Firefighter Resilience Assessment Using a Structured Approach

Khyati Vyas

A thesis submitted to the School of Graduate and Postdoctoral Studies in partial fulfillment of the requirements for the degree of

Master of Health Science in Health Informatics

Faculty of Health Sciences

Ontario Tech University

Oshawa, Ontario, Canada December, 2019

THESIS EXAMINATION INFORMATION Submitted by: Khyati Vyas

Master of Health Science in Health Informatics

Thesis title: Firefighter Resilience Assessment Using a Structured Approach

An oral defense of this thesis took place on December 13, 2019 in front of the following examining committee:

Examining Committee:

Chair of Examining Committee DR. PAUL YIELDER

Research Supervisor DR. CAROLYN MCGREGOR AM

Examining Committee Member DR. MICHAEL WILLIAMS-BELL

Thesis Examiner DR. GRACE VINCENT

The above committee determined that the thesis is acceptable in form and content and that a satisfactory knowledge of the field covered by the thesis was demonstrated by the candidate during an oral examination. A signed copy of the Certificate of Approval is available from the School of Graduate and Postdoctoral Studies.

ii ABSTRACT

This thesis proposes a resilience assessment model to assess the physiological changes experienced during a simulated firefighting environment. Currently, a resilience assessment model that measures the response to job stressors does not exist. The model provides a mechanism to measure the level of progress or decline experienced by public safety personnel, while exposed to job-related stressors. The research design for the model includes analysis of heart rate and electrocardiogram-based RR intervals at baseline and during the training activity. The model was applied to a case study that involved an extreme heat (50° C) search and rescue training task for pre-service firefighters (mean age of 21 years and a standard deviation of 4.6 years with 20% female). The performance of the female participants revealed a 54% higher standard deviation of normal R-R interval

(SDNN) score compared to male participants. Furthermore, the average of the mean heart rate (HR) for males was 18% higher than the females. The SDNN for 93% (39) of males was less than 100ms as compared to the female SDNN where only 22% (2) of participants fell below 100 ms. Applying the model revealed female participants to be more resilient than their male counterparts. Prior studies have not addressed biological sex differences in their studies and this needs to be further explored. Implementation of the resilience assessment model to assess and improve the effectiveness of resilience training programs could benefit firefighters by reducing the incidence of PTSD, depression and workplace injuries.

Keywords: occupational stress; firefighters; heart rate variability; public safety personnel; resilience

iii

CO-AUTHORSHIP STATEMENT

The thesis includes a paper co-authored with research supervisor, Dr. Carolyn McGregor who contributed by providing feedback and supervision. The Use of Heart Rate for the

Assessment of Firefighter Resilience: A Literature Review (Vyas & McGregor, 2018).

AUTHOR’S DECLARATION

I hereby declare that this thesis consists of original work of which I have authored.

This is a true copy of the thesis, including any required final revisions, as accepted by my examiners.

I authorize the University of Ontario Institute of Technology to lend this thesis to other institutions or individuals for the purpose of scholarly research. I further authorize

University of Ontario Institute of Technology to reproduce this thesis by photocopying or by other means, in total or in part, at the request of other institutions or individuals for the purpose of scholarly research. I understand that my thesis will be made electronically available to the public.

The research work in this thesis that was performed in compliance with the regulations of Ontario Tech’s Research Ethics Board/Animal Care Committee under REB Certificate

#14783 and Durham College Research Ethics Board under REB Certificate #156-1718.

KHYATI VYAS

STATEMENT OF CONTRIBUTIONS

1 The work described in Chapter 4 was performed at the in climatic chambers of Ontario

Tech University’s ACE Facility, Oshawa, using the Hexoskin smart wearable device for data acquisition in a pre-firefighting simulated environment supervised by Dr. Michael

Williams-Bell and Dr. Carolyn McGregor. I was responsible for downloading and analyzing data in my computer.

Part of the work described in Chapter 2 has been published as:

Vyas, K., McGregor, C., 2018, “The Use of Heart Rate for the Assessment of Firefighter Resilience: a Literature Review”, 2nd IEEE Life Sciences Conference, Montreal, Canada, 259-262, doi: 10.1109/LSC.2018.8572095

I performed the majority of the literature review, finding articles relevant to the context, and writing of the document.

ACKNOWLEDGEMENTS I would first like to thank my thesis advisor Dr. Carolyn McGregor from the Faculty of Business and IT at Ontario Tech University. She provided me confidence and courage in pursuing my goals and guided me during this journey. She consistently allowed this paper to be my own work but steered me in the right direction whenever she thought I needed it.

I would also like to thank Dr. Michael Williams-Bell who was involved in this research work. His prior research work to make firefighting a safer occupation has always inspired me to work harder and remain constantly driven to accomplishing this thesis work.

I would also like to acknowledge all the members of our Health Informatics lab in

SIRC. They provided motivation when the times I was trying to find solutions to my research questions. Catherine Inibhunu (Ph.D. © of Computer science) from FBIT at

Ontario Tech University who provided advice on the constructive research and Meg

Thavakugathasalin as the second reader of this thesis, and I am gratefully indebted to her very valuable comments on this thesis. Additionally, I would like to thank the Financial

Aid Office of Ontario Tech University for providing financial support during a semester through the TD Bank Financial Group Graduate Scholarship.

Finally, I must express my very profound gratitude to my friends and everyone that provided me with unfailing support and continuous encouragement throughout my years of study and through the process of researching and writing this thesis. This accomplishment would not have been possible without them. Thank you.

vii

TABLE OF CONTENTS

ABSTRACT ...... iii CO-AUTHORSHIP STATEMENT ...... iv AUTHOR’S DECLARATION ...... v STATEMENT OF CONTRIBUTIONS ...... vi ACKNOWLEDGEMENTS ...... vii TABLE OF CONTENTS ...... viii LIST OF TABLES ...... x LIST OF FIGURES...... xi LIST OF ABBREVIATIONS AND SYMBOLS ...... xii Chapter 1. Introduction ...... 1 1.1 Background ...... 1 1.1.1 Physiological Signal Monitoring ...... 3 1.2 Mental Health in the Firefighter Population ...... 4 1.1.2 Resilience Training ...... 7 1.1.3 Application of Resilience Assessment Techniques ...... 8 1.1.4 Research Motivations ...... 10 1.1.5 Structure of Resilience Assessment Model ...... 10 1.1.6 Research Methods ...... 11 1.1.7 Thesis Structure ...... 14 Chapter 2. Literature Review...... 16 2.1 Introduction ...... 16 2.1.1 Method...... 16 2.2 Physical Demands of Firefighter Population ...... 17 2.1.2 Cardiovascular Disease in the Firefighter Population ...... 19 2.1.3 Prior work for Resilience in the Firefighter Population ...... 21 2.1.4 Resilience in other Public Safety Personnel Population ...... 24 2.1.5 Sample Size & Demographic Characteristics ...... 29 2.1.6 Heart Rate Responses ...... 30 2.1.7 Recent Work and Limitations ...... 32 2.1.8 Statistical Analysis ...... 33

viii

2.1.9 Discussion ...... 37 2.1.10 Conclusion ...... 38 Chapter 3. Methodology ...... 40 3.1 Introduction ...... 40 3.2 Physiological Data ...... 40 3.2 Activity Data...... 41 3.2 Data Model ...... 42 Chapter 4. Case Study and Evaluation of Data Model...... 45 4.1 Case Study ...... 45 4.1.1 Activities ...... 45 4.1.2 Physiological and Activity Data acquisition ...... 46 4.1.3 Implementation of the Resilience Assessment Model ...... 47 4.1.4 Findings from the Case Study ...... 48 4.1.5 Table 3. Mean Heart Rate by Gender ...... 50 4.1.6 Table 3.1. Mean N-N by Gender ...... 51 4.1.7 Table 3.2. Min & Max Mean HR by Gender ...... 52 4.1.8 Table 4. SDNN by Gender ...... 54 Chapter 5. Discussion ...... 55 5.1 Introduction ...... 55 5.2 Using HR and HRV within a Resilience Assessment Model ...... 55 5.3 Case Study Resilience Results ...... 56 5.1.1 Biological Sex Differences ...... 58 5.1.2 Limitations ...... 59 5.1.3 Personalized Resilience Assessment ...... 60 5.1.4 Implications for Resilience Assessment and Development...... 61 Chapter 6. Conclusions ...... 63 6.1 Research and Findings ...... 63 6.2 Future Work ...... 65 BIBLIOGRAPHY ...... 66

LIST OF TABLES

CHAPTER 1 Table 1 : Constructive Research method ...………………...... 13 CHAPTER 2 Table 2 : Summary of previous studies …………………...... 26 CHAPTER 4 Table 3 : Mean Heart Rate by Gender …...…………………...... 50 Table 3.1: Mean N-N by Gender …...…………………...... 51 Table 3.2: Min & Max Mean HR by Gender …...…………………...... 52 Table 4 : SDNN by Gender …...…………………...... 54

LIST OF FIGURES

CHAPTER 4 Figure 1 : Findings from the Case Study ……….………...... 49

LIST OF ABBREVIATIONS AND SYMBOLS

ANS Autonomic Nervous System HRV Heart Rate Variability HR Heart Rate PTSD Post-traumatic Stress Disorder PSP Public Safety Personnel SDNN Standard Deviation of normal N-N intervals

xii

Chapter 1. Introduction

1.1 Background

Resilience is an essential trait in public safety personnel (PSP) professions, which

include firefighters, police officers, paramedics, correctional workers, dispatchers,

and paramedics, who experience intense physical demands within their occupation

(Carleton et al., 2018). Firefighters face stressful situations and emergencies, such as

attending to automobile accidents, exposure to traumatic events, suicides, failed

rescue attempts, death of a victim or fellow firefighter, causing a negative impact on

their mental health (Beaton, Murphy, Johnson, Pike, & Corneil, 1999; Jacobsson et

al., 2015; Lim, Childs, & Kathy Gonsalves, 2000). Resilience is defined as “the

ability to adapt and successfully cope with acute or chronic adversity” (Connor,

2006; Lee et al., 2014). Firefighters exposed to traumatic events, along with

correlation to the perceived occupational stress, develop posttraumatic stress disorder

(PTSD) symptoms, whereas improved resilience can decrease the severity of those

symptoms (Lee, Ahn, Jeong, Chae, & Choi, 2014). Resilience provides a buffering

effect by protecting against adverse effects of PTSD (Lee et al., 2014).

While there are several reasons for the need to improve resilience amongst PSP, there

is a lack of an appropriate resilience assessment model that provides a resilience level

score based on physiological responses that assess occupational stress during

workplace training. The physiological data extraction from the wearable device

deployed during simulated firefighting training will educate us on the impact of job

stressors. Furthermore, the measurement of physiological data enables a greater

understanding of the reaction to stress on an individual level. The information will

1 also provide concrete evidence to address improvements to current PSP training. This thesis aims to design a structured approach for resilience assessment that bridges this gap and enables an understanding of how to quantify stress-induced response to stressors in a simulated firefighting environment.

This thesis presents a structured approach to perform resilience assessment for PSP that was applied amongst a pre-service firefighter training cohort using physiological monitoring through a smart wearable device discussed later in proceeding chapters.

Physiological monitoring involves measuring and assessing an individual’s perspiration, heart rate, breathing rate, and/or changes in their core temperature

(Banerjee, Nabar, Gupta, & Poovendran, 2011).

This study implemented heart rate (HR) and heart rate variability (HRV) calculations for physiological data. In reviewing the literature, both values are important to consider when constructing a resilience assessment model quantifying stress. HRV is the variation over a period between successive heartbeats contributing to overall cardiac health and the functioning of the autonomic nervous system (ANS) (Acharya,

Joseph, Kannathal, Lim, & Suri, 2006).

Based on the literature reviewed, many gaps exist in knowledge relating HRV values for optimal utilization and accessibility of techniques that assess resilience (Ramey,

Perkhounkova, Hein, Bohr, & Anderson, 2017). The field of informatics has great potential for designing resilience assessment scales based on physiological data for the firefighter population applicable in other areas of PSP, where such scales can utilize HRV and HR monitoring through smart body wearable garments for the continuous assessment of resilience.

The introduction of job stressors within the training exercise allows initiation of the

resilience assessment model during training. The changes in physiological data are

then analyzed to measure how job-related stress experienced by these individuals is

quantified. Stress-related to physiological and psychological exposure affects human

performance (Winslow et al., 2013). Research undertaken by Ramey et al. (2016) has

considered HRV data in assessing resilience training for law enforcement officers.

While this study did try to address the gap in training for police officers, there is a

paucity of information to substantiate the need to improve the resiliency assessment

for PSP.

1.1.1 Physiological Signal Monitoring

Physiological measurements, such as HR, can provide valuable information on the

cardiovascular strain and physical workload and aid in preventing work-related

deaths (Smith et al., 2016). Based on the findings of on-duty deaths in the United

States, about 45% of fatalities amongst firefighters are associated with heart disease

(Kales, Soteriades, Christophi, & Christiani, 2007).

HRV is the variation in timely sequenced interbeat intervals (Appelhans & Luecken,

2006). Moreover, it is vital to consider HRV while studying the response to stressors

by an individual, as an the association between the autonomic nervous system (ANS)

and the cardiovascular system; hence, there can be a negative influence on their HR

or HRV if stressors are not controlled (Gomes et al., 2013). A low HRV value can

also indicate an autonomic dysregulation linked to mortality and morbidity (Kemp

& Quintana, 2013).

A lower HRV value is an independent marker of developing different cardiovascular

diseases (Appelhans & Luecken, 2006). Lower HRV is also associated with the

presence of PTSD, depression, and other anxiety disorders (Pyne et al., 2016;

Shaffer, McCraty, & Zerr, 2014). Hence, it is essential to consider HRV values while

designing an experiment that aims to measure resilience levels of an individual

working as a PSP.

There have been attempts made to improve the resilience levels of PSP with

resilience training programs that could optimize their job functions, and skills related

to stressful events. Resilience training has been shown to improve stress-related

autonomic response and HRV and cardiovascular disease (CVD) risk factors,

especially amongst young police officers, who are less resilient compared to

experienced officers (Ramey et al., 2016).

1.2 Mental Health in the Firefighter Population

PSP are at high risk of developing mental disorders, such as PTSD, major

depressive disorder (MDD), panic disorder (PD), generalized anxiety disorder

(GAD), social anxiety disorder (SAD), and an alcohol use disorder (AUD) since PSP

personnel experience traumatic events during their occupation (Carleton et al., 2018).

An individual develops PTSD when exposed to a traumatic event and experiences

symptoms such as grief, isolation, suicidal tendencies, and physical distress

(American Psychiatric & American Psychiatric Association, 2013). Prevention

strategies for PTSD, including improving a person’s resilience and providing coping

mechanisms to reduce job-related stress, could potentially minimize the onset of

4 other mental health disorders, such as depression and alcohol use disorders (AUDs)

(Kim, Park, & Kim, 2018).

The prevalence rate of PTSD symptomology in the general population is 3.5% to

5.6% compared to firefighters, where the prevalence is estimated as high as 37%

(Henderson, Van Hasselt, LeDuc, & Couwels, 2016). While there is an extreme rise in PTSD onset and substance abuse, there is a paucity of research regarding the mental health of firefighters (Henderson et al., 2016). In addition, there is a stigma surrounding mental health that creates a barrier to seek assistance, treatment, or new approaches that make mental health treatment more accessible for firefighters and other PSPs (Kim, et al., 2018). Research in this area will ultimately create awareness of the importance of mental health and mindfulness, which are underrated, but essential skills for PSPs.

High levels of posttraumatic stress symptoms (PTSS) can lead firefighters to suicide to avoid a painful situation (Boffa et al., 2017). Attempts of suicide and suicidal ideation (SI) is seen highest amongst firefighter and Emergency Medical Services

(EMS) personnel compared to the rest of the population and other public safety professions (Martin, Tran, & Buser, 2017). Moreover, volunteer firefighters who mostly have a separate full-time career and have a different screening process and less cumulative hours spent on traumatic events compared to career firefighters, experience much higher levels of PTSD and are more likely to consider suicide as an option compared to career firefighters (Stanley, Boffa, Hom, Kimbrel, & Joiner,

2017).

It is necessary to consider the firefighter population because they are exposed to dangerous and stressful situations and are required to perform under intense pressure that includes rescuing victims from vehicular accidents, forest and structural fires or other events generating mental and physical challenges (Gomes et al., 2013).

Moreover, exposure to traumatic incidents can cause negative implications for mental health, leading to PTSD or depression amongst this PSP (Marshall, Milligan-

Saville, Mitchell, Bryant, & Harvey, 2017).

A study performed on predictors of PTSD symptoms revealed firefighters’ years of experience with the fire service, seniority in the fire department, and higher educational background were related to lower PTSD symptoms while interventions could improve their resilience and locus of control serving as a protective mechanism for PTSD (Onyedire, Ekoh, Chukwuorji, & Ifeagwazi, 2017). Another study performed on United States (US) soldiers that returned from Operations Enduring

Freedom and Iraqi Freedom (OEF/OIF), also supports the claim that interventions of strengthening resilience might prove to be effective in reducing the severity of traumatic stress and depressive symptoms among veterans (Pietrzak, Johnson,

Goldstein, Malley, & Southwick, 2009).

Based on prior work with the firefighter population, there is a need for an intervention that monitors their psychological and physiological stress responses during firefighting activities (Gomes et al., 2013). Firefighters, when exposed to various harmful traumatic stressors, might develop symptoms of memory impairment, difficulty performing cognitive tasks, and wrongly assessing risk factors (Regehr &

LeBlanc, 2017). Research that measures adverse outcomes from this exposure and

associates this with occupational stress remains absent (Meyer et al., 2012; Regehr

& LeBlanc, 2017). Also, those firefighters who have served in the military need

careful consideration as risk resilience might be an essential mental health

intervention required for this understudied population deprived of useful resources

to protect from PTSD (Bartlett, Smith, Tran, & Vujanovic, 2018). Based on the

literature, there is an absence of a resilience model to aid firefighters with the

advantage of protection from risk factors (Lee et al., 2014).

1.1.2 Resilience Training

There have been efforts made to improve resilience amongst military veterans, such

as stress inoculation training (SIT), and relaxation breathing techniques, that will

enhance the coping mechanism to deployment-related stressors and proven to reduce

PTSD amongst military veterans (Hourani et al., 2016). The Squad Overmatch Study,

designed to address the gap in the resilience training, includes immersive and live

training techniques within Stress Exposure Training (SET), and consists of a three-

phase training protocol to train the soldiers for coping with combat stressors,

however, their performance is not measured, and the training is not validated

(Johnston, Napier, & Ross, 2015).

Previous work has tried to build resilience using virtual reality simulation and serious

games that provide an opportunity for the learner to engage with the environment to

build resilience skills by improving the coping mechanism to stress (Williams-Bell,

Kapralos, Hogue, Murphy, & Weckman, 2015). There is a need to improve learning

strategies and trainee interaction practices (Tate, Sibert, & King, 1997). Furthermore,

a study involving military training tasks to measure the effects of stress and resilience

by testing performance output through physiological changes such as HR and skin

conductance level, determined that short-term resilience and cortisol levels are

predictors of stress; however, the effectiveness of this technique still needs to be

evaluated (Winslow, Carroll, Martin, Surpris, & Chadderdon, 2015).

Overall, at present, the resilience training programs have considered improving

coping mechanisms to stressful events with relaxation techniques, regulating

emotions, practicing guided imagery, etc., however, most have failed to address

physiological responses from the training that would be more practical and beneficial

for resilience and assessment development (Ramey et al., 2016). There is not enough

evidence that supports a resilience-testing method based on self-reported responses

to stress. Moreover, currently, a resilience assessment model that considers

physiological responses of firefighters that can apply to PSP does not exist.

1.1.3 Application of Resilience Assessment Techniques

The tools used to assess resilience in the past have utilized self-reported

questionnaires. For instance, the Connor-Davidson resilience scale (CD-RISC) is a

self-rated questionnaire considered useful to measure the ability to cope with stress

amongst individuals with PTSD or without PTSD. The higher score shows better

resilience, whereas the Stress Vulnerability Scale (SVS) measures perceived distress

from hindrances, with higher scores inferring an inability to be resilient (Connor,

2006). These tests generate an understanding of an individual’s resilience levels and

every test has different elements. The CD-RISC utilizes an exploratory factor

analysis (EFA) of a 25-item scale and is considered a promising method to measure

resilience, but still needs further development (Campbell-Sills & Stein, 2007).

Similarly, the Pre-deployment PTSD Checklist-Military Version (PCL) used in the

Warriors Achieving Resilience (WAR) study, confirms reduced HRV amongst soldiers along with higher scores on the PCL is related to poor autonomic regulation directly linked to a higher risk of developing post-deployment PTSD symptoms

(Pyne et al., 2016). Pre-deployment resilience training amongst experienced soldiers can reduce Post-deployment PTSD symptoms; however, current tools are mostly self-reported, and future studies are required to improve the process that is more comprehensive for the participants to properly implement resilience training (Pyne et al., 2018). Moreover, an increase in resilience can prevent the onset of PTSD

(DiCorcia & Tronick, 2011; McGregor & Bonnis, 2016; McGregor, Bonnis,

Stanfield, & Stanfield, 2017).

A method to assess resilience in military personnel using HR was proposed by

Grossman and Siddlie (Grossman & Siddle, 2000). They propose that during stressful situations, the balance between the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS) is disrupted, causing an increase in SNS activation. The resultant effect is a physiological response of a sharp and significant rise in HR (Grossman & Siddle, 2000). However, this form of resilience assessment using HR responses is limited to military personnel and needs to be extended to firefighters as well due to the nature of their physically demanding job. Hence, improving resilience levels of firefighters could potentially provide them with a coping mechanism needed to deal with all types of stressors (DiCorcia & Tronick,

2011).

1.1.4 Research Motivations

A resilience assessment model that considers a physiological response to stressors

introduced in a simulated firefighting environment can potentially bridge the gap that

exists between mental health outcomes and research. Despite the mental health

problems faced by PSP, there is still development required for resilience assessment

models and their use in training in the firefighter population. It is crucial to address

this current problem amongst the firefighter population and design a resilience

assessment model that would predict their reaction to stress at any given time.

1.1.5 Structure of Resilience Assessment Model

The resilience assessment model proposed in this thesis utilises time domain

analysis of HRV data and mean HR plus standard deviation as a means to assess

resilience. Primarily, the HRV value obtained will provide information on the impact

of stressors and physiological responses. If an individual shows signs of low HRV,

that could be an indication of reduced resilience levels. In addition, if an individual

is not able to maintain normal range HR during resilience training activities that

could be an indication of reduced resilience levels.

Research Questions and Objectives:

1) Can a resilience assessment model be constructed that enables the

measurement of the change in physiological response during a work-related

training activity using HR and HRV?

2) Does this resilience assessment model enable the capture of quantitative

differences in HR and HRV values between men and women in the pre-

firefighter population?

The main objectives of this thesis are:

1) Develop a model for resilience that is transferable to diverse work

environments.

2) Demonstrate this model using a case study on a simulated work

environment that consists of pre-service firefighters

3) Data captured from participants in the simulation will be used to assess

differences in variability of the HR by gender.

1.1.6 Research Methods

This thesis is situated within the Constructive Research methodology. This

approach facilitates the purposeful creation of tools with a structured path that is

feasible beyond the case study for bringing novel solutions (McGregor, 2018). Using

Constructive Research, the case study measures the feasibility and application in

simulated stressful environments with the inclusion of both male and female genders.

This structured approach is achieved by identifying an existing area that requires

improvement. This is obtained through understanding the current research work and

challenges in the PSP domain. Once the problem has been addressed, the research

then aims to develop an innovative method, technique, or framework that results in

addressing the problem and measuring the progress over time.

The Constructive Research method is applied widely across technical sciences,

mathematics, operational analysis and clinical medicine (Kasanen, Lukka, &

Siitonen, 1993). The critical element of Constructive Research is to build on the

existing knowledge in new ways, by adding new functionality (Crnkovic, 2010). The

11 objectives of this process required having this thesis follow a structured approach as follows.

Firstly, a comprehensive literature review was performed and is presented in Chapter

2. The literature review provided the groundwork and need to discover possible patterns in the physiological data as a potential solution to address the scarcity of resilience assessment models in PSP. The literature review provided insight into the existing challenges faced by firefighters. Also, the review curated a path for this thesis in identifying the gaps in current resilience training programs for firefighters and in extension, PSP. Moreover, leading to motivation for future research by conducting an exploratory case study, utilizing physiological data obtained during simulated firefighting activities.

Identifying the research problem provided a clear indication of the study’s aim, to understand the impact of stressors by looking at the physiological data patterns exhibited during simulated firefighting activities experienced by pre-service firefighters. Therefore, a precise research method and model including the physiological and activity data was completed along with data analysis techniques, presented in chapter 3. Demonstration of the developed research method and model together with its application in the pre-service firefighter population, as well as assessment, are presented in chapter 4. Chapter 5 presents a discussion of the developed resilience assessment model. The construction of the complete framework requires artifacts such as models, diagrams, plans, organization charts, system designs, algorithms, artificial intelligence, and software development methods, which are usually constructs utilized in research and engineering (Crnkovic, 2010).

Finally, chapter 6 focuses on the study’s contribution made to the existing literature.

This is showcased through identifying the importance of monitoring physiological changes during the simulated workplace environment as a means of improving existing resilience training protocols amongst the firefighter population and in extension, other PSPs. A summary of the application of the Constructive Research method as applied to this research is presented in following Table 1.

Phase Constructive Research Firefighter Resilience Assessment

1 Find a practically relevant How can we provide a new structured approach to firefighter resilience

problem assessment using physiological data such as HR and HRV during

firefighter training to learn about the impact of job stressors?

2 Obtain a general and Disciplinary understanding of the prior research work in the firefighter

comprehensive domain, exploring resilience training programs in other public safety

understanding of the topic professions and the impact of stressors on firefighters.

Disciplinary understanding of the research methods applied for

measuring the physiological changes that occur during the simulated

firefighting activities.

Disciplinary understanding of tools used to measure firefighters’

occupational stress.

3 Innovate (i.e., construct a Develop a resilience assessment model that utilizes statistical principles

solution idea) in standard deviation for identifying variability of N-N intervals before

and during the simulated search and rescue activity part of firefighter

training workshop. Incorporate assessment of changes in HR within

the resilience assessment model.

This case study was performed to understand job-related stressors

exposed to firefighter population.

Create resilience assessment using HRV phenomenon, SDNN >100ms

is considered to be suitable for an individual and a drop in that measure

indicates physiological impairment.

4 Demonstrate the solutions Perform a case study with pre-service firefighters using the Hexoskin

Feasibility smart wearable device for data capture.

5 Show theoretical Contribution to Health Informatics:

connections and research Structured approach for resilience assessment and opportunity to

contribution of the improve existing workplace training for firefighter population. The

solution concept significant resilience differences were identified amongst both genders.

Also, further extends the resilience assessment framework presented by

(McGregor, 2016) to measure the physiological response during

firefighter training can be integrated in future with real-time analysis

and implementation of Big Data Analytics platform Athena

(McGregor, 2016) for understanding job-related stressors.

6 Examine the scope of This resilience assessment model can perform the resilience-related

applicability of the measurement and job performance analysis on physiological data

solution within and outside of the firefighter domain.

1.1.7 Thesis Structure

The thesis structure is as follows: Chapter 1 introduces the vital themes of this

thesis, including an overview of PTSD amongst the firefighter population, and the

tools used to measure resilience with the use of scoring and HRV. Chapter 1 also

presents the research motivations as well as the research questions. Chapter 2

consists of the literature review, which outlines the existing research on firefighter

population, including HR as a measure of stress used in the past and a discussion on

using physiological variables as indicators of resilience. Chapter 3 has a detailed

description of the resilience assessment model and the methodology: the data

preparation phase and the data model phase. Chapter 4 displays in detail the case

study using Hexoskin and evaluation of the data model. Chapter 5 provides a

discussion of the analysis and explanations of the research findings. Finally, Chapter

6 concludes the thesis by identifying the limitations of this research work and outlining areas for future research and addressing the possibility of implementing this design in a firefighting program in the future.

Chapter 2. Literature Review

2.1 Introduction

This literature review chapter presents an examination of existing research

exploring resilience and resilience assessment within the context of firefighters and

more broadly in PSP and military communities. The literature review begins with a

discussion on physical and mental challenges faced by firefighters, followed by

existing resilience training programs for PSP, specifically exploring the availability

and accessibility of these mechanisms, and lastly, exploring the need for resilience

assessment amongst the firefighter population. It was important to review previous

studies that conducted physiological monitoring to assess resilience in order to create

a usable model that can incorporate physiological responses during training to

educate PSP on progression.

2.1.1 Method

The literature search was performed to look at previous work that utilized

physiological monitoring and considered HR as part of their data acquisition method

amongst the firefighter population. The articles had “firefighters” and “heart rate” as

keywords in the databases, including the Ontario Tech University library, Google

Scholar, EBOS Medline, Scholar’s Portal, and PubMed, but that search returned only

a few articles around resilience assessment.

Additionally, to understand current work on measuring resilience amongst the

firefighter population, keywords such as “firefighters” and “resilience” were

searched through the same databases. Furthermore, to learn about existing resilience

training programs and resilience assessment methods in public safety personnel, a

combination of keywords such as “police + resilience,” “emergency responders +

resilience/resiliency/resilient,” “military + resilience,” were entered in the database.

A preliminary examination of the article’s title and abstract previewed an indication

of the article’s relevance to the topic. Related articles recommended by the databases,

as well as cited articles in the original articles, were also studied to perform this

extensive literature review.

2.2 Physical Demands of Firefighter Population

Firefighting consists of physically demanding tasks, such as fire suppression, that

can increase HR to an abnormally high level, leading to future cardiovascular

problems (Banes, 2014; MacNeal et al., 2016; Smith et al., 2016). As a result, it is

considered a high-risk occupation (Austin, Dussault, & Ecobichon, 2001).

Firefighting requires individuals to have functional aerobic and muscular fitness,

which declines with age, affecting overall performance (Kirlin, Nichols, Rusk,

Parker, & Rauh, 2017). Considering the nature and asks of the job, firefighters are at

a higher risk of having knee injuries and osteoarthritis, compared to the rest of the

population (Brady et al., 2018). Gomes et al., (2003) found that the challenges

firefighters endure, such as rescuing victims from vehicular accidents, forest and

structural fires, or other events, cause mental and physical problems along with poor

cardiac health over time.

Firefighters suffer from serious injuries, encounter occupational risk factors that

affect their job performance and quality of life (Kong, Suyama, & Hostler, 2013).

Firefighters wear personal protective equipment (PPE) and self-contained breathing

apparatus (SCBA) including pants, jacket, helmet face mask, gloves, and boots for

17 safety. Although PPE is worn to protect them from physical and chemical injuries, firefighters can become fatigued by obstructed movement from the heavy weight and bulkiness of the multiple layers (Son, Bakri, Muraki, & Tochihara, 2014).

Firefighters face a great amount of physical strain along with psychological stress related to their occupation. In certain situations, they risk their lives to rescue people from structural and forest fires, accidents, or motor vehicle accidents (Baker, Grice,

Roby, & Matthews, 2000; Selkirk & McLellan, 2004). The occupation demands a strenuous workload that involves heavy lifting, bending, twisting, and minimal time to evacuate and suppress fires (Austin et al., 2001; Baker et al., 2000; Lusa,

Louhevaara, & Kinnunen, 1994). Moreover, a better understanding of heat stress on cognitive performance, a state of human mental wellness, can immensely help with not only defining occupational exposure limits in hot work environments, but also in improving quality of life in social and occupational areas (Hancock & Vasmatzidis,

2003).

Research work performed on tactical operators revealed that the use of physiological monitoring during training is useful in determining an individual's response to that activity, which can then guide decision-makers to improve the existing training process (McGregor et al., 2015). The study conducted by McGregor et al. (2015) inspired a subsequent study for firefighters to improve existing resilience training.

The need for such training stems from the occupation’s high stress level that evokes physiological and mental strain during tactical tasks, which may cause the body to feel tense (McGregor et al., 2017).

2.1.2 Cardiovascular Disease in the Firefighter Population

The previous section briefly discussed the physical strain on the body of

firefighters. Firefighters, however, also experience excessive amounts of cardiac

demands during strenuous activities can which contribute to premature

cardiovascular deaths leading them to be more than double that of police officers as

well as quadruple that of first responders, and as a result, it is crucial to monitor

physiological responses during firefighting activities (Carey & Thevenin, 2009).

Majority of the firefighter population have untreated hypertension, hyperlipidemia,

and obesity, along with poor dietary habits and physical fitness, and if exposed to

heavy-duty work, such as suppressing fire, could possibly elevate their HR to high

levels causing an increase in the risk of future cardiovascular problems (Banes, 2014;

MacNeal et al., 2016; Smith et al., 2016). When an individual is not under stress, an

equilibrium between the SNS and the PNS exists with a sufficient level of physical

resources (Grossman & Siddle, 2000). During stressful situations, an abnormal

physiological response takes place, creating a sharp and significant rise in HR due to

SNS hyperactivity (Grossman & Siddle, 2000). Based on the literature, maintaining

a steady or average HR is vital to prevent any injuries and sustain a balanced

physiological functioning of the body.

Firefighting involves high-risk activities to be performed by the body, as most of the

injuries are non-fatal and associated with fires involving contact with flames or

smoke (Austin et al., 2001). Cardiovascular disease, along with smoke inhalation,

can trigger myocardial ischemia and is found to be a significant contributor to

morbidity and on-duty death amongst firefighters (Soteriades, Smith, Tsismenakis,

Baur, & Kales, 2011). In the United States, 1.2 million firefighters are at risk of facing a premature death while on-duty due to cardiovascular disease (Choi et al.,

2017). About 45% of on-duty firefighter deaths are linked with heart disease (Kales et al., 2007). Thus, cardiac health management is essential to stop the occurrence of premature death (Choi et al., 2017). Moreover, firefighters with metabolic syndrome

(MetS), which consists of three or more out of five risk factors (i.e. central obesity, high blood pressure, elevated fasting blood glucose, high blood triglycerides, and low high-density lipoprotein cholesterol), increase their likelihood of developing cardiovascular disease, and in extension, their risk of death (Choi et al., 2017; Grundy et al., 2005).

While research has examined and determined firefighters to be prone to developing cardiovascular disease because of the high smoke exposures, parameters such as HR and HRV can be a pre-screening tool used to quantify stress exhibited, which can quantify the likelihood of developing cardiovascular disease. The HR measurement could indicate the cardiovascular strain and physical workload at work, providing information about their fitness level and overall health (Brouha, Maxfield, Smith Jr,

& Stopps, 1963; Maxfield & Brouha, 1963; Smith et al., 2016). Additionally, HRV is a promising indicator of any changes related to the association between the autonomic nervous system (ANS) and the cardiovascular system (Gomes et al., 2013;

Malik et al., 1996). HRV can provide valuable information on the functioning of the cardiovascular system (Gomes et al., 2013). HRV analysis is a non-invasive technique for measuring the function of cardiovascular ANS (Akselrod et al., 1981;

Hadase et al., 2004; Wang et al., 2018). Hence, this research utilizes HR and HRV

measures in the resilience assessment model to understand the stress levels and

coping abilities during various simulated firefighter activities.

The research examined in this section explored the risk of cardiovascular deaths

amongst firefighters; a measure to prevent and to improve their cardiac health could

elevate their chances of survival (Choi et al., 2017). Thus, the prevention of cardiac

diseases is essential and attained through pre-screening tools that protect from

premature death (Choi et al., 2017). The measurement of HR is necessary to

understand the variables contributing to their early demise.

2.1.3 Prior work for Resilience in the Firefighter Population

Resilience could be affected if an individual has a medical history of PTSD or

depression, impairing their ability to cope with stressful situations (Winslow et al.,

2013). Firefighters can benefit by building resilience through learning about

occupational hazards in a simulated environment that can provide a safe, ethical, and

time efficient, as well as cost-effective way that fosters not only their resilience but

also competency level (Williams-Bell et al., 2015). The simulated environment

consists of simulated fire environments, where trainees learn and gain feedback from

this changing simulated environment that teaches many work-related procedures and

improves their ability to assess a situation and follow safety protocols during a

variety of firefighting processes (Williams-Bell et al., 2015).

Moreover, immersive and live training techniques within the Stress Exposure

Training (SET) architecture, where SET consists of a three-phase training module

equipped to give information, skills training, and practice, prepares the trainee to

cope and perform when required to combat stressors (Johnston et al., 2015).

Also, the implementation of virtual reality is considered effective in reducing the rates of PTSD and suicide, through increasing resilience-related cognitive skills

(Ogden et al., 2015).

The stress caused by physiological and psychological exposure negatively impacts human performance (Winslow et al., 2013). Efforts such as stress inoculation training

(SIT) and relaxation breathing techniques are conducted to improve coping mechanisms to deployment-related stressors and reduce PTSD (Hourani et al., 2016).

Moreover, the onset of PTSD can be prevented with an increase in resilience

(DiCorcia & Tronick, 2011; McGregor & Bonnis, 2016; McGregor et al., 2017).

Hence, improving resilience levels of firefighters could potentially provide them with a coping mechanism needed to deal with all types of stressors (DiCorcia &

Tronick, 2011).

Resilience is useful in maintaining mental health by preventing the onset of PTSD

(DiCorcia & Tronick, 2011; McGregor & Bonnis, 2016; McGregor et al., 2017).

Resilience acts as a powerful protective mechanism in preventing the detrimental effects of traumatic stress on developing PTSD (Lee et al., 2014). If firefighters have developed their resilience, the physiological response is that the HR change will reduce and improve coping capacity to handle stressful or traumatic events that prevent the development of any adverse mental health outcomes (Marshall et al.,

2017). It will be beneficial to the firefighter population if a resilience monitoring protocol exists that analyzes their psychological and physiological stress responses while performing firefighting activities (Gomes et al., 2013).

As time progresses, the accumulation of poor health has an adverse effect to the firefighter’s ability on the job. Specifically, the traumatic experiences expose firefighters to being vulnerable to mental health risks such as developing PTSD or depression (Marshall et al., 2017). The high levels of stress leading to the over- activity of the sympathetic nervous system (SNS), which essentially decreases the interaction of the ANS and impacts variability of heart rate (Shin, Lee, Yang, Lee, &

Chung, 2016). HRV is found to be associated with the autonomic and central nervous systems, and as a result, was chosen to be studied as a potential indicator of post- deployment PTSD in the WAR study (Pyne et al., 2016). In related work, a physiological monitoring device was strapped around each participants’ chest that measured HR responses during emergency incident rehabilitation following a firefighting activity in a simulated fire on the 10th floor of a building to find the factors that may influence HR recovery (Smith et al., 2016). The findings from the observational study performed by Smith et al., indicated heavy workload (stair trials), higher ambient temperature (≥30°C), higher age (≥45 years), increased BMI (≥ 30.0 kg.m-2) and more significant movement during rehabilitation (≥0.1 g-force) were associated with higher HR responses during rehabilitation (2016). There is no explicit comparison between before and after the activity. There is a need for a protocol that measures every specific interval of activity to provide a better comparison of pre and post firefighting activities.

Similarly, another study utilized an exclusive shirt capable of capturing continuous

ECG signal, and a framework that runs on a smartphone to get physiological and psychological responses of firefighters before and after stressful events.

The results confirmed that standard HRV measures and self-reported stress are firmly

linked (Gomes et al., 2013). There is still inefficiency in HR monitoring, and no such

standardized method exists that could trace the triggers of stressful physiological

responses. There is a need for resilience assessment at every specific interval of

activity, allowing for better comparison between the pre and post activities of

firefighters.

Several other elements determine the ability of an individual to cope in a stressful

situation. More years of service in the fire department and level of education have

been found as an indication of adequate resilience that was linked to lower PTSD

symptoms (Onyedire et al., 2017). The existing resilience training programs should

focus on improving resilience levels of young or volunteer firefighters, such that they

can effectively handle a stressful situation and remain resilient with average HR and

protected against PTSD like symptoms.

2.1.4 Resilience in other Public Safety Personnel Population

This section introduces prior work performed amongst other public safety

personnel, such as police officers or military professionals. Police officers experience

different types of traumatic and stressful events that may involve violence, traffic

accidents, or life-threatening situations, increasing their susceptibility to PTSD.

Building high-levels of self-resilience could potentially protect them from the onset

of PTSD symptoms (Carlier, Lamberts, & Gersons, 1997; Foa, Hearst-Ikeda, &

Perry, 1995; Lee et al., 2016). There is still a need for future work to evaluate the

effects that remain after receiving initial resilience training (Ramey, Perkhounkova,

Hein, Bohr, & Anderson, 2017).

Simulated training is reported to decrease stress responses amongst special police forces, also known as special weapons and tactics (SWAT) officers, to critical incident situations, and further research is needed in this area that could potentially prepare them for real-life occupational hazards (Andersen et al., 2015). Moreover, police officers operating under high-stress environments could experience more on- the-job accidents or errors compromising public safety. Building their resilience and self-regulation skills could considerably benefit them with improvement in decision- making and reducing errors as well as preventing the onset of disease or the risk of having sudden cardiac death (McCraty & Atkinson, 2012). There is a need for resilience interventions within this population that would help them with stress management, job performance, improving decision-making, and overall health as well as reduce the prevalence of developing chronic disease (Ramey, Perkhounkova,

Hein, Chung, & Anderson, 2017).

Additionally, mindfulness-based resilience training is shown to improve overall health in an experimental group, where some benefits included reduction in sleep disturbance, self-reported aggression, stress levels, increased psychological flexibility and decreased reactivity after receiving training, however, these differences were not present during a three-month follow-up (Christopher et al.,

2018). The younger population is more likely to benefit from the stress-resilience training as it improves their response to specific job stressors and aids in the job

(Ramey et al., 2016). Professionals in law enforcement and military are exposed to psychological stressors and must experience horrifying scenes of death and injury while on-duty (Weltman, Lamon, Freedy, & Chartrand, 2014). Public safety

25 personnel can benefit from resilience training, and if their physiological response to job stressors could be assessed, then the risk of developing some chronic disease or mental health problems may reduce drastically.

The existing problem related to the area of public safety personnel is that resilience development is still understudied. Prior related work has implemented heart monitoring and self-reported responses. However, HRV has not been considered that would be related to reactions to stressors in a simulated environment.

Table 2 below consists of a summary of previous studies related to the firefighter population that collected and analyzed HR as a measure of the stress response.

Reference SUMMARY

HR Methods Data Acquisition Analysis Participants

(Smith et al., 2016) HR monitoring and BioHarness 3, One-way (trail) Simulated fire

HRmean compared Zephyr ANOVA, SPSS fighting activities

Technology,

Annapolis, MD on

elastic chest strap

(Gomes et al., Psychological stress Vital Jacket for SDNN, RMSSDM 4 participants

2013) (self-reports, continuous ECG HRVti, and PSD in 37-41 years

physiological stress Vital Analysis Matlab

(HRV) framework (VA)

in a real-world Smartphone-based

environment framework

Ecological

Momentary

Assessment (EMA)

Self-assessment of

the stressful event

(Choi et al., 2017) HRR difference as I- Work and Spearman 288 participants

RHR and estimated health-based correlation (281 males and 7

maximum HR questionnaire coefficients, Chi- females)

II- a cross- square test Cox's

sectional survey proportional

RHR measured hazard model

(Shin et al., 2016) Survey and clinical Self-administered Analysis of 645 participants

exam questionnaires Covariance

HRV measured Korean (ANCOVA)-

with SA-3000P Occupational univariate linear

HR measurement Stress Scale model

controlled (KOSS) SDNN and

environment Sociodemographic RMSDD

and work-related LF 0.04-0.15 Hz

elements

(Horn, Blevins, HR watch (S625X, Every 5 s and then HRpeak and 9 participants (6

Fernhall, & Smith, Polar Electro Oy, averaged over 60-s HRave for the male and 3 female)

2013) Oulu, Finland) intervals. work cycle

Live-fire training Temperature 15°C HRmin and HRave 20-45 years

exercises for 3 hrs -25°C for rest cycle

Mean ± SD

ANOVA &

Correlations

(Kim & Lee, 2015) Participants wore Real-time Linear regression 12 male

protective monitoring to between Tre and firefighters

equipment (15kg) measure the heat HR

exercise at 32 °C strain by work

and humidity of core body

43% temperature and

HRabsolute in bpm Two 15 min

and HRrelative, exercises on a

%HRmax treadmill at 5.5

km. hr-1

HR, (Tre), and (60%VO2max)

oxygen

consumption

(Ftaiti, Duflot, HR and Tty FF performed Jacket-related 6-well trained

Nicol, & GrÉlot, measured every running exercises effects on both Tty firefighters

2001) minute throughout with Leather and HR analyzed

the exercise jacket (J1), Nomex with two-way Age 38±3 years

delta T jacket (J2), analysis of Body mass 69± 5

Textile jackets variance kg

(J3), (J4), (J5) (ANOVA)

Tukey posthoc test

(Lesniak, Abel, HRV average HRV obtained HRV examined 12 male structural

Sell, & Morris, Simulated fire with a portable SDNN, RMSSD, firefighters

2017) ground test SFGT ECG device for HF and LF factors

(acute) 20.8±4.6 days Mean ± SD, Age: 37.3±7.6 yr

Accelerometer for Pearson product-

19.1±5.8 days to moment and

measure physical correlation

activity coefficients

(Coca, Sinkule, Overall body RPEs Treadmill walking Repeated measure 10 active

Powell, Roberge, & and HR measured at 50% of maximal ANOVA participants (7

Williams, 2008) oxygen males, and 3

consumption with females)

firefighter Treadmill exercise

ensemble (SE) sessions

(Porto et al., 2016) HR measured in Submaximal Multivariate 100 male

exercise testing Bruce-treadmill analysis logistic participants

test regression

Mann-Whitney

test

(MacNeal et al., Basis B1 Health HR measured Kruskal-Wallis 42 participants

2016) Trackers on their The three-phase test and two-tailed

wrists at the urban trial of fire station Fischer’s exact test

three-station notifying patterns,

municipal fire fatigue scale, and a

department survey

HR data of FF

collected with

watches and saved

as Excel files for

analysis

The following section details a synthesis of the selected literature. The extracted data

from the most relevant 11 articles in the form of a summary is present in Table 2.

2.1.5 Sample Size & Demographic Characteristics

Many of the articles reviewed either included only male firefighter participants

(Kim & Lee, 2015; Lesniak et al., 2017; Porto et al., 2016) or failed to specify sex or

gender (Ftaiti et al., 2001). Some studies included female participants, however, the

ratio of female participants compared to male participants were significantly lower

(Choi et al., 2017; Coca et al., 2008; Horn et al., 2013). An example is a study

conducted by Choi et al., (2017) which had a total of 288 participants, with 7

participants being female.

Furthermore, some studies did consider the demographics of the participants, that

included job-related stress and other factors not limited to age, marital status,

smoking status, drinking status, exercise status and body mass index (BMI) (Horn et

al., 2013; Shin et al., 2016; Smith et al., 2016). The Korean Occupational Stress Scale

(KOSS), a self-administered questionnaire, along with other surveys, collected socio-

demographic details that covered work-related factors of the firefighter population

(Shin et al., 2016).

Based on the research work available, there is a gap in the literature relating to female

participation and the use of subjective measures to capture the occupational stress

experienced by the firefighters. The results obtained by considering only male

participants cannot be fully applicable to the female population. To make positive

changes in PSP domain, there has to be an equal inclusion of both genders in

resilience-related activities, such that there is an opportunity for improvement in

existing measurement tools and training techniques.

2.1.6 Heart Rate Responses

The link between heart rate reserve (HRR) and Metabolic syndrome (MetS), where,

HRR was obtained by deriving the difference between resting heart rate (RHR) and

estimated HRmax (Choi et al., 2017). The authors concluded that HRR is inversely

related to MetS, which is a significant risk factor amongst middle-aged firefighters

(Choi et al., 2017). The physiological stress on firefighters is shown to be assessed

through changes in core temperature when firefighters reached peak HR during work

30 compared to break time (Horn et al., 2013). The HR changes are measured during work and break time, with no measurements taken 5 minutes before the start of the activity for comparative analysis, nor is HRV captured throughout the study.

Therefore, this thesis will address an open research area of comparing a simulated firefighting activity, such as search and rescue, with pre-activity baseline measurements.

There is a direct relationship between depression and mortality. Firefighters with depression have an increased risk of mortality (Liao, Al-Zaiti, & Carey, 2014). The use of Beck Depression Inventory and HRV monitoring with a 24-hr ambulatory electrocardiogram (ECG) can be indicated by HRV and reduction in vagal tone (Liao,

Al-Zaiti, & Carey, 2014). Real-time monitoring to investigate HR as a possible indicator of heat strain was performed by measuring HR, rectal temperature (Tre), and oxygen consumption (Kim & Lee, 2015). The monitoring of HR changes while working in hot climates can be useful in understanding the physiological responses from heat stress (Kim & Lee, 2015).

After being exposed to strenuous activities within the context of their occupation, firefighter’s individual body temperature, age and BMI could influence HR during rehabilitation (Smith et al., 2016). Furthermore, there is an increase in HR and HRV responses during these simulated firefighting activities (Smith et al., 2016). Smith et al., (2016), however, fail to draw comparisons from the population’s before and after the activity. The lack of information demonstrates the need for a protocol that can measure every specific interval of activity to provide better parallelisms of pre and post firefighting activities. The existing research shows some exploratory data on the

impact of HR responses within this cohort. However, the studies fail to include HRV

data within the statistical analysis. The collaborative information of both HR and

HRV can be vital to measuring resilience successfully within firefighters and in

extension, PSPs.

Firefighting involves high levels of stress leading to an SNS response during an

activity that impacts variability of heart rate (Shin et al., 2016). HRV can act as an

indicator for the increased risk associated with post-deployment PTSD onset and can

identify with increased low frequency (LF) to high-frequency (HF) ratio (Pyne et al.,

2016). Firefighters with cardiac autonomic impairment (CAI) suffer from

sympathetic hyperactivity and/or a reduced parasympathetic activity contributing to

sudden cardiac death (Porto et al., 2016). Individuals with this condition are unable

to achieve 75% of their age-predicted maximum HR and HR recovery, resulting in

poor physical fitness and reduced job performance (Porto et al., 2016).

2.1.7 Recent Work and Limitations

The psychological stress and fatigue can be reduced by changing the alerting

method of emergency responses to the firefighters with ramp-up tones. This was

performed by generating 2,073,600 data points collected through 16 watches, worn

by the participants 24 hrs. a day for over 3 months, that received HR responses from

the firefighters while they were alerted with the alarm (MacNeal et al., 2016).

However, underlying factors affecting their responses such as BMI or age and/or

shift hours were not mentioned. Resilience levels to stress are dependent on an

individual’s ability to cope with changing environments, can be influenced by other

factors and should be considered while conducting such activities.

Simulated environments involve firefighters performing live-fire training exercises

such as extinguishing the fire, conducting forcible entry, search and rescue,

ventilation tasks, and fire scenarios consisting of firefighting procedures wearing

PPE and SCBA. These were performed in ambient temperatures between 15 °C to

25 °C (Horn et al., 2013). The strenuous activities show the physical and mental

strain that firefighters experience because of firefighting activities can create an

unfavourable environment daily. Overall, studies that consider resilience levels of

firefighters and monitor their HR and HRV in real-time to improve the existing

working conditions is an open research area.

2.1.8 Statistical Analysis

Prior research has utilized several statistical tools to examine the results. Resting

heart rate (RHR) and heart rate reserve (HRR) were examined with Spearman

correlation coefficients (Choi et al., 2017). The sociodemographic characteristics and

health-related factors with MetS, RHR, HRR, and VO2max were investigated by Chi-

square test (Choi et al., 2017).

Time-domain HRV analysis with a standard deviation of all normal beats (SDNN)

intervals, and the square root of the mean of the total of the squares of differences

between successive NN intervals (RMSDD) in milliseconds (ms) were used to

determine HRV metrics in Gomes et al. (2013). In addition, they utilized the

triangular index of HRV (HRVti) with all NN intervals divided by the height of the

histogram of all the NN intervals measured (Gomes et al., 2013). Hence, statistical

tools that allow examination of HR during firefighting activities can be used in future

work.

The summary table 2 shows existing research that utilized HR and HRV to demonstrate firefighter job demands. There is a need to build a structured approach that performs resilience assessment from HR and HRV responses. Prior studies described job-related stress with physiological monitoring, however, research that measures HRV for resilience levels of firefighters when encountered with job stressors does not exist.

To advance the development of a physiology-based resilience measurement, HRV analysis has been applied to some extent. It can be beneficial to introduce the time- domain statistical analysis of HRV data of each individual working in public safety to have an assessment of resilience. This will enable investigation of the connection of the autonomic nervous system with the cardiovascular system and implications of stress. The use of HR in detecting tolerance levels of stress may be strengthened by introducing additional forms of data analysis such as HRV and associated SDNN time domain analysis. There are some limitations in existing resilience training programs and future work that implements HRV analysis for detection of stress on their health is highly recommended.

The commonly used measures to analyze HRV are based on an analysis of the time and frequency domain. The time domain is simple to calculate and time-efficient.

The most used technique is SDNN that is the standard deviation of NN intervals and

SDANN is the standard deviation of the average NN interval calculated across all 5- min sections (Appelhans & Luecken, 2006). The SDNN score for an individual has been shown as a measure of their status of health, such that a lower value of <100

34 ms, indicates chances of experiencing mortality are much greater versus someone with a higher value of HRV (Shaffer, McCraty, & Zerr, 2014).

Higher range SDNN beyond >100 ms, is seen to be protective of such risks indicating an individual is healthy, and if that range were to drop between 50-100 ms, then it suggests the individual now has a predisposition to increased risk of mortality

(Shaffer et al., 2014). As a result, it can be concluded that it is important to have a higher value of HRV, as it is associated with psychological resiliency and autonomic flexibility, and an individual’s ability to adapt efficiently to social or environmental pressures and accomplish complex decision-making cognitive tasks (Pyne et al.,

2016). These are all the attributes required to adhere to a stressful environment, and necessary to perform tasks that have intense physical workload and requires high- level of cognitive functioning such as firefighting, which involves making decisions and rescuing victims while exposed to traumatic events at the same time. Thus, measuring their resilience levels could provide an indication of their endurance level to stress and impact on their health.

Grossman’s research on heart rate ranges of military personnel suggests the physical and mental stress involved in their work lives and provides research evidence that combination of physiological and emotional/psychological increase in HR with 150-

170 bpm range causes an individual to experience cognitive and physiological impairment (Grossman & Siddle, 2000; McGregor & Bonnis, 2017). HR>75 bpm represents activation of the SNS that is known for fight or flight reaction, and average

HR of 75 bpm and lower indicates PNS is dominant and individual is in resting state

(Shaffer et al., 2014).

Firefighters are at greater risk, because while performing heavy duty tasks at work such as fire suppression causes a sudden increase in their HR, contributing to the development of future cardiovascular problems (Banes, 2014; MacNeal, Cone, &

Wistrom, 2016; Smith, Haller, Benedict, & Moore-Merrell, 2016). If the firefighters improved their resilience levels, there is a possibility that their physiological response, such as HR, will reduce and simultaneously improve coping capacity to stressful or traumatic events that prevent negative mental health outcomes (Marshall et al., 2017).

Prior studies have performed physiological monitoring on a firefighter cohort to measure accuracy of smart wearable devices by collecting heart rate (HR), oxygen saturation (SaO2), respiratory rate (RR), minute ventilation ([V]E), tidal volume

(VT), and skin temperature (Tsk) (Coca et al., 2009). As noted in the literature review chapter, physiological monitoring of bodily responses to stressors in public safety personnel has proven to be beneficial in understanding the importance of resilience to improve their job performance.

As a result, this research proposed to study resilience assessment considering physiological response to job stressors that are been introduced in a simulated firefighting activity. Physiological monitoring in real-time through a smart wearable device, Hexoskin, enabled the collection of HR and RR interval data. Simultaneously the firefighting training activity was being used for training pre-service firefighters to build resilience and become well-equipped for future traumatic events.

2.1.9 Discussion

There are different types of stressors in the form of psychological, physical and

emotional stress that can influence an individual’s health and performance.

Physiological stress has been defined as an activation of the hypothalamic-pituitary-

adrenal axis (Gillespie & Nemeroff, 2007; Moore & Cunningham, 2012). Physical

stress is a result of an injury leading to a hormonal response from physical stressors,

such as military combat or feeling electric shock or experiencing illness (Heatherton,

Herman, & Polivy, 1991; Moore & Cunningham, 2012; Popper, Smits, Meiselman,

& Hirsch, 1989). Psychological stress creates hormonal changes seen with the

increased levels of cortisol in the plasma (Del Sal et al., 2009; Smith, Petruzzello,

Chludzinski, Reed, & Woods, 2005). The studies performed related to the bodily

reaction to these stresses. There exists paucity that requires further consideration

when deployed to stressful situations.

Stress is defined in many different ways in the literature, and commonly describes

the negative impact it has on job performance and overall health. The bodily changes

that occur due to stress are described, as “upon immediate disruption of

psychological or physical equilibrium, the body responds by stimulating the nervous,

endocrine, and immune systems. The reaction of these systems causes a number of

physical changes that have both short- and long-term effects on the body” (Norwood

& Rascati, 2012). Furthermore, to combat these changes, resilience is the skill

required to develop an ability to adhere and adapt to stressful events (Straud,

Henderson, Vega, Black, & Van Hasselt, 2018).

Some of the signs of stress are described as physical such as headaches, indigestion,

diarrhea, upset stomach, and tiredness. Emotional stress can be seen in the form of

anxiety, irritability, difficulty making decisions and sadness; additionally, behavioral

stress can cause sleep disturbance, fatigue, change in eating patterns, and

drug/alcohol abuse leading to decrements in job-related performance (Norwood &

Rascati, 2012). Introducing a method to create a resilience measurement model is

likely to provide an overview of the individual’s current state of physiological

response to stress and, ultimately, a means to improve resilience levels.

2.1.10 Conclusion

This chapter presented a literature review of physical and mental challenges faced

by firefighters, existing resilience-training programs for PSP, and the need for

resilience assessment model amongst the firefighter population. None of the studies

reviewed have considered the incorporation of a resilience assessment model in

training firefighters. Majority of the literature considered the values obtained by men

in the field and hence, the results are not transferable to women. There is a need for

research to determine base-line values and stress measurements for the underrated

female firefighter population to invoke a holistic training. Research that recruits more

female participants is required. Also, there is limited research that focuses on HRV

analysis in real-time to determine resilience levels and physiological and/or cognitive

changes during firefighting activities.

Resilience assessment can prove beneficial as it gives information on how they deal

with stressful events. A framework that incorporates body sensor wearables that

monitor heart rate and RR intervals in real-time could provide valuable physiological

38 data to enable the assessment of a firefighter’s resilience and overall health. This will also improve training programs with the ultimate goal of reducing the incidence of

PTSD/depression rates, workplace injuries, and premature deaths amongst firefighters.

Based on the review of the results, it is evident there is no existence of resilience assessment or gold-standard measurement of resilience amongst the firefighter population, and existing resilience training programs amongst other public safety personnel still require development and future research. Thus, resilience assessment of firefighters while exposed to job-related stressors could potentially help with existing working conditions by increasing their resilience, mental health, and make a valuable contribution to future research work. Chapter 3 describes in detail the utilization of activity data and physiological data to build a resilience assessment model that was implemented in the case study to prove the research hypothesis.

Chapter 3. Methodology

3.1 Introduction

This chapter presents a methodology for creating a resilience assessment model

based on the physiological monitoring of an individual during his or her training

activity. Essentially, constructing this resilience assessment model will address the

research questions and measure the change in physiological response, using HR and

HRV, while experiencing a work simulation. These values and changes will then be

stratified by gender to quantify the difference between the two sub-cohorts.

Literature supports the notion that when the body experiences stressors, it will

compromise the body’s capabilities and impact performance (Norwood & Rascati,

2012). Hence, performing an activity in a simulated environment might provide an

opportunity to learn about physiological responses taking place at a given point in

time. In order to study a stress response, it is essential to measure the movement from

and restoration to homeostasis. The resilience-assessment model provides a

mechanism to understand the response to stressors and quantify the level of resilience

for an individual. The components of this methodology are presented below in the

subsections titled: Physiological Data, Different Types of Stress, Activity Data,

Acquired Data, and Data Model.

3.2 Physiological Data

Physiological data obtained in form of HR and variation of HR produced as bodily

functions provides insight regarding an individual’s health status. Moreover, these

physiological changes are measured to understand the inner mechanisms of the body

and the influence of the outside environment to assess whether there is exposure to

stressful situations. The raw data collected through a smart wearable device is

utilized to analyze changes that take place due to a stress response. Examples of

physiological data include HR and RR intervals that enable HRV analysis

(Rodrigues, Paiva, Dias, & Cunha, 2018; Tartare, Zeng, & Koehl, 2018).

HR in a healthy individual at any given time is a combined effect of the neural output

of the parasympathetic nervous system (vagus nerve) that slows down the cardiac

cycle and the sympathetic system that increases it (Shaffer et al., 2014). The

physiological data that is inclusive of HR and RR intervals to derive HRV are

necessary values required to build the resilience assessment model. For the proposed

resilience assessment model, HR and HRV are measured before the training activity,

as baseline data, and during the training exercise, while exposed to a stressor.

As noted in the previous chapter, analysis of HRV involves understanding the heart’s

inter-beat time intervals, providing information on the cardiac system’s ability to

adapt to the changing external or internal events, and importantly HRV analysis gives

an overall assessment of cardiac health and the state of the ANS that regulates cardiac

activity (Germán-Salló & Germán-Salló, 2016). Additionally, HR is valuable in

providing information about the impact of stressors on the body.

3.2 Activity Data

Activity data is the information collected during the training exercise to record

information about the tasks that the trainee is performing at specific time points in

the training activity. During the training, activity stressors are introduced, and

understanding the changes in physiological data during those activities could

provide valuable information and understanding of the impact of the activity on the

body. These activities simulate job-related tasks.

3.2 Data Model

The resilience assessment model contains activity data and physiological data.

Furthermore, it could also provide an understanding of the physiological responses

during a simulated event. The model can be used during a training exercise to enable

training for a job skill and resilience assessment and development as well.

If an individual has a low HRV score at baseline such as SDNN value of <100 ms

then he/she is likely to be highly susceptible to PTSD symptoms post-deployment

following a stressful event (Shaffer et al., 2014). To measure changes in HRV, the

standard deviation of normal R-R intervals (SDNN) is applied to perform the analysis

of resilience. R-R intervals were derived from the electrocardiogram signal. The time

domain measures such as SDNN are most simple to calculate and time-efficient, and

therefore, has been implemented in the resilience assessment model (Shaffer et al.,

2014).

The resilience assessment model measures the changes in HR and HRV through an

assessment of the average and standard deviation during the training exercise activity

and assesses the difference in these values from those collected as a baseline during

an initial resting stage. By examining the individuals’ mean HR and SDNN values

before the activity (i.e. at baseline) and during the activity, it is possible to provide

an assessment of the physiological changes that occur when exposed to stressors

(Gomes et al., 2013). This research extends utilizing the HRV together with the HR

ranges to assess resilience (Grossman & Christensen, 2007).

Higher HRV is related to psychological resiliency and autonomic flexibility, and an individual’s ability to adapt efficiently to changing social or environmental stressors and complete cognitive tasks that require complex decision-making (Pyne et al.,

2016). Whereas, higher HR is known to be an indicator of different problematic symptoms that a body might be experiencing when exposed to a stressful environment.

The HR beats per minute (bpm) is considered to be at a normal and resting level with a measurement of 60-80 bpm, but if an individual is introduced to a fear response causing HR to increase, this could potentially lead to deteriorating hormonal effects

(Grossman & Christensen, 2007). As an illustration, each HR range has a negative influence on the physical and mental components of the body causing an individual to experience unpleasant symptoms. A HR of 115 bpm affects fine motor skills

(Grossman & Christensen, 2007). Moreover, if HR increases to 145 bpm, complex motor skills are affected; whereas at 175-bpm occurrence of cognitive decision- making, blood vessels restriction, vision, auditory, and depth of perception loss were apparent (Grossman & Christensen, 2007). Above 175 bpm, fight or flight causes intense reaction, freezing of body with bladder and bowels stoppage, and loss of gross motor skills (e.g. result of extreme running) (Grossman & Christensen, 2007;

McGregor & Bonnis, 2017). The resilience scores containing HRV SDNN and HR mean for the pre-activity and during the activity were plotted against each other in the case study presented in upcoming chapter 4 to quantify the difference in resilience scores amongst the male and female genders.

Overall, this chapter has presented the resilience assessment model that has been designed to address the research hypothesis. It utilizes physiological data and activity data to understand the response to stressors and quantify resilience for an individual specifically through the assessment of changes to HR and HRV during each activity for everyone. The process described in this chapter is instantiated in a case study in chapter 4.

Chapter 4. Case Study and Evaluation of Data Model

This chapter presents the implementation of the proposed method examined in Chapter

3. The resilience assessment model is stratified by gender. The case study is followed by the presentation of the results of the resilience assessment model.

4.1 Case Study

The case study population for this research was fifty-four volunteers (11 females –

20%), with an average age of 21 years old and standard deviation of 4.6 years,

recruited from the Durham College Pre-Service Firefighter, Education, and Training

(PFET) program while completing the Environmental Stress Workshop (McGregor,

2019). This workshop contains a series of simulated firefighter training scenarios

performed within the Ontario Tech University’s ACE Facility climatic chambers.

Ethical approval was obtained from Durham College and the Ontario Tech

University’s Research Ethics Boards (approval #156-1718 and #14783), and written

informed consent was obtained before each participants’ completion of the search

and rescue scenario (McGregor, 2019).

4.1.1 Activities

Participants underwent three scenarios as part of the firefighter Environmental

Stress Workshop. The following details the three scenarios incorporated in the

workshop:

i. Heat Stress Primary Search and Rescue: Participants were required to wear

personal protective equipment (PPE) and self-contained breathing apparatus

(SCBA) while performing simulated firefighting tasks, including hose carry,

forcible entry, and search and rescue. The environmental conditions were

controlled at 50⁰C and 30% humidity to create a heat stress environment

similar to the one's firefighters experience.

ii. Cold Stress Roof Ventilation and SCBA Operation: Participants were

required to ventilate the roof of a building using a roof prop simulation along

with wearing their SCBA that teaches a trainee about working and using their

equipment in a cold/winter environment. The environmental conditions were

controlled at -20°C and 20% humidity.

iii. Dynamic Cardiopulmonary resuscitation (CPR) Training: This task is

designed to train participants on the techniques required to perform

emergency CPR while in a moving vehicle. This was simulated by placing an

ambulance on a 4-post shaker apparatus, which is rated for human

participation, to simulate driving conditions in a controlled and repeatable

way.

4.1.2 Physiological and Activity Data acquisition

During this workshop, the Hexoskin smart wearable device was used to capture

physiological data, including an electrocardiogram signal. The Hexoskin smart

wearable device consists of a Hexoskin compression shirt that contains a range of

sensors, a data logger that captures data from the two sensors (at the level of the

thorax and navel) within the shirt, and a USB cable to connect the data logger to a

computer once data acquisition is complete.

Research assistants recorded events on event logging sheets while the participants

completed the scenarios and recorded information about the times that each

participant carried out the various training tasks. The various activities and job tasks

recorded during the scenario were used to enable later association of the specific job

task with a related physiological response.

To get the data in the form of a set of .csv files from the Hexoskin smart wearable

device, a systematic process needs to be followed.

i. The Hexoskin compression shirt needs to fit tightly so that it will allow proper

data collection. It needs to make contact with the skin and if it is not tight

then it needs to be tightened with the strap.

ii. When the participants finished the workshop and removed the Hexoskin,

their data was uploaded from the data logger to the Hexoskin dashboard,

which is a cloud-based data storage platform. The detection of R peaks within

the QRS complex from the electrocardiogram was performed by the

Hexoskin cloud-based software. Similarly the derivation of second by second

(1Hz) heart rate data was also performed by the Hexoskin cloud-based

software.

Data for four participants (2 females) was not collected due to issues with the

Hexoskin garment and a further two male participants due to the data logger battery

going flat resulting in data being successfully collected from 50 (9 female)

participants.

4.1.3 Implementation of the Resilience Assessment Model

SDNN of normal N-N intervals together with peak and mean HR was calculated

during the first 5 minutes of wearing the Hexoskin garment for pre-activity (baseline)

data and during the Search and Rescue activity.

The following steps were conducted to analyze the physiological components of HR

and HRV:

i. R-R intervals from the first 5 minutes of wearing the Hexoskin garment and

during the activity were analyzed to calculate SDNN for both these segments.

ii. Each R-R/N-N interval was converted to a beats per minute representation

for the first 5 minutes of wearing the Hexoskin garment and during the

activity. Standard deviation and Mean were calculated using all heart rate

values for each of the two segments, the first 5 minutes and during the

activity.

4.1.4 Findings from the Case Study

Scatter plots were generated for both the HRV and mean HR to compare their

respective values before and during the activity. Search and rescue scenarios for

females (Figure 1a), males (Figure 1b) and all participants combined (Figure 1c) are

presented in Figure 1. A y=x line is drawn to show a reference point of equality

between the axes. Where values exist below the y=x line, the mean HR fell during

the scenario, on the line there was no change and above the line, the mean HR

increased during the scenario. The same process was followed to construct the SDNN

scatterplots (Figures 1d-1f) comparing baseline to during the search and rescue

scenario, which is presented in Figure 1.

Figure 1 a. Mean HR for first 5 mins Preactivity vs. Figure 1 d. Mean SDNN for first 5 mins Preactivity Search and Rescue of Females vs. Search and Rescue of Females

Figure 1 b. Mean HR for first 5 mins Preactivity vs. Figure 1 e. SDNN for first 5 mins Preactivity vs. Search and Rescue of Males Search and Rescue of Males

Figure 1 c. Mean HR for first 5 mins Preactivity vs. Figure 1 f. SDNN for first 5 mins Preactivity vs. Search and Rescue of All Participants Search and Rescue of All Participants

Table 3. Population means calculated from individual participant mean HR (bpm)

and Standard deviation (SD) (bpm), stratified by gender.

4.1.5 Table 3. Mean Heart Rate by Gender

Activity Population Mean

Gender Mean HR (bpm) Std. Dev

(SD)(bpm)

Baseline/Pre- Females 109 5.9

activity

Males 107 9.5

Search and Rescue Females 138 20.8

Males 163 14.6

In this study, the average of the female mean HR at baseline was higher than males

by only 2 bpm; however, the SD was 5.9 bpm and 9.5 bpm, respectively. That is, the

male SD was 61% higher than the female SD. The average of the female mean HR

during the search and rescue scenario was 25 bpm lower than the average of the male

mean HR. That is, the average of the male mean HR was 18% higher. The average

of the male SD was 71% of the average of the female SD during the search and rescue

scenario. Simply put, the SD of females HR values were closer at baseline compared

to males but more varied during the search and rescue than males.

The cluster of mean HR for female participants are closer together on the pre-activity

axis compared to male participants and reflected in the lower SD in the female pre-

activity population in Table 3. Also, 2 female participants remained on the y=x line,

potentially demonstrating an effective resilience response. The mean HR value for

each female participant reveals many of the cluster points are closer to the reference

line suggesting that they may bee less reactive and not as negatively impacted by the

effect of the stressors during the activity.

4.1.6 Table 3.1. Mean N-N by Gender

Activity Mean Heart Rate

Gender Mean Min N-N Mean Max N-N

(ms) (ms)

Baseline/Pre- Females 534.4 731

activity

Males (5 min) 482.3 732.6

Search and Rescue Females 371.8 657.7

Males 324 533.4

Table 3. (3.1 & 3.2) is constructed to illustrate the significant difference between

both genders during pre-activity vs. search and rescue activity. The female

population during pre-activity had a Mean Min N-N value of 534.4 ms. This was

comparatively higher than the Mean Min N-N value of males, which was at 482.3

ms. Similarly, the study found females to have a higher Mean Min N-N value during

search and rescue activity. These values were respectively, 324 ms and 371.8 ms.

The Mean Max N-N value for females was higher than that of males during the search

and rescue. These values were respectively, 657.7 ms and 533.4. Both the Mean Max

N-N (ms) for females and males were comparatively similar to one another. These

values were respectively, 731 ms and 732.6 ms during the pre-activity. It is important

to note that females had higher Mean Min N-N (ms) values during both intervals that

is pre-activity, and search and rescue when compared to males. However, the values

of Mean Max N-N (ms) during search and rescue activity of females at 657.7 ms and

males at 533.4 ms with almost 124 ms difference.

4.1.7 Table 3.2. Min & Max Mean HR by Gender

Activity Min & Max Mean Heart Rate

Gender Mean Min HR Mean Max HR

(bpm) (bpm)

Baseline/Pre- Females 98 117

activity

Males (5 min) 83 126

Search and Females 111 169

Rescue

Males 127 187

As detailed in Table 3.2, mean Min HR of females was found to be 98 bpm and the

males at 83 bpm during pre-activity, whereas mean Max HR is observed in females

at 117 bpm vs. males at 126 bpm. During the search and rescue activity, it was found

that females with mean Min HR at 111 bpm vs. males at 127 bpm, and mean Max

HR of 169 bpm compared to 187 bpm. It is worth to note that the HR value of females

is lower compared to males during the search and rescue activity. The lower HR

value could indicate that females possess better resilience levels to cope with this

52 stressful activity compared to males. However, it could also indicate higher fitness levels amongst the women, therefore, additional data is required to determine the exact cause of the difference in heart rate.

It is interesting to see the changes in HR that occur between both genders, which may give an idea about how job-related stressors could affect both genders differently.

Hence, values of HR bpm and SDNN ms provide evidence of the existing differences in resilience and as a result, these measurements are considered essential to include it in the resilience assessment model.

The male cohort is further away from the reference line in graph Figure 1 e, SDNN for first 5 mins pre-activity versus Search and Rescue of Males. In addition, some male values are seen to be very close to the origin possibly suggesting a low-quality resilience related activity performance before and during the search and rescue activity. This indicates that the male participants in this study may have a higher response to stressors during the Search and Rescue scenario. A higher response to stressors could potentially indicate the population is more affected by the stressors.

The results obtained suggest there may be a difference between both genders. The scatter plot graph Figure 1 d, mean SDNN for first 5 mins pre-activity versus Search and Rescue of Females, shows female points are closer to the reference line, which may indicate an improve resilience response. Moreover, it is important to note that the female values are found to be further away from the origin compared to males as discussed earlier.

In Table 4, the average female SDNN is higher at baseline by almost 100 ms and SD by 30 ms compared to males. Besides, there is a decrease in the average SDNN

amongst males by 121 ms and SD by almost 40 ms, whereas females decreased by

76 ms and 107 ms, respectively.

4.1.8 Table 4. SDNN by Gender

Activity SDNN

Gender Mean (ms) Std Dev (ms)

Baseline Females 280.0 128.0

Males 182.3 99.2

Search and Rescue Females 203.8 20.8

Males 61.1 59.5

From the SDNN graphs (Figure 2), the male cohort has shown a consistent decline

in SDNN values for all but two participants. For males, the cluster closest to the

origin shows a cluster of 11 males (27%) with low SDNN at pre-activity (<100ms)

and during the activity. The next cluster of males has higher SDNN at pre-activity

between 100 and 200ms but similarly, have a decrease in SDNN during the activity,

whereas the third cluster represents the group of males whose baseline was between

200 and 350ms and the SDNN decreased for all but one to less than 100ms. Two

male participants demonstrated higher variability at baseline and during the activity.

Female SDNN was between 300-450 ms at baseline and 150-350 ms during the

activity with three individuals closer to the reference line, indicating they had a less

pronounced response to the stressors. Overall, the SDNN values declined during the

Search and Rescue scenario for all except two female participants who remained

unchanged and one female participant with SDNN significantly increased.

Chapter 5. Discussion

5.1 Introduction

This chapter presents a discussion of relevant literature within the context of the

results presented in the previous chapter. Resilience assessment via physiological

data is a unique and novel concept in the firefighter domain. As a result, and noted

in the literature review, there were very few research articles that describe work

related precisely to the research presented in this thesis. Similar concepts relating to

the resilience assessment involving HR and HRV measured during firefighter

training or active duty are considered within the context of the resilience assessment

model application results proposed in this thesis.

5.2 Using HR and HRV within a Resilience Assessment Model

The application of resilience assessment with HRV measurement has been used in

studies involving law enforcement officers (McCraty & Atkinson, 2012). The

assessment of HR has also been used to support resilience training in police officers

(Andersen et al., 2015). A resilience score using the change in HR and HRV is the

proposed approach within this thesis for enabling resilience assessment during

exposure to the job-related stressors. Specifically, the use of HR and HRV (SDNN

score of R-R intervals) 5 minutes before the activity and during the activity provides

an approach to assess a change in physiological responses and to use that to measure

resilience.

The SDNN value of participants is calculated by taking the standard deviation of

N-N intervals. N-N intervals are R-R intervals that are considered to be valid

intervals between heart beats. Issues with monitoring devices cause some R- R

intervals to not be considered valid as they are outside the expected range. An SDNN

score of less than 100 ms is considered very low and that these individuals need

resilience training to build their resilience (Shaffer et al., 2014).

5.3 Case Study Resilience Results

Firefighters are exposed to job stressors during specific tasks such as searching for

a victim or rescuing their fellow team member from a building collapse. The

resilience assessment model was applied to pre-service firefighters to study the

physiological changes due to job stressors while performing firefighting tasks, as

demonstrated through a search and rescue activity in a hot environment. Based on

the results obtained, it is found that the majority of the participants had higher SDNN

values 5 minutes before entering the search and rescue activity, with SDNN values

decreasing post-activity. Based on the data collected (Figures 1a.-f), 12 out of 50

individuals (24%) showed <100 ms SDNN value pre and during the activity. These

individuals were already at a low spectrum (i.e., unhealthy range) of SDNN. It is

unclear from the study design whether this was their resting heart rate behavior or

whether they were already under stress from being asked to participate in the training

workshop.

It is essential to note the specific findings in this case study, in particular, that 33%

of female participants, could potentially be more resilient as indicated by their SDNN

values being closer to the reference line indicating that it was similar to the prior

activity reading. Hence, this may demonstrate that there was little to no impact from

the training activity simulated job stressor (search and rescue activity) compared to

male participants. Data from male participants revealed that the SDNN for 27% of

56 the cohort, that is 11 out of 41 male participants, was below the threshold of 100ms before the activity as well as during the activity.

An important finding from this research was 93% of the male participants’ SDNN fell <100ms during the activity. This suggests there might be low resilience scores during stressful events for almost all males. This is a major finding from the case study results, also suggests the significance of resilience training required for the young firefighting professionals. As previously mentioned in earlier chapters, a low

SDNN score is associated with many adverse health effects, including association with a higher risk of mortality and a higher score >100ms has a protective mechanism with an indication of better health status (Shaffer et al., 2014). Moreover, a higher

HRV value is also associated with psychological resiliency and autonomic flexibility, and an individual’s ability to adapt efficiently to social or environmental pressures and accomplish complex decision-making cognitive tasks (Pyne et al.,

2016). There is uncertainty regarding whether this result is a product of physical fitness levels, a lesser ability to cope with job-related stress, a male biological response, or some other factor. This requires further research.

With a resilience training program, there are chances of improvement amongst young, inexperienced firefighter trainees that have low baseline values in order to enhance their ability to withstand this stressful event along with improving their resilience scores. These results are similar to the findings that suggest that momentary feeling of a lack of resilience is associated with reduced HRV and negative affect to highly stressful tasks in the daily life of firefighters requiring a flexible approach towards resilience (Schwerdtfeger & Dick, 2019).

5.1.1 Biological Sex Differences

According to the Baltimore Longitudinal Study of Aging, where the authors

performed an experiment involving cognitive tests, sex differences in cognitive

abilities over time showed men tend to have a steeper rate of decline on measures of

mental status and older women have greater resilience to age-related cognitive

decline (McCarrey, An, Kitner-Triolo, Ferrucci, & Resnick, 2016). Similarly, during

this case study that performed resilience assessment on 54 participants of the pre-

service firefighter training program using the resilience model proposed in this

research, the female participants showed higher resilience levels based on their HRV

results, indicating they may not be as affected by the stressors as compared to male

participants. There was a more significant drop in SDNN and a higher rise in HR for

the male participants. The SDNN graph Figure 1 d. shows female SDNN values are

closer to the reference line with minimal to negligible change in HRV, whereas the

majority of the male SDNN values seem to have suffered from a severe drop in HRV

from pre-activity to during the activity. Further research is required to determine

whether this difference by sex is a result of physical fitness, a better ability to cope

with stress, or some other factor. No other study on firefighters or resilience models

has performed this assessment with stratification by biological sex.

Another factor considered when analyzing participant data in this domain is the

understanding that each individual is different and may exhibit different

characteristics that may be related to other contributing elements. It is important to

include female participants in order to compare the responses from stress across

biological sex differences. Studies relating to gender are required; Kim and Lee’s

(2015) study did not specify the gender differences that would distinctively include

the female sex and gender for evaluating the heat strain considering HR responses

during rest periods at work. Data presented in this study suggested the female

population scored better than males, which could be further investigated to determine

if resilience scoring is impacted by gender, age, or other factors.

5.1.2 Limitations

The pre-service firefighter extreme heat search and rescue case study within this

thesis can create challenges when obtaining and analyzing data because of the intense

activity, increased temperature, and complex environmental and physiological

conditions. The activity/stressor data for this study was collected manually on paper

forms. While the internet clock time was used, this approach is still at risk of human

error. To address this limitation, an integration of a real-time data collection

framework that can create a time sync event of when the trainee was exposed to

stressors and completed various activities at specific times during the training

activity should be considered.

The stressors introduced within the search and rescue activity included physical

exertion for the activity, heat, having to breathe through the SCBA and being

blindfolded to simulate reduced visibility from smoke. There was no targeted

completion time set for the activity so no mental stressors other than individual

competitive desires were imposed for completion time. In future work, further

stressors will be introduced. It is possible that wearing a physiological data gathering

garment or the fear of utilizing unknown technology will have influenced HR and

HRV responses.

This research did not correlate the physiological response with fitness levels. As

such, the impact of the physical exertion as correlated with fitness levels has not been

assessed. The data analysis has been limited to the physiological response during the

Search and Rescue task. Time to return to baseline and its correlation with physical

fitness has not been measured and will be reported in future studies. During this

experiment, the analysis performed is based on physiological data and the specific

activity being performed by the participant during the search and rescue scenario.

This same technique can be used for any simulated scenarios.

A distinction was not made in the data collection from study participants regarding

biological sex and gender identification, therefore, the term gender was used in the

analysis. Biological sex and gender information should be collected in future studies

to enable stratification by biological sex and gender.

5.1.3 Personalized Resilience Assessment

This research has proposed a resilience model that enables the assessment of

individual resilience that can be used to build individual resilience for all firefighters

by monitoring their HRV and HR changes in real-time to assess job-related stress.

Thus, this study provides a contribution to existing literature and highlights the

importance of providing a mechanism to enable individuals to improve their

resilience levels to job-related stressors. Moreover, the impact of this research could

provide evidence that can help the firefighter occupation and improve the current

mental health and cardiac health of this public safety population.

It is important to note that previous studies have focused on analyzing the results as

a population, whereas a key contribution of this study is providing an individual

resilience score for their response to occupational stressors in a simulated

environment. This type of individual resilience scoring system utilizing

physiological data of HR and HRV has not been discussed earlier. Also, it is

necessary to consider young firefighter cohort as they are inexperienced compared

to career firefighters in this profession, which, as a result, makes them more

susceptible to stress through exposure to something they have not dealt with before.

As previously studied, several factors can be contributing to resilience including

seniority in the profession, gender differences as well as aging. Therefore, these

characteristics need to be addressed when designing future studies.

5.1.4 Implications for Resilience Assessment and Development

If a resilience assessment model is deployed along with a resilience-training

program that enables repeat exposure to assess resilience development, it will

measure progress made by pre-service firefighters and ultimately determine the

effectiveness of the training received. Maneuvering through the maze of the search

and rescue activity, pre-service firefighters encountered wire entanglements, had to

crawl in confined spaces, visual impairment, bending through the maze, all of which

creates elevated physical and mental stress. A low resilience score could allow public

safety officers to make a judgment on their team members and possibly protect the

individuals that are prone to the likelihood of being affected more from stress. With

this intervention, the risk of occupational injuries could be monitored through the

availability of a resilience score analyzed in real-time.

During a search and rescue activity, it is important to observe SDNN values as a

decrease would suggest the need to improve their resilience levels. Reduced changes

61 in SDNN indicate the capacity to resist job-related physical and cognitive stressors.

If a resilience assessment model is deployed along with a resilience-training program that enables repeat exposure to assess resilience development, it will measure progress made by pre-service firefighters and ultimately determine the effectiveness of the training received.

This research study has the potential to change existing work conditions of the firefighter population, while future work in this direction is likely to improve their overall well-being and reduce depression, as well as PTSD related risks. This same approach could be applied to a resilience-training program for any job-related stressor.

Chapter 6. Conclusions

This thesis presented the construction and application of a resilience assessment model to detect changes in physiological data experienced during job-related stressors. This model can be used to quantify resilience. This is illustrated within the context of a case study involving pre-service firefighters. The resilience assessment model constructed measured the physiological changes in respect to HR and HRV during the simulated training activities within extreme heat 50°C at Ontario Tech University’s ACE Facility.

Moreover, this resilience assessment model captured quantitative differences in HR and

HRV values between men and women in the pre-firefighter population. Lower resilience is assumed to exist due to a lack of experience in the working field. As learned previously, seniority in a profession can be a determining factor for resilience and how an individual interacts with a stressful situation.

6.1 Research and Findings

The literature review concluded that there is no gold standard for resilience

assessment that would utilize physiologically generated results. It further showed an

excellent opportunity to improve the process of assessing resilience in firefighters as

a population as well as individually.

The decrease in SDNN values during the exposure to the search and rescue activity

suggests a decline in physical and cognitive performance, as well as increasing HR

values, contribute towards the overall impact on health. This resilience assessment

model could potentially be utilized for predicting a decline in physiological changes

imposed by heat stress to improve working conditions. These results are supported

63 by the observed measures taken during the physiological monitoring of bodily responses during pre-activity and the search and rescue activity.

The SDNN values of female participants compared to male participants stayed higher, and average HR values were found to be lower in females during the search and rescue activity. It is not yet clear whether this is due to higher resilience levels of the female participants, higher fitness levels or fundamental biological differences between the sexes. Several factors can affect the resilience levels, and further analysis is required to determine if there is an underlying factor such as age, sex, gender, skill set, prior PTSD episode, depression, ethnicity or physical activity status as well as any other health condition responsible for the variation of the physiological responses observed.

This study instantiated a resilience assessment model designed in a manner to interpret the stress response and analyze resilience levels in the simulated firefighting environment. Based on the results, assessment of the changes in physiological measures such as HR and SDNN could suggest that individuals performing the simulated firefighting activity had a lower score compared to the beginning of the activity. This illustrates their body resilience levels are affected by either heat, stress, or each physiological mechanism.

The resilience training of military and law enforcement officers has continued to develop throughout its existence. Future work in the firefighter domain should involve the implementation of resilience assessment model using physiological monitoring devices to continuously monitor an individual’s physiological state and optimize the functionality as a resilience assessment tool.

6.2 Future Work

Future work could utilize data obtained from physiological responses to improve

existing protocols and build the necessary level of resilience required for the

firefighters to cope with an emergency. It is essential to investigate whether the

beneficial effects of resilience also enable firefighters that have suffered from

traumatic events with a protective shield towards developing PTSD (Lee et al.,

2014). Thus, this thesis proposes that a resilience model that utilizes physiological

monitoring of HR, HRV responses collected when exposed to job stressors would

provide insight to resilience on an individualized level. Overall, the results contribute

further understanding of the challenges experienced at an emergency and provide

additional knowledge for occupational stressors to improve the safety of all

emergency responders.

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