Real-time core body temperature estimation from heart rate for first responders wearing different levels of personal protective equipment.

First responders often wear personal protective equipment (PPE) for protection from on-the-job hazards. While PPE ensembles offer individuals protection, they limit one's ability to thermoregulate, and can place the wearer in danger of heat exhaustion and higher cardiac stress. Automatically monitoring thermal-work strain is one means to manage these risks, but measuring core body temperature (Tc) has proved problematic. An algorithm that estimates Tc from sequential measures of heart rate (HR) was compared to the observed Tc from 27 US soldiers participating in three different chemical/biological training events (45-90 min duration) while wearing PPE. Hotter participants (higher Tc) averaged (HRs) of 140 bpm and reached Tc around 39 °C. Overall the algorithm had a small bias (0.02 °C) and root mean square error (0.21 °C). Limits of agreement (LoA ± 0.48 °C) were similar to comparisons of Tc measured by oesophageal and rectal probes. The algorithm shows promise for use in real-time monitoring of encapsulated first responders. PRACTITIONER SUMMARY: An algorithm to estimate core temperature (Tc) from non-invasive measures of HR was validated. Three independent studies (n = 27) compared the estimated Tc to the observed Tc in humans participating in chemical/ biological hazard training. The algorithm's bias and variance to observed data were similar to that found from comparisons of oesophageal and rectal measurements.

Female anthropometric variability and their effects on predicted thermoregulatory responses to work in the heat.

The use of thermoregulatory models for assessing physiological responses of workers in thermally stressful situations has been increasing because of the risks and costs related to human studies. In a previous study (Yokota et al. Eur J Appl Physiol 104:297-302, 2008), the effects of anthropometric variability on predicted physiological responses to heat stress in U.S. Army male soldiers were evaluated. Five somatotypes were identified in U.S. Army male multivariate anthropometric distribution. The simulated heat responses, using a thermoregulatory model, were different between somatotypes. The present study further extends this line of research to female soldiers. Anthropometric somatotypes were identified using multivariate analysis [height, weight, percent body fat (%BF)] and the predicted physiological responses to simulated exercise and heat stress using a thermoregulatory model were evaluated. The simulated conditions included walking at ~3 mph (4.8 km/h) for 300 min and wearing battle dress uniform and body armor in a 30°C, 25% relative humidity (RH) environment without solar radiation. Five major somatotypes (tall-fat, tall-lean, average, short-lean, and short-fat), identified through multivariate analysis of anthropometric distributions, showed different tolerance levels to simulated heat stress: lean women were predicted to maintain their core temperatures (T(c)) lower than short-fat or tall-fat women. The measured T(c) of female subjects obtained from two heat studies (data1: 30°C, 32% RH, protective garments, ~225 w·m(-2) walk for 90 min; data2: 32°C, 75% RH, hot weather battle dress uniform, ~378 ± 32 w·m(-2) for 30 min walk/30 min rest cycles for 120 min) were utilized for validation. Validation results agreed with the findings in this study: fat subjects tended to have higher core temperatures than medium individuals (data2) and lean subjects maintained lower core temperatures than medium subjects (data1).

Applications of real-time thermoregulatory models to occupational heat stress: validation with military and civilian field studies.

A real-time thermoregulatory model using noninvasive measurements as inputs was developed for predicting physiological responses of individuals working long hours. The purpose of the model is to reduce heat-related injuries and illness by predicting the physiological effects of thermal stress on individuals while working. The model was originally validated mainly by using data from controlled laboratory studies. This study expands the validation of the model with field data from 26 test volunteers, including US Marines, Australian soldiers, and US wildland fire fighters (WLFF). These data encompass a range of environmental conditions (air temperature: 19-30° C; relative humidity: 25-63%) and clothing (i.e., battle dress uniform, chemical-biological protective garment, WLFF protective gear), while performing diverse activities (e.g., marksmanship, marching, extinguishing fires, and digging). The predicted core temperatures (Tc), calculated using environmental, anthropometric, clothing, and heart rate measures collected in the field as model inputs, were compared with subjects' Tc collected with ingested telemetry temperature pills. Root mean standard deviation (RMSD) values, used for goodness of fit comparisons, indicated that overall, the model predictions were in close agreement with the measured values (grand mean of RMSD: 0.15-0.38° C). Although the field data showed more individual variability in the physiological data relative to more controlled laboratory studies, this study showed that the performance of the model was adequate.

A real-time heat strain risk classifier using heart rate and skin temperature.

Heat injury is a real concern to workers engaged in physically demanding tasks in high heat strain environments. Several real-time physiological monitoring systems exist that can provide indices of heat strain, e.g. physiological strain index (PSI), and provide alerts to medical personnel. However, these systems depend on core temperature measurement using expensive, ingestible thermometer pills. Seeking a better solution, we suggest the use of a model which can identify the probability that individuals are 'at risk' from heat injury using non-invasive measures. The intent is for the system to identify individuals who need monitoring more closely or who should apply heat strain mitigation strategies. We generated a model that can identify 'at risk' (PSI 7.5) workers from measures of heart rate and chest skin temperature. The model was built using data from six previously published exercise studies in which some subjects wore chemical protective equipment. The model has an overall classification error rate of 10% with one false negative error (2.7%), and outperforms an earlier model and a least squares regression model with classification errors of 21% and 14%, respectively. Additionally, the model allows the classification criteria to be adjusted based on the task and acceptable level of risk. We conclude that the model could be a valuable part of a multi-faceted heat strain management system.

Treatment Tolerability in Patients with Immunoglobulin Light-Chain Amyloidosis.

BACKGROUND: Immunoglobulin light-chain amyloidosis (AL amyloidosis) is a rare and often fatal disease for which there is currently no treatment approved by the US Food and Drug Administration or the European Medicines Agency. Treatment options, which are typically based on therapies for multiple myeloma and are used off-label, are associated with substantial adverse events (AEs). Because the severity of AEs is often determined by clinicians, evaluations of treatment tolerability may not fully consider patients' own experience with treatment. OBJECTIVES: To explore the prevalence of AEs and treatment tolerability problems as reported by patients who received therapies for AL amyloidosis, and to examine the effects of AEs on treatment continuation and on health-related quality of life (HRQOL). METHODS: Patients with AL amyloidosis were recruited for this noninterventional, longitudinal, online survey. The patients responded to survey items regarding demographics, disease characteristics, most recent AL amyloidosis treatment, and HRQOL. The study analyses are based on data collected during the 6-month follow-up survey and are restricted to patients who completed the baseline and 6-month surveys and received treatment for AL amyloidosis within 6 months before the follow-up survey. RESULTS: A total of 100 patients met the inclusion criteria and were included in the study. The patients self-reported having a variety of AEs, which ranged in severity. Overall, 69.4% of patients had problems tolerating their treatment in the past 6 months, of whom 22% discontinued at least 1 therapy. In addition, approximately 33% of patients reduced their AL amyloidosis treatment because of AEs. Most often reported AEs included fatigue (83%), shortness of breath (53%), nausea (52%), and diarrhea (51%). Overall, 50% of the patients reported that their treatment was moderately well-tolerated and 41% said it was very well-tolerated. Those whose treatment was not well-tolerated had significantly worse HRQOL than patients whose treatment was well-tolerated. CONCLUSIONS: Patient-reported experiences should be considered by clinicians when making treatment-related decisions. More research is needed to explore additional factors that may contribute to treatment discontinuation in patients with AL amyloidosis.

Modeling physiological responses to military scenarios: initial core temperature and downhill work.

INTRODUCTION: Previous field studies suggested that a thermoregulatory model developed by the U.S. Army Research Institute of Environmental Medicine (USARIEM) needed an adjustment of initial core temperature (Tcr) for individual variation and a metabolic (M) correction during downhill movements. This study evaluated the updated version of the model incorporating these new features using a dataset collected during U.S. Marine Corps marksmanship training at Quantico, VA. METHODS: Individual anthropometrics, physiological, and environmental time series data were obtained from five Marine men. The study focused on the marksmanship training for approximately 2 h, then 30-min marching including uphill and downhill movements in a moderately hot environment (air temperature: approximately 30 degrees C; dew point: approximately 21 degrees C). The predicted and observed heart rate (HR) and Tcr measurements were compared by root mean square deviations (RMSD). RESULTS: Overall, the current model improved predictions of physiological measures (HR RMSD = 23 bpm, Tcr RMSD = 0.46 degrees C), particularly for marching in the heat (HR RMSD = 21 bpm, Tcr RMSD = 0.32 degrees C). The model under-predicted both HR and Tcr during marksmanship training, indicating that a greater solar effect or non-thermal factors may have required higher M rates during these periods. CONCLUSIONS: Updated features of the model significantly improved physiological predictions. However, accurate M estimates are required for slow movements of subjects under heat stress, such as movements on the firing range. Such improvement should result in more accurate simulations of physiological status and better risk assessment, thereby reducing heat injuries and improving performance of deployed military personnel.

Head and facial anthropometry of mixed-race US Army male soldiers for military design and sizing: a pilot study.

In the United States, the biologically admixed population is increasing. Such demographic changes may affect the distribution of anthropometric characteristics, which are incorporated into the design of equipment and clothing for the US Army and other large organizations. The purpose of this study was to examine multivariate craniofacial anthropometric distributions between biologically admixed male populations and single racial groups of Black and White males. Multivariate statistical results suggested that nose breadth and lip length were different between Blacks and Whites. Such differences may be considered for adjustments to respirators and chemical-biological protective masks. However, based on this pilot study, multivariate anthropometric distributions of admixed individuals were within the distributions of single racial groups. Based on the sample reported, sizing and designing for the admixed groups are not necessary if anthropometric distributions of single racial groups comprising admixed groups are known.

Total energy expenditure estimated using foot-ground contact pedometry.

Routine walking and running, by increasing daily total energy expenditure (TEE), can play a significant role in reducing the likelihood of obesity. The objective of this field study was to compare TEE estimated using foot-ground contact time (Tc)-pedometry (TEE(PEDO)) with that measured by the criterion doubly labeled water (DLW) method. Eight male U.S. Marine test volunteers [27 +/- 4 years of age (mean +/- SD); weight = 83.2 +/- 10.7 kg; height = 182.2 +/- 4.5 cm; body fat = 17.0 +/- 2.9%] engaged in a field training exercise were studied over 2 days. TEE(PEDO) was defined as (calculated resting energy expenditure + estimated thermic effect of food + metabolic cost of physical activity), where physical activity was estimated by Tc-pedometry. Tc-pedometry was used to differentiate inactivity, activity other than exercise (i.e., non-exercise activity thermogenesis, or NEAT), and the metabolic cost of locomotion (M(LOCO)), where M(LOCO) was derived from total weight (body weight + load weight) and accelerometric measurements of Tc. TEE(PEDO) data were compared with TEEs measured by the DLW (2H2(18)O) method (TEE(DLW)): TEE(DLW) = 15.27 +/- 1.65 MJ/day and TEE(PEDO) = 15.29 +/- 0.83 MJ/day. Mean bias (i.e., TEE(PEDO) - TEE(DLW)) was 0.02 MJ, and mean error (SD of individual differences between TEE(PEDO) and TEE(DLW)) was 1.83 MJ. The Tc-pedometry method provided a valid estimate of the average TEE of a small group of physically active subjects where walking was the dominant activity.

Thermoregulatory modeling use and application in the military workforce.

Thermoregulatory models have been used in the military to quantify probabilities of individuals' thermal-related illness/injury. The uses of the models have diversified over the past decade. This paper revisits an overall view of selected thermoregulatory models used in the U.S. military and provides examples of actual practical military applications: 1) the latest military vehicle designed with armor and blast/bulletproof windows was assessed to predict crews' thermal strains levels inside vehicles under hot environment (air temperature [Ta]: 29-43 °C, dew point: 13 °C); 2) a military working dog (MWD) model was developed by modifying existing human thermoregulatory models with canine physical appearance and physiological mechanisms; 3) thermal tolerance range of individuals from a large military group (n = 100) exposed to 35 °C/40% relative humidity were examined using thermoregulatory modeling and multivariate statistical analyses. Model simulation results assist in the decisions for the strategic planning and preventions of heat stress.

Assessment of male anthropometric trends and the effects on simulated heat stress responses.

Assessing temporal changes in anthropometrics and body composition of US Army soldiers is important because these changes may affect fitness, performance, and safety. This study investigated differences in body dimensions (height, weight, percent body fat (%BF)) of US Army male soldiers by comparing 2004 and 1988 databases. Anthropometric somatotypes were identified and physiological responses of the different somatotypes to simulated heat stress (35 degrees C/50%rh, approximately 550 W work rate, carrying 12 kg load including battle dress uniform and body armor, rest for 30 min and walk for 70 min) using a thermal regulatory model were evaluated. A significant increase in body weight (2.4 kg) was observed between the 2004 and 1988 data (P < 0.05, after Bonferroni correction). However, changes in height and circumference measurements for %BF were insignificant, with the magnitude of the changes not exceeding inter-observer errors. Multivariate analyses demonstrated that anthropometric distributions did not differ between the two databases and identified five primary somatotypes: "tall-fat", "tall-lean", "average", "short-lean", and "short-fat." Within each database, anthropometric values differed among the somatotypes. However, simulated physiological responses to heat stress in each somatotype were similar in the 2004 and 1988 populations. In conclusion, an increase in body weight was the primary change observed in this sample of US Army male soldiers. Temporal changes in somatotypes of soldiers over a 16-year period had minimal impact on simulated physiological response to heat stress using a thermal regulatory model.

Thermoregulatory model to predict physiological status from ambient environment and heart rate.

A real-time thermoregulatory model was developed for predicting real-time physiological responses of workers engaged in various tasks for prolonged time. The unique feature of the present model is primarily on metabolic activity inputs derived from minimum non-invasive measures (i.e., heart rate and ambient temperature). In addition, it utilizes individual anthropological characteristics (height, weight, and clothing) as an input to estimate core temperatures (T(c)). The model was validated using data from five laboratory studies (n=63) with varied environments, clothing, and heat acclimation status. Overall, T(c) predictions using this simplified model, corresponded well with measured values (root mean square deviation: 0.05-0.31 degrees C).

Total energy expenditure estimated using a foot-contact pedometer.

BACKGROUND: Total energy expenditure (TEE) assessment using pedometers provide an easy and less expensive method than doubly labeled water (DLW). This study assessed TEE by a new pedometry method (TEEpedo) compared to the doubly labeled water method (TEEdlw). MATERIAL/METHODS: Shipboard sailors (7 men, age: 23.0+/-3.9 yrs; ht: 180.2+/-6.5 cm; wt: 83.8+/-11.8 kg, and 10 women, age: 24.7+/-4.4 yrs; ht: 165.2+/-8.0 cm; wt: 63.5+/-14.0 kg) (Mean +/-SD) were studied for 8 days. The energy cost of activity was estimated using (a) total body weight, (b) foot-ground contact times [Tc] during running, walking, and non-exercise activity [NEAT], and (c) the known proportion of time spent in each activity category. Resting metabolic rate (RMR) was estimated from lean body mass. RESULTS: TEEPEDO was calculated as: TEEpedo (MJ) = (1440 x [%Run Time x ((0.0761 x [Total Body Weight/TcRun]) - 7.598) +%Walk Time x ((0.056 x [Total Body Weight/TcWalk]) - 2.938) + (%NEAT Time x 0.1 x [RMR/Minute])] + RMR)/239. This method, explained 79% of the variance of TEEpedo with a 95% confidence interval of +/-0.81 MJ/day, relative to TEEdlw (12.55+/-3.3MJ/day). Mean TEEpedo (12.65+/-3.1 MJ/day) did not differ from mean TEEdlw (p=0.95). CONCLUSIONS: At TEEs >14 MJ/day, the TEEpedo method underestimated actual TEE, possibly due to unaccounted for upper body exercise. At more moderate TEEs of 9 to 14 MJ/day, the Tc pedometry method provided accurate estimates of TEE.