Personal Health Records (PHR) have been championed as a way to improve the access, delivery and the quality of health care services. They are defined as “real-time, patient-centred records that provide immediate and secure information to authorized users” [1]. PHRs are expected to play an increasingly important role in empowering patients by facilitating better health information exchange between patients and health professionals, and in turn enabling patients to be proactive and engage more effectively as partners in their care [2]. It has been noted that the provision of PHRs will further help with self-care, facilitate the better coordination of healthcare services and improve health outcomes [3, 4]. In this context, the European Commission supports the adoption of PHR within and between its member states, with a strong emphasis on the safety and the security of patients’ health data. To date, most countries within the European Union, with the exception of Germany, have developed and to some extent implemented PHRs [1].
However, even though individuals have physical access to their PHRs, the uptake among certain socioeconomic and migrant populations has been rather slow and socially patterned [5,6,7,8]. Health inequities might thus be worsened by the fact that technologies that facilitate self-management and patient engagement are used more frequently by those who are already healthier and more socioeconomically advantaged [9, 10]. To date, PHRs have been studied through two different approaches. On one side, scholars are concerned with the digital divide, examining disparities in the use of digital technology across different groups [11,12,13,14]. On the other side, research concerned with the use of digital technologies is rooted in the Unified Theory of Acceptance and Use of Technology (UTAUT) approach, predominantly used in the field of social psychology and which explores the individual intentions for the use of Information Communication Technologies (ICT) [15]. The integration of these two approaches can provide a fresh perspective on the ways in which digital technologies may contribute to deepening health inequities.
The notion of the digital divide has been described as a paradigm with two levels. The first level refers to disparities in actual access to digital technology, and the second level goes beyond access and explores the skills and abilities that are required to utilize these technologies [11, 16, 17]. Previous studies have shown that individuals with a higher socioeconomic status are more likely to perform better on both levels of the digital divide. Those with a more advantaged socioeconomic position have a better access to digital technology and also more frequently have the skills required to used them, as compared to individuals from lower socioeconomic strata [7, 12, 18, 19]. Evidence, mainly from the United States, also suggests that racial and ethnic minority patients and those with lower incomes are less likely to have access to and to adopt PHRs [10, 18]. Indeed, it is most likely that those with higher incomes will have earlier access to material goods such as computers, portable health devices or various health monitoring software. Additionally, those with a higher education level are more inclined to use some form of information technology, mostly through their job positions as compared to those from the lower occupational categories whose jobs do not necessarily required contact with ICT.
Van Dijk [16] further distinguishes four broad categories in research on the digital divide: motivational access; physical access; skills and the actual use of digital technologies. He argues that prior to physical access to a digital tool, people need to wish to have access—“a much neglected phenomenon” in the digital divide literature [16]. The disengagement with new technologies is explained as involuntary and related to possibilities and lack of opportunities—some people simply do not have access to an ICT or a certain digital technology [20], however, even in places where everyone has access, some people are still not utilizing ICT [21]. This points to the need to look beyond physical access and examine more challenging notions of ‘choice’ and ‘cultural legitimacy’ linked to peoples’ social positions and lifestyles [22]. Indeed, the notion of choice goes back to the sociologist Pierre Bourdieu (1984) who argues that people from more affluent socioeconomic backgrounds make strategic choices that oftentimes lead to a long-term benefit [23]. In the context of the choice as to whether to access their PHRs or not, we can assume that individuals who are more motivated to use this digital tool could exploit its potential and turn it to their health advantage. Conversely, individuals from lower socioeconomic background express a feeling of cultural illegitimacy about using digital devices and generally feel that “the use of ICT oversteps their social position” [24]: p.9]. Although some of Bourdieu’s concepts such as “choice of necessity” and “cultural illegitimacy” has been evidenced in the utilisation of healthcare services and digital self-tracking apps [24, 25] they have not been studied in the field of use and access to PHR. Thus, while the digital divide approach is useful to understand which groups are disadvantaged in the use of new digital technologies and why, it is important to identify specific behavioural processes that lead to individuals’ acceptance and intention to adopt the PHR. This type of approach its best represented by the UTAUT model.
The Unified Theory of Acceptance and Use of Technology (UTAUT) model by Venketesh et. al., 2003, integrates behavioural elements of eight different models and which aims to explain the intention to use digital technologies through six constructs, known as:
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1.
Performance acceptancy: the degree to which individuals believe that the digital technology will improve their performance;
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2.
Effort expectancy: the ease of use of the digital technology;
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3.
Social influence: whether an individual knows someone who uses that technology;
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4.
Facilitating conditions: the degree of perceived support, such as available help from friends and family in the use of new technology;
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5.
Personal attitudes towards using digital health technologies;
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6.
Anxiety: fear of using digital technologies.
Proponents of this theory argue that digital technologies, even if available, are not always accepted by individuals for various reasons, such as: devices that are hard to use, lack of training and computer skills, not seeing the added value in the technology and low social support [26]. However, results show multiple discrepancies in explaining the factors that contribute to the use of digital devices. Hoogenbosch et al., found that effort and performance expectancy were the only constructs that significantly influence patients’ use of a health PHR [27]. Drawing on the UTAUT model, Hoque R and Sorwar G (2017) revealed that, with the exception of facilitating conditions, none of the constructs were associated with the use of a health technology [28]. In addition, researchers that used this model have also argued that the use and the adoption of digital technologies is moderated by demographic variables, especially age and gender [15, 29]. However, literature on the digital divide has shown that there is also a socioeconomic dimension to these disparities that has to be considered.
In this context, the focus of this paper is therefore to integrate the digital divide literature with the UTAUT concepts to provide a better understanding of the socioeconomic and behavioural determinants that contribute to the three stages of PHR use, mainly:
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Expressing a desire to use their PHR;
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Having physical access to their PHR which is achieved through the availability of PHR, as well as a computer and access to the internet, and lastly;
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Intention to regularly use their PHR.
Indeed, as van Dijk [16] highlights that there is a lack of interdisciplinary research, as well as a need to incorporate social psychology into the digital divide research. We believe that UTAUT can shed light on important mechanisms that determine the higher acceptance and use rates among those from more affluent backgrounds. Hence, this study goes beyond the socioeconomic circumstances of individuals by incorporating the UTAUT model.
In particular, we are interested to know:
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Which demographic and socioeconomic factors determine different stages of PHR use: desire to access, physical access and intention to regularly use PHR?
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What behavioural factors linked to the use and acceptance of technology are associated with the intention to regularly use PHR, and are these determined by the socioeconomic characteristics of the individual?
