lats

This randomized crossover study compared surface electromyographic (sEMG) amplitude across cadence-defined lifting and lowering analytical intervals under four front lat pulldown attachment conditions in resistance-trained men. Sixteen participants performed the exercise with a straight bar, wide V-shaped handle, cambered bar, and rope. Each condition was performed at 70% of its attachment-specific 1-repetition maximum using a metronome-prescribed nominal 2-second lifting and 2-second lowering cadence and standardized grip-width criteria. sEMG was recorded from the latissimus dorsi, middle trapezius, posterior deltoid, biceps brachii, and triceps brachii. Normalized root mean square amplitudes were analyzed with repetition-level linear mixed-effects models, and sensitivity analyses were used to examine robustness. The most consistent attachment-related findings were greater biceps brachii sEMG amplitude in the wide V-shaped handle condition, particularly in the lifting analytical interval, and greater posterior deltoid sEMG amplitude in the rope than cambered-bar condition. Middle trapezius sEMG amplitude was greater in the cadence-defined lowering analytical interval than in the lifting interval. Specifically, the rope exceeded the cambered bar for posterior deltoid sEMG amplitude (mean difference = 8.0 %MVIC, 95% CI = 4.1 to 11.9; p = 0.002), and the wide V-shaped handle showed greater biceps brachii sEMG amplitude in the lifting analytical interval than the straight bar, cambered bar, and rope (mean differences = 11.0 to 15.7 %MVIC; all p < 0.001). Middle trapezius sEMG amplitude was greater in the lowering analytical interval than in the lifting analytical interval (mean difference = 6.2 %MVIC, 95% CI = 3.3 to 9.1; p < 0.001). No consistent between-condition difference was found for latissimus dorsi sEMG amplitude; a small attachment-by-phase interaction in the primary model was attenuated in complete-case sensitivity analysis. Because relative intensity was standardized to attachment-specific 1RM, absolute loads differed among conditions. The results therefore describe differences among attachment-specific relative-load conditions and should not be attributed solely to attachment geometry. The lifting and lowering labels refer to cadence-defined analytical intervals rather than mechanically synchronized phase boundaries, and acute sEMG findings should not be interpreted as evidence of longer-term hypertrophy or strength adaptations.

matched text: “lat”

Introduction Phenotypic profiling of elite athletes remains constrained by supervised frameworks that conflate physiological characteristics with performance outcomes. Unsupervised machine learning offers an outcome-independent alternative for identifying intrinsic athlete profiles. Method This study applied clustering algorithms to comprehensive laboratory data from elite flatwater canoeists. Twenty-one international-level C1 canoeists (12 male, 9 female) underwent a standardized testing battery including body composition, muscle contractile properties, mobility, maximal and explosive strength, and sport-specific neuromuscular assessments via Functional Electromechanical Dynamometry (FEMD). All performance variables were excluded to ensure purely phenotypic segmentation. Preprocessing included asymmetric winsorization, Yeo-Johnson transformation, and robust scaling. K-Means and hierarchical clustering (Ward linkage) were applied with optimal k determined via silhouette coefficient, Davies-Bouldin index, and elbow method. Discriminant variable importance was assessed via Random Forest Classifier; interpretable classification rules were extracted using Decision Tree analysis (max_depth=3) with stratified train-test validation (66.7%–33.3%). Statistical characterization of cluster differences employed ANOVA or Kruskal-Wallis tests with post-hoc comparisons and Benjamini-Hochberg FDR correction; algorithmic concordance was quantified via Adjusted Rand Index (ARI). Results Three phenotypic clusters emerged: High-Neuromuscular Male Phenotype (n=7, 33.3%), Female-Dominant Phenotype (n=10, 47.6%), and Moderate-Neuromuscular Male Phenotype (n=4, 19.0%) (ARI = 0.591). Sex stratification was marked (χ²=18.90, p<0.001): the Female-Dominant Phenotype comprised 90% female athletes; both male phenotypes were exclusively male, encoding sexual dimorphism alongside intra-sex neuromuscular differentiation. FEMD neuromuscular variables dominated discrimination (75.9% of top-15 importance; 14 of 15 variables with p adj < 0.01 following Benjamini-Hochberg FDR correction). Decision Tree classification achieved 100% training accuracy and 71.4% test accuracy (5/7; 95% CI: 29.0%–96.3%; p=0.045 vs. chance), though the small test partition constrains generalizability. Within male phenotypes, Clusters 0 and 2 demonstrated equivalent neuromuscular profiles (all p > 0.05), with phenotypic separation captured by latissimus dorsi sustain time from the non-paddle side; differences relative to the Female-Dominant Phenotype primarily reflect sexual dimorphism. Discussion Unsupervised clustering suggests a tripartite phenotypic structure in elite canoeists consistent with sexual dimorphism and intra-sex neuromuscular differentiation, with FEMD-derived eccentric force as primary discriminant. Classification rules may inform future sex-stratified talent identification and training individualization; external validation with larger cohorts is required.

matched text: “latissimus dorsi”