8JAIV11E1

Campens J., et al. (2022) Identifying subgroups of non-internet-users among community dwelling older people, Journal of Aging & Innovation, 11 (1): 112 ARTIGO ORIGINAL: Campens J., et al. (2022) 130 Identifying subgroups of non-internet-users among community dwelling older people, Journal of Aging & Innovation, 11 (1): 112 - 130

 ARTIGO ORIGINAL

Identifying subgroups of non-internet-users among community dwelling older people Identificação de não usuários de Internet nos idosos morando em casa Identificando no-usuarios de internet entre personas mayores viviendo en casas

Jorrit Campens ¹; Werner Schirmer ²; Anina Vercruyssen ³; Emily Verté 4; Nico De Witte 5 1

Faculty of Psychology and Educational Sciences, Vrije Universiteit Brussel, Brussels, Belgium, School of Healthcare, HOGENT University of Applied Sciences and Arts, Ghent, Belgium, 2 TOR Research Group - Department of Sociology, Vrije Universiteit Brussel, Brussels, Belgium, 3 Centre for Population, Family and Health (CPFH), Antwerp, Belgium, Faculty of Social Sciences, University of Antwerp, Antwerp, Belgium, 4 Department of Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium, Department of Family Medicine and Chronic Care, Vrije Universiteit Brussel, Brussels, Belgium, 5Faculty of Psychology and Educational Sciences, Faculty of Medicine, FRAILTY IN AGEING (FRIA) Research group, Vrije Universiteit Brussel, Brussels, Belgium School of Healthcare, HOGENT University of Applied Sciences and Arts, Ghent, Belgium

Corresponding Author: Jorrit.Campens@vub.be

Authors’ contribution statements The manuscript has been seen and approved by all authors, and all contributed to it significantly.

Funding This study was funded by the Research Foundation – Flanders (FWO), SBO-project [S005221N].

Ethics approval This study was approved by the ethical committee of the Vrije Universiteit Brussel (B.U.N. 143201111521).

Disclosure of potential conflicts of interest The authors have no relevant financial or non-financial interests to disclose.

Abstract Title: Identifying subgroups of non-internet-users among community dwelling older people

112 JOURNAL OF AGING AND INNOVATION, ABRIL, 2022, 11 (1)  ISSN: 2182-696X  http://journalofagingandinnovation.org/ DOI: 10.36957/jai.2182-696X.v11i1-9

ARTIGO ORIGINAL: Campens J., et al. (2022) Identifying subgroups of non-internet-users among community dwelling older people, Journal of Aging & Innovation, 11 (1): 112 - 130 Objective: Internet use is increasing among all age groups. However, older people are less likely to adopt the internet. This study aims to examine the cumulative impact of sociodemographic variables enabling case finding of non-users among community dwelling older people.

Methods: This study uses data from the Belgian Ageing Studies. The sample consists of 64,553 people aged 60 and older. Age, gender, marital status, education, monthly household income and internet use are included. Logistic regression analyses and CHAID-analyses are performed to determine the impact of sociodemographic characteristics on internet use.

Results: According to logistic regression analyses, being female, high age, low education, low income and being widowed showed to be related with lower odds of internet use. CHAID-analyses revealed education, income and age, respectively, as the strongest predictors of non-use. Based on the cumulative impact of these sociodemographic characteristics, diverse subgroups composed almost entirely of non-internet-users could be determined. The highest proportion of non-users (98.6%) was observed in the subgroup of people with no degree/only primary education, who had a net household income of less than 999 euro/month and were 80 years and older.

Conclusions: The lower the education and income and the higher the age, the higher the possibility of being non-internet-user.

These results can assist policy makers and internet training providers to identify non-users when deploying e-inclusion initiatives.

Keywords aging, internet, digital gap, sociodemographic impact

Resumo Título: Identificação de não usuários de Internet nos idosos morando em casa Objetivo: A utilização de Internet está a aumentar em todos os grupos de idade. Contudo, os idosos são menos propensos a utilizar a Internet. Este estudo visa a investigar o impacto cumulativo das variáveis sociodemográficas que permitem de encontrar os não usuários de Internet entre as pessoas idosas que vivem em casa.

Metodologia: Este estudo utiliza dados dos Belgian Ageing Studies (estudos sobre as necessidades dos idosos). A amostra é composta por 64.553 pessoas com idades de 60 anos e mais. Idade, sexo, estado civil, educação, rendimento mensal do agregado familiar e a utilização de Internet foram incluídos neste estudo. Análises de regressão logística e análises CHAID foram realizadas para avaliar o impacto das características sociodemográficas na utilização de Internet.

Resultados: De acordo com as análises de regressão logística, verificou-se que idosos do sexo feminino, de idade elevada, com renda familiar baixa e que são viúvo(a) foram associados com as menores probabilidades de utilização de Internet. As análises CHAID mostraram que a educação, o rendimento e a idade, respetivamente, são os preditores mais fortes da não utilização de Internet. Com base o efeito cumulativo destas características sociodemográficas, vários subgrupos compostos quase inteiramente por não usuários de Internet poderiam ser distinguidos. A maior percentagem de não usuários de Internet (98,6%) foi observada no subgrupo de pessoas sem diploma/ que apenas foram para o ensino fundamental, que tinham um rendimento familiar líquido inferior a 999 euros/mês e que tinham 80 anos de idade ou mais.

Conclusão: Quanto menor for a educação e o rendimento mensal e quanto maior for a idade, maior será a possibilidade de ser um não usuário de Internet. Estes resultados podem ajudar os decisores políticos e os provedores de cursos de Internet a identificar os não-usuários quando eles implementam iniciativas de e-inclusão.

Palavras Chave envelhecimento, Internet, divisão digital, repercussões sociodemográficas

113 JOURNAL OF AGING AND INNOVATION, ABRIL, 2022, 11 (1)  ISSN: 2182-696X  http://journalofagingandinnovation.org/ DOI: 10.36957/jai.2182-696X.v11i1-9

ARTIGO ORIGINAL: Campens J., et al. (2022) Identifying subgroups of non-internet-users among community dwelling older people, Journal of Aging & Innovation, 11 (1): 112 - 130

Introduction The internet is pervasive in most areas of life and its importance in today's society is increasing at high pace (Gao et al., 2020). It has become one of the most eminent means of communication, hobby-related or entertainment activities, administration, shopping, community resources, and the prime source of information (van Boekel et al., 2017). In addition, the internet allows people to communicate with healthcare professionals or informal caregivers and, in this way, enables provision of home-based healthcare (van Boekel et al., 2017). Over the last decades, public administration and business companies have been moving more of their services online or replaced them by automats and self-service solutions (Lombardo et al., 2021), which makes everyday life increasingly difficult without internet. Moreover, during the COVID-19 crisis social distancing rules, quarantine and lockdown protocols were implemented and people were forced to reduce physical interactions and to adopt online alternatives (Seifert et al., 2020).

Taking into account the importance of internet in daily life, the proportion of internet users has increased among all age groups over the last decades. However, the number of older internet users is far smaller than that of younger ones (Eurostat, 2020).

This age gap is often referred to as the grey digital divide (Cruz-Jesus et al., 2012).

For example, one in four (26%) Europeans aged 65 to 74 have never used the internet in 2019, compared to only 6 per cent of people aged 25-64 (Eurostat, 2020). The digital divide is not only about internet access, but is also related to the lack of competencies or skills required for effective internet use (Telstra, 2015). Furthermore, inexperienced older people are often concerned about internet security and expect the internet to be complicated and difficult to understand, both of which can discourage them from using the internet (Friemel, 2016).

Given the increasingly important role the internet plays in daily life, it becomes a prime concern to facilitate internet access and enhance digital skills of older people (Smith, 2014). Not surprisingly, digital inclusion has become a hot topic on recent political agendas worldwide (Choi and Dinitto, 2013; Smith, 2014). Scholars keep pointing out the need for digital inclusion in order to benefit from the growing array of online applications and e-services which enable an independent, socially connected and meaningful life (Neves et al., 2018), even for housebound people (Sum et al., 2008).

114 JOURNAL OF AGING AND INNOVATION, ABRIL, 2022, 11 (1)  ISSN: 2182-696X  http://journalofagingandinnovation.org/ DOI: 10.36957/jai.2182-696X.v11i1-9

ARTIGO ORIGINAL: Campens J., et al. (2022) Identifying subgroups of non-internet-users among community dwelling older people, Journal of Aging & Innovation, 11 (1): 112 - 130

As internet can facilitate participation in society and reduce feelings of disconnectedness from family, friends or the broader community (Barrett et al., 2012) it becomes increasingly important for the majority of older people who desire to age in place and grow old in their own homes, especially for those older people who have physical, mental or mobility limitations that prevent them from getting out of the home (Sum et al., 2008).

In order to facilitate digital inclusion of older people, numerous e-inclusion initiatives have been developed (Jun, 2020). However, the population of older people is diverse and heterogeneous, and chronological age by itself is found to be a poor predictor of digital inclusion and digital skills (Berner et al., 2015; Choi and Dinitto, 2013; König et al., 2018; Silver, 2014). Hence, we argue that it is important to know which segments of older people are non-internet-users and would benefit most from digital inclusion initiatives.

Non-internet-users are often described in terms of sociodemographic characteristics (van Deursen and Helsper, 2015) and differ most notably in terms of education, income and age (Hargittai and Dobransky, 2017; Hunsaker and Hargittai, 2018). The oldest old – those aged 80 and older – and those with lower education levels or lower income are less likely to be an internet user (Berner et al., 2015; Dixon et al., 2014; Friemel, 2016; Hargittai and Dobransky, 2017; Hunsaker and Hargittai, 2018; König et al., 2018; Sum et al., 2008; van Deursen and Helsper, 2015). While these sociodemographic variables reveal clear social disparities in becoming an internet user, differences by gender are less clear (Hunsaker and Hargittai, 2018). Finally, marital status seems to influence internet adoption among older people as well (Hunsaker et al., 2019; van Deursen and Helsper, 2015). van Deursen and Helsper (2015) mention that older people living alone cannot learn about the internet from partners or someone else in the household and are less likely to start using it.

Although research has already examined the relation between internet use among older people and their sociodemographic characteristics, the frequently used regression models do not allow to properly examine the cumulative impact of the sociodemographic characteristics on internet use among older people (Watada et al., 2020). Moreover, regression analyses only identify predictors in the study population as a whole and are not able to identify different sets of predictors for subgroups of a 115 JOURNAL OF AGING AND INNOVATION, ABRIL, 2022, 11 (1)  ISSN: 2182-696X  http://journalofagingandinnovation.org/ DOI: 10.36957/jai.2182-696X.v11i1-9

ARTIGO ORIGINAL: Campens J., et al. (2022) Identifying subgroups of non-internet-users among community dwelling older people, Journal of Aging & Innovation, 11 (1): 112 - 130

population (Gorunescu, 2011; Watada et al., 2020; Ye et al., 2016). Furthermore, previous studies have typically reported the ratio of non-users to users per capita and have not examined the prevalence of non-internet-use in subgroups of the population of older people (Eurostat, 2020). Yet, knowledge about which subgroups have the lowest prevalence rates of internet use can assist policy makers and internet training providers to identify the population of non-users and target those who may particularly benefit from e-inclusion initiatives (Bakken-Gillen et al., 2015). Additionally, most studies on internet use only focus on the youngest old – with a cut-off point at the ages of 75 (Eurostat, 2020) or lower (E.g. Siliquini et al., 2011) – and often exclude people in the highest age groups. Furthermore, most studies on internet use in older people are often based on relatively small samples (Berner et al., 2015; Hargittai and Dobransky, 2017; van Deursen and Helsper, 2015). In order to fill these gaps, this study aims to examine the cumulative impact of sociodemographic variables allowing to identify subgroups of non-internet-users among older people, based on a vast and representative sample of community dwelling people aged 60 and older (N = 64,553) in Flanders (the Dutch speaking part of Belgium) with a proper representation of the oldest old.

Methodology Data collection and participants In this study, we use quantitative data originating from the Belgian Ageing Studies (BAS), an ongoing research project that started in 2004. The BAS-survey collects information of community dwelling people, aged 60 and older, about their perceptions on various aspects related to quality of life and living conditions in later life, using a standardized self-administered questionnaire. Since the project started, older people living in 169 municipalities have participated. Within each participating municipality, a stratified sample using gender and age (60-69, 70-79, 80+), is randomly drawn from population registers. Individual participants are required to be at least 60 years old and to still live at home (i.e. older people living in residential care facilities were excluded from the sample).

The complete BAS-data (N = 86,977) were checked to identify possible irregular response patterns and outliers. Participants who did not respond to at least one of the five sociodemographic characteristics measured in this study or did not answer the 116 JOURNAL OF AGING AND INNOVATION, ABRIL, 2022, 11 (1)  ISSN: 2182-696X  http://journalofagingandinnovation.org/ DOI: 10.36957/jai.2182-696X.v11i1-9

ARTIGO ORIGINAL: Campens J., et al. (2022) Identifying subgroups of non-internet-users among community dwelling older people, Journal of Aging & Innovation, 11 (1): 112 - 130

question concerning internet use were excluded, resulting in a final sample of 64,553 respondents, which corresponds to 74.22 per cent of the BAS database. Among them 30,228 (46.8%) are aged 60-69, while 22,822 (35.4%) are aged 70-79 and 11,503 (17.8%) are 80 and older.

The research was conducted within the standards required by the Declaration of Helsinki and approved by the ethics committee of the institution where the research was conducted.

Measures and statistical strategy In the BAS-survey internet use is assessed by asking respondents “How often do you use internet?”. Response categories were never (1), less than weekly (2), weekly (3), daily (4) and several times a day (5). For the purpose of this study we dichotomised internet use into non-use and use (by grouping together the response categories 2-5).

As this study focusses on sociodemographic characteristics, age, gender (man, woman), marital status (married, never married, divorced, cohabiting, widowed), educational level (no degree or primary education, lower secondary, higher secondary, higher education) and monthly household income (< €999, €1000 - €1499, €1500€1999, ≥€2000) were included. Age was assessed by asking the respondents their age in years and was recoded into three categories; 60-69 years old, 70-79 years old and 80+.

As a first step, chi-square analyses are performed to assess the association between sociodemographic characteristics ‘age’, ‘gender’, ‘marital status’, ‘educational level’, ‘income’ and internet usage. Second, multiple logistic regression analyses are used to determine the impact of each of the five sociodemographic characteristics on internet usage separately, which results in five models. Furthermore, we study the joint effect of these sociodemographic variables in a 6th model.

According to Gorunescu (2011) and Ye et al. (2016), the combination of regression analyses

and

Chi-squared

Automatic

Interaction

Detector

(CHAID)-analysis

contributes to a better understanding of the significance of the potential factors and to the identification of the target population of non-internet-users. Therefore, in the third step, we performed CHAID-analysis that has great value for a number of reasons (Biggs et al., 1991; Guillon et al., 2016; Hsu and Kang, 2007; Kass, 1980): it is a stepwise process that allows to identify the most significant relations between the 117 JOURNAL OF AGING AND INNOVATION, ABRIL, 2022, 11 (1)  ISSN: 2182-696X  http://journalofagingandinnovation.org/ DOI: 10.36957/jai.2182-696X.v11i1-9

ARTIGO ORIGINAL: Campens J., et al. (2022) Identifying subgroups of non-internet-users among community dwelling older people, Journal of Aging & Innovation, 11 (1): 112 - 130

sociodemographic variables and non-internet-use; it is useful to explore predictor variables; it allows to explore the cumulative impact of the sociodemographic variables on non-internet-use; it allows to develop predictive models and generate easy-to-read decision trees; it does not force a universal model for the entire population; it enables partition of the study population into subgroups and to focus on potentially useful subgroups of large datasets; it allows to verify if interacting sociodemographic predictors are different for different subgroups; and finally, it results in prevalence rates of non-internet-use in different subgroups of the population.

CHAID-analysis determines the most significant sociodemographic predictor to partition the sample into subsamples in order to explain the dependent variable (internet use). Subsequently, a new analysis defines, in each subsample, the second best predictor. The software continues to analyse until no more significant predictors remain (Biggs et al., 1991; Hsu and Kang, 2007; Kass, 1980). All statistical analyses were performed using SPSS 25.0 (IBM, SPSS, Armonk, NY: IBM Corp). Given the large sample size, statistical significance was set at p < .001 (Field, 2017).

Results In the sample, 52.4 per cent of the respondents were female. As for age, 46.8 per cent of the respondents were 60-69 years, 35.4 per cent were 70-79 years and 17.8 per cent were 80 years and older. Concerning marital status, 69.2 per cent were married, 20.7 per cent widowed, 4.4 per cent divorced, 3.8 per cent never married and 1.9 per cent cohabiting. As for income level, 56.3 per cent had a net household income of less than 1500 euro/month, while 21.6 per cent had an income between 1500 and 1999 euro/month and 22.1 per cent had an income of at least 2000 euro/month. More than one in three of the respondents (37.4%) had no degree or only primary education, one in two (48.2%) completed secondary education and one in seven (14.4%) had attained the highest educational level (university/higher education college). 44,862 of the 64,553 respondents (69.5%) were non-internet-user (presented in table 1).

Table 1: Respondents’ characteristics N

%

30 711

47.6%

Gender Men

118 JOURNAL OF AGING AND INNOVATION, ABRIL, 2022, 11 (1)  ISSN: 2182-696X  http://journalofagingandinnovation.org/ DOI: 10.36957/jai.2182-696X.v11i1-9

ARTIGO ORIGINAL: Campens J., et al. (2022) Identifying subgroups of non-internet-users among community dwelling older people, Journal of Aging & Innovation, 11 (1): 112 - 130

Women

33 842

52.4%

< €999

12 616

19.5%

€1000 - €1499

23 734

36.8%

€1500 - €1999

13 917

21.6%

≥ €2000

14 286

22.1%

No degree or primary

24 120

37.4%

Lower secondary

18 668

28.9%

Higher secondary

12 438

19.3%

Higher education (university/higher education college)

9327

14.4%

60 - 69

30 228

46.8%

70 - 79

22 822

35.4%

80+

11 503

17.8%

Married

44 657

69.2%

Never married

2438

3.8%

Divorced

2867

4.4%

Cohabiting

1210

1.9%

Widowed

13 381

20.7%

No

44 862

69.5%

Yes

19 691

30.5%

Income

Educational level

Age

Marital status

Internet user

Bivariate statistics between the sociodemographic characteristics and internet use are presented in table 2. Among internet users were significantly more men (59.4%), while there were more women among non-users (57.6%) (χ2 (1) = 1587.93, p < .001).

Concerning income, 44.2 per cent of the users had a net monthly household income of at least 2000 euro and this percentage differs significantly with the 12.4 per cent in non-users (χ2 (1) = 10,053.03, p < .001). Approximately one in three users (32.8%) indicated that they received higher education, which was significantly more than the 6.4 per cent in non-users (χ2 (1) = 12,576.51, p < .001). The majority of internet users 119 JOURNAL OF AGING AND INNOVATION, ABRIL, 2022, 11 (1)  ISSN: 2182-696X  http://journalofagingandinnovation.org/ DOI: 10.36957/jai.2182-696X.v11i1-9

ARTIGO ORIGINAL: Campens J., et al. (2022) Identifying subgroups of non-internet-users among community dwelling older people, Journal of Aging & Innovation, 11 (1): 112 - 130

(69.5%) were younger than 70 years, while the majority of non-internet-users (63.1%) were people aged 70+ (χ2 (2) = 6274.08, p < .001).

Table 2: Sociodemographic differences in internet use Users (n=19 691)

Non-users (n=44 862)

Men

59.4%

42.4%

Women

40.6%

57.6%

Gender

χ2

1587.93*

Income < €999

6.6%

25.2%

€1000 - €1499

24.2%

42.3%

€1500 - €1999

25.0%

20.0%

≥ €2000

44.2%

12.4%

χ2

10 053.03*

Educational level No degree or primary

13.3%

47.9%

Lower secondary

24.8%

30.7%

Higher secondary

29.1%

14.9%

Higher education (university/higher education college)

32.8%

6.4%

χ2

12 576.51*

Age 60 - 69

69.5%

36.9%

70 - 79

24.6%

40.1%

80+

6.0%

23.0%

χ2

6274.08*

Marital status

χ2

Married

79.4%

64.7%

Never married

3.0%

4.1%

Divorced

6.0%

3.8%

Cohabiting

2.8%

1.5%

Widowed

8.8%

25.9%

2667.38*

120 JOURNAL OF AGING AND INNOVATION, ABRIL, 2022, 11 (1)  ISSN: 2182-696X  http://journalofagingandinnovation.org/ DOI: 10.36957/jai.2182-696X.v11i1-9

ARTIGO ORIGINAL: Campens J., et al. (2022) Identifying subgroups of non-internet-users among community dwelling older people, Journal of Aging & Innovation, 11 (1): 112 - 130

*p < .001

Table 3 shows the results of logistic regression models, using internet usage as dependent

variable

(reference

category:

users).

Five

models,

for

each

sociodemographic indicator, and one overall model, containing all indicators at once, were developed. In this latter model (model 6) all sociodemographic characteristics were significantly (p < .001) related to internet usage. The regression analysis indicates that women were more likely to be non-internet-user than men (OR 1.56, 95% CI 1.50-1.63). Similarly, people aged 70-79 and those aged 80 years and older were more likely to be a non-user than those aged 60-69 years (OR 2.38, 95% CI 2.272.49 and OR 4.69, 95% CI 4.36-5.05 respectively). In addition, a positive effect of income and education on internet use was found. Having a monthly household income of €1000 - €1499, €1500 - €1999 and ≥ €2000 was associated with a smaller chance of being a non-user than the reference group (OR 0.57, 95% CI 0.53-0.61, OR 0.37, 95% CI 0.34-0.40 and OR 0.20 95% CI 0.18-0.21 respectively). Similarly, people who obtained lower secondary, higher secondary or higher education were more likely to be an internet user than respondents with a primary degree or no degree (OR 0.47, 95% CI 0.45-0.50, OR 0.23, 95% CI 0.22-0.25 and OR 0.12 95% CI 0.11-0.13 respectively). Finally, widow(er)s were 1.17 times (95% CI 1.10-1.25) more likely to be a non-user than those married. By contrast, those divorced were less likely to be a non-user than those married (OR 0.60, 95% CI 0.55-0.66), which is also the case for people who were cohabiting (OR 0.74, 95% CI 0.64-0.84 respectively). Those never married were not significantly more likely to be non-internet-user than those married (OR 1.10, 95% CI 0.98-1.23).

121 JOURNAL OF AGING AND INNOVATION, ABRIL, 2022, 11 (1)  ISSN: 2182-696X  http://journalofagingandinnovation.org/ DOI: 10.36957/jai.2182-696X.v11i1-9

ARTIGO ORIGINAL: Campens J., et al. (2022) Identifying subgroups of non-internet-users among community dwelling older people, Journal of Aging & Innovation, 11 (1): 112 - 130

In order to examine the cumulative impact of the sociodemographic characteristics on (non-) internet use a CHAID analysis was performed, of which the results can be found in figure 1. The decision tree revealed three levels. Of the five sociodemographic variables that were entered into the CHAID analysis, the educational degree turned out to be the strongest predictor of non-internet-use among older people. Specifically, the subgroup of people with no degree/only primary education were less likely to use internet than the subgroup of people with any additional school education (lower secondary, higher secondary or higher education). Furthermore, income was found to be the second strongest predictor of non-internet-use among those with no degree/only primary education, on the one hand, and those with a lower secondary degree, on the other hand. This was different within the subgroup of people with a higher secondary degree or higher education, where age and not income was the second strongest predictor of (non-) internet use. More specifically, the higher the income (for people with lower education levels) or the lower the age (for people with higher education levels), the greater the chance of being an internet user. The CHAID-analysis further identified age, income and marital status as predictors in the third layer of the decision tree model. However, marital status is only a predictor in the subgroup of higher educated people aged 80 and above, with widow(er)s and people who were never married being less likely to use internet than those who were cohabiting, divorced or married.

122 JOURNAL OF AGING AND INNOVATION, ABRIL, 2022, 11 (1)  ISSN: 2182-696X  http://journalofagingandinnovation.org/ DOI: 10.36957/jai.2182-696X.v11i1-9

ARTIGO ORIGINAL: Campens J., et al. (2022) Identifying subgroups of non-internet-users among community dwelling older people, Journal of Aging & Innovation, 11 (1): 112 - 130

According to the CHAID-analysis, the subgroup of people with no degree/only primary education who had a net household income of less than 999 euro/month and who were 80 and above had the highest proportion of non-internet-users (98.6%). The subgroup with no degree/only primary education, who had a net household income of less than 999 euro/month and who were 60-79 years old had the second highest proportion of non-use (95.8%). Similarly, based on the cumulative impact of the sociodemographic characteristics CHAID-analysis revealed that the third highest proportion of noninternet-use (95.4%) was again observed in the subgroup of people with no degree/only primary education, but this time in the subgroup of those with a net household income of less than 1500 euro/month and who are at least 80 years.

Discussion This study examined the cumulative impact of sociodemographic variables in order to identify subgroups of non-internet-users in the population of community dwelling people aged 60 and older in Flanders (the Dutch speaking part of Belgium). The identification of subgroups of non-users can assist policy makers and internet training providers to target those who may particularly benefit from e-inclusion initiatives.

123 JOURNAL OF AGING AND INNOVATION, ABRIL, 2022, 11 (1)  ISSN: 2182-696X  http://journalofagingandinnovation.org/ DOI: 10.36957/jai.2182-696X.v11i1-9

ARTIGO ORIGINAL: Campens J., et al. (2022) Identifying subgroups of non-internet-users among community dwelling older people, Journal of Aging & Innovation, 11 (1): 112 - 130

Logistic regression revealed that, among people over 60 years old, non-internet-use was related to being female, high age, low education, low income and being widowed, which is in line with previous research (Berner et al., 2015; Choi and Dinitto, 2013; Dixon et al., 2014; Friemel, 2016; Hargittai and Dobransky, 2017; Hunsaker and Hargittai, 2018; König et al., 2018; van Deursen and Helsper, 2015; Vulpe and Craciun, 2020). While previous studies only used regression analyses to investigate the link between the sociodemographic characteristics and non-internet-use in older people (Berner et al., 2015; Friemel, 2016; König et al., 2018; van Deursen and Helsper, 2015), we argue that the combination of regression analyses and CHAID-analysis contributes to a better understanding of the significance of the potential factors and to the identification of the target population of non-internet-users (Gorunescu, 2011; Ye et al., 2016). One reason that makes the findings of our study particularly robust is the large sample size with which the CHAID-analysis was performed. Most studies on internet use in older people are often based on much smaller samples (Berner et al., 2015; Hargittai and Dobransky, 2017). Furthermore, our study was conducted on a stratified sample (age and gender) that enables us to include people aged 80 and above in proportions similar to those in the population, which is important as this group is often absent in previous studies often using a cut-off point at the age of 75 (Eurostat, 2020).

CHAID-analysis showed the educational degree to be the primary and most important risk factor that divides the sample in four subgroups. The subgroup of people with no degree/only primary education is less likely to use internet than the subgroups of people with a lower secondary, higher secondary or higher education degree. This result is in line with previous research showing that higher educated older people are more likely to use internet (Berner et al., 2015; Choi and Dinitto, 2013; König et al., 2018). However, our study is the first to reveal prevalence rates of non-internet-use in subgroups of the population of older people.

According to the CHAID-analysis we conducted, income was the second most important risk factor for non-internet-use, however only in lower educated people (i.e. older people with no degree/only primary education and older people with a lower secondary degree). This result is consistent with studies that show that lower educational degree is associated with lower income, which may limit the ability to purchase internet access and technological devices (Dickson and Ellison, 2000; Silver, 124 JOURNAL OF AGING AND INNOVATION, ABRIL, 2022, 11 (1)  ISSN: 2182-696X  http://journalofagingandinnovation.org/ DOI: 10.36957/jai.2182-696X.v11i1-9

ARTIGO ORIGINAL: Campens J., et al. (2022) Identifying subgroups of non-internet-users among community dwelling older people, Journal of Aging & Innovation, 11 (1): 112 - 130

2014; Zhang, 2013). The cumulative impact of education and income enabled us to identify a subgroup of people with the lowest educational degree and the lowest income with a prevalence rate of non-internet-use of 95.2 per cent.

The CHAID-analysis also showed age as the third most important predictor of noninternet-use among the lower educated people (i.e. older people with no degree/only primary education and older people with a lower secondary degree) and the second most important predictor of non-use among the higher educated people (i.e. higher secondary educated people and higher educated people). The cumulative impact of education, income and age enabled us to find a subgroup composed almost entirely of non-internet-users (98.6%). This group consisted of people with no degree/only primary education, who had a net household income of less than 999 euro/month and were at least 80 years old. The identification of this group is relevant for policy makers and internet training providers if they want to reach non-internet-users in the population of older people and include them in e-inclusion initiatives.

Although logistic regression analysis revealed the highest odds of non-internet-use in people aged 70-79 (2.38) and people aged 80+ (4.69), CHAID-analysis puts new perspectives about the link between age and non-internet-use since age is not a predictor in the first layer of the CHAID-decision tree. This nuance is consistent with Morris and Brading (2007) who state that the gap between internet users and nonusers is not only related to age but also to income and education attainment. Similarly, CHAID-analysis has put the gender gap into perspective. Although logistic regression analysis showed that women are more likely to be non-internet-user, gender was not a significant predictor of non-internet-use according to CHAID-analysis. This is in line with research that indicates that the gender gap in internet use largely disappears since more and more older women start using the internet (Bimber, 2000; Dixon et al., 2014; König et al., 2018). Finally, the predictive value of marital status is less important according to CHAID-analysis. Compared with logistic regression analysis, which revealed that widow(er)s are at greater risk of non-use than married older people, CHAID-analysis indicates that this is only the case in the highest educated people aged 80 and over. In line with van Deursen and Helsper (2015), we can conclude that more research is needed to further investigate the relation between marital status and/or household composition and internet use and to understand why marital status is a predictor of non-internet-use only among the oldest old who are higher educated.

125 JOURNAL OF AGING AND INNOVATION, ABRIL, 2022, 11 (1)  ISSN: 2182-696X  http://journalofagingandinnovation.org/ DOI: 10.36957/jai.2182-696X.v11i1-9

ARTIGO ORIGINAL: Campens J., et al. (2022) Identifying subgroups of non-internet-users among community dwelling older people, Journal of Aging & Innovation, 11 (1): 112 - 130

Limitations and further research Some limitations need to be considered. A first limitation is the cross-sectional design of this study, which makes it impossible to determine causal relationships and assess evolutions over time. A second shortcoming might be that the sample only contains Dutch speaking older adults in Flanders. As a consequence, we cannot make statements about the prevalence of non-internet-use among non-Dutch speaking older people in Flanders or among those in the French and German-speaking part of Belgium. Third, we dichotomised respondents into non-internet-users and internet users by grouping together the respondents who indicated they use internet less than weekly, weekly, daily or several times a day. With this binary approach between noninternet-users and users, this study does not focus on the variety in frequency of internet use, nor on the types of internet activities older people use. In line with Neves et al. (2018), we recommend further research to focus on the continuum between noninternet-use and use. Moreover, Vulpe and Craciun (2020) recommend broadening the scope by investigating why people are (non-) user and which and to which degree internet features are used. Although our study shows there are demographic factors (education, income and age) influencing non-internet-use, more research is needed to investigate other factors as well (Gallagher et al., 2008). In addition, more in-depth research is needed to investigate the role internet plays in older people’s lives (van Deursen and Helsper, 2015).

Conclusions Digital inclusion of older people has become a hot topic on recent political agendas worldwide. E-inclusion initiatives have been developed in order to reduce the proportion of non-internet-users among older people. In order to provide accurate case finding of older people who are non-internet-users and will likely benefit from einclusion initiatives, this study examined the cumulative impact of sociodemographic variables allowing to identify subgroups of non-internet-users in the population of community dwelling people aged 60 and older. According to logistic regression analysis, women, the oldest old, the lowest educated, those in the lowest income class and those who are widowed are at greater risk of non-internet-use in Flanders, Belgium. The decision tree based on CHAID modeling reveals that education is the

126 JOURNAL OF AGING AND INNOVATION, ABRIL, 2022, 11 (1)  ISSN: 2182-696X  http://journalofagingandinnovation.org/ DOI: 10.36957/jai.2182-696X.v11i1-9

ARTIGO ORIGINAL: Campens J., et al. (2022) Identifying subgroups of non-internet-users among community dwelling older people, Journal of Aging & Innovation, 11 (1): 112 - 130

strongest predictor of non-internet-use in older people. The CHAID analysis further identified income and age as predictors in the second layer of the decision tree model.

Based on the cumulative impact of education, income and age, we identified a subgroup composed almost entirely of non-internet-users (98.6%). This group consisted of people with no degree/only primary education who had a net household income of less than 999 euro/month and who were 80 and above – exactly that growing age group of which most other datasets do not have information. Our findings can assist policy makers and internet training providers to identify the population of nonusers and target those who are at the greatest risk of non-internet-use and pay more attention to the growing segment of ‘oldest old’. Given that private and public companies as well as governments and authorities are increasingly moving their services online, our results can benefit policy makers to identify subgroups of older people excluded from these services.

Acknowledgements I would like to express my deep gratitude to the founders of the Belgian Ageing Studies for using their data.

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