Japanese Journal of Twin Studies (2023), 2 (1), 8-15

Review Article (invited as an award recipient in 2022)

Behaviour genetic studies of kaigo yobo: Focus on the
difference between tojikomori and depression
Fujio Inui 1,
1

Tenri University, Japan
Abstract
As Japan is one of the most rapidly ageing countries in the world, there is an increasing need for long-termcare. Accordingly, all municipalities in Japan have been required to start kaigo yobo programmes, which aim
to prevent disability and increase healthy life expectancy among the aged. A typical kaigo yobo programme
provides interventions for various conditions, including tojikomori (a Japanese term for the state of being
housebound) and depression. However, no studies have found that tojikomori and depression interventions
have the same effect, and the genetic factors for tojikomori remain unclear. Therefore, using the classicaltwin method, this study aims to investigate the genetic and environmental correlations between tojikomori
and depressive symptoms and to clarify the mechanisms underlying the onset of tojikomori. Two analyses
were performed: the first using structural equation modelling and the second investigating the associations
between tojikomori and depressive symptoms in regard to genetic and environmental factors. The findings
revealed that only two genetic factors explained all phenotypes loaded, which suggested that decomposing
the relationships between the phenotypes correlated with depression into genetic and environmental factorsof many other phenotypes makes it possible to determine the high-risk genetic backgrounds represented
by combinations of other phenotypes. These results suggest that describing the genetic and environmental
correlations between target (e.g. tojikomori, depression) and surrounding phenotypes could lead to thedevelopment of more effective individualised kaigo yobo programmes.
Keywords:
kaigo yobo, twin study, tojikomori, depression(Received 22 September 2023; accepted 22 September 2023; First Published online 31 March 2024)

Author for Correspondence: Fujio Inui, Email: inui.f@sta.tenri-u.ac.jp


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Behaviour genetic studies of kaigo yobo

Introduction
Life expectancy has been steadily increasingin Japan. As of 2019, life expectancy was 81.41
years for males and 87.45 years for females.
However, various age-related health problems
are also becoming a social problem in regard
to healthy life expectancy. In 2019, according
to the Ministry of Health, Labour and Welfare(MHLW) in Japan, healthy life expectancy was
72.68 years for males and 75.38 years for females.
Additionally, the ageing rate in Japan is rapidly increasing compared with other developed
countries. The ageing rate was 7% in 1970, 14%
in 1994, 21% in 2007 and 28.9% in 2022. Bycomparison, the ageing rate was 7% in France
in 1889, and 126 years later, in 1990, had only
reached 14%. Rapid ageing is accompanied by theincreasing need for “long-term care” and expected
increases in medical and long-term care costs forthe aged (Ministry of Health, 2022). Since 2000,
long-term care services have been separate frommedical services and covered by long-term careinsurance. However, in 2025, “baby boomers”
will reach the mean age of 75 years, making thissituation more critical. In addition to the rapid
increase in the numbers of individuals requiringlong-term care, it is predicted that there will be ashortage of social resources because of decreases
in the working population and insurance income.
The Japanese Long-Term Care Insurance Act
has two primary objectives: to provide insurance
and to provide preventive care. Article 2 of the
Act states that “(1) Long-Term Care Insurance
shall provide necessary insurance benefits for a
Condition of Need for Long-Term Care or for a
Needed Support Condition of the insured person.
(2) Insurance benefits as set forth in the preceding
paragraph shall be provided in order to contributeto reduction or prevention of aggravation of a
Condition of Need for Long-Term Care or a
Needed Support Condition, and shall be provided
with careful attention to cooperation with medicalcare.”
Since 2006, all municipalities in Japan havebeen required to start what are referred to as kaigo
yobo programmes, which aim to help prevent disability and increase healthy life expectancy among
the aged, thereby alleviating the need for longterm care insurance. For kaigo yobo, the MHLW

recommends focusing on the following six different functions: physical function, nutrition, oral
function, tojikomori (a Japanese term for the stateof being housebound), depression and dementia.
A typical kaigo yobo intervention is to provide
a group session that includes all six functions.
However, no evidence has been published to suggest that tojikomori and depression interventions
have the same effect. For example, a common
intervention for an acute treatment period for
depression is rest. By contrast, a common intervention for tojikomori in the aged is to promote
going out and increasing contact with others; theseintervention methods contradict each other.
The development of disease in humans
has a complex association with genetic andenvironmental backgrounds. However, thegenetic factors for tojikomori remain unclear.
Understanding individual differences between
tojikomori and depression is therefore essential for the implementation of interventions or
preventive care by health professionals. The
conventional epidemiological research methods
used for comparing completely different genetic
backgrounds have yet to clarify the influenceof genetic and environmental factors. However,behavioural genetics, especially the twin study
method, enables the relative importance of genetic
and environmental factors to be estimated for each
symptom.
What is tojikomori?
The exact meaning of tojikomori differs
slightly from that of “housebound”, which is
generally caused by impaired physical and/or
mental functions; no such limitations exist or
are observed in relation to tojikomori. Therefore,“social isolation” is a more accurate descriptionof tojikomori because this term encompasses not
only the state of being housebound physically,
but also the state of having less communication
with others. However, the terms tojikomori and“housebound” are frequently used interchangeably. In this article, the term tojikomori is based on
the following factors provided by (Kondo & Hirai,
2007): (1) the extent of daily activity, (2) the frequency of going out, (3) the frequency of contact
with others and (4) mobility. The term describes
older adults in Japan who are housebound but
9Fujio Inui 1,

have no impairments in physical function or major
mental problems. Thus, the definition of tojikomori in this article is as follows: (1) A decreased
frequency of going out (less than once per week),(2) a decreased frequency of contact with others(less than once per week) and (3) no impairments
in physical function or major mental problems.
Previous studies (Hirai, Kondo, & Hanibuchi,
2008; Watanabe et al., 2005) have reported theprevalence of tojikomori in Japan to be between
8% and 21%. Longitudinal studies have reported
that prolonged tojikomori leads to physical dysfunction and mental problems such as decreased
activities of daily living (ADL) and instrumental
ADL, cognitive function and depression (Herr,
Latouche, & Ankri, 2013; Imuta et al., 1998;
Shinkai et al., 2005; Watanabe et al., 2005).
Several causes of tojikomori have been identified, including physical factors (e.g. decreased
walking ability, instrumental ADL, cognitive
function), psychological factors (e.g. decreased
subjective health, tendency for depression, lackof ikigai [purpose in life]) and social factors (e.g.
old age, not participating in group activities, lower
social and domestic roles) (Yasumura, 2009).
These previous Japanese studies have indicated that tojikomori leads to a negative spiralof frailty that can be prevented. However, to ourknowledge, there are no such reports from othercountries; instead, loneliness and social isolation
have recently become issues in the same context.
Whether tojikomori is the same as loneliness andsocial isolation still needs to be clarified. This is
one reason for using the term tojikomori insteadof housebound, loneliness or social isolation in
the present article. What is clear, however, is thatstaying at home weakens physical and mental
function, and individuals who choose to stay athome or communicate less frequently with others
might have a reason to avoid something more
serious, such as the onset of conditions such as
depression. Therefore, to clarify the relationshipbetween tojikomori and depression, the correlation with the genetic and environmental factors
needs to be examined to clarify the mechanisms
underlying the onset of tojikomori. Furthermore,
expanding this clarification to other phenotypes
might promote the development of more effective
kaigo yobo programmes.

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A longitudinal twin study on tojikomori anddepressive symptoms in Japanese older adults(Inui et al., 2016)
This study was conducted to investigate thegenetic and environmental correlations between
tojikomori and depressive symptoms. Two analyses were performed using the classical twin
method.
The first analysis performed was structural
equation modelling (SEM) (Rijsdijk & Sham,2002) to evaluate the relative importance of genetic
and environmental factors for the phenotypes
measured, that is, a cross-sectional evaluation of
univariate measures. In SEM analyses, phenotypicvariance is assumed to be divisible into four components: 1) an additive genetic component (A), 2)
a non-additive genetic component (D), 3) a sharedenvironmental component (C) and 4) a non-sharedenvironmental component (E). Component A
reﬂects the sum of the additive allelic effects ofmany segregating genes, whereas component D
refers to the interaction of alleles. Component
C, common to both types of twins, is shared bysiblings in a family and expected to contributeequally to the similarities in monozygotic anddizygotic twin pairs. Component E, unique to each
twin, is speciﬁc to each individual and expected tocontribute to differences within pairs. Component
E also includes measurement errors. Because
components C and D cannot be estimated in thesame model with twins reared together, ACE and
ADE models were ﬁtted as alternatives. The aimof genetic modelling is to identify a model that ﬁts
the data well and has as few explanatory parameters as possible. In the present study, we selected
the ACE model for further discussion because
it provides a better ﬁt to the data than the ADEmodel. The ACE model considers the following
possible combinations of parameters: ACE, AE,
CE and E (Jang, 2005). The full model (i.e. theACE model), which includes all latent variables,
was tested against nested sub-models (i.e. AE,
CE and E) with a reduced number of parameters.
The fits of the alternative models were comparedwith the difference in −2 log likelihood (−2LL),
which is asymptomatically distributed as χ2 with
degrees of freedom equal to the difference in thenumber of parameters. The fits of the different
models were also tested by analysing Akaike’sBehaviour genetic studies of kaigo yobo

information criterion (Akaike, 1987), in whicha smaller value indicates a better model. The
theoretically most acceptable and parsimonious
model obtained estimates with 95% confidence
intervals (CIs).
The results from the first analysis indicatedthat the AE model was the best-ﬁtting model forall phenotypes. Regarding tojikomori, 29% of thevariance was explained by additive genetic effects
and 71% by unique environment (including error)
effects at baseline in 2008. A 2012 follow-up
showed similar results: 25% of the variance
was explained by additive genetic effects and
75% by unique environment (including error)
effects. Regarding depressive symptoms, 27% of
the variance was explained by additive genetic
effects at baseline and 28% at follow-up. Those
results demonstrated that tojikomori is affected byadditive genetic factors, which marks the first time
the heritability of tojikomori has been estimated.
To clarify the mechanisms underlying the onsetof tojikomori, the other phenotypes geneticallyand environmentally related to tojikomori need to
be clarified. Especially at this point, the greatest
concern is whether depression and tojikomori
share the same genetic background.
The second analysis investigated the associations between tojikomori and depressive
symptoms in regard to genetic and environmental
factors. A Cholesky decomposition (also known
as a triangular decomposition) is useful fordecomposing the relations between phenotypes
genetically or environmentally. In a Cholesky
decomposition, the first latent factor (i.e. A1, C1
1

or E1) loads on all the variables, the second on
all but the first variable (i.e. excludes phenotype1), and so on. In this study, three phenotypes were
loaded to investigate the longitudinal influenceof tojikomori on depression (i.e. tojikomori atbaseline as phenotype 1, tojikomori at follow-up
as phenotype 2 and depressive symptoms as phenotype 3). Furthermore, to investigate the effectof depression on tojikomori, the variables were
exchanged (i.e. depressive symptoms at baseline
as phenotype 1, depressive symptoms at follow-up
as phenotype 2 and tojikomori at follow-up as
phenotype 3).
The main results from the second analysis areshown in Figures 1 (effect of depression on tojikomori) and 2 (effect of tojikomori on depression).
As shown in Figure 1, tojikomori was influenced
by some common genetic factors with depressive
symptoms (i.e. A1: 11% of the total variance), but
not by others (i.e. A3: 30% of the total variance).
Latent factor A2 was removed because its value
could not be estimated, which indicates no additive
genetic influence of depressive symptoms overtime. This finding suggests that for the preventionof and interventions for tojikomori, persons in a
genetic high-risk group for depressive symptoms
need to be identified. As shown in Figure 2,
tojikomori was influenced by genetic factors that
increased with time, which was an interestingfinding. In contrast to depressive symptoms,
genetic influences do not typically change overtime. Supposing that tojikomori is explained bya single genetic factor (not a single gene, but asingle factor that includes multiple genes), in this
1

A1

22％

A3

30%

42％

11%

GDS2008

78%

1

GDS2012

44%

12%

5%

tojikomori

53%
1%

1
E1

SAS2012

Common genetic
factor between
depression and

1
E2

E3

Fig. 1. Longitudinal effect of depression on future tojikomori. Modified from (Inui et al., 2016).

11

Fujio Inui 1,

1

1
A1

34％

1
A2

24％

17%

SAS2008

SAS2012

66%

47%

12%

A3

3%

Common genetic
factors between
tojikomori anddepression

34%

7%
GDS2012
1%

52%
3%

1

1
E1

1
E2

E3

Fig. 2. Longitudinal effect of tojikomori on future depression. Modified from (Fujio Inui, 2016; Inui et al., 2016).

model, only A1 explained the genetic factor atfollow-up, and thus, a22 from A2 might be moreminor or even insignificant. However, A2 existedwith 17% of 41% of the additive genetic effectof the tojikomori phenotype at follow-up. This
result has no evident interpretation but could havesignificant implications in the future as researchon tojikomori progresses.
Figure 3 shows standard interventions (e.g.
group sessions) that aim to promote going outsideand communicating more frequently with others
to reduce tojikomori symptoms. However, someinterventions may worsen situations for those witha high-risk genetic background for depression.
Figure 4 shows future possibilities regarding
individualised kaigo yobo programmes given thepossibility of identifying high-risk genetic backgrounds for depression in terms of tojikomori. Theintervention shown considers the genetic risk ofdepression. However, at present, no method exists
to examine the genetic risk of depression in termsof tojikomori, so it is still important to clarify thephenotypic correlations in relation to genetic andenvironmental effects to elucidate the mechanisms
underlying the onset and worsening of symptoms.
What is the genetic background of depression?
A previous meta-analysis of twin research studies reported that the heritability rate for depression
was 37% (95% CI: 31%−42%) (Sullivan, Neale,& Kendler, 2000); however, heritability was still
missing. Another study reported that “GWASs
using large sets of samples, including thousandsof patients with different forms of depressive
disorders (DDs) and tens of thousands of patients

Genetic high-risk
group for depression

Genetic low-risk
group for depression

Risk ofdeveloping or
worsening ofdepression

Normal intervention
for Tojikomori

Tojikomori
Present

FutureFig. 3. Normal intervention for tojikomori.

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Behaviour genetic studies of kaigo yobo

Genetic high-risk
group for depression

Able to decrease
the risk ofdeveloping or
worsening ofdepression

Consider the genetic
risk for depression
Genetic low-risk
group for depression

Normal intervention

Tojikomori
Present

FutureFig. 4. An individualised intervention for tojikomori.

in meta-analyses, have failed to identify any specific loci responsible for predisposition to DDs”
(Shadrina, Bondarenko, & Slominsky, 2018).
Omics research may solve this mystery in the near
future. However, it is also essential to consider
other methodologies. The classical twin method
still provides important answers. Several studies
using the classical twin method have confirmed
some “strange” results. For example, one study
reported the genetic structure of four temperaments
1and depression; when the phenotypic correlations
decomposed to genetic and environmental factors,
the genetic factors that explained depression
disappeared (Ono et al., 2002). In this study(Fujio
Inui, 2016) showed similar results. Figure 5 shows
the relationship between self-efficacy, fatigue anddepressive symptoms decomposed to genetic andenvironmental factors. As shown in the figure,the genetic factors that explain depression havedisappeared. In the present study, only two genetic

1
A1

1

1
A2

A2

0

0.29

(0.17 , 0.40)

0.17

0.26

(0.05 , 0.29)(0.14 , 0.41)

0.12(0.03 , 0.27)

A3

0

0

0.20

0.32

(0.08 , 0.32)(0.16 , 0.45)

0.05(0.00 , 0.15)

GSE-2014

CFQ-2016

CFQ-2014

CES-D-2014
0.05

0.02
0.71

(0.60 , 0.83)

0.57

0.01

(0.46 , 0.67)(0.00 , 0.04)(0.00 , 0.05)(0.01 , 0.11)

0.12(0.07 , 0.20)

0.11(0.05 , 0.18)

0.01(0.00 , 0.03)

0.59(0.49 , 0.69)

0.43(0.34 , 0.52)

1

1
E1

1
E2

1
E2

E3

Fig. 5. Relationship between self-efficacy, fatigue and depressive symptoms decomposed to genetic and environmental
factors. Modified from (Fujio Inui, 2016).

13

Fujio Inui 1,

factors explained all phenotypes loaded. Fromthese results, even without hunting for depression
genes, decomposing the relationships between thephenotypes correlated with depression into genetic
and environmental factors of many other phenotypes, it is possible to determine the high-risk
genetic backgrounds represented by combinationsof other phenotypes.
Aiming for individualised kaigo yobo
programmes

KAKENHI Grant Nos. JP16K21530, JP16K15978,
JP17H04134, JP19K10673, JP19H04048,
JP19KK0244, JP20K11091, 22K10569 and
23K10308, and University Grants from theJapanese Ministry of Education, Culture, Sports,
Science and Technology, and Kio University’s
Overseas Research Programme.
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Describing the genetic and environmental
correlations between target phenotypes (e.g.
tojikomori, depression) and other surrounding
phenotypes could lead to the development of
individualised kaigo yobo programmes. For example, previous studies have found that depressive
symptoms are genetically correlated with social
activities (Nishihara et al., 2011), tojikomori (Inuiet al., 2016), general self-efficacy and fatigue(Fujio Inui, 2016).
Although these genetic relations remain insufficient to describe the high-risk backgrounds fordepression, theoretically, this may be possible
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