Sweat Characteristics in Individuals With Varying Susceptibilities of Exercise-Associated Muscle Cramps.

ABSTRACT: Szymanski, M, Miller, KC, O'Connor, P, Hildebrandt, L, and Umberger, L. Sweat characteristics in individuals with varying susceptibilities of exercise-associated muscle cramps. J Strength Cond Res 36(5): 1171-1176, 2022-Many medical professionals believe dehydration and electrolyte losses cause exercise-associated muscle cramping (EAMC). Unlike prior field studies, we compared sweat characteristics in crampers and noncrampers but accounted for numerous factors that affect sweat characteristics including initial hydration status, diet and fluid intake, exercise conditions, and environmental conditions. Sixteen women and 14 men (mean ± SD; age = 21 ± 2 year, body mass = 69.1 ± 11.6 kg, height = 171.4 ± 9.9 cm) self-reported either no EAMC history (n = 8), low EAMC history (n = 10), or high EAMC history (n = 12). We measured V̇o2max, and subjects recorded their diet. At least 3 days later, subjects ran at 70% of their V̇o2max for 30 minutes in the heat (39.9 ± 0.6° C, 36 ± 2% relative humidity). Dorsal forearm sweat was collected and analyzed for sweat sodium concentration ([Na+]sw), sweat potassium concentration ([K+]sw), and sweat chloride concentration ([Cl-]sw). Sweat rate (SWR) was estimated from body mass and normalized using body surface area (BSA). Dietary fluid, Na+, and K+ ingestion was estimated from a 3-day diet log. We observed no differences for any variable among the original 3 groups (p = 0.05-p = 0.73). Thus, we combined the high and low cramp groups and reanalyzed the data against the noncramping group. Again, there were no differences for [Na+]sw (p = 0.68), [K+]sw (p = 0.86), [Cl-]sw, (p = 0.69), SWR/BSA (p = 0.11), dietary Na+ (p = 0.14), dietary K+ (p = 0.66), and fluid intake (p = 0.28). Fluid and electrolyte losses may play a more minor role in EAMC genesis than previously thought.

Comparison of formulaic equations to determine energy expenditure in the critically ill patient.

OBJECTIVE: Inappropriate energy intake can negatively affect patient outcome during critical illness. Measuring energy expenditure via indirect calorimetry (IC) is the most accurate method of determining needs. Often predictive equations are used because IC is not available at all institutions or for all populations. METHODS: This study compared 24-h IC measures with five previously published formulaic equations and nomograms using kilocalorie per kilogram of body weight to determine their accuracy in predicting energy needs in critically ill adults receiving nutrition support. Two different weight categories were analyzed: body mass indexes below 25 kg/m(2) and below 30 kg/m(2). RESULTS: The Harris-Benedict equation using adjusted body weight multiplied by a stress factor of 1.6 and the Swinamer equation predicted measured energy expenditure (MEE) within 20% of IC values 80% of the time for the entire population studied. For those individuals at the lower weight range, the Harris-Benedict equation using actual weight reference weight via the Hamwi equation and via adjusted weight times a stress factor of 1.6 and the Swinamer equation predicted MEE within 20% of IC values 89% of the time. The Frankenfield equation overestimated MEE; whereas the Penn State and Ireton-Jones equations underestimated energy needs in the population studied. CONCLUSIONS: Predictive equations such as the Harris-Benedict equation multiplied by a stress factor of 1.6 and the Swinamer equation may be accurate enough for short-term nutrition support of critically ill patients when IC is unavailable.

Evaluation of the BOD POD for estimating percent fat in female college athletes.

The purpose of this investigation was to examine the accuracy of percent body fat (%BF) estimates obtained by air displacement plethysmography (ADP) using the BOD POD Body Composition System compared with hydrostatic weighing (HW) in a group of female college athletes (n = 80). In addition, %BF estimates by skinfold measures (SF) were also obtained for comparison. A lean subset (n = 39) of the sample was also examined. Mean %BF estimated for the entire sample by ADP (21.2 +/- 5.9%) was significantly greater than that determined by HW (19.4 +/- 6.4%) and SF (18.8 +/- 5.5%). Results from the lean subset also revealed that %BF determined by ADP (17.1 +/- 3.7%) was significantly higher than %BF estimates by HW (14.3 +/- 2.8%) and SF (15.2 +/- 3.2%). The regression equation for the entire sample (%BF HW = 0.937%BF ADP - 0.452, r(2) = 0.73, standard error of estimates (SEE) = 3.34) did not differ from the line of identity. In contrast, the line of identity differed significantly from the regression equation for the lean subset of female athletes (%BF HW = 0.48%BF ADP + 6.115, r(2) = 0.41, SEE = 2.18). The results of this investigation indicate that ADP significantly overestimated %BF by 8% in female athletes and by 16% for a leaner subset of the sample compared with HW. It appears that %BF estimates by SF may be more accurate than those obtained by ADP for female college athletes, regardless of body composition. Coaches and trainers evaluating body composition should consider the use of SF before ADP when measuring %BF in female college athletes. Sports scientists should continue to examine the possible gender and body composition bias for ADP.