healthcare
ArticleInformation Usage and Compliance with Preventive Behaviors
for COVID-19: A Longitudinal Study with Data from the
JACSIS 2020/JASTIS 2021
Taro Kusama 1,2, * , Sakura Kiuchi 2 , Kenji Takeuchi 1,2 , Takaaki Ikeda 2,3 , Noriko Nakazawa 2 ,Anna Kinugawa 2 , Ken Osaka 2 and Takahiro Tabuchi 4
1

2

3

4

*

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
Citation: Kusama, T.; Kiuchi, S.;
Takeuchi, K.; Ikeda, T.; Nakazawa, N.;
Kinugawa, A.; Osaka, K.; Tabuchi, T.
Information Usage and Compliancewith Preventive Behaviors for
COVID-19: A Longitudinal Studywith Data from the JACSIS
2020/JASTIS 2021. Healthcare 2022,
10, 521. https://doi.org/10.3390/
healthcare10030521
Academic Editor: Rajiv Kishore

Division for Regional Community Development, Liaison Center for Innovative Dentistry,
Tohoku University Graduate School of Dentistry, Sendai 980-8575, Japan; kenji.takeuchi.c4@tohoku.ac.jp
Department of International and Community Oral Health, Tohoku University Graduate School of Dentistry,
Sendai 980-8575, Japan; sakura.kawamura.r2@dc.tohoku.ac.jp (S.K.); t.ikeda0110@gmail.com (T.I.);
noriko.umehara.s6@dc.tohoku.ac.jp (N.N.); kinugawa.annna.r3@dc.tohoku.ac.jp (A.K.);
ken.osaka.e5@tohoku.ac.jp (K.O.)
Department of Health Policy Science, Graduate School of Medical Science, Yamagata University,
Yamagata 990-9585, Japan
Cancer Control Center, Osaka International Cancer Institute, Osaka 541-8567, Japan; tabuchitak@gmail.com
Correspondence: taro.kusama.a2@tohoku.ac.jp; Tel.: +81-22-717-7639

Abstract: The influence of different types of information sources on individual preventive behaviors
remains unclear. We aimed to investigate the associations between individual information usage toobtain information about COVID-19 and compliance with preventive behaviors. This longitudinal
study was based on an Internet survey conducted in August–September 2020 and February 2021. We
used compliance with four preventive behaviors for COVID-19, “wearing a mask”, “ventilation”,“social distancing”, and “avoiding crowds” as outcome variables, and 20 types of information
sources based on people or institutions (Medical worker, Government, etc.) and media (TV news,
Twitter, etc.) as predictors. Absolute differences and 95% confidence intervals were estimated using
generalized estimating equations adjusted for possible confounders. Among the 18,151 participants
aged 20–79, the mean age was 51.7 (SD = 15.9) in 2020, and 51.3% were male. In total, compliance with“wearing a mask”, “ventilation”, “social distancing”, and “avoiding crowds” was seen in 86.2%, 46.9%,
45.4%, and 62.6% of individuals in 2020, and 89.3%, 38.2%, 47.2%, and 61.6% of individuals in 2021,
respectively. In the multivariate analysis, “medical workers”, “professionals”, “the government”,“Twitter”, “news websites”, and “TV news” were positively associated with compliance with two or
more preventive behaviors (p < 0.05). The type of information source may play an important role inproviding information for COVID-19 prevention.

Received: 26 January 2022
Accepted: 11 March 2022
Published: 13 March 2022

Keywords: COVID-19; preventive behavior; media; longitudinal

Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims inpublished maps and institutional affil-

1. Introduction

iations.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), is an ongoing public health threat [1]. Althoughvaccinations have been developed to prevent COVID-19 [2], non-pharmaceutical interventions to prevent infection are still required because of the delayed progression of vaccination
uptake and the mutation of the pathogenicity of SARS-CoV-2 [3]. Previous studies havesuggested the effectiveness of physical distancing, wearing a mask, and building ventilationin preventing COVID-19 [4,5], and many political sectors have emphasized compliancewith these preventive behaviors [6].
Previous studies have suggested that exposure to specific information sources, suchas the media, government, professionals, and other people, affects individual preventive
behaviors such as vaccination uptake and behavioral prevention strategies [7]. Social media

Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Healthcare 2022, 10, 521. https://doi.org/10.3390/healthcare10030521

https://www.mdpi.com/journal/healthcare

Healthcare 2022, 10, 521

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has recently emerged as a vehicle for strong social impacts and is commonly regarded asa source of misinformation, including for COVID-19 [8,9]. Exposure to social media may
affect individual knowledge and risk perception for COVID-19 prevention. A previous
study among young adults suggested that individual knowledge and risk perception wereassociated with engagement in preventive behaviors for COVID-19 [10]. Several studies
have shown the associations between social media use and non-compliance with preventive
behaviors for COVID-19; however, only a few studies have investigated the associations
between preventive behaviors for COVID-19 and multiple information sources [11–14]. In
addition, most previous studies were cross-sectional and did not consider longitudinalchanges in compliance with preventive behaviors.
The present study aims to evaluate the association of the use of multiple information sources based on people or institutions and media to obtain the information about
COVID-19 prevention with the compliance of four preventive behaviors, including “wearing a mask”, “ventilation”, “social distancing”, and “avoiding crowds” for COVID-19 overa period of two years
2. Materials and Methods
2.1. Study Design and Participants
The present study is a longitudinal study using the two-point panel data obtained
from the Japan COVID-19 and Society Internet Survey (JACSIS) and the Japan Society
and New Tobacco Internet Survey (JASTIS). The JACSIS aimed to evaluate the healthconditions and social determinants of the COVID-19 pandemic in Japan. JASTIS aimed toevaluate the status of new tobacco products and their related factors in Japan. Both surveys
were administered on internet questionnaires. JACSIS, the baseline survey in 2020, wasdistributed to 224,389 candidates registered as panelists at a Japanese internet researchcompany (Rakuten Insight, Inc., Tokyo, Japan) from 25 August to 30 September 2020. The
selected sample (n = 28,000) is representative of the sociodemographic distribution of theJapanese population (sex, age, and residential prefecture). In 2021, the JASTIS survey
was distributed to the participants from the eighth to the twenty-sixth of February. The
participants who provided unreliable responses (i.e., selected “all” in questions regarding
current using drugs or chronic diseases) were excluded from the analysis. Participants
below 20 years of age or who answered their educational attainment as “other” werealso excluded.
2.2. Outcome Variables: Compliance with Preventive Behaviors for COVID-19
We used compliance with preventive behaviors for COVID-19 in the 2020 and 2021
surveys as the outcomes. We selected four preventive behaviors: “wearing a mask”,“ventilation”, “social distancing”, and “avoiding crowds”. These outcomes were thepreventive behaviors mandated by the Japanese governments’ campaign called “3Cs”, andmultiple media outlets distributed the message [15]. The campaign calls public attention
to avoid “closed spaces”, “crowded places”, and “close-contact settings”. We selected thefollowing item from the JACSIS and JASTIS surveys to extrapolate our outcome variable,“In the last month, how did you often take any of the following preventive measures?” Theoutcome variable was extrapolated from four responses assessing preventive measures
behaviors, namely “I wore a mask in a public place”, “I opened the window to ventilate
the room”, “I tried to keep a social distance (at least 2 m from people)”, and “I tried not
to go to places where people were crowded”. The questionnaire items used an ordinalscale, and participants selected their responses from “always”, “sometimes”, “seldom”,
and “never”. We defined those who answered “always” as having compliance with each offour preventive measures.
2.3. Predictors: Information Usage to Obtain Information about COVID-19
To evaluate information usage, we used the types of information sources based on people or institutions and media that individuals use to obtain information about COVID-19 as

Healthcare 2022, 10, 521

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predictors. We selected the following item from the JACSIS and JASTIS surveys: “Did you
get information about the new coronavirus infection from each of the following sources?”
Participants were able to select the following 20 types of information sources: (1) family,(2) friends, (3) workplaces and schools, (4) medical workers, (5) celebrities, (6) professionals,(7) government, (8) academic institutions, (9) video sharing sites (YouTube, etc.), (10) LINE,(11) Twitter, (12) Facebook, (13) Instagram, (14) news websites, (15) newspapers, (16) magazines, (17) books, (18) TV news, (19) TV tabloid shows, and (20) radio. Items one to eightare people and institutions from which participants sourced information about COVID-19.
Items nine to twenty are the media-based information sources, while nine to thirteen are“social network services (SNS)”. For each information source, participants responded “yes”
or “no”. We also included the time point of the response (2020 or 2021) as a predictor.
2.4. Covariates
We selected several covariates based on the previous studies and clinical knowledge [11,16–18]. All covariates were measured in 2020. The covariates included in theanalysis were sex (male or female), age (20–29, 30–39, 40–49, 50–59, 60–69, or 70–79), equivalent income by quartile (Q1: <22,500 USD, Q2: 22,500–31,800 USD, Q3: 31,800–47,500 USD,
Q4: >47,500 USD (1USD ≈ 100 JPY)), educational attainment (junior high school or highschool, vocational school or junior college, or university or graduate-school), family structure (living with others or living alone), and health literacy measured using the Communicative and Critical Health Literacy (CCHL) scale. The CCHL consists of five questions
regarding health literacy, and the average of the responses on a five-point Likert scale (1–5)
presents the respondents’ health literacy [19].
2.5. Statistical Analysis
We estimated the absolute differences in percentages and 95% confidence intervals(95% CIs) for each preventive measure based on the generalized estimating equations fitting
the Gaussian distribution and identity link with a Huber–White sandwich estimator for
standard errors [20]. The time-variant variables were compliance with each preventive measure and the time point of the response, and the time-invariant variables were information
usage and all covariates. Two-point panel data were created for each participant in a longformat. The same values were inserted for time-invariant variables; thus, two-row data foreach participant were inserted, with the outcome variable measured at either baseline orfollow-up. Further, we inserted predictors and covariates measured at baseline as baseline
and follow-up values. We built two models. Model 1 included each predictor of information
usage or time point, adjusting all covariates separately to evaluate the association of eachpredictor with preventive behavior conditional on the included covariate. Model 2 included
all predictors related to the types of people- or institution-based information sources (items
one to eight) or media-based information sources (items nine to twenty) separately, withtime points and all covariates. Model 2 aimed to evaluate the association of each predictor with preventive behavior conditional on other predictors and covariates. To reduce
selection bias, all statistical analyses employed the inverse probability weighting method
using the propensity score estimated from the Comprehensive Survey of Living Conditions,a representative Japanese sociodemographic random sample [21]. We also checked thedifferences in baseline characteristics between the original population and the analyzed
population using standardized differences [22]. A standardized difference > 0.1 indicates a
non-negligible difference between them. We used STATA 16.1 for all analyses.
2.6. Ethical Issues
JACSIS in 2020 and JASTIS in 2021 followed the procedures approved by the Ethics
Committee on Research of Human Subjects at the Research Ethics Committee of the Osaka International Cancer Institute (No.20084-2). Additionally, we followed the STROBE
statement to report our observational study.

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3. Results

Healthcare 2022, 10, 521

The flowchart of participant inclusion is presented in Supplementary Figure S1. Initially, of the 28,000 participants who answered the JACSIS survey in 2020, 26,646 wereaged 20 years or older and 24,208 were eligible at baseline. In the JASTIS 2021 survey,
20,061 (82.9%) participants responded. Finally, 18,151 participants were included in theanalysis after excluding unreliable responses. We could not find a negligible difference inthe distribution of baseline characteristics between the original and analyzed populations(Supplementary Table S1). The mean age of the participants was 51.7 (SD = 15.9) in 2020,
and 51.3% were male. Table 1 shows the distribution of the participants’ characteristics. The
proportions of those who had compliance with “wearing a mask”, “ventilation”, “social
distancing”, and “avoiding crowds” were 86.2%, 46.9%, 45.4%, and 62.6% in 2020, and89.3%, 38.2%, 47.2%, and 61.6% in 2021, respectively. The proportion of each predictor of
information usage is presented in Table 1 (unweighted) and Figure 1 (weighted). The mostused information source was “TV news” (84.2%) and the least used was “Instagram” (7.7%).
Table 2 shows the proportions of those who complied with each preventive behavior based
6 of 13
on information usage and covariates. The proportion of usage of each information sourceby sociodemographic and socioeconomic status is presented in Supplementary Table S2.

FigureFigure 1.
1. Weighted
Weighted proportions
proportions ofofthe
theuseuseof
ofinformation
informationsources
sources(n(n== 18,151).
18,151).

Table 2. The compliance with preventive behaviors for COVID-19 based on the characteristics in2020 and 2021 (n = 18,151).

Wearing a Mask

Ventilation

SocialDistancing

Avoiding

Healthcare 2022, 10, 521

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Table 1. The characteristics of the participants (n = 18,151).
2020

Characteristics
Preventive behaviors
Wearing a mask
Ventilation
Social distancing
Avoiding crowds
Information sources
FamilyFriends
Workplace/School
Medical workers
Celebrities
Professionals
Government
Academic institution
Video sharing site
LINE
Twitter
Facebook
Instagram
News website
Newspaper
Magazines
Books
TV news
TV tabloid show
Radio
Gender

2021

n

%

n

%

Yes
No
Yes
No
Yes
No
Yes
No

15,641
2510
8513
9638
8231
9920
11,356
6795

86.2
13.8
46.9
53.1
45.4
54.6
62.6
37.4

16,218
1933
6927
11,2248569
9582
11,187
6694

89.3
10.7
38.2
61.8
47.2
52.8
61.6
38.4

Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Male
Female10,488
7663
8486
9665
7448
10,7033603
14,5483847
14,3046293
11,8588000
10,151
1939
16,2122952
15,1993852
14,2992775
15,3761271
16,880975
17,176
12,833
5318
9288
8863
2202
15,9491234
16,917
15,3362815
12,0556096
3695
14,456
9306
8845

57.8
42.2
46.8
53.2
41.0
59.0
19.9
80.1
21.2
78.8
34.7
65.3
44.1
55.9
10.7
89.3
16.3
83.7
21.2
78.8
15.3
84.7
7.0
93.0
5.4
94.6
70.7
29.3
51.2
48.8
12.1
87.9
6.8
93.2
84.5
15.5
66.4
33.6
20.4
79.6
51.3
48.7

—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—

—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—

Healthcare 2022, 10, 521

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Table 1. Cont.
2020

Characteristics
Age

20–29
30–39
40–49
50–59
60–69
70–79
IncomeQ1 (Lowest)
Q2
Q3
Q4 (Highest)
Not answered
Education
Junior high school/High school
Vocational school/Junior college
University/Graduate-school
Family structure
Living with others
Living alone
Health literacy measured by CCHL

2021

n

%

n

%

1963
2554
3713
3312
3476
3133
3440
4026
3421
3747
3517
5208
4046
8897
14,664
3487
Mean
3.5

10.8
14.1
20.5
18.2
19.1
17.3
19.0
22.2
18.8
20.6
19.4
28.7
22.3
49.0
80.8
19.2
SD
0.7

—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—

—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—

—

—

Note: All predictors and covariates were measured only at baseline. CCHL, Communicative and Critical
Health Literacy.

Table 2. The compliance with preventive behaviors for COVID-19 based on the characteristics in 2020
and 2021 (n = 18,151).
Wearing a Mask
(Yes: %)
Information sources
FamilyFriends
Workplace/School
Medical workers
Celebrities
Professionals
Government
Academic institution
Video sharing site
LINE
Twitter
Facebook
Instagram
News website
Newspaper
Magazines
Books
TV news

Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No

Ventilation(Yes: %)

Social Distancing(Yes: %)

Avoiding Crowds(Yes: %)

2020

2021

2020

2021

2020

2021

2020

2021

90.3
80.5
90.3
82.5
88.7
84.4
90.0
85.2
90.3
85.1
92.3
82.9
91.8
81.7
87.6
86.0
88.1
85.8
89.6
85.3
86.6
86.1
82.7
86.4
81.0
86.5
90.2
76.5
90.3
81.9
88.4
85.9
84.6
86.3
90.0
65.6

92.5
85.0
92.3
86.7
90.8
88.3
91.3
88.9
92.0
88.7
93.8
87.0
93.5
86.1
89.1
89.4
89.4
89.3
91.1
88.9
87.7
89.7
85.8
89.6
82.3
89.8
92.7
81.4
92.5
86.0
89.7
89.3
86.4
89.6
92.4
73.0

50.5
41.9
52.0
42.4
48.6
45.7
56.2
44.6
52.8
45.3
53.7
43.3
52.2
42.7
54.9
45.9
52.3
45.9
53.5
45.1
50.4
46.3
52.7
46.5
55.4
46.4
48.7
42.7
49.8
43.9
53.5
46.0
57.1
46.2
48.1
40.4

41.7
33.4
43.0
33.9
42.0
35.5
47.5
35.9
43.7
36.7
43.2
35.5
42.9
34.4
47.5
37.1
44.3
37.0
45.5
36.2
44.4
37.0
45.9
37.6
51.1
37.4
39.4
35.2
38.7
37.6
46.1
37.1
49.8
37.3
38.6
35.9

47.9
41.9
46.1
44.7
42.8
47.1
55.2
42.9
48.7
44.5
50.3
42.7
50.1
41.6
54.1
44.3
50.3
44.4
48.6
44.5
46.3
45.2
48.9
45.1
49.4
45.1
46.7
42.1
48.4
42.2
52.8
44.3
57.3
44.5
46.7
37.8

49.4
44.3
47.9
46.6
44.8
48.9
55.8
45.1
49.1
46.7
51.6
44.9
51.8
43.6
54.6
46.3
50.6
46.6
49.4
46.6
47.5
47.2
48.5
47.1
49.3
47.1
48.3
44.7
50.2
44.1
53.7
46.3
57.9
46.4
48.5
40.2

67.1
56.3
65.8
59.7
62.2
62.9
70.9
60.5
66.1
61.6
69.2
59.0
68.7
57.8
67.5
62.0
65.6
62.0
65.9
61.7
61.1
62.8
60.4
62.7
60.6
62.7
65.4
55.8
65.7
59.3
65.8
62.1
66.5
62.3
65.2
48.0

65.4
56.5
64.1
59.5
61.1
62.0
69.0
59.8
63.8
61.0
67.1
58.7
67.3
57.2
66.9
61.0
64.3
61.1
64.1
61.0
60.9
61.8
60.0
61.8
61.9
61.6
63.8
56.5
64.5
58.6
64.5
61.2
66.1
61.3
64.0
48.6

Healthcare 2022, 10, 521

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Table 2. Cont.
Wearing a Mask
(Yes: %)

Ventilation(Yes: %)

Social Distancing(Yes: %)

Avoiding Crowds(Yes: %)

2020

2021

2020

2021

2020

2021

2020

2021

90.7
77.3
88.7
85.5

92.8
82.5
90.9
89.0

48.4
44.0
52.9
45.4

38.5
37.4
41.1
37.4

47.1
42.0
51.5
43.8

48.7
44.3
52.1
46.0

65.6
56.5
67.3
61.4

64.8
55.4
64.9
60.8

GenderMale
Female

79.7
93.0

84.8
94.1

38.2
56.1

30.9
45.6

40.9
50.0

43.5
51.1

56.3
69.1

55.6
68.0

Age
20–29
30–39
40–49
50–59
60–69
70–79

75.4
81.6
85.3
87.9
90.5
91.1

76.5
85.3
89.1
91.9
93.6
93.6

42.9
44.4
46.6
47.7
46.1
51.8

39.4
40.3
39.5
38.9
35.4
36.4

36.5
37.1
41.8
46.5
51.8
53.4

37.0
40.2
44.6
47.9
53.5
54.6

52.2
57.1
59.4
62.1
67.4
72.4

51.1
56.9
60.0
60.3
65.9
70.8

IncomeQ1 (Lowest)
Q2
Q3
Q4 (Highest)
Not answered

84.8
88.0
84.7
86.0
87.0

87.6
91.1
88.7
89.1
90.0

47.7
46.9
44.3
45.1
50.6

36.2
38.1
36.2
39.1
41.1

45.0
45.0
41.0
46.2
49.4

46.6
47.1
44.0
47.1
51.2

62.0
63.9
60.5
60.3
64.9

61.5
62.9
59.0
60.4
64.2

Education
Junior high school/High school
Vocational school/Junior college
University/Graduate-school

86.5
89.5
84.5

89.6
92.1
88.0

47.2
52.9
44.0

36.1
44.1
36.7

44.5
47.4
45.0

46.8
50.2
46.1

61.7
66.3
61.2

61.7
66.0
59.6

Family structure
Living with others
Living alone

87.2
81.9

90.3
85.5

47.3
45.1

39.0
34.5

46.4
41.0

48.4
42.3

63.8
57.5

63.0
56.0

Health literacy measured by CCHL
(Rounded)
1
2
3
4
5

69.6
81.9
80.8
91.5
91.9

75.9
83.4
85.2
93.9
93.2

44.8
45.5
43.2
49.2
60.5

36.3
37.7
36.3
38.9
49.1

44.4
39.5
40.8
48.9
60.0

45.9
42.8
42.9
50.5
60.4

54.1
58.3
57.3
66.9
74.5

54.1
55.7
56.6
66.2
71.8

Yes
No
Yes
No

TV tabloid show
Radio

Note: CCHL, Communicative and Critical Health Literacy.

Table 3 shows the association of each predictor of information usage and compliancewith each preventive behavior. In model 2, which included all predictors, most predictors
were associated with significantly higher compliance with at least one preventive measure.
Especially, for people- and institution-based information sources, “medical workers” (5.9%for ventilation (95% CI [1.2, 10.6] and 10.8% for social distancing (95% CI [6.3, 15.2])), “professionals” (5.7% for wearing a mask (95% CI [2.1, 9.3]) and 5.0% for ventilation (95% CI[0.1, 10.0])), and “government” (3.9% for wearing a mask (95% CI [1.3, 6.5]) and 6.7% foravoiding crowds (95% CI [3.4, 9.9])) were associated with significantly higher compliancewith two of the four types of preventive behaviors. For media-based information sources,“Twitter” (3.8% for wearing a mask (95% CI [0.6,7.0]), 4.7% for ventilation (95% CI = 4.7(95% CI [1.0, 8.3]), and 6.4% for social distancing (95% CI [3.0, 9.8])), “news website”(7.3% for wearing a mask (95% CI [5.1, 9.5]), 5.5% for ventilation (95% CI [1.5, 9.5]), and5.5% for avoiding crowds (95% CI [2.1, 8.8]), and “TV news” (12.3% for wearing a mask
(95% CI [8.2, 16.3]), 5.6% for avoiding crowds (95% CI [0.4, 10.7]) were associated with
two or three of the four types of preventive behaviors. In contrast, “Celebrities” (−4.7%
for avoiding crowds (95% CI [−9.0, −0.3])), “Instagram” (−18.9% for wearing a mask
(95% CI [−28.8, −8.9])), “Facebook” (−6.8% for social distancing (95% CI [−12.2, −1.3])),and “newspaper” (−2.8% for ventilation (95% CI [−5.9, −0.3])) were negatively associated with compliance with at least one preventive behavior, respectively. Regarding
changes due to time, compliance with ventilation decreased at follow-up by −9.3% (95% CI[−11.2, −7.5%]); meanwhile, compliance with social distancing increased by 3.0% (95% CI[1.2, 4.8]).

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Table 3. The association between each information source and preventive behavior for COVID-19 (n = 18,151).
Wearing a Mask

Ventilation

Social Distancing

Avoiding Crowds

Absolute Difference
in % [95%CI] †Model 1 ‡Model 2 §Model 1 ‡Model 2 §Model 1 ‡Model 2 §Model 1 ‡Model 2 §Time (2020→2021)

1.8 [−0.6, 4.2]

1.8 [−0.6, 4.2]

−9.3 [−11.2, −7.5] ***−9.3 [−11.2, −7.5] ***3.0 [1.2, 4.8] **3.0 [1.2, 4.8] **−1.2 [−3.7, 1.2]

−1.2 [−3.7, 1.2]

People/institution
FamilyFriends
Workplace/School
Medical workers
Celebrities
Professionals
Government
Academic institution

5.3 [2.5, 8.1] ***5.4 [2.9, 7.9] ***6.7 [3.8, 9.6] ***2.0 [−1.6, 5.6]
2.1 [−1.4, 5.7]
6.6 [4.6, 8.6] ***5.5 [2.4, 8.7] **−1.3 [−7.8, 5.2]

1.9 [−1.4, 4.2]
1.8 [−0.5, 4.1]
4.4 [1.7, 7.2] **−1.0 [−3.5, 1.4]
−2.7 [−6.2, 0.8]
5.7 [2.1, 9.3] **3.9 [1.3, 6.5] **−5.5 [−11.0, 0.0]

6.0 [3.0, 9.1] ***6.4 [3.1, 9.6] ***4.9 [2.0, 7.8] **9.6 [4.7, 14.5] ***7.0 [3.2, 10.9] ***8.6 [4.8, 12.4] ***5.3 [2.0, 8.6] **9.4 [1.9, 16.9] *2.4 [−1.8, 6.6]
2.1 [−2.1, 6.3]
0.2 [−3.1, 3.4]
5.9 [1.2, 10.6] *0.8 [−3.8, 5.3]
5.0 [0.1, 10.0] *1.0 [−2.6, 4.5]
4.2 [−3.3, 11.7]

1.5 [−1.5, 4.4]
−0.7 [−3.6, 2.2]
0.3 [−2.4, 2.9]
11.2 [7.1, 15.4] ***1.9 [−1.2, 5.1]
4.8 [1.2, 8.4] **5.1 [1.9, 8.3] **7.8 [0.2, 15.4] *1.4 [−2.9, 5.6]
−3.7 [−7.7, 0.2]
−1.7 [−4.8, 1.3]
10.8 [6.3, 15.2] ***−2.2 [−6.4, 2.0]
3.0 [−1.7, 7.7]
2.9 [−0.4, 6.3]
3.8 [−4.3, 11.8]

8.1 [5.3, 10.9] ***4.2 [1.3, 7.0] **3.7 [0.8, 6.6] *6.5 [2.2, 10.7] **1.6 [−1.8, 5.0]
6.8 [3.3, 10.2] ***8.9 [5.4, 12.4] ***4.8 [−1.0, 10.6]

6.7 [2.7, 10.7] **−0.9 [−4.6, 2.8]
−0.5 [−3.7, 2.7]
3.7 [−0.5, 7.8]
−4.7 [−9.0, −0.3] **4.7 [−0.1, 9.4]
6.7 [3.4, 9.9] ***−0.3 [−6.1, 5.5]

MediaVideo sharing site
LINE
Twitter
Facebook
Instagram
News website
Newspaper
Magazines
Books
TV news
TV tabloid showRadio

4.1 [1.8, 6.4] **3.7 [0.7, 6.8] *4.1 [−0.6, 8.7]
−2.6 [−9.0, 3.7]
−10.9 [−19.7, −2.0] *11.7 [8.5, 14.8] ***6.3 [3.5, 9.1] ***0.7 [−4.3, 5.7]
1.7 [−2.1, 5.6]
17.9 [13.6, 22.3] ***9.1 [5.9, 12.4] ***3.7 [1.4, 5.9] **2.4 [−0.6, 4.2]
1.6 [−0.7, 4.0]
3.8 [0.6, 7.0] *0.9 [−6.4, 8.3]
−18.9 [−28.8, −8.9] ***7.3 [5.1, 9.5] ***3.1 [0.9, 5.3] *−4.5 [−10.6, 1.6]
4.5 [−2.4, 11.4]
12.3 [8.2, 16.3] ***2.3 [−0.4, 5.0]
1.0 [−1.4, 3.3]

6.3 [2.3, 10.3] **4.4 [0.6, 8.1] *7.6 [3.5, 11.6] ***2.1 [−5.3, 9.5]
2.6 [−3.0, 8.1]
7.2 [3.2, 11.1] ***0.7 [−2.2, 3.7]
9.0 [2.1, 15.8] *8.0 [−1.2, 17.2]
3.3 [−0.4, 7.0]
2.8 [−0.9, 6.4]
9.3 [5.0, 13.6] ***2.9 [−0.7, 6.6]
0.9 [−2.3, 4.2]
4.7 [1.0, 8.3] *−3.3 [−10.8, 4.1]
−3.8 [−11.2, 3.6]
5.5 [1.5, 9.5] *−2.8 [−5.9, −0.3] *5.5 [−2.7, 13.6]
1.3 [−9.0, 11.6]−0.5 [−5.2, 4.2]
−0.3 [−4.8, 4.2]
7.8 [3.7, 11.8] ***5.4 [1.6, 9.2] **2.2 [−0.9, 5.2]
6.3 [2.8, 9.9] ***−1.6 [−6.7, 3.5]
−0.5 [−5.9, 4.8]
−0.1 [−3.3, 3.2]
2.6 [−0.7, 5.9]
9.1 [3.6, 14.7] **9.7 [2.1, 17.4] *2.1 [−0.9, 5.1]
1.3 [−2.6, 5.1]
4.3 [0.0, 8.7]

4.5 [0.7, 8.3] *−0.2 [−2.9, 2.6]
6.4 [3.0, 9.8] ***−6.8 [−12.2, −1.3] *−5.4 [−12.1, 1.3]
−2.5 [−6.2, 1.2]
0.6 [−2.8, 3.9]
6.4 [0.1, 12.6] *5.6 [−2.2, 13.5]
1.7 [−2.8, 6.1]
−0.6 [−5.6, 4.3]
2.0 [−2.3, 6.4]

4.4 [0.4, 8.3] *1.9 [−1.6, 5.5]
3.5 [−0.9, 7.9]
0.4 [−6.7, 7.6]
−0.6 [−8.6, 7.5]
8.3 [5.0, 11.6] ***3.3 [−0.4, 7.0]
5.3 [0.1, 10.4] *3.8 [−3.9, 11.5]
10.4 [6.2, 14.5] ***6.7 [3.0, 10.3] ***4.7 [1.0, 8.5] *2.4 [−1.4, 6.2]
−1.4 [−4.6, 1.8]
1.3 [−3.9, 6.5]
−0.3 [−8.3, 7.6]
−5.0 [−14.9, 4.8]
5.5 [2.1, 8.8] **0.2 [−3.2, 3.6]
2.4 [−4.4, 9.1]
0.8 [−7.9, 9.4]
5.6 [0.4, 10.7] *2.4 [−1.9, 6.7]
2.5 [−0.6, 5.7]

† The estimates were adjusted for age, gender, income, educational attainment, family structure, and health literacy. ‡ Model 1: Each predictor included separately in the model.
§ Model 2: All predictors of people-, institution-, or media-based information sources included simultaneously with time in the model. Note: * p < 0.05, ** p < 0.01, *** p < 0.001.

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4. Discussion
The present study evaluated the association between the various types of information
usage and compliance with four preventive behaviors: “wearing a mask”, “ventilation”,“social distancing”, and “avoiding crowds”, and how their compliance changed over time.
The results of the present study showed that most information sources were positively
associated with compliance with at least one preventive behavior. Information sources
including “medical workers”, “professionals”, “government”, “Twitter”, “news website”,
and “TV news” were especially positively associated with compliance with two or morepreventive behaviors for COVID-19.
Previous studies have also suggested positive or negative associations between theinformation sources and compliance with preventive behaviors. Although the classificationof the information sources in the present study was defined differently than in previous
studies, the findings of the present study corroborated previous studies that stated thatthe use of information sources is associated with preventive behaviors for COVID-19.
For information sources, reliable sources, such as the government, were reported to be
associated with a higher probability of adhering to preventive behaviors [11,23]. For
media, TV news and news websites have also been reported to influence compliancewith preventive behaviors [16,18]. In contrast, the association between social media andcompliance with preventive behaviors differed according to the type of social media usedin previous studies [10,21,22]. We investigated the association between information source
usage and preventive behavior by employing more detailed categories of information
sources, especially social media. Our results add to the knowledge that even on SNS,the direction of association with compliance with preventive behavior differed by thetype of information source (e.g., Twitter was positively and Instagram and Facebook were
negatively associated with preventive behavior). In addition, the association differedaccording to the type of preventive behavior.
A previous review suggested that exposure to the media directly changes individual
health behavior through (1) changes in emotion and perception of the behavior, (2) lowering obstacles to change the behavior, and (3) recognizing the social norms related tothe behavior [24]. In addition, as several major health behavior theories include knowledge as a fundamental factor to change health behavior, we considered that providing
knowledge is also an important mechanism between exposure to information and preventive behaviors [25,26]. Previous studies also suggested that exposure to social media
influenced preventive behavior by increasing negative emotions toward COVID-19; meanwhile, exposure to mass media influenced preventative behavior compliance through social
norms [13,27]. Another study suggested that media exposure affects preventive behavior
compliance through negative emotions and knowledge [17]. A previous study also suggested that knowledge is associated with both risk perception and preventive behavior for
COVID-19 [10]. Therefore, as described in the previous review, information sources andmedia, including social media, can influence individual behavior by providing emotionalchanges, knowledge, and social norms [24].
We observed that the degree or direction of the association of compliance with preventive behaviors for COVID-19 differed according to the types of information sources. We
considered that these differences were caused by the amount of beneficial information on
preventive behaviors provided by each source or media. In the present study, most of theinformation sources were not significantly associated with all four preventive behaviors.
Therefore, we postulate that if the information source provided information or messages oncertain preventive behaviors, the recipients would be more likely to engage in preventive
behaviors. Many researchers are worried about “misinformation” or the “infodemic” [28].
We consider that the lack of beneficial information about effective preventive measuresand “information overload”, which conceal important information, may also affect public
compliance with preventive behaviors for COVID-19 [29,30].

Healthcare 2022, 10, 521

10 of 13We also observed that compliance with ventilation decreased at the follow-up survey.
The baseline survey was conducted in summer, and the follow-up survey was conducted in
winter. Therefore, we considered that this finding could be attributed to people refraining
from adhering to ventilation guidelines owing to low outside temperatures in winter.
A ventilation system that can maintain room temperature would contribute to higher
compliance with ventilation in winter.
In the public health viewpoint, our result suggested that “medical workers”, “professionals”, “the government”, “Twitter”, “news website”, and “TV news” were positively
associated with compliance with multiple preventive behaviors. These information sourcesplay an important role in providing beneficial information for preventing COVID-19.
However, not all people received information from these information sources. Therefore,
developing a strategy of increasing access to these sources or media may increase compliance for preventive behavior against COVID-19. Our descriptive results also suggestthat the proportion of usage of each information source differed by sociodemographic andsocioeconomic status. It may also be important to consider which information source ismost effective in providing beneficial information to a specific population.
The present study has several limitations. First, our survey was based on an Internet
survey, which may have led to selection bias. However, in order to mitigate this limitation, we conducted an inverse probability weighting method based on the distribution
obtained from nationally representative data in order to increase the representativenessof the study sample [31]. Self-reported questionnaires are vulnerable to information bias.
The self-reported responses are usually different from the actual condition; therefore, theprevalence is underestimated or overestimated. In addition, such non-differential misclassification of the actual condition leads to underestimation of the association [32]. In addition,
the accuracy of the questions employed in this study has not been confirmed previously,
and has also increased the degree of misclassification. Third, although we included theconfounders, unknown or residual confounders could have impacted the study findings.
Further analysis that is robust to unmeasured confounders and employs more precise
measurements would contribute to checking the robustness of the results of the present
study. Fourth, our study did not consider attitudes or willingness to engage in preventive
behaviors. A previous study investigating the association of socioeconomic status with
anxiety or preventive behavior for radiation exposure suggested that people with low
socioeconomic status tended to have more anxiety than those with high socioeconomic
status, but were less engaged in preventive behavior [33]. Based on knowledge, attitude,
and behavior models, there is a gap between attitude and behavior [34]; therefore, future
research that considers attitudes toward preventive behavior and obstructing factors toengage in preventive behavior is required. Fifth, we could not consider the correlations between the use of each information source. Supplementary Table S3 presents the correlations
between the usage of each information source, and we observed moderate correlations
between several variables (phi coefficient > 0.4). A future study considering the patterns of
information usage using unsupervised machine learning techniques would elucidate theeffects of combining multiple information sources. Finally, although we considered temporal changes in compliance with preventive behaviors, we could not consider temporalchanges in information usage.
In contrast, our study also had some strengths. The sample size of the present result
was relatively large, and it contributed to the statistical power to detect the differences in
estimates in various types of information sources. In addition, the present study used alongitudinal design and could measure individual responses twice. Therefore, we could
analyze the associations between temporal changes and interpersonal correlations, and it
provided more valid results than cross-sectional studies.
Our research partially revealed the longitudinal effects of information usage on thepreventive behavior for COVID-19. For future research on this topic, a two-year follow-up
to measure the change in preventive behavior and information usage would be insightful. Repeated measurements with accurate measures would also enable us to conductHealthcare 2022, 10, 521

11 of 13a fixed-effect model or marginal structural model, which would contribute to eliminating confounding factors. In addition, future research which evaluates the interaction oreffect modification of information usage on preventive behavior by sociodemographic
or socioeconomic factors would contribute to providing beneficial knowledge on publichealth strategies.
5. Conclusions
The present study showed that various sources of information sources, especially“medical workers”, “professionals”, “the government”, “Twitter”, “news website”, and“TV news”, were associated with a higher probability of compliance with multiple preventive behaviors for COVID-19. These information sources played a key role in providing
beneficial information for preventing COVID-19.
Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/healthcare10030521/s1. Supplementary Figure S1: The participants flow for analytic sample; Supplementary Table S1: The differences of baseline characteristics
between original and analyzed population; Supplementary Table S2: The characteristics of participants using each information source; Supplementary Table S3: The correlation between the use of
information sources.
Author Contributions: Conceptualization and design, T.K., S.K., K.T., T.I., N.N., A.K., K.O. and T.T.;
data acquisition, T.T. All authors have contributed to the analysis and interpretation of data, draftingof the article, and critical revision. All authors have read and agreed to the published version of
the manuscript.
Funding: This study was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI(grant numbers 17H03589, 19K10671, 19K10446, 18H03107, 18H03062, 19H03860, 21H04856); theJSPS Grant-in-Aid for Young Scientists (grant number 19K19439); the Research Support Program to
Apply the Wisdom of the University to Tackle COVID-19 Related Emergency Problems, Universityof Tsukuba, Health Labor Sciences Research Grant (grant numbers 19FA1005, 19FG2001, 19FA1012);
and the Japan Agency for Medical Research and Development (AMED; grant number 2033648). The
findings and conclusions of this article are the sole responsibility of the authors and do not represent
the official views of the research funders.
Institutional Review Board Statement: The study was conducted in accordance with the Declarationof Helsinki, and approved by the Ethics Committee on Research of Human Subjects at the Research
Ethics Committee of the Osaka International Cancer Institute (No.20084-2).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: Data are available upon reasonable request.
Conflicts of Interest: The authors declare no conflict of interest.

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