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Success in extreme environments comes with a cost of subtle performance decrements that if not mitigated properly can lead to lifethreatening consequences. Identification and prediction of performance decline could alleviate deleterious consequences and enhance success in challenging and high-risk operations. The Rim-to-Rim Wearables at the Canyon for Health (R2R WATCH) project was designed to examine the cognitive, physiological, and biological markers of performance decline in the extreme environment of the Grand Canyon Rim-to-Rim (R2R) hike. The study utilized commercial off-the-shelf cognitive and physiological monitoring techniques, along with subjective self-assessments and hematologic measurements to determine subject performance and changes across the hike. The multiyear effort collected these multiple data streams in parallel on a large sample of participants hiking the R2R, leading to a rich and complex data set. This article describes the methodology and its evolution as devices and measurements were assessed after each data collection event. It also highlights a subset of the patterns of results found across the data streams. Subsequent work will draw on this data set to focus on building more sophisticated, predictive statistical models and dive deeper into specific analyses (such as the physiological and biological profiles of hikers who were left behind by their hiking partners).

In the past decade, nuclear chemists and physicists have been conducting studies to investigate the signatures associated with the production of special nuclear material (SNM). In particular, these studies aim to determine how various processing parameters impact the physical, chemical, and morphological properties of the resulting special nuclear material. By better understanding how these properties relate to the processing parameters, scientists can better contribute to nuclear forensics investigations by quantifying their results and ultimately shortening the forensic timeline. This paper aims to statistically analyze and quantify the relationships that exist between the processing conditions used in these experiments and the various properties of the nuclear end-product by invoking inverse methods. In particular, these methods make use of Bayesian Adaptive Spline Surface models in conjunction with Bayesian model calibration techniques to probabilistically determine processing conditions as an inverse function of morphological characteristics. Not only does the model presented in this paper allow for providing point estimates of a sample of special nuclear material, but it also incorporates uncertainty into these predictions. This model proves sufficient for predicting processing conditions within a standard deviation of the observed processing conditions, on average, provides a solid foundation for future work in predicting processing conditions of particles of special nuclear material using only their observed morphological characteristics, and is generalizable to the field of chemometrics for applicability across different materials.

While there is wide agreement that physical activity is an important component of a healthy lifestyle, it is unclear how many people adhere to public health recommendations on physical activity. The Physical Activity Guidelines (PAG), published by the CDC, provides guidelines to American adults, but it is difficult to assess compliance with these guidelines. The PAG further complicates adherence assessment by recommending activity to occur in at least 10 min bouts. To better understand the measurement capabilities of various instruments to quantify activity, and to propose an approach to evaluate activity relative to the PAG, researchers at Iowa State University administered the Physical Activity Measurement Survey (PAMS) to over 1000 participants in four different Iowa counties. In this paper, we develop a two-part Bayesian measurement error model and apply it to the PAMS data in order to assess compliance with the PAG in the Iowa adult population. The model accurately accounts for the 10 min bout requirement put forth in the PAG. The measurement error model corrects biased estimates and accounts for day-to-day variation in activity. The model is also applied to the nationally representative National Health and Nutrition Examination Survey.

Metabolic Syndrome (MetS) is a serious condition that can be an early warning sign of heart disease and Type 2 diabetes. MetS is characterized by having elevated levels of blood pressure, cholesterol, waist circumference, and fasting glucose. There are many articles in the literature exploring the relationship between physical activity and MetS, but most do not consider the measurement error in the physical activity measurements nor the correlations among the MetS risk factors. Furthermore, previous work has generally treated MetS as binary, rather than directly modeling the risk factors on their measured, continuous space. Using data from the National Health and Nutrition Examination Survey (NHANES), we explore the relationship between minutes of moderate to vigorous physical activity (MVPA) and MetS risk factors. We construct a measurement error model for the accelerometry data, and then model its relationship between MetS risk factors with nonlinear seemingly unrelated regressions, incorporating dependence among MetS risk factors. The novel features of this model give the medical research community a new way to understand relationships between MVPA and MetS. The results of this approach present the field with a different modeling perspective than previously taken and suggest future avenues of scientific discovery.

Background The challenges of accurate estimation of energy intake (EI) are well-documented, with self-reported values 12%-20% below expected values. New approaches rely on gold-standard assessments of the other components of energy balance, energy expenditure (EE) and energy storage (ES), to estimate EI. Objectives The purpose of this study was to evaluate the validity, repeatability, and measurement error of consumer devices when estimating energy balance in a free-living population. Methods Twenty-four healthy adults (14 women, 10 men; mean ± SD age: 30.7 ± 8.2 y) completed two 14-d assessment periods, including assessments of EE and ES using gold-standard [doubly labeled water (DLW) and DXA] and commercial devices [Fitbit Alta HR activity monitor (Alta) and Fitbit Aria wireless body composition scale (Aria)], and of EI by dietician-administered recalls. Accuracy and validity were assessed using Spearman correlation, interclass correlation, mean absolute percentage error, and equivalency testing. We also applied linear measurement error modeling including error in gold-standard devices and within-subject repeated-measures design to calibrate consumer devices and quantify error. Results There was moderate to strong agreement for EE between the Fitbit Alta and DLW at each time point (rs = 0.82 and 0.66 for Times 1 and 2, respectively). There was weak agreement for ES between the Fitbit Aria and DXA (rs = 0.15 and 0.49 for Times 1 and 2, respectively). Correlations between methods to assess EI ranged from weak to strong, with agreement between the DXA/DLW-calculated EI and dietary recalls being the highest (rs = 0.63 for Time 1 and 0.73 for Time 2). Only EE from the Fitbit Alta at Time 1 was equivalent to the DLW value using equivalency testing. Conclusions Commercial devices provide estimates of energy balance in free-living adults with varying degrees of validity compared to gold-standard techniques. EE estimates were the most robust overall, whereas ES estimates were generally poor.

This phase I, open-label, single-dose study evaluated the pharmacokinetics, safety, and tolerability of renally excreted drug dexpramipexole in subjects with normal and impaired renal function, i.e. mild, moderate, severe renal impairment, or end-stage renal disease (ESRD) requiring hemodialysis when matched by age and sex. Dexpramipexole area under the curves (AUCs), but not Cmax , were significantly increased with the severity of renal impairment after a single dose administration. The geometric mean ratio of dose-normalized AUC(0-72) was 1.4, 1.7, 2.7, and 4.5, respectively, in mild, moderate, severe renal impairment, and ESRD subjects when compared to healthy subjects. There was a strong association between renal function (eGFR) and dexpramipexole CLr. The slope (90% confidence interval(CI)) of eGFR and renal clearance (CLr) in the regression model was 3.1 (2.4, 3.7). Dexpramipexole elimination in ESRD subjects during both dialysis and non-dialysis (i.e., interval between dialysis) was insignificant. Single 75 mg and 150 mg doses of dexpramipexole were well tolerated, and the safety profile was comparable across renal function groups. Extensive drug accumulation may occur with repeated dosing in patients with significant renal impairment. It is recommended that dexpramipexole not to be given to patients with severe renal impairment or in those with ESRD.