KAATSU Cuff Tightness and Limb Anthropometry: Effect on Blood Flow Restriction

abstractKAATSU resistance training involves low loads (20%1RM) and partial blood flow restriction (BFR). When applying a BFR cuff, the initial cuff tightness (ICT) is important. ICTs can potentially impact the degree of BFR (%BFR) caused by the subsequent inflation to the target training pressures. It’s known that limb anthropometrics can affect the amount of BFR that is produced at specific pressures. Understanding the interaction between limb anthropometrics and ICT is an important first step in standardizing BFR dose between individuals for KAATSU training prescription. Purpose: To determine what limb anthropometrics (circumference, muscle or fat composition) have the greatest effect on %BFR with various ICTs. Methods: Forty-two volunteers (26 men, 16 women) provided informed consent. Caliper skin folds (anterior and posterior), Gulick tape circumferences, and peripheral quantitative computed tomography (pQCT) scans were performed on the randomly assigned ipsilateral arm and leg at the level of the KAATSU cuff. %BFR was measured via pulse-wave Doppler ultrasound at baseline (no cuff) and at 5 ICT pressures (20, 30, 40, 50 and 60mmHg). Variable relationships were assessed using Pearson correlations and stepwise linear regression. Results: The dependent variable for regression analysis was %BFR at each ICT. pQCT-determined muscle (R2= .147, .614, .445, .360, & .232, respectively) and fat composition (R2= .138, .587, .429, .338, & .220, respectively) were significant (p<.05) determinants of BFR at all ICT pressures in the arm. At 30mmHg, circumference was also a determinant (R2=.163). There were no significant correlations between %BFR and any of the ICT pressures for the leg. pQCT fat composition and sum of skin folds correlated significantly (r=.915, p<.05). pQCT circumference and Gulick circumference measures correlated significantly (r=.991, p<.05). Conclusion: Arm anthropometrics impact the %BFR created by 5 ICTs in the arm. Skin fold measures and circumference measures were highly correlated with pQCT data. As a result, skin fold and Gulick circumference measures can be used to predict arm composition at the level of the cuff and may inform prescription of appropriate ICTs that result in more consistent initial %BFR across individuals

Initial KAATSU Cuff Tightness: Effect of Limb Anthropometrics on Blood Flow Restriction

abstractINTRODUCTION KAATSU training involves low load (20%1RM) resistance exercise combined with partial blood flow restriction (BFR). BFR is achieved by positioning a specially designed pneumatic cuff around the proximal aspect of the limb, cinching it to an initial cuff tightness (ICT), then inflating the cuff to a higher restrictive training pressure. ICTs can potentially impact the degree of BFR (%BFR) caused at the higher training pressures, yet many studies use the same ICTs for all subjects (1). Identifying that discrepancies in %BFR exist between subjects with different limb anthropometrics is an important step in moving toward standardization of BFR dose for KAATSU training prescription. The purpose of this study was to identify variation in %BFR between subjects experiencing the same ICT and what limb anthropometrics (circumference, muscle, and fat composition) may be determinants. METHODS Forty-two volunteers (26 men, 16 women) provided informed consent. Caliper skin folds, Gulick tape circumferences, and peripheral quantitative computed tomography (pQCT) scans were performed on the randomly assigned ipsilateral arm and leg at the level of the KAATSU cuff application. %BFR was measured via pulse-wave Doppler ultrasound at baseline (no cuff) and at an ICT of 30 mmHg. Variable relationships were assessed using Pearson correlations and stepwise linear regression. RESULTS The average %BFR (avg±st. dev.) for the arm and leg was 16.01±11.42% and 16.75±9.27% with a range of 46.66% and 36.41%, respectively. The dependent variable for regression analysis was %BFR. In the arm, pQCT-determined muscle (R2=0.614) and fat composition (R2=0.587) were significant (p<0.05) determinants of %BFR. Circumference was also a determinant (R2=0.163). There were no significant correlations between %BFR and the anthropometrics for the leg. pQCT fat composition and sum of skin folds correlated significantly (r=0.915, p<0.05). pQCT circumference and Gulick circumference measures correlated significantly (r=0.991, p<0.05). DISCUSSION Conflicting BFR training results have been reported in the literature. A potential cause could be universal ICT usage causing some individuals to receive an inadequate training stimulus. Individuals using a 30 mmHg ICT will experience different %BFR when limb anthropometrics vary. Thus a method of assigning ICTs specific to individuals’ anthropometric characteristics is needed to ensure equally potent stimuli. Skin fold measures and circumference measures were highly correlated with pQCT data. As a result, skin fold and Gulick circumference measures can be used to predict arm composition at the level of the cuff and may inform prescription of appropriate ICTs that result in more consistent initial %BFR across individuals

Subject factors influencing blood flow restriction in the arm at low cuff pressures

BACKGROUND Limb circumference predicts the pressure needed for complete occlusion. However, that relationship is inconsistent at moderate pressures typical of effective blood flow restriction (BFR) training. The purpose of this study was to investigate the influence of subject factors on BFR at low restriction pressures in the arm. METHODS Fifty subjects had arm anthropometrics assessed by peripheral quantitative computed tomography (pQCT), skin folds (sumSKF) and Gulick tape (GulCirc) at cuff level. Blood flow was measured with ultrasound at baseline and five restrictive pressures (20,30,40,50, and 60mmHg). Relationships between subject characteristics and BFR were assessed using Pearson correlations and hierarchical regression. RESULTS Blood flow decreased (p<0.05) at each incremental pressure. Regression models including muscle composition (%Muscle), pQCT circumference, and systolic blood pressure (SBP), were significant at all 5 pressures (R2 = 0.18 to 0.49). %Muscle explained the most variance at each pressure. Regression models including sumSKF, Gul circ, and SBP, were significant at 30–60mmHg (R2 = 0.28 to 0.49). SumSKF explained the most variance at each pressure. CONCLUSIONS At low pressures (20–60mmHg), there is considerable variability in the magnitude of BFR across individuals. Arm composition factors (muscle, fat) explained the greatest variance at each cuff pressure, and may be the most important consideration when using BFR protocols