Background

Chronic disease is a major problem worldwide. There are four main types of chronic disease, including cardiovascular disease (myocardial infarction and cerebrovascular events), cancer, chronic respiratory disease (chronic obstructed pulmonary disease and asthma) and diabetes.1 Annually, 38 million people die from chronic disease, and more than half of these deaths (28 million) occur in low- and middle-income countries.1 The burden of chronic disease, from 2011 to 2025, in these low- and middle-income countries, is expected to amount to US$7 trillion and US$12 billion, respectively.1 However, between 10 and 50% of chronic disease hospitalizations are potentially preventable.2 Chronic disease patients who engage in self-care and the self-management of their illnesses have better health outcomes, fewer hospitalizations and complications.

Patients can play an integral role in the management of their chronic diseases.3,4 Self-care is the process of maintaining health through health-promoting practices in both healthy and ill states, including self-care maintenance, self-care monitoring and self-care management.5 Patients can limit worsening symptoms when they understand the principles of chronic disease management and learn to undertake simple interventions.6,7 The ability of the patient to control their illness through an effective therapeutic plan is significantly influenced by social and behavioral factors.5,8 However, impaired cognition, poor understandings of health information, illiteracy and sleep disturbances often prevent effective self-care behaviors.9,10 A key predictor of successful behavioral change is the confidence in one's ability to carry out self-monitoring and to implement appropriate actions to achieve self-efficacy in health. Bandura's11 conceptual framework of self-efficacy is often referenced to describe the relationship between self-confidence and the development of self-care behaviors.

The concept of self-efficacy has been adopted in chronic disease management in an attempt to educate and change habits. Self-efficacy is a powerful predictor of self-care performance.12 According to Bandura11, there are three factors of human interaction that influence self-efficacy: behavioral, personal and environmental factors. If patients have higher self-efficacy, they will be more likely to display self-care behaviors.13,14 With chronic disease, this means not only will they adhere to treatment recommendations, they will also maintain these behaviors. A number of chronic disease education programs are based on Bandura's theory that self-efficacy improves knowledge, self-care skills, quality of life (QoL) and psychological health.15,16 Self-efficacy may also influence how individuals with chronic disease perceive their cognitive functioning over time.17

Literacy is the ability to read and write, including skills for accessing knowledge through technology and the ability to read and comprehend complex instructions.18 In 2013, the number of illiterate adults worldwide was estimated to be 757 million.19 Literacy is an important factor in understanding how to engage in self-care, self-managing illness and accessing the healthcare system, as most educational instruction is presented in print form. Therefore, low literacy is a fundamental barrier to effective self-care.20,21 For example, inadequate literacy can affect the patient's ability to read instructions on a medicine bottle and this may lead to the exacerbation of illness, a higher burden of symptoms, poorer QoL and an increased risk of hospitalization and premature death.22-24 Any patient-centered education intervention for teaching self-care for chronic disease needs to consider a variety of delivery formats to account for low literacy and also for patients who speak English as a second language.25

Health literacy is defined as the ability to read and understand health information and materials for performing self-care and making effective health decisions.26 Health literacy influences a patient's self-care ability. Adequate health literacy is associated with greater knowledge about the disease and better health outcomes.27 For individuals with a chronic illness, having an adequate level of health literacy is essential for obtaining and understanding the health information and services they need to engage in managing and making decisions about their own health.28,29 Berkman et al.28 reported, in a systematic review, an association between low levels of health literacy, poor health outcomes and poorer utilization of healthcare services.

Patient education interventions have been developed for improving knowledge and self-care skills in patients with low health literacy.30 However, printed-education materials and traditional patient education only have a minor beneficial effect on professional practice outcomes. The information contained is insufficient and uncertain to have a positive effect on patient outcomes.31,32 Presently, information and communication technologies can serve as a means to promote the principles of self-care and to improve health outcomes.33,34 Technology-assisted psychological interventions have been shown to be efficacious in improving self-management and health status.35-37 Emerging technologies have been positively linked to improving patient engagement.38 Real or simulated experiences and the process of debriefing also enhance learning. Therefore, technology has the potential to be part of the learning environment for teaching self-care for chronic disease.37

An avatar is defined as a moveable online manifestation or animation of a person used to enhance interaction in the virtual reality environment or in virtual cyberspace.39 Avatar-based education technologies enable users to actively participate in the creation of a unique online persona and allow educators to easily present an activity that would otherwise be difficult to read or demonstrate with a static picture.40 Avatar technology is, therefore, particularly useful for patients with low literacy, low health literacy and English as a second language. In addition, avatar technology has been developed for collaborative or simulation-based education, which is a contemporary intuitive approach to distance teaching and an important tool in modern education practices.40,41 Virtual activities are more interactive than real-world teaching activities.42 Avatar-based technologies for supporting education are now widespread and on the increase.42

The use of avatar technology for patient education aids in chronic diseases such as cancer, diabetes and depression for improving knowledge, self-care behaviors and QoL.43-45 Findings from related studies demonstrate positive outcomes in clinical practice. For example, an avatar-based intervention embedded into an online self-management program improved over-active bladder health-related QoL (HRQoL) and symptoms in women.46 In another study, an avatar-based interactive application improved knowledge and stoma care self-efficacy in hospitalized patients with a new ileostomy.43 Avatar technology also reduced depressive symptoms over time in a group of young adults44 and prevented relapse in smoking in hospitalized cancer patients.47

A preliminary search in the JBI Database of Systematic Reviews and Implementation Reports, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and PubMed found no systematic reviews looking at the use of avatar-based technology for patient education to evaluate the effect on outcomes such as knowledge and self-care behaviors for chronic disease. Therefore, this review will search for evidence to determine the effectiveness of patient education, using avatar technology, on knowledge and self-care behaviors in chronic disease.

Inclusion criteria

Types of participants

The current review will consider studies that include patients with any chronic diseases. For example, cardiovascular diseases, chronic respiratory diseases, type 1 or type 2 diabetes or any form of cancer.1 In addition, this review will consider for inclusion studies evaluating patients of all ages (children and adults), races, ethnicities and genders.

Types of interventions

The current review will consider studies that investigate the effectiveness of using avatar technology in patient education interventions. In the first encounter (orientation to the technology), the avatar technology will have been delivered by a nurse, physician and/or allied health educator via an iPad, other tablet device, computer or mobile phone in a hospital or in the community. Patients will then continue to use/view the technology at home.

Comparator

The current review will consider studies that use usual care and paper-based education materials.

Usual care in the chronic disease field includes standard medical care via outpatient or specialist clinics, education through printed materials and/or verbal information from a physician or nurse.48 Care from a family physician or general practitioner, self-management programs, mainstream community services and generic telephone advice are also included. Self-management programs for usual care in chronic disease normally provide education using adult learning principles. These education programs may utilize a variety of education formats such as audio, video and written delivered in group and individual sessions.49 Other computer/internet-based program that do not use an avatar will not be used as comparators.

Outcomes

The current review will consider studies that include the following outcomes as measured by valid and reliable instruments.

Primary outcomes

Knowledge is defined as a theoretical or practical understanding of health information acquired through experience or education. There are many tools in the chronic disease field that measure knowledge including: the Michigan Diabetes Knowledge Test, which is used to measure diabetes knowledge through a 14-item scale questionnaire50; the Dutch Heart Failure Knowledge Scale, which measures the knowledge of heart failure patients through a 15-item self-administered questionnaire51; the Bristol chronic obstructive pulmonary disease (COPD) Knowledge Questionnaire designed to measure knowledge of COPD through a 20-minute self-administered instrument comprising 13 domains, each consisting of five statements with response choices52 and the Cancer Knowledge Test, which is used to measure knowledge about cancer and its treatment through 18 multiple-choice questions.53

Self-care behavior is defined as the process of maintaining health through the use of health-promoting practices and the self-management of illness in both healthy and ill states.5 There are many tools in the chronic disease field that measure self-care behaviors including the European Heart Failure Self-care Behavior Scale, which measures heart-failure-related self-care behaviors through a nine-item questionnaire54 and the Summary of Diabetes Self-care Activities Measure, which is used to assess diabetes self-management through a brief self-report questionnaire.55

Secondary outcomes

Self-efficacy is defined as the use of individual management to educate and change one's habits.12 There are several tools that can potentially be used to measure self-efficacy including the Self-Efficacy for Diabetes Scale, which measures the perception of one's ability to manage diabetes in medical, general and diabetes-specific situations through a 34-item questionnaire and a six-point scale,56 and the Exercise Self-Regulatory Efficacy Scale, to measure the self-regulatory efficacy of individuals with COPD to exercise through a 16-item questionnaire with a level of confidence ranging from 0 (not at all confident) to 100% (highly confident).57

Health-related QoL (HRQoL) is defined as the overall impact of a medical condition on the physical, mental and social wellbeing of an individual.58 Examples of tools used to measure HRQoL in the chronic disease field include: the Medical Outcome Study 36-item Short Form Health Survey, which is designed to measure the health status of individuals through a combination of a physical component summary and a mental component summary, including 36 questions in a self-administered questionnaire with scores for each dimension ranging from 0 to 10059; the Minnesota Living with Heart Failure Questionnaire, which measures the effects of symptoms, functional limitations and psychological distress on individual QoL through 21 questions on a scale from 0 to 560 and the St. George's Respiratory Questionnaire, which is a disease-specific QoL assessment tool validated for both COPD and asthma through 50 items containing 76 weighted responses.61

Re-admission is defined as a subsequent acute admission for the same patient within 30 days of discharge of the initial admission, with at least one day between the discharge and the new admission.62 In this review, re-admission includes both emergency and elective admissions to hospitals and unplanned general physician units as measured by a medical self-report form.

Adherence to medication is defined as patients taking medications as prescribed by their healthcare providers with respect to timing, dosage and frequency.63 Medication adherence can be measured through: the Medication Adherence Questionnaire, also known as the Morisky-4 or morisky medication adherence scale-4 (MMAS-4) scale64; the Brief Medication Questionnaire, which measures adherence to medication in diabetic patients, through a self-report tool for screening adherence and barriers to adherence, and includes a five-item scale65 and the Hill-Bone Compliance Scale to measure adherence to hypertension medication through a 14-item scale in three subscales with each item being measured through a four-point Likert-type scale.66

Types of studies

The current review will consider both experimental and epidemiological study designs including randomized controlled trials, non-randomized controlled trials, quasi-experimental studies, before and after studies, prospective and retrospective cohort studies, case-control studies and analytical cross-sectional studies for inclusion. If there is a lack of the best evidence in this area (randomized controlled trials), this review will also consider descriptive epidemiological study designs including case series, individual case reports and descriptive cross-sectional studies for inclusion to report on current best evidence in relation to the effectiveness of using avatar technology in patient education for the field of chronic disease.

Search strategy

The search strategy aims to find both published and unpublished studies. A three-step search strategy will be utilized and has been adapted from the Joanna Briggs Institute Reviewers Manual (2014 edition) guidelines.67 An initial limited search of MEDLINE and CINAHL will be undertaken, followed by an analysis of the text words contained in the title and abstract and of the index terms used to describe each article. A second search using all identified keywords and index terms will then be undertaken across all the included databases. Third, the reference list of all the identified reports and articles will be searched for additional studies. Studies published in English from 2005 to present will be considered for inclusion in the review, as this is the timeframe in which these technologies have begun to make an impact on teaching and learning outcomes.68 This review will exclude publications in languages other than English, as there are no facilities available for translation. In addition, this review will exclude all qualitative studies on this topic, as the aim is to determine the effectiveness of using avatar technology in patient education.

The databases to be searched include:

PubMed, CINAHL, PsycINFO, Cochrane Central Trials Register of Controlled Trials ProQuest, Web of Science and Embase.

The search for unpublished studies will include:

TROVE, Networked Digital Library of Theses and Dissertations, PQDT Open, World Health Organization, National Institute for Health and Care Excellence, Clinicaltrials.gov, Open-Grey and Google Scholar.

Initial keywords to be used will be:

Chronic disease, heart diseases, diabetes mellitus, cerebrovascular disorders, asthma, pulmonary disease chronic obstructive, cancer, avatar, user-computer interface, gaming, computer simulation, three-dimension, virtual environment, patient education, health education, consumer health information, knowledge, self-care behaviors, self-efficacy, quality of life and medication adherence.

Assessment of methodological quality

All quantitative papers on avatar technology patient education in the field of chronic disease selected for retrieval will be assessed by two independent reviewers for methodological validity prior to inclusion in the review using standardized critical appraisal instruments from the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI) (Appendix I). Any disagreements that arise between the reviewers will be resolved through discussion or with a third reviewer.

Data extraction

Data will be extracted from papers included in the review by two independent reviewers using the standardized data extraction tool from JBI-MAStARI (Appendix II). The data extracted will include specific details about the interventions, populations, study methods and outcomes of significance to the review question and specific objectives.

Data synthesis

Quantitative data will, where possible, be pooled in a statistical meta-analysis using JBI-MAStARI. All results will be subject to double data entry. Effect size expressed as an odds ratio (for categorical data) or weighted mean differences (for continuous data) and their 95% confidence intervals will be calculated for analysis. Heterogeneity will be assessed statistically using the standard Chi-square measure and will also be explored through subgroup analyses based on the different study designs included in this review. Where statistical pooling is not possible, the findings will be presented in narrative form including tables and figures to aid in the presentation of the data where appropriate.

Acknowledgements

The current review was supported by the 2015 "12 Weeks to Publication" Workshop, School of Nursing and Midwifery, Flinders University, South Australia.

Appendix I: Appraisal instruments

Appendix II: Data extraction instruments

References