Business School
EFFORTLESSNESS, SELF-DETERMINATION, AND INCENTIVES
Youth personal health information management with consumer technology: an integrative review
Taru Honkonen Master’s thesis University of Eastern Finland Economics and Business Administration Master's Degree Programme in Health and Business December 2020
Abstract
UNIVERSITY OF EASTERN FINLAND Faculty
Faculty of Social Sciences and Business Studies
Department
Business School
Author
Taru Honkonen Supervisor
Ulla-Mari Kinnunen
Title
Effortlessness, self-determination, and incentives – youth personal health information management with consumer technology: an integrative review
Main subject
Health and Business LevelMaster’s degree Date11 December 2020 Number of pages
94 + 11
Health care has become more consumer-oriented over the past two decades. The utilization of consumer technology in personal health management is expected to make an impact in the preventive health care domain. In addition to the nearly ubiquitous internet, the acceleration of chronic diseases, access to care, and the aging population are commanding the development aiming for enhanced cost-effectiveness. However, the world has now more young people than ever before with increasing health concerns.
The youth population as the digital natives have gained increased interest in consumer health informatics research but evidence on the effect of consumer technology in youth health empowerment is yet scarce.
The objective of the research is to find out how adolescents and young adults perceive technology-based personal health record solutions and which elements influence youths’ behavioral intention or use behavior of personal health record solutions, and whether the use of such solutions is increasing youths' health empowerment. The research aims to provide an up to date outlook for health care practice, youth community policymakers as well as businesses in the field on how to position and utilize personal health record interventions in youth health. The research is carried out as an integrative literature review through an online research database search to databases of Cochrane Library, Scopus, Web of Science, and ProQuest. The search yielded 487 potentially relevant articles which were screened based on the title, abstracts, and key terms of the articles, and reduced to 97 articles for reading the full texts. Based on the study selection criteria, 25 articles were eligible for the final data analysis.
The extended model of Unified Theory of Acceptance and Use of Technology (UTAUT2) by Venkatesh et al. was used as the priori framework in the deductive-based data analysis. The model has seven determinants affecting the behavioral intention or use behavior of technology in a consumer context. The results were analyzed using the constant comparative method and thematic analysis.
Based on the results, the determinants of performance expectancy and effort expectancy have the predominant influence on youths’
behavioral intention or use behavior of personal health record solutions. The use or the potential use of the solutions is wanted to happen with minimal effort and without consuming time on the use. The desire for autonomy, self-efficacy, privacy, control of own health image, possibilities for use personalization, and lack of incentives influenced the behavioral intention and use behavior.
Participatory approach on youth personal health record development can have a positive influence on use behavior. Further research is needed to clarify the usefulness of personal health records for youth with different needs and contexts of use. New determinants of self-determination and privacy are suggested for future UTAUT2 model utilization in youth health.
Key words
Consumer health informatics, Personal health records, Youth, Integrative literature review, UTAUT2
TABLE OF CONTENTS
1 INTRODUCTION ... 5
1.1 Gaze to youth ... 6
1.2 The objective of the research ... 9
2 FROM HEALTH INFORMATICS TO CONSUMER HEALTH INFORMATICS ... 13
2.1 Evolution of consumer health informatics ... 13
2.2 Consumer health informatics ... 16
2.2 Technology Acceptance Model (TAM) and the extended versions ... 17
2.3 The Unified Theory of Acceptance and Use of Technology (UTAUT) and the extended version (UTAUT2) ... 21
3 METHODOLOGY ... 27
3.1 A review as a research method ... 27
3.2 An integrative literature review ... 29
3.3 The literature search strategy and study selection ... 30
3.4 The constant comparative method ... 40
4 RESULTS ... 43
4.1 Performance expectancy and effort expectancy ... 44
4.2 Social influence, facilitating conditions and habit ... 49
4.3 Hedonic motivation and price value ... 54
4.4 Moderators of age, gender and experience ... 59
4.5 New determinants of privacy and self-determination ... 61
4.6 Provider reflections – findings for research and policy ... 63
4.7 Improvements in adolescents’ and young adult’s health empowerment ... 65
4.8 Summary of the key results ... 66
5 DISCUSSION AND CONCLUSION ... 73
5.1 Summary of the study ... 73
5.2 Key findings ... 74
5.3 Reliability and limitations of the study ... 76
5.4 Implications for practice and future research ... 78
REFERENCES ... 83
APPENDICES ... 95
APPENDIX 1. Research articles included for the final data analysis ... 95
1 INTRODUCTION
Health care has become more consumer-oriented over the past two decades. One of the
compelling elements in the cultural change from the institutional health care orientation towards a participatory health care system can be found from the phenomenon of the pervasive information.
Continuous diffusion of digitalization and the nearly ubiquitous internet has accelerated
consumers access to health information. Starting from the personal computers in the early 1990s and continuing to expansion of the internet, medical knowledge and the latest clinical science have become a public domain through various online research databases and health information web portals. (Weaver & Zielstorff 2011).
Across the modern health care sector, a significant amount of work and resources are targeted to digitalize citizens’ health and social care services. Acceleration of chronic diseases, access to care, aging population, and diminishing of health care expenditure are some of the perceived challenges in the health care sector that countries around the world are facing with a rapid speed (Griffiths et al. 2006). Policymakers are setting a great deal of anticipation and resources to develop the so-called eHealth services to overcome these global challenges. One of the reasons for enthusiasm towards intuitive and interactive eHealth solutions among health and social care providers is bursting from the opportunity to utilize widely spread consumer technology
(Griffiths et al. 2006). Ideally, easily accessible and affordable technology used by people around the world could make a global effectiveness leap in health care settings as well.
Organizations in the health and social care sector are acquiring smarter information technology solutions, more powerful data repositories, more dynamic cloud computing services, and artificial intelligence integrated agile software applications for saving, accessing, and further utilization of health-related data. Veritably, organizations and businesses regardless of the field, are
transformed into data-driven and the ever-increasing data is seen as a goldmine when processed into actionable knowledge. Expectations for technology-based solutions solving future challenges in the health and social care sector are high, although, Ball et al. (2011) have pointed out that information technology as a tool cannot provide magical care improvements. However, technology can provide significant assistance for health care (Ball et al. 2011, 5-9).
One of the key elements in digitalization is that different tasks in society are aimed to make simpler and with fewer resources. When a technology-based solution is acquired hoping to make processes more efficient in the health care sector, an evidence-based decision is of high
importance. The origin comes from the evidence-based medicine in which it means that decision should be made according to the best available scientific evidence with emphasizing the role of patient preferences and values in the decision-making process (Pearson et al. 2005).
When the patient is put into the middle, the possibility of information technology expands. The patient is also a consumer surrounded by technology. The quantified self-boom with health applications, smart wearables, and intelligent fabrics is a growing trend. Aligned with the self- care or the so-called “body hacking” (Duarte 2014), changing health care services and health promotion initiatives closer to the consumer are widely recognized as essential elements in reinventing health care (Whetton 2005, 21-25). In today’s world, it seems that the possibility to access, collect and save precious health data is tempting, although, it is suggested that the gap between the generation of data and our capability to understand it is ever increasing. According to estimates, the amount of data in the world’s databases doubles every 20 months, but rarely the information hidden behind the data is exploited. (Witten et al. 2011, 4-5). Nevertheless, the scattered data has its potential for consumers, businesses as well as for health care organizations when processed into actionable knowledge.
1.1 Gaze to youth
Aging population are often referred to as the burden for health care expenditure. How about the youths? The youth population as the digital natives is the biggest user group of internet and mobile applications in the Europe (European Union Eurostat 2020). According to the World Health Organization (WHO), the world has now more young people than ever before. Of the 7,2 billion world population, up to 42% are younger than 25 years (WHO 2020c). Adolescent health has gained increased global health initiatives, but it is still lacking a comprehensive approach. In addition, a fallacy that adolescents are healthy is still persistently distorting the understanding of the current adolescent global health status (Simelela 2020). According to the European
Comission’s Digital Economy and Society Index (DESI), Finland has the strongest human and
digital knowledge capital among European Union (EU) member states alongside with
Luxemburg, Netherlands and Sweden. The DESI measures the individual’s skills to use services in a digital society, such as the capability to interact online and to consume digital goods as well as the individual’s ability to enhance productivity and economic growth through technology use.
In terms of digital public services in the EU member states, Finland is succeeding well as it was ranked with Estonia and Netherlands in the top three countries utilizing and developing the so- called eGovernment services for their citizens. (European Comission 2018). In addition, 98 percentages of Finland’s youth population (16 to 24-years-old) are using internet many times per day and the majority (92%) uses social media services as well as instant messaging-like services.
(Statistics Finland 2020). Consequently, it seems that the digital infrastructure to engage youth in health empowerment through technology is ready to be utilized.
Health issues concerning adolescent health are related to this specific age period in life of cognitive and psychosocial growth during which adolescents establish patterns of behavior that influence also their health behavior. Such behaviors can be related to physical activity, mental health, diet, substance use and sexual activity. It is recognized that youths’ have age-appropriate information needs in terms of health education to promote healthy life choices, increase sexual education and create youth accessible health services. Accordingly, adolescents face specific barriers in accessing health information due to restrictive laws and policies or parental control, as well as lack of confidentiality or limited knowledge available. (WHO 2020a). In addition, WHO has highlighted the need for design that actively and meaningfully participates adolescents in developing health interventions that fit youths’ needs and encourage youth to take a leadership in the global youth health agenda (WHO 2018).
Despite the existing research evidence questioning the trustworthiness of the available health information in the internet, previous studies have shown that consumers are using the internet for health information seeking (Gray et al. 2005, Cline & Haynes 2001). It is also suggested that adolescents’ health behavior with reflection to health literacy and health decision-making should be further researched with comprehensive definitions and measures creating new theoretical frameworks and research designs. (Fleary et al. 2018). Earlier studies have also recognized the need for novel strategies to enhance adolescents’ participation and activation in personal health management interventions. A large-scale retrospective cross-sectional research on
sociodemographic disparities in patient portal enrollment and activation in primary care
pediatrics in the United States found out that the target area’s health care personnel had not been planned a strategy for adolescent activation although there were no specific rules against offering enrollment directly to adolescents. However, the health care staff was trained to offer the portal enrollment typically only to parents. The researchers suggested that the requirement of adolescent consent for proxy user enrollment in the participating pediatric settings might have caused a barrier for the responsible staff to advertise and offer the portal directly to adolescents
themselves. They also pointed out that future studies should research the impact of adolescent patient portal access and the effect of it on youths’ self-management. (Ketterer et al. 2013).
Again, how about the youths? It seems that research has been conducted regarding the
technology-based health empowerment among youth from the perspective of the provider or the parents (Armani et al. 2016, Werner et al. 2017, Ronis et al. 2015, Bratt et al. 2018, Rand et al.
2015). This gives reasons to explore the perceptions of the youth themselves more. In addition, it is suggested that more detailed information about different personal health record solution user groups are needed (Coughlin et al. 2017) and that research should focus on understanding the effects that motivate for use also in the long-term (Archer et al. 2011). Despite the conducted research in the field of consumer health informatics, the engagement for personal health record solution use has remained low. Thus, strategies for successful adoption of web-based personal health record platforms aiming for enhancing patient motivation and at the same time engaging professionals to promote the use are needed (Ozok et al. 2017). Current research suggests that there is a potential in consumer health informatics to have an impact on consumer’s health but in the context of youth health and from youths’ own perspective, the research is not yet mature enough.
1.2 The objective of the research
The objective of the study is twofold. Firstly, the aim is to add knowledge about the status of consumer health informatics and personal health records in the context of youths’ health
management. Secondly and first and foremost, the objective is to investigate in more detail how personal health record solutions are perceived among adolescents and young adults, and whether such solutions have an influence on youths’ health empowerment. By locating and synthesizing available evidence-based knowledge with an integrative literature review, the research aims to answer the following research questions:
1. How adolescents and young adults perceive personal health record solutions?
2. Which elements influence adolescents’ and young adults’ behavioral intention or use behavior of personal health record solutions?
3. Is behavioral intention or use behavior of personal health record solutions increasing adolescents’ and young adults’ health empowerment?
The extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model by Venkatesh et al. (2012) works as the prior framework against which the elements affecting youths’ behavioral intention or use behavior of personal health record solutions are investigated.
By supplementing earlier research with the results of this study, an attempt is to provide an up to date outlook for health care practice and youth community policymakers on how to position and utilize personal health record interventions in youth health. At the same time, results could possibly be valuable for companies developing health informatics solutions. The research will also increase the knowledge of the researcher, and hopefully, the results from the research will contribute ideas for future consumer health informatics research. The possible new views for health care practice are presumably targeted at a national level.
American Medical Informatics Association (AMIA) defines consumer health informatics (CHI) as “the field devoted to informatics from multiple consumer-patient views”, which consists of patient focused informatics, health information literacy and consumer education. The focus is on information structures and processes that empower consumers to manage their own health.
Personal health records, internet-based solutions and consumer-friendly language in health- related topics are amongst the key areas in the research arena of the field. In consumer health
informatics the objective is to analyze and develop methods on how to integrate consumers’
preferences into health information systems. The discipline is in the intersection between clinical informatics and public health informatics combined with study fields of health promotion, nursing informatics, public health and health education as well as communication science.
(AMIA 2013).
A personal health record (PHR) can be defined as “an electronic health record that is maintained by the patient, typically combining information from a variety of encounters with multiple providers” (AMIA 2013). The proposed definition reflects the reality of PHRs’
ambiguity, suggesting that a PHR can mean a variety of solutions or platforms that are used by patients managing their health or searching for health-related information. However, what is common nowadays, is that PHR’s are web-based, although paper-based, such as paper-based health diaries still exist as well and can be found useful (Self et al. 2015). In this research, the focus is in web based PHR solutions. In addition, the consumer context has broadened the definition even more, bringing the consumer platforms into the picture, for example, the social media as one of the most prominent new platforms (Moorhead et al. 2013).
In addition to the manifold of platforms and technologies used in personal health records, the PHR solutions are generally divided into two categories; a tethered PHR or a standalone PHR.
The tethered PHR refers to a PHR solution or a platform that is connected with the patient’s care provider’s or health care organization’s electronic health record (EHR) solution, through which the patient can access their own health records and see, for example, laboratory test results.
(ONC 2019). The functions and access that a patient can have in a tethered PHR varies depending on the health care organization’s practices as well as country’s legislation and health care funding system. In Finland, for instance, the “My Kanta” (“Omakanta”) is a form of a tethered PHR (The Social Insurance Institution of Finland 2019). A standalone PHR is more complex concept and can refer to a variety of solutions or applications with which the user or a patient can manage and record own health-related information from diet to exercise and from medications to moods. The main difference in a standalone PHR is that the user fills in the information by him or herself and has the control over with whom to share that information. The user can decide to share the information or access in a standalone PHR with care providers or for instance with family
members. In some cases, a standalone PHR also receives data from external sources, like form the care provider. (ONC 2019).
To have and to give access to a PHR or to give the so-called proxy access for patient’s surrogate, is a complex legislative challenge, especially in the case of under-aged people. Essén et al.
(2018) conducted a policy analysis to compare patients’ rights to access their electronic health records in Australia, Denmark, Estonia, Finland, France, the Netherlands, New Zealand, Norway, Sweden and the United States, and found out that individuals encounter greatly different access rights to their health records depending on where they live. They discovered three particular areas with the biggest differences between the countries; security in terms of system login processes, adolescence access and the data sets of the medical data that the patients have the right to access.
The high variability in adolescents’ access rights were grouped according to the policy the country obeyed in terms of parental access; a default parent access (Estonia and France), default blocked access (Norway, Sweden), default mixed access (Finland) and a case-by-case approach (Australia, Netherlands, New Zealand, United States). Within these policies each country has different interpretations about the ages of access by adolescents. Especially the case-by-case approach evaluating the youth’s maturity for access were seen as a risk for unequal rights among adolescents’ while increasing the administrative burden at the same time. (Essen et al. 2018).
Each country has also a mixture of other legislation which aims to protect everyone’s right to access and meaningfully use their health-related data as well as protect the own health
information. However, youths’ right to access and control their own health data appears to be an area in need for clarification and development in order to ultimately empower the youth in health management with the help of technology. The youth population in this research is considered as persons from 10 years to 24 years old, which is discussed in more detail in the methodology section.
The structure of the thesis is as follows. The introduction aimed for explaining the topic of the research, the rationale and relevance for conducting the study highlighting the gaps in the current research. In addition, the key objectives and the main concepts were described. Next the
theoretical background of this research is presented and previous research relevant to the research questions reviewed and discussed. The theoretical background is shaped according to illustrate the interdisciplinary nature of health informatics and one of its evolutionary orientations towards
consumer health informatics research. Therefore, some of the well-known and widely applied theoretical approaches in the field of health care informatics, information systems research, and human health behavior research are presented. The theoretical background continues with presenting the Unified Theory of Acceptance and Use of Technology (UTAUT) model (Venkatesh et al. 2003) and the extended version of it, the UTAUT2 (Venkatesh et al. 2012), which performs as the theoretical model and as the basis for the data analysis in this research.
The chapter is finalized with a summative figure of the research’ theoretical approach in the health informatics research field. In the chapter 3, the methodological approach of an integrative literature review is explained, and the method of the data collection as well as the method for the data analysis described. Results of the research are presented in chapter 4, and lastly, the
conclusion of the research is drawn, and some critical discussion, as well as implications for future research, made.
2 FROM HEALTH INFORMATICS TO CONSUMER HEALTH INFORMATICS 2.1 Evolution of consumer health informatics
Seldom have I found myself thinking that paper is a technology. Paper can be used in many ways, for many purposes and it comes in a variety of forms. The paper-example demonstrates the interdisciplinary in health care information technology research rather well. Until the technology
“disappears” as a technology in the same way that paper did, the need for understand and investigate how it is used in health care exists. (Englebardt & Nelson 2002, 498-499).
Health informatics is an applied field of study, thus assimilating theories from a wide range of sciences. The prevalent ones come from the fields of information and computer science, but also cognitive science is widely present complemented with multiple theories from medicine and other study fields to guide health care practice. Many theories supporting health care informatics incorporates with; the systems theory with the perspective of closed or open systems,
information theories, such as the Blum’s Model, learning theories like behavioral learning theories and change theories such as the diffusion of innovation and the planned change.
(Englebardt & Nelson 2002, 3-25). It is important to make a short review of these theoretical emphases as the later presented theoretical model that was chosen as the priori framework for the research questions is built on and further developed based on the origins of these interdisciplinary theories. Secondly, the overview clarifies the diverse nature of health informatics as a science field and rationalizes the place for this research.
In systems theory the so-called closed systems do not interact with the environment such as a chemical reaction in a glass structure with no interaction with the environment outside the glass.
In contrast to closed systems, open systems take input, such as information, from the
environment, processes it and then returns the output to the environment. In health informatics research the concepts of open systems are applied for instance when researching how health workers use computers at their work or in a more general level, when investigating the interaction between a specific technology and health care professionals using it. (Englebardt & Nelson 2002, 5-10).
There are many definitions for the term information. One of the well-known information theories applied in the field of health informatics is originated from the Blum’s model. The
model identifies three types of medical computing applications based on the objects they process;
data, information, or knowledge. Uninterpreted elements are defined as data, like person’s name or weight. Whereas information is perceived as a collection of processed data, such as patient’s medical record and lastly, knowledge can be created through understanding the relationship between the identified data and the information. (Blum 1986). However, by testing the Blum’s model, Graves and Corcoran (1989) recognized that knowledge can be found at an empirical, ethical, personal and aesthetic levels as well. Followed by these new levels, Nelson and Joos (1989) complemented the model with a fourth element, wisdom. This further developed model is known as the Nelson Data-to-Wisdom Continuum and it has been applied in the research context of automated health care information systems. The idea in the model is that the more complex the data and the purpose of the system, the more intelligence the system needs to be. A decision support system that helps a physician to make a diagnosis for the patient can be described as a highly automated system. If the system utilizes for instance artificial intelligence techniques like neural networks or data mining in order to make the system itself more intelligent, Nelson et al.
designates such systems as expert systems with certain wisdom and knowledge base built in the system. (Englebardt & Nelson 2002, 12-15).
The concept of open systems with different levels of information can be placed to humans as well. When a human becomes the processor of information, the theoretical framework takes a form of learning theories. Behavioral and cognitive learning theories are manifold and widely applied in health informatics research. The information input-output process is considered from the perspective of how people learn, and which elements influence the learning process.
Utilization of learning theories is proven to be important in health informatics education and for instance when designing a new software application for health care. It is crucial to identify learning styles and methods when educating health care professionals or patients to use
computerized information systems and applications. One of the well-known behavioral learning theories by the famous physiologist Ivan Pavlov and his research on dogs’ digestion provides two key concepts to explain learning in informatics; pairing and reinforcement. Just like the dogs learned their mealtime by hearing the bell, the same learning event of classical conditioning - pairing and reinforcement- can appear among users of health information systems. (Englebardt &
Nelson 2002, 15-17).
When imagining a real-life situation in which a clinical nurse has changed a working place and finds out that the new employer is about to change their electronic health record system to a system the nurse has had bad user experiences from the hospital he or she worked before. The name of the unpleasant health record system can work as the conditioned stimulus triggering anxiety or frustration in the nurse’s mind, leading to unconditioned response as lower enthusiasm towards the new workplace. Although such a form of a negative reinforcement at a general level is crucial in learning process, it is important to focus on positive reinforcement when, for
instance, educating patients to use a new information system (Englebardt & Nelson 2002, 15-17).
Although we are living in a modern computerized world, frustration and dissatisfaction towards electronic health record systems are common among the users (Laitinen et al. 2010, Nelson 2016). With a positive reinforcement it is possible to strengthen the learner’s motivation to the correct direction instead of focusing on what is done wrong. If the learner gets only reminders of what he or she is doing wrong or if the user repeatedly faces an error in a computer system, the negative reinforcement can increase the anxiety towards the use of a new information system.
(Englebardt & Nelson 2002, 15-20).
The unifying element in all the presented theories is change. Whether it is a change in a system, change in an information, change in a form of human learning or change in health engagement, the outcome is always a change to direction or another. One of the well-known change theories is called the planned change by the founding father of the organizational development research, a social psychologist Kurt Lewin. The philosophy behind Lewin’s idea in chance is that motivation to change is inseparably related to action. The three main steps in the planned change are
unfreezing, moving and refreezing. To initiate a change, the first phase is to locate the need for it and secondly lower possible anxieties related to change in order to get the change started and moving forward and lastly achieving the wanted change. (Englebardt & Nelson 2002, 21-22).
The theory of planned change could be put in practice for example when a health care organization aims to outgrow from paper-based patient communication and to transfer the practice into computer-based communication solutions. The theoretical emphasis in planned change is in action research in which research questions are often related to practical issues or improvement of an actual problem and the role of the researcher is noticeably collaborative in the study settings (Eriksson 2008, 193-198). In the planned change the focus is in motivational action, whereas the diffusion of innovation aims to explain how an individual or communities
reacts to new ideas or practices (Rogers, Everett 1995). As are people, so are their reactions to innovations diverse as well. In the diffusion of innovation, people are divided in groups of innovators, early adopters, early majority, late majority and laggards based on the way they respond and acts upon new ideas. The model has been widely applied in the health research and according to Rogers, a noteworthy and successful field in diffusion of innovation applied
research has been in studies focusing on HIV prevention with successful results especially during the AIDS epidemic in the United States in the 1980s. (Rogers 1995, Rogers, 2004).
2.2 Consumer health informatics
Continuing from the diverse evolvement of health informatics research, Eysenbach and Jadad (2001) took a step forward and made a statement that consumers health informatics should be regarded as a total new academic discipline. They discovered that consumer health informatics represents a total new era for evidence-based medicine with internet as the hyper-distributor of information. Accordingly, they suggested that in its broadest sense consumer health informatics research should take a stand on analyzing consumer preferences, develop methods for educational health promotion activities, investigate participatory telecommunication effectiveness, and to study these interventions form the perspective of public health. (Eysenbach & Jadad 2001).
Researchers agree that patient-physician relationship has met an irreversible new form with consumers’ increased technological possibilities to take part. Although the shift from an
authoritative health communication model towards shared decision making is receiving positive interest among physicians, the relationship reality has not yet succeeded, as preferable evidence and models to act upon it are yet under development. Lack of financial incentives and lack of time are seen influential for some health care providers both in developed and in developing countries to favor the traditional paternalistic consumer-provider interaction. (Eysenbach & Jadad 2001).
Consumer health informatics (CHI) is growing, yet scarce research field. In their brief scoping review Lai et al. (2017) discovered that within past few years the patient-generated health data and consumer health informatics research have accelerated in a form of pilot studies testing consumer technology solutions in health care context but further research is needed to understand the impact of patient-generated health data on clinical outcome. For example, randomized control
trials concentrating on this topic are limited. Although there is a growing interest towards development of various kind of patient-generated health data solutions, there are still gaps in the research on how the data from such solutions is utilized by the patients or consumers and by the physicians. (Lai et al. 2017). The research evidence so far provides marginally recommendations for policymakers and thus a re-examination of current evidence is suggested. In addition, open questions remain on themes such as standards for interchangeability of the patient-generated health data between the patients and the physicians as well as the overall sense-making of the usage and effectiveness of this data. (Lai et al. 2017). These findings support the need for further investigation of the topic through wider lenses both theoretically and methodologically.
As presented, the profound theoretical approach in the health informatics research is often a multidisciplinary. The underpinning elements for the health informatics research utilizes concepts and frameworks build upon amongst system theories, information theories, learning theories and change theories. Next, some of the widely applied theoretical models in technology acceptance research are presented and the theoretical approach of this research comprised.
2.2 Technology Acceptance Model (TAM) and the extended versions
In the field of information systems research one of the widely applied and further developed theoretical model in an organizational context aiming to explain and predict how an individual accepts the use of technology is called the Technology Acceptance Model (TAM) originally proposed and validated by Davis (Davis 1986; 1989) and later framed by Davis et al. (Davis et al.
1989). The fundamental of the model is that a person chooses to use, or not to use, a computer system based on the following two main premises:
• The personal assumption of how much the system will help the person to better manage the job the system is designed for is called the perceived usefulness.
• Whereas the perceived ease of use can be capsulized; the easier the system is to use, the more likely the person will use it. (Davis 1989).
The usefulness and ease of use of information systems had been studied earlier but from variety of theoretical spectrums including system utilization (Schultz et al. 1975), expectancy (Vroom
1964), self-efficacy (Bandura 1982), cost-benefit paradigm in behavioral decision making (Beach & Mitchell 1978), adoption of innovation (Rogers et al. 1971), information formats and channels (Larcker & Lessig 1980, Swanson 1987) and in marketing context human-computer interaction (Hauser & Simmie 1981). Leaning on these previous studies, Davis further studied the two premises and found out that no matter how easy the system is to use, the usefulness within the context the system is designed for, weighs more. However, ease of use can predict the usefulness in a way that the easier the system is to use, the less effort is needed to use it and hence more time to be allocated into actual job performance. He also suggested that external variables affect the usage as well and that the role of user’s beliefs and motivation for the use should be further studied. (Davis 1989).
Based on these validated new findings the model for technology acceptance began to form. By incorporating the theory of reasoned action (TRA) from social psychology, the model was
completed including the element of person’s behavioral intention to use the system. According to TRA (Ajzen et al. 1980), person’s specific behavior is determined by his or her behavioral intention which is resulted from to the person’s positive or negative attitudes towards the performance of behavior and from the person’s subjective norms. The subjective norm refers to the degree of which the person’s behavioral intention and use behavior are influenced by how the people that are important to the user weighs the importance of the use by the user. (Davis et al.
1989). With the theoretical base from TRA, the technology acceptance model (TAM) was formed and has continued evolving over 30 years gaining a solid and a robust role predicting user
acceptance within information systems research in variety of science fields (Rondan-Cataluña et al. 2015).
The extended Technology Acceptance Models (TAM2, TAM3)
The later developed extension of the technology acceptance model was TAM2 (Venkatesh &
Davis 2000) which highlighted social forces and cognitive factors affecting the individual’s perceived usefulness towards a system. In addition to the existing subjective norm, a
voluntariness of use and the effect of the use for the person’s image in the social setting he or she uses the system, were proposed as the new antecedents predicting the perceived usefulness. The voluntariness of the use as an added moderator for use intention appeals in TAM2 through the subjective norm meaning that in a mandatory job-like settings the use intention is inevitable when
others are demanding the use, whereas if the usage is voluntary, the effect of social influence diminishes. Beyond social influences, the extended model suggested that people are cognitively comparing what the system is capable to do compared to what the person needs to get done in their job. This resulted in theorizing that job relevance (the system is applicable for the job), output quality (the system performs of the highest quality outputs) and result demonstrability (tangibility of the results the system provides are persuasive) will affect positively on perceived usefulness of the system. In addition, Venkatesh & Davis (2000) discovered that over a time with increased experience with system use, social influence factors effect on the judgment towards the perceived usefulness decreased but the effect with the use to person’s image and status in the social setting where the system was used, continued to affect the perceived usefulness. On the other hand, the cognitive factors were discovered yet influencing the perceived usefulness despite increased usage experience.
As a chronological continuum in their research on information system implementation, Venkatesh and Bala (2008) combined the TAM2 with the perceived ease of use introducing a third version of the model, TAM3, which aimed to be more comprehensive in terms of
determinants affecting the individuals’ information system adoption and use. In comparison to the original TAM and the critique towards it as being overly plain and missing actionable tools and guidance (Lee et al. 2003), the TAM3 targeted providing more thorough view on practical implementation as a guidance. The notable extensions in TAM3 are the so-called anchors and adjustments that the model suggests an individual has prior the system use. Accordingly, person’s general computer beliefs and experiences affect the perceived ease of use and should be noticed in the process of information system implementation. The presented new anchors were computer self-efficacy, facilitating conditions, computer playfulness and computer anxiety. The self-
efficacy beliefs mean the presumptions a person has over his or her own capability to use a system, whereas facilitating conditions are perceptions of external control reflecting the person’s experiences based on the earlier technology acquisition decisions made in the organization. The computer playfulness and computer anxiety reflect person’s natural motivation and feelings towards the system usage meaning that people who tend to use information systems “for fun”
may have the same perception towards the organizational usage as well. As an adjustment, the effect of playfulness decreases when the perceived enjoyment of the system increases. At the other end, the anchor of computer anxiety means that the person may have a negative attitude
towards a system as a basic assumption. The objective usability as an adjustment adapts the user’s perception on the perceived ease of use according to the use experience and can decrease the computer anxiety. The idea in the link between anchors and adjustments is that along the increasing use experience, the possible presumptions will transform into actual system use experiences. (Venkatesh & Davis 2000).
Despite the wide adoption of TAM, TAM2 and TAM3 models, research questioning the
effectiveness of the two extension exists. Rondan-Cataluña et al. (2015) discovered in their linear and non-linear comparison tests with the models applied in research on technology acceptance, that TAM developments did not provide significant improvements for explaining the use of new technologies better than the first TAM. By removing the variable of attitudes towards behavioral intension, the actual use of technology remained constant. The finding suggested that attitude is not of importance in the context of obligated technology use and that the extended models lack the ability to be applied in the context of voluntary use. Behind the observation affects the risk of biased conclusion if a consumer-focused technology use has been tested with a model designed for mandatory use context. (Rondan-Cataluña et al. 2015).
Nonetheless, TAM has been proven to be an applicable model for researching the technology acceptance in health and social care setting as well. In their methodological review on technology acceptance model usage in health care, Holden and Karsh (2010) find out that TAM can estimate a substantial portion of the health information technology use and acceptance, although stating that comparison of the model relevance is challenging due to the many variations of the models.
Through years of contributions towards TAM development, Venkatesh et al. (2003) discovered that the multitude of the model variations are placing researchers as well as organizations into a challenging position being confronted to choose between the models and at the same delimiting the use of redeeming variables in alternative models. With the objective to synthesis and
incorporate similarities in various theories across the user acceptance of new technologies, a unified model comprised from eight substantial previous theories was created, called the Unified Theory of Acceptance and Use of Technology (UTAUT). (Venkatesh et al. 2003).
2.3 The Unified Theory of Acceptance and Use of Technology (UTAUT) and the extended version (UTAUT2)
Similarly like in TAM, the behavioral intention is the main component affecting the use behavior in UTAUT. However, prior the intention or use, four direct determinants with variable
moderating effects of age, gender, experience and voluntariness of use, are crucial (Venkatesh et al. 2003):
• Performance expectancy comprises premises from earlier theories, such as perceived usefulness, external motivation as well as outcome and benefit expectations. The
underlying idea is to evaluate to what extend a person believes that the system helps him or her to succeed in job performance. The performance expectancy is estimated to be the most influential element affecting the acceptance of the use. It is also suggested that the influence of expected performance on behavioral intention is moderated by age and sex being particularly stronger for younger men. (Venkatesh et al. 2003)
• Effort expectancy is defined as the degree of effortless and ease of use that the user associates with the system use. This construct is also drawn upon previous theories, namely from perceived ease of use and a level of complexity related with the system use.
Effort expectancy is suggested to have a significant role towards use behavior both in voluntary and mandatory settings but decreasing over time when usage becomes routine.
Older women are generally expected to face stronger effect on use with effort expectancy due to less use experience with a system. (Venkatesh et al. 2003)
• Social influence integrates the earlier described variables of subjective norms and social factors as one determinant meaning the degree a person feels that important other people believe that he or she should use the system. Effect of social influence is not seen relevant in voluntary use but becomes significant in mandatory use and more so for older women, although the effect is expected to decrease over time when use experience increases.
(Venkatesh et al. 2003)
• Facilitating conditions means the organizational and technical infrastructure that the user believes to either support or not support the system use. Ideally, organizations should aim at minimizing barriers by supporting system users through training and securing
appropriate technical infrastructure enabling coherent system use. It is suggested that if
performance expectancy and effort expectancy are prevalent, facilitating conditions do not have an effect in predicting the person’s behavioral intention to use a system. However, age and use experience are suggested being moderators for facilitating conditions influencing on use behavior among older more experienced persons that are used to rely on stronger support. (Venkatesh et al. 2003)
According to Venkatesh et al. (2003) UTAUT model is valid as a suitable new model explaining 70 percent of the usage intention effects, which is more than all the eight previous models have been able predict. However, as well as TAM, the UTAUT is distinctively designed from an internal organizational perspective with the uttermost aim to enhance and understand the factors influencing an information system implementation process and acceptance in an organizational context (Venkatesh et al. 2012). As the lack of the previous models fit for consumer context (Rondan-Cataluña et al. 2015) were observed through the UTAUT work as well, a further developed extension of UTAUT2 model was presented as a response to the need for technology acceptance evaluation in the consumer context.
The extended Unified Theory of Acceptance and Use of Technology (UTAUT2)
As the UTAUT model focuses on understanding employees’ or organizational perceptions of technology use, the UTAUT2 aims to investigate the hidden individuals’ factors in a voluntary consumer technology context, which has steadily become a flourishing business field as well.
The widespread consumer technology is challenging the role of behavioral intention as a
determinant for use in contrast to habit. In addition, a consumer usually bears the decision of the cost of the technology usage. Another distinct differentiative dimension in the consumer context technology use compared to organizational use is, motivation. The Figure 1. shows the crucial differences between the UTAUT and the extended UTAUT2 model.
FIGURE 1. UTAUT and UTAUT2 models in a combined figure, adapted from Venkatesh et al.
(Venkatesh et al. 2012, Venkatesh et al. 2003)
The system use from motivational perspective in an organization is strongly related to external factors in a form of extrinsic motivation (social influence) whereas person’s intrinsic motivation has a crucial role in voluntary consumer technology use. Due to the voluntariness in consumer context, Venkatesh et al. (2012) removed the voluntariness of use and extended the model for UTAUT2 with three new predictors for intention to use a technology:
• Hedonic motivation means the fun and pleasure the use of technology brings to the user.
In human behavior and psychological research hedonic motivation is explained that an individual rather seeks pleasure and rewards than pursuits for punishments (Gray, J. A.
1981).
• Price value can be defined as the cognitive tradeoff in person’s mind for value for the money. The price value is adopted from theories of marketing (Dodds et al. 1991). In the extended model it means that if the user finds the benefit from the use of a technology higher than the cost, the price value has a positive impact on behavioral intention.
(Venkatesh et al. 2012)
• Habit is seen as the level of which person is performing or recognizes performing a behavior automatically. Habit is viewed before behavior. Experience is necessary for forming a habit towards a technology use, but experience does not automatically generate a habit. Habit or prior use is often seen a significant predictor for future use of a
technology. (Venkatesh et al. 2012).
In the consumer context the performance expectancy is detached from a work environment and placed to a more general level predicting the degree a person believes that use of technology will benefit him or her to perform any specific activity. Facilitating conditions in UTAUT2 are expected to influence on both the behavioral intention and the use behavior. The perspective, however, is different than in UTAUT in which the available support to use technology in an organization is thought to be more as a default. In the consumer context facilitating conditions varies depending on external environment and available instructions for use, for example, from an application provider or consumer’s own access to internet or knowledge to use a specific device.
When Venkatesh et al. (2012) developed and tested this new extended model with an online survey concerning attitudes towards mobile internet usage, they hypothesized that moderators of age, gender and experience will also have a role in the technology acceptance. Facilitating conditions are expected to be moderated by age, gender and experience on behavioral intention with an assumption that it is stronger among older women in the beginning of the use. In the consumer context, the hedonic motivation is expected to be moderated by age, gender, and experience from the perspective of novelty seeking and attractiveness of the use. When the user experience increases, the more normal the use will become and the newness of the technology as an inspiring element fades away. According to Venkatesh et al. (2012), younger men tend to have more enthusiasm towards new technology in the early stages of use, which is presumed to
accumulate hedonic motivation. It is also argued that in the consumer context especially older women pay more attention to the price value of a product or a service, which is why age and gender are seen as the moderators for price value. It also assumed that habit as the determinant on behavioral intention or use behavior will have moderating effects of age, gender and experience in a way that older men with more experience of technology use will engage more easily to use or use intention of consumer technology as the experience has developed into a habit. In addition, it
was also suggested that the moderator of experience will effect on behavioral intention in a way that when the user have gained enough experience about a, for example, smartphone use, the more likely he or she will use a smartphone in the future as well. (Venkatesh et al. 2012).
In the history of the technology acceptance model development, the UTAUT2 represents the newest version of it with the consumer perspective in the center. Despite the model’s relative newness as a theoretical framework, it has been used in research with promising results regarding its fit to consumer context. For example, Rondan-Cataluña et al. (2015) compared the model with the previous models and suggested that model can be validated as better suited for researching consumer perspectives. A lot of consumer-focused research have been conducted regarding e.g.
acceptance of social networks and web sites by using the previous versions of the model, although the models are designed for mandatory organizational context. Hence, the UTAUT2 model is expected to provide a more suitable framework for technology acceptance research for consumer-driven context. (Rondan-Cataluña et al. 2015).
Summary of the theoretical approach of the study
The previous chapters comprised the evolution of health informatics research towards consumer health informatics, as well as some of the widely used theoretical models regarding technology acceptance research. The Figure 2. is meant to summarize the theoretical approach of the research comprising the suggested gap in the current research.
FIGURE 2. Summary of the theoretical approach in this research
Based on the presented theoretical arguments in this chapter, the UTAUT2 model (Figure 1.) was chosen as a suitable theoretical priori framework for this research’ data analysis with a deductive style. By testing the theory in youth consumer health informatics context, the aim is to find out whether the presented determinants of performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit, including the moderators, effect on youths’ personal health record solution behavioral intention or use
behavior. However, an important notion in this study is that age as a moderator is to some extend predefined giving the specific pre-determined age limit of the research population, although there is an age range.
The chapter 3 will describe the methodological approach of the research.
3 METHODOLOGY
3.1 A review as a research method
A literature review can be defined as “the synopsis of other research” (Coughlan et al. 2013, 2).
Although searching the available research is a distinguishable action performed in the process, the critical evaluation of the literature with a fundamental aim to provide a comprehensive picture of the knowledge emerged with pre-existing knowledge relating to a specific topic or
phenomenon, is crucial. Depending on the objective of the literature review, a goal with a review can be anything from advancing practice to generating new ideas, concepts or theories.
(Coughlan et al. 2013, 8-31).
According to Greenhalgh (2014, 28-30), majority of published research articles belongs to bin due to critical mistakes or inaccuracies in the used research methods and are therefore unqualified for guiding the practice. The observation of poor-quality studies is not new in the history of published research. (Mills 1993) referred an expression of “data torturing” describing that if research data is maneuvered enough different ways, it can be made to prove whatever the researcher wants to prove. The well-known medical journal Lancet raised alike concerns about shortage of high-evidence research in the field of health care stating that on an estimation, 85 percent of research investments are lost because of inefficient research questions, inappropriate or incoherent research methods/designs, incomplete study reports and failures in publishing the research which leads to research waste. The argument of such “waste production” was based on statistics of conducted clinical trials but suggested to comply in other forms of research as well.
However, the message was that the portion of research waste could be avoided by enhancing the rigorousness of the research and the research process. (Chalmers & Glasziou 2009).
Undeniably however, not all the research is of bad quality. The rather provocative view on the lack of high-quality research exemplifies the scientific community’s devotion to the quality of the research. When standards are high, the amount of trashed papers increases. Greenhalgh suggests that it is better to trash a paper if the quality is low before starting to look the results of the research (Greenhalgh 2014, 29). To ensure that practice and policymakers are provided with the best available evidence, it is crucial to have joint rules on how to obtain the quality in research. In health and social care research variety of collaborations are invigilating and advancing the quality
of research such as, Cochrane Collaboration (Cochrane), Joanna Briggs Institute (JBI), AGREE Collaboration (AGREE) and National Collaborating Centre for Methods and Tools (NCCMT).
Some of these collaborations has a form of specialties such as dissemination on systematic reviews and clinical trials (Cochrane), evidence-based health care practice (JBI) and practice guidelines (AGREE). This type of collaborative actions have conducted many research method- or objective-based guidelines and critical appraisal tools for scientists, educational institutions and for policymakers with the aim of guiding the work by following the verified best practices on one end when conducting a research, and on the other end when implementing these evidence- based results in to practice. (Aveyard et al. 2016, 109-114).
The Cochrane Collaboration (founded in 1992) is seen as one of the most influential factors for the development of literature review as a research method. The professor Cochrane was one of the earliest medical scientists pointing out the lack of high-quality evidence in treatments and interventions established in health care. Further developed systematic approaches for different kind of reviews for reviewing the research in variety of methodologies are notably followed and adapted from the Cochrane Handbook for Systematic Reviews. (Aveyard et al. 2016, 6-9). Well out-carried systematic reviews are of high-quality, however the key emphasis in systematic reviews is in quantitative data and randomized clinical trials. Thus, other types of review methods have been developed in order to fulfill the review research quality needs for various research designs and methods. (Coughlan et al. 2013, 15-16). The need for different kind of literature review methods is also a reflection of the increased variety in methodological approaches in health and social sciences research. The mixed method research has accelerated as the traditional dichotomy between the quantitative and qualitative study methods has moderated due to the increased interdisciplinary of sciences. The conventional line in health research between the quantitative and qualitative study methods is originated from the different point of views of constructivism and logical empiricism. These philosophical tendencies are traditionally put in confrontation in academic research. Inductive qualitative studies are commonly connected with constructivism in consideration to idealism, relativism and subjectivity, whereas logical
empiricism is associated with deductive quantitative research with perspective in materialism, realism and objectivity. (Pluye et al. 2009).
Characteristic and utterly crucial part with all literature review types is a standardized and transparent review process as well as the conventional goal and a definition of attempting to produce a reliable summary and synthesis of research of other research. (Coughlan et al. 2013, 15-29). Although systematic reviews have gained applauds as a research method and a status as a benchmark among literature reviews, there are other ways of reviewing the literature as well.
Alternatives types are not necessarily as strict, nor standardized as systematic reviews, however a protocol-driven form of conducting the review like with systematic reviews, is recommendable due to it logicality and transparency. Such elements strengthen the validity of the review as well.
Whereas, the lack of structure and the lack of review protocol, for instance, in a narrative or in a traditional literature review, are the main reasons why such alternatives are seen as least
significant in comparison. (Coughlan et al. 2013, 11-14).
Next the rationale for choosing an integrative literature review followed by the work of
Whittemore and Knafl (2005) for the research method is reasoned in detail and the steps of the data collection and the method of the data analysis of the research is presented.
3.2 An integrative literature review
The main objective with this research is to find out how personal health record solutions are perceived among youth population and which elements affect youths’ behavioral intention or use behavior of personal health record solutions. In addition, the aim is to find out whether the usage influences youths’ health empowerment. Given the explorative nature in the research, a research method that supports combining evidence from variety of methodologies is appropriate for this study (Aveyard et al. 2016, 27). In addition, the interdisciplinary nature of health informatics as a research area and the research focus on health care practice evidence seeking, a protocol-driven and a well-structured integrative literature review with proven evidence integrity suits well for this purpose (Coughlan et al. 2013, 13). Integrative reviews are seen as the broadest type of research reviews. With an integrative literature review, both empirical and theoretical, such as experimental or non-experimental research are important sources for reviewing. In addition, different views of phenomena and thoughts of new perspectives can be presented with the results in integrative literature reviews. Contradictory though, the diversity of reviewed literature makes the comparison between the studies difficult in integrative reviews. In addition, the complexity in
incorporating evidence from diverse methodologies can result in lack of rigor or inaccuracies and a risk of bias in the interpretation increases. (Whittemore & Knafl 2005).
The strength with integrative literature review is that it allows a variety of research types for inclusion, which other reviews, such as systematic reviews, traditionally do not. Hence, with an integrative review the researcher does not need to focus on evidence solely, for example, from randomized control trials as systematic reviews traditionally tend to rely on. With integrative review methodology the researcher can control not to over-emphasizing on certain research methodology. However, major challenges may occur in the phase of analyzing and synthesizing primary research conducted with different research methods. (Whittemore & Knafl 2005).
3.3 The literature search strategy and study selection
Although a widely accepted golden standard about the steps on how to conduct an integrative literature review do not exist, there are well-established recommendation that apply (Webb et al.
2008, 137-138). Many of the recommendations are adapted from the systematic review process but perhaps one of the best-known frameworks is the one developed by Whittemore and Knafl (2005). When undertaking a rigorous and comprehensive review it is important to define the focus of the review and its boundaries by describing each phase of the review process regardless the selected review style. In systematic reviews this presentation of the review process is called the review protocol and should be made prior the review. (Webb et al. 2008, 137-139).
The review process of this integrative literature review is presented next. The process was conducted based on the work of Whittemore and Knafl (2005) but modified to certain extent to match the scope of this research. The review had the following six stages:
• Problem identification: review questions for the literature search with the PICO terms
• Literature search: research database search and the search query with key terms
• Study selection: inclusion and exclusion criteria with flowchart
• Evaluating the quality of included studies: selecting studies for the inclusion of high- quality
• Data analysis: data reduction, data display and data comparison
• Conclusion drawing, verification and synthesis (Whittemore & Knafl 2005)
Problem identification
The first stage of problem identification was formulated through the research question. A thorough review of previous literature resulted in locating a research gap which was then converted into review questions. The process of developing the review question is crucial not only due to its significant effect on the research objective but also in order to create applicable search terms for the search strategy stage. (Coughlan et al. 2013, 34-36). In addition, Whittemore and Knafl (2005) have highlighted that with a clear review question, the boundaries for the review process are set. With reflections to the aid for the search strategy, a PICO approach was used in formulating the review question for the literature search. The PICO approach is widely used in health and social care research. Depending whether the research question is a qualitative or a quantitative, the PICO can refer to as P=population, I=intervention/Issue/Interest,
C=Comparison/Context, and the possible O=Outcome. The idea with the PICO formulation is to identify appropriate range of synonyms and key terms for the literature search stage. (Aveyard et al. 2016, 68-71). Based on the research questions, this research had both a qualitative and a quantitative review questions for the literature search. The research question derived qualitative PICO formulation (Table 1.) and the quantitative PICO formulation (Table 2.) are presented below.
The research questions:
1. How adolescents and young adults perceive personal health record solutions?
2. Which elements influence adolescents’ and young adults’ behavioral intention or use behavior of personal health record solutions?
3. Is behavioral intention or use behavior of personal health record solutions increasing adolescents’ and young adults’ health empowerment?
TABLE 1. The qualitative PICo formulation
1. How adolescents and young adults perceive personal health record solutions?
1. Population Interest or phenomena Context
PICO term Adolescents and young adults (healthy or with illness)
Personal health records -
Alternative terms
(synonyms) Teenage, Youth, Young,
Teen, Pubescent, Juvenile Personal health informatics, Consumer health informatics, Patient-generated health data, Patient portal, Electronic health record portal, EHR patient portals
TABLE 2. The quantitative PICO formulation
2. Which elements influence adolescents’ and young adults’ behavioral intention or use behavior of personal health record solutions?
3. Is behavioral intention or use behavior of personal health record solutions increasing adolescents’ and young adults’ health empowerment?
2. Population Intervention Comparator Outcome
PICO term Adolescents and young adults (healthy or with an illness)
Personal health records
No personal health record solutions
Elements affecting
Alternative terms
(synonyms) Teenage, Youth, Young, Teen, Pubescent, Juvenile
Personal health informatics, Consumer health informatics, Patient- generated health data, Patient portal, Electronic health record portal, EHR patient portals
UTAUT2 determinants
Other determinants
3. Population Intervention Comparator Outcome
PICO term Adolescents and young adults (healthy or with an illness)
Personal health
record solutions No personal health
record solutions Increased health empowerment
