Older adults’ technology use and its association with
health and depressive symptoms: findings from the
2011 National Health and Aging Trends Study
Jeehoon Kim, PhD, MSWa,
*, Hee Yun Lee, PhDb
, Cho Rong Won, MSWb
,
Tina Barr, PhDc
, Joseph R. Merighi, PhDd
a
Department of Sociology, Social Work, and Criminology, Idaho State University, Pocatello, ID b
School of Social Work, University of Alabama, Tuscaloosa, AL c
Department of Social Work, University of North Carolina at Pembroke, Pembroke, NC d
School of Social Work, University of Minnesota, Twin Cities, St. Paul, MN
ABSTRACT
Background: Information and communication technology (ICT) provides older
adults with access to information and resources that benefit their health.
Purpose: To explore ICT use among older adults and examine the influence of
information technology (IT), communication technology (CT), or ICT use on older
adults’ self-rated health status and depressive symptoms.
Method: A sample of community-dwelling Medicare beneficiaries aged 65 and
older in the United States (N = 4,976) from the 2011 National Health and Aging
Trends Study.
Findings: Older adults who embraced ICT and used this technology for a variety of
purposes were more likely to report better health status, and were less likely to
experience major depressive symptoms than nonusers.
Discussion: In accordance with the Health Information Technology for Economic
and Clinical Health Act, nursing professional can play an important role by
responding to older adults’ diverse technology preferences and effectively incorporating them into nursing practice.
Cite this article: Kim, J., Lee, H.Y., Won, C.R., Barr, T., & Merighi, J.R. (2020, xxx). Older adults’ technology
use and its association with health and depressive symptoms: findings from the 2011 National Health
and Aging Trends Study. Nurs Outlook, 00(00), 1 13. https://doi.org/10.1016/j.outlook.2020.05.001.
ARTICLE INFO
Article history:
Received 22 October 2019
Received in revised form
27 April 2020
Accepted 3 May 2020
Keywords:
Communication technology
Depressive symptoms
Information technology
Older adults
Self-rated health status
Introduction
Information and communication technology (ICT)
refers to the use of various computer-and mobile
device-based technologies (e.g., Internet, email, text
messages, Twitter, and other technologies) in order to
communicate, access, and store information between
individuals and organizations (Blaschke, Freddolino, &
Mullen, 2009; Elliot, Mooney, Douthit, & Lynch, 2014;
Selwyn, Gorard, Furlong, & Madden, 2003).
Although older adults lag behind younger populations in Internet use, studies have found regular use of
ICT in this population (Choi & Dinitto, 2013b; Gell,
Rosenberg, Demiris, Lacroix, & Patel, 2015; Hilt & Lipschultz, 2004). Also, the rate had increased from 1% in
*Corresponding author: Jeehoon Kim, Department of Sociology, Social Work, and Criminology, Idaho State University, 921 S. 8th Ave
Stop 8114, Pocatello, ID 83209.
E-mail address: kimjeeh@isu.edu (J. Kim).
0029-6554/$ -see front matter 2020 Elsevier Inc. All rights reserved.
https://doi.org/10.1016/j.outlook.2020.05.001
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2000 to 67% in 2016 according to the data from the Pew
Internet Research Projects (Anderson & Perrin, 2017).
Specifically, 42% of older adults had smartphones and
34% used social networking sites in 2016 (Anderson &
Perrin, 2017). In addition, one study reported that older
adults exchanged an average of 64 text messages per
month (Nielsen, 2011).
Use of ICT by older adults varies according to sociodemographic factors. Older adults who are ICT users are
more likely to be non-Latino white (Choi & Dinitto
2013a, 2013b; Werner, Carlson, Jordan-Marsh, & Clark,
2011), younger, married, and have a higher level of education (Choi & Dinitto, 2013b; Gell et al., 2015; Selwyn
et al., 2003). Selwyn et al. (2003) found that older adults
in England and Wales were more likely to use ICT if
they had access to technology in home or family settings. Also, research has indicated that older adults are
most likely to use ICT in order to stay in touch with
family and friends (Carpenter & Buday, 2007; Vroman,
Arthanat, & Lysack, 2015). On the other hand, the major
reasons older adults reported not using ICT were their
disinterest or lack of motivation (Carpenter & Buday,
2007; Selwyn et al., 2003). Some evidence has shown
that older adults’ use of ICT was negatively associated
with limited mobility (Gell et al., 2015; Wright & Hill,
2009), memory, and visual acuity (Gell et al., 2015).
Although older adults are increasing their use of ICT,
there is limited research on the relationship between
their ICT use and health status. A number of studies
have shown that older adults used the Internet to
access and communicate about health information
(Choi & Dinitto, 2013b; Crabb, Rafie, & Weingardt, 2012;
Gell et al., 2015). For example, the Pew Research Center
found that 27% of adults aged 65 and older used the
Internet to search for health and medical information
(Zickuhr & Madden, 2012). Some evidence shows that
online health information seeking behavior is linked
to better health status (Cotten & Gupta, 2004), but the
association between specific types or purpose of Internet use and their health status has not been examined.
Research also shows mixed results regarding the
impact of ICT use on the mental health of older adults.
Some findings indicate that ICT use may increase protective factors for older adults’ mental health, such as
reduced loneliness and increased social contacts. For
example, studies have reported that going online was
negatively associated with loneliness (Cotten, Anderson, & McCullough, 2013; Sum, Mathews, Hughes, &
Campbell, 2008), and reduced the probability of a
depression state (Cotten, Ford, Ford, & Hale, 2014).
Also, as one of the common communication technologies, video chat was associated with lower risk of
developing depression (Teo, Markwardt, & Hinton,
2018). In a study of Taiwanese nursing home residents,
depressive symptoms were decreased after a videoconference intervention that taught them how to use
the Internet and to arrange videoconferencing
appointments with their distant relatives (Tsai & Tsai,
2011). Shapira, Barak, and Gal (2007) also found an
improvement in older adults’ well-being, including
feelings of depression and loneliness, in their ICT
intervention study. They reasoned that learning how
to use and communicate through ICT led to a psychological process of personal empowerment. Additionally, Fang, Chau, Wong, Fung, and Woo (2017)
examined an association between ICT use and psychological well-being among adults aged 50 and above in
Hong Kong. They found that ICT use was positively
associated with psychological well-being only among
those aged 75 and over, and this relationship was further facilitated through contact with family members
(Fang et al., 2017).
In contrast, some research on the relationship
between ICT and depression in older adults reported
little or no impact. Slegers, van Boxte, and Jolles (2008)
found no evidence that computer training and Internet
usage had either a negative or positive influence on the
well-being, mood, or social network of communitydwelling older healthy individuals in their 1-year follow-up intervention study. In White and colleagues’
(2002) intervention study, older adults in nursing
homes or congregate housing who were trained to use
the Internet reported less loneliness, less depression,
and more confidence compared to the control group,
but the changes were not statistically significant.
Moreover, Billipp (2001) found that interactive computer use with the right training conditions increased
self-esteem and reduced depression in older adults,
but the effect of interactions between the older adults
and the computer-skills trainers was not factored into
the analysis. Elliot and colleagues (2014) also found
that ICT use was not associated with depression in
older adults. As evidenced by the mixed outcomes
in the literature, research has not yet revealed a consistent relationship between specific types of ICT
uses and mental health status in the older adult
population.
Recently, there has been an increased reliance on
ICT in the health care delivery system. Nurses and
their health care colleagues have incorporated these
technologies to improve patient care, reduce costs,
and increase the efficiency of their practice
(Fagerstrom, Tuvesson, Axelsson, & Nilsson, 2016 € ).
However, nurses have not uniformly embraced ICT in
their practice (Fagerstrom et al., 2016 € ; Lupianez-Villa- ~
nueva, Hardey, Torrent, & Ficapal, 2011; While &
Dewsbury, 2011) despite some patients reporting that
ICT afforded them more frequent and personalized
treatment (Pols, 2010). As the number of older adults
requiring health care services continues to increase,
coupled with evidence that this population is expanding its use of ICT, it will be beneficial for nurses to
understand how ICT can be employed to help older
adults access and better utilize health care services.
Purpose
Given the unclear picture from existing data regarding
the relationship between ICT use and health status
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2 Nurs Outlook 00 (2020) 1 1 3
and depression in older adults, additional evidence is
needed to guide health and mental health care professionals who work with this population. A more thorough understanding of the type, level, purpose, and
impact of technology use among older adults is needed
to better inform health-directed interventions for this
growing population. This study, therefore, investigated (a) the level and purpose of ICT use among older
adults and (b) the association between levels of ICT
use and older adults’ self-rated health status and
depressive symptoms.
Methods
Data and Sample
This cross-sectional study used data from the first
round of the National Health and Aging Trends Study
(NHATS). NHATS is a nationally representative panel
study of Medicare beneficiaries aged 65 and older
residing in the contiguous United States. The data
were sampled from the Medicare enrollment database
as of September 30, 2010, using a stratified three-stage
sample design and an oversampling of the oldest age
group and Black, non-Latino individuals. The first
round of NHATS was conducted in 2011 with a 71%
response rate and 8,077 completed cases; these participants were re-interviewed annually in 2012, 2013, and
2014 (Montaquila, Freedman, Edwards, & Kasper,
2012). The data were publicly available and obtained
from the NHATS research team after signing a user
agreement.
This study used a community-based sample
(N = 7,197), which excluded study respondents living
in nursing homes (N = 468) or other residential care
facilities (N = 412). In accordance with our study aims,
we included only the sample respondents who were
able to access ICT at home or elsewhere and knew
how to use computer. From 5,809 older ICT users, we
additionally excluded cases in which a proxy respondent completed the survey (N = 258), as some measures in the current study should be self-reported, and
cases for missing variables (N = 575). Therefore, the
current study sample was comprised of 4,976 older
adults residing in the community.
Measures
Outcome Variables
Health Status. Self-reported general health status was
measured on a five-point Likert scale (1 = poor, 2 = fair,
3 = good, 4 = very good, and 5 = excellent).
Depressive Symptoms. Levels of depressive symptoms
were measured by the Patient Health Questionnaire 2
(PHQ-2; Kroenke, Spitzer, & Williams, 2003). The PHQ-2
asks about frequencies of two depressive symptoms,
depressed mood and anhedonia, and has been validated as a brief screening tool for detecting major
depression in older adults using a nationally representative sample (Li, Friedman, Conwell, & Fiscella, 2007).
In the NHATS surveys, the respondents were asked to
evaluate how often, in the past month, (a) they had little interest or pleasure in doing things; and (b) they felt
down, depressed, or hopeless. Both questions were
measured on a four-point scale from 0 (not at all) to 3
(nearly every day), and these questions were to summed
for a total score ranging from 0 to 6. Kroenke et al.
(2003) used a PHQ-2 total score of 3 to determine a positive screen for major depressive symptoms. We
adopted this cut point to categorize the older adult
respondents into three depressive symptom groups:
no depressive symptoms (0), mild depressive symptoms (1 or 2), and major depressive symptoms (3 6).
The no depressive symptoms group was used as a
base outcome in the multinomial logistic regression
model.
Explanatory Variables
Information and Communication Technology Use. In the
NHATS surveys, the respondents were asked whether
and how often, in the past month, they ever sent messages by email or text (communication technology,
CT), and if they had ever used the Internet or gone
online for any reason other than emailing or texting
(information technology, IT). The respondents provided details about their IT use, including if they used
the Internet for completing certain instrumental activities of daily living (i.e., shopping, paying bills/banking,
or ordering prescriptions) and seeking health-related
information (i.e., contacting medical providers, handling health insurance matters, or getting information
about their health conditions). Respondents who used
both CT and IT were identified as ICT users. A fourgroup classification was used in the current study: ICT
users, CT only users, IT only users, and nonusers. The
nonuser group was used as a referent category in multivariate analyses.
Control Variables
Health Conditions. We measured three aspects of
health conditions: cognitive function, number of
chronic diseases, and physical capacity. Cognitive
function, which consisted of self-reported memory
status at the time of interview, was measured on a
five-point scale (1 = poor, 2 = fair, 3 = good, 4 = very
good, and 5 = excellent). Number of chronic diseases
was measured by summing nine possible chronic
health conditions for which each individual had ever
been diagnosed by a doctor (range = 0 5, where 5
equals five to nine chronic conditions). Physical capacity was measured at the lower and higher level of functional capacities. The respondents were asked in the
past month, (a) whether they had difficulty with physical movements and (b) if they responded affirmatively,
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whether it was without help from another person or
without any assistive devices (e.g., cane, walker). Physical movements included six pairs of less challenging
and more challenging tasks (e.g., walking three and six
blocks, going up 10 and 20 stairs). We used the twodimensional physical capacity measures developed
and validated by Freedman et al. (2011): (a) inability to
do any less challenging task and (b) ability to do all
more challenging tasks (1 = yes for each).
Participation in Activities. We measured respondents’
social network size as well as participation in social
and physical activities. Social network size was measured by the number of people the respondents talked
with most often about important things in the past
year (range = 0 5). For participation in social activities,
we measured in the past month, (a) whether respondents visited family or friends and (b) whether respondents attended clubs, classes, or organized activities
(1 = yes for participation in each activity). Participation
in physical activity was measured by combining
responses for the following items: (a) whether the
respondent had ever walked for exercise in the past
month and (b) whether the respondent had spent time
on vigorous activities (e.g., working out) in the past
month. Physical activity participation was classified
into three groups: inactive, walking for exercise only,
and participating in vigorous activities alone or with
walking. The inactive group was used as a referent category in the multivariate analyses.
Sociodemographic Variables. We included age, gender
(1 = male), marital status (1 = married or partnered),
being employed for payment (1 = yes), race/ethnicity,
level of education, and income. Race/ethnicity was
categorized into four groups: non-Latino white, African American, Latino, and other races. The Latino
group was used as a referent category in the multivariate analyses. Education was measured on a five-point
scale (1 = less than 12th grade, 2 = high school diploma
or GED, 3 = Associate’s degree or some college, 4 = Bachelor’s degree, and 5 = graduate degree). Total income
was reported either as collected amount of possible
income sources and assets (56%), or as one of five
bracketed ranges (13%). Montaquila, Freedman, and
Kasper (2012) provided five imputed values of a total
annual income amount for missing cases and bracketed responses, using a cyclical n-partition hot deck
treatment. Due to high skewness, we used log-transformed values of the five imputed annual income variables in the multivariate analyses.
Statistical Analysis
Since the NHATS data were obtained using a complex
survey design, we used weighted statistics with
Table 1 – Weighted Means (SE) or Percentages for Outcome and Control Variables by ICT User Groups
M (SE) or % ICT CT Only IT Only Nonusers Total p Value*
Outcome variables
Health Status (range: 1 5) 3.7 (0.03) 3.3 (0.08) 3.3 (0.06) 3.1 (0.03) 3.4 (0.02) < .001
Depression: no symptoms 67.2 61.6 55.1 52.0 59.5 < .001
Mild depression 25.8 24.1 31.1 30.3 28.1
Major depression 7.0 14.3 13.8 17.7 12.5
Control variables
Age (range: 65 100) 71.8 (0.12) 73.8 (0.39) 72.6 (0.30) 75.9 (0.16) 73.7 (0.09) < .001
Married/partnered (1 = Yes) 70.9 66.3 68.9 55.7 64.1 < .001
Gender (1 = Male) 47.3 42.2 53.7 42.9 45.7 < .001
Race and ethnicity: Latino 3.0 4.3 5.1 9.5 6.0 < .001
White, non-Latino 90.9 83.6 85.1 75.4 83.5
Black, non-Latino 3.8 6.6 7.1 11.8 7.6
Others, non-Latino 2.3 5.5 2.7 3.3 2.9
Education (range: 1 5) 3.4 (.04) 2.8 (.08) 2.8 (.06) 2.2 (.03) 2.8 (.04) < .001
Paid work (1 = Yes) 30.9 24.6 23.4 12.7 22.3 < .001
Cognitive function (1 5) 3.7 (.02) 3.5 (.06) 3.4 (.04) 3.2 (.02) 3.5 (.01) < .001
Chronic diseases (0 5) 2.0 (.03) 2.3 (.10) 2.4 (.07) 2.5 (.03) 2.3 (.02) < .001
Unable to do any less challenging task (1 = Yes) 17.0 28.7 24.7 42.8 29.1 < .001
Able to do all more challenging tasks (1 = Yes) 49.5 38.7 38.0 28.4 39.1 < .001
Visiting family or friends (1 = Yes) 93.9 92.1 89.5 85.7 90.0 < .001
Attending clubs or organized activities (1 = Yes) 54.1 39.1 36.2 27.0 40.4 < .001
Physical activity participation: inactive group 17.5 30.2 24.7 37.3 27.1 < .001
Walking only for exercise 24.4 25.7 28.7 33.9 28.8
Vigorous activity 58.1 44.0 46.6 28.8 44.1
Size of social network (range: 0 5) 2.2 (.05) 2.0 (.09) 1.9 (.07) 1.8 (.04) 2.0 (.04) < .001
Unweighted observations n = 1,802 n = 254 n = 388 n = 2,532 n = 4,976
Notes. M, mean; SE, standard errors.
* ICT user groups differences were examined by F statistic for continuous variables and by adjusted Wald F test for categorical variables, weighted estimates.
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4 Nurs Outlook 00 (2020) 1 1 3
analytic sample weights for all data analyses. Using
Stata 13.0 (StataCorp, 2013), we also conducted Taylor
series linearization method and subpopulation analyses to compute correct variance and standard errors
(SE). The adjusted Wald F-statistics estimated the differences in ICT user groups in sample characteristics
(Table 1) and purposes of ICT use (Table 2). Using mi
estimate command, we estimated model F tests (equal
fraction of missing information, FMI) from five data
sets (i.e., each data set included a different imputed
income variable from five imputed values, and other
variables remained the same), and adjusted coefficients and SEs for the variability between imputations
in multiple regression analysis (Table 3) and multinomial logistic regression analysis (Table 4). Confidence
intervals (CIs) were set at 95%.
Findings
Description of Sample
Table 1 provides descriptive statistics of the study
sample. Respondents’ age ranged from 65 to 100, with
a mean of 73.7 years. The majority (83.5%) of respondents were white, nearly two-thirds (64.1%) were married or partnered, and just over half (54.3%) were
women. The respondents’ average level of education
was an associate degree, and less than a quarter
(22.3%) were employed for payment.
Regarding their health conditions, respondents
reported an average of two or more chronic diseases,
and the average self-reported cognitive function (i.e.,
memory status) was good to very good (M = 3.5,
SE = 0.01). On average, the sample respondents also
reported their health status as good to very good
(M = 3.4, SE = 0.02). Approximately 29% of respondents
were not able to do any physically less challenging
task presented, and 39.1% were able to do all of more
physically challenging tasks. About 40% of respondents had experienced depressive symptoms either as
major depression (12.5%) or mild depression (28.1%).
Ninety percent of the respondents reported visiting
family and friends, whereas less than half (40.4%) had
attended clubs, classes, or organized activities within
the past month. The respondents reported talking
with two persons, on average, about important aspects
of their lives in the past year (i.e., a size of social network). Regarding physical activity, in the month prior
to taking the survey, 44.1% of respondents had
engaged in vigorous activity and 28.8% had walked
only for exercise.
Level of ICT Use
In addition to providing the overall characteristics of
the sample, Table 1 presents differences in covariates
of ICT use by the four ICT user groups: ICT users, IT
only users, CT only users, and nonusers. Approximately 50% of respondents reported using digital technology in the past month; 36.2% of older adults used
ICT, 5.1% used only CT, and 7.8% used only IT. Overall,
ICT users were identified as being younger, white,
married or partnered, female, and a paid employee,
having a higher educational level, a larger social network size and good overall self-reported health, and
actively engaged in social or physical activities as compared to CT only users, IT only users, and nonusers (all
at p < .001).
While non-Latino whites were more likely to use the
Internet for ICT, the respondents of other races/ethnicities were disproportionately represented in the CT
Table 2 – Older Adults’ Information and Communication Technology Use for Specific Purposes
Information Technology Use ICT IT Only Total p Value*
n = 1,802 n = 388 n = 2,190
IT use for personal
tasks (%)
Shop for groceries or personal items (1 = yes) 35.4 13.8 32.0 < .001
Pay bills or do banking (1 = yes) 49.3 17.8 44.2 < .001
Order or refill prescriptions (1 = yes) 19.9 5.5 17.6 < .001
IT use for health
related information (%)
Contact any of medical providers (1 = yes) 17.9 5.5 15.9 < .001
Handle Medicare or health insurance matters (1 = yes) 13.6 2.3 11.8 < .001
Get information about health conditions (1 = yes) 40.1 15.4 36.2 < .001
Communication Technology Use ICT CT Only Total p Value*
n = 1,802 n = 254 n = 2.056
Rarely 15.1 37.9 17.4 <.001
Frequency of sending messages by email or texting (%) Some days 28.4 35.6 29.1
Most days 56.5 26.5 53.5
Notes. n, unweighted observations.
* ICT user groups differences were examined by adjusted Wald F test, weighted estimates.
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only user group, and African Americans and Latinos
were overrepresented among nonusers. Men were less
likely to be ICT users than women, but were overrepresented among IT only users. Women were disproportionately represented among CT only users.
Nonusers (17.7%) were more than twice as likely as
ICT users (7%) to have major depressive symptoms.
Older adults with mild depressive symptoms were
overrepresented in the IT only users (31.1%) and nonusers (30.3%). Nonusers were more than twice as likely
to not socialize with family and friends compared to
ICT users (14.3% vs. 6%). About 54% of ICT users
attended clubs or organized activities compared to
only 27% of nonusers. Nonusers (37.4%) and CT only
users (30.2%) were more likely to be physically inactive
than other groups. Older adults who walked only for
exercise were overrepresented among the IT only
users.
Purpose of ICT Use
As seen in Table 2, older adults who used IT were more
likely to use the Internet for the personal tasks of paying bills or banking (44.2%) and shopping for groceries
or personal items (32%) and to seek information about
health conditions (36.2%). More than half (53.5%) of CT
users texted or emailed on “most” days, while 29.1%
used CT on “some” days in the past month. Older ICT
users were actively using technology for various purposes than IT or CT only users.
Self-reported Health Status
Table 3 presents the estimated coefficients and standard errors predicting self-reported health status of
older adults from multiple regression analysis after
controlling other covariates. The model explained
44.4% of the variance in self-reported health status of
older adults. As expected, level of educational attainment (b = .04, p < .001) and annual income (b = .03, p <
.05) were positively associated with self-reported
health status. Being non-Latino white (b = .09, p < .001)
and a paid employee (b = .03, p < .01) as well as
advanced age (b = .07, p < .001) were also associated
with better health status. Older men were more likely
to report worse health status than older women
(b = -.10, p < .001).
Overall, all variables from the health conditions
domain were highly associated with better selfreported health status: higher cognitive function
(b = .20, p < .001), fewer number of chronic diseases
(b = -.29, p < .001), and better physical capacity at the
lower end (b = -.20, p < .001) and higher end (b = .09, p
< .001). Participation in social or physical activity was
also associated with better health status: visiting family or friends (b = .03, p < .05), attending clubs or organized group activities (b = .05, p < .001), and regularly
engaging in vigorous physical activity (b = .08, p < .001)
in the past month. Among the ICT use groups (i.e., IT
use, CT use, and ICT use), ICT users were more likely
to report better health status than nonusers (b = .05,
p < .01).
Depressive Symptoms
Table 4 presents odds ratios (ORs) to predict levels of
depressive symptoms of older adults from multinomial logistic regression analysis after controlling other
covariates. Among sociodemographic variables,
advanced age (OR = 0.98, CI: 0.97 0.99; OR = 0.96, CI:
0.94 0.97), being married or partnered (OR = 0.84, CI:
0.72 0.98; OR = 0.69, CI: 0.54 0.88), and level of educational attainment (OR = 0.91, CI: 0.84 0.98; OR = 0.89,
CI: 0.81 0.97) were associated with a decreased likelihood of experiencing mild and major depressive
symptoms, respectively. Cognitive function (OR = 0.72,
CI: 0.66 0.78; OR = 0.60, CI: 0.54 068) was also associated with a decreased likelihood of experiencing mild
and major depressive symptoms, whereas having
more chronic diseases (OR = 1.18, CI: 1.11 1.25;
Table 3 – Multiple Regression Analysis of Selfreported Health Status among Older Adults,
Weighted Estimates (N = 4,976)
Variable B (SE) b
ICT use groups
Nonusers (Reference)
IT use only -.01 (.06) -.00
CT use only -.01 (.07) -.00
ICT use .13 (.04)** .05
Age .01 (.00)*** .07
Race and ethnicity
Latino (Reference)
White, Non-Latino .25 (.06)*** .09
Black, Non-Latino .08 (.07) .02
Other, Non-Latino .07 (.09) .01
Married/partnered (1 = yes) .04 (.03) .02
Gender (1 = male) -.22 (.03)*** -.10
Education .07 (.01)*** .04
Log of annual income .02 (.01)* .03
Paid work (1 = yes) .09 (.03)** .03
Size of social network .01 (.01) .01
Cognitive function .23 (.02)*** .20
Number of chronic diseases -.23 (.01)*** -.29
Unable to do any less challenging
task (1 = yes)
-.47 (.03)*** -.20
Able to do all more challenging
tasks (1 = yes)
.23 (.03)*** .09
Visiting family or friends (1 = yes) .11 (.05)* .03
Attending clubs or organized activities (1 =yes)
.12 (.03)*** .05
Physical activity participation
Inactive group (Reference)
Walking only for exercise .03 (.03) .01
Vigorous physical activity .18 (.04)*** .08
Constant 1.47 (.21)***
Equal FMI F statistic F(20, 54) = 280.03***
R2 .444
Notes. CT, communication technology; FMI, fraction of missing
information; ICT, information and communication technology; IT,
information technology; SE, standard errors.
*p < .05. **p < .01. ***p < .001.
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6 Nurs Outlook 00 (2020) 1 1 3
OR = 1.24, CI: 1.16 1.33) and worse physical capacity at
the lower end (OR = 1.34, CI: 1.08 1.68; OR = 2.03, CI:
1.54 2.68) were associated with an increased likelihood of experiencing both mild and major depressive
symptoms. Attending clubs or organized activities in
the past month (OR = 0.44, CI: 0.65 0.92; OR = 0.54, CI:
0.42 0.69) was also associated with a decreased likelihood of experiencing both mild and major depressive
symptoms.
Some covariates were differently associated with
levels of depressive symptoms. For example, size of
social network (OR = 1.09, CI: 1.03 1.17) and a high
level of physical capacity (OR = 0.59, CI: 0.48 0.73)
were associated with a likelihood of experiencing mild
depressive symptoms. However, being male (OR = 1.34,
CI: 1.03 1.75), a paid employee (OR = 0.65, CI:
0.47 0.89), walking for exercise (OR = 0.61, CI:
0.47 0.79), and engaging in vigorous physical activity
(OR = 0.50, CI: 0.38 0.65) were associated with a likelihood of experiencing major depressive symptoms.
Among three types of ICT use groups, ICT users
(OR = 0.7, CI: 0.56 0.91) were less likely to experience
major depressive symptoms than nonusers.
Discussion
This study explored differences in the levels and purposes of ICT use among older adults and examined the
influence of different levels of ICT use (i.e., ICT, IT only,
and CT only) on respondents’ self-reported health status and levels of depressive symptoms. Our findings
revealed that 49.1% of respondents used ICT, IT, or CT.
Of note, the sample included only older adults who
were able to access ICT at home or elsewhere and knew
how to use computer. Research examining older adults’
use of technology using the same data from the 2011
NHATS (Elliot et al., 2014; Gell et al., 2015) did not apply
this inclusion criterion, which explains the higher ICT
use level in this study. However, a recent report from
the Pew Research Center revealed that 67% of adults 65
and older in the United States reported going online in
2016 (Anderson & Perrin, 2017). This finding, which
points to an upward trend in Internet use among older
adults, is likely to continue to increase over time, and it
lends support for utilizing ICT to better manage older
adults’ physical and mental health.
Table 4 – Multinomial Logistic Regression Predicting Levels of Depressive Symptoms among Older Adults,
Weighted Estimates (N = 4,976)
Mild Depressive Symptoms Group * Major Depressive Symptoms Group*
Variable OR 95% CI OR 95% CI
ICT use groups
Nonusers (Reference) 1 1
IT use only 1.20 0.90 1.60 1.10 0.73 1.65
CT use only 0.85 0.58 1.25 1.03 0.64 1.68
ICT use 1.05 0.87 1.26 0.7** 0.56 0.91
Age 0.98*** 0.97 0.99 0.96*** 0.94 0.97
Race and ethnicity
Latino (Reference) 1 1
White, non-Latino 1.21 0.86 1.71 0.87 0.54 1.39
Black, non-Latino 1.28 0.91 1.79 0.87 0.53 1.43
Other, non-Latino 1.09 0.65 1.82 0.99 0.48 2.08
Married/partnered (1 = yes) 0.84* 0.72 0.98 0.69** 0.54 0.88
Gender (1 = male) 1.01 0.85 1.22 1.34* 1.03 1.75
Education 0.91* 0.84 0.98 0.89* 0.81 0.97
Log of annual income 0.99 0.93 1.07 1.01 0.94 1.09
Paid work (1 = yes) 0.84 0.68 1.03 0.65** 0.47 0.89
Size of social network 1.09** 1.03 1.17 1.08 0.99 1.17
Cognitive function 0.72*** 0.66 0.78 0.60*** 0.54 0.68
Number of chronic diseases 1.18*** 1.11 1.25 1.24*** 1.16 1.33
Unable to do any less challenging task (1 = yes) 1.34* 1.08 1.68 2.03*** 1.54 2.68
Able to do all more challenging tasks (1 = yes) 0.59*** 0.48 0.73 0.78 0.57 1.07
Visiting family or friends (1 = yes) 1.06 0.85 1.33 0.85 0.62 1.15
Attending clubs or organized activities (1 = yes) 0.77** 0.65 0.92 0.54*** 0.42 0.69
Physical activity participation
Inactive group (Reference) 1 1
Walking only for exercise 0.87 0.70 1.10 0.61*** 0.47 0.79
Vigorous physical activity 0.84 0.67 1.04 0.50*** 0.38 0.65
Equal FMI F statistic F(42, 53.5) = 36.15***
Notes. CI, confidence intervals; ; FMI, fraction of missing information; OR, odds ratio.
* Base outcome = no depressive symptoms group.*p < .05. **p < .01. ***p < .001.
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Nurs Outlook 00 (2020) 1 1 3 7
The current study corroborates prior research findings that older adults’ technology use is significantly
associated with race/ethnicity, marital status, gender,
employment status, and social and physical activity.
For example, non-Latino white older adults were more
likely to use some forms of ICT compared to their Black
and Latino counterparts (Choi & Dinitto, 2013a, 2013b;
Werner et al., 2011). Whereas the majority (83.5%) of
Medicare beneficiaries were non-Latino white in our
sample, coupled with a study inclusion criterion that
required some forms of access to ICT, previous studies
using samples with a majority of racial and ethnic
minority respondents have reported low utilization of
ICT among older adults. For example, Werner et al.
(2011) reported rates of computer use for email (23%)
and general Internet use (26%) in a sample that was
62.6% non-white. Similarly, Choi and Dinitto (2013a)
reported a 17% ICT use rate in a sample of low-income
older adults in which 57.7% identified as non-white,
racial and ethnic minorities. Such racial and ethnic
disparity in ICT use is noteworthy.
The finding that married or partnered older adults
were more likely to use ICT than single older adults is
in line with other studies (Choi & Dinitto, 2013b; Gell
et al., 2015; Vroman et al., 2015). It can be argued that
older adults living alone might experience enhanced
social benefits by using ICT because it can reduce
social isolation. However, married or partnered older
adults appear to be taking advantage of ICT at higher
rates. There are two possible explanations for this outcome: (a) married or partnered older adults may help
each other learn how to use ICT and subsequently
increase their motivation to use this technology; and
(b) it is also possible that older married or partnered
couples may have a higher level of income or access to
ample resources so that they can afford an Internet
service and digital devices (e.g., computer, tablet, or
smartphone).
Among ICT users in this study, older women were
more likely to use ICT than older men. Previous findings on gender disparities in ICT use have revealed
mixed results. Perrin and Duggan (2015) reported little
difference by gender for Internet use across all age
groups. For older adults, Gell et al. (2015) found that
men were more likely to use ICT, yet other studies
found no significant association between gender and
ICT use (Choi & Dinitto, 2013a, 2013b). A nuanced
understanding of gender differences in ICT use can be
achieved by investigating the older adult’s purpose for
ICT use. For example, our findings revealed that
women were more likely to use only CT, whereas men
were more likely to use only IT. These findings lend
support for older women’s ICT use more so for social
rather than instrumental purposes (Ihm & Hsieh,
2015). Moreover, older ICT users utilized technology
for various purposes while IT users were limited to certain activities such as banking (44.2%), shopping (32%),
and health condition-information search (36.2%). To
increase older adult IT user’s motives for utilizing
technology that go beyond a single application for
activities such as banking, a health-related mobile
application (app) can be introduced so they can easily
access health information without needing to execute
complicated and multistep web searches. Additionally,
to accommodate a CT user’s context in which more
than half (53.5%) texted or emailed on “most” days and
29.1% on “some” days in the past month, health care
providers could send daily or weekly health-related
information via text, email, or the institution’s official
mobile application. An easy-to-use app is feasible for
both patients and institution because it can protect
patients’ personal information while tailoring to each
patient’s health-related issue and delivery preference.
Efforts to increase ICT use among older adults may initially need to be adjusted to the current and potential
users’ main motivations for technology use before
moving to a more integrated use of CT and IT.
Older ICT users in this study were more likely to be
socially active (i.e., visited family or friends and
attended clubs or organized activities) because those
who are more socially engaged may recognize the benefits of ICT use and have the resources to use technology for sharing information and communicating with
others in their social network. Using ICT with peers
and the organizations with which they are involved
might reinforce their continued use (Kim, Gajos,
Muller, & Grosz, 2016), more so than for those who are
not as socially active and have fewer opportunities to
connect with others through ICT. Programs and classes that teach older adults to use ICT should consider
doing so in a social context that incorporates social
network members and community representatives.
Interestingly, our findings revealed that physical
inactivity was higher among nonusers, followed by CT
only users, whereas physical activity participation was
higher among ICT users. It is unclear how physical
activity participation is differently associated with levels of ICT use due to the limited information we have
about this relationship. However, it is assumed if older
adults who may experience physical activity challenges are able to use CT, they can continue socializing
with others online using a CT tool. The current study
also found that older adults were less likely to use ICT
when they experienced more limitations in physical
capacity, such as their ability to walk or climb stairs
independently (Gell et al., 2015). Our findings underscore the need to carefully consider older adults’ physical capacity and engagement in physical activities in
understanding ICT use, and how these factors may
affect their desire and ability to use ICT. More research
is warranted in uncovering a relationship between
physical capacity, physical activity participation, and
levels of ICT use among older adults.
Additionally, this study found that self-rated health
status differed based on levels of ICT use (i.e., IT use,
CT use, and ICT use). Specifically, there was a positive
relationship between ICT use and self-rated health status. Gell et al. (2015) reported that higher self-rated
health status was significantly associated with more
technology use. Similarly, Cresci et al. (2010) found
ARTICLE IN PRESS
8 Nurs Outlook 00 (2020) 1 1 3
that older adults who used computers were significantly healthier than older adults who did not use
computers. Yet, other studies found that technology
use was not significantly associated with older adults’
self-rated health status (Carpenter & Buday, 2007) or
ill-health (Elliot et al., 2014), but positively associated
with chronic health conditions (Choi & Dinitto, 2013a).
Interestingly, Gracia and Herrero (2009) found that
the significant negative relationship between Internet
use and self-rated poor health status went away after
social class was factored into their statistical model.
They posited that the relationship between the digital
divide (Internet users vs. nonusers) and health status
would reflect socioeconomic inequalities in health
among older adults. On the contrary, the current study
found a positive association between ICT use and selfreported health status after controlling socioeconomic
status variables (i.e., annual income, education level,
and employment for payment). Unlike Gracia and
Herrero’s (2009) focus on the digital divide, the present
study attempted to unveil digital inequalities in ICT
use and explored how they were reflected in a relationship with self-reported health status among older
adults who were able to access to the Internet and
knew how to use computer. Because the study sample
is comprised of Medicare beneficiaries residing in the
community, it is also possible that their overall health
status would be better than that of older adults living
in nursing homes or other care facilities. More
research is needed to explore the relationship between
digital inequalities and health status with diverse
samples of older adults who might have less access to
resources based on educational level, racial and ethnic
minority status, and care needs.
Regarding depressive symptoms, some studies have
found a negative association between technology use
and depression (Cotten et al., 2014; Shapira et al., 2007)
and loneliness (Cotten et al., 2013; Shapira et al., 2007;
Sum et al., 2008) in older adults. The present study
revealed that ICT use was negatively associated with
experiencing major depressive symptoms after controlling other covariates; however, it was not associated with experiencing mild depressive symptoms.
Our finding regarding major depressive symptoms is
in contrast to those of other studies that analyzed
NHATS data and did not find a link between ICT use
and depression (Choi & Dinitto, 2013b; Elliot et al.,
2014). Choi and Dinitto (2013a) found that low-income
homebound older adults who reported a diagnosis of
depression used the Internet more than older adults
who were not depressed. The discrepancies between
these findings may be attributed to differences in samples (e.g., community-based sample vs. low-income
homebound older adults), respondent eligibility criteria, or analytic strategies. However, they highlight a
need for further research regarding the relationship
between ICT use and depressive symptoms in older
adults. A more thorough understanding of the diversity in ICT use among older adults and its association
with different levels of depressive symptoms may be
used to support better access to mental health care
services in this population by utilizing digital technology in the current health care system.
Overall, the mixed outcomes suggest that the link
between technology use and health is complex and
merits further investigation. The combination of
access to information and use of the Internet to build
social support might strengthen health status, as Elliot
et al. (2014) speculated. Alternately, as evidenced by
Choi and Dinitto (2013a), older adults with depression
were more likely to use the Internet than those without depression, and older adults who experience poor
health but are able to access and use technology may
be motived to use technology for health-related purposes (e.g., searching for health information or communicating with health care providers). More research
needs to test potential moderating factors that may
affect the relationships between technology use and
physical and mental health status. Also, the ability of
older adults to use ICT in this study may be due, in
part, to their higher cognitive functioning based on
their self-reported health status. As noted previously,
the community-dwelling Medicare beneficiaries in the
current study are likely to have better health status
than persons with more health care needs (e.g., homebound older adults, nursing home residents), or racial
and ethnic minority older adults. Future research
needs to replicate our study to elucidate how specific
types of technology use (i.e., ICT, IT, and CT) are associated with health status using diverse samples of
older adults and detailed measures of digital technology use.
In order to increase ICT use in the older adult population, with a particular focus on augmenting health
care service delivery and promoting patient self-management, tailored interventions are needed to introduce older adults to the variety and power of
technologies that are available. These interventions
can consist of didactic and experiential learning exercises that will serve to enhance their self-efficacy
(Davis, 1989; Kim et al., 2016), reduce distrust in technology (Knowles & Hanson, 2018), and leverage peer
support to improve uptake (Kim et al., 2016). These
interventions will also focus on teaching older adults
various ways that they can obtain health-related information, communicate with their health care provider,
and become active participants in their care (self-management). To make the learning exercises as successful and useful as possible, it is also recommended that
older adults play a role in their design and implementation (Knowles & Hanson, 2018). It is noteworthy that
some older adults are limited to using technologies for
only IT or CT purposes. Therefore, health care providers should consider the sociodemographic and
health characteristics of their patients as they begin to
integrate specific technologies into the health treatment plan. Then, based on the outcome of this simple
technology use, they can eventually integrate ICT in
the health management and treatment of older adult
patients. Ultimately, the long-term goal is for health
ARTICLE IN PRESS
Nurs Outlook 00 (2020) 1 1 3 9
care programs to reduce the gaps between different
levels of technology use as well as disparities between
nonusers and users in the older population through
the gradual integration of technology into the overall
treatment approach.
The need for older adults to have easily accessible
and remote access to health care resources and providers through digital technology has never been more
pressing as the world contends with the novel coronavirus (COVID-19) pandemic. In a recent study of online
epidemic-related consultations from Internet hospitals in China, Gong, Xu, Cai, Chen, and Wang (2020)
reported the discrepancy between counselees’ motivation for visits and the doctors’ recommendation for
offline visits, thus indicating improper medical-seeking behaviors among potential patients with varying
degrees of symptoms of, or similar to COVID-19. Nursing professionals can play a pivotal role in facilitating
epidemiological screening for older adults, those who
would fall in a high-risk group for infection and mortality, using digital technology. ICT-based screening
and consultation with nursing professionals can help
older adults access adequate health-related information, engage in social/physical distancing while seeking proper medical behaviors, and reduce the
likelihood of being potentially infected by avoiding
interactions with multiple individuals. Also, ICT in this
context would provide a lifeline to older adults who
wish to communicate with their providers, help reduce
social isolation, offer information and resources in
addition to what is shared in the media, and promote
a sense of agency by giving older adults resources to
help them manage during a public health crisis.
Limitations
This study had several limitations. First, according to
our research objectives, the current study sample was
limited to only community-dwelling older adults who
were able to access ICT at their home or elsewhere and
knew how to use computer. Additionally, the study
sample did not include older adults who used a proxy
to complete the survey, due to their health conditions
or language barriers. Therefore, these selection criteria
may have excluded the most vulnerable older adults
who have limited resources, complex health conditions, or language challenges. Second, the measures of
CT use in the NHATS survey did not discriminate
between emailing and texting. Third, other types of
technology use, such as ebooks, Twitter, and other
social networking sites, were not captured because the
2011 NHATS survey did not collect this information.
Also, it did not factor in the level of accessibility (e.g.,
Wi-Fi speed) and mode of technology (i.e., smart
phones, basic phones, computers, or tablets). Unlike
the CT measure, the IT use variables (e.g., grocery
shopping or contacting health care providers) measured only the respondents’ purposes of Internet
usage, not the frequency of such activities. Fourth, the
cross-sectional research design limits inferences
regarding causality or exploring a bi-directional relationship between ICT use and health and depressive
symptoms among older adults.
Finally, as a successor to the 1982 to 2004 National
Long-Term Care Survey, the NHATS has continued to
study disability and health trends among older Medicare beneficiaries since 2011. The current study used
data from the 2011 NHATS, which is comprised of the
first cohort sample and the baseline data for the 2012
to 2014 NHATS. In 2015, the sample was replenished
by adding an aged-in group, then this second cohort
sample was used the second baseline data for the following 2016-2018 NHATS. Since our main research aim
is to uncover ICT use patterns by levels and purposes,
we selected to use first baseline data from the 2011
NHATS. By understanding ICT use patterns among
older adults in the most available earlier cohort from
the NHATS, the findings will be used as a groundwork
for future research studies such as a trends study by
comparing 2011 and 2015 NHATS, and panel studies
using the follow-ups data in 2011-2014 NHATS.
Conclusion
As one of the few studies to examine differentiated
technology use among older adults, our findings elucidate the association between ICT use and the health
and mental health of this population. Empowering
older adults to use ICT as a way to access health information and interface with their health care providers
may enhance their sense of agency regarding the use
of technology as a health management tool and help
them achieve optimal health outcomes. These efforts
are critically important as the prevalence of older
adults who have or are at risk of developing multiple
chronic conditions increases, especially at times with
an increasing need for epidemic prevention and treatment around the world.
Promoting ICT utilization among older adults is only
part of a complex equation that helps to improve population health. Nurses, who constitute the largest
group of health care professionals in the United States
(American Associaton of Colleges of Nursing, 2011),
can incorporate ICT into their practice as a means to
engage in better health promotion efforts and provide
nursing care access to the greatest number of community members and patients. Studies have shown the
benefit of nurse-led technology-based interventions
among older adults in terms of reported user satisfaction, better quality of life (Chau et al., 2012), reduced
nursing home/hospital admission (Chau et al., 2012;
Chau et al., 2012; Tappenden, Campbell, Rawdin,
Wong, & Kalita, 2012) and mortality (Tappenden et al.,
2012), and improved health status (Chau et al., 2012;
Rabins et al., 2000; Tappenden et al., 2012). Limited
health care resources coupled with increased demand
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10 Nurs Outlook 00 (2020) 1 1 3
for health care services support the use of technology
as an important supplement to traditional in-person
interactions. By incorporating ICT into nursing practice, patients can benefit from access to high quality
health information, prevention strategies, and frequent communication with their nurse. Timely assessments and interventions, and enhanced service
coordination can be of consequences (While & Dewsbury, 2011). Further, strengthening the patient nurse
relationship through ICT use has the potential to
improve the health outcomes of a population that is
disproportionately affected by multiple chronic conditions, and provide vital access to health care services
for older adults residing in rural settings.
Fortunately, with the implementation of the Health
Information Technology for Economic and Clinical
Health Act (HITECH Act) (2009), an electronic health
record (EHR) allows nurses to efficiently communicate
updated health information (e.g., lab results, medication lists, and clinical summaries) to patients, and it
also provides patients with digital access to their
health history. Also, the EHR can be used to remind
patients about preventive or follow-up care, as well as
provide patient-specific educational resources. This
type of electronic communication, when done consistently and in a way that meets a patient’s health information needs, can help foster a strong and trusting
relationship between the nurse and patient.
As electronic communications gain a great foothold
in health care systems, efforts are needed to support
older adults with more health care needs in accessing
and using digital technology. Our study points to disparities in older adults’ ICT use based on sociodemographic and health characteristics. In response to
these disparities, health care providers need to obtain
a holistic understanding of an older adult’s health conditions, their technology use intentions, and their protective factors for maintaining health and mental
health as they relate to differences in ICT use. Thus,
provisions in the Health Information Technology for
Economic and Clinical Health Act grant the authority
to tailor older adults’ technology preferences by diversifying the information delivery system. Text, email,
or mobile applications can be used as a delivery
medium of choice for older adults given the complexity of conducting Internet searches and the possible
resistance to using advanced technology. For example,
reminders for preventive or follow-up care could be
linked to the patients’ mobile calendar app, so they
could easily access such information. Also, a simple
touch of an EHR mobile application can simplify the
process in accessing relevant information along with
training of the app operation from the hospital may
increase accessibility and intimacy to ICT.
The role of ICT in the health care delivery system will
continue to expand as more patients depend on technology to monitor and manage their health. This is
particularly salient for the older adult population.
Nurses are uniquely positioned to become leaders and
innovators in the use of ICT to support the health and
well-being of their patients. Achieving this leadership
will require expanding ICT knowledge and skill development in nursing education, conducting rigorous
research to examine how ICT influences nursing practice and nurses’ work lives (While & Dewsbury, 2011),
and perceiving the role of ICT as a core value of the
profession (Fagerstrom et al., 2016 € ).
Acknowledgment
This research project was funded by the Endowed
Research Fund from the University of Alabama, School
of Social Work.
Supplementary materials
Supplementary material associated with this article
can be found in the online version at doi:10.1016/j.out
look.2020.05.001.
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