The physiological strain index does not reliably identify individuals at risk of reaching a thermal tolerance limit

Purpose The physiological strain index (PSI) was developed to assess individuals’ heat strain, yet evidence supporting its use to identify individuals at potential risk of reaching a thermal tolerance limit (TTL) is limited. The aim of this study was to assess whether PSI can identify individuals at risk of reaching a TTL. Methods Fifteen females and 21 males undertook a total of 136 trials, each consisting of two 40–60 minute periods of treadmill walking separated by ~ 15 minutes rest, wearing permeable or impermeable clothing, in a range of climatic conditions. Heart rate (HR), skin temperature (Tsk), rectal temperature (Tre), temperature sensation (TS) and thermal comfort (TC) were measured throughout. Various forms of the PSI-index were assessed including the original PSI, PSIfixed, adaptive-PSI (aPSI) and a version comprised of a measure of heat storage (PSIHS). Final physiological and PSI values and their rate of change (ROC) over a trial and in the last 10 minutes of a trial were compared between trials completed (C, 101 trials) and those terminated prematurely (TTL, 35 trials). Results Final PSIoriginal, PSIfixed, aPSI, PSIHS did not differ between TTL and C (p > 0.05). However, differences between TTL and C occurred in final Tsk, Tre–Tsk, TS, TC and ROC in PSIfixed, Tre, Tsk and HR (p < 0.05). Conclusion These results suggest the PSI, in the various forms, does not reliably identify individuals at imminent risk of reaching their TTL and its validity as a physiological safety index is therefore questionable. However, a physiological-perceptual strain index may provide a more valid measure.

The Physiological Strain Index Modified for Trained Heat-Acclimatized Individuals in Outdoor Heat

Purpose: To determine if the Physiological Strain Index (PSI), in original or modified form, can evaluate heat strain on a 0–10 scale, in trained and heat-acclimatized men undertaking a competitive half-marathon run in outdoor heat. Methods: Core (intestinal) temperature (TC) and heart rate (HR) were recorded continuously in 24 men (mean [SD] age = 26 [3] y, VO2peak = 59 [5] mL·kg·min−1). A total of 4 versions of the PSI were computed: original PSI with upper constraints of TC 39.5°C and HR 180 beats·min−1 (PSI39.5/180) and 3 modified versions of PSI with each having an age-predicted maximal HR constraint and graded TC constraints of 40.0°C (PSI40.0/PHRmax), 40.5°C (PSI40.5/PHRmax), and 41.0°C (PSI41.0/PHRmax). Results: In a warm (26.1–27.3°C) and humid (79–82%) environment, all runners finished the race asymptomatic in 107 (10) (91–137) min. Peak TC and HR were 39.7°C (0.5°C) (38.5–40.7°C) and 186 (6) (175–196) beats·min−1, respectively. In total, 63% exceeded TC 39.5°C, 71% exceeded HR 180 beats·min−1, and 50% exceeded both of the original PSI upper TC and HR constraints. The computed heat strain was significantly greater with PSI39.5/180 than all other methods (P < .003). PSI >10 was observed in 63% of runners with PSI39.5/180, 25% for PSI40.0/PHRmax, 8% for PSI40.5/PHRmax, and 0% for PSI41.0/PHRmax. Conclusions: The PSI was able to quantify heat strain on a 0–10 scale in trained and heat-acclimatized men undertaking a half-marathon race in outdoor heat, but only when the upper TC and HR constraints were modified to 41.0°C and age-predicted maximal HR, respectively.

Wearable Sensor Application for Integrated Early Warning and Health Surveillance

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.