Inter-device reliability of wearable technology for quantifying jump height in collegiate athletes.

The purpose of this study was to evaluate the inter-device reliability of three VERT devices (Mayfonk Athletic, Florida, USA) when worn on the waist (W), left-hip (LH), and right-hip (RH) during single- and double-leg counter movement jumps (CMJ) in collegiate athletes. Thirty-two female and twenty-eight male NCAA Division II athletes (n = 60) participated in the present study. Jump height (JH) values for double-leg CMJs were analyzed by each device using a one-way repeated measures ANOVA whereas a 2 (jump leg) x 3 (wear location) repeated measures ANOVA was employed to evaluate single-leg CMJs. Reliability of the VERT devices were based upon intraclass correlation coefficients (ICC). Double-leg CMJs revealed an excellent ICC between all three VERT devices (ICC = 0.969). However, JH for RH and LH (45.69 ± 9.84 and 45.82 ± 10.45 cm, respectively) were on average lower than W (50.44 ± 12.37cm; both p < 0.001). The ICCs were excellent for right- and left-leg CMJs (ICC = 0.939 and 0.941, respectively). However, an interaction was observed (p < 0.001). No differences existed for left- or right-leg when VERT was worn on the waist. However, JH was higher when VERT devices were worn on the opposite hip of the jump leg (i.e., LH>RH for right-leg CMJs; RH>LH for leftleg CMJs; all p < 0.001). Results suggest that LH and RH are interchangeable for double-leg CMJs, but not with waist despite excellent reliability. In addition, all wear locations provided excellent ICCs for single-leg CMJs. However, waist provides more consistent JH values for right- and left-leg CMJs while RH and LH show more variability.

Effects of independent versus dependent stressful life events on major symptom domains of schizophrenia.

We evaluated two models to link stressful life events (SLEs) with the psychopathology of schizophrenia spectrum disorders (SSD). We separated SLEs into independent (iSLEs, unlikely influenced by one's behavior) and dependent (dSLEs, likely influenced by one's behavior). Stress-diathesis and stress generation models were evaluated for the relationship between total, i- and d- SLEs and the severity of positive, negative, and depressive symptoms in participants with SSD. Participants with SSD (n = 286; 196 males; age = 37.5 ± 13.5 years) and community controls (n = 121; 83 males; 35.4 ± 13.9 years) completed self-report of lifetime negative total, i- and d- SLEs. Participants with SSD reported a significantly higher number of total SLEs compared to controls (B = 1.11, p = 6.4 × 10-6). Positive symptom severity was positively associated with the total number of SLEs (ß = 0.20, p = 0.001). iSLEs (ß = 0.11, p = 0.09) and dSLEs (ß = 0.21, p = 0.0006) showed similar association with positive symptoms (p = 0.16) suggesting stress-diathesis effects. Negative symptom severity was negatively associated with the number of SLEs (ß = -0.19, p = 0.003) and dSLEs (ß = -0.20, p = 0.001) but not iSLEs (ß = -0.04, p = 0.52), suggesting stress generation effects. Depressive symptom severity was positively associated with SLEs (ß = 0.34, p = 1.0 × 10-8), and the association was not statistically stronger for dSLEs (ß = 0.33, p = 2.7 × 10-8) than iSLEs (ß = 0.21, p = 0.0006), p = 0.085, suggesting stress-diathesis effects. The SLE - symptom relationships in SSD may be attributed to stress generation or stress-diathesis, depending on symptom domain. Findings call for a domain-specific approach to clinical intervention for SLEs in SSD.

Effect of total body water estimates via bioimpedance on bod pod-based three-compartment body fat models.

BACKGROUND/OBJECTIVES: Previous research has compared 2- and 3-compartment (2C and 3C, respectively) models against criterion 4-compartment (4C) models while utilizing the same body density (Db) method for all measures. This design induces an inherent bias and obscures the added benefit of a 3C model over the simpler 2-compartment (2C) models. PURPOSE: The purpose of this study was to determine the effect of total body water estimates via single-frequency (SF-BIA) and multi-frequency (MF-BIA) bioimpedance analysis on body fat estimates derived from air displacement plethysmography (ADP)-derived 3C models. SUBJECTS/METHODS: A sample of 95 females and 82 males (n = 177) participated in this study. Underwater weighing, dual energy X-ray absorptiometry, and bioimpedance spectroscopy were used to calculate percent fat (%Fat) via a criterion 4C model (4CCRITERION). %Fat was predicted via 3CMFBIA (ADP and MF-BIA), 3CSFBIA (ADP and SF-BIA), and a stand-alone 2-compartment (2C) model, based upon ADP, when using Siri and Brozek body density conversion formulas (2CSIRI and 2CBROZEK. respectively). RESULTS: The standard error of estimate (SEE) was lowest for 3CSFBIA when evaluated in females and males (2.72% and 2.31%, respectively) and highest for 2CSIRI (3.98% and 3.84%, respectively). Similarly, the total error (TE) for females and males was lowest for 3CSFBIA (3.21% and 2.67%, respectively) and highest for 2CSIRI (4.58% and 4.48%, respectively) and 2CBROZEK (4.65% and 4.33%, respectively). CONCLUSIONS: Results suggest that SF-BIA and MF-BIA can improve the estimation of %Fat, beyond simpler 2C models, when integrated with ADP in a more advanced 3C model. Furthermore, the present study revealed that 3CSFBIA was the best overall prediction model based upon TE values. The current study results support the integration of ADP and bioimpedance technology as part of a 3C model for the improvement of %Fat estimates over simpler 2C models.

Evaluation of Load Velocity Profiles with Varying Warm-up Sets and Relative Intensities.

The purpose of this study was to determine whether the number of warm-up sets and relative intensity impacts the prediction of velocity-based one-repetition maximum (1RM) values. Twenty resistance-trained individuals (males: n = 10, females: n = 10) participated in this study. Warm-up sets consisted of subject's bench-pressing loads at 50 (five-repetitions), 70 (three-repetitions), and 90% (one-repetition) of estimated 1RM. A maximum of four attempts were performed to determine 1RM, while recording mean concentric velocity (MCV) using a linear position transducer during warmup and 1RM trials in order to develop load-velocity profiles. Specifically, four different velocity-based 1RM equations (EQ) were developed from the warm-up sets of 50, 70, and 90% (MCV-EQ1), 50 and 90% (MCV-EQ2), 70 and 90% (MCV-EQ3), and 50 and 70% (MCV-EQ4). Constant error (CE) for the MCV prediction equations were not statistically significant for any comparisons (CEs = 0.80 to 2.96kg, all p > 0.05). Correlation coefficients between the MCV prediction methods and measured 1RM were near perfect for all comparisons (r ≥ 0.98, all p < 0.001). The standard error of estimate (SEE) and 95% limits of agreement (LOAs) were lowest for MCV-EQ1 (7.86 kg and ± 15.00 kg, respectively) and slightly higher for MCV-EQ3 (9.24 kg and 17.74 kg, respectively). Nonetheless, SEEs and 95% LOAs for MCV-EQ2 (8.10 kg and ± 15.55kg, respectively) and MCV-EQ4 (8.38 kg and ± 16.08 kg, respectively) were similar as MCV-EQ1. Current study results indicated that an additional warm-up set only slightly increases the accuracy of velocity-based 1RM estimations. Furthermore, larger differences in relative intensity will help produce slightly more accurate 1RM values.