www.nature.com/scientificreports

OPEN

The association between street
connectivity and depression
among older Japanese adults:
the JAGES longitudinal study
Yu‑Ru Chen 1*, Masamichi Hanazato
Katsunori Kondo 2,42, Chie Koga

2

, Kazushige Ide

2,3&

Mental health is important in older age; neighborhood environment is considered a protective factor
of depression. Research has established that a critical indicator of neighborhood environment, street
connectivity, is related to older people’s health. However, little is known about the relationshipbetween street connectivity and depression. We examined the relationship between street
connectivity and depression among older people. Using Japan Gerontological Evaluation Study
2013–2016, the target population comprised 24,141 independent older people without depression(Geriatric Depression Scale scores below 5) in 2013. The outcome variable was depression in 2016;
the explanatory variable was street connectivity calculated by intersection density and space syntax
within 800 m around the subject’s neighborhood in 2013. We used logistic regression analysis tocalculate the odds ratio and 95% confidence interval for the new occurrence of depression among
participants in 2016. This analysis demonstrated incidence of new depression after 3 years that is 17%
and 14% lower among participations living in high-intersection density and high-street-connectivity
areas, respectively, than those living in low-intersection density and low-street-connectivity areas.
The association held after adjusting for physical activities and social interaction. Given the establishedconnection between street connectivity and mental health, the findings can contribute to healthyurban planning.
Mental health and well-being are as important in older age as at any other time of life. In 2017, 264 million
people worldwide experienced a mental disorder, and the estimate in Japan is the second highest in Asia at 5.06
million suffering from depressive ­disorders1,2. It is a super-aged society and a country with a high proportion of
adults aged ≥ 75 years. Previous studies documented that this population is experiencing serious psychologicaldistress, such that depression is a common cause of suicide in older people of both sexes in ­Japan3. Depression is
also associated with increased mortality mainly associated with frailty among older m­ en4. In a systematic reviewof the relationship between depression and frailty, depressed older adults were more likely to experience frailty
than those without depression, specifically ­men4,5.
Depression is a complex interplay between individual and environmental traits and characteristics, whereindividual characteristics refer to social, psychological, and biological factors, such as childhood adversity, loss,and ­unemployment2,6. Among the older people, depression is associated with genetic risk, non-genetic biologicalfactors, physical illness, anxiety, sleep disturbance, elder abuse, and social risk f­ actors7,8. Environmental contributions are related to the neighborhoods, where communities of individuals and neighborhood environment
characteristics, such as walkability, community facilities, transportation, types of greenness, and proximity to anelementary school, have been linked with depression among older p­ eople5,9–11. Previous studies have reported
that the mechanisms of the association between neighborhood environment and mental health can be explaned
by loneliness, socially supportive relationships, and physical ­activity12–14. Thus, the presence of neighborhood
environments that provide for physical and social activities are protective factors against ­depression7.

1

Graduate School of Medical and Pharmaceutical Sciences, Chiba University, 1‑8‑1 Inohana, Chuo‑ku,
Chiba‑shi, Chiba 260‑8672, Japan. 2Center for Preventive Medical Sciences, Chiba University, 1‑33 Yayoi‑cho,Inage‑ku, Chiba‑shi, Chiba 263‑8522, Japan. 3Department of Community General Support, Hasegawa Hospital,
Yachimata‑shi, Chiba 289‑1103, Japan. 4Center for Gerontology and Social Science, National Center for Geriatrics
and Gerontology, 7‑430 Morikoka‑cho, Obu‑shi, Aichi 474‑8511, Japan. *email: yuruchen@chiba-u.jp

Scientific Reports |(2022) 12:13533| https://doi.org/10.1038/s41598-022-17650-w

1Vol.:(0123456789)

www.nature.com/scientificreports/
Among the neighborhood environmental correlations of depression outcomes, walkability refers to whether
the neighborhood environment encourages people to walk, where previous studies report on the relationships
between walkability and h­ ealth15,16. For instance, in a cross-sectional study in European countries, improving walkability decreased loneliness in older adults with ­depression17, whereas older people in Japan living inneighborhoods with good walkability reported low prevalence of knee and lower-back p­ ain18. Walkability canbe measured using several indicators, where the commonly used indicators in the research are land-use mix,
residential density, and street ­connectivity15,16. According to a systematic review on neighborhood environmentand depression in older adults, 39% of papers (n = 73) are related to urbanization population/residential density,whereas 19% are related to diverse access to/availability of destinations. Both are associated with depression.
However, only 1% focused on connectivity and found no association with d­ epression9. Another systematic review
described three out of 13 studies on connectivity and found inconsistent results between street connectivity anddepression due to the cross-sectional study design, which may be due to reverse causation and require further­research19. Furthermore, among the walkability indices, urban designers can easily measure and plan street
connectivity compared with land-use mix and residential density. Lotfi and Koohsari (2011) reported that theavailability and comparability of land-use mix data may be related to the difficulty of collecting ­data20. For thesereasons, we focused on street connectivity among the walkability indices.
The concept of street connectivity has received increased attention from researchers, planners, and planning­authorities21.We propose that street connectivity may influence depression due to street connectivity, which
has been found to be associated with physical activity, social interaction, and cognitive function among older
people. Two longitudinal studies of American and Australian adults identified that street connectivity is positively associated with walking for t­ ransport22,23. Moreover, superior street connectivity can encourage walking
behavior and contribute to the formation of social networks among adults, such as gaining more friends andmeeting ­neighbors24. Following previous s­ tudies25,26, we considered that higher street connectivity, where small
street blocks and more frequent intersections were created, enhanced the walking behavior, extended spaces forbuilding development, and encouraged the creation of more spots where people may meet. Furthermore, in aprevious study, older people from 11 European countries reported that high levels of physical activity at baseline
is related to low rates of depression after two of observation y­ ears27. Further, social interaction was found toexert a protective effect on depression among older people, and it has been reported that older Japanese males
who frequently meet with their friends experience lower rates of depression over a 4-year p­ eriod28. Researchersin Singapore identified an association between street connectivity and the cognitive function of older ­adults29.
Moreover, a longitudinal study conducted over a 2-year period in the United States demonstrated that highstreet connectivity was associated with small declines in attention among older people without mild Alzheimer’s
disease. Meanwhile, in a systematic review and meta-analysis of 29 longitudinal studies, associations between
cognitive function and subsequent depression were ­significant30. However, in terms of the relationship between
street connectivity and depression, we found inconsistent results among previous s­ tudies25,31, and no longitudinalstudies were conducted. Findings from cross-sectional studies conducted in Singapore and China suggest the
influence of street connectivity on depression in a negative a­ ssociation25,31. In contrast, Zhang et al. reported
that connectivity is associated with greater increases in depressive symptoms among older Chinese ­people32.
Another cross-sectional study found no significant difference between street connectivity and depression amongolder Australian ­men33. Moreover, previous studies have certain limitations. First, previous researchers commonly highlighted the disadvantage of the cross-sectional study design, because it does not help establish causal
relationships, such as those between built environments and depression among older ­people21,29,31–33. Second,
the sample sizes were limited due to the inclusion of only m­ en33 or of a small population for g­ eneralization25.
Third, the mechanisms explaining the associations between street connectivity and depression remain unclear.
Therefore, the current study aimed to examine the association between street connectivity and depression
among a large group of older Japanese adults of both sexes in a 3-year follow-up longitudinal study. We tested
the following hypothesis: high street connectivity is associated with a lower likelihood of depression amongolder adults.

Results

Table 1 presents the chi-squared results for the study participants’ characteristics. Among the 24,141 older
adult participants, 9.5% (n = 2292) became depressed during the 3-year follow-up period, of whom 52.1% were
women. The following characteristics were common among the respondents who reported depression onset:
age ≥ 75 years, < 10 years of education, low income, married status: as a widowed, divorced or never married,
living alone, not using a car, do not walk, going out, seeing friends regularly, and without social participation.
Residents in areas with low street connectivity and low land value reported depression.
Table 2 presents the odds ratios (ORs) and 95% confidence intervals (CIs) of Models 1–3 from the logistic
regression analysis for the association between street connectivity (intersection density and space syntax connectivity) and depression (the results of the full models are available in the Supplementary materials). For intersection density, in Model 1, we found a significantly negative association between an area with high-intersection
density (OR = 0.83, 95% CI: 0.72–0.96) and depression onset controlled for demographic and socioeconomic
covariates during the follow-up period. After adjusting for the factors of physical activities in Model 2 andadding social interaction as covariates in Model 3, the results of depression onset demonstrated a resemblance
to model 1, which are both significantly negatively related to high-intersection density. Regarding space syntax connectivity, our results reveal that older adults living in areas with moderate (0.85, 0.76–0.95) and high(0.86, 0.75–0.97) street connectivity were 15% and 14%, respectively, less likely to report depression onset thanthose living in areas with low connectivity in Model 1. After adding physical activities in Model 2 and social
interaction in Model 3, the results of depression onset were found to be similar to those in Model 1, which are

Scientific Reports |Vol:.(1234567890)(2022) 12:13533 |https://doi.org/10.1038/s41598-022-17650-w

2www.nature.com/scientificreports/

Depression
No
Characteristics (Total numbers)

No

χ2
Yes

%

No

%

p-Value

Sex
Male (n = 11,711)

10,614

48.6

1097

47.9

Female (n = 12,430)

11,235

51.4

1195

52.1

0.514

Age group, years
65–69 (n = 8185)

7523

34.4

662

28.9

70–74 (n = 8272)

7542

34.5

730

31.8

75–79 (n = 4815)

4325

19.8

490

21.4

80–84 (n = 2175)

1891

8.7

284

12.4

≥ 85 (n = 694)

568

2.6

126

5.5

< 0.001

Education level, yearsa≤ 9 (n = 8672)

7675

35.1

997

43.5

≥ 10 (n = 15,223)

13,949

63.8

1274

55.6

Missing (n = 246)

225

1.0

21

0.9

< 0.001

Equivalent household income, million yen
Low (< 2.00) (n = 8938)

7899

36.2

1,039

45.3

Middle (2.00–3.99) (n = 9108)

8374

38.3

734

32.0

High (≥ 4.00) (n = 2994)

2814

12.9

180

7.9

Missing (n = 3101)

2762

12.6

339

14.8

< 0.001

Marital status
Married (n = 18,734)

17,038

78

1696

74

Widowed (n = 4011)

3588

16.4

423

18.5

Divorced (n = 605)

531

2.4

74

3.2

Never married (n = 433)

378

1.7

55

2.4

Other/missing (n = 358)

314

1.4

44

1.9

< 0.001

Living situationb
Lives with others (n = 20,768)

18,836

86.2

1932

84.3

Lives alone (n = 2261)

2011

9.2

250

10.9

Missing (n = 1112)

1002

4.6

110

4.8

0.023

Driving statusc
Not a car user (n = 4483)

3948

18.0

535

23.3

Car user (n = 19,609)

17,858

81.7

1751

76.4

Missing (n = 49)

43

0.3

6

0.3

< 0.001

Years of residence< 10 (n = 1429)

1268

5.8

161

7.0

10–19 (n = 2285)

2053

9.4

232

10.1

20–29 (n = 2425)

2210

10.1

215

9.4

30–39 (n = 4131)

3749

17.2

382

16.7

40–49 (n = 5771)

5251

24.0

520

22.7

≥ 50 (n = 7743)

6993

32.0

750

32.7

325

1.5

32

1.4

Missing (n = 357)

0.133

Frequency of going outd
A few times per year (n = 344)

287

1.3

57

2.5

Weekly (n = 3984)

3488

16.0

496

21.6

Daily (n = 19,602)

17,889

81.9

1713

74.7

Missing (n = 211)

185

0.8

26

1.1

< 0.001

Duration of daily walking, mine
Low (< 30) (n = 4417)

3864

17.7

553

24.1

Moderate (30–59) (n = 8884)

8023

36.7

861

37.6

High (≥ 60) (n = 10,577)

9725

44.5

852

37.2

Missing (n = 263)

237

1.1

26

1.1

< 0.001

Frequency of seeing friends
Less than once per month (n = 5043)

4416

20.2

627

27.4

More than once per month (n = 18,447)

16,865

77.2

1,582

69.0

Missing (n = 651)

568

2.6

83

3.6

< 0.001

Continued

Scientific Reports |(2022) 12:13533 |https://doi.org/10.1038/s41598-022-17650-w

3Vol.:(0123456789)

www.nature.com/scientificreports/

Depression
No
Characteristics (Total numbers)

No

χ2
Yes

%

No

%

p-Value< 0.001

Social participation
Less than onces per month (n = 8378)

7366

33.7

1012

44.0

More than once per month (n = 14,039)

12,962

59.3

1077

47.0

Missing (n = 1724)

1521

7.0

203

9.0

Quintiles of population density, people/km2< 3322 (n = 6104)

5485

25.1

619

27.0

3322–4528 (n = 6395)

5772

26.4

623

27.2

4528–9213 (n = 5609)

5107

23.4

502

21.9

> 9213 (n = 6033)

5485

25.1

548

23.9

0.088

Quintiles of land value, yen/m2< 35,500 (n = 6123)

5475

25.1

648

28.3

35,500–65,100 (n = 5974)

5422

24.8

552

24.1

65,100–144,000 (n = 6043)

5501

25.2

542

23.6

> 144,000 (n = 6001)

5451

24.9

550

24.0

0.009

Tertiles of intersection density, intersections/km2—800 m circular buffer
Low (≤ 155) (n = 8112)

7275

33.3

837

36.5

Moderate (156–216) (n = 8041)

7273

33.3

768

33.5

High (≥ 217) (n = 7988)

7301

33.4

687

29.9

0.001

Tertiles of space syntax connectivity, numbers
Low (< 2.7) (n = 8048)

7191

32.9

857

37.4

Moderate (2.7–3.0) (n = 8067)

7356

33.7

711

31.0

High (≥ 3.0) (n = 8026)

7302

33.4

724

31.6

< 0.001

Table 1.  Characteristics of the Study Participants Classified by Depression Status (n = 24,141). χ2chi-squared
test. a How many years of formal education have you completed? 1 = Less than 6 years; 2 = 6 to 9 years; 3 = 10
to 12 years; 4 = 13 years or more; 5 = Others. b How many people are in your household, including yourself?
Whom do you live with? Circle all that apply. Live by myself/Live with (1) spouse, (2) son(s), (3) daughter(s),(4) spouse(s) of child(ren), (5) grandchild(ren), (6) brother(s) or sister(s), and (7) Others. c Car user includes
both self-driving and someone else drives. d How often do you go out (including to the field or immediateneighborhood, for shopping, to the hospital?) 1 . four times or more per week 2. Two or three times per week
3. Once per week 4. One to three times per month 5. Several times per year 6. Rarely. e How long do you walk a
day on average? 1 = Less than 30 min; 2 = 30–59 min; 3 = 60–89 min; 4 = 90 min or more.

Model ­1a
Characteristics

Model ­2bModel ­3c

OR (95% CI)

Intersection density (intersections/km2)
Low (≤ 155)

Ref.

Ref.

Ref.

Moderate (156–216)

0.97 (0.86–1.09)

0.97 (0.86–1.10)

0.99 (0.87–1.11)

High (≥ 217)

0.83 (0.72–0.96)

0.82 (0.71–0.95)

0.83 (0.72–0.96)

Low (< 2.7)

Ref.

Ref.

Ref.

Moderate (2.7–3.0)

0.85 (0.76–0.95)

0.85 (0.76–0.96)

0.85 (0.76–0.96)

High (≥ 3.0)

0.86 (0.75–0.97)

0.85 (0.75–0.97)

0.84 (0.74–0.96)

Space syntax connectivity (numbers)

Table 2.  Logistic regression results for associations between depression and explanatory variables (Model 1­ a,Model ­2b, and Model 3­ c; for full tables see Supplementary Tables 2-1-1, 2-1-2, 2-1-3, 2-2-1, 2-2-2, 2-2-3). Ref.
Reference group, OR Odds ratio, 95% CI 95% Confidence interval. p-Value = significant p values (< 0.05) are in
bold. a Model 1 controlled for sex, age, education, equivalent household income, marital status, living situation,
driving status, land value, and population density. b Model 2 controlled for sex, age, education, equivalenthousehold income, marital status, living situation, driving status, land value, population density, frequencyof going out, and duration of walking time. c Model 3 controlled for sex, age, education, equivalent householdincome, marital status, living situation, driving status, land value, population density, frequency of seeing
friends, and social participation.

Scientific Reports |Vol:.(1234567890)(2022) 12:13533 |https://doi.org/10.1038/s41598-022-17650-w

4www.nature.com/scientificreports/
both significantly negatively related to moderate- and high-street connectivity. In particular, we found the ORof depression onset in individuals who went out daily, walked for 30–59 min per day, walked for ≥ 60 min perday, seeing friends more than once per month, and engaged in social participation more than once per month
to be 0.67 (95% CI 0.50–0.90), 0.81 (0.72–0.91), 0.69 (0.61–0.77), 0.76 (0.68–0.84), and 0.69 (0.63–0.76) times,
respectively, less prone to depression onset than those did not.
The results for street connectivity and depression stratified by sex, population density, and latitude demonstrated that the point estimates of depression remain suppressed in the same direction. However, these results
were partly significant for intersection density: male, residents in areas between 40° and 45° north, and areas
with a high population density areas; for space syntax connectivity: both male and female, residents in areas
between 30° and 35° N and between 40° and 45° N, areas with high and low-population density (Supplementary
Tables 3-1, 3-2, 4-1, 4-2, 5-1, 5-2). We found that the interaction of street connectivity (intersection density andspace syntax connectivity) with sex, population density, or latitude is nonsignificant (data not shown).

Discussion

This longitudinal study evaluated associations between street connectivity (intersection density and space syntax
connectivity) and the onset of depression during a 3-year follow-up study of older Japanese adults. Intersection
density and space syntax connectivity were calculated differently, but they both represent connectivity. Ourfindings revealed an association between higher street connectivity and lower odds of depression onset. The
results may have implications for urban design and highlight possible unintended benefits of intervention using
built environments for depression risk among older adults. Given that poor mental health can lead to physicalhealth diseases related to suicide and enormous economic burdens, the present result points to protective factors for approaches for preventing mental ­disorders34. Physical activities and social interaction were significantly
negatively related to depression; however, after controlling for both factors, the relationship between street
connectivity and depression onset remained. A plausible mechanism of this association operates through three
facets, namely, physical activity, experiences of pedestrian movement, and sidewalks and cognitive function.
In a previous research, high street connectivity was associated with increased physical activity. For example,
an Australian study reported that increases in street connectivity were associated with increased time spentwalking among middle-aged r­ esidents23. Koohsari et al. (2017) mentioned that areas with better street connectivity are beneficial for walking for transport, partially because more commercial destinations are availableand ­accessible35. Increased accessibility was associated with low depression and less stressful environments for­aging36. Moreover, previous research found that older people living in areas with better access to health clinics
and services were less likely to report depressive s­ ymptoms31,37. Our results indicate that frequent going out
and walking exert a protective effect against depression. However, the association between street connectivity
and depression onset remained significant after regression analyses was conducted by adding physical activities
sequentially. We deemed that the factors of physical activities used are likely prone to bias from measurement,
which is an aspect that we were unable to capture. For example, the measure for walking time is self-reported
instead of objectively measured data, such as accelerometer assessment. In addition, the following responses
to the questions on walking time are as follows: “less than 30 min,” “30–59 min,” “60–89 min,” and “90 min or
more.” Thus, we deemed that the potential source of bias appeared in the measurement, whereas the categories
of variables that may influence the relationship (street connectivity and depression) remained after adjustingfor physical activities.
The association between street connectivity and depression remained after adjustment for the factors ofphysical activities and social interaction, which suggests that additional unobserved factors may explain thisrelationship, such as greenness, public transportation, and other experiences of pedestrian movement. Leslie andCerin (2008) reported that perceived street connectivity is positively associated with social networks, including friends and ­neighbors24. Furthermore, in Japan, the findings of longitudinal studies indicated that frequentmeetings with friends could prevent depression in a population of older ­adults28. In another Japanese study,community-level civic participation was inversely associated with the onset of depressive symptoms amongolder ­people38. Although the current study found that frequently seeing friends and attending activity groups isimportant and related to mental health, we were unable to establish that social interaction is a possible explanation for our findings. As such, the relationship between street connectivity and social interaction amongolder adults and the association between this relationship and depression requires further investigation. For
other possibilities of the underlying mechanism between street connectivity and depression, we considered theexperiences of pedestrian movement, such as traffic volume, seating facilities, access to public transportation,and greenness at the eye level of ­pedestrians32,39–42. According to a previous study in Hong Kong, high street
connectivity was associated with increased depressive symptoms among older adults due to areas with heavy
traffic volumes and less seating ­facilities32. Furthermore, James et al. (2017) reported that mental health could
be negatively affected by high traffic density or poor quality of pedestrian infrastructure regardless of living in aneighborhood with a walkable ­environment39. Cerin et al. (2019) mentioned that well-connected street networks
promote the availability of public ­transportation40, Yang et al. (2018) reported that more bus lines and stations
effectively reduce depression among older American a­ dults41. Other experiences of pedestrian movement, suchas exposure to greenness, especially eye-level greenspace in tree-planting zones, may indicate a path between
street connectivity and depression. Scholars proposed that eye-level greenspace quality plays an important rolein mental health due to the benefit of greenspace, including capacity-restoring effect and stimulus on mental
well-being. The findings of the China study suggest that the exposure of pedestrians to green space during walking is beneficial to mental health because the majority of daily street greenery can provide shade protection andoffer a restorative ­environment42.

Scientific Reports |(2022) 12:13533 |https://doi.org/10.1038/s41598-022-17650-w

5Vol.:(0123456789)

www.nature.com/scientificreports/
Recently, scholarly interest in the relationships between street network design and cognitive function has
been ­increasing26,43,44, where one component of high street connectivity may be the increased number of sidewalks in street networks. In Japan, older adults living in urban areas with many sidewalks displayed low risks ofdementia than those living in urban areas with few s­ idewalks44. Although street connectivity is concerned with
the interconnectedness of segments; on the other hand, sidewalks denotes width, continuity, and o­ bstacles44; inaddition, simply adding sidewalks to the existing roads does not alter connectivity. However, high street connectivity and sidewalk environment support walking for transport; thus, we conducted a Pearson’s correlation
coefficient analysis using street connectivity values and sidewalk area data from Tani et al.44. We identified a highcorrelation (r = 0.63, p < 0.001) between sidewalk areas and street connectivity.
In a 2-year longitudinal study, increased neighborhood connectivity was associated with high cognitive function in older ­adults26. Researchers highlighted that additional intersections denote less direct routes and moreavailable ground area to cover. Thus, they suggested that the greater cognitive complexity required to navigate
while walking strengthens the cognitive function of individuals who regularly walk in such a­ reas15,26. Althoughprevious research pointed to a bidirectional relationship between cognitive function and depression, a 36-monthfollow-up study reported that older people with dementia at baseline experienced a high prevalence and cumulative incidence of depressive symptoms than participants without mild Alzheimer’s d­ isease45. Meanwhile, asystematic review and meta-analysis of 29 longitudinal studies found that the associations between cognitive
function and subsequent depression were ­significant30. Thus, we deemed that certain environmental factors
related to cognitive function could also influence depression.
This study has several strengths. First, to our knowledge, this is the first longitudinal investigation of therelationship between street connectivity measured by intersection density and space syntax and depression
among older adults. Second, the current sample size is larger than those in previous studies, as larger sample
sizes provide more accurate mean values and a smaller margin of error. A systematic review focused on relationships between the neighborhood environment and depression among older people and reported that out of 73
articles, the sample size of 66% of the studies is less than ­25009. Third, we could use a large population-based
longitudinal data set in which the effect of reverse causation was being controlled. Moreover, our data covered
17 municipalities from northern and southern Japan, encompassing both urban and rural areas, and thus, wewere able to capture the full range of street connectivity across the areas we studied.
Nevertheless, this study has some limitations. First, space syntax calculations of street connectivity only
count numbers of connected streets, so we could not consider individual area characteristics, such as the width,safety, or even existence of sidewalks. However, correlation analysis did show positive associations between
street connectivity and sidewalk area. Second, we should have included climate factors such as snowfall andamount of rain as adjustment variables because the weather in the northern areas might have influenced thestreet construction, and climate factors might be associated with seasonal ­depression46. However, our sensitivity
analysis of the findings stratified by latitude showed that the depression point estimate remained in the same
direction of suppression. Third, the onset of depression between the baseline and the follow-up was based on
responses to a self-administered questionnaire, which means that respondents could have experienced recall
bias when answering or might not have answered truthfully because of social desirability b­ ias47. Fourth, the lossof 60% of the participants should be noted because we excluded individuals without residential information andwith GDS 5 and more at baseline to focus on the onset of depression. Fifth, street layout values were measured
using an 800 m circular buffer, which may lead to bias. As Koohsari et al. (2021) noted, a circular buffer may not
genuinely represent the attributes of the built environment to which participants were e­ xposed48. Additionally,
we cannot deny the possibility that certain participants would live outside the buffer, as the buffer is imposed by
the administrative unit. However, 97% of administrative units are smaller than the 800 m circular buffer, and 3%of the administrative units have a roughly 50% probability that the participants reside inside the 800 m buffer.
We could not access the actual addresses of the participants due to reasons of confidentiality; thus, future studies
should replicate the findings across types and sizes of spatial b­ uffers47,48. Sixth, this study classified the level ofstreet connectivity based on distribution and classified it into three quartiles instead of using scientific criteria.
The reason for this that previous research is insufficient to scientifically construct a cutoff for street connectivity.
To test the appropriateness of the tertile, we analyzed the data per quintile, which was negatively significantly
associated with depression onset but did not indicate a clear dose response (supplementary materials Tables 6-1,
6-2). The point estimates of depression remained suppressed in the same direction when examined as continuous
values but were statistically nonsignificant. Notably, a previous study found a curvilinear association between
intersection density and physical activity in an inverted U shape, thereby reporting that a high probability ofwalking behavior is associated with the threshold value of intersection d­ ensity49. Greater street connectivity may
not provide better mental health it may lead to increased concerns about traffic safety. We considered that thisassociation does not necessarily appear, no matter what level of connectivity values we compare, but appears
when we take the low-connectivity area as a reference and compare it with a higher one. Thus, a precise analysisof the cutoff for street connectivity is required in relation to depression.
Future researchers should focus on areas with low connectivity to further investigate the mechanisms that
operate between street connectivity and other built environment elements regarding depression risk in older
adults, including more measures related to street layout and neighborhood-level covariates. We established withthis study that older adults in Japan who lived in areas with high street connectivity had a lower risk of depression
onset during a 3-year study period from 2013 to 2016. We found that the association between street connectivity
and depression held after adjustment for the factors of physical activities and social interaction, which suggeststhat additional unobserved factors may explain this relationship. With a high population of older adults in Japan,
understanding how street layouts can influence mental health will be essential for promoting their long-termwell-being and supporting the development of urban design. The clear connections between street connectivity
and good mental health make this a valuable area for exploration.
Scientific Reports |Vol:.(1234567890)(2022) 12:13533 |https://doi.org/10.1038/s41598-022-17650-w

6www.nature.com/scientificreports/

Figure 1.  Flow of participants in the study.

Methods

Study participants. We set the baseline in 2013 to perform a 3-year follow-up longitudinal study using
panel data from the Japan Gerontological Evaluation Study (JAGES). The JAGES is a population-based epidemiological research initiative conducted on older people every 3 years since 2010. It focuses on the social
determinants of health and the social/built ­environment50. The self-administered questionnaires were mailed toindependent individuals aged ≥ 65 years across 24 municipalities in Japan in 2013 and 2016. The 2013 baseline
JAGES was conducted between October and December 2013 with a total of 173,643 questionnaires distributed
to older people and 126,781 returned and completed (response rate: 73.0%). The follow-up survey was conducted between October and December 2016 with a total of 154,496 questionnaires distributed, where 109,672
were returned and completed (response rate: 71.0%). 61,267 respondents completed the questionnaire in both
years with an average follow-up period of 1,106 ± 9.4 days. In selecting the respondents, we used three exclusion
criteria. First, we excluded any respondents who required assistance with daily living activities, with missing dataon age, and whose age difference between baseline and after 3 years did not fall within the range of 2–4 years.
Second, we excluded respondents with missing data on residential years or residence information (Chocho-aza
point data: the smallest administrative unit used for the 1995 population census of Japan, roughly comparable
to a European parish or a block group in the United States)51. Respondents not living in the same place at bothsurvey time points were excluded, because they lack a consistent exposure to the same neighborhood over the
3-year period. Third, we excluded respondents with missing scores from the 15-item Geriatric Depression Scale(short form; GDS-15) in 2013 and 2016 and those with baseline (2013) GDS-15 score above 5­ 52. We excluded
the last group, because the study focuses on the potential association between street connectivity and depression
onset instead of effects on existing depression. After excluding the aforementioned groups, 24,141 data of older
Japanese people residing in rural and urban areas across the country (Fig. 1) were used for analysis.

Scientific Reports |(2022) 12:13533 |https://doi.org/10.1038/s41598-022-17650-w

7Vol.:(0123456789)

www.nature.com/scientificreports/

Representative point ofparticipant’s residence
800 m radiusFigure 2.  Chocho-aza point data and 800 m buffer.

Outcome variable.

The outcome variable was depression, which was assessed using GDS-1553; the GDS15 score ranges from 0 to 15 points. A score of 5 or more indicates depression. GDS-15 was developed for selfadministration using a simple yes/no format, and its validity and reliability have been confirmed among older
Japanese ­populations52. We defined participants as having developed depression during the follow-up period iftheir GDS-15 score was 5 or over at the 2016 survey.

Explanatory variable. Our explanatory variable was tertiles of street connectivity within 800 m of thecenter of each participant’s residence (representative point). We were unable to use the actual addresses of theparticipants. We used the latitude and longitude of a representative point (chocho-aza point data) in the participant’s neighborhood, which were identified through geocoding to the smallest administrative u­ nit51. We set
800 m as the buffer to be consistent with previous research findings on the correlation between the behavior ofolder people and environmental ­outcomes43,54. Regarding street connectivity, two objective variables included:intersection density and space syntax connectivity (Both using 2013 street network data from Zenrin Co., Ltd.
and simplified the data in ArcGIS ver. 10.3.1 (Environmental Systems Research Institute, ESRI, Redlands, CA,USA)). Intersection density was commonly used in previous studies, we computed it as the number of intersections (three-way or more) per network buffer 800 m circular b­ uffer25,32,33. Space syntax connectivity measures
the number of street segments that immediately directly connect to a street ­segment55. We used DepthMapX
software (settings = Tulip Bin: 1024, Radius type: Metric) (University College London, n.d.), where the connectivity for each street segment was calculated using space syntax ­theory56,57, a method used to analyze urban
space and developed areas at the University of London in the ­1970s58. This method focuses on the street elementsof connectivity and relationships and analyzes them mathematically using ­graphs59,60. Koohsari et al. opine thatintersection density is a typical measure, and space syntax provides an alternative way to measure street connectivity. In active research predicting pedestrian movement (such as in a natural movement)61 and public healthhas used space syntax to investigate the relationships between street layout and health p­ roblems26,43,62. However,
we found no previous study that has used it with depression. Third, we assigned street connectivity values to eachstreet segment using QGIS ver. 3.863,64 to calculate the mean street connectivity within 800 m of the center ofthe representative point of each participant’s residence (Fig. 2). The top panel of Fig. 3 displays a street map of apart of one target area, whereas the bottom panel presents a DepthMapX street connectivity diagram of the same
area. In the diagram, red and blue indicate high and low street connectivity, respectively. Figure 4a,b present
areas with low and high connectivity in both large metropolitan and small urban areas.
Covariates. We controlled for demographics, socioeconomics, physical activity, social interaction, andregional characteristics which may likely correlate with depression. Demographic and socioeconomic covariates
included sex (male or female), age (65–69, 70–74, 75–79, 80–84, or ≥ 85 years), equivalent household income(< 2, 2–3.99, or ≥ 4 million yen per year or missing), educational attainment (≤ 9 or ≥ 10 years or missing), marital
status (married, widowed, divorced, never married, or missing), living status (living alone, living with others, or
missing), driving status (car user, including receiving rides from others or non-car-user) and years of residence(< 10, 10–19, 20–29, 30–39, 40–49, and ≥ 50 years, or missing). We included the factors of physical activity andsocial interaction such as frequency of going out (annually, none, weekly, daily, or missing), duration of daily
Scientific Reports |Vol:.(1234567890)(2022) 12:13533 |https://doi.org/10.1038/s41598-022-17650-w

8www.nature.com/scientificreports/

Figure 3.  OpenStreetMap (top) and space syntax connectivity map (bottom).

Figure 4.  Space syntax connectivity maps of research municipalities. Panels (a) and (b) show areas of both
low and high connectivity in both large metropolitan areas and small urban areas. High-population areas show
higher street connectivity than low population areas.
walking (< 30, 30–59, ≥ 60 min, or missing), frequency of seeing friends (more than once per month, less thanonce per month, or missing), and social participation (more than once per month, less than once per month, or
missing). Social participation refers to individuals who participated in any of the 14 organized and regular activities (groups or clubs of volunteer, sports, leisure activity, senior citizen, neighborhood or resident association,
study or cultural group, long-term care prevention or health-building activities, local events; activities that teachskills or pass on experiences to others, support older people requiring protection, long-term care, parents raisingchildren, improvement in local living arrangements, or another group/organization) at least once per month.

Scientific Reports |(2022) 12:13533 |https://doi.org/10.1038/s41598-022-17650-w

9Vol.:(0123456789)

www.nature.com/scientificreports/
The regional factors we measured were population density by quintile (< 3322 people/km2 school district,3322–4528, 4528–9213, or > 9213) and land value by quintile (< 35,500 yen/m2, 35,500–65,100, 65,100–144,000,
or > 144,000).The former was adjusted for urbanity, whereas the latter was controlled for neighborhood socioeconomic status. All covariates were assessed using a questionnaire in the baseline s­ urvey10,16. We categorized thecovariates with missing data as missing, and analysis included participants with missing data on the covariates.

Statistical analysis. The methods we used to analyze the study data included descriptive analysis and thechi-squared test to calculate the differences between depression and each variable. To identify the intermediatefactors, we consider physical activities and social interaction as pathways between connectivity and depression.
We tested three models using logistic regression analysis via forced entry and controlled for covariates in themodel to examine the associations between street connectivity (intersection density and space syntax connectivity) and depression in older Japanese people. In Model 1, the explanatory variable, outcome variable, andcovariates (sex, age, equivalent household income, educational attainment, marital status, living status, years ofresidence, driving status, population density, and land value) are included. Model 2 further adjusts the frequencyof going out and duration of daily walking as variables of physical activities in Model 1. Finally, in Model 3, weadded the frequency of seeing friends and social participation as social interaction variables to Model 1. We
also used sensitivity analysis stratified by gender due to the fact that women suffer from depression more than­males65. Moreover, stratification analyses of population density and latitude were conducted to evaluate thedifferences between areas and climates, where we deem that high-connectivity areas exist in areas with highpopulation density or without heavy snow. Furthermore, research reported that the prevalence of depression
is associated with seasonality, especially autumn and w­ inter46. The interaction among sex, population density,
latitude, and street connectivity are calculated. We calculated all statistics using Stata/MP 16.1 software (StataCorp, College Station, TX, USA) with a significance level of 5%.
Ethics approval and consent to participate. This study used data from the JAGES, which is a pro-

ject conducted by the Nihon Fu-kushi University Center for Well-Being and Society. The JAGES project wasapproved by the Ethics Committee on Research of Human Subjects at Nihon Fukushi University (application
number 10–05), and the JAGES 2013 survey has also been approved by the Ethics Committee at the Chiba
University Faculty of Medicine (application number 13–14). Furthermore, JAGES 2016 was approved by the
National Center for Geriatrics and Gerontology and Chiba University (receipt number 992, 2493). The JAGES
project was approved by the Ethics Committee on Research of Human Subjects at Nihon Fukushi University(application number 10–05). The JAGES 2013 survey was approved by the Ethics Committee at the Chiba University Faculty of Medicine (application number 13–14), whereas the JAGES 2016 was approved by the National
Center for Geriatrics and Gerontology and Chiba University (receipt number 992 and 2493). The participants
were informed that participation was voluntary and that completing and returning the questionnaire via mailindicated consent to participate. We created an anonymized dataset. All methods were conducted according torelevant guidelines and regulations or according to the Declaration of Helsinki.

Data availability

The data that support the findings of this study are available from JAGES but restrictions apply to the availabilityof these data, which were used under license for the current study, and so are not publicly available. Data are
however available from the authors upon reasonable request and with permission of JAGES. Please contact thedata management committee of JAGES and email dataadmin@jages.net requests for access.
Received: 13 November 2021; Accepted: 28 July 2022

References

1. James, S. L. et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 Diseases and Injuries
for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. The Lancet 392,
1789–1858 (2018).
2. Depression. WHO: World Health Organization. (2021). Available from: https://​www.​who.​int/​news-​room/​fact-​sheets/​detail/​depre​
ssion.
3. Ministry of Health, Labor and Welfare (MHLW). Comprehensive Survey of Living Conditions. (2020). Available from: https://​www.​mhlw.​go.​jp/​stf/​seisa​kunit​suite/​bunya/​hukus​hi_​kaigo/​seika​tsuho​go/​jisat​su/​jisat​suhak​usyo2​020.​html.
4. Almeida, O. P. et al. Depression, frailty, and all-cause mortality: A cohort study of men older than 75 years. J. Am. Med. Dir. Assoc.
16, 296–300 (2015).
5. Chu, W., Chang, S. F., Ho, H. Y. & Lin, H. C. The relationship between depression and frailty in community-dwelling older people:
A systematic review and meta-analysis of 84,351 older adults. J. Nurs. Scholarsh. 51, 547–559 (2019).
6. Diez Roux, A. V. & Mair, C. Neighborhoods and health. Ann. N. Y. Acad. Sci. 1186(1), 125–145 (2010).
7. Fiske, A., Wetherell, J. L. & Gatz, M. Depression in older adults. Am. J. Nurs. 112, 22–30 (2012).
8. Koga, C., Tsuji, T., Hanazato, M., Suzuki, N. & Kondo, K. Elder abuse and depressive symptoms: Which is cause and effect? Bidirectional longitudinal studies from the JAGES. J. Interpers. Violence https://​doi.​org/​10.​1177/​08862​60520​967135 (2020).
9. Barnett, A., Zhang, C. J. P., Johnston, J. M. & Cerin, E. Relationships between the neighborhood environment and depression inolder adults: A systematic review and meta-analysis. Int. Psychogeriatr. 30, 1153–1176 (2017).
10. Nishida, M., Hanazato, M., Koga, C. & Kondo, K. Association between proximity of the elementary school and depression in
Japanese older adults: A cross-sectional study from the JAGES 2016 Survey. Int. J. Environ. Res. Public Health 18(2), 500 (2021).
11. Nishigaki, M., Hanazato, M., Koga, C. & Kondo, K. What types of greenspaces are associated with depression in urban and rural
older adults? A multilevel cross-sectional study from JAGES. Int. J. Environ. Res. Public Health 17(24), 9276 (2020).
12. Domènech-Abella, J. et al. The association between perceived social and physical environment and mental health among older
adults: Mediating effects of loneliness. Aging Ment. Health 25, 962–968 (2021).

Scientific Reports |Vol:.(1234567890)(2022) 12:13533 |https://doi.org/10.1038/s41598-022-17650-w

10www.nature.com/scientificreports/
13. Evans, G. W. The built environment and mental health. J. Urban Health 80(4), 536–555 (2003).
14. Diez Roux, A. V. & Mair, C. Neighborhoods and health. Ann. N. Y. Acad. Sci. 1186(125–145), 2010. https://​doi.​org/​10.​1111/j.​
1749-​6632.​2009.​05333.x (2010).
15. Frank, L. D., Schmid, T. L., Sallis, J. F., Chapman, J. & Saelens, B. E. Linking objectively measured physical activity with objectively
measured urban form: Findings from SMARTRAQ. Am. J. Prev. Med. 28, 117–125 (2005).
16. Wang, H. & Yang, Y. Neighbourhood walkability: A review and bibliometric analysis. Cities 93, 43–61 (2019).
17. Hernandez, R. et al. The cross-sectional and longitudinal association between perceived neighborhood walkability characteristics
and depressive symptoms in older latinos: The “¡Caminemos!” study. J. Aging Health 27, 551–568 (2015).
18. Okabe, D. et al. Neighborhood walkability in relation to knee and low back pain in older people: A multilevel cross-sectional study
from the jages. Int. J. Environ. Res. Public Health 16, 1–13 (2019).
19. Sokale, I. O., Conway, S. H. & Douphrate, D. I. Built environment and its association with depression among older adults: A
systematic review. Open Public Health J. 15, (2022).
20. Lotfi, S. & Koohsari, M. J. Neighborhood walkability in a city within a developing country. J. Urban Plan. Dev. 137(4), 402–408.
https://​doi.​org/​10.​1061/​(ASCE)​UP.​1943-​5444 (2011).
21. Southworth, M. Designing the walkable city. J. Urban Plan. Dev. 131, 246–257 (2005).
22. Hirsch, J. A. et al. How much are built environments changing, and where?: Patterns of change by neighborhood sociodemographic
characteristics across seven U.S. metropolitan areas. Soc. Sci. Med. 169, 97–105 (2016).
23. Bentley, R. et al. A longitudinal study examining changes in street connectivity, land use, and density of dwellings and walking fortransport in Brisbane, Australia. Environ. Health Perspect. 126(5), 057003 (2018).
24. Leslie, E. & Cerin, E. Are perceptions of the local environment related to neighbourhood satisfaction and mental health in adults?.
Prev. Med. 47, 273–278 (2008).
25. Lam, W. W. Y., Loo, B. P. Y. & Mahendran, R. Neighbourhood environment and depressive symptoms among the elderly in Hong
Kong and Singapore. Int. J. Health Geogr. 19, 1–10 (2020).
26. Watts, A., Ferdous, F., Diaz Moore, K. & Burns, J. M. Neighborhood integration and connectivity predict cognitive performanceand decline. Gerontol. Geriatr. Med. 1, 2333721415599141 (2015).
27. Lindwall, M., Larsman, P. & Hagger, M. S. The reciprocal relationship between physical activity and depression in older European
adults: A prospective cross-lagged panel design using SHARE data. Health Psychol. 30, 453–462 (2011).
28. Misawa, J. & Kondo, K. Social factors relating to depression among older people in Japan: Analysis of longitudinal panel data fromthe AGES project. Aging Ment. Health 23, 1423–1432 (2019).
29. Ng, T. P. et al. The neighborhood built environment and cognitive function of older persons: Results from the Singapore longitudinalageing study. Gerontology 64, 149–156 (2018).
30. Scult, M. A. et al. The association between cognitive function and subsequent depression: A systematic review and meta-analysis.
Psychol. Med. 47(1), 1–17 (2017).
31. Zhang, T., Chiu, R. L. H. & Ho, H. C. Suburban neighborhood environments and depression: A case study of Guangzhou, China.
J. Transp. Health 15, 100624 (2019).
32. Zhang, C. J. et al. Cross-sectional associations of objectively assessed neighbourhood attributes with depressive symptoms in older
adults of an ultra-dense urban environment: The Hong Kong ALECS study. BMJ Open 8(3), e020480 (2018).
33. Saarloos, D., Alfonso, H., Giles-Corti, B., Middleton, N. & Almeida, O. P. The built environment and depression in later life: The
health in men study. Am. J. Geriatr. Psychiatry 19, 461–470 (2011).
34. Hosman, C. M. H., Jané Llopis, E., Saxena, Shekhar., World Health Organization, Department of Mental Health and Substance
Abuse, & Prevention Research Centre (Netherlands). Prevention of mental disorders: Effective interventions and policy options :summary report. (World Health Organization, 2004).
35. Koohsari, M. J. et al. Built environmental factors and adults’ travel behaviors: Role of street layout and local destinations. Prev.
Med. 96, 124–128 (2017).
36. La Gory, M. & Fitpatrick, K. The effects of environmental context on elderly depression. J. Aging Health 4(4), 459–479 (1992).
37. Wang, Y., Chen, Y. C., Shen, H. W. & Morrow-Howell, N. Neighborhood and depressive symptoms: A comparison of rural andurban chinese older adults. Gerontologist 58, 68–78 (2018).
38. Yamaguchi, M. et al. Community social capital and depressive symptoms among older people in Japan: A multilevel longitudinal
study. J. Epidemiol. 29, 363–369 (2019).
39. James, P., Hart, J. E., Banay, R. F., Laden, F. & Signorello, L. B. Built environment and depression in low-income african americans
and whites. Am. J. Prev. Med. 52, 74–84 (2017).
40. Cerin, E. et al. Associations of objectively-assessed neighborhood characteristics with older adults’ total physical activity andsedentary time in an ultra-dense urban environment: Findings from the ALECS study. Health Place 42, 1–10 (2016).
41. Yang, Y. et al. Examining the possible impact of daily transport on depression among older adults using an agent-based model.
Aging Mental Health 23(6), 743–751 (2019).
42. Wang, R. et al. Dynamic greenspace exposure and residents’ mental health in Guangzhou, China: From over-head to eye-level
perspective, from quantity to quality. Landsc. Urban Plan. 215, 104230 (2021).
43. Koohsari, M. J. et al. Cognitive function of elderly persons in Japanese Neighborhoods: The role of street layout. Am. J. Alzheimer’s
Dis. Other Dement. https://​doi.​org/​10.​1177/​15333​17519​844046 (2019).
44. Tani, Y., Hanazato, M., Fujiwara, T., Suzuki, N. & Kondo, K. Neighborhood sidewalk environment and incidence of dementia inolder Japanese adults. Am. J. Epidemiol. 190, 1270–1280 (2021).
45. Helvik, A. S. et al. The course of depressive symptoms with decline in cognitive function - A longitudinal study of older adults
receiving in-home care at baseline. BMC Geriatr. 19, 1–14 (2019).
46. Wirz-Justice, A., Ajdacic, V., Rössler, W., Steinhausen, H. C. & Angst, J. Prevalence of seasonal depression in a prospective cohort
study. Eur. Arch. Psychiatry Clin. Neurosci. 269, 833–839 (2019).
47. Kanamori, S. et al. Frequency and pattern of exercise and depression after two years in older Japanese adults: The JAGES longitudinal study. Sci. Rep. 8, 1–9 (2018).
48. Koohsari, M. J., Nakaya, T., Mccormack, G. R. & Shibata, A. Landscape and Urban Planning Traditional and novel walkable built
environment metrics and social capital. Landsc. Urban Plan. 214, 104184 (2021).
49. Cerin, E. et al.. Urban design, transport, and health 2 Determining thresholds for spatial urban design and transport features that
support walking to create healthy and sustainable cities: findings from the IPEN Adult study. vol. 10 www.​thela​ncet.​com/​lance​tgh (2022).
50. Advancing universal health coverage through knowledge translation for healthy ageing Lessons learnt from the Japan Gerontological Evaluation Study. (2018).
51. Nakaya, T. et al. Associations of all-cause mortality with census-based neighbourhood deprivation and population density in Japan:
A multilevel survival analysis. PLoS ONE 9(6), e97802 (2014).
52. Yasavage, J. A. & Sheikh, J. I. 9/geriatric depression scale (GDS) recent evidence and development of a shorter version. Clin. Gerontol. 5, 119–136 (1986).
53. Burke, W. J., Roccaforte, W. H. & Wengel, S. P. The short form of the geriatric depression scale: A comparison with the 30-item
form. J. Geriatr. Psychiatry Neurol. 4, 173–178 (1991).

Scientific Reports |(2022) 12:13533 |https://doi.org/10.1038/s41598-022-17650-w

11Vol.:(0123456789)

www.nature.com/scientificreports/
54. McCormack, G. R. & Shiell, A. In search of causality: A systematic review of the relationship between the built environment andphysical activity among adults. Int. J. Behav. Nutr. Phys. Act. https://​doi.​org/​10.​1186/​1479-​5868-8-​125 (2011).
55. Hillier, B. & Hanson, J. The Social Logic of Space (Cambridge University Press, Cambridge, 1984).
56. Marcus, L. H. Place Syntax Tool | GIS Software for Analysing Geographic Accessibility with Axial Lines. New Dev. Space Syntax
Softw. 35–41 (2003).
57. Turner, A. Depthmap: A program to perform visibility graph analysis. In: Proceedings of the 3rd International Symposium on Space
Syntax 30, 7–11 (2001).
58. Hillier, B. Space is the Machine: A Configurational Theory of Architecture (Cambridge University Press, Cambridge, 1966).
59. Hillier, B. & Iida, S. Network and Psychological Effects in Urban Movement. 475–490 (2005).
60. Takano, Y. & Sasaki, Y. A Study on identifying the townscape types in urban areas by applying Space Syntax theory -A Case study
on eastern side of Setagaya ward, Tokyo. J. City Plan. Inst. Jpn. 42(3), 127–132 (2007).
61. Koohsari, M. J., Oka, K., Owen, N. & Sugiyama, T. Natural movement: A space syntax theory linking urban form and function
with walking for transport. Health Place https://​doi.​org/​10.​1016/J.​HEALT​HPLACE.​2019.​01.​002 (2019).
62. Nichani, V., Koohsari, M. J. & McCormack, G. R. Associations between the traditional and novel neighbourhood built environment
metrics and weight status among Canadian men and women. Can. J. Public Health 112(1), 166–174 (2021).
63. QGIS Development Team. QGIS geographic information system. Open source geospatial foundation project. [Internet]. Qgis.
osgeo.org. 2019. Available from: http://​qgis.​osgeo.​org/.
64. Gil, J. Space Syntax Toolkit for QGIS. 1–24 (2015).
65. Zunzunegui, M. V. et al. Gender differences in depressive symptoms among older adults: A cross-national comparison: The CLESA
project. Soc. Psychiatry Psychiatr. Epidemiol. 42, 198–207 (2007).

Acknowledgements

The authors would like to thank everyone who contributed to the Japan Gerontological Evaluation Study (JAGES).
Special thanks to the members of the Healthy Places Design Laboratory for their dedicated support and guidance.

Author contributions

Y.C. and M.H. conceived the presented idea, performed the literature review, analyzed the data, performed theanalytic calculations, and drafted the full-text papers. C.K. and K.I. verified the analytical methods, provided
statistical analysis, and screened the titles, abstracts, and full-text papers. K.K. collected the data and provided
support and guidance. All authors have critically revised the manuscript and approved the final version.

Funding

This study used data from the Japan Gerontological Evaluation Study, which was supported by the MEXT
(Ministry of Education, Culture, Sports, Science and Technology–Japan)-Supported Program for the Strategic
Research Foundation at Private Universities (2009–2013), the Japan Society for the Promotion of Science (KAKENHI grant numbers JP18390200, JP22330172, JP22390400, JP23243070, JP23590786, JP23790710, JP24390469,
JP24530698, JP24683018, JP25253052, JP25870573, JP25870881, JP26285138, JP26882010, JP15H01972,
19K04785, 20K13721, 22K04450, and 20H00557, a large-scale survey), Health, Labour and Welfare Sciences
Research Grants (H22-Choju-Shitei-008, H24-Junkanki [Seishu]-Ippan-007, H24-Chikyukibo-Ippan-009,
H24-Choju-Wakate-009, H25-Kenki-Wakate-015, H25-Choju-Ippan-003, H26-Irryo-Shitei-003 [Fukkou],
H26-Choju-Ippan-006, H27-Ninchisyou-Ippan-001, H28-choju-Ippan-002, H28-Ninchisyou-Ippan-002, H30Kenki-Ippan-006, H30-Junkankitou-Ippan-004), the Japan Agency for Medical Research and Development(JP17dk0110017, JP18dk0110027, JP18ls0110002, JP18le0110009), Research Funding for Longevity Sciencesfrom the National Center for Geriatrics and Gerontology (24–17, 24–23, 29–42, 30–22), the Research Instituteand Academia (OPERA, JPMJOP1831), and the Japan Science and Technology Agency (JST). The views andopinions expressed in this article are those of the authors and do not necessarily reflect the official policies orpositions of the respective funding organizations.

Competing interests

The authors declare no competing interests.

Additional information

Supplementary Information The online version contains supplementary material available at https://​doi.​org/​
10.​1038/​s41598-​022-​17650-w.
Correspondence and requests for materials should be addressed to Y.-R.C.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps andinstitutional affiliations.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or
format, as long as you give appropriate credit to the original author(s) and the source, provide a link to theCreative Commons licence, and indicate if changes were made. The images or other third party material in thisarticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to thematerial. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly fromthe copyright holder. To view a copy of this licence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.
© The Author(s) 2022

Scientific Reports |Vol:.(1234567890)(2022) 12:13533 |https://doi.org/10.1038/s41598-022-17650-w

12