982147

research-article2020

JAGXXX10.1177/0733464820982147Journal of Applied GerontologyNakagomi et al.

Original Manuscript

Can Online Communication Prevent
Depression Among Older People?
A Longitudinal Analysis

Journal of Applied Gerontology
﻿1­–9© The Author(s) 2020
Article reuse guidelines:
sagepub.com/journals-permissions
https://doi.org/10.1177/0733464820982147
DOI: 10.1177/0733464820982147
journals.sagepub.com/home/jag

Atsushi Nakagomi1 , Koichiro Shiba1,
Katsunori Kondo2,3, and Ichiro Kawachi1

Abstract
Evidence on the association between internet usage and incidence of depression remains mixed. We examined the associations
between different categories of internet usage and developing clinical depression. We used data from the 2013 and 2016
waves of the Japan Gerontological Evaluation Study (JAGES) comprising 12,333 physically and cognitively independent adults
aged ≥65 years. Participants were engaged in seven categories of internet usage: communication with friends/family, social
media, information collection about health/medicine, searching for medical facilities, purchase of drugs and vitamins, shopping,and banking. We found that internet use for communication had a protective influence on the probability of developing
clinical depression defined as the Geriatric Depression Scale scores ≥5 or self-reported diagnosed depression. Our findings
support the role of online communication with friends/family in preventing clinical depression among older people. Online
communication could be particularly useful in the COVID-19 crisis because many families are geographically dispersed and/or socially distanced.
Keywords
depression, internet use, online communication, social interaction, social distancing, COVID-19

Introduction
Late-life depression is a serious public health issue, associated with high mortality, morbidity, and decreased physical,
cognitive, and social function (Blazer, 2003; Fiske et al.,2009; Wei et al., 2019). The proportion of the global population with depression in 2015 was estimated at more than 7%
among females aged 65 to 79 years, and above 5% among
males (World Health Organization, 2017). In addition, latelife depression often remains undetected among community-dwelling people. Among older people aged 50 years orolder, bereavement, disability, prior depression, and female
gender are important risk factors for depression (Cole &Dendukuri, 2003). Modifiable protective factors that havebeen shown to reduce the incidence of depression include
the provision of emotional support and promoting social
interactions (Berkman et al., 2014; Cole & Dendukuri,
2003; Gariepy et al., 2016).
A growing number of older adults, defined as people aged
65 years or older, are connected to the internet today. For
example, the proportion of internet users aged 65 years orolder in the United States reached 73% in 2019 (Pew
Research Center, 2019). Similar trends are found in Japan:

The proportion of internet users among Japanese people aged
65 years or older has reached 63.6% in 2019 (Ministry ofInternal Affairs and Communications, 2020). The internet
has changed daily life in several ways. It enables people tocommunicate with friends and family remotely; to search for
useful information easily (e.g., health-related information);
to perform instrumental activities such as shopping and banking from home; and especially in the era of the COVID-19
pandemic, to telecommute to work.
Several studies have focused on the role of the internet inalleviating depressive symptoms among older people, but with
mixed findings. Cotten et al. (2014) reported that internet
Manuscript received: August 19, 2020; final revision received:
October 20, 2020; accepted: November 27, 2020.
1

Harvard T.H. Chan School of Public Health, Boston, MA, USA
Chiba University, Japan
3
National Center for Geriatrics and Gerontology, Obu, Japan
2

Corresponding Author:
Atsushi Nakagomi, Takemi Program in International Health, Harvard T.H.
Chan School of Public Health, 677 Huntington Avenue, Boston,
MA 02115, USA.
Email: bay2item@yahoo.co.jp

2usage was found to reduce the probability of depression by
33% in a longitudinal study using the data from 2002 to2008 among retired people over the age of 50 years in the
United States. Another longitudinal study using relatively
recent data from 2012 to 2015 among Korean adults aged 50
years or older showed that internet use lowered depression,
partially mediated by an improvement in social relationshipsatisfaction (Jun & Kim, 2017). A cross-sectional study conducted in China in 2016 also showed that internet usage wasassociated with lower depression levels among adults aged
60 years or older (Wang et al., 2019). By contrast, somestudies reported no direct association between internet usage
and depression. For example, two cross-sectional studies
and a longitudinal study conducted between 2008 and 2011
among older people showed no significant association
between internet use and depression/depressive symptoms(Elliot et al., 2014; Erickson & Johnson, 2011; Hamer &Stamatakis, 2014). A meta-analysis that retrieved studies
that had been published from 2001 to 2012 showed the overall mean weighted effect size of computer and internet interventions for depression was not statistically significant (M.
Choi et al., 2012). Another meta-analysis published in 2010
even suggested a small detrimental effect of internet usageon psychological well-being (Huang, 2010), although thisstudy included the general population and age was assessed
as a moderator while no significant effect modification was
found. It should be pointed out, however, that these negative
findings were based on relatively old data. Considering the
rapidly increasing availability of the internet among older
people, studies using data reflecting current situations are
required to examine the role of internet use today.
The inconsistencies across studies of the impacts of internet use on depression may be attributable partly to the challenges of teasing out reverse causality—for example, people
with depression/depressive symptoms might be less likely to
use the internet. As the existing evidence on the relationshipbetween internet use and depression is mostly based oncross-sectional data, the use of longitudinal data to establish
temporality is warranted.
Another explanation is insufficient differentiation
between alternative uses of the internet. Although older people use the internet for various purposes, many studies
focused on a crude comparison between users versus nonusers. van Boekel et al. (2017) identified four groups ofusages in a latent class analysis based on the internet activities among older adults aged 65 years and older in the
Netherlands: practical users (engaging in practical andfinancial activities), minimizers (low-frequency use), maximizers (high-frequency use for various activities, including“chatting, video calling, sending messages.”), and social
users (engaging in social media and online games). Practicalusers and maximizers reported higher psychological wellbeing than minimizers and social users. Szabo et al. (2019)
reported that social use (connecting with friends/family)
indirectly affected well-being via decreased loneliness andJournal of Applied Gerontology 00(0)instrumental (e.g., banking) and informational uses (e.g.,
reading health-related information) indirectly affected wellbeing via engagement in a wider range of activities. Thesefindings suggest that the impacts of internet use amongolder people might vary according to purposes, althoughthese studies did not focus on depression. Because of theclose and bidirectional relationship between loneliness anddepression, online communication might be an effective
tool for preventing loneliness and depression in older people(National Academies of Sciences, Engineering, andMedicine, 2020). Especially, during the COVID-19 pandemic, loneliness among older people is a serious problem
because of their greater vulnerability to the virus, and hence
their greater need to comply with social distancing guidelines (Killgore et al., 2020). Thus, it is of particular interest
whether online communication with friends/family can substitute for face-to-face communication particularly forsocially isolated people (Ball et al., 2019).
Using data from two waves of the Japan Gerontological
Evaluation Study (JAGES) (Kondo, 2016), we assessed specific categories of internet usage among older people definedas aged 65 years or older and how each relates to the incidence of depression longitudinally. We hypothesized thatinternet use for communication (connecting with friends/
family) would be more protectively related to depression
than internet use for other purposes such as informational(e.g., health-related information) or instrumental uses (e.g.,
shopping and banking).

MethodStudy Population
We used data from the 2013 and 2016 waves of the JAGES(Kondo, 2016). JAGES is a nationwide population-based
gerontological cohort in Japan focusing on the social determinants of health and functional disability. It originated froma smaller survey called the Aichi Gerontological Evaluation
Survey (AGES) conducted in Aichi prefecture in 1999.
Following the first survey, five waves were conducted in2003 to 2004, 2006 to 2007, 2010 to 2011, 2013, and 2016.
The project was renamed JAGES in 2010 because the survey
covered all the major islands in Japan (except for Shikoku).
In fact, the surveys conducted in 2013 and 2016 included
137,736 older adults from 30 municipalities and 180,021
older adults from 39 municipalities from rural and urban
areas in Japan, respectively (response rate = 71.1% and70.2%). JAGES is based on questionnaires mailed to older
people, defined as aged 65 years or older, who were physically and cognitively independent and were not receiving
benefits from the national public long-term care insurance
scheme. A census was done for all residents in municipalities
with fewer than 5,000 eligible residents, while random sampling was used in large municipalities with 5,000 or moreeligible residents. Our data were drawn from respondents

Nakagomi et al.
who entered JAGES in the 2013 wave and who were followed up in the 2016 wave (n = 61,267). The 2013 wave of
the JAGES data set consisted of five modules. The questions
about internet usage were included in one of the five modules. The subsample consisted of 12,333 older adults residing in both rural and urban areas. Among them, our analytic
sample was people without clinical depression at baseline,n = 9,199 in the observed (unimputed) data set. Participants
were informed that returning the questionnaire indicatedtheir consent to participate.

Clinical Depression
Depressive symptoms were assessed by the short form of theGeriatric Depression Scale (GDS), consisting of 15 questions (GDS-15; score range: 0–15; Cronbach’s α = .80;
Burke et al., 1991; Nyunt et al., 2009). Higher scores indicate
more depressive symptoms. Participants with GDS-15 scoresof 5 or greater were considered to be indicative of clinicallysignificant depression (area under the receiver operating
characteristic curve = 0.98; Nyunt et al., 2009). Clinicaldepression was defined as GDS-15 scores ≥5 or self-reported
diagnosis of depression.

Internet Usage and Categories of Internet Use
As part of the study, participants indicated whether they used
the internet in the past year. In addition, participants wereasked to indicate the use for different purposes on a yes/nobasis to the following seven categories: (a) communication
with friends/family, (b) social media including Facebook
and Twitter, (c) information collection about health andmedicine, (d) searching for medical facilities, (e) purchaseof drugs and vitamins, (f) shopping, and (g) online banking.
Participants could select more than one purpose when they
engaged in them.
However, communication with friends/family andengagement in social media might overlap for some participants. To address the issue of misclassification, we conducted a sensitivity analysis in which the seven purposes ofinternet use were collapsed into three categories: (a) socialuse (communication with friends/family and social media),(b) instrumental use (shopping, purchase of drugs and vitamins, and banking), and (c) informational use (informationcollection about health and medicine and searching for medical facilities) (Szabo et al., 2019). These categories were analyzed as binary variables: set to 0 if participants answered“No” to any of the items, otherwise set to 1.

Control Variables
We adjusted for a series of demographic factors, physicalhealth conditions, socioeconomic status, and physical social
interaction at baseline as potential confounders.

3
Demographic factors included age and gender because
both of them can affect depression levels and the probabilityof internet use (World Health Organization, 2017).
Physical health is often associated with depression (Hsu,
2009), and people with physical problems might seek information about health problems on the internet or might communicate online with family/friends to share their concerns.
Hence, we included the instrumental activities of daily living,which is a validated 13-item self-reported index (score range:
0–13; Koyano et al., 1991), and the number of chronic diseases (diabetes mellitus, cardiovascular diseases, respiratory
diseases, stroke, and cancer) as indicators of physical health.
Socioeconomic status has been also associated withdepression/depressive symptoms (Gero et al., 2017;
Hoogendijk et al., 2014; Shiba et al., 2017) and internet use(Nicole & Eszter, 2009): Thus, educational attainment (<10or ≥10 years), occupational status (currently employed or
unemployed), and annual equivalized income (<2 million
yen or ≥2 million yen) were evaluated. Marital status (married or unmarried) and living arrangement (living alone ornot) were assessed because of the connections with depression (Honjo et al., 2018; Yan et al., 2011) and their expected
effect on internet use.
As an index of physical social interaction, we included thefrequency of meeting friends (<once a week; ≥once aweek), which was associated with depression (Chao, 2011;
Schwarzbach et al., 2014), because people who met theirfriends frequently might be more likely to use the internet tocommunicate online with their friends. Some people also
might use the internet to compensate for fewer offline interactions (e.g., with families that live far away).
Emotional social support was also associated with depression (Schwarzbach et al., 2014). However, people with noemotional social support might not use the internet for communication because of a lack of resources (i.e., friends/family to talk online) or older people may use the internet as away to seek such support (Quan-Haase et al., 2017). It wasassessed with the following question: “Do you have someone who listens to your concerns and complaints?”

Statistical Analysis
All data were analyzed using STATA 16.0 software (STATA
Corp. LLC, College Station, TX, USA). All continuous
values were expressed as mean (standard deviation [SD]),and categorical variables were reported as percentages inTables 1 and 2. Pearson’s correlation coefficients were calculated among the purposes of internet use. We used logistic
regression models to examine how categories of internet
use at baseline relate to the onset of clinical depression at
the follow-up survey. Our analytic sample in the logistic
regression was people without clinical depression at baseline, n = 9,199 in the observed (unimputed) data set. Ourbasic model was as follows:4

Journal of Applied Gerontology 00(0)Table 1. Characteristics of Internet Nonusers and Users Without Clinical Depression at Baseline.
VariableInternet nonusers (n = 6,236)Internet users (n = 2,167)709 (11.7%)
73.7 (5.6)
2,490 (39.9%)
11.9 (1.5)
0.3 (0.6)
1,618 (26.5%)765 (12.9%)
2,387 (47.0%)
3,245 (52.6%)
1,419 (25.3%)
3,342 (55.8%)
205 (3.4%)

162 (7.7%)
71.0 (4.7)
1,339 (61.8%)
12.2 (1.1)
0.3 (0.6)323 (15%)182 (8.5%)
1,411 (71.8%)
1,852 (85.7%)640 (30.5%)
1,164 (54.6%)
65 (3.0%)

Developing clinical depression
Age
Gender (male)
Instrumental activities of daily living
Number of diseases
Unmarried
Living alone
Income (2 million yen)
Educational attainment (10 years or more)
Current working
Meeting friends ≥once a week
No emotional social support
Purpose of internet use
Communication with friends/familySocial media
Information collection
Searching for medical facilities
Purchase of drugs and vitamins
Shopping
Banking

1,129 (52.1%)
374 (17.3%)
903 (41.7%)
366 (16.9%)
68 (3.1%)
514 (23.7%)
387 (17.9%)

Note. Data were missing in 796 people for the variable internet use/purposes among people without clinical depression at baseline (n = 9,199).

Logit Pr[Depression 2016 = 1 | Internet use 2013 , X 2013 ]= α 0 + β0*Internet use 2013
+ β1 * X 2013 + ε,
where logit probability of clinical depression in 2016 wasregressed on internet use in 2013 and a vector of confounding factors in 2013 (X2013). α0 represents the intercept and ε
the residual term. The associations of interest are expressedin the coefficients β0, or eβ0 in terms of odds ratio (OR).
Seven categories of internet usage were included simultaneously in the logistic model. We did not include product terms
in the logistic regression model because we found no evidence of significant effect modification by all confounding
variables (age, gender, instrumental activities of daily living,
the number of chronic diseases, educational attainment,
occupational status, marital status, living arrangement, thefrequency of meeting friends, and emotional social support)
in the association between internet use for seven purposesand developing clinical depression.
After estimating the logistic regression models, averagemarginal effects (AMEs) were obtained for clear interpretations as we cannot straightforwardly interpret and compare
coefficients/OR as we can in linear regression models (Mood,
2010). AMEs estimate the average of predicted changes in
the probability of developing clinical depression for a oneunit increase in a particular variable controlling for othercovariates.
We conducted two sensitivity analyses. First, we applieda logistic regression model in which the seven purposes ofinternet use were collapsed into three categories: social use(communication with friends/family and social media),
instrumental use (shopping, purchase of drugs and vitamins,
and banking), and informational use (information collection
about health and medicine and searching for medical facilities). Second, we conducted linear regression analyses inwhich the GDS score (continuous variable) in 2016 was used
as an outcome variable. We did not exclude people with clinical depression at baseline and included GDS in 2013 toadjust for depressive symptoms at baseline in the sensitivity
analysis.
We conducted multiple imputation analyses with theMarkov chain Monte Carlo method under the assumptionthat data were missing at random to address potential bias
due to missing data (Rubin, 1997). Data were missing in
9.9% for the variable internet use/purposes, 15.5% for GDS
in 2013, and 18.0% for GDS in 2016. The missing data forother variables were less than 10% except for equivalized
income (17.3%). We created 20 imputed data sets and combined the effect estimates using Rubin’s rule (Rubin, 1996).

Results
Of internet nonusers, 11.7% developed clinical depression,
whereas 7.7% of internet users developed clinical depression(Table 1). Older adults who were younger and male and had
higher household income and educational attainment weremore likely to use the internet. The most common categoryof internet use was communication with friends/family(52.1%), whereas social media was used by only 17.3% ofNakagomi et al.

5

Table 2. Characteristics of People Using the Internet for Each Purpose.
People using the Internet for

Variable
Developing clinicaldepression
Age
Gender (male)
Instrumental activities
of daily living
Number of diseases
Unmarried
Living alone
Income (2 million yen)
Educational attainment
Current working
Meeting friends ≥once a week
No emotional social
supportCommunication
with friends/
family(n = 1,129)

Socialmedia(n = 374)

Informationcollection(n = 903)

Searchingfor medical
facilities(n = 366)

Purchase ofdrugs/
vitamins(n = 68)

Shopping(n = 514)

Banking(n = 387)

71 (6.4%)

32 (8.9%)

76 (8.6%)

38 (10.7%)

8 (12.5%)

42 (8.4%)

28 (7.4%)

71.2 (4.7)
700 (62.0%)
12.3 (1)70.6 (4.7)
272 (72.7%)
12.1 (1.3)

71.2 (4.7)
549 (60.8%)
12.3 (1)71.8 (4.7)
217 (59.3%)
12.3 (1)

71.1 (4.5)
39 (57.4%)
12.2 (1.1)

70.6 (4.6)
305 (59.3%)
12.3 (1)71 (4.6)
307 (79.3%)
12.2 (1.1)

0.3 (0.5)157 (13.9%)
98 (8.8%)771 (74.5%)
992 (88.3%)
319 (29.0%)653 (58.5%)

0.3 (0.6)
49 (13.2%)
36 (9.8%)240 (70.2%)319 (85.5%)
137 (37.4%)
200 (54.9%)

0.3 (0.6)
132 (14.7%)
82 (9.2%)
612 (74.2%)
778 (86.3%)
266 (30.4%)450 (50.6%)

0.4 (0.6)
67 (18.5%)
41 (11.5%)229 (69.4%)
308 (84.4%)
91 (25.6%)
180 (49.9%)

0.3 (0.5)
8 (11.8%)
4 (6.2%)33 (55.9%)
54 (79.4%)
16 (24.6%)
31 (47.0%)

0.3 (0.6)
78 (15.2%)
34 (6.7%)345 (73.6%)
447 (87.1%)
145 (29.2%)
285 (56.0%)

0.4 (0.6)
46 (11.9%)
24 (6.3%)
278 (79.0%)350 (90.4%)
119 (31.9%)
199 (52.4%)

28 (2.5%)

20 (5.4%)

22 (2.5%)

11 (3.0%)

1 (1.5%)13 (2.5%)

16 (4.2%)

Note. Columns show the characteristics of people who used the internet for the seven purposes.

internet users. Men and women used the internet for communication almost equally (men: 52.3%; women: 51.8%).
The second most common purpose was information collection about health and medicine (41.7%).
Of internet users for communication purposes, 6.4%
developed clinical depression, whereas 7.4% to 12.5% ofinternet users for other purposes developed clinical depression (Table 2). Internet banking was used mainly by men(79.3%). Other characteristics were roughly comparable
among the seven different purposes of usage. We found amoderate association between internet use for informationcollection about health and medicine and searching for medical facilities (correlation coefficient = .36, p < .01), whileother bivariate correlations were weak or null (Supplemental
Table 1).
The logistic regression models showed that internet use
for “communication with friends/family” at baseline wasassociated with lower odds of developing clinical depression
at follow-up (OR: 0.68; 95% confidence interval [CI] =[0.51, 0.89], p = .005), whereas any other purposes did notshow significant association with clinical depression in thefollow-up survey (Table 3, Supplemental Table 2). The AMEof internet usage for communication on developing clinicaldepression was −0.033 (95% CI = [−0.054, −0.013]): The
predicted probability of developing clinical depression was
on average 3.3% points lower in internet users for communication with friends/family (predicted probability: 11.45%[10.62%–12.29%]) than in people not engaging in communication with friends/family via the internet (predicted probability: 8.11% [6.18%–10.05%]). A sensitivity analysis inwhich the seven categories of internet use were collapsed
into three categories showed that social use (communication
with friends/family and social media) at baseline was associated with lower odds of developing clinical depression at
follow-up (OR: 0.74; 95% CI = [0.59, 0.94], p = .01),
whereas instrumental use or informational use did not show
significant association with clinical depression in the followup survey (Supplemental Table 3).
Another sensitivity analysis applying linear regression
model using the GDS as a continuous variable (score range:0–15) showed the same trend, although the association was
marginally significant: Internet users for communication
with friends/family showed lower GDS at follow-up by 0.14
after adjusting for GDS and confounding factors at baseline(β: −0.14 [−0.27 to 0.001], p = .052; Supplemental Table 4).

Discussion
This study examined distinct purposes for internet use amongpeople aged 65 years or older and how each relates to clinicaldepression longitudinally. Our findings illustrate that internet use for communication has a protective influence on theprobability of developing clinical depression. However,other purposes of internet use showed no protective association with developing clinical depression.
It is of particular interest whether online communication
can be an alternative to face-to-face communication for people who do not or cannot interact with others in person. In
this study, the frequency of meeting friends was included
as an index of physical social interaction. We found6

Journal of Applied Gerontology 00(0)Table 3. Associations Between Internet Usage for Seven Purposes and Developing Clinical Depression at Follow-Up.
Purposes of Internet usage
Communication with friends/familySocial media
Information collection
Searching for medical facilities
Purchase of drugs and vitamins
Shopping
Bankingβ [95% CI]

AME [95% CI]

OR [95% CI]−0.39 [−0.66, −0.12]
0.00 [−0.40, 0.40]−0.01 [−0.34, 0.32]
0.33 [−0.13, 0.79]
0.15 [−0.78, 1.08]−0.03 [−0.42, 0.36]−0.11 [−0.52, 0.30]−0.033 [−0.054, −0.013]
0.00 [−0.038, 0.038]−0.001 [−0.032, 0.031]
0.036 [−0.018, 0.09]
0.017 [−0.083, 0.117]−0.002 [−0.039, 0.034]−0.01 [−0.047, 0.026]

0.68 [0.51, 0.89]
1.00 [0.67, 1.49]
0.99 [0.71, 1.38]
1.39 [0.88, 2.21]
1.16 [0.46, 2.94]
0.97 [0.66, 1.44]
0.89 [0.59, 1.35]

Note. Controlled for age, gender, instrumental activities of daily living, number of diseases, marital status, living arrangement, equivalized income,educational attainment, current working, the frequency of meeting friends, and emotional social support. AMEs can be interpreted as the higher/lower
probability of developing clinical depression than the reference group (internet nonusers). AME = average marginal effects; CI = confidence intervals;
OR = odds ratio.

the protective association of online communication with
developing clinical depression with no effect modification ofconfounding variables including the frequency of meeting
friends, suggesting that people can benefit from online communication equally, regardless of whether they meet theirfriends in person. The same might apply for interaction withfamily members. Although we did not assess the frequencyof meeting with family members in this study, living alone
would reflect it to some extent: People living alone have lessinteraction with family members. Our findings suggest thatpeople living alone can benefit from online communication
as well as people living with their family.
Although we did not examine pathways linking online
communication with the prevention of depression, some of
the findings from a longitudinal mediation study by Szabo
et al. (2019) might be applicable to our study, that is, virtualinteractions with others affected well-being via a decreased
sense of loneliness as well as increased social engagement.
The findings suggest a role for online communication in creating offline social networks, which is also a plausible pathway linking online communication with the prevention ofdepression, considering the close relationship between
loneliness/social engagement and depression (National
Academies of Sciences, Engineering, and Medicine, 2020).
However, direct mechanisms, not via offline social activities,
may also be involved. For example, video talking with family/friends and online social activities/events might increase
the range and diversity of social connections (Barbosa Neveset al., 2019). In the era of COVID-19, the direct effects of
online communication need to be investigated urgently.
This study did not show a significant relationship betweensocial media use and developing clinical depression. Social
media also provides opportunities to communicate with others, but communication on social media (e.g., posting, reading, and checking messages/pictures) may differ fromphone/video talking and sending messages to friends/family.
Nevertheless, some participants might engage in both formsof media (phone/video talking as well as social media) tocommunicate with friends/family. The sensitivity analysis inwhich communication with friends/family and social media

usage were collapsed into a single category of “social use”
supported our main finding, that is, online communication
might prevent developing depression. However, further studies are required to examine which type of online communication can be effective to prevent depression.
Methods of connecting to the internet (e.g., phone, tablet,and computer) might determine or pose barriers for how
people use the internet for communication. For example, if
people do not have a smartphone, they would not be able tocommunicate with friends using apps available only on
smartphones. The increasing availability of smartphones
may provide more opportunities for older people to communicate with their friends. However, larger screen sizes fortablets and computers might be more suitable for some older
adults to text, read, and use apps. Future interventions needto take into consideration what type of devices is appropriate
for promoting online communication among older people.
In contrast to some previous findings, informational uses
(information collection and searching for medical facilities)
and instrumental uses (shopping and banking/brokers) were
not associated with developing clinical depression. van
Boekel et al. (2017) identified four groups of internet usages
in a latent class analysis, and practical users (engaging inpractical and financial activities) showed better psychological well-being, although it was a cross-sectional association.
Szabo et al. (2019) also reported that instrumental (e.g.,banking) and informational uses (e.g., reading health-related
information) indirectly affected well-being via volunteeringin a wider range of activities in a longitudinal study. There
are two explanations for this inconsistency. First, the outcome in the previous studies was well-being but not depression or depressive symptoms. Second, our study measured
individual’s information collection about health, medicine,and medical facilities in contrast with the study by Szabo
et al. (2019) which also measured searching for information
about music/entertainment. Online information seeking
about things of their interest might encourage people toengage in community activities and volunteering (N. G. Choi
& Dinitto, 2013), but it would not be applicable to people
seeking information about health-related problems.

Nakagomi et al.

7

Limitations

negative perceptions of the internet. However, if we can convince older people to use the internet even once, the experience might increase self-efficacy (Adams et al., 2005). In
one well-known case study of a rural agribusiness collective
in a remote area of Japan, community developers demonstrated that older people (older than 70 years) could be successfully trained in the use of the internet (Hashimoto, 2012).
This was accomplished by developing specialized hardware(e.g., large roller-ball mouses) and easy-to-use interfaces that
could be navigated by older people. Today, many suchdevices and apps have been developed, making it easier for
older people to use the internet. We should also focus on nonengineering aspects such as providing the information theywant in an accessible way and activities they can enjoy easily
to convince older people to adopt the internet. Online communication with friends/family could be an example of a persuasive portal to the internet. Our findings support the role ofonline communication with friends/family in preventing
clinical depression among older people. Online communication could be particularly useful among older adults today
as families are often geographically dispersed and, becauseof their vulnerability to the COVID-19, an alternative toface-to-face communication is required in the era of social
distancing.

There are some limitations to this study. First, we could not
fully identify the causality of the relationships. Although we
excluded people with clinical depression at baseline to deal
with reverse causality and adjusted for a series of potential
confounders, there may still be unmeasured confounders.
Second, we could not measure internet usage and confounding factors between the two study periods. Thus, we had toassume that these variables had remained the same during
the time period, although they might have changed over theperiod. Third, the proportion of internet users among people aged 65 years or older in this study (25.8%) was lower
than that reported in the Communications Usage Trend
Survey conducted by the Ministry of Internal Affairs andCommunications in 2013 (50.5%; Ministry of Internal
Affairs and Communications, 2013). The Communications
Usage Trend Survey might overestimate the number of internet users because the questionnaires of the survey were
mailed and e-mailed, whereas those of JAGES were mailed
only. Although the ratio of mail and e-mail in responses to
the Communications Usage Trend Survey is not available, itis plausible that internet users are more likely to reply to thequestionnaires than internet nonusers, leading to a greater
proportion of internet users than reality. However, JAGES
might underestimate the proportion of internet users becausesome mobile phone internet users might be misclassified asinternet nonusers in this study because some of them mightanswer “no” to the question “Did you use the internet or
email in the past year?” Unfortunately, we could not identify
which device was used for online communication. However,
if online communication via mobile phones plays a protective role in developing depression, which is a plausible
assumption, the association between online communicationand developing clinical depression could be underestimated.
Fourth, the types of online communication were not identified. Some might talk to friends/family online and others
might send emails and/or messages to them. It remains
unclear whether there is a specific type of online communication to prevent depression. Finally, we did not assess thefrequency of internet use. The impact of online communication is presumably small among less frequent users. In
addition, addictive use of the internet, particularly social
media, has become an area of increasing research interest(Longstreeta & Brooks, 2017). Further studies are needed tofigure out the appropriate frequency of online communication for preventing depression.

Conclusion
In a few decades, almost all older people, at least in developed
countries, will be internet users because of a birth cohort
effect, that is, the generation who grew up with the internet
are aging. However, for current older people who have neverused the internet as a young adult, there are several obstacles
to use the internet, such as a lack of knowledge/support andAuthors’ Note
This project was conducted with the support of the Takemi Programin International Health at Harvard T.H. Chan School of Public
Health. The views and opinions expressed in this article are those of
the authors and do not necessarily reflect the official policy or position of the respective funding organizations.

Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.

Funding
The author(s) disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article: This
work was supported by the Japan Society for the Promotion of
Science (JSPS) KAKENHI Grant Numbers (JP15H01972, JP15H04781,
JP15H05059, JP15K03417, JP15K03982, JP15K16181, JP15K17232,
JP15K18174, JP15K19241, JP15K21266, JP15KT0007, JP15KT0097,
JP16H05556, JP16K09122, JP16K00913, JP16K02025, JP16K12964,
JP16K13443, JP16K16295, JP16K16595, JP16K16633, JP16K17256,
JP16K19247, JP16K19267, JP16K21461, JP16K21465, JP16KT0014,
JP22330172, JP22390400, JP22390400, JP22592327, JP23243070,
JP23590786, JP23790710, JP24390469, JP24530698, JP24653150,
JP24683018, JP25253052, JP25713027, JP25870573, JP25870881,
JP26285138, JP26460828, JP26780328, JP26882010, JP20H00557);
Health Labour Sciences Research Grants (H22-Choju-Shitei-008,
H24-Junkankitou-Ippan-007, H24-Chikyukibo-Ippan-009, H24Choju-Wakate-009, H25-Kenki-Wakate-015, H25-Irryo-Shitei-003,
H26-Choju-Ippan-006, H27-Ninchisyou-Ippan-001, H28- ChojuIppan-002, H28- Ninchisyou-Ippan-002, H30-Junkankitou-Ippan-004);
the Research and Development Grants for Longevity Science fromthe Japan Agency for Medical Research and Development (AMED;

8

Journal of Applied Gerontology 00(0)16dk0110017h0002, 16ls0110002h0001, JP20dk0110034); theResearch Funding for Longevity Sciences from National Center
for Geriatrics and Gerontology (24-17, 24-23, 30-22, 20-19); andGrants from Japan Science and Technology Agency (JST-OPERA:JPMJOP1831).

Research Ethics
Ethical approval for the study was obtained from the Ethics
Committee at Chiba University (approval number: 2493) and the
National Center for Geriatrics and Gerontology (approval number:
992).

ORCID iD
Atsushi Nakagomi

https://orcid.org/0000-0002-3908-696X

Data Availability
Data are from the Japan Gerontological Evaluation Study (JAGES)
study. All enquiries are to be addressed at the data management
committee via e-mail: dataadmin.ml@jages.net. All JAGES data
sets have ethical or legal restrictions for public deposition due toinclusion of sensitive information from the human participants.

Supplemental Material
Supplemental material for this article is available online.

References
Adams, N., Stubbs, D., & Woods, V. (2005). Psychological barriers to Internet usage among older adults in the UK. Medical
Informatics and the Internet in Medicine, 30(1), 3–17. https://doi.org/10.1080/14639230500066876
Ball, C., Francis, J., Huang, K. T., Kadylak, T., Cotten, S. R., &Rikard, R. V. (2019). The physical-digital divide: Exploring
the social gap between digital natives and physical natives.
Journal of Applied Gerontology, 38(8), 1167–1184. https://doi.
org/10.1177/0733464817732518
Barbosa Neves, B., Franz, R., Judges, R., Beermann, C., &Baecker, R. (2019). Can digital technology enhance social
connectedness among older adults? A feasibility study. Journalof Applied Gerontology, 38(1), 49–72. https://doi.org/10.1177/0733464817741369
Berkman, L. F., Kawachi, I., & Glymour, M. M. (2014). Social epidemiology (2nd ed.). Oxford University Press.
Blazer, D. G. (2003). Depression in late life: Review and commentary. The Journals of Gerontology, Series A: Biological
Sciences and Medical Sciences, 58(3), 249–265. https://doi.
org/10.1093/gerona/58.3.m249
Burke, W. J., Roccaforte, W. H., & Wengel, S. P. (1991). The shortform of the Geriatric Depression Scale: A comparison with the
30-item form. Journal of Geriatric Psychiatry and Neurology,
4(3), 173–178. https://doi.org/10.1177/089198879100400310
Chao, S. F. (2011). Assessing social support and depressive symptoms in older Chinese adults: A longitudinal perspective. Aging& Mental Health, 15(6), 765–774. https://doi.org/10.1080/136
07863.2011.562182
Choi, M., Kong, S., & Jung, D. (2012). Computer and internet
interventions for loneliness and depression in older adults:
A meta-analysis. Healthcare Informatics Research, 18(3),191–198. https://doi.org/10.4258/hir.2012.18.3.191

Choi, N. G., & Dinitto, D. M. (2013). Internet use among older
adults: Association with health needs, psychological capital,and social capital. Journal of Medical Internet Research, 15(5),Article e97. https://doi.org/10.2196/jmir.2333
Cole, M. G., & Dendukuri, N. (2003). Risk factors for depression among elderly community subjects: A systematic review
and meta-analysis. American Journal of Psychiatry, 160(6),1147–1156. https://doi.org/10.1176/appi.ajp.160.6.1147
Cotten, S. R., Ford, G., Ford, S., & Hale, T. M. (2014). Internet
use and depression among retired older adults in the UnitedStates: A longitudinal analysis. Journal of Gerontology. Series
B: Psychological Sciences & Social Sciences, 69(5), 763–771.
https://doi.org/10.1093/geronb/gbu018
Elliot, A. J., Mooney, C. J., Douthit, K. Z., & Lynch, M. F. (2014).
Predictors of older adults’ technology use and its relationship todepressive symptoms and well-being. Journal of Gerontology.
Series B: Psychological Sciences & Social Sciences, 69(5),667–677. https://doi.org/10.1093/geronb/gbt109
Erickson, J., & Johnson, G. M. (2011). Internet use and psychological wellness during late adulthood. Canadian Journal on
Aging, 30(2), 197–209. https://doi.org/10.1017/s071498081
1000109
Fiske, A., Wetherell, J. L., & Gatz, M. (2009). Depression in older
adults. Annual Review of Clinical Psychology, 5, 363–389.
https://doi.org/10.1146/annurev.clinpsy.032408.153621
Gariepy, G., Honkaniemi, H., & Quesnel-Vallee, A. (2016). Social
support and protection from depression: Systematic review
of current findings in Western countries. British Journal of
Psychiatry, 209(4), 284–293. https://doi.org/10.1192/bjp.bp.115.169094
Gero, K., Kondo, K., Kondo, N., Shirai, K., & Kawachi, I. (2017).
Associations of relative deprivation and income rank withdepressive symptoms among older adults in Japan. Social
Science & Medicine, 189, 138–144. https://doi.org/10.1016/j.
socscimed.2017.07.028
Hamer, M., & Stamatakis, E. (2014). Prospective study of sedentary behavior, risk of depression, and cognitive impairment.
Medicine & Science in Sports & Exercise, 46(4), 718–723.
https://doi.org/10.1249/mss.0000000000000156
Hashimoto, K. (2012). Agri-business for elderly people through theInternet. The example of the Irodori leaf business. NETCOM:
Réseaux, communication et territoires, 26, 235–250. https://doi.org/10.4000/netcom.1012
Honjo, K., Tani, Y., Saito, M., Sasaki, Y., Kondo, K., Kawachi, I.,& Kondo, N. (2018). Living alone or with others and depressive symptoms, and effect modification by residential social
cohesion among older adults in Japan: The JAGES longitudinal study. Journal of Epidemiology, 28(7), 315–322. https://doi.org/10.2188/jea.JE20170065
Hoogendijk, E. O., van Hout, H. P., Heymans, M. W., van der
Horst, H. E., Frijters, D. H., Broese van Groenou, M. I., Deeg,
D. J., & Huisman, M. (2014). Explaining the association
between educational level and frailty in older adults: Resultsfrom a 13-year longitudinal study in the Netherlands. Annals of
Epidemiology, 24(7), 538–544.e532. https://doi.org/10.1016/j.
annepidem.2014.05.002
Hsu, H. C. (2009). Physical function trajectories, depressive
symptoms, and life satisfaction among the elderly in Taiwan.
Aging & Mental Health, 13(2), 202–212. https://doi.org/10.1080/13607860802342201

Nakagomi et al.
Huang, C. (2010). Internet use and psychological well-being:
A meta-analysis. Cyberpsychology, Behavior, and Social
Networking, 13(3), 241–249. https://doi.org/10.1089/cyber.2009.0217
Jun, H. J., & Kim, M. Y. (2017). What accounts for the relationship between internet use and suicidal ideation of Korean older
adults? A mediation analysis. Journal of Gerontology. Series
B: Psychological Sciences & Social Sciences, 72(5), 846–855.
https://doi.org/10.1093/geronb/gbw163
Killgore, W. D. S., Cloonan, S. A., Taylor, E. C., & Dailey, N. S.
(2020). Loneliness: A signature mental health concern in theera of COVID-19. Psychiatry Research, 290, Article 113117.
https://doi.org/10.1016/j.psychres.2020.113117
Kondo, K. (2016). Progress in aging epidemiology in Japan: The
JAGES project. Journal of Epidemiology, 26(7), 331–336.
https://doi.org/10.2188/jea.JE20160093
Koyano, W., Shibata, H., Nakazato, K., Haga, H., & Suyama, Y.
(1991). Measurement of competence: Reliability and validityof the TMIG Index of Competence. Archives of Gerontology
and Geriatrics, 13(2), 103–116.
Longstreeta, P., & Brooks, S. (2017). Life satisfaction: A key to managing internet & social media addiction. Technology in Society,50, 73–77. https://doi.org/10.1016/j.techsoc.2017.05.003
Ministry of Internal Affairs and Communications. (2013). The communications usage trend survey. https://www.soumu.go.jp/
johotsusintokei/statistics/statistics05b1.html
Ministry of Internal Affairs and Communications. (2020). The communications usage trend survey. https://www.soumu.go.jp/
johotsusintokei/statistics/statistics05b1.html
Mood, C. (2010). Logistic regression: Why we cannot do what
we think we can do, and what we can do about It. European
Sociological Review, 26(1), 67–82. https://doi.org/10.1093/esr/
jcp006
National Academies of Sciences, Engineering, and Medicine. (2020).
Social isolation and loneliness in older adults Opportunities
for the health care system. The National Academies Press.
Nicole, Z., & Eszter, H. (2009). Digital distinction: Status-specifictypes of Internet usage. Social Science Quarterly, 90(2),274–291. https://doi.org/10.1111/j.1540-6237.2009.00617.x
Nyunt, M. S., Fones, C., Niti, M., & Ng, T. P. (2009). Criterionbased validity and reliability of the Geriatric Depression
Screening Scale (GDS-15) in a large validation sample ofcommunity-living Asian older adults. Aging & Mental Health,13(3), 376–382. https://doi.org/10.1080/13607860902861027
Pew Research Center. (2019, April 22). 10% of Americans don’t
use the internet. Who are they? https://www.pewresearch.org/

9fact-tank/2019/04/22/some-americans-dont-use-the-internetwho-are-they/
Quan-Haase, A., Mo, G. Y., & Wellman, B. (2017). Connected seniors:
How older adults in East York exchange social support online
and offline. Information, Communication & Society, 20(7),967–983. https://doi.org/10.1080/1369118X.2017.1305428
Rubin, D. B. (1996). Multiple imputation after 18+ years. Journalof the American Statistical Association, 91, 473–489. https://ci.nii.ac.jp/naid/20001008568/
Rubin, D. B. (1997). Multiple imputation for nonresponse in surveys. John Wiley.
Schwarzbach, M., Luppa, M., Forstmeier, S., Konig, H. H., &Riedel-Heller, S. G. (2014). Social relations and depression inlate life-a systematic review. International Journal of Geriatric
Psychiatry, 29(1), 1–21. https://doi.org/10.1002/gps.3971
Shiba, K., Kondo, N., Kondo, K., & Kawachi, I. (2017). Retirement
and mental health: Dose social participation mitigate the association? A fixed-effects longitudinal analysis. BMC Public
Health, 17(1), Article 526. https://doi.org/10.1186/s12889017-4427-0
Szabo, A., Allen, J., Stephens, C., & Alpass, F. (2019). Longitudinal
analysis of the relationship between purposes of internet use
and well-being among older adults. Gerontologist, 59(1),58–68. https://doi.org/10.1093/geront/gny036
van Boekel, L. C., Peek, S. T., & Luijkx, K. G. (2017). Diversity inolder adults’ use of the Internet: Identifying subgroups throughlatent class analysis. Journal of Medical Internet Research,
19(5), Article e180. https://doi.org/10.2196/jmir.6853
Wang, Y., Zhang, H., Feng, T., & Wang, H. (2019). Does internet
use affect levels of depression among older adults in China?
A propensity score matching approach. BMC Public Health,19(1), Article 1474. https://doi.org/10.1186/s12889-019-7832-8
Wei, J., Hou, R., Zhang, X., Xu, H., Xie, L., Chandrasekar, E. K.,
Ying, M., & Goodman, M. (2019). The association of late-life
depression with all-cause and cardiovascular mortality amongcommunity-dwelling older adults: Systematic review andmeta-analysis. British Journal of Psychiatry, 215(2), 449–455.
https://doi.org/10.1192/bjp.2019.74
World Health Organization. (2017). Depression and other common
mental disorders. Global health estimates. https://www.who.
int/publications-detail/depression-global-health-estimates
Yan, X. Y., Huang, S. M., Huang, C. Q., Wu, W. H., & Qin, Y.
(2011). Marital status and risk for late life depression: A metaanalysis of the published literature. Journal of International
Medical Research, 39(4), 1142–1154. https://doi.org/10.1177/
147323001103900402