Use of Communication Technology to Maintain Intergenerational Contact: Toward An Understanding Of “Digital Solidarity”

Abstract

For more than half a century, gerontologists and family scholars have been concerned with describing and explaining patterns of contact between parents and their adult children – a dimension of intergenerational relations that is often referred to as associational solidarity (Silverstein and Bengtson, 1997; Silverstein and Giarrusso, 2010). It is not surprising that associational solidarity has been a central focus of studies of later-life families, because such contact is essential in the exchange of both expressive and instrumental support between the generations (Hank, 2007; Swartz, 2009). In this paper, we extend the study of associational solidarity by considering older mothers’ use of technology to maintain contact with adult children – what we refer to as ‘digital solidarity’.

Full text

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NINE
Use of communication technology
to maintain intergenerational
contact: toward an understanding of
‘digital solidarity’
Siyun Peng, Merril Silverstein, J. Jill Suitor, Megan Gilligan,
Woosang Hwang, Sangbo Nam, and Brianna Routh
Introduction
For more than half a century, gerontologists and family scholars
have been concerned with describing and explaining patterns of
contact between parents and their adult children – a dimension of
intergenerational relations that is often referred to as associational
solidarity (Silverstein and Bengtson, 1997; Silverstein and Giarrusso,
2010). It is not surprising that associational solidarity has been a central
focus of studies of later-life families, because such contact is essential
in the exchange of both expressive and instrumental support between
the generations (Hank, 2007; Swartz, 2009). In this paper, we extend
the study of associational solidarity by considering older mothers’ use
of technology to maintain contact with adult children – what we refer
to as ‘digital solidarity’.
The role of digital solidarity in the study of
intergenerational solidarity
The intergenerational solidarity paradigm (Bengtson and Roberts,
1991; Silverstein and Bengtson, 1997) incorporates six interrelated
components of family solidarity: (a) aectional (emotional closeness);
(b) associational (frequency of contact); (c) normative (norms of
obligation); (d) consensus (agreement about values); (e) structural
(geographical proximity); and (f) functional (exchange of support).
We propose that digital solidarity adds a new dimension to the
concepts of both associational solidarity and functional solidarity by

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Connecting families?
augmenting face-to-face and telephone communication, and enabling
the provision of expressive and, in some contexts, instrumental forms
of support. Our development of the concept of digital solidarity rests
on it being a form of communication that is instant and virtual (and,
after purchase of the required device, inexpensive). As such, it serves
to enhance intergenerational cohesion in ways that both complement
and supplement more traditional forms of communication that
require greater investments of time and eorts of coordination. Digital
communication can be more frequent, while still being perceived as
less intrusive than phone or face-to-face contact, and consequently
may be particularly valuable to help frail older parents stay in regular
contact with their adult children.
In this chapter, we focus on the role of communication technology
(CT) in associational solidarity. Although we propose that digital
solidarity is an important dimension of functional solidarity as well,
our data do not provide details on the type of content of the emails and
texts exchanged between mothers and their adult children necessary
to study this dimension.
The role of digital solidarity in older adults’ use of CT has been
demonstrated by the finding that older adults who use the internet are
most likely to do so as a means of engaging with family and friends to
socialize and exchange support (Thayer and Ray, 2006). This suggests
that understanding patterns of older mothers’ use of CT with their adult
children may provide valuable information that can be used to create
interventions designed to increase internet use by women in later life.
Such increased use would be beneficial across a wide array of contexts
beyond the family, including managing finances, healthcare, and
recreation, all of which contribute to older adults’ independence and
quality of life (Khosravi et al, 2016; Vuori and Holmlund-Rytkönen,
2005).
Despite the potential benefit of technology use by older adults, recent
national surveys have reported that only 64% of adults 65 and older use
the internet, compared with 87% of adults aged 50‒64 (Pew Research
Center, 2017). Such a pattern of adoption of new technologies by age
is not surprising, but it is troubling, considering that use of the internet
has the potential to play an increasingly important role in individuals’
social lives and well-being as they age. In particular, older adults who
adopt CT expand their opportunities for such interactions at a time
when their opportunities for in-person interactions may decline due
to changes in mobility and access to driving.
It is commonly believed that skill deficits and high costs account for
older adults’ lower use of CT, but some studies have found that low

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motivation and interest are the key factors that deter older adults from
using CT (Lee et al, 2011; Melenhorst et al, 2006). Given that most
CT use by older adults involves communication with family members,
we suggest that a good starting point for understanding these processes
is to compare older adopters and non-adopters who are especially
likely to be motivated to interact with family members – specifically,
older mothers with adult children. Studies have shown consistently
that older mothers are highly invested in their adult children and
have high rates of contact and exchanges of emotional support with
them (Suitor et al, 2015). Thus, older mothers provide an excellent
opportunity for understanding why some older individuals adopt CT
whereas others do not.
The data we use to address this question were collected as part of the
Longitudinal Study of Generations (LSOG) and the Within-Family
Dierences Study (WFDS), studies that provide extensive information
on mothers and their ospring, allowing us to examine the ways in
which the combination of demographic and socioemotional factors
shape older adults’ use of CT. Further, because the data for these two
studies were collected several years apart (WFDS in 2008; LSOG in
2016), we can also take cohort eects into consideration by examining
changes in the rates of emailing and text messaging as the availability
of smart phones and tablets became more widespread.
Explaining older mothers’ use of CT with their adult
children
Theoretical and empirical scholarship has emphasized the combination
of cohort membership, demographic characteristics, and socioemotional
factors in understanding associational solidarity (Treas and Gubernskaya,
2012; Ward et al, 2013). Given that we conceive of digital solidarity as a
new dimension of associational solidarity, we propose that the same set
of factors will play a role in digital solidarity as in traditional dimensions
of associational solidarity (Silverstein et al, 2012; Suitor et al, 2015).
However, as we will discuss below, in some cases we suggest that factors
that may shape traditional face-to-face and telephone contact may
reduce use of CT between mothers and their adult ospring.
Because the literature has shown that characteristics of parents and
children influence these processes, we will consider characteristics of
mothers and ospring separately. Given that we are studying which
mothers use CT with ospring and not whether they use CT with
particular ospring, we consider aggregate, rather than individual-level,
characteristics of adult children.
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Mothers’ characteristics
Cohort membership and older mothers’ CT use with adult children
One of the tenets of theories of intergenerational solidarity (Bengtson
and Roberts, 1991) and the life course (Elder, 1985; Riley et al, 1994;
Settersten, 2003) is the salient role of cohort membership. A cohort
refers to a group of individuals who share a defining characteristic
(typically people who experienced a common event in a selected
time period).
The history of the development of the communication technologies
we are investigating makes mothers’ cohort membership a crucial
characteristic to take into consideration when predicting which
mothers use CT to interact with their adult children. The mothers
who participated in the WFDS and LSOG represent cohorts that
dier markedly by the point at which CT – particularly emailing and
texting – became common. The mothers who participated in the
WFDS were born, on average, in the early 1930s, whereas the mothers
who participated in the LSOG were born between the mid-1940s and
mid-1950s. Although neither of these two groups of women were
exposed to communication technology prior to adulthood, CT became
widely available at earlier stage of lives of the LSOG cohort. Further,
the introduction of the iPhone in 2007 and iPad in 2010 increased the
ease of emailing and texting for mothers in the LSOG (interviewed in
2016) compared to mothers in the WFDS (interviewed in 2008), thus
further increasing the likelihood that LSOG mothers would use CT
with their adult ospring. These dierences in exposure to computer-
related technologies led us to hypothesize that members of the LSOG
cohort would be substantially more likely than members of the WFDS
cohort to report that they used CT to interact with their adult children.
Demographic characteristics of mothers
Education. Studies have shown that better educated mothers have less
contact with children, probably because friends rather than relatives
may be more important in the social networks of the more highly
educated (Fischer, 1982; Grundy and Shelton, 2001; Tomassini et al,
2004). However, a recent Pew study revealed that 65% of older adults
who have a college degree reported owning a smartphone compared
to only 27% of those with a high school diploma or below in 2016
(Anderson and Perrin, 2017). Further, occupations requiring higher
levels of education often require employees to adapt to technology use

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(Fairlie, 2004). Operational skills, openness to change, and supportive
learning environments are motivators for technology adoption among
older adults (Hill et al, 2008). These motivators may be more relevant
among families with higher educational backgrounds and occupations
requiring more education. Thus, we hypothesized that education would
be a positive predictor of mothers’ use of CT with adult children.
Age. Mothers’ need for support increases with age, which could be
expected to fuel greater contact (Suitor et al, 2015). However, studies
of technology have found that internet use decreases with age (Elliot
et al, 2014; Gell et al, 2015), suggesting that age may be negatively
associated with contact in the case of mothers’ use of CT with their
children. However, these studies did not dierentiate cohort eects
from age. In fact, older adults make up the fastest growing consumer
segment of the internet (Hart et al, 2008). This implies that age itself
is not a barrier to CT and that many older adults are eager to adopt
CT use (Neves et al, 2013). The dierence of CT use in age mainly
reflects the cohort eects of age on exposures to CT.
As a result of these competing arguments, we do not propose a specific
single hypothesis regarding mothers’ age, when health and cohort are
controlled for. Given the collinearity between cohort and age, it is
important to emphasize that the analytic approach we take allows us
to explore the role of age within two contiguous cohorts – in other
words, net of cohort eects.
Race. The preponderance of studies over the past two decades has shown
greater cohesion in non-White than White families (Kaufman and
Uhlenberg, 1998; Sechrist et al, 2007; Silverstein and Bengtson, 1997;
Suitor et al, 2015). However, this greater cohesion does not necessarily
translate into higher levels of contact and, in fact, has yielded mixed
findings in this regard (Ajrouch et al, 2001; Krause, 2006).
The literature on race and CT shows a much more consistent picture,
with much higher rates of internet usage among Whites compared
to other racial and ethnic groups (Gell et al, 2015). This may be due
to limited access to technology throughout the life course, as well as
educational and occupational dierences for low-income minority
populations (US Census Bureau, 2012). For example, a study of older
adults found that non-technology users were more likely to be low-
income and Black/Latino (Choi and DiNitto, 2013). Although the
socioeconomic digital divide is decreasing among younger generations,
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White youth are nevertheless still more likely to have greater breadth
and depth of experiences using technology than are their Black/
Latino counterparts (Warschauer and Matuchniak, 2010). Thus, we
hypothesized that Whites would be more likely to use CT with their
children than would non-Whites.
Marital status. Studies have found that older adults who are married
are more likely to use CT in general (Elliot et al, 2014; Gell et al,
2015). The literature on parent‒adult child contact does not provide
a consistent basis for arguing that older mothers’ use of CT with
their ospring will or will not conform to this pattern (Suitor et al,
2015). Thus, in the absence of clear findings, we oer a tentative
expectation that married mothers will be more likely to engage in CT
communication with their adult children than unmarried mothers.
Mothers’ health. Health is a characteristic of mothers that may
dierentially impact on traditional forms of communication and use
of CT. Typically, contact between mothers and adult children increases
when mothers experience health declines (Hank, 2007; Suitor et al,
2015). However, such health declines are likely to present obstacles to
mothers’ initial or continued use of CT to communicate with their
children. The broader literature on technology use has shown that older
adults in worse health are less likely to use CT (Elliot et al, 2014; Gell
et al, 2015). This is particularly the case when poor health reduces
vision and manual dexterity. Further, poor health may not only deter
people from learning CT but can also force users to stop using CT.
Thus, we propose that older mothers in poor health will be less likely
to use CT with their adult children.
Characteristics of adult children
Gender. Classic and contemporary theories of gender role development
argue that girls and women are socialized to be the kin-keepers in the
family (Coser, 1991; Gilligan, 1982; Rossi and Rossi, 1990). Consistent
with this argument, empirical research has found that mothers have
closer and more active ties with their daughters than their sons
throughout the life course in both childhood and adulthood (Suitor
et al, 2015). Additionally, although the gender divide in technology
usage often favours men for many forms of technology, women are
more likely than men to use technology to communicate (Kimbrough
et al, 2013). Thus, we propose that mothers with a greater proportion

165
of daughters in their families will be more likely to use CT with their
adult ospring.
Relationship quality. As individuals age, they increasingly perceive
that their life expectancy is finite and emphasize socioemotional
dimensions of their lives (Carstensen, 1993). This theory can be used
to suggest that older mothers would be likely to interact more with
adult children with whom they have closer relationships and interact
less with ospring with whom they have conflictual relationships. In
fact, empirical evidence regarding relationship quality and contact
between older mothers and their adult children is consistent with this
argument (Lawton et al, 1994; Silverstein et al, 1995). However, these
studies considered ‘traditional’ telephone and face-to-face contact. In
the case of CT, it is not clear how relationship quality would aect
contact – further, because closeness and tension are conceptually
dierent dimensions of relationship quality, rather than simply opposite
points on a continuum (Suitor et al, 2011, 2015), closeness and tension
may have dierent eects on CT use. We propose that mothers who
report higher average levels of emotional closeness to their ospring
will be more likely to use CT with their adult children. However,
we do not propose specific hypotheses regarding mothers’ average
tension with their ospring and CT use. On one hand, mothers who
have higher average tension may be less likely to use CT with their
children, consistent with a general trend to seek less contact with
these ospring. However, it is possible that mothers with high average
tension with their ospring are more likely to use CT as a way of
maintaining normative levels of contact without having to engage in
direct telephone or face-to-face contact.
Geographic proximity. Living further from adult children has been found
to substantially decrease face-to-face and phone contact (Hank, 2007;
Kalmijn, 2006). However, the broader literature on communication
has found that individuals separated by distance are more likely to use
communication technologies (Hampton and Wellman, 2003; Treas
and Gubernskaya, 2012). If use of CT is compensatory for geographic
distance, we expect mothers whose children, on average, live further
away, will use CT more than those whose ospring live closer.
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Connecting families?
Summary
In summary, we hypothesize that CT use will be more likely among
mothers born in the later cohort, as well as mothers who are better
educated, White, married, and in better health. Further, we hypothesize
that CT use will be more prevalent among mothers who have a greater
proportion of daughters, and who overall feel closer to, and live farther
away from, their adult children. We also explore the roles of mothers’
age and level of mother‒child tension on CT use with children.
Methods
Longitudinal Study of Generations (LSOG)
Data for this investigation derive from the LSOG, a multigenerational
and multi-panel study of 418 three-generation families that began
in 1971 and has continued for eight additional waves up to 2016.
The original sample of three-generation families was identified by
randomly selecting grandfathers from the membership of a large health
maintenance organization in Southern California. For the current
analysis, we rely on data from the 2016 survey which was administered
to 684 members of the third generation, for an eective follow-up rate
of 73.2%. The large majority (79.8%) of this sample responded via a
web survey with the remainder responding with a mail-back paper
survey. We selected 669 respondents who were 60‒72 years of age at
the time of the survey, corresponding to early and middle waves of
the baby-boom generation.
The analytic sample for this study included 241 older mothers
who reported on 708 adult children. The sample was restricted to
respondents (a) who were female, (b) who had at least two children. We
restricted the number of children to match the WFDS dataset, which
only included older adults who had at least two children. Using these
criteria, 366 respondents were excluded. Further, 57 respondents were
omitted because they were missing data on the dependent variable. We
compared people with missing data on the dependent variable with
those who did not have missing data on the dependent variable and
found no substantial demographic dierences, with one exception:
people with higher education were more likely to be missing data
for the dependent variable. Five cases were excluded using listwise
deletion to handle missing data on the independent variables, because
there were fewer than 1% missing on any variable in the analysis

167
(Allison, 2010). Mothers’ and children’s demographic characteristics
are presented in Table 9.1.
Within-Family Differences Study (WFDS)
The data for this study were collected as part of the WFDS, which
involved selecting a sample of mothers 65–75 years of age with at least
two living adult children from Massachusetts City and town lists (for a
more detailed description of the WFDS, see Suitor et al, 2013). The
first wave of interviews took place with 566 women between 2001
and 2003; the second wave of data collection was from 2008 to 2011.
At the second wave, 420 mothers were interviewed, representing 86%
of mothers who were living at T2.
The analytic sample for this study includes 325 older mothers who
reported on 1,196 adult children. Thirty-eight respondents were
omitted because they were missing data on the dependent variable.
We compared people with missing data on the dependent variable
with those who did not have missing data on the dependent variable
Table 9.1 Demographics on mothers and adult children
Data Source
LSOG WFDS
Mothers N = 241 N = 325
Using CT with children (in %)
Married (in %)
Ethnicity (in %)
Non-White
White
Education (in %)
Less than high school
High school graduate
Post-high school
Some college
College graduate
Graduate school
Age (SD)
Number of children (SD)
Subjective health (SD)
95.4
81.8
3.3
96.7
1.7
13.3
7.1
34.9
17.4
25.7
64.0 (2.7)
2.9 (1.3)
3.1 (0.8)
31.1
39.7
27.4
72.6
17.2
35.4
7.4
14.8
12.9
12.3
77.7 (3.1)
3.7 (1.6)
3.2 (1.1)
Adult children N = 708 N = 1,196
Characteristics (in %)
Daughters
Married
Closeness (SD)
Tension (SD)
Distance to mother (SD)
50.4
75.3
4.3 (1.5)
1.7 (1.1)
4.5 (2.0)
55.1
69.5
6.2 (1.2)
2.1 (1.5)
4.5 (1.8)
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168
Connecting families?
and found no substantial demographic dierence, with one exception:
people with lower education were more likely to be missing data on
the dependent variable. Fifty-seven cases were excluded using listwise
deletion to handle missing data on the independent variables, because
there were fewer than 5% missing on any variable in the analysis
(Allison, 2010). Mothers’ and children’s demographic characteristics
are presented in Table 9.1.
Combined data sets
In order to combine the two datasets, we transformed education,
subjective health, closeness, tension, and distance variables to make
them consistent across the datasets.
Measures
Dependent variables
In the LSOG data, older mothers’ use of CT with children was measured
by combining mothers’ email contact with a child and mothers’ contact
with a child via texting. Mothers’ email contact with a child was
measured by asking the respondents the following question regarding
each of their children: ‘During the past year, how often have you had
contact with this child by email?’ Similarly, mothers’ contact with a
child via texting was measured by asking: ‘During the past year, how
often have you had contact with this child by texting?’ We coded
mothers’ use of CT with children as 0 if mothers had not used email/
texting to communicate with any of their children in the past year or
1 if mothers had used email/texting with at least one child.
In the WFDS data, older mothers’ use of CT with children was measured
by asking the respondents the following question regarding each of
their children: ‘Have you emailed or instant messaged with your child
in the past year?’ We coded mothers’ use of CT with children as 0 if
mothers had not used CT to communicate with any of their children
in the past year or 1 if mothers had used CT with at least one child.
Results show that 31.1% of the 325 mothers used CT with at least one
adult child in the WFDS, whereas 95.4% of the 241 mothers used CT
with at least one adult child in the LSOG (see Table 9.1).

169
Mothers’ characteristics
Race was coded as 0 = non-White or 1= White. Family size was
measured using the number of living adult children in the family. We
controlled for family size because mothers’ opportunities to use CT
with children increase with the number of ospring. Marital status was
coded as 0 = not married or 1 = married. Age was a continuous variable
ranging from 60 to 72. Subjective health was recoded as 1= poor, 2 = fair,
3 = good, or 4 = excellent. Mothers’ educational attainment was recoded
as 1 = less than high school, 2 = high school or vocational school
graduate, 3 = post-high school training not college, 4 = some college
(1‒3 years), 5 = college graduate, 6 = graduate or professional school.
Aggregate children’s characteristics
Because we are concerned with mothers’ likelihood of using CT to
communicate with any of their adult children, rather than with a
particular child, we consider aggregate demographic and relational
characteristics of children in each family.
Gender was coded as 0 = son; 1 = daughter. Marital status was coded
as 0 = not married or 1 = married. Mothers’ closeness with children was
transformed to range from 1 (not at all close) to 6 (extremely close).
Mothers’ tension with children was transformed to range from 1 (no
tension at all) to 6 (a great deal). Mothers’ residential distance to children
was transformed into 1 = same house, 2 = same neighbourhood, 3 =
within an hour, 4 = 1‒2 hours, 5 = more than 2 hours away.
Children’s characteristics were transformed either into family averages
(percentages or means). In the case of variables that were dichotomous
prior to transformation (child’s gender and marital status) aggregate
percentages could range from 0 to 100; in the case of ordinal or interval
variables (closeness, tension, and distance), the mean of the aggregated
variables was used.
Analytic plan
We present the multivariate analyses using only the combined dataset
because separate analyses using the WFDS and the LSOG datasets
produced consistent results. In the combined data set, we created
a variable called data source (0 = LSOG, 1 = WFDS) to serve as an
indicator of cohort membership. We conducted the analyses using
logistic regression with STATA 14.
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Results
Table 9.2 presents the results of the logistic regression models predicting
older mothers’ use of CT with children. As shown in Model 1, mothers
in better health (OR = 1.93; p < 0.01), with higher education (OR
= 1.43; p < 0.01), and who were White (OR = 2.76; p < 0.01) were
more likely to use CT than their counterparts. Mothers were also
more likely to use CT with adult children when, on average, their
adult children lived further away (OR = 1.54; p < 0.05). Mothers who
were members of the earlier cohort (WFDS) were much less likely to
use CT with children than were mothers who were members of the
more recent cohort (LSOG) (OR = 0.08; p < 0.01).
Table 9.2 Logistic regression model predicting mothers’ use of CT with adult
children (N = 566 mothers who have at least 2 children; combined data:
age = 60-83)
Predictors Model 1 Model 2
OR 95% CI OR 95% CI
Data Source
Data source (0 = LSOG; 1=WFDS) 0.08** 0.02-0.31 0.75 0.07-8.11
Mother Characteristics
Race (0 = non-White; 1 = White) 2.76** 1.30-5.84 2.90** 1.36-6.22
Family size 1.05 0.90-1.23 1.04 0.89-1.22
Age 0.92 0.85-1.00 0.92 0.85-1.00
Married 1.04 0.61-1.78 1.04 0.60-1.78
Education 1.43** 1.22-1.68 1.43** 1.20-1.69
Self-rated health 1.93** 1.34-2.78 3.60** 1.82-7.14
Average Children Characteristics
Percentage of daughter 0.81 0.34-1.94 0.85 0.36-2.01
Percentage of married children 0.84 0.33-2.13 0.78 0.31-1.96
Tension with children 1.12 0.84-1.51 1.12 0.84-1.49
Emotional closeness with children 1.20 0.85-1.69 1.21 0.85-1.72
Distance to children 1.54* 1.09-2.17 1.56* 1.11-2.18
Interaction
Data source X Self-rated health 0.44* 0.20-0.97
Model Statistics
BIC 497.873 500.35
AIC 441.471 439.61
*p < .05. **p < .01.

171
Next, we conducted a series of interaction tests to assess whether the
associations between data source and older mothers’ use of CT were
moderated by other factors. As shown in Model 2, we found that the
interaction term of data source and health was statistically significant
(OR = 0.44; p < 0.05), indicating that mothers’ health played a more
important role in CT use among members of the more recent cohort
(LSOG) than the earlier cohort (WFDS). We further illuminate this
pattern in Figure 9.1, which shows that the slope of the eect of health
on mothers’ CT use is steeper in the LSOG than in the WFDS. There is
no statistically significant dierence in probability of using CT between
the two data-sets when mothers were in poor health. We will discuss
the implications of these findings in the discussion.
Figure 9.1 Interaction plot of data source and self-reported health
0
.2
.4
.6
.8
1
Pr(CT Use with Children)
Poor Fair Good Excellent
Mothers’ Self-Rated Health
LSOG
WFDS
Discussion
Our goals in this paper were threefold. Our first goal was to introduce
the concept of digital solidarity as an extension of the classic
intergenerational solidarity paradigm. Our second was to document
older mothers’ use of email and texting for communicating with their
adult children. Our final goal was to explore the roles of cohort,
demographic characteristics, and socioemotional factors in older
mothers’ use of digital communication. To address these questions,
we used data from the Longitudinal Study of Generations and the
Within-Family Dierences Study.
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172
Connecting families?
We proposed that digital solidarity is a new aspect of associational
solidarity with implications for functional solidarity, two key
components of the solidarity model (Bengtson and Roberts, 1991;
Silverstein and Bengtson, 1997), the conceptual framework that
has guided the study of intergenerational relations for nearly half a
century. We consider this an important extension because it expands
the applicability of solidarity as a conceptual tool into the twenty-
first century, when digital communication has increasingly become
a primary mode of contact and support provision for adults (Treas
and Gubernskaya, 2012). Evidence that digital solidarity represents a
unique construct was found in its distinctive relationship with social
advantage of various forms. The well-known digital divide, although
not desirable, has consequences for how families maintain contact, and
calls attention to the dierent strategies that older parents and adult
children use to stay in touch.
We found that use of CT to communicate with adult children was
shaped by cohort membership, with more recent cohorts of older
mothers being substantially more likely to use this technology with
their children – 95.4% ages 60‒72 in 2016 (LSOG) vs 31.1% of mothers
aged 73‒83 in 2008 (WFDS). This dierence was confirmed using
logistic regression analysis in which we controlled for other factors
that predicted mothers’ use of CT. In fact, mothers interviewed in
2008 (WFDS) had 92% less likelihood of using CT with children than
mothers who were interviewed in 2016 (LSOG) (OR = 0.08; p <
0.01). Although it would be tempting to attribute this pattern to age,
the analysis showed that age did not aect CT use beyond eects of
cohort membership. Thus, we suggest the pattern greatly reflects the
timing of the introduction of smartphones and tablets around 2010.
Cohort and mothers’ health interact to further shape CT use, with the
eect of cohort disappearing among mothers with poor health. This
suggests that despite CT development between 2008 and 2016, CT use
among older mothers in the worst health did not increase. Thus, poor
health presents a persistent barrier to the use of digital communication
precisely when older adults may need the most support from family.
Drawing from the literature on intergenerational relations, we
considered several demographic variables as potential predictors of
mothers’ CT use with adult children. Consistent with our hypotheses,
we found that education and race predicted mothers’ CT use; better
educated and White mothers were more likely to use CT with their
ospring than their counterparts who were less educated or were
non-White, respectively. Thus, these patterns also mirror those found

173
in studies of CT use outside of the intergenerational context (Elliot
et al, 2014; Gell et al, 2015). However, marital status and age did not.
Finally, we proposed that several socioemotional and relational
characteristics of children would shape their mothers’ use of CT.
Because we were concerned with whether mothers used CT with any
of their ospring, rather than which specific children mothers used
CT with, it was necessary to aggregate these characteristics. Contrary
to our hypotheses, neither the proportion of daughters nor average
parent‒child closeness or tension predict CT use. However, as expected,
mothers whose children, on average, lived further from them were
more likely to use CT to communicate. This suggests that CT can
potentially compensate for barriers to contact imposed by geographic
dispersion of the multigenerational family.
Taken together, we found that mothers’ characteristics played a larger
role in CT use than did aggregate characteristics of their ospring. We
find the absence of eects for aggregate demographic and relational
characteristics surprising, given that such factors have been found to
play a role in other aspects of patterns and consequences of mother-
child relations (Pillemer et al, 2017; Suitor et al, 2007). We speculate
that aggregate measures may not fully capture nuanced variation in
CT use at the relationship level.
Future directions
The present analyses provided new insights into patterns and predictors
of older mothers’ use of emailing and texting with their adult ospring.
However, we hope that many questions beyond the scope of this chapter
will be pursued in future research.
First, neither the LSOG nor WFDS collected data on CT in earlier
waves, thus limiting our ability to explore the trajectory of CT use by
older mothers. Such panel data we would allow us to further examine
cohort eects due to the introduction of smartphones and tablets.
Moreover, panel data would allow us to dierentiate between two
pathways of barriers to CT use: whether poor health acts as a barrier
to CT by stopping non-users from adopting CT or by forcing users
to quit using CT.
As noted above, children’s aggregate demographic characteristics and
relationship quality did not predict mothers’ CT use with them. Perhaps
these factors would prove more fruitful when studying which particular
children mothers engage in CT with. Given the important role of
adult children’s CT use in their mothers’ adoption of this technology,
knowing which children mothers are most likely to communicate with
Use of communication technology to maintain intergenerational contact

174
Connecting families?
using CT may shed light that could be used in developing interventions
designed to increase mothers’ use of CT in other contexts.
We also hope that future research will explore more fully the role
of race in CT. Given that older Black women are more likely to have
chronic conditions that would limit their mobility (Fuller-Thomson
et al, 2009), and thus their in-person interaction, adopting CT with
their ospring to facilitate meeting health care needs may be especially
important.
Finally, we focused only on mothers. However, older men who are
unmarried have been found to be at greater risk of social isolation and
unmet need for care than their female counterparts. Thus, priority
should be given to studying CT use of men with the goal of finding
ways to increase their access to and use of these technologies.
Implications
Lower use of CT among mothers who were in poorer health, non-
White, and less educated suggest disparities in the use of CT and
unequal distribution of its benefits. Beyond the interpersonal benefits
mothers may receive from using CT with adult children, learning
new technologies can enhance cognitive functioning and physical
well-being (Chan et al, 2016; Schulz et al, 2015). Given that our
findings suggest that CT may not be equally accessible, adjustment
should be made to the accessibility of technologies to older adults
(Neves et al, 2017). For example, although studies have shown that
older adults with some impairments, such as pain and diculties with
breathing, use CT to improve their communication and reduce health-
related tasks (Gell et al, 2015), our study suggests that those in poor
health have the lowest rate of CT usage, and this rate did not increase
following the introduction of smartphones and tablets. This suggests
that intervention studies are needed to understand how redesigning
devices and applications may make CT more accessible to older adults
whose health prevents them from adopting current CT technologies.
Such increased access could enhance their social connection to family
members, allow greater monitoring of vulnerabilities by their ospring,
and give them opportunities for securing digital assistance with their
health conditions.

175
In brief
1. In this paper, we extend the study of associational solidarity by considering
older mothers’ use of technology to maintain contact with adult children
– what we refer to as ‘digital solidarity’. Digital solidarity may be the key
to facilitating older adults’ CT use and to increase their wellbeing.
2. The introduction of smartphones and tablets around 2010 may
have dramatically increased the proportion of older mothers using
communication technology (CT) with their adult children from 31.1% in
2009 to 95.4% in 2016.
3. Mothers who are least likely to use CT with their adult children are those
who are in an older cohort, have worse health, less education, live closer to
children, and are non-White. This suggests that a select group of families
may benefit from CT.
4. Our study suggests that women in poor health have the lowest rate of CT
use, and this rate did not change after the introduction of smartphones
and tablets.
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