ARTICLE

Socio-economic and technological aspects of
mental health of older persons: the role of strong
and weak ties in Ghana

Padmore Adusei Amoah1* , Annabella Osei-Tutu2 and Stephen Baffour Adjei3

1Department of Applied Psychology; Institute of Policy Studies; School of Graduate Studies, Lingnan
University, Tuen Mun, Hong Kong (SAR), 2Department of Psychology, University of Ghana, Accra, Ghana
and 3Department of Interdisciplinary Studies, Akenten Appiah-Menka University of Skills Training and
Entrepreneurial Development, Kumasi, Ghana
*Corresponding author. Email: pamoah@LN.edu.hk, padmoreamoah@yahoo.com

(Accepted 15 November 2021; first published online 27 January 2022)

Abstract
Research indicates that social capital can influence the extent to which socio-economic
status (SES) and information and communications technology (ICT) affect mental health.
This study uses empirical data to examine the veracity of this claim by examining the effect
of SES and ICT use on the mental health of older persons in Ghana, as well as the mod-
erating role of bonding (i.e. strong ties) and bridging (i.e. weak ties) social capital in these
associations. Data were drawn from 409 older persons from four regions in Ghana as part
of a broader cross-sectional survey. Ordinal logistic regression analyses showed that SES
and ICT use had positive associations with mental health after adjusting for other
socio-demographic factors. Bridging social capital modified the association between SES
and mental health positively. Bonding social capital also moderated the relations between
ICT use on mental health positively. We argue that the prevalent nature of resources
embedded in strong ties and the diversity of support that emerge from weak ties account
for the difference in their influence observed in this study. Thus, while advances in socio-
economic and technological conditions can enhance older persons’ mental health, equal
attention must be paid to the characteristics of their strong and weak ties as they possess
the resources to make socio-technological policies even more meaningful.

Keywords: social capital; mental health; health; socio-economic status; information and communications
technology; Ghana

Introduction
Health-related challenges are inevitable among older persons (Wahl et al., 2012;
Maharaj, 2013; World Health Organization, 2015; Amoah et al., 2019). However,
social and technological determinants of health such as socio-economic inequal-
ities, poor infrastructure, deficiencies in access to information and communications

© The Author(s), 2022. Published by Cambridge University Press. This is an Open Access article, distributed under the
terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unre-
stricted re- use, distribution and reproduction, provided the original article is properly cited.

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https://orcid.org/0000-0002-8730-5805
https://orcid.org/0000-0001-9407-8770
https://orcid.org/0000-0002-9672-9079
mailto:pamoah@LN.edu.hk
mailto:padmoreamoah@yahoo.com
http://creativecommons.org/licenses/by/4.0/
https://doi.org/10.1017/S0144686X21001859


technology (ICT), unresourceful social networks and weak social service systems
exacerbate poor health outcomes of older persons in low- and middle-income
countries (LMICs) (Maharaj, 2013; Gyasi et al., 2018, 2020; Amoah, 2019).
Notwithstanding, research in LMICs has devoted little attention to the interactive rela-
tionships between some of these critical factors as regards their influence on health
outcomes in later life, particularly mental health. To rectify this deficiency, this
study investigates the associations of the use of ICT and socio-economic status
(SES) with the mental health of older persons in Ghana. The study further examines
the influence that bonding social capital (i.e. strong social ties) and bridging social cap-
ital (i.e. weak social ties) have on the associations of ICT use and SES with the mental
health of older persons. The investigation of these associations is depicted in Figure 1.

Ghana’s population, of about 30 million people, has been described as ‘young’
(Kpessa-Whyte, 2018; World Bank, 2020). However, it is projected that Ghana
will have one of the highest percentages of people over 60 years in sub-Saharan
Africa by 2030 (United Nations, 2015; Van der Wielen et al., 2018). Social embed-
dedness such as friendship and kinship attachments are important for defining per-
sonhood in Ghana (Adjei, 2019). Hence, older persons and other vulnerable groups
in Ghana mostly rely on family and other social networks for support in health and
social life because of inadequate welfare services (Amoah, 2019, 2020; Gyasi et al.,
2020). Research suggests that the way in which older people use social relationships
and social networks can have important implications for their mental health con-
ditions, such as depression, anxiety and overall wellbeing (Amoah, 2019; Bai et al.,
2020; Gyasi et al., 2020).

In the present study, we focus on SES, ICT use and social capital primarily
because their impact on mental health is strongly influenced by older persons’
circumstances. For instance, while access to ICT – hardware infrastructure and soft-
ware programs that enable individuals, households and organisations to transmit
and receive information electronically (Karakara and Osabuohien, 2019) – is con-
sidered fundamental to the mental health of populations (Kim et al., 2020), its pro-
vision and utilisation vary from place to place (Forsman et al., 2018). Such variation
is attributed to the digital divide between and within countries (e.g. among low-
and high-income groups and across age groups) (Ohemeng and Ofosu-Adarkwa,
2014; Karakara and Osabuohien, 2019). For example, access and utilisation of
internet-enabled equipment and platforms (e.g. computers and smartphones) are
common in high-income countries, but telephones, radio and television remain
fundamental to the ICT needs in many LMICs such as Ghana (Ohemeng and
Ofosu-Adarkwa, 2014; Karakara and Osabuohien, 2019). Similarly, variations in
SES markers such as educational attainment, income and occupation persistently
change and reinforce inequalities in general and mental health outcomes, particu-
larly among older persons (Adler and Newman, 2002; Read et al., 2016; Srivastava
et al., 2021). These variations and inequalities cause the impact of social capital to
be critical to the way SES and ICT use affect the mental health and wellbeing of
older persons (Vonneilich et al., 2012; Srivastava et al., 2021; Wang et al., 2021).
However, little research on the role of different kinds of social capital in these rela-
tionships has been carried out in Ghana. As indicated earlier, the purpose of the
current study is to fill this empirical gap. The rest of the paper provides details
about the theoretical perspectives and literature review related to the study and

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hypotheses. We then present the methodology, results from our analyses, as well as
the discussion and conclusion.

ICT use, SES and mental health: theoretical and empirical perspectives

The relationship between SES and the mental health of older persons can be
explained by the fundamental cause theory of health. The theory posits that SES is
strongly associated with various kinds of health conditions and outcomes due to
its effect on disease risk factors, access to basic resources (e.g. money, knowledge,
ICT and social networks) to minimise the impact of health risks and its evident
reproducible effect on health (Link and Phelan, 1995; Adler and Newman, 2002).
Correspondingly, high SES enables individuals to manage their socio-physical envir-
onments through access to needed services while avoiding harmful physical environ-
ments and behaviours that cause stress and agony (Adler and Newman, 2002; Phelan
et al., 2010). Ageing well is thus associated with favourable conditions derived from
SES (Hu et al., 2005). Older persons with high SES tend to perceive their mental
health in satisfactory terms (Hu et al., 2005; Read et al., 2016; Srivastava et al.,
2021). This is because SES influences mental health and other primary determinants
of mental health such as ICT (Phelan et al., 2010; Wahl et al., 2012).

According to the person–environment interchange theory (P-E theory), ICT (e.g.
internet, mobile phones, smart technologies and e-health) represents ‘new’ ways to
experience ageing (Wahl et al., 2012; Wahl, 2015). The theory conceives older per-
sons’ health as a product of the interactive exchanges they have with their circum-
stances. It argues that the influence of older persons’ socio-physical environments
on their health depends on their agency – proactive and reactive capabilities to man-
age the socio-physical environments for themselves. Besides, their health is also

Figure 1. A heuristic model of the study.
Note: Points A, B, C and D are the primary relationships of interest to this study.

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influenced by their sense of belonging – the extent of attachment that individuals
have to their socio-physical environment (Wahl et al., 2012; Wahl, 2015). ICT is con-
sidered a primary resource for older persons to engage productively with their envir-
onments because it enhances agency (e.g. using the internet to seek health
information for themselves) (Lee et al., 2020). ICT use also enables them to ‘stay con-
nected and age well’ (Wahl, 2015). These benefits of ICT use help to reduce risk fac-
tors of psychological distress such as social isolation and loneliness, which are
prevalent in later life and thereby promote good mental health (Ihm and Hsieh,
2015; Khosravi et al., 2016; Kim et al., 2020). Several empirical studies have estab-
lished a connection between ICT use and older persons’ mental health through
mechanisms such as inclusivity and social connectivity (Ihm and Hsieh, 2015;
Khosravi et al., 2016; Francis et al., 2019; Forsman et al., 2018; Kim et al., 2020).

Social capital and mental health of older persons

Despite the positive association between ICT use, SES and mental health, the rela-
tionships are seldom linear. Social capital – either from strong or weak sources –
can buffer the degree of influence that ICT use and SES have on mental health
of older persons (Read et al., 2016; Kim et al., 2020). Social capital is conceptualised
as the resources (e.g. information, emotional support and tangible support) that
emerge from the different kinds of social networks a person possesses and the asso-
ciated norms of exchange (Putnam, 1993, 2000; Szreter and Woolcock, 2004;
Harpham, 2008). There are other kinds of social capital aside from bonding and
bridging social capital such as linking social capital and cognitive social capital
(e.g. trust) (see Putnam, 2000; Szreter and Woolcock, 2004; Amoah and Phillips,
2017). Our emphasis on bonding and bridging social capital reflects the primary
characteristics of the phenomenon as rooted in Granovetter’s (1973) theory of
strength of weak ties (see also Szreter and Woolcock, 2004). Granovetter (1973:
1371) posits that while strong social ties are fundamental to health-related well-
being and access to instrumental and other forms of support, ‘those to whom we
are weakly tied are more likely to move in circles different from our own and
will thus have access to information different from that which we receive’.
Categorising social ties as either strong or weak is predicated on a ‘combination
of the amount of time, the emotional intensity, the intimacy (mutual confiding),
and the reciprocal services which characterise the tie’ (Granovetter, 1973: 1361).
Thus, bonding social capital (which emerges from strong ties) describes the
resources obtained through frequent and trusting relationships among groups/net-
works with homogenous characteristics and identity, and of equal or near-equal
status such as families, close neighbours and close friends (Putnam, 2000; Szreter
and Woolcock, 2004; Amoah and Phillips, 2017). Meanwhile, bridging social cap-
ital embodies weak ties that connect people of heterogeneous characteristics in
terms of ethnicity, social class, geographical and social space, religion and occupa-
tional backgrounds (Halpern, 2005). Examples include distant neighbours (both in
social and geographic terms), business associates and a friend of a friend (Amoah
and Phillips, 2017).

Some existing empirical studies have distinguished bonding and bridging social
capital and have established their role in mental health in different contexts

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(Hogarty and Wieland, 2005; De Silva et al., 2007; Flores et al., 2018; Bai et al.,
2020). Better cognitive functioning has been associated with higher bonding social
capital (being married or co-habiting). In contrast, depressive symptoms have been
linked to lower social capital (not being married or co-habiting) (Ramlagan et al.,
2013). Weak ties are also associated with positive self-worth (Zhai, 2021).
Furthermore, a systematic review of the literature shows that while low SES contri-
butes to poor psychological health, the association is weaker when the influence of
social capital is examined (Read et al., 2016). A study among middle-aged and older
persons found that social capital shapes the association between SES and mental
health (Phongsavan et al., 2006). Likewise, there is evidence that social capital, espe-
cially strong ties, help to overcome the digital divide by facilitating access to and use
of the internet and other technology, which can lead to better mental health (Chen,
2013). However, social capital can exclude individuals from the benefits of ICT due
to limited resources in their social circles (Claudine and McCreadie, 2010; Chen,
2013). Therefore, Adler and Newman (2002) conclude that factors relating to the
social environment can have a greater impact on health than elements of the phys-
ical environment. This is particularly true for older persons in LMICs, where fam-
ilies and other social networks are often the primary source of care and sustenance
(Maharaj, 2013; Amoah, 2019; Gyasi et al., 2020).

Hypotheses

Based on the fundamental cause theory and the P-E theory, as well as the empirical
literature above, it is hypothesised that:

(1) High SES and ICT use will be positively associated with the mental health of
older persons.

(2) Bonding social capital will positively modify the relationship between SES,
ICT use and mental health.

(3) Bridging social capital will positively moderate the relationship between
SES, ICT use and mental health among older persons in Ghana.

The formulation of these hypotheses arises because high bonding and bridging
social capital can provide resources that make SES useful to older persons and
improve their access to and use of ICT, which will ultimately empower them to
manage their socio-physical circumstances to minimise stressful conditions (Link
and Phelan, 1995; Putnam, 2000; Vonneilich et al., 2012; Wahl et al., 2012;
Chen, 2013; Read et al., 2016). Figure 1 depicts the variables and the primary rela-
tionships of interest to this study: points A, B, C and D.

Methods
Data were derived from a cross-sectional survey, which gathered information on the
multi-dimensional aspects of wellbeing from adults who were 18 years and older.
The survey was conducted from July to August 2018 in four of the ten administra-
tive regions of Ghana, which has been increased to 16. This study analyses a sub-
sample of the broader study with participants aged 50 years and older at the time of
the survey. We used a minimum age of 50 years because of early onset of

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age-related frailty and low life expectancy among populations in LMICs (Gyasi
et al., 2020). The Council for Scientific and Industrial Research and Lingnan
University Research Ethics Committee approved the study protocols.

Sampling and participants

A pragmatic multi-stage cluster sampling approach (Bryman, 2012) was employed in
the data-gathering to have a regional, district, community (rural and urban) and
household balance in the data. The first cluster comprised the four regions. Four
regions were selected to consider geography (e.g. rural and urban areas), religious
and ethnic compositions, and socio-economic characteristics. A simple random sam-
pling was used in each region to select a conveniently determined number (N = 23)
of administrative districts for inclusion, which formed the next cluster. The regions
were: Ashanti (eight districts), Greater Accra (six districts), Eastern Region (four dis-
tricts) and Upper East (five districts). While the four regions did not comprehen-
sively cover all the socio-economic circumstances that exist among the whole
population in Ghana, the inclusion of places such as the Greater Accra and
Ashanti regions helped to capture an adequate range of characteristics of the popu-
lation because of the diversity of people in these regions (Ghana Statistical Service,
2012). Thus, to ensure diversity in the sample, 36 rural communities and 70
urban areas were altogether selected across the 23 districts using a combination of
convenient and purposive sampling techniques. Specific rural and urban areas
were selected to become the sub-clusters for the survey. However, not all persons
in these selected areas were surveyed. After demarcating the selected areas into sub-
clusters, a person in every second house in the selected rural areas and a fifth house
in the selected urban areas who were available and offered to participate in the study
were included. This approach is consistent with techniques adopted in related studies
in Ghana (Amoah, 2018; Gyasi et al., 2020).

Questionnaires were proportionally distributed to the regions, districts and com-
munities based on the available size of the sampling frame. Trained field assistants
administered the questionnaires. The sample was based on the general adult popu-
lation across the selected regions using a sample error of 0.05, 95 per cent confi-
dence level and 50 per cent for the proportion of participants giving a positive
response to a question (Monette et al., 2008). While a sample of 384 would have
sufficed, the study gathered 1,381 respondents across the regions. Of these, 409
were aged 50 years and older and are included in this article. Other details of
this survey have been reported elsewhere (see Amoah, 2020).

Measures

Dependent variable: mental health
The General Health Questionnaire (GHQ-12) was used to measure mental health
(Goldberg and Williams, 1988; Fridh et al., 2014). The GHQ-12 instrument asks
about recent negative (six items) and positive (six items) mental conditions relating
to anxiety, ability to cope with daily demands and activities, and depression, which
are measured on a four-point Likert scale: ‘much less than usual’, ‘less than usual’,
‘same as usual’ and ‘better than usual’ (Fridh et al., 2014). The full instrument can
be found in Table 1. All the negative items were reverse-coded so that higher scores

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Table 1. Descriptive statistics of mental health of older persons

In the last few weeks, have you…
Much less
than usual

Less than
usual

Same as
usual

Better than
usual Mean (SD)

Frequencies (%)

1. been able to concentrate on daily tasks? (positive) 25 (6.1) 124 (30.3) 195 (47.7) 65 (15.9) 2.73 (0.79)

2. recently been able to enjoy your normal day-to-day activities? (positive) 21 (5.1) 107 (26.2) 211 (51.6) 70 (17.1) 2.81 (0.78)

3. lost much sleep due to worrying? (negative) 62 (15.2) 134 (32.8) 150 (36.7) 63 (15.4) 2.52 (0.93)

4. been able to face up to your problems? (positive) 38 (9.3) 113 (27.6) 176 (43.0) 82 (20.0) 2.74 (0.88)

5. felt that you were playing a useful part in things? (positive) 45 (11.0) 99 (24.2) 169 (41.3) 96 (23.5) 2.77 (0.93)

6. been feeling unhappy or depressed? (negative) 88 (21.5) 129 (31.5) 155 (37.9) 37 (9.0) 2.34 (0.91)

7. been capable of making decisions about things? (positive) 51 (12.5) 93 (22.7) 185 (45.2) 80 (19.6) 2.72 (0.92)

8. been losing confidence in yourself? (negative) 88 (21.5) 134 (32.8) 133 (32.5) 54 (13.2) 2.37 (0.9)

9. felt yourself to be constantly under strain? (negative) 99 (24.2) 127 (31.1) 139 (34.0) 44 (10.8) 2.31 (0.96)

10. been thinking of yourself as a worthless person? (negative) 97 (23.7) 132 (32.3) 140 (34.2) 40 (9.8) 2.30 (0.91)

11. felt that you couldn’t overcome your difficulties? (negative) 92 (22.5) 124 (30.3) 148 (36.2) 45 (11.0) 2.36 (0.95)

12. been feeling reasonably happy, all things considered? (positive) 56 (13.7) 94 (23.0) 189 (46.2) 70 (17.1) 2.67 (0.92)

Notes: N = 409. SD: standard deviation.

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represented good mental health. For purposes of our analyses, three of the items
(items 3, 7 and 12) were excluded from the regression analysis to improve the reli-
ability of the scale. This approach and others to scale purification have been used in
different studies (Preston and Colman, 2000; Wieland et al., 2017). The three items
showed a negative corrected item-total correlation indicating their variation from
other items. We posit that participants interpreted these questions differently.
The remaining nine items showed adequate reliability with a Cronbach’s alpha of
0.75. The nine mental health items were dichotomised for ease of interpretation.
Following previous studies, responses to ‘much less than usual’ and ‘less than
usual’ were given a value of ‘0’ and responses to ‘same as usual’ and ‘better than
usual’ were given a value of ‘1’. The results were summed to have the lowest of 0
and the highest of 9. The summed responses were used to represent mental health
in the regression analyses (described under the data analyses sub-section below).
However, to provide an explicit overview of mental health in the descriptive
analyses, the summed responses were categorised into poor mental health (scores
0–7) and good mental health (scores 8–9) as a percentile equivalence to predom-
inant approaches used in existing studies (Fridh et al., 2014).

Independent variables
ICT use: ICT use was measured using an eclectic approach by generating the mean of
the extent of use of three of prevalent media devices, namely mobile phone, television
and FM radio, among older persons and most people in Ghana (Karakara and
Osabuohien, 2019). Participants were asked to rate the frequency with which they
used the three devices, regardless of ownership. For each device, participants
responded on a five-point Likert scale: ‘never’, ‘less than once a month’, ‘a few
times a month’, ‘a few times a week’ and ‘every day’. The instrument was adapted
from the Afrobarometer Round 7 Survey in Ghana (Afrobarometer, 2018). A
Cronbach’s alpha of 0.77 was observed, which assured a decent consistency in the
use of the different devices and overall reliability of the scale.

SES: SES was measured using the one-item MacArthur Scale of Subjective Social Status,
which has been used widely in health-related research (Adler and Newman, 2002; Sanders
et al., 2006; Nobles et al., 2013). Participants were asked to rate their perceived social and
economic positions in relation to others from 1 (low) to 10 (high). To provide a precise
overview of conditions in the descriptive analyses, SES was categorised into three classes:
low SES (scores 1–4), average SES (scores 5–7) and high SES (scores 8–10). However, in
the regression analyses, the actual responses were used.

Moderators: social capital variables
Bonding and bridging social capital were measured using a modified form of the
short version of the Adapted Social Capital Assessment Tool (S-ASCAT) (De
Silva et al., 2006; Harpham, 2008). In this study, the response options in the ori-
ginal instrument were specifically grouped to reflect the relevant types of social cap-
ital of interest (i.e. bonding and bridging). For bonding social capital, participants
were asked to indicate if they received emotional, instrumental or informational
support from these individuals within their communities in the past 12 months:
nuclear family/household (e.g. mother, father, siblings), extended family, close

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friends, close neighbours, people from same association (e.g. religious group, sports
and youth groups) and others (as specified by participants based on the definition
provided). Participants could select as many categories as applicable to them.
Likewise, for bridging social capital, participants were asked to select as many
options as applied to them if they had received any emotional, instrumental or
informational support from people such as a friend of a friend, people of other reli-
gious affiliations, people of other ethnicities, people in other communities/towns/
abroad, neighbours they are not close with, foreigners and others (as specified by
participants). The options for bonding and bridging social capital were derived
through a contextual assessment in a prior study (Amoah, 2017) as De Silva
et al. (2006) did for the original instrument. The responses for bonding and bridg-
ing social capital were summed to obtain a score for each (De Silva et al., 2006;
Harpham, 2008; Amoah et al., 2021). Thus, higher numbers indicated more bond-
ing/bridging social capital and vice versa. The two social capital variables showed
only moderate correlation (see Appendix 1 in the online supplementary material),
indicating that they measured different aspects of social capital.

Covariates
The study collected several demographic and objective socio-economic characteris-
tics of participants, including age (in years), sex (male or female), educational
attainment, monthly income/stipend and employment status, to ensure the robust-
ness of the analyses (see Nobles et al., 2013). Others included marital status, ethni-
city, residence area (urban or rural), region of residence, household size and
religion. Table 2 shows the details of how the various characteristics were measured.

Data analyses

The analyses comprised three steps. The first step was a descriptive analysis of the
variables involved in the study. The second step was Spearman’s correlation ana-
lysis to identify potential predictors of mental health (see Appendix 1 in the online
supplementary material). The final step was an ordinal logistic regression analysis
to identify relations between (a) SES and mental health, and (b) ICT use and men-
tal health; and whether bonding and bridging social capital modify the relation-
ships. To ensure the robustness of the results, seven models were constructed.
However, three of these models, which contain the main results, are presented in
Table 2. The first model included the socio-demographic correlates of mental
health. The second model comprised the first model in addition to the independent
variables and the moderators. The third model included the first two models and
the interaction terms between the moderators and the independent variables. All
seven models, including the other four, which tested the associations of the inde-
pendent variables (i.e. ICT use and SES), the social capital variables as well as
the interaction terms with mental health are presented in Appendix 2 in the online
supplementary material. In all the models, marital status, religious affiliation and
ethnicity were excluded as they are theoretically linked to bonding and bridging
social capital, respectively (Szreter and Woolcock, 2004; Harpham, 2008).

Further to the regression analyses, simple slope analyses were conducted to
check the robustness of the significant interaction terms. The slope analyses

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Table 2. Descriptive statistics of variables used in the study

Variable Frequency Percentage

Age:

50–59 218 53.3

60–64 83 20.3

65+ 108 26.4

Mean age (18–85 years) (SD) 59.56 (8.29)

Sex:

Male 214 52.3

Female 195 47.7

Region of residence:

Ashanti 102 24.9

Greater Accra 41 10.0

Eastern Region 70 17.1

Upper East 196 47.9

Locality:

Urban 207 50.6

Rural 202 49.4

Educational attainment:

Never been to school 155 37.9

Primary school 97 23.7

MSLC 94 22.9

O Level 23 5.6

A Level 13 3.2

Vocational/technical 5 1.2

Tertiary 18 4.4

Postgraduate 4 1.0

Ethnicity:

Asante 92 22.5

Other Akans 60 14.7

Ewe 21 5.1

Ga-Adangbe 18 4.4

Dagomba 12 2.9

Northern ethnicities 206 50.4

Religious affiliation:

Christian 272 66.5

(Continued )

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Table 2. (Continued.)

Variable Frequency Percentage

Islam 50 12.2

Traditional religion 60 14.7

No religion 22 5.4

Monthly income (if employed, GH¢):

Mean (SD) 453.21 (454)1

Minimum–maximum 30–2,2002

Employment status:

Full-time employee 44 10.8

Part-time employee 29 7.1

Self-employed 153 37.4

Pension/retired 79 19.3

Student 3 0.7

Housewife 17 4.2

Unemployed 84 20.5

Household size:

Mean (SD) 6.5 (3.28)

Minimum–maximum 1–25

Marital status:

Married 275 67.2

Divorced 14 3.4

Windowed 60 14.7

Separated 14 3.4

Living together as married 3 0.7

Single 28 6.8

ICT use (mobile phone use):

Never 60 14.7

Less than once a month 24 5.9

A few times a month 31 7.6

A few times a week 43 10.5

Every day 251 61.4

ICT use (television):

Never 107 26.2

Less than once a month 31 7.6

A few times a month 35 8.6

(Continued )

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evaluated the associations between the independent variables at different levels of
the moderators (either high or low). This was done at one standard deviation
below and above the mean (Dawson, 2014). Variables with missing values were
replaced using the linear trend at point technique for continuous and ordinal vari-
ables, and missing categorical values were replaced by the response mean (Cokluk
and Kayri, 2011). A template provided by De Coster and Iselin (2005) was used to

Table 2. (Continued.)

Variable Frequency Percentage

A few times a week 66 16.1

Every day 170 41.6

ICT use (FM radio):

Never 63 15.4

Less than once a month 19 4.6

A few times a month 22 5.4

A few times a week 65 15.9

Every day 240 58.7

Mean (SD) of all ICT 3.78 (1.21)

Minimum–maximum 1–5

Socio-economic status:

Low 168 41.1

Average 197 48.2

High 40 9.8

Mean (SD) 4.78 (2.14)

Minimum–maximum 1–10

Bonding social capital:

Mean (SD) 3.21 (1.66)

Minimum–maximum 0–6

Bridging social capital:

Mean (SD) 2.54 (1.60)

Minimum–maximum 0–6

Mental health:

Poor 334 81.7

Good 75 18.3

Mean (SD) 5.23 (2.09)

Minimum–maximum 0–9

Notes: N = 409. 1. US $88.10 (88.3). 2. US $5.84–427.90. SD: standard deviation. ICT: information and communications
technology. MSLC: Middle School Leaving Certificate.

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compute the odds ratios for each regression estimate. Statistical significance was set
at p < 0.05.

Results
The majority of participants were men (52.3%). They were mostly aged between 50
and 59 (53.3%) years. Almost as many participants lived in rural (49.4%) as urban
(50.6%) areas. Most of them had never been to school (37.9%). Most participants
were self-employed (37.4%). The participants were also mostly married (67.2%), as
shown in Table 2.

Regarding the independent and moderating variables, most participants had
used some form of ICT device on several occasions every week (for details, see
Table 2). Their SES was generally poor because only 10 per cent of the older per-
sons rated their status as high. This corresponded with low mental health as over
81.7 per cent of participants described their status as poor. Further details in
Table 1 corroborate this result as most people rated their mental health modestly.
Bonding social capital was more prevalent than bridging social capital (Table 2).

Table 3 shows the results from the inferential statistics of the study. From all
three models of the table, older persons aged 50–59 were more likely to experience
good mental health than those who were 65 and older (B = 0.562, p < 0.05). There
was also evidence from Table 3 (Model 3) that older persons with some education
(vocational/technical: B = 1.732, p < 0.05; tertiary: B = 0.1.204, p < 0.05) were more
likely to experience good mental health compared to those who had never been to
school. It was also found that older people who were employees (full-time employee:
B = 0.848, p < 0.05; part-time employee: B = 0.937, p < 0.05) were more likely to have
higher mental health scores compared to those who were unemployed.

Regarding the variables primary to this study’s objectives, Table 3 (Model 3)
shows that SES (B = 0.127, p < 0.01), ICT use (B = 0.230, p < 0.05) and bonding
social capital (B = 0.257, p < 0.01) were positively associated with mental health.
Bridging social capital showed an inconsistent association with mental health. It
showed significant association with mental health when the analyses excluded:
(a) independent variables and interactions, and (b) bonding social capital and its
interaction terms (see Appendix 2 in the online supplementary material).
However, the two categories of social capital did not moderate both primary
relationships as expected. The association between SES and mental health was
moderated by bridging social capital (B = 0.311, p < 0.01), while bonding social
capital modified the influence of ICT use on mental health positively (B = 0.232,
p < 0.05). The subsequent simple slope analyses confirmed these results. At
high levels of bonding social capital, ICT use was associated with mental health
(B = 0.462, t = 4.405, p = 0.000).

However, at low levels of bonding social capital, no association was found
between ICT use and mental health (B =−0.002, t =−0.016, p = 0.987)
(Figure 2). Likewise, the slope analyses indicated that at high levels of bridging
social capital, a positive and significant association was observed between SES
and mental health (B = 0.0376, t = 3.178, p = 0.002), but a non-significant associ-
ation was observed at low levels of bridging social capital (B =−0.120, t =−1.200,
p = 0.231), as shown in Figure 3.

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Table 3. The role of social capital (SC) in the relationship between information and communications technology (ICT) use, socio-economic status (SES) and mental
health by ordinal logistics regression

Model 1 Model 2 Model 3

Estimate
(95% CI)

Adjusted
odds ratio

Estimate
(95% CI)

Adjusted
odds ratio

Estimate
(95% CI)

Adjusted
odds ratio

Age (Ref. 65+):

50–59 0.550*
(0.035, 1.065)

1.733 0.609*
(0.070, 1.148)

1.839 0.562*
(0.021, 1.103)

1.754

60–64 0.442
(−0.095, 0.980)

1.556 0.591*
(0.038, 1.144)

1.806 0.558
(0.005, 1.112)

1.747

Sex (Ref. Female):

Male 0.068
(−0.293, 0.428)

1.070 −0.002
(−0.387, 0.347)

0.98 −0.085
(−0.456, 0.286)

0.919

Educational attainment
(Ref. Never been to school):

Primary school 0.307
(−0.175, 0.789)

1.359 0.124
(−0.363, 0.611)

1.132 0.094
(−0.397, 0.585)

1.099

MSLC 0.569*
(0.016, 1.122)

1.766 0.330
(−0.231, 0.891)

1.391 0.387
(−0.181, 0.955)

1.473

O Level 0.229
(−0.568, 1.026)

1.257 −0.073
(−0.880, 0.734)

0.929 −0.061
(−0.876, 0.753)

0.941

A Level −0.192
(−1.251, 0.867)

0.825 −0.466
(−1.532, 0.601)

0.628 −0.734
(−1.816, 0.348)

0.479

Vocational/technical 1.606*
(0.004, 3.208)

4.983 1.589
(−0.021, 3.199)

4.899 1.732*
(0.110, 3.354)

5.652

Tertiary 1.274**
(0.368, 2.179)

3.575 1.034*
(0.112, 1.956)

2.812 1.204*
(0.274, 2.135)

3.333

(Continued )

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Table 3. (Continued.)

Model 1 Model 2 Model 3

Estimate
(95% CI)

Adjusted
odds ratio

Estimate
(95% CI)

Adjusted
odds ratio

Estimate
(95% CI)

Adjusted
odds ratio

Postgraduate 1.010
(−0.854, 2.875)

2.746 1.229
(−0.638, 3.097)

3.418 1.351
(−0.526, 3.228)

3.861

Region of residence
(Ref. Upper East):

Ashanti −0.333
(−0.806, 0.140)

0.717 −0.498*
(−0.989, −0.007)

0.607 −0.499*
(−0.997, −0.002)

0.607

Greater Accra −0.381
(−1.006, 0.243)

0.682 −0.625
(−1.275,.025)

0.535 −0.508
(−1.161, 0.145)

0.602

Eastern Region 3.248***
(2.648, 3.848)

25.739 3.279***
(2.664, 3.894)

26.549 3.433***
(2.762, 4.103)

30.969

Employment status
(Ref. Unemployed):

Full-time employee 0.794*
(0.106, 1.483)

2.212 0.829*
(0.123, 1.536)

2.291 0.848*
(0.139, 1.558)

2.334

Part-time employee 0.943*
(.143, 1.743)

2.568 0.898*
(0.089, 1.708)

2.454 0.937*
(0.120, 1.754)

2.552

Self-employed 0.160
(−0.336, 0.656)

1.174 0.102
(−.397, 0.602)

1.107 0.113
(−0.402, 0.629)

1.119

Pension/retired 0.499
(−0.109, 1.107)

1.647 0.306
(−0.316, 0.928)

1.358 0.197
(−0.429, 0.823)

1.218

Student −0.333
(−2.454, 1.788)

0.717 −0.273
(−2.424, 1.878)

0.761 −0.236
(−2.395, 1.924)

0.789

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Housewife −0.798
(−1.769, 0.173)

0.450 −0.825
(−1.797, 0.147)

0.438 −0.920
(−1.896, 0.056)

0.399

SES 0.112*
(0.025, 0.223)

1.119 0.127*
(0.038, 0.371)

1.135

ICT use (index) 0.241**
(0.077, 0.441)

1.272 0.230**
(0.065, 0.479)

1.259

Bonding SC 0.226**
(0.083, 0.469)

1.254 0.257**
(0.110, 0.463)

1.293

Bridging SC 0.116
(−0.494, 0.056)

1.123 0.097
(−0.053, 0.319)

1.101

SES × Bonding SC −0.018
(−0.291, 0.254)

0.982

ICT use (index) × Bonding SC 0.232*
(0.140, 0.435)

1.261

SES × Bridging SC 0.311**
(0.051, 0.571)

1.365

ICT use (index) × Bridging SC −0.040
(−0.280, 0.200)

0.961

Nagelkerke pseudo R2 0.350 0.398 0.419

Notes: Model 1 = socio-demographic correlates of mental health. Model 2 = Model 1 + independent (SES and ICT use) and moderating (bonding and bridging SC) variables. Model 3 = Model 1 +
Model 2 + interaction variables. The full model comprising the robustness checks are shown in Appendix 2 in the online supplementary material. CI: confidence intervals. Ref.: reference category.
MSLC: Middle School Leaving Certificate.
Notes: * p < 0.05, ** p < 0.01, *** p < 0.001.

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Discussion
Factors influencing the mental health of older persons in LMICs are numerous and
complex. This study aimed to expand understanding of these complexities by exam-
ining the moderating roles of strong social ties and weak social ties in the associ-
ation of SES and ICT use with mental health among older persons in Ghana.
Consistent with our first hypothesis, older persons with high ICT use and high
SES were more likely to perceive their mental health in satisfactory terms than
those with low ICT use and low SES. While we found evidence of the moderating
role of social capital, some inconsistencies were observed with regard to our second
and third hypotheses. None of the social capital categories significantly moderated
the two primary associations. Instead, bridging social capital moderated the associ-
ation between SES and mental health, while bonding social capital modified the
influence of ICT use on mental health. These observations are discussed below
in light of our hypotheses.

Figure 2. Bonding social capital moderates the relations between information and communications
technology (ICTs) use and mental health. The template for Figure 2 was obtained from Dawson (2014).

Figure 3. Bridging social capital moderates the relations between socio-economic status (SES) and men-
tal health. The template for Figure 3 was obtained from Dawson (2014).

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The positive association between SES and mental health augments the widely
held view that SES is a fundamental cause of health outcomes, even among poten-
tially vulnerable groups such as older persons (Adler and Newman, 2002; Phelan
et al., 2010; Read et al., 2016). Congruent to the tenets of the fundamental cause
theory, the association between SES and mental health involved different resources
(Link and Phelan, 1995; Phelan et al., 2010). Bridging social capital moderated the
association between SES and mental health positively, as proposed in our third
hypothesis. This finding offers a substantial empirical basis concerning how social
capital can attenuate widening social inequalities, which are injurious to mental
health, especially in LMICs where older persons are more vulnerable to the vagaries
of their socio-physical environments (Maharaj, 2013; Amoah et al., 2019).
Concerning mental health, vulnerable people tend to place significant value on sup-
port emerging through weak social ties as opposed to personal or strong ones
(Cohen, 2004; Hogarty and Wieland, 2005). Perceived interconnectedness can min-
imise psychological distress of older persons by boosting perceived socio-economic
conditions (Putnam, 2000; Cohen, 2004; Song, 2011). It must be emphasised that
the role of bridging social capital in this study is imputable to its fundamental char-
acteristics, such as the provision of new resources (including economic, financial
and even emotional support) compared to strong ties (Granovetter, 1973). The
resources emerging from weak ties can offer older persons a new sense of purpose
in life by raising their status in societies and thereby improving their mental health.
Additionally, an abundance of weak social ties encourages a sense of belonging,
which facilitates overall wellbeing (Granovetter, 1973; Wahl et al., 2012). This is
why scholars argue that weak ties such as bridging social capital help people to
‘get ahead’ instead of the inward-looking bonding social capital, which enables peo-
ple to ‘get by’ (Putnam, 2000).

Consistent with our first hypothesis, we observed a positive association between
ICT use and mental health. The use of ICT helps older persons to understand,
change and adapt to their socio-physical environments more easily by paving the
way for healthy ageing as put forward by P-E theory (Wahl et al., 2012; Wahl,
2015). This result also aligns with the theses of other research findings (e.g. Ihm
and Hsieh, 2015; Khosravi et al., 2016; Francis et al., 2019; Forsman et al., 2018;
Kim et al., 2020).

Nonetheless, our results have indicated that strong ties (bonding social capital)
modify the association between ICT use and mental health. The contribution of
bonding social capital to the mental health of older persons is invaluable across
contexts, and this is even so in the case of high-income countries (Ramlagan
et al., 2013). ICT involves technical and financial implications which older persons
may not be able to afford because many older persons rely on their strong social ties
to access and use ICT (Claudine and McCreadie, 2010; Chen, 2013; Olsson et al.,
2019). Moreover, the influence of bonding social capital in the association between
ICT use and mental health relates to the more accessible nature of such strong ties
compared to the support derived from weak ties (Granovetter, 1973). Strong social
ties are usually the first point of contact for information, instrumental support and
decisions about everyday activities, including access to ICT devices/services (Szreter
and Woolcock, 2004; Chen, 2013; Amoah et al., 2018). Indeed, Chen (2013) argues
that while ICT devices and services can promote social connectivity, the right social

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capital must be in place to close the existing digital divide (see also Claudine and
McCreadie, 2010). According to the present study, bonding social capital is most
likely the right social capital, especially when it comes to the effects of ICT on men-
tal health. For instance, it is argued that ‘once people overcome informational,
motivational, and technical barriers and gain Internet access, bridging social capital
is less crucial in affecting online communication’ (Chen, 2013: 23). Because this
study focused on commonly used ICT in Ghana, it is understandable that bonding
social capital would be more influential in its association with mental health. Strong
social ties are said to exert more peer pressure than weak ties regarding technology
uptake (Chen, 2013). Thus, bonding social capital must be an integral part of mea-
sures to improve older persons’ mental health through modern technology.

The results and the preceding discussions generate curiosity about what caused
the roles of bonding and bridging social capital to differ from the propositions sta-
ted in our hypotheses. Such differences in the moderating effect of the two social
capital categories reinforce their theoretical relevance by offering critical insight
into the debates concerning their distinctiveness and similarities (Halpern, 2005;
Kawachi and Berkman, 2014). Firstly, this study adds to existing empirical and
theoretical works in its support of the appropriateness of distinguishing between
different kinds of social capital in analysing the implication of their effects on
health (Szreter and Woolcock, 2004; Ferlander, 2007; Nyqvist et al., 2013). The
differences in the moderating roles of bonding and bridging social capital in this
study expand understanding of how each of these resources can help to improve
the mental health of older persons. Second, it is possible that these social capital
categories are best examined and utilised through an analysis of their functions
in a given situation instead of the much-adopted approach focusing on the network
characteristics, at least among older persons. Our position contests perspectives
such as:

while bonding–bridging distinction may be important in some cases, maybe we
don’t need to worry quite so much about always measuring both bonding and
bridging social capital – if an individual or community is rich in one, they will
probably be rich in the other, too. (Halpern, 2005: 21)

Thus, this study posits that the actual evidence of these forms of social capital may
lie in their functions rather than in their mere availability, as demonstrated by their
distinctive influence on the mental health of older persons in this study. Such func-
tional diversity may have contributed to the different influence of bonding and
bridging social capital on the associations between SES, ICT and mental health
of older persons; and invites further studies into the role that these social phenom-
ena play in improving mental health (Nyqvist et al., 2013).

Limitations of the study

While the study expands our knowledge and understanding of the complex nature
of older persons’ mental health, it must be emphasised that the results are based on
a cross-sectional study that limits the potential for drawing causal inferences. The
sampling process was also not based entirely on a probability approach because of
challenges, including lack of a complete address system and highly nucleated and

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scattered settlement patterns, making randomisation almost impossible in a house-
hold survey like this one. Thus, the sample is based on a balanced instead of a rep-
resentative one. Besides, the study relied on and tested only two categories of social
capital; it excluded the cognitive components of the phenomenon. Such an
approach certainly offers a partial perspective on the importance of social capital
to the mental health of older persons. Future studies should extend this work by con-
sidering other aspects of social capital, such as trust. In addition, ICT use was based
on selected devices/services, and albeit justified, cannot be said to represent the entir-
ety of older persons’ experiences because of differences in specific definitions of ICT
and SES between regions and communities in Ghana. These limitations imply that
the results of the study cannot be generalised to the entire country. However, the
results have significant implications for understanding the mental health of older per-
sons in Ghana. It is also helpful to note that while important relations were found
between socio-demographic characteristics of the older persons and their mental
health, such results have not been discussed and factored into the conclusions
drawn from this study. This leaves an important gap for future studies to fill.

Conclusions
Few studies have examined the socio-technological aspects of mental health in
Ghana. This study has attempted to expand the knowledge base in this research
area. The findings indicate that both strong and weak social capital are important
phenomena in understanding the influence of SES and ICT use on mental health.
The mental health of older persons, irrespective of their material and immaterial
assets, is conditioned by social capital. Specifically, it was apparent from our discus-
sions that the prevalent nature of strong ties and the diversity of resources embed-
ded in weak ties resulted in their unique effect on the relations between ICT use and
mental health, and SES and mental health, respectively. While the findings cannot
be generalised due to some of the limitations elaborated above, they point to the
potential areas for interventions to address problems with the mental health of
older persons that can be attributed to their SES and access to and use of techno-
logical resources. Therefore, the generation of both strong and weak social ties and
the support that emerges from these networks hold promising potential for the
mental health of older persons, and they must be given attention in health promo-
tion interventions.

Supplementary material. The supplementary material for this article can be found at https://doi.org/10.
1017/S0144686X21001859

Financial support. This work was supported by the Research Seed Fund of Lingnan University, Hong
Kong (fund code 102338); and the Lam Woo Research Fund-Individual Grant (grant code LWI20014)
of Lingnan University, Hong Kong. The funders did not play any role in preparing the manuscript or
in the decision to publish it.

Ethical standards. The Council for Scientific and Industrial Research (CSIR) (RPN 006/CSIR-IRB/2018)
and Lingnan University Research Ethics Committee (EC-052/1718) approved the study protocols.

Conflict of interest. The authors declare no conflicts of interest.

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Cite this article: Amoah PA, Osei-Tutu A, Adjei SB (2023). Socio-economic and technological aspects of
mental health of older persons: the role of strong and weak ties in Ghana. Ageing & Society 43, 2650–2672.
https://doi.org/10.1017/S0144686X21001859

2672 PA Amoah et al.

https://doi.org/10.1017/S0144686X21001859 Published online by Cambridge University Press

https://www.worldbank.org/en/country/ghana/overview
https://www.worldbank.org/en/country/ghana/overview
https://doi.org/10.1017/S0144686X21001859
https://doi.org/10.1017/S0144686X21001859

	Socio-economic and technological aspects of mental health of older persons: the role of strong and weak ties in Ghana
	Introduction
	ICT use, SES and mental health: theoretical and empirical perspectives
	Social capital and mental health of older persons
	Hypotheses

	Methods
	Sampling and participants
	Measures
	Dependent variable: mental health
	Independent variables
	Moderators: social capital variables
	Covariates

	Data analyses

	Results
	Discussion
	Limitations of the study

	Conclusions
	References