Aerobic Exercise Performance During Load Carriage and Acute Altitude Exposure.

Coffman, KE, Luippold, AJ, Salgado, RM, Heavens, KR, Caruso, EM, Fulco, CS, and Kenefick, RW. Aerobic exercise performance during load carriage and acute altitude exposure. J Strength Cond Res 34(4): 946-951, 2020-This study quantified the impact of combined load carriage and acute altitude exposure on 5-km running time-trial (TT) performance and self-selected pacing strategy. Furthermore, this study developed a velocity prediction tool (nomogram) for similar aerobic exercise tasks performed under various combinations of altitude and load stress. Nine volunteers (6M/3F, age: 24 ± 7 years, height: 171 ± 6 cm, body mass: 72 ± 7 kg, and V[Combining Dot Above]O2peak: 50.5 ± 5.2 ml·min·kg) completed a randomized, repeated-measures design protocol. Volunteers performed 3 familiarization (FAM) trials at sea level (SL; 250 m) with no-load carriage. Experimental testing included 3 self-paced, blinded 5-km running TT on a treadmill while carrying a 30% body mass external load at SL, moderate altitude (MA; 2000 m), and high altitude (HA; 3000 m). At SL, load carriage resulted in a 36% decrement in 5-km exercise performance in comparison with FAM trials (43 ± 7 vs. 32 ± 3 minutes; p < 0.001). Time required to complete the 5-km distance while carrying an external load was increased by 11% when performed at HA vs. SL (48 ± 7 vs. 43 ± 7 minutes; p = 0.001). TT pace was not different among experimental conditions (load carriage at SL, MA, and HA) until after 1 km of the running distance had been completed. Heart rate was not different among experimental conditions throughout the entire TT (170 ± 17 b·min). These data quantify the anticipated reduction in aerobic exercise performance under various combinations of acute altitude exposure and load carriage conditions. The self-paced running TT approach used presently allowed for development of an altitude-load nomogram for use in recreational, occupational, or military settings.

Planning Military Drinking Water Needs: Development of a User-Friendly Smart Device Application.

Potable water is essential to maintain health and sustain military operations, but carrying and transporting water is a major logistical burden. Planning for group drinking water needs is complex, requiring understanding of sweat losses on the basis of intensity of activity, clothing biophysical parameters, and environmental conditions. Use of existing prediction equations is limited to tabled doctrine (e.g., Technical Bulletin, Medical 507) or to individuals with extensive expertise in thermal biophysics. In the present project, we translated the latest updated equations into a user-friendly Android application (Soldier Water Estimation Tool, SWET) that provides estimated drinking water required from 5 simple inputs based upon a detailed multiparametric sensitivity analysis. Users select from multiple choice inputs for activity level, clothing, and cloud cover, and manually enter exact values for temperature and relative humidity. Total drinking water needs for a unit are estimated in the Mission Planner tool on the basis of mission duration and number of personnel. In preliminary user acceptability testing, responses were overall positive in terms of ease of use and military relevance. Use of SWET for water planning will minimize excessive load (water) carriage in training and mission settings, and will reduce the potential for dehydration and/or hyponatremia to impair Warfighter health and performance.

Effect of WBGT Index Measurement Location on Heat Stress Category Classification.

UNLABELLED: The location of the wet bulb globe temperature (WBGT) index measurement may affect heat stress flag category classification. PURPOSE: This study aimed to compare WBGT measurements at three locations along the Boston Marathon race course and compare WBGT estimates for meteorological stations and 72-h advanced WBGT forecasts. METHODS: WBGT was measured hourly from 1000 to 1400 h at approximately 7 km, approximately 18 km, and approximately 30 km on the Boston Marathon race course. Simultaneous WBGT estimates were made for two meteorological stations southeast of the course via a commercial online system, which also provided 72-h advanced forecasts. RESULTS: The measurement difference (mean ± SD) among course locations was 0.2°C ± 1.8°C WBGT (ANOVA, P > 0.05). The difference between course and stations was 1.9°C ± 2.4°C WBGT (t-test, P < 0.05). Station values underestimated (n = 98) or overestimated (n = 13) course values by >3°C WBGT (>0.5 flag category) in 111 of 245 paired comparisons (45%). Higher black globe and lower wet bulb temperatures explained over- and underestimates, respectively. Significant underestimates of WBGT resulted in misclassification of green (labeled white) and black (labeled red) course flag categories (χ2, P < 0.05). Forecast data significantly underestimated red (labeled amber) and black (labeled red) course flag categories. CONCLUSIONS: Differences in WBGT index along 23 km of the Boston Marathon race route can be small enough to warrant single measurements. However, significant misclassification of flag categories occurred using WBGT estimates for meteorological stations; thus, local measurements are preferred. If the relation between station WBGT forecasts and the race sites can be established, the forecast WBGT values could be corrected to give advanced warning of approximate flag conditions. Similar work is proposed for other venues to improve heat stress monitoring.

Validation of equations used to predict plasma osmolality in a healthy adult cohort.

BACKGROUND: Plasma osmometry and the osmol gap have long been used to provide clinicians with important diagnostic and prognostic patient information. OBJECTIVE: We compared different equations used for predicting plasma osmolality when its direct measurement was not practical or an osmol gap was of interest and identified the best performers. DESIGN: The osmolality of plasma was measured by using freezing point depression by microosmometer and osmolarity calculated from biosensor measures of select analytes according to the dictates of each formula tested. After a rigid analytic prescreen of 36 originally published equations, a bootstrap regression analysis was used to compare shrinkage and model agreement. RESULTS: Sixty healthy volunteers provided 163 plasma samples for analysis. Of 36 equations considered, 11 equations met the prescreen variables for the bootstrap regression analysis. Of the 11 equations, 8 equations met shrinkage and apparent model error thresholds, and 5 equations were deemed optimal with an original model osmol gap <5 mmol. CONCLUSIONS: The use of bootstrap regression provides a unique insight for osmolality prediction equation performance from a very large theoretical population of healthy people. Of the original 36 equations evaluated, 5 equations appeared optimal for the prediction of osmolality when its direct measurement was not practical or an osmol gap was of interest. Note that 4 of 5 optimal equations were derived from a nonhealthy population.