Health and Social Development, Faculty of (Okanagan)
Nursing, School of (Okanagan)
DSpace
UBCO
Phillips, Robyn Elaine
Master of Science in Nursing - MSN
University of British Columbia
Atrial fibrillation (AF) is the most common diagnosed cardiac arrhythmia worldwide and contributes to one in four cerebrovascular accidents (also called strokes) in people over the age of 40 years old in Canada. The purpose of this pilot study was to assess the efficacy of a credible, educational website, used to educate people about their AF with the aim of increasing their AF knowledge and self-efficacy. A mixed-methods design was used to collect both quantitative and qualitative data. Participants were initially recruited through dedicated cardiac rehabilitation clinics; due to the COVID-19 pandemic, recruitment was changed to online using social media (Instagram, Facebook) and by word-of-mouth. A total of nine participants completed the educational website intervention for a total of four weeks, and three participated in the follow-up semi-structured interview. Participants baseline demographics were collected following consent; AF knowledge and self-efficacy measurements were collected pre- and post-website intervention. AF knowledge was measured using the Knowledge about Atrial Fibrillation Survey and self-efficacy was measured using the Chronic Disease Self-Efficacy Scale. While using the website participants were given alias logins to allow for confidentiality to be maintained and data on website usage was collected using Google Analytics. The results of the AF knowledge and self-efficacy scores were compared using paired t-tests and showed no statistical significance between before and after scores, likely due to the small sample size. Participants used the website in various ways, from low to high website usage, which, did not have an impact on AF knowledge or self-efficacy scores. Participant’s semi-structured interviews gave light to the value participants saw in an AF website, the quality of the information on the website and recommendations for change to the website. This study illustrates that there is a place in AF education for a web-based educational tool. Researchers, patient educators and practitioners should use this study to guide AF patient education.
PATIENTS WITH ATRIAL FIBRILLATION: EXPLORING THE USE OF AN EDUCATIONAL WEBSITE by Robyn Elaine Phillips BScN., McMaster University, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN NURSING in The College of Graduate Studies THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) November 2021 © Robyn Elaine Phillips, 2021 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled: Patients with Atrial Fibrillation: Exploring the Use of an Educational Website submitted by Robyn Elaine Phillips in partial fulfillment of the requirements of the degree of Master of Science in Nursing. Dr. Kathy L. Rush, Faculty of Health and Social Development, School of Nursing Supervisor Dr. Mike Chiasson, Faculty of Management Supervisory Committee Member Ms. Anne Pistawka, Central Okanagan Association for Cardiac Health Supervisory Committee Member Dr. Stephen Berg, Okanagan School of Education University Examiner iii Abstract Atrial fibrillation (AF) is the most common diagnosed cardiac arrhythmia worldwide and contributes to one in four cerebrovascular accidents (also called strokes) in people over the age of 40 years old in Canada. The purpose of this pilot study was to assess the efficacy of a credible, educational website, used to educate people about their AF with the aim of increasing their AF knowledge and self-efficacy. A mixed-methods design was used to collect both quantitative and qualitative data. Participants were initially recruited through dedicated cardiac rehabilitation clinics; due to the COVID-19 pandemic, recruitment was changed to online using social media (Instagram, Facebook) and by word-of-mouth. A total of nine participants completed the educational website intervention for a total of four weeks, and three participated in the follow-up semi-structured interview. Participants baseline demographics were collected following consent; AF knowledge and self-efficacy measurements were collected pre- and post-website intervention. AF knowledge was measured using the Knowledge about Atrial Fibrillation Survey and self-efficacy was measured using the Chronic Disease Self-Efficacy Scale. While using the website participants were given alias logins to allow for confidentiality to be maintained and data on website usage was collected using Google Analytics. The results of the AF knowledge and self-efficacy scores were compared using paired t-tests and showed no statistical significance between before and after scores, likely due to the small sample size. Participants used the website in various ways, from low to high website usage, which, did not have an impact on AF knowledge or self-efficacy scores. Participant’s semi-structured interviews gave light to the value participants saw in an AF website, the quality of the information on the website and recommendations for change to the website. This study illustrates that there is a place in AF iv education for a web-based educational tool. Researchers, patient educators and practitioners should use this study to guide AF patient education. v Lay Summary Atrial fibrillation (AF) is the most common diagnosed irregular heartbeat worldwide and can be difficult for people to understand and self-manage. The goal of this study was to explore how a website made for people with AF, can be an educational tool for people with AF. The website aims to teach people about their heart condition and help them feel more confident in caring for themselves. The results of this study showed that there was no difference in AF knowledge or self-efficacy before and after using the website. Although there were no significant changes, we found that people with AF valued having a website that they trust and covers a large amount of information they need to know to successfully to care for themselves. vi Preface This thesis is the original, unpublished work of author Robyn Phillips, supervisor Dr. Kathy Rush, and committee members Dr. Mike Chiasson and Anne Pistawka. This thesis was completed by Robyn Phillips with editorial, guidance, and support from the research supervisor and committee members, and research coordinator Lindsay Burton. This study was approved by the UBC Okanagan Behavioural Research Ethics Board, Ethics ID # (H19-01710). vii Table of Contents Abstract ........................................................................................................................................ iii Lay Summary ................................................................................................................................. v Preface ...........................................................................................................................................vi Table of Contents ........................................................................................................................ vii List of Tables ............................................................................................................................... xii List of Figures ............................................................................................................................ xiii List of Abbreviations ..................................................................................................................xiv Glossary ........................................................................................................................................ xv Acknowledgements .....................................................................................................................xvi Dedication .................................................................................................................................. xvii Chapter 1: Introduction and Background to the Problem .................................................... 1 1.1 Prevalence of Atrial Fibrillation ................................................................................ 1 1.2 Knowledge Gaps on Atrial Fibrillation ..................................................................... 2 1.3 Educational Approaches to Bridge the Gap .............................................................. 4 1.3.1 Group Education.................................................................................................... 4 1.3.2 Technology in AF Education ................................................................................ 4 1.4 Social Cognitive Theory ............................................................................................ 5 1.5 Research Purpose ...................................................................................................... 7 1.6 Research Question(s) ................................................................................................. 7 viii 1.7 Conclusion ................................................................................................................. 7 Chapter 2: Review of Literature .............................................................................................. 9 2.1 Overview of Atrial Fibrillation.................................................................................. 9 2.2 Patient Knowledge and Understanding of Atrial Fibrillation ................................. 13 2.2.1 Lack of Awareness of AF Diagnosis ................................................................... 14 2.2.2 Poor Understanding of AF and the Complications of AF ................................... 14 2.2.3 Lack of Understanding of Treatment and Medication ........................................ 16 2.2.3 Lack of Knowledge of Lifestyle Risk Factors Associated with AF .................... 17 2.2.4 Factors Contributing to Knowledge Gaps ........................................................... 17 2.3 Self-Efficacy in AF Patients .................................................................................... 19 2.4 Addressing the Knowledge Gaps ............................................................................ 20 2.4.1 Individualized Education ..................................................................................... 20 2.4.2 Group Education Programs ................................................................................. 21 2.4.3 Technology Use in AF Education ....................................................................... 23 2.5 Conclusion ............................................................................................................... 24 Chapter 3: Research Methodology ........................................................................................ 26 3.1 Mixed Methods Design ........................................................................................... 26 3.2 Sampling .................................................................................................................. 27 3.2.1 Sample and Recruitment ..................................................................................... 27 3.2.2 Inclusion and Exclusion Criteria ......................................................................... 29 3.2.3 Sample Size ......................................................................................................... 29 ix 3.3 Intervention ............................................................................................................. 31 3.4 Participant Consent.................................................................................................. 33 3.5 Data Collection ........................................................................................................ 34 3.6 Data Analysis .......................................................................................................... 39 3.7 Risks and Benefits ................................................................................................... 40 3.8 Conclusion ............................................................................................................... 40 Chapter 4: Research Findings ................................................................................................ 41 4.1 Description of Sample ............................................................................................. 41 4.2 Knowledge About Atrial Fibrillation Survey Results ............................................. 46 4.3 Knowledge About Atrial Fibrillation Survey Domain Results ............................... 48 4.4 Chronic Disease Self-Efficacy Results.................................................................... 49 4.5 Website Usage & Google Analytics Results ........................................................... 53 4.6 Website Usage and Knowledge about Atrial Fibrillation Survey Results .............. 56 4.7 Website Usage and Chronic Disease Self-Efficacy Scores ..................................... 57 4.8 Combining the Results ............................................................................................ 58 4.9 Correlational Analysis ............................................................................................. 60 4.10 Semi-Structured Interviews Analysis ...................................................................... 61 4.10.1 Value of Website ............................................................................................. 62 4.10.2 Quality of the Information ............................................................................... 63 4.10.3 Quality and Usability of Website .................................................................... 64 x 4.10.4 Recommendations for Change ........................................................................ 65 Chapter 5: Discussion ............................................................................................................. 67 5.1 Introduction ............................................................................................................. 67 5.2 Knowledge............................................................................................................... 67 5.3 Knowledge and Information Seeking ...................................................................... 68 5.4 Knowledge and Website Usage ............................................................................... 69 5.5 Self-Efficacy ............................................................................................................ 69 5.6 Self-Efficacy and COVID-19 .................................................................................. 70 5.7 Self-Efficacy and Website Usage ............................................................................ 71 5.8 Self-Efficacy and SMART Goals ............................................................................ 73 5.9 Educational Preferences .......................................................................................... 74 5.10 Limitations............................................................................................................... 76 5.11 Future Implications.................................................................................................. 77 5.12 Conclusion ............................................................................................................... 80 References ..................................................................................................................................... 81 Appendices.................................................................................................................................... 93 Appendix A ......................................................................................................................... 93 Appendix B.......................................................................................................................... 94 Appendix C.......................................................................................................................... 95 xi Appendix D ....................................................................................................................... 101 Appendix E ........................................................................................................................ 107 Appendix F ........................................................................................................................ 111 Appendix G ....................................................................................................................... 114 Appendix H ....................................................................................................................... 116 xii List of Tables Table 3.1 ................................................................................................................................. 30 Table 3.2 ................................................................................................................................. 35 Table 4.1 ................................................................................................................................. 42 Table 4.2 ................................................................................................................................. 43 Table 4.3 ................................................................................................................................. 46 Table 4.4 ................................................................................................................................. 47 Table 4.5 ................................................................................................................................. 49 Table 4.6 ................................................................................................................................. 50 Table 4.7 ................................................................................................................................. 54 Table 4.8 ................................................................................................................................. 55 Table 4.9 ................................................................................................................................. 59 Table 4.10 ............................................................................................................................... 60 xiii List of Figures Figure 1.1: Social Cognitive Theory. Representation of relations between efficacy and outcome expectancies. .............................................................................................................. 6 Figure 4.1 ................................................................................................................................ 44 Figure 4.2 ................................................................................................................................ 45 Figure 4.3 ................................................................................................................................ 45 Figure 4.4 ................................................................................................................................ 47 Figure 4.5 ................................................................................................................................ 51 xiv List of Abbreviations AF/Afib Atrial Fibrillation BPM Beats per minute COACH Central Okanagan Association for Cardiac Health CVA Cerebrovascular Accident ECG Electrocardiogram INR International Normalized Ratio KAFS Knowledge about Atrial Fibrillation Survey NOAC Non-vitamin K Oral Anticoagulants SARS-CoV-2 COVID-19 SD Standard Deviation SMART Goals Specific Measurable Attainable Realistic Timely xv Glossary Atrial Fibrillation: An irregular heartbeat characterized by chaotic electrical impulses in the atria of the heart usually resulting in a high heart rate 80-180 beats per minute. Cerebrovascular Accident: Occurs when part of the blood supply to part of the brain is interrupted or reduced preventing brain tissue from receiving oxygen and nutrients, damaging brain cells. More commonly known as: stroke. Electrocardiogram: A test which measures the electrical activity of the heart. Hawthorne Effect: The effect on the dependent variable resulting from subjects’ awareness that they are participants under study. Internationalized Normalized Ratio: A blood test result that is calculated based the prothrombin time (time it takes for blood to form a clot) and is used to monitor individuals taking anticoagulant medication such as warfarin. xvi Acknowledgements I would like to begin by acknowledging my supervisor, Dr. Kathy Rush. Kathy, your wisdom, guidance, and patience are incomparable. You have helped me through every step of the MSN program, from providing me with my acceptance to the program to guiding me through my current research. I am immensely grateful for the opportunity to work with you and learn from you over the past few years. To my committee Anne Pistawka and Dr. Mike Chiasson, thank you for your support and guidance through the turmoil of the many changes with process of this study. To Lindsay Burton, you have been one of my biggest supporters and have helped me through so much during the process of this study. Thank you for every email, late night texts and last-minute zooms. To my family and friends, thank you for always supporting and believing in me every step of the way. xvii Dedication To my mom, Wendy Phillips. You have always taught us that we can do anything we want in life. Thank you for always believing in us and teaching us to pave our own paths in life. To my brother, Scott Phillips. Your ambition and eagerness to continuously learn in life is contagious. Thank you for sharing this passion with me and role modeling that we can do anything we put our minds to. 1 Chapter 1: Introduction and Background to the Problem Atrial fibrillation (AF) is the most common diagnosed cardiac arrhythmia worldwide (Morillo et al., 2017). Self-managing the condition can be challenging and requires that patients are well educated about their disease. Yet many patients may not receive the education they need to be able to successfully self-manage their AF (Rush, Burton, et al., 2019). Busy primary practitioners/ physicians often do not have the time to fully educate patients, leaving them to seek out knowledge and information from other sources (Rush, Burton, et al., 2019). In today’s technological world, the Internet has become an alternate resource for patients to self-educate (Pandya & Bajorek, 2016). Although this self-education has benefits, harms may occur if the available online information is inaccurate, or unreliable. Providing evidence-based, up-to-date, and credible educational resources are imperative for patients with AF. An educational website dedicated to patients with AF provides one such resource. The purpose of this pilot study is to assess the efficacy of an educational website resource for patients with AF. 1.1 Prevalence of Atrial Fibrillation AF affects millions of lives daily (Morillo et al., 2017). The global prevalence of AF is estimated to be 2% in the general population, and up to 8% in those who are 75 years of age and older (Bowyer et al., 2017). In Canada, AF affects about 200,000 Canadians and the prevalence doubles in those who are over 55 years old (O’Reilly et al., 2013; Heart and Stroke Foundation of Canada, 2020). AF is predicted to affect 6-12 million people in the United States of America (USA) by the year 2050 and 17.9 million Europeans by the year 2060 (Bowyer et al., 2017; Morillo et al., 2017). There are several reasons for the expected rise in AF: growth in the aging population, advances in technology that enhance detection, 2 increased survival and sedentary lifestyles over a lifespan that are associated with AF (Jacob, 2017; Morillo et al., 2017). AF is more common in men than women; however, women have a greater mortality risk than men (Hardin & Steele, 2008; Macario et al., 2016). As the prevalence of AF increases, there is a parallel increase in the economic burden on the health care system. A study using primary data from two national Canadian administrative databases for the fiscal year 2007-2008, showed 64,214 patients admitted to acute care facilities were for an AF-related complication, 8815 were for same-day operations for AF, and 76,964 were emergency department visits due to AF - related complications or illnesses (O’Reilly et al., 2013). Of all hospital admissions in Canada, 2.6% were AF-related and increased to 5.6% in those 50 years and older (O’Reilly et al., 2013), suggesting a relatively heavy use of the health care system by AF patients. The total cost of these AF-related hospital admissions and visits was $815 million CAN with most of the costs ($558 million CAN) resulting from known AF complications such as stroke and congestive heart failure (O’Reilly et al., 2013). More recently, AF and atrial flutter have been among the top 20 reasons for patients visiting the emergency department before admission to the hospital in 2017-2019 (Canadian Institute for Health Information, 2019). This high AF patient usage of the health care system suggests patients are challenged to properly manage their cardiac condition perhaps due to a lack of knowledge and understanding of their condition. 1.2 Knowledge Gaps on Atrial Fibrillation Research has shown that patients with AF have substantial knowledge gaps related to their condition making it difficult for them to self-manage and putting them at risk of complications (Desteghe et al., 2016; McCabe et al., 2008). Knowledge gaps that have been identified in the literature include: a lack of understanding of their diagnosis and the nature 3 of AF, the progression of AF, the medical treatment of AF, and the complications that could occur (Clarkesmith et al., 2017; Desteghe et al., 2016, 2018; Salmasi, de Vera, et al., 2019). Studies have highlighted that patients with AF are unaware of their condition and many patients in the same studies did not know that having AF is a risk for thromboembolism and stroke (Desteghe et al., 2016, 2018; Salmasi, de Vera, et al., 2019). Patients throughout the literature were seen to have a poor understanding of the need for anticoagulation and its importance in preventing a stroke, as well as important side effects that may occur due to anticoagulation (Clarkesmith et al., 2017; Desteghe et al., 2016, 2018; Salmasi, de Vera, et al., 2019). Patients have also demonstrated poor understanding of lifestyle factors that impact AF and a need for further education (Desteghe et al., 2018). Overall, evidence has shown that patients lack vital knowledge and understanding of AF; this knowledge is essential for successful management AF to reduce patient complications and hospitalization. Further, low self-efficacy may contribute to an AF patient’s lack of knowledge and understanding of AF; in turn leading to lack of confidence in self-management. AF self-efficacy has been defined as “a belief in one’s capacity to execute behaviors necessary to manage one’s AF condition” optimally (Macario et al., 2016, p.294). Self-efficacy has been understudied in patients with AF but has been shown to be important in patients’ self-management of their conditions (Macario et al., 2016). Education is important in promoting self-efficacy through gaining understanding and confidence in how to manage one’s condition. To optimally care for themselves, patients with AF require a clear understanding of their cardiac condition; health care professionals and educational tools are vital resources to help patients gain knowledge to successfully manage their AF. 4 1.3 Educational Approaches to Bridge the Gap 1.3.1 Group Education Over the years, researchers have attempted to bridge the knowledge gap for patients with AF by trialing different educational interventions. Patients with AF participating in group education have shown improvements in knowledge about AF and ability to self-manage their warfarin dosing (Jenner et al., 2015). Although knowledge improvements through group education have been seen, group education may not always be ideal for all patients. Some AF patients who live in rural communities are faced with challenges related to a lack of availability of group education and specialist care (Rush, Burton, et al., 2019). Group education can also create challenges for patients with time constraints and busy schedules. This approach to education may not be individualized to the patient’s learning needs or geared to the patient’s learning style and does not allow for flexibility and self-paced learning. 1.3.2 Technology in AF Education Challenges with one-on-one and group education make technology a viable alternative option for offering patient education for AF. Technology (such as dedicated websites) allows patients to self- pace their education and tailor study material on AF according to their educational needs (Desteghe et al., 2018). However, there has been limited use of technology-based education with the AF population. As technology advances and the Internet becomes more popular than ever, AF patients lacking knowledge of their condition begin to self-educate by reaching out to information sources available online (Rush et al., 2018). Online resources provide accessibility that other forms of education cannot; however, the nature of online information can be questionable (Desteghe et al., 2018). Research has 5 shown that the credibility of websites and online resources for patients with AF is undermined when information and content is inaccurate (Pandya & Bajorek, 2016). Although not specific to AF, one study found that 33% of web-based videos, specifically educating patients on the treatment of hypertension, presented misleading information (Kumar et al., 2014). The lack of credible websites and online education modules can make it difficult for patients to gain needed information, further widening the knowledge gaps for AF patients. As a result of these potentially misleading online platforms, it is important for health care professionals to ensure patients with AF have access to quality, credible online resources. 1.4 Social Cognitive Theory Social cognitive theory [SCT], formerly known as social learning theory, was developed originally in the 1960’s by Albert Bandura (LaMorte, 2019). The aim of the theory was to understand how individuals maintain and regulate behavioral change through control and reinforcement, to achieve long-term, goal-directed behavior (LaMorte, 2019). There are now 6 constructs to SCT that include: reciprocal determinism, behavioral capability, observational learning, reinforcements, expectations (outcomes), and self-efficacy (LaMorte, 2019). The two main constructs of SCT are self-efficacy and outcome expectations (Bandura, 1998; Young et al., 2014). Bandura (1998) describes self-efficacy as the beliefs people have in themselves to be able to organize and follow through with actions to produce an expected outcome, such as management of AF. Bandura (1998) explains that one’s self-efficacy belief is the main driver of personal change in health promotion and a lack of self-efficacy limits acting in a way for positive change. In SCT, self-efficacy contributes to motivation for change, affect and behavior action (Bandura, 1998). Outcome expectations are described as one’s beliefs about how the behavior may affect them in the 6 end, positively or negatively and the consequences that may result from the behavior (Young et al., 2014). Self-efficacy beliefs and outcome expectations are related and mutually impact one another; Figure 1.1, depicts how Bandura represents the relationship between the person, behavior, and outcomes in SCT (Bandura, 1998). Bandura (1998) states, “In given domains of functioning, efficacy beliefs vary in level, strength and generality. The outcomes that flow from a given course of action can take the form of positive or negative physical, social, and self- evaluative effects” (p. 627). Figure 1.1: Social Cognitive Theory. Representation of relations between efficacy and outcome expectancies. Retrieved from: Bandura, A. (1998). Health promotion from the perspective of social cognitive theory. Psychology and Health, 13(4), 623-649. doi: 10.1080/08870449808407422 SCT is important to consider when exploring the self-efficacy and knowledge of AF patients’ as it could help explain why some patients may improve their knowledge and change their lifestyle habits and why others may not. SCT is multifaceted and explains that self-efficacy is influenced by external social constructs (Bandura, 1998). With SCT, AF patients can gain or strengthen their self-efficacy by witnessing other AF patients’ be successful in self-management. Bandura (1998) emphasizes that belief in learning efficacy is necessary to enhance knowledge and skills in everyday life. Lane et al. (2015) align with Bandura’s social cognitive theory as they explain that a patient’s coping response to their 7 treatment and illness, adherence to treatment and overall health outcomes are all affected by the patient’s beliefs and knowledge about their health condition. This emphasis on a patient’s belief in their ability to care for themselves, having support mechanisms in place and knowledge of AF, supports the importance of measuring self-efficacy for AF patients. SCT guided this research, to offer explanation of the nature of relationships between knowledge and self-efficacy and patients’ AF knowledge gains. 1.5 Research Purpose The purpose of this pilot mixed methods research study was to assess the efficacy of using a credible, educational website focused on educating patients about their AF. The quantitative arm of the study examines changes in patients’ AF knowledge and self-efficacy before and after the intervention, and their website usage during the intervention and relationships between patients AF knowledge and self- efficacy. The qualitative arm of the study seeks to explore patients’ experiences with use of the educational website after they complete the online intervention. 1.6 Research Question(s) The primary research question is: Does the use of an educational website dedicated to patients with atrial fibrillation increase their knowledge and self-efficacy? Secondary research questions are: Is there a relationship between website usage and AF patients’ knowledge and self-efficacy? Is there a relationship between AF knowledge and self-efficacy? What are the patients’ experiences of using the educational website? 1.7 Conclusion This research contributes to the body of knowledge related to current educational approaches with patients with AF. Despite the recurring lack of knowledge reported in the 8 AF population there have been few studies that have tested educational interventions. This research presents potential benefits of credible virtual education that could be streamlined with not only patients with AF, but patients with other chronic health conditions and those who live in rural communities where specialty care is minimal. Chapter 2 presents a literature review on AF, education, and the types of patient educational delivery modalities in healthcare, the outcomes associated with different types of education, and how education impacts patient knowledge and self-efficacy. Chapter 3 overviews the methodology of the study, outlining the study design, sampling/recruitment, data collection tools and briefly discusses the design of the website itself, as well as an overview and timeline of the recruitment procedures. Chapter 4 presents the research findings and results of the study and chapter 5 discusses the findings of the research, and draws implications of the study for research, education, and practice. 9 Chapter 2: Review of Literature A review of literature was carried out to gain an understanding of what is known about educational approaches for patients with AF, what is known about self-efficacy and educational approaches with AF patients and existing knowledge gaps. This chapter provides an overview of the pathophysiology of AF, the prevalence of the condition, and synthesizes current empirical research related to educational support for patients with AF and associated knowledge gaps. This chapter also examines what educational approaches have been trialed in literature to bridge the knowledge gap. The literature was searched from Medline EBSCOhost, and CINAHL, using search terms: atrial fibrillation, patient education, knowledge gaps, and self-efficacy, searched separately as well as combined with the Boolean operator ‘AND’ for each database. The articles were then reviewed for inclusion based on focus topic of AF, and whether they addressed specific AF related concepts (knowledge gaps, and self-efficacy). 2.1 Overview of Atrial Fibrillation AF is the most common cardiac arrhythmia and is exhibited by supraventricular tachycardia with rapid electrical impulses and inadequate emptying of the atria resulting in decreased cardiac output (Hardin & Steele, 2008). AF occurs when atrial systole is uncoordinated, characterized by chaotic electrical impulses of the atria, deflecting in different directions resulting in disorganized atrial depolarizations without effective atrial contraction (Hannon & Porth, 2010; Hardin & Steele, 2008). The increase in electrical impulses results in irregular ventricular responses, usually presenting as tachycardia ranging from 80-180 beats per minute (Hannon & Porth, 2010; Hardin & Steele, 2008). 10 AF is typically classified into three major categories: paroxysmal, persistent, and permanent (Bennett, 2013; Heart and Stroke Foundation of Canada, 2020). Paroxysmal AF is when the heart rhythm goes into AF temporarily and spontaneously returns to normal sinus rhythm within seven days of onset, and usually within 48 hours (Bennett, 2013; Heart and Stroke Foundation of Canada, 2020). Persistent AF is when the heart rhythm is in AF for more than seven days and would continue to stay in AF but can be restored to normal sinus rhythm with treatment (such as cardioversion) (Bennett, 2013; Heart and Stroke Foundation of Canada, 2020). Permanent AF occurs when the heart rhythm has been in AF for greater than a year either because treatment has not been attempted yet, or the treatment has failed to revert the heart back to normal sinus rhythm (Bennett, 2013; Heart and Stroke Foundation of Canada, 2020). AF can also be classified as lone AF, which is idiopathic in nature and occurs in younger adults, usually younger than sixty years of age (Bennett, 2013). Paroxysmal lone AF can occur in some patients in a single episode or recurrent episodes that may last for hours or seconds (Bennett, 2013). Risk factors associated with AF can be both cardiac and non-cardiac related in nature (Bennett, 2013). Cardiac related risk factors include hypertension, myocardial infarction, cardiomyopathy, myocarditis, pericarditis, atrial septal defect, heart valve disease, coronary artery disease, cardiac or thoracic surgery, and congestive heart failure (Bennett, 2013; Hardin & Steele, 2008; Heart and Stroke Foundation of Canada, 2020; Jacob, 2017). Non-cardiac related risk factors for AF include age, hyperthyroidism, chronic obstructive pulmonary disease, infection, or disease to the lungs (such as cancer), obstructive sleep apnea, smoking, pulmonary embolism, alcoholism, obesity, diabetes mellitus, and high-endurance sport training (Bennett, 2013; Hardin & Steele, 2008; Heart and Stroke 11 Foundation of Canada, 2020; Jacob, 2017). The most significant complication associated with AF is the development of a thromboembolism, which can result in a cerebrovascular accident (also known as: stroke), pulmonary embolism, or renal embolism; all of which may be life threatening (Hardin & Steele, 2008; Heart and Stroke Foundation of Canada, 2020). Identifying and mitigating risk factors for developing AF is the primary goal in reducing the occurrence of AF (Jacob, 2017). The prevalence of AF is on the rise both nationally, and globally. The Heart and Stroke Foundation of Canada estimates AF affects about 200,000 Canadians and is the cause of one in four of all strokes in Canada for those who are 40 years of age and older (Heart and Stroke Foundation of Canada, 2020). In the USA and European countries, one in four middle-aged adults are likely to develop AF over their lifespan (Zulkifly et al., 2018). Zulkifly et al. (2018) found that AF was the most prevalent in order of continents, with the highest in Australia, followed by Africa, Europe, South America, North America, and the lowest prevalence in Asia. AF affects about 2% of the general population and increases to 8% in seniors aged 75 years and older (Bowyer et al., 2017). This staggering increase is only expected to rise with continuing growth in the aging population. Statistics Canada (2018) projects that the population of seniors aged 65 years and older in Canada, is expected to rise rapidly until the year 2031; with projections that seniors will make up 23-25% of the total Canadian population by 2036, compared to only 14.1% of seniors in 2010. As the senior population increases, the prevalence of AF is expected to drastically increase globally. Clinically, individuals can present with AF in three different ways: typical symptoms (palpitations with or without associated symptoms); atypical symptoms such as fatigue, shortness of breath, chest pain, lightheadedness, syncope, decreased exercise tolerance 12 without palpitations; and asymptomatic presentation where AF is detected during routine physical examination, preoperative evaluation, emergency department or clinical visit (Siontis et al., 2016). These symptoms of AF are associated with anxiety and depression, uncertainty, and impaired quality of life (Bowyer et al., 2017; Rush, Hatt, et al., 2019). Treatment options for those who develop AF are often guided by the clinical presentation of the individual and their history of AF. The primary goals of treating AF are to control the patient’s heart rate and/or restore normal rhythm and prevent thrombus formation to protect the patient from systemic embolism (Bennett, 2013; Hardin & Steele, 2008; Prystowsky et al., 2015). First, antiarrhythmic medications are used to try to slow the ventricular response and revert it back to normal sinus rhythm. Antiarrhythmic drugs may not eliminate the AF and are only modestly effective but are a first choice of treatment in the primary clinical setting (Prystowsky et al., 2015). If antiarrhythmic medications are not successful, rate control medications may be used in addition to the antiarrhythmics (Prystowsky et al., 2015). Rate control is important even when patients are asymptomatic during AF due to the complications (e.g., heart failure) that can arise when increased heart rate is sustained for long periods of time (Prystowsky et al., 2015). If drug therapy does not convert the patient to normal sinus rhythm or if the patient’s cardiac status becomes unstable or the patient develops pulmonary edema because of their AF, another treatment choice is cardioversion (Hannon & Porth, 2010; Hardin & Steele, 2008; Prystowsky et al., 2015). Cardioversion is the “acute termination of sustained AF using electric current shock or antiarrhythmic drugs” (Prystowsky et al., 2015, p.284). Anticoagulation is important before and after cardioversion due to a high risk of stroke both at the time of the cardioversion and for weeks following the procedure (Prystowsky et al., 2015). Anticoagulation is important due to the potential 13 dislodgement of a thrombosis and due to hypercoagulation that occurs in the blood following cardioversion (Bennett, 2013; Prystowsky et al., 2015). Anticoagulation is recommended for three weeks prior to the cardioversion and to be continued for four weeks post-procedure to minimize risk of stroke (Bennett, 2013; Prystowsky et al., 2015). Anticoagulant medications include vitamin K antagonists (VKAs) (e.g., warfarin) and non-vitamin K antagonists’ oral anticoagulants (NOACs) to prevent stroke in AF patients (Steffel et al., 2018). NOACs have become the anticoagulant of choice for AF patients because of their improved efficiency with fewer food and medication interactions; as well, NOACs are more predictable in their effects on the patient, requiring less routine coagulation monitoring (Steffel et al., 2018). These NOACs medications are: apixaban, edoxaban, rivaroxaban, and dabigatran (Steffel et al., 2018). Often antithrombotic medications are started during an acute episode of AF and can be used long-term when patients have chronic AF to prevent stroke (Hardin & Steele, 2008). A final option for treatment is catheter ablation therapy. Catheter ablation therapy is used to cauterize the abnormal tissue of the heart that is causing the irregular and chaotic electrical impulses (Hardin & Steele, 2008). Catheter ablation is recommended when those with paroxysmal AF are not responding to drug therapy and those with persistent AF (Bowyer et al., 2017; Prystowsky et al., 2015). It is important during treatment of AF that health care professionals take the opportunity to educate their patients on their condition to help the patient make informed decisions regarding their treatment plan and lifestyle adjustments. 2.2 Patient Knowledge and Understanding of Atrial Fibrillation Research has identified that many patients with AF do not have a clear understanding of their medical condition and lack the knowledge to properly self-manage their condition. Several areas in which patients have described a lack of knowledge include a lack of 14 awareness of AF diagnosis, poor understanding of AF, risk factors, complications (e.g., stroke), medication, and treatment plan (McCabe, 2018; Salmasi, de Vera, et al., 2019). 2.2.1 Lack of Awareness of AF Diagnosis Knowledge and understanding of AF often begins with receiving an AF diagnosis. However, evidence suggests that patients lack an awareness that they have AF. In recent studies, one in four AF patients reported that they were unaware of having the medical condition called ‘atrial fibrillation’ answering ‘no’ or ‘I don’t know’ to being asked if they had ever been diagnosed with AF (Desteghe et al., 2016, 2018). When patients in an emergency room (ER) setting were examined 33% of participants stated this was their first episode of AF and 22% stated they sought treatment for their symptoms 2-14 days after onset of symptoms (Koponen et al., 2008). The length in delay of treatment seeking may be related to a lack of awareness of their AF diagnosis and of the symptoms associated with AF episodes. A systematic review examining 21 studies related to knowledge gaps among AF patients, found that four studies (n= 748 participants) discussed a lack of awareness of one’s own AF diagnosis (Salmasi, de Vera, et al., 2019). Of these studies, the pooled percentage of participants who were aware of their AF diagnosis was less than 50% (Salmasi, de Vera, et al., 2019). The lack of awareness of AF diagnosis is also related to the patients understanding of their health. Reading et al. (2017) found that lack of health literacy was significantly associated with patients having a reduced awareness of their AF diagnosis. 2.2.2 Poor Understanding of AF and the Complications of AF It is important that AF patients have a clear understanding of what AF is, how it progresses, how it may impact their daily lives, what treatment options are available and how 15 to self-manage the condition. Despite the importance of each of these areas of knowledge to optimal AF self-management, evidence has shown that patients lack an understanding of AF and its side effects. In two studies it was reported that between 20% and 27.1% of patients were unable to identify the definition of AF and one third of the patients in both studies, did not know that AF could cause a thromboembolism and stroke (Desteghe et al., 2016, 2018). Desteghe et al. (2018) reported that only 27.5% of patients understood that AF could be asymptomatic, and 41.7% of participants understood that AF could be detected by regularly taking their own pulse (Desteghe et al., 2018). Similarly, when examining knowledge of patients with AF who were seen in the ER, Koponen et al. (2008) found that patients had better knowledge in some areas of AF than others. Patients had moderate knowledge on how to detect AF but showed poor understanding that AF could be asymptomatic, and that AF may reoccur even when taking antiarrhythmic medication (Koponen et al., 2008). During a 3-month follow-up after the ER visit, Koponen et al. (Koponen et al., 2008) assessed whether patients sought further education post-ER discharge for their AF and found that patients did not increase their knowledge and understanding of AF. Salmasi, de Vera, et al.’s (2019) recent systematic review confirmed Koponen and colleague’s findings, reporting from 21 studies in the review, just above 50% of the pooled proportion of participants knew that AF and stroke were related, and that AF could reoccur. When Lindberg et al. (2017) explored the experiences of older adults living with chronic AF they found that participants expressed uncertainty about what it meant to live with AF. The participants exhibited a lack of understanding about the condition, the impact of anticoagulation therapy on procedures (e.g., dental), and the importance of communicating about their condition to other health care professionals, such as the dentist (Lindberg et al., 16 2017). AF patients also expressed significant limitations to their activities of daily living due to their symptoms, feelings of dependence and burnout associated with frequent hospital visits for monitoring, and the anxiety associated with potential complications (Altiok et al., 2015). 2.2.3 Lack of Understanding of Treatment and Medication Literature has shown that patients with AF are unaware of the treatment goals for their AF and the use of anticoagulants for thromboembolism prevention. Between 15.8% and 23.8% of patients were unaware that oral anticoagulants were prescribed for the prevention of blood clot formation and ischemic stroke (Desteghe et al., 2016, 2018). Patient narratives also showed considerable misunderstanding of the relationship between their AF condition and the need for anticoagulation therapy and management to prevent stroke (Clarkesmith et al., 2017). Similarly, Xu et al. (2010) found that 27% of their participants thought that if there was no embolic complication, then it was not necessary for them to take their anticoagulant. Studies testing knowledge about the risk of bleeding with the use of oral anticoagulation [OAC] therapy found that nearly one third of the participants did not know that an increased risk of bleeding could occur because of their OAC therapy (Desteghe et al., 2016, 2018). Obamiro et al. (2018) found that only 24% of patient’s could identify three side effects of their anticoagulants, only 26% identified that missing a dose of medication could have negative side effects, and 33% did not know that bleeding was the most important adverse effect of anticoagulant use. In contrast, evidence has shown that 90 to 94% of patients who were taking anticoagulation medication understood that the goal of the medication was to ‘thin the blood’ and 6 to 9% of patients believed that the anticoagulation was to control the arrhythmia (Amara et al., 2015; Hernández Madrid et al., 2016). 17 Along with misunderstanding the use of anticoagulation therapy, only 53.9% of users of non-vitamin K antagonist anticoagulants (NOAC) were aware that the use of NOAC requires monitoring of the renal system (Amara et al., 2015); shining light on the lack of knowledge regarding the use of medication on the body systems. When exploring how patients understood their medications and the impact on their daily lives, Lindberg et al. (2017) found that the older adult participants took their anticoagulation therapy very lightly. The participants did not worry about potential food-medication interactions, viewing their anticoagulant as ‘just a medicine among others’, and from time to time had forgotten to take it, and often relied on family and medication organizers to remember to take their anticoagulant (Lindberg et al., 2017). Knowledge about medication and treatment goals are important in prompting patient understanding to successfully self-management AF. 2.2.3 Lack of Knowledge of Lifestyle Risk Factors Associated with AF Patient’s knowledge of the risk factors that can lead to, or exacerbate, AF have been observed to be relatively poor. Evidence has shown that approximately 60% of patients with AF were not aware that being overweight could lead to, and exacerbate AF (Desteghe et al., 2016, 2018). AF patients seen in the ER demonstrated very limited knowledge that AF could be triggered due to stressful lifestyle factors and only 35% of participants understood that it was still possible to exercise normally with AF (Koponen et al., 2008). This is important as those who do not exercise are at risk for being overweight that can further exacerbate their AF. 2.2.4 Factors Contributing to Knowledge Gaps Gaps in patient’s knowledge have been attributed to factors related to the health care provider and the patient. These factors include a lack of physician time, insufficient patient- 18 provider communication, inadequate education resources and limited consideration of the social determinants of health for patients with AF (Clarkesmith et al., 2017; Desteghe et al., 2016, 2018; Lindberg et al., 2017; Reading et al., 2017; Rush, Burton, et al., 2019). Lane et al. (2015) explain that making a diagnosis of AF can be challenging, resulting in delayed diagnosis and treatment. The delay or misdiagnosis has been due to the transience of symptoms in some cases, but as well due to physician’s providing incomplete diagnostic procedures (Lane et al., 2015). Similarly, physicians in rural hospitals identified “patient education as challenging for those whose AF was discovered incidentally, who had cognitive impairment, experienced failed treatment, or were highly symptomatic” (Rush, Burton, et al., 2019, p. 5). Patients have reported wanting better communication regarding their experience with AF and adequate education from their health care providers to help them make better informed decisions (Lane et al., 2015). Patients turn to their health care providers to provide them with high quality, unbiased resources such as websites, decision aids and brochures to give them direction with their AF (Lane et al., 2015). Evidence indicates that the social determinants of health related to a lack of understanding of AF were education, age, socioeconomic status, and gender. Health literacy is one’s ability to gather, comprehend and interpret health information to be able to make informed decisions about their health (Reading et al., 2017). Reading et al. (2017) found compared to their non-AF counterparts, patients with AF who had poorer health literacy were female, single, making less than $25,000/year and had less than a high school education. Similarly, Desteghe et al. (2018) reported that patients with AF who had lower baseline knowledge, were those who had no access to online resources, had a lower educational degree, were older, and had a higher risk of bleeding and stroke risk. Overall, older adults, 19 those with less education and lower socioeconomic status have been seen to be at greater risk for lack of knowledge and understanding of AF, which can lead to poor patient outcomes and higher demands on the health care system. 2.3 Self-Efficacy in AF Patients Self-efficacy can impact and be impacted by AF in patients. Self-efficacy related to AF self-management is defined as “a belief in one’s capacity to execute behaviors necessary to manage one’s AF condition as optimally as possible” (Macario et al., 2016, p. 264). Lindberg et al. (2017) found that older adult patients with AF experienced feelings of dependence on the health care system and having their daily lives scheduled that had a negative impact on AF self-efficacy. They expressed feeling less independence because of their AF and greater reliance on the health care system for monitoring aspects of their condition and frequent appointments, such as anticoagulant therapy (Lindberg et al., 2017). Conversely, a survey of 332 women with AF found that the women had the greatest agreement that it was important for them to take an active role in their own health and AF. Increased physical activity and attendance at support groups for AF increased women’s self-efficacy (Macario et al., 2016). Health care provider actions are key to promoting and increasing self-efficacy in newly diagnosed AF patients. A qualitative study found that when health care providers took the time to provide explanations, education, reassurance, validation of symptoms and provided prompt treatment with patients who were newly diagnosed with AF, it decreased patients’ feelings of uncertainty and fear related to their AF (McCabe et al., 2015). An increase in patient self-efficacy occurred when nurses provided positive encouragement (Siouta et al., 2016). Patient confidence developed through discussing problems and doubts 20 about their ability to care for their AF (Siouta et al., 2016). AF patients demonstrated feelings of involvement with decisions about their care when they acquired knowledge about the disease process of AF and treatment, and when they were able to build a trusting relationship with their physician and nurses (Siouta et al., 2016). This enhanced decision-making increases AF patient self-efficacy to optimally self-manage their AF. Improving self-efficacy is important to help patients become more independent, self-motivated to care for their AF, and self-directed in learning to meet their own informational needs. 2.4 Addressing the Knowledge Gaps Despite the documented prevalence of knowledge gaps among patients with AF and recommendations to address them, there has been limited research focusing on educational interventions to enhance knowledge. 2.4.1 Individualized Education Individualized or one-to-one education has been a traditional approach to education. Interventional studies have been shown to be effective in increasing patient knowledge of AF. One German study used a complex educational intervention that included interventions for both the patients and guidelines for the general practitioners [GPs] and health care assistants in the GPs practice, which was tailored for the patient and the type of anticoagulant they were prescribed (Maikranz et al., 2017). The patient’s complex intervention included practice-based case management with treatment monitoring, education on the type of anticoagulant specific to each patient, general patient education on AF, and encouragement to self-manage where applicable (Maikranz et al., 2017). The study found that the patient intervention group showed significant knowledge improvements 12 months after the intervention and maintained the knowledge 24 months post-education intervention (Maikranz 21 et al., 2017). In contrast, the control group received usual care according to the current evidence-based guideline on oral anticoagulation therapy at that time in Germany (Maikranz et al., 2017). The control group had little improvement in knowledge at 12 months and even a slight decline at 24 months while receiving usual care (Maikranz et al., 2017). Similarly, Bowyer et al. (2017) compared a nurse-led educational intervention prior to patients receiving AF ablation therapy to a control group, that only received the AF ablation. The multi-component intervention that had education as a key component, was implemented at five points in time for each participant: face-to-face on admission and prior to discharge, and telephone calls at two weeks, one month and three months post-AF ablation (Bowyer et al., 2017). The nurse-led education intervention was associated with a significant decrease in AF symptoms, improved quality of life associated with improved physical functioning and vitality compared to the control group (Bowyer et al., 2017). 2.4.2 Group Education Programs Research has shown the effectiveness of group educational sessions and programs specific for AF patients in addressing their knowledge gaps. Jenner et al. (2015) conducted a feasibility study to evaluate the impact of a group educational program to support patients with the self-management of their warfarin therapy and adjustments to their warfarin dosing based on the results of their International Normalized Ratio [INR]. In this study healthcare professionals educated the participants in 13 two-hour group-training sessions (Jenner et al., 2015). Prior to the education sessions, there was a 34.9% (15 of 44) pass rate on the pre-AF/Warfarin therapy knowledge test, and following the educational sessions there was a statistically significant increase in the pass rate of 95.3% (41 of 44) (p < 0.001) (Jenner et al., 2015). Patients reported feeling able to self-manage their warfarin and 92.9% of participants 22 expressed they felt comfortable to self-adjust their warfarin dosing and 100% of the class reported that the training left them feeling prepared to manage their own warfarin (Jenner et al., 2015). The majority of participants in this study expressed that group education was preferred over one-on-one instruction (Jenner et al., 2015). Similarly, Khan et al. (2004) educated patients in groups of two or three on AF, as to the clinical benefits and side effects of anticoagulation therapy, as well as the importance of INR monitoring, drug interactions and diet. These participants were then separated into two groups, where one group was further educated to self-monitor their INR and one group received INR monitoring at the anticoagulant clinic for INR monitoring (Khan et al., 2004). The control group continued with usual anticoagulation clinic care and were unaware of being in the study to reduce Hawthorne effect (Khan et al., 2004). Participants in both education groups saw improvements with their INR’s staying in therapeutic range (Khan et al., 2004). Participants who received the group education alone saw improvements in maintaining INR in a therapeutic range from a mean of 61.1 % of the time (SD 15.1) six months prior to the study to a mean of 70.4% (SD  24.5) six months after the study began (p=.054) (Khan et al., 2004). The intervention group that received the combination of group education and education to self-monitor their INR had a significant increase in of the percentage of time maintaining their INR in therapeutic range (Khan et al., 2004). This group had a significant mean increase from 57.0% (SD  17.0) six months prior to the study, to 71.1% (SD  14.5) (p<0.01)(Khan et al., 2004). In contrast, the usual care control group saw no changes in the stability of staying within the INR range (Khan et al., 2004). Findings suggest that education alone can improve stability with their anticoagulation therapy, especially with group education sessions. 23 2.4.3 Technology Use in AF Education Technology is only just starting to be used in the education of AF patients. There are many avenues for use of technology in the education of AF patients, such as through online platforms like websites, interactive smart phone applications, and wearable technology, such as smart watches. In one study, an online tailored education platform was used to educate patients who were undergoing current cardioversion or pulmonary vein isolation (Desteghe et al., 2018). This online platform (website) was designed specifically for AF patients and had educational information about OAC treatment, different options for anticoagulation, and procedure- related information tailored to each patient dependent on the type of procedure the patient was undergoing (Desteghe et al., 2018). The website was designed to accommodate different learning styles such as visual and auditory learners using text boxes, imagery, and video education (Desteghe et al., 2018). The online tailored education group was seen to have significant knowledge improvement by the time of the procedure (p=.001) and continued to have improved knowledge 6 (p=.010) and 12 weeks post-procedure (p <0.001) (Desteghe et al., 2018). The comparison group in this study, were those who were willing to participate in the study but did not have access to a computer, tablet, or smartphone and received standard care (Desteghe et al., 2018). The comparison group received information from the cardiologist, general AF brochures and booklets with procedure information (Desteghe et al., 2018). The comparison group had no improved knowledge by the time of procedure and had minimal improvement from baseline to 6 and 12 weeks, post-procedure (Desteghe et al., 2018). This study is the first to use an online platform specific for AF patients and was found to be the preferred delivery of AF education for three out of four patients (72.7%) (Desteghe et al., 2018). 24 Mobile applications have been increasing in popularity to educate patients with many different health conditions including AF. Guo et al. (2017) used a randomized, controlled trial to test a mobile health app (mAF App) in supporting patients with AF. The mAF App was designed to include many aspects of AF management including: personal health record, where the app was able to record such information as medical history, medications, treatment, and laboratory tests; clinical decision support, where the app was able to educate the patient based on their risk score for stroke; patient education program(Guo et al., 2017). The patient education program additionally included 8 components and was self-directed (Guo et al., 2017). The mAF App also included: patient involvement with self-care, where the patients were encouraged to utilize the app by recording their blood pressure and heart rate in the app, as well as their feedback about AF treatment (Guo et al., 2017). The study had structured follow-up with the mAF App users at 1, 3, 6, 9, and 12 months after consultation/ discharge (Guo et al., 2017). Similar to the online platform, this study was the first of its kind to use a mobile health app for AF education. Ninety percent of participants found the mAF App to be accepted, user-friendly and helpful (Guo et al., 2017). The mAF App was successful in addressing knowledge gaps in AF patients, with statistically significant improvements in patients’ AF knowledge with usage of the mAF app over time (Guo et al., 2017). The mAF app was also successful with increasing patient’s adherence to medication therapy and their understanding of the need for anticoagulation therapy from the app education they received (Guo et al., 2017). 2.5 Conclusion AF is a cardiac condition that has been shown throughout the literature to cause many challenges for patient education, knowledge and understanding. Many different education 25 modalities: such as group education and nurse- led clinics, have been trialed to successfully address these challenges for patients to understand their AF however, knowledge deficits remain a growing concern. As the prevalence of AF rises, it is becoming increasingly important for health care educators to create tools to help patients navigate their AF in a self-directed platform. This pilot study is one of the first to examine the feasibility of the implementation of an online platform for patients about AF. An online AF education platform is a universal education tool for AF patients to self-direct their knowledge, education and understanding of AF, that provides an alternative modality for helping to improve their self-efficacy and self-management of AF. 26 Chapter 3: Research Methodology Mixed methods provide a comprehensive approach to addressing research questions. This chapter describes the use of mixed methods use within the field of nursing research and its appropriateness for this study. This chapter also presents the study design, sampling/recruitment, data collection, and data analysis that were used in this study. 3.1 Mixed Methods Design The field of nursing has increasingly turned to the use of mixed methods research design to strengthen the depth and breadth of understanding of phenomena within nursing (Doorenbos, 2014). Mixed methods have been defined as “research in which the investigator collects and analyzes data, integrates findings and draws inferences using both qualitative and quantitative methods in a single study” (Tashakkori & Creswell, 2007, p. 4). Mixed methods are used when a single research method is inadequate to answer the research question and strengthen the overall study, drawing on multiple sources of data to encompass various results (Creswell, 2009; Morse, 1991). Triangulation within mixed methods research combines qualitative and quantitative methods in one study to have more comprehensive results and a reduced potential for bias that may result from only using one research method (Creswell, 2009; Doorenbos, 2014; Morse, 1991). Both qualitative and quantitative data can complement each other to provide greater understanding of nursing phenomena (Doorenbos, 2014). Three common mixed method designs used within health science and nursing research are triangulation (or convergent design), sequential explanatory design, and exploratory design (Doorenbos, 2014; Ivankova et al., 2006; Zheng, 2015). A sequential explanatory design is used when a study starts with the quantitative method and then is 27 followed with the qualitative method within the same study (Ivankova et al., 2006; Zheng, 2015). “The qualitative data are collected and analyzed second in the sequence and help explain, or elaborate on, the quantitative results obtained in the first phase” (Ivankova et al., 2006, p. 5). This approach allows the quantitative data to provide a general understanding of the phenomena and then the qualitative data can refine and explain the statistical results by gaining greater insight into the participants views and experiences (Ivankova et al., 2006). The current study used a mixed methods sequential explanatory design which began with the collection of quantitative data and then collected qualitative data to understand participants’ experiences and learnings with online education. Mixed methods were appropriate because of the exploratory nature of this pilot study of the website intervention and the need to understand and interpret quantitative data (e.g., knowledge, self-efficacy, and website usage) and qualitative (e.g., post-implementation interviews about patient experience in using the website) data on the effectiveness of an online educational platform. This information helped gain an understanding of patient experiences with this type of education and may inform future research to improve patient knowledge and self-efficacy. 3.2 Sampling Following harmonized ethics approval (REB# H19-01710) from the University of British Columbia Okanagan and Interior Health Authority recruitment commenced. 3.2.1 Sample and Recruitment Convenience sampling was used to recruit participants, allowing for interested AF patients to voluntarily participate in the study. According to (Onwuegbuzie & Collins, 2007), convenience sampling is appropriate to use when researchers choose a setting or group of individuals who are willing and conveniently available to participate in the study. 28 Recruitment was highly challenging and required several successive strategies that included invitations through two specific cardiac rehabilitation programs, online social media, and word-of-mouth. An overview of these strategies follows. In the initial stages of the recruitment our focus was on recruiting from cardiac rehabilitation programs to explore this populations experience with online learning in conjunction with in-person educational sessions. Recruitment began with adults over the age of 19 attending rehabilitation programs offered through the Central Okanagan Association for Cardiac Health (COACH) in Kelowna, B.C., and the Vascular Improvement Clinic in Kamloops, B.C. Prospective participants with AF were invited to participate in the study through a letter of invitation/permission to contact distributed by a staff member who used a script for introducing the study. Consenting participants received a follow-up telephone call from the student researcher with information about the study and were given an opportunity to ask questions. However, with the declaration of the SARS-CoV-2 (COVID-19) and social distancing measures that restricted group gatherings and work from home orders, cardiac rehabilitation education sessions and exercise programs were halted and recruitment from these locations curtailed. Six people agreed to participate in the study during a 6-month recruitment period (November 12th, 2019, to April 30th, 2020), however only one participant completed the study. Following an ethics amendment (May 6th, 2020), recruitment proceeded through word-of-mouth and online social media to cast a broad net to all adult patients with AF. A dedicated Facebook Page and Instagram account were created for participant recruitment. Facebook Page Reach is defined by (Facebook for Business, 2021a) as “the estimated number of people who saw any content from your Page or about your Page, including posts, 29 stories, ads, social information from people who interact with your Page and more” (Facebook for Business, 2021a). Over a 6-month period (May 6th, 2020 to November 17th, 2020) the estimated Facebook Page Reach was 18,224. The Instagram reach, similar to Facebook Page Reach, which is “the number of unique accounts that saw any of your posts or stories at least once” reached an estimated 245 (Facebook for. Business, 2021b). A Facebook ad was run once over the 6-month period with a reach of 17,100 views and were on screen a total of 23,400 times. Recruitment for the post-implementation follow-up interviews occurred in conjunction with the online survey. At the end of the survey, participants were asked if they wanted to participate in a follow-up interview to talk about their experiences and learnings from using the website. The student researcher contacted those participants indicating interest in the follow-up interview to arrange a co-determined time and date for the interview. 3.2.2 Inclusion and Exclusion Criteria Participant inclusion criteria included: 1) adults 19 years of age or older with a diagnosis of AF (both primary or secondary diagnoses); 2) access to a computer/tablet/laptop with reliable internet; 3) the ability to speak, read and comprehend English; and 4) the ability to give their own consent to participate in the study. Participant exclusion criteria included: 1) inability to use a computer/tablet/laptop. 3.2.3 Sample Size In similar studies that have measured the use of an online educational platform for patients with AF, the sample sizes have ranged from 35 participants receiving the online intervention (Desteghe et al., 2018) to 113 participants receiving the mAF App intervention 30 (Guo et al., 2017). Based on the meta-analysis by Wantland et al. (2004) examining web-based interventions on knowledge and/or behavioral outcomes, a moderate effect size of 0.35 was used to calculate the sample size. For the quantitative arm of the study, the sample size of 36 was determined by a power analysis with power of 0.80 with an alpha of 0.05, calculated using G*Power (Appendix A) (Faul et al., 2007). In anticipation of potential drop out aim was to recruit between 45- 50 participants. For the qualitative design of this study, Onwuegbuzie and Collins (2007) recommend a sample size of 12 participants for interviewing participants to achieve data saturation. Despite the desired sample size to achieve the goals of the study, recruitment challenges made these sample targets unrealistic. The study recruited a total of n= 17 participants; however, dropouts resulted in a final total of n=9 participants who completed the quantitative arm of the study, and of there, n=3 who participated in the qualitative arm. Table 3.1 presents an overview of recruitment totals and where participants dropped out of the study. Table 3.1 Participant Recruitment and Participant Dropout Participant Overview Total number of participants (n=x) Total number of participants drop out with reasons (if given) Participants who agreed to participate on initial intake. n= 17 Participants who completed baseline data. n= 14 Three participants dropped out between agreeing to participate and completing consent with baseline data. One of three contacted the student researcher to say they were no longer able to participate due to family health matters. Participants who completed intervention. n=12 Of the 14 participants given access to the website, two participants did not access the website and 31 did not respond to follow-up emails. Participants who completed post-intervention data. n= 9 Of the 12 who accessed the website, three more people did not complete the post-intervention questionnaire, even after given follow-up emails and reminders. Participants who completed the semi-structured interview. n=3 Of the initial 14 participants who completed the baseline data, upon initial intake, 8 participants checked that they were interested in the follow-up interview. Of those 8, two dropped out of the study, and upon follow-up in the final questionnaire three changed their minds and no longer wanted to participate in the follow-up interview. Note. This table represents the participant totals recruited for the study and shows where dropouts occurred with reason when given. 3.3 Intervention The participants in the study were exposed to an AF specific educational website that was designed by a team of researchers and clinicians, with the support of information technologists and a digital marketing strategist and input from individuals with AF. This website was developed to enhance AF patients’ knowledge and understanding of their condition using a reliable and credible electronic resource. The information available on the website was also designed to cover the content included in the ‘Knowledge About Atrial Fibrillation Survey’ (McCabe, Sendin, et al., 2020) which was one of the tools used in the pre/post survey further described below. A flow diagram providing an overview of the website is shown in Appendix B. The website consists of eight main pages that the participant can select, and four of the main pages include subheadings with more information within the main page. The first page is an introduction to the website explaining what AF is, 32 the types of AF, signs, and symptoms of AF and when the patient should go to the hospital. The second page provides education about several risk factors associated with AF including lipids, thyroid, blood pressure, obesity, diabetes, and sleep apnea. The third main page is lifestyle, which includes subheadings: nutrition, exercise, alcohol and caffeine, stress management and smoking. The lifestyle section introduces patients to the importance that lifestyle choices can have on AF and to SMART goals to help with motivation for lifestyle changes as part of AF self-management. Each subheading provides information about how AF can impact each lifestyle choice and gives health suggestions to help the patient make informed decisions, such as resources to help with smoking cessation. The next main page on the website is symptom management. Management presents information about different AF treatment options such as medications, ablation, and cardioversion. The complications and long-term effects of AF page gives details about heart failure and stroke. The next three pages are patient stories, frequently asked questions and resource links and PDF’s as additional resources. Following online consent, participants were granted access to the website. Participants were assigned login credentials associated with generic researcher produced email addresses, allowing for the use of individual level website analysis, while protecting participant privacy and confidentiality. The participants had access to the website for one month (four weeks). This timeline was based on two studies that used web-based interventions, one specific to patients with AF and one specific to older adults. Although not specific to AF, one study used a computer-tailored physical activity intervention to try to increase physical activity levels in three age groups (44 years old and younger, 45-59, 60 years old and older) (Ammann et al., 2013). The participants were exposed to the website for 33 one month and were assessed at baseline, one week and after one month of exposure to the website (Ammann et al., 2013). Ammann et al. (2013) found that overall, all three age groups had a significant increase in the number of physical activity sessions and the minutes per session between baseline and one-month follow-up. Similarly, Guo et al. (2017) exposed patients with AF to a mobile health technology application that patients were able to access on their mobile smart phone for three months after hospital discharge. Data were collected on the patient’s knowledge, quality of life scores, antithrombotic drugs, and other medications at three points in time, baseline, one month and three months (Guo et al., 2017). Patients exhibited improvements in AF knowledge at one-month compared to baseline and had statistically significant improvements at one month in both quality of life (p<0.05) and drug adherence to therapy (p<0.05) when compared to baseline (Guo et al., 2017). 3.4 Participant Consent Before entering the study, participants from the cardiac rehabilitation centers were given the letter of invitation/permission to contact. The participants were asked to give their name and phone number to be contacted by the student researcher. Online social media and social contact participants were invited to join the study through the online letter of invitation (Appendix H) and through conversation with the student researcher if the potential participant vocalized interest in the study and met inclusion criteria. Participants were given information about the study and an opportunity to ask questions through an online, or telephone conversation. If the participant wanted to join the study, they then receive a follow-up email with access to the online consent form. Consent was obtained electronically before participants gained access to the pre-study surveys through Qualtrics. Following consent and completion of the surveys, participants were then given access to the website for 34 the four weeks. After the use of the website, the participants were then sent the post-intervention surveys online through Qualtrics. At the end of the surveys, participants were asked if they consented to a follow-up semi-structured interview with the student researcher to learn about participants experiences and learnings using the website. 3.5 Data Collection Participants who were interested in the study and who met the study inclusion criteria were sent a personalized email with a link that gave them access to the consent form, demographics and health history questionnaire, AF knowledge measurement tool, chronic disease self-efficacy tool and a unique user ID to allow access to the website. Data collection occurred at two points in time for the quantitative arm of the study: before and after the intervention. Participant demographics and health history (Appendix E) were only collected at baseline while Knowledge About Atrial Fibrillation and Chronic Disease Self-Efficacy Scale administered through Qualtrics, were collected before and after the intervention. Qualitative data were collected post-intervention with participants who had completed the intervention and agreed to be interviewed. There was also continuous data collection on website usage with Google Analytics (Table 3.2). Measurements collected included AF knowledge, Chronic Disease Self-Efficacy Scale, and website usage. 35 Table 3.2 Data Collection Schedule and Measurement Tool Variable Measurement Tool t1 t2 AF Knowledge KAFS – Knowledge About Atrial Fibrillation Survey X X Self- efficacy Chronic Disease Self-Efficacy Scale X X Demographics X Health History X Follow-up interview Interview guide X Website Usage Google Analytics t1 – Baseline, t2 – 1 month Qualtrics, an online survey program, was used to administer the study measures. AF knowledge was measured using McCabe’s recently developed ‘Knowledge About Atrial Fibrillation Survey’ (KAFS) (presented in Appendix C). The KAFS was developed using research on AF patient knowledge gaps, values, and management preferences (McCabe, 2011). The survey was designed for newly diagnosed AF patients to gain insights into their AF knowledge gaps. The KAFS includes questions about the pathophysiology, causes and progression of AF, the signs and symptoms and symptom management of AF and treatments and actions to take if experiencing AF. The KAFS (Appendix C), a 28- item multiple-choice survey, provides three answer choices, with one preferred response. The current study used a modified KAFS with 25-items, removing three questions focused on lifestyle factors that make AF difficult to control: a question about high blood pressure and controlling AF, obstructive sleep apnea’s impact on controlling AF and the impact of exercise and AF control. The 25-item modified KAFS was used due to the accidental omission of the final three questions when posting to the online survey platform. The omission of these three questions did not significantly impact the results of the KAFS as the participants were 36 assessed on their interpretation of their ability to control and manage their AF in the self-efficacy measurement tool. The KAFS questions are categorized into eight domains that consisted of questions that fit into each domain (McCabe, Sendin, et al., 2020). The domains were: ‘What is AF and what happens in AF? (basic anatomy and physiology explained in plain language)’ (3 questions); ‘Common symptoms of AF and emphasis that AF is often asymptomatic’ (2 questions); ‘Consequences of untreated AF’ (5 questions); ‘Recurrent nature of AF trajectory to expect’ (3 questions); ‘Treatment’ (5 questions); ‘Monitoring response to treatment and signs and symptoms that require professional evaluation’ (6 questions); ‘Risk factors that make AF more difficult to treat (3 questions- all that were omitted from the current study); and “Psychological response to living with AF’ (one question) (McCabe, Sendin, et al., 2020). The KAFS was scored out of 25, and the more questions the participant answered correctly the higher their AF knowledge score. If a participant did not answer the question, it was marked as incorrect, as guided by McCabe, Sendin, et al. (2020). The lead investigator involved with the KAFS Pamela McCabe, Ph.D., R.N. granted permission to use the KAFS in the study. McCabe, Sendin, et al. (2020) have recently tested the KAFS for validation and reported the initial testing phases. There is beginning evidence of construct validity through tests of convergent validity, known groups validity and exploratory factor analysis (McCabe, Sendin, et al., 2020). Convergent validity resulted in a positive correlation (r=0.60, P<.001) between the “Knowledge about Atrial Fibrillation and Self-Management Survey (KASFM)” (McCabe, Sendin, et al., 2020) and Koponen’s “Knowledge about Atrial Fibrillation” test (McCabe, Sendin, et al., 2020). Known group validity was evaluated through independent t- 37 test to determine the difference in KAFSM scores between those who had been recently diagnosed with AF and those who had undergone ablation procedure for their AF (McCabe, Sendin, et al., 2020). The independent t-tests results found that those who underwent ablation therapy had significantly higher knowledge scores than those who were recently diagnosed with AF (P<.001)(McCabe, Sendin, et al., 2020). Exploratory factor analysis also showed a single factor solution; internal consistency reliability was found to be 0.86 (McCabe, Sendin, et al., 2020). Additionally, participants completed the Chronic Disease Self-Efficacy Scale (Appendix D), to measure their confidence in self-managing a chronic disease such as AF. The Chronic Disease Self-Efficacy Scale was developed to evaluate self-efficacy in the Chronic Disease Self-Management Program (Lorig et al., 1996). This scale used a 33-item survey 10-point Likert scale, where one equals ‘not at all confident’ and ten equals ‘totally confident’ with higher scores reflecting higher self-efficacy. The survey assessed participant’s confidence in their ability to: exercise regularly (3-items); get information about their disease (1-item); obtain help from community, family, and friends (4-items); communicate with physician (3-items); manage disease in general (5-items); do chores (3- items); social/ recreational activities (2-items); manage symptoms (5-items); manage shortness of breath (1-item); and control/manage depression (6-items) (Lorig et al., 1996). When testing for construct validity of the self-efficacy scale, Lorig et al. (1996) explained that they used item convergent and discriminant validity tests as part of a multi-trait scaling analysis in their overall conceptual framework in which hypothesized correlations between self-efficacy and various measures were determined (Lorig et al., 1996). Lorig et al. (1996) found that all items for self-efficacy met criteria of item convergence (all 38 item-scale correlations were >.50, with a set minimum of .40). Additionally, reported internal consistency reliability for each subscale were all greater than 0.70; from a range of 0.77 to 0.92 internal consistency coefficients (Lorig et al., 1996, p. 27; Self-Management Resource Center, n.d.). The test-retest reliability coefficients ranged from 0.82 to 0.89 (Lorig et al., 1996, p. 27; Self-Management Resource Center, n.d.). Google Analytics was used to collect website usage data for each participant. Google Analytics is a web-based software that allows the website owner or the researcher to gather information about how the users of the website interact with, and behave on the website (Google, 2021b). The use of Google Analytics to collect the data from website users has been utilized across many fields of study and is mainly used among online retailers to track the activity of their customers and the effectiveness of advertising (Clark et al., 2014). Google Analytics is the most readily available, simple, and powerful tool to use for analyzing complex online behavior (Clark et al., 2014) and has been effective in measuring the usability of a web-based physical activity intervention in older adults (Ammann et al., 2013). To maintain anonymity each participant was assigned a participant ID, which was linked to an alias email address that was then linked to a google analytics User ID allowing the participants to remain anonymous while using the website. Google Analytics uses JavaScript tracking codes which are embedded in each page of the website and each time a user visits the page, a tracking code collects data about the user’s interactions with the webpage (Google, 2021a). Google Analytics tracks webpage usage by tracking ‘tag’ signaling (Clark et al., 2014). Tags are small website codes that provide useful insights on website usage (Clark et al., 2014). Google analytics tags allow for the collection of information about website ‘events’ including form submission, the time spent on the page, 39 clicks and mouse movements, files downloaded, or external links clicked, as well as videos played on the webpage (Clark et al., 2014). Specifically, this study set up Google Analytics Tags to measure the events of page engagement of scroll depth from 0-100% vertical scroll depth of the page, clicks of outbound links, PDF download tracking and YouTube video plays/pauses/ from 0-100% of the video played. The Google Analytics Tags are outlined in specific detail related to coding of the tags in Appendix F. The qualitative data collection occurred through semi-structured individual telephone interviews. Interviews occurred at a pre-determined convenient time for the participants and the student researcher and lasted for 15-30 minutes. The interview questions were developed to gain insight into participants’ views of the use of a website as a form of education delivery, as well as the usability of the online platform. The questions were guided by the Perceived Health Web Site Usability Questionnaire (Nahm et al., 2006) to understand participants’ experiences and responses to use of technology. To gain further insight with some participant responses the student researcher prompted the participant with questions, such as ‘please tell me more’. Appendix G outlines the semi-structured interview questions that guided the telephone interview with the participants. 3.6 Data Analysis The quantitative data were analyzed using IBM SPSS Statistics 26 software. Sample descriptions were collected from the demographic and health history questionnaire (Appendix E). Means and standard deviations were computed for the main study variables (e.g., AF knowledge and chronic disease self-efficacy). Paired t-tests with an alpha of 0.05, were used to measure changes in KAFS scores and the Chronic Disease Self-Efficacy scores before and after the website intervention. Google analytics were used to measure total time 40 spent on the website, total number of website sessions, total number of pageviews, total number of events and the average time each participant spent on the website per session. Spearman’s rank correlational analyses were also computed to examine relationships between AF knowledge, self-efficacy and website usage. The qualitative interviews were digitally recorded and the student researcher subsequently transcribed and thematically analyzed them. A thematic analysis was used in which the student researcher and the supervisor analyzed the transcribed data. They began with open coding the data and clustered similar codes into themes and used a consensual process to label the clustered codes to appropriately reflect participants accounts. 3.7 Risks and Benefits Participating in this study had no more risk than participants might encounter in their everyday lives. Participant identity was concealed through the use of a unique User ID to log in to the website. The User ID was linked to a google analytics participant ID that allowed their use of the website to be tracked. These multiple layers of ID allowed for confidentiality in the user’s identification as well. Although participants were informed, they might not gain direct benefit to study participation, there was the possibility of gains in AF knowledge that might enhance their ability to care for themselves. Participants were compensated with a $20.00 gift card of their choice that was delivered by mail or my e-gift card. 3.8 Conclusion Overall, this study used a mixed methods design and social cognitive theory to guide the research in data collection and analysis. The following chapters present the research findings, discussion, and conclusion. 41 Chapter 4: Research Findings This chapter will present the results of this mixed-methods pilot study to answer the proposed research questions for both the quantitative and qualitative arms of the study. The primary quantitative research question was: Does the use of an educational website dedicated to patients with AF increase their knowledge and self-efficacy? Secondary quantitative research questions included: Is there a relationship between AF knowledge and self-efficacy? How did participants use the website? Is there a relationship between website usage and AF patients’ knowledge and self-efficacy? Finally, this chapter will discuss themes found from the qualitative arm of the study to answer the question: “What are the patients’ experiences of using the educational website?”. Before presenting the findings, the study sample will be described. 4.1 Description of Sample In total nine participants completed the intervention and post-intervention questionnaire. Participants were numbered one through nine, based on the chronological order in which they were recruited. Demographics, descriptive statistics, and health history are displayed in Tables 4.1, 4.2, 4.3, and Figures 4.1, 4.2 and 4.3. All the participants in this pilot study were Caucasian, six males and three females: with a mean age 53 (SD 16.1), with an AF history from less than six months (three participants) to having AF for more than five years (four participants). All the participants had either some college or university education and were married or remarried (66.7%). Annual income for all the participants ranged from $25,000 to over $75,000; and most of the participants owned their home and lived with a partner (66.7%). Four of the participants had only one other chronic medical condition such as arthritis, diabetes, hypertension, sleep apnea, or previous stroke, in addition 42 to AF and eight participants had been referred to a cardiologist. Six participants were from British Columbia, Canada, and three were from the United Kingdom: two from England and one from Northern Ireland. Table 4.1 Descriptive Statistics Female (N=3) Male (N=6) Overall (N=9) Age Mean (SD) 50.3 (19.1) 55.5 (16.1) 53.8 (16.1) Median [Min, Max] 53.0 [30.0, 68.0] 53.5 [38.0, 84.0] 53.0 [30.0, 84.0] Marital Status Divorced 1 (33.3%) 1 (16.7%) 2 (22.2%) Married or remarried 2 (66.7%) 4 (66.7%) 6 (66.7%) Single 0 (0%) 1 (16.7%) 1 (11.1%) Education College/University graduate 1 (33.3%) 2 (33.3%) 3 (33.3%) Some College 2 (66.7%) 4 (66.7%) 6 (66.7%) Income $25,000- $50, 000 2 (66.7%) 1 (16.7%) 3 (33.3%) $51,000-$75,000 1 (33.3%) 3 (50.0%) 4 (44.4%) Over $75,000 0 (0%) 2 (33.3%) 2 (22.2%) Residence Apartment/Condominium for all ages 2 (66.7%) 1 (16.7%) 3 (33.3%) Own detached home 1 (33.3%) 5 (83.3%) 6 (66.7%) Living Arrangements Live alone 1 (33.3%) 0 (0%) 1 (11.1%) Live with partner 2 (66.7%) 4 (66.7%) 6 (66.7%) Live with other family members 0 (0%) 2 (33.3%) 2 (22.2%) Total of Medical Conditions (Excluding AF) Mean (SD) 0.333 (0.577) 0.833 (0.753) 0.667 (0.707) Median [Min, Max] 0 [0, 1.00] 1.00 [0, 2.00] 1.00 [0, 2.00] Years with AF 1-3 years 1 (33.3%) 0 (0%) 1 (11.1%) Less than 6 months 1 (33.3%) 2 (33.3%) 3 (33.3%) More than 5 years 1 (33.3%) 3 (50.0%) 4 (44.4%) 43 Table 4.1 Descriptive Statistics Female (N=3) Male (N=6) Overall (N=9) 3-5 years 0 (0%) 1 (16.7%) 1 (11.1%) Total Medications Currently Taking Mean (SD) 1.00 (1.00) 2.67 (1.37) 2.11 (1.45) Median [Min, Max] 1.00 [0, 2.00] 2.50 [1.00, 5.00] 2.00 [0, 5.00] Rated Overall Health Excellent 2 (66.7%) 2 (33.3%) 4 (44.4%) Good 1 (33.3%) 3 (50.0%) 4 (44.4%) Fair 0 (0%) 1 (16.7%) 1 (11.1%) Rated Mental Health Excellent 2 (66.7%) 2 (33.3%) 4 (44.4%) Good 1 (33.3%) 3 (50.0%) 4 (44.4%) Fair 0 (0%) 1 (16.7%) 1 (11.1%) Table 4.2 Perceived Health Participant Overall Health Overall Mental Health 1 Excellent Good 2 Good Excellent 3 Excellent Excellent 4 Excellent Excellent 5 Excellent Excellent 6 Good Good 7 Good Good 8 Good Good 9 Fair Fair 44 Figure 4.1 Years with Atrial Fibrillation Note. The participants each had unique experiences with their AF and ranged from newly diagnosed participants to long-standing AF. In addition to AF, four participants reported having at least one other chronic condition such as arthritis, diabetes, hypertension, sleep apnea, or previous stroke. One participant reported having two other chronic conditions (diabetes and hypertension) in addition to AF. Four participants did not report any other chronic condition in their health history. 3 Participants 33%1 Participant11%1 Participant11%4 Participants45%Less than 6 months1-3 Years3-5 YearsMore than 5 Years 45 Figure 4.2 Referral to Cardiologist Figure 4.3 Cardiac Medications Note. None of the participants were taking warfarin and six participants were taking NOACs (novel oral anticoagulants). 6 Participants67%2 Participants 22%1 Participant11%Referred by Family DoctorReferred by EmergencyRoom PhysicianNo Referral1 Participant 11%5 Participants 56%2 Participants22%1 Participant11%No medications1-2 Medications3-4 Medcations5 or more Medications 46 Unique Participant 1: The only participant (Participant 1) was prescribed five cardiac medications, had not been referred to a cardiologist while the other participants were prescribed fewer or no cardiac medications but had been referred to a cardiologist. Table 4.3 Participant AF Treatment Treatment Number of Participants No treatment 1 Treated with medication only 2 Treated with medication and cardioversion 4* Treated with medication, cardioversion, and ablation 2** * One of the total number of participants was awaiting cardioversion at the time of the study. **One of the total number of participants was awaiting ablation at the time of the study. Three participants had been to the emergency department once in the last year and one of the three had been there twice in the last year due to their AF. Of these, two of the participants had been admitted to the hospital for five days or less, and one of the two had more than four hospital admissions in the last year due to their AF. 4.2 Knowledge About Atrial Fibrillation Survey Results All participants completed the modified Knowledge About Atrial Fibrillation Survey (KAFS) pre- and post-website intervention. The KAFS results are displayed in Figure 4.4. The pre-website intervention AF knowledge scores ranged from 72% to 92% with a mean of 84% (SD 7.5%). The post-website intervention scores ranged from 64% to 92% with a mean score of 82.67% (SD 8.5%). One participant had an improved knowledge score, four participants knowledge scores stayed the same, and four participants had a decrease in their AF knowledge scores. Three participants had decreases in knowledge scores that ranged from 4%-8%. The paired t-test (Table 4.4) found no statistical significance between the pre- 47 website intervention knowledge scores and the post-website intervention knowledge scores (p=.397). Figure 4.4 Knowledge About Atrial Fibrillation Survey Scores Note. Figure displays the KAFS scores matched with the participant’s years with AF diagnosis. Pre-knowledge average score= 84%; post-knowledge average score= 82.67%. Table 4.4 Paired t-test for KAFS Scores Paired Differences t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pre-KAFS & Post-KAFS Scores 0.01333 0.04472 0.01491 -0.02104 0.04771 0.894 8 0.397 0%10%20%30%40%50%60%70%80%90%100%Participant # (Years with AF)Pre-Knowledge Scores Post-Knowledge Scores 48 Unique Participant 1: Participant 1 was the only participant shown to have increased knowledge scores from pre-intervention to post-intervention. This participant was a newly diagnosed AF participant, who had not been referred to a cardiologist but had attended a cardiac rehabilitation program. 4.3 Knowledge About Atrial Fibrillation Survey Domain Results The KAFS questions were categorized by McCabe, Sendin, et al. (2020) into eight domains. The domains varied between participants and were averaged as an overall score per domain. The domain with the lowest knowledge score pre-intervention was the domain of ‘Consequences of untreated AF’, assessed through five questions with an overall average score of 60% (SD 22%). The domains ‘What is AF and what happens in AF? (basic anatomy and physiology explained in plain language)’ (three questions) and ‘Psychological response to living with AF’ (one question), had the highest collective knowledge scores with pre-intervention scores of 100% for all participants. Post-intervention domain results showed overall increases in knowledge scores in three domains; ‘Common symptoms of AF and emphasis that AF is often asymptomatic’ (three questions), ‘Consequences of untreated AF’ (five questions), and ‘Recurrent nature of AF and trajectory to expect’ (three questions). The ‘Consequences of untreated AF’ remained the lowest overall score of 71% post-intervention, and domains of ‘Common symptoms of AF and emphasis that AF is often asymptomatic’ and ‘Psychological response to living with AF’ both had scores of 100% post-intervention. Three domains had decreases in mean knowledge scores, these domains included: ‘What is AF and what happens in AF? (basic anatomy and physiology explained in plain language)’, ‘Treatments’, and ‘Monitoring response to treatment and signs and symptoms that require professional evaluation’. Paired t-tests were not conducted on these mean scores for pre- and 49 post-intervention due to the small number of questions asked per domain and the small sample size. 4.4 Chronic Disease Self-Efficacy Results All participants completed the Chronic Disease Self-Efficacy Scale before and after the website intervention. The Chronic Disease Self-Efficacy Scale measures participants confidence in managing aspects of their chronic disease where the higher the score the higher the participants self-efficacy (highest possible score 330). At baseline measurement, the participants had a mean score of 260 (SD 43) with a minimum score of 202 and a maximum score of 319. Post-website intervention mean self-efficacy scores had a slight decrease to 258.78 (SD 37.76) with a minimum score of 201 and a maximum score of 322. A paired t-test (p> 0.05) showed no statistical significance (p=.923) (Table 4.5) in the Chronic Disease Self-Efficacy scores before and after the intervention. Table 4.5 Paired t-test for Chronic Disease Self-Efficacy Paired Differences t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pre & Post-Chronic Disease Self-Efficacy Scale 1.22222 36.86386 12.28795 -27.11385 29.55830 0.099 8 0.923 The subscales of the Chronic Disease Self-Efficacy Scale were examined for any differences between pre- and post-website intervention. Paired t-tests (Table 4.6) found no 50 significant difference in any of the sub-scale scores before and after the intervention. Figure 4.5 presents each sub-scale with participants’ pre- and post-website intervention scores. Table 4.6 Paired t-test for Chronic Disease Self-Efficacy Overall and Sub-Scales Paired Differences t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pre & Post Chronic Disease Self-Efficacy Overall 1.222 36.864 12.288 -27.114 29.558 0.099 8 0.923 Pre & Post Self-Efficacy- Exercise -0.444 6.064 2.021 -5.106 4.217 -0.220 8 0.831 Pre & Post Self-Efficacy- Get Information on Disease -0.556 1.878 0.626 -1.999 0.888 -0.887 8 0.401 Pre & Post Self-Efficacy- Obtain help from community, family, friends -2.222 4.868 1.623 -5.964 1.519 -1.370 8 0.208 Pre & Post Self-Efficacy- Communication with Physician 0.222 5.449 1.816 -3.966 4.411 0.122 8 0.906 Pre & Post Self-Efficacy- Manage Disease in General 1.333 9.513 3.171 -5.979 8.646 0.420 8 0.685 Pre & Post Self-Efficacy-Do Chores 0.889 1.537 0.512 -0.292 2.070 1.735 8 0.121 Pre & Post Self-Efficacy- Do social/recreational activities 2.222 3.801 1.267 -0.699 5.144 1.754 8 0.117 Pre & Post Self-Efficacy- Manage Symptoms 2.000 9.192 3.064 -5.066 9.066 0.653 8 0.532 Pre & Post Self-Efficacy- Manage Shortness of breath -0.333 1.118 0.373 -1.193 0.526 -0.894 8 0.397 51 Pre & Post Self-Efficacy- Control/ manage depression -1.889 18.510 6.170 -16.117 12.339 -0.306 8 0.767 Figure 4.5 Chronic Disease Self-Efficacy Sub-Scales 52 53 4.5 Website Usage & Google Analytics Results All participants had access to the website for a total of four weeks but the activity on the website varied greatly between participants. Table 4.7 outlines each participant’s website usage from Google Analytics according to their gender and age, years of AF diagnosis, total time spent on the website, total number of sessions, total number of pageviews, total number of events, and the average time spend on the website per session. The average total time spent on the website was 00:42:19 (hh:mm:ss) (SD00:38:36); ranging from a minimum of 00:02:10 to a maximum of 01:47:58. Participants spent an average of four sessions (SD2.598) on the website with a range of one to eight sessions. The overall average time per session on the website was 00:09:57 (SD00:08:08) ranging from 00:01:12 to 00:27:00. Participants used the website in different ways. Some participants viewed as few as three pages on the website while others viewed up to 30 website pages. Participant page events ranged from 13 events on the website to as many as 225 events. The page events measured page engagement of scroll depth from 0-100% of vertical scroll on the page, clicks of outbound links, PDF download tracking and YouTube video plays/pauses/ from 0-100% of the video played. Table 4.8 reports the total number of users per webpage. Overall, the main landing page had the most users with 9 users in total. The pages titled: Lifestyle, Complications of AF, and Patient stories had the highest viewing with eight users each, followed by the pages titled Medications, Exercise, Alcohol and Caffeine each with seven users. The page titled “Patient Stories” was the main landing page of the patient stories, which gave the user a brief overview of four different AF patient’s stories they could then select to read in detail. The pages that were only viewed by two users were: Cardioversion, Tumultuous Atrial Fibrillation (patient story), Blood Pressure, 54 Mystery of AFib (patient story), Smoking (lifestyle), and From Friends to Family During the Ups and Down (patient story). Table 4.7 Google Analytics of Website Usage Participant Gender/ Age Years of AF Diagnosis Total Time Spent on Website Total Sessions Total Pageviews Total Events Average Time on Website per Session Average SD 00:42:19 SD 00:38:36 4 SD 2.598 11.33 SD 8.972 87.89 SD 75.661 00:09:57 SD 00:08:08 1 Male/59 Less than 6 months 0:53:39 3 6 115 0:17:53 2 Male/84 More than 5 years 1:35:37 8 21 225 0:11:57 3 Male/58 More than 5 years 0:29:51 3 9 64 0:09:57 4 Female/68 More than 5 years 1:47:58 4 12 174 0:27:00 5 Female/53 Less than 6 months 0:03:36 3 6 13 0:01:12 6 Male/49 Less than 6 months 0:33:17 5 12 33 0:06:39 7 Female/30 1-3 years 0:02:10 1 3 27 0:02:10 8 Male/38 3-5 years 0:06:48 1 3 23 0:06:48 9 Male/45 More than 5 years 0:47:56 8 30 122 0:06:00 55 Table 4.8 Page Titles and Total Number of Users Page Title Total Number of Users Virtual Atrial Fibrillation Clinic – The Virtual Atrial Fibrillation Clinic Resource Center has been designed specifically for people with newly diagnosed atrial fibrillation (AFib) as well as people with longer term AFib having some management challenges. 9 Lifestyle – Virtual Atrial Fibrillation Clinic 8 Complications of AFib – Virtual Atrial Fibrillation Clinic 8 Patient Stories – Virtual Atrial Fibrillation Clinic 8 Medications – Virtual Atrial Fibrillation Clinic 7 Exercise – Virtual Atrial Fibrillation Clinic 7 Alcohol and Caffeine – Virtual Atrial Fibrillation Clinic 7 Management – Virtual Atrial Fibrillation Clinic 6 Risk Factors – Virtual Atrial Fibrillation Clinic 6 Resources – Virtual Atrial Fibrillation Clinic 5 My SMART Goals – Virtual Atrial Fibrillation Clinic 5 Frequently Asked Questions – Virtual Atrial Fibrillation Clinic 5 Stroke – Virtual Atrial Fibrillation Clinic 5 Obesity – Virtual Atrial Fibrillation Clinic 4 Stress Management – Virtual Atrial Fibrillation Clinic 4 Sleep Apnea – Virtual Atrial Fibrillation Clinic 4 Ablation – Virtual Atrial Fibrillation Clinic 4 Heart Function – Virtual Atrial Fibrillation Clinic 4 Thyroid – Virtual Atrial Fibrillation Clinic 3 Nutrition – Virtual Atrial Fibrillation Clinic 3 Cardioversion – Virtual Atrial Fibrillation Clinic 2 Tumultuous Atrial Fibrillation – Virtual Atrial Fibrillation Clinic 2 Blood Pressure – Virtual Atrial Fibrillation Clinic 2 Mystery of AFib – Virtual Atrial Fibrillation Clinic 2 Smoking – Virtual Atrial Fibrillation Clinic 2 From Friends to Family During the Ups and Downs – Virtual Atrial Fibrillation Clinic 2 56 4.6 Website Usage and Knowledge about Atrial Fibrillation Survey Results Participants’ website usage varied according to length of time per session and number of sessions. Participant 1, who demonstrated the only knowledge score increase (from 76% to 84%), used the website for a total time of 00:53:39, averaged 00:17:53 on the website per session, had a total number of three sessions, looked at six pages and had 115 events. This participant was a newly diagnosed participant with AF for less than six months. Conversely, four participants had a decrease in knowledge scores and all averaged 00:06:48 per session or less on the website. Of these four participants with the decrease, participant 9 had the highest total time on the website (00:47:56), while participant 7 spent the least amount of time on the website (00:02:10); both had a 4% decrease in their knowledge scores. These four participants accessed the website from one to eight sessions, viewing a range of three to 30 pages and a range of 23 to 122 events. Participant 8 had the largest knowledge score decrease of 9% from 72% pre-intervention to 64% post-intervention. History of AF did not appear to influence these four participants’ website usage with variability in lengths of time with AF: one participant newly diagnosed (less than six months), one diagnosed for one to three years, one with AF for three to five years and one had AF for more than five years. Unique participant 8: This participant was unique in the background they included when contacting the student research about the study. This participant was recruited through social media and was very interested in learning more about AF due to suffering a stroke at the age of 38 related to their AF. Despite the overwhelming interest in learning more about their AF, the participant only accessed the website once in the four weeks, using it for a total of six minutes and 48 seconds, with a total of 23 events. At a closer look the participant scrolled through each 57 main landing page (risk factors, lifestyle, management, complications, and patient stories) but did not go into the sub-pages within the main landing pages. Four participants had no change in their knowledge scores. The amount of time spent on the website varied from a low of 00:03:36 to a high of 01:47:58. Three of the four had AF for more than five years, and one was newly diagnosed, less than six months. Their knowledge scores remained unchanged before and after the intervention and ranged from 76% (website used for a total 00:29:51) to 92% (website used for 00:03:36). Total sessions for this group ranged from three sessions to eight, with a minimum of six page views with 13 events, to a high of 21 page views with 225 events. 4.7 Website Usage and Chronic Disease Self-Efficacy Scores Similar to the knowledge scores, the website usage and chronic disease self-efficacy score changes varied between participants. Three participants had an overall increase, while six participants had decreases in their in self-efficacy scores after using the website. Two of the three participants (participants 2 and 4) who had increases in self-efficacy scores were high users of the website, (multiple sessions (4-8), high total website time (01:47:58;01:35:37), multiple pageviews (12-21) with high total events (174-225), and above average time per session (00:11:57, 00:27:00). The third participant with an increased self-efficacy score was Participant 5, who unlike the other two had low website usage; lower total time spent on the website (00:03:36), fewer total sessions (3), pageviews (6), events (13), and had the lowest time per session (00:01:12). The remaining participants had a decrease in their self-efficacy scores, and varied use of the website. The largest decrease in the self-efficacy score was Participant 7, with a 60-point drop in score from pre-to post-intervention corresponding to the lowerest website usage (one session of short duration (00:02:10), three page views and 26 events). The rest of the 58 participants had a self-efficacy score decrease of 4- 33 points, with total time website usage varying from 00:06:18 to 00:53:39. 4.8 Combining the Results Overall, participants varied in their use of the website. Table 4.9 provides an overview of each participant with their KAFS scores, Chronic Disease Self-Efficacy scores and Google Analytics Website Usage. One participant (Participant 1) had relatively high website use of 00:53:39, increased their knowledge score but decreased their self-efficacy score. Three participants (Participants 2,4,5) had no change in their knowledge scores and had increased self-efficacy scores with varying website usage as little as 00:03:36 to as high as 01:47:58. One participant (Participant 3) had no change in their knowledge score and a decrease in their self-efficacy, using the website for 00:29:51, with three sessions, nine page views, and 64 events. Four participants (Participants 6, 7, 8, 9) all had decreases in their knowledge and self-efficacy scores, used the website as little as 00:02:10 to a high of 00:47:56. Two of the four (Participants 7 and 8) only used the website once, and the other two had multiple sessions of five and eight sessions, with lows of three pageviews with 23 events and high of 30 pageviews with 122 events. 59 Table 4.9 Participant Overview of KAFS, Chronic Disease Self-Efficacy Scores and Google Analytics Website Usage Participant Gender/Age Years of AF Diagnosis KAFS Scores Pre/Post Score (Increase/Decrease/No Change) Self-Efficacy Score (Increase/Decrease) Total Time Spent on Website Total Number of Sessions Total Number of Pageviews Total Number of Events Average Time on Website per Session Average SD Pre= 84%/ Post= 82% (p=.397) SD .05333 Pre= 260 Post=258 (p=.923) SD36.864 00:42:19 SD 00:38:36 4 SD 2.598 11.33 SD 8.972 87.89 SD 75.661 00:09:57 SD 00:08:08 1 Male/59 Less than 6 months 76%/84% (Increase) 319/301 (Decrease) 0:53:39 3 6 115 0:17:53 2 Male/84 More than 5 years 88%/88% (No change) 304/322 (Increase) 1:35:37 8 21 225 0:11:57 3 Male/58 More than 5 years 76%/76% (No change) 291/284 (Decrease) 0:29:51 3 9 64 0:09:57 4 Female/68 More than 5 years 88%/88% (No change) 206/263 (Increase) 1:47:58 4 12 174 0:27:00 5 Female/53 Less than 6 months 92%/92% (No change) 202/249 (Increase) 0:03:36 3 6 13 0:01:12 6 Male/49 Less than 6 months 92%/88% (Decrease) 234/201 (Decrease) 0:33:17 5 12 33 0:06:39 7 Female/30 1-3 years 88%/84% (Decrease) 293/234 (Decrease) 0:02:10 1 3 27 0:02:10 8 Male/38 3-5 years 72%/64% (Decrease) 246/234 (Decrease) 0:06:48 1 3 23 0:06:48 9 Male/45 More than 5 years 84%/80% (Decrease) 245/241 (Decrease) 0:47:56 8 30 122 0:06:00 60 4.9 Correlational Analysis The secondary research questions examined if there was a relationship between AF knowledge, self-efficacy, and website usage. Spearman’s rank correlational analyses (Table 4.10) were conducted to examine relationships between: AF knowledge and website usage, self-efficacy and website usage, and AF knowledge and self-efficacy. When examining the relationship between AF knowledge and website usage the correlational analysis found no relationship between the two variables (p=.598). Similarly, when examining the relationship between self-efficacy and website usage, no relationship was found (p=.110). Finally, a spearman’s rank correlational analysis was computed on the relationship between AF knowledge and self-efficacy, also resulting in no relationship between the two variables (p=.734). Table 4.10 AF Knowledge & Time Spent on Website Post-KAFS Scores Total Time Spent on Website in Seconds Spearman's rho Post-KAFS Scores Correlation Coefficient 1.000 .204 Sig. (2-tailed) . .598 N 9 9 Total Time Spent on Website in Seconds Correlation Coefficient .204 1.000 Sig. (2-tailed) .598 . N 9 9 61 Table 4.10 Self-Efficacy and Website Usage Total Time Spent on Website in Seconds Post-Chronic Disease Self-Efficacy Scale Spearman's rho Total Time Spent on Website in Seconds Correlation Coefficient 1.000 .569 Sig. (2-tailed) . .110 N 9 9 Post-Chronic Disease Self-Efficacy Scale Correlation Coefficient .569 1.000 Sig. (2-tailed) .110 . N 9 9 AF Knowledge & Self-Efficacy Post-Chronic Disease Self-Efficacy Scale Post-KAFS Scores Spearman's rho Post-Chronic Disease Self-Efficacy Scale Correlation Coefficient 1.000 .132 Sig. (2-tailed) . .734 N 9 9 Post-KAFS Scores Correlation Coefficient .132 1.000 Sig. (2-tailed) .734 . N 9 9 4.10 Semi-Structured Interviews Analysis The student researcher conducted semi-structured telephone interviews with three participants who participated in the website intervention. The interviewees were Participant 2, an 84-year-old male; Participant 4, a 68-year-old female; and Participant 3, a 58-year-old 62 male. All three participants had had AF for more than five years. Four main themes emerged: value of website, quality of the information, quality and usability of the website and suggested changes for the website. 4.10.1 Value of Website Participants ascribed considerable value to the website even though they all had some familiarity with AF, but what they valued was variable. Participants described valuing the website as a refresher, as affording one stop shopping (everything in one place), as complementary to provider education, and as an impetus for self-reflection. Two participants described the website as refreshing and reinforcing what they already knew and was an impetus for self-reflection. Participant 2 elaborated, “Most of it…the stuff that was there I was familiar with. So going through the website it would be a refresher of what I knew and…it would make me realize that there were things in there that I kind of shoved into the background of my memory”. He added there wasn’t anything in particular that he found was not helpful on the website and stated, “the whole thing was a good lesson”. For other participants such as participant 4, the website reinforced what she already knew and the self-management she was already practicing. For example, she perceived that prior to the study she already had a good knowledge base of AF and its contributing factors and was already implementing some of the things suggested on the website, such as “weight loss, and um curtailing alcohol consumption… increasing your activity level where you can”. Similarly, Participant 2 agreed that in addition to his doctor’s education, the website was a reinforcement tool and “a good review of things that I remembered and partly remembered and forgot over the years”. 63 Participant 3 described the value of the website in raising his consciousness about what “I need to do” (“I think it helped me to understand what I need to do”). He explained that “for me, the most helpful was the little video explaining everything.. I think it was four minutes, five minutes…short video really interesting and straight to the point”. He indicated that using the website made him consider lifestyle factors (e.g., alcohol intake, physical activities, etc.) and enhanced awareness of how his lifestyle and nutrition may be impacting his AF, “it made me take consciousness… really one thing I need to improve is...the way I eat, my food”. He explained that the SMART goals on the website prompted him to think about lifestyle goals: “What would be my goal okay, eat better and maybe take more time for myself and my family right… so I’m slowly, without setting goals I think I’m slowly getting my mind to that so, ya it did help ya”. 4.10.2 Quality of the Information Participants described the information on the website as good quality and easy to understand. Of the website and videos, participant 2 stated “I went through every bit of it. It was actually, uhh, quite good!”. Similarly, participant 3 felt like the information was very precise, simple, and not too complicated to understand. Another participant lauded the quality of the content presented on the website. The participant explained that the information was easy to understand and laid out clearly, specifically the information on stroke risk factors and prevention. Participant 4 stated: “I thought the stuff on there was really good about recognizing and managing your modifiable risk factors and coping with your non-modifiable risk factors for stroke 64 and uhh..those kind of, which is the most, I guess the biggest risk for AFib and the most daunting”. Participant 4 went on to elaborate about the website’s clear integration of many facets of stroke risk and prevention and “how important their lifestyle is towards managing those, that risk”. The participant also pointed out that the page on: “stroke prevention and AF risk tool…that kind of brought a whole lot of stuff together in terms of the…CHADs-2 scoring and the Has-bled scoring and showing how the different drug treatments change your risk factor and risk level.. I found that really interesting and useful and reassuring to see it all laid out so clearly like that”. 4.10.3 Quality and Usability of Website Participants praised the quality and the usability of the website. Participant 4 who admitted to spending a lot of time over the years researching online, exclaimed, “it’s really nice to see a website like that put together, with all the information in one place, and um, certainly clarifies a lot of things”. She added that the website would be a real time saver for someone who may be new to AF and might be trying to put all the information together to have it laid out on one website. Participants found the website easy to understand and navigate. Participant 4 went on to explain that she found “the pages and text with lots of good information on them, well written and understandable”. Participant 3 shared that he found the website user friendly and that it was “easy to navigate…not too complicated, clear, simple, not too fussy, not too flashy, it was just perfect”. Participants all resoundingly agreed that they would recommend the website to someone with AF. According to participant 2 “I would refer them to it, that’s for sure! As I 65 say, it’s going to give them a greater, a great percentage of the information they need”. Participant 3 would like to share it with those without AF to gain an understanding of risk factors that may lead to AF, “Definitely. Even someone like a family member, like my sons, because… I talk to them about it because they might, it might be genetic, so I asked them to be checked by their physician and it’s just something I could say well if you want to find out more just go to that website, so that’s an easy fixer”. 4.10.4 Recommendations for Change Participants all had suggestions and recommendations for change to the website. Some suggestions were generic and others more specific. Although one participant found the website content was presented in a user-friendly, easy to understand way, another participant suggested that some simplification might be needed. Participant 2 said: “I would have a look at would be ‘simplification’…like as if you’re going to talk about sinus nodes, some kind of diagram to show where the heck it is, and what it does and what, for example, if ablation goes in there, what is it, and what’s it going to do to that sinus node?” Participant 4 recommended the addition of information on the website regarding the difference between vagally-mediated AF and adrenergically-mediated AF, as her AF is vagally-mediated, and she explained that it mainly occurs when she is asleep. A couple of the participants reported there were some general errors including malfunctioning links, a spelling mistake on the non-modifiable risk factors page and a section in the SMART goals that highlighted alcohol and caffeine in the title, but caffeine was not mentioned on the page. The participant stated, “I just assumed it was a work-in- 66 progress…and there will be more added”. One participant also felt that the website would be very beneficial if it could be translated to French. The participant is French-Canadian and reported that he has friends who have AF and are seeking out educational material in French to learn more about AF. Two of the participants expressed that they felt it would be helpful to have some ongoing connection with the website and other people with AF, such as a blog style forum or an educational newsletter. Participant 3 requested that an e-mail newsletter or information letter may be helpful to keep up to date on AF news, self-management tips and care. Similarly, Participant 4 found value in connection through a blog previously and suggested a similar forum would be helpful. “There used to be a yahoo site that was.. an AFib site and people would put in comments about some of their experiences and get other people’s feedback on it or information that they had learned, and I used to read that every day and now that sites been discontinued…so ya something like that I think would be good”. Overall, the semi-structured interviews gave valuable insight to the use of a website for AF patients. Participants found value with the website and the quality of information presented. Participants felt that a website would be best used as complimentary and reinforcement tool to their in-person education. Participants recommended helpful changes and additions to the website to allow for optimal website implementation and usage. 67 Chapter 5: Discussion 5.1 Introduction The purpose of this mixed-methods pilot study was to assess the efficacy of using an educational website developed with the focus of educating patients about their atrial fibrillation (AF). Study of technology in atrial fibrillation education is still nascent and is being explored through different modalities such as online platforms (e.g., current study), interactive smart phone applications, and wearable technologies like smart watches and portable electrocardiograms (ECGs). Findings have shown that the current dedicated AF website did not significantly increase participants’ knowledge and self-efficacy scores; therefore, the findings at this point do not support social cognitive theory. This chapter considers the findings of the pilot study and how they relate to the current literature. Implications for practice, education and research are presented. Limitations of the research study are considered, followed by a conclusion. 5.2 Knowledge The lack of significant increase in knowledge scores following the educational website intervention may reflect pre-implementation Knowledge About Atrial Fibrillation Survey (KAFS) scores that were relatively high with a participant mean score of 84%. There is no published evidence using the KAFS to determine how participants in the current study compared to other patients with AF. However, the relatively high pre-implementation scores are somewhat unexpected given the low knowledge scores of patients with AF reported in other studies (McCabe et al., 2017). McCabe, Sendin, et al. (2020) found that the KAFS scores were higher among those who had had ablation therapy than those who were newly diagnosed with AF (those who were newly diagnosed 6 months or less). McCabe, Sendin, et 68 al. (2020) stated that the KAFS “could discriminate patients expected to have more knowledge about AF and self-management from those who would be expected to have less” (p. 40); however, this study did not see that same pattern. In the current study participants who were newly diagnosed with AF (less than six months) had better scores pre/post-intervention (76-92% pre-intervention, 84-92% post-intervention) than those who had had AF for one year or longer (72-88% pre-intervention, 64-88% post-intervention). One participant in this study who had ablation therapy, scored the same both pre- and post-intervention, 88%. Although knowledge score results varied, McCabe, Sendin, et al. (2020) explained that the KASF is an appropriate knowledge and self-management measurement tool for patients with AF who vary in age and educational backgrounds. A larger sample size may have provided sufficient power to better reflect McCabe’s expected results. 5.3 Knowledge and Information Seeking Six of the nine participants who had either a decrease or no change in knowledge scores, had experienced AF for greater than one year. Researchers have suggested that the more familiar that users are with a topic, the less they tend to learn about the topic in website informational search sessions (Gadiraju et al., 2018). Gadiraju et al. (2018) found a strong negatively linear relationship between the knowledge gain of users in informational search sessions and the topic familiarity; R=-.82, R2=.78, p<.001. While familiarity with the topic areas may have had some influence on current study participants, it is unlikely. First, two of the six participants with a decrease/no change in scores had the lowest pre-intervention knowledge scores (72% and 76%) of all participants. Second, two of the three participants who had had AF for less than 6 months and who might be expected to be less familiar with the AF content had the highest pre-intervention knowledge scores (both 92%); these two also 69 had a decrease or no change in knowledge scores post-intervention. Finally, the three participants in the qualitative follow-up were all familiar with AF from having it but spoke highly of the value of the website in advancing their knowledge about some aspects of AF. Despite the lack of increase in knowledge and self-efficacy, nevertheless participants had very positive qualitative feedback on the website. 5.4 Knowledge and Website Usage The small sample size did not allow for analysis of relationships between Google Analytic metrics and knowledge and self-efficacy scores. However, the total time spent on the website, time per session and the other usage metrics, did not appear to influence knowledge scores as those with the longest times had increases, decreases or no change in scores. Gadiraju et al. (2018) similarly found no relationship between length of session and knowledge gains. Although not specific to AF, evidence has been mixed as to the relationship between knowledge and website usage. In their web-based study, (Heinrich et al., 2012) found no significant correlation between total time (1 hour) diabetic patients spent on the website and knowledge regardless of age, sex, or education level (p=.42). In contrast, Pacaud et al. (2012) found that higher total website usage for web-based and control groups (who had access to web-searching), was associated with higher diabetes knowledge scores (r=0.265, p=.029). 5.5 Self-Efficacy Although not significant, self-efficacy scores trended towards a decrease with all but three participants having lower scores post-implementation than at baseline. AF self-efficacy has been defined as “a belief in one’s capacity to execute behaviors necessary to manage one’s AF condition” (Macario et al., 2016, p. 264) and has been shown to be important in 70 patients’ self-management of their chronic conditions. Knowledge has been shown to be associated with self-efficacy in other chronic disease such as type 2 diabetes; where higher diabetes knowledge scores were positively correlated with higher general self-efficacy scores (p<0.001) (Qiu et al., 2020). Education is important in promoting self-efficacy and confidence through gains in self-management knowledge. In a recent pilot study researchers transferred their AF educational program, the AF-At-Home Program, to online because of the COVID-19 pandemic (Rosman et al., 2021). The program included two one-hour weekly sessions delivered through online videoconferencing, with 40-minutes of didactic education with 20-minutes of group discussion and questions and answers (Rosman et al., 2021). The group (n=68) was given digital education materials and website links to video recordings of the education portion of the sessions (Rosman et al., 2021). Rosman et al. (2021) found self-management behaviors such as monitoring heart rate (p<.001), heart rhythm (p=.003) and blood pressure (p=.013) all significantly increased over the 4-weeks of education. Rosman et al. (2021) found participants self-confidence significantly improved in three areas: following medication recommendations (p<.001), symptom identification (p=.007), management (p<.001) and awareness of when to seek emergency medical care for acute exacerbations (p=.001). Such group and interactive approaches might complement the stand along website used in the current pilot study to improve patients’ self-management efficacy of their AF. 5.6 Self-Efficacy and COVID-19 Although the Chronic Disease Self-Efficacy Scale assesses confidence in several behaviors involved in managing a chronic disease it is not specific to atrial fibrillation and some items may have been less relevant. The Chronic Disease Self-Efficacy Scale used in 71 this study measured participants’ confidence in their ability to self-manage in several areas (e.g., exercise, getting disease information, symptoms, depression, chores/social/recreational activities, communicating with physician) (Lorig et al., 1996). Some of the results of the Chronic Disease Self-Efficacy scale could be explained by the current SARS-CoV-2 (COVID-19) pandemic. The areas of the Chronic Disease Self-Efficacy Scale which saw a decrease for four or more participants were ‘manage disease in general’, ‘do social/recreational activities’, ‘manage symptoms’, and ‘control or manage depression’. COVID-19 brought with it many restrictions to everyday activities due to social distancing, self-isolations, ‘sheltering in place’ (Hall et al., 2021) and restrictions on whom people could see or visit outside their home. Kroll et al. (2021) explain that social isolation coupled with feelings of loneliness in older adults can have significant adverse effects such as increased risks of hypertension, heart disease, anxiety, depression or worse (p. 38). Although the current study did not focus on older adults, the decrease in self-efficacy related to managing or controlling depression, managing symptoms and the disease (AF) in general, may be due to social isolation and restrictions related to COVID-19. Along with sheltering in place, many physicians and nurse practitioners’ appointments were switched to virtual care, or telehealth which may have had an impact on the participants self-efficacy to manage their AF and its related symptoms. The decreased confidence in social or recreational activity participation is not unexpected, and likely the result of the restrictions for participants in both Canada and the United Kingdom (UK). 5.7 Self-Efficacy and Website Usage During the one-month intervention, all participants logged into the website for at least one session, with most logging in from 3-8 sessions. Although there was no pre-determined 72 threshold for success, other research suggests this represents success. For example, Weng et al. (2021) used a platform with patients with AF that included a physician treatment plan, content through text-based and video media and an interactive component allowing patients to send questions that were answered by a research nurse. Patients also received weekly email reminders with hyperlinks to the online platform (Weng et al., 2021). Weng et al. (2021) set their threshold of success of “90% of patients using the platform at least once, and 75% of patients using the platform at least twice during the 6-month follow-up period” (p.64). The website tested in the current study was a standalone resource with no clinician interaction but with text, video content and SMART goals, etc., yet all participants used it reflecting 100% success rate according to Weng et al.’s criteria. High website usage during this study may also be related to the COVID-19 pandemic. According to Statistic Canada, 75% of Canadians aged 15 years and older, reported using the internet more during the pandemic than previously (Bilodeau et al., 2021). Similarly in 2020, adults in the UK used the internet more than in 2019 with the largest increase in usage among those who are 55 years old and over (Ofcom, 2021). With COVID-19 restrictions in place, adults spent more time on the internet for various uses such as: work, education, news, and social interactions (Bilodeau et al., 2021; Ofcom, 2021). Thirty percent of Canadians reported using the internet for online training and learning from November 2020 to March 2021 (Bilodeau et al., 2021). Change in online use because of COVID-19 was not assessed in the current study but may have contributed to greater time spent online for education and training and accounting for the high website usage. 73 5.8 Self-Efficacy and SMART Goals SMART goals were used in this study to encourage participants to think about where they could implement change in their lifestyle to improve AF self-management. SMART goals are goals that are Specific, Measurable, Attainable, Realistic and Timely. Only one participant (Participant 2) utilized the SMART goals interactive function on the website, and this participant had a non-significant 18-point increase in their self-efficacy score from pre-to post-intervention. However, this participant did not write SMART goals according to the correct format, simply writing: “no smoking, one drink per quarter, and my life is stress free at this time”. When it comes to health-related goals, people generally have more success with their goals and improve self-efficacy, when they use positive framing and mastery of a new skill (e.g., goals to increase steps per day, or learn healthier recipes) rather than negative framing (e.g., no more junk food) (Bailey, 2019). Hughes et al. (2020) also explain that when participants are asked to set goals for chronic conditions, they emphasize the importance of goals being both personal and meaningful and including biomedical aspects such as symptom management. The above participant wrote goals that were mainly related to lifestyle, personal and meaningful goals to manage AF triggers; however, and were negatively framed and therefore, the participant was not expected to have great increase in self-efficacy. Similarly, Desteghe et al. (2018) found that participants who used the online platform felt more motivated to take better care of their health. In the current study, another participant explained that although he did not use the SMART goals function on the website, having the option to write about his goals motivated him to self-reflect on how he could better manage his health and lifestyle (specifically eating habits) to improve self-management of his AF. 74 5.9 Educational Preferences Understanding how individuals with AF prefer to learn about their AF has been well researched (McCabe, Kumbamu, et al., 2020; Rush, Burton, et al., 2019; Rush, Hatt, et al., 2019; Salmasi, Kwan, et al., 2019). The current study found that participants valued the website as a credible source for a “one stop shop” for their AF education and as a reinforcement of what they had learned from their physician. Similarly, McCabe, Kumbamu, et al. (2020) explored patients’ preferences with initial AF education and found that patients consistently vocalized that they would prefer written material after the in-person education to reinforce what they were taught from the physician. Participants in the current study had comparable responses about the use of a website for education as a reinforcement of provider communicated information. Comparably, participant 2 in the current study when asked about using a website to educate patients with AF responded, “I would probably put more trust in my doctors…and use this as a reinforcement or well as I found out there, it was a good review of things that I remember and partly remembered and forgot over the years”. Salmasi, Kwan, et al. (2019) also found that patients with AF expressed that in-class and online education would be complementary; where, patients could learn online then use in-class time to ask questions about what they learned. When it came to specific topics that the website covered and recommendations for change, findings from the current study resonated with another study of AF patients’ educational needs (McCabe, Kumbamu, et al.,2020). Similar to McCabe and colleagues, participants wanted website content related to understanding AF triggers and specifically whether it was vagally- or adrenergically-mediated. According to McCabe, Kumbamu, et al. 75 (2020) understanding the cause and triggers allows patients to “feel some sense of control over AF” (McCabe, Kumbamu, et al., 2020, p. 450). When examining patient preferences for online resources with AF, one participant (Participant 4) in the current study recommended that there be access to an online discussion forum for people with AF to share their stories and experiences with AF. Social support is a main construct posted to promote self-efficacy in social cognitive theory (Bandura, 1998), but was only an indirect component of the current standalone website. Bandura (1998) explains that self-beliefs to be successful in self-management can be created or strengthened through witnessing others succeed in similar circumstances. The patient stories on the website were an avenue for the current study participants to utilize to help strengthen their self-efficacy through reading others experiences with AF; however, these stories were rarely fully read, just the abstract of each story. Participant 3 suggested that there be some social support through monthly emails and that family be given access to the website. McCabe, Kumbamu, et al. (2020) had comparable results for the benefit of a monitored online discussion site. Participant 4 suggested that in the past she was a high user of an AF forum, that has since been changed to a social media page and suggested that she preferred when the webpage forum was solely dedicated to people with AF. The participant explained that she put great value in the previous blog she read and explained that it helped her to learn about other people’s experiences and information that they had learned. Research has shown that these types of online communities can be beneficial, however do need to be carefully implemented (Ziebland & Wyke, 2012). Ziebland & Wyke (2012) suggest that blogs and other online communities can add to patient experiences in a positive way, such as reassurance and decreasing the feelings of loneliness, but also caution that the online 76 community should be carefully implemented and monitored to reduce misleading and harmful information. 5.10 Limitations There is no study without limitations and this pilot study is no exception. This study was limited by a small sample size due to recruitment challenges, resulting in insufficient statistical power to achieve significant quantitative findings. Another limitation may have been related to recruitment that occurred in two different health care systems (UK and Canada). This can be seen as a limitation when assessing AF knowledge as the participants may have had different experiences in managing their AF and the type of education they received. This study was also limited by human error in relation to the Knowledge about Atrial Fibrillation Scores. The student researcher omitted three of the questions from the original questionnaire when transferring the questions to the online survey generator. However, the omission of the same three questions occurred both at pre and post implementation, therefore, it does not change the overall total score possible at the two points. Another limiting factor in the study could also be the Chronic Disease Self-Efficacy tool as it was not specific to AF self-efficacy. Self-efficacy measurements within this pilot study may have also been limited through two areas: one the relatively short timeframe for using the website and two, the lack of social supports accompanying the intervention. If a participant has low self-efficacy and AF knowledge to begin the study, it may take time to build their self-efficacy and their knowledge. The four-week timeframe may not have allowed for enough time to build knowledge and self-efficacy. Additionally, due to the COVID-19 pandemic, participants’ 77 self-efficacy may have been challenged because of social distancing and the lack of social support shown to influence a person’s belief in their chronic disease self-management abilities. The current study aimed to assess the standalone website without any social supports that according to Bandura’s social cognitive theory, may have limited self-efficacy development in the current study. A limiting factor of Google Analytics was the continuous collection of data, meaning it still collected usage data if the study webpage remained open in the background while the participant opened another webpage. This may have skewed the results and made it appear that the website was used for longer than its actual use. Google analytics was also limited for this study as it is setup in an e-commence and advertising perspective (Clark et al., 2014) and is not directly used for gathering data about website users for educational purposes; therefore, making some of the data collected futile and difficult to sort through for the results of the study. 5.11 Future Implications The current study can be used to inform multiple different areas of health care including clinical practice, nursing practice, and research. To begin, the current study can be used to inform clinical practice as participants in this study gave positive feedback that a website can be a viable resource to support people with AF. This type of a resource may be offered through many different clinical settings such as physician offices, pharmacies, nurse-led clinics, urgent/primary care clinics, emergency departments, and cardiac rehabilitation programs. Further, this study suggests that both those with new diagnosis and those that have been living with AF longer found value in this web-based support. This suggests that there is a role for ongoing support for those living with AF. Participants in both the current study and 78 McCabe, Kumbamu, et al. (2020), explained that it would be useful to have this type of a website after they visit their physician as a reinforcement of education, and support for self-management. Websites and online-based educational tools have been shown to be effective in AF knowledge increase and self-management (Desteghe et al., 2018; Rosman et al., 2021). A dedicated AF website could also be helpful in bridging the AF primary care gaps seen in rural vs urban settings. Research has shown that rural AF patients have difficulty accessing both primary and specialty care (Rush, Burton, et al., 2019), and a dedicated AF website may be feasible for providing education for rural AF patients. Participant 3 in this study was also French-Canadian and added that there would be great value in having a resource such as this website, developed in French and suggested that finding primarily French resources is difficult in the AF community. Implementing a credible, dedicated AF website into clinical guidelines for patients with AF in Canada would also help ensure that rural and urban AF patients are receiving access to the same education. Secondly, this study can be used to inform nursing practice and educate caregivers of those with AF. Nurses have an important role in health promotion and health education for both patient and families. A website such as this, can be used as an educational tool for nurses to share with AF patients and their family members. Sharing this type of educational tool with patient families can also help build patient self-efficacy and can support social cognitive theory within the AF community. Nurses can support AF patients with guidance through use of SCT, to encourage patient story sharing, whether that be virtual in an online forum or in a group session, as sharing success stories can increase patient self-efficacy (Bandura, 1998). Additionally, hosting online group question and answer sessions with AF patients in addition to online educational resources has been shown to provide effective 79 social supports (Rosman et al., 2021), as has the use of interactive features that allow participants to interact with a nurse online and as questions (Weng et al., (2021). Nurses can use this tool in a variety of different settings from acute care to community care and public health. Informing care givers about the website will also assist AF patients, specifically those with lifestyle risk factors for AF, as it will allow care givers and family members to self-educate about AF to help support the person with AF in their lives. Nurses and nursing students can also use this educational website for professional development and continued education. Sharing this tool with newly diagnosed and long-standing AF patients will help streamline AF education and knowledge throughout patient populations. Finally, future research should continue to aim to bridge the knowledge gaps of AF patients. The use of a dedicated AF website can be a viable option to help educate patients about their AF. Future research should seek to study the use of an educational website in conjunction with traditional AF education such as group education, one-on-one education, and nurse-led education clinics. Researching the integration of a dedicated AF website into the clinical settings of both urban and rural care areas may provide valuable insights about the website’s value within these settings. Along with researching the implication of a complimentary website, researchers should focus on the potential benefits that an online community such as a blog or AF forum may have for AF patients. Researchers should also seek to understand how AF patients interact with the internet after diagnosis. Understanding how AF patients interact and feel about using the internet to self-educate about AF can provide valuable information about whether patients would use this type of resource and trust it. Research has shown that AF websites can be highly variable and limited with the 80 information they deliver (Pandya & Bajorek, 2016); therefore, understanding patient perspectives of a credible and tried website would be of value for implication of the website. Future research should aim to explore the impact of self-efficacy on AF patients. Self-efficacy was explored in this study, but the tool used to measure self-efficacy was not focused on AF patients. Future research should seek to find how self-efficacy impacts AF patients and how AF patients can increase self-efficacy for optimal self-management. 5.12 Conclusion In this chapter, the findings of the study were discussed in relation to the current literature, a summary of the limitations were presented, and future implications of the research were considered. This mixed-methods study found that although there were no statistically significant changes in AF knowledge and self-efficacy between participants, there is great value in the use of a website to be an educational tool for people with AF and a supplement to their in-person education. 81 References Altiok, M., Yilmaz, M., & Rencüsoʇullari, I. (2015). Living with atrial fibrillation: An analysis of patients’ perspectives. Asian Nursing Research, 9(4). https://doi.org/10.1016/j.anr.2015.10.001 Amara, W., Larsen, T. B., Sciaraffia, E., Hernández Madrid, A., Chen, J., Estner, H., Todd, D., Bongiorni, M. G., Potpara, T. 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International Journal of Clinical Practice, 72(3). https://doi.org/10.1111/ijcp.13070 93 Appendices Appendix A G*Power Output for Power Analysis 94 Appendix B 95 Appendix C Knowledge About Atrial Fibrillation Survey (KAFS) 1. Atrial fibrillation or AFib is a condition in which: a. there is too much pressure in the heart b. the heartbeat is not regular c. there are blockages in the heart’s blood vessels 2. When the heart is in AFib: a. the heart beat is slower than normal b. the top part of the heart (atria) quivers c. the bottom part of the heart (ventricles) do not get enough oxygen 3. In AFib the top part of the heart (atria) and bottom part of the heart (ventricles): a. do not get enough oxygen b. do not beat in coordination with each other c. beat too slowly 4. Which of the following statements is true? a. The longer AFib has been present the more difficult it is to control b. People can always feel when they are in AFib c. People without other heart disease are unlikely to get AFib 5. When the heart is in AFib there is an increased risk for stroke because blood clots can form in the top part of the heart and travel to the brain when: a. the heart beats too fast b. the bottom part of the heart (ventricles) beats too slowly c. the top part of the heart (atria) does not pump blood as it should 96 6. Over time people who have AFib may develop poor pumping function of the bottom part of the heart (ventricles) when: a. the top part of the heart muscle (atria) becomes stiff b. the bottom of the heart beats too fast c. the top part of the heart gets enlarged 7. People who have been diagnosed with AFib have the greatest risk for stroke if they also: a. have high blood pressure and are over 75 years old b. consume excess alcohol and do not exercise regularly c. have lung disease and drink an excess amount of caffeinated beverages 8. Three common symptoms of AFib include: a. lightheadedness, headache, blurred vision b. tiredness, shortness of breath, feeling of racing heart beat c. shortness of breath, arm pain, tingling in the hands 9. If you experience a sudden loss of feeling or numbness in your arm, but it lasts for only 15 minutes, it is important to: a. call your healthcare provider at the clinic b. call 911 and seek emergency care c. make an appointment to see your healthcare provider the very next day 10. People who have their AFib on and off, meaning that sometimes the heart is in Afib and sometimes it is not: a. don’t need medication to treat AFib b. may have AFib and not have symptoms c. are not at risk for stroke 97 11. Sudden onset of any of the following symptoms: blurred vision, numbness on one side of the body, or slurred speech: a. are signs that atrial fibrillation is out of control b. are symptoms of high blood pressure c. are symptoms of a stroke 12. If you suddenly feel weak and about to faint: a. sit down and have someone call 911 for you or if you are alone call 911 yourself b. just sit down and let it pass—these are expected side effects of medications to treat AFib c. try to get an appointment with your healthcare provider the next day 13. Beta blockers (metoprolol or others) may be prescribed for people with AFib mainly to: a. convert them from AFib to a regular rhythm (sinus rhythm) b. reduce the risk of developing blood clots c. keep the heart from beating too fast 14. Medications used to treat AFib: a. are prescribed only to help the person stay in a regular rhythm b. may be prescribed to either control the heart rate and/or keep the person in a regular rhythm c. have only a few side effects to watch for 15. Taking medication to keep the heartbeat at a normal rate is important because: a. heart rates that are too high can lead to decreased pumping function of the bottom part (ventricles) of the heart b. heart rates that are too high can lead to high blood pressure 98 c. high heart rates can lead to a stroke 16. Calcium channel blockers (diltiazem, Cardizem©) are prescribed for people with AFib to: a. convert them to a regular rhythm b. prevent blood clots c. slow the heart rate 17. Which of the following are true about people who have been cardioverted from AFib to a regular rhythm: a. they will not need to take medicines to keep them in a regular rhythm b. they may develop AFib again even if they take their medications c. they do not need to take medication to prevent blood clots 18. A person who has had AFib and converted to a regular rhythm: a. does not need to worry about having a stroke related to AFib b. should not participate in vigorous exercise c. may have AFib later and not feel it 19. It is important for patients who have been diagnosed with AFib to: a. adjust their medication based on the number of symptoms they have b. check their blood pressure once a day c. take their medications as prescribed even if they don’t have symptoms of AFib 20. If a person diagnosed with AFib finds their heart rate is greater than 120 beats per minute most of the time: a. there is no need to be concerned b. they should call their healthcare provider about the heart rate 99 c. they should stop their exercise program 21. If a person whose treatment goal is to stay in regular (sinus) rhythm feels an irregular pulse: a. they should take an extra dose of their medication that day b. they should call their health care provider that day for directions about what to do c. they should call 911 to be taken to the nearest emergency department 22. If a person experiences a short period of lightheadedness with position changes when starting to take beta blockers or calcium channel blockers, it would be important to: a. make position changes slowly and let their healthcare provider know if the lightheadedness does not get better b. reduce the dose of the medication c. drink more fluids 23. If a person who has been cardioverted to a regular rhythm experiences shortness of breath and feels more tired than usual, they should: a. not be concerned because those are normal side effects from medications to treat AFib b. let their healthcare provider know about these symptoms at their next visit c. check their pulse to see if the rhythm is regular; the symptoms may mean that they are back in AFib 24. In addition to taking medication to control AFib, it is important for persons diagnosed with AFib to do the following: a. reduce social and recreational activities in case they might trigger an episode of AFib 100 b. limit their exercise program to walking only c. discuss concerns about stressful aspects of their life with their health care provider 25. Which of the following is most true about a cardioversion procedure? a. It is less successful at returning the person to a normal rhythm if the heart has been in AFib for more than a year. b. Most people don’t require medication to stay in a normal rhythm after they have been cardioverted to a normal rhythm. c. The electrical shock used in a cardioversion procedure injures the heart. 101 Appendix D Chronic Disease Self- Efficacy Scale We would like to know how confident you are in doing certain activities. For each of the following questions, please choose the number that corresponds to your confidence that you can do the tasks regularly at the present time. Exercise Regularly Scale 1. How confident are you that you can do gentle exercises for muscle strength and flexibility three or four times per week (range of motion, using weights, etc.)? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 2. How confident are you that you can do aerobic exercise such as walking, swimming, or bicycling three or four times each week? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 3. How confident are you that you can exercise without making symptoms worse? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident Getting Information About Disease Item 1. How confident are you that you can get information about your disease from community resources? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident Obtain Help from Community, Family, Friends Scale 1. How confident are you that you can get family and friends to help you with the things you need (such as household chores like shopping, cooking, or transport)? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 102 2. How confident are you that you can get emotional support from friends and family (such as listening or talking over your problems)? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 3. How confident are you that you can get emotional support from resources other than friends or family, if needed? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 4. How confident are you that you can get help with your daily tasks (such as housecleaning, yard work, meals, or personal hygiene) from resources other than friends or family, if needed? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident Communicate With Physician Scale 1. How confident are you that you can ask your doctor things about your illness that concern you? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 2. How confident are you that you can discuss openly with your doctor any personal problems that may be related to your illness? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 3. How confident are you that you can get work out differences with your doctor when they arise? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident Manage Disease in General Scale 103 1. Having an illness often means doing different tasks and activities to manage your condition. How confident are you that you can do all the things necessary to manage your condition on a regular basis? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 2. How confident are you that you can judge when the change in your illness mean you should visit a doctor? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 3. How confident are you that you can do the different tasks and activities needed to manage your health condition so as to reduce your need to see a doctor? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 4. How confident are you that you can reduce the emotional distress caused by your health condition so that it does not affect your everyday life? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 5. How confident are you that you can do things other than just taking medication to reduce how much your illness affects your everyday life? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident Do Chores Scale 1. How confident are you that you can complete your household chores, such as vacuuming and yardwork, despite your health problems? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 2. How confident are you that you can get your errands done despite your health problems? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 104 3. How confident are you that you can get your shopping done despite your health problem? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident Social/ Recreational Activities Scale 1. How confident are you that you can continue to do your hobbies and recreation? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 2. How confident are you that you can continue to do things you like to do with friends and family (such as social visits and recreation)? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident Manage Symptoms Scale 1. How confident are you that you can reduce your physical discomfort or pain? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 2. How confident are you that you can keep the fatigue caused by your disease from interfering with the things you want to do? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 3. How confident are you that you can keep the physical discomfort or pain of your disease from interfering with the things you want to do? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 4. How confident are you that you can keep any other symptoms or health problems you have from interfering with the things you want to do? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 5. How confident are you that you can control any symptoms or health problems you have so that they don’t interfere with the things you want to do? 105 not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident Manage Shortness of Breath Item 1. How confident are you that you can keep your shortness of breath from interfering with what you want to do? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident Control/ Manage Depression Scale 1. How confident are you that you can keep from getting discouraged when nothing you do seems to make any difference? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 2. How confident are you that you can keep from feeling sad or down in the dumps? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 3. How confident are you that you can keep yourself from feeling lonely? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 4. How confident are you that you can do something to make yourself feel better when you are feeling lonely? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 5. How confident are you that you can do something to make yourself feel better when you are feeling discouraged? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident 6. How confident are you that you can do something to make yourself feel better when you feel sad or down in the dumps? not at all confident 1-2-3-4-5-6-7-8-9-10 totally confident Scoring 106 The score for each item is the number circled. If two consecutive numbers are circled, code the lower number (less self-efficacy). If the numbers are not consecutive, do not score the item. The score for each scale is the mean of the items. For scales of 1-2 items, do not score the scale if any item is missing; for scales with 3-4 items, do not score the scale if more than 1 item is missing; for scales with 5-6 items, do not score the scale if more than 2 items are missing. Higher number indicates higher self-efficacy. 107 Appendix E Patients with Atrial Fibrillation: Exploring Website Educational Support as a Supplement to Care Demographic Information The purpose of this survey is to gather demographic data and the health history of patients with atrial fibrillation (AF). 1. Age: ______ Year 2. Sex: □ Female □ Male 3. Marital Status: □ Single (never married) □ Widowed □ Divorced □ Married or Remarried □ Separated □ Other (specify) 4. Race/Ethnicity: □ African-Canadian/Black □ Caucasian □ Asian □ Hispanic □ Indigenous □ Other (specify)_________________________ 5. Highest Level of Education: □ Less than High School □ Some College □ Partial High School □ College/University Graduate □ Completed High School □ Other (specify)_________________________ 6. Income: □ Less than $25,000 □ $51,000 - $75,000 □ $25,000 - $50,000 □ Over $75,000 108 7. Place of residence: □ Your own detached home □ Apartment/Condominium for all Ages □ Seniors’ Apartment Complex □ Assisted living □ Other ____________ (specify) 8. Living arrangements: □ Live alone □ Live with children □ Live with partner □ Live with other family members □ Live with friends □ Other __________________(specify) 9. Chronic Health Problems: □ Arthritis □ Coronary Heart Disease □ Diabetes □ Dementia (e.g. Alzheimer’s) □ High Blood Pressure □ Sleep Apnea □ Thyroid Disease □ Chronic Obstructive Pulmonary Disease (COPD) □ Stroke □ Eye Problems (Other than corrective lenses) □ Other ______________________ (specify) 11. Time since AF diagnosis: ______ (months, years) 12. Have you had a referral to a heart specialist for your atrial fibrillation: □ Yes □ No 13. Who referred you? □ Family Doctor □ Nurse Practitioner □ Community Nurse □ Other: _______ 14. Who referred you to the cardiac program? □ Family Doctor □ Nurse Practitioner □ Community Nurse □ Other: _______ If ‘some’ or ‘a lot’ please describe what education you have received about your AF: 15. Medications You are Currently Taking, check all that apply (these are classification names with some examples and not an inclusive list of medications): 109 □ Warfarin (blood thinner) □ Novel oral anticoagulants (e.g., Pradaxa, Xarelto, Eliquis) □ Antiplatelet (e.g., ASA, Plavix) (makes your blood less sticky) □ Diuretic (e.g., Lasix) (water pill) □ Calcium Channel Blocker (e.g., Cardizem, Adalat, Norvasc) (controls blood pressure) □ -Blocker (e.g., Metoprolol, Atenolol) (slows the beating of the heart) □ -Blocker (e.g., Terazosin, Prazosin) (controls blood pressure) □ Angiotensin-Converting Enzyme Inhibitor (e.g., Ramipril) (controls blood pressure) □ Angiotensin II receptor blocker (e.g., Valsartan, Losartan) (controls blood pressure) □ Statin (e.g.,. Lovastatin, Simvastatin) (controls cholesterol) □ Antiarrhythmics (e.g., Amiodarone, Digoxin) (controls heart rhythm) □ Other ________________ 16. Treatment history for AF (check all that apply): □ Medication □ Cardioversion (An electrical shock that restores normal heart rhythm) □ Ablation (Scarring of heart tissue to restore normal heart rhythm) □ Continuous Positive Airway Pressure (CPAP) □ Other __________________ (specify) 17. Number of visits to the emergency room in the last year for Atrial Fibrillation □ None □ One □ Two □ Three □ > Four 18. Number of hospital admissions in the last year for Atrial Fibrillation: □ None □ One □ Two □ Three □ > Four 19. Average length of stay in hospital: □ Five days or less □ Greater than five days 110 20. How would you rate your overall health? □ Excellent □ Good □ Fair □ Poor 21. How would you rate your overall mental health? □ Excellent □ Good □ Fair □ Poor 111 Appendix F Tag Manager Guide – Atrial Fibrillation Website This document outlines the tracking that has been placed on the website using Google Analytics Manager. Account ID- GTM- P2T32SQ Google Analytic Tracking Tag: Google Analytic Tracking- All Pages Trigger: All pages Variable: vAFC- Google Analytics (UA- 109654273-1) Google Analytic Tracking: • Base tracking for all visits User ID tracking has been enabled in this analytic view. This allows tracking of logged in WordPress users based on their logged in user id. Outbound Link Tracking Tag: Outbound Link Tag Trigger: Outbound link Trigger “Click_target” contains “_blank” “Click URL” does not match RegEX (ignore case) \.pdf$ Google Analytic Event Tracking: Category: Page Engagement Action: Click outbound link Label: {{Click Text}} – {{Click URL}} – {{Page URL}} {{Page Copy Text}} – {{Destination URL}} – {{WebPage URL}} 112 PDF Download Tracking Tag: PDF Download Tag Trigger: PDF Download Trigger “Click URL” matches RegEX “\.pdf$” Google Analytic Event Tracking: Category: Page Engagement Action: Download PDF Label: {{Click URL}} – {{Page URL}} {{PDF URL}} – {{WebPage URL}} Scroll Depth Tracking Tag: Scroll Depth Tag Trigger: Scroll Depth Tag Trigger Vertical Scroll Depth Percentages- 25, 50, 75, 90, 100 Google Analytic Event Tracking: Category: Page Engagement Action: Scroll depth Label: {{Scroll Depth Threshold}} – {{Page URL}} YouTube Event Tracking: Tag: YouTube Event Trigger: YouTube event 113 Google Analytic Event Tracking: Category: youtube Action: play|pause|0%|25%|50%|75%|100% Label: {{YoutTube URL- Video Title}} 114 Appendix G Semi- Structured Interview Questions 1. How did you feel using this website helped you understand and improve (or change) your knowledge about your AF condition? 2. What about the website did you find the most helpful or valuable to living with/managing AF? Least helpful/ valuable? a. How have you used the website information in living with AF? b. Do you feel the website has made a difference in your self-management of your AF? c. Do you feel the website has impacted your confidence in your ability to self- manage your AF? 3. If you watched the videos, how satisfied were you with the quality of the video information provided? 4. How do you feel about self-directed learning with the use of online education for AF? 5. Did you find the website complemented your in-person education sessions? If so, how? 6. Do you think the website could be used on its own rather than being complementary to in-person education sessions? 7. Overall, if you were to tell someone else with AF about the website what would you say? a. What would you say about getting around and finding things on the website? What difficulties did you find, if any? 115 b. What would you say about your ease in reading the information? Understanding the information? What difficulties did you find, if any? c. How do you think the website could be changed to be more helpful? d. Would you recommend this website to someone else? 116 Appendix H Atrial Fibrillation Patients Needed! Do you have Atrial Fibrillation? Do you want to learn more about your heart condition? If you answered ‘YES’ to these questions: YOU are invited to participate in a research study exploring the use of an educational website for patients with Atrial Fibrillation. Researchers from the University of British Columbia Okanagan want to find out if using the website helps people learn more about atrial fibrillation and builds their confidence in managing their condition. You will receive a $20.00 CAD gift card of your choice for your participation! To participate you must: • Have a diagnosis of Atrial Fibrillation • Be 19 years of age or older • Have no difficulty with memory or recall • Have access to the internet Contact Student Researcher, Robyn Phillips to learn more! Robyn Phillips BScN, RN, MSN Student Phone: 250-807-9713 Email: Robyn.Phillips@alumni.ubc.ca
Thesis/Dissertation
eng
Nursing
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Rush, Kathy L.
Patients with atrial fibrillation : exploring the use of an educational website
Text