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Personal Health Records (PHR) systems provide individuals with access and control over their health information and consequently can support individuals in becoming active participants, rather than passive recipients, in their own care process. In spite of numerous benefits suggested for consumers’ utilizing PHR systems, research has shown that such systems are not yet widely adopted or well known to consumers. Bearing in mind the potential benefits of PHRs to consumers and their potential interest in these systems—and that similar to any other type of information system, adoption is a prerequisite for realizing the potential benefits of PHR systems—research is needed to understand how to enhance the adoption rates for PHR systems.
This research seeks to understand how individuals’ intentions to adopt PHR systems are affected by their self-determination in managing their own health—the extent of their ability to take an active role in managing their own health. As such, this research aims to develop and empirically validate a theoretical model that explains PHR systems adoption by the general public through the integration of theories from the information systems and psychology literatures.
This research employs a cross-sectional survey method targeted at the Canadian general public without any prior experience in using PHR systems. A partial least squares approach to structural equation modeling was used to validate the proposed research model of this study (N=159).
Individuals with higher levels of ability to manage their own health (self-determination) are more likely to adopt PHR systems since they have more positive perceptions regarding the use of such systems. Further, such self-determination is fueled by autonomy support from consumers’ physicians as well as the consumers’ personality trait of autonomy orientation.
This study advances our theoretical understanding of PHR systems adoption. It also contributes to practice by providing insightful implications for designing, promoting, and facilitating the use of PHR systems among consumers.
A personal health record (PHR) system is an information system that comprises data as well as supporting tools and functionalities related to an individual’s health. The most cited definition of a PHR system [
Personal health records are created, owned, updated, and controlled by individual consumers or others authorized by them. Ideally, they contain a summary of the consumer’s lifelong health information such as their history of previously undertaken health procedures, major illnesses, allergies, home monitoring data (eg, blood pressure), family history, immunizations, medications, and laboratory test results [
When successfully implemented and used, PHR systems have the potential to facilitate a transformative advancement in health care delivery and management. Such advancements are likely to be in the form of “improved interactions between patients and care providers,” increased “opportunities to realize innovation in care management,” “a shift in the locus of control of health information” to a more shared control between patients and care providers, and improved “efficiency in care” [
In spite of numerous benefits suggested for consumers’ utilizing PHR systems [
By providing individuals with access and control over their health information, PHR systems can support individuals in becoming active participants, rather than passive recipients, in their own health care process [
Hence, this research seeks to understand how the extent of individuals’ ability to take a more active role in managing their health could affect their intentions to adopt electronic PHR systems. We further seek to understand some of the important personal and environmental antecedents that support the individual in accepting and practicing such an active role in their own health management. This is accomplished by proposing a PHR systems adoption model through the integration of theories from the information systems and psychology literatures. This model is then validated through an empirical study involving a stratified sample of 159 Canadians, leading to important results with implications for theory and practice.
Several studies have investigated the factors responsible for the lack of PHR adoption (eg, [
To understand how to enhance PHR adoption rates, we integrated mainstream IS adoption models with Self-Determination Theory (SDT), which is a theory of motivation from the field of psychology. SDT sheds light on the mechanisms through which individuals become able and motivated to take active (rather than passive) roles when engaging in different types of behaviors including individual health care [
This paper builds, in part, on existing studies on PHR systems in order to develop and validate an adoption model for PHR systems while maintaining novelty by being the first to observe the following unique set of characteristics: (1) it (the paper) is targeted at the general public and not a specific segment of the population, (2) it focuses on integrated PHR systems—systems that gather and present data from multiple sources (eg, consumer, care provider, health care organizations) into a single view, generally through secure Internet access [
A PHR system has the potential to empower individual owners to play a more active role in their health management [
SDT represents a broad framework for the understanding of human motivation and personality. SDT begins with the assumption that human beings are active organisms with evolved tendencies toward growing, mastering new skills, applying their talents responsibly, learning, and integrating new experiences into a sense of self. As such, they tend to behave in a “self-determined” way [
Research guided by SDT shows the importance of “environmental” (eg, physician behavior) and “consumers’ personality characteristics” in explaining differences in self-determination and motivation, “within” and “between” individuals respectively [
Throughout the years, SDT has been successfully applied [
How an individual could take an active (rather than passive) role in their health management, according to SDT.
Construct definitions.
Construct | Definition |
Autonomous Causality Orientation | A person’s tendency toward being autonomous (ie, self-determined) in general, across different domains and times [ |
Physician Autonomy Support | The extent to which physicians obtain and acknowledge patients’ perspectives, support their ideas, offer choices in treatment options, and offer relevant information without trying to pressure them [ |
Basic Needs Satisfaction | A measure of self-determination in a given context assessed through the satisfaction/thwarting of the three basic needs for autonomy, competence, and relatedness [ |
Self-Efficacy | An individual’s belief of having the capability to use computers [ |
Complexity | The degree to which a PHR system is perceived as relatively difficult to understand and use [ |
Perceived Usefulness | The extent to which an individual believes that a PHR system can be used advantageously in managing their health [ |
Behavioral Intention | A measure of the strength of an individual’s intention to use a PHR system for managing their health [ |
The proposed theoretical model of this study (
The definitions of all the constructs in the model are shown in
The PHR Technology Adoption component of the model (
TAM holds that an individual’s behavioral intention to use an IS is mainly determined by their beliefs regarding the usefulness (Perceived Usefulness – PU) and ease of use (Perceived Ease of Use – PEOU) associated with using that IS [
As noted from
In a review of technology adoption models/constructs, Venkatesh [
Finally, a construct that has been consistently shown to determine user perceptions of IS, especially in the early stages of adoption, is that of self-efficacy [
The second component of the proposed research model (
According to SDT, and as seen in
H1: A higher level of perceived physician autonomy support positively influences an individual’s level of BNS in the context of health management.
Several studies that have employed SDT in different contexts have shown a positive association between autonomous causality orientation and BNS. Example contexts include weight loss [
H2: A higher level of an individual’s autonomous causality orientation is positively associated with their level of BNS in the context of health management.
Hypotheses H3 and H4 in the proposed research model in
Based on the four justifications above, it is reasonable to hypothesize that consumers with higher levels of self-determination (associated with higher BNS) in managing their health would have more positive beliefs (eg, higher PU and lower CPLX) regarding the use of a technology that supports their reaching their goal.
In a survey of consumers’ perceptions regarding the use of PHR systems, PHR system functionalities that were rated highest (in terms of usefulness) among survey respondents were the ones that aligned with the satisfaction of SDT’s three basic needs (for autonomy, competence, and relatedness) [
H3: A higher level of BNS in the context of health management positively influences an individual’s perceived usefulness of PHR systems.
H4: A higher level of BNS in the context of health management negatively influences an individual’s perceptions of complexity of PHR systems.
In summary, the proposed research model suggests that behavioral intention to start using a PHR system is influenced by an individual’s perceptions regarding usefulness (ie, PU) and effort (ie, CPLX) associated with using the system. Prior research on IS adoption suggests that these perceptions (ie, internal beliefs about the system or individual reactions to using the system) mediate the influence that external variables might have on behavioral intention [
Research model and hypotheses (arrows in bold demonstrate the main focus of this study; dashed lines on the right side of the model are included for statistical testing but are not specifically hypothesized as they have been repeatedly established in IS literature).
Modeling of basic needs satisfaction as a second-order construct.
In order to test the hypotheses in the proposed research model in
In order to reduce the effect of common method variance and to reduce the cognitive load on participants, the entire survey was divided into two parts, such that each part would be completed by participants in one sitting. For each participant, the two sittings were on average 36 hours apart. Each of the survey parts contained only approximately half of the questions. Using LimeSurvey, an open source survey app, the two parts of the survey for this study were programmed and were hosted on the McMaster University (Hamilton, Ontario, Canada) website. Finally, for the purpose of this study, PHR systems were introduced to participants using an online video clip that was shown to participants at the beginning of Part 2.
On entering the website for Part 1 and signing the consent form to participate in the study, participants were presented with a set of questions to determine their eligibility for this study. Only persons living in Canada (the target population of this study), above the age of 18 years (ethical consideration), with a family physician (the measurement items of physician autonomy support relate directly to the participants’ family physician), and with no prior experience in using electronic PHR systems of any type (our focus is on pre-usage stage of PHR system adoption) were considered eligible to participate in this study. Ineligible participants were prevented from starting the survey.
Since this study was targeted at individuals with no prior experience in using PHR systems (ie, this study was focused on a pre-usage stage of PHR adoption), an online video clip was created (described in
In order to ensure content validity, measurement scales were selected from the existing literature, and in some cases, were slightly adapted to reflect the context of this study. The full measurement instrument can be found in
Participants were recruited through a commercial market research firm with a consumer panel that includes over 400,000 Canadians (the target population of this study). Invitations to take part in this study were balanced based on participant location, age, and gender, according to the 2011 Canadian Census Profile provided by Statistics Canada [
Prior to conducting data collection for the study, a pre-test of the instrument was conducted by inviting PhD students and three IS faculty members at McMaster University to complete the survey and provide their feedback on the instrument. Their feedback and responses to survey questions resulted in minor revisions to the questions as well as data collection procedures. On finalizing the online survey, a pilot was conducted through the same commercial market research firm with the purpose of diagnosing any possible flaws in the data collection procedures. As a result, 20 participants filled out the survey. The pilot study did not result in any changes in either data collection procedures or the measurement instrument. Therefore, the 20 data cases were included in the final dataset for this study. Finally, prior to conducting any sort of data collection, an ethics application was approved by the research ethics board of McMaster University.
There are two criteria that would impose minimum sample size requirements on this research [
The recruitment of participants and the administration of the survey ran from August 1-17, 2012. In order to obtain the 139 cases, and following the recruitment firm’s prior experience, a total of 6423 persons were invited, of whom 508 individuals completed Part 1, and 173 completed Part 2 as well. Thus, the response rate for Part 1 of the survey was 7.91%; for Part 2, it was 34.06%. Although the response rate fits within the acceptable range for this type of research [
In order to examine the possible existence of nonresponse bias, respondents were compared to two groups of nonrespondents (ie, those invitees who did not complete Part 1 and those who did not complete Part 2). The comparisons were conducted based on socioeconomic information as suggested by Sivo et al [
Before conducting the main analyses of this study, this dataset was investigated for data anomalies (eg, participant’s gaming patterns), univariate outliers, multivariate outliers, and cases with missing data [
Structural equation modeling (SEM) was used to validate our proposed research model. SEM allows for the analysis and investigation of unobservable variables that are indirectly measured from observable variables [
We conducted and reported PLS analyses following a two-step approach as suggested by Chin [
As mentioned earlier, the BNS construct was modeled and measured as a second-order factor. The procedures of measurement model evaluation for the second-order factor must be the same as those performed for the first-order factor [
The measurement model evaluation (
The PLS modeling of the second-order factor (BNS) was done following Agarwal and Karahanna [
The survey for this study was designed following the guidelines suggested by Podsakoff et al [
The characteristics of the study participants (N=159) are presented in
Our research model was further examined, and as a result, confirmed in terms of predictive relevance, and goodness-of-fit (
Participants in this study were also asked questions about their individual characteristics as well as several control variables (ie, age, gender, extent of daily Internet use, Internet experience in years, education level, perceived health status, chronic illness, frequency of doctor visit, years with family doctor, family health responsibility, prior use of paper-based health records, information privacy concerns, information security concerns, household income, and retirement status). The impact of these individual and control variables on the results of this study was assessed by examining variations in the R2 for endogenous variables in the model or changes in the support for the hypothesized relations. Results of these examinations showed that the control variables and the individual characteristic variables did not change our findings. Results of control variable analysis are presented in
PLS results for the proposed research model: Significant at (a) .05; (b) .01; (c) .001 (ns=non-significant path).
Frequency statistics of participant characteristics.
Characteristics | Freq. | % | CCa | % Dev. from CCb | |
Female | 83 | 52.20 | 51 | 2.35 | |
Male | 76 | 47.80 | 49 | 2.44 | |
18-34 | 48 | 30.2 | 27 | 11.85 | |
35-49 | 32 | 20.1 | 26 | 22.69 | |
50+ | 79 | 49.7 | 45 | 10.44 | |
Alberta | 18 | 11.3 | 10.5 | 7.61 | |
British Columbia | 22 | 13.8 | 13.5 | 2.22 | |
Manitoba | 6 | 3.8 | 3.5 | 8.57 | |
New Brunswick | 2 | 1.3 | 2.5 | 48 | |
Newfoundland | 1 | 0.6 | 1.5 | 60 | |
Nova Scotia | 5 | 3.1 | 2.5 | 24 | |
Ontario | 61 | 38.4 | 38.5 | 0.25 | |
Prince Edward Island | 0 | 0 | 1 | 100 | |
Quebec | 39 | 24.5 | 23.5 | 4.25 | |
Saskatchewan | 5 | 3.1 | 3 | 3.33 | |
Secondary school or less | 23 | 14.47 | |||
Some university or college | 36 | 22.64 | |||
University or college degree | 71 | 44.65 | |||
Some graduate work | 4 | 2.52 | |||
Graduate degree | 25 | 15.72 | |||
Less than 40,000 | 35 | 22.01 | |||
40,000-79,999 | 68 | 42.77 | |||
80,000-119,999 | 36 | 22.64 | |||
120,000-159,999 | 17 | 10.69 | |||
˃160,000 | 3 | 1.89 |
aCC: % in the 2011 Canadian census.
bDev: deviation.
cNot included in sample stratification.
Descriptive statistics of participant characteristics.
Characteristics | Min. | Max. | Mean | SD |
Age in years | 19 | 82 | 48.16 | 16.11 |
Internet experience in years | 3 | 26 | 16.60 | 6.52 |
Time spent online hours per day | 1 | 12 | 3.67 | 2.43 |
Findings from this study are discussed here in two parts. First, the appropriateness of the research model of this study in terms of explaining PHR system adoption is discussed, followed by a discussion of the results of hypotheses.
In terms of the appropriateness of our research model for explaining pre-usage adoption intentions, the overall R2 of the endogenous construct (behavioral intention) in the research model (.650) indicates that a large portion of the variance (65%) in this construct was explained by the factors in the model, thus indicating the high explanatory power of the research model. Further, the cross-validated redundancy for the endogenous variables in the research model (Q2), as well as the absolute and relative goodness-of-fit indices, is indicative of the model appropriately explaining an individual’s adoption of PHR systems.
In terms of our hypotheses, consistent with prior research on IS adoption, PU and CPLX of PHR systems were shown to be the key antecedents of behavioral intention to use such systems. In addition, self-efficacy was shown to be a significant predictor of CPLX. In contrast, the association between self-efficacy and PU was not statistically significant. Compeau and Higgins [
As argued in the theoretical development section of this paper, CPLX was incorporated in our model instead of the commonly used PEOU construct as representative of effort associated with using a PHR system. However, PEOU data were also collected, and we ran our research model with PEOU as well. We found no difference between having either CPLX or PEOU. However, having CPLX in the model yielded stronger associations and higher explained variances compared to PEOU, which supports our theoretical arguments to use it instead of PEOU.
BNS was shown in this study to be significantly associated with PU (beta coefficient=.165,
BNS was also shown to have a significant negative association with CPLX (beta coefficient=-.136,
Physician autonomy support was shown to be a significant predictor of BNS in the context of health management (beta coefficient=.481,
Finally, the personality trait of autonomous causality orientation was shown to be associated with BNS in the context of personal health management (beta coefficient=.421,
From an academic perspective, this research contributes to the literature by developing and validating a research model that explains the adoption of PHR systems, from an SDT perspective. As such, the current study highlights the importance of considering the changing role of consumers from passive recipients of care to active partners in their own care when considering the adoption of PHRs. Although this model is specific to using PHR systems for managing one’s health, such a role change supported by information technology could be observed in contexts other than health care (eg, self-service technologies). Findings of this research highlight the importance of considering how information systems can facilitate the changing way people engage in certain behaviors when trying to understand the adoption of such systems. Accordingly, this study is the first to apply SDT in order to understand PHR system adoption.
Our findings also showed the importance of physician autonomy support in the adoption of PHR systems by individuals. Similarly, the importance of considering the role of personality traits of autonomous orientation in PHR system adoption was shown. Finally, the measurement scales for the constructs of SDT were adapted and validated for the context of health management and can be used in similar future studies.
This study provides valuable implications and contributions to practice in terms of the development, promotion, and facilitation of PHR systems use by consumers. The major findings in terms of the supported hypotheses, the academic value added of testing each hypothesis, and the practical implications of the findings are summarized as follows.
Perceived usefulness positively influences behavioral intention, complexity negatively influences behavioral intention, and self-efficacy negatively influences complexity. This study provided empirical support for a relationship not previously validated in the context of using PHR systems for health management. In addition, the study adapted and validated self-efficacy scales for PHR systems. As for practical results, we suggest considering features deemed useful by consumers in designing PHR systems (eg, monitoring and tracking features), promoting PHR systems (highlight those features in advertisements), and facilitating PHR system use (eg, provide incentive for health care providers to communicate with patients through PHR systems), designing PHR systems that are easy to use and maintain, training consumers in using PHR systems, providing technical support and facilitating usage, and providing technical features that would reduce the ongoing effort of keeping the system up to date (eg, automatic data population, smart data population, compatibility with external devices such as blood sugar readers).
BNS negatively influences complexity, BNS positively influences perceived usefulness, physician autonomy support positively influences BNS, and autonomous causality orientation is positively associated with BNS. This study adapted and validated SDT scales for the context of personal health management and provided empirical support for relationships not previously investigated as well as providing empirical support for relationships not previously validated in the context of personal health management. In terms of practice, the results suggest that health care providers must generally allow their patients to take part in their health management. That said, it must be noted that according to SDT, people with different personality orientations are motivated through different regulation mechanisms. For example, for individuals with a personality orientation toward being controlled (rather than being autonomous), rewards and punishments may promote a higher level of self-determination in health management [
This research was carried out in a Canadian context; thus, findings from the research will not be immediately transferrable to other countries with different demographics, health care system characteristics, and cultures. For example, the role of culture is believed to be influential in research related to SDT (eg, [
Data collection for this study was conducted by employing a cross-sectional survey design. Given that perceptions and intentions (CPLX, PU, and behavioral intention) regarding the use of PHR systems could change over time, collecting data at one point could pose a threat of temporal instability in the findings. Nevertheless, the focus of the study was on only one particular stage in the adoption process where individuals had no prior experience with using PHR systems (ie, pre-usage), and the selected method of data collection was deemed to be the best approach in this case.
The current study was focused on the pre-usage stage of PHR system adoption process. In this stage, consumers may not have a full understanding of the nature of the change in their roles (from passive to active) when using an actual PHR system. Therefore, possible venues of future research may include the development and validation of a theoretical adoption model for later stages of the adoption process (ie, initial use, continued use). Using PHR systems might influence an individual’s level of BNS in health management [
Finally, several other factors could impact PHR adoption such as trust, security, privacy, and social influence. Although influences of some of these variables were investigated through their use as control variables in this study, future studies should explore their role more formally.
Positioning of the study in relation to the literature.
Measurement instruments and data collection.
Statistical analyses.
basic needs satisfaction
complexity
information systems
perceived ease of use
personal health record
partial least squares
perceived usefulness
self-determination theory
structural equation modeling
technology acceptance model
None declared.