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.

The Impact of Nutritional Supplementation on Sweat Metabolomic Content: A Proof-of-Concept Study.

Sweat is emerging as a prominent biosource for real-time human performance monitoring applications. Although promising, sources of variability must be identified to truly utilize sweat for biomarker applications. In this proof-of-concept study, a targeted metabolomics method was applied to sweat collected from the forearms of participants in a 12-week exercise program who ingested either low or high nutritional supplementation twice daily. The data establish the use of dried powder mass as a method for metabolomic data normalization from sweat samples. Additionally, the results support the hypothesis that ingestion of regular nutritional supplementation semi-quantitatively impact the sweat metabolome. For example, a receiver operating characteristic (ROC) curve of relative normalized metabolite quantities show an area under the curve of 0.82 suggesting the sweat metabolome can moderately predict if an individual is taking nutritional supplementation. Finally, a significant correlation between physical performance and the sweat metabolome are established. For instance, the data illustrate that by utilizing multiple linear regression modeling approaches, sweat metabolite quantities can predict VO2 max (p = 0.0346), peak lower body Windage (p = 0.0112), and abdominal circumference (p = 0.0425). The results illustrate the need to account for dietary nutrition in biomarker discovery applications involving sweat as a biosource.

Rate normalization for sweat metabolomics biomarker discovery.

As the demand for real-time exercise performance feedback increases, excreted sweat has become a biosource of interest for continuous human performance assessment. For sweat to truly fulfill this requirement, analyte concentrations must be normalized to adequately assess day-to-day differences within and among individuals. In this manuscript, data are presented highlighting the use of accurate localized sweat rate as a means for ion and global metabolomic data normalization. The results illustrate large sweat rate variability among individuals over the course of two distinct exercises protocols. Furthermore, the data show sweat rate is not symmetrical at similar locations among right and left forearms of individuals (p = 0.0007). Sweat ion conductivity analysis suggest overall sweat rate normalization reduces variability collectively among ion values and participants with principal component analysis showing 77.8% of variation in the data set attributable to sweat rate normalization. Global metabolomic analysis of sweat illustrated overall rate normalization increases the variability among test subjects with 72.7% of the variation explained by sweat rate normalization. Finally, overall rate normalized metabolomic features of sweat significantly correlated (ρ ≥ 0.7, ρ ≤ -0.7) with measured performance metrics of the individual, establishing the potential for sweat to be used as a biosource for performance monitoring. Collectively, these data illustrate the importance of accurate localized sweat rate determination, for analyte data normalization, in support for the use of sweat in biomarker discovery efforts to predict human performance.

Metabolomic stability of exercise-induced sweat.

Due to increased interest in the use of excreted sweat for biomarker discovery, data must be generated supporting sample collection and handling methods to allow for controlled, large-scale biomarker discovery studies to be performed. In this manuscript, twelve amino acids were quantitated from exercise-induced excreted sweat held at room temperature or a simulated body temperature of 37 °C for up to 90 min. The data illustrate a large dynamic range exists among amino acids in sweat. Additionally, the amino acid quantities vary across individuals and among the same individual under different storage conditions, with alanine, arginine, and threonine showing a significant statistical difference between sampling events (p < 0.05). Furthermore, the results establish amino acids are relatively invariant, at both storage temperatures tested, for up to 90 min illustrated by <10% (15/156) of the amino acids measurements demonstrating change greater than 10% from the time zero value. An untargeted metabolomics approach was also applied to the data set to evaluate global changes to the metabolome. The results show more than 88% of all data points fall within the established limits, regardless of temperature condition and ionization mode. Collectively, this study demonstrates that sweat is largely invariant at two distinct temperatures for up to 90 min. These results establish sweat collection and sample handling is possible for up to 90 min with minimal changes in metabolite abundances.

The proteomic and metabolomic characterization of exercise-induced sweat for human performance monitoring: A pilot investigation.

Sweat is a biofluid with several attractive attributes. However, investigation into sweat for biomarker discovery applications is still in its infancy. To add support for the use of sweat as a non-invasive media for human performance monitoring, volunteer participants were subjected to a physical exertion model using a treadmill. Following exercise, sweat was collected, aliquotted, and analyzed for metabolite and protein content via high-resolution mass spectrometry. Overall, the proteomic analysis illustrates significant enrichment steps will be required for proteomic biomarker discovery from single sweat samples as protein abundance is low in this medium. Furthermore, the results indicate a potential for protein degradation, or a large number of low molecular weight protein/peptides, in these samples. Metabolomic analysis shows a strong correlation in the overall abundance among sweat metabolites. Finally, hierarchical clustering of participant metabolite abundances show trends emerging, although no significant trends were observed (alpha = 0.8, lambda = 1 standard error via cross validation). However, these data suggest with a greater number of biological replicates, stronger, statistically significant results, can be obtained. Collectively, this study represents the first to simultaneously use both proteomic and metabolomic analysis to investigate sweat. These data highlight several pitfalls of sweat analysis for biomarker discovery applications.

The effect of load uncertainty on anticipatory muscle activity in catching.

To investigate how the CNS copes with load uncertainty in catching, anticipatory postural adjustments (APAs) in one-handed catching of balls of known and unknown weights were compared. Twenty-nine (n = 29) men (mean age = 21.1 years) participated, all of whom had engaged in a sport activity requiring hand-eye coordination. Participants' muscle activity in the biceps brachii, triceps brachii, wrist flexor group, and bilateral erector spinae at L4-5 was recorded using electromyography (EMG) while they caught visually identical balls of four different weights (0.5, 1.33, 2.17, and 3.0 kg). EMG integrals were computed for the 1 s prior to ball drop (pre-drop period), and the interval between ball drop and catch (drop period). Uncertainty about ball weight had no effect on APA activity during the pre-drop period. During the drop period, however, load uncertainty did influence APA activity in the biceps brachii, triceps brachii, and the wrist flexor muscles (i.e., the effect of ball weight on APA magnitude depended on the presence or absence of load knowledge). In the known ball weight condition, participants exhibit greater APA magnitude with increases in ball weight. In contrast, under the unknown ball weight condition, APA magnitude was relatively consistent across ball weights and at a level similar to the APA magnitude for an intermediate weight (i.e., the second heaviest ball of four) in the known weight condition. In catching balls of unknown weights, the CNS appears to scale APA magnitude to afford the greatest chance of catching the ball, regardless of the weight.