The purpose of this investigation was to compare changes in circulating lymphocyte subset cell counts between high-intensity interval exercise (HIIE), sprint interval exercise (SIE), and moderate-intensity continuous exercise (MICE). Recreationally active men (n ​= ​11; age: 23 ​± ​4 ​yr; height: 179.9 ​± ​4.5 ​cm; body mass: 79.8 ​± ​8.7 ​kg; body fat %:12.6 ​± ​3.8%; V̇O 2 max: 46.6 ​± ​3.9 ​ml⋅kg −1 ⋅min −1 ) completed a maximal graded exercise test to determine maximal oxygen uptake (V̇O 2 max) and three duration-matched cycling trials (HIIE, SIE, and MICE) in a randomized, counterbalanced fashion. HIIE consisted of fifteen 90-s bouts at 85% V̇O 2 max interspersed with 90-s active recovery periods. SIE consisted of fifteen 20-s bouts at 130% maximal power and 160-s active recovery periods. MICE was a continuous bout at 65% V̇O 2 max. Total exercise duration was 53 ​min in all three trials, including warm-up and cool-down. Blood was collected before, immediately post, 30 ​min, 2 ​h, 6 ​h, and 24 ​h post-exercise. Changes in lymphocyte subset counts, and surface expression of various markers were analyzed via flow cytometry. Changes were assessed using mixed model regression analysis with an autoregressive first order repeated measures correction. Significant decreases were observed in absolute counts of CD56 dim NK cells, CD19 + B cells, CD4 + T cells, and CD8 + T cells 30 ​min and 24-h post-exercise in all three trials. Despite resulting in greater total work and oxygen consumption, MICE elicited similar changes in lymphocyte subset counts and receptor expression compared to both SIE and HIIE. Similarly, while the two interval trials resulted in differing oxygen consumption and total work, no differences in the lymphocyte response were observed. Though both forms of exercise resulted in declines in circulating lymphocyte cell counts, neither exercise type provides an immune-related advantage when matched for duration.

Background and Objectives: Acute resistance exercise (RE) reduces vagal modulation and increases sympathovagal balance, which increases the risk for arrythmias. Few studies have examined sex differences in autonomic modulation after acute RE. The purpose of this investigation was to examine sex-specific responses to acute RE on autonomic modulation. Materials and Methods: Twenty-one resistance-trained individuals (men n = 11, women n = 10) between the ages of 19 and 25 y were analyzed for autonomic modulation in response to acute RE and a control (CON). Measures of autonomic modulation were collected at rest, 15 (R15), and 30 (R30) min following both conditions. Heart rate (HR), log transformed root mean square of successive differences (lnRMSSD), total power (lnTP), low-frequency power (lnLF), high-frequency power (lnHF), sample entropy (SampEn), and Lempel-Ziv entropy (LZEn) were measured at all time points. A three-way repeated analysis of variance (ANOVA) was used to analyze sex (men, women) across condition (RE, CON) and time (Rest, R15, R30). Results: The results are similar for all heart rate variability (HRV) variables at rest for both conditions (RE, CON). SampEn was significantly higher in men compared to women at rest for both conditions (p = 0.03), with no differences in LZEn (p > 0.05). There were no significant (p > 0.05) three-way interactions on any variables. Condition by time interactions demonstrated that both sexes increase in HR (p = 0.0001) and lnLF/HF ratio (p = 0.001), but decreases in lnRMSSD (p = 0.0001), lnTP (p < 0.0001), lnLF (p < 0.0001), lnHF (p = 0.0001), and LZEn (p = 0.009) at R15 and R30 compared to rest following acute RE and were different from CON. Condition by time interaction (p = 0.017) demonstrated that SampEn was attenuated at R15 compared to rest, and the CON, but not R30 following acute RE. Conclusion: Although SampEn is more complex at rest in men compared to women, autonomic modulation responses between sexes following acute RE appear to be similar.

This study aimed to compare the predictability of indoor track and XC performances using CS and either D' or AWC/C. METHODS: 9 female and male NCAA Division I cross-country and track runners from Furman University participated in this study. Each athlete completed a 3-minute All-Out running sprint (3MAO) on a track wearing a VO2 mask. Distance, time, velocity, and VO2 were collected to account for metabolic demand. Height, body mass, and calculated air resistance were used to account for energy cost of transport (C). AWC, C, Race time, and CS were used to predict race speed with a linear regression (RStudio, Boston, MA) and was compared with a linear regression using D', Race Time, and CS to predict race speed. Each regression was completed for indoor races, cross country races, and both races combined. RESULTS: (See Table)CONCLUSIONS: AWC had a better explanation of the variability based on the higher R 2 value (0.82 vs. 0.66) for the combination of indoor track and outdoor XC races. XC race predictions were comparable with either method, while indoor track only performances were better predicted with D'. Researchers and coaches should consider athletic season (indoor track or XC) when choosing a model for performance prediction. XC, Indoor Track Predictions based on D' or AWC/C Facility Anaerobic Parameter: AWC/C (R^2) D' (R^2) Indoor = 0.67(/Time+ CS) + 1.75 (0.81) = 1.22(+CS) -0.76 (0.96) XC = 0.76(/Time + CS) + 0.93 (0.87) = 0.78(+ CS) + 0.94 (0.89) Indoor+XC = 0.79(/Time + CS) + 0.85 (0.82) = 0.97(+CS) + 0.15 (0.66)

PURPOSE:To compare changes in back squat one repetition maximum (1RM) between training programs with different proximities to failure using the repetitions in reserve (RIR)-based rating of perceived exertion (RPE) scale. METHODS: Fourteen males (height: 175.77±5.72cm, body mass: 80.15±13.12kg, squat 1RM relative to body mass: 1.72±0.25) with ≥2yrs of back squat experience were assigned to one of two groups: 1) 4-6 RPE, n=7 (4-6 RIR) or 2) 7-9 RPE, n=7(1-3 RIR) for 8wks. Pre and post 1RM strength testing was performed 48 hours before the first training session and 48 hours after the last training session, respectively. Both groups performed the back squat 3x/wk on non-consecutive days (e.g., Mon., Wed., Fri.) using the same number of sets and repetitions on an undulating resistance training program, which linearly decreased repetitions throughout. Weeks 1-3 consisted of 10, 8, and 6 repetitions on sessions 1, 2, and 3, respectively; weeks 4-5 consisted of 9, 7, and 5 repetitions; and weeks 6-7 consisted of 8, 6, and 4 repetitions. Week 1 served as an introductory week in which fewer sets were performed at a lower RPE. Week 8 served as a taper with 4 and 2 repetition days on session 1 and session 2, respectively, followed by post-testing in session 3. In weeks 2-7 (i.e., main training period), 10 weekly sets were performed for each back squat and bench press. Subjects were instructed to select a load in which the set ended with 4-6 RPE or 7-9 RPE. A repeated measures ANOVA was used to assess 1RM changes and independent t-tests compared average intensity (% of 1RM) and total relative volume (% of 1RM × reps). RESULTS: Significant increases in back squat 1RM were observed in both the 4-6 RPE group (142.29 ± 50.05 to 156.07 ± 44.65 kg; p<0.01; +11.53%; p<0.01; +11.68%; g=0.26); however, no significant group × time interactions were observed (p>0.05). The 7-9 RPE group trained with significantly higher average relative intensity (82.2 ± 5.18 vs. 72.0 ± 4.57 %1RM, p=.002) and performed significantly higher relative volume (338.7 ± 21.46 vs. 380.1 ± 25.83kg, p=.007). CONCLUSIONS: Our findings indicate that resistance training with 4-6 RPE and 7-9 RPE produce similar back squat strength improvements.

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