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Figueiredo, PS, Looney, DP, Pryor, JL, Doughty, EM, McClung, HL, Vangala, SV, Santee, WR, Beidleman, BA, and Potter, AW. Verification of maximal oxygen uptake in active military personnel during treadmill running. J Strength Cond Res 36(4): 1053-1058, 2022-It is unclear whether verification tests are required to confirm "true" maximal oxygen uptake (V[Combining Dot Above]O2max) in modern warfighter populations. Our study investigated the prevalence of V[Combining Dot Above]O2max attainment in U.S. Army soldiers performing a traditional incremental running test. In addition, we examined the utility of supramaximal verification testing as well as repeated trials for familiarization for accurate V[Combining Dot Above]O2max assessment. Sixteen U.S. Army soldiers (1 woman, 15 men; age, 21 +/- 2 years; height, 1.73 +/- 0.06 m; body mass, 71.6 +/- 10.1 kg) completed 2 laboratory visits, each with an incremental running test (modified Astrand protocol) and a verification test (110% maximal incremental test speed) on a motorized treadmill. We evaluated V[Combining Dot Above]O2max attainment during incremental testing by testing for the definitive V[Combining Dot Above]O2 plateau using a linear least-squares regression approach. Peak oxygen uptake (V[Combining Dot Above]O2peak) was considered statistically equivalent between tests if the 90% confidence interval around the mean difference was within +/-2.1 ml[middle dot]kg-1[middle dot]min-1. Oxygen uptake plateaus were identified in 14 of 16 volunteers for visit 1 (87.5%) and all 16 volunteers for visit 2 (100%). Peak oxygen uptake was not statistically equivalent, apparent from the mean difference in V[Combining Dot Above]O2peak measures between the incremental test and verification test on visit 1 (2.3 ml[middle dot]kg-1[middle dot]min-1, [1.3-3.2]) or visit 2 (1.1 ml[middle dot]kg-1[middle dot]min-1 [0.2-2.1]). Interestingly, V[Combining Dot Above]O2peak was equivalent, apparent from the mean difference in V[Combining Dot Above]O2peak measures between visits for the incremental tests (0.0 ml[middle dot]kg-1[middle dot]min-1 [-0.8 to 0.9]) but not the verification tests (-1.2 ml[middle dot]kg-1[middle dot]min-1 [-2.2 to -0.2]). Modern U.S. Army soldiers can attain V[Combining Dot Above]O2max by performing a modified Astrand treadmill running test. Additional familiarization and verification tests for confirming V[Combining Dot Above]O2max in healthy active military personnel may be unnecessary. [ABSTRACT FROM AUTHOR]

Introduction: The Load Carriage Decision Aid (LCDA) is a U.S. Army planning tool that predicts physiological responses of soldiers during different dismounted troop scenarios. We aimed to develop an equation that calculates standing and walking metabolic rates in healthy military-age adults for the LCDA using a meta-regression. Methods: We searched for studies that measured the energetic cost of standing and treadmill walking in healthy men and women via indirect calorimetry. We used mixed effects meta-regression to determine an optimal equation to calculate standing and walking metabolic rates as a function of walking speed (S , m·s−1). The optimal equation was used to determine the economical speed at which the metabolic cost per distance walked is minimized. The estimation precision of the new LCDA walking equation was compared with that of seven reference predictive equations. Results: The meta-regression included 48 studies. The optimal equation for calculating normal standing and walking metabolic rates (W·kg−1) was 1.44 + 1.94 S 0.43 + 0.24 S 4. The economical speed for level walking was 1.39 m·s−1 (~ 3.1 mph). The LCDA walking equation was more precise across all walking speeds (bias ± SD, 0.01 ± 0.33 W·kg−1) than the reference predictive equations. Conclusion: Practitioners can use the new LCDA walking equation to calculate energy expenditure during standing and walking at speeds <2 m·s−1 in healthy, military-age adults. The LCDA walking equation avoids the errors estimated by other equations at lower and higher walking speeds. [ABSTRACT FROM AUTHOR]

Introduction: Military load carriage can cause extreme energy expenditure (EE) that is difficult to estimate due to complex terrain grades and surfaces. Global Positioning System (GPS) devices capture rapid changes in walking speed and terrain but the delayed respiratory response to movement is problematic. We investigated the accuracy using GPS data in three different equations to estimate EE during complex terrain load carriage., Methods: Twelve active duty military personnel (age, 20 ± 3 yr; height, 174 ± 8 cm; body mass, 85 ± 13 kg) hiked a complex terrain trail on multiple visits under different external load conditions. Energy expenditure was estimated by inputting GPS data into three different equations: the Pandolf-Santee equation, a recent GPS-based equation from de Müllenheim et al.; and the Minimum Mechanics model. Minute-by-minute EE estimates were exponentially smoothed using smoothing factors between 0.05 and 0.95 and compared with mobile metabolic sensor EE measurements., Results: The Pandolf-Santee equation had no significant estimation bias (-2 ± 12 W; P = 0.89). Significant biases were detected for the de Müllenheim equation (38 ± 13 W; P = 0.004) and the Minimum Mechanics model (-101 ± 7 W; P < 0.001)., Conclusions: Energy expenditure can be accurately estimated from GPS data using the Pandolf-Santee equation. Applying a basic exponential smoothing factor of 0.5 to GPS data enables more precise tracking of EE during non-steady-state exercise.