THE EFFECTS OF TRANSCRANIAL DIRECT CURRENT STIMULATION (tDCS) ON PERIPHERAL FATIGUE OF THE LEG EXTENSORS DURING A THORSTENSSON FATIGUE PROTOCOL

Jake A. Deckert1, Trent J. Herda1, Philip M. Gallagher1 & Joseph P. Weir1, FACSM 1University of Kansas, Lawrence, Kansas Transcranial Direct Current Stimulation (tDCS) of the brain has been shown to have profound effects on many physiological and psychological processes, including effects on the autonomic nervous system and fatigue. PURPOSE: The purpose of this study was to investigate the effects tDCS on parasympathetic and sympathetic nervous system modulation and their influence on a maximum effort fatiguing exercise protocol. METHODS: Twenty recreationally active subjects (10 male; 10 female) volunteered to participate in this study. Each individual visited the lab on four occasions. The first visit was a familiarization visit. Visits two through four consisted of a sham treatment, an anodal parasympathetic stimulation treatment, and an anodal sympathetic stimulation treatment, in random order. The subjects sat in a dark, quiet environment for 30-min while receiving the appropriate stimulation. The anode was placed on the T3 area, equidistant between the ear and the CZ point, while the cathode was placed on the contralateral side of the skull, just supraorbital. Following stimulation, the subject completed 50 maximum intensity isokinetic (Biodex medical Systems, Inc., Shirley, New York) leg extensions at an angular velocity of 180°s-1, followed my passive flexion. Autonomic modulation was quantified using time and frequency domain indices of heart rate variability. The data were analyzed using 1x3 repeated measures ANOVAs. RESULTS: For the heart rate variability data there were no significant effects for high frequency power (F1.8,33.2 = 0.80, p = 0.44, Eta2 = 0.04), low frequency power (F1.9,35.4 = 0.98, p = 0.38, Eta2 = 0.05), inter-beat interval (F1.8,35.0 = 0.58, p = 0.55, Eta2 = 0.03), root mean square of successive differences (F2.0,38.0 = 1.32, p = 0.28, Eta2 = 0.07), variance (F2.0,38.0 = 1.69, p = 0.20, Eta2 = 0.08), or SD-1 (F2.0,38.0 = 1.32, p = 0.28, Eta2 = 0.07). Likewise, there was no significant effect of tDCS on mean torque (F2.0,37.5 = 0.73, p = 0.49, Eta2 = 0.04) or peak torque (F2.0,38.0 = 0.22, p = 0.80, Eta2 = 0.01). CONCLUSION: In contrast to previously published studies, the results of this study showed no effects of tDCS on cardiovascular autonomic modulation or fatigue during high intensity exercise. Discrepancies between these results and other studies may be due to differences in stimulation protocol, brain area of stimulation, and/or exercise modality

IMMUNOENDOCRINE RESPONSE TO MARINE CORPS MARTIAL ARTS TRAINING

Jacob A. Siedlik, Jake A. Deckert, Trent J. Herda, Joseph P. Weir, FACSM, Philip M. Gallagher & John P. Vardiman Department of Health, Sport, and Exercise Science, University of Kansas, Lawrence, Kansas Military training programs are rigorous and involve periods of intense physical activity in a high psychologically stressful environment. Quantifying the interplay between exposure to acute physical and psychological stress events and the lymphocyte subpopulations in the peripheral circulation may aid the development of training strategies for military and first responder personnel. PURPOSE: This study’s purpose is to map the trajectory of the immunoendocrine response to training in the Marine Corps Martial Arts Program. METHODS: 10 male marines (age 20 ±1.4y, body mass 74.76 ± 8.96kg, height 177.5 ± 7.44cm) were recruited for participation. Subjects were observed 3 times during a 9-week period. Serial blood samples for cortisol, norepinephrine (NE), epinephrine (EPI) and absolute CD4+ and CD8+ cells were collected before training and during the recovery period (Immediate Post, 15, 30, 45 and 60min). Variables were quantified using summary measures (area-under-the-curve (AUC), time to peak value and peak value) and analyzed using RMANOVAs. Pearson product moment correlations were calculated. RESULTS: There were no significant differences across visits for any of the summary or baseline measures. EPI (69±46.54pcg/ml, 70.6±46.12pcg/ml, 58.5±42.57pcg/ml), NE (880.3±670pcg/ml, 886.4±353.22pcg/ml, 874.1±578.12pcg/ml), CD4+ (744.4±182.15cells/ul, 944.9±326.46cells/ul, 900.6±217.58cells/ul), and CD8+ (664.8±204.89cells/ul, 939.1±443.69cells/ul, 833±238.8cells/ul) cells all reached peak values immediately post training. Times to peak value for cortisol (22.02±6.71mcg/dl, 20.91±5.92mcg/dl, 19.66±3.85mcg/dl) were 18, 7.5, and 9 minutes for Visits 1-3 respectively. As the time intervals between blood collections were 15 minutes, these are interpreted as a peak between 15-30min for Visit 1 and peaks between 0-15min for Visits 2-3. For Visits 1 and 2, CD4+ and CD8+ cells were significant correlated (.728, p=.017 and .712, p=.021). CONCLUSION: The lack of significant differences in AUC values across visits suggests the subject’s acute physiological responses to the training stress are not attenuated with repeated exposures. The observed decrease in the CD4/CD8 ratio immediately post training is not associated with an immunosuppressive response but is driven by an increase in CD8+ cells. Future research should investigate signaling molecules that may preferentially mobilize CD8+ cells in response to acute stress exposure. Supported by a grant through the Office of Naval Research

EFFECT OF THORSTENSSON TEST DATA COLLECTION WINDOW ON SYNERGIST BETWEEN-MUSCLE EMG AMPLITUDE RELATIONSHIPS

Tércio A.R. Barros1, Anthony B. Ciccone2, Jake A. Deckert2, Cory R. Schlabs2, Max J. Tilden2 , Trent J. Herda2 & Joseph P. Weir2, FACSM; 1University of Nebraska, Lincoln, Nebraska; 2University of Kansas, Lawrence, Kansas; [email protected] Repeated maximal effort isokinetic knee extension tests are common in fatigue research. The theory of common drive dictates that surface electromyographic (EMG) amplitude should be highly correlated between synergist muscles. However, researchers collect EMG data from different ROM (range-of-motion) windows. Different data collection windows will inherently result in different datasets from each trial. This may change the interpretation of the same test. PURPOSE: Quantify the relationship magnitudes of EMG RMS between the knee extensor muscles and determine if those relationships are affected by the ROM in which data is collected. METHODS: Nine healthy males and nine healthy females (age=21.1±1.4 y; height=173.8±12.4 cm; mass=72.1±14.7 kg) completed one bout of 50 repeated maximal effort concentric knee extensions at 180°/s with passive flexion on an isokinetic dynamometer. Position and EMG were sampled at 10k Hz. Custom LabVIEW software was used to analyze data. For the vastus lateralis (VL), rectus femoris (RF), and vastus medialis (VM), EMG data were captured in 3 different ROM windows: full ROM (F), 120°-150° (M), and load range (L). EMG amplitude was quantified via normalized root mean square (RMS) of the EMG signal in each ROM window. Between-muscle EMG amplitude Pearson correlations of the VL-VM, VL-RF, and RF-VM combinations over each window were calculated. Pearson correlation coefficient (r) values were analyzed via a two-way 3 (window) x 3 (muscle combination) ANOVA. Alpha was set at .05. RESULTS: There was no significant interaction between window and muscle. There was no main effect of muscle. There was a main effect of window where the F and LR windows yielded stronger between-muscle correlations than the M window. RMS amplitude data from F windows yielded stronger between-muscle correlations than LR windows. CONCLUSIONS: When processing repeated isokinetic knee extension data, surface EMG RMS data from the full concentric range of motion results in the strongest between-muscle correlations of synergist muscles. Assuming the task does not deviate from the theory of common drive, EMG RMS data gathered from larger ROM windows are probably better representative of the EMG amplitude during repeated maximal effort isokinetic knee extensions

EFFECT OF THORSTENSSON DATA COLLECTION WINDOW AND MUSCLE ON EMG MEDIAN POWER FREQUENCY SLOPE

Max J. Tilden1, Anthony B. Ciccone2, Jake A. Deckert2, Cory R. Schlabs2, Tércio A.R. Barros2, Trent J. Herda2 & Joseph P. Weir2, FACSM 1University of Kansas, Lawrence, Kansas; 2University of Nebraska, Lincoln, Nebraska; e-mail: [email protected] Repeated maximal effort isokinetic knee extension tests are commonly used to examine fatigue. Electromyographic (EMG) median power frequency (MPF) is thought to be related to peripheral fatigue. Multiple synergist muscles contribute to knee extension torque, and multiple range-of-motion (ROM) windows have been used to collect surface EMG data. MPF data taken from different ROM windows could alter the interpretation of magnitude of muscle-specific fatigue. PURPOSE: Quantify the EMG MPF slopes of three knee extensor muscles over three ROM windows commonly used in isokinetic tests. METHODS: Nine healthy males and nine healthy females (age=21.1±1.4 y; height=173.8±12.4 cm; mass=72.1±14.7 kg) performed 50 maximal effort concentric knee extensions at 180°/s, with passive flexion, on an isokinetic dynamometer. Custom LabVIEW software collected position and EMG data for each repetition at 10 Hz. For the vastus lateralis (VL), rectus femoris (RF), and vastus medialis (VM), normalized EMG MPF data were captured in 3 different ROM windows: full ROM (F), 120°-150° (M), and load range (L). Pearson correlations of normalized EMG MPF and repetition number were calculated for each muscle over each ROM window. Pearson correlation coefficient (r) values were analyzed via a two-way (3) muscle x (3) window ANOVA. Alpha was set at .05. RESULTS: There was no significant (p=.516) interaction between muscle and window. There was no significant (p=.577) main effect of window on EMG MPF slope across repetitions. There was a significant (p=.022) main effect of muscle, where normalized EMG MPF slopes for RF were more negative than VL (95% CI for difference: -.244 to -.103) and VM (95% CI for difference: -.212 to -.016). There was no significant (p=???) difference between VL and VM EMG MPF slopes (95% CI for difference: -.074 to .102). CONCLUSION: EMG MPF slope data suggests that, during a 50-repetition repeated maximal effort knee extension test, the RF muscle experiences more fatigue than the VL and VM muscles. Furthermore, when analyzing EMG MPF, ROM window does not affect the interpretation of MPF slope

EFFECT OF FATIGUE INDEX CALCULATION METHOD ON THE QUANTIFICATION OF FATIGUE

Cory R. Schlabs1,Anthony B. Ciccone1, Jake A. Deckert1, Max J. Tilden1,Tércio A.R. Barros2, Trent J. Herda1 & Joseph P. Weir1, FACSM 1University of Kansas, Lawrence, Kansas; 2 University of Nebraska, Lincoln, Nebraska; e-mail: [email protected] Repeated maximal effort isokinetic knee extension tests are common in exercise physiology research. However, not all researchers analyze torque data from the same range-of-motion (ROM). Furthermore, fatigue quantification methods of the torque data also differ between studies. The lack of consistent between-study measurement windows and analysis methods may lead to differing interpretations of the same data. PURPOSE: Determine if there is an effect of torque analysis method on the quantification of fatigue index (FI) during repeated maximal effort isokinetic knee extensions. METHODS: Nine healthy males and nine healthy females (age=21.1±1.4 y; height=173.8±12.4 cm; mass=72.1±14.7 kg) completed one bout of 50 repeated maximal effort concentric knee extensions at 180°/s with passive flexion on an isokinetic dynamometer. Position and torque were sampled at 10k Hz. Custom LabVIEW software was used to analyze data. Torque was defined as either peak torque (PT), torque at 135 degrees (T135), torque integral (TI) for the full ROM (TIF), TI for the middle ROM (TIM), or TI for isokinetic load range (TIL). FI was calculated using the following formula: [(start torque – end torque) / start torque]. Four types of FI were calculated using different starting and end torques, respectively: the average torque of repetitions (reps) 1-3 and 48-50 (F3), the average torque of reps 1-5 and 46-50 (F5), the highest three rep torque average and the average torque of reps 48-50 (P3), and the highest five rep torque average and the average torque of reps 46-50 (P5). A four (FI method) x five (torque variable) ANOVA was used. RESULTS: There was a significant interaction between FI method and torque variable. For all torque variables, P3 was greater than F3, F5, and P5. Collapsed across torque variables, the greatest difference was between P3 and F3 (~6%). For torque variables T135, TIM and TIL, F3 was equal to F5. For PT and TIF, F3 was equal to F5 and to P5. For T135 and TIF, P5 was greater than F3 and F5. For TIM and TIL, P5 was less than F3 and F5. For PT, P5 was greater than F5. CONCLUSIONS: Using the data from the same test, for all torque variables, the quantification of fatigue is affected by the repetitions chosen for analysis with the most noticeable effect being P3 suggesting greater fatigue than the other three FI’s for all torque analysis methods