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E-inclusion: Beyond individual socio-demographic characteristics

  • Patrícia Silva ,

    Contributed equally to this work with: Patrícia Silva, Alice Delerue Matos, Roberto Martinez-Pecino

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Communication and Society Research Centre, Institute of Social Sciences, University of Minho, Braga, Portugal

  • Alice Delerue Matos ,

    Contributed equally to this work with: Patrícia Silva, Alice Delerue Matos, Roberto Martinez-Pecino

    Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Department of Sociology and Communication and Society Research Centre, Institute of Social Sciences, University of Minho, Braga, Portugal

  • Roberto Martinez-Pecino

    Contributed equally to this work with: Patrícia Silva, Alice Delerue Matos, Roberto Martinez-Pecino

    Roles Conceptualization, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Department of Social Pyschology, Universidad de Sevilla, Seville, Spain


The changing demographic structure of the population, resulting in unparalleled growth of the elderly population, means that e-inclusion of this population group is considered to be a social and political priority in the context of the Information Society. Most research studies have only considered individual variables -such as age, gender, education, income and health- in the explanatory models of e-inclusion of senior citizens, while ignoring macro variables, such as the welfare systems and public policies in each country. Simultaneously, most studies focus on small-scale samples, lack international comparisons and do not consider the combined effect of several variables that influence Internet use. This study aims to analyse possible differences between two countries that have different welfare systems and public policies, after controlling for the effects of the individual variables that have been identified in the literature as relevant for Internet use. The study focuses on a sample of 8639 individuals, aged 50 years and over, residing in Portugal and Estonia, who participated in the SHARE project (Survey of Health, Ageing and Retirement in Europe). The results of the logistic regression analysis demonstrate that welfare systems and public policies have an impact on the likelihood of Internet use, thus reinforcing the importance of developing public policies to foster e-inclusion of senior citizens.


The Internet has redesigned our daily lives, blurring the boundary between the local and the global, in terms of fostering access to knowledge and information, and disseminating ideas and values. It has also changed the way that people interact, and the manner in which they build their social networks [13]. However, some individuals remain on the margins of this "revolution" [4]. The "digital divide" between those who have access to information and communication technologies (ICT) and those who don’t, has concerned the public authorities, given that it is an obstacle to achieving the Information Society, which is considered to be economically more competitive and that fosters greater social cohesion, participation and control of citizens [57].

Studies on the "digital divide" identify senior citizens as one of the groups most vulnerable to e-Exclusion [3]. This "digital divide" results from inequalities in Internet access, types of use of Internet technology, knowledge of its technical features, ability to assess information quality and, among other factors, the diversity of forms of use [4]. Indeed, there is a major political, social and economic interest in identifying the factors that affect Internet use in order to avoid the "digital divide" and, in particular, to avoid the marginalization of senior citizens, given that they constitute a fast-growing population group [8].

Despite the relevance of the “digital divide”, scientific literature still lacks a comprehensive explanation of technology acceptance and, in particular, internet’ adoption by the elderly [9]. Studies of older age groups have shown, in general, that certain socio-demographic characteristics of individuals -such as age, gender, education, health status and income—condition the use of technology [1019]. The influence of socio-demographic variables on Internet use has been shown by studies based on different technology acceptance models -like the Unified Theory of Acceptance and Use of Technology (UTAUT)- (e.g.[9,20]).

Literature evidences the importance of individual socio-demographic aspects employing qualitative as well as quantitative methodologies. In this sense studies using qualitative methodologies have focused in-depth on small sample sizes ranging from 10 to 24 participants [2123] or slightly bigger [24]. Studies with quantitative methodologies, mainly surveys, also analyze the relevance of individual socio-demographic characteristics such as: age, gender, education [2527], income [2527], health [25,27] in the USA, Europe and worldwide (e.g. [25,26,28,29]). All in all, these studies confirm the influence of individual socio-demographic characteristics on information technology use. (For more detail see table in annex [4,19,2541]).

On the other hand, several authors argue that inequalities in ICT use, when observed individually, are no more than a reflection of inequalities in social structures [42], and are therefore related to the economic, political, historical and social characteristics of the respective countries [43]. In this vein, there is a need for comparative studies between countries with different welfare regimes to evidence the extent of these factors [44]. Analyses with large sample sizes, that include a comparison between countries considering the simultaneous effect of various different factors, as in this work, are particularly important and needed since it is thereby possible to discuss the possible effect of different policies and welfare systems. Taking into consideration the literature review conducted, the main objective of this work was to analyse the existence of eventual differences in Internet use amongst individuals aged 50 years and over, resident in countries with different welfare systems and public policies, after having controlled for the socio-demographic variables that are identified in the literature as being important to explain Internet use.

Materials and methods

This study focuses on 8639 individuals, aged 50 years and over, interviewed in wave 4 of the European project, SHARE (Survey of Health, Ageing and Retirement in Europe), in two countries with different welfare systems and public policies: Portugal and Estonia.

Details on the SHARE study in Europe have been described elsewhere[45]. Briefly, in wave 4 (2010–2011), a survey was conducted with representative samples of the non-institutionalised population aged 50 + in 16 European countries.

To achieve representation of this population, SHARE employs a sample design which involves baseline samples of the household population aged 50 and older at a particular point in time in each country, supplemented by regular refreshment samples of the sub -population of individuals who have turned 50 since the original baseline sample was selected [45].

Interviews were face-to-face and took place in the household. Trained interviewers conducted interviews using exactly the same questionnaire in all countries on a computer assisted personal interviewing program (CAPI).

The SHARE project, coordinated internationally by the Max Planck Institute for Social Law and Social Policy (Germany), has been approved by the Ethics Council of the Max-Planck-Society for the Advancement of Science and by the Ethics Committees of the institutions responsible for the study in the participating countries.

Given that the SHARE project has national samples of different sizes (in this study Portugal N = 1972 and Estonia N = 6667) and a sample design that is not uniform in the different countries, calibrated individual weights were used in the descriptive statistical analysis that aimed to present a comparative study of regular Internet users, and non-users. We used the chi-square test to assess the interdependence between the two qualitative variables. The sample means were also compared using Student’s t-tests for independent samples. Statistical test results with p < .05 were considered to be significant. The results from these tests were also complemented with effect size measures (Cohen’s d/Phi). The interpretation of results was based on Cohen (1988)[46].

In order to identify the determinants of Internet use, a binary logistic regression analysis, using the Enter method, was subsequently carried out. These analyses were performed using SPSS software, version 23.

The following variables and corresponding methods were used in this study:

Internet use: a dichotomous variable related to regular use of the Internet in order to send and receive e-mails, or for other purposes such as shopping, browsing information or making travel reservations. This variable has the status of a dependent variable in binary logistic regression analysis -wherein an affirmative answer to the question on Internet use is the reference category.

Socio-demographic variables: age; gender; number of years of schooling and self-perception of financial stress. In the latter case, this variable distinguished between individuals who reported that they experienced "major" or "some difficulties" in paying monthly expenses, and those who claimed that it is "easy" or "very easy" to meet such expenses, according to their incomes.

Health variables: mental health was assessed using the EURO-D scale and included a category that encompassed individuals who reported significant symptoms of depression, i.e. who obtained a score of equal to or higher than 3 points in the Euro-D 12-items scale [47] and another that encompasses individuals who recorded lower scores.

Physical health was addressed using two variables: the ADL scale and mobility limitations. The first variable, i.e. the ADL scale (limitations with activities of daily living), evaluates the functional dependence of individuals, taking into account the perceived difficulties in performing some activities (dressing, including putting on socks and shoes, walking across the room, bathing or showering, eating, such as cutting up food, getting in and out of bed, using the toilet, including getting up or down). This scale distinguishes between individuals who reported having one or more difficulties in basic activities of daily living, from those who stated no functional limitation.

The second variable, related to physical health, evaluates limitations in terms of mobility level, i.e. the ability to walk 100 metres; sit down for around two hours; get up from a chair after having been seated for some time; climbing several flights of stairs without resting; climbing a flight of stairs without resting; leaning, kneeling or crouching; placing or raising the arms above shoulder level; pulling or pushing large objects; lifting or carrying weights over 5 kilograms; picking up a small coin from the top of a table. This last variable distinguishes individuals who do not present any mobility limitations from those who have one or more limitations.

Macro-social-variables: country of residence, that distinguishes between individuals residing in Portugal and Estonia. In the regression analyses, Portugal was considered to be the reference category because it is the country where there are lower rates of Internet use.

Results and discussion

Individuals resident in Portugal and Estonia, aged 50 years and older, who use the Internet are a minority group. In Portugal only 15.2% of respondents regularly use this technology. In Estonia the percentage of Internet users was 35.4%.

Table 1 shows the descriptive analyses of the socio-demographic, economic and health characteristics of Internet users and non-users in the whole sample.

Table 1. Characteristics of Internet users and non-users in the sample.

Our results are consistent with findings from other studies, that suggest the existence of differences between the groups of Internet users and non-users according to age, gender, years of schooling, financial status, mental health (symptoms of depression) and physical health (functional capacity and mobility) [15,17,48].

The average ages of the two groups (Internet users and non-users) are statistically different (Internet users have 60.07 years on average, while non-users have 67.14 years) with large effect size, in line with different studies [16,25,27,38].

The group of Internet users is mainly composed of males (59.30%) while the group of non-users is essentially composed of females (with small effect size). With regard to education, on average, Internet users have a higher number of years of schooling (on average, 11.76 years for users, compared to 5.31 years for non-users, with large effect size) like found in other studies [17,19,25,26,49].

Internet users also report having fewer financial problems: 61.1% state that they can meet monthly expenses without difficulty, compared to 41.7% for non-users (with small-medium effect size).

The group of Internet users is positively differentiated from the group of non-users, with regard to mental and physical health, with fewer symptoms of depression, fewer functional limitations in performing daily activities and fewer limitations on the level of mobility (with small-medium effect size).

After carrying out this comparative descriptive analysis of Internet users and non-users, residing in the two countries, a logistic regression analysis was undertaken, to assess whether, after controlling for the effect of the various individual variables, differences still persist, related to the macro-social aspects of the two countries (Table 2).

Table 2. Determinants of Internet use, according to the characteristics of individuals aged 50+ years, living in Portugal and Estonia.

The socio-demographic, economic, health and macro-social variables entered in the regression model explain 46% of the variance in Internet use by individuals aged 50 years or more.

We will first discuss the individual variables and then the macro-social variables, which constitute the main goal for this study. With regard to the individual variables, there is an inverse relationship between age and Internet use. The probability of using the Internet decreases by 10.3% for each year of additional age (OR = .897; 95% CI: .890 to .904). This is consistent with previous studies [16,25,27,38].

Some studies on gender have identified the persistence of inequalities in the use of ICT between male and female senior citizens [15,50]. On the other hand, in contrast to these studies, other studies suggest that the number of females using the Internet is tending to increase [51], moving towards growing gender parity [5254]. Indeed, our results are consistent with the findings of these recent research studies, since we found no statistically significant gender differences, in relation to the probabilities of Internet use (p = .345).

The level of education, as expected, is strongly associated with Internet use, wherein individuals with more years of schooling tend to have a higher level of Internet use. These results mirror the results obtained in other studies, that have highlighted the importance of education in determining levels of Internet use amongst senior citizens [17,19,25,26,49]. Also, as was expected, and in accordance with the literature review [17,18,26], absence of economic difficulties, is associated with a higher probability of using this technology, which implies that cost of computer equipment and the price of Internet access continue to be significant factors of discrimination. Similarly, with regard to health, our results are consistent with previous research, that establishes a correlation between the existence of health problems with lower Internet use [17,25,27]. Hence, European citizens aged 50 years and more in the two countries under analysis that have significant symptoms of depression, i.e. 3 or more symptoms of depression in the EURO-D scale, are 18.5% less likely to use the Internet (OR = .855; 95% CI: .750 to .973) compared to those with under 3 symptoms. In turn, individuals who have limitations in performing daily activities are also distinguished from those who do not have these limitations, since the former are 20.9% less likely to use the Internet (OR = .791, 95% CI: .647 to .967). Also people who have one or more mobility limitations are 14.8% less likely to use this technology (OR = .852; 95% CI: from .7427 to .971) than those who don’t have such limitations.

Other studies have also shown a relation between physical or health problems and technology [27].

Finally, focusing on the main goal of this study, the country of residence constitutes an important factor for Internet use. Although Portugal has fewer Internet users than Estonia, once the effects of individual variables have been controlled for, the residence of persons aged 50 years and over in Portugal is actually associated with a higher probability of Internet use.

In Portugal, the majority of individuals aged 50 years and over tend to have a set of socio-demographic characteristics that are strongly related to the profile of an Internet non-user, because, in addition to health-related limitations which are an issue to consider [55], older Portuguese citizens, especially those who spent their childhood and youth under the dictatorship regime, had limited or no educational opportunities in the past, and today have limited pensions [56] because they only made limited pension contributions, which thus hinders their access to paid Internet services. However, the probabilities of Portuguese citizens aged 50 years and over using the Internet, are actually higher than in Estonia, after having controlled for the socio-demographic and health characteristics of the population in the study. These results are particularly interesting and relevant for definition of public policies and may have been affected by the significant investments made in Portugal over the past decade in technology programs [57]; adult education (e.g. the New Opportunities programme, EFA courses, etc.); the provision of specific IT training by senior citizen universities, parish councils and NPOs, often available free of charge, and the creation of public spaces for free Internet access. Another factor that has probably contributed to this result is the incentive to use the Internet, which has been achieved in Portugal, in particular, through increasing computerisation of administrative acts (eGovernment).

The results presented herein highlight the need to analyse e-inclusion, taking into consideration not only socio-demographic, economic and health variables, at an individual level, but also the macro-social variables related to the country of residence. In this sense, future research should include, in addition to the variables that are traditionally included in studies on technology-use, variables related to the importance that different public policies may have on reducing the "digital divide" and fostering the e-inclusion of individuals aged 50 years and over.

This study presents certain limitations that must be taken into account in future research. SHARE’s database, which is of great value for carrying out comparative analyses of Europeans aged 50 years and over, has significant information gaps in relation to Internet use by these individuals. It would therefore be advisable to collect data on other variables associated to the "digital divide", such as different types of Internet use and the frequency with which individuals use this technology in the various countries participating in the project. It would also be advisable for future research with SHARE data to include variables of the user acceptance of information technology models well integrated in the Unified Theory of Acceptance and Use of Technology (UTAUT) [58] and its extension UTAUT2 [59]. This information would make it possible to go further the results of this study, which points to the importance of the macro-social context and public policies, in the construction of a more inclusive Information Society.

Supporting information

S1 Table. Quantitative studies analysing the influence of socio-demographic characteristics on technology use.



  1. 1. Giddens A. Sociologia. 6th ed. Polity Press. Lisboa: Fundação Calouste Gulbenkian; 2001.
  2. 2. Heo J, Oh J, Subramanian S V., Kim Y, Kawachi I. Addictive internet use among Korean adolescents: A national survey. PLoS One. 2014;9: 1–9. pmid:24505318
  3. 3. Zhou R, Fong PSW, Tan P. Internet use and its impact on engagement in leisure activities in china. PLoS One. 2014;9: 1–8. pmid:24586902
  4. 4. Aroldi P, Colombo F, Carlo S. New Elders, Old Divides: ICTs, Inequalities and Well Being amongst Young Elderly Italians. Comunicar. 2015;23: 47–55.
  5. 5. European Commission. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. Brussels; 2007.
  6. 6. Commission E. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. European i2010 initiative on e-inclusion “To be part of the information society.” Brussels; 2007.
  7. 7. European Parliament. European Parliament resolution of 5 may 2010 on a new digital agenda for Europe: 2010.
  8. 8. Martinez-Pecino R, Cabecinhas R, Loscertales F. University senior students on the web. Comunicar. 2011;19: 89–95.
  9. 9. Niehaves B, Plattfaut R. Internet adoption by the elderly : employing IS technology acceptance theories for understanding the age-related digital divide. Eur J Inf Syst. 2014;23: 708–726.
  10. 10. Lee B, Chen Y, Hewitt L. Age differences in constraints encountered by seniors in their use of computers and the internet. Comput Human Behav. 2011;27: 1231–1237.
  11. 11. Rosenthal RL. Older Computer-Literate Women: Their Motivations, Obstacles, and Paths to Success. Educ Gerontol. 2008;34: 610–626.
  12. 12. Eastman JK, Iyer R. The elderly’s uses and attitudes towards the Internet. J Consum Mark. 2004;21: 208–220.
  13. 13. Wilson K, Wallin J, Reiser C. Social Stratification and the Digital Divide. Soc Sci Comput Rev. 2003;21: 133–143.
  14. 14. Browne H. Accessibility and Usability of Information Technology by the Elderly. In: University of Maryland [Internet]. 2000 pp. 1–7.
  15. 15. Dias I. O uso das tecnologias digitais entre os seniores: Motivações e interesses. Sociol Probl e Práticas. 2012;68: 51–77.
  16. 16. Demoussis M, Giannakopoulos N. Facets of the digital divide in Europe: Determination and extent of internet use. Econ Innov New Technol. 2006;15: 235–246.
  17. 17. Carpenter BD, Buday S. Computer use among older adults in a naturally occurring retirement community. Comput Human Behav. 2007;23: 3012–3024.
  18. 18. Choi NG, Dinitto DM. The digital divide among low-income homebound older adults: Internet use patterns, ehealth literacy, and attitudes toward computer/internet use. J Med Internet Res. 2013;15: 1–14. pmid:23639979
  19. 19. Selwyn N, Gorard S, Furlong J, Madden L. Older adults’ use of information and communications technology in everyday life. Ageing Soc. 2003;23: 561–582.
  20. 20. Tsai HS, Larose R. Broadband Internet adoption and utilization in the inner city : A comparison of competing theories q. Comput Educ. Elsevier Ltd; 2015;51: 344–355.
  21. 21. Sourbati M. “It could be useful, but not for me at the moment”: older people, internet access and e-public service provision. New Media Soc. 2009;11: 1083–1100.
  22. 22. Larsson E, Larsson-Lund M, Nilsson I. Internet Based Activities (IBAs): Seniors’ Experiences of the Conditions Required for the Performance of and the Influence of these Conditions on their Own Participation in Society. Educ Gerontol. 2013;39: 155–167.
  23. 23. Quan-Haase A, Martin K, Schreurs K. Interviews with digital seniors: ICT use in the context of everyday life. Information, Commun Soc. 2016;19: 691–707.
  24. 24. Hill R, Michael PB, Williams M. Older people and internet engagement: Acknowledging social moderators of internet adoption, access and use. Inf Technol People. 2008;21: 244–266.
  25. 25. Yu RP, Ellison NB, McCammon RJ, Langa KM. Mapping the two levels of digital divide: Internet access and social network site adoption among older adults in the USA. Information, Commun Soc. 2016;19: 1–20.
  26. 26. Friemel TN. The digital divide has grown old: Determinants of a digital divide among seniors. New Media Soc. 2014; 1–19.
  27. 27. Lissitsa S, Chachashvili-bolotin S. Does the wind of change blow in late adulthood? Adoption of ICT by senior citizens during the past decade. Poetics. Elsevier B.V.; 2015;52: 44–63.
  28. 28. Van Deursen Ajam, van Dijk Jagm, Peters O. Rethinking Internet skills: The contribution of gender, age, education, Internet experience, and hours online to medium- and content-related Internet skills. Poetics. Elsevier B.V.; 2011;39: 125–144.
  29. 29. Wong Y, Chen H, Law C. Empowerment of Senior Citizens via the Learning of Information and Communication Technology. Ageing Int. 2013;39: 144–162.
  30. 30. Stoica V. Age Based Digital Divide in the City of Iasi. Rev Cercet si Interv Soc. 2015;51: 202–2015.
  31. 31. Van Deursen Ajam, Van Dijk Jagm. Internet skill levels increase, but gaps widen: a longitudinal cross-sectional analysis (2010–2013) among the Dutch population. Information, Commun Soc. 2014;18.
  32. 32. Casado-Muñoz R, Lezcano F, Rodríguez-Conde J. Active Ageing and Access to Technology: An Evolving Empirical Study. Comunicar. 2015;45: 37–46.
  33. 33. Gilleard C, Jones I, Higgs P. Connectivity in Later Life: The Declining Age Divide in Mobile Cell Phone Ownership. Sociol Res Online. 2015;20: 3. Available:
  34. 34. Correa T, Straubhaar JD, Chen W, Spence J. Brokering new technologies: The role of children in their parents’ usage of the internet. New Media Soc. 2013;17: 483–500.
  35. 35. Agudo-Prado S, Pascual-Sevillano MÁ, Fombona-Cadavieco J. Uses of digital tools among the elderly. Comunicar. 2012;20: 193–201.
  36. 36. Wei L. Number Matters: The Multimodality of Internet Use as an Indicator of the Digital Inequalities. J Comput Commun. 2012;17: 303–318.
  37. 37. Lee C. The Role of Internet Engagement in the Health-knowledge Gap. J Broadcast Electron Media. 2009;53: 365–382. pmid:25530667
  38. 38. Loges WE, Jung J-Y. Exploring the Digital Divide: Internet Connectedness and Age. Communic Res. 2001;28: 536–562.
  39. 39. Diño MJS, de Guzman AB. Using Partial Least Squares (PLS) in Predicting Behavioral Intention for Telehealth Use among Filipino Elderly. Educ Gerontol. 2015;41: 1–16.
  40. 40. Ramón-Jerónimo M, Peral-Peral B, Arenas-Gaitán J. Elderly Persons and Internet Use. Soc Sci Comput Rev. 2013;31: 389–403.
  41. 41. Jung Y, Peng W, Moran M, Jin S-AA, McLaughlin M, Cody M, et al. Low-Income Minority Seniors’ Enrollment in a Cybercafé: Psychological Barriers to Crossing the Digital Divide. Educ Gerontol. 2010;36: 193–212.
  42. 42. Ono H, Zavodny M. Digital inequality: A five country comparison using microdata. Soc Sci Res. 2007;36: 1135–1155.
  43. 43. Guillen MF, Suarez SL. Explaining the Global Digital Divide: Economic, Political and Sociological Drivers of Cross-National Internet Use. Soc Forces. 2005;84: 681–708.
  44. 44. Orviska M, Hudson J. Dividing or uniting Europe? Internet usage in the EU. Inf Econ Policy. Elsevier B.V.; 2009;21: 279–290.
  45. 45. Malter F, Börsch-supan A. Share Wave 4 Innovations & Methodology. Munich: Munich Center for the Economics of Aging (MEA); 2013.
  46. 46. Cohen J. The t test for means. Statistical Power Analysis for the Behavioral Sciences. 1988. pp. 19–74.
  47. 47. Prince MJ, Reischies F, Beekman ATF, Fuhrer R, Jonker C, Kivela SL, et al. Development of the EURO-D scale—A European Union initiative to compare symptoms of depression in 14 European centres. Br J Psychiatry. 1999;174: 330–338. pmid:10533552
  48. 48. Gell NM, Rosenberg DE, Demiris G, Lacroix AZ, Patel K V. Patterns of Technology Use Among Older Adults With and Without Disabilities. Gerontologist. 2013; 1–11. pmid:24379019
  49. 49. Peacock SE, Künemund H. Senior citizens and Internet technology: Reasons and correlates of access versus non-access in a European comparative perspective Sylvia. Eur J Ageing. 2007;4: 191–200. pmid:28794788
  50. 50. Bimber B, Barbara S. Measuring the Gender Gap on the Internet. Soc Sci Q. 2000;81.
  51. 51. Horrigan JB. New Internet Users: What They Do Online, What They Don ‘ t, and Implications for the “Net”s Future. Pew Internet and American Life Project. 2000. pp. 1–27.
  52. 52. Fox S. Older Americans and the internet. Pew Internet and American Life Project. Pew Internet and American Life Project.; 2004. pp. 1–16.
  53. 53. Lenhart A, Rainie L, Fox S, Horrigan J, Spooner T. Who’s not online: 57% of those without Internet access say they do not plan to log on. PEW Internet & American Life Project. Washington; 2000. pp. 1–18.
  54. 54. Martinez-Pecino R, Delerue Matos A, Silva P. Portuguese older people and the Internet: Interaction, uses, motivations, and obstacles. 2013 pp. 331–346.
  55. 55. Santoni G, Angleman S, Welmer A-K, Mangialasche F, Marengoni A, Fratiglioni L. Age-related variation in health status after age 60. PLoS One. 2015;10. pmid:25734828
  56. 56. Hartog J, Pereira PT, Vieira J a. C. Changing returns to education in Portugal during the 1980s and early 1990s: OLS and quantile regression estimators. Appl Econ. 2001;33: 1021–1037.
  57. 57. Ministério da Ciência T e ES. Ligar Portugal. Um programa de acção integrado no plano tecnológico do XVII Governo: Mobilizar a Sociedade de Informação e do Conhecimento. 2005;
  58. 58. Venkatesh V, Morris M, Davis G, Davis F. User Acceptance of Information Technology: Toward a Unified View. MIS Q. 2003;27: 425–478.
  59. 59. Venkatesh V, Thong J, Xu X. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Q. 2012;36: 157–178.