Whole-Body Sweat Rate Prediction: Outdoor Running and Cycling Exercise.

Our aim was to develop and validate separate whole-body sweat rate prediction equations for moderate to high intensity outdoor cycling and running, using simple measured or estimated activity and environmental inputs. Across two collection sites in Australia, 182 outdoor running trials, and 158 outdoor cycling trials were completed at a wet-bulb globe temperature ranging from ~15 to ~29˚C, with ~60-min whole-body sweat rates measured in each trial. Data were randomly separated into model development (running: 120; cycling: 100 trials) and validation groups (running: 62; cycling: 58 trials), enabling proprietary prediction models to be developed and then validated. Running and cycling models were also developed and tested when locally measured environmental conditions were substituted with participant subjective ratings for black globe temperature, wind speed, and humidity. The mean absolute error for predicted sweating rate was 0.03 and 0.02 L·h-1 for running and cycling models, respectively. The 95% confidence intervals for running (+0.44 and -0.38 L·h-1) and cycling (+0.45 and -0.42 L·h-1) were within acceptable limits for an equivalent change in total body mass over 3 h of ±2%. The individual variance in observed sweating described by the predictive models was 77% and 60% for running and cycling, respectively. Substituting measured environmental variables with subjective assessments of climatic characteristics reduced the variation in observed sweating described by the running model by up to ~25%, but only by ~2% for the cycling model. These prediction models are publicly accessible (https://sweatratecalculator.com) and can guide individualized hydration management in advance of outdoor running and cycling.

Early Prediction of Impending Exertional Heat Stroke With Wearable Multimodal Sensing and Anomaly Detection.

We employed wearable multimodal sensing (heart rate and triaxial accelerometry) with machine learning to enable early prediction of impending exertional heat stroke (EHS). US Army Rangers and Combat Engineers (N = 2,102) were instrumented while participating in rigorous 7-mile and 12-mile loaded rucksack timed marches. There were three EHS cases, and data from 478 Rangers were analyzed for model building and controls. The data-driven machine learning approach incorporated estimates of physiological strain (heart rate) and physical stress (estimated metabolic rate) trajectories, followed by reconstruction to obtain compressed representations which then fed into anomaly detection for EHS prediction. Impending EHS was predicted from 33 to 69 min before collapse. These findings demonstrate that low dimensional physiological stress to strain patterns with machine learning anomaly detection enables early prediction of impending EHS which will allow interventions that minimize or avoid pathophysiological sequelae. We describe how our approach can be expanded to other physical activities and enhanced with novel sensors.

ACSM Expert Consensus Statement on Exertional Heat Illness: Recognition, Management, and Return to Activity.

ABSTRACT: Exertional heat stroke is a true medical emergency with potential for organ injury and death. This consensus statement emphasizes that optimal exertional heat illness management is promoted by a synchronized chain of survival that promotes rapid recognition and management, as well as communication between care teams. Health care providers should be confident in the definitions, etiologies, and nuances of exertional heat exhaustion, exertional heat injury, and exertional heat stroke. Identifying the athlete with suspected exertional heat stroke early in the course, stopping activity (body heat generation), and providing rapid total body cooling are essential for survival, and like any critical life-threatening situation (cardiac arrest, brain stroke, sepsis), time is tissue. Recovery from exertional heat stroke is variable and outcomes are likely related to the duration of severe hyperthermia. Most exertional heat illnesses can be prevented with the recognition and modification of well-described risk factors ideally addressed through leadership, policy, and on-site health care.

Indicators to assess physiological heat strain - Part 1: Systematic review.

In a series of three companion papers published in this Journal, we identify and validate the available thermal stress indicators (TSIs). In this first paper of the series, we conducted a systematic review (registration: INPLASY202090088) to identify all TSIs and provide reliable information regarding their use (funded by EU Horizon 2020; HEAT-SHIELD). Eight databases (PubMed, Agricultural and Environmental Science Collection, Web of Science, Scopus, Embase, Russian Science Citation Index, MEDLINE, and Google Scholar) were searched from database inception to 15 April 2020. No restrictions on language or study design were applied. Of the 879 publications identified, 232 records were considered for further analysis. This search identified 340 instruments and indicators developed between 200 BC and 2019 AD. Of these, 153 are nomograms, instruments, and/or require detailed non-meteorological information, while 187 can be mathematically calculated utilizing only meteorological data. Of these meteorology-based TSIs, 127 were developed for people who are physically active, and 61 of those are eligible for use in occupational settings. Information regarding the equation, operating range, interpretation categories, required input data, as well as a free software to calculate all 187 meteorology-based TSIs is provided. The information presented in this systematic review should be adopted by those interested in performing on-site monitoring and/or big data analytics for climate services to ensure appropriate use of the meteorology-based TSIs. Studies two and three in this series of companion papers present guidance on the application and validation of these TSIs, to guide end users of these indicators for more effective use.

Indicators to assess physiological heat strain - Part 2: Delphi exercise.

In a series of three companion papers published in this Journal, we identify and validate the available thermal stress indicators (TSIs). In this second paper of the series, we identified the criteria to consider when adopting a TSI to protect individuals who work in the heat, and we weighed their relative importance using a Delphi exercise with 20 experts. Two Delphi iterations were adequate to reach consensus within the expert panel (Cronbach's α = 0.86) for a set of 17 criteria with varying weights that should be considered when adopting a TSI to protect individuals who work in the heat. These criteria considered physiological parameters such as core/skin/mean body temperature, heart rate, and hydration status, as well as practicality, cost effectiveness, and health guidance issues. The 17 criteria were distributed across three occupational health-and-safety pillars: (i) contribution to improving occupational health (55% of total importance), (ii) mitigation of worker physiological strain (35.5% of total importance), and (iii) cost-effectiveness (9.5% of total importance). Three criteria [(i) relationship of a TSI with core temperature, (ii) having categories indicating the level of heat stress experienced by workers, and (iii) using its heat stress categories to provide recommendations for occupational safety and health] were considered significantly more important when selecting a TSI for protecting individuals who work in the heat, accumulating 37.2 percentage points. These 17 criteria allow the validation and comparison of TSIs that presently exist as well as those that may be developed in the coming years.

Exertional heat stroke: nutritional considerations.

NEW FINDINGS: What is the topic of this review? The potential role of nutrition in exertional heat stroke. What advances does it highlight? Certain nutritional and dietary strategies used by athletes and workers may exert a protective effect the pathophysiological processes of exertional heat stroke, whereas others may be detrimental. While current evidence suggests that some of these practices may be leveraged as a potential countermeasure to exertional heat stroke, further research on injury-related outcomes in humans is required. ABSTRACT: Exertional heat stroke (EHS) is a life-threatening illness and an enduring problem among athletes, military servicemen and -women, and occupational labourers who regularly perform strenuous activity, often under hot and humid conditions or when wearing personal protective equipment. Risk factors for EHS and mitigation strategies have generally focused on the environment, health status, clothing, heat acclimatization and aerobic conditioning, but the potential role of nutrition is largely underexplored. Various nutritional and dietary strategies have shown beneficial effects on exercise performance and health and are widely used by athletes and other physically active populations. There is also evidence that some of these practices may dampen the pathophysiological features of EHS, suggesting possible protection or abatement of injury severity. Promising candidates include carbohydrate ingestion, appropriate fluid intake and glutamine supplementation. Conversely, some nutritional factors and low energy availability may facilitate the development of EHS, and individuals should be cognizant of these. Therefore, the aims of this review are to present an overview of EHS along with its mechanisms and pathophysiology, discuss how selected nutritional considerations may influence EHS risk focusing on their impact on the key pathophysiological processes of EHS, and provide recommendations for future research. With climate change expected to increase EHS risk and incidence in the coming years, further investigation on how diet and nutrition may be optimized to protect against EHS would be highly beneficial.

Wearable Sensors and Machine Learning for Hypovolemia Problems in Occupational, Military and Sports Medicine: Physiological Basis, Hardware and Algorithms.

Hypovolemia is a physiological state of reduced blood volume that can exist as either (1) absolute hypovolemia because of a lower circulating blood (plasma) volume for a given vascular space (dehydration, hemorrhage) or (2) relative hypovolemia resulting from an expanded vascular space (vasodilation) for a given circulating blood volume (e.g., heat stress, hypoxia, sepsis). This paper examines the physiology of hypovolemia and its association with health and performance problems common to occupational, military and sports medicine. We discuss the maturation of individual-specific compensatory reserve or decompensation measures for future wearable sensor systems to effectively manage these hypovolemia problems. The paper then presents areas of future work to allow such technologies to translate from lab settings to use as decision aids for managing hypovolemia. We envision a future that incorporates elements of the compensatory reserve measure with advances in sensing technology and multiple modalities of cardiovascular sensing, additional contextual measures, and advanced noise reduction algorithms into a fully wearable system, creating a robust and physiologically sound approach to manage physical work, fatigue, safety and health issues associated with hypovolemia for workers, warfighters and athletes in austere conditions.

ACSM Expert Consensus Statement on Exertional Heat Illness: Recognition, Management, and Return to Activity.

ABSTRACT: Exertional heat stroke (EHS) is a true medical emergency with potential for organ injury and death. This consensus statement emphasizes that optimal exertional heat illness management is promoted by a synchronized chain of survival that promotes rapid recognition and management, as well as communication between care teams. Health care providers should be confident in the definitions, etiologies, and nuances of exertional heat exhaustion, exertional heat injury, and EHS. Identifying the athlete with suspected EHS early in the course, stopping activity (body heat generation), and providing rapid total body cooling are essential for survival, and like any critical life-threatening situation (cardiac arrest, brain stroke, sepsis), time is tissue. Recovery from EHS is variable, and outcomes are likely related to the duration of severe hyperthermia. Most exertional heat illnesses can be prevented with the recognition and modification of well-described risk factors ideally addressed through leadership, policy, and on-site health care.

Unifying the Estimation of Blood Volume Decompensation Status in a Porcine Model of Relative and Absolute Hypovolemia Via Wearable Sensing.

Hypovolemia remains the leading cause of preventable death in trauma cases. Recent research has demonstrated that using noninvasive continuous waveforms rather than traditional vital signs improves accuracy in early detection of hypovolemia to assist in triage and resuscitation. This work evaluates random forest models trained on different subsets of data from a pig model (n = 6) of absolute (bleeding) and relative (nitroglycerin-induced vasodilation) progressive hypovolemia (to 20% decrease in mean arterial pressure) and resuscitation. Features for the models were derived from a multi-modal set of wearable sensors, comprised of the electrocardiogram (ECG), seismocardiogram (SCG) and reflective photoplethysmogram (RPPG) and were normalized to each subject.s baseline. The median RMSE between predicted and actual percent progression towards cardiovascular decompensation for the best model was 30.5% during the relative period, 16.8% during absolute and 22.1% during resuscitation. The least squares best fit line over the mean aggregated predictions had a slope of 0.65 and intercept of 12.3, with an R2 value of 0.93. When transitioned to a binary classification problem to identify decompensation, this model achieved an AUROC of 0.80. This study: a) developed a global model incorporating ECG, SCG and RPPG features for estimating individual-specific decompensation from progressive relative and absolute hypovolemia and resuscitation; b) demonstrated SCG as the most important modality to predict decompensation; c) demonstrated efficacy of random forest models trained on different data subsets; and d) demonstrated adding training data from two discrete forms of hypovolemia increases prediction accuracy for the other form of hypovolemia and resuscitation.

Wearable Sensors Incorporating Compensatory Reserve Measurement for Advancing Physiological Monitoring in Critically Injured Trauma Patients.

Vital signs historically served as the primary method to triage patients and resources for trauma and emergency care, but have failed to provide clinically-meaningful predictive information about patient clinical status. In this review, a framework is presented that focuses on potential wearable sensor technologies that can harness necessary electronic physiological signal integration with a current state-of-the-art predictive machine-learning algorithm that provides early clinical assessment of hypovolemia status to impact patient outcome. The ability to study the physiology of hemorrhage using a human model of progressive central hypovolemia led to the development of a novel machine-learning algorithm known as the compensatory reserve measurement (CRM). Greater sensitivity, specificity, and diagnostic accuracy to detect hemorrhage and onset of decompensated shock has been demonstrated by the CRM when compared to all standard vital signs and hemodynamic variables. The development of CRM revealed that continuous measurements of changes in arterial waveform features represented the most integrated signal of physiological compensation for conditions of reduced systemic oxygen delivery. In this review, detailed analysis of sensor technologies that include photoplethysmography, tonometry, ultrasound-based blood pressure, and cardiogenic vibration are identified as potential candidates for harnessing arterial waveform analog features required for real-time calculation of CRM. The integration of wearable sensors with the CRM algorithm provides a potentially powerful medical monitoring advancement to save civilian and military lives in emergency medical settings.

Controversies in exertional heat stroke diagnosis, prevention, and treatment.

During the past several decades, the incidence of exertional heat stroke (EHS) has increased dramatically. Despite an improved understanding of this syndrome, numerous controversies still exist within the scientific and health professions regarding diagnosis, pathophysiology, risk factors, treatment, and return to physical activity. This review examines the following eight controversies: 1) reliance on core temperature for diagnosing and assessing severity of EHS; 2) hypothalamic damage induces heat stroke and this mediates "thermoregulatory failure" during the immediate recovery period; 3) EHS is a predictable condition primarily resulting from overwhelming heat stress; 4) heat-induced endotoxemia mediates systemic inflammatory response syndrome in all EHS cases; 5) nonsteroidal anti-inflammatory drugs for EHS prevention; 6) EHS shares similar mechanisms with malignant hyperthermia; 7) cooling to a specific body core temperature during treatment for EHS; and 8) return to physical activity based on physiological responses to a single-exercise heat tolerance test. In this review, we present and discuss the origins and the evidence for each controversy and propose next steps to resolve the misconception.

Exercise-heat stress with and without water replacement alters brain structures and impairs visuomotor performance.

Effects of exercise-heat stress with and without water replacement on brain structure and visuomotor performance were examined. Thirteen healthy adults (23.6 ± 4.2 years) completed counterbalanced 150 min trials of exercise-heat stress (45°C, 15% RH) with water replacement (EHS) or without (~3% body mass loss; EHS-DEH) compared to seated rest (CON). Anatomical scans and fMRI Blood-Oxygen-Level-Dependent responses during a visuomotor pacing task were evaluated. Accuracy decreased (P < 0.05) despite water replacement during EHS (-8.2 ± 6.8% vs. CON) but further degraded with EHS-DEH (-8.3 ± 6.4% vs. EHS and -16.5 ± 10.2% vs. CON). Relative to CON, EHS elicited opposing volumetric changes (P < 0.05) in brain ventricles (-5.3 ± 1.7%) and periventricular structures (cerebellum: 1.5 ± 0.8%) compared to EHS-DEH (ventricles: 6.8 ± 3.4, cerebellum: -0.7 ± 0.7; thalamus: -2.7 ± 1.3%). Changes in plasma osmolality (EHS: -3.0 ± 2.1; EHS-DEH: 9.3 ± 2.1 mOsm/kg) were related (P < 0.05) to thalamus (r = -0.45) and cerebellum volume (r = -0.61) which, in turn, were related (P < 0.05) to lateral (r = -0.41) and fourth ventricle volume (r = -0.67) changes, respectively; but, there were no associations (P > 0.50) between structural changes and visuomotor accuracy. EHS-DEH increased neural activation (P < 0.05) within motor and visual areas versus EHS and CON. Brain structural changes are related to bidirectional plasma osmolality perturbations resulting from exercise-heat stress (with and without water replacement), but do not explain visuomotor impairments. Negative impacts of exercise-heat stress on visuomotor tasks are further exacerbated by dehydration.

Wearable technology for compensatory reserve to sense hypovolemia.

Traditional monitoring technologies fail to provide accurate or early indications of hypovolemia-mediated extremis because physiological systems (as measured by vital signs) effectively compensate until circulatory failure occurs. Hypovolemia is the most life-threatening physiological condition associated with circulatory shock in hemorrhage or sepsis, and it impairs one's ability to sustain physical exertion during heat stress. This review focuses on the physiology underlying the development of a novel noninvasive wearable technology that allows for real-time evaluation of the cardiovascular system's ability to compensate to hypovolemia, or its compensatory reserve, which provides an individualized estimate of impending circulatory collapse. Compensatory reserve is assessed by real-time changes (sampled millions of times per second) in specific features (hundreds of features) of arterial waveform analog signals that can be obtained from photoplethysmography using machine learning and feature extraction techniques. Extensive experimental evidence employing acute reductions in central blood volume (using lower-body negative pressure, blood withdrawal, heat stress, dehydration) demonstrate that compensatory reserve provides the best indicator for early and accurate assessment for compromises in blood pressure, tissue perfusion, and oxygenation in resting human subjects. Engineering challenges exist for the development of a ruggedized wearable system that can measure signals from multiple sites, improve signal-to-noise ratios, be customized for use in austere conditions (e.g., battlefield, patient transport), and be worn during strenuous physical activity.

Wearable knee health system employing novel physiological biomarkers.

Knee injuries and chronic disorders, such as arthritis, affect millions of Americans, leading to missed workdays and reduced quality of life. Currently, after an initial diagnosis, there are few quantitative technologies available to provide sensitive subclinical feedback to patients regarding improvements or setbacks to their knee health status; instead, most assessments are qualitative, relying on patient-reported symptoms, performance during functional tests, and physical examinations. Recent advances have been made with wearable technologies for assessing the health status of the knee (and potentially other joints) with the goal of facilitating personalized rehabilitation of injuries and care for chronic conditions. This review describes our progress in developing wearable sensing technologies that enable quantitative physiological measurements and interpretation of knee health status. Our sensing system enables longitudinal quantitative measurements of knee sounds, swelling, and activity context during clinical and field situations. Importantly, we leverage machine-learning algorithms to fuse the low-level signal and feature data of the measured time series waveforms into higher level metrics of joint health. This paper summarizes the engineering validation, baseline physiological experiments, and human subject studies-both cross-sectional and longitudinal-that demonstrate the efficacy of using such systems for robust knee joint health assessment. We envision our sensor system complementing and advancing present-day practices to reduce joint reinjury risk, to optimize rehabilitation recovery time for a quicker return to activity, and to reduce health care costs.

Acoustical Emission Analysis by Unsupervised Graph Mining: A Novel Biomarker of Knee Health Status.

OBJECTIVE: To study knee acoustical emission patterns in subjects with acute knee injury immediately following injury and several months after surgery and rehabilitation. METHODS: We employed an unsupervised graph mining algorithm to visualize heterogeneity of the high-dimensional acoustical emission data, and then to derive a quantitative metric capturing this heterogeneity-the graph community factor (GCF). A total of 42 subjects participated in the studies. Measurements were taken once each from 33 healthy subjects with no known previous knee injury, and twice each from 9 subjects with unilateral knee injury: first, within seven days of the injury, and second, 4-6 months after surgery when the subjects were determined to start functional activities. Acoustical signals were processed to extract time and frequency domain features from multiple time windows of the recordings from both knees, and k-nearest neighbor graphs were then constructed based on these features. RESULTS: The GCF calculated from these graphs was found to be 18.5 ± 3.5 for healthy subjects, 24.8 ± 4.4 (p = 0.01) for recently injured, and 16.5 ± 4.7 (p = 0.01) at 4-6 months recovery from surgery. CONCLUSION: The objective GCF scores changes were consistent with a medical professional's subjective evaluations and subjective functional scores of knee recovery. SIGNIFICANCE: Unsupervised graph mining to extract GCF from knee acoustical emissions provides a novel, objective, and quantitative biomarker of knee injury and recovery that can be incorporated with a wearable joint health system for use outside of clinical settings, and austere/under resourced conditions, to aid treatment/therapy.

Quantitative model of hematologic and plasma volume responses after ascent and acclimation to moderate to high altitudes.

Despite decades of research, the magnitude and time course of hematologic and plasma volume (PV) changes following rapid ascent and acclimation to various altitudes are not precisely described. To develop a quantitative model, we utilized a comprehensive database and general linear mixed models to analyze 1,055 hemoglobin ([Hb]) and hematocrit (Hct) measurements collected at sea level and repeated time points at various altitudes in 393 unacclimatized men (n = 270) and women (n = 123) who spent between 2 h and 7 days at 2,500-4,500 m under well-controlled and standardized experimental conditions. The PV change (ΔPV) was calculated from [Hb] and Hct measurements during a time period when erythrocyte volume is stable. The results are 1) ΔPV decreases rapidly (~6%) after the 1st day at 2,500 m and [Hb] and Hct values increase by 0.5 g/dl and 1.5 points, respectively; 2) ΔPV decreases an additional 1%, and [Hb] and Hct increase an additional 0.1 g/dl and 0.2 points every 500-m increase in elevation above 2,500 m after the 1st day; 3) ΔPV continues to decrease over time at altitude, but the magnitude of this decrease and subsequent increase in [Hb] and Hct levels is dependent on elevation and sex; and 4) individuals with high initial levels of [Hb] and Hct and older individuals hemoconcentrate less at higher elevations. This study provides the first quantitative delineation of ΔPV and hematological responses during the first week of exposure over a wide range of altitudes and demonstrates that absolute altitude and time at altitude, as well as initial hematologic status, sex, and age impact the response.