Effect of Chronic Hyperthermia in Firefighters on Cognitive Function and Postural Stability

A thesis submitted to the Graduate School of the University of Cincinnati in partial fulfilment of the requirements for the degree of

Master of Science

in the Department of Environmental Health of the College of Medicine

2019

by

Nell Wickstrom

B.S. Miami University, 2015

Committee Chair: Amit Bhattacharya, Ph.D

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ABSTRACT Purpose: The purpose of this pilot study is to measure the effect of chronic heat on postural balance characteristics and decision making by comparing firefighters with a long work experience to firefighters who have a much shorter work experience, in terms of years.

Methods: Eight fulltime, male firefighters (33.92 years old ± 1.51) participated in this study.

Firefighters perceived judgement of others’ fall risk was assessed while undergoing a functional magnetic resonance imaging (fMRI) as they viewed actors in various states of imbalance while walking or standing still. Firefighter’s postural balance was assessed with a wearable, inertial sensor system quantifying spatiotemporal parameters of gait as well as angular velocity, angular displacement, and angular acceleration for phase plane analysis along the medial-lateral (ML) and anterior-posterior (AP) planes. Participants performed static tests while on a force platform to assess these parameters during one-and two-feet balance tests with eyes open and closed. An instrumented timed up and go test (iTUG) was performed to assess dynamic parameters of gait and phase plane analysis. Firefighters perceived judgement of their own fall risk was determined during static and dynamic testing.

Results: Firefighters were divided into two groups to compare firefighters with less than 8 years of work experience [Group 1: (3.00 years ± 2.83)] to firefighters with greater than 8 years

[Group 2: (13.92 years ± 2.62)]. Between variance of double stance times during dual task was statistically significant (p = 0.013) between Group 1 (5.19e-04 ± (3.36e-04) and Group 2 (4.36e-

3 ± (2.50e-03)). Firefighters who have worked more years (Group 2) have a larger variability between trials for the amount of time they have both feet on the ground during the double stance gait cycle. Significant associations (p-values ranging between 0.018 and 0.061) were found between gait dual task objective variables and gait PSPSI analysis, but not with respect to group.

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No significant correlations (p-values ranging between 0.165 and 0.980) were found between perceived judgement (as measured by PSPSI metric) of fall risk and history of firefighting.

Significant associations (p-values ranging between 0.000173 and 0.100) were found between the two groups for static and dynamic postural balance. Postural sway analysis determined a significant increase in variance (p = 0.000173) along the AP plane for Group 1 firefighters

(40.29 ± (1.32)) with less years of work experience in comparison with Group 2 (11.36 ± (7.83)).

Variability may indicate central nervous system (CNS) impairments; however, a small sample size may be a contributing factor.

Conclusions: Greater double stance variability during dynamic task for firefighters with more years worked may be due to underlying CNS impairments affecting postural stability. Nearly all firefighters portrayed similar concordance when comparing how they perceived fall risk of others revealing the number of years worked as a firefighter had no effect on their perception of fall risk. A consensus model was created from these curves to express a gold standard of perception to be used for future studies. Gait and postural sway results were inconclusive due to small sample size. Future studies are recommended.

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ACKNOWLEDGMENT There are many individuals that have supported me as I have pursued my degree. I’d first like to thank my academic advisor, Dr. Amit Bhattacharya, for his continual support and guidance. Dr.

Bhattacharya has continually aided me throughout the degree process to which I am endlessly grateful. I would also like to thank Dr. Marepalli Rao for his assistance with statistical analysis.

Dr. Rao has donated so much of his time to teach and guide me, and I am extremely grateful for his patience throughout this process.

I would also like to thank all of the colleagues at the Ergonomics-Biomechanics Laboratory, particularly Cyndy Cox, Lorenna Altman, Rachel Zeiler, Ashley Turner, Nick Ferrara, and

Kerrie Dailey. This study was truly a team effort, and no single individual could have performed the extensive methods and collected all of the data alone. Thank you all so much for your persistence, time, and effort. Thank you also to Chris Dicesare for developing a custom computer program for this study.

Thank you to the National Institute for Occupational Safety and Health and all those involved with providing the funding for grant number 200-2015-M-87462. Without this funding, this research would never have been accomplished.

Finally, I’d like to thank my family for their endless support throughout my career.

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Table of Contents ABSTRACT ...... 2 ACKNOWLEDGMENT ...... 5 1.0 INTRODUCTION ...... 10 2.0 PURPOSE ...... 11 3.0 BACKGROUND ...... 12 4.0 METHODS ...... 14 4.1 Background Information on the Present Study ...... 14 4.2 Subjects ...... 14 4.3 Study Design ...... 15 4.3.1 Questionnaire / Screening ...... 15 4.3.2 Magnetic resonance imaging (MRI) ...... 15 4.3.3 Static and Dynamic Balance Assessments ...... 17 4.4 Data Analysis ...... 21 4.4.1 Phase Plane Analysis ...... 21 4.4.2 Sway Force Plate Independent Variables ...... 26 4.4.3 Gait Independent Variables ...... 27 4.4.4 Statistical Analysis Plan ...... 29 5.0 RESULTS ...... 34 5.1 Static and Dynamic fMRI Judgement Tasks ...... 35 5.1.1 fMRI Judgement Pairwise Concordance ...... 35 5.1.2 fMRI Judgement Compared to Questionnaires ...... 40 5.2 Sway Analysis ...... 41 5.2.1 Sway PSPSI Analysis...... 42 5.2.2 Sway Classification Analysis ...... 45 5.3 Gait Analysis ...... 47 5.3.1 Gait PSPSI Analysis ...... 47 5.3.2 Gait Classification Analysis ...... 49 6.0 DISCUSSION ...... 53 6.1 Static and Dynamic fMRI Judgement Tasks ...... 53 6.2 PSPSI Analysis ...... 54 6.3 Sway Analysis ...... 55 6.4 Gait Analysis ...... 57

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6.5 Strengths, Limitations, and Alternative Approaches ...... 60 7.0 CONCLUSION ...... 61 REFERENCES ...... 63 Appendix A –Questionnaires ...... 67 Appendix B – Medical IRB Research Protocol ...... 78 Appendix C – Postural Balance Metrics ...... 96 Appendix D – Phase Plane Plots ...... 99 Appendix E – Pairwise Concordance Plots ...... 104 Appendix F – Statistical Power and Effect Size ...... 143

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Table of Figures Figure 1 Functional magnetic resonance imaging (fMRI) set-up...... 16 Figure 2 Example of range of animated postural movement within and beyond the stability boundary.38 ...... 16 Figure 3 Scale to determine subjective rating for how far out of balance a person appears during static and dynamic judgement tasks ...... 17 Figure 4 Inertial sensor placement on the body6 ...... 18 Figure 5 Body planes and axis spatial coordinate system of the human body.1 ...... 23 Figure 6 Axis orientation of LEGSys sensors on a human body ...... 23 Figure 7 Sway Angular Velocity versus Angular Displacement across the mediolateral plane. . 25 Figure 8 Gait Angular Velocity versus Angular Displacement across the mediolateral plane. ... 26 Figure 9 Angular anterior-posterior velocity versus angular anterior posterior displacement. .... 27 Figure 10 Subject’s Gait walk during iTUG test trial...... 28 Figure 11 Static Quadratic Regression Model by direction to showcase variability in degree and Perception as a result of direction...... 30 Figure 12 Dynamic Pairwise Concordance comparisons between all firefighters...... 36 Figure 13 Dynamic Consensus Model curve for Firefighters F002 through F008...... 37 Figure 14 Static Forward Directional Pairwise Concordance comparisons between all firefighters...... 37 Figure 15 Static Forward Consensus Model curve for Firefighters F002 through F008...... 38 Figure 16 Static Sideways Directional Pairwise Concordance comparisons between all firefighters...... 38 Figure 17 Static Sideways Consensus for Firefighters F002 through F008...... 39 Figure 18 Static Backwards Directional Pairwise Concordance comparisons between all firefighters...... 39 Figure 19 Static Backwards Consensus Model curve for Firefighters F001 through F008...... 40

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Table of Tables

Table 1 Characterization of how various test conditions were challenged and/or dependent on the visual, proprioception, and vestibular systems...... 20 Table 2 Subject Demographics ...... 35 Table 3 Correlation Analysis between fMRI Perception Data and Firefighter History ...... 41 Table 4 Fall History and Time Duration before Falls during Sway Trials ...... 42 Table 5 Comparison between total PSPSI scores during one legged conditions (L & M) in which falls occurred between groups...... 43 Table 6 Average PSPSI comparison for each condition of sway to average sway force plate variables and firefighter group...... 44 Table 7 Comparison of force plate variables and PSPSI variables for each condition between Group 1 and Group 2...... 45 Table 8 Sway Classification Tree Criteria ...... 46 Table 9 Sway p-value output of parametric and nonparametric testing...... 47 Table 10 Comparison between average PSPSI of dual task to average dual task parameters and firefighter group ...... 48 Table 11 Comparison of gait sensor traditional variables and PSPSI variables for each condition between Group 1 and Group 2...... 49 Table 12 Gait Classification Tree Criteria ...... 50 Table 13 Gait p-value output of parametric and nonparametric testing...... 52

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1.0 INTRODUCTION Environmental heat stress is a common occupational hazard amongst firefighters.34

Firefighters are routinely exposed to high heat stress for periods of unpredictable duration as they perform both physically and mentally demanding tasks.3 (Bhattacharya) Sources of heat exposure can include wearing protective equipment, performing physically demanding tasks, and working in live fire and high heat environments.3, 19 During firefighting search and rescue activities, maintaining adequate motor and cognitive function, such as sustaining upright functional static and dynamic balance and having appropriate judgement, is imperative to the health and safety of the individual firefighter, the crew, and the public. Firefighters must be able to remain vigilant, make important decisions, and remember geographical locations in order to navigate the fire, while being under extreme emotional stress.3

Previous studies have investigated the acute physiological and cognitive effects of heat stress on firefighters.18, 31-32, 44 Prolonged heat stress causes failures in the transmission of the neural drive at neuromuscular, spinal, and cortical function leading to mental fatigue and a decline in physical and cognitive performance.34-35 A study conducted by Nybo et al found that cerebral activity alterations may be associated with hyperthermia induced fatigue during prolonged exercise.32 There has also been evidence that heat, smoke, sleep deprivation, and shift work can all have deleterious effects on motor and cognitive functions.12, 44

Gap in the Literature: There is little information on the chronic effect of heat stress on firefighters. Information on the prolonged impact of firefighting activities when exposed to high heat on physiological and cognitive responses is limited. Understanding how postural balance and decision making are chronically affected after firefighting for a number of years may be critical to the health and safety of firefighters.

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2.0 PURPOSE The purpose of this study was to assess brain function in firefighters to determine the accuracy of decision-making skills and assess how static and dynamic postural balance (with and without mental task) are affected in firefighters with varying degrees of history of exposures to hyperthermia.

• Hypothesis 1: A long-term history of chronic exposure to heat associated with

firefighting will be affiliated with a reduced ability to accurately judge static and dynamic

postural balance.

• Hypothesis 2: A long-term history of chronic exposure to heat associated with

firefighting will be affiliated with poorer performance on static and dynamic postural

balance tests and gait (with and without the inclusion of a mental task).

• Hypothesis 3: A long-term history of chronic exposure to heat associated with

firefighting will be affiliated with increased upper body sway implying poorer postural

balance.

• Specific Aim 1: Measure firefighters’ subjective judgement of postural balance of static

and dynamic tasks when undergoing fMRI and compare their subjective judgement to

objective measures.

• Specific Aim 2: Measure how static and dynamic postural balance and dual-task

performances are affected when exposed to combined cognitive and motor challenges

(with and without mental tasks) under normothermic conditions in a laboratory testing

environment.

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• Specific Aim 3: Measure the association of upper body balance under normothermic

conditions with whole body postural balance in a laboratory testing environment using

phase plane analysis.

3.0 BACKGROUND The assessment of postural balance is imperative to assess and treat individuals with motor impairment.10 James et al found that perceived exertion parameters did not correlate with objective measures of postural balance parameters. This may suggest that firefighters may not perceive postural instability as a result of heat stress which may increase fall risk.19 This current study seeks to evaluate firefighter’s perception on fall risk of individuals in various states of static and dynamic imbalance conditions compared to their own postural balance as measured objectively in a laboratory setting.

The impact of heat stress on postural balance is of particular importance to firefighters due to the dangerous conditions they encounter when responding to a fire. Postural equilibrium is challenged by visual, vestibular, or proprioception changes which result in displacement of the body from its equilibrium. If the center of gravity is shifted outside of the base of support, instability is detected from sensory afferents via the vestibular system, vision, and somatosensory/proprioception system that is impacted from muscle and joint inputs. The impact of these sensory afferents become particularly important when one or more of the afferents is excluded. During firefighting activities, firefighters may enter situations where vision is reduced due to low or no light, and surfaces are uneven or slick. This situation would increase the importance of the role of the vestibular system to effectively maintain balance.11 The displacement of center of gravity and center of pressure can be used to evaluate overall postural balance.2 This can be evaluated with the use of a force platform. A force platform uses pressure

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receptors to evaluate linear movement of ground reaction forces generated by an individual standing or walking across them.22 Postural stability occurs when the center of gravity stays within the stability boundary of an individual. The stability boundary is defined by the area around the foot, or feet maintaining contact with the ground. Postural balance is maintained as a result of changes in the body’s spatial position and orientation to continually correct and maintain the center of gravity and center of pressure within the stability boundary to keep the body in safe limits and limit the risk of fall. The measurement of maximum displacement of the center of pressure an individual can move without falling (stabilogram) is a useful predictor of postural balance.2

The assessment of human gait is also of importance, as it requires dynamic balance to perform successfully. Human gait occurs subconsciously, and yet it necessitates the integration of complex neuromuscular-skeletal system and the coordination of muscles acting throughout many joints within the body. Gait can be negatively affected by traumatic injury, neurological damage, gradual degeneration, and fatigue. The motor patterns at the hips, knees, and ankles are needed to absorb and generate energy and the central nervous system (CNS) functions to integrate and coordinate commands with proprioceptive feedback, vestibular and visual inputs to maintain correct motor patterns of force at each joint. Human gait rotates between single and double stance as an individual takes a single step forward. Each step is essentially a controlled fall as the CNS relies on afferent sensory feedback to maintain dynamic balance.11 When the sensory inputs, neuro-muscular system, and/or cognitive systems are challenged, dynamic balance may be negatively affected resulting in slips and falls.11, 44

Performing physical and mental tasks cohesively, also known as a performing a dual task, can further affect human gait which may lead to an increase in fall-risk.27-28 Previous studies

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have revealed that impaired working memory in subjects with mild cognitive impairment performing a dual task is associated with a slower gait and increased gait variability.25-28

Assessing dual-tasks enables the ability to expose gait impairments not obvious under a single- task test condition. This may aide in falls risk identification in people with mild cognitive impairment.29 Firefighting activities typically necessitate the need for dual-task activities to maintain their own safety while effectively performing their duties such as navigating the terrain, maintaining communications, and performing aid.3

4.0 METHODS

4.1 Background Information on the Present Study The data used in this study is a part of a larger pilot study currently being conducted by

Dr. Amit Bhattacharya, Ph.D., and Dr. Kim M. Cecil, Ph.D. titled the Effect of Hyperthermia on

Brain Function and Impact on Functional Outcomes (NIOSH 200-2015-M-87462). Twenty-five full-time, male firefighters, between 30-45 years of age are anticipated to participate in this pilot study. The medical international review board (IRB) research protocol for the larger study is available in Appendix B. The present study does not include some of the methods addressed in the larger pilot study. The present study had a narrower age range between 30-35 years of age.

Subjects were recruited within the Cincinnati metropolitan area.

4.2 Subjects A total of 8 healthy, male firefighters between the ages of 30 to 35 years old (33.92 ±

1.51) participated in this study. This age range was narrow because the human brain is age dependent. Impairments to proprioception, visual, and vestibular systems increase with age, resulting in age-associated changes in compensatory responses.14 Participants were screened for

MRI contraindications which includes any type of electronic, mechanical, or magnetic implant, metal objects, or foreign body (BB, bullet, shrapnel, metallic slivers). Participants with known

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cardiac disease, neuropsychiatric diagnosis, use of psychotropic medications, and substance abuse (self-reported) were excluded.

4.3 Study Design 4.3.1 Questionnaire / Screening All subjects were given a prescreening questionnaire prior to the test date to assess their chronic history of heat exposure. The information gathered included but is not limited to: the years they have spent as a part-time and full-time firefighter; the average non-fire related runs they have undergone per week in the last year; and how many times total they have worked in a structure fire or heavy smoke conditions in the last year. On the day of visit, all subjects were given a questionnaire to assess their acute history of heat exposure. The information gathered included: the amount of heavy smoke or structure fires undergone in the past week; and the amount of EMS runs undergone in the past week. The prescreening questionnaire and day of visit questionnaire are available in Appendix A.

4.3.2 Magnetic resonance imaging (MRI) Magnetic resonance imaging (MRI) was used to evaluate the brain using a 3 Tesla Philips

MR scanner equipped with a 32-channel head coil. The MRI assessment was conducted at

Cincinnati Children’s Hospital Medical Center (CCHMC). All subjects within the pilot study underwent an anatomical MRI using a Three dimensional (3D) Standard T1 weighted sequence to visualize the whole brain. A functional MRI (fMRI) was then performed while participants were asked to judge static and dynamic postural stability through a series of pictures and videos.

Each participant would lie in the MR scanner in a comfortable, supine position (Figure 1) with their right arm resting beside their trunk with a trac-ball (an MRI compatible device that works similar to a computer mouse) attached to their hand.

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Figure 1 Functional magnetic resonance imaging (fMRI) set-up. During the static balance judgement task, subjects would view images of actors in different degrees of stability/balance when leaning forward, backward, and side-to-side. Figure 2 shows an example of the range of postural movement when leaning forward and backward within and beyond the stability boundary. The images were shown in random order. Participants were asked to judge how likely the actors are to fall based on a scale (Figure 3). Telemetry obtained during stimulus development provides objective information about the actor’s stability/fall risk based on their position. The actors’ angular degrees of stability were used to compare the participant’s responses to how they perceived the actors were likely to fall.

Figure 2 Example of range of animated postural movement within and beyond the stability boundary.38

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Figure 3 Scale to determine subjective rating for how far out of balance a person appears during static and dynamic judgement tasks During the dynamic balance judgement task, participants viewed short videos (2-5 seconds in duration) of actors walking with various degrees of stability. Using the same scale as during the static balance judgement, subjects then indicated how likely the actor was to fall following each video. Videos were played in random order. All of the viewed walking videos had prior measurements of the actors’ percent out, or the percent of the trial that occurs outside of the stability boundary. This was an objective measurement used to compare the participant’s responses to how they perceived the actors were likely to fall.

4.3.3 Static and Dynamic Balance Assessments The static and dynamic balance assessments were conducted in Dr. Bhattacharya’s laboratory at the University of Cincinnati. The tests were performed under normothermic conditions (i.e. room temperature). During all of the tests, each participant wore five inertial, wearable, and wireless sensors (BioSensics LEGSys+TM Watertown, MA) equipped with 3-D accelerometers and 3-D gyroscopes of the X, Y, and Z planes on the torso, and the left and right shanks and thighs (Figure 4).6 These sensors provide the ability to measure spatiotemporal parameters of gait since they enable the recording of gait and postural transitions.43 Each sensor contains an Invensense MPU-9150 Motion Processing Unit which uses a three-axis capacitative

Micro-Electro-Mechanical-Systems (MEMS) accelerometer. These accelerometers are best suited for measuring low-frequency vibration, motion, and stead-state acceleration. Limitations

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for this type of accelerometer include poor signal to noise ratio, limited bandwidth, and they are restricted to smaller acceleration levels.17 This will be used to quantify phase plane-based assessment of dynamic postural balance. The dynamic postural balance metrics will be based on phase plane plots among Linear Acceleration and Angular Velocity in 3 mutually perpendicular directions.

Figure 4 Inertial sensor placement on the body6 For each subject and prior to performing the static and dynamic assessments, preliminary testing is performed on the accelerometers to insure the 3-D gyrometers and 3-D accelerometers are working properly.

Static Balance Assessments A force platform (AMTI, model OR6-6-1000) was used to attain postural balance metrics. The postural balance metrics will include: 1) Sway area defined as the area within the x- y movement of the CP obtained during the 30 second postural balance test and 2) Sway length defined as the total distance travelled by the CP obtained during the 30 second postural balance test. Foot placement can affect balance. Due to this, subjects’ footprints were drawn as they stood feet parallel, shoulder-width apart in the center of the force plate prior to testing on a piece of paper resting on the force plate. Subjects were asked to stand in their footprints during each

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test to standardize their foot position. The inertial sensors and force platform technologies were synchronized so that all data were collected simultaneously.

The static balance tests were used to assess participant’s postural sway as they stand on a force platform under four conditions: standing on two legs with eyes open (Condition A), standing on two legs eyes closed (Condition B), standing on one leg with eyes open (Condition

L), standing on one leg with eyes closed (Condition M). Two trials were completed for each condition. The first trial was completed in order (beginning with Condition A and ending with

Condition M). The second trial was completed in reverse order (beginning with Condition M and ending with Condition A) in order to avoid an order effect to prevent fatigue from being a factor.

Each test takes 30 seconds in duration to perform. The conditions were determined to challenge the various sensory afferents associated with balance (visual, proprioception, and vestibular systems). Table 1 illustrates how each test condition either challenges or remains dependent on the three sensory afferents. The visual system was challenged during the eyes closed conditions.

Proprioception was challenged during the one-legged test conditions.

The force plate is used to gather data on x-y linear movements of the participants’ center of pressure (CP) which will subsequently be used to quantify the static postural balance. These forces and moments are processed using a custom software (“Posture60” Copyright All Rights

Reserved, University of Cincinnati, 1987-2010), which enables the calculation of x-y coordinates of the subjects’ CP movement during testing.4,5

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Table 1 Characterization of how various test conditions were challenged and/or dependent on the visual, proprioception, and vestibular systems.

Test Condition Sensory Afferents

Challenged Dependent

Two legs, eyes open Visual

Proprioception

Vestibular

Two legs, eyes closed Visual Proprioception

Vestibular

One leg, eyes open Proprioception Visual

Vestibular

One leg, eyes closed Visual Vestibular

Proprioception

At the end of each trial, the subject was asked to rate their perceived sense of postural sway and instability (PSPSI) they experienced throughout the trial. This questionnaire is available in Appendix A. During the one-legged tests, if a subject placed their raised foot on the ground in order to regain balance during the test, the test was recorded as a ‘Fall’. If the subject needed assistance from an observing investigator anytime throughout the test, a ‘Fall” was also recorded.

Dynamic Balance Assessments Participants performed six trials of an instrumented Timed Up and Go test (iTUG) while wearing five inertial sensors. Six channels of data are obtained wirelessly using the Inertial link sensor system for calculating outcomes of Dynamic Gait associated with the iTUG test. Data collected includes linear acceleration and angular velocity in 3 mutually perpendicular directions.

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The outcomes collected from the multi-axis sensor during the iTUG test include: 1) Turn

Duration (TD, sec), 2) Peak Turning Velocity (PTV, degrees/sec), and gait variables. The iTUG test is widely used to assess balance and mobility.10, 43 The use of the five inertial sensors additionally aids the test’s ability to assess balance and gait during a dynamic task. During the iTUG test, a participant would stand up from a chair, walk at a normal pace 7 meters across the room, turn around a cone, walk back to the chair, and sit down. Three trials were performed doing only the iTUG test.

An additional three trials were performed with a dual mental task demanding working memory. During these iTUG trials, the participant performed the iTUG test while also carrying out a mental task. The subject counted backwards or performed serial subtractions on the number three, starting from a randomly assigned number between 500 and 700 while performing the iTUG test. This dual task is comparable to the math part of the Trier Social Stress Test.

4.4 Data Analysis 4.4.1 Phase Plane Analysis Phase plane analysis was conducted for the static and dynamic balance assessments during static balance tests on a force platform as well as dynamic balance during an iTUG test using a custom analysis software (“BAS”, University of Cincinnati, 2019). BAS Version 0.991 was used to analyze traditional gait parameters and gait phase plane variables. Version 0.9 was used to conduct sway phase plane analysis. The analysis uses digitized kinematic data collected from LEGSys wireless sensors worn throughout testing. Phase plane and kinematic variable definitions are available in Appendix C. Phase plane analysis uses measures that incorporate both angular position and angular velocity of the center of pressure to characterize balance and evaluate overall angular motion of the body. A phase plane plot is created by plotting the time derivative of a parameter against that parameter.37 Evaluated parameters were velocity versus

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displacement and acceleration versus displacement along the mediolateral and anterior-posterior planes of the human body. These plots can give insight of both static and dynamic factors affecting balance by providing information about neuromuscular control of a body segment.37, 39

Phase plane graphs uniquely observe movements based on the displacement and timing of segmental motion. This type of analysis can broaden the understanding of typical and aberrant motion as well as help to identify underlying movement impairments that may give rise to abnormal motions.39

There is limited research on phase plane analysis during dynamic task. Gait is comprised of a series of cycles including single leg stance and double leg stance. Single leg stance occurs when one leg alone is on the ground and supports the weight of the body as the other leg is swinging to take a next step. Double leg stance occurs when both feet are on the ground and therefore are both supporting the weight distribution of the body during the walking cycle.42 The postural stability boundary is constantly changing during gait due to changes in center of mass.

The postural stability boundary during the gait task is defined as the area underneath a single foot during the single stance. Studies have shown that during single leg stance, the postural stability boundary is smallest leading to an increased risk of falls. The center of mass changes throughout the swing phase as each body segment adjusts to keep the center of mass inside the stability boundary.9, 23-24 Phase plane analysis was used to investigate the relative relationship from one side of the body to the other and to determine the relational association between all of the body segments.

Phase plane analysis investigates how an individual’s spatial coordinate system is affected when performing static and dynamic tasks.37 Figure 5 shows the orientation of the planes along the x, y, and z axis in order to further understand variables associated with phase

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plane analysis adapted from Annis et al.1 The axis orientation is based on the directional plane of the wireless sensors as seen in Figure 6.

Figure 5 Body planes and axis spatial coordinate system of the human body.1

Figure 6 Axis orientation of LEGSys sensors on a human body

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Phase Plane Plots Phase plane plots were created and calculated using BAS software by analyzing outputs from the LEGSys chest sensor. These plots were used to calculate sway area, sway length, and

Percent Out during sway testing. Phase plane plots represent the calculated variables by analyzing angular displacement, velocity, and acceleration across the mediolateral and anterior posterior planes. All the different plot types can be viewed in Appendix D. Total sway length refers to the total length of the phase plane plot during a 30 second trial. A large sway length represents increased movement and indicates a decrease in balance or stability. Total sway area refers to the total area of the phase plane plot during a 30 second trial. An increase in sway area demonstrates an increase in postural movement, which suggests a decreased ability to maintain upright balance or stability. As sway area increases, linear acceleration and linear velocity generally increases in order to maintain upright balance. Total sway area was calculated as changes in each parameter with respect to time.19 Figure 7 displays a two-dimensional (2D) and three-dimensional (3D) plot of angular mediolateral velocity and angular mediolateral displacement. The 3D plot illustrates sway length inside of an individual’s stability boundary.

Sway area is identified as the total area inside the stabilogram identified by the red line. Sway length is the total distance traveled identified by the blue lines.

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Figure 7 Sway Angular Velocity versus Angular Displacement across the mediolateral plane. Similarly to sway phase plane plots, gait phase plane plots were created to assess sway area, sway length, and percent out. The data was gathered with the LEGSys Waist sensor and analyzed by BAS. Gait phase plane plots assess phase plane dimensional changes during the walking, iTUG task across the mediolateral and anterior posterior planes. Figure 8 displays a two-dimensional (2D) and three-dimensional (3D) plot of angular mediolateral velocity and angular mediolateral displacement during the iTUG task. Each blue line identified in the plot characterizes a single stance step. The 3D plot shows when the body moves outside of the stability boundary while walking, demonstrating a greater fall risk. Examples of all gait phase plots are available in Appendix C.

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Figure 8 Gait Angular Velocity versus Angular Displacement across the mediolateral plane. 4.4.2 Sway Force Plate Independent Variables Traditional static sway variables, measured with a force platform, were evaluated for each subject and separated by test condition (A, B, L, M). The static sway variables calculated by the force platform include total sway length, total sway area, and excursion in the mediolateral

(ML) and anterior-posterior (AP) direction. ML Excursion evaluates the range of body movement in the mediolateral direction. AP Excursion evaluates the range of body movement in the anterior posterior direction.19 These variables are also defined in Appendix C. Summary statistics calculated for sway variables include mean, standard deviation, and coefficient of variance. A natural logarithm was calculated for sway area and sway length.

Figure 9 illustrates a sway phase plane plot that demonstrates these variables. All of these variables were calculated using Posture60. Excursion was calculated as the difference between the greatest displacement and the least displacement along the linear horizontal and vertical axis.

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Figure 9 Angular anterior-posterior velocity versus angular anterior posterior displacement. 4.4.3 Gait Independent Variables Traditional dynamic gait variables were measured using LEGSys sensors and were evaluated for each subject and separated by single task and dual task. The dynamic gait variables calculated include cadence, single stance time, double stance time, turn duration, peak linear acceleration during turn along the x-axis, peak angular velocity during turn along the x-axis, and stride length. Cadence is defined as the speed at which a person can walk, expressed in steps per minute (steps/min).36 Single stance time is the amount of time the stance foot is in contact with the ground throughout the swing phase, when only one foot is touching the ground, expressed in seconds (s). Double stance is the amount of time both feet are touching the ground, expressed in seconds.9 Single and double stance times were calculated for each step during the test. Peak linear acceleration, expressed in meter per second squared (m/s2), and velocity, expressed in degree per second (deg/s), during turn along the x-axis evaluated how quickly each subject turned around the cone for each trial. Stride length is the distance between one foot and the next during a single step, expressed in inches.9 These variables are also defined in Appendix C. The

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average stance times and stride lengths, standard deviation, and variance were summarized using

BAS for each trial.

Studies have shown that during walking, individuals can adjust their gait as a result of external cues. Compensatory gait adjustments to limit fall risk as a direct result of an increase in balance demand, include a decrease in walking speed and single stance times and increases in stride length and double stance time. A longer double stance period indicates the body requires a longer amount of time to regain stability from one step to another.9, 33, 36

Figure 10 illustrates an aerial view of one subject’s walking path when performing the iTUG test as analyzed by BAS. Each step represents the subject’s single stance on their left or right feet. Each footprint also represents the subject’s single stance stability boundary. The subject stands up from a chair (not seen here), walks 7 meters, turns around a cone, walks back 7 meters, and sits back in his chair. The blue dots within each foot print represent changes in the center of pressure in relation to the stability boundary. This figure further demonstrates how walking causes changes in postural stability and necessitates the body to compensate respectively in order to limit fall risk.

Figure 10 Subject’s Gait walk during iTUG test trial.

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4.4.4 Statistical Analysis Plan Statistical analysis was performed using R Project for statistical computing.40 The purpose of analysis was to determine the statistical relationship of the impact of chronic heat in firefighters on Firefighter’s subjective ratings of perception for static and dynamic tasks, gait variables, and sway variables associated with loss of balance. Statistical models developed for different analysis strategies are further documented below.

Static and dynamic fMRI judgement tasks to perceive fall risk A quadratic regression consensus model was performed on both the static and dynamic results for each firefighter. Pairwise concordance was performed based on quadratic regression models of each firefighter to compare a single firefighter’s subjective rating of perception to each other firefighter’s subjective rating of perception. The difference in areas under each firefighter’s quadratic regression curve was calculated for comparison and can be viewed in Appendix E.

Concordance measures were compared between firefighters using Tukey’s Multiple Testing

Honest Significant Difference Procedure. This procedure determines if there is differences between each firefighter’s perceived judgement of fall risk when viewing individuals walking or leaning in varying degrees of instability.

For the dynamic judgement task, a natural logarithm of each firefighter’s subjective rating of perception (log(Perception)) collected along the x-axis of the judgement scale (Figure

3) was compared to the objectively measured parameter of Percent Out of each actor’s walk.

For the static judgement task, a natural logarithm of each firefighter’s subjective rating of perception (log(Perception)) collected along the x-axis of the judgement scale (Figure 3) was compared to the objectively measured degree of angular deviation by which each actor was leaning from the vertical axis. Degree of angular deviation was determined using Kinovea software.20 Static results were further separated by direction (forward, backwards, and sideways)

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based on which direction the actor was leaning in the viewed picture. There are different ranges of degrees the actors were leaning based on each direction, as seen in Figure 11. Actors’ range of deviation in the forward direction was from 0 to 27 degrees. In contrast, the range of deviation in the backwards direction was from 0 to 16 degrees and for the sideways direction the range was from 0 to 17 degrees. Based on this plot, direction has a large influence on perception which justifies splitting data according to direction.

Figure 11 Static Quadratic Regression Model by direction to showcase variability in degree and Perception as a result of direction. The R-squared (R2) parameter for each firefighter’s dynamic and static directional quadratic regression models was calculated. This variable was compared (cor.test) to the questionnaire results for total years worked as a firefighter (a total of part-time and full-time years as a firefighter), the number of heavy smoke or structure fire runs performed in the last

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year, and the number of non-fire runs performed in the last year, relating to all other runs that were not involved with heavy smoke or structure fire.

Static and Dynamic Laboratory Assessments of Postural Gait and Sway

Evaluated gait variables included: cadence, single-stance duration, double-stance duration, turn duration, peak linear acceleration in the x direction during the turn, peak angular velocity in the x direction during the turn, and stride length. An analysis of within and between variance were calculated for the following gait variables: cadence, single-stance duration, double-stance duration, and stride length. Gait trials were divided between single task (3 trials) and dual task (3 trials) for each subject. Analysis was conducted separately for these tasks. The following equations were used to determine both variance types:

1) A weighted mean was calculated using the following equation:

Weighted mean (WM) = (n1m1 + n2m2 + n3m3) / (n1 + n2 + n3)

Where: n = number of steps; and

m = mean of specific variable

2) Between variance was calculated using the following equation:

2 2 2 Between variance (BV) = [n1(m1 – WM) + n2(m2 – WM) + n3(m3 – WM) ] / (t – 1)

Where: n = number of steps;

m = mean of specific variable;

WM = Calculated weighted mean;

T = number of trials

3) Within variance was calculated using the following equation:

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Within variance (WV) = [(n1 - 1)var1+ (n2 - 1)var2+ (n3 - 1)var3)] / [(n1 + n2 + n3) – 3]

Where: n = number of steps;

Var = Calculated variance for each trial

The summary statistics calculated for turn duration, peak linear acceleration in the x direction during the turn, and peak angular velocity in the x direction during the turn were mean, standard deviation, and coefficient of variance.

Classification Analysis Subjects were divided into two groups to evaluate how their chronic heat exposure is affected by sway, gait, and phase plane parameters. The groups’ identity was based on years of experience and were divided into Group 1 and Group 2 respectively: less than 8 years of firefighting experience and greater than 8 years of firefighting experience. Only two firefighters had less than 8 years of experience (3 ± 2.83 years). Six firefighters had greater than 8 years of experience (13.92 ± 2.62 years). Due to the numerous variables assessed and the small sample size, an exploratory data mining technique was used to distinguish between both groups. This preliminary study is performing this exploratory technique to find which variables are perfect classifiers to predict the group identity for further evaluation in future studies.

Parametric and Non-parametric Analysis Parametric (t-test) and nonparametric (Permutation Test) analysis was performed for all classified variables. Traditional statistical methods, including parametric analysis, require assumptions about underlying distribution. This is limited by the small sample size of the data.

When calculating a t-test, a normal distribution is required. A t-test evaluates if the population means are the same. A two-sample permutation test, which is a type of non-parametric analysis, does not require the underlying distribution and is therefore not as limited by the small sample

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size. A permutation test builds a sampling distribution by resampling the observed data. This test is typically used in experimental studies when testing the null hypothesis in which there is no difference between groups. Permutation tests evaluate patterns of all the data to investigate possible alternative groups there could have been and where the mean-difference in the observed data is relative to all of the other alternative group differences independent of group assignment.21, 41

PSPSI Analysis Subjects’ PSPSI subjective ratings were collected after each trial during sway and gait testing. Subject’s total perceived rating per trial based on the questionnaire was calculated.

PSPSI total rating scores could range between 0 (representing the subject perceived very little postural instability) to 8 (representing the subject felt a lot of postural instability).

For sway analysis, subjects’ PSPSI was averaged between Trial 1 and Trial 2 for each condition and compared to objective measurements of Sway for each condition using a linear regression model. Therefore, their PSPSI was compared to sway length, sway area, AP excursion, and ML excursion for all conditions. This analysis was conditional due to limitations of the force plate. The force plate is unable to analyze sway variables when a fall occurs so any trials in which a fall occurs were unable to be analyzed.

For gait analysis, single task and dual task were assessed separately. Total PSPSI was averaged between trials for both single and dual task conditions and compared to objective measurements of Gait using a linear regression model. Single and dual task average PSPSI was compared to average cadence, single stance duration, double stance duration, stride length, turn duration, acceleration during turn along the x-axis, and angular velocity during turn along the x- axis.

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Parametric and non-parametric analysis was conducted for these variables to assess how these measurements compare between Group 1 and Group 2 to assess differences in variables based on the number of years firefighters have worked.

Statistical Power Due to the small sample size of this study, statistical power of the sample size was calculated for all variables that were perfect classifiers based on the classification analysis.

Statistical power determines how capable a study is able to detect statistical effect based on a p- value. Based on a one-way analysis of variance test using R, an appropriate sample size was calculated for classifiers with a weak power rating less than 80% with a significance level of

0.05. This determines how many subjects are necessary to be a part of each group within the study to maintain a strong power rating for the different variables. A table of all classified variables’ power and needed sample size to reach a power of 80% is available in Appendix F.

5.0 RESULTS The demographics for all firefighters participating in this study are given in Table 2.

Firefighters were further divided into Group 1 and Group 2 based on the number of years they have worked as a firefighter to indicate differences in occupational chronic heat exposure between groups. Parametric and non-parametric analysis was performed on these demographics to assess if the groups are statistically different. The number of years worked as a firefighter is statistically different between groups based on the parametric test (p = 0.032). The t-test p-value has a p-value between 0.05 and 1.0 for the variables of years worked as a firefighter (p = 0.059) and number of non-fire runs each firefighter has gone on in the last year (p = 0.085). Static and dynamic postural balance data were collected for all firefighters. All but one firefighter had no exposure to structure fires or heavy smoke in the past week prior to testing. Therefore, acute heat exposure was not analyzed.

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Table 2 Subject Demographics

Demographics Group 1 Group 2 T-test P-value Permutation Test P- value Number of Firefighters 2 6 Age (years) 33.95 ± (2.39) 33.91 ± (1.44) 1 1.17 BMI 29.41 ± (4.11) 27.84 ± (2.87) 0.687 0.502 Years worked 3.00 ± (2.83) 13.92 ± (2.62) 0.059’ 0.032* Non-fire runs in last year 787.50 ± (159.10) 391.61 ± (336.77) 0.085’ 0.182 Fire runs in last Year 16.00 ± (19.80) 11.33 ± (14.11) 0.798 1 Group 1 consists of all firefighters that have less than 8 years of work experience as a firefighter. Group 2 consists of all firefighters that have greater than 8 years of work experience as a firefighter. *P-values less than 0.05. ‘P-values between 0.05 and 1.0 5.1 Static and Dynamic fMRI Judgement Tasks This section assesses comparisons between firefighter’s perceived judgement of fall risk between each firefighter as well as between questionnaire outcomes.

5.1.1 fMRI Judgement Pairwise Concordance This section assesses each single firefighter perceived judgment ratings, when viewing dynamic and static pictures and videos of actors in varying stages of imbalance. Each firefighter’s perception analysis was compared to all the other individual firefighters using pairwise concordance analysis.

Dynamic, static – forward, and static – sideways concordance plots (Figures 12, 14, and

16 respectively) all show that Subject F001 is an outlier disagreeing with all the other firefighters. Based on concordance in these plots, there is not much difference between subjects

F002 through F008. Therefore, a quadratic regression consensus curve was created to represent a gold standard of perception when rating fall risk based on all subjects, sans F001. These curves, represented in Figures 13, 15, and 17, can be utilized in future studies to compare other firefighters against this curve to establish how well future firefighters perceive fall risk.

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For static – backwards concordance, no outliers were found, as evidenced in Figure 18.

Therefore, for the backwards direction, all subjects are concurrent. Since there is not much difference between subjects F001 and F008, a quadratic regression consensus curve was created to represent a gold standard of perception when rating fall risk in the backwards direction, as shown in Figure 19. This curve can also be used in future studies to see how future subjects perceive fall risk when leaning in the backwards direction.

Figure 12 Dynamic Pairwise Concordance comparisons between all firefighters.

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Figure 13 Dynamic Consensus Model curve for Firefighters F002 through F008.

Figure 14 Static Forward Directional Pairwise Concordance comparisons between all firefighters.

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Figure 15 Static Forward Consensus Model curve for Firefighters F002 through F008.

Figure 16 Static Sideways Directional Pairwise Concordance comparisons between all firefighters.

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Figure 17 Static Sideways Consensus for Firefighters F002 through F008.

Figure 18 Static Backwards Directional Pairwise Concordance comparisons between all firefighters.

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Figure 19 Static Backwards Consensus Model curve for Firefighters F001 through F008. 5.1.2 fMRI Judgement Compared to Questionnaires A quadratic regression model was created for all eight firefighters. The R2 output from the regression model was correlated to the firefighter’s work history gathered from the questionnaires. R2 evaluates the quadratic regression model’s goodness of fit. The correlation test evaluates an association between paired samples. The output of the correlation test is shown in

Table 3. Based on this Table, all p-values are greater than 0.05. Therefore, there is no statistical significance between firefighter work history and firefighter’s perceived evaluation of fall risk when viewing static and dynamic pictures and videos of actors in various states of imbalance.

Many negative correlations were determined between firefighter history and dynamic perception data. There is a negative correlation between total years worked and all Dynamic and Static

Perception outputs. There are also negative correlations comparing Dynamic R2 to total structure fire runs and non-fire runs in the last year. Static – Sideways R2 has a negative correlation

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between total structure fire runs in the last year. All other correlations were positive as seen in this table.

Table 3 Correlation Analysis between fMRI Perception Data and Firefighter History

Firefighter Dynamic R2 Static – Forward Static – Static – Sideways 2 2 2 History R Backwards R R Cor.1 P-value Cor.1 P-value Cor.1 P-value Cor.1 P-value Total years -0.416 0.305 -0.172 0.684 -0.542 0.165 -0.305 0.463 worked Total Structure -0.105 0.804 0.287 0.491 0.010 0.980 -0.121 0.776 fire runs in last year Total Non-fire -0.028 0.948 0.350 0.396 0.087 0.839 0.413 0.309 runs in last year 1Correlation between firefighter history attained from questionnaires and Quadratic Regression Model R2 output from fMRI Perception Data. 5.2 Sway Analysis Sway static testing was analyzed with traditional linear motion variables using the data gathered from a force platform as well as phase plane variables investigating angular motion analyzed with wearable sensors.

Table 4 demonstrates the fall history for each subject during the Sway trials. No falls occurred if subjects were able to successfully maintain the condition for a full 30 seconds during the trial. Based on this table, the majority of falls occurred during Condition M, in which the subjects stood on one leg with their eyes closed where posture was primarily maintained by the vestibular system. Only two firefighters were able to successfully complete Condition M during one of their trials. Based on the average fall times, subjects were able to maintain their balance for a longer period of time during Condition L trials compared to Condition M. Condition M trials demonstrated participants had poorer postural stability, due to their shorter fall time, during

Trial 2 (T2) as compared to Trial 1 (T1). This may be a result of fatigue effect on the vestibular system. Since Condition M challenged or impaired the visual and proprioception sensory

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afferents and relied mainly on the vestibular system (Table 1), fall risk was greatest during this condition. No falls occurred during any of the two-legged trials (Condition A and B).

Table 4 Fall History and Time Duration before Falls during Sway Trials Subject Condition A: Condition B: Two Condition L: Condition M: ID Two legs, eyes legs, eyes closed One leg, eyes One leg, eyes open open closed T1 (s) T2 (s) T1 (s) T2 (s) T1 (s) T2 (s) T1 (s) T2 (s) F001 30 30 30 30 FALL 30 FALL FALL (26.03) (12.95) (6.57) F002 30 30 30 30 FALL 30 FALL FALL (26.75) (19.13) (2.80) F003 30 30 30 30 30 30 FALL FALL (12.18) (2.88) F004 30 30 30 30 30 30 30 FALL (18.53) F005 30 30 30 30 30 30 FALL 30 (26.00) F006 30 30 30 30 30 30 FALL FALL (19.83) (17.63) F007 30 30 30 30 30 30 FALL FALL (4.99) (5.25) F008 30 30 30 30 FALL 30 FALL FALL (19.05) (6.92) (7.35) Mean1 30 30 30 30 23.94 30 14.57 8.72 SD2 0 0 0 0 4.25 0 7.50 6.62 CV3 NA NA NA NA 5.63 NA 1.94 1.32 1Average fall time for each condition. 2Standard Deviation of fall time for each condition. 3Coefficent of Variance of fall time for each condition. 5.2.1 Sway PSPSI Analysis Table 5 provides the each subject’s total PSPSI scores for Trial 1 and Trial 2 for the one legged conditions in which falls occurred separated by group experience. Based on this table only subjects within Group 2 experienced falls during Condition M. When falls occurred, their perceived rating of postural instability was higher (PSPSI score of 4.5 to 7) in comparison to their Trial 2 scores (PSPSI score of 1 – 3) in which they did not fall.

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Table 5 Comparison between total PSPSI scores during one legged conditions (L & M) in which falls occurred between groups.

PSPSI Condition L PSPSI Condition M T1 T2 T1 T2 Group 1 0.5 0.5 4 7* 6 3 7.5* 4.5* Group 2 4.5* 1 7* 7.5* 7* 2.5 8* 8* 0.5 0.5 8* 8* 1.5 1 6* 5 3.5 0 7* 7.5* 4.5* 3 7.5* 7.5* *Trial in which a fall has occurred. Group 1 represents all firefighters with less than 8 years worked as a firefighter. Group 2 represents all firefighters with greater than 8 years worked as a firefighter. Score of “0” indicates subject’s total perceived rating of postural instability was very low during the trial. Score of “8” represents subject’s total perceived rating of postural instability was very high during the trial. Table 6 compares subject’s personal perceived sense of balance during sway testing for each condition to all average force plate variables. Their PSPSI was also compared to the divided firefighter groups to test if there was an association between personal perception and experience as a firefighter. Condition M was not analyzed due to limitations of the force plate data to analyze falls. Based on this table, there was no significant difference between subject’s personal perceived sense of balance and force plate parameters or between Firefighter Groups classified by experience.

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Table 6 Average PSPSI comparison for each condition of sway to average sway force plate variables and firefighter group.

Test Condition Variable P-value Condition A: Firefighter Group 0.243 Two legs, eyes open Sway Area mean 0.301 Sway Length mean 0.877 ML Excursion mean 0.201 AP Excursion mean 0.425 Condition B: Firefighter Group 0.766 Two legs, eyes closed Sway Area mean 0.319 Sway Length mean 0.924 ML Excursion mean 0.274 AP Excursion mean 0.345 Condition L: Firefighter Group 0.362 One leg, eyes open Sway Area mean 0.264 Sway Length mean 0.308 ML Excursion mean 0.623 AP Excursion mean 0.323 Group 1 represents all firefighters with less than 8 years worked as a firefighter. Group 2 represents all firefighters with greater than 8 years worked as a firefighter. Parametric and non-parametric analysis was performed on each average force plate variable to assess differences between Group 1 and Group 2 as seen in Table 7. Based on this table, there was no significant difference between Group 1 and Group 2 for average force plate variables or PSPSI data for each analyzed condition.

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Table 7 Comparison of force plate variables and PSPSI variables for each condition between Group 1 and Group 2.

Test Condition Variable Group 1 Group 2 T-test Permutation Test mean ± (SD) mean ± (SD) p-value p-value PSPSI mean 0.37 ± (0.18) 0.12 ± (0.14) 0.257 0.249 Sway Area mean -0.15 ± (0.91) -0.04 ± (0.46) 0.889 0.817 Condition A: Sway Length mean 2.949 ± (0.01) 3.02 ± (0.23) 0.456 0.756 Two legs, eyes open ML Excursion mean 0.92 ± (0.77) 0.89 ± (0.43) 0.958 0.933 AP Excursion mean 1.63 ± (0.45) 1.79 ± (0.39) 0.704 0.528 PSPSI mean 0.37 ± (0.18) 0.71 ± (0.29) 0.145 0.356 Sway Area mean 0.63 ± (0.14) 0.52 ± (0.74) 0.625 0.742 Condition B: Two Sway Length mean 3.30 ± (0.06) 3.48 ± (0.21) 0.109 0.732 legs, eyes closed ML Excursion mean 1.06 ± (0.39) 1.11 ± (0.41) 0.884 0.887 AP Excursion mean 2.64 ± (0.002) 2.48 ± (0.52) 0.48 0.726 PSPSI mean 2.50 ± (2.82) 1.17 ± (0.62) 0.625 0.713 Sway Area mean 1.97 ± (0.08) 1.78 ± (0.30) 0.413 0.698 Condition L: Sway Length mean 4.75 ± (0.11) 4.53 ± (0.28) 0.306 0.397 One leg, eyes open ML Excursion mean 3.34 ± (0.51) 3.13 ± (0.79) 0.742 0.696 AP Excursion mean 4.06 ± (0.85) 3.83 ± (0.74) 0.782 0.792 Group 1 represents all firefighters with less than 8 years worked as a firefighter. Group 2 represents all firefighters with greater than 8 years worked as a firefighter. 5.2.2 Sway Classification Analysis Table 8 lists all the sway independent variables that are perfect classifiers to divide the data into two groups: less than eight years working as a firefighter (Group 1) and greater than eight years working as a firefighter (Group 2). Sway force plate variables and sway phase plane sensor variables are represented in this table. Fourteen independent variables out of 34 sway force plate variables were able to predict the group identity. These variables were measured via a force platform using “Posture60”. This software is unable to analyze trials in which a fall occurs.

As a result, Condition M (one leg, eyes closed) was unable to be analyzed. Therefore this analysis was conditional based on absence of falls due to limitations of “Posture60”.

One independent variable out of 202 sway phase plane variables was able to predict the group identity. “BAS” evaluates sway force plate variables based on sensor feedback. Condition

M for one subject was unable to be analyzed due to sensor feedback errors. This subject was one

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of the two subjects categorized into Group 2. Classification and p-value analysis require at least two subjects in a group for comparisons. Therefore, Condition M was unable to be analyzed.

Table 8 Sway Classification Tree Criteria

Test Condition Dependent Variables1 Classification Criteria Units2 Group SD < 0.055 1 Sway Area SD cm2 SD ≥ 0.055 2 Sway SD < 0.063 1 Condition A: ML Excursion SD cm SD ≥ 0.063 2 Two legs, eyes open CV ≥ 33 1 AP Excursion CV cm CV < 33 2 Sway Length SD SD < 0.03; or SD ≥ 0.03 and mean < 3.3 and ≥ 3.2 1 cm Sway Length Mean SD ≥ 0.03 and mean ≥ 3.3; or SD ≥ 0.03 and mean < 3.2 2 Sway Sway Length CV SL CV ≥ 142; or SL CV ≥ 49 and < 142 and SA CV ≥ 4.2 cm 1 Condition B: Sway Area CV SL CV < 142 and SA CV < 4.2; or SL CV < 49 and SA CV ≥ 4.2 cm2 2 Two legs, eyes closed ML Excursion SD SD < 0.24 and mean ≥ 2.1 but < 2.7 1 SD ≥ 0.24; or SD < 0.24 and mean ≥ 2.7; or SD <0.24 and cm AP Excursion mean mean < 2.1 2 Phase Plane Condition ml displacement vs ml CV < 5.3 1 Unitless B: Two legs, eyes closed acceleration length CV CV ≥ 5.3 2 Sway Length mean SL ≥ 4.7 and SA < 2 cm 1 Sway Sway Area mean SL < 4.7; or SL ≥ 4.7 and SA ≥ 2 cm2 2 Condition L: ML Excursion mean ML ≥ 3 and < 3.9 and AP < 4.7 1 One leg, eyes open cm AP Excursion mean ML < 3; or ML ≥ 3.9; or ML < 3.9 and AP ≥ 4.7 2 1Coefficent of variance (CV); Standard deviation (SD) 2Centimeters (cm); Degrees (⁰); seconds (s) *Group 1 represents all firefighters with less than 8 years worked as a firefighter. Group 2 represents all firefighters with greater than 8 years worked as a firefighter.

Parametric (T-test) and nonparametric (Permutation test) analysis was conducted for all fourteen independent sway variables listed in Table 8 to evaluate statistical significance. This analysis is illustrated in Table 9. The variable of anterior-posterior excursion when performing the test on two legs with eyes open was the only variable of significance based on Table 6. The standard deviation (t-test p-value = 0.009) and coefficient of variance (t-test p-value = 1.73 x 10-

4) for this variable both have p-values less than 0.05, thereby rejecting the null hypothesis that there is no difference between AP excursion variability during Condition A and total years worked as a firefighter. The permutation p-value was also less than 0.05 for Condition A, AP

Excursion CV (p = 0.036). This rejects the null hypothesis that there is no difference between

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distributions for this variable and years worked as a firefighter. The single, phase plane classifier was not statistically significant. There were two p-values between 0.05 and 1.00. Although not statistically significant with this sample size, a larger sample size for future studies may increase significance.

Table 9 Sway p-value output of parametric and nonparametric testing.

Test Variable Group 1 Group 2 T-test Permutation Condition Test mean ± (SD) mean ± (SD) p-value p-value Condition A: Sway Area SD 0.03 ± (0.03) 0.18 ± (0.15) 0.064' 0.250 Two legs, AP Excursion SD 0.04 ± (0.01) 0.21 ± (0.10) 0.009** 0.107 eyes open AP Excursion CV 40.29 ± (1.32) 11.36 ± (7.83) 1.73E-04*** 0.036* Condition B: Sway Length SD 0.04 ± (0.03) 0.07 ± (0.03) 0.426 0.393 Two legs, Sway Length Mean 3.30 ± (0.06) 3.48 ± (0.21) 0.109 0.321 eyes closed Sway Length CV 127.19 ± 105.84 ± 0.535 0.179 (60.27) (20.93) Sway Area CV 3.84 ± (1.05) 2.40 ± (2.31) 0.288 0.464 ML Excursion SD 0.12 ± (0.13) 0.19 ± (0.15) 0.593 0.536 AP Excursion mean 2.64 ± (2.47e- 2.48 ± (0.52) 0.480 0.714 03) Phase Plane ml displacement vs ml 4.73 ± (0.44) 58.27 ± 0.270 0.286 Condition B acceleration length CV (105.69) Condition L: Sway Length mean 4.75 ± (0.11) 4.70 ± (0.31) 0.778 0.786 One leg, eyes Sway Area mean 1.97 ± (0.08) 1.92 ± (0.29) 0.727 0.857 open ML Excursion mean 3.34 ± (0.51) 3.34 ± (0.68) 0.997 1.000' AP Excursion mean 4.06 ± (0.85) 3.98 ± (0.74) 0.923 0.964 Group 1 represents all firefighters with less than 8 years worked as a firefighter. Group 2 represents all firefighters with greater than 8 years worked as a firefighter. ***P-values less than 0.001. **P-values less than 0.01. *P-values less than 0.05. ‘P-values between 0.05 and 1.0 5.3 Gait Analysis 5.3.1 Gait PSPSI Analysis Table 10 compares subject’s personal perceived sense of balance during the dual task gait condition to all average traditional gait variables. Their PSPSI was also compared to the firefighter groups (Group 1 and Group 2) to test if there was an association between personal

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perception and experience as a firefighter. The single task gait condition was not analyzed due to

PSPSI homogeneity across all subjects. Based on this table, firefighter group bears no significance to individual perception. However, five out of seven dual task variables show significant correlations to perception (p-values ranging between 0.018 and 0.061) demonstrating that these variables are significantly influenced by subjects’ perception.

Table 10 Comparison between average PSPSI of dual task to average dual task parameters and firefighter group

DUAL TASK VARIABLES P-VALUE

FIREFIGHTER GROUP 0.525

CADENCE MEAN 0.056’

SINGLE STANCE MEAN 0.057’

DOUBLE STANCE MEAN 0.061’

STRIDE LENGTH MEAN 0.624

TURN DURATION MEAN 0.045*

LINEAR ACCELERATION OF TURN ALONG X-AXIS MEAN 0.018*

ANGULAR VELOCITY OF TURN ALONG X-AXIS MEAN 0.198

Group 1 represents all firefighters with less than 8 years worked as a firefighter. Group 2 represents all firefighters with greater than 8 years worked as a firefighter. *P-values less than 0.05. ‘P-values between 0.05 and 1.0 Parametric and non-parametric analysis was performed on each average dual task traditional gait sensor variables to assess differences between Group 1 and Group 2 as seen in

Table 11. Based on this table, there was no significant difference between Group 1 and Group 2 for dual task PSPSI data. Only one variable (angular velocity of turn along x-axis mean) out of six gait variables portrayed a significant difference between Group 1 and Group 2 (permutation p-value = 0.076), however this variable had no significant associations to Perception (Table 10).

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Therefore, group classification is not relevant to subject’s individual ratings of perceptions for each test condition regardless of gait task parameters.

Table 11 Comparison of gait sensor traditional variables and PSPSI variables for each condition between Group 1 and Group 2.

Gait Dual Task Variables Group 1 Group 2 T-test Permutation Test mean ± (SD) mean ± (SD) PSPSI mean 0.25 ± (0.35) 0.53 ± (0.56) 0.474 0.523 Cadence mean 105.87 ± (0.20) 102.80 ± (13.83) 0.611 0.65 Single Stance mean 0.45 ± (0.61) 0.47 ± (0.05) 0.606 0.746 Double Stance mean 0.24 ± (0.05) 0.25 ± (0.07) 0.762 0.682 Stride length mean 53.52 ± (1.48) 55.52 ± (5.40) 0.444 0.605 Turn duration mean 2.64 ± (0.39) 2.39 ± (0.42) 0.527 0.436 Linear acceleration of turn along x-axis mean -0.13 ± (0.38) 0.09 ± (0.21) 0.559 0.26 Angular velocity of turn along x-axis mean -117.62 ± (36.46) -33.24 ± (45.57) 0.107 0.076 Group 1 represents all firefighters with less than 8 years worked as a firefighter. Group 2. represents all firefighters with greater than 8 years worked as a firefighter.

5.3.2 Gait Classification Analysis Table 12 lists all the gait independent variables that are perfect classifiers to divide the data into Group 1 and Group 2 based on the number of years each subject has worked as a firefighter. Traditional gait variables and gait phase plane variables are represented in this plot identifying 20 perfect classifiers. Five variables out of 50 traditional gait variables were able to predict the group identity. Three of these 5 variables were concerned with dual task, in which the individual performed a mental task as well as the dynamic iTUG task. Fifteen out of 179 phase plane variables were able to predict the group identity. Thirteen of these 15 variables were concerned with dual task. Therefore, the majority of the predicting variables presented in Table 7 are for dual task.

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Table 12 Gait Classification Tree Criteria

Test Condition Dependent Variables Classification Criteria Unit Group mean < -80 1 Angular velocity of turn along x-axis mean deg/s Gait mean ≥ -80 2 Single Task SD ≥ 25 1 Angular velocity of turn along x-axis SD deg/s SD < 25 2 ml displacement vs ml acceleration length mean < 39e3 1 Gait Phase deg sen variance mean mean ≥ 39e3 2 Plane ml displacement vs ml acceleration area mean < 5776 1 Single Task deg mean mean ≥ 5776 2 BV < 774e-6 1 Double Stance BV s2 Gait BV ≥ 774e-6 2 Dual Task Angular velocity of turn along x-axis mean mean < -76 and WV < 0.0028 deg/s 1 Double Stance WV mean ≥ -76; or mean < -76 and WV ≥ 0.0028 s2 2 ap displacement vs ap velocity length sen CV ≥ 21 1 Unitless total CV CV < 21 2 ml displacement vs ml velocity percent out SD ≥ 215 1 Unitless sen variance SD SD < 215 2 ml displacement vs ml velocity area CV Area CV < 5.1 and var CV < 5 1 ml displacement vs ml velocity percent out Unitless Area CV ≥ 5.1; or Area CV < 5.1 and Var CV ≥ 5 2 sen variance CV ap displ vs ap accel length sen total SD CV < 821 and SD < 98 1 Gait Phase ap displacement vs ap acceleration percent Unitless CV ≥ 821; or CV < 821 and SD ≥ 98 2 Plane out sen mean Dual Task ap displ vs ap accel length sen total CV Mean ≥ 22 and SD < 12e3 1 Unitless ap displ vs ap acceleration area mean Mean < 22; or mean ≥ 22 and SD ≥ 12e3 2 SD < 1373 and ≥ 414 1 ap displ vs ap accel area SD Unitless SD≥ 1373; or SD < 414 2 ml displ vs ml accel percent out sen var mean Var mean ≥ 22 and total mean ≥ 15e3 1 Var mean < 22; or Var mean ≥ 22 & Total Unitless ml displ vs ml accel length sen total mean mean < 15e3 2 ml displacement vs ml acceleration area CV Area CV < 5.2 and Var CV < 31 1 Unitless ml displ vs ml accel percent out sen var CV Area CV ≥5.2; or Area CV < 5.2 and Var CV ≥ 31 2 *Group 1 represents all firefighters with less than 8 years worked as a firefighter. Group 2 represents all firefighters with greater than 8 years worked as a firefighter.

Parametric and non-parametric analysis was performed on all gait variables (Table 13).

Six out of the 20 total predicting gait variables had a p-value of less than 0.05, portraying statistical significance. Two variables had a statistically significant t-test p-value. These variables were Double Stance BV during the dual task (p = 0.013) and mediolateral displacement versus mediolateral velocity area CV during the gait phase plane dual task (p = 0.034). This demonstrates that there is statistical difference between Group 1 and Group 2. Four variables

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were statistically significant based on the permutation p-values (ranging from 0.035 to 0.038).

Eight variables had p-values between 0.05 and 0.1 (p-values ranging from 0.058 to 0.90).

Although not statistically significant currently, a larger sample size may produce significant values for these variables in future studies.

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Table 13 Gait p-value output of parametric and nonparametric testing.

Test Variable Group 1 Group 2 T-test Permutation Condition Test mean ± (SD) mean ± (SD) p-value p-value Single Task Angular velocity of -130.73 ± (51.02) -30.83 ± (48.21) 0.161 0.036* turn along x-axis mean Angular velocity of 38.78 ± (17.66) 9.72 ± (7.60) 0.243 0.036* turn along x-axis SD Phase Plane ml displ vs ml accel 3.20e04 ± (312.85) 1.67e05 ± (1.47e5) 0.075’ 0.141 Single Task length variance mean ml displacement vs ml 4734.43 ± (761.46) 9415.01 ± 0.083’ 0.288 acceleration area mean (5269.57) Dual Task Double Stance BV 5.19e-04 ± (3.36e-04) 4.36e-3 ± (2.50e- 0.013* 0.107 03) Angular velocity of -117.62 ± (36.46) -33.24 ± (45.57) 0.107 0.071’ turn along x-axis mean Double Stance WV 1.36e-03 ± (1.38e-03) 1.46e-03 ± (1.29e- 0.939 0.929 03) Phase Plane ap displacement vs ap 24.37 ± (3.72) 14.21 ± (4.78) 0.077’ 0.038* Dual Task velocity length CV ml displ vs ml velocity 321.81 ± (99.06) 150.80 ± (16.23) 0.245 0.035* percent out variance SD ml displacement vs ml 4.14 ± (0.35) 10.23 ± (5.16) 0.034* 0.130 velocity area CV ml displ vs ml velocity 3.43 ± (0.57) 5.46 ± (3.02) 0.171 0.414 percent out variance CV ap displ vs ap accel 674.03 ± (192.09) 1188.92 ± (509.70) 0.090’ 0.182 length SD ap displ vs ap accel 95.25 ± (1.04) 94.02 ± (5.56) 0.627 0.808 percent out mean ap displ vs ap accel 28.13 ± (6.66) 15.18 ± (6.41) 0.161 0.075’ length CV ap displ vs ap 7220.22 ± (377.49) 8511.31 ± 0.565 0.750 acceleration area mean (5108.81) ap displ vs ap accel 956.34 ± (504.78) 1561.60 ± (690.58) 0.294 0.298 area SD ml displ vs ml accel 27.75 ± (7.78) 19.97 ± (24.56) 0.522 0.804 percent out var mean ml displ vs ml accel 1.68e04 ± (1485.85) 2.33e04 ± 0.085’ 0.286 length mean (7236.28) ml displacement vs ml 4.91 ± (0.31) 7.53 ± (2.62) 0.058’ 0.243 acceleration area CV ml displ vs ml accel 1.95 ± (0.64) 3.24 ± (2.53) 0.297 0.576 percent out var CV ***P-values less than 0.001. **P-values less than 0.01. *P-values less than 0.05. ‘P-values between 0.05 and 1.0

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6.0 DISCUSSION This study was performed to investigate how exposure to chronic heat in firefighters may affect postural balance. Firefighters are exposed to extreme heat throughout their career.

Understanding the effect of chronic exposure to extreme heat on postural balance may help to further understand the long-term implications of occupational hazards of firefighting.

6.1 Static and Dynamic fMRI Judgement Tasks Nearly all firefighters portrayed similar concordance when comparing how they perceived fall risk of other individuals. Based on these results, years worked as a firefighter had no effect on their perception of fall risk. This was further validated by Table 3, since no significance was found between each firefighter’s static and dynamic quadratic regression model

R2 portraying goodness of fit and the questionnaire outcomes. Therefore, there was no statistical significance between years worked as a firefighter, the number of non-fire runs each subject did in the last year, nor the number of structure fire or heavy smoke runs each subject performed in the last year. Both positive and negative correlations were determined between R2 and the questionnaire outcomes, albeit none were significant. Therefore, there is enough variability that no significant correlation was found between firefighters’ perception of fall risk and firefighting history. However, the findings are inconclusive due to small sample size.

Prior to this study, there was no standard for how to best perceive fall risk during the static and dynamic fMRI tests performed. Consensus model regression curves of firefighters’ perception results was created demonstrating the homogeneity between firefighters for dynamic and static tests. These curves are important since they can represent a gold standard for future studies. Future firefighters can compare their perceived judgement of fall risk to this curve to evaluate how they compare to these subjects’ consensus. Subjects that have a smaller curve area and test below this consensus curve would demonstrate they judged actors were less likely to

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fall. Subjects that have a higher curve area and test above the consensus curve would demonstrate they were more conservative in their judgement and perceived fall risk was much higher.

6.2 PSPSI Analysis Impaired perception and other nonmotor systems including cognition and orientation can potentially impair balance. These intrinsic factors can increase the risk of falling. Workers are generally able to avoid loss of balance in comparison to those with medical conditions such as

Parkinson’s disease and multiple sclerosis. However, if their perception of risk is inaccurate, their ability to maintain upright balance suffers in static and dynamic conditions. If an individual is unable to properly estimate postural sway in the medio-lateral and anterior-posterior direction or cannot accurately assess slipperiness, their likelihood of falling increases.14 Therefore, negative associations between a firefighter’s subjective sense of their own balance and their tested static and dynamic objective measures may indicate a disconnect between higher CNS and quantitative measures of static and dynamic balance.

Sway PSPSI analysis indicated no correlation between each firefighter’s own rating of perception and their years of experience nor objective sway force plate variables. However, if firefighters fell during the task, the variables assessed with the force plate were unable to be analyzed. This is important since a fall occurrence may indicate a disconnect between the somatosensory system and perception. Studies have found that if an individual perceives fall risk, their body will compensate and make anticipatory postural adjustments to activate postural muscle before disruptions in balance occur to limit the risk of fall.14 Therefore, if an individual has a higher perception rating but still falls, there may be a disconnect between their perception and the CNS since anticipatory postural adjustments were not enough to prevent the fall. Table 5

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compares Group 1 and Group 2 to indicate when subjects fell with respect to the corresponding total perception rating for each trial. Only subjects in Group 2 fell during Condition L. These subjects had higher ratings of perception compared to most other subjects during this condition which could indicate a disconnect between these firefighters. It is recommended for this phenomenon to be further reviewed in future studies. This can be especially important because if there is a disconnect between firefighter’s own ratings of perception and their balance, then they may not be able to accurately judge other individuals’ postural stability. This can be critical for the firefighter occupation if they are not able to identify a victim in a fire with potential loss of balance.

Gait PSPSI analysis showed significant correlations between dual task traditional variables and perception (p-values ranging between 0.018 and 0.061) but was not associated with

Group 1 and Group 2. Based on this data (Table 10), it does not matter which group the firefighter belongs in when reviewing the PSPSI data. Instead, the significant variables influence on perception is of importance.

6.3 Sway Analysis Increases in postural sway parameters, such as sway area and sway length suggest an impact on neuromuscular functionality leading to a decrease in postural stability. An increased sway length may correspond to increased muscular activity needed to maintain postural balance.

An increased sway area around the stability boundary can also indicate deterioration in neuromuscular functionality, leading to an increase in fall risk.14, 19 Since we are reviewing a correlation to long term heat exposure, increased sway area and length may indicate how heat can negatively affect postural balance.

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Fourteen variables were classified into Groups 1 and 2 based on total years of work experience as a firefighter using classification tree analysis (Table 5). Of these variables, two were statistically significant based on a p-value of less than 0.05. These variables were the standard deviation of anterior-posterior excursion (t-test p-value = 0.009) and the coefficient of variance for AP excursion during Condition A (t-test p-value = 1.73E-04; permutation p-value =

0.036). Based on the classification criteria for Condition A, the coefficient of variance is greatest in Group 1 for AP Excursion variables. This variable is statistically significant based on a t-test and permutation test (Table 6). This means that there is both a significant difference in the means of both groups as well as the distributions of both groups. This can be interpreted that Group 1 has the highest variability as a result of less experience. For that same variable, the standard deviation is lowest in Group 1 and highest in Group 2. However, standard deviation is based on the sample of data. In contrast, coefficient of variance normalizes the standard deviation with respect to the mean. Having a lower standard deviation does not necessarily mean there is low variability. This may indicate that CV is a better metric to use for analysis of variance.

Due to the significant increase in variance along the anterior posterior plane, firefighters with less years of work experience may have a decreased ability to maintain controlled movements, signifying decreased postural balance. This result is contrary to the conjecture that as work experience and therefore exposure to heat increases, postural balance decreases.

However, these results were restricted because of the small sample size of this study. . As sample size decreases, variability is likely to increase since the smaller sample size is not representative of the entire population. Group 1 only has two subjects in this group. As a result, this group is more likely to have variability within the Group compared to Group 2 which has six subjects

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within the group. Not all variables were able to be assessed due to the limited number of subjects per group.

Sway testing of Condition M (one leg, eyes closed) was unable to be analyzed. This was an important test, because the condition is mainly dependent on the vestibular system in order to maintain balance, since visual and proprioception sensory afferents are challenged (Table 1).

Challenging certain balance parameters can influence a person’s ability to maintain postural balance and therefore illustrates how the individual is able to compensate without those parameters.19 A larger study is recommended to further analyze this condition.

6.4 Gait Analysis Sway Area and Sway length are also affected during walking. Falls generally occur when walking and thereby performing whole body segments. These falls are associated with risk of serious injury.13 In order to successfully complete the gait cycle, the body must coordinate feedback from the central nervous system with proprioceptive feedback, vestibular and visual inputs.11 Phase plane and traditional gait analysis were conducted to study movement control and coordination when walking.39 Phase plane analysis was conducted to investigate body coordination during single stance of the gait cycle in which one foot is planted on the ground and the other is swinging forward to make a step. Phase plane analysis investigates how momentum and control of the center of gravity position helps to maintain postural stability, rather than characterizing only displacement of center of gravity. (Reilly)

Twenty-two total gait variables were able to be classified into Group 1 and Group 2 based on how many years subjects had worked as a firefighter (Table 7). Only two subjects were able to be classified into Group 1. The remaining six were classified into Group 2. Six of these variables were statistically significant (Table 8). The mean (p = 0.036) angular velocity of the

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turn along the x-axis (vertical axis based on Figure 5) and standard deviation (p = 0.036) of the angular velocity of the turn along the x-axis during single task were both statistically significant based on permutation tests. For these variables, Group 1 was classified by having a greater mean, but a larger standard deviation compared to Group 2. This demonstrates that Group 1 had larger variability while also turning faster around the cone compared to Group 2. Dingwell et al found that people who are at an increased risk of falling tend to slow down or walk slower to improve stability. Therefore, individuals naturally slow down when stability is challenged. Studies have also correlated slower gait speeds with limiting impairments.13 Therefore, because Group 2 walked slower around the turn with smaller variability, they may have poorer postural stability.

To compensate, they slow down their walk to decrease fall risk. However, Group 1 firefighters had greater variability, which is also associated with poorer postural stability. The small sample size within Group 1 may be a contributing factor. Since this group is only based upon two subject’s data, variability may be greatly affected. Therefore, a broader sample size is recommended to gain clearer associations. The average linear acceleration along the turn of the x-axis during dual task also had the most significant correlation to perception (p = 0.018). Group

1 also had smaller ratings of perception in comparison to Group 2. This may indicate that firefighters with less experience perceive a lower risk of fall and therefore turn around the cone at a greater speed.

Between variance of double stance times during dual task was statistically significant (t- test p-value = 0.013) between groups. Group 1 had lower between variability for this variable in comparison to Group 2. Therefore, firefighters who have worked more years have a larger variability between trials for the amount of time they have both feet on the ground during the gait cycle of double stance. Brach et al reported that increased double stance period is an independent

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predictor of future mobility disability and found that impairments to the central nervous system increased stance time variability in slow walkers and that specific patterns of gait variability may have underlying causes.8Therefore, greater variability in firefighters with a greater number of years worked may be due to underlying CNS impairment affecting postural stability.

There were three phase plane dual task variables that portrayed statistical significance

(Table 8). Two of these variables were significant based on nonparametric analysis. The permutation test was significant for the coefficient of variance of ap displacement vs ap velocity length (p = 0.038). Based on classification criteria, variability was higher for Group 1 compared to Group 2 for this variable. Therefore, firefighters who have worked for less years have greater variability in displacement and velocity length along the anterior-posterior plane. The permutation test also showed statistical significance for standard deviation of displacement vs ml velocity percent out variance (p = 0.035). Based on classification criteria, variability is greater for Group 1 compared to Group 2 for this variable. Therefore, firefighters who have worked for less years have greater variability in displacement and velocity percent out variance along the anterior-posterior plane.

One phase plane dual task variable was significant based on parametric analysis. The coefficient of variance of ml displacement vs ml velocity area was significant (p = 0.034). Based on classification analysis (Section 5.3.2), this variable fluctuated between high and low variability between groups. The variable worked in conjunction with coefficient of variance of ml displacement vs ml velocity percent out variance. If velocity area CV and percent out CV are both below 5 and 5.1 respectively, they were classified into Group 1. However, if velocity area

CV was greater than 5 or velocity area CV was less than 5 and percent out CV is greater than

5.1, they were classified into Group 2 (Table 7). By reviewing the mean and standard deviation

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of coefficient of variance of ml displacement vs ml velocity area (Table 8), Group 1 had a significantly smaller mean and standard deviation. Based on these results, Group 1 had a lower variability compared to Group 2. Therefore, firefighters who have worked for longer than eight years have greater variability in displacement and velocity area along the mediolateral plane. The vestibular system is within the inner ear and made up of three semicircular canals and otolith organs filled with fluid. These ducts are rotated approximately 90 degrees to each other in order to detect the angular rotation of the head. When the head turns, fluid within these canals compensates and moves as well bending the sensory hair cells and relaying the information to the brain.16 Since the variability is larger in the ML plane, the vestibular-semicircular canal aligned with this plane may be detrimentally impacted.

6.5 Strengths, Limitations, and Alternative Approaches This pilot study is one of the first studies to analyze the impact of chronic heat. It also compares firefighter’s innate sense of perception of postural instability when they themselves do a task versus how they perceive others postural instability when carrying out a task.

Sample size was a major limitation of this study. There are a total of 1,225 fire departments in the state of Ohio according to an analysis conducted from 2012-2014,30 but only

12.5% of the Ohio fire departments are registered as career.15 A total of 19,210 firefighters are employed in this state.7 It is unknown how many full-time firefighters are in the Cincinnati metropolitan area within Ohio but based on these statistics, there are a limited number of full- time firefighters available to participate in this study. Due to the limited age range, full-time work requirement, and screening to safely admit firefighters for the MRI, recruitment for this study was restricted resulting in a small sample size. Because of the small sample size, this study may also be prone non-response bias. A small sample size is less likely to represent the entire

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population leading to increasing variability. If this study was expanded to encompass a larger geographic location or allow the participation of part-time and volunteer firefighters, a much greater number of firefighters would have been capable of participating in this study.

7.0 CONCLUSION Firefighters who have worked a greater number of years have statistically significant variability of double stance duration when performing a dynamic task. Studies have shown that increased double stance period is an independent predictor of future mobility disability and found that impairments to the central nervous system increased stance time variability in slow walkers.8 Therefore, greater variability in firefighters with a greater number of years worked may be due to potential underlying CNS impairment affecting postural stability. Gait phase plane analysis has indicated firefighters who have worked for longer than eight years have greater variability in displacement and velocity area along the mediolateral plane. This may suggest that the vestibular-semicircular canal aligned with the medial-lateral plane may be detrimentally impacted.

No statistically significant correlations were found between firefighter’s perceived judgement of fall risk of other individuals and firefighting history. Most firefighters, excluding one outlier, had a similar consensus when perceiving fall risk of other individuals in various states of imbalance. A consensus model was created from these curves to express a gold standard of perception to be used for future studies. Significant associations (p-values ranging between

0.018 and 0.061) were found between gait dual task objective variables and gait PSPSI analysis, but not with respect to Group.

Sway analysis determined a significant increase in variance along the anterior posterior plane for firefighters with less years of work experience. Therefore, less experienced firefighters

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may have a decreased ability to maintain controlled movements, signifying decreased postural balance. This result is contrary to the conjecture that as work experience and therefore exposure to heat increases, postural balance decreases. Because of the small sample size, it is possible that these outcomes are due to chance. Some results may confirm or reject the hypothesis of this study when an alternative hypothesis is true, skewing the results. As sample size decreases, the margin of error increases, leading to less conclusive results. Due to the conflicting variabilities between groups and small sample size, future studies are recommended with a larger sample size to further analyze these variables.

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33Pan, C., Chiou, S., Hsiao, H., et al. (2011) Ergonomic hazards and controls for elevating devices in construction. In Bhattacharya, A., McGlothlin, J. (Eds.), Occupational Ergonomics: Theory and Applications (pp. 654 – 688). Boca Raon, FL: CRC Press. 34Qian, S., Li, M., Liu, K. et al. (2015) Environmental heat stress enhances mental fatigue during sustained attention task performing: Evidence from an ASL perfusion study. Behavioural Brain Research, 280, 6-150. https://doi.org/10.1016/j.bbr.2014.11.036. 35Racinais, S., Gaoua, N., Grantham, J. (2008) Hyperthermia impairs short-term memory and peripheral motor drive transmission. Journal of Physiology, 586(19). https://doi- org.proxy.libraries.uc.edu/10.1113/jphysiol.2008.157420 36Redfern, M., Rhoades, T. (2011) Fall prevention in industry using slip resistance testing. In Bhattacharya, A., McGlothlin, J. (Eds.), Occupational Ergonomics: Theory and Applications (pp. 525 – 537). Boca Raon, FL: CRC Press. 37Riley, P., Benda, B., Gill-Body, K., et al. (1995) Phase plane analysis of stability in quiet standing. Journal of Rehabilitation Research and Development, 32(3), 227-235. Retrieved from https://search-proquest- com.proxy.libraries.uc.edu/docview/215297637/fulltextPDF/E3C82B474BC1438DPQ/1?accoun tid=2909

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38Slobounov, S., Wu, T., Hallett, M. (2006) Neural basis subserving the detection of postural instability: An fMRI study. Motor Control, 10, 69-89. Retrieved from http://web.b.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=1&sid=bc5e76c9-e013-400c-9b61- bb9e5555c63d%40pdc-v-sessmgr01 39Spinelli, B.A., Wattananon, P., Silfies, S., et al. (2015) Using kinematics and dynamical systems approach to enhance understanding of clinically observed aberrant movement patterns. Man Ther, 20(1), 221-226. doi:10.1016/j.math.2014.07.012. 40The R Project for Statistical Computing. Retrieved from https://www.r-project.org/ 41Ugarte, M.D., Militino, A.F., Arnholt, A.T. (2016) Probability and statistics with R. New York, NY: CRC Press. 42Umberger, B.R. (2010) Stance and swing phase costs in human walking. J R Soc Interface, 7(50), 1329-1340. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2894890/ 43Zampieri, C., Salarian, A., Carlson-Kuhta, P., et al. (2009) The instrumented timed up and go test: potential outcome measure for disease modifying therapies in Parkinson’s disease. J Neurol Neruosurg Psychiatry, 81, 171-176. doi:10.1136/jnnp.2009.173740. 44Zare, S., Hemmatjo, R., Allahyari, T., et al. (2017) Comparison of the effect of typical firefighting activities, live fire drills and rescue operations at height on firefighters’ physiological responses and cognitive function. Journal of Ergonomics, 61(10). Retrieved from https://www- tandfonline-com.proxy.libraries.uc.edu/doi/abs/10.1080/00140139.2018.1484524

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Appendix A –Questionnaires

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SURVEY TO DETERMINE ELIGIBILTY TO PARTICIPATE IN THE FIREFIGHTER STUDY

Thank you for your interest in the Firefighter fMRI Study. The questions in this survey will help to determine if you qualify to participate in the research study. By answering these questions, you are giving permission for the use and disclosure of your health information for the purposes of this research study. All information will be kept confidential unless disclosure is required by law. You do not have to give this permission and you do not have to answer these questions. However, if you do not complete these questions, you will not be considered for participation in the study.

Health History

Has a doctor or other health professional told you that you currently have or have had any of the following conditions? 1) Heart disease or condition ❏No (0) ❏Yes (1) ❏Unknown (2) 2) Psychiatric disorders (e.g., bipolar, major depressive disorder) ❏No (0) ❏Yes (1) ❏Unknown (2) 3) Substance abuse ❏No (0) ❏Yes (1) ❏Unknown (2) 4) Stroke ❏No (0) ❏Yes (1) ❏Unknown (2) 5) Epilepsy or seizures ❏No (0) ❏Yes (1) ❏Unknown (2) 6) Tremors ❏No (0) ❏Yes (1) ❏Unknown (2) 7) Other neurological disease or condition (e.g., Tourette’s syndrome, traumatic brain injury) ❏No (0) ❏Yes (1) ❏Unknown (2) If yes, what is the name of the disease or condition? ______8) Disorder that affects your balance or causes dizziness such as Meniere’s disease or inner ear disorder

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❏No (0) ❏Yes (1) ❏Unknown (2) If yes, what is the name of the disease or condition? ______9) Brain Tumor ❏No (0) ❏Yes (1) ❏Unknown (2) 10) Are you taking medications for any of the following conditions: a seizure disorder, depression, problems with attention or hyperactivity, anxiety or other emotional or behavior problems? ❏No (0) ❏Yes (1) (Condition1: ______Medication:______) (Condition 2: ______Medication:______) ❏Unknown (2) 11) Are you taking medications for heart problems? ❏No (0) ❏Yes (1) (Condition 1: ______Medication:______) (Condition 2: ______Medication:______) ❏Unknown (2)

Work History

12) How many years have you worked as a fulltime firefighter? ______

13) How many additional years have you worked as a part-time firefighter? ______

14) On average, how many hours per week have you worked as a firefighter over the past 6 months? ______

15) On average, how many heavy smoke or structure fire runs per week have you gone on over the past 6 months? ______

16) On average, how many EMS runs per week have you gone on over the past 6 months? ______

17) If you work at an airport, on average, how many aircraft runs per week have you gone on over the past 6 months? ______

18) How many times total in the last 6 months have you worked in a structure fire or heavy smoke conditions? ______

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19) How many times total in the last year have you worked in a structure fire or heavy smoke conditions? ______

20) When did you have your last fire department medical exam? ______

Which fire department? ______

Comments: ______

Other Information

21) How would you best describe your race? ❏African American ❏American Indian ❏Asian ❏Caucasian ❏Pacific Islander

❏Other ______❏Unknown

22) How would you best describe your ethnicity? ❏Hispanic or Latino ❏Non-Hispanic or Latino ❏Unknown

Contact Information

If you wish to be considered for the study, please complete your contact information below. If you do not wish to participate, you do not need to fill this out and you do not need to submit the survey.

What is your first name? ______What is your last name? ______What is your primary fire station? ______What is your preferred contact method?

❏Cell phone ❏Text ❏Home phone ❏Work phone ❏Email ❏Other ______Cell Phone: Home Phone: Work Phone: Email:

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Other:

What is the best time to reach you? ______

Thank you for your time. After we review these results, we will contact you. If you have any questions, please feel free to contact Ashley Turner by phone (630-306-2259) or by email ([email protected]).

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FIREFIGHTER fMRI STUDY SWAY DAY OF VISIT QUESTIONNAIRE

IRB PROTOCOL #: 2016-2342 SUBJECT ID:

TO BE ASKED IMMEDIATELY BEFORE LAB TEST SESSION Today’s Date _____/______/_____ Sway Test Start Time: __ __: __ __ AM / PM

1. What is the highest grade you have completed in school? ❏ Elementary school

❏ Junior High (8th and 9th grade)

❏ High School

❏ Junior college (1-2 years college)

❏ College graduate

❏ Graduate School

2. Marital Status: ❏ Single ❏ Married ❏ Divorced/separated ❏ Widowed

3. a. What time did you go to bed? __ __ : __ __ AM / PM b. What time did you wake up? __ __ : __ __ AM / PM

c. Did you wake up during the night for more than 15 minutes? ❏No (0) ❏Yes (1) ❏ Unknown (2)

d. If yes, for how many minutes? ______e. Total number of hours slept: ………………………………………………… ______

f. Total number of hours between waking and sway test time: ...... ______

4. a. At what time was your last meal? __ __ : __ __ AM / PM b. Total number of hours between last meal and sway test time: ...... ______

5. Have you had caffeine in the last 12 hours? ❏No (0) ❏Yes (1) ❏ Unknown (2)

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If Yes, please indicate type and how much: # of oz.

1. Coffee (1 cup = 8 oz)

2. Pop – Type: ______

(1 can = 12 oz.)

3. Tea: Hot Tea (1 cup = 8 oz)

Iced Tea (1 glass = 16 oz)

4. Energy drink:______

5. Other:

6. Do you currently smoke or use or other tobacco products? ❏No (0) ❏Yes (1) ❏ Unknown (2)

If no, go to question 7.

a. How many cigarettes or tobacco products in the last 12 hours? ...... ______b. Time of last cigarette or tobacco product: _____ : ______AM PM

7. Have you had any alcoholic drinks in the last 12 hours? ❏No (0) ❏Yes (1) ❏ Unknown (2) If yes: a. How many? ...... ______

b. Time of last alcoholic drink: _____ : ______AM PM

8. Have you exercised or had any strenuous activity today? ❏No (0) ❏Yes (1) If yes: a. What type? ______b. What time did you start? _____ : ______AM PM c. What time did you finish? _____ : ______AM PM d. Total number of minutes of strenuous exercise today:…………………… ...... ______

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e. Total number of hours between end of exercise and sway test time: ...... ______

9. What is your current job title? ______

10. How long have you been working at your current job title? …………………… ...... ______

11. When was the start and end of the last shift you worked?

Start date & time: ______End date & time: ______

12. Do you work a 24 on/48 off schedule? ❏No (0) ❏Yes (1) If no, what is your schedule? ______

13. How many hours do you normally average per week in your job (not overtime)? ...... ______[If 24 on/48 off, average is 60]

14. How many hours overtime do you work in an average week? ...... ______

15. How many times do you have full-gear training in an average week? ...... ______

16. How many heavy smoke or structure fire runs have you gone on in the past week?

a. When was the last run? Date & time: ______

17. How many EMS runs have you gone on in the past week? ...... ______

a. When was the last run? Date & time: ______

18. If you work in an airport, how many aircraft runs have you gone on in the past week? ...... ______

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a. When was the last run? Date & time:______Work Comments: ______

19. Have you been sick in the last week?

❏None (0) ❏Allergies (1) ❏Cold/Cough (2) ❏Stomachache/vomiting (3) ❏Headache (4) ❏Other illness (5) Describe: ______

20. Are you feeling sick today? ❏No (0) ❏Yes (1) If yes, describe: ______

21. Are you currently experiencing any of the following? (Mark all that apply.)

❏ Vertigo or spinning dizziness (4) ❏ Light-headedness (3) ❏ Light-headedness/“blacking out” with change in position (sitting to standing, etc.) (2)

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❏ Problems with maintaining balance (1) ❏ None (0)

22. Are you having any pain or discomfort today? ❏No (0) ❏Yes (1) ❏ Unknown (2) If yes, describe: ______

23. Have you taken any medications (prescription or non-prescription) in the last 3 days (72 hours)? ❏No (0) ❏Yes (1) ❏ Unknown (2) If yes, list them below:

Strength/ Number of How Often? Date and Time of Name of Medication Dose pills taken at (# times/day) Last Dose (mg) each dose

Administered by:______Scored by: ______

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Rating of Perceived Sense of Postural Sway and Instability

After each sway test, you will be asked the following questions about how you felt during the test. The first question asks about body sway. Body sway is movement from side to side or back and forth. Even while standing still, everyone experiences body sway. When asked these questions, please indicate the number that best describes how you felt – 0 means “little or none” all the way up to 2 which means “a lot.” Remember that there are no right or wrong answers. This is a subjective rating of your experience for a particular test. I will ask you each question after each test. It may get repetitive but you may feel different after each one. The first question is [read all questions.]… Remember to give me a number for your answer. 1. How much did you feel your body sway (i.e., rotate, pivot)? a little some a lot 0 0.5 1 1.5 2

2. Did you have any difficulty in maintaining balance (how much did you or your muscles compensate for your movement)? a little some a lot 0 0.5 1 1.5 2

3. Did you feel at any time that you would fall? a little some a lot 0 0.5 1 1.5 2

4. What would you say was the overall difficulty of this task? a little some a lot 0 0.5 1 1.5 2

Note: Facilitator should show and read each question out loud to the participant after each trial. Participant should respond with a number between 0 and 2 from the rating scale. Facilitator will record response on the data sheet.

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Appendix B – Medical IRB Research Protocol

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UNIVERSITY OF CINCINNATI – MEDICAL IRB RESEARCH PROTOCOL

Title: Effect of Hyperthermia on Brain Function and Impact on Functional Outcomes

Co-PIs: Amit Bhattacharya, Ph.D., and Kim M. Cecil, Ph.D. Co-Investigators: Darren S. Kadis, PhD, W.A. Jetter, PhD, Jagjit Yadav, PhD, and W. Jon Williams, PhD (NIOSH) Department: Environmental Health and CCHMC Funding Agency: National Institute for Occupational Safety and Health (NIOSH)

Abstract

The proposed study is designed to accomplish several objectives dealing with the impact of chronic history of heating associated with firefighting on brain function, balance, and gait. The hypothesis to be tested is that there will be differences in brain function, balance, and gait functions between firefighters with a chronic history of participating in long-term firefighting compared to firefighters who have shorter term history of firefighting. To accomplish the objectives of the study, we will recruit firefighters in a narrow range of age group (30-45 years old) with a wide range of heat exposures between <1 year to >15 years. This will allow investigating the impact of heat exposure durations on brain function, balance, and gait outcomes while minimizing age associated innate physiological changes in their brain’s neural architecture.

The objectives of the study are to collect the following data in all firefighters with a range of firefighting experience 1) characterize patterns of neural activation associated with judgment of postural stability; 2) characterize patterns of neural activation associated with an executive function task; 3) characterize the ability to perform a complex gait task and postural balance test; and 4) characterize a subset of specific biomarkers in order to better understand the impact of history of heat exposure (short-term versus chronic) on immune status/function. This will help in understanding the biological/immune mechanism influencing postural balance and dynamic balance under dual-task demands (combined cognitive and motor challenges). Objectives 1 and 2 will be satisfied using functional magnetic resonance imaging (fMRI). The data will be combined to develop a more comprehensive picture of the neural mechanisms for control of postural stability. For objective 3, postural balance and gait functions will be quantified with force platform posturography and inertial link sensors. Objective 4 will be accomplished by collection of a blood sample to measure various biomarkers of heat stress and immune function

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status (see details in section 2 Background and Significance). Data will provide a better understanding of the mechanisms underlying compromised judgment due to chronic exposure to heat stress associated with firefighting.

Purpose of the Study: The purpose of the pilot study is to provide transformative information about the impact of chronic heating on brain function and the mechanisms underlying altered balance, gait, postural judgment and decision making among firefighters. The results will provide useful data to design a comprehensive study with a larger sample size.

1. Specific Aims and Hypotheses

Hypotheses

a. A history of longer term chronic exposure to heat associated with firefighting will be associated with decreased ability to accurately judge static and dynamic postural balance. b. A history of longer term chronic exposure to heat during firefighting will be associated with decreased ability to make cognitive decisions. c. A history of chronic exposure to heat during firefighting will be associated with poorer performance on the static and dynamic postural balance tests and gait (with and without mental task). d. A history of chronic exposure to heat during firefighting will be associated with detrimental changes in immune function status.

Specific Aims

The hypotheses will be accomplished by carrying out the following specific aims:

a. Measure ability to judge static and dynamic postural balance postures under normothermic conditions and characterize changes in patterns of neural activation using fMRI. b. Measure ability to accurately make cognitive decisions under normothermic conditions and characterize changes in patterns of neural activation using fMRI. c. Measure static and dynamic postural balance and dual-tasks performances under combined cognitive and motor challenges (with and without mental tasks) while exposed to normothermic conditions. d. Measure specific biomarkers related to immune function status.

2. Background and Significance

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In the U.S. about 1.1 million firefighters expose themselves daily to a myriad of risk factors while carrying out their routine occupational tasks [1, 2]. There is evidence that over-exertion, including heat exposure immediately after a prolonged sedentary period, which is not uncommon among fire fighters on a typical day at the fire station, potentially triggers a sequence of neurological and cardiovascular events that may lead to sudden death. It also appears the use of self-contained breathing apparatus (SCBA) and specialized fire-protective garment impacts the firefighters’ ability to maintain safe upright functional postural balance and gait especially in hot environment. This places an undue demand on their cognitive functions jeopardizing their life and the people they are attempting to rescue [3-9]. Furthermore, shift work in conjunction with high workload such as rotating shifts has been known to contribute to workplace associated fatigue, deleteriously impacting both motor and cognitive functions. One common underlying reason behind deleterious effects on motor and cognitive functions is cumulative buildup of fatigue due to shift work in conjunction with high workload. The above discussion forms the basis for the hypotheses for our proposed study. Studies designed to better understand the effects of heat stress on the brain in firefighters may shed light on developing strategies to reduce its deleterious effects.

Biomarkers of Heat stress in firefighters: In order to understand whether firefighters’ heat stress level and neurological (cognitive, motor) outcomes are linked with their immune function status, we will analyze for neurological indicators (Balance, Gait), heat stress indicator, and the immune status end points. In other human studies, heat stress is known to induce protein indicators such as heat shock protein (HSP)[10]. HSP induction due to heat stress has been linked with alteration of immune response[11]. One of the mechanisms reported in animal and human studies is via upregulation of toll-like receptors[12, 13], which are the immune cell surface receptors (belonging to pattern recognition receptors); these receptors have role in both innate and adaptive immunity as well as in neural and cognitive functions in CNS[14]. In this pilot study, we will compare firefighters exposed to short-term and long-term (chronic) heat stress regimes and will quantitatively measure HSP (heat stress indicator as demonstrated in non-firefighter human studies) as well as the immune cells receptor TLR 9 (which has been suggested to have a role in both immunity and CNS functions) in their blood.

4. Preliminary Studies

Our team has published preliminary studies assessing individualized prediction of heat stress in firefighters, especially the effects of protective gear and long work shifts [15-17]. We also have an established body of work illustrating our expertise with measures of postural balance, postural sway and gait outcomes associated with occupational exposures as well as disease [18-23].

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Researchers have previously documented increased activation within the parietal lobe, the cingulate cortex and cerebellar lobe (areas known to support attention, decision making, balance and gait) as participants judge postural stability of actors in pictures. Additional engagement of basal ganglia structures has been documented in participants viewing animated depictions of stable and unstable postures [24] . 5. Investigator Experience

Co-Principal Investigator: Amit Bhattacharya, PhD: Professor, Environmental Health, College of Medicine (COM) and secondary appointments in Mechanical and Biomedical engineering, College of Engineering and Applied Science (CEAS); Disciplines: heat stress exposure assessment, biomechanics, balance and gait. Co-Principal Investigator: Kim M. Cecil, PhD Professor, Radiology, secondary appointments in Pediatrics, Neuroscience and Environmental Health, COM, employment with Cincinnati Children’s Hospital Medical Center (CCHMC); Disciplines: magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS). Co-Investigators: 1. Darren Kadis, PhD Assistant Professor, Neurology, Pediatric Neuroimaging Research Consortium, Neuroscience, Pediatrics, COM, employment with CCHMC; Disciplines: Neuroimaging (MRI, fMRI) and Neuropsychology; 2. William A. Jetter, PhD Fire Chief and Adjunct Professor, Fire Science program in Aerospace Dept., CEAS; Discipline: Fire Science; and 3. Jon Williams, PhD, NPPTL Labs NIOSH; Discipline: Exercise physiology. 4. Jagjit Yadav, PhD, Professor Environmental Health- Disciplines: Immunotoxicology and infections as applied to environmental and occupational health.

6. Experimental Design and Methods

6.a. Test Protocol Objectives 1 and 2. The MRI procedures will be performed within the Schubert Research Clinic at CCHMC. It is located on the first floor of the Clinical Science Pavilion (Location T).

Objective 3. The postural balance and gait procedures will be performed in the Biomechanics- Ergonomics Research Laboratories within the UC Department of Environmental Health.

Objective 4. Blood samples will be analyzed for immune function in Dr. Yadav’s lab located in UC Department of Environmental Health.

The following test protocol will be carried out under normothermia condition: without heating the torso of the subjects, i.e. at room temperature.

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6.a.1. MRI: Our study design will evaluate the brain using a 3 Tesla Philips MR scanner equipped with a 32-channel head coil. We will have constant visual observation with the participant in the scanner via line of sight and camera from the rear of the magnet. Any signs of distress will result in suspending the experiment, checking on the participant and when necessary, removing the participant from the MRI environment.

Our imaging protocol will include the following:

1) Anatomical MRI - Three dimensional (3D) – Standard T1 weighted sequence to visualize the whole brain. 2) Go/No-Go fMRI – Go/No-Go tasks are widely used to assess sustained attention and inhibition (processes related to decision making; these are constituent of executive functions). Subjects view letters and/or images on a screen, and are asked to quickly press a button each time a new letter or image appears (“go” trials). The pacing is rapid and interstimulus intervals appear random. Subjects will be asked to withhold button-presses for a subset of trials (“no-go”) where a predefined target (e.g., the letter ‘X’) or set of targets appears. The task requires sustained visual attention to correctly button-press on “go” trials, and inhibition to withhold a trained/practiced response during the “no-go” trials.

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3) Postural Stability (Static and Dynamic) Judgement fMRI – Each subject will lie comfortably supine in the MR scanner with their right arm resting beside their trunk with response button attached to their hand. See Figure 1 from Slobounov et al. [24-27] While lying in the MR scanner, each subject will view the video (projected on a screen) of motor tasks (Static and Dynamic).

For the static balance judgement task, participants will view images of actors in various degrees of stability/balance. The participant will press one of two buttons to indicate whether the actor is in balance or about to fall. Telemetry obtained during stimulus development provides objective information about the actor’s position stability/fall risk. Images will be shown in random order.

For the dynamic gait judgement task, participants will view brief videos (2-5 seconds in duration) of actors walking with various degrees of stability. Participants will indicate, by button pressing, whether the walker is stable or unstable. Videos will be played in random order.

Figure 1: General view of fMRI set-up (top); range of animated postural movement within and beyond the stability boundary (bottom) [24].

Before initiating the experiment, the subject will be given appropriate cognitive training for discriminating unstable and stable postures by presenting appropriate still and dynamic pictures of a human in unstable and stable postures in static and dynamic environments as described above. Outcomes: Scores of correct recognition of unstable postures; verbal report rating of subjects’ ego-centric motion as being able to experience self-motion when observing projected videos.

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4) Resting State fMRI – Subjects are asked to relax and “think of nothing” while viewing a fixation cross centered on the screen. We characterize patterns of intrinsic connectivity through correlation of voxel time-series of resting state data.

We estimate the scanning protocol will take approximately one hour; however, the participant may be in the scanning environment for up to 1.5 hours.

Outcomes: Activation patterns associated with the Go-No-Go decision-making task; Activation patterns associated with judging stable positions and intrinsic connectivity in the brain at different brain temperatures.

6.a.2. Motor tasks (Static and Dynamic Balance) assessments: 1) Quantitate in all participants their limits of postural stability using quantitative posturography on dry surface; 2) Quantitate in all participants their perception of postural instability while carrying out the test of their limits of postural stability described in item #1; 3) Quantitate gait function in all participants while carrying out the iTUG test with and without mental task.

The above test protocol will be carried out under normothermia condition - i.e. in room temperature. Anthropometric measurements will be obtained and questions about sleep, fatigue, body discomfort, and work history, and a Day of Visit questionnaire will be asked.

Outcomes: Limits of postural stability, measures of postural stability, subjective perception of degree of perceived sense of loss of balance.

In the following, details of the motor tasks are presented.

6.a.2.a. Limits of Postural Stability Test (Static Balance): These tests will be performed by the participants standing first on two feet and then standing on a single preferred foot on a force platform for 30 seconds as per our previous protocol with firefighters and others[3, 20] - first with eyes open and then with eyes closed. The data from the force plate will provide x-y movements of the participants’ center of pressure (CP) which will be used to quantify the static postural balance as per our previous studies. The postural balance metrics will include 1) Sway

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area defined as the area encompassed by the x-y movement of the CP obtained during the 30 second postural balance test and 2) Sway length defined as the total distance travelled by the CP obtained during the 30 second postural balance test. In addition, for this test each participant will also wear inertial wearable sensors (3-D accelerometers + 3D Gyroscope) on the body for quantifying dynamic movements and accelerations of the body during the test which will be used to quantify phase plane based assessment of dynamic postural balance as per previous studies [3, 28, 29]. The dynamic postural balance metrics will be based on phase plane plots among Linear Acceleration and Angular Velocity in 3 mutually perpendicular directions [29] [28]. At the end of each of the above tasks, the subject will be asked to rate their perception of their balance. If a participant places the raised foot on the ground to regain balance during the test, the test will be recorded as a “FALL”. A “FALL” is also recorded if the participant needs assistance from an observing investigator during the test. Investigators will stand next to the subject to prevent any injury if the subject were to lose balance. 6.a.2.b. Gait Function Test: Instrumented Timed Up and Go test (iTUGT) with and without dual task demand: iTUGT without dual task demand: The iTUG test is designed to assess balance control status during dynamic task of getting up from chair, walking, and turning and has been used successfully by our group[30] and several other authors[31]. In this test, a wireless inertial link sensor system will be attached to the torso and the extremities, adapted from the protocol of our previous work [30]. Six channels of data are obtained wirelessly by the Inertial Link sensor system for calculating outcomes of Dynamic Gait associated with the iTUGT test: Linear Acceleration and Angular Velocity in 3 mutually perpendicular directions. The outcomes of multi-axis sensor based iTUG tests are: 1) Turn Duration (TD, sec), 2) Peak Turning Velocity (PTV, degrees/sec), and gait variables. Each subject will perform the following tasks while wearing the Inertial Link sensor system: arise from a chair, walk across the room, turn, walk back, and sit down. iTUGT with dual task demand on working memory: For this iTUGT test, while carrying out the above protocol, subjects will be counting backwards or performing serial subtractions of the number three, starting from a randomly assigned number between 500 and 700 which is comparable to the math part of the Trier Social Stress Test. Previous study has shown that impaired working memory in subjects with mild cognitive impairment is associated with gait slowing and increased gait variability [32-37]. At the end of each of the above tasks, the subject will be asked to rate their perception of their balance. Outcomes: Performance time (PT), Dynamic balance metrics (Linear Acceleration and Angular Velocity in 3 mutually perpendicular directions, Turn Duration (TD, sec), Peak Turning Velocity (PTV, degrees/sec), and gait variables. 6.a.3. Immune Biomarker Assessment: The following biomarkers will be measured in the blood or serum. 1). Heat shock protein (HSP) marker: This serum protein marker, HSP will be measured using commercially available ELISA

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kit from Sigma Aldrich, USA, following manufacturer’s instructions. 2) TLR9 expression level in blood cells: Blood will be collected in PAXGENE tubes (special tubes for RNA processing). Total RNA will be isolated using Qiagen RNAeasy kit per manufacturers’ instructions. Expression levels of TLR9 will be measured by qRT-PCR analysis using gene-specific primers. The Ct value of the TLR gene and a house-keeping gene (such as GAPDH) will allow us to calculate fold-change in TLR9 levels due to heat stress, using our established qRT-PCR protocol. In addition, the PCL-5 questionnaire will be self-administered to evaluate perceived level of stress.

6.b. Data/Statistical Analyses and Power Considerations This is a pilot study to establish feasibility and preliminary data to determine measurement effect sizes. Sample size was adjusted to accommodate as many participants as the budget would allow. Data from this pilot study will be acquired to perform a sample size determination for future funding applications.

6.c. Data Storage and Confidentiality Every effort will be made to maintain the privacy and confidentiality of the study records. In order to maintain confidentiality, each participant will be assigned an identification number known only to the P.I.s, Co-P.I., and the study coordinators. Electronic data will be stored in password protected folders on a secure server and on electronic media stored in a locked file accessed only by the Principal Investigator and authorized research team members. Paper documents will be stored in a locked cabinet in a locked room accessed only by the Principal Investigator and authorized research team members. Identifiers will be kept in a locked area in files separate from the main data accessible only by the P.I.s and authorized research team members. No identifying information will be included in scientific reports and presentations. The University of Cincinnati Medical Center and CCHMC have strict policies for data security and confidentiality which will be followed by the research team.

6.d. Setting Cincinnati Children’s Hospital Medical Center (CCHMC) and the University of Cincinnati.

6.e. Laboratory methods and facilities All MRI-related research activities will take place at the Schubert Clinic within the first floor of the Clinical Sciences Pavilion (Location T) at CCHMC. All postural balance and gait related research activities will take place at the Biomechanics-Ergonomics Research Laboratories within the UC Department of Environmental Health. Venipuncture will take place in the private exam room in the Biomechanics-Ergonomics Research Laboratories. The Immune Biomarker

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Assessment will be carried out in Dr. Yadav’s lab within the UC Department of Environmental Health.

Facilities: Cincinnati, Children’s Medical Center The CCHMC Imaging Research Center (IRC) occupies approximately 4,000 square feet of research dedicated imaging laboratory space in the Basement level of location R Research Building, approximately 4,000 square feet of translational research space in the first floor of the location T Clinical Sciences Research Pavilion, and 3000 square feet of office space on the first floor in the location S Research Building. The location R laboratory space houses a 3T Philips Achieva MR scanner, a 1.5T Philips Ingenia MR scanner, an animal-only 7T Bruker Biospec MR scanner, a 1.5T GE Optima MR scanner, an ImTek micro CT, a chemistry lab, a hyperpolarized gas laboratory, an electronics laboratory, a machine shop, a participant/family waiting area, a participant changing room, and two testing rooms for performing consent procedures and completing written or computerized assessments. The location T Clinical Sciences Research Pavilion houses a 3T Philips Ingenia MR scanner, a participant/family waiting area, a participant changing room, and two testing rooms for performing consent procedures and completing written or computerized assessments. The 3T Philips MR scanners within the IRC R and T buildings are available for the imaging studies outlined in the proposal. Our preference for this study is to use the location T Philips Ingenia 3T MR scanner with a wide 70 cm bore size. CCHMC has additional MR scanners available for use if an unlikely event occurs which prevents the use of the IRC 3T MR scanners. The Ingenia incorporates dStream digital broadband technology with digitization of MR signal inside the receive coil via DirectDigital radiofrequency technology. The number of coils determines the number of radiofrequency channels. For brains, we currently employ a 32 coil setup. The Omega HP gradient system allows for a 45 mT/m peak amplitude with a slew rate of 200 mT/m/ms. The system also has ancillary equipment to support fMRI studies including a full audiovisual paradigm system.

Motor testing will be done using a force plate and portable sensors. Under the guidance of P.I. Dr. Bhattacharya, postural balance and gait assessment of workers, including firefighters, have been done in the field as well as in the Biomechanics-Ergonomics Research Labs at the University of Cincinnati for the past 25 years.

Dr. Yadav’s laboratory space (1100 sq ft.) consists of one double-module room (440 sq. ft.) and four single module (220 sq. ft) rooms in the Department of Environmental Health (Kettering Laboratory Complex), and is fully equipped for conducting immunology/toxicology/molecular biology/microbiology work.

6.f. Estimated Period of Time to Complete the Study Data collection, analysis, and overall study period will be 4 years. Testing for each research participant will take 3 to 4 hours.

7. Human Subjects

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Human Participants Characteristics, Eligibility, and Exclusion Criteria: The research team will recruit approximately 25 participants from local fire departments in the Greater Cincinnati region. Recruitment will start with the help of our stakeholder Fire Chiefs who are collaborating on this study. PI has already met with them and discussed the proposed study plan and recruitment strategies. Fire Chief Dr. Jetter has been working with P.I. (Bhattacharya) on firefighter heat stress studies since 2005. The recruitment of firefighters will not involve firefighters’ supervisors such as the fire chief. We have been recruiting firefighters for research studies at UC by distributing flyers about the project at the fire departments and interested firefighters can contact our office to obtain additional information about the study. This process will minimize any potential coercion (real or perceived) from their supervisors. During the initial phone call, firefighters will be asked a few basic screening questions and then if interested, they will be sent a link to complete an online survey about their health to determine if they can participate. REDCap software will be used for data capture and is a secure, HIPAA-capable web-based data management tool. Data servers housing the REDCap database for this study are housed in the CCHMC data center, and passwords are assigned to study-related personnel. Access to the servers is limited to personnel directly involved in the study. Any materials used for recruitment will be submitted to the Internal Review Board for expedited review before use. All interested active firefighters (full-time) will be allowed to enroll in the study. The study will be open to healthy males of age 30 years up to age 45 (with a range of firefighting experience) who have successfully completed a screening test. This being a pilot study, the project will be limited to male participants which will provide results without gender bias. In the future, a larger study can be developed to include both male and female firefighters.

Interested participants will be screened for MRI contraindications, which include any type of electronic, mechanical or magnetic implant, metal objects or foreign body (e.g., BB, bullet, shrapnel, metallic slivers). Participants with known cardiac disease, neuropsychiatric diagnosis, use of psychotropic medications, and substance abuse (self-report) will be excluded.

8. Potential Risks and Protections

In the Imaging Research Center, there are no adverse effects identified to date from undergoing imaging studies. The suggested guidelines for the operation of clinical MR systems established by the FDA address three areas of control. They are: • Static magnetic field • Gradient switching speed • Radio frequency power absorption by the participant a. Static Magnetic Field – The 3.0 Tesla static magnetic field strength of the scanners proposed in this protocol is below the 8 Tesla limit for clinical diagnostic MR scanners set by the FDA guidelines. The FDA has concluded that magnetic field below 8 T does not by itself impose

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a risk to human participants. The FDA guideline for Criteria for Significant Risk Investigation of Magnetic Resonance Diagnostic Devices issued in 2003 also suggests magnetic field strengths up to 4T should be safe for infants. In short, the FDA has approved the use of magnetic field strengths of up to 4 Tesla for MR scanning of infants and children in a research environment. b. Gradient Speed – The FDA suggested rate of change of magnetic field (dB/dt) is 20T/sec. A 3T/sec limit is maintained by the scanner’s security system on all three gradients. It measures the gradient currents in time steps of 100 microseconds. In the event that the maximum allowed values are exceeded, a signal is given to the operator console and the scan cannot be initiated. The operator must reduce the gradient strengths by increasing the slice thickness and/or the field of view before the scan can proceed. c. RF Absorption Rate – The FDA guidelines for the specific RF absorption rate (SAR) imparted by the MRI device is set by limiting the patient’s core temperature rise to less than 1 degree Celsius. In the absence of core temperature monitoring equipment, the manufacturers have continued to use the previously established FDA limits of 2 W/Kg (average) and 8 W/Kg (peak). The security system of the scanners proposed in this protocol limits the SAR to 1 W/Kg. In order to monitor this value the RF energy at the output of the amplifier is measured over a period of 10 seconds. In addition to the average output power, the peak value is also monitored. In the event that one of these values is exceeded, the transmitter power supply is turned off automatically within 3 to 5 seconds. These measures ensure that the MRI system is well within the current FDA regulations on SAR.

During the postural balance test, study team members will stand near to the participant to help prevent any remote possibility of a fall-related injury. Dr. Amit Bhattacharya, PI, has been conducting postural balance testing conditions in his lab and at field sites for over 25 years and has tested over 400 participants ranging in ages 5-95 with no adverse health effects to study participant. For the gait test, non-invasive wearable inertial sensors will be used which will be attached on the legs, arms and chest with hypoallergenic tapes. Therefore, chances of skin rashes are minimal.

Venipuncture: The subject will be brought to a private area for the blood draw. The blood draw can cause temporary discomfort or bruising at the skin puncture site and in rare instances (less than 1%), fainting or an infection can occur. To minimize these risks, blood will be drawn only by qualified phlebotomists, using aseptic techniques, with participants in a seated or recumbent position. A sterile bandage will cover the phlebotomy site after the procedure and the arm will be elevated to ensure that bleeding has stopped. The subject will be observed for any lightheadedness, bruising or bleeding during and after the procedure. If the subject is lightheaded, he will be reclined and monitored until symptoms resolve. If the subject is asymptomatic after the phlebotomy procedure, he will be released. Any subject with any side

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effects during or after phlebotomy will not be used again to obtain the blood products. In addition, no more than three attempts to access a vein will be done.

Incidental Findings on MRI We have established practices for identifying and handling incidental imaging findings. A board certified neuroradiologist will review all imaging studies for incidental findings of clinical significance using the research Picture Archiving and Communication System (PACS). This service is provided by the CCHMC Department of Radiology and is performed in accordance with department policy for the ethical and appropriate conduct of imaging research investigations. Based upon the imaging literature and Dr. Cecil’s experience over the past two decades, estimates are that between 4-10% of typical children and adults participating in research neuroimaging examinations have findings that require further neurological or clinical imaging evaluation. Dr. Cecil will contact the participant to inform them of the findings, if present. The consent process will allow the participant to decide and provide their primary care physician’s contact information. If the participant agrees, the participant’s primary physician will be informed of any significant findings identified during the course of the study to then provide appropriate management and care. Discomfort – During the MRI testing, mild to moderate discomfort may occur due to noise produced by the MR scanner. Earplugs and sound isolating headphones will be provided to reduce noise exposure to safe levels. In addition, participants with known claustrophobic tendencies will be excluded. Visual and audio contact will be maintained at all times. Any participant who experiences discomfort or exhibits distress will be removed immediately from the scanner.

Emergency Procedures: In the event of a medical emergency in the magnet room, the individual experiencing the emergency will be pulled out of the MR room immediately by staff, and Basic Life Support will be initiated. A staff member will call the code internal emergency number (6- 8888) to initiate a code blue to the appropriate location (see below). Once the code team arrives, Advanced Life Support will be initiated as necessary.

Precautions – a. Protection Against Risk– All participants and family members that enter the imaging facility will be screened for compatibility with the MRI system. All participants will be provided with ear protection in the form of foam ear-plugs plus insulated headphones. In addition, participants will be given a “panic” button to hold during the scans. In the event that a participant becomes uncomfortable, he or she can press the panic button to notify the operator of the need for immediate attention. Intercom contact will be opened immediately and the participant can be removed from the scanner if needed.

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b. Method of Monitoring Study Conduct –The study staff will report adverse events to the University of Cincinnati Review Board. c. Maintaining Data Quality and Confidentiality – The main threat to the quality of MR images is patient motion. Each set of images will be checked for motion immediately following the scan. In some cases scans may be repeated, but often, image frames that are contaminated by motion can be deleted from the data set during the statistical image analysis stage. Confidentiality will be maintained by storing all scan data on secure, password-protected servers, which are located behind an electronic “firewall” in the CCHMC data center. Access to all hard copy records is restricted to study related personnel.

Risk/Benefit Analysis – The study poses minimal risk but provides no direct benefit to participants. There is no known risk to individuals from MRI. The benefit to the study is to the community at large and not specifically to the individual participants participating in the project.

9. Biological Material

Blood will be collected and then analyzed in Dr. Yadav’s lab in the Kettering Laboratory Complex. The samples/specimens collected during this protocol will be archived into a freezer repository located in a locked room in the PI laboratory for future use. In terms of Identifiers, the samples/specimens will be assigned codes and their corresponding information including the participant’s identity will be stored as hard copies safely in locked cabinet and as electronic records on a password protected computer and secure data storage server, as described in the Data Storage and Confidentiality section above.

10. Payment

Subjects will receive a reloadable debit card of $100 total [$50 for participation in the research study procedures and $50 for blood draw] as compensation for the study visit. This amount of compensation is entirely reasonable and fair given that the study visit will require approximately 4 hours of time and participants will be responsible for their own transportation.

11. Subject Costs

There are no financial costs to the subject for participating in this study. They will be responsible for their own transportation.

12. Informed Consent

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Each person who agrees to participate will complete a consent process that will include the following: 1) reading of the consent, 2) discussion with a research team member about consent form and questions, and 3) signing of the form. Upon completion of the consent process, the forms will be kept in a locked box until they are returned to the office of the PI where the consent forms will be kept for 3 years after the completion of the study under lock and key.

Reference List

[1] U.S.F. Administration, National Fire Department Census quick facts, http://apps.usfa.fema.gov/census/summary, DOI (2015). [2] J. Hylton, G. , US Volunteer Firefighter Injuries 2011-2013, National Fire Protection Association Fire Analysis and Research Division, DOI (2015). [3] K. James, Mani, A., Kincer, G. et al, Effects of Heat Stress on Firefighters' Postural Balance During Live Fire Fighting., Presented at the American Industrial Hygiene Annual Conference, Montreal, Canada, May 18-23, DOI (2013). [4] A. Mani, Musolin, K., James, K., Kincer, G, Alexander, B., Succop, P., Lovett, W., Jetter, WA, Bhattacharya, A., Risk factors associated with live fire training: buildup of heat stress and fatigue, recovery and role of micro-breaks, Occupational Ergonomics, 11 (2013) 109-121. [5] L. Nybo, B. Nielsen, Hyperthermia and central fatigue during prolonged exercise in humans, J. Appl. Physiol (1985. ), 91 (2001) 1055-1060. [6] L. Nybo, B. Nielsen, Perceived exertion is associated with an altered brain activity during exercise with progressive hyperthermia, J. Appl. Physiol (1985. ), 91 (2001) 2017-2023. [7] L. Nybo, Brain temperature and exercise performance, Exp. Physiol, 97 (2012) 333-339. [8] L. Nybo, CNS fatigue provoked by prolonged exercise in the heat, Front Biosci. (Elite. Ed), 2 (2010) 779-792. [9] M. Febbraio, Does muscle function and metabolism affect exercise performance in the heat?, Exerc.Sport Sci.Rev., 28 (2000) 171-176. [10] F.T. Amorim, I.T. Fonseca, C.A. Machado-Moreira, C. Magalhaes Fde, Insights into the role of heat shock protein 72 to whole-body heat acclimation in humans, Temperature (Austin, Tex.), 2 (2015) 499-505. [11] Chen W, Syldath U, e.a. Bellmann K, Human 60-kDa heat-shock protein: a danger signal to the innate immune system, Journal of immunology (Baltimore, Md.: 1950), 162 (1999) 3212- 3219. [12] J. Zhou, An, H., Xu, H., Liu, S., and Cao, X., Heat shock up-regulates expression of Toll- like receptor-2 and Toll-like receptor-4 in human monocytes via p38 kinase signal pathway, Blackwell Publishing Ltd, Immunology, 114 (2005) 522-530. [13] K. Ohashi, V. Burkart, S. Flohe, H. Kolb, Cutting edge: heat shock protein 60 is a putative endogenous ligand of the toll-like receptor-4 complex, Journal of immunology (Baltimore, Md. : 1950), 164 (2000) 558-561. [14] V. Khariv, Pang, K, Servatius, RJ, David, BT, Goodus, MT, Beck, KD, Heary, RF, Elkabes, S, Toll-like receptor 9 deficiency impacts sensory and motor functions, Brain, Behavior, and Immunity, 32 (2013) 164-172.

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[15] A. Mani, Rao, M.B., James, K., Aljaroudi, A., Bhattacharya, A., Predictive modeling along with pre-cooling interventions can reduce heat stress in firefighters, Poster presented at the 2014 American Industrial Hygiene Conference, San Antonio, TX. , DOI (2014). [16] A. Mani, Rao, M.B., James, K., Bhattacharya, A., Individualized Prediction of Heat Stress in Firefighters: A Data-driven Approach using Classification and Regression Trees, J.Occup Env Hygiene, 12 (2015) 845–854 [17] T.M. Sobeih, K.G. Davis, P.A. Succop, W.A. Jetter, A. Bhattacharya, Postural balance changes in on-duty firefighters: effect of gear and long work shifts, J Occup. Environ. Med, 48 (2006) 68-75. [18] T. Larsh, Mani, A, Bhattacharya, A, Duker, A, Raghavan, R, Cox, C,Revilla, F., "Effect of Subthalamic Nucleus Deep Brain Stimulation on Gait Stability during Dual Tasking Conditions in Patients with Parkinson's Disease", American Academy of Neurology 65th Annual Meeting, March 16-23, San Diego, CA, DOI (2013). [19] A. Mani, K. Dunning, T. Larsh, C. Cox, A. Shukla, A. Bhattacharya, F.J. Revilla, Dynamic fall-risk predictors in Parkinson's disease, Presented at American Academy of Neurology 66th Annual Meeting, at the Pennsylvania Convention Center, in Philadelphia, PA.April 26 to May 3, DOI (2014). [20] F.J. Revilla, T.R. Larsh, A. Mani, A.P. Duker, C. Cox, P. Succop, M. Gartner, T.C. Jarrin, A. Bhattacharya, Effect of dopaminergic medication on postural sway in advanced Parkinson's disease, Front Neurol., 4 (2013) 202. [21] A. Bhattacharya , Watts, NB., Davis, K., Kotowski, S., Shukla, R., Dwivedi, A., Coleman, R.,, Dynamic Bone Quality – A Non-Invasive Measure Of Bone's Biomechanical Property in Osteoporosis, Journal of Clinical Densitometry: Assessment of Skeletal Health, vol. 13 (2), 228- 236, 13 (2010) 228-236. [22] A. Bhattacharya, Shukla, R., Auyang, ED., Dietrich KN.,Bornschein, RL., Effect of succimer chelation therapy on postural balance and gait outcomes in children with early exposure to environmental lead, Neurotoxicology, 28 (2007) 686-695. [23] A. Bhattacharya, Shukla, R., Dietrich, KN.,Bornschein, RL.,, Effect of early lead exposure on the maturation of children's postural balance: a longitudinal study, Neurotoxicol. Teratol, 28 (2006) 376-385. [24] S. Slobounov, T. Wu, M. Hallett, Neural basis subserving the detection of postural instability: an fMRI study, Motor Control, 10 (2006) 69-89. [25] S. Slobounov, C. Cao, W. Sebastianelli, E. Slobounov, K. Newell, Residual deficits from concussion as revealed by virtual time-to-contact measures of postural stability, Clin Neurophysiol, 119 (2008) 281-289. [26] S. Slobounov, M. Hallett, C. Cao, K. Newell, Modulation of cortical activity as a result of voluntary postural sway direction: an EEG study, Neurosci. Lett, 442 (2008) 309-313. [27] S. Slobounov, M. Hallett, S. Stanhope, H. Shibasaki, Role of cerebral cortex in human postural control: an EEG study, Clin Neurophysiol, 116 (2005) 315-323. [28] P.O. Riley, B.J. Benda, K.M. Gill-Body, D.E. Krebs, Phase plane analysis of stability in quiet standing, J Rehabil. Res. Dev, 32 (1995) 227-235. [29] A. Bhattacharya, Effect of Live Firefighting on the Firefighters' Cardiovascular and Neuromuscular Systems using Wearable and Ingestible Sensor Technology, Presented at the 51st Lucien Brouha Work Physiology Symposium, Savannah, GA April 23-25, DOI (2013).

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[30] A. Bhattacharya, Watts NB, Dwivedi A, Shukla R, Mani A, Diab D, “Combined Measures of Dynamic Bone Quality and Postural Balance -- A Fracture Risk Assessment Approach in Osteoporosis” J Clin Densitom. , 19 (2016) 154-164. [31] C. Zampieri, A. Salarian, P. Carlson-Kuhta, K. Aminian, J.G. Nutt, F.B. Horak, The instrumented timed up and go test: potential outcome measure for disease modifying therapies in Parkinson's disease, J Neurol.Neurosurg.Psychiatry, 81 (2010) 171-176. [32] B.R. Bloem, V.V. Valkenburg, M. Slabbekoorn, M.D. Willemsen, The Multiple Tasks Test: development and normal strategies, Gait.Posture., 14 (2001) 191-202. [33] M. Montero-Odasso, H. Bergman, N.A. Phillips, C.H. Wong, N. Sourial, H. Chertkow, Dual-tasking and gait in people with mild cognitive impairment. The effect of working memory, BMC. Geriatr, 9 (2009) 41. [34] M. Montero-Odasso, A. Casas, K.T. Hansen, P. Bilski, I. Gutmanis, J.L. Wells, M.J. Borrie, Quantitative gait analysis under dual-task in older people with mild cognitive impairment: a reliability study, J. Neuroeng. Rehabil, 6 (2009) 35. [35] M. Montero-Odasso, S.W. Muir, M. Speechley, Dual-task complexity affects gait in people with mild cognitive impairment: the interplay between gait variability, dual tasking, and risk of falls, Arch. Phys. Med Rehabil, 93 (2012) 293-299. [36] M. Montero-Odasso, J. Verghese, O. Beauchet, J.M. Hausdorff, Gait and cognition: a complementary approach to understanding brain function and the risk of falling, J. Am. Geriatr. Soc, 60 (2012) 2127-2136. [37] S.W. Muir, M. Speechley, J. Wells, M. Borrie, K. Gopaul, M. Montero-Odasso, Gait assessment in mild cognitive impairment and Alzheimer's disease: the effect of dual-task challenges across the cognitive spectrum, Gait. Posture, 35 (2012) 96-100.

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Appendix C – Postural Balance Metrics

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Phase Plane Variables (Gait & Sway) Variable Anatomical Planes Definition Medial-Lateral (ML) Angular Displacement Versus Anterior-Posterior Total area of the phase plane plot with Sway Area (SA) Angular Velocity (AP) respect to time. Angular Displacement Versus ML Angular Acceleration AP Angular Displacement Versus ML Total distance traveled of center of gravity Angular Velocity AP with respect to time from the initial start of Sway Length (SL) Angular Displacement Versus ML the test at the center of pressure to the final Angular Acceleration AP location of the center of gravity at the Angular Displacement Versus ML Angular Velocity AP The percent of the trial that occurs outside of Percent Out Angular Displacement Versus ML of the stability boundary. Angular Acceleration AP Kinematic Variables (Gait & Sway) Definition Total area of the phase plane plot with Angular Displacement Versus ML versus AP Sway Area (SA) Angular Displacement respect to time. Total distance traveled of center of gravity Angular Displacement Versus ML versus AP with respect to time from the initial start of Sway Length (SL) Angular Displacement the test at the center of pressure to the final Angular Displacement Versus The percent of the trial that occurs outside of ML versus AP Pecent Out Angular Displacement of the stability boundary. The ratio of the minimum distance between the stabilogram and functional stability ML versus AP Index of proximity to Angular Displacement Versus boundary and the radial distance to the point stabiilty boundary (ipsb) Angular Displacement on functional stability boundar used to Angular Displacement Versus The ratio of the area of the envelope around ML versus AP Stability area ratio (sar) Angular Displacement the stabilogram and the area of the functional Weighted residence time Angular Displacement Versus The estimated time in which the center of ML versus AP index (wrti) Angular Displacement pressure is within the functional stability *All variables are unitless.

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Sway Force Plate Variables Units Definition Total area of movement patterns within the outer centimeter (cm)2 Sway Area (SA) perimeter of X-Y coordinates of the center of pressure. Sway Length (SL) centimeter (cm) Total distance traveled by the center of pressure. The maximum distance the body moved in the medio- ML lateral (ml) direction. Excursion (Range) centimeter (cm) The maximum distance the body moved in the anterior- AP posterior (ap) direction. Gait Variables Units Definition Cadence steps/minute The rate at which a person can walk. The amount of time a person stood on a single foot second (s) Single stance during each step of the walk. The amount of time a person stood on both feet during second (s) Double stance each step of the walk. The amount of time a person turned around the cone second (s) Turn duration during the instrumented Timed Up and Go task. The highest instance of acceleration when turning with degree/second Peak linear acceleration relationship to angular direction during the (deg/s) during turn instrumented Timed Up and Go test. The highest instance of velocity when turning with degree/second Peak linear velocity during relationship to angular direction during the (deg/s) turn instrumented Timed Up and Go test. Stride length inches (in) The distance between one foot and the next during a *Medial-lateral (ML) anatomical plane **Anterior-posterior (AP) anatomical plane

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Appendix D – Phase Plane Plots

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Sway Phase Plane Plots

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Gait Phase Plane Plots

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Appendix E – Pairwise Concordance Plots

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Dynamic Plot Comparisons

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Static - Forward Plot Comparisons

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Static - Backwards Plot Comparisons

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Static - Sideways Plot Comparisons

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Appendix F – Statistical Power and Effect Size

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Test Condition Variable Power Sample Size Sway Area SD 36.67% 9.56 Sway Condition A: AP Excursion SD 77.92% 4.14 Two legs, eyes open AP Excursion CV 100.00% Sway Length SD 17.67% 22.37 Sway Length Mean 28.63% 12.54 Sway Condition B: Sway Length CV 18.28% 21.40 Two legs, eyes Sway Area CV 16.18% 25.16 closed ML Excursion SD 9.07% 66.12 AP Excursion mean 8.26% 82.03 Sway Phase Plane ml displacement vs ml acceleration length CV Condition B 13.79% 31.58 Sway Length mean 5.60% 435.78 Sway Condition L: Sway Area mean 5.85% 308.64 One leg, eyes open ML Excursion mean 5.00% 1421850.00 AP Excursion mean 5.15% 1716.80 Angular velocity of turn along x-axis mean 66.23% 5.04 Gait Single Task Angular velocity of turn along x-axis SD 71.28% 4.63 Gait Phase Plane ml displacement vs ml acceleration length sen variance mean 33.79% 10.44 Single Task ml displacement vs ml acceleration area mean 31.68% 11.20 Double Stance BV 71.70% 4.59 Gait Dual Task Angular velocity of turn along x-axis mean 67.56% 4.93 Double Stance WV 5.09% 2881.76 ap displacement vs ap velocity length CV 79.78% 4.01 ml displacement vs ml velocity percent out sen variance SD 80.90% ml displacement vs ml velocity area CV 50.73% 6.76 ml displacement vs ml velocity percent out sen variance CV 99.99% ap displ vs ap accel length sen total SD 35.68% 9.84 ap displacement vs ap acceleration percent out sen mean 6.56% 169.07 Gait Phase Plane ap displ vs ap accel length sen total CV 64.91% 5.16 Dual Task ap displ vs ap acceleration area mean 7.13% 124.53 ap displ vs ap accel area SD 22.34% 16.69 ml displ vs ml accel percent out sen var mean 8.07% 87.03 ml displ vs ml accel length sen total mean 31.66% 11.21 ml displacement vs ml acceleration area CV 38.67% 9.03 ml displ vs ml accel percent out sen var CV 13.36% 33.14 *Sample size is based on number of subjects needed per group to attain p-value of 0.05 with 80% power. **Sample size was not calculated for variables with statistical power greater than 80%.

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