∼128 comparisons) on each force plate system separately with rest between jumps. Baseline testing occurred 3 times and demonstrated poor test-retest reliability for modified reactive strength index (mRSI) and rate of force development (RFD). ForceDecks and HD comparisons yielded acceptable agreement for concentric/propulsive relative force and net impulse, jump height, eccentric/braking RFD, and mRSI, but systematic and proportionate bias existed for RFD. Sparta Science jump height and reactive strength index (RSI) demonstrated systematic overestimations compared with HD and FD, but jump height had acceptable agreement according to concordance correlation coefficients (CCC = 0.92-0.95). Agreement between SS load (eccentric RFD) and HD braking RFD was acceptable (CCC = 0.91), whereas agreement between SS load and FD deceleration RFD was considered acceptable (CCC = 0.81-0.87) but demonstrated systematic and proportionate bias. ForceDecks (CCC = 0.89) and HD (CCC = 0.85) average relative concentric/propulsive force yielded acceptable agreement with SS explode (average relative concentric force), but SS explode demonstrated systematically lower values than FD and HD. Sparta Science drive (concentric impulse) yielded acceptable agreement with HD relative propulsive impulse (CCC = 0.85), but not FD concentric impulse. Human performance practitioners need to be aware of inconsistencies among testing procedures and analyses across force plate systems, such as differences in metric definitions and units of measurement, before making comparisons across systems.

Psychophysiology, Cognitive Function, and Musculoskeletal Status Holistically Explain Tactical Performance Readiness and Resilience

ABSTRACT

Thompson, AG, Ramadan, JH, Alexander, JS, and Galster, SM. Psychophysiology, cognitive function, and musculoskeletal status holistically explain tactical performance readiness and resilience. J Strength Cond Res 37(12): 2443-2456, 2023-This study aimed to advance the techniques used in quantifying holistic readiness and resilience within military personnel. Tactical performers, instructors, and applied human performance scientists designed a weeklong competition to reflect realistic operational demands, test specific underlying performance constructs, and elucidate how modernized assessments could drive programmatic action. By placing first in their installation's local preliminary competition, 34 active-duty Marines earned the opportunity to compete in a series of 7 intense events for the title of champion. All inferential statistics were set to a p ≤ 0.05 level of significance. Morning heart rate variability identified top from bottom quartile finishers before a single competition event. By day 3, morning countermovement jump force production (normalized reactive strength index-modified) and cognitive psychomotor vigilance were significant indicators of performance resilience and final competition group rank. Heart rate variability also tracked performer readiness across time, identifying within-group and between-group differences among top, bottom, and field. Collectively, these holistic assessments proved significant markers of acute and chronic tactical performance capabilities. In summary, the incorporation of psychophysiological monitoring, cognitive performance testing, and musculoskeletal force plate evaluations could help inform selection and support needs, drive workload or recovery modulation, and provide critical metrics for evaluating training efficacy and operational readiness. Defense organizations should consider routinely incorporating and actioning similar holistic status monitoring strategies in training and operational settings. Moreover, leveraging other tactical competitions may provide key opportunities for advancing the standard of practice through additional scientific investigation.

Assessment of Countermovement Jump: What Should We Report?

The purpose of the present study was (i) to explore the reliability of the most commonly used countermovement jump (CMJ) metrics, and (ii) to reduce a large pool of metrics with acceptable levels of reliability via principal component analysis to the significant factors capable of providing distinctive aspects of CMJ performance. Seventy-nine physically active participants (thirty-seven females and forty-two males) performed three maximal CMJs while standing on a force platform. Each participant visited the laboratory on two occasions, separated by 24-48 h. The most reliable variables were performance variables (CV = 4.2-11.1%), followed by kinetic variables (CV = 1.6-93.4%), and finally kinematic variables (CV = 1.9-37.4%). From the 45 CMJ computed metrics, only 24 demonstrated acceptable levels of reliability (CV ≤ 10%). These variables were included in the principal component analysis and loaded a total of four factors, explaining 91% of the CMJ variance: performance component (variables responsible for overall jump performance), eccentric component (variables related to the breaking phase), concentric component (variables related to the upward phase), and jump strategy component (variables influencing the jumping style). Overall, the findings revealed important implications for sports scientists and practitioners regarding the CMJ-derived metrics that should be considered to gain a comprehensive insight into the biomechanical parameters related to CMJ performance.

Analyzing Force-Time Curves: Comparison of Commercially Available Automated Software and Custom MATLAB Analyses

ABSTRACT

Merrigan, JJ, Stone, JD, Galster, SM, and Hagen, JA. Analyzing force-time curves: Comparison of commercially available automated software and custom MATLAB analyses. J Strength Cond Res 36(9): 2387-2402, 2022-With the growing prevalence of commercial force plate solutions providing automated force-time curve analysis, it is critical to understand the level of agreement across techniques. Thus, this study directly compared commercial and custom software analyses across force-time curves. Twenty-four male and female subjects completed 6 trials of countermovement, squat, and drop jumps, and isometric mid-thigh pulls on the same force plate. Vertical ground reaction forces were analyzed by automated software from Vald Performance, Hawkin Dynamics, and custom MATLAB scripts. Trials were visually assessed to verify proper landmark identifications. Systematic and proportional bias among analyses were compared via least products regressions, Bland-Altman plots, and percent error. Hawkin Dynamics had subtle differences in analysis procedures and demonstrated low percent errors across all tests (<3% error), despite demonstrating systematic and proportional bias for several metrics. ForceDecks demonstrated larger percent differences and greater biases for several metrics. These errors likely result from different identification of movement initiation, system weight, and integration techniques, which causes error to subsequent landmark identifications (e.g., braking/propulsive phases) and respective force-time metrics. Many metrics were in agreement between devices, such as isometric mid-thigh pull peak force consistently within 1 N across analyses, but some metrics are difficult and incomparable across software analyses (i.e., rate of force development). Overall, many metrics were in agreement across each commercial software and custom MATLAB analyses after visually confirming landmarks. However, because of inconsistencies, it is important to only compare metrics that are in agreement across software analyses when absolutely necessary.

Simplifying External Load Data in NCAA Division-I Men's Basketball Competitions: A Principal Component Analysis

The primary purpose was to simplify external load data obtained during Division-I (DI) basketball competitions via principal component analysis (PCA). A secondary purpose was to determine if the PCA results were sensitive to load demands of different positional groups (POS). Data comprised 229 observations obtained from 10 men's basketball athletes participating in NCAA DI competitions. Each athlete donned an inertial measurement unit that was affixed to the same location on their shorts prior to competition. The PCA revealed two factors that possessed eigenvalues >1.0 and explained 81.42% of the total variance. The first factor comprised total decelerations (totDEC, 0.94), average speed (avgSPD, 0.90), total accelerations (totACC, 0.85), total mechanical load (totMECH, 0.84), and total jump load (totJUMP, 0.78). Maximum speed (maxSPD, 0.94) was the lone contributor to the second factor. Based on the PCA, external load variables were included in a multinomial logistic regression that predicted POS (Overall model, p < 0.0001; AUC_centers = 0.93, AUC_guards = 0.88, AUC_forwards = 0.80), but only maxSPD, totDEC, totJUMP, and totMECH were significant contributors to the model's success (p < 0.0001 for each). Even with the high significance, the model still had some issues differentiating between guards and forwards, as in-game demands often overlap between the two positions. Nevertheless, the PCA was effective at simplifying a large external load dataset collected on NCAA DI men's basketball athletes. These data revealed that maxSPD, totDEC, totJUMP, and totMECH were the most sensitive to positional differences during competitions. To best characterize competition demands, such variables may be used to individualize training and recovery regimens most effectively.

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.

Using Random Forest Regression to Determine Influential Force-Time Metrics for Countermovement Jump Height: A Technical Report

ABSTRACT

Merrigan, JJ, Stone, JD, Wagle, JP, Hornsby, WG, Ramadan, J, Joseph, M, and Hagen, JA. Using random forest regression to determine influential force-time metrics for countermovement jump height: a technical report. J Strength Cond Res 36(1): 277-283, 2022-The purpose of this study was to indicate the most influential force-time metrics on countermovement jump (CMJ) height using multiple statistical procedures. Eighty-two National Collegiate Athletic Association Division I American football players performed 2 maximal-effort, no arm-swing, CMJs on force plates. The average absolute and relative (i.e., power/body mass) metrics were included as predictor variables, whereas jump height was the dependent variable within regression models (p < 0.05). Best subsets regression (8 metrics, R2 = 0.95) included less metrics compared with stepwise regression (18 metrics, R2 = 0.96), while explaining similar overall variance in jump height (p = 0.083). Random forest regression (RFR) models included 8 metrics, explained ∼93% of jump height variance, and were not significantly different than best subsets regression models (p > 0.05). Players achieved higher CMJs by attaining a deeper, faster, and more forceful countermovement with lower eccentric-to-concentric force ratios. An additional RFR was conducted on metrics scaled to body mass and revealed relative mean and peak concentric power to be the most influential. For exploratory purposes, additional RFR were run for each positional group and suggested that the most influential variables may differ across positions. Thus, developing power output capabilities and providing coaching to improve technique during the countermovement may maximize jump height capabilities. Scientists and practitioners may use best subsets or RFR analyses to help identify which force-time metrics are of interest to reduce the selectable number of multicollinear force-time metrics to monitor. These results may inform their training programs to maximize individual performance capabilities.

Effects of Maximal Effort Running on Special Agents' Loaded and Unloaded Drop Jump Performance and Mechanics

The purpose was to investigate the effect of load and fatigue on landing forces and mechanics. Thirteen Department of State special agents first completed drop jump testing, a maximal treadmill test, and another round of drop jump testing. During drop jump testing, agents performed 3 maximal effort drop jumps from 30 cm with body mass only (unloaded) or a 15 kg weight-vest (loaded). A force plate was used to collect force–time data, while two laptops were placed 3 m from the force plate from frontal and sagittal planes. Two-way analyses of variance were used to analyze the effect of load and fatigue on landing forces and Landing Error Scoring System (LESS) with alpha of p < 0.05. Dropping from 30 cm with 15 kg resulted in greater landing impulse, which was driven by increases in contact time. The loaded condition also resulted in lower jump height and reactive strength indexes. After the maximal graded treadmill test there were no further changes in drop jump ground reaction forces or performance. However, relative aerobic capacity was related to impulse changes following the treadmill test in unloaded (R² = 0.41; p = 0.018) and loaded conditions (R² = 0.32; p = 0.044). External loads of 15 kg increased impulse and contact time and resultantly decreased drop jump height and reactive strength indexes. It is encouraged that training protocols be aimed to concomitantly improve aerobic capacity and lower body power. Plyometric training with progressive overloading using external loads may be helpful, but further research is warranted.

Applying Force Plate Technology to Inform Human Performance Programming in Tactical Populations

Force plate assessments, such as countermovement jumps and isometric mid-thigh pulls, examine performances (e.g., jump height, force, power) and movement strategies (e.g., asymmetries, durations), and are best suited to characterize and monitor physical capabilities, not predict injuries. To begin applying force plate technologies, users must first; (1) develop a data management plan to visualize and capture data over time; (2) select appropriate force plates for their scenario; (3) design appropriate testing protocols to ensure valid and reliable data. Force plate assessments may be added to existing testing, serve as separate testing batteries for annual profile testing to compare individuals and understand initial physical capabilities, or for more frequent testing (i.e., monthly or weekly) to monitor training-related adaptations or neuromuscular fatigue. Although these assessments inform evidence-based program designs, human performance practitioners must understand the considerations for conducting appropriate force plate testing, as well as proper visualizations and management of force plate data. Thus, the aim of this review is to provide evidence-based practices for utilizing force plates in tactical populations (e.g., military, firefighters, police). This includes best practices to implement testing for performance profiling, training adaptations, and monitoring neuromuscular fatigue and force asymmetries. Of note, due to the large amount of force-time metrics to choose from, this article provides general examples of important metrics to monitor and training recommendations based on changes to these force-time metrics, followed by specific examples in three case studies.