Introduction

The United Nations defines older adults as persons aged 60 years and older (United Nations Development Programme (UNDP), 2017). Globally, there were an estimated 1.05 billion persons aged 60 years and older as of 2020 (He et al., 2020), and this population is projected to increase to 1.4 billion in 2030 and 2.1 billion in 2050 (World Health Organization, 2022). Though Africa has the lowest proportion of older adults (5.6%) as of 2020, it is projected to experience the highest growth of older adults population by 2050 (216.1%) (He et al., 2020). Asia is projected to experience 114.2% growth of the older adult population by 2050, while Europe will experience 29.1% growth by 2050 (He et al., 2020). In Ghana, there were 2.0 million (6.5%) older adults as of 2021, and this number is projected to increase to 2.6 million (10.8%) in 2050 (Ghana Statistical Service (GSS), 2024).

The emergence of the internet has transformed human interaction, the acquisition of knowledge, and engagement with the global society (Nie et al., 2002; Papanis et al., 2010; Szymkowiak et al., 2021). Globally, internet use is less prevalent among older adults than individuals in other age groups (Cotten et al., 2022). Nevertheless, the utilisation of the Internet has a beneficial influence on the physical and general wellbeing of older adults (Aggarwal et al., 2020; Hou et al., 2022). For instance, previous studies on older adults have linked frequent internet use with improved life satisfaction (Lam et al., 2020; Lee, 2024), quality of life (Mohan and Lyons, 2024), and reduced depression and anxiety (Cotten et al., 2014; Hofer and Hargittai, 2024; Lam et al., 2020; Mohan and Lyons, 2024; Zhang et al., 2021). In addition, internet use enhances social connections between older adults and their significant others, which reduces their social isolation and loneliness (Cotten et al., 2013; Gatto and Tak, 2008; Khvorostianov et al., 2012; Mellor et al., 2008; Tsai et al. 2010). Internet use allows older adults to obtain information that aligns with their interests and utilises telemedicine services (Ilali et al., 2023; Khanassov et al., 2024; Wang et al., 2024).

Previous studies on internet use among older adults have identified intrapersonal (age, sex, and educational status) (Chang et al., 2015; Davidson and Schimmele, 2019; Lee, 2024; Sun et al., 2020), interpersonal (marital status, household size, and household income) (Choi and Dinitto, 2013; Davidson and Schimmele, 2019; Lee, 2024; Sun et al., 2020), institutional (religious attendance) (Choi and Dinitto, 2013), and community (place of residence) (Davidson and Schimmele, 2019; Lee, 2024) factors as predictors of internet use. Studies have also documented functional limitations (such as sight and hearing impairments) as barriers to internet use among older adults (Gatto and Tak, 2008; Gitlow, 2014).

Previous studies used varied dependent variables to measure internet use among older adults. For instance, Chang et al. (2015) measured internet use by whether older adults currently use the internet. Also, Davidson and Schimmele (2019) measured internet use by whether older adults used the internet at least once in the past month. Lee (2024) measured internet use by how often older adults used the internet. Furthermore, most studies used binary logistic regression to examine the factors influencing internet use among older adults (Chang et al., 2015; Choi and Dinitto, 2013; Davidson and Schimmele, 2019; Rennoch et al., 2024; Sun et al., 2020). However, Lee (2024) used the ordinary least-squares regression (OLS) method to analyse the correlates of internet use among older adults.

While there are previous studies on internet use worldwide, these studies have focused mainly on adolescents (Bozkurt et al., 2018; Casaló and Escario, 2019; Chi et al., 2020; Dong et al., 2020; Schemer et al., 2021) and youths (Aslanidou and Menexes, 2008; Bakker and De Vreese, 2011; Prievara et al., 2019; Tomczyk et al., 2020) to the disadvantage of older adults. The situation in Ghana is similar. In Ghana, studies on internet use have focused primarily on the youth (Kyei-Arthur et al., 2024; Dzogbenuku and Kumi, 2018; Essel et al., 2022; Ofori and Appiah-Nimo, 2019; Osei Asibey et al., 2017) and the general populace (Alemna and Adanu, 2005; Badu and Markwei, 2005; Siaw et al., 2020; Twumasi et al., 2021). In addition, the limited studies on internet use among older adults have primarily been conducted in high-income countries (Chang et al., 2015; Choi and Dinitto, 2013; Cotten et al., 2014; Lam et al., 2020; Matthews et al., 2019; Wang et al., 2021) rather than in low- and middle-income countries, including Ghana. The limited studies on older adults in low- and middle-income countries limit the understanding of internet use among older adults in these contexts to guide the design of context-specific interventions. Furthermore, studies on internet use among older adults have used surveys (Chang et al., 2015; Choi and Dinitto, 2013; Cotten et al., 2014; Lam et al., 2020; Matthews et al., 2019; Wang et al., 2021) rather than relying on census data that includes all sub-groups of older adults living in a specific geographic area, which minimises the potential for sampling bias and improve representativeness. Also, the census data has a large sample size, enhancing the statistical power of analysis and yielding more accurate estimations (Andrade, 2020).

To the best of our knowledge, this is the first study on Internet use among older adults in Ghana using census data. This study examined the prevalence and predictors of internet use among Ghanaian older adults. The findings of this study will contribute to the limited studies on internet use among older adults in Ghana and sub-Saharan Africa. It will also help policymakers develop appropriate interventions to promote internet use among Ghanaian older adults.

Theoretical framework

This study will be guided by the socio-ecological model, which posits that human behaviour is influenced by five levels of influence: intrapersonal, interpersonal, institutional, community and policy (McLeroy et al., 1988). Studies have documented that the sex and age of older adults (intrapersonal level factors) influence their internet use (Lee, 2024; Sun et al., 2020), while being widowed (interpersonal level factor) (Lee, 2024) and religious attendance (institutional level factor) (Choi and Dinitto, 2013) influence older adults internet use. In addition, residing in an urban area (community level factor) influence older adults’ internet use (Davidson and Schimmele, 2019). Furthermore, government policies and regulations can influence internet use. However, this study could not examine policy level factors, since the secondary data used for this study did not capture such variables.

Methods

Data source and study design

This study was a secondary analysis of the Ghana 2021 Population and Housing Census (PHC). A quantitative design was used during the Ghana 2021 PHC to collect data from all persons residing in Ghana. The Ghana 2021 PHC conducted a comprehensive enumeration of all individuals residing within the geographical boundaries of Ghana, regardless of their nationality. The Ghana 2021 PHC encompasses various subject areas, such as information and communication technology, literacy and education, fertility and child survival, and sanitation. This study used a 10% representative sample of the Ghana 2021 PHC, which is publicly available by the Ghana Statistical Service (GSS). This study focused on internet use among Ghanaian older adults.

Study setting

Ghana is a sub-Saharan African country geographically surrounded by Burkina Faso to the North, the Gulf of Guinea to the South, Togo to the East, and Cote d’Ivoire to the West. Ghana has 16 regions and an estimated population of 30,832,019 in 2021 (Ghana Statistical Service (GSS), 2021). Of the total population of 30,832,019, 6.5% (1,991,736) were older adults (60 years and older).

Measurement of variables

Dependent variable

The dependent variable for the study was internet use. During the Ghana 2021 PHC, older adults were asked whether they used the internet through the following five information and communications technology (ICT) devices in the last three months (April–June 2021) preceding the census: mobile phone, laptop, desktop, tablet, and digital television. The responses were “yes” and “no” for each ICT device. All the responses were merged to create an internet use variable with scores ranging from 0 to 5. The internet use variable was then recategorised as a dichotomous variable. Older adults with scores ranging from 1 to 5 were classified as having used the internet, while those with zero (0) scores were classified as never using the internet.

Independent variables

The independent variables were age (60–74, 75–84, and 85+), sex (male and female), educational status (no formal education and formal education), marital status (currently married, ever married, and never married), religious affiliation (no religion, and religiously affiliated), and place of residence (urban, and rural). Other variables included sight impairment (no difficulty and having difficulty), hearing impairment (no difficulty and having difficulty), household wealth status (poor, middle, and rich), and ecological zone (Coastal, Middle, and Northern).

Wealth index and ecological zone were other independent variables considered for this study. Wealth index was computed from household ownership of 19 assets, including a laptop, bicycle, television, and radio. The principal component analysis method was used to compute the wealth index into five categories: poorest, poor, middle, rich and richest. The wealth index was recategorised as poor (poor and poorest), middle, and rich (richest and rich). The methodology for calculating the wealth index is detailed in prior research (ICF International, n.d.).

The Ghana 2021 PHC had 16 regions: Ahafo, North East, Savannah, Northern, Upper West, Upper East, Western North, Bono East, Bono, Western, Central, Volta, Greater Accra, Ashanti, Oti and Eastern. The 16 regions were recoded as ecological zone with three categories: Coastal (Western North, Western, Central, Volta, Greater Accra, and Oti regions), Middle (Ahafo, Bono East, Bono, Ashanti, and Eastern regions), and Northern (North East, Savannah, Northern, Upper West, and Upper East regions). The variables included in this study were based on previous studies on internet use among older adults.

Statistical analyses

Descriptive statistics were used to describe the socio-demographic characteristics of older adults. With the aid of the Statistical Package for Social Sciences (SPSS) version 26, Pearson’s chi-square test (χ2) was used to examine the association between socio-demographic characteristics of older adults and internet use since the variables were categorical (Agresti, 2018). Older adult’s internet use was coded as “1” for having used the internet and ‘0’ for never using the internet. Internet use was entered as a column variable, while socio-demographic characteristics of older adults were entered as row variables. All variables were deemed statistically significant at p-value ≤ 0.05. Variables considered statistically significant after conducting Pearson’s chi-square test were considered for the multivariate analysis. Multivariate binary logistic regression was used to determine the predictors of internet use among Ghanaian older adults. The dependent variable (internet use) was entered as “dependent” in SPSS, while the socio-demographic characteristics of older adults were entered as “covariates”. The enter method was used to examine the predictors of internet use among Ghanaian older adults.

Results

Socio-demographic characteristics of older adults

Table 1 shows the socio-demographic characteristics of older adults. More than half of the older adults were aged 60–74 (72.3%), were females (56.8%), and resided in urban areas (52.5%). Slightly more older adults (50.3%) had a formal education. The majority of older adults were religiously affiliated (94.1%) and had no difficulty hearing (89.2%) and seeing (77.2%). In addition, two-fifths of the older adults (40.9%) were from rich households. Slightly more than two-fifths of older adults (43.7%) resided in the Coastal ecological zone, while about 39% resided in the Middle ecological zone.

Table 1 Socio-demographic characteristics of older adults.
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Prevalence of internet use among older adults

Table 2 shows that 52.9% of older adults used the internet three months preceding the census. More older adults aged 60–74 (60.9%, χ2 (2, N = 197,379) = 14,591.15, p-value < 0.001) used the internet than those aged 75–84 (37.3%) and 85 and older (20.4%). More males (67.6%, χ2 (1, N = 197,379) = 13,006.59, p-value < 0.001) than females (56.8%) used the internet. Also, older adults with formal education (92.9%, χ2 (1, N = 197,379) = 128,304.89, p-value < 0.001) used the internet more than those with no formal education (12.5%).

Table 2 Association between socio-demographic characteristics and whether internet was used in the last 3 months.
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Regarding marital status, more older adults who were currently married (60.0%, χ2 (2, N = 197,379) = 4974.67, p-value < 0.001) used the internet than those who had ever married (44.1%) and never married (2.3%). A higher proportion of older adults (53.7%, χ2 (1, N = 197,379) = 743.34, p-value < 0.001) who were religiously affiliated used the internet than those who were not religiously affiliated (40.7%). Most older adults in urban areas (65.0%, χ2 (1, N = 197,379) = 12,788.79, p-value < 0.001) used the internet more than those in rural areas.

In terms of disability, most older adults with no sight impairment (55.2%, χ2 (1, N = 197,379) = 1,338.11, p-value < 0.001) and hearing impairment (55.4%, χ2 (1, N = 197,379) = 3859.38, p-value < 0.001) used the internet than those with sight impairment and hearing impairment. Most older adults from poor households (76.4%, χ2 (2, N = 197,379) = 25,727.52, p-value < 0.001) used the internet more than their counterparts from Middle (51.5%) and rich (34.5%) households. A higher proportion of older adults residing in the Coastal ecological zone (61.9%, χ2 (2, N = 197,379) = 20,607.09, p-value < 0.001) used the internet than older adults living in the Middle (58.5%) and the Northern (18.0%) ecological zones.

Types of ICT devices used to access the internet

Table 3 shows the ICT devices older adults use to access the internet. From Table 3, more than half of the older adults (52.5%) used mobile phones to access the internet, while about 3% used television to access the internet. Also, about 1% of older adults used laptops to access the internet.

Table 3 Type of information and communication technology device used to access the internet.
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Predictors of internet use among older adults

Table 4 presents the predictors of internet use among older adults. On the one hand, the internet was more likely to be used among older adults who had a formal education (AOR = 62.92, 95% CI = 60.82–65.09) and those who were religiously affiliated (AOR = 1.95, 95% CI = 1.82–2.08).

Table 4 Predictors of internet use among older adults in Ghana.
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On the other hand, the internet was less likely to be used among older adults aged 75–84 (AOR = 0.66, 95% CI = 0.63–0.68), those aged 85 and older (AOR = 0.35, 95% CI = 0.33–0.37), females (AOR = 0.51, 95% CI = 0.49–0.53), those who were ever married (AOR = 0.80, 95% CI = 0.78–0.83), those who were never married (AOR = 0.80, 95% CI = 0.73–0.89), and those residing in rural areas (AOR = 0.51, 95% CI = 0.49–0.52). Similarly, older adults with difficulty seeing A = 0.82, 95% CI = 0.78–0.85) and hearing (AOR = 0.76, 95% CI = 0.72–0.81) were less likely to use the internet. In terms of household wealth, older adults from Middle (AOR = 0.45, 95% CI = 0.44–0.47) and rich (AOR = 0.33, 95% CI = 0.32–0.34) households were less likely to use the internet than those from poor households. Older adults residing in the Northern ecological zone (AOR = 0.71, 95% CI = 0.68–0.75) were less likely to use the internet than respondents from the Coastal ecological zone.

Based on the adjusted odds ratios of the various variables in Table 4, the most important to least important variables predicting internet use among older adults in Ghana were educational status (have formal education vs. no formal education): AOR = 62.92, religious affiliation (religiously affiliated vs. no religion): AOR = 1.95, sight impairment (having difficulty vs. no difficulty): AOR = 0.82, marital status (ever married vs. currently married): AOR = 0.80, marital status (never married vs. currently married): AOR = 0.80, hearing impairment (having difficulty vs. no difficulty): AOR = 0.76, ecological zone (Northern vs. Coastal): AOR = 0.71, age (75–84 vs. 60–74): AOR = 0.66, sex (female vs. male): AOR = 0.51, place of residence (rural vs. urban): AOR = 0.51, household wealth status (Middle vs. Poor): AOR = 0.45, age (85+ vs. 60–74): AOR = 0.35, and household wealth status (Rich vs. Poor): OR = 0.33.

Discussion

Guided by the socio-ecological model, this study examined the prevalence and predictors of internet use among Ghanaian older adults using the Ghana 2021 PHC. The prevalence of internet use among older adults was 52.9%. On the one hand, the prevalence of internet use among older adults in this study is lower than in studies conducted in the United States. For instance, studies by Chang et al. (2015), Anderson and Perrin (2017), and Wang et al. (2021) reported 57.5%, 67.0%, and 69.1% prevalence of internet use among older adults in the United States, respectively.

On the other hand, the prevalence of internet use among older adults in this study is higher than the prevalence of internet use reported in China (38.6%) (Sun et al., 2020) and the United States (50.6% and 42.7%, respectively) (Choi and Dinitto, 2013; Gell et al., 2015). The difference in the prevalence of internet use could be due to several factors, including the measurement of internet use, the age categorisation of older adults, and the years the data were collected. For instance, Choi and Dinitto’s (2013) study examined internet use among older adults aged 65 and older, and they measured internet use by whether older adults used their cell phones or computers for various purposes, including sending e-mails, ordering prescriptions, and contacting medical providers, among others. The authors used the first wave of the National Health and Aging Trends Study (NHATS) conducted in 2011. Additionally, Chang et al.’s (2015) study examined internet use among older adults aged 60–99, and internet use was measured by respondents currently using the internet. The authors collected their data between April and June 2012. However, the current study examined internet use among older adults aged 60 and older and examined internet use through mobile phones, laptops, desktops, tablets, and digital televisions in the last three months (April–June 2021) preceding the census. Caution should be exercised when comparing findings on the prevalence of internet use across different studies due to the disparities in the measurement of internet use. In order to improve the comparability of findings, future research should consider adopting a standardised measurement of internet use.

This study revealed that mobile phones were the primary ICT devices older adults used to access the internet. According to Kemp (2021), there were 41.69 million mobile phones in Ghana as of 2021, which was higher than the number of people residing in Ghana (30.8 million) over the same period. The availability of mobile phones in Ghana may explain the emergence of mobile phones as the primary ICT devices used to access the internet. Moreover, the utilisation of mobile phones serves as a handy means for older adults, particularly those facing mobility challenges, to obtain information and socially connect with others (Busch et al., 2021; Navabi et al., 2016). Mobile phones also enable older adults to engage in economic activities, including conducting mobile money transactions (Beneito-Montagut et al., 2022; Kuranchie, 2022). This finding is similar to previous studies, which found mobile phones to be the primary ICT device older adults use to access the Internet (Dabalen and Mensah, 2023).

This study revealed that intrapersonal, interpersonal, institutional, and community factors significantly influenced internet use. Age was a significant predictor of internet use. Older adults aged 60–74 were more likely to use the internet than those aged 75–84 and 85 and older. This finding is similar to previous studies among older adults, which found that internet use declines with increasing age (Chang et al., 2015; Choi and Dinitto, 2013; Czaja and Lee, 2007; Davidson and Schimmele, 2019; Matthews et al., 2019; Rennoch et al., 2024; Slegers et al., 2012; Sun et al., 2020; Wang et al., 2021; Werner et al., 2011; Wright and Hill, 2009). One possible reason is that older adults aged 60–74 are more likely to be exposed to digital technology during their working years, making them more adept and accustomed to utilising the internet (Charness and Boot, 2009). Furthermore, older adults aged 75–84 and 85+ are more vulnerable to cognitive decline, which can impede their ability to utilise ICT devices and access the internet (Gatto and Tak, 2008; Gitlow, 2014).

Similar to other studies (Choi and Dinitto, 2013; Dabalen and Mensah, 2023; Davidson and Schimmele, 2019; Gell et al., 2015; Matthews et al., 2019; Rennoch et al., 2024; Slegers et al., 2012; Sun et al., 2020); this study found that older female older adults were less likely to use the internet than older male adults. Males tend to have more financial resources compared to women (Meriküll et al., 2021; Ruel and Hauser, 2013), which may influence their internet use through their ability to access the internet and ICT devices, including mobile phones to access the internet. Also, men tend to have more educational opportunities than women (Evans et al., 2020), which may make them more digitally literate and enhance their internet usage.

Older adults with a formal education were more likely to use the internet than those without a formal education. Similar to previous studies (Chang et al., 2015; Dabalen and Mensah, 2023; Echt and Burridge, 2011; Slegers et al., 2012; Werner et al., 2011), this finding underscores the role of formal education in internet use. Formal education enhances the acceptance of technological innovations (Mann et al., 2016). Therefore, it is unsurprising that older adults with formal education were more likely to use the Internet.

Also, similar to previous studies (Rennoch et al., 2024), this study revealed that marital status was a significant predictor of internet use. Older adults who were currently married were more likely to use the internet than those who were ever married and never married. A probable explanation is that a spouse’s presence can support married older adults in overcoming barriers to internet access, potentially boosting their internet usage. Also, married older adults may possess greater financial means than never and ever married older adults, increasing their ability to connect to the internet.

Older adults who were religiously affiliated were more likely to use the internet than those without religious affiliation. During the emergence of COVID-19, there has been a shift from in-person participation in religious activities to online involvement in religious activities (Adogla-Bessa, 2020; Africanews, 2021). This online participation in religious activities enhances internet use for people of all ages, and this could explain why older adults who were religiously affiliated were more likely to use the Internet.

Similar to other studies (Davidson and Schimmele, 2019; Hale et al., 2010; Hanson, 2010; Wright and Hill, 2009), older adults dwelling in urban areas were more likely to use the internet than those dwelling in rural areas. Internet penetration in Ghana is higher in urban areas than in rural areas (Dabalen and Mensah, 2023; Twumasi et al., 2021), which may explain why older adults who dwell in urban areas are more likely to use the internet. In addition, older adults with difficulty seeing and hearing were less likely to use the internet than their counterparts without difficulty seeing and hearing. Studies have linked the decline in auditory and visual acuities with less frequent internet use (Charness and Boot, 2009; Echt and Burridge, 2011). The decline in auditory and visual acuities may explain why older adults with difficulty seeing and hearing are less likely to use the internet. Previous studies have identified sight and hearing impairments as barriers to internet use among other adults (Gell et al., 2015; Gitlow, 2014; Vaportzis et al., 2017).

Surprisingly, older adults from middle and rich households were less likely to use the internet than those from poor households. This finding contradicts previous studies, which showed that individuals from poor households were less likely to use the internet (Echt and Burridge, 2011; Matthews et al., 2019). Future qualitative studies could explore why older adults from poor households use the internet more than those from middle and rich households.

Regarding ecological zone, older adults dwelling in the Northern ecological zone were less likely to use the internet than those dwelling in the Coastal ecological zone. Internet penetration is more prevalent in the Coastal ecological zone than in the Northern ecological zone (Dabalen and Mensah, 2023), which may explain why older adults in the Coastal ecological zone were more likely to use the internet than those in the Northern ecological zone.

Strengths and limitations

This study has several notable strengths. The study utilised data from the census of Ghanaian older adults, ensuring the representativeness of older adults and enabling the generalisability of its findings. Also, the findings of this study make a valuable contribution to the limited body of literature on internet use by older adults residing in low- and middle-income countries, including Ghana. Nevertheless, this study has some limitations. First, this study did not capture older adults’ perspectives on the facilitators of and barriers to internet use, which could help develop context-specific interventions to promote internet use among older adults in Ghana. Second, this study was cross-sectional; therefore, causality between internet use and socio-demographic characteristics of older adults cannot be established. Third, since this study involved secondary data analysis, some essential determinants of internet use among older adults (e.g. frequency of internet use and online social activities) were not considered since the secondary data did not capture them. This study recommends that future studies on older adults’ internet usage use longitudinal mixed-method approaches to enhance the understanding of the complex nature of internet use among older adults. Also, information on other essential determinants of internet use (such as frequency of internet use and online social activities) should collected during the data collection.

Implications for policy and practice

This study found that a little more than half of Ghanaian older adults (52.9%) used the internet. Though the prevalence of internet use among older adults is relatively high, policymakers should promote internet use among older adults by creating an enabling environment for internet use among older adults since it could enable them to participate in social activities within their families and communities, enhancing their mental health and general wellbeing. With the proliferation of live-stream social activities (Dover and Kelman, 2018), such as weddings, naming and funeral ceremonies, and religious activities, older adults can actively engage in these activities without needing to travel to the locations of these activities physically. The Government of Ghana (GoG), through its ministries, especially the Ministry of Communications and Digitisation, should collaborate with telecommunication companies in Ghana (such as MTN Ghana and Telecel Ghana) to design affordable data bundle packages for older adults to increase their internet use. In addition, future studies should investigate the perspectives of older adults on the facilitators of and barriers to internet use. This insight will facilitate the development of targeted interventions to enhance internet usage among older adults.

Internet use will also allow older adults to seek health information and healthcare remotely. Thus, internet use could remove any physical barriers to accessing healthcare, which could enhance healthcare access for older adults. This will improve Ghana’s achievement of Sustainable Development Goals (SDGs) 3, specifically Targets 3.4 and 3.8, which address promoting mental health and wellbeing and achieving universal health coverage for all persons of all ages, including older adults.

Educational status and religious affiliation were the primary factors that predicted internet usage among older adults. Stakeholders should actively embark on awareness campaigns to promote digital literacy among older adults, particularly those without formal education. Efforts to raise awareness should emphasise the advantages of internet usage for older adults and address older adults’ barriers to internet access to improve their internet usage. These initiatives will help policymakers facilitate lifetime learning for older persons, as outlined in SDG 4, which seeks to encourage lifelong learning opportunities for everyone.

Furthermore, stakeholders should partner with religious institutions nationwide to encourage internet use among their members, particularly the older adult population. These institutions’ places of worship and facilities could serve as platforms for conducting awareness campaigns on internet usage. Moreover, stakeholders should develop targeted initiatives addressing the needs of older adults with varying educational backgrounds and religious affiliations, which will help facilitate their internet usage.

Conclusion

Approximately 53% of Ghanaian older adults used the internet three months preceding the census. Mobile phones were the primary ICT devices older adults used to access the internet. The study demonstrated that older adults’ age, sex, educational status, marital status, and religious affiliation were significant predictors of internet use. In addition, other significant predictors of internet use include place of residence, sight impairment, hearing impairment, household wealth status, and ecological zone. Policymakers should consider these intrapersonal, interpersonal, institutional, and community factors when developing interventions to promote internet use among older adults to enhance their health and general wellbeing.