Assessing Human Health Through Real-time Data Collection at the Grand Canyon

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1 Assessing Human Health Through Real-time Data Collection at the Grand Canyon. Principal Investigator: Glory Aviña, PhD MBA Sandia National Laboratories Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy s National Nuclear Security Administration under contract DE-AC4-94AL8. SAND C DISTRIBUTION STATEMENT A: Approved for public release. Distribution is unlimited.

2 Purpose of this Study 1) Markers for Health: identify physiological, cognitive markers most related to health and task performance 2) Data Processing: determine key methodologies for data processing from GOTS/COTS devices 3) BSVE Integration: integrate data into the Biosurveillance Ecosystem (BSVE) 2

3 Experimental Design Choose Wearables Data Grand Canyon R2R Data Extraction Data Analysis Data Interpretation Presentation of Findings Proposed 3 year project 3

Park Services Grand Canyon R2R ER Medicine University of New

4 Interdisciplinary Approach Systems Biology Device Security Data Science Sandia- CA Sandia- NM Cognitive Science National Park Services Grand Canyon R2R ER Medicine University of New Mexico Military Participants Relevant Data Microsensors Participants

5 Grand Canyon Rim-2-Rim Hike Altitude and Temperature Change Extreme Environment Physical Strain 14.3 miles, 6, feet to the bottom 9.6 miles, 4, feet back to the South Rim Source:

6 Three tiers for R2R WATCH

7 R2R WATCH Team Rob Abbott, PhD (Org 1463), Computer Scientist Clifford Anderson-Bergman, PhD (Org 8962), Statistician Glory Emmanuel Aviña, PhD MBA (Org 8962), Principal Investigator Kristin Divis, PhD (Org 1463), Cognitive Psychologist Cathy Branda, PhD (Org 892), Sr Manager, SNL Program Manager Patricia Benguerel (Org 832), Project Controller Victoria Newton (Org 163), Project Support

8 Collecting Physiological and Cognitive Data Suunto Spartan Ultra Garmin Vivoactive HR Garmin etrex 1 LifeBeam SmartHat Garmin Tempe Wahoo Tickr-X Suunto Smart Sensor ipod Touch 6 Fitbit Charge HR Omron bio-impedance Garmin Fenix 3 HR Myontec Mbody EMG SensorPush Pre / Post Weigh-in

9 Collecting Physiological and Cognitive Data Brain Baseline 9

10 Participants Voluntary hikers already doing Rim-to-Rim hike Military Personal 1

11 Data Data Extraction Device Security Data Analysis Data Interpretation Presentation of Findings Recommendations on Wearables Statistical Findings 11

12 OCTOBER 216 DATA COLLECTION

13 Sandia National Laboratories Team 7 wearable devices packages 3+ wearable devices The University of New Mexico Health Sciences Center

14 Add Pics from the South Rim

16 Differences between Military & Civilians Military

17 Military vs Civilian: Cognitive Differences First Test Military Civilian Go/No-go: Time 27 ms (sd = 66) 4 ms (sd = 87) Accuracy.964 (sd =.61).97 (sd =.7) Military performed better in 1 / 12 measures Flanker: Time 94 ms (sd = 19) 92 ms (sd = 98) Accuracy.93 (sd =.14).943 (sd =.17) VSTM: Time 112 ms (sd = 26) 1139 ms (sd = 39) Accuracy.779 (sd =.82).732 (sd =.93) Last Test Military Civilian Go/No-go: Time 488 ms (sd = 92) 3 ms (sd = 71) Accuracy.94 (sd =.6).949 (.7) Flanker: Time 496 ms (sd = 64) 9 ms (sd = 71) Accuracy.97 (sd =.26).96 (sd =.98) VSTM: Time 978 ms (sd = 411) 188 (sd = 343) Accuracy.779 (sd =.82).72 (sd =.16) Military s advantage increased in later trials, especially with response time: Could be less fatigue or better learning

18 Overall Device Performance

19 Elevation in Feet Cognitive-HR Connection to examine fatigue Goal of Model 1: validate that BrainBaseline captures fatigue effect Take advantage of structure of experiment for robust measures Know subjects start experiencing light fatigue hiking down the Canyon, heavy fatigue hiking back up the Canyon Capture this in two variables: Percent descended, percent ascended Important to validate, but results do not extend to a non-controlled environment Down: 6% Up: % Down: 1% Up: 7% Time in Hours

(decrease) In 13/14 estimated effects, this trend was observed

20 Hypotheses Fatigue would have a positive effect on response time (increase) Fatigue would have a negative effect on accuracy (decrease) In 13/14 estimated effects, this trend was observed (p-value from sign test:.9). Brain Baseline scores decline as fatigue increases

21 Brainbaseline-Fatigue Connection

Hypothesis: heart rate responds more quickly to changes in workload as fatigue sets in Detecting changes in workload - Breaks North This is everywhere that somebody s pedometer (wrist-based

22 Hypothesis: heart rate responds more quickly to changes in workload as fatigue sets in Detecting changes in workload - Breaks North This is everywhere that somebody s pedometer (wrist-based accelerometer) read for at least 8% of samples in any -minute period GPS was not used to detect breaks, only indicate where they were at the time This appears to be a reasonably promising, if crude, way to detect changes in workload But maybe it would be easier to just get a sample of more controlled data to start with

23 g m /d L g m /d L g m /d L m g /d L U /L U /L U /L mmo l/l mmo l/l mmo l/l m g /d L m g /d L m g /d L m g /d L Complete Metabolic Panel Standard Clinical Laboratory tests used commonly in most patients who receive blood tests. n = 1 matched samples, 6 total subjects p values from paired T-test between pre and post crossing samples. Red circles highlight significant differences. S o d iu m P o tassiu m C h lo r id e C a lc iu m G lu c o s e C r e a tin in e B U N p r e p =. 77 p o s t p r e p o s t p = p r e p o s t p = < p r e p o s t p = < p r e p o s t p = p r e p o s t p = < p r e p o s t p = <.1 P r o te in A lb u m in G lo b u lin T o ta l B ilir u b in A lkalin e P h o s p h a ta s e A S T (S G O T ) A L T (S G P T ) p p o r e s t p =.2 3. p r e p o s t p = <.1 2. p r e p o s t p = <.1. p r e p o s t p =.1 p r e p = p o s t p r e p o s t p = <.1 p p o r e s t p =.11

24 m m o l/l p r e S o d iu m pos t Slightly elevated levels are less clinically significant Sudden drops in sodium to these levels (over hours) can lead to seizures and death Sodium levels did not change significantly as a population. This is in part because half of the subjects sodium concentrations increased, while the other half decreased. This is has important implications as decreases in sodium can lead to catastrophic outcomes including seizure, coma, and death. In less severe cases it can lead to nausea and declines in performance. Previous literature implicates human behavioral factors like amounts of food and water ingestion as significant predictors. Molecular mechanisms through Arginine Vasopressin have also been implicated. p =.77

25 Funding for Wearables At The Canyon for Health is provided by the Defense Threat Reduction Agency (CB139). THANK YOU Glory Emmanuel Aviña, Principle Investigator Catherine Branda, Program Manager

26 ACKNOWLEDGEMENTS Funding for Wearables At The Canyon for Health is provided by the Defense Threat Reduction Agency (CB139).

CHARACTERIZATION OF WIND VELOCITY DISTRIBUTIONS WITHIN A FULL-SCALE HELIOSTAT FIELD

CHARACTERIZATION OF WIND VELOCITY DISTRIBUTIONS WITHIN A FULL-SCALE HELIOSTAT FIELD Jeremy Sment and Clifford K. Ho Concentrating Solar Technologies Department Sandia National Laboratories P.O. Box 800,