Warren S. Roberts

Intensity and physiological strain of competitive ultra-endurance exercise in humans

Abstract The aim of this study was to determine the magnitude and pattern of intensity, and physiological strain, of competitive exercise performed across several days, as in adventure racing. Data were obtained from three teams of four athletes (7 males, 5 females; mean age 36 years, s = 11; cycling [Vdot]O2 peak 53.9 ml · kg−1 · min−1, s = 6.3) in an international race (2003 Southern Traverse; 96 – 116 h). Heart rates (HR) averaged 64% (95% confidence interval: ± 4%) of heart rate range [%HRR = (HR − HRmin)/(HRmax − HRmin) × 100] during the first 12 h of racing, fell to 41% (±4%) by 24 h, and remained so thereafter. The level and pattern of heart rate were similar across teams, despite one leading and one trailing all other teams. Core temperature remained between 36.0 and 39.2°C despite widely varying thermal stress. Venous samples, obtained before, during, and after the race, revealed increased neutrophil, monocyte and lymphocyte concentrations (P < 0.01), and increased plasma volume (25 ± 10%; P < 0.01) with a stable sodium concentration. Standardized exercise tests, performed pre and post race, showed little change in the heart rate – work rate relationship (P = 0.53), but a higher perception of effort post race (P < 0.01). These results provide the first comprehensive report of physiological strain associated with adventure racing.

Human thermoregulatory system state estimation using non-invasive physiological sensors

Small teams of emergency workers/military can often find themselves engaged in critical, high exertion work conducted under challenging environmental conditions. These types of conditions present thermal work strain challenges which unmitigated can lead to collapse (heat exhaustion) or even death from heat stroke. Physiological measurement of these teams provides a mechanism that could be an effective tool in preventing thermal injury. While indices of thermal work strain have been proposed they suffer from ignoring thermoregulatory context and rely on measuring internal temperature (IT). Measurement of IT in free ranging ambulatory environments is problematic. In this paper we propose a physiology based Dynamic Bayesian Network (DBN) model that estimates internal temperature, heat production and heat transfer from observations of heart rate, accelerometry, and skin heat flux. We learn the models conditional probability distributions from seven volunteers engaged in a 48 hour military field training exercise. We demonstrate that sum of our minute to minute heat production estimates correlate well with total daily energy expenditure (TDEE) measured using the doubly labeled water technique (r2 = 0.73). We also demonstrate that the DBN is able to infer IT in new datasets to within ±0.5 °C over 85% of the time. Importantly, the additional thermoregulatory context allows critical high IT temperature to be estimated better than previous approaches. We conclude that the DBN approach shows promise in enabling practical real time thermal work strain monitoring applications from physiological monitoring systems that exist today.