International Journal ofEnvironmental Researchand Public Health

ArticleCommunity-Level Sports Group Participation and HealthBehaviors Among Older Non-Participants in a Sports Group:
A Multilevel Cross-Sectional Study
Taishi Tsuji 1, * , Satoru Kanamori 2,3 , Yasuhiro Miyaguni 4 and Katsunori Kondo 4,5
1
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3

4

5

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Citation: Tsuji, T.; Kanamori, S.;
Miyaguni, Y.; Kondo, K.
Community-Level Sports Group
Participation and Health Behaviors
Among Older Non-Participants in a
Sports Group: A Multilevel
Cross-Sectional Study. Int. J. Environ.
Res. Public Health 2021, 18, 531.
https://doi.org/10.3390/
ijerph18020531

Faculty of Health and Sport Sciences, University of Tsukuba, Bunkyo City, Tokyo 112-0012, Japan
Graduate School of Public Health, Teikyo University, Itabashi City, Tokyo 173-8605, Japan;
satoru_kanamori@med.teikyo-u.ac.jp
Department of Preventive Medicine and Public Health, Tokyo Medical University, Shinjuku City,
Tokyo 160-8402, Japan
Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu City,
Aichi 474-8511, Japan; y.miyaguni@ncgg.go.jp (Y.M.); kkondo@chiba-u.jp (K.K.)
Center for Preventive Medical Sciences, Chiba University, Chiba City, Chiba 263-8522, Japan
Correspondence: tsuji.taishi.gn@u.tsukuba.ac.jp

Abstract: This study validates the relationship between community-level sports group participationand the frequency of leaving the house and transtheoretical model stages of behavior change for exercise among older individuals who did not participate in a sports group. We used cross-sectional datafrom the 2016 Japan Gerontological Evaluation Study. The proportion of sports group participants atthe community level was calculated using the data from 157,233 older individuals living in 1000 communities. We conducted a multilevel regression analysis to examine the relationship between the
proportion of sports group participants and the frequency of leaving the house (1 day/week orless) and the transtheoretical model stages of behavior change for exercise. A statistically significant
relationship was observed between a high prevalence of sports group participation and lower riskof homeboundness (odds ratio: 0.94) and high transtheoretical model stages (partial regression
coefficient: 0.06) as estimated by 10 percentage points of participation proportion. Older individuals,
even those not participating in a sports group, living in a community with a high prevalence of sports
group participation are less likely to be homebound; they are highly interested and have numerous
opportunities to engage in exercise.
Keywords: multilevel analysis; social capital; contextual effect; housebound; exercise epidemiology

Received: 15 November 2020
Accepted: 7 January 2021
Published: 10 January 2021
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1. Introduction
Older individuals engaging in sports and exercises while participating in groups areat lower risk of functional disability, depressive symptoms, and falls than those engaging
in sports and exercises alone without participating in such groups [1–3]. The suspectedmechanisms underlying health outcomes that are more likely to be obtained through sports
group participation rather than individual exercise involve the benefits of physical (e.g., inducing good adherence and long duration of exercise) [4–6], psychological (e.g., enjoyment,enhanced self-esteem, and decreased stress) [4,7], and social (e.g., receiving social support,social capital, and social influence) factors [4,7,8]. Furthermore, community-level sports
group participation had a preventive effect on depressive symptoms [9] and cognitive
impairment [10] in older individuals. This result indicated that regardless of participation
in a sports group, older individuals living in communities with a high prevalence of sports
group participation were less likely to present with depressive symptoms and develop cognitive impairment than those living in a community with a low prevalence of participation.
However, the reason why older individuals who do not participate in community sports
groups remain healthy is not fully elucidated.

Int. J. Environ. Res. Public Health 2021, 18, 531. https://doi.org/10.3390/ijerph18020531

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Civic participation, such as sports group participation, is a component of social capital [11]. One of the pathways between community-level social capital and individual-level
health outcomes is social contagion, referring to the notion that behaviors spread more
quickly through a tightly knit social network [12]. The social contagion extends to older
individuals who may be encouraged by sports group participants to increase their interest
and prepare to engage in sports and exercise activities. As a result, even if individuals do
not join a sports group, several older individuals may frequently go out, have an interestin engaging in exercise and sports, and acquire those habits. A decrease in the frequencyof going out and being homebound are known markers of complex comorbidities andvulnerability, and a specific population who is at risk possesses these characteristics [13].
Furthermore, in reference to the transtheoretical model stages of behavior change (i.e., precontemplation, contemplation, preparation, action, and maintenance stages), staying at
a higher stage is considered a healthy lifestyle [14]. From the public health and healthpromotion perspectives, establishing an effective approach to progress the stage of behavior
change is required [15].
This multilevel cross-sectional study examines whether the frequency of leaving thehouse was maintained and the transtheoretical model stages of behavior change for exercise
were high among older individuals, even those who do not participate in the sports group,
living in a community with numerous sports group participants.
2. Materials and Methods
2.1. Study Design and Participants
We used cross-sectional data from the Japan Gerontological Evaluation Study (JAGES),
which is an ongoing cohort study exploring social, environmental, and behavioral factors correlated with health loss, particularly functional decline or cognitive impairment,
among individuals aged 65 years or older [16]. We mailed a set of questionnaires to279,661 community-dwelling individuals between October 2016 and January 2017, andthese individuals were randomly selected from 39 municipalities, including metropolitan,
urban and semi-urban, and rural communities, in 18 prefectures from as far north as
Hokkaido (i.e., the northernmost prefecture) and as far south as Kyushu (i.e., the southernmost region) in Japan. The exclusion criteria were as follows: individuals receiving support
and long-term care certification under the Japanese long-term care insurance system [17];those having limitations in performing activities of daily living, which is defined as inability
to walk, bathe, or use the toilet without assistance; and those living in communities with≤30 respondents. One community was defined primarily by the school district. In theprocedure of generating community-level variables of sports group participation, respondents who did not answer the extent of sports group participation were considered as“non-participants” in a sports group regardless of the status of individual exercise, andaggregated individual-level sports group participation according to community area wasconsidered a community-level independent variable. With regard to the procedure of
conducting the main statistical analysis, we further excluded respondents with incomplete
information on sex, extent of sports group participation, or each outcome variable or those
who participated in a sports group 1 day/month or more.
2.2. Measurements
2.2.1. Frequency of Leaving the House
The participants were assessed for the frequency of leaving the house (including to
one’s garden, immediate neighborhood, shopping complex, and hospital). The choices forthe answers were as follows: ≥4 days/week, 2–3 days/week, 1 day/week, 1–3 days/month,a few times/year, and zero. We defined going out 1 day/week or less as mostly homebound [13,18], and this was previously associated with a higher mortality risk amongcommunity-dwelling older individuals [18].

Int. J. Environ. Res. Public Health 2021, 18, 531

3 of 112.2.2. Transtheoretical Model Stages of Behavior Change for Exercise
The transtheoretical model stages of behavior change assess an individual’s readinessto act on a healthier behavior [14]. In this study, the participants were asked the question,“which of the following best describes your current lifestyle?” Here, regular exercise isdefined as performing exercise once a week or more for at least 20 min per session. The
choices for the answers were as follows: (1) not engaging in regular exercise and thelack of intention to start exercising in the future, (2) have not started exercising yet but
committed to taking action within 6 months, (3) performing minimal exercises but notconsistent, (4) exercising consistently for less than 6 months, and (5) exercising consistently
for 6 months or more. We referred to the question item that was validated for Japanese
adults [19]; however, we revised the frequency of regular exercise from twice a week toonce a week, considering that the participants of this study were older individuals. The
series had five stages, that is, precontemplation, contemplation, preparation, action, andmaintenance, and an individual will go through these stages in adopting a healthy behavior
or quitting an unhealthy behavior [14,20,21].
2.2.3. Community-Level Sports Group Participation
The participants were assessed for the frequency of sports group participation. The
choices for the answers were as follows: ≥4 days/week, 2–3 days/week, 1 day/week,
1–3 days/month, a few times/year, and zero. We defined participating 1 day/month ormore as participation in a sports group [9,22], and aggregated individual-level sports group
participation according to the community area defined primarily by the school district
was considered a community-level independent variable. A study has indicated a strong
correlation between the proportion of older individuals with poor self-rated health anddepressive symptoms living in areas with community-level sports group participation oncea month or more (r = −0.233 and −0.355, respectively) compared with community-level
sports group participation once a week or more (r = −0.210 and −0.314, respectively) [22].
2.2.4. Covariates
We evaluated confounding variables between community-level sports group participation and individual-level health status [9]. Data on basic demographic characteristics,
including sex and age, were collected. The participants were divided into the following
age groups: 65–69, 70–74, 75–79, 80–84, and ≥85 year groups. Then, they were queriedon their household members and categorized as living with others or living alone. Drinking and smoking status (none, past, or current) and education (<10, 10–12, or ≥13 years)
were classified according to each answer. Annual equivalent income was calculated by
dividing the household income by the square root of the number of household members
and categorized into the following three groups: JPY <2,000,000; JPY 2,000,000–3,999,999;
and JPY ≥4,000,000. To obtain information about disease status in treatment, the participants were asked if they were currently receiving any medical treatment or had secondary
diseases; the answer was either yes or no. If the participants did not respond to theindividual-level covariates, corresponding observations were assigned to the missing categories. As community-level covariates, population density per km2 of inhabitable area andmean annual household income for each community area were calculated and categorized
into the following quartile categories: ≥10,000; 7000–9999; 2500–6999; and <2500 individuals per km2 and JPY ≥2,713,000; JPY 2,468,000–2,712,999; JPY 2,250,010–2,467,999; andJPY <2,250,010, respectively.
2.3. Statistical Analysis
This analysis was conducted from January 2019 to March 2019. The multilevel analysis framework assumes that the health outcome of an individual partly depends on thecommunity in which an individual lives. Multilevel models estimate the variations inoutcome between communities (random effects) and the effects of community-level variables while adjusting for individual- and community-level characteristics (fixed effects).

Int. J. Environ. Res. Public Health 2021, 18, 531

4 of 11Multilevel logistic regression analysis was conducted to calculate the odds ratio (OR) and
95% confidence interval (CI) for being mostly homebound, and multilevel mixed-effects
linear regression analysis was performed to calculate the partial regression coefficient (B)and 95% CI for the transtheoretical model stages of behavior change for exercise. The OR
and B of community-level sports group participation was estimated as a 10-percentage
point change in aggregated sports group participation. The following three models ofanalysis were used: the null model, crude model including community-level sports group
participation, and fully adjusted model (the crude model + all covariates). We calculated
the proportional changes in the variance of the crude and fully adjusted models to the nullmodel to estimate how much the community-level variances of the frequency of leaving
the house and transtheoretical model stages of change were explained by the exposure andcovariates. Furthermore, we conducted a subgroup analysis by age groups (65–69, 70–79,
and ≥80 years) to investigate whether the relationships were consistent or not. Analyses
were performed using Stata/MP 14.2 (Stata Corp., College Station, TX, USA).
3. Results
Figure 1 shows the flow of the participants in this study. We received responses from
196,438 individuals, with a response rate of 70.2%. We excluded 38,707 respondents who
received support and long-term care certification or who have limitations in performing
activities of daily living and 498 respondents who lived in communities with ≤30 respondents. Thus, 157,233 respondents (mean and standard deviation for age: 73.8 ± 6.0 years)
living in 1000 communities. They were considered analytic participants for generating
community-level variables of sports group participation and mean annual household income. Furthermore, we excluded respondents with incomplete information on sex (n = 23)
or extent of sports group participation (n = 25,641) or those who participated in a sports
group 1 day/month or more (n = 40,879). Individuals with missing information on eachoutcome were excluded as well. Finally, 89,847 participants were considered analytic participants for the frequency of leaving the house analysis. With regard to the transtheoreticalmodel stage analysis, the analytical sample comprised 10,487 participants because the question item was distributed to one-eighth of the participants who were randomly allocated.
Table 1 shows the demographic characteristics of the participants according to eachanalysis. Among the 89,847 analytic participants of the frequency of leaving the house
analysis, 7364 (8.2%) reported going out 1 day/week or less and were categorized as mostly
homebound. Among the 10,487 analytic participants for the transtheoretical model stage
analysis, approximately 40% (n = 4356) were at the precontemplation stage, whereas 20%(n = 2260) were at the action or maintenance stage. Table 2 shows the descriptive statistics
for the community-level variables. The mean proportion of sports group participants was
26.7% (standard deviation: 7.6%; range: 2.0–50.5%).
Table 3 and Table S1 show the results of the multilevel regression analyses. According
to the analysis of the frequency of leaving the house, regardless of models including covariates, a higher prevalence of sports group participation is statistically significantly associatedwith a lower risk of homeboundness (OR: 0.94; 95% CI: 0.89–0.996 in the adjusted model), as
estimated by 10-percentage points of participation proportion. The community-level variance decreased by 22.5% with the addition of the proportion of sports group participantsand 36.3% with the addition of all covariates to the null model. In addition, communitylevel sports group participation was positively associated with the transtheoretical modelstages of behavior change for exercise (B: 0.06; 95% CI: 0.01–0.12 in the adjusted model),and the community-level variance decreased by 23.1% and 38.5%, respectively. Table S2shows the subgroup analysis by age groups. The direction of relationships was consistentamong age groups although the 95% CIs were widened due to the smaller sample size.

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Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW

5 of 115 of 11

Figure 1. Flow of participants throughout the study.
Figure 1. Flow of participants throughout the study.

Total
Sex
Men
Women

Table 1 shows the demographic characteristics of the participants according to each
1. Characteristicsof the participants.
analysis.Table
Among
the 89,847 analytic
participants of the frequency of leaving the house
analysis, 7364 (8.2%) reported going out 1 day/week or less and were categorized asAnalytic Participants for the Frequency
Analytic Participants for the
mostly homebound.
the Analysis
10,487 analytic participants
for thetranstheoreticalmodelof LeavingAmongthe House
Transtheoretical
Model
Stages Analysis
stage analysis, approximately 40% (n = 4356) were at the precontemplation stage, whereasn%n%
20% (n = 2260) were at the action or maintenance stage. Table 2 shows the descriptive
89,847100.0%
statistics for thecommunity-level100.0%
variables. The mean10,487
proportion of sports
group participants was 26.7% (standard deviation: 7.6%; range: 2.0–50.5%).
45,84751.0%
Table 1. Characteristicsof the participants.
44,00049.0%

Age (years)
65–69
70–74
75–79
80–84
≥85

30,924
24,10818,821
10,698
Total
5296

Sex
Men
Women
Age (years)

5356
5131

Analytic Participants for the Frequency
of Leaving the House
34.4%
3713
26.8% Analysis
2791
20.9%
n
% 2166
11.9%
1232
89,847100.0%
5.9%
585
45,84744,000

51.0%
49.0%

51.1%
48.9%

Analytic Participants for
the Transtheoretical
35.4%
26.6%
Model Stages
Analysis
20.7% %
n
11.7%
10,487
5.6%100.0%
5356
5131

51.1%
48.9%

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Table 1. Cont.
Analytic Participants for the Frequency
of Leaving the House Analysis

Analytic Participants for theTranstheoretical Model Stages Analysis

n

%

n

%

17,593
69,631
2623

19.6%
77.5%
2.9%

2117
8093
277

20.2%
77.2%
2.6%

76,657
9873
3317

85.3%
11.0%
3.7%

8957
1161
369

85.4%
11.1%
3.5%

43,81910,22834,504
1296

48.8%
11.4%
38.4%
1.4%

5062
1201
4066
158

48.3%
11.5%
38.8%
1.5%

50,11527,754
11,402
576

55.8%
30.9%
12.7%
0.6%

5853
3222
1306
106

55.8%
30.7%
12.5%
1.0%

29,02436,67923,142
1002

32.3%
40.8%
25.8%
1.1%

3230
4319
2831
107

30.8%
41.2%
27.0%
1.0%

35,851
28,2007970
17,826

39.9%
31.4%
8.9%
19.8%

4208
3327
957
1995

40.1%
31.7%
9.1%
19.0%

82,483
7364

91.8%
8.2%

4356
1215
2822
365
1729
2.4

41.5%
11.6%
26.9%
3.5%
16.5%
(1.5)

Disease status in treatment
No
Yes
Missing
Living with others
Yes
No (living alone)
Missing
Drinking status
None
Past
Current
Missing
Smoking status
None
Past
Current
Missing
Education (years)
<10
10–12
≥13
MissingAnnual household equivalent income(Japanese Yen)
<2,000,000
2,000,000–3,999,999≥4,000,000
Missing
Frequency of leaving the house>1 day/week≤1 day/week
Transtheoretical model stages
1. Precontemplation
2. Contemplation
3. Preparation
4. Action
5. Maintenance
Mean (standard deviation)

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Table 2. Characteristics of community-level variables (n = 1000).
Proportion of Sports Group Participants (%)
Mean (standard deviation)(Minimum–Maximum)

26.7

(7.6)
(2.0–50.5)

Population Density(Persons Per km2 of Inhabitable Area), n≥10,000
7000–9999
2500–6999
<2500

257
258
238
247
Mean Annual Household Equivalent Income(Japanese Yen), n≥2,713,000
2,468,000–2,712,999
2,250,010–2,467,999<2,250,010
Missing

249
249
249
249
4

Table 3. Point estimates and 95% confidence intervals (CIs) estimated from multilevel regression analyses in each model.
Null Model

Adjusted Model 1

Crude Model

Outcome: mostly homebound (leaving the house 1 day/week or less)(number of communities = 1000, number of participants = 89,847)
Fixed effects
Proportion of sports group participants
Per 10 percentage points, OR (95% CI)

0.82(0.79–0.86)

0.94(0.89–0.996)

0.079
22.5

−0.011

0.065
36.3−0.011

Random effects
Community-level varianceΩµ, (standard error)
Proportional changes in variance, %0.102−0.012

Outcome: transtheoretical model stages of behavior change for exercise(number of communities = 988, number of participants = 10,487)
Fixed effects
Proportion of sports group participants
Per 10 percentage points, B (95% CI)

0.09(0.05–0.14)

0.06(0.01–0.12)

0.02
23.1

−0.0090.016
38.5−0.008

Random effects
Community-level varianceΩµ, (standard error)
Proportional changes in variance, %0.026−0.009

OR: odds ratio, CI: confidence interval. 1 Adjusting for age, sex, population density, mean annual household equivalent income (communitylevel), disease status in treatment, living status, drinking status, smoking status, education, and annual household equivalent income(individual-level).

4. Discussion
To the best of our knowledge, this is the first study to assess the contextual relationshipbetween community-level prevalence of sports group participation and health behavior
in older individuals, particularly those who do not participate in a sports group. A 10percentage point increase in community-level sports group participation was associatedwith a 6% reduction in the risk of being homebound and a 0.06 higher in the transtheoreticalmodel stages of behavior change for exercise after adjusting for potential confounders.
These favorable associations can occur at any age group in the older population.

Int. J. Environ. Res. Public Health 2021, 18, 531

8 of 11Social contagion might be considered a pathway that confirmed the results of thisstudy supporting our hypothesis. Sometimes the behavior spreading via the network
can promote healthy lifestyle changes (e.g., the spread of smoking cessation) [23,24]. In
relation to this study, in communities where sports group participation is active, even if
individuals do not participate in the group, they may have numerous opportunities towatch or cheer during the activities or participate in occasional events. As a result, they
have numerous opportunities to go out, which may increase their interest in engaging in
exercise and sports. Furthermore, Seino and colleagues have reported that communitylevel informal neighbor relationships were positively associated with individual-level
moderate-to-vigorous physical activities among older men [25]. Both social participation,such as that in sports groups, and neighbor relationships are categorized as structuralsocial capital; the affluence of this type of resource may enhance the frequency of going out
and physical activities among older individuals living in the community.
Another possible pathway is collective efficacy, which is the group-level analog of theconcept of self-efficacy and refers to the ability of the collective to mobilize to undertake acollective action [12,26,27]. Facilities, built environment, industries, systems, and bylaws
for health promotion may develop to reflect the opinions and actions of communities withnumerous sports groups and their participants. The group-level mechanisms of widespread
sports group participation may result in positive spillover effects [9].
The transtheoretical model of behavior change is an integrative theory of therapy
providing strategies or processes of change to guide an individual [14]. The strategies
for enhancing the stages are mostly individual-oriented approaches, such as raising an
individual’s consciousness and enhancing self-efficacy [28–30]. Naturally, the importanceof controlling the environment is also mentioned; however, it is focused on the environment relatively close to an individual, which directly correlated with their consciousness
and activities. A systematic review assessing the efficacy of dietary or physical activity
interventions based on the transtheoretical model stages of behavior change in overweight
and obese individuals has reported that the interventions led to sustained weight loss
during the intervention period [15]. Another review has concluded that the transtheoreticalmodel is a useful and suitable behavior model in creating, developing, and evaluating interventions to acquire and improve physical activity habits in older individuals [29]. However,evidence about whether such interventions could improve the stages of behavioral change
and sustain them over time is limited. Meanwhile, the results of this study indicated thatthe stages may be improved by arranging a community environment where sports groups
are active, even without working on older individuals directly. Assuming that there are
10-percentage points for more sports group participants in the community area, then 6 of
100 non-participants living in the area will be one stage higher. The accumulation of thisclinical significance would not be a negligible contribution to public health.
The study strength is its large, nationwide, population-based sample enabling communityand individual-level multilevel analyses for clarifying the contextual relationship of sports
group participation limited only to non-participants in a sports group. However, this study
had several limitations. First, reverse causality could occur because of the nature of thecross-sectional design, and longitudinal studies must be conducted to resolve this limitation.
Second, selection bias might have affected the results due to the relatively low response
rate at 70.2%. According to our previous study, the response rate and percentage of sports
group participants with older age were significantly lower than those with younger age [31].
Therefore, the participants in this study might be relatively at low risk of homeboundness
and might stay at high transtheoretical model stages of behavior change for exercise, andthose relationships might have been underestimated. Third, the frequency of leaving thehouse and the transtheoretical model stages of behavior change for exercise are assumed to
be associated with the levels of physical activity/inactivity; however, we did not collect the
information using a valid index. If we evaluate the levels of physical activity/inactivity,
we could further define the pathway linking community-level sports group participationand health outcomes among older individuals living in the community. Fourth, we couldInt. J. Environ. Res. Public Health 2021, 18, 531

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not consider the geographic characteristics of the communities which could contribute tophysical activity [32]. This might be one of the unmeasured confounders and diminish therelationships identified in this study.
5. Conclusions
Older individuals, even those who do not participate in a sports group, living in
a community area with a high prevalence of sports group participation were less likely
to be homebound and have higher transtheoretical model stages of behavior change forexercise than those living in an area with a low prevalence of participation. Promoting
sports groups in a community may be an effective population-based strategy for increasing
the frequency of leaving the house and enhancing interest in and providing opportunities
to engage in exercise and sports even for non-participants in a sports group. A policy toincrease sports groups, giving priority to communities where many older individuals withhealth problems live, may be effective.
Supplementary Materials: The following are available online at https://www.mdpi.com/1660-460
1/18/2/531/s1, Table S1: All point estimates and 95% confidence intervals estimated from multilevel
regression analyses in the fully adjusted model. Table S2: Point estimates and 95% confidence
intervals (CIs) estimated from multilevel regression analyses stratified by age groups.
Author Contributions: Conceptualization, T.T.; formal analysis, T.T.; investigation, T.T., Y.M. andK.K.; writing—original draft preparation, T.T.; writing—review and editing, S.K., Y.M. and K.K.;
project administration, K.K.; funding acquisition, T.T. and K.K. All authors have read and agreed tothe published version of the manuscript.
Funding: This study was funded by JSPS (Japan Society for the Promotion of Science) KAKENHI
Grant Number (JP15H01972, JP16K16595, JP20K19534, JP20H00557), Health Labour Sciences Research
Grant (H28-Choju-Ippan-002, H30-Junkanki-Ippan-004), Japan Agency for Medical Research and Development (AMED) (JP17dk0110017, JP18dk0110027, JP18ls0110002, JP18le0110009, JP20dk0110034,
JP20dk0110037), Open Innovation Platform with Enterprises, Research Institute and Academia(OPERA, JPMJOP1831) from the Japan Science and Technology (JST), a grant from Innovative Research Program on Suicide Countermeasures (1-4), a grant from Sasakawa Sports Foundation, a
grant from Japan Health Promotion & Fitness Foundation, a grant from Chiba Foundation for Health
Promotion & Disease Prevention, the 8020 Research Grant for fiscal 2019 from the 8020 Promotion
Foundation (adopted number: 19-2-06), a grant from Niimi University (1915010), grants from Meiji
Yasuda Life Foundation of Health and Welfare and the Research Funding for Longevity Sciencesfrom National Center for Geriatrics and Gerontology (29-42, 30-22).
Institutional Review Board Statement: Ethical approval for the study was obtained from the Ethics
Committee at of Chiba University, Japan (Approval number: 2493) and the National Center for
Geriatrics and Gerontology, Japan (Approval number: 992).
Informed Consent Statement: JAGES participants were informed that participation in the study
was voluntary and that completing and returning the questionnaire via mail indicated their informed
consent to participate in the study.
Data Availability Statement: Data are from the JAGES study. All enquiries are to be addressedat the data management committee via e-mail: dataadmin.ml@jages.net. All JAGES datasets have
ethical or legal restrictions for public deposition due to inclusion of sensitive information from thehuman participants.
Acknowledgments: We would like to thank the study participants and the JAGES team for data collection.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the designof the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, orin the decision to publish the results.

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