{"@context":{"@language":"en","Affiliation":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","AggregatedSourceRepository":"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider","Campus":"https:\/\/open.library.ubc.ca\/terms#degreeCampus","Creator":"http:\/\/purl.org\/dc\/terms\/creator","DateAvailable":"http:\/\/purl.org\/dc\/terms\/issued","DateIssued":"http:\/\/purl.org\/dc\/terms\/issued","Degree":"http:\/\/vivoweb.org\/ontology\/core#relatedDegree","DegreeGrantor":"https:\/\/open.library.ubc.ca\/terms#degreeGrantor","Description":"http:\/\/purl.org\/dc\/terms\/description","DigitalResourceOriginalRecord":"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO","FullText":"http:\/\/www.w3.org\/2009\/08\/skos-reference\/skos.html#note","Genre":"http:\/\/www.europeana.eu\/schemas\/edm\/hasType","GraduationDate":"http:\/\/vivoweb.org\/ontology\/core#dateIssued","IsShownAt":"http:\/\/www.europeana.eu\/schemas\/edm\/isShownAt","Language":"http:\/\/purl.org\/dc\/terms\/language","Program":"https:\/\/open.library.ubc.ca\/terms#degreeDiscipline","Provider":"http:\/\/www.europeana.eu\/schemas\/edm\/provider","Publisher":"http:\/\/purl.org\/dc\/terms\/publisher","Rights":"http:\/\/purl.org\/dc\/terms\/rights","RightsURI":"https:\/\/open.library.ubc.ca\/terms#rightsURI","ScholarlyLevel":"https:\/\/open.library.ubc.ca\/terms#scholarLevel","Supervisor":"http:\/\/purl.org\/dc\/terms\/contributor","Title":"http:\/\/purl.org\/dc\/terms\/title","Type":"http:\/\/purl.org\/dc\/terms\/type","URI":"https:\/\/open.library.ubc.ca\/terms#identifierURI","SortDate":"http:\/\/purl.org\/dc\/terms\/date"},"Affiliation":[{"@value":"Education, Faculty of","@language":"en"},{"@value":"Kinesiology, School of","@language":"en"}],"AggregatedSourceRepository":[{"@value":"DSpace","@language":"en"}],"Campus":[{"@value":"UBCV","@language":"en"}],"Creator":[{"@value":"Nguyen, Vienna","@language":"en"}],"DateAvailable":[{"@value":"2024-05-03T17:49:38Z","@language":"en"}],"DateIssued":[{"@value":"2024","@language":"en"}],"Degree":[{"@value":"Master of Science - MSc","@language":"en"}],"DegreeGrantor":[{"@value":"University of British Columbia","@language":"en"}],"Description":[{"@value":"Background: Cardiorespiratory fitness (CRF) testing in preventative health (PH) settings offers healthcare practitioners a feasible tool to stratify chronic disease risk in patients. While this has been established as a powerful tool of assessing mortality risk, it is still not included as part of routine healthcare. Understanding of CRF testing in PH populations could better inform best practices in its implementation. \r\nObjective: This study aimed to provide insight on exercise testing in PH populations exploring the CRF and clinical profiles of patients who underwent a maximal exercise test. Three questions were addressed: 1) What is the proportion of exercise tests in which maximal exercise capacity (MEC) is achieved? 2) Are there pre-test characteristics that affect the proportion of exercise tests in which MEC is achieved? 3) What determines exercise test termination in PH populations? \r\nMethods: A retrospective chart review was conducted for all patients completing an exercise test between 2022-2024. Exercise tests from 619 patients were included. The frequency of achieving MEC in a PH population was determined. Exercise mode, test protocol, and pre-test demographics were explored as predictors of achieving MEC. As well, reasons for exercise test termination were discussed.\r\nResults: In a PH population, 77% of individuals achieved MEC. Older age showed a widespread effect on achievement of MEC. Patients over the age of 70 were less likely to reach MEC in both cis-males (p<.001) and cis-females (p=.033), individuals who are active (p=.008), sedentary (p=.010), unknown PA behaviour (p=.012), those with elevated CVD risk (p<.001), those using treadmills (p<.001), and those following the Bruce-ramp protocol (p=<.001). The most common reason for termination an exercise test was fatigue or dyspnea (81%). \r\nConclusion: When working with older adults, exercise clinicians should take extra care in understanding their experience with exercise, as well as their CVD risk factors and impact on exercise, and select the exercise test mode and protocol best suited to their patient. Further understanding and assessment of the CRF profiles of PH patients will contribute to the methodological rigour required in the evaluation of exercise programming and support future explorations of meaningful impacts on health outcomes.","@language":"en"}],"DigitalResourceOriginalRecord":[{"@value":"https:\/\/circle.library.ubc.ca\/rest\/handle\/2429\/88201?expand=metadata","@language":"en"}],"FullText":[{"@value":"  EXERCISE TESTING IN PREVENTATIVE HEALTH POPULATIONS: PREDICTORS OF ACHIEVING MAXIMAL EXERCISE CAPACITY by Vienna Nguyen BSc., York University, 2019 A THESIS SUBMITTED IN PARTIAL FULFULLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in The Faculty of Graduate and Postdoctoral Studies (Kinesiology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April 2024  \u00a9 Vienna Nguyen, 2024 ii  The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Exercise testing in preventative health populations: Predictors of achieving maximal exercise capacity  submitted by Vienna Nguyen  in partial fulfilment of the requirements for the degree of Master of Science in Kinesiology  Examining Committee: Dr. Robert Boushel, Director and Professor, Kinesiology, UBC (Vancouver) Supervisor  Dr. Michael Koehle, Professor, Kinesiology, UBC (Vancouver) Supervisory Committee Member  Jasmin Ma, Assistant Professor, Kinesiology, UBC (Vancouver) Supervisory Committee Member     iii  Abstract Background: Cardiorespiratory fitness (CRF) testing in preventative health (PH) settings offers healthcare practitioners a feasible tool to stratify chronic disease risk in patients. While this has been established as a powerful tool of assessing mortality risk, it is still not included as part of routine healthcare. Understanding of CRF testing in PH populations could better inform best practices in its implementation.  Objective: This study aimed to provide insight on exercise testing in PH populations exploring the CRF and clinical profiles of patients who underwent a maximal exercise test. Three questions were addressed: 1) What is the proportion of exercise tests in which maximal exercise capacity (MEC) is achieved? 2) Are there pre-test characteristics that affect the proportion of exercise tests in which MEC is achieved? 3) What determines exercise test termination in PH populations?  Methods: A retrospective chart review was conducted for all patients completing an exercise test between 2022-2024. Exercise tests from 619 patients were included. The frequency of achieving MEC in a PH population was determined. Exercise mode, test protocol, and pre-test demographics were explored as predictors of achieving MEC. As well, reasons for exercise test termination were discussed. Results:  In a PH population, 77% of individuals achieved MEC. Older age showed a widespread effect on achievement of MEC. Patients over the age of 70 were less likely to reach MEC in both cis-males (p<.001) and cis-females (p=.033), individuals who are active (p=.008), sedentary (p=.010), unknown PA behaviour (p=.012), those with elevated CVD risk (p<.001), those using iv  treadmills (p<.001), and those following the Bruce-ramp protocol (p=<.001). The most common reason for termination an exercise test was fatigue or dyspnea (81%).  Conclusion: When working with older adults, exercise clinicians should take extra care in understanding their experience with exercise, as well as their CVD risk factors and impact on exercise, and select the exercise test mode and protocol best suited to their patient. Further understanding and assessment of the CRF profiles of PH patients will contribute to the methodological rigour required in the evaluation of exercise programming and support future explorations of meaningful impacts on health outcomes.            v  Lay Summary Aerobic fitness is one of the greatest ways to understand a person\u2019s health, and their subsequent risk for developing chronic disease. This can be tested in routine healthcare. Currently, there is little research on testing aerobic fitness in patients who want to prevent chronic disease and include PA in their lifestyle. The goal of this project was to provide a better understanding of aerobic fitness testing for preventative health. Exercise tests from 619 patients in a preventative healthcare centre were studied to learn what factors made them more likely to exercise to their maximal effort in an exercise test. It was found that patients above 70 years of age did not reach their maximal effort as often as younger individuals. Exercise clinicians working with older adults should understand patient experience, health, and preferences to accurately assess aerobic fitness.          vi  Preface This research study was designed by myself, Vienna Nguyen, with the help of my supervisor and committee (Dr. Robert Boushel, Dr. Michael Koehle, and Dr. Jasmin Ma), and Joshua Bovard, an Advisor and Kinesiology Team Manager at the partnering healthcare centre, TELUS Healthcare Centre. Data extraction for the retrospective chart review, as well as data analysis, interpretation, and writing were completed by myself. Patient data collected as part of the healthcare centre services were carried out by clinicians at TELUS Healthcare Centre. All methods executed in this thesis were approved by the University of British Columbia\u2019s Research Ethics Board (H23-01891).            vii  Table of Contents Abstract .......................................................................................................................................... iii Lay Summary .................................................................................................................................. v Preface............................................................................................................................................ vi Table of Contents .......................................................................................................................... vii List of Tables .................................................................................................................................. x List of Figures ................................................................................................................................ xi List of Abbreviations .................................................................................................................... xii Acknowledgements ...................................................................................................................... xiv Dedication ..................................................................................................................................... xv Chapter 1: Introduction ................................................................................................................... 1 1.1. Review of the Literature ................................................................................................. 2 1.2. Summary ....................................................................................................................... 19 1.3. Objectives ..................................................................................................................... 20 1.4. Research Questions ....................................................................................................... 20 1.5 Hypotheses .............................................................................................................................. 21 Chapter 2: Methods ....................................................................................................................... 22 viii  2.1. Sample ............................................................................................................................... 22 2.2. Study Overview ................................................................................................................. 22 2.3. Clinical Standard Operating Procedure ............................................................................. 22 2.4. Outcome Measures ............................................................................................................ 23 2.5. Data Extraction .................................................................................................................. 28 2.6. Clinician Expertise ............................................................................................................. 29 2.7. Procedure ........................................................................................................................... 29 Chapter 3: Data and Statistical Analysis ....................................................................................... 30 3.1. Achievement of MEC ........................................................................................................ 30 3.2. Predictors of Achieving MEC............................................................................................ 30 3.3. Insight on Maximal Test Termination ............................................................................... 30 Chapter 4: Results ......................................................................................................................... 31 4.1. Participant Characteristics and CVD Risk ......................................................................... 31 4.2 Predictors of Achieving Maximal Capacity ........................................................................ 32 4.3. Insight on Maximal Test Termination ............................................................................... 50 Chapter 5: Discussion ................................................................................................................... 52 ix  5.1. Main Findings .................................................................................................................... 52 5.2. Methodological Considerations ......................................................................................... 60 5.3. Limitations ......................................................................................................................... 61 5.4. Future Directions ............................................................................................................... 63 Chapter 6: Conclusion................................................................................................................... 66 References ..................................................................................................................................... 67 Appendix ....................................................................................................................................... 76            x  List of Tables Table 4.1. Participant characteristics and CVD risk. .................................................................... 31 Table 4.2. Comparison of maximal and non-maximal exercise tests, stratified by age, sex, PA behaviour, CVD risk status, exercise mode, and treadmill protocol. ........................................... 33 Table 4.3. Significant relationships between factors influencing the ability to reach maximal exercise capacity. .......................................................................................................................... 34 Table 4.4 Relationship between age and sex, PA behaviour, CVD risk, exercise mode, and treadmill protocol in achievement of MEC. ................................................................................. 35 Table 4.5. Frequency of Listed Reason for Test Termination ...................................................... 51           xi  List of Figures Figure 4.1. Impact of age on sex impacting ability to achieve MEC............................................ 36 Figure 4.2. Impact of sex on age impacting ability to achieve MEC. ........................................... 37 Figure 4.3. Impact of age on PA level impacting ability to achieve MEC. .................................. 38 Figure 4.4. Impact of age on CVD risk impacting ability to achieve MEC. ................................ 39 Figure 4.5 Impact of age on exercise test mode impacting ability to achieve MEC. ................... 40 Figure 4.6 Impact of age on treadmill exercise test protocol impacting ability to achieve MEC. 41 Figure 4.7 Impact of sex on PA behaviour impacting ability to achieve MEC. ........................... 42 Figure 4.8 Impact of sex on CVD risk impacting ability to achieve MEC. .................................. 43 Figure 4.9 Impact of CVD risk on sex impacting ability to achieve MEC. .................................. 43 Figure 4.10 Impact of sex on exercise mode impacting ability to achieve MEC. ........................ 44 Figure 4.11 Impact of sex on treadmill exercise test protocol impacting ability to achieve MEC........................................................................................................................................................ 45 Figure 4.12 Impact of CVD risk on PA behaviour impacting ability to achieve MEC. ............... 46 Figure 4.13 Impact of PA behaviour on CVD risk impacting ability to achieve MEC. ............... 47 Figure 4.14 Impact of exercise test mode on PA behaviour impacting ability to achieve MEC. . 48 Figure 4.15 Impact of treadmill protocol on exercise test mode impacting ability to achieve MEC. ............................................................................................................................................. 49 Figure 4.16 Impact of exercise test mode on treadmill protocol impacting ability to achieve MEC. ............................................................................................................................................. 50  xii  List of Abbreviations ACSM American College of Sports Medicine BMI Body mass index BP Blood pressure CAD Coronary artery disease CEP Clinical exercise physiologist CI Confidence interval CPX Cardiopulmonary exercise testing CR Category-ratio CRF Cardiorespiratory fitness CV  Cardiovascular CVD Cardiovascular disease DBP Diastolic blood pressure ECG Electrocardiogram EMR Electronic medical record HDL-C High density lipoprotein cholesterol HR Heart rate HRR Heart rate recovery LDL-C Low density lipoprotein cholesterol MET Metabolic equivalent PA Physical activity PH Preventative health RPE Rate of perceived exertion SBP Systolic blood pressure xiii  SCORE Systematic Coronary Risk Evaluation T2DM Type two diabetes mellitus USAFSAM United Sates Air Force School of Aerospace Medicine VO2 Oxygen uptake VO2max Aerobic capacity                xiv  Acknowledgements I would like to express my sincere gratitude to you, Dr. Robert Boushel, for your wisdom, guidance, and unconditional support throughout this thesis project. Your vision of, passion for, and contributions to the future of exercise in health are truly inspiring and I look forward to continuing our work together. To Josh Bovard, thank you for the opportunity to work with your team, for the countless hours in making this project meaningful, and for sharing your inexhaustible knowledge of Excel spreadsheets. To my committee members, Dr. Michael Koehle and Dr. Jasmin Ma, thank you for sharing your wealth of knowledge and experiences as both researchers and clinicians. Your commitment to my aspirations beyond graduate school have been invaluable. To the TELUS Healthcare Centre Kinesiology Team (aka the Healthy Not Wealthy crew), thank you for your commitment to your patients, furthering the field of clinical exercise, and your friendship these past few years. As well, I would like to express my sincere gratitude for the opportunity to conduct this thesis work on the traditional, unceded, and ancestral territories of the x\u02b7m\u0259\u03b8k\u02b7\u0259y\u0313\u0259m (Musqueam), S\u1e35wx\u0331w\u00fa7mesh (Squamish), and s\u0259lilw\u0259ta\u026c (Tsleil-Waututh) Nations.       xv  Dedication This work is dedicated to my family, friends, and all those that I\u2019ve leaned on throughout this journey. To my friends \u2013 thank you for always being there to celebrate the wins, grieve the lows, and pushing me to the end. Your passions for health within and beyond this field are truly inspirational. To my partner \u2013 thank you for being the constant warmth in my life, and for taking over as our home cook and cleaner on more than enough occasions. I promise I will not go back to school any time soon. To my parents, thank you for your unconditional love and support. While my academic journey has been a rollercoaster, your positivity and encouragement have not wavered. I hope to be as good as you both when I grow up. Finally, to my sister \u2013 thank you for being my number one cheerleader, every step of the way. Thank you for always believing me and never letting me lose faith. I would not be here without you today.         1  Chapter 1: Introduction Addressing cardiorespiratory fitness (CRF) in healthcare settings engages patients in having an active role in their health. Over the last few decades, our knowledge base on exercise in healthcare has grown substantially while the need for its implementation is still unmet (Lion et al., 2019). Exercise is recommended in treatment plans for 26 different chronic diseases (Pedersen & Saltin, 2015) while CRF predicts the risk of developing chronic conditions and premature mortality (Blair et al, 1989; Blair et al, 1995; Cooper et al, 1989; Harber et al, 2017; Lee et al, 2012; Morris et al, 1953, Morris and Crawford, 1958;; Wilhemsen et al, 1981). Low CRF is associated with various morbidities including cardiovascular disease (CVD), stroke, hypertension, type two diabetes mellitus (T2DM), lung cancer, colorectal cancer, dementia, depression and anxiety, placing a $2.6-5.1 billion demand on the Canadian healthcare system in 2021 alone where the prevalence of low CRF was estimated to be 45.5% (Chaput et al., 2023). Consistency in prioritizing CRF in healthcare for chronic disease prevention is imperative - for risk reduction and cost demands simultaneously.   CRF is one of the most powerful predictors for risk of future adverse health events in individuals who are apparently healthy, at risk for cardiovascular disease, among most other populations. CRF testing in settings that routinely assess disease risk to prevent adverse health outcomes, or preventative health (PH) settings, offer healthcare practitioners a feasible tool to stratify chronic disease risk and gauge exercise capacity in their patients. With this information available, practitioners can educate their patients on the role of CRF in health, offer strategies in lifestyle management, work with their patients in creating a tailored exercise prescription, and provide detailed referrals to exercise professionals (Fletcher et al, 2013). Despite the clinical significance of CRF, only 26.2% of Canadian family physicians assess patient fitness in routine 2  exams while 10.9% of physicians refer out to other healthcare professionals to conduct fitness assessments. This is substantially lower compared to other PH services offered in routine healthcare, such as blood pressure monitoring, cancer screens, and smoking cessation counselling. Of note, information regarding method of fitness assessment was not captured (Petrella et al., 2007).   While CRF testing has been established as a powerful tool for assessing mortality risk, it is not a PH service offered in routine care. Further evidence is needed to show that incorporating exercise testing into preventative care will improve health outcomes. Concerns have also been raised around risk classification protocols. While CRF reference standards have been established for both treadmill and cycle ergometry testing (Kaminsky et al 2015; Kaminsky et al 2016), there are no standardized age- and sex- predicted abnormal value cut-off for asymptomatic individuals (Lauer et al 2005; Kligfield and Lauer, 2006). As well, the majority of the extensive work done in establishing CRF as a strong and independent predictor of adverse health outcomes has been shown outside of healthcare settings. While many studies looked at individuals that were clinically referred (Goraya et al., 2000; Myers et al., 2002; Roger et al., 1998), few have focused on a PH population. Recently, Sydo and colleagues (2023) explored the prognostic usefulness of exercise testing in in a primary prevention population. Exercise capacity was established as a powerful tool in assessing risk for both CV and non-CV mortality. Further understanding of maximal exercise test administration in PH populations, specifically those measuring achieved METs, could better inform best practices in its incorporation into standard care. 1.1. Review of the Literature CRF describes the integrated efficiency of the circulatory, respiratory, and muscular systems in delivering and utilizing oxygen for energy transfer in sustained physical activity (PA) 3  and exercise, and can be assessed through various types of aerobic capacity (VO2max) tests (Lamoureux et al., 2019; Laukkanen et al., 2022; Ross et al., 2016, Harb et al 2020). There is a large body of epidemiological evidence that highlights CRF as one of the best predictors for future risk of adverse health outcomes in apparently healthy individuals (Blair et al., 1989, 1995; Harber et al., 2017; Lee et al., 2012; A. J. N. Morris & Crawford, 1958; J. Morris et al., 1953; Wilhelmsen et al., 1981), individuals at risk for CVD, and almost all other populations, independent of traditional risk factors (Goraya et al, 2000; Albers et al, 2006, Fletcher et al, 2013). In an updated meta-analysis of 37 studies including over 2.2 million adults objectively measured for CRF, those with higher levels of CRF had a 45% reduced risk of all-cause mortality. As well, every 1-MET increase in exercise capacity is associated with an 11% decrease in risk of all-cause mortality (Laukkanen et al., 2022).  Exercise capacity is commonly assessed through maximal exercise tests and is expressed in metabolic equivalents (MET), referring to amount of energy required to complete a task. This can be measured through ventilatory expired gas analysis or estimated through workload parameters such as speed, incline, or resistance depending on the mode of exercise test (Ross et al., 2016). Age- and sex- predicted MET level classification tools have been created to assess disease and mortality risk (Kaminsky et al., 2017; Ross et al., 2016). While the Bruce, Naughton, and Balke protocols are most often used to assess exercise capacity in clinical settings, they may not be appropriate for all populations. Individuals with health limitations impacting treadmill performance could terminate the exercise test before reaching true physiological exhaustion and impact their measurements (Fletcher et al., 2013). Several studies include the use of cycle ergometers to assess CRF (Erikssen et al., 2004; Laukkanen et al., 2022) and maintain the clinical value of measuring exercise capacity.  4  In this review, and elsewhere in this thesis, both gender (men\/women) and sex (male\/female) terms are discussed as reported in the original literature. While comparisons discussed refer to sex-based differences, gender terms are included if used in the original work.  1.1.1. Prognostic Value of CRF Testing 1.1.1.1. CRF in Risk Assessment CRF is often excluded when screening patients for risk of mortality while it has shown similar, if not greater, prognostic power to traditional risk factors. Healthy men and women from the Cooper Clinic were included in a study examining the relationship between physical fitness and all-cause mortality. Low levels of fitness showed comparable relative risk of mortality (1.58, 95% CI [1.32, 1.89]) to smoking (current or quit in the past two years) (1.08, 95% CI [0.90,  1.28]), serum cholesterol (\u22656.20 mmol\/L) (1.65, 95% CI [1.39, 1.97]), systolic blood pressure (SBP) (\u2265140 mm Hg) (1.36, 95% CI [1.05, 1.77]), having either parent die of coronary heart disease (1.56, 95% CI [1.26, 1.92]), and serum glucose level (\u22656.7 mmol\/L) (1.58, 95% CI [1.21, 2.08]). Similar impact was also shown for improving CRF compared to traditional risk factors in both individual- (Riffenburgh & Gillen, 2020) and population- attributable risk (Kirch, 2008). Blair and colleagues (1989) showed  the individual-attributable risk for men and women were 36.7% and 48.4%, respectively; the population-attributable risk for men and women were 9% and 15.3%. In other words, if the population as a whole saw an improvement in CRF, men and women would see a reduction in deaths by 9% and 15.3%, respectively (Kirch, 2008). In a similar analysis of participants with and without CVD, exercise capacity was found to be the best age-adjusted predictor of survival among both participants with (hazard ration for death = 0.91, 95% CI [0.88-0.94]) and without CVD (hazard ratio for death = 0.84, 95% CI [0.79-0.89]). Put differently, every 1-MET increase in exercise capacity in participants that were both disease-free 5  and living with CVD resulted in a 16% and 9% increase in chances of survival, respectively. In normal participants, the next best age-adjusted predictor of survival, showing a similar inverse relationship, was pack-years of smoking. In those with CVD, the next best age-adjusted predictor of survival was having a history of congestive heart failure. This was followed by a history of MI, pack-years of smoking, left ventricular hypertrophy on ECG at rest, pulmonary disease, and exercise-induced ST-segment depression (Myers et al., 2002). Recently, Sydo and colleagues (2023) explored the prognostic power of CRF testing in a primary care population, looking at those clinically referred for an exercise test through their family physician. Individuals with any baseline CVD, history of cancers, lung diseases, kidney diseases, or autoimmune diseases were excluded. Demonstrated by Cox regression analyses, exercise capacity was a significant predictor of both non-CV deaths (HR, 1.36; 95% CI, 1.19-1.69; p<0.0001) and CV deaths (HR, 2.11; 95% CI, 1.18-2.40; p=0.0044). Not only can CRF testing contribute to overall risk screening, it can strengthen existing assessments. Measuring CRF significantly improves the measurement, as well as classification, of CVD mortality risk when used alongside traditional risk factors (Erikssen et al., 2004; Gupta et al., 2011; Ross et al., 2016). This association with CRF was found to be stronger in determining risk of developing diabetes, where classic risk factors alone could not significantly predict incidence (Erikssen et al., 2004). In asymptomatic women who were classified as low risk using the Framingham risk scores (Mora, Redberg, Cui, Whiteman, Flaws, Sharrett, Blumenthal, et al., 2003), those with heart rate recovery (HRR) and\/or exercise capacity values lower than the median were at higher risk of all-cause mortality. For every 1-MET decrease in exercise capacity, the age-adjusted hazard ratio for CV death was 1.20 (95% CI, 1.18-1.30). In contrast, both exercise-induced ST-segment depression at least 1.0 mm (hazard ratio=1.02; 95% 6  CI [0.57-1.80]) and at least 2.0 mm (hazard ratio=0.97; 95% CI [0.14-6.93]) were not found to be predictors of all-cause mortality (Mora, Redberg, Cui, Whiteman, Flaws, Sharrett, Blumenthal, et al., 2003). Aktas and colleagues (2004) argue for the use of the European SCORE (Knuuti et al., 2020) with CRF testing instead of the Framingham Risk Score in assessing CVD risk, looking at age, sex, total cholesterol, systolic blood pressure, and smoking status. Using the European SCORE classification, individuals with a higher assessed risk were also more likely to have impaired exercise capacity, abnormal HRR, and frequent ventricular ectopy during recovery. Here, ST-segment changes, diabetes, body mass index, triglyceride levels, HDL-C, black race, and use of cardiac medications were not significant predictors of death. In contrast to the Framingham Risk Score (Mora, Redberg, Cui, Whiteman, Flaws, Sharrett, Blumenthal, et al., 2003), CRF testing was only clinically meaningful for assessing mortality risk in individuals who had a higher risk at baseline according to their SCORE assessment. Recommendations were made to use the European SCORE in determining necessity for CRF testing (Aktas et al., 2004). Laukkanen and colleagues (2007) also found that adding CRF to CVD mortality risk assessments, such as the SCORE and Framingham Risk Score, provides greater prognostic value. Wickramasinghe and colleagues (2014) created a 30-risk prediction tool through a cohort of 16 544 participants from the Cooper Center Longitudinal Study. Both CRF and traditional risk factors (age, BMI, systolic blood pressure, prevalence of T2DM, total cholesterol, and smoking habits) were included to assess risk of CVD death. CRF was found to be a strong predictor of long-term risk \u2013 while a similar risk of CVD death was observed across fitness levels 10 years after testing, those with lower CRF levels were associated with a significantly lower 30-year risk for CVD death. These findings underline the importance for preventative CRF screening early on.   7  Individuals with low CRF are also at further risk of mortality if classified as obese. Compared to presence of CVD, T2DM, and CVD risk factors (high serum cholesterol, hypertension, and current cigarette smoking), having low CRF was still the most common predictor for all-cause mortality in men classified as obese (Wei, Kampert, et al., 1999). These findings suggest the added importance of assessing CRF in individuals classified as overweight or obese to further stratify disease and mortality risk. While the logistical incorporation of CRF testing into routine health screening requires further research, its place in standard care has been demonstrated. 1.1.1.2. Impact of CRF on Mortality CRF is a strong, graded, and independent predictor of CV and all-cause mortality. The strength in association between CRF and mortality has been assessed through exercise capacity on maximal treadmill tests, reflected by the maximal METs achieved. Adults less than 65 years of age in Minnesota, USA saw a 20-25% reduction in risk of death and cardiac events with every 1-MET level increase in workload achieved on an exercise test (Roger et al., 1998). Following the same protocols, adults aged 65 years and older showed an 18% reduction in risk of overall mortality and cardiac events with every 1-MET level improvement. Of interest, the relationship between nursing home placement and exercise capacity was also examined. Elderly participants had a 12% lower likelihood of being placed in a nursing home for each 1-MET increase in exercise capacity (Goraya et al., 2000). When looking at men who were referred for an exercise test due to clinical reasons, every 1-MET increase in exercise capacity resulted in a 12% increase in survival rate when pooling non-CVD and CVD groups (Myers et al., 2002). Healthy women from the St. James Women Take Heart Project saw that each 1-MET improvement in exercise capacity resulted in a 17% decrease in risk of mortality (Gulati, Pandey, Arnsdorf, Lauderdale, 8  Thisted, Wicklund, Al-hani, et al., 2003). Reviewing the current knowledge on association between health outcomes and CRF, the American Heart Association (Ross et al., 2016) outlined those with an aerobic capacity of less than five METs were at higher risk for mortality. Meanwhile, the ability to achieve eight-to-ten METs or greater on a maximal exercise test had protective effects. More than half the reduction in mortality risk lies between the lowest CRF group (less than five METs) and next one (five-to-seven METs), showing small improvements in CRF have a clinical impact on survival. These findings are especially encouraging for less fit individuals who are looking to begin exercising. This is also useful for practitioners in giving patients exercise capacity goals for decreasing disease risk. 1.1.1.3. Clinical Impact of CRF Testing The strong, independent, and inverse relationship between CRF and all-cause mortality risk, as well as protective role of high CRF, has been well established and demonstrates the importance of exercise testing and prescription in clinical care. However, the inclusion of CRF in routine risk assessment has yet to be established. Several concerns have been raised around protocol standardization. Since prognostic value has been established according to the MET level achieved on maximal exercise tests, protocol and mode selection must elicit maximal physiological responses in individuals. Age- and sex-predicted risk classification tools must also be established for meaningful interpretation of test results (Kligfield & Lauer, 2006). Questions have also been raised about the direct impact of exercise testing on health outcomes. If we conduct CRF testing in patients for PH, will the test results provoke changes in lifestyle habits that could impact their health? While the health benefits of exercise and PA are well understood, Lauer and Froelicher (2005) expressed a further need evidence that knowing one\u2019s exercise capacity through clinical testing would improve their health outcomes long term.  9  While there is limited research on the inclusion of CRF assessments in primary care practice or PH screening, the impact of changing physical fitness on risk of all-cause and CV mortality has been explored. Blair and colleagues (1995) investigated this relationship across five years in 9777 men through preventive medical examinations. After adjusting for age, men who were deemed fit at both time points (baseline and follow-up assessment) through a maximal exercise test had the lowest rates of all-cause death, while men who were deemed unfit at both time points had the highest rates of all-cause death. Those who showed an improvement in fitness had a 44%, 95% CI [25%, 59%] reduction in mortality risk, with a 7.9% (p=0.001) decrease in mortality risk with each minute increase on their maximal treadmill time. Recently, Kokkinos and colleagues (2023) took a sample group from the Exercise Testing and Health Outcomes Study out of Washington, DC and explored the impact of changing CRF on mortality risk. 93 060 male and female participants, aged 30-95 years, with and without known CVD were included. Their findings were able to show reciprocal changes in mortality risk with changes in CRF, establishing a required improvement in CRF to achieve health benefits. An increase of at least one MET from baseline CRF was associated with an improvement in mortality risk in all levels of fitness except for those in the highest fitness group; an improvement of two or greater METs from baseline was required to see benefits. Meanwhile, a decrease of at least one MET from baseline CRF was associated with an increased risk of mortality in least- and low-CRF groups; moderate- and high-CRF groups saw on increase in risk with a change in CRF of two or more METs below their baseline. A similar pattern has been demonstrated in patients from a cardiac rehabilitation program (Martin et al., 2013). Individuals who improved their CRF after the 12-week program saw a 13% (P<.001) reduction in long-term mortality risk, while those starting out with a low level of CRF saw a 30% (P<.001) reduction in risk. Shah and colleagues 10  (2016) have also made the argument for addressing CRF early on. In young adults aged 18-30 years, each one-minute decrease in exercise test duration after seven years was associated with a 21% (HR, 1.21; 95% CI, 1.07-1.37; P=.002) increase in risk of all-cause mortality and 20% (HR, 1.20; 95% CI, 1.06-1.37; P=.006) increase risk of CVD.  Individuals increasing their CRF between health exams clearly lowers their risk of adverse health outcomes compared to those who see a decline in CRF (Ross et al., 2016). While the impact of including CRF assessments in routine PH still requires exploration, knowing the specific dose to health benefits is still valuable. Equipping patients with information about their own CRF and providing concrete goals for improvement could impact their PA behaviour and in turn, health outcomes.  1.1.2. Technical Considerations of CRF Testing 1.1.2.1. Maximal Exercise Testing The American Heart Association has recommended all adults, including those living with chronic diseases, regularly undergo maximal exercise testing as they would any other routine PH service. Cardiopulmonary exercise testing (CPX) is considered the gold standard, utilizing standard exercise testing procedures in conjunction with ventilatory expired gas analysis. This involves assessment of measured oxygen uptake (VO2), carbon dioxide production, and minute ventilation, offering clinicians an objective assessment of patient effort (Ross et al., 2016). To determine that an individual has reached their physiological maximum, primary and secondary criteria has been outlined. Firstly, exercise test administrators will look for a plateau in VO2 despite an increase in workload. If no plateau occurs, secondary criteria is assessed looking at respiratory exchange ratio, HR, and rate of perceived exertion (RPE). Cut-offs for these criteria vary (Knaier et al., 2019). 11  As well, CPX offers additional assessment of prognostic factors related to ventilatory and circulatory function, strengthening risk assessment in patients. This includes screening for exercise oscillatory ventilation, as well as measurement of oxygen uptake efficiency slope, exercise ventilatory power, circulatory power, and noninvasive determination of cardiac output. While these measures may offer a more comprehensive assessment of risk for adverse events in patients, there is still limited evidence indicating their additional clinical value. There also lacks normative data or established threshold values for meaningful assessment of risk using these measures (Guazzi et al., 2012). When ventilatory gas analysis is unavailable, maximal CRF tests rely on patient perception of fatigue where patients are encouraged to work until they can no longer sustain the prescribed intensity. Exercising heart rate (HR) is monitored and used in calculations estimating VO2max and not relied upon to terminate the test (Ross et al., 2016). HRR, hemodynamic responses, and CRF can also still be assessed without CPX measures (Guazzi et al., 2012). While CPX measures will provide a more accurate measure of VO2max, there is still epidemiological benefit to estimation through maximal exercise test workload. Kokkinos and colleagues derived new estimation equations with only 5.1% (Kokkinos et al., 2017) and 0.51% (Kokkinos et al., 2018) overestimation in aerobic capacity when performing exercise tests without gas analysis on a treadmill and cycle ergometer; respectively. As well, maximal CRF tests requiring relatively high intensity efforts have been found to better estimate aerobic capacity (Bernard et al., 1997; Wis\u00e9n & Wohlfart, 1999). The risks of type 1 (indication of achievement of maximal exertion when individual did not) and type 2 errors (indication of failure to reach maximal exertion when individual did) and their clinical implications should be considered when determining if individuals reach maximal exertion on an exercise test. When CPX measures are available, type 1 errors happen when individuals have been declared to reach 12  maximal oxygen uptake when they have not. In contrast, type 2 errors occur when individuals have reached maximal oxygen uptake but are declared to have not. Recommendations have been made for more conservative (higher) secondary criteria cut-offs to limit type 1 errors (Knaier et al., 2019). While this is in the context of measuring oxygen uptake, consideration of type 1 and type 2 errors still apply to maximal testing without CPX measures. Wagner and colleagues (2020) sought to determine age-dependent cut-offs for secondary exhaustion criteria in generally healthy men and women aged 20-91 years. They found the commonly used HR cut-off of 85% of an individual\u2019s age-predicted maximal HR could lead to large type 1 errors, specifically in those aged 40-59 years. Lower criterion did not have as large of an effect with older adults, although type 1 errors were still present. This outlines the importance of encouraging individuals to exercise to volitional exhaustion, regardless of HR, as long as it is safe to do so.  Using CPX measures will introduce risk of error when determining a plateau for oxygen uptake, and achievement of cut-offs for age-predicted maximal HR, respiratory exchange ratios, RPE, and blood lactate concentration. When conduction maximal exercise testing without CPX measures, error risk considerations are also made for age-predicted maximal HR and RPE criteria cut-offs, as well as determination of volitional fatigue. Irrespective of CPX measure availability, criteria cut-offs must find the balance between risk of type 1 and type 2 measures. Truly understanding a patient\u2019s maximal exercise capacity (MEC) allows for optimal clinical care, such as exercise prescription that is best tailored to patient ability.  Submaximal exercise tests do offer a less intensive option for assessing CRF in PH, using a percentage of age-predicted maximal HR as the test end-point. Laukkanen and colleagues (2020) conducted submaximal exercise tests in 58 892 participants between 40-69 years of age. Cycle ergometer workloads of 50% and 35% of the participant\u2019s predicted maximal workload 13  were used. Estimated CRF was found to be linearly associated with mortality risk. As well, the addition of CRF to a 5-year mortality risk score improved its predictive capabilities. However, patient consideration is advised as submaximal exercise testing could greatly under- or over-estimate an individual\u2019s VO2max, depending on how close their true maximal HR is to the predicted value due to variation in individual maximal HRs (Wis\u00e9n & Wohlfart, 1999). Whaley and colleagues (1992) compared age-predicted and true maximal HRs in 2010 men and women, aged 14-77 years, and looked at age, resting HR, body weight, and cigarette smoking habits as predictors for the under- or over-achievement of their predicted maximal HR. They found men and women who were older, had lower body weight, and were non-smokers were more likely to exceed their predicted maximum HR. Tanaka and colleagues (2001) also found maximal HRs to be underestimated in adults over the age of 40 years old. In submaximal exercise tests, using a prescribed HR as an endpoint would prematurely terminate the test and underestimate an individual\u2019s CRF level. There is also a large variation in individual HR at lower intensity, submaximal work rates making it difficult to discriminate HRs between stages (Greiwe et al., 1995). In addition, submaximal CRF test protocols cannot be used accurately with those who are taking HR-modulating medications, such as beta-blockers (Ross et al., 2016). 1.1.2.2. Age  Patient age could impact their ability to reach maximal exertion on an exercise test. Older individuals may be apprehensive in continuing at relatively higher exercise intensities due to fear of falling, feelings of discomfort during high intensity exercise, or fear of adverse health events from increasing their effort. When looking at the perceptual and ventilatory responses to graded treadmill exercise in adults aged 40 \u2013 80 years, Ofir and colleagues (2008) found differences in age group in feelings of breathlessness. Men and women aged 60 years and older had higher 14  perceived breathlessness compared to their younger counterparts when exercising at the same relative intensity (%VO2max). Women in this older group had significantly higher ratings of breathlessness compared to men, most likely due to their reduced baseline ventilatory capacity (Ofir et al., 2008). Fear of falling in relation to PA behaviour and intensity was assessed in community-dwelling adults aged 65 years and older. Those with greater fears of falling had significantly lower objectively-measured step counts, as well as light and moderate-to-vigorous PA minutes, compared to individuals with low fears of falling. Understanding factors that may impede patient ability to reach maximal capacity could help clinicians in protocol and mode selection, and patient safety education and reassurance, when conducting an exercise test (Sawa et al., 2020).  Considerations should also be made for exercise intensity. In a PA preferences survey of 1885 individuals, Gavin and colleagues (2015) found a decline with aging for interest in activities involving higher levels of PA intensity. It has also been found that older adults were more likely to prefer slower-paced PA (Alley et al., 2018). 1.1.2.3. Sex  The role of gender in achieving maximal exercise capacity is unclear. Several studies have looked at gender or sex differences in accuracy of RPE scoring. Winborn and colleagues (1988) investigated the impact of sex and exercise experience on submaximal exercise performance, measuring estimated VO2max, as well as HR and RPE at comparable workloads. Overall, males had lower HRs when working at relative workloads (percent-estimated-VO2max) compared to females. Individuals with high athletic experiences also showed lower HRs at relative workloads (Winborn et al., 1988). Similarly, Rascon and colleagues (Rascon et al., 2020) found females had higher HRs at both low and high intensity exercise compared to males. As well, females showed higher percentages of their HRmax at low, moderate, and high intensity 15  exercise. In both studies, there were no significant differences in RPE indications between males and females (Rascon et al., 2020; Winborn et al., 1988). Faulkner and Eston (Faulkner & Eston, 2007) put 49 men and women aged 19-50 years through two graded exercise tests and also found that gender did not impact RPE accuracy. Discrepancies between men and women in reaching maximal exercise capacity could be observed in PH populations, most likely differing from healthy, athletic, and\/or undergraduate individuals.  1.1.2.4. PA Behaviour Individuals who are physically active may be more likely to reach maximal capacity on exercise tests when compared to those who are sedentary. In the study by Winborn and colleagues (1988) described above, previous exercise experience was found to be the strongest predictor of RPE scoring accuracy. In males specifically, individuals with low exercise experience recorded lower RPE scores for their relative workloads. The researchers believed this could be accounted for bias due to a desire to appear fitter, compared to highly experienced males who were more likely to respond realistically. In contrast, Hughes and colleagues (Hughes et al., 1984) found physical inactivity to be associated with lower RPE. After further exploration, this relationship was explained by lower PA levels, not lower CRF, compared to their physically active counterparts (Hughes et al., 1984). It is unclear whether or not this would translate to PH populations reaching maximal exercise capacity. It could be that those who are physically active are more likely to have experienced relatively high intensities and be more comfortable with the sensations of exercise. They may also be more used to physically pushing themselves through higher perceived exertional levels.   16  1.1.2.5. Mode The prognostic value of CRF testing extends across most populations of varying health status, age, and ability. As such, CRF tests should accommodate all patient populations while still producing accurate results. Regardless of mode selection, care should be taken to ensure standardized execution of the exercise test. Participants should use treadmills with front rails, side rails, or both to offer stability, without grasping tightly. Doing so reduces the exercise workload on the participant by supporting their bodyweight, therefore overestimating their aerobic capacity. If needed, participants can place two fingers on handrails for balance support. Treadmills should also have variable speed and grade, and be calibrated for accurate conversion to METs. Cycle ergometers can be used for those with gait or balance concerns, those who have orthopedic issues, or those who live in larger bodies. They are also less expensive, take up less space, and are easier for recording health measures due to the participant\u2019s limited upper body movement. An electronically-braked cycle ergometer could be considered for better control of power output as they adjust resistance depending on the pedaling speed. Participants should maintain 50-80 revolutions per minute (rpm) to achieve the highest VO2 and HR measures. Attention should also be paid to the participant\u2019s force of grasp on the handles (Balady et al., 2010; Fletcher et al., 2013). When working with patients who are limited by weight-bearing activities and\/or lower-body function, arm ergometers may also be used understanding that they may further underestimate exercise capacity (Forman et al., 2010).    While there is a consistent relationship between both treadmill and cycle ergometers in predicting exercise capacity, individuals using a cycle ergometer could see a 10-20% lower VO2 max score compared to their treadmill counterparts (Fletcher et al., 2013; Fleury, 2005; Forman et al., 2010; Maeder et al., 2005). This limitation can be explained by quadriceps fatigue and 17  discomfort, typically leading to test termination before reaching the individual\u2019s true physiological maximum (Fletcher et al., 2013). As well, the total amount of working muscle is most likely greater while performing treadmill exercise (Miyamura & Honda, 1972). However, recent objective analyses of CRF and mortality risk deemed maximal tests on both the treadmill and cycle ergometer valid to be pooled for comparison (Laukkanen et al., 2022). Mode of exercise should be selected according to the context of the participant.  1.1.2.6. Protocol Selection Maximal exercise test protocols can differ depending on the population and practitioner. Participants typically start with a warm-up period, progressively increase their intensity at varying time and load increments, and finish with a recovery period. Graded CRF tests will follow step or ramp protocols (Fletcher et al., 2013). Step protocols used have included the Bruce, modified Bruce, or Naughton protocol (Goraya et al., 2000; Roger et al., 1998), while others have used the Balke (Blair et al., 1995; Wei, Kampert, et al., 1999) or modified Balke protocol (Carnethon et al., 2009; Sieverdes et al., 2010; Sui et al., 2008; Wei, Gibbons, et al., 1999). Varying in intensity, these protocols involve increases in workload at certain prescribed time points after steady state has been achieved. Participant context should be considered as larger workload increments could limit the accuracy of VO2max estimation, leading to overestimation (Fletcher et al., 2013; Myers & Froelicher, 1990). Of note, those using non-Bruce protocols have been significantly older, more likely to be women, have a higher burden of comorbidities, and are more likely to be taking cardiac medications. This is most likely due to less aggressive workloads required of other protocols.  Recommendations have been made to consider ramp protocols, depending on the participant. This method of assessing CRF involves continuous, gradual increases in workload 18  where no steady state is achieved. Ramp protocols can be individualized to the person and could be more accurate in estimating exercise capacity as they have been shown to elicit more gradual physiological responses (Myers & Froelicher, 1990; Panza et al., 1991). This holds especially true in those with lower functional capacity (Fletcher et al., 2013; Myers & Froelicher, 1990). Participants have also been found to prefer a ramp progression over step progression, specifically comparing the Bruce protocol and Bruce-ramp protocol (Kaminsky & Whaley, 1998). However, when comparing step and ramp protocols in adults aged 60 years and older, no differences were found between estimated aerobic capacities when gas exchange analysis is not used (Bader et al., 1999).   Concerns have also been raised for individuals who cannot reach their physiological maximal effort before other limitations arise. Although maximal exercise tests are recommended to be between 8-12 minutes to achieve fatigue-limited exercise, data can still be useful outside of this range. Treadmill exercise tests could last 5-26 minutes, while cycle ergometer tests could last 7-26 minutes. Workload and warm-up protocols should be adjusted accordingly (Midgley et al., 2008). It is up to the discretion of the practitioner to choose an exercise test protocol that accurately and safely assesses the patient\u2019s CRF. 1.1.2.7. Population  Exercise testing has historically been conducted in populations clinically referred for diagnostic purposes where the focus is on the exercise ECG and development of signs and symptoms. Findings from exercise stress tests are used by cardiologists to screen for and diagnose CVD. While they are non-invasive, inexpensive, and relatively safe and simple to administer, exercise stress tests are not recommended for CVD screening. This is due to limitations in the diagnostic power of ST-segment analysis in asymptomatic individuals (Fowler-19  brown et al., 2004; Lauer & Froelicher, 2005). Several research groups have pushed for the shift away from the ST segment and using exercise tests solely to diagnose.   The prognostic value of CRF testing in determining risk of future adverse health events has been established in primary prevention populations. Studies looking at asymptomatic women (Gulati et al., 2003), asymptomatic men (Erikssen et al., 2004), and all asymptomatic individuals (Aktas et al., 2004; Blair et al., 1989) have found having higher CRF levels predicted better health outcomes. Primary care patients who underwent maximal exercise testing and were followed up with, found the same protective effect of having higher levels of CRF (Syd\u00f3 et al., 2023).  Similarly, this strong relationship between CRF and mortality risk has been previously established in men clinically referred for exercise testing, including individuals with and without established CVD. Here, use of beta-blockers did not impact the prognostic power of CRF, even in those with a blunted HR response to exercise (Myers et al., 2002).    Great prognostic value has been highlighted in other exercise test measures such as exercise capacity, HR, and BP responses. In addition to assessment of risk for adverse health outcomes, focusing on CRF in exercise testing allows for informed exercise prescription by qualified exercise professionals (Ross et al., 2016). This shift in testing indication could alter and expand the population exposed to exercise testing. Understanding the CRF assessment responses of a primary prevention population could better inform testing practices.  1.2. Summary  The clinical significance of CRF testing to has been well established in the literature. Epidemiological studies have demonstrated the inverse association between CRF level and all-cause mortality and CVD, as well as shown the protective effect of increasing CRF and becoming physically active. Measuring patients\u2019 CRF in clinical settings can support healthcare 20  practitioners in stratifying chronic disease risk and provide patients with a tangible goal for health outcomes. Despite the clear benefit of assessing CRF, healthcare settings have yet to include testing in standard practice. While several clinical populations have been explored in establishing best practice guidelines for exercise testing, including protocol and mode selection, safety measures, and interpretation and application of results, standardization of procedures for PH populations have yet to be established and the inclusion of routine CRF testing in asymptomatic individuals is still debated. Further understanding of CRF profiles in PH patients could provide clarity on effective practices 1.3. Objectives  This study examined electronic medical records (EMR) from an interdisciplinary healthcare centre that has established CRF testing as a routine PH service. This project sought out to understand how often a maximal level of exertion is achieved during exercise testing in a primary prevention and longitudinal care clinic. By understanding the necessary considerations for supporting patient achievement of MEC, clinicians can rely on CRF testing to consistently predict adverse health outcomes, as well as effectively prescribe exercise and track progress. Exercise mode, test protocol, and pre-test demographics were explored for effect on the percentage of tests in which maximal level of exertion is achieved. Reasons for exercise test termination were also identified. 1.4. Research Questions 1. In a preventative health and longitudinal care clinic, what is the proportion of exercise tests in which maximal exercise capacity (MEC) is achieved? 2. Are there pre-test characteristics that affect the proportion of exercise tests in which MEC is achieved? 21  a) Does patient age predict patient achievement of MEC? b) Does patient sex predict patient achievement of MEC? c) Does patient PA behaviour predict patient achievement of MEC? d) Does exercise test modality predict patient achievement of MEC? e) Does exercise test protocol predict patient achievement of MEC? f) Does disease risk predict patient achievement of MEC? 3. What determines test termination in PH populations? 1.5 Hypotheses 1. H0: Patient ability to reach MEC will not be impacted by age. H1: Patient ability to reach MEC will be impacted by age. 2. H0: Patient ability to reach MEC will not be impacted by sex. H1: Patient ability to reach MEC will be impacted by sex. 3. H0: Patient ability to reach MEC will not be impacted by PA behaviour. H1: Patient ability to reach MEC will be impacted by PA behaviour. 4. H0: Patient ability to reach MEC will not be impacted by the mode of exercise test. H1:  Patient ability to reach MEC will be impacted by the mode of exercise test. 5. H0: Patient ability to reach MEC using a treadmill will not be impacted by the exercise test protocol. H1:  Patient ability to reach MEC using a treadmill will be impacted by the exercise test protocol. 6.  H0: Having one or more risk factors will not impact patient ability to reach MEC. H1:  Having one or more risk factors will impact patient ability to reach MEC.  22  Chapter 2: Methods 2.1. Sample Participants in the study were recruited though an interdisciplinary PH facility in Vancouver, British Columbia, Canada. Eligible participants were at least 18 years of age and have undergone a CRF test in the facility upon recommendation from their family physician from January 1, 2022 to January 31, 2024. Consent for inclusion in research was obtained as part of the healthcare centre intake process. Consent for exercise testing was obtained by patients\u2019 primary care providers and confirmed by the testing clinician prior to starting the test. 2.2. Study Overview All eligible patients underwent a retrospective chart review of relevant clinical and exercise test data. CRF tests conducted from January 2022 \u2013 January 2024 were included to reflect the most recent update in protocols and ensure all tests included followed the same protocols. 2.3. Clinical Standard Operating Procedure Most healthcare patients completed an exercise test as part of their annual preventative health assessment. This includes separate appointments with their nurse practitioner, family physician, dietician, and kinesiologist, as well as laboratory visit to complete physician-requested bloodwork. While these are the services offered, some patients decline to complete certain parts of their health assessment. In this sample, 120 patients chose to complete the exercise test recommended from the physician but opted out of completing a kinesiology assessment with the clinical exercise physiologist (CEP). In these cases, PA behaviour, among other factors not included in this current study, are not recorded in the EMR.  23  2.4. Outcome Measures 2.4.1. Maximal Test Criteria  The participating healthcare clinic requires approximately two out of the three following criteria to be met: a) Reach a rate of perceived exertion (RPE) of 9 on the Borg Category-Ratio (CR) 10 Scale b) Achieve within 10 bpm or 5% of their age-predicted maximal HR; c) Reach volitional exhaustion where the participant has expressed an inability to continue exercising, despite strong encouragement to continue by the clinician. Clinicians indicated \u201cyes\u201d, \u201clikely\u201d, \u201cmaybe\u201d, \u201cunlikely\u201d, or \u201cno\u201d on the exercise test report, pertaining to the patient reaching maximal effort. In this analysis, any tests indicating \u201clikely\u201d were grouped into the \u201cyes\u201d group. Tests indicating \u201cunlikely\u201d and \u201cmaybe\u201d were grouped into the \u201cno\u201d group. \u201cYes\u201d and \u201cno\u201d were the final groups for analysis. Indication of a maximal test is ultimately decided by clinician discretion.  2.4.2. Exercise Test Mode The CRF test mode (treadmill or electronically-braked cycle ergometer) used for each patient will be recorded. Those using a treadmill with excessive handrail holding will be differentiated. Selection of mode was based off clinician expertise. Patient context, ability, comfort, and history, among other factors, were considered.  2.4.3. Exercise Test Protocol The CRF test protocol used for each patient was recorded. Selection of protocol was based off clinician expertise with the goal of patient achievement of maximal effort. Patient context, ability, comfort, and history, among other factors, were considered. Generally, staged 24  protocols change in workload at given intervals where ramp protocols continuously progress in workload (Bader et al., 1999). Staged treadmill protocols used included the Bruce, Naughton, Balke-Ware, United States Air Force School of Aerospace Medicine (USAFSAM), and custom protocols. Clinicians could also opt to complete a modified and\/or ramped version of the listed protocols. All cycle ergometer protocols, as well as treadmill custom protocols, were individualized to the patient.  2.4.4. Disease Risk Profile  Patients will be classified as elevated and low risk of disease. The lower-risk patient group will include those who do not have any of the cardiovascular comorbidities and\/or risk factors listed below (Hedman et al., 2020), while the elevated risk group will have at least one or more of the outlined risk factors. The following criteria directly follow\u2019s Hedman and colleagues\u2019 (2020) criteria: a) Previous coronary artery disease (CAD): Previous myocardial infarction, cardiac procedures\/interventions and\/or coronary artery stenosis >50% at imaging b) Atrial fibrillation: Atrial fibrillation registered on ECG at rest or during exercise at time of exercise testing c) Family history of CAD: An immediate family member having ischemic heart disease before the age of 60 d) Hypertension: Previous diagnosis of hypertension and\/or use of any anti-hypertensive medication (not including beta-blockers) e) Smoking: current smoker or previous smoker with history of >10 pack-years of smoking f) Hypercholesterolemia: Total cholesterol >12.2 mmol\/, statin use, or both g) Diabetes: A diagnosis at time of test per medical records 25  h) Cardiac medications: Use of any of the following as per medical records, and verified at time of test: statins, anti-hypertensives (incl. angiotensin converting enzyme-inhibitors, angiotensin receptor antagonists, calcium channel blockers), aspirin and beta-blockers 2.4.5. PA Behaviour  Patients were classified as \u201cphysical active\u201d or \u201csedentary\u201d. Individuals considered physically active are those who participate in at least 30 minutes of moderate-intensity PA on at least three days per week for the previous three months. Those who did not meet this criteria were placed in the \u201csedentary\u201d group (Liguori & American College of Sports Medicine, 2021). 2.4.6. Reasons for Test Termination  Patients were instructed and encouraged to exercise to their maximal exertion, unless otherwise indicated by their physician. After an exercise test, clinicians record the reason for test termination in the test report. Reasons for test termination were categorized into six groups described below. Exercise tests could have been terminated for more than one reason. Categories were determined by the clinician lead. Exercise tests will also be categorized by the number of reasons for termination indicated.  a) Inability to Continue Due to Physical Discomfort. Patients in this category terminated their exercise test due to physical discomfort preventing further continuation of the test despite strong encouragement from the clinician. Inability to continue due to physical discomfort was said to be present if exercise reports outlined volitional exhaustion, fatigue, leg fatigue, or dyspnea\/shortness of breath as reasons for terminating the exercise test. b) Abnormal Hemodynamic Response. 26  Exercise tests were stopped by the clinician if a patient\u2019s blood pressure response was considered abnormal based on established guidelines. An absolute indication for test termination was a drop in systolic blood pressure greater than 10 mmHg with repeated measurements when accompanied by other evidence of ischemia, despite an increase in workload. If there was no other evidence of ischemia, it was the clinician\u2019s discretion to continue or terminate the test. A relative indication for test termination was a systolic blood pressure above 250 mmHg or diastolic blood pressure above 115 mmHg, whereby it was the clinician\u2019s discretion whether to terminate the test (Fletcher et al., 2013; Liguori & American College of Sports Medicine, 2021). c) Exercise Electrocardiogram Abnormalities. Exercise tests were stopped by the clinician if the following ECG abnormalities were present: i. ST elevation: ST-segment elevation (>1.0 mm) in leads without preexisting Q waves because of prior MI (other than aVR, aVL, and V1) ii. ST depression: Marked ST displacement (horizontal or downsloping of >2 mm, measured 60 to 80 ms after the J point [the end of the QRS complex]) in a patient with suspected ischemia iii. Arrhythmias compromising maintenance of cardiac output: Sustained ventricular tachycardia or other arrhythmia, including 2nd or 3rd degree AV block, that could affect normal maintenance of cardiac output during exercise iv. Arrhythmias \u2013 other: Arrhythmias other than sustained ventricular tachycardia, including multifocal ectopy, ventricular triplets, supraventricular tachycardia, and bradyarrhythmias that have the potential to become more complex or to affect 27  hemodynamic stability. Includes development of bundle branch block that cannot be immediately differentiated from ventricular tachycardia d) Signs and\/or symptoms. Signs and symptoms contraindicative to exercise were considered present if the following were listed in the exercise test report: i. Dyspnea at unexpected exertion ii. Breathing abnormalities iii. Chest pain iv. Ataxia, dizziness, or near syncope v. Poor perfusion \u2013 cyanosis or pallor vi. Leg cramps or claudication e) Stopped by Clinician. Exercise tests in this category have been stopped by the clinician for reasons outside of abnormal hemodynamic and\/or electrocardiographic responses and\/or presence of signs and symptoms contraindicative to exercise. This included technical difficulties with testing equipment or excessive patient handrail use. In some cases, clinicians terminated the exercise test when patients reached an unspecified \u201ctarget heart rate\u201d for reasons unknown.  f) Other Reasons. Patients in this category did not wish to continue their exercise test prior to reaching a maximal level of exertion. Other reasons were said to be present if exercise reports outlined discomfort with equipment, lack of motivation, balance issues, fear of falling, or discomfort with speed and\/or incline of treadmill as reasons for test termination. 28  2.5. Data Extraction 2.5.1. Laboratory Measures  Bloodwork involved in determining CVD risk consists of LDL-C, HDL-C, HbA1C levels and fasting plasma glucose, and is collected using standard laboratory procedures in-office or through an external referral, typically within 2 weeks of CRF testing. Patient body mass, waist circumference, and resting BP were measured by the lab and diagnostic screening coordinators. 2.5.2. Exercise Test Variables 2.5.2.1. HR  During the CRF test, HR was recorded using an electrocardiogram or Polar H10 chest strap (Polar Electro Oy, Kempele, Finland). 2.5.2.2. Age-Predicted Maximal HR Age-predicted maximal HR was calculated with the following formula: Age-Predicted Maximal HR (bpm) = 207 \u2013 0.7*Age 2.5.2.3. BP  BP was assessed using auscultation by a clinical exercise physiologist. Exertional hypotension was defined by a SBP decrease below resting or by \u226510 mmHg. Exaggerated SBP responses are indicted in patients who reach \u2265210 mmHg (men) or \u2265 190 (women). Exaggerated DBP responses are indicted in patients who rise >10 mmHg above resting or >90 mmHg. Abnormal SBP recovery was indicated in patients who see an increase in SBP after exercise test cessation, as well as those who did not see a decrease from peak SBP (Liguori & American College of Sports Medicine, 2021). 29  2.5.2.4. RPE  RPE during the CRF test was assessed using the Borg CR-10 Scale (Noble et al., 1983). This will be used to capture overall perceived exertion and presented as a visual to patients during the test. Patients can rate >10 for maximal intensity, as well as in-between two numbers. 2.6. Clinician Expertise  All clinicians carrying out maximal exercise tests held a minimum of a Bachelor\u2019s degree in exercise science. Clinicians were either candidates for or certified as CEPs through the American College of Sports Medicine (ACSM) or the Canadian Society for Exercise Physiology. Several team members also hold graduate degrees and further certifications in clinical exercise. In addition, the team of CEPs regularly undergo continuing education, evaluation, and are required to observe and assist on 50 exercise tests before conducting one independently as well as 25 exercise tests per year to maintain competency, in alignment with ACSM guidelines (Liguori & American College of Sports Medicine, 2021). A senior CEP and physician were always available on-site in case further guidance was required.  2.7. Procedure This study followed a retrospective study design. Patient outcomes of interest already occurred before being enrolled in the study, and data was extracted from their EMRs. CRF tests results were extracted from each chart, along with the patient chart number, birth date, and sex. CRF tests reports include resting standing or seated HR and BP (depending on exercise mode), HR and BP at each CRF test stage, CRF test workload at each stage, cool-down HR and BP, RPE, volitional fatigue determination, percent maximum HR reached, maximal test determination, reason for stopping test, test protocol, and test mode. CV comorbidities and risk factors were also pulled to create a subgroup of lower-risk patients. 30  Chapter 3: Data and Statistical Analysis  Statistical analyses were run using IBCM SPSS Statistics software version 29.0.1 (IBM Corporation, Armonk, NY). 3.1. Achievement of MEC  The proportion of participants reaching their MEC in this PH population was calculated. 3.2. Predictors of Achieving MEC  Chi-squared tests of independence were used to assess the associations, if any, of age, sex, exercise test mode, treadmill test protocol, PA behaviour, and CVD risk on achieving maximal effort.  Further chi-squared tests were performed to identify relationships between variables in impacting the achievement of MEC. Finally, multiple comparisons tests were conducted for variables with more than two groups 3.3. Insight on Maximal Test Termination  Frequency distributions were used to describe patients\u2019 reasons for termination their exercise test before reaching maximal effort. Exercise reports allowed for multiple reasons to be listed, reflected in the presentation of data.      31  Chapter 4: Results 4.1. Participant Characteristics and CVD Risk  619 males (n=431) and females (n=188) underwent a retrospective chart review in this study. Participant characteristics and CVD risk are presented in Table 4.1. 76.9% of the study sample achieved MEC.  Table 4.1. Participant characteristics and CVD risk.  All n (%) Max tests n (%) 476 (77) Not max n (%) 143 (23) Sex (female)  188 (30) 129 (27) 59 (41) Age (years) 58.7 \u00b112.0 57.6\u00b111.5 62.4\u00b112.9 Height (centimetre) 173.8\u00b19.9 174.4\u00b19.9 171.9\u00b19.5 Body mass (kilogram) 81.6\u00b117.3 82.6\u00b117.5 78.2\u00b116.6 BMI (kg\u00b7m-2)  26.8\u00b14.3 27.0\u00b14.3 26.2\u00b14.4 PA behaviour  - - -  Active 378 (61) 299 (63) 79 (55)  Sedentary 121 (20) 91 (19) 30 (21)  Unknown 120 (19) 86 (18) 34 (24) CVD risk factors - - -  CAD  22 (4) 9 (1) 9 (9)  Atrial fibrillation  18 (3) 9 (2) 9 (6)  Stroke\/claudication\/COPD 3 (1) 2 (0) 1 (0)  Family history of CAD  97 (16) 68 (14) 29 (20)  Hypertension  194 (31) 139 (29) 55 (39)  Smoking  55 (9) 36 (8) 19 (13)  Hypercholesterolemia  255 (41) 190 (40) 65 (46)  Diabetes  35 (6) 28 (6) 7 (5)  Cardiac medications  303 (49) 224 (47) 79 (55) Abbreviations: BMI = body mass index, CAD = coronary artery disease, COPD = chronic obstructive pulmonary disorder. Continuous variables presented as Mean \u00b1 SD. Sex presented as Number of female participants (% of maximal test category). PA behaviour presented as Number 32  of participants in PA behaviour category (% of maximal test category). CVD risk factors presented as Number of participants with risk factor (% of maximal test category). 4.2 Predictors of Achieving Maximal Capacity  Associations with MEC were found for age, sex, CVD risk, exercise test mode, and treadmill protocol, and summarized in Table 4.2. Younger patients had a higher percentage of maximal tests compared to older patients (p<.001). Males had a higher percentage of maximal tests than females (p=.001). Individuals with low CVD risk had a higher percentage of maximal tests than those with elevated CVD risk (p=.022). Patients using a treadmill while holding onto handrails had less maximal tests compared to other modes (p=.002). In treadmill exercise tests, there were more individuals using the Bruce-ramp protocol who reached MEC compared to other treadmill protocols.  Further exploration determined relationships between variables influencing the achievement of MEC, as described in Table 4.3 and expanded on below. Participant age group contributed to the impact of sex, PA behaviour, CVD risk, exercise mode, and treadmill protocol on ability to achieve MEC, further outlined in Table 4.4.      33  Table 4.2. Comparison of maximal and non-maximal exercise tests, stratified by age, sex, PA behaviour, CVD risk status, exercise mode, and treadmill protocol.  All (n = 619)  n (%) Maximal (n = 476)  n (%) p-value Age    <.001  <50 years old 138 112 (81)   50-70 years old 370 299 (81)   \u226570 years old 111 65 (59)  Sex    .001  Female 188 129 (69)   Male 431 347 (81)  PA behaviour   .367  Sedentary 121 91 (52)   Active 378 299 (63)   Unknown    CVD risk status  - - .022  Low 250 204 (82)   Elevated 369 272 (74)  Exercise mode  - - .002  Treadmill 418 321 (77)   Treadmill with excessive handrail use 98 67 (68)   Cycle ergometer 103 88 (85)  Treadmill protocol  - - .033  Bruce-ramp protocol 468 357 (76)   Other 51 34 (67)  Abbreviations: CVD = cardiovascular disease, PA = PA. Significance was set at alpha level .05.        34  Table 4.3. Significant relationships between factors influencing the ability to reach maximal exercise capacity.  Age Sex PA behaviour CVD risk status Exercise mode Test protocol Age - * * * * * Sex  - * * * * PA behaviour   - * * NS Risk status    - NS NS Exercise mode     - * Test protocol      - * = Significant association. The table shows significant association between age and sex, age and PA behaviour, age and CVD risk status, age and exercise mode, age and test protocol, sex and PA behaviour, sex and CVD risk status, sex and exercise mode, sex and test protocol, PA behaviour and CVD risk status, PA behaviour and exercise mode, PA and exercise mode, and mode and test protocol. Significance was set at alpha level .05.  35  Table 4.4 Relationship between age and sex, PA behaviour, CVD risk, exercise mode, and treadmill protocol in achievement of MEC.   All (n=619)  Maximal (n=476)  Non-maximal (n=143)  Statistical Differences*   <50 y (n=138) 50-70 y (n=370) >70 y (n=111)  <50 y 50-70 y >70 y  <50 y 50-70 y >70 y  Sex               Male 95 263 73  78 (82) 224 (85) 45 (62)  17 (18) 39 (15) 28 (38)  p<.001  Female 43 107 38  34 (79) 75 (70) 20 (53)  9 (21) 32 (30) 18 (47)  p=.033 PA Behaviour               Active 88 224 66  74 (84) 182 (81) 43 (65)  14 (16) 42 (19) 23 (35)  p=.008  Sedentary 27 72 22  22 (82) 58 (81) 11 (50)  5 (19) 14 (19) 11 (50)  p=.010  Unknown 23 74 23  16 (70) 59 (80) 11 (48)  7 (30) 15 (20) 12 (52)  p=.012 CVD risk mode               Low 93 137 20  77 (83) 114 (83) 13 (65)  16 (17) 23 (17) 7 (35)  p=.136  Elevated 45 233 91  35 (78) 185 (79) 52 (57)  10 (22) 48 (21) 39 (43)  p<.001 Exercise mode               Treadmill 109 250 59  88 (81) 202 (81) 31 (53)  21 (19) 48 (19) 28 (47)  p<.001  Treadmill with excessive handrail use 8 57 33  6 (75) 42 (74) 19 (58)  2 (25) 15 (26) 14 (42)  p=.261  Cycle ergometer 21 63 19  18 (86) 55 (87) 15 (79)  3 (14) 8 (13) 4 (21)  p=.663 Treadmill protocol               Bruce-ramp protocol 107 287 74  87 (81) 229 (80) 41 (55)  20 (19) 58 (20) 33 (45)  p<.001  Other treadmill protocols 11 21 19  8 (73) 16 (76) 10 (53)  3 (27) 5 (24) 9 (47)  p=.256 * = Statistical significance of variable and age. Abbreviations: PA = PA, CVD = cardiovascular disease. 36  4.2.1. Age and Sex  As outlined in Figure 4.1, males over 70 years of age were less likely to reach MEC compared to males under 50 years (p=.003) and those between 50-70 years of age (p<.001). Females over 70 years of age were more likely to reach MEC compared to those under 50 years of age (p=.012). As well, in individuals between 50-70 years of age, males were more likely to reach MEC compared to females (p<.001), shown in Figure 4.2.  Figure 4.1. Impact of age on sex impacting ability to achieve MEC. * = significant difference between age groups <50 years and >70 years. \u2020 = significant difference between age groups 50-70 years and >70 years. Relationship observed regardless of sex.    0%10%20%30%40%50%60%70%80%90%100%M FFrequency of Maximal Test (%)Sex<50 y 50-70 y >70 y**\u2020 \u2020 37   Figure 4.2. Impact of sex on age impacting ability to achieve MEC. * = significant difference between males and females in age group 50-70 years.  4.2.2. Age and PA Behaviour Active individuals over 70 years of age were less likely to reach MEC compared to those under 50 years (p=.006) and between 50-70 years of age (p=.006). Sedentary individuals over 70 years of age were less likely to reach MEC compared to those under 50 years (p=.019) and between 50-70 years of age (p=.005). When PA behaviour is unknown, individuals over 70 years of age were less likely to reach MEC compared to those between 50-70 years (p=.003). Relationships can be observed in Figure 1.3.    0%10%20%30%40%50%60%70%80%90%100%<50 y 50-70 y >70 yFrequency of Maximal Test (%)Age GroupM F*38   Figure 4.3. Impact of age on PA level impacting ability to achieve MEC. * = significant difference between age groups 50-70 years and >70 years for all levels of PA. \u2020 = significant difference between age groups <50 years and >70 years in both sedentary and active individuals. Note: 120 participants did not have PA behaviour indicated (Unknown).  4.2.3. Age and CVD Risk Status  In those with elevated CVD risk, individuals over 70 years of age were less likely to reach MEC compared to those under 50 years (p=.018) and between 50-70 years of age (p<.001), as shown in Figure 4.4.    0%10%20%30%40%50%60%70%80%90%100%Sedentary Active UnknownFrequency of Maximal Test (%)PA Behaviour<50 y 50-70 y >70 y\u2020* \u2020 * * 39   Figure 4.4. Impact of age on CVD risk impacting ability to achieve MEC.  * = significant difference between age groups 50-70 years and >70 years for individuals with elevated CVD risk. \u2020 = significant difference between age groups <50 years and >70 years for individuals with elevated CVD risk.  4.2.4. Age and Exercise Mode In those using a treadmill for their exercise test, individuals over 70 years of age were less likely to reach MEC compared to those under 50 years (p<.001) and between 50-70 years of age (p<.001). Relationships can be observed in Figure 4.5.   0%10%20%30%40%50%60%70%80%90%100%Low ElevatedFrequency of Maximal Test (%)CVD Risk<50 y 50-70 y >70 y*\u2020  40   Figure 4.5 Impact of age on exercise test mode impacting ability to achieve MEC.  * = significant difference between age groups 50-70 years and >70 years for individuals using a treadmill. \u2020 = significant difference between age groups <50 years and >70 years for individuals using a treadmill. 4.2.5. Age and Test Protocol  In those using the Bruce-ramp protocol for their treadmill exercise test, individuals over 70 years of age were less likely to reach MEC compared to those under 50 years (p<.001) and between 50-70 years of age (p<.001). Relationships can be observed in Figure 4.6. 0%10%20%30%40%50%60%70%80%90%100%Treadmill Excessive Handrail BikeFrequency of Maximal Test (%)Mode<50 y 50-70 y >70 y* \u2020  41   Figure 4.6 Impact of age on treadmill exercise test protocol impacting ability to achieve MEC.   * = significant difference between age groups 50-70 years and >70 years for individuals using the Bruce-ramp protocol. \u2020 = significant difference between age groups <50 years and >70 years for individuals using the Bruce-ramp protocol.  4.2.6. Sex and PA Behaviour  When PA behaviour is unknown, males are more likely to reach maximal capacity compared to females (p=.006) as shown in Figure 4.7. 0%10%20%30%40%50%60%70%80%90%100%Bruce-ramp Other TMFrequency of Maximal Test (%)Treadmill Protocol<50 y 50-70 y >70 y* \u2020 42   Figure 4.7 Impact of sex on PA behaviour impacting ability to achieve MEC. * = significant difference between males and females when PA behaviour is not indicated (Unknown). Note: 120 participants did not have PA behaviour indicated (Unknown). 4.2.7. Sex and CVD Risk Status  As shown in Figure 4.8 in individuals with elevated CVD risk, males were more likely to reach MEC compared to females (p=.037). As well, in females, those at low risk of CVD were more likely to reach MEC compared to females as shown in Figure 4.9.  0%10%20%30%40%50%60%70%80%90%100%Sedentary Active UnknownFrequency of Maximal Test (%)PA BehaviourM F*43   Figure 4.8 Impact of sex on CVD risk impacting ability to achieve MEC. * = significant difference between males and females in individuals with elevated CVD risk.  Figure 4.9 Impact of CVD risk on sex impacting ability to achieve MEC. * = significant difference between those with low and elevated CVD risk in females. 0%10%20%30%40%50%60%70%80%90%100%Low ElevatedFrequency of Maximal Test (%)CVD RiskM F*0%10%20%30%40%50%60%70%80%90%100%M FFrequency of Maximal Test (%)SexLow Elevated*44  4.2.8. Sex and Exercise Mode  In those using a treadmill for the exercise test, males were more likely to reach MEC compared to females (p=.003), as shown in Figure 4.10.   Figure 4.10 Impact of sex on exercise mode impacting ability to achieve MEC. * = significant difference between those with males and females in those using a treadmill. 4.2.9. Sex and Test Protocol  In those using the Bruce-ramp protocol for their treadmill exercise test, males were more likely to reach MEC compared to females (p=.006), as shown in Figure 4.11.     0%10%20%30%40%50%60%70%80%90%100%Treadmill Excessive Handrail BikeFrequency of Maximal Test (%)ModeM F*45   Figure 4.11 Impact of sex on treadmill exercise test protocol impacting ability to achieve MEC. * = significant difference between males and females while using the Bruce-ramp protocol on the treadmill. 4.2.10. PA Behaviour and CVD Risk Status  When PA behaviour is unknown, those at low risk for CVD were more likely to reach MEC compared to those at elevated risk (p=.026) as shown in Figure 4.12. As well, in individuals at elevated CVD risk, those who were sedentary were more likely to reach MEC compared to those who had unknown PA behaviour (p=.016). This can be seen in Figure 4.13.    0%10%20%30%40%50%60%70%80%90%100%Bruce-ramp Other TMFrequency of Maximal Test (%)Treadmill ProtocolM F*46   Figure 4.12 Impact of CVD risk on PA behaviour impacting ability to achieve MEC.  * = significant difference between those with low and elevated CVD risk when PA behaviour is not indicated (Unknown). Note: 120 participants did not have PA behaviour indicated (Unknown). 0%10%20%30%40%50%60%70%80%90%Sedentary Active UnknownFrequency of Maximal Test (%)PA BehaviourLow Elevated*47   Figure 4.13 Impact of PA behaviour on CVD risk impacting ability to achieve MEC. * = significant difference between those who are sedentary and unknown PA behaviour when CVD risk is elevated. 4.2.11. PA Behaviour and Exercise Mode  In active individuals, those who used a cycle ergometer were more likely to reach MEC compared to those who used a treadmill (p=.022) and treadmill with excessive handrail grip (p=.005). Relationships can be observed in Figure 4.14. 0%10%20%30%40%50%60%70%80%90%100%Low ElevatedFrequency of Maximal Test (%)CVD RiskSedentary Active Unknown*48    Figure 4.14 Impact of exercise test mode on PA behaviour impacting ability to achieve MEC. * = significant difference between those using a treadmill with excessive handrail holding and bike when individuals are active. \u2020 = significant difference between those using a treadmill and bike when individuals are active. Note: 120 participants did not have PA behaviour indicated (Unknown). 4.2.12. Mode and Test Protocol  In those using other treadmill protocols, individuals using a treadmill without gripping the handrails were more likely to reach MEC compared to those who were (p=.031). This can be seen in Figure 1.15. As well, those gripping the handrails excessively were more likely to reach MEC when using the Bruce-ramp protocol compared to other treadmill protocols (p=.031) as shown in Figure 4.16. 0%10%20%30%40%50%60%70%80%90%100%Sedentary Active UnknownFrequency of Maximal Test (%)PA BehaviourTreadmill Excessive Handrail Bike* \u2020  49   Figure 4.15 Impact of treadmill protocol on exercise test mode impacting ability to achieve MEC. * = significant difference between those using the Bruce-ramp protocol and other treadmill protocols when individuals are using a treadmill with excessive handrail holding. 0%10%20%30%40%50%60%70%80%90%100%Treadmill Excessive HandrailFrequency of Maximal Test (%)Treadmill ProtocolBruce-ramp Other TM*50   Figure 4.16 Impact of exercise test mode on treadmill protocol impacting ability to achieve MEC. * = significant difference between those using a treadmill and those holding onto treadmill handrails excessively when other treadmill protocols are used. 4.3. Insight on Maximal Test Termination  Clinicians indicated one or more reasons for terminating the exercise test on patient reports. The frequency of each reason listed is summarized in Table 4.4.    0%10%20%30%40%50%60%70%80%90%100%Bruce-ramp Other TMFrequency of Maximal Test (%)Treadmill ProtocolTreadmill Excessive Handrail*51  Table 4.5. Frequency of Listed Reason for Test Termination  Reason  N=619                            n (%) Physical discomfort 504 (81) Other reasons 137 (22) Stopped by clinician 40 (7) Exercise ECG 11 (2) Abnormal hemodynamic response 7 (1) Signs and symptoms 7 (1)  The most common reason for test termination indicated was fatigue (N=504), followed by other reasons (N=137) as defined in the methods.            52  Chapter 5: Discussion 5.1. Main Findings  This project sought to further our understanding of exercise testing in clinical settings and support the optimization of achieving maximal exercise capacity (MEC) in a preventative health (PH) population. The assessment of cardiorespiratory fitness (CRF) is clearly deserving of inclusion in routine health exams. Much like the measurement of blood pressure (BP), lipids, and blood sugars, among other traditional risk factors for chronic disease, CRF provides prognostic value to the patient health profile in predicting risk for future adverse health outcomes (Blair et al., 1989; Gupta et al., 2011; Myers et al., 2002; Syd\u00f3 et al., 2023). Literature describing the strength of CRF as a risk factor, and predicted outcomes of improving CRF, established their findings through maximal exercise (Goraya et al., 2000; Gulati et al., 2003; Myers et al., 2002; Roger et al., 1998). By mirroring this practice in patients, clinicians have access to a measurement tool that is consistent with the great body of literature on CRF as a predictor of disease risk and mortality. Furthermore, patients are provided with a safe and supportive setting to exercise at a high intensity. As healthcare settings, specifically those prioritizing PH care, look to include or improve upon exercise testing as part of their routine health exams, the elicitation of maximal capacity in patients must be considered. How do we ensure patients reach their MEC? What factors should influence the selection of exercise test procedures?  The purpose of this thesis was to investigate the pre-test demographics and factors that impact patient ability to achieve MEC in a PH population. A retrospective chart review was conducted for any patients who completed an exercise test between January 1, 2022 and January 31, 2024. Demographic, exercise test, PA behaviour, and CVD risk data were pulled for each patient included. 76.9 % of the study sample reached MEC. Patient ages ranged from 23- to 87-53  years old, with an average age of 58.68 (SD=12.02) years. Findings are limited by availability of patient data captured in the EMR. While all clinicians follow standard charting procedures, patient circumstances may impact clinician ability to record all applicable data points. For example, PA behaviour was not recorded for 120 individuals as some patients declined their kinesiology assessment and chose to only complete the exercise test. While not addressed in this thesis, this observation is important to consider in future exploration. In a healthcare setting where a kinesiology assessment is included as part of the enrolment fee, there are still individuals that choose not to receive this kind of care. As we argue the importance of addressing CRF in healthcare, how do we convince patients that a consultation regarding their PA behaviour is no different than their routine bloodwork? This work aims to find what pre-test characteristics impact the achievement of MEC with the hopes of providing both patients and clinicians meaningful data on CRF. 5.1.1. Predictors of achieving MEC Patient age was established as a clear impacting factor for the achievement of MEC. Individuals over the age of 70 years old has a significantly lower proportion of maximal exercise tests across most factors investigated in this study. This included males, females, active and sedentary individuals, those using treadmills, as well as those using the Bruce-ramp treadmill protocol. In line with the maximal test criteria for this current study\u2019s clinical protocols, a significantly higher number of older adults not reaching MEC is most likely due to the individual stopping the test before meeting the RPE criteria of eight or greater, and\/or reaching volitional exhaustion. As mentioned above, this could be due to a number of reasons that were not comprehensively captured by clinicians. Higher perceived feelings of breathlessness (Ofir et al., 2008), greater fear of falling (Sawa et al., 2020), and preferences for lower exercise intensities 54  (Alley et al., 2018; Gavin et al., 2015) may have impacted older adults\u2019 ability to reach MEC. These feelings of discomfort for many reasons during the exercise test may have led to patients choosing to terminate the exercise test before reaching their MEC. Another consideration should be made for one of the criteria measurements for a maximal test. While the current study\u2019s clinical protocols use a Borg CR-10 (Noble et al., 1983), this may not have been the best measurement tool for older adults. When comparing the Borg 6-20 scale, Borg CR-10 scale, and OMNI scale in a systematic review, Lopes and colleages (2020) found the Borg 6-20 scale presented more cross-cultural adaptation in older adults. Cultural background was not included in this current study but would be interesting to explore in the future for impact on RPE scale utilization.  Significant associations with MEC were also found for sex, exercise test mode, treadmill protocol, and CVD risk. While it is informative knowing these factors impact a person\u2019s ability to reach MEC, further inquiry was required to make meaningful conclusions on considerations for exercise testing. Further analyses were conducted to highlight relationships, if any, between the independent variables, or pre-test demographics and factors, in impacting the achievement of MEC. Patient sex was established as another factor impacting the achievement of MEC. Overall, a greater proportion of males reached MEC compared to females. Specifically, a higher proportion of males reached MEC when PA behaviour was unknown, had an elevated CVD risk, a treadmill was used, and specifically with the Bruce-ramp protocol during their exercise test. This was an interesting finding as several studies have found no sex or gender differences in ratings of perceived exertion at high intensity exercise (Chase et al., 2008; Green et al., 2003; Kim & Lee, 2011). However, the females and women in these previous studies were generally 55  younger, more active, and had higher CRF levels (sex and gender terms were presented as dichotomous variables; no papers differentiated sex versus gender). Females in a PH population may differ in exercise experience and comparatively, this current study. Bengoechea and colleagues (2005) found disparities in perceptions of environmental correlates to PA. Women are less likely to view their neighbourhood as safe to walk in at night or perceive easy access to places for PA. This may impact the exercise habits, and in turn experience with high intensity, in women. As well, findings in this current study would also suggest that aging may account for sex differences in reaching MEC. When looking at adults between 60-67 years of age, Van Uffelen and colleagues (2017) found women to be less likely to prefer activities that were competitive, vigorous, required skill or practice, and were done outdoors.   CVD risk was established as another factor that impacts the achievement of MEC. Individuals with low CVD risk were more likely to reach MEC only in females and those with unknown PA behaviour information. Here, CVD risk appeared to have little impact on whether or not a person reaches MEC However, it would be valuable to explore the mechanisms leading to sex differences in the impact of CVD risk on reaching MEC in this PH population. Joshi and colleagues (2010) explored exercise responses of individuals with and without T2DM. Those with T2DM showed attenuated cardiac function and microvascular utilization in response to exercise, compared to those without T2DM. Looking at women specifically, Huebschmann and colleagues (2009) conducted submaximal exercise tests at different set loads (20 watts and 30 watts). Women with T2DM perceived a harder effort during the higher intensity load when compared to women without T2DM. Here, the researchers believed that individual CRF may have impacted differences between groups. Overall, this aligns with the wealth of literature on the prognostic benefit of exercise testing in different populations with elevated CVD risk 56  (Guazzi et al., 2012; Sui et al., 2008; Syd\u00f3 et al., 2023; Wei, Kampert, et al., 1999) where tests are symptom- and\/or fatigue-limited. Establishing this in a PH population gives clinicians the confidence to safely encourage patients to exercise to volitional exhaustion. This could especially be beneficial for individuals with elevated CVD risk who would benefit from high intensity exercise. Allowing the patient to explore their physical abilities in a safe, supervised environment could give them the confidence to incorporate exercise into their lifestyle and lower their risk for adverse health outcomes.   Exercise test mode was also found to impact the achievement of MEC. A higher proportion of maximal tests in this current study were done on the cycle ergometer, compared to the treadmill and treadmill with excessive handrail holding. However, only active individuals had a significantly higher proportion of maximal tests while using the cycle ergometer. In sedentary patients, as well as those with unknown PA behaviour, exercise test mode did not impact their ability to reach MEC. It is important to note that central and peripheral fatigue were included in the same indication for test termination, ie. there was no way to tell from the patient chart if they were limited by lower-limb fatigue or volitional exhaustion. PH patients may have felt more comfortable exerting themselves to maximal effort while seated in a cycle ergometer. Patient comfort during the exercise test is not indicated in their chart could be valuable to capture in future studies. While this difference was seen in active individuals, specific details on their experience and history of exercise is unknown. Those who are meeting exercise guidelines may not be necessarily comfortable with exercising at very high intensities. This is important for clinics looking to acquire equipment for exercise testing as cycle ergometers are typically cheaper, take up less space, and potentially easier for clinicians to attain exercise test measures such as BP and HR. As well, they are more inclusive of those that are limited by musculoskeletal 57  issues, balance concerns, and fears of falling. Patient preference for exercise test mode, aligning with their exercise experience and history, could be important factors to consider test procedure selection (Muscat et al., 2015). Unfortunately, neither cycle ergometers nor treadmills are appropriate for CRF testing in individuals with limited lower-limb function. Clinical settings that offer CRF testing should consider alternative methods for this population, such as the use of arm ergometers. Ilias and colleagues (2009) investigated the predictive capability of arm exercise testing in 359 veterans who were unable or unwilling to complete a treadmill exercise test. The average follow-up time was about five years where the research team found exercise capacity to be a significant predictor of death. The bounds of this exercise mode should be considered as arm crank ergometers are not meant to assess whole body VO2max and may not compare to other methods in assessing CRF. Deguchi and colleagues (2023) compared arm crank ergometers and cycle ergometers in collegiate rowers and cyclists. They found the arm crank ergometer underestimated VO2max in all athletes. In healthy, non-athletic populations, those using an arm crank ergometer achieved VO2max values that were on average 70% of the VO2max values measured on cycle ergometers (Larsen et al., 2016). Despite the differences in physiological response, arm ergometers still hold prognostic benefit in assessing risk for adverse health outcomes. While they may not offer accurate measurement of whole-body aerobic capacity, patients who are limited by their lower limbs still have an opportunity to exercise at a high intensity, most likely with equipment they would be using outside of a test setting.  Finally, exercise test protocol appeared to have some impact in patients reaching MEC. Older adults on the treadmill appeared to reach MEC less often compared to those under 70 years old when using the Bruce-ramp protocol. Females on the treadmill reached MEC less often than males when using the Bruce-ramp protocol. Treadmill patients excessively holding onto 58  handrails reached MEC less often that those refraining from holding onto handrails. As well, individuals using other protocols reached MEC less often than those using the Bruce-ramp protocol when excessively holding onto handrails on the treadmill. Differences in groups most likely are accounted for by the impact of age and sex. When selecting exercise test protocol, considerations for patient exercise experience should be made to ensure they are best set up to achieve maximal capacity. 5.1.2. Insight on Maximal Exercise Test Termination Reasons for terminating the exercise test were indicated on the exercise test report, where more than one reason could have been listed. 81.4% (n=504) of patients indicated fatigue\/dyspnea as the reason for stopping their test. As mentioned above, it is unclear what kind of fatigue or physical discomfort that patient experienced. Further specification of patient feelings of fatigue, dyspnea, and\/or discomfort could better determine whether the exercise test was maximal. Chase and colleagues (2008) explored the relationship between differences in exercise test measures, as well as health outcomes, and reasons for exercise test termination in patients with heart failure. Dyspnea and fatigue were compared, with both groups showing similar resting ejection fraction and medications prescribed. Individuals who reported dyspnea as their reason for terminating the exercise test had a higher severity of heart failure compared to those terminating for fatigue. Patients were tracked for two years following their initial exercise test. Those who terminated their test due to dyspnea were at a significantly higher risk of adverse events. While this population differed from the current study sample, further assessment of reasons for exercise test termination in a PH population could provide prognostic value and support exercise prescription and delivery.  59  One fifth of patients in this current study also terminated the test due to miscellaneous reasons such as musculoskeletal limitations, balance issues, and fear of falling. Patient consultation and discussion before selecting an exercise test mode and protocol could increase their chances of achieving MEC. In the healthcare clinic studied, exercise tests are ordered by the family physician, not the clinical exercise physiologist. Considerations could be taken for further involvement of the CEP to allow for a more comprehensive assessment of the patient context before selection of exercise test parameters. This could also strengthen the patient-CEP relationship, and instill trust in the clinician to take the patient through higher intensity exercise. Valuable information on the risk of adverse health outcomes can come from analyzing the reasons an individual terminates their exercise test. When investigating the prognostic significance of reasons for test termination on long-term mortality in men, Bodegard and colleagues (2005) found impaired breathing to negatively impact health outcomes. Men who terminated their test due to difficulties in breathing had significantly higher rates of CHD-, pulmonary-, and total-mortality. This finding was true for both men who had perceived, as well as observed difficulty breathing by the clinician. After adjusting for confounders such as age, smoking, SBP, fasting blood glucose, and CRF, impaired breathing was still found to hold significant negative prognostic value. The literature exploring reasons for test termination as a predictor of adverse health outcomes could warrant its consistent and accurate inclusion in the patient exercise test report and EMR.  5.1.3. Main Findings Summary  The patients in this project have provided a wealth of knowledge that contribute to our understanding of the CRF profiles in a PH population. Several exercise test factors and pre-test characteristics can impact an individual\u2019s ability to reach MEC, with age and sex showing to be 60  great predictors in this current study. While further work can be done in finding the most suitable exercise test mode and protocol that could facilitate reaching MEC, clinicians, knowing this, can build in time into their practice to better understand the patient when selecting their assessments. For example, a fear of falling on the treadmill could be addressed through a combination of choosing a cycle ergometer protocol, as well as building rapport and trust with the patient.   While submaximal exercise testing has shown prognostic benefit in assessing mortality risk (Laukkanen et al., 2020), the HR cut-offs used for estimating CRF increase the risk of type one errors (Wagner et al., 2020). As well, HR-based estimation equations can not be used in individuals taking medications that impact their HR response to exercise (Ross et al., 2016). In this current population, 49% of patients were taking cardiac medications while 31% of patients were hypertensive. A substantial proportion of the study population were most likely taking medications that would prevent them from getting an accurate assessment of their CRF if a submaximal exercise test was used. By conducting maximal exercise testing where CRF is estimated using workload-based equations, patients in a PH population have access to an accurate assessment of CRF. This not only provides a more comprehensive health patient profile, but can inform meaningful exercise prescriptions. In the CRF tests used in the current study, clinicians are able to learn the patient\u2019s RPE, HR, BP, and percentage of their predicted VO2max, for a given workload, even in those taking medications that affect their physiological response to exercise. This provides exercise clinicians valuable data to effectively prescribe exercise intensity and track progress. 5.2. Methodological Considerations  Several methodological considerations should be addressed in this study. First, while BMI was included in the reporting of patient demographics, it was not included in the analysis of 61  impact on achieving MEC. The purpose of this study was to examine the pre-test characteristics that impact patient ability to reach MEC. The outcomes of this study could influence decision making when selecting exercise test methods, as well as inform patient exercise prescription. While obesity has been shown to be associated with CRF (Sui et al., 2008; Wei, Kampert, et al., 1999), caution should be taken in using BMI to make clinical decisions (Campos 2013; Hunger et al., 2020), specifically on exercise testing methods. As well, the study only investigated the impact of CVD risk on the ability to achieve MEC. Other medical diagnoses were not considered but could also impact CRF testing. A more comprehensive indicator of clinical complexity would be valuable to investigate in the future.  5.3. Limitations  There are several research limitations to address in this retrospective chart review study. All patients included have undergone the same CRF testing protocols previously established by the interdisciplinary clinic, therefore introducing single-group threat. Other methods of assessing CRF in PH were not addressed in this study. Working with established protocols could have introduced instrumentation threat. While the treadmills and cycle ergometers used are standard for CRF testing, they may not have elicited volitional fatigue in all populations. Other modes of exercise may be more appropriate for individuals of varying abilities. As a convenience sample was used for this study, the patients included may not have been representative of the general population. While this private healthcare clinic is similar to an interdisciplinary primary care clinic in structure, it differs from public health in access to resources, clinicians, and clientele. Further investigation is required to assess the feasibility of CRF testing in a public health setting.  It is also important to consider the enrolment fee for the interdisciplinary healthcare centre, which may have attracted patients from a higher 62  socioeconomic status. Patients also had the option to decline their CRF test as part of their annual health assessment. Individuals included in this study may be more active than a general PH population, and be more comfortable working to maximal exhaustion. As this was a clinical environment, patient resting states before participation in a CRF test were also harder to control. Outcome measures requiring HR and BP data are subject to confounding variables such as hydration, food and caffeine intake, sleep, previous activity, and baseline stress levels.  Due to the nature of an interdisciplinary clinic, data entered in patient EMRs could have come from several family physicians, nurses, and CEPs. This may have presented a lack of homogeneity in presenting patient history, diagnoses, health measures, and CRF test results. It was also assumed that data in the chart was up-to-date and correct. Finally, the study sample should be considered. There was a substantially higher proportion of males, treadmill exercise tests, and Bruce-ramp protocols used. Future investigations could use purposeful sampling methods to achieve a more even distribution when further investigating the impact of these pre-test characteristics.  Finally, the achievement of MEC was determined by the CEP as defined in the methods. Two of the three criteria used to indicate a maximal test \u2013 RPE and volitional exhaustion \u2013 were based on subjective observations from the patient and CEP. Patient knowledge of their RPE and clinician experience in recognizing volitional exhaustion are required to limit the risk of type one (indication of maximal test when there was not one) and type two (indication of no maximal test when there was one) errors as no CPX measures were taken to verify if patients reached their physiological maximal aerobic capacity. However, the inclusion of these measures does not guarantee error-free measures. The primary and secondary criterion used with CPX measures 63  could present different sources for the risk of type one and two errors, as described in the background above. 5.4. Future Directions  Exploration beyond this study could further optimize maximal exercise testing in PH populations. While this sample size was substantial for establishing relationships between pre-test characteristics and achieving MEC, this population could be extended to include more cycle ergometer tests, more females, more tests using protocols other than the Bruce-ramp protocol, and more patients with available PA behaviour data. Greater detail of maximal testing criteria could also be included in exercise test reports to verify criteria met and provide better understanding of patient responses to maximal testing. CPX measures may also provide valuable insight, especially for patients holding on to handrails on the treadmill. This could prevent the overestimation of exercise capacity, as well as allow for more accurate determinations of maximal exercise.   While the focus of this study was maximal exercise testing in PH populations as exercise capacity has been established as a strong predictor of adverse health outcomes (Blair et al., 1989, 1995; Harber et al., 2017; Lee et al., 2012; A. J. N. Morris & Crawford, 1958; J. Morris et al., 1953; Wilhelmsen et al., 1981), other prognostic measures collected from CRF testing have been shown to predict disease risk and would be valuable to collect. Assessing HRR during CRF tests can be a powerful, non-invasive prognostic tool in PH settings to stratify risk of CVD mortality and evaluate autonomic functioning (Hammond & Froelicher, 1985). Individuals who failed to show a rapid decrease in HR early in recovery after terminating their exercise test are at higher risk for overall mortality. This risk is sustained after presence of CV risk factors, changes in HR during exercise, medication use, exercise capacity, 64  and presence or absence of myocardial perfusion defects were accounted for (Cole et al., 2000; Myers et al., 2007; Tabachnikov et al., 2022). The prognostic significance of HRR was also evaluated in a primary prevention population (Sydo et al., 2018). Abnormal HRR, defined as a decrease of less than 13 beats per minute one minute after exercise cessation, was found to be a significant predictor of total, CV, and non-CV death. This was shown in all ages, males and females, and those with and without hypertension. In contrast to the above-mentioned studies, HRR did not predict death in individuals taking beta blockers, smokers, or those with normal CRF.  Blood pressure responses to exercise also presents another value measure. Manolio and colleagues (1994) found participants who had exaggerated BP responses in their exercise test, defined as women who reached \u2265190 mm Hg and men who reached \u2265210 mm Hg in SBP, were 1.7 times more likely to develop hypertension after five years. This same group also had significantly higher maximal DBP compared to those who did not develop hypertension. When looking at age- and sex-specific percentiles, normotensive individuals who have an exaggerated BP response (\u226595th percentile) at the second stage of an exercise test are associated with a 2- to 4-fold higher risk at developing hypertension (Singh et al., 1999). Similar patterns are observed when using a cycle ergometer where an exaggerated BP response with increasing exercise HR is associated with a 3- to 4-fold increase in risk (Miyai et al., 2002). Exaggerated SBP responses have also been associated with treadmill exercise test duration. In their study, Manolio and colleagues (1994) found those who developed hypertension after five years had shorter exercise test durations compared to participants who did not. Across all study participants, each 60-second increase in exercise test duration correlated with a 12% risk reduction for developing hypertension. Finally, elevated SBP recovery (\u226595th age- and sex-specific percentile at the three-65  minute mark following test termination) in men is also associated with a higher risk of developing hypertension.   Considerations for EMR structure are required for further exploration of exercise testing in clinical settings. While this current study was able to provide insight on variables impacting patient ability to reach MEC, the reach of this project could be limited by researcher time constraints in the future. For example, PA behaviour was extracted from the EMR through a review of clinician notes in a chart text box. This was feasible for 620 patients, but will be extremely time-consuming for a larger sample size without restructuring the EMR to automatically pull this information from patient charts.   The long-term impact of CRF testing should also be explored in PH populations. While the prognostic value of CRF has been well-established for future health, it is unknown if incorporating routine CRF testing into PH populations can directly impact CRF. Within this setting, establishing a strong relationship with a CEP could facilitate this change. While physicians currently indicate exercise testing for patients, it would be interesting to see the impact of having a CEP in healthcare indicate the exercise test and begin building rapport with their patient earlier.      66  Chapter 6: Conclusion CRF has been well established as a powerful indicator of patient health status, comparable to regularly screened measures such as resting HR and BP, blood parameters such as lipids and blood glucose, and health risk assessments, such as smoking and alcohol consumption habits. Optimizing CRF testing in PH can meaningfully impact patient health outcomes through informed exercise prescriptions and accurate assessments of CRF. Personalizing test characteristics to align with a patient\u2019s clinical context could allow for consistency in achievement of MEC. In this current study, the achievement of MEC was impacted by age, sex, exercise test mode, treadmill protocol, and CVD risk. Specifically, individuals over the age of 70 were less likely to reach MEC in both males and females, all levels of PA behaviour, those with elevated CVD risk, those using treadmills, and those following the Bruce-ramp protocol. When working with older adults, exercise clinicians should take extra care in understanding their experience with exercise, as well as their CVD risk factors and impact on exercise, and select the exercise test mode and protocol best suited to their patient. Enhancing the decision-making process in CRF testing allows for better assessment of patient risk for adverse events across the life course and effective exercise prescriptions. As well, supporting patients in exercising to their maximal capacity in a safe environment could build their confidence in their own abilities. Further understanding and assessment of the CRF profiles of PH patients will contribute to the methodological rigour required in the evaluation of exercise programming and support future explorations of meaningful impacts on health outcomes. Finally, involving patients in the assessment and tracking of their CRF could empower them to engage in their own healthcare and adopt healthy lifestyle habits. 67  References Aktas, M. K., Pothier, C. E., Lang, R., & Lauer, M. S. (2004). Global risk scores and exercise testing for predicting all-cause mortality. JAMA, 292: 1462-1468. Alley, S. J., Schoeppe, S., Rebar, A. L., Hayman, M., & Vandelanotte, C. (2018). Age differences in PA intentions and implementation intention preferences. Journal of Behavioral Medicine, 41(3), 406\u2013415. https:\/\/doi.org\/10.1007\/s10865-017-9899-y Bader, D. 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Physical Therapy Review, 4, 7\u201320. https:\/\/doi.org\/10.1179\/ptr.1999.4.1.7         76  Appendix Retrospective chart review data extraction sheet ID  Test-date  Age_y  Gender_M_F  Height_cm  Weight_kg  Max-test_Y_N  Mode  Protocol  Sedentary_Y_N  Previous-coronary-artery-disease_Y_N  Atrial-fibrillation_Y_N  Family-history-of-coronary-artery-disease_Y_N  Hypertension_Y_N  Smoking _Y_N  Hypercholesterolemia_Y_N  Diabetes_Y_N  Cardiac-medications_Y_N  Reason-for-test-termination     ","@language":"en"}],"Genre":[{"@value":"Thesis\/Dissertation","@language":"en"}],"GraduationDate":[{"@value":"2024-11","@language":"en"}],"IsShownAt":[{"@value":"10.14288\/1.0442262","@language":"en"}],"Language":[{"@value":"eng","@language":"en"}],"Program":[{"@value":"Kinesiology","@language":"en"}],"Provider":[{"@value":"Vancouver : University of British Columbia Library","@language":"en"}],"Publisher":[{"@value":"University of British Columbia","@language":"en"}],"Rights":[{"@value":"Attribution-NonCommercial-NoDerivatives 4.0 International","@language":"*"}],"RightsURI":[{"@value":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/","@language":"*"}],"ScholarlyLevel":[{"@value":"Graduate","@language":"en"}],"Supervisor":[{"@value":"Boushel, Robert","@language":"en"}],"Title":[{"@value":"Exercise testing in preventative health populations : predictors of achieving maximal exercise capacity","@language":"en"}],"Type":[{"@value":"Text","@language":"en"}],"URI":[{"@value":"http:\/\/hdl.handle.net\/2429\/88201","@language":"en"}],"SortDate":[{"@value":"2024-12-31 AD","@language":"en"}],"@id":"doi:10.14288\/1.0442262"}