Social Science & Medicine 321 (2023) 115777

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Social Science & Medicine
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Video call and depression among older adults during the COVID-19
pandemic in Japan: The JAGES one-year longitudinal study
Ryunosuke Shioya a, Atsushi Nakagomi a, *, Kazushige Ide a, Katsunori Kondo a, b
a

Department of Social Preventive Medical Sciences, Center for Preventive Medical Sciences, Chiba University, 1-33, Yayoi-Cho, Inage-Ku, Chiba-Shi, Chiba, 263-8522,
Japanb
Department of Gerontological Evaluation, Center for Gerontology and Social Science, Research Institute, National Center for Geriatrics and Gerontology, 7-430,
Morioka-Cho, Obu-Shi, Aichi, 474-8511, JapanA R T I C L E I N F O

A B S T R A C T

Handling Editor: Social Epidemiology Editorial
OfficeEvidence of video call on preventing late-life depression during the COVID-19 pandemic is limited. We examined
the associations of social contact (in-person, voice call, and video call) with incidence of depressive symptoms
and evaluated whether specific factors (particularly, age and change in the frequency of in-person contact) affectthese associations. We used longitudinal data from the 2019 and 2020 waves of the Japan Gerontological
Evaluation Study, including 10,523 participants aged ≥65 years in 10 municipalities. Depressive symptoms were
measured by the 15-item Geriatric Depression Scale (GDS-15) score ≥5 in main analysis, and GDS-15 score ≥4,10, or continuous variable in sensitivity analyses. Social contact represented frequency changes before andduring the pandemic: non-contact (reference), decreased-contact, maintained-contact, and increased-contact. We
employed modified Poisson regression analysis. Compared to non-contact of video call, the association ofincreased-contact of video call with depressive symptoms was insignificant in main analysis (GDS-15 ≥ 5: risk
ratio (RR) = 0.89, 95% confidence interval (CI): 0.79–1.01), whereas significant in sensitivity analyses (GDS-15≥ 4: RR = 0.89, 95% CI: 0.82–0.98; GDS-15 ≥ 10: RR = 0.71, 95% CI: 0.53–0.97; GDS-15 = continuous variable:
В = − 0.17, 95% CI: − 0.33 to − 0.002). In-person contact was significantly associated with lower incidence ofdepressive symptoms (non-contact: reference; maintained-contact: RR = 0.92, 95% CI: 0.85–0.99; increasedcontact: RR = 0.84, 95% CI: 0.77–0.91), whereas voice call was not. Age and change in the frequency of inperson contact did not show significant effect modifications on the associations of video call with incidence ofdepressive symptoms after Bonferroni correction for multiple testing. In conclusion, this study suggests that theevidence supporting video call as a way to protect against depressive symptoms among older adults during the
pandemic appears weak compared to the evidence for in-person contact.

Keywords:
Health policy
Social connectedness
Online communication
Social distancing
Japan

1. Introduction
Late-life depression is a serious public health problem affecting a
rapidly aging population worldwide. Depression among older adults is
common, with a global prevalence of more than 5% for women and 4%
for men in 2015 (World Health Organization, 2017). Late-life depression
is associated with high risks of mortality, suicide, physical disability,
and cognitive impairment (Fiske et al., 2009). The World Health Orga­nization regarded depression as the most significant contributor to
global disability (World Health Organization, 2017).
The coronavirus disease 2019 (COVID-19) pandemic has led to se­vere psychosocial consequences globally, including late-life depression(Briggs et al., 2021; Noguchi et al., 2021; Sepúlveda-Loyola et al., 2020;
Zaninotto et al., 2022). Social distancing was mandatory during the
COVID-19 pandemic, thereby increasing the risks of social disconnect­
edness (characterized as “scarcity of contact” (Cornwell and Waite,2009)) and perceived isolation (i.e., loneliness (National Academies of
Sciences & Medicine, 2020)) (Cudjoe and Kotwal, 2020), which
contribute to depression among older adults (National Academies of
Sciences & Medicine, 2020; Santini et al., 2020).
As a countermeasure, online communication may effectively protect
mental health among older adults (Galea et al., 2020). Internet usage
among older adults is increasing worldwide. The proportion of Internet
users over 65 years reached 75% in the USA (Pew Research Center,* Corresponding author. 1Yayoi-Cho, Inage-Ku, Chiba-Shi, Chiba, 263-8522, Japan.
E-mail address: bay2item@yahoo.co.jp (A. Nakagomi).
https://doi.org/10.1016/j.socscimed.2023.115777
Received 2 August 2022; Received in revised form 7 January 2023; Accepted 13 February 2023
Available online 16 February 2023
0277-9536/© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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2022) and 53% in Japan (Ministry of Internal Affairs and Communica­
tions, 2022) in 2021. Several longitudinal studies before the COVID-19
pandemic reported that Internet use for connecting friends/family
indirectly affected well-being via increased social engagement anddecreased loneliness (Szabo et al., 2019), and Internet use for commu­
nication purposes was associated with a lower incidence of depression(Kondo et al., 2021; Lam et al., 2020; Nakagomi et al., 2022).
Video call is one of the promising online communication modes toprevent depression among older adults during the COVID-19 pandemic(Hajek and König, 2021). Previous studies reported the increase in video
call as an alternative to in-person contact among older adults during the
COVID-19 pandemic (Arpino et al., 2021; Freedman et al., 2022;
Greenwood-Hickman et al., 2021). Given the ability to convey rich so­cial cues, video call is closer to in-person contact than other online
communication modes (Fox and McEwan, 2017). Indeed, a longitudinal
study before the COVID-19 pandemic showed that video call was
significantly related to reduced depressive symptoms among American
adults aged over 50 years (Teo et al., 2019).
However, the effect of video call on preventing late-life depression
during the COVID-19 pandemic could differ compared to before thepandemic (Hajek and König, 2021). Indeed, a longitudinal study using
data from the 2015 and 2020 waves of the National Social Life, Health,and Aging Project (NSHAP) reported no significant associations ofretrospectively reported video call contact during the COVID-19
pandemic with depressive feelings among American adults aged over
55 years (Hawkley et al., 2021).
As a possible reason for the inconsistent findings regarding video callbefore and during the COVID-19 pandemic (i.e., the association of video
call with reduced depressive symptoms among American adults was
significant before the pandemic (Teo et al., 2019), whereas insignificant
during the pandemic (Hawkley et al., 2021)), the effect of video call on
preventing late-life depression could vary depending on specific factors.
Given the pandemic situation that restricts in-person contact, forexample, those with less in-person contact during the pandemic could
particularly benefit from video call contact. However, the relevant evi­
dence during the COVID-19 pandemic remains scarce, and such an effectmodification (i.e., the effect modification by the change in the frequency
of in-person contact with regard to the association of video call withdepressive symptoms among older adults) has not yet been investigated.
Additionally, age could also influence the association of video callwith late-life depression. For example, the effect of video call to prevent
depression could be greater especially among older individuals due tothe tighter restriction of in-person contact because of their vulnerability
to COVID-19. On the other hand, a cross-sectional online survey during
the COVID-19 pandemic showed that video call was associated with lessloneliness and higher quality of life among people aged 60–69 years, but
not among people over 70 years (Bonsaksen et al., 2021) probably
because of a lack of skills to use digital devices among the older group.
Although such age-related differences could also be observed in late-life
depression, the evidence is inconclusive; therefore, further explorationis needed.
Furthermore, the previous study during the COVID-19 pandemic wasonly conducted in one country (i.e., the USA) (Hawkley et al., 2021),
therefore it is unclear whether such findings could be generalizable tothe population in other contexts (e.g., country/culture). From the cul­tural perspective, for example, a normative imperative is founded onconnecting with others in Eastern interdependent countries (e.g.,
Japan), whereas it is founded on independence from others in Western
independent countries (e.g., the USA) (Markus and Kitayama, 1991).
These cultural differences could affect the relationship between social
contact and late-life depression as in a previous systematic review(Schwarzbach et al., 2014). Although this systematic review mainly
focused on in-person contact before the COVID-19 pandemic(Schwarzbach et al., 2014), the effect of video call on preventing late-life
depression during the pandemic could also vary between culturesconsidering the characteristics of video call (i.e., its similarity toin-person contact (Fox and McEwan, 2017)) and the abovementioned
cultural differences (Markus and Kitayama, 1991). In terms of general­
izability, further investigations in different contexts from the previous
study (e.g., Japan) are needed.
We built two hypotheses according to the abovementioned previous
studies. Hypothesis I: Considering its similarity to in-person contact
compared to other online communication modes (e.g., voice call) (Foxand McEwan, 2017), video call would be effective in preventing late-life
depression during the pandemic situation that restricts in-person con­tact. Hypothesis II: The protective effect of video call would differ bysome factors such as age and change in the frequency of in-person
contact during the pandemic: older people and people with lessin-person contact during the pandemic could particularly benefit fromvideo call contact. Using longitudinal data from the 2019 and 2020
waves of the Japan Gerontological Evaluation Study (JAGES) (Kondo,
2016; Kondo et al., 2018), this study aimed to assess the associations ofsocial contacts through each communication mode (in-person, voice
call, and video call) with depressive symptoms among older adults
during the COVID-19 pandemic and to evaluate whether specific factors(particularly, age and a change in the frequency of in-person contact are
relevant for the examination) affect the associations of video call withdepressive symptoms in this age group.
2. Methods
2.1. Study participantsThis is a one-year longitudinal study using the data from the 2019and 2020 waves of the JAGES (Kondo, 2016; Kondo et al., 2018). The
JAGES is a nationwide cohort study of older adults in Japan, focusing onsocial determinants of health and long-term care prevention (Kondo,
2016; Kondo et al., 2018). We conducted a baseline survey (2019 wave)
in 10 voluntary municipalities (i.e., whose officials agreed to participate
in the survey) before the COVID-19 pandemic from November 2019 toJanuary 2020 (Fig. 1) (Sato et al., 2022). Based on the list of residents
aged ≥65 years in each municipality, we mailed self-administeredquestionnaires to 88,476 independent individuals aged ≥65 years
without long-term care certification (i.e., those who are physically andcognitively independent and not certified for eligibility to receive ben­efits from the public long-term care insurance service in Japan). The
eligible individuals were randomly selected from each subdistrict(basically, elementary school or junior high school districts) of themunicipalities whose officials voluntarily agreed to participate in thesurvey, and 62,973 returned the questionnaires (response rate: 71.2%).
We conducted a follow-up survey (2020 wave) during the pandemic
from November 2020 to February 2021 and the second state of emer­
gency declaration from the Japanese government that requests people tomaintain social distancing and nonessential activities. We mailed
follow-up questionnaires to 12,705 individuals randomly selected fromthe group of baseline survey respondents, and 10,860 returned thequestionnaires (response rate: 85.5%). We excluded 337 respondents
whose age or sex was inconsistent between the two waves. Finally, we
included 10,523 participants. Fig. 1 shows a flowchart of participants
selection and temporal orders of pandemic-related events involved inour study.
Conforming to the principles of the Declaration of Helsinki, thisstudy was approved by the Institutional Review Board of the affiliateduniversity and research institution. All participants provided written
informed consent in the questionnaires.
2.2. Outcome variable
We measured depressive symptoms using the 15-item GeriatricDepression Scale (GDS-15) (Yesavage and Sheikh, 1986), which is avalidated tool for screening depression among older adults (Park andKwak, 2021). Its score ranges from 0 to 15, and we adopted a cutoff
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Fig. 1. A flowchart of participants selection and temporal orders of pandemic-related events.

value of 5, which is often used across several countries for screening
depression (Pocklington et al., 2016).
In sensitivity analyses, we adopted a cutoff value of 4 according to
the previous meta-analysis (Pocklington et al., 2016), a cutoff value of
10 for severe depressive symptoms, and the GDS-15 as a continuous
variable.

from the variable representing the frequency during the pandemic.
Positive values for the composite variable indicated an increase in fre­quency (increased-contact), whereas negative values indicated adecrease in frequency (decreased-contact). The remainder showed nochange in frequency. We further divided the remainder into two groups
according to the social contact status at baseline: non-contact (therespondent answered “none” at both time points) or maintained-contact(the respondent answered having the same frequency of social contact at
both time points). Thus, we classified changes in the frequency of eachsocial contact (in-person, voice call, and video call) as follows: noncontact (never: reference), decreased-contact (decrease), maintainedcontact (keep), and increased-contact (increase).

2.3. Exposure variables
We examined changes in the frequency of each social contact (inperson, voice call, and video call) before and during the COVID-19
pandemic. We asked the participants how frequently they had inperson contact at both baseline and follow-up surveys with thefollowing question: “How often do you meet friends?” During the followup survey, we asked them how often they experienced social contact
through voice call or video call with the following questions: “How often
did you use voice call to interact with family or friends before the spreadof COVID-19 (i.e., before January 2020 in Japan)?”; “How often did you
use video call to interact with family or friends before the spread of
COVID-19?”; “How often do you use voice call for interactions withfamily or friends at present (i.e., from November 2020 to February2021)?”; and “How often do you use video call for interactions withfamily or friends at present?” We provided the participants with thesame answer choices for each question coded as follows: “0: none; 1: a
few times/year; 2: 1–3 time(s)/month; 3: once a week; 4: 2–3 times/
week; and 5: 4 times/week or more.” Then, we created composite var­iables for each social contact (in-person, voice call, and video call) by
subtracting the variable representing the frequency before the pandemic

2.4. Control variables
The individual-level confounders included sociodemographic fac­
tors, physical health conditions, and social support, because these fac­
tors could correlate with social contact modes and depression amongolder adults (Fiske et al., 2009; Hunsaker and Hargittai, 2018; Lee andCoughlin, 2015; World Health Organization, 2017; Xiu-Ying et al., 2012;
Yan et al., 2011). Sociodemographic factors included age, sex, educa­
tional attainment (≥10 or < 10 years), household equivalized income(≥2 million or < 2 million yen/year), occupational status (employed or
unemployed), marital status (married or unmarried), and living
arrangement (living together or alone). Physical health conditions
consisted of current diseases (exist or none), activities of daily living(independent or dependent), and higher-level functional capacity
measured using the Tokyo Metropolitan Institute of Gerontology Index
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of Competence (TMIG) (score range: 0–13) (Koyano et al., 1991).
Furthermore, emotional support (exist or none) and instrumental sup­port (exist or none) constituted the social support. To evaluate
emotional support, we used the question “Do you have someone who
listens to your concerns and complaints?” For instrumental support, weused the question “Do you have someone who takes care of you when
you are ill in bed for several days?”
The municipality-level confounders included population density(person/km2) and whether a state of emergency has been declared in theliving municipality (never or once or more) (Fig. 1). We selected thesefactors because a rural setting could be related to a higher incidence ofdepression (Kanamori et al., 2021) and the limited Internet use amongolder adults (Kondo et al., 2021) and a state of emergency declaration
could change the communication modes of older adults (Arpino et al.,2021; Freedman et al., 2022; Greenwood-Hickman et al., 2021) andmake them depressed due to restricted in-person contact (Briggs et al.,2021; Noguchi et al., 2021; Sepúlveda-Loyola et al., 2020; Zaninotto
et al., 2022). We calculated the population density of each municipality
using the Statistical Observations of Municipalities in 2019 from the
Ministry of Internal Affairs and Communications.
We included the GDS-15 levels at baseline survey corresponding tooutcome variables (binary variables (GDS-15 ≥ 5 or not, GDS-15 ≥ 4 ornot, and GDS-15 ≥ 10 or not) and a continuous variable) as the prioroutcome values.

TX, USA) for statistical analyses, with a statistical significance of p <0.05. As for the assessment of effect modifications, the risk of type I error
could occur due to multiple testing. To address this problem, we
employed Bonferroni correction. We defined the more conservative pvalue cutoff for Bonferroni correction as p = 0.0031 (0.05/16) bydividing the nominal significance level of the test (α = 0.05) by the
number of tests.
3. Results
Table 1 presents the participant characteristics. The average age was75.1 years (standard deviation, 6.0), and the male and female pro­portions were equal (50.1% and 49.9%, respectively). The proportion ofdepressive symptoms (GDS-15 ≥ 5) increased from 20.6% to 25.9%
before and during the COVID-19 pandemic. Supplementary Tables 1–3
summarize the characteristics of participants by each social contact (inperson, voice call, and video call). The most common change in thefrequency of in-person contact was “keep” (37.5%), followed by“decrease” (31.9%). Regarding voice call and video call, the mostcommon change was “keep” (67.1%) and “never” (76.8%), respectively.
Table 1
Characteristics of participants (n = 10,523).
Characteristics
Individual-level variables
Depressive symptomsa at follow-up, n (%)
GDS-15 < 5
GDS-15 ≥ 5Depressive symptomsa at baseline, n (%)
GDS-15 < 5
GDS-15 ≥ 5Age (years), mean (SD)Sex, n (%)
Male
FemaleEducational attainment, n (%)≥10 years<10 years
Household equivalized income, n (%)≥2 million yen/year<2 million yen/year
Occupational status, n (%)
Unemployed
Employed
Marital status, n (%)
Married
Unmarried
Living arrangement, n (%)
Living together
Living alone
Current diseases, n (%)
Exist
None
Activity of daily living, n (%)
Independent
Dependent
Higher-level functional capacityb, mean (SD)
Emotional social support, n (%)
Exist
None
Instrumental social support, n (%)
Exist
None
Municipality-level variables
Population density (person/km2), mean (SD)State of emergency declared, n (%)
NeverOnce or more times

2.5. Statistical analysis
We described participants’ characteristics, presenting all continuous
variables as mean and standard deviation and all categorical variables aspercentages.
The proportion of the outcome variable was more than 10% (25.9%)
in our study; thus, we employed modified Poisson regression models(Zhang and Yu, 1998). Using modified Poisson regression models withcluster-robust standard errors, we estimated the risk ratio (RR) and 95%
confidence interval (CI) to examine the associations of the changes in thefrequency of each social contact (in-person, voice call, and video call)with the incidence of depressive symptoms (Zou, 2004). All exposure
variables were analyzed using the same model simultaneously as well asother control variables because the exposure variables could confoundeach other (e.g., those who decreased in-person contact during the
pandemic may increase voice call and/or video call to maintain social
connectedness) (Arpino et al., 2021; Freedman et al., 2022; Green­
wood-Hickman et al., 2021).
We conducted subgroup analyses by all confounders: social contacts(in-person and voice call), age, sex, educational attainment, household
equivalized income, occupational status, marital status, living arrange­
ment, current diseases, activities of daily living, higher-level functional
capacity, emotional support, instrumental support, population density,and whether a state of emergency has been declared in the living mu­
nicipality. Then, we included interaction terms between the change inthe frequency of video call and all confounders in the modified Poisson
regression models to assess the effect modifications. For ease of inter­
pretation, we converted all continuous variables into dichotomous
variables in subgroup analyses (age: 65–74 years or over 75 years, TMIG
score: 13 or 0–12, population density: ≥3000 or < 3000 person/km2).
We conducted three sensitivity analyses. For the first and second
analysis, we adopted GDS-15 scores of 4 and 10 as the cutoff values ofthe outcome variable, respectively. Lastly, we employed linear regres­sion models using the GDS-15 as a continuous variable.
We imputed missing data using multiple imputation analyses with
the Markov chain Monte Carlo method, assuming that data were missing
at random. The percentage of missing data for each variable was 0.0%–
18.6%. We created 50 imputed data sets and combined the estimatedparameters using Rubin’s rule (Kenward and Carpenter, 2007; Rubin,
1996).
We used STATA 16.0 software (STATA Corp. LLC, College Station,

Mean (SD), n (%)

6694 (74.1%)
2345 (25.9%)7093 (79.4%)
1839 (20.6%)
75.1 (6.0)
5269 (50.1%)5254 (49.9%)8097 (79.2%)
2122 (20.8%)5424 (57.1%)
4081 (42.9%)6991 (72.0%)2721 (28.0%)7528 (72.7%)
2828 (27.3%)8705 (83.7%)
1691 (16.3%)
8037 (80.3%)
1975 (19.7%)
9647 (96.3%)
368 (3.7%)
11.4 (1.7)
9750 (94.2%)
598 (5.8%)9733 (94.1%)
613 (5.9%)
6804.8 (2950.7)
824 (7.8%)
9699 (92.2%)

Abbreviations: SD, standard deviation; GDS-15, geriatric depression scale-15.
a
We measured depressive symptoms using GDS-15 (score range: 0–15), andadopted a cut-off value of 5 for screening depression.
b
We measured higher-level functional capacity using the Tokyo Metropolitan
Institute of Gerontology Index of Competence (score range: 0–13).
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Table 2 presents the associations of the change in the frequency ofeach social contact (in-person, voice call, and video call) with the inci­
dence of depressive symptoms. The increased frequency of video call (i.
e., “increase” in video call contact; see “2.3. Exposure variables” of“Methods”) was not significantly associated with the incidence ofdepressive symptoms (“never” in video call contact: reference, RR =0.89, 95% CI: 0.79–1.01, p = 0.068). The changes in the frequency of inperson contact were significantly associated with a lower incidence ofdepressive symptoms (“never” in in-person contact: reference; “keep” inin-person contact: RR = 0.92, 95% CI: 0.85–0.99, p = 0.020; “increase”
in in-person contact: RR = 0.84, 95% CI: 0.77–0.91, p < 0.001), whereas
the changes in the frequency of voice call were not significantly asso­
ciated with the incidence of depressive symptoms.
Supplementary Tables 4–6 present the results of the sensitivity an­alyses. An increased frequency of video call (“increase” in video callcontact) was significantly associated with a lower incidence of depres­sive symptoms (“never” in video call contact: reference; GDS-15 cutoff of
4: RR = 0.89, 95% CI: 0.82–0.98, p = 0.015; GDS-15 cutoff of 10: RR =0.71, 95% CI: 0.53–0.97, p = 0.029; GDS-15 as a continuous variable:
unstandardized coefficient (В) = − 0.17, 95% CI: − 0.33 to − 0.002, p =0.048).
Fig. 2 shows the results of subgroup analyses adjusted for covariates
at baseline. The associations of an increased frequency of video call(“increase” in video call contact) with the incidence of depressive
symptoms by age were as follows: “never” in video call contact: refer­ence; 65–74 years: RR = 0.80, 95% CI: 0.67–0.96, p = 0.015; over 75
years: RR = 0.99, 95% CI: 0.87–1.13, p = 0.900; p for interaction =0.016 (p-value cutoff for Bonferroni correction = 0.0031). Within thesubgroup of decreased in-person contact, the association of an increased
frequency of video call (“increase” in video call contact) with the inci­
dence of depressive symptoms was as follows: “never” in video callcontact: reference; RR = 0.87, 95% CI: 0.74–1.01, p = 0.069; p forinteraction (“keep” in in-person contact: reference) = 0.531.

associations of increased video call with the incidence of depressive
symptoms by age or a change in the frequency of in-person contact were
not significant after Bonferroni correction for multiple testing. On theother hand, the maintained or increased in-person contact was signifi­
cantly related to the lower incidence of depressive symptoms, whereas
voice call was not. In summary, our results suggest that the evidence
supporting video call as a way to protect against depressive symptoms
among older adults appears weak compared to the evidence for inperson contact.
Contrary to hypothesis I, the primary analysis showed that video call
was not significantly associated with the incidence of depressive
symptoms among older adults during the COVID-19 pandemic. Ourfindings of each social contact (in-person, voice call, and video call) aresimilar to the results of a previous study during the COVID-19 pandemic,
which showed that a retrospectively reported decrease in in-person
contact was significantly related to the deterioration of depressive
symptoms among older American adults, whereas a retrospectively re­ported increase in video call or voice call was not significantly related todepressive symptoms (Hawkley et al., 2021). We hypothesized thatvideo call would be protective against late-life depression during the
pandemic because video call features high social presence (i.e., “thefeeling of being there with a ‘real’ person” (Oh et al., 2018)) and theability to convey rich social cues, thereby closer to in-person contact
than other online communication modes (e.g., voice call) (Fox andMcEwan, 2017). However, our primary analysis showed that video call
was not significantly associated with depressive symptoms among older
adults. This suggests that video call is not equivalent or superior toin-person contact in preventing late-life depression during the
pandemic.
On the other hand, our results of video call are inconsistent with aprevious study before the COVID-19 pandemic that showed a robust
association of video call with reduced depressive symptoms among
American adults aged over 50 years (Teo et al., 2019). This inconsis­
tency supports the possibility that the effect of video call to prevent
late-life depression could differ before and during the pandemic. Given
the insignificant findings of effect modifications after Bonferroni
correction in this study, other effect modifiers that we have not inves­tigated could exist. A possible candidate for the unmeasured effectmodifiers is the interlocutors with whom older adults make video call.
For example, previous studies before the pandemic reported that social
contact with friends was associated with lower odds ratio for depression
among older adults (Misawa and Kondo, 2019; Werner-Seidler et al.,2017). Although we failed to assess the interlocutors in terms of change
because of the lack of data, with whom older adults increased video callcould matter to prevent depression. The evidence during the pandemic is
still scarce, thereby further investigations are warranted.
Contrary to hypothesis II, age-specific effect modification was not
significant after Bonferroni correction, and video call was not signifi­
cantly associated with depressive symptoms among those aged over 75
years. We hypothesized that older adults could particularly benefit fromvideo call because of strict restrictions on in-person contact due to theirvulnerability to COVID-19. Possible explanations include the barriers
that older adults face while using advanced technology. As age in­creases, most of them are less likely to use the Internet (Hunsaker andHargittai, 2018; Kondo et al., 2021) and have sufficient Internet skills(Hunsaker and Hargittai, 2018). Furthermore, they experience fear andanxiety while using new technology (O’Connell et al., 2022). Thus, older
adults may benefit insufficiently from video call because of suchbarriers.
In this study, the change in the frequency of in-person contact
showed no effect modification, and the associations of video call withdepressive symptoms by the change in the frequency of in-person con­tact were insignificant. Thus, the results contradicted hypothesis II:
older people with less in-person contact could particularly benefit fromvideo call during the pandemic.
Our results on in-person contact corroborate previous studies

4. Discussion
Our primary and sensitivity analyses of video call showed mixed
findings. The association of increased video call with the incidence ofdepressive symptoms among older adults during the COVID-19
pandemic was not significant in the primary analysis, whereas signifi­
cant in the sensitivity analyses. In our subgroup analyses, theTable 2
Associations of the Changes in the Frequency of Each Social Contact with
Depressive symptoms (GDS-15 ≥ 5) at Follow-up (n = 10,523).
RR

95％CI

Change in frequency of in-person contact (Ref.: Never)Decrease
1.00
0.95–1.07
Keep
0.92
0.85–0.99
Increase
0.84
0.77–0.91
Change in frequency of voice call contact (Ref.: Never)Decrease
1.08
0.98–1.19
Keep
0.96
0.89–1.02
Increase
1.05
0.97–1.12
Change in frequency of video call contact (Ref.: Never)Decrease
1.09
0.92–1.30
Keep
0.93
0.84–1.04
Increase
0.89
0.79–1.01

p-value
0.874
0.020<0.001
0.114
0.195
0.208
0.314
0.191
0.068

Note. Modified Poisson regression models adjusted for covariates at baseline:
depressive symptoms (GDS-15 ≥ 5), age, sex, educational attainment, household
equivalized income, occupational status, marital status, living arrangement,
current diseases, activity of daily living, higher-level functional capacity,
emotional social support, instrumental social support, population density, andstate of emergency declared. Changes in the frequency of social contact indi­cated: non-contact (never), decreased-contact (decrease), maintained-contact(keep), and increased-contact (increase). Abbreviations: Ref, Reference; RR,
risk ratio; CI, confidence interval; GDS-15, geriatric depression scale-15.
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Fig. 2. Subgroup analyses on the associations of the changes in the frequency of video call with depressive symptoms (GDS-15 ≥ 5) by all confounders.
Modified Poisson regression models estimated risk ratios of the changes in the frequency of video call (non-contact (reference), never; decreased-contact, decrease;maintained-contact, keep; increased-contact, increase) for depressive symptoms (GDS-15 ≥ 5) adjusting for covariates at baseline: depressive symptoms (GDS-15 ≥5), age, sex, educational attainment, household equivalized income, occupational status, marital status, living arrangement, current diseases, activity of daily living,higher-level functional capacity, emotional social support, instrumental social support, population density, and state of emergency declared. Abbreviations: GDS-15,
geriatric depression scale-15; TMIG, Tokyo Metropolitan Institute of Gerontology Index of Competence. *p for interaction = 0.016.

conducted during the COVID-19 pandemic (Hawkley et al., 2021;
Noguchi et al., 2021), suggesting that in-person contact consistently
matters for older adults to prevent depressive symptoms regardless ofthe pandemic.
This study has several limitations. First, we cannot exclude thepossibility of reverse causation. For example, some participants might

have increased their video call frequency because of their low levels ofdepressive symptoms. Second, residual confounding might remain
because of unmeasured confounding (e.g., other contact modes such associal networking services). To address these issues, further studies (e.g.,
randomized control trials) are required. Third, we retrospectively re­ported the frequency of video call and voice call at baseline; thus,
6

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misclassification could be possible (i.e., recall bias). Fourth, the questionof in-person contact could have been misinterpreted: the term “Howoften do you meet friends?” could be interpreted as not only in-person
contact but also voice/video call contact. Fifth, we cannot apply our
results to older adults with long-term care certification because we
excluded them from our survey. According to an annual report by the
Ministry of Health, Labour and Welfare in Japan, 18.7% of older adults
aged ≥65 years were certified as requiring long-term care insurance
services in 2020. Finally, our findings, as with previous studies in the
USA (Hawkley et al., 2021; Teo et al., 2019), are less generalizable
because the surveys were only conducted in one country. However, thisstudy is novel in terms of providing evidence in a different context (i.e.,country and culture) from the previous studies. Furthermore, theresponse rates of our surveys were over 70%, which is higher than thatof the similar study during the pandemic (58.1%) (Hawkley et al.,2021).

Health, Labour and Welfare (H28-Chouju-Ippan-002, 19FA1012, 19FA
2001, 21GA0101, 22FA1010, 22FA 2001). Some of the municipalities
where the study was conducted partially funded the surveys. The
funding organizations had no role in the study design, data collection,
analysis and interpretation of data, and preparation of the manuscript.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.socscimed.2023.115777.
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5. Conclusions
Our study provides crucial insights into public health policies/
practices to maintain older adults’ mental health during prolonged
pandemics or newly emerging pandemics in the future. In this study, inperson contact showed a robust association with a lower incidence ofdepressive symptoms among older adults during the COVID-19
pandemic, whereas video call did not show such a robust association
with depressive symptoms. As observed during the COVID-19 pandemic,
the next emerging pandemic may strictly limit in-person contact amongolder adults because they are generally vulnerable to infectious diseases.
In a situation that limits in-person contact, digital inclusion of older
adults (e.g., providing opportunities for continued support and training
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connections. However, policymakers/practitioners should bear in mind
that the evidence supporting video call as a way to protect against
depressive symptoms among older adults appears weak compared to theevidence for in-person contact and is still inconclusive at the moment. In
the future, interventional studies evaluating the causality between video
call and late-life depression and its effect modifiers are warranted.
Credit author statement
Ryunosuke Shioya: Conceptualization, Methodology, Formal anal­ysis, Software, Writing – original draft. Atsushi Nakagomi: Conceptu­
alization, Methodology, Investigation, Writing – review & editing,
Supervision, Funding acquisition. Kazushige Ide: Conceptualization,
Methodology, Writing – review & editing. Katsunori Kondo: Investiga­
tion, Writing – review & editing, Supervision, Funding acquisition,
Project administration. All authors approved the final manuscript.
Data availability
Data will be made available on request.
Acknowledgments
This study was supported by the Japan Society for the Promotion of
Science (21K17302). This study used the data of JAGES which was
supported by the Japan Society for the Promotion of Science(15H01972, 20H00557, 20K18931, 21K10323), National Center for
Geriatrics and Gerontology (29–42, 30–22, 20–19, 21–20), Japan
Agency for Medical Research and Development (JP18dk0110027,
JP18ls0110002, JP18le0110009, JP19dk0110034, JP20dk0110034,
JP21lk0310073, JP21dk0110037), Open Innovation Platform with En­
terprises, Research Institute and Academia (OPERA, JPMJOP 1831)
from the Japan Science and Technology (JST), and Japanese Ministry of7

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