Login / Signup

Cross-validation of equations to predict whole-body sweat sodium concentration from regional measures during exercise.

Lindsay B BakerRyan P NuccioAdam J ReimelShyretha D BrownCorey T UngaroPeter John D De ChavezKelly A Barnes
Published in: Physiological reports (2021)
We have previously published equations to estimate whole-body (WB) sweat sodium concentration ([Na+ ]) from regional (REG) measures; however, a cross-validation is needed to corroborate the applicability of these prediction equations between studies. The purpose of this study was to determine the validity of published equations in predicting WB sweat [Na+ ] from REG measures when applied to a new data set. Forty-nine participants (34 men, 15 women; 75 ± 12 kg) cycled for 90 min while WB sweat [Na+ ] was measured using the washdown technique. REG sweat [Na+ ] was measured from seven regions using absorbent patches (3M Tegaderm + Pad). Published equations were applied to REG sweat [Na+ ] to determine predicted WB sweat [Na+ ]. Bland-Altman analysis of mean bias (raw and predicted minus measured) and 95% limits of agreement (LOA) were used to compare raw (uncorrected) REG sweat [Na+ ] and predicted WB sweat [Na+ ] to measured WB sweat [Na+ ]. Mean bias (±95% LOA) between raw REG sweat [Na+ ] and measured WB sweat [Na+ ] was 10(±20), 0(±19), 9(±20), 22(±25), 23(±24), 0(±15), -4(±18) mmol/L for the dorsal forearm, ventral forearm, upper arm, chest, upper back, thigh, and calf, respectively. The mean bias (±95% LOA) between predicted WB sweat [Na+ ] and measured WB sweat [Na+ ] was 3(±14), 4(±12), 0(±14), 2(±17), -2(±16), 5(±13), 4(±15) mmol/L for the dorsal forearm, ventral forearm, upper arm, chest, upper back, thigh, and calf, respectively. Prediction equations improve the accuracy of estimating WB sweat [Na+ ] from REG and are therefore recommended for use when determining individualized sweat electrolyte losses.
Keyphrases
  • spinal cord
  • type diabetes
  • randomized controlled trial
  • physical activity
  • metabolic syndrome
  • high intensity
  • neuropathic pain
  • polycystic ovary syndrome
  • big data
  • soft tissue