Health and Social Development, Faculty of (Okanagan)
Nursing, School of (Okanagan)
DSpace
UBCO
Lukey, Alexandra Marie
Master of Science in Nursing - MSN
University of British Columbia
Background: Heart failure (HF) is a condition that afflicts more than 26 million patients worldwide and is associated with high rates of morbidity, mortality, and hospital admissions. Education delivered online is an acceptable and scalable strategy to support patient self-care. Further, gamification, otherwise known as serious games, is gaining acceptance as a valid method of creating behaviour change in education and eHealth. The purpose of this study was to design and pilot test the efficacy of utilizing a theory-informed, web-based, gamified, self-care education website for patients living with HF. Story-based scenarios and rewards were the gamification tactics chosen for the website. Research Design: This study was a three-part, mixed-methods study that included embedded exploratory and explanatory qualitative phases during the intervention design and testing. After the design and initial iteration of the website, 19 patients participated in a pre-post-test design using the Self-Care of Heart Failure Index (SCHFI) and the Dutch Heart Failure Knowledge Scale. Usability measures were tracked using Google Analytics and the System Usability Scale (SUS). Finally, six post-test explanatory qualitative interviews were conducted. Results: There were significant increases in HF knowledge following the intervention despite high baseline knowledge scores (p = 0.04). Post-intervention self-reported HF self-care behaviours were also significantly improved; maintenance (p = 0.005), management (p = 0.003) and confidence (p = 0.003). However, knowledge and self-care scores were not correlated. Patients scored the website as highly usable and described acceptance and varied support for distinct gamification techniques used on the website. Discussion: Although the sample may not have been representative, patients successfully engaged independently with the online self-care education. Explanatory patient interviews confirmed the theoretical explanation for self-care behaviour change in patients' discussion of practised decision making using the engaging content. Behaviour changes independent of knowledge gains is supported by improved decision-making confidence through narrative, scenario-based learning. Using this embedded qualitative, mixed-methods approach, we found that a gamified website that uses narrative and reward-based gamification techniques has the potential to improve HF patient knowledge and self-care. However, more research is needed to confirm the clinical benefit of these results and to address technology literacy inequities.
GAMIFICATION OF EHEALTH SELF-CARE EDUCATION IN A HEART FAILURE POPULATION by Alexandra Marie Lukey B.S.N. University of British Columbia Okanagan A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE DEGREE OF MASTER OF SCIENCE IN NURSING in The College of Graduate Studies THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) October 2021 © Alexandra Marie Lukey ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, the thesis entitled: GAMIFICATION OF EHEALTH SELF-CARE EDUCATION IN A HEART FAILURE POPULATION submitted by Alexandra Marie Lukey in partial fulfillment of the requirements for the degree of Master of Science in Nursing Examining Committee: Dr. Kathy Rush, School of Nursing, University of British Columbia Okanagan Supervisor Dr. Martha Mackay, School of Nursing, University of British Columbia Supervisory Committee Member Dr. Mohammad Khalad Hasan, Computer Science, University of British Columbia Okanagan Supervisory Committee Member Dr. Susan Holtzman, Psychology, University of British Columbia Okanagan Additional Examiner iii Abstract Background: Heart failure (HF) is a condition that afflicts more than 26 million patients worldwide and is associated with high rates of morbidity, mortality, and hospital admissions. Education delivered online is an acceptable and scalable strategy to support patient self-care. Further, gamification, otherwise known as serious games, is gaining acceptance as a valid method of creating behaviour change in education and eHealth. The purpose of this study was to design, and pilot test the efficacy of utilizing a theory-informed, web-based, gamified, self-care education website for patients living with HF. Story-based scenarios and rewards were the gamification tactics chosen for the website. Research Design: This study was a three-part, mixed-methods study that included embedded exploratory and explanatory qualitative phases during the intervention design and testing. After the design and initial iteration of the website, 19 patients participated in a pre-post-test design using the Self-Care of Heart Failure Index (SCHFI) and the Dutch Heart Failure Knowledge Scale. Usability measures were tracked using Google Analytics and the System Usability Scale (SUS). Finally, six post-test explanatory qualitative interviews were conducted. Results: There were significant increases in HF knowledge following the intervention despite high baseline knowledge scores (p = 0.04). Post-intervention self-reported HF self-care behaviours were also significantly improved; maintenance (p = 0.005), management (p = 0.003) and confidence (p = 0.003). However, knowledge and self-care scores were not correlated. Patients scored the website as highly usable and described acceptance and varied support for distinct gamification techniques used on the website. Discussion: Although the sample may not have been representative, patients successfully engaged independently with the online self-care education. Explanatory patient interviews iv confirmed the theoretical explanation for self-care behaviour change in patients' discussion of practised decision making using the engaging content. Behaviour changes independent of knowledge gains is supported by improved decision-making confidence through narrative, scenario-based learning. Using this embedded qualitative, mixed-methods approach, we found that a gamified website that uses narrative and reward-based gamification techniques has the potential to improve HF patient knowledge and self-care. However, more research is needed to confirm the clinical benefit of these results and to address technology literacy inequities. v Lay Summary Heart failure is a difficult disease to manage at home and has high rates of hospitalizations. Patient and caregiver education is an important way to reduce hospitalizations for heart failure patients. In this study, we created and pilot-tested a website for heart failure patients that was set up like a game to help them learn about caring for themselves with this disease. The website used a point system and stories to increase patient engagement. A group of patients tested the website and filled out surveys about heart failure before and after using the website. We also interviewed a subset of these patients to understand their experiences. Our findings from this study show that people living with heart failure improved their heart failure knowledge and self-care after using the website. We also found that the stories and points on the website increased some patient's subjective motivation to learn. vi Preface This thesis is the original, unpublished work by the author, Alexandra Lukey. This study was granted approval by the University of British Columbia Behavioural Research Ethics Board- Certificate Number: H20-02390. vii Table of Contents Abstract .................................................................................................................................................. iii Lay Summary .......................................................................................................................................... v Preface ..................................................................................................................................................... vi List of Tables ............................................................................................................................................ x List of Figures ......................................................................................................................................... xi Acknowledgements ................................................................................................................................xii Chapter 1: Introduction ................................................................................................................. 1 1.1. Background ....................................................................................................................................... 1 1.2. Purpose of Study ............................................................................................................................... 6 1.3. Theoretical Support ......................................................................................................................... 6 1.4. Research Question ............................................................................................................................ 7 Chapter 2: Review of Related Literature ....................................................................................... 9 2.1. Theory of Heart Failure Self-Care.................................................................................................. 9 2.2. Factors that Influence Self-Care of Heart Failure ...................................................................... 11 2.2.1 Patient Factors ........................................................................................................................................... 11 2.2.2. System/Provider Factors ........................................................................................................................... 14 2.3. Self-Care Education Interventions for Heart Failure ................................................................. 15 2.3.1. Traditional Approaches ............................................................................................................................ 15 2.3.2. eHealth Approaches .................................................................................................................................. 16 2.4. Gamification for Increasing Patient Engagement in Education ................................................ 20 2.5. Gamification in eHealth ................................................................................................................. 23 Chapter 3: Methods ...................................................................................................................... 27 3.1. Research Design .............................................................................................................................. 27 3.1.1. Mixed Methods Research ......................................................................................................................... 27 3.1.2. Choice of Qualitative Description ............................................................................................................ 29 3.1.3. Usability Testing ...................................................................................................................................... 31 3.2. Study Protocol................................................................................................................................. 32 3.2.1. Design of the Intervention ........................................................................................................................ 34 3.2.2. Testing the Intervention with Heart Failure Patients ................................................................................ 38 3.2.3. Measures ................................................................................................................................................... 41 3.2.4. Patient Perspectives .................................................................................................................................. 43 3.2.5 Interpretation of the Data ........................................................................................................................... 44 3.3. Ethics ............................................................................................................................................... 45 3.3.1. Informed Consent ..................................................................................................................................... 45 3.3.2. Benefits and Risk ...................................................................................................................................... 45 3.3.3. Privacy and Confidentiality ...................................................................................................................... 46 3.3.4 Compensation ............................................................................................................................................ 47 viii Chapter 4: Results ........................................................................................................................ 48 4.1 Technical Development Process ..................................................................................................... 48 4.2 Website Development ...................................................................................................................... 50 4.3 Health Care Provider Population .................................................................................................. 52 4.3.1 Website Education Content ....................................................................................................................... 52 4.3.2 Heart Failure Patient Decision Making ..................................................................................................... 53 4.3.3 Gamification Ambivalence ........................................................................................................................ 54 4.4 Website Iterations Based on Clinicians’ Feedback ...................................................................... 57 4.5 Patient Study: Sample ..................................................................................................................... 57 4.5.1 Participants’ Website Engagement ............................................................................................................ 60 4.5.2 Heart Failure Knowledge After Website Use ............................................................................................ 63 4.5.3 Change in Self-Reported Heart Failure Self-Care Behaviour ................................................................... 66 4.5.4 Correlations Between Measures ................................................................................................................ 68 4.6 Qualitative Interviews: Sub-Sample Description ......................................................................... 69 4.6.1 Website Usability Impressions .................................................................................................................. 70 4.6.2 Knowledge Insights ................................................................................................................................... 71 4.6.3 Behaviour change ...................................................................................................................................... 72 4.6.4 Self-Care Motivation ................................................................................................................................. 74 4.6.5 Techniques of Gamification ...................................................................................................................... 74 4.6.6 Caregiving and Self-Care .......................................................................................................................... 77 4.6.7 System-Level Barriers to Self-Care ........................................................................................................... 78 Chapter 5: Discussion .................................................................................................................. 79 5.1 Overview of the Study ..................................................................................................................... 79 5.2 Exploring Healthcare Provider Perceptions ................................................................................. 79 5.3 Usability, Knowledge and SCHFI Scores ...................................................................................... 80 5.3.1 Usability .................................................................................................................................................... 81 5.3.2 Knowledge and Behaviour Change ........................................................................................................... 82 5.4 Gamification and Subjective Self-Care Motivation ..................................................................... 85 5.5 Explanatory Discussion ................................................................................................................... 87 5.6 Strengths and Limitations .............................................................................................................. 88 5.7 Recommendations for Future Research ........................................................................................ 90 5.8 Implications for Nursing Practice and Policy ............................................................................................... 93 Conclusion .............................................................................................................................................. 94 References .................................................................................................................................... 95 Appendix A .......................................................................................................................................... 111 Appendix B ........................................................................................................................................... 115 Appendix C .......................................................................................................................................... 116 Appendix D .......................................................................................................................................... 117 Appendix E ........................................................................................................................................... 118 ix Appendix F ........................................................................................................................................... 119 Appendix G .......................................................................................................................................... 121 Appendix H .......................................................................................................................................... 123 Appendix I ............................................................................................................................................ 124 Appendix J ........................................................................................................................................... 129 x List of Tables Table 3.1 Research Phase Methodology ....................................................................................... 29 Table 4.1 Demographics at Baseline ............................................................................................ 58 Table 4.2 Website Usage .............................................................................................................. 61 Table 4.3 Demographic Differences in Website Use.................................................................... 62 Table 4.4 System Usability Score ................................................................................................. 63 Table 4.5 Heart Failure Knowledge Scores, by Time ................................................................... 64 Table 4.6 Change in Heart Failure Knowledge Score, by Gender ................................................ 65 Table 4.7 Change in Heart Failure Knowledge Score, by Age ..................................................... 65 Table 4.8 Change in Heart Failure Knowledge Score, by Education ........................................... 66 Table 4.9 Change in Heart Failure Self-Care Index Scores Over Time ....................................... 67 Table 4.10 Change in Heart Failure Self-Care Index Scores, by Gender ..................................... 67 Table 4.11 Change in Heart Failure Self-Care Index Scores, by Age .......................................... 68 Table 4.12 Change in Heart Failure Self-Care Index Scores, by Education................................. 68 xi List of Figures Figure 3.1 Study Flow Chart ......................................................................................................... 34 Figure 4.1 Login Page ................................................................................................................... 49 Figure 4.2 Home Page................................................................................................................... 49 Figure 4.3 Sample Scenario .......................................................................................................... 50 xii Acknowledgements During this research, I was generously supported by the CIHR Frederick Banting and Charles Best Canada Graduate Scholarship, the Dorothy Anna Proudfoot Memorial Scholarship in Nursing (#22507) and the University Graduate Fellowship (#26301). First, I would like to thank my supervisor Dr. Kathy Rush. Your guidance and steady encouragement have allowed me to accomplish things I never dreamed possible for myself. Thank you for your patience and willingness to help me explore ideas and challenge myself. Thank you to my committee members, Dr. Martha Mackay, and Dr. Khalad Hasan. Thank you, Dr. Mackay, for your wise clinical guidance. Your expertise was invaluable to the research. Thank you, Dr. Hasan, for your valuable technical advice and your leap of faith in taking me on as a student without a computer science background. A special thanks to my wonderful family and partner: Sherry, Evan, Cass and Torsten. These past two years brought many surprises (some good, some not so good). Your unwavering support allowed me to muddle my way through. Next, I would like to thank the heart failure team at St. Paul's Hospital, whose commitment to providing the best care for their patients was demonstrated in their willingness to assist with this project. I would also like to thank the patients who participated in this research. This work would not be possible without their generosity with their time. Finally, I would like to offer gratitude and acknowledge the lands where this work took place. I conducted work for this study on the traditional and unceded territory of the Syilx (Okanagan) Peoples as well as the unceded territory of the Coast Salish Peoples, including the territories of the xwməθkwəy̓əm (Musqueam), Skwxwú7mesh (Squamish), Stó:lō and Səl̓ílwətaʔ/Selilwitulh (Tsleil- Waututh) Nations, to whom I offer thanks. 1 Chapter 1: Introduction This chapter will introduce my thesis research and provide a brief background of the problem this study seeks to address. First, I will define heart failure (HF) and provide a background of HF self-care. Then, I will state the purpose of the study and list the chosen research questions. I will also discuss the theoretical support behind the planning of the study and design of the intervention, which I will elaborate on in Chapter 2. 1.1. Background HF has become a global disease affecting more than 26 million patients worldwide and is associated with high rates of morbidity and mortality (Ambrosy et al., 2014). Further, this syndrome is expected to dramatically increase in prevalence due to population ageing and a worldwide shift towards increases in chronic disease (Savarese & Lund, 2017). Hospital readmissions for HF are common and increase healthcare expenditures (Takeda et al., 2012). Rates of HF readmission have been cited to be as high as 59% within 6 months of hospital discharge, a trend that has remained stable in the last two decades, despite improvements to medical therapies (Ambrosy et al., 2014). Further, a cohort study tracked patient readmission rates for HF patients in Canada from 2005 until 2015. In this study, of 239,031 patients discharged alive after an acute care hospitalization for HF, 43,498 (18.2%) were readmitted within 30 days (Samsky et al., 2019). A contributing factor to hospital readmissions is the lack of HF patient self-care at home (Jaarsma et al., 2017). A significant mediating factor in patients’ poor HF self-care is inadequate education in the successful management of their disease (Massouh et al., 2019). HF is a syndrome with multiple causes, where the heart no longer pumps adequately to perfuse the body (Coronel, 2001). The self-care required to manage the many symptoms and complications that 2 may occur as a result of HF is complex (Riegel & Dickson, 2008). In their recent study, Vidán and colleagues measured six primary self-care behaviours in patients with HF (2019). These included the use of a scale, weight registration, diuretic identification, knowledge of salted foods, edema identification, and treatment adjustment. Of a sample of 415 patients, only 5.3% could perform all six tasks correctly. Further, poor self-care ability was associated with a higher one-year mortality risk in this sample (Vidán et al., 2019). Therefore, improving self-care is a valuable strategy to improve outcomes of patients living with HF to reduce hospital admissions and mortality. Self-care is a set of learned behaviours that allows people to maintain their health at home and requires knowledge and skill to carry out effectively (LeBlanc & Jacelon, 2018). To engage in self-care, patients must also engage in decision-making about their own care at every step along the continuum of wellness to illness (LeBlanc & Jacelon, 2018). It is well understood that education influences confidence in self-care (Massouh et al., 2019). Not as well understood is the effectiveness of novel ways of delivering self-care education through technology and, specifically, technology that makes use of Self-Determination Theory (Deci & Ryan, 2008). A review of multiple models of patient education teaching strategies in the HF population found verbal teaching sessions to be the most common, but least effective, model of education, unless combined with other methods of review. Studies in this review addressed methods of education delivered through multimedia formats, such as CD-ROMs and compared these to more traditional methods, such as mailouts. The authors of the review concluded difficulty in determining the most effective educational strategy, due to variation in use of outcome measures and types of intervention (Boyde et al., 2011). 3 In-person teaching sessions may also have limitations due to limited healthcare provider time. This creates the need for patients to spend time learning and reviewing material independently. However, there is a need for engaging resources for patients to independently review self-care education materials (Makris et al., 2020). Those that are offered for self-review are often limited to static text material which do not facilitate patient engagement and may contribute to a lack of motivation to adequately learn the material (Charlier et al., 2016). There is a growing body of evidence in the field of patient education to support the use of eHealth, or information and communications technologies including online and mobile technology, for increasing patients’ self-care knowledge. Technological interventions have been used in a variety of ways to enhance self-care, ranging from symptom tracking and education to healthcare provider communication, and with diverse patient populations, including those with HF (McDermott & While, 2013). A recent review of HF eHealth platforms found receiving HF self-care interventions online or through a mobile device was effective and acceptable to patients. (Athilingam & Jenkins, 2018). The use of technology for education specifically has shown the potential to improve care through low-cost and easily accessible interventions, allowing greater repetition and more patient engagement than traditional educational methods (Whitehead & Seaton, 2016). A 2018 systematic review found virtually-delivered education had comparable or superior outcomes compared to usual care for patients with chronic diseases. The results supported virtual education across measures, including clinical indicators, knowledge, self-care, quality of life, and health care utilization (Rush et al., 2018). Improving patient engagement and motivation through e-learning resources is a fairly novel area of research that has borrowed theory from fields such as education and psychology. One such method of increasing patient engagement with education is the concept of 4 gamification. Gamification is a set of strategies that uses elements of game design for non-recreational purposes. It is gaining acceptance as a valid method of creating behaviour change in education and eHealth (Sardi et al., 2017). Gamification may be defined as adding game design elements in non-game contexts, such as education. It is also termed “serious games” in the literature (Charlier et al., 2016). The effect of adding gamified components into non-game contexts such as education is theorized to increase the persuasiveness of the education, therefore increasing engagement and possibly self-efficacy (Cugelman, 2013). Although researchers have been exploring the integration of gamification into eHealth platforms for about a decade, rigorous evidence is limited (Cugelman, 2013). Further, little is known about the best techniques of gamification, since game design elements are extremely varied (Sardi et al., 2017). There are also very few studies that have applied gamification techniques to HF patient education. At the time of writing, there is only one study published that has implemented gamification for education purposes in the HF population. This platform used the format of a casino slot game and knowledge-testing questions, which some participants found to be monotonous and therefore less engaging (Radhakrishnan et al., 2016). Other applications of gamification of technology in the HF population did not integrate education but rather were based on behaviour tracking or were used to increase exercise (Dithmer et al., 2016; Jaarsma et al., 2021). There are two other HF eHealth interventions published as prototypes; however, both currently lack key pieces of working technology necessary for deployment (Arulnathan et al., 2019; Radhakrishnan et al., 2020) There are many techniques of gamification, yet evidence is lacking as to the optimal techniques of gamification, particularly for specific populations, such as those with HF (Sardi et 5 al., 2017). The application of gamification as a technique relevant to education with the HF population is in a nascent stage. Exploring gamification with patients with HF is important, given that it has been shown to circumvent barriers to retention of health information and behaviour change (Cugelman, 2013). Although there is a growing body of evidence to support the integration of technology into HF care, there is a gap in the literature and a lack of studies specifically exploring gamification in the HF population. I used a “narrative story” game-design approach, which engages patients through principles of goal achievement and rewards as they move through the stories. Previous research has suggested that gamification using stories may increase motivation to achieve learning goals (Bopp, 2017). Further, in education research, a story-based game design was shown to increase motivation of students who had been previously unmotivated when using static-text learning resources (Garneli et al., 2017). For the purpose of this research, I hypothesized that story-based gamification would work to increase learning, through active engagement , using rewards and inherently making the learning more enjoyable. Specifically, I hypothesized that using gamification to engage HF patients in self-care learning materials would be associated with increased self-care knowledge and confidence, which would in turn be evident by increased self-care behaviour. This translation of knowledge to behaviour is supported by research relating knowledge to confidence in self-care (Massouh et al., 2019). Further, we delivered this education through a web-based platform which allowed patients to engage in the education at their own convenience, while maintaining interactivity. Overall, finding ways to improve patient participation in, and retention of the self-care education material is highly relevant to improving the outcomes for patients with HF. 6 1.2. Purpose of Study The purpose of this study was to design and explore the efficacy of a web-based, gamified self-care education tool for patients living with HF. We tested this tool for usability and efficacy for improving self-care behaviour and knowledge of HF patients as well as subjective patient motivation. 1.3. Theoretical Support Education that has been informed by theories is more likely to be effective than education that has not been informed by theories (Athilingam et al., 2016). Behaviour change interventions grounded in theory are more likely to be effective since theories provide explanation and reduce uncertainty that the intervention will be effective (Michie et al., 2018). This research integrated several theoretical perspectives, including self-care theory and self-determination theory. There is a growing body of literature on HF self-care as well as patient education. This research was informed by the Middle Range Theory of Self Care, which was a theory developed from the Situation-Specific Theory of Heart Failure and translated to chronic disease self-care in general (Riegel et al., 2012). It describes the process of maintaining health with health promoting practices within the context of the management required of a chronic illness and includes three categories of behaviors: self-care maintenance, self-care monitoring, and self-care management (Riegel et al., 2012). Next, the constructs of intrinsic and extrinsic motivation found in the Theory of Self-Determination may support gamification as a technique to increase patient motivation to learn (Deci & Ryan, 2008; Sardi et al., 2017). Namely, Ryan and Deci describe that intrinsically driven actions are motivated by enjoyment in the absence of an external reward (2000). Gamification that engages patients and increases the enjoyment of learning may increase 7 intrinsic motivation. We used these theories, which are elaborated in Chapter 2, in the design of the study and intervention. 1.4. Research Question I sought to answer the following research questions with the goal of improving HF patients’ self-care through the novel education technique of gamification. This research explored several research questions, each of which built on the preceding questions. 1. Will HF patients engage in online self-care education material that is gamified? 2. Will this education result in an increase in HF-related knowledge? 3. If there is an increase in knowledge of self-care activities/practices, does this increase in knowledge also translate to an increase in self-care behaviour? 4. What is the usability of the website designed for this study as measured by patient interview and usage analytics? 5. Does engaging patients with educational material that incorporates narrative gamification increase subjective motivation to learn? Usability was tested throughout the study both with the clinicians and patient participants and will be discussed in greater detail in the methods chapter. I hypothesized that the online intervention would be effective, as demonstrated by improvements in knowledge scores, and potentially HF self-care behaviour after the intervention. The chapters of this thesis will follow the following format. In Chapter 2, I will discuss the challenges related to self-care for people with HF, including individual-level factors and systemic influences of self-care. This chapter will present an overview of the current literature pertaining to traditional and eHealth interventions previously developed to support HF self-care. A review of gamification in eHealth literature and how self-care and self-determination theory influenced this study will also be discussed. Chapter 3 will present the protocol for this mixed-method study with embedded exploratory and explanatory qualitative phases. I will also discuss 8 the reasoning behind the methods chosen and the ethical considerations. Chapter 4 will present the study results, beginning with the intervention development process, quantitative testing, and qualitative patient interviews. Lastly, Chapter 5 will relate results to current evidence and discuss limitations, conclusions, and next steps for the research. 9 2 Chapter 2: Review of Related Literature I will provide an overview of the literature related to my research questions in Chapter 2. First, I will describe the Situation-Specific Theory of Heart Failure as the theoretical basis for this research. Barriers to HF self-care, including both patient-related and systemic barriers, will be described next. Then, I will provide an overview of the literature of both traditional and eHealth approaches to improve HF patient self-care. Moving closer to my research question, I will then describe the theoretical basis and current evidence related to gamification use in eHealth. Finally, I will describe the limited existing literature related to gamification and eHealth in the HF population. 2.1. Theory of Heart Failure Self-Care The Situation-Specific Theory of Heart Failure Self Care breaks self-care into separate processes which range in complexity (Riegel & Dickson, 2008). Although self-care is the umbrella term, the self-care behaviours comprising the theory have been categorized into three concepts including “self-care maintenance, self-care monitoring, and self-care management” (Jaarsma et al., 2017, p. 71). Maintenance is simply following the directions/instructions, such as taking prescribed medications, given by the patient’s care team. However, patients must also monitor themselves for changes in their disease process such as increases in fluid retention. Once changes are recognized and linked to the disease process, the self-care management phase involves patients taking actions based on changes in their condition, such as changing a diuretic dose in response to symptoms. Self-care at every stage is complex and requires patients to make informed decisions about their care (Riegel et al., 2009a). Although regarded as less complex than the other phases, maintaining preventative behaviours such as taking medications, dietary changes, and exercise 10 regimens requires knowledge and experience to perform correctly. A patient may be competent in the maintenance phase and possess the ability to take their medication correctly. However, the patient may not have the knowledge to perform self-monitoring behaviours, such as weighing themselves daily. Without this knowledge, they will not adequately recognize changes in their HF status. Therefore, knowledge is required to link actions to symptom changes. Patients must have the skill and knowledge not only to follow their team’s self-care directions, but also have the ability to make decisions to act on changes related to their disease. This type of decision making is naturalistic. This means it occurs when people make decisions in the context of a real-world setting (Riegel et al., 2009a). In the home setting, HF patients are required to make decisions daily regarding their disease management. Further, patients must have the skill and knowledge to act when changes occur. Evidence indicates that patients with HF have gaps in their knowledge and understanding of HF. In their survey, a team of American and Lebanese researchers (Massouh et al., 2019) found that patients with HF (n = 100) had gaps in their knowledge about HF. Although 80% understood edema was linked to fluid accumulation, less than half could relate this to the importance of fluid restriction. In the same study, only 25% of patients knew that a cold or flu could exacerbate their HF. Further, a scientific statement released by the American Heart Association stated that patients with HF often struggle to link changes in weight to symptoms such as shortness of breath or edema. In their mixed-methods study, Sedlar et al. (2021), found that knowledge of self-care behaviours was the most common factor (84%) impacting the self-care of the 80 participants. Thus, depending on skill and knowledge, a patient may be competent in simpler self-care behaviours, such as following a medication regimen, but may lack more complex skills such as 11 recognizing and acting upon symptom changes. HF self-care is a complex, multistep process. Effective self-care requires patients to understand the relationship between symptoms and the disease process (Riegel et al., 2009b). 2.2. Factors that Influence Self-Care of Heart Failure Self-care behaviours do not exist separately from the outside circumstances of a patient’s life and are influenced by factors such as experience, motivation, culture, support and access to services (Riegel et al., 2012). For the purpose of this review, factors that influence self-care will be categorized according to those that relate to the individual patient and those influenced by the health system. 2.2.1 Patient Factors There are many self-care behaviours that are influenced by individual patient factors. Some of these factors are modifiable and some are not. Non-modifiable factors include physiological changes, symptoms, presence of co-morbidities and sociodemographic factors. Modifiable risk factors include knowledge, confidence, skill, and motivation to perform self-care. First, of particular importance in the HF population is the influence of symptoms on self-care (LeBlanc & Jacelon, 2018). Symptoms may inhibit a person's ability to perform self-care, due to lack of energy or other physiological changes. Several symptoms of HF, such as anxiety, depression, “impaired cognition,” fatigue and shortness of breath, may impact a patient's ability to perform self-care (Jaarsma & Strömberg, 2019; Riegel & Dickson, 2008). For instance, if a person's energy is low, they may find it challenging to prepare healthy meals or exercise. Further, mild cognitive impairment (MCI) is reported to be as prevalent as 25-75% in the HF population and is known to negatively impact self-care behaviour (Currie et al., 2015). This is 12 supported by a 2020 review article of HF patient adherence strategies. In this review, the authors classified patient nonadherence as either intentional or unintentional. Patient factors such as depression, anxiety and impaired cognition were cited as contributing to passive or unintentional nonadherence (Makris et al., 2020) Comorbid conditions may also complicate self-care. The complexity of self-care increases for patients who suffer from concurrent conditions. Patients who have multiple comorbidities may have competing demands or experience conflicting advice (LeBlanc & Jacelon, 2018). For instance, patients who have multiple comorbidities often have an increased number of medications to take and keep track of. Further, diet advice and symptom monitoring may become increasingly complex with multiple comorbidities (Riegel et al., 2009a). Since comorbidities within this population are common, these must be taken into consideration in the complexity and load of self-care. Next, sociodemographic factors influence self-care. These include, but are not limited to, gender, age, socio-economic status, and amount of social support (LeBlanc & Jacelon, 2018). In their multinational analysis of more than 2,000 patients, LeBlanc and Jacelon showed no relevant gender differences in subjective assessment of self-care in HF patients. However, factors such as marital status and education level, which may be related to gender, showed some differences. For example, married women were 37% less likely to report adequate self-care maintenance than unmarried women (Lee et al., 2009). Another study found differences in mediating factors of self-care maintenance for people with HF. The study found self-care confidence was an important mediating factor for both genders. However, social support was more predictive of self-care maintenance proficiency for men, while self-care knowledge was more strongly predictive of self-care maintenance in women (Mei et al., 2019). 13 The presence of social support, including both emotional and instrumental support with activities of daily living, has been found to be a significant mediator in patients displaying confidence in self-care (Massouh et al., 2019). Lastly, those individuals of low socioeconomic status or less than high school education performed worse in self-care than those with less financial strain and more education (Dickens et al., 2018; Sedlar et al., 2021). Modifiable factors have also been shown to influence an individual’s self-care ability. Modifiable individual patient characteristics that may influence self-care include knowledge, experience, skill, motivation, and confidence in self-care (Riegel & Dickson, 2008). An important mediator of engagement in self-care is perceived self-care ability, as well as the belief that the actions will lead to results (Riegel & Dickson, 2008). This is also related to self-efficacy. The effect on self-efficacy is described in the 2016 update to the Situation Specific Theory of Heart Failure. Self-efficacy is described as the ability to perform the required behaviour despite barriers. Self-efficacy is also influenced by life experience. High self-efficacy has been shown to increase self-care behaviour and quality of life (Riegel et al., 2016). Confidence in one’s own self-care ability, which leads to effective self-care, is mediated by many factors such as experience level and social support (Massouh et al., 2019). Evidence indicates that a number of interventions that may increase a patient's knowledge, may influence confidence in self-care (Massouh et al., 2019). One such intervention is education, which has been found to increase a patient’s confidence despite other barriers such as depression or a lack of social support (Massouh et al., 2019). Knowledge and experience are necessary, but insufficient, for patients to engage in self-care (Jaarsma et al., 2017). However, since knowledge mediates confidence, interventions that increase knowledge may be able to lessen other barriers to self-care. Massouh et al. (2019) also reported that confidence in self-care ability contributed to 14 success in both self-care maintenance and self-care management for patients with HF. As an independent, modifiable mediator of confidence, HF knowledge is a feasible area for targeting interventions. There are many individual-level factors that inhibit self-care, such as uncontrolled symptoms and lack of social support. Yet there are also many modifiable factors that influence self-care, such as skill level and knowledge, which may be improved through educational interventions (LeBlanc & Jacelon, 2018; Mei et al., 2019). 2.2.2. System/Provider Factors In addition to individual patient factors, there are system-level barriers and enablers of self-care. Access to knowledgeable health care providers and specialized services are not equal throughout populations (LeBlanc & Jacelon, 2018). Patients living in more rural areas may not have the same access to rehabilitative programs or specialists (Jaarsma et al., 2017). Wait times to see specialists or barriers to attending in person appointments may inhibit self-care (Jaarsma et al., 2017). Further, limited clinician time may limit self-care education in clinical settings (Riegel et al., 2009b). Barriers to clinicians spending time on self-care support include lack of reimbursement for patient education and counselling and increasing medical complexity of patients. Fee-for service reimbursements are often linked to individual disease outcomes rather than prevention or complexity of care, such as the education necessary for HF patients (Riegel et al., 2009b; Suter et al., 2017). Although current Canadian guidelines have recently emphasized the importance of teaching self-care (Howlett et al., 2016), these systemic barriers continue to impact care. 15 Finally, a challenge to clinicians is a lack of theory-driven interventions (Jaarsma et al., 2017), which are important to support patients in HF self-care. Interventions that are validated through research and are theory-driven will assist both patients and providers in promoting self-care. Additionally, interventions to support clinicians in self-care promotion may help to bridge the gap in service delivery. 2.3. Self-Care Education Interventions for Heart Failure There have been a range of HF interventions developed for the purpose of improving self-care. There are two main categories of interventions that have been developed for self-care in HF: traditional or analog and e-health approaches. Traditional approaches to HF self-care education include in-person individual teaching, group sessions, and static text resources. E-health interventions include all interventions that integrate technology to improve self-care, such as telehealth, mobile-based and web-based interventions. Evidence has been mixed as to the efficacy of these interventions on self-care. This section will review the types of HF patient self-care interventions. 2.3.1. Traditional Approaches In an American Heart Association “State of the Science” publication, Promoting Self-Care in Persons with Heart Failure, it was noted that traditional approaches to HF education were commonly described; these included in-person patient counselling, discharge teaching, and in-person group education sessions (Riegel et al., 2009b). The efficacy of the interventions included in the review varied, with only two (Krumholz et al., 2002; Martensson et al., 2005) of eight included studies reporting statistically significant improvements in self-care or clinical outcomes. Further, the insufficiency of knowledge transfer alone to create behaviour change was highlighted in the review (Riegel et al., 2009b). Cardiac rehabilitation programs aimed at 16 improving self-care behaviours, which included HF patients, have shown mixed results. A review of 18 studies found that only a small number showed significant behaviour change. Methods to increase self-care behaviour included early post-discharge appointments, nurse-led follow ups and group rehab programs (Karmali et al., 2014). 2.3.2. eHealth Approaches The use of technology in patient education and patient disease self-management is a rapidly advancing field of knowledge, with the potential to improve chronic disease management, including HF. Most technological tools used to improve patient self-care are not meant to replace face-to-face interactions with healthcare providers, but rather supplement these interactions or be used in areas that are resource scarce (Fox, 2009). The growing burden of chronic diseases on the healthcare system and on health care providers’ time may be eased through supplementing traditional patient teaching methods with the use of technological tools while maintaining quality (Whitehead & Seaton, 2016). Currently, HF eHealth interventions such as telemonitoring, are being designed to work in conjunction with usual care as part of disease management programs. However, within the eHealth literature, there has also been discussion about whether interventions will replace aspects of standard care (Wagenaar et al., 2019). As healthcare resources are ever scarcer, interventions to improve the efficiency of care delivery without significantly increasing the use of resources will be an important component of improving patient outcomes. Specifically, interactive educational tools have been shown to improve patients’ retention of health information (Fox, 2009). In one study, patients felt that the interactive component of the educational intervention was more personalized and this, in turn, increased their satisfaction (Finkelstein & Bedra, 2013). Interactive educational tools that utilize technology have the 17 potential to supplement provider interactions with patients by removing the time constraints of traditional patient teaching methods. Tools with which patients can interact independently have the potential to improve self-management (Charlier et al., 2016). One study in the review of HF-related apps used theory-based development and beta testing with a small sample size (n = 10) of HF patients who had previously received HF education. This app incorporated a mixture of text and audio content, which included an avatar that asked skill-testing questions. Feedback from participants was positive, with many agreeing that they enjoyed the app and found the educational components helpful (Athilingham et al., 2016). Another study of hospitalized patients, Austin et al. (2012) tested the use of audio education, followed by daily at-home automated voice messages, and optional inputting and tracking of symptom data. This study showed a significant decrease in hospital readmissions; a “readmission rate of 10% for 60 patients was compared with the [hospital] baseline CHF readmission rate of 21% for roughly 1200 patients per year” (Austin et al., 2012, p. 444). Another interactive HF education website that has been studied is the European Society of Cardiology/Heart Failure Association website 'heartfailurematters.org.' The website was tested in a three-group, randomized control trial comparing the heartfailurematters.org website and an e-health-adjusted care pathway to usual care (Wagenaar et al., 2019). The two groups using heartfailurematters.org or the e-health-adjusted care pathway had significantly higher self-care scores at the three-month follow-up than the group receiving only usual care. However, these differences disappeared by one year, suggesting that continued use is needed to sustain effects. In a systematic review that assessed the effectiveness of chronic disease self-management mobile and tablet apps, Whitehead and Seaton (2016) found statistically significant improvements in clinical outcomes such as HbA1c and a six-minute walking test in six of the 18 nine studies reviewed. In addition, some studies also used qualitative questions to learn about patient experience and acceptance of the intervention, which overall showed positive results (Athilingam et al., 2016). Two recent meta-analyses on eHealth interventions for behaviour change and cardiovascular disease showed mixed results on outcomes (Akinosun et al., 2021; Indraratna et al., 2020). Akinosun et al. found that, overall, digital interventions aimed at health behaviour change were more effective in increasing healthy behaviours such as physical exercise but were less effective at reducing unhealthy behaviour such as smoking (2021). Indrarata et al. assessed mobile phone interventions (MPIs) specifically, and importantly found MPIs were associated with significantly lower rates of hospitalization (n = 1595; Odds Ratio (OR) 0.77, 95% confidence interval CIs [0.62 - 0.97] (2020). The growth in eHealth interventions across patient populations has extended to patients with HF. Evidence is accumulating to support these interventions; however, there has yet to be consensus on the best eHealth approaches for patients with HF. A recent review found 26 commercially available apps for self-management of HF (Athilingam & Jenkins, 2018). The few studies that had tested apps were often limited in power or had a high risk of bias. The apps included a range of features such as symptom tracking, video messages, MP3 recordings and medication management (Athilingam & Jenkins, 2018). Qualitative data from the studies showed that patients found the interventions to be useful. Overall, the review concluded that mobile, phone-based HF interventions showed promise as low-cost solutions to engaging patients in HF self-care but recommended more rigorous research on the topic. Another study reviewed the quality of a group of HF mobile apps using the Mobile Apps Rating Scale (MARs) (Creber et al., 2016). Criteria included engagement, functionality, 19 aesthetics, and information that were individually and collectively scored to determine the quality (score) and subjective satisfaction (Creber et al., 2016). Overall, the review found that use of apps in HF management promoted patient engagement in their care. Creber and colleagues recommended that future apps should focus on creating behaviour change as well as lasting and measurable health outcomes in the HF population. Finally, a review of mobile HF interventions found that the interventions had mixed effects on self-care outcomes (Cajita et al., 2016). Although two of the three studies showed significant improvements in self-care, the third study showed no difference. Further, one of the studies that showed increased self-care employed a quasi-experimental design, with the possibility of confounding factors influencing changes in self-care. There is growing support for the use of technology to support self-care in the HF population, but evidence is lacking as to the explanatory mechanisms to behind the demonstrated benefit of this technology (Chib & Lin, 2018). In one qualitative study, participants described that knowing the connection between symptoms and the status of their HF influenced health behaviour (Myers et al., 2020). To date, the majority of studies have tested accessibility, usability, usage, and data quality, rather than health outcomes (Chib & Lin, 2018). Many eHealth HF interventions are not theory-based and lack rigorous research evaluation. More evidence is needed to support practices in developing e-health patient education. Existing research has shown the feasibility of using e-health education interventions to increase HF patients’ engagement in their care. Further, patients are accepting of e-health integration into their care (Radhakrishnan et al., 2016). Overall, findings indicate that the most promising eHealth self-management interventions use interactive education that integrates technology to increase patient understanding of the disease (Clark et al., 2016; Jaarsma & 20 Strömberg, 2019; Riegel et al., 2019). If and how increases in HF patients’ knowledge translates to changes in health behaviour requires further study. Next steps for eHealth interventions research in HF include research on the best methods of utilizing interactive technology for education and linking interventions to disease outcomes. 2.4. Gamification for Increasing Patient Engagement in Education The Theoretical Basis of Gamification: Self-Determination Theory Gamification is an educational tactic that is increasing in popularity in the fields of business, marketing, and e-learning, and is also being adopted in the area of digital health (Sardi et al., 2017). Gamification has been argued to be superior to traditional methods of delivering patient education, due to its ability to engage patients in the material and increase their motivation to learn (Charlier et al., 2016). In simplistic terms, motivation has been defined as, “the process to make a start, guide, and maintain goal-oriented behaviours” (Gopalan et al., 2017, p. 020043-1). The motivation to learn new information is regulated by several factors such as challenge, curiosity, competition and recognition (Gopalan et al., 2017). Motivation may also be increased through the psychological principles of reward and goal achievement. The theoretical support that gamification may increase motivation emerges from Self-Determination theory, a macro theory of human motivation (Deci & Ryan, 2008). Rather than describing motivation by quantity, Deci & Ryan describe types of motivation as a determinant of behaviour change which includes autonomous, controlled, intrinsic and extrinsic motivation. Autonomous motivation is self-endorsed, while controlled motivation is based on external contingencies. While both may direct behaviour, evidence has shown that when behaviour is autonomous, there is greater long-term persistence of behaviour change (Deci & Ryan, 2008). 21 Learning that is extrinsically motivated is any learning that leads to a separate outcome independent of the learner’s enjoyment and satisfaction with the action of learning (Ryan & Deci, 2000). Therefore, it could be argued that all patients with HF engage in extrinsic learning with the desired outcome of improvement in their symptoms and condition. Motivation to learn about one’s self-care is extrinsic in nature, since the outcome of increased health and well-being creates an automatic outside motivation (Gopalan et al., 2017). Intrinsic motivation is defined as motivation to engage in learning that is enjoyable on its own and not motivated by outside factors or outcomes (Ryan & Deci, 2000). Another way of stating this is that intrinsic motivation “come[s] from the experience of carrying out an activity rather than from results of the activity that are experienced subsequently” (Beswick, 2017, p. 64). Therefore, when a person is said to be motivated intrinsically, they are doing an activity for the sake of their interest and or enjoyment of it. Learning that results from intrinsic motivation is usually of high quality and increased creativity; however intrinsic motivation is not required for learning to be of high quality (Ryan & Deci, 2000). Nonetheless, Ryan and Deci (2000) make the argument that motivation is not homogenous and dichotomous; easily fitting into one of the two categories of extrinsic or intrinsic motivation. How extrinsic motivation affects behaviour varies by level of learner, autonomy, and internalization of the goal that the learning is meant to achieve. (Ryan & Deci, 2000). For instance, a learner who has internalized the value of the goals will be self-regulated and experience higher autonomy, in turn creating greater motivation than a learner who passively complies with the learning requirements/activities, which differentiates autonomous from controlled motivation. This personal commitment to self-regulation of the learning shares many qualities of intrinsic motivation due to higher personal commitment, despite being extrinsically 22 motivated (Ryan & Deci, 2000). Therefore, if the learner is able to fully grasp the meaning and the worth of what they are learning, there will be reduced conflict because the motivation will be self-determined and autonomous (Deci & Ryan, 2008). According to Ryan and Deci (2000), learning materials that are enjoyable and engaging, such as gamified material, may increase a patient’s intrinsic motivation to engage in education. Education material that is inherently engaging may make the act of learning more pleasant. However, it may also increase extrinsic motivation when game design, such as rewards and progress tracking, are incorporated. As discussed by Garneli et al. (2017), story-based gamification may be extrinsically motivating due to goal achievement, yet autonomous if the goals are self-endorsed (Deci & Ryan, 2008). Overall, if informed by the Theory of Self-Determination, gamification may take advantage of both intrinsic and extrinsic motivation to increase engagement in topics that are not traditionally viewed as games. Gamification Techniques To strengthen the argument for integrating gamification in patient education, recommendations have been made to increase the theory to support gamification. In particular, in the arena of behaviour change, the game architecture that is used must be linked to a behaviour change mechanism (Cugelman, 2013). Basing the game design on psychological principles as well as theory and evidence will be necessary for gamification to reach its full potential in the field of patient education. For instance, there are elements of game design that may increase the entertainment value of the game, but which are not persuasive in nature. Cugelman (2013) defines persuasion as elements that “exert persuasive force on people, encouraging them to shift their beliefs, attitudes, and actions” (p. 2). Examples of persuasive architecture include elements such as goal setting, overcoming challenges and giving the user constant feedback on their 23 performance (Cugelman, 2013). Architectural elements of game design that are persuasive in nature are directly linked to a validated psychological behaviour change ingredient such as goal setting, action planning and prompting of self-monitoring behaviour (Ryan & Deci, 2000). Use of narrative for gamification purposes may help to engage learners as well as create desired behaviour change. Narrative has been defined as the use of games that include a story or a plot line. Further, elements of narrative gamification include actor, choice of possibility, interactivity, narrator, plot, sequence of events, space, story/data, time, and user experience (Palomino et al., 2019). Narratives may be used as a persuasion tactic in game design. Through choice and interactivity, narratives may be used to condition behaviours of the user. Through these elements, users engage in creating a unique experience by exercising choice that is bound within the logic and rules of the system with which they are interacting. Therefore, narrative use in game design for patient education may be effective in creating behaviour change. 2.5. Gamification in eHealth One way to engage patients in interacting with HF educational content is through gamification. While there is limited research of gamification of eHealth interventions in the HF population, gamified patient education has been studied in other chronic diseases. A systematic review and meta-analysis focussed on how serious games improve knowledge and self-management of chronic diseases in young people living with conditions such as asthma, diabetes, and cancer. This study showed promising results. It found six studies that showed significant knowledge gains and two studies that showed improved self-management as an outcome of games (Charlier et al., 2016). The authors of this review concluded that gamification uses mechanisms of both intrinsic and extrinsic motivation as primary reasons for the improvements shown in the studies. Although this review focussed on young people and lacked randomized 24 control trials, it showed that there is preliminary evidence to support gamified education in the management of chronic diseases including HF. In a more comparable population, a study that compared a gamified hypertension intervention to a non-gamified intervention found significant improvement in blood pressure at four months in both the intervention and the control group (Hickman et al., 2015). The gamified intervention included scenarios where participants interacted with healthcare provider avatars on screen. Patients selected responses to educational questions which they could “ask” the avatars. The control group was delivered an attention-control screen-based education program with comparable content to the gamified content. Both groups had a statistically and clinically significant reduction in blood pressure, which slightly favoured the gamified group; however, the between group difference was not great enough to reach statistical significance. The authors indicated that this study was not sufficiently powered to reveal between group differences. Therefore, further study is needed (Hickman et al., 2015). This study is one example of an initial support of gamification; however, variance will occur with different education tools and patient populations. At the time of writing, only one published platform could be found that has implemented gamified education in the HF population using technology (Radhakrishnan et al., 2016). This platform used only a reward-based gamification technique using achievement badges. This education tool was designed to be played like a casino slot game, which some patients found engaging, while others found to be repetitive. Another study focused specifically on using gamification techniques to increase physical activity in a HF population, but did not include education (Jaarsma et al., 2021). Participants used a home-based exercise game on a Nintendo Wii. Except for an improvement in heel raise at six months, there were no significant differences 25 in the study's primary endpoints (submaximal aerobic exercise capacity) between (the control group and those using the game, after controlling for confounders. Two studies have published findings from prototype exploration in the HF population. First, a study by Arulnathan et al., shared feedback on the prototype of a gamified self-monitoring platform for HF patients (2019). The proposed platform included telemonitoring and integrated goal setting and point rewards as gamification techniques. With initial prototype data collection, 16 out of 23 participants (70%) stated that they would use the platform. However, results have yet to be published on the functional platform because the proposed Bluetooth technology was not yet functional. Another proposed prototype (Radhakrishnan et al., 2020) explored patients' perceptions of a sensor-controlled digital game for HF self-management. The sensors included activity trackers and weight scales. In the interviews from this qualitative study, which explored participants’ preferences in multiple modes of gamification, most participants preferred progress tracking, mini-quizzes, and the option to involve a family member. In addition, over half of the participants chose to earn rewards, either in game-based points or rewards that translated to tangible world outcomes, such as gift cards or coupons. This study was carried out at a trade show and was exploratory in nature; therefore there is yet to be any published work on a developed intervention. Overall, this chapter has described the challenges that patients face living with the diagnosis of HF. This chapter then described how education is an important factor to improving self-care. Finally, it proposed that using gamification in education delivered in eHealth format may improve patient engagement in education material which may improve behaviour change 26 through increased motivation. The next chapter will describe the methods that were used to study these phenomena. 27 3 Chapter 3: Methods In this chapter, I will describe the methodology to address the research question: Does narrative-based, gamified, online-delivered heart failure self-care education improve the knowledge and self-care behaviours of patients living with heart failure? I will describe the research design, the study protocol, chosen measures, and data analysis. Methodological limitations of the study and ethical considerations for protecting participants will also be outlined. 3.1. Research Design We used a mixed methods design for this study. Specifically, a quantitative, quasi-experimental, pre-post-intervention design with an embedded qualitative element (Creswell & Creswell, 2018). The qualitative portion of the study used qualitative description (Sandelowski, 2010). Usability testing was also included throughout the research phases. 3.1.1. Mixed Methods Research Mixed-methods research is a highly useful technique for solving real-world practice problems within healthcare. Although many researchers argue that either qualitative or quantitative research methods are superior depending on their world view, mixed methods offer a third choice with which to find middle ground. This is particularly useful for understanding participants’ viewpoints in the context of an experiment (Creswell & Creswell, 2018). Since the intervention used in this study sought to improve HF patient self-care, a mixed-method design was ideal to show efficacy through quantitative measures, while including the richness of patient and nurse perspectives to help interpret the findings (Creswell & Creswell, 2018). A common worldview that is employed in mixed-methods research is one of pragmatism. Rather than subscribe strictly to one school of thought or another, such as postpositivist or 28 constructivism, pragmatism takes a more fluid approach. Pragmatism looks for the most relevant method for answering the research question at hand, allowing the researcher to pick and mix methods as necessary (Creswell & Creswell, 2018). In mixed-methods research, pragmatism allows for the most flexibility in determining solutions to real world problems. Mixed methods experimental (intervention) design may be used when it would be useful to the study to also include participants’ perspectives throughout the experiment. Including qualitative methods in the evaluation may be included at one or multiple points within the study and may be before, during or after the intervention depending on the goal of the qualitative feedback (Creswell & Creswell, 2018). Another way to describe this method is as an embedded mixed methods design. An embedded element may be added at any stage of the research. For this study, the qualitative element embedded prior to testing the intervention with patients assisted in the development of the intervention by obtaining clinicians’ perspectives and feedback. This is consistent with the exploratory stage of an embedded model (Creswell & Creswell, 2018), in which qualitative data collection is initially used to develop an intervention. Consistent with Creswell and Creswell’s (2018) explanatory stage of an embedded mixed methods model, we used the qualitative element embedded after the quantitative data collection to understand and provide greater explanation for the reasons behind the quantitative findings. We gathered feedback from patients in the post-intervention qualitative element about their experiences with the intervention, centering on their perspectives about gamification elements and their influence on learning, to better explain the mechanisms underlying the quantitative findings. 29 Therefore, the weighting of this mixed methods study stresses the quantitative data; the qualitative feedback in the first phase sought to improve the intervention while the qualitative data after the intervention sought to explain the quantitative results. Therefore, this study has been categorized as qual QUANT qual. Below is a table that shows how the qualitative methodology was embedded in the quantitative study design (Creswell & Creswell, 2018). Table 3.1 Research Phase Methodology Design Component Phase Pre-Intervention Intervention Post-Intervention Method Qualitative exploratory Quantitative Qualitative explanatory Data collection Focus group 1. Dutch Heart Failure Scale 2. Heart Failure Self Care Index 3. System Usability Score Interviews Analytic strategy Qualitative description: Deductive thematic analysis Statistical Analysis Qualitative description: Inductive thematic analysis Purpose To improve an intervention prior to the use in a study. Measure intervention efficacy through validated measures. To better explain the results that are collected and analyzed of the quantitative phase Usability testing 3.1.2. Choice of Qualitative Description We used qualitative description, which is an appropriate qualitative approach, for this mixed methods study. It has high applicability to clinical interventions (Neergaard et al., 2009). Qualitative description is an appropriate methodology for developing a practical educational intervention in which actionable feedback from experts is sought. It is a highly suitable method for both the development and evaluation of clinical interventions (Neergaard et al., 2009). The desired feedback for intervention development drew on direct clinician experience working with 30 the HF population in responding to website content and did not require a high level of interpretation for purposes of improving the website. Therefore, data were described as closely to the original language as possible (Sullivan-Bolyai et al., 2005). A common misconception related to qualitative description is that this methodology does not promote/suggest using theory to support research questions (Sandelowski, 2010). However, researchers who use qualitative description may begin with a theory or framework to guide the collection and analysis of data (Willis et al., 2016) but the researcher does not try to fit the data into the framework, nor does qualitative description require researchers to commit to a theory throughout the progression of the research. Central Principles Qualitative description is useful for exploring the who, what, when, where of a phenomenon. Description may be used to create a comprehensive summary of events or perspective in the language of the participants (Sandelowski, 2010). In a continuum of purely descriptive to high interpretative, this method has been described as being “closer” to the data than other qualitative methods such as phenomenology or ethnography (Sandelowski, 2010). However, this does not mean that qualitative description does not require interpretation by the researcher. When creating themes and synthesizing findings, interpretation is still necessary. Sandelowksi argues, “[t]here is nothing trivial or easy about getting the facts, and the meanings participants give to those facts, right and then conveying them in a coherent and useful manner (2010, p. 336). In order to create themes that are reflective of the phenomena occurring, some interpretation is necessary (Willis et al., 2016). The goal of qualitative description is to create a “rich and straight description of an event” (Neergaard et al., 2009, p. 2). Qualitative description 31 seeks to create results both the participants and the researchers can agree upon. Often the language and labels that are found in the initial data are used, rather than interpreted. Finally, Sandelowksi states that the boundaries of qualitative research are highly permeable (2010). Qualitative description may include tones of other qualitative methodologies but should not be used as a category for research that is poorly designed. Qualitative description may be an appropriate method for situations of high uncertainty. When conditions of a study are dealing with novel phenomena where little is known, this method may be useful rather than forcing a more precise design which would give a false impression of certainty (Sandelowski, 2010). This makes qualitative description an ideal choice in exploring the use of gamification for education in the HF population because to date, there has been little research in this area. 3.1.3. Usability Testing It is important that interventions are appropriately targeted towards the population that will be using them. Healthcare applications that are easily usable for patients and health care providers provide benefits such as “improved productivity, enhanced user well-being, avoidance of stress, increased accessibility and reduced risk of harm” (Maramba et al., 2019, p. 96). Usability of eHealth interventions has been measured using both qualitative and quantitative measures. Several key metrics in usability testing are task completion, task duration and mouse clicks (Harrati et al., 2016). We measured task completion (scenario) and task duration (website session and time on each page) for this intervention using Google Analytics (Harrati et al., 2016). Google Analytics has the capability to automatically log task-related data such as task completion time and task duration. I activated these features to track efficiency and errors. Further, patients completed the System Usability Scale in the post-test measures (Lewis, 2018). The system usability score is a list of 10 items that participants rate on a Likert scale 32 from strongly agree to strongly disagree designed to test perceived usability of websites or software. Possible scores range from 0-100, with higher scores representing higher perceived usability. Items focus on the user’s likelihood to use the system, system complexity and ease of use (Lewis, 2018). Further, the SUS is the most widely used scale for perceived usability since its inception in the 1980s. The scale has undergone updates and improvements, but consistently it has shown high reliability (coefficient alpha of 0.91) and validity when tested against other measures (Lewis, 2018). Qualitative data are also deemed to be highly valuable in determining usability (Broekhuis et al., 2019) and were gathered during the pre-intervention focus group with the HF clinicians and during post-intervention interviews with patients. Usability questions centred around learnability, memorability, and overall satisfaction with the intervention. We included usability in the patient interview guide for the post-intervention interviews. This step was conducted over video conference to reduce time burden for participants as well as comply with current COVID-19 research guidelines. Videoconferencing increased the ease of scheduling for both the research team and participants. The combination of tracking quantitative measures such as task completion, time spent and click through rates, together with qualitative data, provided a comprehensive approach to usability testing of the intervention. 3.2. Study Protocol This research study consisted of several phases including intervention design, iteration, and testing with embedded qualitative methods. The first phase included the development of the online gamified HF education website. This phase included iterating the intervention, based on feedback gathered from a focus group of HF clinician, including HF specialist nurses. I analyzed this feedback through deductive thematic analysis with a focus on improving the intervention. 33 The analytic framework for the deductive analysis was driven by the theories of self-care (self-care constructs and naturalistic decision making), self-determination (intrinsic vs. extrinsic motivation and gamification) and website improvement (usability, considerations for older adults and content). Patients completed the pre-tests of the quantitative measures at the time of study orientation. Then, the patients were given de-identified login information to access the website intervention for 14 days, which is comparable to other HF pilot studies (Foster, 2018). The patients were encouraged to complete all seven educational modules, however there was no penalty for not completing. After the 14 days of website access, I asked patients to complete the quantitative post-test measures. Then I sought qualitative feedback from a subset of patients who agreed to participate in phone interviews after the 14-day quantitative measures were collected. I analyzed these interviews through inductive thematic analysis with a focus on patient experience with using the website. The flow chart below outlines the stages of the research, which will be explained in greater detail in later sections. This chart also demonstrates the quantitative nature of the study with embedded qualitative methods. 34 3.2.1. Design of the Intervention The first phase involved the development of the online gamified HF education website. We chose a web-based intervention rather than a mobile application due to the scope of the project and resources available. The website was accessible by the internet on mobile devices and tablets. HF clinicians who work in the heart function and pre-transplant clinics provided feedback about the website, which was used to finalize the content and design of the intervention. The in-depth procedures for this step are outlined below. Sampling/Recruitment We invited all nurses who work in the heart function and pre-transplant clinics to participate. Later, one non-nurse clinician also joined the study, requesting to participate after hearing about the study through word of mouth. HF clinic nurses have specialized knowledge and experience in the care of HF patients and are highly involved in the education of these patients. Therefore, they were ideal candidates for providing feedback not only on the content, but also the structure of the education. We recruited the sample of HF clinicians from the heart Figure 3.1 Study Flow Chart 35 function and pre-transplant clinics at St Paul’s Hospital. St. Paul's hospital is one of five tertiary cardiac centers for the province of British Columbia and is the only quaternary cardiac centre. The nurse manager informed all nurses who work at the heart function and pre-transplant clinics of the study by a pre-scripted email and invited them to review the consent included with the email (see Appendix A). Nurses who were interested in participating agreed to have the nurse manager share their email with me for follow-up contact. I then sent the focus group information to the clinicians by email. The email included a link to the consent form which was hosted online through Qualtrics software. Nurses had the option to schedule a phone call to ask questions about the form. Data Collection Once nurses gave consent to participate in the study, I granted them access to the intervention website. I then instructed them to review the scenarios as well as to give feedback on the presentation of the content (see Appendix B for interview guide). The primary data collection method was a focus group conducted over videoconference and one individual interview by videoconference. Following discussion with the heart function clinic manager, this format was chosen as a more feasible scheduling option to reduce risks of gatherings related to COVID-19. We chose focus groups as a method of data collection for this stage of the research for several reasons. First, they involve active interaction between the participants, which is particularly useful when exploring new or complex ideas (Jayasekara, 2012). Second, focus groups are ideal when the data being sought is related to suggestions and feedback (McLafferty, 2004). Third, they allow for gaining broad perspectives as well as discussion and possibly consensus (Sandelowski, 2010). We held the focus group approximately two weeks after the 36 clinicians were given access to the website. Prior to the focus group, I sent the clinicians a copy of the focus group questions to allow time for reflection and to promote rich feedback during the focus group. Providing the clinicians with the questions in advance was intended to mitigate groupthink. Groupthink is a phenomenon that can occur when participants influence other’s opinions, which often creates the pressure to conform (Madigosky & Schaik, 2016). I recorded and transcribed the focus group session. I also composed field notes about the session. Data Analysis I used thematic analysis to analyze the data. One of the strengths of thematic analysis is the flexibility it allows the researcher when analyzing the data through the lens of theory. Braun and Clarke state that through “its theoretical freedom, thematic analysis provides a flexible and useful research tool, which can potentially provide a rich and detailed, yet complex, account of data” (2006, p. 78). Thematic analysis allows the researcher to choose between two approaches dependent on the research question: deductive or inductive. A deductive approach to analysis is committed to a specific epistemological position, which I used for the qualitative data collected from the nurses. When analyzing inductively, the researcher codes and analyzes data in a way that is not theoretically driven (Braun & Clarke, 2006). When thematic analysis is deductive or theoretically driven, the data is coded and analyzed based on the research question and existing theory. By analyzing data with theory in mind, the researcher codes and analyzes the data based on an accepted theoretical framework. Therefore, the data and findings may be influenced by this theory. I analyzed the data for themes based on established theory and research related to gamification and HF self-care principles. Theoretical thematic analysis is, “driven by the researcher’s theoretical or analytic interest in the 37 area” (Braun & Clarke, 2006, p. 84). Thus, the analysis sought to describe specific feedback regarding improvement of the website rather than exploring the overall experience of the nurses. An inductive approach involves analysis “essentially independent of theory and epistemology and can be applied across a range of theoretical and epistemological approaches” (2006, p. 78). I used inductive analysis for the patient's post-intervention interviews. A decision that must be made when using thematic analysis is at what level themes are to be analyzed—whether semantic/explicit or latent/interpretive (Boyatzis, 1998). In general, during thematic analysis the researcher chooses only one level. Since the focus of the qualitative component of this research was on intervention development, I used a semantic approach in data analysis. This approach focussed on the explicit meaning of the data collected from the nurses and patients rather than taking a more interpretive approach (Braun & Clarke, 2006). A semantic approach is consistent with the expectation in qualitative description to represent the data in original language (Sandelowski, 2010), and generate themes that will stay as close to the language of the participants as possible. Qualitative data analysis should include the analysis of both verbal and nonverbal data with the goal of summarizing these findings (Sandelowski, 2000). Braun and Clarke describe six phases of thematic analysis which are outlined below (2006). 1. Familiarize yourself with the data: Includes transcribing, reading, and re-reading the data, may write initial ideas 2. Generate initial codes: Code features in a systematic way across the data set 3. Search for themes: Bring together codes into initial themes. Gather the data relevant to each theme 4. Review the themes: Check if the themes work in relation to the codes and the entire data set. Generate a thematic map of the analysis 38 5. Define and name themes: Refine the themes and the overall story that the analysis tells 6. Produce the report: The final opportunity for analysis. Select extract examples. Relate the analysis back to the research question I used the themes generated during data analysis to make changes to the intervention prior to testing it with patients. This feedback was also included in the overall synthesis of findings at the final stage of research. 3.2.2. Testing the Intervention with Heart Failure Patients The next phase of the research was a one-group, pre-and post-test design. Before and after the intervention, participants filled out quantitative measures of knowledge and self-care behaviour. Following the intervention, I invited a subset of participants to provide qualitative feedback on the intervention and their experiences with using it. Sampling/recruitment We recruited the sample from the St Paul’s Hospital Heart Function Clinic, the Pre-Heart Transplant Clinic, and through social media channels. Patient educators and nurses identified prospective patients at St. Paul's Hospital who met the inclusion criteria. Patients recruited through social media were self-selected. I used three strategies for patient recruitment. 1. Clinic nurses used a short script to invite eligible patients to learn more about the study during routine pre-visit phone calls (Appendix D). If the patient consented to learn more about the study, the nurses shared the patient's contact information with me. 2. The clinics’ clerk emailed invitations to the heart function and pre-transplant patients in the pre-visit package (Appendix E). 3. I posted invitations to participate through social media accounts made specifically for the research study. I created accounts for Twitter, Instagram, and Facebook (Appendix F). 39 Patients who received invitational material from the heart function or heart transplant clinics had the option of either contacting the researcher directly or giving verbal consent to have the clinic share their contact information for follow up. Patients who were recruited through social media contacted me by email or by reaching out through the three respective social media pages to initiate communication. I then set up a telephone call with interested patients to discuss the consent form. At this time, patients had the option of completing the online consent form with over-the-phone support or taking time to think it over and completing the link at a later time. The study consent form was hosted through UBC Qualtrics software. The sample was a targeted and non-probability convenience sample. Previous studies using the Self-care of Heart Failure Index (SCHFI) have found small to medium effects of the intervention on self-care maintenance, management, or confidence (Ware et al., 2020). The recommended power for pilot studies is 80%. I performed a power calculation to determine sample size using G*Power software. For a two-tailed, matched pairs t-test with alpha set at 0.05 and power of 0.8, the recommended sample size was 35. Therefore, this is the sample size that I strove for, allowing for some flexibility because this study was a pilot. After attrition of four patients, nineteen patients completed the study (Whitehead et al., 2016) The patients who were identified by the heart function and pre-transplant nurses as possible research participants were pre-screened according to the inclusion criteria. I screened participants who were recruited through social media for inclusion and exclusion criteria. Participants recruited over social media self-reported their diagnosis of HF and no clinical confirmation was obtained. Below is the full list of inclusion and exclusion criteria. Inclusion Criteria a) Confirmed or self-reported diagnosis of HF 40 b) At least nineteen years of age c) Able to speak, read and understand English d) Cognitively able to give consent to participate in the study e) Demonstrable basic computer skills Exclusion Criteria a) Acute mental illness, substance use disorder, or palliative care goals (determined from clinic nurses or me) b) Score below 27 on the Mini-Mental State Examination (MMSE) scores of 26 and below may indicate mild cognitive impairment (Prochota et al., 2019) c) Lack of manual dexterity or vision necessary to operate a computer, iPad, or mobile device d) Inconsistent access to a computer or mobile device with internet connection for the 14-day study duration e) Patients were asked to demonstrate basic computer skills during the consent process. Those who lacked computer literacy sufficient to navigate website and answer the data collection tools via email were excluded. Quantitative Data Collection I asked patients who completed the online consent form to complete a demographic questionnaire and baseline measures of HF knowledge and self-care. Appendix G includes other demographic data that were collected. During the two-week educational module/intervention, I tracked patients’ time using the platform and task completion through the online tracking analytic software. At the end of the 14-day intervention, patients repeated baseline measures as 41 well as a usability survey. I administered demographics and survey tools using UBC Qualtrics online survey software. This software is recommended by the Research Ethics Board of UBC. 3.2.3. Measures The Self-Care of Heart Failure Index (SCHFI) (Riegel et al., 2009a) measures the naturalistic decision-making processes that occur when patients with HF engage in self-care. This 22-item scale measures maintenance, monitoring and management behaviours. Since its inception, the SCHFI has been used widely with HF patients at all stages of the disease and in multiple contexts, including both inpatient and outpatient HF clinical settings. In the testing and update of the SCHFI, it was shown to be valid and reliable. The SCHFI has shown evidence of concurrent validation in comparison to the European Heart Failure Self-care Behaviours Scale (Jaarsma et al., 2003). When tested after an update in 2009, (n = 154), internal reliability (alpha coefficient) was found to be .553 for the self-care maintenance scale, .597 for the self-care management scale, and .827 for the self-care confidence scale (Riegel et al., 2009a). Data from three mixed methods studies were used to triangulate construct validity of the SCHFI (Dickson et al., 2008; Riegel et al., 2006, 2007). Further, during a study where the scale was administered at monthly intervals, researchers also tested for any repetition or learning effects (Riegel et al., 2009a). No learning effects were observed with repeat administration. Social desirability, also tested in the scale on a sample of 34 patients using the Crowne-Marlowe Social Desirability Scale, was found to be low: none of the scores on the SCHFI significantly correlated with social desirability (Crowne & Marlowe, 1960; Riegel et al., 2009a). Therefore, this scale was optimal for repeated use in the same population to show change over time. For statistical analysis, a change in the scale greater than one half of a SD was considered clinically 42 relevant. Also, because this scale is in the public domain, we did not need permission to use the scale. Second, since this is an educational intervention, we also used a knowledge scale. We used the Dutch Heart Failure Knowledge scale to measure knowledge change related to the intervention. It has been used widely to test heart-failure-related knowledge (van der Wal et al., 2005). This scale is a 15-item, self-administered scale that has been shown to be valid in differentiating patients who have received education counselling from those who have not. In the development and testing of the Dutch Heart Failure Knowledge scale, in a sample of 902 patients with HF, internal consistency was low with a Cronbach’s Alpha of .62. This was assessed through patients hospitalised for HF and who had participated in the Coordinating study evaluating Outcomes of Advising and Counselling in Heart Failure (COACH) study, a multicenter randomised study evaluating outcomes of a counselling program for HF (Jaarsma et al., 2004). Construct validity was tested using the “known groups” technique to determine the tool’s ability to distinguish between groups that theoretically should have different scores on the tool. Patients who had received comprehensive education had significantly higher knowledge scores than those who had not had the education. The tool showed construct validity (t= -7.14; p = 0.0001) through change in knowledge scores using known groups technique (van der Wal et al., 2005). Google Analytics data were extracted to measure participant website usage. While there are many metrics available, we chose to track total time participants used the website and the number of scenarios each participant visited. We increased the validity of the data tracked on individual participants using Monster Insights to link Google Analytics participant IDs to the website IDs to ensure data was accurately attributed to the correct participant. Primary data such 43 as the number of page visits and time spent on the page is highly reliable. We did not use any secondary data such as age or gender tracked from Google Analytics, which is less reliable (Tonkin et al., 2010) . Quantitative Data Analysis I analyzed data using Microsoft Excel and SAS Version 9.4 with descriptive statistics to summarize pre- and 14-day post-tests scores of HF knowledge and self-care behaviour. I hypothesized that HF knowledge and self-care scores would increase after the 14-day time period of website use. I conducted paired t-tests to determine the statistical significance of any observed change in scores following the intervention. Also, I used independent t-tests to compare effects across demographic segments based on age, gender, and education. Further, I tracked time spent on pages and task completion using Google Analytics. Next, I performed Pearson’s correlation to explore associations between time using the intervention, knowledge, and self-care behaviour scores (Plichta et al., 2012). I also hypothesized that there would be a positive correlation between time spent and improvements in knowledge and behaviours. Therefore, data analysis also gathered evidence for time spent on the education material in relation to outcomes. 3.2.4. Patient Perspectives The purpose of this phase was to better understand the participants’ experiences of using the intervention and to gain feedback for possible future improvements to the intervention. Sampling and Recruitment I invited participants who completed the quantitative portion of the study to participate in a semi-structured telephone interview following the intervention. Please see Appendix H for the interview guide. During the initial consent process, I invited patients to participate voluntarily in a follow-up interview. If the patient consented to be contacted for the follow-up interview, I sent 44 a second consent form at the end of the 14-day intervention. I recruited six participants, purposefully selected for demographic variability as well as variability in time spent on intervention (two lower users, two intermediate users, two high users). This increased the variability of the responses as there were both high and low users included in the interviews. This number may not achieve representativeness of the overall sample but was chosen due to feasibility in relation to the timeline of the study and is similar to other HF eHealth pilot testing studies (Athilingam et al., 2016). Qualitative Data Collection I used a semi-structured interview guide to elicit an understanding of participants’ experiences of story-based gamification and how this approach may have altered their level of motivation. I conducted semi-structured interviews over the phone or over a UBC secure video-chat application, which I recorded and subsequently transcribed verbatim. Qualitative Data Analysis Similar to the first phase of this research, I employed thematic analysis as I method for data analysis (Braun & Clarke, 2006). Data were coded and analyzed for themes, using thematic analysis. In contrast to pre-intervention qualitative analysis, which was exploratory in nature, post-intervention data analysis sought to explain the quantitative data results. Additionally, focus was on exploring participants experiences of using the gamified HF education website. Data analysis for this stage was inductive and involved a more theory-naïve perspective. This approach to thematic analysis shares some similarity to grounded theory (Braun & Clarke, 2006). 3.2.5 Interpretation of the Data The final phase of the study was to interpret the results of the quantitative testing along with the embedded qualitative data. I interpreted post-test, evaluation interviews to understand 45 participants’ perspectives and to explain quantitative results of knowledge and behaviour change. Interpretation of the data in this order follows the Creswell and Creswell methodology of using qualitative data to explain the results of quantitative testing of an intervention in an embedded mixed-methods research design (2018). Therefore, I analyzed quantitative data, then I analyzed the qualitative data to expand on these results in the discussion of results. 3.3. Ethics Ethical considerations for this mixed methods research included considerations for both the quantitative and qualitative phases. 3.3.1. Informed Consent All participants gave consent to participate in the study. The consent form was presented in plain, clear language. We included details about the research procedures as well as the time required to participate in the study in the consent form. Further, we informed research participants that (a) consent was given voluntarily; (b) consent was an ongoing process; (c) consent could be withdrawn at any time without negative consequences. The patient consent forms were separated by Part 1 and Part 2 of the Study. The Part 1 consent form was the consent to test the website as well as complete the survey measures at 0 and 14 days of the intervention (Appendix I). The Part 1 consent form also gave patients the option of consenting to be contacted for a follow-up interview after the 14 days of website use. If the patient agreed to the follow up interview, they completed the Part 2 consent which described the details of the post-evaluation interview (Appendix J). 3.3.2. Benefits and Risk There was a potential benefit to the research participants in this study. Use of the intervention may have increased self-care knowledge related to HF. This may have improved the 46 participants’ long-term self-care behaviour, which could result in improved HF outcomes. There may also be the potential benefit of having knowledge that they were helping other patients with HF for the future. There were no significant identified risks to participants in this study. We took measures to reduce risk of any damages due to privacy concerns which will be discussed in the next section. There was a small potential risk that post-intervention interviews could illicit patient discomfort or negative emotions. To mitigate this risk, I asked participants to let me know if they were in any way uncomfortable during interviews so the interview could be paused or stopped altogether. I also reminded participants that they could withdraw from the study at any time. 3.3.3. Privacy and Confidentiality During the consent process, I made participants aware of any risks to their privacy and confidentiality. The consent forms included information about website use and measures to protect their privacy. I gave participants anonymous emails attached to unique identifiers to log in to the website. This ensured that no personal identifying information was attached to website use. I also made participants aware that the data that were tracked in conjunction with their anonymous email was stored in a server in the United States through Google Analytics. At study completion I will download Google Analytics data to be securely stored on an UBC server and will submit a data deletion request to Google Analytics. I collected demographics and the responses to the pre-and post-test tools through Qualtrics software, which is UBC-approved for the use of research. The consent form informed participants about the storage of the results of the intervention feedback and the recorded interview. For example, they were told their data would be kept for at least five years. I sent all research-related files and emails through a secure UBC 47 web address. Also, I utilized a secure UBC workspace for the storage of all the online files. Further, all work done on the study was completed on a password -protected and encrypted laptop. 3.3.4 Compensation Healthcare providers and patients who chose to participate in the study were not paid for the time they spent on the study as they participated during working hours. However, we gave all participants (healthcare providers and patients) a $10.00 gift certificate to Starbucks as a thank you for their participation in the study. We gave patients who completed the qualitative interview an additional $10.00 gift certificate. All healthcare providers who gave feedback and all patients who completed the pre- and post-measures were given the gift certificate. 48 4 Chapter 4: Results In this results chapter, I will present results from data analysis for each of the three phases of this mixed-methods study. First, I will present exploratory qualitative data results from the HF healthcare provider focus group and interview as well as subsequent website changes. Next, I will show the quantitative results from 19 patients who completed both pre- and post-test study measures. These results will include the usability, knowledge, and self-care behaviour scores. Finally, using qualitative data collected from a subset of patients who participated in the quantitative testing phase, I will describe the patients' experiences with the website and the results found in the quantitative results. 4.1 Technical Development Process The website was created and hosted on the web hosting service Bluehost. WordPress, which is an open-source content management system, was used to design and modify the website. I created the initial design of the website created using the WordPress theme "Twenty." I then adapted this template to the needs of the website. I used WordPress software plugins to create user registration and reward tracking features. I limited user registration to the manual creation of usernames by website administrators using temporary email addresses to reduce the use of participants’ personal information. The website is public, so anyone can navigate to the home page. However, educational content was set to private, so only study participants had access. In addition to the seven educational scenarios, pages created included "How To," "Contact," "My Rewards," and "Login" pages. On the "Home" and "How To" pages, I embedded a video on the page that walked patients through the website using screen recording. 49 Figure 4.1 Login Page Figure 4.2 Home Page 50 Figure 4.3 Sample Scenario I linked Google Analytics to the website to track the anonymous logins I created for the study participants. In addition, I set the Google Analytics "Goals" feature to flag individual users when they passed milestones such as visiting a webpage or passing 10 minutes of active website usage. 4.2 Website Development I completed the initial website design and development, which was iterated after feedback from my thesis committee. Three primary resources were used to create learning outcomes on which to base the stories. The concepts covered by the Self-Care of Heart Failure Index and the Dutch Heart Failure Knowledge Scale were used in this study as a basis to create the education outcomes (Riegel et al., 2009a; van der Wal et al., 2005). In addition, education was also based on the Heart Failure Zones, a widely used decision support tool used as part of the discharge teaching for HF patients at St. Paul's Hospital (Vancouver Coastal Health et al., 2016). From these documents, I created a list of learning outcomes. I divided learning outcomes across the seven scenarios, with storylines incorporating several learning outcomes per scenario. Usability and the target population were key considerations during the website design process. According to Ambrosy et al., the mean age of people hospitalized for HF ranges from 51 70 to 75 years (2014). However, there is a high level of heterogeneity of technological skills and use among older adults. While many older adults are high technology users, it is known that worldwide, internet use is lower in the older adult population (van Boekel et al., 2017). Therefore, the likely range of technical skills of participants was an important consideration in website design. There is an initiative in Human-Computer Interaction design to seek to first design for those in underserved populations (Erete et al., 2018). For this study, I sought to design for older adults with the lowest level of computer literacy. A high-contrast colour scheme, ample white space, clear buttons, large font size, and reduced scrolling were all recommendations used to improve the accessibility of the website for older adults (National Library of Medicine, N.D.) Further, a "How-To" video was embedded on the website, which showed meme clicking through the website step by step. Additionally, I wrote the stories in plain language to be accessible to patients from varied educational backgrounds. Finally, all text was checked to be at or below a reading level of Grade 7 using the online tool "hemingwayapp.com." I paid attention to inclusivity in the design of the website and stories. For instance, I used gender-neutral language throughout the scenarios. When referring to patients’ family members or a romantic partner in the stories, gender-neutral terminology was also used to include varied gender identities and sexual orientations. Next, rather than modelling the main character after a human, I designed a cartoon heart as the protagonist. This was intentionally chosen to be inclusive of race and ethnicity. The stories do not begin to cover the richness and diversity of HF patient life experiences. Nonetheless, the narrative of the stories and the website's design sought to be inclusive of gender, race, class, sexual orientation, and education. 52 4.3 Health Care Provider Population Participants from the focus group included clinicians from both the heart function and heart transplant clinics of St. Paul's Hospital in Vancouver, B.C. In addition, one individual interview was conducted with a non-nurse clinician who works closely with this population but was unable to participate in the focus group. Six HF clinicians were present for the focus group, which was conducted over Zoom. Nurses who work at the heart function and transplant clinics are experienced clinicians with specialized knowledge of HF. 4.3.1 Website Education Content The focus group began with a discussion of the nurses’ thoughts about the content and organization of the website content/materials. The first concern nurses raised was that patients would come to the education materials with a varied background in HF education knowledge. Therefore, the first suggestion was to have a document available for patients to prime them on the basics of HF. One participant highlighted this concern; "You're just jumping right into scenarios [when] you don't know what you're basing the education on or what education they've had" (Participant 3). To further address the differences in patients’ educational backgrounds, nurse participants suggested that the title of each scenario should be related to the learning outcomes. The suggestion of clear titles was intended to allow patients to pick scenarios dependent on their current knowledge of separate topics. In addition, nurse participants voiced their support for the "Key Takeaways" section at the end of each scenario. The Key Takeaways slide at the end of each scenario could also be adapted to introduce each scenario, which would provide patients with learning objectives before starting the module. 53 Several nurse participants lauded the repetition of key concepts as a strength of the educational scenarios. For example, nurse Participant 5 described the importance of repetition and how self-learning could help address patient's desire for more time with their clinicians. "Imagine you had as much time with your doctor as you wanted and felt super comfortable in this idyllic world. What would you want to know? What would you ask?" The degree of depth of the educational content emerged as another issue. Because the website was designed to provide foundational knowledge (e.g., diuretics), clinicians thought the content was not always as in-depth and personalized as needed for some situations/patients. For example, the non-nurse clinician noted that HF medications are a complex topic and require additional teaching for many HF patients to understand. To overcome this limitation, a suggestion was made to provide an option to learn in greater depth about topics by linking to additional resources. Therefore, if a patient wanted to learn more about a topic, they could be directed to a vetted educational resource describing the topic in greater depth and detail. 4.3.2 Heart Failure Patient Decision Making Nurse participants discussed elements of HF patients' naturalistic decision-making throughout the focus group—one aspect of the discussion centered on the importance of scenarios reflecting real life. In several instances, nurse participants recommended that scenarios be made more challenging to reflect more realistic options such as "choosing between a hot dog and a turkey sandwich, like something that's actually comparing apples to apples" (Participant 2). Other nurse participants agreed that decisions should be challenging when creating scenarios for patients and should reflect real life. Also related to naturalistic decision-making in HF, nurse participants highlighted the duality of balancing patient autonomy and quality of life with HF self-care recommendations. 54 Nurse participant 6 elaborated, "It's about treating their heart failure, but also trying to improve their quality of life and their quality of life may be that they really do want this hot dog." Nurse participants highlighted the challenge of scenario-based learning, where context and ambiguity are difficult to address fully. This ambiguity present in patient's lives was reflected in nurse participants' discussion of patient decision-making in which there is no obvious right or wrong choice. Nurse participant 2 expressed that the decision-making options need to be "two things that both look like the right things to do. But then they chose the slightly worse option because that's real life. Right? Like, that's what our patients are dealing with." Nurses advocated for these realistic options to align with their approach to patients in the decision-making process. According to nurse participant 6, "We don't do black and white here, really with our patients… We don't necessarily always come from a place of no, but a place of how can we make this work for you?" One nurse participant stressed the importance of personalization of treatment plans for individual patients. "We might want them to do something different [than standard of care]. So how are we going to show that in this kind of forum?" (Participant 4). Overall, the challenge of including real-world and individual contexts for patients in the educational scenarios was a significant theme of the focus group. 4.3.3 Gamification Ambivalence Many nurse participants expressed ambivalence towards the gamification approach to education for HF patients. At the same time, as they described positive feelings, they conveyed uncertainty. Generally, participants voiced support for an e-learning approach for HF patients. The changing healthcare landscape was addressed by one nurse participant when she said, "I really like a multimedia way of teaching heart failure. I think that's really great, especially these 55 days. We probably should have had this a long time ago." (Participant 6). Moreover, nurse participants expressed the desire for more interactive components to be added to the website, such as voiceover, sound effects or other side games, in addition to the story-based gamification. Specifically, the desire to add voiceover was discussed as an option to increase website accessibility. Conversely, participants described uncertainty that HF patients would have the skills to operate and learn from the website independently. One participant reflected on her interactions with recent patients: I'm just thinking of some of our more elderly patients who can't even click on Zoom, right? And I am aware that the research shows that that's not always the case, but I think you're going to be focusing this on a subset of our patients instead of most of our patients. (Participant 2) Nurse participants also expressed uncertainty about the use of gamification and engaging the patient population in the HF clinic. While nurse participants did not indicate a definitive lack of patient engagement in gamified learning, they expressed some doubt. Doubt arose from the potential lack of fit between the gaming approach and patient interest, particularly with older adults. For example, when Participant 5 posed a question to me, "[Our] patients are usually older people, do you think they enjoy video games?", other participants shared the stance of uncertainty about how older HF patients would react to gamification. The participants had several concerns related to the gamification technique of using rewards to increase motivation. First, when testing the website, two of the participants thought points were linked to patients' decisions as they worked through each scenario. While an explanation of the points was presented on a website page, the lack of clear instructions resulted 56 in HF nurse confusion. However, this resulted in more extensive discussion related to nurses' concerns about rewards-based gamification and patient education. First, nurses were worried that rewards could be harmful to patient confidence and emotional health. They may feel shame or [it might] make them feel insecure about their health literacy and think, you know, I just don't get this. Obviously, I don't get this, and I'm going to fail. Like, look at this. I scored poorly. I just really worry about what that could do to people's emotional health. (Participant 6) Second, nurse participants worried that patients might think that their performance on the scenarios would impact their healthcare provider's view of them. For example, Participant 5 expressed this when she said, "I felt worried that, like, are the patients going to think, is the doctor looking at my trophies or the notes and does this have some effect on me?" A third potential issue using the combination of story-based learning and points was that if points were linked to patient performance in scenarios, patients would be discouraged from experimenting with different scenario choices, limiting learning. Once it was clarified that points were not related to the patient's performance on the scenario but were linked to time spent on the website, the participants' attitudes became more neutral. However, one participant expressed the view that the reward features were not contributing to the value of the website when she stated, "I don't think it's really necessary, to be honest." (Participant 6) Overall, the discussion related to the reward gamification technique revealed the importance of not linking rewards to patient performance in scenarios when combined with story-based learning. 57 4.4 Website Iterations Based on Clinicians’ Feedback Several changes were made to the website based on clinicians’ feedback. First, due to the confusion related to rewards, the "How-To" video was moved to the front page to introduce/orient patients to all parts of the website before beginning the scenarios. One scenario's name was changed to be more reflective of the content covered in the scenario. However, after further discussion with my committee, a decision was made not to add an additional description at the beginning of each scenario, to entice patients to complete the full scenario rather than only reading the first pages. Feedback related to specific sections having too-obvious decisions was addressed by increasing the difficulty of the decision trees. There were numerous educational content changes made, as suggested by the clinical experts in heart failure. However, no other significant structural or design changes were made to the website. 4.5 Patient Study: Sample Baseline participant demographics collected at Time 1 are listed below in Table 4.1. Twenty-three people consented to participate but only 19 completed both pre-and post-measures. The majority of the participants were from Canada (n = 11, 57.9%). However, there was international participant representation with participants residing in the United States (US), New Zealand, and the United Kingdom (UK). The average age for the cohort was 63.3 years and ranged from 31- to 80-years-old. Fourteen participants had more than high school education, and one participant had only partial high school education. There were more women (n = 11, 57.8%) than men (n = 8, 42.1%), with no participants self-identifying as non-binary or using the optional textbox to self-describe their gender. The majority of participants (n = 10, 52.6%) self-described 58 their overall health as "Fair," and the majority (n = 9, 47.4%) described their mental health as "Good," with no participants self-describing their mental health as "Poor." All except for three participants listed at least one comorbidity in addition to HF, with the maximum number of comorbidities listed as seven. The mean time since HF diagnosis was 52.9 months, ranging from 2 to 156 months. The difference between diagnosis time between men and women was statistically significant (p = 0.05); men had a longer average time since diagnosis than women. One participant listed no current medications. The most common medication listed by participants was blood pressure medication (n = 14 73.7%), followed by medication to control heart rate (n = 12, 63.2%) and blood thinners (n = 11, 57.9%). Nine participants had had at least one hospital admission in the previous year, with one participant reporting two and another participant reporting three hospital admissions. Table 4.1 Demographics at Baseline All n =19 Women n = 11 (57.8) Men n = 8 (42.1) Age, years, M (SD) 63.3 (14.5) 66.4 (8.7) 59 (19.8) Marital status (%) • Widowed • Single • Separated • Married or remarried • Divorced 3 (15) 3 (15.8) 1 (5.3) 10 (52.6) 2 (10.53) 3 (27.3) 2 (18.2) 0 (0) 5 (45.5) 1 (9.1) 0 (0) 1 (12.5) 1 (12.5) 5 (62.5) 1 (12.5) Race/ethnicity (%) • Asian • Caucasian • Not answered 2 (11.1) 16 (84.2) 1 (5.30 0 (0) 11 (100) 0 (0) 2 (25) 5 (62.5) 1 (12.5) Highest level of education (%) • College/University • Some college • High school • Partial high school 9 (47.4) 5 (26.3) 4 (21.1) 1 (5.3) 3 (27.3) 4 (36.4) 4 (36.4) 0 (0) 6 (75) 1 (12.5) 0 (0) 1 (12.5) 59 Table 4.2 Demographics at Baseline Housing circumstances (%) • Apartment/condo • Detached home • Other 2 (10.5) 14 (73.7) 3 (11.3) 1 (9.1) 8 (72.7) 1 (9.1) 1 (12.5) 6 (75) 1 (12.5) Living arrangements (%) • Lives alone • Lives with children • Lives with other family • Lives with partner 6 (31.69) 1 (5.3) 3 (15.8) 9 (57.4) 4 (36.4) 1 (9.1) 2 (18.2) 4 (36.4) 2 (2) 0 (0) 1 (12.5) 5 (62.5) Country of residence (%) • Canada • New Zealand • UK • United States 11 (57.9) 2 (10.5) 2 (10.5) 4 (21.1) 5 (45.5) 1 (9.1) 1 (9.1) 4 (36.4) 6 (75) 1 (12.5) 1 (12.5) 0 (0) Self-reported number of co-morbidities M (SD) 2.6 (1.9) 2.54 (1.6) 2.6 (1.94) Self-reported chronic health problems (%) • High blood pressure • Coronary heart disease • Arthritis • Diabetes • Atrial fibrillation • Valvular heart disease • Stroke • Sleep apnea • Thyroid disease • COPD • Cancer • Other 9 (40.9) 9 (40.9) 6 (27.3) 5 (22.7) 4 (18.2) 3 (13.6) 3 (13.6) 2 (9.1) 2 (9.1) 1 (4.5) 1 (4.5) 9 (40.9) 7 (63.6) 6 () 4 (36.4) 3 (27.3) 4 (36.4) 2 (18.2) 3 (27.3) 1 (9.1) 2 (18.2) 0 (0) 0 (0) 4 (36.4) 2 (25) 2 (25) 2 (25) 2 (25) 0 (0) 1 (12.5) 0 (0) 1 (12.5) 0 (0) 0 (0) 1 (12.5) 5 (62.5) Self-reported time since heart failure diagnosis, months, Median (IQR) 24 (5.5, 72) 9.5 (3.3, 60) 36 ( 6.3 ,66) Medications per participant M (SD) 5 (1.5) 4.5 (1.5) 5.5 (1.5) 60 4.5.1 Participants’ Website Engagement I analyzed data from Google Analytics for patterns of website usage. Participants spent an average total time of 33 minutes 1 second on the website (SD = 28:31) during the two weeks that they had access. The average duration of sessions per participant was 13:57 with 11 page views, and the number of sessions per user ranged from one to seven throughout the two-week Table 4.3 Demographics at Baseline Medication (%) • Blood thinners • Blood pressure medication • Diuretics • Medication to control heart rate • Medication to control heart rhythm • Statin • Other 11 (57.9) 14 (73.7) 9 (47.4) 12 (63.2) 7 (36.8) 10 (52.6) 5 (26.3) 8 (72.7) 9 (81.8) 6 (54.5) 9 (81.8) 4 (36.4) 7 (63.6) 3 (27.3) 3 (37.5) 5 (62.5) 3 (37.5) 3 (37.5) 3 (37.5) 3 (37.5) 2 (25) Hospital admission in the last year (%) • None • One • Two • Three 10 (52.6) 7 (36.8) 1 (5.3) 1 (5.3) 5 (45.5) 5 (45.5) 1 (9.1) 0 (0) 5 (62.5) 2 (25) 0 (0) 1 (12.5) Average length of hospital admission % • < 5 days • > 5 days • Not Applicable 3 (15.8) 8 (42.1) 8 (42.1) 1 (9.1) 5 (45.5) 5 (45.5) 2 (25) 3 (37.5) 3 (37.5) Self-reported overall health (%) • Poor • Fair • Good 2 (10.5) 10 (52.6) 7 (36.8) 0 (0) 7 (63.6) 4 (36.4) 2 (25) 3 (37.5) 3 (37.5) Self-reported mental health (%) • Fair • Good • Excellent 8 (42.1) 9 (47.4) 2 (10.5) 5 (45.5) 5 (45.5) 1 (9.1) 3 (37.5) 4 (50) 1 (12.5) 61 study period. Thirteen participants visited four or more scenarios. Four participants visited three or fewer scenarios. Finally, two participants who completed both pre-and post-test measures logged in but did not visit any of the scenarios. Table 4.4 Website Usage Pageviews Time Spent minutes, M Homepage 134 00:45 My Rewards 33 00:32 How to Get Rewards 11 00:52 Heart Racing 40 02:16 What is Heart Failure 24 04:01 Out to Dinner 32 01:59 Going Home 64 01:03 Get Moving 24 04:01 Achoo! 30 01:38 The Camping Trip 29 02:13 I tested differences in website usage by selected demographic characteristics. The independent samples t-test (two-tailed) showed no significant difference between groups, based on age, education, or gender. 62 Table 4.5 Demographic Differences in Website Use Total Website Use Time, minutes, M Mean Difference 95% CI p-value Total Cohort 33.01 Age ≤70 29.98 6.4 -21.65, 34.45 0.65† >70 36.38 Education ≤ High school 52.85 25.13 -6.98, 57.24 0.12† >High school 27.72 Gender Female 26.37 15.78 -11.62, 43.18 0.24† Male 42.15 ≤ High school= education of high school or less >High school= more than high school education † Independent t-test: two sample assuming equal variances Similarly, I compared usability scores across demographic characteristics in the group. The average usability score across the sample was 79.36, which, according to Lewis (2018), places the website in the 80th to 85th percentile of usability. Where either Fischer's measures of kurtosis or skewness were greater than ±1.96, I used the Mann-Whitney U test (Plichta et al., 2012). For the SUS, there were no significant differences observed across demographic characteristics. While the difference between age groups ≤ 70 versus >70 years, was not significant the older group scored the website more usable than the younger group. 63 Table 4.6 System Usability Score SUS Grade, M Percentile Range Mean Difference 95% CI p-value Total Cohort 79.36 85-89 Age ≤ 70 72.83 60-65 12.42 -4.97, 29.80 0.09‡ >70 85.25 96-100 Education ≤ High school 73.13 65-69 7.90 -44.75, 28.94 0.69‡ >High school 81.03 90-95 Gender Female 77.50 80-84 4.44 -21.87, 12.99 0.97 ‡ Male 81.94 90-95 ‡ Mann-Whitney U 4.5.2 Heart Failure Knowledge After Website Use I measured HF knowledge before and after the two-week intervention period, using the Dutch Heart Failure Knowledge Scale (van der Wal et al., 2005). Since groups were related, where Fischer's measures of kurtosis or skewness were greater than ±1.96, the Wilcoxon signed-rank test was used (Plichta et al., 2012). Baseline (Time 1) knowledge scores were relatively high as a group average of 12.79, SD (1.18), out of the highest possible score of 15, compared to initial testing of the tool where average scores were 10.9 SD (2.3) (van der Wal et al., 2005). As a group, total post-test scores were 0.77 higher than pre-test scores, which was significant at (p = 0.04). There were no significant differences between individual knowledge constructs. Of the three constructs covered in the scale, HF in general had the greatest overall score increase, with mean post-test scores 0.88 points higher. Mean change in mean knowledge score of HF symptoms/symptom recognition was 0.37 and the mean HF treatment score increased by 0.32. 64 Table 4.7 Heart Failure Knowledge Scores, by Time Construct, M (SD) Time Mean Change 95% CI p-value 1 2 Total Score 12.79 (1.18) 13.47 (1.22) 0.77 0.02, 1.35 0.04† HF symptoms/symptom recognition 4.58 (0.61) 4.95 (0.23) 0.37 -0.33, 0.33 0.06‡ HF in general 3 (0.75) 3.11 (0.88) 0.88 -0.28, 0.5 0.58† HF treatment 5.21 (0.85) 5.53 (0.7) 0.32 -0.11, 0.74 0.14† † Paired t-test: two sample assuming equal variances ‡ Wilcoxon signed-rank test I analyzed knowledge score change for differences between men and women (Table 4.6). Total scores were not significantly different between men and women. However, a significant difference was found between the change in score HF in general between men and women, with a difference of average score change of 0.9 (95% CI, 0.23-1.58), men having a higher gain in knowledge than women. There were no differences when comparing between age or education level of participants. Next, I completed Pearson's correlation to evaluate the relationship between time spent on the website and change in knowledge level. No significant relationship was found (r = 0.13, p = 0.70). 65 Table 4.8 Change in Heart Failure Knowledge Score, by Gender Construct, M (SD) All (n = 19) Women (n = 11) Men (n = 8) Mean difference, men vs women 95% CI p-value Total Score 0.68 (1.38) 0.27 (1.42) 1.25 (1.16) 0.98 -2.23, 0.28 0.14‡ HF symptoms/symptom recognition 0.37 (0.68) 0.27 (0.79) 0.5 (0.53) 0.26 -0.87, 0.41 0.39‡ HF in general 0.11 (0.81) -0.27 (0.65) 0.63 (0.74) 0.9 0.23, 1.58 <0.01† HF treatment 0.32 (0.89) 0.36 (1.92) 0.25 (0.89) 0.11 -0.78, 1 0.79† † Independent t-test: two sample assuming equal variances ‡ Mann-Whitney U Table 4.9 Change in Heart Failure Knowledge Score, by Age Construct, M (SD) All (n = 19) ≤ 70 Years (n = 10) >70 Years (n = 9) Mean difference, ≤ 70 vs >70 95% CI p-value Total Score 0.68 (1.38) 0.9 (1.20) 0.44 (1.59) 0.46 -0.93, 1.85 0.77 ‡ HF symptoms/symptom recognition 0.37 (0.68) 0.3 (0.48) 0.44 (0.88) 0.14 -0.82, 0.53 0.72‡ HF in general 0.11 (0.81) 0.3 (0.82) -0.11 (0.78) 0.41 -0.37, 1.19 0.34‡ HF treatment 0.32 (0.89) 0.4 (0.97) 0.22 (0.83) 0.18 -0.7, 1.06 0.90‡ ‡ Mann-Whitney U 66 Table 4.10 Change in Heart Failure Knowledge Score, by Education Construct, M (SD) All (n = 19) ≤ High school (n = 4) >High school (n = 15) Mean difference, ≤ High school vs >High school 95% CI p-value Total Score 0.68 (1.38) 0.25 (2.36) 0.8 (1.08) 0.55 -4.20, 3.10 0.98‡ HF symptoms/symptom recognition 0.37 (0.68) 0.5 (1.29) 0.33 (0.49) 0.17 -1.84, 2.18 0.78‡ HF in general 0.11 (0.81) -0.5 (1.0) 0.27 (0.70) 0.77 -2.27, 0.74 0.11‡ HF treatment 0.32 (0.89) 0.25 (0.96) 0.33 (0.90) 0.08 -1.16, 1 1.0‡ ‡ Mann-Whitney U 4.5.3 Change in Self-Reported Heart Failure Self-Care Behaviour In addition to the change in knowledge scores, I measured changes to self-reported HF self-care behaviour using the SCHFI (Riegel et al., 2009a). It is not recommended to report the SCHFI as a whole score but rather to report changes to individual constructs for meaningful interpretation. There were significant changes across all three constructs of maintenance (p = 0.005), management (p = 0.003) and confidence (p = 0.003). Further, all three significant scores were as a result of score increases after the intervention periods. 67 Table 4.11 Change in Heart Failure Self-Care Index Scores Over Time Construct, M (SD) Time Mean Change 95% CI p-value 1 2 Maintenance 75.03 (18.27) 83.14 (17.31) 8.01 (12.40) 2.13, 14.09 0.005‡ Management 46 (26.01) 63.5 (20.96) 17.5 (14.29) 6.31, 25.51 0.003† Confidence 55.01 (17.77) 65.68 (18.69) 10.67 (14.4) 4.21, 17.12 0.003† † Paired t-test: two sample assuming equal variances ‡ Wilcoxon signed-rank test I assessed differences in score changes between men and women across the self-care constructs. The largest difference between men and women was the mean change in maintenance scores, 1.83 (95% CI, -14.11, -10.44), and the smallest difference in confidence scores, 0.18 (95% CI, -13.33, 13.69). However, no statistically significant differences were observed between groups. I also assessed the between group differences of age and education level in the sample. There were no significant differences between ≤ 70 years and > 70 years old groups or ≤ high school and >high school education. However, two constructs of the SCHFI showed noticeably larger differences compared to confidence: maintenance 9.77 (95% CI, -1.47, 21.0) and management 16.33(95% CI, -0.21, 32.88), with younger participants showing more change. Table 4.12 Change in Heart Failure Self-Care Index Scores, by Gender Construct, M (SD) Total (n = 19) Women (n =11) Men (n = 8) Mean difference men vs, women 95% CI p-value Maintenance 8.01 (12.40) 7.34 (13.33) 9.17 (11.81) 1.83 -14.11, 10.44 0.54‡ Management 17.5 (14.29) 15.63 (13.74) 16.67 (18.93) 1.04 -22.01, 24.09 0.92† Confidence 10.67 (14.4) 10.59 (14.73) 10.77 (12.29) 0.18 -13.33, 13.69 0.98† † Independent t-test: two sample assuming equal variances ‡ Mann-Whitney U 68 Table 4.13 Change in Heart Failure Self-Care Index Scores, by Age Construct, M (SD) Total (n = 19) ≤ 70 Years (n = 10) >70 Years (n = 9) Mean difference, ≤ 70 vs >70 95% CI p-value Maintenance 8.01 (12.40) 12.74 (15.14) 2.97 (5.64) 9.77 -1.47, 21.0 0.09§ Management 17.5 (14.29) 23.33 (12.52) 7.00 (11.51) 16.33 -0.21, 32.88 0.08‡ Confidence 10.67 (14.4) 10.54 (16.67) 10.79 (12.29) 0.25 -13.60, 13.11 0.97† † Independent t-test: two sample assuming equal variances § Independent t-test: two sample assuming unequal variances ‡ Mann-Whitney U Table 4.14 Change in Heart Failure Self-Care Index Scores, by Education Construct, M (SD) All (n = 19) ≤ High school (n = 4) >High school (n = 15) Mean difference, ≤ High school vs >High school 95% C p-value Maintenance 8.01 (12.40) 3.34 (7.21) 9.39 (13.36) 6.05 -20.88, 8.78 0.41‡ Management 17.5 (14.29) 11.66 (12.58) 17.50 (15.35) 5.84 -5.83, 14.78 0.55‡ Confidence 10.67 (14.4) 10.0 (12.22) 10.84 (14.08) 0.84 -17.20, 15.50 0.91† † Independent t-test: two sample assuming equal variances ‡ Mann-Whitney U 4.5.4 Correlations Between Measures I used Pearson's correlation to examine the relationship between the amount of time participants spent on the website and each HF self-care construct score as well as the correlation between knowledge change and behaviour change. Overall, no statistically significant correlations were found between time spent on the website and the differences in self-care scores: maintenance (r = -0.10, p = 0.34), management (r = -0.10, p = 0.33), confidence (r = - 69 0.21, p = 0.20). The possible outliers impacting this score include the two participants who spent <5 minutes each on the website. There was also no significant correlation between change in knowledge score and change in behaviour score, despite significant independent changes to each (r = 0.07, p = 0.61). 4.6 Qualitative Interviews: Sub-Sample Description I invited participants to participate in a follow-up interview, with an aim to include participants with a range of demographic characteristics and website usage and usability scores. In particular, I invited participants who rated the website highest and lowest in usability scores. However, we were somewhat limited by participants consenting to the follow-up interviews. Not all participants who were invited (n =12) to the follow-up interviews agreed to do the interview. Six participants consented to interviews. The average age of participants was 64 years, with a range of 40 to 79 years. The majority of the participants who agreed to interviews were women (n = 4). One male participant completed the follow-up interview, and one caregiver completed an interview instead of the male participant but talked about both her experience as well as her husband's experience. While not part of the questions asked of participants, all spoke of receiving previous HF education. Further, this subsample unintentionally included one retired nurse and a volunteer paramedic. The average usability score of the subsample was 83.33, with a range of 42.4 to 97.5 out of a possible score of 100. None of the participants who were the lowest users of the website consented to follow-up interviews. Therefore, the interviews do not include the full range of patient experiences with the website. 70 4.6.1 Website Usability Impressions Overall, patients found the website highly usable, except for one participant who expressed more difficulty; this evaluation was consistent with their SUS scores. One participant reflected most patients’ overall impression of the website as, "If you've ever touched the computer before. The website was very easy to navigate." (Participant 15). Another participant also addressed the prerequisite for basic computer skills to use the website when she said, "I could see where it might be challenging for somebody who was basically computer illiterate, but it's easy enough to get through and learn" (Participant 18). The participant who described more difficulty using the website addressed website navigation as the primary reason for her trouble. This participant talked about there not being a prescribed ordering of scenarios causing confusion when she said, "I just kind of went for the helter-skelter. Yeah, like I think they should be numbered like one, two, three, four and five" (Participant 5). Another participant also suggested that there could be increased clarity in the navigation if scenarios were based on symptoms. "You could have them, follow the prompts for that particular type of event. Like I had chest pain today or I had a heart flutter" (6). Several participants suggested the option to have the scenarios also available in print. While most participants said that the website was an acceptable format for learning HF self-care, one stated a strong preference for learning materials to be provided in print when she said, "You have to log into the bloody computer—I don't live on my computer" (Participant 15). Another participant highlighted the concern that, while she was proficient with computers, many HF patients may lack the skills to use the website. This participant suggested that the website could be translated into "a mini-book thing for the doctors to hand out to the patients that aren't computer literate" (Participant 5). 71 4.6.2 Knowledge Insights The depth and breadth of educational content covered by the website was a common theme participants discussed. Four participants talked about liking the basic nature of the education provided. Participant 16, who was diagnosed with HF 11 months prior, elaborated: "It wasn't too much. It was just challenging enough. You know how do you say it? TMI? Sometimes with that, you just zone out. And I didn't feel that tendency to zone out." While the education content was mostly a review for participants, most also expressed that the review was valuable. In particular, the wife and caregiver of one participant described the need for online education resources that are evidence-based when she said, "Google it, and you can get ten different answers…Whereas, if you've got something that's set up by an actual, you know, health center. You know that the information that you're getting is the right thing" (Participant 5). Conversely, two other participants spoke about their desire for more in-depth education. One participant who had been living with a diagnosis of HF for over ten years, stated that she valued the basic education but also would not use the website frequently when she said, "It's not something that you would go back to again and again and again unless there was new content" (Participant 18). In discussion with participants, it was clear that patients had both high background knowledge or had previous HF education which was consistent with their high baseline knowledge scores. Despite this, participants who expressed that the education was too simplistic lauded the website's value for newly diagnosed HF patients, such as Participant 19, who said, "For people that are brand new, this might be wonderful for them. If they're open to it. You know so many people are shell-shocked by the whole process." While this participant was diagnosed with HF only 18 months prior, she spoke to receiving previous HF education. Overall, 72 while participants described the educational support of the website in covering the basics of HF, they recommended adding content that also included greater depth of education. 4.6.3 Behaviour change Despite the participants’ high baseline knowledge, they described that going through the scenarios still prompted behaviour change. Although they acknowledged that most of the education was not new, participants found the scenarios prompted them to re-evaluate their self-care decision-making. As stated by participant 6: It would certainly make me think about it when I might not have otherwise… You know, I tend to have a bit of chest pain, but it's usually gone pretty quick. So I thought about it, and I did reach out to my doctor. Another participant said that the scenarios prompted her to increase the frequency of weighing herself, despite knowing that she should be doing this before the study. Additionally, one study participant talked about how the scenario about family visiting reminded her to give herself permission to take care of herself. I know it's important that I spend time with them, but if I don't take this break. I'm not going to be around to spend time…Those general reminders and those gentle nudges and it's not being selfish it's, you know, taking care [of yourself] (Participant 18). Participants also talked about the challenges of making behaviour changes for their HF self-care. The contrast between naturalistic decisions and "perfect world" scenarios was frequently mentioned in interviews. In these conversations, patients expressed a high degree of uncertainty in making decisions about potential HF exacerbations. For example, in a scenario where one participant was experiencing chest pain, he explained, "We're trying to figure out how bad it is. My wife said, 'Do you want me to call?' And I was like, 'No. I'm all right for the 73 moment. I think?' (laughs)" (Participant 6). The wife and caregiver of another participant discussed her husband's recent hospital admission: "Maybe that step could have been averted somewhere in between there? If he had, you know, knew enough to say okay. Should I call right now?" (Participant 5). Overall the difficulty of making decisions related to HF exacerbations was prevalent throughout interviews. Another theme in the interviews was that participants described the behaviour change related to HF self-care as challenging. As described by Participant 16: "Everybody wants to prevent further complications, but the work of doing it is hard." One participant recounted her frustration with the advice she had received from a doctor about trying to lose weight. In this conversation, the participant talked about starting an exercise class and making changes to eat more healthily. She also recognized that her sedentary job and commute were making the weight loss difficult. This interaction exemplifies how self-care decision-making and advice must be responsive to barriers in patients’ lives. This participant elaborated: [I said] I don't know what else to do. And she looked at me like she does, you know with her earplugs in, she goes, 'Eat less and exercise more.' I just went, 'The hell did you not understand about what I just told you?' It told me she wasn't listening at all (Participant 19). Overall, patients talked about knowledge and behaviour change as two distinct concepts. Participants also discussed how naturalistic decision-making about HF exacerbations is a challenge. Lastly, when making decisions in the context of real-life, participants conveyed that barriers could create further difficulty in maintaining adequate self-care and must be taken into consideration when offering education. 74 4.6.4 Self-Care Motivation What motivated participants to learn and engage in HF self-care behaviour was varied across participants. Fear was discussed as a motivating force. However, fear as a motivating factor was not always perceived to manifest as positive behaviour change. When talking about the outcomes of the scenarios, one participant said, "Well, you know it's pretty basic like so do you want to live? Then pay attention. It's what it comes down to. Do you want to continue living, then you have to do what all these people say" (Participant 15). Another participant discussed how fear after her heart attack led to behaviour that did not support her recovery: "After my heart attack. I was just spot on. I was so diligent. To the extent that my internal medicine doctor said. You're overdoing it" (Participant 16). On the other hand, this same participant talked about the more positive reasons for her motivation to keep herself well and free from HF symptoms. In the interview, she said, "I don't want my whole life to be about my cardiac health. I would rather do my sewing. Yeah. To care for my husband and enjoy my bird watching, You know, life-affirming activities" (Participant 16). 4.6.5 Techniques of Gamification There was varying discussion about the techniques of gamification that were used in the website design. The primary technique of gamification used in the website design was the use of story. Several participants described how the scenarios simulated real-life experiences, which led to learning or behaviour change, such as Participant 5, “You know, you can kind of put yourself in those shoes… like this could be a real-life situation." Another participant described the autonomy in learning offered by the scenarios when he said, " I liked the choices, [it] was probably the thing that I liked the most about it (Participant 6). 75 Participants also described that the use story as a vehicle for learning created a less polarizing learning environment. In the words of participant 16: It's easy to feel defensive. And so, I thought that the format was good because it eased my need to be defensive or already make excuses for myself. So I thought it was very friendly in that way it wasn't accusatory or rigid (Participant 16). Interestingly, this participant was a retired nurse of 40 years who, as a home health nurse, had done significant patient teaching throughout her career. She reflected on how her current experience as a patient shaped her view of patient teaching: "People get weary. Weary of listening to education, but to be able to make it palatable. It's so important. And now I know that from a firsthand point of view" (16). Overall, participants described the non-confrontational nature of the education as being beneficial to their experience. Conversely, participants described a potential risk of learning through stories or scenario-based education. They felt the pressure to pick the "right" answers, as opposed to answers that will best support their learning. Participant 5 raised this concern when she said, "A lot of people might know what answer they should pick. It might not necessarily be what [they] would do." During the interviews, most participants described trying out multiple paths through scenarios; not only picking what they thought were correct answers. One participant described difficulty placing herself in the scenarios, suggesting that story-based learning is not suitable for all patients. During scenarios, this participant talked about not having directly experienced specific symptoms, and her preference for use of “if this were to happen” language rather than the story, which presents the scenario in the present tense. This participant also suggested that there be a third option added to the decision-making that might allow participants to create their own answers. The suggestion to create an "or other" option was 76 also brought up by Participant 5, who also highlighted how stories could be modified to fit personal preference to increase the relatability of the stories. "You know we're all so different...Some people might hate the picnic! Not sure who would hate a picnic, but you know" (Participant 6). The second technique of gamification that was employed on the website was using gems and trophies as rewards, based on time spent on the website. There were varying opinions about the reward-based gamification technique. Two study participants expressed strong support for the reward-based gamification technique included on the website, such as participant 5 who exclaimed, "The ones that make it like a little game? Oh yeah, I like that kinda shit. And you know that probably motivated me to do all of them." Another participant shared her strong preference to keep the rewards on the website when she said, "You know, we don't always pat ourselves on the back. So when there's a virtual patting on the back, it feels good" (Participant 16). Both participants who strongly supported rewards-based gamification were over 70 years old and were diagnosed less than one year ago. Another participant stated that she liked having the points, but they did not influence her motivation to use the website because it was not obvious enough when she had gained points or reached a new level. Her suggestion was to add a notification or an animation that alerts users when they have gained gems or reached a new trophy level. Only one participant stated that she did not see any value to having the reward gamification technique included on the website when she said, "I didn't really care. I'm not a stick and the carrot kind of person. Yeah. I mean some people find that useful, not me" (Participant 19). When asked if there were other ways of making the website interactive that she would like better, this participant described adding a social or challenge gamification technique. 77 "You could, you know, partner up people. I have a Fitbit, and you can challenge people; you know other people who are wearing the Fitbit [to do] an amount of steps this week" (Participant 19). Overall, both strong positive and negative opinions were expressed from participants about gamification techniques included on the website. 4.6.6 Caregiving and Self-Care Caregiving and the gendered influence of self-care were themes that became evident across several interviews. Interviews included experiences from both a HF patient (Participant 16) who is a caregiver and the wife of a HF patient who has a caregiving role. Participant 16 discussed the complexity of caregiving in the context of living with HF. On one hand, she talked about how acting as a primary caregiver for her husband creates a barrier to her own self-care. When talking about preparing healthy meals participant 16 said, "When you're up through the night caring for somebody, you don't have the time or energy." However, she also described her caregiving role as a motivating factor in her self-care: My goal is to be diligent with my care. I'm not afraid of dying. But I care for my husband, who has advanced dementia. In order to be better and care for him, I need to care for myself so that I can be in better shape to do that (Participant 16). Additionally, the wife of an HF patient who tested the website with her husband offered her insight on using the website as a caregiver of a person with HF. She described using the website and going through the scenarios as a caregiver to be valuable. The role of gender concerning self-care and caregiving was raised by two female participants in the interviews. Participant 18 elaborated in her interview: 78 Women have typically been the family caretaker, you know, making sure the men get to the doctor, and the kids get taken care of in the house, you know what I mean. So where, you know, where do I fit in? [18]. 4.6.7 System-Level Barriers to Self-Care Next, several system-level barriers to HF self-care were also identified throughout interviews with participants. Some patients with HF described their experience of patient education in the hospital as part of their beginning interactions with the health system. One participant expressed that she would have found using the website helpful during her initial hospital stay. This participant had a unique perspective due to her dual role as a retired nurse and now as a person living with HF. "I think it would be good for patients in the hospital. As even a first-time exposure. My hospital experience was very lean on patient teaching. I mean I would've expected much more" (Participant 16). Another participant shared a similar experience she had discussing the same issue when she said, "These people aren't being given the information that they need when they leave. I mean, give them the info about the god damn pills" (Patient 19). Participants also critiqued the health care system factors in response to the education provided by the website. For example, the direction provided by the website often suggested to patients to "contact their primary care provider" when experiencing a change in symptoms or other low to moderately urgent concerns. When discussing the advice to contact her husband's doctor, participant 5 described this concern: "How do you get in to see a doctor when, you know, you can't get a hold of your doctor or when you've got a three-week waiting list?" 79 5 Chapter 5: Discussion In this chapter, I will discuss results related to clinicians’ attitudes toward online gamified HF education. Next, the impact of website usability on results and the relationship between theory and outcomes of behaviour and knowledge change will be discussed in the context of current scientific understanding of HF self-care. Finally, I will discuss patients’ experiences with the website and will further explain quantitative results based on qualitative patient interviews. 5.1 Overview of the Study This study was a three-part, mixed-methods study, which included embedded exploratory and explanatory qualitative phases during the intervention design and testing. The purpose of this study was to design, and pilot test a gamified HF self-care education website. We evaluated the efficacy of the website, usability, change in knowledge and change in behaviour. Nurse input during the design phase touched on themes such as naturalistic decision making and technology adoption of older adults. Results from the intervention testing phase showed significant differences in both total knowledge scores and self-care behaviours after the two-week intervention. In addition, patients participating in post-intervention interviews described the gamified website as engaging, but suggested changes to improve the value of the website. 5.2 Exploring Healthcare Provider Perceptions I completed a focus group and one individual interview as the exploratory phase of this mixed-methods study. Deductive thematic analysis unveiled several important themes related to the clinician's perspective on website design and use in the HF population. Nurses discussed uncertainty about the technological skills of patients, which similarly has been reflected in the literature on the variability of internet skills among older adults (Hargittai et al., 2019). Although this study required patients to navigate the website 80 independently, enablers of technology use such as social and technical support may allow those with lower internet skills to adopt the technology (Lee & Coughlin, 2015). Additionally, during the focus group, nurses also recognized the need to adopt technology to serve patients due to the ease and accessibility of online learning. In addition, according to Sardi et al. (2017) gamification is relatively new as a patient educational approach, and therefore nurses also expressed ambivalence about the HF population's acceptance and the efficacy of gamification techniques in the HF population. Another relevant theme of the focus group was the difficulty of naturalistic decision-making in the population. Nurses stressed the importance of creating challenging scenarios that reflect the real-life dilemmas that patients encounter. Since naturalistic decision-making accounts for the person, problem, and environmental factors, decision-making for HF patients often includes high degrees of unpredictability and uncertainty (Riegel et al., 2016). Therefore, nurses expressed that decisions presented to patients should reflect that degree of real-life variability and challenge. Also related to naturalistic decision making, nurses stressed the importance of designing storylines where decisions are not framed as "good" or "bad," but also included emphasis on patient quality of life. 5.3 Usability, Knowledge and SCHFI Scores The sample of patients who participated in the quantitative testing of the website was overall similar to the general HF population but may not be fully representative for several reasons. First, in a review of epidemiological studies, the average age of those admitted to the hospital with a primary HF diagnosis was 70-75 years. (Ambrosy et al., 2014). This contrasts with the younger age of the current sample, with a mean age in years of 63.3 SD (14.5).The cohort included both newly diagnosed patients as well as those with a more extended history of 81 HF, and also included those with recent hospital admissions and multiple co-morbidities. Further, there were no participants over 80 years old, with only four participants older than 75, making any comparison between age groups limited. Overall, our sample may be younger than the average age of HF patients. Second, there were very few participants who had less than high school (n = 1) education. Therefore, groups based on education may not be truly comparative of education levels. Previous work has shown that education is correlated with higher internet skills in older adults (Hargittai et al., 2019). Finally, since patients were self-selecting to participate in an online study, computer skills in this sample may have been higher than a truly representative sample of HF patients. While the findings showed that HF patients are willing to engage independently with online gamified self-care education, there was also significant attrition (17.4%). Of twenty-three patients who consented, four patients later dropped out of the study; one patient only completed the consent form and the other patients filled out at least partial pre-test measures. No patients who dropped out of the study logged into the website. The attrition rate may suggest that not all patients are willing to engage in online education, despite showing initial interest in the website. However, in a recent meta-analysis of digital technology interventions for cardiovascular disease, over half of the 25 studies had a rate of high attrition, suggesting that this is a common challenge (Akinosun et al., 2021). 5.3.1 Usability Participants who completed the study gave the website high usability scores. The average SUS score from participants was in the 80-85th percentile of usability, which can also be interpreted as a letter grade of "A-." For comparison, previous work on benchmarking usability of public facing websites found that in sample of 174 websites, the mean usability score was a 82 score of 67 or "C" (Sauro, 2011). Therefore, the website's usability scores were high from this group of participants and therefore usability is unlikely to detract from the meaningful interpretation of outcomes of interest such as knowledge and behaviour change. While Harrati et al. (2016) have critiqued the SUS as insufficient as a standalone measure of usability, this high score, is supported in the post-intervention subjective qualitative interviews and Google Analytics tracking. 5.3.2 Knowledge and Behaviour Change The first outcome of interest we measured was the change in knowledge score. There was a significant increase in total knowledge scores from pre- to post-website use. However there were no significant differences between individual knowledge constructs scores before and after the intervention. Increased time spent on the website was also not associated with increased knowledge scores. Compared to scores used to initially test the Dutch Knowledge Scale (van der Wal et al., 2005), patients in the current study had a high average pre-intervention score. van der Wal et al., (2005) reported a mean pre-education test score of 6.8 for newly diagnosed patients completing the Dutch knowledge scale, compared to a mean of 12.92 (SD 1.33) for this study cohort. The higher pre-intervention knowledge scores in the current patient cohort may be due to the inclusion of both newly diagnosed patients and those with a longer time since diagnosis. There was one significant difference between men and women in knowledge. Men had greater knowledge gains in "General HF Knowledge" than did women. A possible explanation is that men had HF for longer than women (p = 0.05) and over time may have lost foundational knowledge from their original HF education related to HF. Next, all three behaviour scores of maintenance, management and confidence, were statistically significantly improved after website use. According to Riegel et al. (2009a) an 83 increase in mean self-care scores above 8-points for all three constructs as observed in this study suggests that the results are clinically relevant. While Riegel et al. (2009a) described an eight-point change in each adjusted score as clinically relevant, this must be interpreted with caution. Through triangulation of testing results, Riegel et al. state that for HF self-care to be "adequate", self-care scores must be ≥ 70 in each of the three constructs. Therefore, in this study sample, self-care maintenance was adequate at both pre and post-test times. However, self-care was below the cut-off of 70 for management and confidence at both the pre-test and post-test time points, despite the statistical and potentially clinically significant improvement in all three self-care constructs. This result is comparable to recent literature describing self-care skills in the HF population. Vidán et al., (2019) found that out of a sample of 415 HF patients only 5.3% could perform all six HF skills (use of a scale, weight registration, diuretic identification, knowledge of salted foods, edema identification, and treatment adjustment) correctly. Further, Riegel et al. (2009a) provided no elaboration of the clinical indicators that should be expected to change but recommended further testing on the direct clinical benefit of improved self-care. Across sociodemographic characteristics of age, gender and education level, there were no significant differences in behavior change. Despite finding significant improvements in both knowledge and self-care behaviour, these scores were not significantly correlated. Participants had low self-care scores in spite of achieving high baseline knowledge scores. This suggest that they already possessed most of the knowledge necessary to perform adequate self-care, not that knowledge is unrelated to self-care. In fact, the relationship between HF knowledge and self-care behaviour is well established in the Situation-Specific Theory of Heart Failure Self-Care, and knowledge has also been found to be an important predictor of self-care behaviour in the literature (Riegel et al., 2009a). Lee et al. 84 (2018), showed the influence of self-care knowledge on behaviour, where study participants who had low knowledge of HF and its signs and symptoms also had poor self-care, compared to those with more comprehensive understanding of HF. Previous research also supports that behaviour change from online HF education is possible (Wagenaar et al., 2019). Website use was associated with behaviour change, although behaviour change was not correlated with knowledge change. However, there were significant improvements in knowledge scores, despite high baseline knowledge, suggesting that website use may have triggered a recollection of knowledge or new learning in some patients. However, since behaviour and knowledge scores were not correlated, knowledge gain cannot fully explain the behaviour change observed in this study. Thus, behaviour change despite high baseline knowledge scores suggests that participants already knew the actions they were supposed to take to perform self-care but were not applying this knowledge. Alternatively, it appears that by applying previously held knowledge to simulated experiences on the website, participants may have overcome some previous barriers between self-care knowledge and behaviour. In support of this, recently Jaarsma and Strömberg (2019) made a similar argument that knowledge alone is not enough to improve HF self‐care behaviour, based on the 'Middle Range Theory of Self-care in Chronic Illness' (Jaarsma et al., 2017; Riegel et al., 2012). A website alone may have little impact on other self-care barriers presented in the theory, such as functional or cognitive abilities, support from others, or access to care. However, a website that engages patients in practicing decision making through story-based gamification may reduce barriers to self-care, such as a lack of experience and skills, motivation, or 85 confidence. Online learning that is interactive and simulates real-life experience may mediate these barriers. The Situation-Specific Theory of Heart Failure Self-Care discusses how processes (experience, skills, knowledge or values) impact the three HF self-care constructs of maintenance, symptom recognition, and management (Riegel et al., 2016). An important mediating factor of self-care confidence is self-efficacy, which is often influenced by experiential learning. Findings from this study suggest that despite similar barriers to self-care as the patient population with HF, those participating in the current study’s simulated, naturalistic decision-making process may have increased self-efficacy and thus self-care confidence, that overcame barriers and improved self-care. The importance of self-care confidence is further supported by Massouh et al. (2019) who found that self-care confidence mediated the association between social support and HF knowledge on self-care maintenance and management. Massouh et al. recommended that self-care confidence be a key target in designing interventions to support HF patients' behaviour change. The results of this study add to the evidence that theory-based, online patient education can create significant behaviour change within the HF population. 5.4 Gamification and Subjective Self-Care Motivation Post-intervention interviews were designed to be explanatory in nature and to help interpret the quantitative results. Representing a wide range and variety of experiences, patients found the gamified website engaging, and some reported a subjective increase in motivation to engage in the education content. Patients who participated in interviews reported either neutral or positive opinions of learning through the scenarios. Previous work has shown that while story-based learning may not outperform more traditional learning, participants’ attitudes towards 86 story-based learning are more positive than traditional paper-based learning (Garneli et al., 2017). Participants described the scenarios as simulating real-life decisions. Autonomy is discussed in the Theory of Self-Determination as an important factor in motivation (Deci & Ryan, 2008). Ryan and Deci discuss the internalization and integration of values and behavioural regulations. In the context of extrinsic motivation, internalizing a goal such as keeping oneself symptom-free, and increasing feelings of autonomy in active decision making, rather than passive compliance with clinical recommendations, may increase motivation. Interview participants described liking the choices posed as they moved through the education, suggesting that the narrative element with practiced decision making may increase feelings of autonomy and internalization of learning goals. Learning that is inherently more enjoyable, such as gamified learning, is also hypothesized to increase intrinsic motivation in the learner, in contrast to learning that is solely goal orientated, and therefore only extrinsically motivated (Ryan & Deci, 2000). This is reflected in participants’ descriptions of the education through stories as friendlier (Participant 16), than traditional methods, which may increase intrinsic motivation to engage in the education. Qualitative data revealed variation in the perceived benefit of rewards-based gamification. While all patients who were interviewed accepted gamification as a concept, there was variation for preference and perceived efficacy of individual gamification techniques. Those who were influenced by the gems/points described them as strongly influencing their motivation. Yet, for others, the points had little to no impact on their motivation. This suggests there is variation as to the impact of externally motivating factors on individual patients. This variation in patient preference in the techniques of gamification is also reported in the literature (Radhakrishnan et al., 2020). 87 According to a 2017 systematic review, virtual rewards are the most common gamification tactic used in eHealth (Sardi et al., 2017). The reward-based gamification technique used on the current study’s website was added to influence both intrinsic and extrinsic motivation. For those who enjoy gaining points, this tactic may increase intrinsic motivation through enjoyment. However, rewards are also linked to extrinsic motivation through the externalization of achievement outside of inherent enjoyment. Interestingly, the participant who expressed the strongest negative feelings towards the gamification techniques, still had increases to both her knowledge score (2 points) and average self-care score (3.3 points). Overall, there was variation in preferences for gamification. However, story-based gamification and reward techniques subjectively increased learner enjoinment and motivation to learn, suggesting intrinsic and extrinsic motivation influences. 5.5 Explanatory Discussion This final section will seek to use qualitative results to explain the results of the quantitative phase of the study. First, our qualitative results confirmed that participants in the study had high previous HF knowledge or had received previous HF patient teaching. High background knowledge notwithstanding, we found significant increases to knowledge scores after website use. Second, in agreement with the 'Situation-Specific Theory of Heart Failure Self-Care,' participants described the story-based gamification as a simulation of naturalistic decision making. This finding could explain the significant gains in self-care scores which are not correlated with knowledge gains. Finally, according to the 'Situation-Specific Theory of Heart Failure Self-Care,' experience and skills, confidence and motivation influence self-care behaviours (Riegel et al., 2016). Therefore, an intervention that improves one or more of these areas, such as the website tested in this study, could influence HF self-care behaviour. 88 A recent qualitative study, which looked at HF patients’ motivation and treatment adherence, found that making connections between behaviour and health was a key factor in treatment adherence. The observed behaviour changes in our study after using the Heart SCaPE website suggests that the scenarios used may allow patients to grasp the cause and effect of self-care behaviour and HF status/symptoms more readily, through simulating real-life scenarios. Further, Myers et al., also found that increased self-efficacy was a key factor in patients transitioning from non-adherence to adherence (2020). Therefore, increased confidence, through practicing naturalistic decision making in scenarios, may help to explain the additional improvement to self-care maintenance and management scores. 5.6 Strengths and Limitations There were some limitations to this study. First, the sample selected for this study was a convenience sample and therefore may not represent the population of patients with HF. For example, delivery of the intervention in English excluded the many non-English speaking patients that St. Paul's Hospital serves. The patients recruited over social media were self-selected. Therefore, we relied on them to confirm a diagnosis of HF without verification of a clinician. Also, due to the heterogeneity of the sample, the care that patients received before the website intervention was not consistent throughout the sample. The sample size was also too small to reach adequate power to confirm generalizability. Therefore, while results suggest trends, they cannot be interpreted as conclusive. Generalizability is also limited by the younger age and overall high education level of the sample. Further limiting the conclusions that may be drawn from our results is the lack of a control group receiving non-gamified education for comparison with the gamified intervention group. A lack of a comparable control group precludes making causational claims about the 89 effects of gamification. In addition, past studies have shown attenuation of self-reported self-care behaviour effects over time after eHealth interventions (Wagenaar et al., 2019). We did not follow patients past the two-week mark to determine the lasting impacts on self-care. Therefore, we cannot comment on the long-term behaviour change impacts of this intervention. Next, the use of self-reported measures increased the risk of social desirability effects and resultant inaccurate scores (Le Grande et al., 2008). To mitigate this effect, the two questionnaires chosen for use in the study have low social desirability scores; however, this is still a factor that must be considered in interpreting the data. The website itself can also be interpreted as a limitation of the study. We created only seven scenarios that were focused on the basics of HF self-care education. However, more comprehensive education is also needed for patients who are more experienced with HF or want more in-depth education. Further, website design was limited by resources for the study. Interactive components of the website were limited by time for website design, cost, and my website design skills and experience. Finally, follow-up qualitative results do not reflect the opinions of the lowest website users. These participants did not elect to participate in the follow-up interview; therefore, we were limited in learning about the reasons for their very low website use. Therefore, the results of the explanatory qualitative data are skewed towards participants with positive opinions of the website. Further, interviews provided only limited data on the subjective impact of the gamification on motivation. Only a subset of the sample completed the interviews and data on motivation were limited to the subjective description from participants. Therefore our ability to discuss conclusions about the effect of gamification on motivation is limited. 90 5.7 Recommendations for Future Research Recommended Changes Beyond the Scope of the Project Some changes that the HF nurses recommended were not implemented in this iteration of the website, due to time and resource limitations. However, these valuable recommendations should be considered for future iterations of the website. For example, one key recommendation to increase the accessibility of the website was to add audio to allow patients the option of having the slide read out loud to them. For those who have limited or impaired vision, this feature would increase the accessibility of the website. In addition, the suggestion to add a primer document with the basics of HF could also be useful to address the disparities of health education backgrounds of patients when visiting the website for the first time. Another significant recommendation was to add more emphasis on the emotional aspects of HF. The content was designed to address key concepts described in the Self-Care of Heart Failure Index and Dutch Heart Failure Knowledge Scale (Riegel et al., 2009a; van der Wal et al., 2005). Neither of these tools includes an explicit focus on emotional support for HF patients. However, the importance of emotional support for people managing HF is known to be important (Whittal et al., 2021). Therefore, this would be essential to address on the next iteration of the website. The recommendation to add content about medications should be considered in future development. For this project, the decision was made to limit education about different medications, due to variability in medication regimens and the related need for personalized patient teaching. Adding more in-depth education about HF medications would require further pharmacist consultation yet would be another valuable addition. Another suggestion to increase the depth of education was to include links to resources to give patients the option for more in- 91 depth learning about the subjects if they chose. This could address the critique that scenarios address only basic concepts of HF self-care. Personalization While current interactive education interventions are designed for maximum acceptance, increasing the efficacy of interventions to improve self-care may be achieved through personalization of the intervention to individual patient learning preferences and clinical characteristics. This study used story-based learning and reward gamification techniques because these tactics aligned with the education design and desired outcomes. However, as found in previous research (Radhakrishnan et al., 2020), there is variation in the form of gamification that patients find the most engaging and, therefore, motivating. For instance, it may be possible to create interventions based on cohorts of patients. For example, do patients over 70 tend to prefer leaderboards? Or is one gender more likely to enjoy stories? This study was not large enough to identify age or gender-based trends in preference for gamification elements. However, important trends may be present, the identification of which may improve the specificity of intervention designs. Another strategy for personalizing gamified online education for HF patients is to customize how gamification of education is integrated into a multifactor self-care strategy, or any combination of up to seven of those strategies (Makris et al., 2020). Makris et al. list electronic reminders, enhanced health education, financial incentives, gamification strategies, community drivers, persona-based modeling, and relief of polypharmacy burden as seven categories of strategies that may improve HF patient adherence to self-care. These merit investigation. Additionally, eHealth solutions are usually not designed to function independently from usual care (Wagenaar et al., 2019) and therefore require careful integration. However, there 92 is currently little direction on how to best combine these strategies for individual patient needs in the context of available resources within the health system. Finally, while current resources often constrain eHealth intervention development to a single condition, more robust future eHealth education may further individualize HF patient education to include common HF comorbidities and medications. Feedback from the healthcare professionals interviewed for this study included the desire for patients to learn about medications, as this is a common area of patient questions. With the trend towards personalized medicine (Akinosun et al., 2021), self-care education interventions present many opportunities for future research. Caregiver Inclusion This study focused on education for HF patients. However, it is well known that including a support person in patient education is also important. Another step to improve HF education could be tailored, gamified HF education for the patient’s support person. Other studies have shown patients’ desire to have their support person included in their self-care and education (Makris et al., 2020; Radhakrishnan et al., 2020). Further research into the most effective methods of including the support person into gamified education or tailoring a portion of the education to the support person would be another step to more efficacious eHealth self-care interventions. Integration of Emerging Technologies Further research will be influenced by improvements and advancements in technology innovations. For instance, two prototypes in the literature review received overall positive feedback from patients; however, supporting technology (sensor and Bluetooth integration) is not yet functional on either platform (Arulnathan et al., 2019; Radhakrishnan et al., 2020). However, 93 as technology improves and adoption increases, these barriers will likely be removed. Therefore, future work on integrating patient tracking through automatic sensors and Bluetooth connectivity to support self-care will require further study. Another emerging technology that could facilitate HF patient self-care education using gamification is immersive gaming. Public consumers are widely adopting virtual reality (VR) or augmented reality (AR) gaming. AR/VR has been used in multiple studies to prepare patients for surgery with early, but promising results (Mahajan et al., 2021). However, the potential uses of these technologies for patient self-care education are largely untapped (Akinosun et al., 2021). 5.8 Implications for Nursing Practice and Policy The results of this study suggest the utility of integrating gamified eHealth HF education to improve self-care. These results have several implications for nursing clinical practice and policy. Gamified eHealth interventions have broad applicability in a practice setting to supplement in-person education provided by healthcare providers. HF patients have frequent interactions with health care providers both in community and acute care settings. Such eHealth interventions could be used in an inpatient setting to support and reinforce the patient education that nurses provide. In the community, eHealth education interventions might better prepare patients for their clinic visits or may reduce community nurses and other healthcare providers’ time to teach basic concepts. However, little evidence is available on how to effectively integrate eHealth education interventions to benefit both patients and their healthcare providers. Consultation with clinical leadership is needed as to how to use eHealth education interventions to support clinical practice. Planning as to how eHealth interventions, such as the Heart SCaPE website, should be integrated into practice is needed. There are potential unintended consequences of eHealth interventions, such as duplication of services, which may 94 increase cost rather than increasing efficiency. For example, if clinical staff is required to set up and teach the patient how to use such eHealth tools, workload may actually be increased (Versluis et al., 2020). When adding tools, it is essential that they perform the desired function rather than adding to workload and potentially cost in the health system. Therefore, program planning related to the integration of eHealth education tools into clinical practice for the maximum benefit of patients and the health system is needed. Conclusion Overall, this 3-phase mixed-methods study found that a gamified website that uses narrative and reward-based gamification techniques has the potential to improve knowledge and HF patient self-care. This study supports previous evidence that patient education should be designed to be theory-based, engaging and patient-centred. However, further research is needed to understand the long-term impacts of online gamified patient education, how to sustain behaviour change and finally, to address the disparity of those who have limited computer literacy or access. 95 6 References Akinosun, A. S., Polson, R., Diaz - Skeete, Y., De Kock, J. H., Carragher, L., Leslie, S., Grindle, M., & Gorely, T. (2021). Digital Technology Interventions for Risk Factor Modification in Patients With Cardiovascular Disease: Systematic Review and Meta-analysis. JMIR MHealth and UHealth, 9(3), e21061. https://doi.org/10.2196/21061 Ambrosy, A. P., Fonarow, G. C., Butler, J., Chioncel, O., Greene, S. J., Vaduganathan, M., Nodari, S., Lam, C. S. P., Sato, N., Shah, A. N., & Gheorghiade, M. (2014). The global health and economic burden of hospitalizations for heart failure: Lessons learned from hospitalized heart failure registries. Journal of the American College of Cardiology, 63(12), 1123. https://doi.org/10.1016/j.jacc.2013.11.053 Arulnathan, A., Vaaheesan, S., & Denecke, K. (2019). A Mobile Application for Self-Monitoring for Patients with Heart Failure. Studies in Health Technology and Informatics, 259, 113. Athilingam, P., & Jenkins, B. (2018). Mobile Phone Apps to Support Heart Failure Self-Care Management: Integrative Review. JMIR Cardio, 2(1), e10057. https://doi.org/10.2196/10057 Athilingam, P., Osorio, R. E., Kaplan, H., Oliver, D., O’neachtain, T., & Rogal, P. J. (2016). Embedding Patient Education in Mobile Platform for Patients With Heart Failure: Theory-Based Development and Beta Testing. CIN: Computers, Informatics, Nursing, 34(2), 92–98. https://doi.org/10.1097/CIN.0000000000000216 Austin, L. S., Landis, C. O., & Hanger, K. H. (2012). Extending the Continuum of Care in Congestive Heart Failure: An Interactive Technology Self-management Solution. JONA: The Journal of Nursing Administration, 42(9), 442–446. https://doi.org/10.1097/NNA.0b013e3182668342 Beswick, D. (2017). Cognitive Motivation: From Curiosity to Identity, Purpose and Meaning. Cambridge University Press. https://doi.org/10.1017/9781316822920 96 Bopp (2017). Storytelling as a Motivational Tool in Digital Learning Games. Didactics of Microlearning. Münster: Waxmann. Boyatzis, R. E. (1998). Transforming qualitative information: Thematic analysis and code development. Sage Publications. http://ubc.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV07D4IwEL74WEwcFDWCj3Rw0EFTCoUyG4k_wJ3waEcX_f_xWoqvoGO5lNIj7T3a7zuAgB3o_mtPEGFS-uibyxh7BHlYccGKUipWxrzyDe32_6s631C71tMZNF8YfeCG3A1i8axmYCJ17anzt4YfWPadRhh9tk25gxcOXxubdAQ9DUAYQ0deHXBr_Cyxa_BGtpYoejeBzaXxOvELSQ2PNDzexNKhaqVPwUtPl-N5j8NkNlmT2SmwGQxzfcX9ejdQuGoOhGq4jch5wQsRKomOBGqSKlpEUZIrGrvgtLzJheX700ZrGVpuH8Pf_-Lkl1gXuYoS5rUOuYBBjdzTiYol9BUuJLky6lybn_IAWFSWwQ Boyde, M., Turner, C., Thompson, D. R., & Stewart, S. (2011). Educational Interventions for Patients With Heart Failure: A Systematic Review of Randomized Controlled Trials. The Journal of Cardiovascular Nursing, 26(4), E27–E35. https://doi.org/10.1097/JCN.0b013e3181ee5fb2 Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa Broekhuis, M., van Velsen, L., & Hermens, H. (2019). Assessing usability of eHealth technology: A comparison of usability benchmarking instruments. International Journal of Medical Informatics, 128, 24–31. https://doi.org/10.1016/j.ijmedinf.2019.05.001 Cajita, M. I., Gleason, K. T., & Han, H.-R. (2016). A Systematic Review of mHealth-Based Heart Failure Interventions. The Journal of Cardiovascular Nursing, 31(3), E10–E22. https://doi.org/10.1097/JCN.0000000000000305 97 Charlier, N., Zupancic, N., Fieuws, S., Denhaerynck, K., Zaman, B., & Moons, P. (2016). Serious games for improving knowledge and self-management in young people with chronic conditions: A systematic review and meta-analysis. Journal of the American Medical Informatics Association, 23(1), 230–239. https://doi.org/10.1093/jamia/ocv100 Chib, A., & Lin, S. H. (2018). Theoretical Advancements in mHealth: A Systematic Review of Mobile Apps. Journal of Health Communication, 23(10–11), 909–955. https://doi.org/10.1080/10810730.2018.1544676 Clark, A. M., Wiens, K. S., Banner, D., Kryworuchko, J., Thirsk, L., McLean, L., & Currie, K. (2016). A systematic review of the main mechanisms of heart failure disease management interventions. Heart, 102(9), 707–711. https://doi.org/10.1136/heartjnl-2015-308551 Coronel, R. (2001). Defining heart failure. Cardiovascular Research, 50(3), 419–422. https://doi.org/10.1016/S0008-6363(01)00284-X Creber, R. M. M., Maurer, M. S., Reading, M., Hiraldo, G., Hickey, K. T., & Iribarren, S. (2016). Review and Analysis of Existing Mobile Phone Apps to Support Heart Failure Symptom Monitoring and Self-Care Management Using the Mobile Application Rating Scale (MARS). JMIR MHealth and UHealth, 4(2), e74. https://doi.org/10.2196/mhealth.5882 Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (Fifth). SAGE Publications, Inc. http://ubc.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwhV3JbsMgEB1luVSqqiZpVSdO5GNySEWMl_E5qtUP6N1i8RxzSf5fHQiW3chSj4AYAQLeAPMeADL9FMeHPcFoaVKLlUJUqs1OOq9IVYxGhkRq9f-hOo9Uu9HXGYZ6BqxsClNZ4qz_1Y3nlpRVgfLkDmZOU0-i0y0bJAoM-k9dYfE37aVYe16-A5_6FWaOkLCASXtZQnTn0yZhTV6TfRCOPqzgpQujS6yPy3iDdf31c_4- 98 ssUm3NM0ofXpOzwrF91-uXkWnP2ARGfcAPZVGM3zrLSkNJVGYGuIKBeWIliOWIogHuZ2A9a4_3YKKdajlTbwxD4B3m8ZYpgTr4J26_u-8yP6C6ZTfko Crowne, D. P., & Marlowe, D. (1960). A new scale of social desirability independent of psychopathology. Journal of Consulting Psychology, 24(4), 349–354. https://doi.org/10.1037/h0047358 Cugelman, B. (2013). Gamification: What it is and why it matters to digital health behavior change developers. JMIR Serious Games, 1(1), e3. https://doi.org/10.2196/games.3139 Currie, K., Rideout, A., Lindsay, G., & Harkness, K. (2015). The Association Between Mild Cognitive Impairment and Self-care in Adults With Chronic Heart Failure: A Systematic Review and Narrative Synthesis. Journal of Cardiovascular Nursing, 30(5), 382–393. https://doi.org/10.1097/JCN.0000000000000173 Deci, E. L., & Ryan, R. M. (2008). Self-determination theory: A macrotheory of human motivation, development, and health. Canadian Psychology/Psychologie Canadienne, 49(3), 182–185. https://doi.org/10.1037/a0012801 Dickens, C., Vaughan-Dickson, V., & Piano, M. R. (2018). Factors Associated with Heart Failure Self-Care in a Low Socioeconomic Status, Urban Patient Population. Journal of Cardiac Failure, 24(8), S7–S7. https://doi.org/10.1016/j.cardfail.2018.07.025 Dickson, V. V., Deatrick, J. A., & Riegel, B. (2008). A Typology of Heart Failure Self-Care Management in Non-Elders. European Journal of Cardiovascular Nursing, 7(3), 171–181. https://doi.org/10.1016/j.ejcnurse.2007.11.005 Dithmer, M., Rasmussen, J. O., Gronvall, E., Spindler, H., Hansen, J., Nielsen, G., Sorensen, S. B., & Dinesen, B. (2016). “The Heart Game”: Using Gamification as Part of a Telerehabilitation 99 Program for Heart Patients. GAMES FOR HEALTH JOURNAL, 5(1), 27–33. https://doi.org/10.1089/g4h.2015.0001 Erete, S., Israni, A., & Dillahunt, T. (2018). An Intersectional Approach to Designing in the Margins. ACM Interactions. Retrieved April 15, 2021 from https://interactions.acm.org/archive/view/may-june-2018/an-intersectional-approach-to-designing-in-the-margins Finkelstein, J., & Bedra, M. (2013). Is internet search better than structured instruction for web- based health education? Studies in Health Technology and Informatics, 190, 65-67 Foster, M. (2018). HF app to support self-care among community dwelling adults with HF: A feasibility study. Applied Nursing Research, 44, 93–96. https://doi.org/10.1016/j.apnr.2018.10.007 Fox, M. (2009). A systematic review of the literature reporting on studies that examined the impact of interactive, computer-based patient education programs. Patient Education and Counselling, 77(1), 6–13. https://doi.org/10.1016/j.pec.2009.02.011 Garneli, V., Giannakos, M., & Chorianopoulos, K. (2017). Serious games as a malleable learning medium: The effects of narrative, gameplay, and making on students’ performance and attitudes: Serious games as a malleable learning medium. British Journal of Educational Technology, 48(3), 842–859. https://doi.org/10.1111/bjet.12455 Gopalan, V., Bakar, J., Zulkifli, A., Alwi, A., & Mat, R. (2017). A review of the motivation theories in learning. AIP Conference Proceedings, 1891(1). https://doi.org/10.1063/1.5005376 Hargittai, E., Piper, A., & Morris, M. (2019). From internet access to internet skills: Digital inequality among older adults. Universal Access in the Information Society, 18(4), 881–890. https://doi.org/10.1007/s10209-018-0617-5 100 Harrati, N., Bouchrika, I., Tari, A., & Ladjailia, A. (2016). Exploring user satisfaction for e-learning systems via usage-based metrics and system usability scale analysis. Computers in Human Behavior, 61, 463–471. https://doi.org/10.1016/j.chb.2016.03.051 Hickman, R. L., Clochesy, J. M., Pinto, M. D., Burant, C., & Pignatiello, G. (2015). Impact of a Serious Game for Health on Chronic Disease Self-Management: Preliminary Efficacy Among Community Dwelling Adults with Hypertension. Journal of Health and Human Services Administration, 38(2), 253-275. Howlett, J., Chan, M., Ezekowitz, J., Harkness, K., Heckman, G., Kouz, S., Leblanc, M., Moe, G.., O’Meara, E., Abrams, H., Ducharme, A., Grzeslo, A., Hamilton, P., Koshman, S., Lepage, S., McDonald, M., McKelvie, R., Rajda, M., Swiggum, E., … Zieroth, S. (2016). The Canadian Cardiovascular Society Heart Failure Companion: Bridging Guidelines to Your Practice. Canadian Journal of Cardiology, 32(3), 296–310. https://doi.org/10.1016/j.cjca.2015.06.019 Indraratna, P., Tardo, D., Yu, J., Delbaere, K., Brodie, M., Lovell, N., & Ooi, S. (2020). Mobile Phone Technologies in the Management of Ischemic Heart Disease, Heart Failure, and Hypertension: Systematic Review and Meta-Analysis. JMIR MHealth and UHealth, 8(7), e16695. https://doi.org/10.2196/16695 Jaarsma, T., Strömberg, A., Mårtensson, J., & Dracup, K. (2003). Development and testing of the European Heart Failure Self-Care Behaviour Scale. European Journal of Heart Failure, 5(3), 363. https://doi.org/10.1016/s1388-9842(02)00253-2 Jaarsma, T., Wal, M. H. L., Hogenhuis, J., Lesman, I., Luttik, M. A., Veeger, N. J. G. M., & Veldhuisen, D. J. (2004). Design and methodology of the COACH study: A multicenter randomised Coordinating study evaluating Outcomes of Advising and Counselling in Heart 101 failure. European Journal of Heart Failure, 6(2), 227–233. https://doi.org/10.1016/j.ejheart.2003.09.010 Jaarsma, T., Cameron, J., Riegel, B., & Stromberg, A. (2017). Factors Related to Self-Care in Heart Failure Patients According to the Middle-Range Theory of Self-Care of Chronic Illness: A Literature Update. Current Heart Failure Reports, 14(2), 71–77. https://doi.org/10.1007/s11897-017-0324-1 Jaarsma, T., & Strömberg, A. (2019). We told you so: ‘Knowledge is not enough to improve heart failure self‐care behaviour.’ European Journal of Heart Failure, 21(11), 1443–1444. https://doi.org/10.1002/ejhf.1580 Jaarsma, T., Klompstra, L., Ben Gal, T., Ben Avraham, B., Boyne, J., Bäck, M., Chialà, O., Dickstein, K., Evangelista, L., Hagenow, A., Hoes, A. W., Hägglund, E., Piepoli, M. F., Vellone, E., Zuithoff, N. P. A., Mårtensson, J., & Strömberg, A. (2021). Effects of exergaming on exercise capacity in patients with heart failure: Results of an international multicentre randomized controlled trial. European Journal of Heart Failure, 23(1), 114–124. https://doi.org/10.1002/ejhf.1754 Jayasekara, R. S. (2012). Focus groups in nursing research: Methodological perspectives. Nursing Outlook, 60(6), 411–416. https://doi.org/10.1016/j.outlook.2012.02.001 Karmali, K. N., Davies, P., Taylor, F., Beswick, A., Martin, N., & Ebrahim, S. (2014). Promoting patient uptake and adherence in cardiac rehabilitation. The Cochrane Database of Systematic Reviews, 6, CD007131. https://doi.org/10.1002/14651858.CD007131.pub3 Krumholz, H. M., Amatruda, J., Smith, G. L., Mattera, J. A., Roumanis, S. A., Radford, M. J., Crombie, P., & Vaccarino, V. (2002). Randomized trial of an education and support intervention 102 to prevent readmission of patients with heart failure. Journal of the American College of Cardiology, 39(1), 83–89. https://doi.org/10.1016/S0735-1097(01)01699-0 LeBlanc, R. G., & Jacelon, C. S. (2018). Self‐care among older people living with chronic conditions. International Journal of Older People Nursing, 13(3), e12191-n/a. https://doi.org/10.1111/opn.12191 Lee, C., & Coughlin, J. F. (2015). PERSPECTIVE: Older Adults’ Adoption of Technology: An Integrated Approach to Identifying Determinants and Barriers: Older adults’ adoption of technology. Journal of Product Innovation Management, 32(5), 747–759. https://doi.org/10.1111/jpim.12176 Lee, C. S., Riegel, B., Driscoll, A., Suwanno, J., Moser, D. K., Lennie, T. A., Dickson, V. V., Cameron, J., & Worrall-Carter, L. (2009). Gender differences in heart failure self-care: A multinational cross-sectional study. International Journal of Nursing Studies, 46(11), 1485–1495. https://doi.org/10.1016/j.ijnurstu.2009.04.004 Lee, K. S., Moser, D. K., & Dracup, K. (2018). Relationship between self-care and comprehensive understanding of heart failure and its signs and symptoms. European Journal of Cardiovascular Nursing, 17(6), 496–504. https://doi.org/10.1177/1474515117745056 Le Grande, M. R., Elliott, P. C., Worcester, M. U. C., Murphy, B. M., & Goble, A. J. (2008). An Evaluation of Self-report Physical Activity Instruments Used in Studies Involving Cardiac Patients. Journal of Cardiopulmonary Rehabilitation and Prevention, 28(6), 358–369. https://doi.org/10.1097/HCR.0b013e31818c3d90 Lewis, J. R. (2018). The System Usability Scale: Past, Present, and Future. International Journal of Human–Computer Interaction, 34(7), 577–590. https://doi.org/10.1080/10447318.2018.1455307 103 Mahajan, U. V., Sunshine, K. S., Herring, E. Z., Labak, C. M., Wright, J. M., & Smith, G. (2021). Virtual reality in presurgical patient education: A scoping review and recommended trial design guidelines. The American Journal of Surgery, Journal Article. https://doi.org/10.1016/j.amjsurg.2021.03.022 Makris, E., Hu, L., Jones, G. B., & Wright, J. M. (2020). Moving the Dial on Heart Failure Patient Adherence Rates. Patient Preference and Adherence, Volume 14, 2407–2418. https://doi.org/10.2147/PPA.S283277 Madigosky, W., & Schaik, S. (2016). Context matters: Groupthink and outcomes of health care teams. Medical Education, 50(4), 387–389. https://doi.org/10.1111/medu.12989 Maramba, I., Chatterjee, A., & Newman, C. (2019). Methods of usability testing in the development of eHealth applications: A scoping review. International Journal of Medical Informatics, 126, 95–104. https://doi.org/10.1016/j.ijmedinf.2019.03.018 Martensson, J., Strömberg, A., Dahlström, U., Karlsson, J.-E., & Fridlund, B. (2005). Patients with heart failure in primary health care: Effects of a nurse-led intervention on health-related quality of life and depression. European Journal of Heart Failure, 7(3), 393–403. https://doi.org/10.1016/j.ejheart.2004.01.016 Massouh, A., Skouri, H., Cook, P., Huijer, H. A. S., Khoury, M., & Meek, P. (2019). Self-care confidence mediates self-care maintenance and management in patients with heart failure. Heart & Lung : The Journal of Critical Care. https://doi.org/10.1016/j.hrtlng.2019.07.008 McDermott, M. S., & While, A. E. (2013). Maximizing the healthcare environment: A systematic review exploring the potential of computer technology to promote self-management of chronic illness in healthcare settings. Patient Education and Counselling, 92(1), 13–22. https://doi.org/10.1016/j.pec.2013.02.014 104 McLafferty, I. (2004). Focus group interviews as a data collecting strategy. Journal of Advanced Nursing, 48(2), 187–194. https://doi.org/10.1111/j.1365-2648.2004.03186.x Mei, J., Tian, Y., Chai, X., & Fan, X. (2019). Gender differences in self-care maintenance and its associations among patients with chronic heart failure. International Journal of Nursing Sciences, 6(1), 58–64. https://doi.org/10.1016/j.ijnss.2018.11.008 Michie, S., Carey, R. N., Johnston, M., Rothman, A. J., de Bruin, M., Kelly, M. P., & Connell, L. E. (2018). From Theory-Inspired to Theory-Based Interventions: A Protocol for Developing and Testing a Methodology for Linking Behaviour Change Techniques to Theoretical Mechanisms of Action. Annals of Behavioral Medicine, 52(6), 501–512. https://doi.org/10.1007/s12160-016-9816-6 Myers, S. L., Siegel, E. O., Hyson, D. A., & Bidwell, J. T. (2020). A qualitative study exploring the perceptions and motivations of patients with heart failure who transitioned from non-adherence to adherence. Heart & Lung, 49(6), 817–823. https://doi.org/10.1016/j.hrtlng.2020.09.010 National Library of Medicine. (2020, November 25). Making Your Website Senior-Friendly. https://nnlm.gov/mar/guides/making-your-website-senior-friendly Neergaard, M. A., Olesen, F., Andersen, R. S., & Sondergaard, J. (2009). Qualitative description – the poor cousin of health research? BMC Medical Research Methodology, 9(1), 52. https://doi.org/10.1186/1471-2288-9-52 Palomino, P., Toda, A. M., Oliveira, W., Cristea, A. I., & Isotani, S. (2019). Narrative for Gamification in Education: Why Should you Care? 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), 97–99. https://doi.org/10.1109/ICALT.2019.00035 Plichta, S. B., Kelvin, E. A., & Munro, B. H. (2012). Munro’s statistical methods for health care research (6th ed). Wolters Kluwer Health/Lippincott Williams & Wilkins. 105 Prochota, B., Szwamel, K., & Uchmanowicz, I. (2019). Socio-clinical variables affecting the level of self-care in elderly patients with heart failure. European Journal of Cardiovascular Nursing, 18(7), 628–636. https://doi.org/10.1177/1474515119855600 Radhakrishnan, K., Toprac, P., O’Hair, M., Bias, R., Kim, M. T., Bradley, P., & Mackert, M. (2016). Interactive Digital e-Health Game for Heart Failure Self-Management: A Feasibility Study. Games for Health Journal, 5(6), 366–374. https://doi.org/10.1089/g4h.2016.0038 Radhakrishnan, K., Baranowski, T., O’Hair, M., Fournier, C. A., Spranger, C. B., & Kim, M. T. (2020). Personalizing Sensor-Controlled Digital Gaming to Self-Management Needs of Older Adults with Heart Failure: A Qualitative Study. Games for Health Journal, 9(4), 304–310. https://doi.org/10.1089/g4h.2019.0222 Riegel, B., Dickson, V. V., Hoke, L., McMahon, J. P., Reis, B. F., & Sayers, S. (2006). A Motivational Counseling Approach to Improving Heart Failure Self-Care: Mechanisms of Effectiveness. The Journal of Cardiovascular Nursing, 21(3), 232–241. https://doi.org/10.1097/00005082-200605000-00012 Riegel, B., Vaughan Dickson, V., Goldberg, L. R., & Deatrick, J. A. (2007). Factors Associated With the Development of Expertise in Heart Failure Self-Care. Nursing Research, 56(4), 235–243. https://doi.org/10.1097/01.NNR.0000280615.75447.f7 Riegel, B., & Dickson, V. (2008). A Situation-Specific Theory of Heart Failure Self-care. The Journal of Cardiovascular Nursing, 23(3), 190–196. https://doi.org/10.1097/01.JCN.0000305091.35259.85 Riegel, B., Lee, C. S., Dickson, V. V., & Carlson, B. (2009a). An Update on the Self-care of Heart Failure Index. Journal of Cardiovascular Nursing, 24(6), 485–497. https://doi.org/10.1097/JCN.0b013e3181b4baa0 106 Riegel, B., Moser, D. K., Anker, S. D., Appel, L. J., Dunbar, S. B., Grady, K. L., Gurvitz, M. Z., Havranek, E. P., Lee, C. S., Lindenfeld, J., Peterson, P. N., Pressler, S. J., Schocken, D. D., & Whellan, D. J. (2009b). State of the Science: Promoting Self-Care in Persons With Heart Failure: A Scientific Statement From the American Heart Association. Circulation, 120(12), 1141–1163. https://doi.org/10.1161/CIRCULATIONAHA.109.192628 Riegel, B., Jaarsma, T., & Strömberg, A. (2012). A middle-range theory of self-care of chronic illness. ANS. Advances in Nursing Science, 35(3), 194–204. https://doi.org/10.1097/ANS.0b013e318261b1ba Riegel, B., Dickson, V. V., & Faulkner, K. M. (2016). The Situation-Specific Theory of Heart Failure Self-Care: Revised and Updated. The Journal of Cardiovascular Nursing, 31(3), 226–235. https://doi.org/10.1097/JCN.0000000000000244 Riegel, B., Dunbar, S. B., Fitzsimons, D., Freedland, K. E., Lee, C. S., Middleton, S., Stromberg, A., Vellone, E., Webber, D. E., & Jaarsma, T. (2019). Self-care research: Where are we now? Where are we going? International Journal of Nursing Studies, 116(Journal Article), 103402–103402. https://doi.org/10.1016/j.ijnurstu.2019.103402 Rush, K. L., Hatt, L., Janke, R., Burton, L., Ferrier, M., & Tetrault, M. (2018). The efficacy of telehealth delivered educational approaches for patients with chronic diseases: A systematic review. Patient Education and Counseling, 101(8), 1310–1321. https://doi.org/10.1016/j.pec.2018.02.006 Ryan, R. M., & Deci, E. L. (2000). Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020 107 Samsky, M. D., Ambrosy, A. P., Youngson, E., Liang, L., Kaul, P., Hernandez, A. F., Peterson, E. D., & McAlister, F. A. (2019). Trends in Readmissions and Length of Stay for Patients Hospitalized With Heart Failure in Canada and the United States. JAMA Cardiology, 4(5), 444. https://doi.org/10.1001/jamacardio.2019.0766 Sandelowski, M. (2010). What’s in a name? Qualitative description revisited. Research in Nursing & Health, 33(1), 77–84. https://doi.org/10.1002/nur.20362 Sandelowski, M. (2000). Whatever happened to qualitative description? Research in Nursing & Health, 23(1), 334-340. Sardi, L., Idri, A., & Fernández-Alemán, J. L. (2017). A systematic review of gamification in e-Health. Journal of Biomedical Informatics, 71, 31–48. https://doi.org/10.1016/j.jbi.2017.05.011 Sauro, J. (2011). A practical guide to the System Usability Scale (SUS): Background, benchmarks & best practices. Denver, CO: Measuring Usability LLC. Savarese, G. & Lund, L. H., (2017). Global Public Health Burden of Heart Failure. Cardiac Failure Review, 3(1), 7–11. https://doi.org/10.15420/cfr.2016:25:2 Sedlar, N., Lainscak, M., & Farkas, J. (2021). Self‐care perception and behaviour in patients with heart failure: A qualitative and quantitative study. ESC Heart Failure, 8(3), 2079–2088. https://doi.org/10.1002/ehf2.13287 Sullivan-Bolyai, S., Bova, C., & Harper, D. (2005). Developing and refining interventions in persons with health disparities: The use of Qualitative Description. Nursing Outlook, 53(3), 127–133. https://doi.org/10.1016/j.outlook.2005.03.005 Suter, E., Mallinson, S., Misfeldt, R., Boakye, O., Nasmith, L., & Wong, S. T. (2017). Advancing team-based primary health care: A comparative analysis of policies in western Canada. BMC Health Services Research, 17(1), 493. https://doi.org/10.1186/s12913-017-2439-1 108 Takeda, A., Taylor, S. J. C., Taylor, R. S., Khan, F., Krum, H., & Underwood, M. (2012). Clinical service organisation for heart failure. COCHRANE DATABASE OF SYSTEMATIC REVIEWS, 9, CD002752. https://doi.org/10.1002/14651858.CD002752.pub3 Tonkin, S., Whitmore, C., Cutroni, J. (2010). Performance marketing with Google Analytics: Strategies and techniques for maximizing online ROI. Wiley Pub. van Boekel, L. C., Peek, S. T., & Luijkx, K. G. (2017). Diversity in Older Adults’ Use of the Internet: Identifying Subgroups Through Latent Class Analysis. Journal of Medical Internet Research, 19(5), e180. https://doi.org/10.2196/jmir.6853 Vancouver Coastal Health, Providence Healthcare & BC Heart Failure Network (2016, September). Heart Failure Zones. https://vch.eduhealth.ca/PDFs/FD/FD.780.H431.pdf van der Wal, M. H. L., Jaarsma, T., Moser, D. K., & Veldhuisen, D. J. (2005). Development and Testing of the Dutch Heart Failure Knowledge Scale. European Journal of Cardiovascular Nursing, 4(4), 273–277. https://doi.org/10.1016/j.ejcnurse.2005.07.003 Versluis, A., van Luenen, S., Meijer, E., Honkoop, P. J., Pinnock, H., Mohr, D. C., Neves, A. L., Chavannes, N. H., & van der Kleij, R. M. J. J. (2020). SERIES: EHealth in primary care. Part 4: Addressing the challenges of implementation. European Journal of General Practice, 26(1), 140–145. https://doi.org/10.1080/13814788.2020.1826431 Vidán, M. T., Martín Sánchez, F., Sánchez, E., Ortiz, F., Serra‐Rexach, J. A., Martínez‐Sellés, M., & Bueno, H. (2019). Most elderly patients hospitalized for heart failure lack the abilities needed to perform the tasks required for self‐care: Impact on outcomes. European Journal of Heart Failure, 21(11), 1434–1442. https://doi.org/10.1002/ejhf.1559 Wagenaar, K. P., Broekhuizen, B. D. L., Jaarsma, T., Kok, I., Mosterd, A., Willems, F. F., Linssen, G. C. M., Agema, W. R. P., Anneveldt, S., Lucas, C. M. H. B., Mannaerts, H. F. J., Wajon, E. M. C. 109 J., Dickstein, K., Cramer, M. J., Landman, M. A. J., Hoes, A. W., & Rutten, F. H. (2019). Effectiveness of the European Society of Cardiology/Heart Failure Association website ‘heartfailurematters.org’ and an e-health adjusted care pathway in patients with stable heart failure: Results of the ‘e-Vita HF’ randomized controlled trial: E-health tools in heart failure: e-Vita HF study. European Journal of Heart Failure, 21(2), 238–246. https://doi.org/10.1002/ejhf.1354 Ware, P., Ross, H. J., Cafazzo, J. A., Boodoo, C., Munnery, M., & Seto, E. (2020). Outcomes of a Heart Failure Telemonitoring Program Implemented as the Standard of Care in an Outpatient Heart Function Clinic: Pretest-Posttest Pragmatic Study. Journal of Medical Internet Research, 22(2), e16538. https://doi.org/10.2196/16538 Whitehead, A. L., Julious, S. A., Cooper, C. L., & Campbell, M. J. (2016). Estimating the sample size for a pilot randomised trial to minimise the overall trial sample size for the external pilot and main trial for a continuous outcome variable. Statistical Methods in Medical Research, 25(3), 1057–1073. https://doi.org/10.1177/0962280215588241 Whitehead, L., & Seaton, P. (2016). The Effectiveness of Self-Management Mobile Phone and Tablet Apps in Long-term Condition Management: A Systematic Review. Journal of Medical Internet Research, 18(5), e97. https://doi.org/10.2196/jmir.4883 Whittal, A., Störk, S., Riegel, B., & Herber, O. R. (2021). Applying the COM-B behaviour model to overcome barriers to heart failure self-care: A practical application of a conceptual framework for the development of complex interventions (ACHIEVE study). European Journal of Cardiovascular Nursing, 20(3), 261–267. https://doi.org/10.1177/1474515120957292 Willis, D. G., Sullivan-Bolyai, S., Knafl, K., & Cohen, M. Z. (2016). Distinguishing Features and Similarities Between Descriptive Phenomenological and Qualitative Description Research. 110 Western Journal of Nursing Research, 38(9), 1185–1204. https://doi.org/10.1177/0193945916645499 111 6.1 Appendix A Heart Failure Self-Care Patient Education: Heart SCaPE Study Nurse/Pharmacist Consent Form Principle Investigator: Kathy L. Rush, PhD, RN Professor School of Nursing, University of British Columbia Okanagan Phone: 250-807-9561 Email: Kathy.Rush@ubc.ca Co- Investigators: Contact Person: Alexandra Lukey, RN, MSN Student, University of British Columbia, Okanagan Phone: 250-540-4302 Email: alukey@mail.ubc.ca Martha Mackay, PhD, RN Clinical Associate Professor School of Nursing, University of British Columbia Khalad Hasan, PhD Assistant Professor Computer Science, University of British Columbia Okanagan Study Information and Purpose The purpose of this study is to learn more about the best ways to deliver education to heart failure patients. You are being invited to take part in this study because you are a nurse or pharmacist who works in the Heart Function Clinic at St. Paul’s Hospital The purpose of this research is to evaluate a web-based education tool. The aim of this tool is to supplement heart failure education with the aim of improving cardiac health overall as well as the patient’s knowledge of the disease. This research study is being conducted for the purpose of a graduate thesis to fulfill to requirements for the UBC Okanagan Master of Science in Nursing Funding This research is funded by the Canadian Institute of Health Research (CIHR) Canada Graduate Scholarship Master’s program. Who can participate? To participate in this research, you must be a nurse (Registered Nurse of Nurse Practitioner) or Pharmacist who works at either the St. Paul’s Heart Function Clinic or Pre-Transplant clinic. 112 What does the study involve? If you agree to take part in this study, you will be given an anonymous login to the patient education website. This will ensure no personal identifying information is attached to use of the website. We then ask that you spend time looking over the education modules. Once you have had an opportunity to review the modules you will asked to take part in a videoconference focus group. The purpose of this focus group is to gain feedback on the overall design and content of the website. This feedback will be to improve the website before patients use it. You will receive the questions in advance of the focus group in advance, so you can reflect. The focus group will run for a maximum time of 60 minutes. The focus group session will be audio-recorded for transcription and analysis. If you are unable to take part in the focus group, there will be an option of scheduling a telephone appointment to share feedback. Results will be shared through publications and presentations, and will be publically available on the internet through cIRcle: (https://circle.ubc.ca) the UBC digital repository for research and teaching material. No identifying information regarding participants will be shared. Risks There is a time commitment to taking part in this study. We do not believe that there is a risk to you in participating in this study. Participation is voluntary. There are no increased risks of COVID-19 exposure because all steps of this study will be completed online. Benefits You may not benefit directly by participating. However, by participating in this study, you will be contributing to knowledge development for the benefit of heart failure patients. A $10 gift certificate to Starbucks will be provided to all participants as a small thank-you. Only nurses or pharmacists who provide website feedback will receive this gift certificate. Confidentiality We will do several things to keep your identity confidential. For all the online videoconferencing, we will be using the UBC hosted version of Zoom videoconferencing software. All audio recordings will be stored on the host device and will not be stored in Zoom’s cloud server. All research-related files and emails will be sent through a secure UBC web address. We will encourage participants not to discuss the content of our conversations with people outside the group. However, we cannot control what participants do with the information discussed. For that reason, we can only offer limited confidentiality. The word-for-word recordings and transcriptions of group discussions will be password protected on encrypted computer. Any hard copies will be kept in a secure location and will be made available only to members of the research team. Data will be stored for five years following publication and then will be destroyed. Only code numbers will be used to identify materials relating to your personal information and your name. Code numbers and consent will be stored separately from the information you provide. Made up names will be used to track the information you provide during the focus group. All information will be kept in a secure location. The data may be used in future analysis and publications beyond this study. After five years, any electronic and printed data will be erased or shredded. 113 The information you provide will be shared with others who study and work with patients with heart failure. Some of your information may be communicated as word-for-word quotes to illustrate points. It will be communicated in written scholarly papers and oral presentations. Your name will not be used in any reporting of the results from this study. We hope that results from the study will be used to guide future research. If you would like a report of the findings, please include your mailing address in the space provided at the bottom of this form. You will also be given a copy of the signed consent form. We will track data related to website usage in through your anonymous email. This will be stored in a server in the United States through Google Analytics. Google is located in the USA and, as such, is subject to U.S. laws. In particular, the US Patriot Act allows authorities to access the records of internet service providers. Using the website does not ask for personal identifiers or any information that may be used to identify you. The web survey company servers record the incoming IP address of the computer you used to access the survey, but no connection is made between your data and your computer's IP address. If you choose to participate in the study, you understand that the data of your website usage will be stored and accessed in the USA. The security and privacy policy for Google analytics can be found at the following link: https://policies.google.com/privacy?hl=en-US Withdrawal from the Study Participation in this study is voluntary. You may decide not to take part without any consequences to your employment. If you do take part, you may decide not to answer any questions that you do not wish to answer. You can also withdraw your participation at any time during the focus group. If you choose to withdraw after you have taken part in the focus group discussions your data will remain in the study. This is necessary because your data is combined with that of other people, and cannot be removed without altering its meaning. Contact for Information about the Study If you have any concerns, questions or would like further information about the study, contact Dr. Kathy Rush at (250) 807-9561 or by e-mail: "Rush, Kathy" <kathy.rush@ubc.ca>. Or Alexandra Lukey (250) 540-4302 or by email alukey@mail.ubc.ca Contact for Concerns about the Rights of Research Participants You have the right to ask questions, and have those questions answered. If you have any concerns or complaints about your rights as a research participant and/or your experiences while participating in this study, please reference the study number H20-02390 when contacting the Complaint Line so the staff can better assist you. Contact the Research Participant Complaint Line in the UBC Office of Research Services at 1-877-822-8598 or the UBC Okanagan Research Services Office at 250-807-8832. It is also possible to contact the Research Participant Complaint Line by email (RSIL@ors.ubc.ca). 114 Consent The Heart SCaPE Study: Nurse/Pharmacist Consent Form H20-02390 Taking part of this study is completely optional and therefore you have the right to refuse or withdraw at any point in this study without any negative consequences to your employment. Your signature below indicates that you have understood all the above points and that you consent to be a part of this study. Your signature below indicates that you have received a copy of this consent for your records. I agree to participate in testing the website and the focus group. By participating I agree to allow my conversation to be audio recorded and directly quoted, maintaining confidentiality, in presentations and publications of research results. YES NO (Please circle your choice) Your signature below indicates that you consent to participate in the study Participants Signature Date Printed Name of the Participant Address of email of Participant (Optional if you would like a copy of the transcription) Please tick box if you would like a copy of the study results sent to you and indicate if you would like this emailed or mailed on the line below. Thank you for your participation 115 Appendix B Heart Failure Self-Care Patient Education: Heart SCaPE Study Nurse Interview Guide (Focus Group) ● What are your overall impressions of the modules as an educational tool for patients with HF? Do you think that the content is at an appropriate level for the patients? Is the content clear? ● What do you think of the flow of each of the modules and decision-making options provided? ● What are your thoughts on the game-like approach? ● Responses to modules 1-7 o Is there content missing o Is there anything that should be taken out? o Other comments ● Do you recommend any changes to the presentation of the units such as how they are ordered or how they are presented on the website? ● How easy was it to use the website and do you have any suggestions for improvement? How easy would it be for the HF patient population to use? ● Are there any other suggestions that you have to improve the website or the intervention content? 116 Appendix C Mailed Invitation Pamphlet Heart SCaPEDo you haveheart failure? Ifso, this study isfor YOU!Contact Em ail :Ca l l or Te xt : V 2.0 11/02/20 page 1 of 2 117 Appendix D PRE-VISIT PHONE-CALL RECRUITMENT SCRIPT Heart Failure Self-Care Patient Education: Heart SCaPE Study (AKA Alex’s Thesis) Nurse: There is a research study going on right now through this heart function clinic about a new way to make heart failure education more interactive for patients. Are you interested in hearing more about the study? If so, would you agree to my giving the researcher your contact information? If the patient asks for further information: I’m sorry but I don’t have enough information about the study details to answer your questions, but Alex will be happy to explain the study and answer any questions you have before you agree to participate. If the patient requests to contact Alex directly: Please provide them with her contact information. Call, text or email are all welcome. Email: alukey@mail.ubc.ca Call/Text: 250-540-4302 118 6.2 Appendix E Email Attachment for Invitation 6.3 6.4 6.5 6.6 6.7 Do you have heart failure?If so, this studyis for YOU!Heart Failure Self-Care Patient Education Study (Heart SCaPE)Name: Email:Phone:WHAT DO I NEED TO TAKEPART?WHAT IS IT?Check this box if youwould like to takepart - Then bring it tothe receptionist atyour nextappointment OR email/call/text :contact info is belowContact:Em a il :Ca l l or T e xt : HOW MUCH TIME WILLTHIS TAKE?V 3.0 05/06/21 Page 1 of 1 119 6.8 Appendix F Twitter Mock-up Facebook Mock-up Instagram Mock-up 120 6.9 121 6.10 Appendix G Heart Failure Self-Care Patient Education: Heart SCaPE Study Demographic Information 1. Age: ______ Years 2. Gender: □ Female □ Male □ Other (specify)_____________________ 3. Marital Status: □ Single (never married) □ Widowed □ Divorced □ Married or Remarried □ Separated □ Other (specify) 4. Race/Ethnicity (select all that apply): □ African-Canadian □ 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 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. In what country and city do you live in? 122 Country __________________(specify) City __________________(specify) 10. Chronic Health Problems: □ Arthritis □ Coronary Heart Disease □ Heart Valve Disease □ Atrial Fibrillation □ Diabetes □ High Blood Pressure □ Sleep Apnea □ Thyroid Disease □ COPD □ Cancer □ Stroke □ Other ______________________ (specify) 11. Time since Heart Failure diagnosis: ______ (months, years) 12. Medications You are Currently Taking: □ Blood Thinners □ Blood Pressure Medication □ Water Pills □ Medication(s) to control heart rate □ Medication(s) to control heart rhythm □ Cholesterol medication □ Other: ___________________ 13. Number of hospital admissions in the last year for Heart Failure: □ None □ One □ Two □ Three □ Four or more 14. Average length of stay in hospital: □ Five days or less □ Greater than five days 15. How would you rate your overall health? □ Excellent □ Good □ Fair □ Poor 15. How would you rate your overall mental health? □ Excellent □ Good □ Fair □ Poor 6.11 6.12 123 6.13 Appendix H Heart Failure Self-Care Patient Education: Heart SCaPE Study Post Intervention Interview Guide (Patients) General ● What was your experience like using the website? Please explain for your answer ● Talk about what it was like to use the education website during the past month. What did you like best about the website? What did you like least? ● What kinds of challenges did you come across over the 30 days? Gamification ● The modules were written like stories following a person with HF through a situation. What was it like for you to learn in a story format? How did you find the stories in your learning about living HF? What were some of your takeaways from the stories? ● How motivated or unmotivated did you feel to use the website and why? ● Did the stories affect the things you do to take care of yourself? If there were changes in your self-care, what were they and why do you think that might be? ● What did you think of having the rewards (gems and trophies) on the website? ● Do you think that having rewards changed your motivation in any way and why? 124 6.14 Appendix I Heart Failure Self-Care Patient Education: Heart SCaPE Study Patient Consent Form: Part 1 Principle Investigator: Kathy L. Rush, PhD, RN Associate Professor School of Nursing, University of British Columbia Okanagan Phone: 250-807-9561 Email: Kathy.Rush@ubc.ca Co-Investigators: Contact Person: Alexandra Lukey, RN, MSN Student, University of British Columbia, Okanagan Phone: 250-540-4302 Email: alukey@mail.ubc.ca Martha Mackay, PhD, RN Clinical Associate Professor School of Nursing, University of British Columbia Khalad Hasan, PhD Assistant Professor Computer Science, University of British Columbia Okanagan Study Information and Purpose The purpose of this study is to learn more about the best ways to deliver education to heart failure patients. You are being invited to take part in this study because you have a diagnosis of heart failure. You also have less than three months of education from either the heart function or pre-transplant clinics. The purpose of this research is to test a web-based education tool. This tool aims to provide a way for patients with heart failure to apply heart failure education to real-life situations. This tool may improve their cardiac health and knowledge. This research study is being done to complete a graduate thesis for the UBC Okanagan Master of Science in Nursing Funding This research is funded by the Canadian Institute of Health Research (CIHR) Canada Graduate Scholarship Master’s program. Who can participate? To take part in this research, you must: a) Have a diagnosis of heart failure 125 b) Have less than three months of education from the heart clinics c) Be at least 19 years old d) Able to speak, read and understand English e) Have access to a computer with an internet connection f) Skills to use a website, read and answer email What does the study involve? • If you agree to take part in this study, we will ask you to use a website for up to two weeks. The website has self-care education about heart failure and is organized according to seven, 15-20 minutes modules. Each module is a guided scenario that asks you to make decisions in response to the information you will be given. • We will ask you to complete some surveys before and after you use the site. The approximate time required to fill out these surveys is 10 minutes. • The first step will be to set up a videoconference with screen sharing capability to show you how to use the website. • Next, you will be asked to fill in two surveys by email. These surveys will ask questions about your current knowledge of heart failure and how you take care of yourself. You will also be asked to fill out a form with basic personal information, such as your age and marital status. This information will not be linked to any of the data collected during the study. • You will then be given a login to enter the website. The login uses a made-up name to protect your identity. It will give you access to the website for 1 days. • During this time, we encourage you to go through all seven modules (chapters); but, this is not a requirement. After the 14 days, you will be asked to fill in the same two surveys you completed before using the website. You will receive these surveys by email. • Last, you will have the option of participating in an interview to share your experiences. You will be asked questions about how easy or difficult the website was to use and what you thought worked or didn't work about it. If you agree, you will be contacted after the 14 days of website use. Results will be shared through publications and presentations and will be publicly available on the internet through cIRcle: (https://circle.ubc.ca) the UBC digital repository for research and teaching material. No identifying information regarding participants will be shared. Risks There is a time commitment to taking part in this study. We do not believe that there is a risk to you in participating in this study. Participation is voluntary. There are no increased risks of COVID-19 exposure because all steps of this study will be completed online. Benefits You may not benefit directly by participating. There is a possibility that use of the intervention may increase self-care knowledge related to heart failure. This may improve long-term self-care behaviour, which could result in improved heart failure outcomes. By taking part in this study, you are also helping to improve the care of future heart failure patients. A $10 gift certificate to Starbucks will be provided to all participants as a small thank- 126 you. Only participants who complete both before and after surveys will receive this gift certificate. Confidentiality We will do several things to keep your identity confidential. For all the online videoconferencing, we will be using the UBC hosted version of Zoom videoconferencing software. All audio recordings will be stored on the host device and will not be stored in Zoom's cloud server. All research-related files and emails will be sent through a secure UBC web address. The UBC Zoom servers are also located in Canada. All surveys will be collected through "Qualtrics" software. Qualtrics is fully compliant with the BC Freedom of Information and Protection of Privacy Act (FIPPA). The survey data collected on Qualtrics is kept secure and is stored and backed up in Canada. Any hard copies will be kept in a secure location and will be made available only to members of the research team. Data will be stored for five years following publication and then will be destroyed. Only code numbers will be used to identify materials relating to your personal information and your name. Code numbers and consent will be stored separately from the information you provide. All information will be kept in a secure location. The data may be used in future analysis and publications beyond this study. After five years, any electronic and printed data will be erased or shredded. The information you provide will be shared with others who study and work with patients with heart failure. Some of your information may be communicated as word-for-word quotes to illustrate points. It will be communicated in written scholarly papers and oral presentations. Your name will not be used in any reporting of the results from this study. We hope that the results from the study will be used to guide future research. If you would like a report of the findings, please include your mailing address in the space provided at the bottom of this form. You will also be given a copy of the signed consent form. We will track data related to website usage through your anonymous email. This will be stored in a server in the United States through Google Analytics. Google is located in the USA and, as such, is subject to US laws. In particular, the US Patriot Act allows authorities to access the records of internet service providers. Using the website does not ask for personal identifiers or any information that may be used to identify you. The web survey company servers record the incoming IP address of the computer you used to access the survey, but no connection is made between your data and your computer's IP address. If you choose to participate in the study, you understand that the data of your website usage will be stored and accessed in the USA. The security and privacy policy for Google Analytics can be found at the following link: https://policies.google.com/privacy?hl=en-US Withdrawal from the study Participation in this study is voluntary. You may decide not to take part without any consequences to your current or future care. If you do take part, you may decide not to answer any questions that you do not wish to answer. You can also withdraw your participation at any time. However, once the data analysis is complete, it will not be possible to withdraw your data. 127 Contact for Information about the Study If you have any concerns, questions or would like further information about the study, contact Dr Kathy Rush at (250) 807-9561 or by email: "Rush, Kathy" <kathy.rush@ubc.ca>. Or Alexandra Lukey (250) 540-4302 or by email alukey@mail.ubc.ca Contact for Concerns about the Rights of Research Participants You have the right to ask questions and have those questions answered. If you have any concerns or complaints about your rights as a research participant and/or your experiences while participating in this study, please reference the study number H20-02390 when contacting the Complaint Line so the staff can better assist you. Contact the Research Participant Complaint Line in the UBC Office of Research Services at 1-877-822-8598 or the UBC Okanagan Research Services Office at 250-807-8832. It is also possible to contact the Research Participant Complaint Line by email (RSIL@ors.ubc.ca). 128 Consent The Heart SCaPE Study: Patient Consent Form: Part 1 H20-02390 Taking part of this study is optional, and so you have the right to withdraw at any point in this study without any negative consequences to your care. Your signature below indicates that you have understood all the above points and that you consent to be a part of this study. Your signature below indicates that you have received a copy of this consent for your records. I agree to participate in this study [Pre-test survey, accessing education website, post-test survey] YES NO (Please circle your choice) I agree to be contacted for a follow-up interview after using the website [20-minute interview] YES NO (Please circle your choice Your signature below indicates that you consent to participate in the study Participants Signature Date Printed Name of the Participant Address of email of Participant (Optional if you would like a copy of the transcription) Please tick the box if you would like a copy of the study results sent to you and indicate if you would like this emailed or mailed on the line below. Thank you for your participation 129 6.15 Appendix J Heart Failure Self-Care Patient Education: Heart SCaPE Study Patient Consent Form: Part 2 Principle Investigator: Kathy L. Rush, PhD, RN Associate Professor School of Nursing, University of British Columbia Okanagan Phone: 250-807-9561 Email: Kathy.Rush@ubc.ca Co- Investigators: Contact Person: Alexandra Lukey, RN, MSN Student, University of British Columbia, Okanagan Phone: 250-540-4302 Email: alukey@mail.ubc.ca Martha Mackay, PhD, RN Clinical Associate Professor School of Nursing, University of British Columbia Khalad Hasan, PhD Assistant Professor Computer Science, University of British Columbia Okanagan Study Information and Purpose The purpose of this study is to learn more about the best ways to deliver education to heart failure patients. You are being invited to take part in this study because you have a diagnosis of heart failure. You also have less than 3 months of education from either the heart function or pre-transplant clinics. The purpose of this research is to test a web-based education tool. The aim of this tool is to provide a way for patients with heart failure to apply heart failure education to real-life situations. This may improve their cardiac health and knowledge. This research study is being done to complete a graduate thesis for the UBC Okanagan Master of Science in Nursing Funding This research is funded by the Canadian Institute of Health Research (CIHR) Canada Graduate Scholarship Master’s program. Who can participate? To take part in this research, you must: a) Have a diagnosis of heart failure b) Have less than three months of education from the heart clinics 130 c) Be at least 19 years old d) Able to speak, read and understand English e) Have access to a computer with an internet connection f) Skills to use a website, read and answer email What does the study involve? • This is the second part of the study. If you agree to take part in the interview you will be asked to share your experiences using the website. You will be asked open-ended questions about how easy or difficult the website was to use and what you thought worked or didn't work about it. • This discussion is planned to take at most 20 minutes. The interview will be arranged to be flexible with your schedule. The interview may take place over the phone or over videoconference (Zoom) by your preference. • We will record the interview for transcription purposes. You may be provided with a copy of this recording on request. Results will be shared through publications and presentations, and will be publicly available on the internet through cIRcle: (https://circle.ubc.ca) the UBC digital repository for research and teaching material . No identifying information regarding participants will be shared. Risks There is a time commitment to participating in this study. We do not believe that there is a risk to you in participating in this study. Participation is entirely voluntary. You may decline to answer any of the questions you do not wish to answer or stop the interview at any time. There are no increased risks of COVID-19 exposure because all steps of this study will be completed online. Benefits You may not benefit directly by participating. However, by taking part in this study, you are helping to improve the care of future heart failure patients. You may also gain knowledge from using the website which may improve your own self-care. An additional $10 gift certificate to Starbucks will be provided to all participants as a small thank-you for completing the interview. Confidentiality We will do several things to keep your identity confidential. For all the online videoconferencing, we will be using the UBC hosted version of Zoom videoconferencing software. All audio recordings will be stored on the host device and will not be stored in Zoom’s cloud server. All research-related files and emails will be sent through a secure UBC web address. . The UBC Zoom servers are also located in Canada. The word-for-word recordings and transcriptions of the interview will be password protected on encrypted computer. Any hard copies will be kept in a secure location and will be made available only to members of the research team. Data will be stored for five years following publication and then will be destroyed. Only code numbers will be used to identify materials relating to your personal information and your name. Code numbers and consent will be stored separately from the information you provide. Made up names will be used to track the information you provide during your interview. All information will be kept in a secure location. The data may be used in 131 future analysis and publications beyond this study. After five years, any electronic and printed data will be erased or shredded. The information you provide will be shared with others who study and work with patients with heart failure. Some of your information may be communicated as word-for-word quotes to illustrate points. It will be communicated in written scholarly papers and oral presentations. Your name will not be used in any reporting of the results from this study. We hope that results from the study will be used to guide future research. If you would like a report of the findings, please include your mailing address in the space provided at the bottom of this form. You will also be given a copy of the signed consent form. Withdrawal from the Study Participation in this study is voluntary. You may decide not to take part without any consequences to your current or future care. If you do take part, you may decide not to answer any questions that you do not wish to answer. You can also withdraw your participation at any time. However, once the data analysis is complete, it will not be possible to withdraw your data. Contact for Information about the Study If you have any concerns, questions or would like further information about the study, contact Dr. Kathy Rush at (250) 807-9561 or by e-mail: "Rush, Kathy" <kathy.rush@ubc.ca>. Or Alexandra Lukey (250) 540-4302 or by email alukey@mail.ubc.ca Contact for Concerns about the Rights of Research Participants You have the right to ask questions, and have those questions answered. If you have any concerns or complaints about your rights as a research participant and/or your experiences while participating in this study, please reference the study number H20-02390 when contacting the Complaint Line so the staff can better assist you. Contact the Research Participant Complaint Line in the UBC Office of Research Services at 1-877-822-8598 or the UBC Okanagan Research Services Office at 250-807-8832. It is also possible to contact the Research Participant Complaint Line by email (RSIL@ors.ubc.ca). 132 Consent The Heart SCaPE Study: Patient Consent Form: Part 2 H20-02390 Taking part of this study is completely optional and therefore you have the right to refuse or withdraw at any point in this study without any negative consequences to your care. Your signature below indicates that you have understood all the above points and that you consent to be a part of this study. Your signature below indicates that you have received a copy of this consent for your records. I agree to participate in an evaluation interview. By participating I agree to allow my conversation to be audio recorded and directly quoted, maintaining confidentiality, in presentations of research results. YES NO (Please circle your choice) Your signature below indicates that you consent to participate in the study Participants Signature Date Printed Name of the Participant Address of email of Participant (Optional if you would like a copy of the transcription) Please tick box if you would like a copy of the study results sent to you and indicate if you would like this emailed or mailed on the line below. Thank you for your participation
Thesis/Dissertation
eng
Nursing
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Rush, Kathy L.
Gamification of eHealth self-care education in a heart failure population
Text