Establishing a Physiological Strain Index Criterion During the Final Sixty Minutes of Heat Tolerance Testing

2019 ◽  
Vol 51 (Supplement) ◽  
pp. 566
Author(s):  
Douglas M. Jones ◽  
Andrew J. Ordille ◽  
Katherine M. Wilson ◽  
Jay H. Heaney
Keyword(s):  
Heat Tolerance ◽  
Physiological Strain ◽  
Strain Index ◽  
Physiological Strain Index ◽  
Tolerance Testing

scholarly journals Interpretations of Physiological Strain Index During Heat Tolerance Testing

2019 ◽  
Vol 51 (Supplement) ◽  
pp. 566
Author(s):  
Andrew J. Ordille ◽  
Douglas M. Jones ◽  
Katherine M. Wilson ◽  
Jay H. Heaney
Keyword(s):  
Heat Tolerance ◽  
Physiological Strain ◽  
Strain Index ◽  
Physiological Strain Index ◽  
Tolerance Testing

Comparison of Physiological Strain Index and Core Temperature Rise for Classifying Heat Tolerance Among Warfighters

2019 ◽  
Vol 51 (Supplement) ◽  
pp. 565-566
Author(s):  
Jay H. Heaney ◽  
Andrew J. Ordillie ◽  
Katherine M. Wilson ◽  
Douglas M. Jones
Keyword(s):  
Core Temperature ◽  
Heat Tolerance ◽  
Temperature Rise ◽  
Physiological Strain ◽  
Strain Index ◽  
Physiological Strain Index

Physiological Strain Index (PSI) May be Used to Classify Level of Strain During a Heat Tolerance Test

2016 ◽  
Vol 48 ◽  
pp. 972
Author(s):  
Christina K. Cooper ◽  
Doug M. Jones ◽  
Dale S. Bergquist-Turori ◽  
Michael J. Buono ◽  
Jay H. Heaney
Keyword(s):  
Heat Tolerance ◽  
Tolerance Test ◽  
Physiological Strain ◽  
Strain Index ◽  
Physiological Strain Index

scholarly journals Predicting Compensable versus Uncompensable Heat Stress from Physiological Strain Index

2004 ◽  
Vol 36 (Supplement) ◽  
pp. S316
Author(s):  
Thomas E. Bernard ◽  
Candi D. Ashley ◽  
Victor Caravello
Keyword(s):  
Heat Stress ◽  
Physiological Strain ◽  
Strain Index ◽  
Physiological Strain Index ◽  
Uncompensable Heat Stress

scholarly journals Wearable Sensor Application for Integrated Early Warning and Health Surveillance

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Lauren E Charles ◽  
Devin P Wright ◽  
Zhuanyi Huang ◽  
Cree White ◽  
Fnu Anubhav ◽  
...  
Keyword(s):  
Health Status ◽  
Real Time ◽  
Early Warning ◽  
Wearable Sensors ◽  
Environmental Parameters ◽  
Sensor Data ◽  
Wearable Sensor ◽  
Physiological Strain ◽  
Strain Index ◽  
Physiological Strain Index

Objective: The Wearable Sensor Application developed by Pacific Northwest National Laboratory (PNNL) provides an early warning system for stressors to individual and group health using physiologic and environmental indicators. The application integrates health monitoring parameters from wearable sensors, e.g., temperature and heart rate, with relevant environmental parameters, e.g., weather and landscape data, and calculates the corresponding physiological strain index. The information is presented to the analyst in a group and individual view with real-time alerting of abnormal health parameters. This application is the first of its kind being developed for integration into the Defense Threat Reduction Agency's Biosurveillance Ecosystem (BSVE).Introduction: Wearable devices are a low cost, minimally invasive way to monitor health. Sensor data provides real-time physiological indictors of an individual’s health status without the requirement of health care professionals or facilities. Information gleamed from wearable sensors can be used to better understand physiological stressors and prodromal symptoms. In addition, this data can be used to monitor individuals that are in high risk of health-related problems.However, raw data from wearable sensors can be overwhelming to process and laborious to monitor for an individual and, even more so, for a group of individuals. Often specific combination of ranges of sensor readings are indicative of changes to health status and need to be evaluated together or used to calculate specific signal parameters. In addition, the environment surrounding the individual needs to be considered when interpreting the data. To address these issues, PNNL has developed an application that collects, analyzes, and integrates wearable sensor data with geographic landscape and weather information to provide a real-time early alert and situational awareness tool for monitoring the health of groups and individuals.Methods: The prototype application described here was a product of PNNL’s BSVE Application Development Competition. The final product that will be deployed in the BSVE is currently under development by PNNL and will vary slightly in the exact design and architecture described.Data. Wearable sensor data was collected from the Rim2Rim (R2R) Watch Study of individuals hiking the Grand Canyon in Arizona [1]. Weather information was obtained from nearby weather stations and mapping features were derived from Google Maps.Calculations. A physiological Strain Index (PSI) was calculated using core temperature estimates derived through a Kalman Filter approach and heart rate [2,3].Application. The prototype backend application development was based in Python with a MongoDB. The front-end development was built using a scalable architecture and modular approach with components in React and D3.Results: A prototype application was developed this past summer through the PNNL BSVE App Competition (Fig 1). The application was aimed at visualizing wearable sensor data from the Grand Canyon R2R hike dataset. Simulated real-time analysis was used to calculate health status of individuals hiking based on measured physiological parameters and to alert to individuals with signs of physiologic health stress. Visualization tools were incorporated to enable sensor data for individuals and the group to be viewed simultaneously along with pertinent weather, geographic, and elevation data.Many features described in the prototype application will be incorporated into the final BSVE application. The key changes will be 1) the ability to select given time periods for viewing historical data as well as the real-time data collection, 2) environmental data and map view will come from BSVE internal data sources, and 3) the alerts will provide more information and have their own page for reviewing.Conclusions: The Wearable Sensor Application developed by PNNL for integration into the BSVE provides an early warning system for individual and group health using physiologic and environmental parameters. The application highlights health status from wearable sensors and relevant environmental parameters while monitoring a calculated physiological strain index. With this tool, an analyst can easily monitor the health of individuals and groups with the aid of real-time alerting tool for early detection of abnormal health parameters.

Predicting Compensable versus Uncompensable Heat Stress from Physiological Strain Index

2004 ◽  
Vol 36 (Supplement) ◽  
pp. S316
Author(s):  
Thomas E. Bernard ◽  
Candi D. Ashley ◽  
Victor Caravello
Keyword(s):  
Heat Stress ◽  
Physiological Strain ◽  
Strain Index ◽  
Physiological Strain Index ◽  
Uncompensable Heat Stress

scholarly journals Effects of human head hair on performance and thermoregulatory responses during 10-km outdoor running in healthy men

2016 ◽  
Vol 18 (2) ◽  
pp. 155
Author(s):  
Angelo Ruediger Pisani Martini ◽  
João Batista Ferreira-Júnior ◽  
Daniel Barbosa Coelho ◽  
Diego Alcântara Borba ◽  
Leonardo Gomes Martins Coelho ◽  
...  
Keyword(s):  
Heart Rate ◽  
Rectal Temperature ◽  
Heat Storage ◽  
Human Head ◽  
Physiological Strain ◽  
Strain Index ◽  
Healthy Men ◽  
Thermoregulatory Responses ◽  
Head Hair ◽  
Physiological Strain Index

DOI: http://dx.doi.org/10.5007/1980-0037.2016v18n2p155 The aim of the present study was to evaluate the effects of human head hair on performance and thermoregulatory responses during 10-km outdoor running in healthy men. Twelve healthy males (29.5 ± 3.7 years, 174.9 ± 4.3 cm, 72.7 ± 3.2 kg and VO2max 44.6 ± 3.4 ml.kg-1.min-1) participated in two self-paced outdoor 10-km running trials separated by 7 days: 1) HAIR, subjects ran with their natural head hair; 2) NOHAIR, subjects ran after their hair had been totally shaved. Average running velocity was calculated from each 2-km running time. Rectal temperature, heart rate and physiological strain index were measured before and after the 10-km runs and at the end of each 2 km. The rate of heat storage was measured every 2 km. The environmental stress (WBGT) was measured every 10 min. The running velocity (10.9 ± 1 and 10.9 ± 1.1 km.h-1), heart rate (183 ± 10 and 180 ± 12 bpm), rectal temperature (38.82 ± 0.29 and 38.81 ± 0.49oC), physiological strain index (9 ± 1 and 9 ± 1), or heat storage rate (71.9 ± 64.1 and 80.7 ± 56.7 W.m-1) did not differ between the HAIR and NOHAIR conditions, respectively (p>0.05). There was no difference in WBGT between the HAIR and NOHAIR conditions (24.0 ± 1.4 and 23.2 ± 1.5ºC, respectively; p=0.10). The results suggest that shaved head hair does not alter running velocity or thermoregulatory responses during 10-km running under the sun.

Can gender differences during exercise-heat stress be assessed by the physiological strain index?

1999 ◽  
Vol 276 (6) ◽  
pp. R1798-R1804 ◽  
Author(s):  
Daniel S. Moran ◽  
Yair Shapiro ◽  
Arie Laor ◽  
Sharona Izraeli ◽  
Kent B. Pandolf
Keyword(s):  
Gender Differences ◽  
Heat Stress ◽  
Sweat Rate ◽  
Physiological Strain ◽  
Strain Index ◽  
Wide Acceptance ◽  
Physiological Strain Index ◽  
Lower Strain ◽  
And Gender ◽  
C 50

A physiological strain index (PSI) based on rectal temperature (Tre) and heart rate (HR) was recently suggested to evaluate exercise-heat stress. The purpose of this study was to evaluate PSI for gender differences under various combinations of exercise intensity and climate. Two groups of eight men each were formed according to maximal rate of O2 consumption (V˙o 2 max). The first group of men (M) was matched to a group of nine women (W) with similar ( P > 0.001)V˙o 2 max (46.1 ± 2.0 and 43.6 ± 2.9 ml ⋅ kg−1 ⋅ min−1, respectively). The second group of men (MF) was significantly ( P < 0.001) more fit than M or W with V˙o 2 max of 59.1 ± 1.8 ml ⋅ kg−1 ⋅ min−1. Subjects completed a matrix of nine experimental combinations consisting of three different exercise intensities for 60 min [low, moderate, and high (300, 500, and 650 W, respectively)] each at three climates {comfortable, hot wet, and hot dry [20°C 50% relative humidity (RH), 35°C 70% RH, and 40°C 35% RH, respectively]}. No significant differences ( P > 0.05) were found between matched genders (M and W) at the same exposure for sweat rate, relativeV˙o 2 max(%V˙o 2 max), and PSI. However, MF had significantly ( P < 0.05) lower strain than M and W as reflected by %V˙o 2 max and PSI. In summary, PSI applicability was extended for exercise-heat stress and gender. This index continues to show potential for wide acceptance and application.

scholarly journals Effect of a Simulated Tactical Occupation Stressor on Physiological Strain Index and Blood Pressure

2018 ◽  
Vol 50 (5S) ◽  
pp. 731
Author(s):  
Cody E. Morris ◽  
Lee J. Winchester ◽  
Andrew J. Hussey ◽  
Ariel S. Tomes ◽  
Wesley A. Neal ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Physiological Strain ◽  
Strain Index ◽  
Physiological Strain Index
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