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More advanced methods and models are needed to evaluate the participation of patients and citizens in the shared health care model that eHealth proposes.
The goal of our study was to design and evaluate a predictive multidimensional model of eHealth usage.
We used 2011 survey data from a sample of 13,000 European citizens aged 16–74 years who had used the Internet in the previous 3 months. We proposed and tested an eHealth usage composite indicator through 2-stage structural equation modelling with latent variables and measurement errors. Logistic regression (odds ratios, ORs) to model the predictors of eHealth usage was calculated using health status and sociodemographic independent variables.
The dimensions with more explanatory power of eHealth usage were health Internet attitudes, information health Internet usage, empowerment of health Internet users, and the usefulness of health Internet usage. Some 52.39% (6811/13,000) of European Internet users’ eHealth usage was more intensive (greater than the mean). Users with long-term health problems or illnesses (OR 1.20, 95% CI 1.12–1.29) or receiving long-term treatment (OR 1.11, 95% CI 1.03–1.20), having family members with long-term health problems or illnesses (OR 1.44, 95% CI 1.34–1.55), or undertaking care activities for other people (OR 1.58, 95% CI 1.40–1.77) had a high propensity toward intensive eHealth usage. Sociodemographic predictors showed that Internet users who were female (OR 1.23, 95% CI 1.14–1.31), aged 25–54 years (OR 1.12, 95% CI 1.05–1.21), living in larger households (3 members: OR 1.25, 95% CI 1.15–1.36; 5 members: OR 1.13, 95% CI 0.97–1.28; ≥6 members: OR 1.31, 95% CI 1.10–1.57), had more children <16 years of age (1 child: OR 1.29, 95% CI 1.18–1.14; 2 children: OR 1.05, 95% CI 0.94–1.17; 4 children: OR 1.35, 95% CI 0.88–2.08), and had more family members >65 years of age (1 member: OR 1.33, 95% CI 1.18–1.50; ≥4 members: OR 1.82, 95% CI 0.54–6.03) had a greater propensity toward intensive eHealth usage. Likewise, users residing in densely populated areas, such as cities and large towns (OR 1.17, 95% CI 1.09–1.25), also had a greater propensity toward intensive eHealth usage. Educational levels presented an inverted U shape in relation to intensive eHealth usage, with greater propensities among those with a secondary education (OR 1.08, 95% CI 1.01–1.16). Finally, occupational categories and net monthly income data suggest a higher propensity among the employed or self-employed (OR 1.07, 95% CI 0.99–1.15) and among the minimum wage stratum, earning ≤€1000 per month (OR 1.66, 95% CI 1.48–1.87).
We provide new evidence of inequalities that explain intensive eHealth usage. The results highlight the need to develop more specific eHealth practices to address different realities.
In recent years, there has been considerable development in the field of eHealth services. With eHealth, a wide range of new opportunities has emerged to improve people’s health status through the use of information and communication technologies (ICTs) in general and the Internet in particular [
With new developments in wireless technologies, Web 2.0, and Media 3.0, eHealth has continued to profoundly change health care, which is shifting from an individual approach (care of acute health problems) toward a population approach (disease prevention and management through online communities) [
In the context and objectives of the digital agenda for Europe, the eHealth Action Plan 2012–2020 promotes patient-centered care, thereby empowering citizens to make health decisions [
Since any impact fluctuates over time and in a given context [
Thus, the main aim of this work was to model and predict eHealth usage in Europe. We designed a multidimensional model for this purpose. The model has 9 dimensions and 88 indicators. We constructed an eHealth usage composite indicator by means of a structural equation modelling (SEM) analysis of a sample of 13,000 European Internet users in 2011. We then conducted a study to establish the indicator’s main predictors, especially the Internet users’ sociodemographic variables and health status. The results obtained provide new evidence of eHealth usage in Europe and have implications for the design of public health policies.
Data for this study were drawn from the Strategic Intelligence Monitor on Personal Health Systems Phase 2 (SIMPHS2) research project “Citizens and ICT for health in 14 European countries: results from an online panel” [
Our study used survey data for a sample of 13,000 European citizens aged 16–74 years who had used the Internet in the previous 3 months (
The questionnaire used in the survey contained 47 questions grouped into 5 dimensions (
The SIMPHS2 research project followed the Checklist for Reporting Results of Internet E-Surveys criteria [
From an empirical perspective, explanatory factors determining eHealth usage raise two particular difficulties. First, the approach to the concept requires a multidimensional basis that is not usually captured in a single variable. In fact, the most common approaches found in the literature perform partial analyses of its various dimensions. This type of analysis has the disadvantage of not taking a full snapshot of the explanatory factors, which gives rise to the second difficulty: statistical modelling. In other words, eHealth usage can be interpreted as a latent, nonobservable concept, which therefore calls for statistical techniques that allow variables of this type, which are not directly measurable, to be used [
In the empirical literature, SEM with latent variables has been used to overcome this problem. A general SEM is a formal mathematical model. It is a set of linear equations that encompasses various types of models, such as regression analysis models, simultaneous equation systems, factor analysis, and path analysis. The main advantage of this method of analysis is the incorporation of different types of variables into the SEM. Directly observable and measurable variables, and theoretical or latent variables representing concepts that are not directly observed can therefore be incorporated. When the variable to be explained (dependent) is latent, it must be continuous, whereas dependent observed variables can be continuous, censored, binary, ordered, or categorical (ordinals), or combinations of any of these variable types [
This method of analysis allows us to define eHealth usage as a latent variable, thus enabling us to calculate the specific explanatory effect of the variables that it comprises. Hence, besides building an overall explanatory model of the determinants of eHealth usage, it is also possible to identify which of its explanatory dimensions are more important. In addition, SEM enables the relationships between the different observable variables included in the model (indirect effects) to be estimated. In this initial approach, however, only the direct effects are presented, that is to say, the coefficients of causality between the individual indicators and their latent dimensions, and later between the estimated dimensions and the latent variable (eHealth usage). In this context, and in order to capture the factors that explain eHealth usage in a large sample of European Internet users, we proposed and tested a 2-stage SEM with latent variables and measurement errors for 2011.
We applied the 2-stage empirical estimation methodology as follows: in the first stage, we tested the causal relationships among 88 indicators and the 9 latent dimensions describing eHealth usage in Europe, and in the second stage, we tested the causal relationships among the indicators constructed for those 9 dimensions (based on the coefficients from the first stage) and the latent construct of eHealth usage. Finally, after applying the coefficients obtained from the second stage, we constructed an eHealth usage indicator and determined its mean values (total and for the 9 dimensions). This methodology involved the design and statistical testing of 10 empirical models: 9 models for the first stage and 1 model for the second stage.
Several eHealth definitions highlight growing patient empowerment (access to information and ability to use it) and point to the potential of eHealth to facilitate doctor-patient communication, partnership, and shared decision making [
Additionally, we calculated the logistic regression to model the predictors of eHealth usage using health status and sociodemographic independent variables. For each independent variable, we calculated odds ratios (ORs) and their 95% CIs. We used IBM SPSS Amos v.22 (IBM Corp) for all calculations.
Flow diagram detailing the multidimensional model of eHealth usage. ICT: information and communication technologies.
Explanatory factors of eHealth usage in Europe (first stage)a in 2011.
Dimension/variable | Standardized |
Error | ||||||||
0.698 | <.001 | |||||||||
1. | Look for information about a physical illness | 0.536 | <.001 | 1.732 | <.001 | |||||
2. | Look for information about wellness or lifestyle | 0.545 | <.001 | 1.955 | <.001 | |||||
3. | Buy medicine or vitamins online | 0.779 | <.001 | 2.558 | <.001 | |||||
4. | Participate in an online support group with people | 0.774 | <.001 | 2.761 | <.001 | |||||
5. | Participate in social networking sites | 0.790 | <.001 | 2.301 | <.001 | |||||
6. | Use email or Web to communicate with a doctor’s office | 0.713 | <.001 | 3.301 | <.001 | |||||
7. | Click on a health or medical Web’s privacy policy | 0.682 | <.001 | 3.750 | <.001 | |||||
8. | Describe a medical condition to get advice from an online doctor | 0.783 | <.001 | 2.645 | <.001 | |||||
9. | Describe a medical condition to get advice from other online users | 0.822 | <.001 | 1.905 | <.001 | |||||
10. | Bookmark or favorite a health website | 0.725 | <.001 | 2.426 | <.001 | |||||
11. | Look to see what company is providing the information on a health website | 0.681 | <.001 | 2.661 | <.001 | |||||
12. | Look for information about a mental health issue | 0.749 | <.001 | 2.209 | <.001 | |||||
13. | Disclose medical information on social networking sites | 0.821 | <.001 | 2.329 | <.001 | |||||
14. | Disclose medical information on websites to share files | 0.814 | <.001 | 2.516 | <.001 | |||||
Goodness-of-fit indexes: NFIb: 0.986; RFIc: 0.979; IFId: 0.987; TLIe: 0.980; CFIf: 0.987; RMSEAg: 0.041 | ||||||||||
1.982 | <.001 | |||||||||
15. | Make an Internet appointment with health care professionals | 0.743 | <.001 | 1.609 | <.001 | |||||
16. | Receive an email from doctor, nurse, or health care organization | 0.781 | <.001 | 1.343 | <.001 | |||||
17. | Have an online consultation through videoconference with health care professionals | 0.813 | <.001 | 1.675 | <.001 | |||||
18. | Receive online the results of clinical or medical test | 0.801 | <.001 | 1.484 | <.001 | |||||
19. | Use medical information through an Internet provider | 0.776 | <.001 | 2.098 | <.001 | |||||
20. | Use medical information through an Internet health care organization | 0.812 | <.001 | 1.656 | <.001 | |||||
21. | Use a game console to play games related to health or wellness | 0.739 | <.001 | 2.056 | <.001 | |||||
22. | Use a health/wellness app on mobile phone | 0.790 | <.001 | 1.643 | <.001 | |||||
23. | Use electronic devices to transmit clinical or medical information | 0.758 | <.001 | 1.811 | <.001 | |||||
24. | Email about health promotion or health prevention | 0.670 | <.001 | 1.906 | <.001 | |||||
Goodness-of-fit indexes: NFI: 0.971; RFI: 0.953; IFI: 0.971; TLI: 0.954; CFI: 0.971; RMSEA: 0.074 | ||||||||||
0.237 | <.001 | |||||||||
25. | Secure handling of personal information | 0.672 | <.001 | 0.287 | <.001 | |||||
26. | Information in own language | 0.580 | <.001 | 0.407 | <.001 | |||||
27. | Updated information | 0.737 | <.001 | 0.246 | <.001 | |||||
28. | Interactivity | 0.520 | <.001 | 0.579 | <.001 | |||||
29. | Involvement of health professionals | 0.867 | <.001 | 0.150 | <.001 | |||||
30. | Clear statement of who is responsible for sponsoring the site | 0.586 | <.001 | 0.614 | <.001 | |||||
31. | Involvement of health organizations | 0.728 | <.001 | 0.322 | <.001 | |||||
32. | Involvement of governments | 0.382 | <.001 | 0.794 | <.001 | |||||
Goodness-of-fit indexes: NFI: 0.973; RFI: 0.934; IFI: 0.973; TLI: 0.935; CFI: 0.973; RMSEA: 0.075 | ||||||||||
0.296 | <.001 | |||||||||
33. | Lack of digital skills | 0.583 | <.001 | 0.574 | <.001 | |||||
34. | Lack of access to ICTh for health applications | 0.632 | <.001 | 0.452 | <.001 | |||||
35. | Lack of motivation and interest | 0.666 | <.001 | 0.382 | <.001 | |||||
36. | Lack of awareness | 0.730 | <.001 | 0.329 | <.001 | |||||
37. | Lack of health literacy | 0.714 | <.001 | 0.352 | <.001 | |||||
38. | Lack of trust | 0.832 | <.001 | 0.199 | <.001 | |||||
39. | Lack of liability | 0.810 | <.001 | 0.242 | <.001 | |||||
40. | Lack of privacy | 0.762 | <.001 | 0.279 | <.001 | |||||
41. | Lack of security | 0.800 | <.001 | 0.232 | <.001 | |||||
42. | Lack of reliability | 0.804 | <.001 | 0.219 | <.001 | |||||
Goodness-of-fit indexes: NFI: 0.979; RFI: 0.953; IFI: 0.980; TLI: 0.953; CFI: 0.980; RMSEA: 0.074 | ||||||||||
0.720 | <.001 | |||||||||
43. | ICT for health could increase other ICT uses | 0.751 | <.001 | 0.555 | <.001 | |||||
44. | ICT for health could lead to greater patient satisfaction | 0.819 | <.001 | 0.372 | <.001 | |||||
45. | ICT for health could improve health status | 0.782 | <.001 | 0.471 | <.001 | |||||
46. | ICT for health could improve the ability to take care of one’s own health | 0.816 | <.001 | 0.385 | <.001 | |||||
47. | ICT for health could change behaviors toward a healthy lifestyle | 0.769 | <.001 | 0.469 | <.001 | |||||
48. | ICT for health could avoid travelling expenses and time | 0.740 | <.001 | 0.567 | <.001 | |||||
49. | ICT for health could improve the quality of health care services | 0.803 | <.001 | 0.407 | <.001 | |||||
50. | Internet health could substitute for offline consultations with the physicians | 0.604 | <.001 | 1.022 | <.001 | |||||
51. | Internet health complements offline consultations with the physicians | 0.704 | <.001 | 0.687 | <.001 | |||||
52. | Quality of Internet health is aligned with the quality of offline services | 0.626 | <.001 | 0.796 | <.001 | |||||
53. | Personal information could be shared with physicians through Internet due to privacy | 0.273 | <.001 | 1.202 | <.001 | |||||
54. | Patients could be more comfortable with a remote monitoring system to track health | 0.626 | <.001 | 0,895 | <.001 | |||||
55. | Patients could be willing to pay to access Internet health services | 0.512 | <.001 | 1.140 | <.001 | |||||
Goodness-of-fit indexes: NFI: 0.979; RFI: 0.953; IFI: 0.980; TLI: 0.953; CFI: 0.980; RMSEA: 0.074 | ||||||||||
0.037 | <.001 | |||||||||
56. | Use a search engine to find information | 0.242 | <.001 | 0.600 | <.001 | |||||
57. | Send emails with attached files | 0.344 | <.001 | 0.987 | <.001 | |||||
58. | Post messages to chatrooms, newsgroups, or an online discussion forum | 0.626 | <.001 | 1.290 | <.001 | |||||
59. | Use the Internet to make telephone calls | 0.520 | <.001 | 1.377 | <.001 | |||||
60. | Use peer-to-peer file sharing for exchanging pictures, videos, or movies | 0.637 | <.001 | 1.042 | <.001 | |||||
61. | Create a webpage | 0.552 | <.001 | 0.856 | <.001 | |||||
62. | Use websites to share pictures, videos, or movies | 0.681 | <.001 | 1.103 | <.001 | |||||
63. | Use a social networking site | 0.436 | <.001 | 1.972 | <.001 | |||||
64. | Purchase goods or services online | 0.472 | <.001 | 0.801 | <.001 | |||||
65. | Keep a blog or weblog | 0.564 | <.001 | 0.939 | <.001 | |||||
66. | Use instant messaging or chat websites | 0.564 | <.001 | 1.638 | <.001 | |||||
67. | Do home banking | 0.184 | <.001 | 1.577 | <.001 | |||||
68. | Use online software | 0.612 | <.001 | 1.230 | <.001 | |||||
69. | Use the Internet through mobile phone | 0.523 | <.001 | 1.833 | <.001 | |||||
70. | Use online gaming or playing games console | 0.371 | <.001 | 1.976 | <.001 | |||||
Goodness-of-fit indexes: NFI: 0.942; RFI: 0.912; IFI: 0.944; TLI: 0.914; CFI: 0.944; RMSEA: 0.051 | ||||||||||
0.656 | <.001 | |||||||||
71. | Better informed about the advice of the health care professionals | 0.792 | <.001 | 0.389 | <.001 | |||||
72. | Better understanding of personal health | 0.830 | <.001 | 0.301 | <.001 | |||||
73. | Better informed on what is available, so that can make own choices | 0.802 | <.001 | 0.341 | <.001 | |||||
74. | Better understand the relevance of personal health | 0.817 | <.001 | 0.323 | <.001 | |||||
75. | Know more about the opinions of people who are in similar situations | 0.708 | <.001 | 0.505 | <.001 | |||||
76. | Better understand personal health through online discussions or experiences | 0.733 | <.001 | 0.524 | <.001 | |||||
77. | Play a more active role in exchanges with health care professionals | 0.728 | <.001 | 0.547 | <.001 | |||||
Goodness-of-fit indexes: NFI: 0.993; RFI: 0.985; IFI: 0.993; TLI: 0.986; CFI: 0.993; RMSEA: 0.046 | ||||||||||
0.673 | <.001 | |||||||||
78. | Better equipped to implement the advice of health care professionals | 0.819 | <.001 | 0.332 | <.001 | |||||
79. | Better equipped to make own choices without the advice of a physician | 0.791 | <.001 | 0.433 | <.001 | |||||
80. | Better equipped to make positive changes through other people | 0.805 | <.001 | 0.353 | <.001 | |||||
81. | More confident in playing a more active role in relationship with physician | 0.834 | <.001 | 0.319 | <.001 | |||||
82. | More confident about choices on possible treatments and solutions | 0.863 | <.001 | 0.265 | <.001 | |||||
83. | More confident in discussions with the people in one’s life | 0.795 | <.001 | 0.387 | <.001 | |||||
Goodness-of-fit indexes: NFI: 0.998; RFI: 0.991; IFI: 0.998; TLI: 0.992; CFI: 0.998; RMSEA: 0.041 | ||||||||||
0.665 | <.001 | |||||||||
84. | Make decisions on health, albeit without going against the physicians | 0.760 | <.001 | 0.486 | <.001 | |||||
85. | Take a more active role in health by deciding solutions or alternative approaches | 0.840 | <.001 | 0.317 | <.001 | |||||
86. | Make decisions about health on the basis of own preferences | 0.825 | <.001 | 0.384 | <.001 | |||||
87. | Take a more active role in health by continuing to talk with people | 0.775 | <.001 | 0.414 | <.001 | |||||
88. | Make decisions about health by relying on the experiences of other people | 0.783 | <.001 | 0.452 | <.001 | |||||
Goodness-of-fit indexes: NFI: 0.997; RFI: 0.988; IFI: 0.997; TLI: 0.992; CFI: 0.988; RMSEA: 0.048 |
aRegression analysis: structural equation modelling; direct effects.
bNFI: normed fit index.
cRFI: relative fit index.
dIFI: incremental fit index.
eTLI: Tucker-Lewis index
fCFI: comparative fit index.
gRMSEA: root mean square error of approximation.
hICT: information and communication technology.
In the health Internet usage dimension, the standardized coefficient variability is 0.3 points. The variables with the greatest explanatory power in this dimension are related to describing a medical condition to get advice from other Internet users (0.822), as well as disclosing medical information on social networking sites (0.821) or on websites (0.814). In contrast, less explanatory variables are related to finding information about physical illness (0.536) or wellness and lifestyle (0.545). In the health care Internet usage dimension, the standardized coefficient variability is 0.14 points, between the explanatory variables related to online consultation through videoconference with health care professionals (0.813), using medical information through an Internet health care organization (0.812), and receiving emails about health promotion or health prevention (0.670). In the drivers of health care Internet usage dimension, the standardized coefficient variability is high and reaches about 0.5 points. The variable with the greatest explanatory power is the involvement of health professionals (0.867), and the variable with the least explanatory power is the involvement of governments (0.382). In the barriers to health care Internet usage dimension, variability is 0.25 points, between the lack of trust (0.832), liability (0.810), reliability (0.804), and security (0.800) and the lack of digital skills (0.583). In the usefulness of health Internet usage dimension, the explanatory variable variability is around 0.3 points, from the perceptions that ICT for health could lead to greater patient satisfaction (0.819), could improve the ability to take care of one’s own health (0.816), and could improve the quality of health care services (0.803) to the willingness to pay to access Internet health services (0.512). In the ICT usage dimension, variability is the highest, and is around 0.5 points, from using the Internet to share pictures, videos, or movies (0.681), peer-to-peer file sharing (0.637), posting messages to chat rooms, newsgroups, or online discussion forums (0.626), and using online software (0.612) to using a search engine to find information (0.242) and home banking (0.184). Finally, in the information health Internet usage, health Internet attitudes, and empowerment of health Internet users dimensions, the explanatory variable variability is minimal, and all the obtained coefficients are in the range from 0.7 to 0.8 points.
Explanatory factors of eHealth usage in Europe (second stage)a in 2011.
Dimension/variable | Standardized |
Error | ||||||||
3.538 | <.001 | |||||||||
1. | Health Internet usage | 0.099 | <.001 | 360.143 | <.001 | |||||
2. | Health care Internet usage | 0.029 | <.001 | 161.145 | <.001 | |||||
3. | Drivers of health care Internet usage | 0.311 | <.001 | 8.003 | <.001 | |||||
4. | Barriers to health care Internet usage | 0.221 | <.001 | 21.665 | <.001 | |||||
5. | Usefulness of health Internet usage | 0.547 | <.001 | 37.930 | <.001 | |||||
6. | Information and communication technology usage | 0.240 | <.001 | 31.880 | <.001 | |||||
7. | Information health Internet usage | 0.859 | <.001 | 5.221 | <.001 | |||||
8. | Health Internet attitudes | 0.940 | <.001 | 2.146 | <.001 | |||||
9. | Empowerment of health Internet users | 0.855 | <.001 | 3.446 | <.001 | |||||
Goodness-of-fit indexes: NFIb: 0.981; RFIc: 0.961; IFId: 0.981; TLIc: 0.962; CFIf: 0.981; RMSEAg: 0.053 |
aRegression analysis: structural equation modelling; estimated coefficients: direct effects.
bNFI: normed fit index.
cRFI: relative fit index.
dIFI: incremental fit index.
eTLI: Tucker-Lewis index
fCFI: comparative fit index.
gRMSEA: root mean square error of approximation.
The standardized coefficients obtained for the indicators of the 9 dimensions of eHealth usage in Europe highlight different explanatory capabilities. The dimensions with more-explanatory power are health Internet attitudes (0.940), information health Internet usage (0.859), empowerment of health Internet users (0.855), and usefulness of health Internet usage (0.547). ICT usage (0.240), and drivers of (0.311) and barriers to (0.221) health care Internet usage fall in the middle. Finally, the health Internet usage (0.099) and health care Internet usage (0.029) standardized coefficients have the least eHealth usage explanatory power. After applying the coefficients obtained from the second stage, we constructed an eHealth usage composite indicator and determined its mean values (
eHealth usage composite indicator descriptive statistics, 2011.
Dimension/variable | Mean | SD | Minimum | Maximum | Skewness | Kurtosis | |
1. | Health Internet usage | 25.37 | 19.07 | 10.21 | 91.93 | 1.832 | 2.705 |
2. | Health care Internet usage | 14.50 | 12.70 | 7.68 | 69.15 | 2.768 | 7.756 |
3. | Drivers of health care Internet usage | 16.41 | 2.98 | 5.07 | 20.29 | –1.352 | 2.530 |
4. | Barriers to health care Internet usage | 23.21 | 4.77 | 7.33 | 29.33 | –0.963 | 1.141 |
5. | Usefulness of health Internet usage | 28.99 | 7.37 | 8.83 | 44.13 | –0.458 | 0.375 |
6. | Information and communication technology usage | 19.12 | 5.82 | 7.33 | 36.64 | 0.566 | –0.005 |
7. | Information health Internet usage | 20.78 | 4.47 | 5.41 | 27.05 | –0.870 | 1.180 |
8. | Health Internet attitudes | 18.22 | 4.29 | 4.91 | 24.54 | –0.714 | 0.756 |
9. | Empowerment of health Internet users | 14.35 | 3.58 | 3.98 | 19.92 | –0.637 | 0.491 |
eHealth usage composite indicator | 80.85 | 14.24 | 24.19 | 117.06 | –0.541 | 0.716 |
To capture the main predictors of eHealth usage in Europe, we performed a logistic regression using independent variables for European Internet users’ health status and sociodemographic circumstances. The first step in this analysis was to recode the eHealth usage composite indicator. We therefore constructed a dichotomous eHealth usage indicator, based on the mean of the composite indicator obtained. The dichotomous eHealth usage indicator takes the value 1 when the eHealth usage composite indicator is equal to or greater than the mean, and the value 0 when less than the mean. The mean value of this dichotomous composite indicator was 0.524 points (SD 0.499, minimum to maximum range 0–1, skew –0.097, kurtosis –1.991). Some 52.39% (6811/13,000) of European Internet users’ eHealth usage was more intensive (greater than the mean).
eHealth usage composite indicator histogram.
Logistic regression models for odds of dichotomous eHealth usage composite indicator reporting a value of 1 (eHealth usage composite indicator greater than or equal to eHealth usage composite indicator mean) by health status, 2011.
ORa | 95% CI | ||||
Very poor health | 0.91 | 0.61–1.34 | |||
Poor health | 1.30 | 1.12–1.51 | |||
Neither good nor poor health | 0.99 | 0.91–1.10 | |||
Good health | 0.94 | 0.88–1.01 | |||
Very good health | 1.02 | 0.94–1.11 | |||
Yes | 1.20 | 1.12–1.29 | |||
No | 0.83 | 0.77–0.89 | |||
Yes | 1.11 | 1.03–1.20 | |||
No | 0.90 | 0.84–0.97 | |||
Diabetes | 1.01 | 0.88–1.16 | |||
Allergy | 0.82 | 0.77–0.88 | |||
Asthma | 0.87 | 0.78–0.98 | |||
Hypertension | 0.86 | 0.79–0.94 | |||
Long-standing muscular problem | 0.78 | 0.72–0.85 | |||
Cancer | 0.93 | 0.77–1.12 | |||
Cataract | 0.91 | 0.73–1.13 | |||
Migraine or frequent headache | 0.83 | 0.77–0.90 | |||
Chronic bronchitis, emphysema | 0.69 | 0.59–0.79 | |||
Osteoporosis | 0.63 | 0.51–0.77 | |||
Stroke, cerebral hemorrhage | 0.95 | 0.72–1.23 | |||
Peptic, gastric, or duodenal ulcer | 0.78 | 0.68–0.91 | |||
Chronic anxiety or depression | 0.72 | 0.66–0.79 | |||
Yes | 1.44 | 1.34–1.55 | |||
No | 0.69 | 0.65–0.75 | |||
Yes | 1.58 | 1.40–1.77 | |||
No | 0.64 | 0.57–0.71 |
aOR: odds ratio.
Logistic regressions models for odds of dichotomous eHealth usage composite indicator reporting a value of 1 (eHealth usage composite indicator greater than or equal to eHealth usage composite indicator mean) by sociodemographic conditions, 2011.
ORa | 95% CI | ||
Male | 0.82 | 0.76–0.88 | |
Female | 1.23 | 1.14–1.31 | |
16–24 | 0.97 | 0.89–1.06 | |
25–54 | 1.12 | 1.05–1.21 | |
55–74 | 0.86 | 0.78–0.94 | |
1 | 0.75 | 0.69–0.83 | |
2 | 0.87 | 0.81–0.94 | |
3 | 1.25 | 1.15–1.36 | |
4 | 1.07 | 0.98–1.16 | |
5 | 1.13 | 0.97–1.28 | |
≥6 or more | 1.31 | 1.10–1.57 | |
0 | 0.82 | 0.77–0.88 | |
1 | 1.29 | 1.18–1.41 | |
2 | 1.05 | 0.94–1.17 | |
3 | 0.97 | 0.79–1.20 | |
4 | 1.35 | 0.88–2.08 | |
≥5 | 0.77 | 0.34–1.71 | |
0 | 0.84 | 0.76–0.92 | |
1 | 1.33 | 1.18–1.50 | |
2 | 0.97 | 0.84–1.14 | |
3 | 0.99 | 0.44–2.24 | |
≥4 | 1.82 | 0.54–6.03 | |
National of 13 sample countries | 0.78 | 0.68–0.91 | |
National of other EUb member state | 1.28 | 1.09–1.50 | |
National of non-EU country | 1.25 | 0.90–1.73 | |
Native of 13 sample countries | 1.02 | 0.89–1.17 | |
Native of other EU member state | 0.80 | 0.67–0.95 | |
Native of non-EU country | 1.31 | 1.06–1.61 | |
Densely populated area (cities and large towns) | 1.17 | 1.09–1.25 | |
Intermediate area (towns) | 0.92 | 0.86–0.99 | |
Thinly populated area (villages and rural) | 0.90 | 0.83–0.97 | |
Primary or lower secondary education | 0.87 | 0.80–0.95 | |
Upper secondary education | 1.08 | 1.01–1.16 | |
Tertiary education | 1.01 | 0.94–1.08 | |
Employed or self-employed | 1.07 | 0.99–1.15 | |
Unemployed | 0.98 | 0.87–1.10 | |
Student | 0.96 | 0.87–1.05 | |
Not in the labor force (retired, inactive) | 0.94 | 0.86–1.03 | |
1–1000 | 1.66 | 1.48–1.87 | |
1001–2000 | 0.78 | 0.69–0.98 | |
2001–3000 | 0.78 | 0.68–0.91 | |
3001–4000 | 0.80 | 0.64–0.99 | |
≥4001 | 0.85 | 0.66–1.12 |
aOR: odds ratio.
bEU: European Union.
From the viewpoint of residence and nationality, residence in other European Union countries (OR 1.28, 95% CI 1.09–1.50), and residence (OR 1.25, 95% CI 0.90–1.73) or birth (OR 1.31, 95% CI 1.06–1.61) outside the European Union determined higher probabilities of intensive eHealth usage. In contrast, European Internet users had a lower propensity toward more intensive eHealth usage if they had citizenship (OR 0.78, 95% CI 0.68–0.91) or were born in 1 of the 13 countries in the sample (OR 1.02, 95% CI 0.89–1.17). By municipality type, eHealth usage was more intensive among users residing in densely populated areas, such as cities and large towns (OR 1.17, 95% CI 1.09–1.25). Internet users residing in intermediate areas, such as towns (OR 0.92, 95% CI 0.86–0.99), or in less densely populated areas, such as village and rural areas (OR 0.90, 95% CI 0.83–0.97), had a lower propensity toward intensive eHealth usage.
Finally, European Internet users’ educational levels and occupational category presented an inverted U shape in relation to more intensive eHealth usage. Regarding levels of completed education, the propensity toward intensive eHealth usage was greater among those with a secondary education (OR 1.08, 95% CI 1.01–1.16). In contrast, users with primary (OR 0.87, 95% CI 0.80–0.95) and tertiary (OR 1.01, 95% CI 0.94–1.08) education had a lower propensity. In terms of occupational category, the propensity toward intensive eHealth usage was greater among the employed or self-employed (OR 1.07, 95% CI 0.99–1.15). Users who were unemployed (OR 0.98, 95% CI 0.87–1.10), students (OR 0.96, 95% CI 0.87–1.05), or not in the labor force (OR 0.94, 95% CI 0.86–1.03) had lower probabilities of more intensive eHealth usage. In explaining more intensive eHealth usage as a consequence of users’ net monthly income, the results suggest a higher propensity among the minimum wage stratum, earning ≤€1000 per month (OR 1.66, 95% CI 1.48–1.87).
The widespread use of ICTs in general and of the Internet in particular, together with the economic and social changes arising therefrom, are creating a fast-paced and significant change in relationships formed among the stakeholders of the health care system. One of the main manifestations of this disruptive process of change is the watering down of the traditional doctor-patient relationship model. Health Internet (eHealth) usage creates new dynamics that put the patient at the heart of the health care process. Doctor-patient interaction is no longer limited to time and place or to a few minutes in a doctor’s office; nowadays, digital flows of information, communication, and knowledge go beyond the scope of health care centers and pervade the daily lives of citizens.
In this new context, the importance of evaluating the extent to which eHealth usage empowers citizens and involves them in their health status has been noted in the literature [
This is why the goal of our study was to design and evaluate a predictive multidimensional model of eHealth usage, comprising 9 dimensions and 88 indicators. To that end, we used a broad sample of 13,000 European Internet users. Although we did not use a population sample, the results obtained are very useful, for two reasons. First, obtaining new evidence centered solely on Internet users allowed us to focus the analysis better, particularly with regard to inequalities (health status, sex, age, nationality, territory, education, and occupational category) that determine intensive eHealth usage. Second, the predictors we obtained provided evidence that complements studies that have taken a population approach.
In recent years, eHealth usage has increased considerably [
Regarding eHealth predictors, while differences between European Internet users’ perceived general health status and more intensive eHealth usage were not significant, long-term health problems or illnesses in the user or a family member did determine predictive power. European Internet users with long-term health problems or illnesses or receiving long-term treatment, or who had family members or cared for people with long-term health problems or illnesses had a greater propensity toward more intensive eHealth usage. Likewise, the study also highlighted that the existence of certain illnesses among the European Internet user population had high explanatory power with respect to intensive eHealth usage. These health problems or illnesses were diabetes, stroke or cerebral hemorrhage, cancer, and cataract. In contrast, users with health problems or illnesses related to chronic bronchitis and emphysema, and to osteoporosis had a lower propensity toward intensive eHealth usage.
These results, which are clearly consistent with other studies of social networking sites, virtual communities, and support group usage by patients with chronic illnesses [
Our results suggest that women, those aged 25–54 years, and households with more members, more children <16 years of age, and more members >65 years of age were most likely to use eHealth intensively. In contrast, men, people in the age groups 16–24 years and 54–74 years, and households with fewer members or with fewer dependents were less likely to use eHealth intensively.
The decisive importance of women [
Aging of the population poses a broad set of challenges for health care systems, which a more widespread implementation of eHealth could help to meet. Without doubt, the main challenge for sustainable health that Europe faces over the coming years is the aging of the population. This is a complex mix of genetic, environmental, lifestyle, and socioeconomic factors, with the rates of associated chronic illnesses. Indeed, the European population is changing dramatically because of longer life expectancy and lower fertility rates. The number of European citizens over the age of 80 years is expected to double by 2025, which will give rise to increasingly complex needs in terms of clinical care, health care, and social care. In this context, eHealth practices could become one of the main tools for delivering health care to older citizens, especially through female caregivers. While the new patient-centered model has increasingly underscored the empowerment of patients and users in health care, the aging care model should be characterized by interaction between an active and informed patient or caregiver and a proactive and versatile medical team [
From the perspective of nationality and territory, significant results were also obtained from the study. European Internet users had a greater propensity toward more intensive eHealth usage if they resided in other European Union countries or outside the European Union, and if they were born outside the European Union. Similarly, European Internet users’ residence in densely populated areas (cities or large towns) also better predicted eHealth usage. In this context, a fairer promotion of eHealth usage in Europe should also consider the territorial dimension, with special emphasis on connecting national health systems and a greater Internet presence and usage in less densely populated areas.
Finally, the results obtained also provide us with significant information about educational, occupational, and income categories, which are crucial for redressing some of the social inequalities in eHealth usage. Users’ educational levels explain more intensive eHealth usage, in an inverted U form. Thus, users with a secondary education had a greater propensity toward intensive eHealth usage. In this sense, the study provides new evidence (beyond population studies) in relation to middle-educated (secondary education) Internet users, who perceived the usefulness of eHealth usage. The education dimension also determines a new area of health inequality, and hence the need to promote Internet usage among the less educated population. The results related to occupational and income categories suggest a higher propensity among the employed or self-employed and among the minimum wage stratum earning ≤€1000 per month. Users who were integrated into the labor market, whether self-employed or employed, clearly had a greater propensity, whereas those who were not (students, unemployed, and not in the labor force) had a lower propensity to use eHealth. In this context, in order to achieve a more equitable eHealth usage, Internet usage among groups not actively integrated into the labor market should be promoted more vigorously. Regarding income, and in order to overcome inequalities, promoting eHealth usage skills (especially through education and learning) for workers with lower wages would also be very useful.
Our study has several limitations. First, there was a time lag between the year we obtained the data and the year we wrote the paper. However, we felt that the availability of a single database of 13,000 Internet users in Europe deserved an analysis despite the time lag. In future research, and as they become available, we will use newer data and introduce dynamic comparisons. Second, the study provides information only from the perspective of health users. In the future, we intend to address the issue of eHealth usage by health professionals. By doing so, we will be able to improve our multidimensional approach and obtain results and conclusions for all actors involved in eHealth usage. Third, the empirical methodology could also be improved by looking at the intensity of eHealth usage (not simply usage or mean usage) and at a higher number of predictors.
The results obtained highlight the need for more in-depth research to be conducted into the link between eHealth usage and predictors, and the different health care systems in Europe. By doing so, it will be possible to increase the resolution of our results and to establish whether the intensity of eHealth usage varies depending on the health care systems, or the extent to which health care systems determine the prediction of eHealth usage. Similarly, strategic and public policy actions resulting from the research could be adapted more precisely to each health care system. Finally, the study results could be supplemented by the construction of a composite indicator of eHealth usage by health care professionals. The design, validation, and prediction of composite indicators of eHealth usage that take into consideration the perspectives of both users (ie, patients) and professionals in the different European health care systems would provide us with a very comprehensive view of the issue and would allow us to round off our multidimensional approach. We shall focus our efforts on all of these approaches in the near future.
Statistical information based on SIMPHS2 online survey.
Strategic Intelligence Mapping on Personal Health Systems Phase 2 (SIMPHS2) Questionnaire.
Health Internet uses descriptive statistics and frequency statistics. 2011.
Health care Internet uses descriptive statistics and frequency statistics. 2011.
Drivers of health care Internet uses and frequency statistics. 2011.
Barriers of health care Internet uses descriptive statistics and frequency statistics. 2011.
Usefulness of health Internet uses descriptive statistics and frequency statistics. 2011.
ICT uses descriptive statistics and frequency statistics. 2011.
Information health Internet uses descriptive statistics and frequency statistics. 2011.
Health Internet attitudes descriptive statistics and frequency statistics. 2011.
Empowerment of health Internet user’s descriptive statistics and frequency statistics. 2011.
comparative fit index
information and communication technology
incremental fit index
normed fit index
odds ratio
relative fit index
root mean square error of approximation
structural equation modelling
Strategic Intelligence Monitor on Personal Health Systems Phase 2
Tucker-Lewis index
This work used data from the Strategic Intelligence Monitor on Personal Health Systems Phase 2 (SIMPHS2) research project, carried out by the Institute for Prospective Technological Studies (IPTS) in cooperation with the European Commission Directorate General for Information Society and Media. The funding sources had no involvement in this study. The authors would like to thank Ioannis Maghiros, Head of Unit, Information Society, at IPTS for his support. Open access microdata are available from the Joint Research Centre (http://is.jrc.ec.europa.eu/pages/TFS/SIMPHS2deliverables.html).
All authors contributed substantially to the design, data analysis, and interpretation of the findings. Joan Torrent-Sellens participated in formulating the research question, study design, literature review, data analysis and statistical modelling, interpretation of the findings, and drafting the manuscript. Ángel Díaz-Chao participated in data analysis and statistical modelling. Ivan Soler-Ramos participated in the design and data analysis. Francesc Saigí-Rubió contributed to formulating the research question, study design, literature review, interpretation of results, and drafting the manuscript. He is the guarantor of the paper. All the authors have read, revised, and approved the final manuscript.
None declared.