International Journal ofEnvironmental Researchand Public Health
ArticleNeighborhood Walkability in Relation to Knee and
Low Back Pain in Older People: A Multilevel
Cross-Sectional Study from the JAGES
Daichi Okabe 1, * , Taishi Tsuji 2 , Masamichi Hanazato 2 , Yasuhiro Miyaguni 3,4 , Nao Asada 1
and Katsunori Kondo 2,4
1
2
3
4

*

Advanced Preventive Medical Sciences, Graduate School of Medical and Pharmaceutical Sciences, Chiba
University, 1-8-1 Inohana, Chuo-ku, Chiba-shi, Chiba 260-8670, Japan; nao.asadacar@gmail.com
Center for Preventive Medical Sciences, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba-shi, Chiba
260-8670, Japan; tsuji.t@chiba-u.jp (T.T.); hanazato@chiba-u.jp (M.H.); kkondo@chiba-u.jp (K.K.)
Institute for Health Economics and Policy, 1-5-11 Nishi-Shimbashi, Minato-ku, Tokyo 105-0003, Japan;
y.miyaguni@ncgg.go.jp
Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology,
7-430 Morioka-cho, Obu City, Aichi 474-8511, Japan
Correspondence: daichiokabe01@gmail.com; Tel.: +81-90-4194-8521

Received: 16 October 2019; Accepted: 15 November 2019; Published: 20 November 2019


Abstract: Few studies have focused on a relationship between the built environment andmusculoskeletal pain. This study aimed to investigate an association between neighborhood
walkability and knee and low back pain in older people. Data were derived from the Japan
Gerontological Evaluation Study (JAGES) 2013, a population-based study of independently living
people ≥65 years old. A cross-sectional multilevel analysis was performed, of 22,892 participants in
792 neighborhoods. Neighborhood walkability was assessed by residents’ perceptions and population
density. Dependent variables were knee and low back pain restricting daily activities within thepast year. The prevalence of knee pain was 26.2% and of low back pain 29.3%. After adjusting forsociodemographic covariates, the prevalence ratio (PR) of knee and low back pain was significantly
lower in neighborhoods with better access to parks and sidewalks, good access to fresh food stores,
and higher population densities. After additionally adjusting for population density, easier walking
in neighborhoods without slopes or stairs was significantly inversely correlated with knee pain (PR0.91, 95% confidence interval 0.85–0.99). Neighborhoods with walkability enhanced by good access toparks and sidewalks and fresh food stores, easy walking without slopes or stairs, and high population
densities, had lower prevalences of knee and low back pain among older people. Further studies
should examine environmental determinants of pain.
Keywords: neighborhood walkability; built environment; musculoskeletal pain; knee pain; low backpain; older people; multilevel analysis

1. Introduction
Musculoskeletal diseases, including osteoarthritis (OA), are major public health problems. Between
one in three and one in five people live with painful musculoskeletal conditions, making these diseases
the second highest contributor to global disability. Low back pain alone is the leading cause of disability
worldwide [1]. A strong relationship exists between musculoskeletal pain and a reduced capacity
to engage in physical activity. This often results in functional decline, frailty, reduced quality of life,and loss of independence [2]. The prevalence and impact of musculoskeletal diseases are particularlyhigh in older people. While OA may be treated surgically when severe, it is now considered amenable
Int. J. Environ. Res. Public Health 2019, 16, 4598; doi:10.3390/ijerph16234598

www.mdpi.com/journal/ijerph

Int. J. Environ. Res. Public Health 2019, 16, 4598

2 of 13

to prevention and treatment in the early stages [3]. For example, weight loss for obesity, prevention of
injury, and exercise have all been shown to be effective in reducing knee and lower back pains [4,5]Although strong evidence supports the benefits of regular exercise, physical inactivity remains highly
prevalent worldwide [6]. In fact, the number of daily steps people take in Japan is decreasing year by
year, despite the fact that walking, the most frequent type of exercise, is recommended by national
health policy [7,8]. For many, however, it is difficult to get regular exercise, and there are limitations to
the effects of policy pronouncements at the individual level where a number of other factors are in play.
One of these factors, the built environment, has been found to exert a noticeable influence on
health [9–11]. The World Health Organization recommends improving the built environment as
a way to promote healthy aging [12]. The built environment is related to physical activity [13,14],
most notably in terms of neighborhood walkability [15,16]. Neighborhood walkability is a measure of
how friendly the residential built environment is to walk in. It is generally expressed as a composite
index of population density, land-use diversity, and pedestrian-friendly design [17]. Neighborhood
walkability has been shown to be related to time spent walking [18], physical activity [15], obesity [19],and depression [20]. These are all factors which are also well known to be associated, in one way or
another, with musculoskeletal pain.
However, few studies have investigated an association between the built environment andmusculoskeletal pain. If neighborhood walkability is associated in some way with musculoskeletal
pain, it would become clear that not only individual factors but environmental factors can be addressedin policies designed to prevent musculoskeletal pain. Therefore, we aimed to examine whether
neighborhood walkability is related to knee and low back pain, focusing on older people in Japan.
2. Methods
2.1. Study Design and Participants
The present study is based on the Japan Gerontological Evaluation Study (JAGES), an ongoing
population-based cohort study in Japan [21]. In 2013, self-reported questionnaires were mailed to193,694 community-dwelling, independently-living individuals aged 65 years or older, of whom
137,736 responded to the survey (response rate, 71.1%). Participants with missing values for ID, age,or sex (n = 7996); who needed assistance in activities of daily living (n = 4247); or people living incommunities with less than 30 respondents (n = 2108) were excluded from the analysis. A total of
123,385 participants’ responses from 792 communities were used to evaluate neighborhood walkability.
About one-fifth of the total participants (n = 24,806) was randomly selected, including some fromeach of the 792 communities, to complete a survey module enquiring about pain. The module was aplanned part of the JAGES. Because long-term exposure to neighborhood walkability was considered
to be beneficial, we excluded residents who had lived in their neighborhood for 3 years or less(n = 732). Responses were also excluded if data on knee and low back pain was missing (n = 1182).
This left responses from 22,892 participants that were included in the subsequent analysis (Figure 1).
Our research protocol and informed consent method were approved by the Ethics Committee of Nihon
Fukushi University (number 13–14).
2.2. Outcome Variables
Data on the presence of knee and low back pain within the last year were collected in the survey
by asking the following two questions. “In the past year, have you had knee pain that restricts yourdaily activities? In the past year, have you had low back pain that restricts your daily activities?”
A response of “yes” was defined as the presence of pain.
2.3. Neighborhood Walkability
Many studies have established the predictive value of residents’ perceptions as a measure ofneighborhood walkability [22,23]. Previously studied relationships include those between access toInt. J. Environ. Res. Public Health 2019, 16, 4598

3 of 13parks and body mass index (BMI) [24], food environment and mortality rate [25], and walking up
slopes and diabetes control [26]. While some studies demonstrate that objective measures affect various
health outcomes [19], two studies reported that subjective walkability rather than objective geographic
information system-based data was associated with health outcomes [25,27]. Subjective walkability
has the advantage of easily grasping the actual situation; for example, it can change depending on
factors such as the size, number, and design during the evaluation of parks and sidewalks. Moreover,there are few studies on walkability in Japan, and the validity of objective indicators has not been
sufficiently
verified.
Therefore,
weInt. J. Environ. Res.
Public Health2019, 16,x used subjectively assessed walkability as an explanatory variable.
3 of 14

Figureof participant
selection
for the studyof neighborhood
walkability andwalkability
musculoskeletal
Figure 1.1.Flow
Flowof participant
selection
for thestudy of neighborhoodandpain (n = 22,892). ID, identification; ADL, activities of daily living.
musculoskeletal pain (n = 22,892). ID, identification; ADL, activities of daily living.

We evaluated neighborhood walkability by asking about access to parks and sidewalks, access to
2.2. Outcome Variables
fresh food stores, and easy walking without slopes or stairs. Three questions were posed about theData on thepresenceofof
knee
low back house.
pain within
thedo
last
year
were
collected
surveyneighborhood
within
1 km
the andparticipant’s
“How
you
feel
about
access into theparks
andbyaskingthefollowingtwoquestions.
“In
the
past
year,
have
you
had
knee
pain
that
restricts
yoursidewalks when walking? How many stores or facilities selling fresh fruit and vegetables are locateddailyyou?
activities?
In thehad without
low backslopes
pain that
restricts
your daily
activities?”near
How doyoupastfeel year,
abouthaveeasy youwalking
or stairs?”
Responses
were
given onAaresponse ofLikert“yes”scale,
was defined
the presence
four-pointwith 1 =asnone,
2 = a few,of3 pain.
= some, and 4 = many. The average of the points ineach neighborhood was used to compare each walkability variable, resulting in a minimum of 1 and a
2.3. Neighborhood
Walkability
maximumof 4 continuous
points. To assess neighborhood walkability, we used the data derived fromall 123,385
participants
ratherthan only
smaller subset
22,892) ofperceptionsindividualsas
who
responded
Many studies have establishedthethepredictive
value (nof =residents’a measureoftothequestions
about
knee
and
low
back
pain.
neighborhood walkability [22,23]. Previously studied relationships include those between access to
also
used
population
densitya variablebecauseand
it ismortality
one of themainassociatedparksWe
and
body
mass
index (BMI)[24],asfood
environmentrate[25],factors
and walkingupwithneighborhood
as it
includesfactorssuch
as land-use thatmix, objective
access to public
transport,
slopesand diabeteswalkability,
control [26].
While
some
studiesdemonstratemeasuresaffectandnumberof outcomeswalkable destinations[17,28].
The population
densityof each of rather
the 792thancommunities
varioushealth[19], two studies
reported
that subjective
walkability
objective
includedfor
analysis
wascalculated
using
the
2010
censusandLand
Utilization
TertiaryDatageographic information system-based data was associated with health outcomes [25,27].Mesh
Subjective(asof 2010) of
National
Land
Information
fromsituation;
the Ministryof Land, Infrastructure,
walkability
hasthetheadvantage
ofNumericaleasily grasping
the actualfor example,
it can change
Transport,
andTourism
in
Japan
based
on
the
1:25,000
Topographic
Map
of
Japan
[29]. These
depending on factors such as the size, number, and design during the evaluation
ofcalculations
parks andexcluded
undeveloped
areas(e.g.,
rivers,lakes,
forest,andwasteland).
Quartilesofpopulation
densitysidewalks. Moreover, there are few studies on walkability in Japan, and the validity of objective
2(persons/km
usedsufficiently
for analysis.
indicators has) were
not beenverified. Therefore, we used subjectively assessed walkability as

an explanatory variable.
We evaluated neighborhood walkability by asking about access to parks and sidewalks, accessto fresh food stores, and easy walking without slopes or stairs. Three questions were posed about theneighborhood within 1 km of the participant’s house. “How do you feel about access to parks andsidewalks when walking? How many stores or facilities selling fresh fruit and vegetables are locatedInt. J. Environ. Res. Public Health 2019, 16, 4598

4 of 132.4. Covariates
For individual covariates, sociodemographic data, behavior, and health status were used.
Sociodemographic covariates included sex, age (65–69, 70–74, 75–79, 80–84, and ≥85 years old),
educational background (<10, 10–12, and ≥13 years), equivalent annual income (<2, 2–3.9, and ≥4
million yen per year), and past occupation (white-collar workers, blue-collar workers, primary industry
workers, or never worked before) [30,31]. Primary industry workers in agriculture, forestry, andfisheries were considered separately from other blue-collar workers because those three occupations are
known to be strongly associated with musculoskeletal pain [32]. Behaviors and health status covariates
assessed included time spent walking (<30, 30–59, and ≥60 min a day), frequency of physical activity(<2, 2–3, and ≥4 times a week), driving status (driving a car by themselves or not) [33], BMI (<18.5,
18.5–24.9, and ≥25 kg/m2 ), and depression (none, mild, severe) [34]. Physical activity referred tomedium intensity exercise, such as walking quickly, dancing, and golf [35]. Participants were classified
in three groups based on scores from the Japanese version of the Geriatric Depression Scale-15 [36,37]:
not depressed (<5), mildly depressed (5–9), or severely depressed (≥10) [38,39]. Missing data wascounted and listed as missing.
2.5. Statistical Analysis
We first calculated the association between each neighborhood walkability factor and knee or
low back pain using Pearson’s correlation coefficient. Multilevel Poisson regression models werethen analyzed to investigate the association between neighborhood walkability and pain. An initialmodel was specified to assess the crude association between neighborhood walkability and kneeor low back pain. This was then adjusted in Model 1 using sex, age, equivalent annual income,educational background, and past occupation as individual confounders to evaluate the influenceof sociodemographic factors. Model 2 was additionally adjusted for walking time, physical activity,
driving status, BMI, and depressive symptoms as potential confounders. As population density
strongly affects various aspects and is easy to correlate with other walkability [28,40]. For example,
in order to clarify that it is not just the influence of population density, we additionally adjusted the
population density in Model 3. Using Appendix A, we identified whether covariates affected theoutcomes. Stata 14.0 (StataCorp LP, College Station, TX, USA) was used, and prevalence ratios (PR)and 95% confidence intervals (CI) were calculated from the regression models. The significance level
was set at 0.05. Participants with missing covariate data were still included in the analysis.
3. Results
The prevalence of knee pain and low back pain was 26.2% (n = 6257) and 29.3% (n = 6989),
respectively (Table 1). The largest proportion by age was 70 to 74 years old (30.3%), followed by those
65 to 69 years old (28.0%). Approximately two-thirds of the participants had normal BMIs and no
depression. More than a third (38.7%) walked >60 min; another third (35.2%) walked 30 to 59 min;and 23.9% walked <30 min. About half drove a car.
The means for the three subjective neighborhood walkability factors ranged from 2.56 to 2.97
(Table 2). The mean population density was 6543 persons/km2 (22–31,565 persons/km2 ). Reports
by neighborhood of knee pain ranged from 15.6% to 51.4%, and of low back pain, from 13.6% to51.4%. The Pearson correlations between neighborhood walkability factors were all significant.
The correlations were relatively high between access to parks and sidewalks and access to fresh foodstores; access to parks and sidewalks and population density; and access to fresh food stores andpopulation density (0.44 to 0.59). There were significant negative correlations between knee pain andaccess to parks and sidewalks (−0.21); knee pain and population density (−0.33); and low back pain
and population density (−0.17).

Int. J. Environ. Res. Public Health 2019, 16, 4598

5 of 13

Table 1. Characteristics of older Japanese adults surveyed in JAGES 2013 with regard to neighborhood
walkability (n = 22,892).
n

%

Sex
Male
Female11,114
12,77546.5
53.5

Age, years
65–69
70–74
75–79
80–84
85+

6690
7231
5330
3147
1491

28
30.3
22.3
13.2
6.2

Educational
background, years
13+
10–12
<10
Missing

4713
8819
9974
383

19.7
36.9
41.8
1.6

Equivalent annual
income, yen
4.0+ million
2.0–3.9 million<2.0 million
Missing

2025
7140
9875
4849

8.5
29.9
41.3
20.3

8481
9494

37.1
41.5

1291

5.6

1170
2456

5.1
10.7

Walking time, min
60+
30–59
<30
Missing

9241
8401
5704
543

38.7
35.2
23.9
2.3

Physical activity
Daily
WeeklyAnnually
None
Missing

6858
6517
3379
4999
2136

28.7
27.3
14.1
20.9
8.9

Driving statusNo
Yes

10,854
12,967

45.6
54.4

Body mass index,
kg/m2
18.5–24.9<18.5
25+
Missing

16,006
1665
6153
65

67
7
25.8
0.3

Depression
None (GDS < 5)
Mild (GDS of 5–9)
Severe (GDS ≥ 10)
Missing

14,223
3603
1143
3923

62.1
15.7
5
17.1

Knee pain
Yes
Missing

6314
646

26.2
2.8

Low back pain
Yes
Missing

7050
657

29.3
2.9

Characteristics

Past occupation
White-collar worker
Blue-collar worker
Primary industry
workerNever worked
Missing

GDS = Geriatric Depression Scale; JAGES = Japan Gerontological Evaluation Study.

Int. J. Environ. Res. Public Health 2019, 16, 4598

6 of 13

Table 2. Pearson correlations between neighborhood walkability factors and pain.
Mean

SD

MedianMinimum

R

MaximumI(i) Access to parks andsidewalks (score)(ii) Access to fresh
food stores (score)(iii) Easy walking
without slopes orstairs (score)(iv) Population density(persons/km2 )(v) Knee pain (%)(vi) Low back pain (%)

II

III

IV

2.94

0.29

2.96

1.94

3.81

1

2.97

0.39

3.05

1.65

3.85

0.52 *

1

2.56

0.38

2.61

1.44

3.29

−0.17 *

0.08 *

1

6543

4727

6719

22

31,5650.44 *

0.59 *

0.24 *

1

29
32.9

6.8
6.9

27.7
32

15.6
13.6

51.4
51.4

−0.21 *
−0.14

−0.14
−0.14

0.02
−0.03

−0.33 *
−0.17 *

VVI

1
0.63 *

1

For neighborhood factors (i to iv), n = 792, while for pain (v to vi), n = 148, calculated only for areas with more than
30 responses about pain. For factors i–iii, the average points on a scale from 1 to 4 (1 = none, 2 = few, 3 = some,
4 = many) were calculated for each community and then combined for analysis of each factor. * p < 0.05. SD =standard deviation.

In the Crude regression model, knee pain was significantly less prevalent with access to parks
and sidewalks, access to fresh food stores, and a high population density (Table 3). After adjustment
for sociodemographic confounders (Model 1) and behavior and activity covariates (Model 2), all three
walkability factors remained statistically significant. After adjusting for population density in Model3, the only statistically significant factor associated with less knee pain was ease of walking withoutslopes or stairs (PR = 0.91, 95% CI = 0.85–0.99).
Table 3. Association between neighborhood walkability and knee pain by multilevel Poisson regression
analysis (n = 22,892).
Crude Model

Access to parks and sidewalks
Access to fresh food stores
Easy walking without slopes or stairs
Population density

Model 1 aModel 2 b

Model 3 c

PR (95% CI)

PR (95% CI)

PR (95% CI)

PR (95% CI)

0.69 (0.63–0.76) *0.81 (0.76–0.87) *1.02 (0.94–1.10)
0.91 (0.89–0.93) *0.84 (0.76–0.93) *0.90 (0.84–0.96) *0.96 (0.89–1.04)
0.96 (0.94–0.98) *0.85 (0.77–0.94) *0.90 (0.84–0.96) *0.95 (0.88–1.02)
0.95 (0.93–0.98) *0.92 (0.81–1.03)
0.95 (0.87–1.03)
0.91 (0.85–0.99) *-PR = prevalence ratio; 95% CI = 95% confidence interval. a Model 1 was adjusted for sex, age, equivalent annual
income, educational background, and past occupation. b Model 2 was adjusted for the covariates in Model 1 plus
walking time, physical activity, driving status, BMI, and depressive symptoms. c Model 3 was adjusted for thecovariates in Model 2 plus population density. * p < 0.05.

For low back pain, the initial results were similar to those with knee pain (Table 4). However,with Models 1 and 2, only access to fresh food stores and population density remained significantly
associated with less low back pain. After adjusting for population density, ease walking without slopes
or stairs fell just short being statistically significant.
Table 4. Association between neighborhood walkability and low back pain by multilevel Poisson
regression analysis (n = 22,892).
Crude Model

Access to parks and sidewalks
Access to fresh food stores
Easy walking without slopes or stairs
Population density

Model 1 aModel 2 b

Model 3 c

PR (95% CI)

PR (95% CI)

PR (95% CI)

PR (95% CI)

0.81 (0.74–0.89) *0.85 (0.80–0.90) *1.02 (0.95–1.09)
0.92 (0.91–0.94) *0.94 (0.85–1.03)
0.92 (0.86–0.98) *0.98 (0.91–1.05)
0.96 (0.94–0.98) *0.96 (0.88–1.06)
0.92 (0.86–0.98) *0.96 (0.89–1.03)
0.96 (0.94–0.98) *1.08 (0.97–1.20)
0.98 (0.91–1.06)
0.93 (0.87–1.00)-

PR = prevalence ratio; 95% CI = 95% confidence interval. a Model 1 was adjusted for sex, age, equivalent annual
income, educational background, and past occupation. b Model 2 was adjusted for the covariates in Model 1 plus
walking time, physical activity, driving status, BMI, and depressive symptoms. c Model 3 was adjusted for thecovariates in Model 2 plus population density. * p < 0.05.

Int. J. Environ. Res. Public Health 2019, 16, 4598

7 of 13

4. Discussion
In a large and diverse, population-based sample, we found that subjectively perceived
neighborhood walkability was associated with a lower prevalence of knee and low back pain.
This relationship remained after adjusting for sociodemographic variables (Model 1). Although we
adjusted for walking time, physical activity, driving status, BMI, and depressive symptoms as potential
mediators, the association remained similar (Model 2). Even after adjusting for population density toeliminate that as a factor, one factor contributing to better walkability—ease of walking without slopes
or stairs—was significantly negatively associated with knee pain (Model 3). To our knowledge, this is
the first study indicating that features of the built environment may be correlated with the prevalenceof musculoskeletal pain in a large-scale survey of older adults.
Earlier studies of neighborhood walkability indicated a negative association with obesity [19],which is a risk factor for knee and low back pain [3,41]. A population-based study of 9046 adults
in Japan reported that living in a rural area was associated with a high prevalence of knee pain andlow back pain [42]. However, that study did not adjust for occupation. The jobs of primary industry
workers tend to place a heavy burden on the knee and low back, and many of these individuals live inrural areas. In our study, after adjusting for past occupation, we found that higher population density,
access to parks and sidewalks and fresh food stores, and easy walking without slopes or stairs wererelated to lower prevalences of knee pain and low back pain.
The sociodemographic factors we assessed are considered key not only in regard to physicalactivity [43] and obesity [44] but to knee and low back pain, as we found relatively large changes
in the PRs from the Crude Model to Model 1 after adjusting for sociodemographic factors. In fact,
an association between low back pain and socioeconomic status, such as educational background,past occupations, and income, has been reported [31]. A longer time spent walking, greater physicalactivity, a lower BMI, and the absence of depression are factors known to be negatively related toknee and low back pain. Therefore, we initially hypothesized that these factors would be potential
mediators, and as shown in the Appendix A, these factors were actually related to knee pain andlow back pain. However, after adjusting for these covariates in Model 2, little change was seen in
our results. Therefore, walking time, physical activity, BMI, and depression were thought to largely
depend on sociodemographic status, and other factors should still be considered. Social environment
variables such as social capital and safety may also be involved, as the social environment has beenshown to be associated with cognitive function and social participation [45,46].
As a mechanism that might mediate the relationship between neighborhood walkability and pain,social interaction and the greenness provided by parks and sidewalks have been considered. Social
interaction increases for people who frequently use parks [47] and can have a positive psychosocial
influence. Good access to parks and sidewalks is likely to increase exposure to greenness which has
also been shown to be associated with less obesity [48]. A fresh food store may be a place people
would go every day, which would therefore encourage daily walking [25] as well as meeting friends.
Such access to fresh food would also support a healthy diet that can be beneficial in preventing obesity.
The relationship between walking up slopes or stairs and health is controversial [35,49]. However,to the extent that such features might hinder walking and physical activity among older adults, a flatterenvironment might be better in terms of walkability. Higher population density can lead to more
walkable destinations, a better land-use mix, and better access to public transport and healthcare
services [28]. We found that, compared with knee pain, low back pain was not significantly associatedwith access to parks and sidewalks or easy walking without slopes or stairs in Models 1–3. A previous
review indicated that low back pain was strongly influenced by awkward posture among agricultural
workers [50]. It may be, therefore, that knee pain is more closely linked with walking than is low
back pain.
Strengths of this study include the focus on the association between the built environment andmusculoskeletal pain in a large-scale population-based study. Past research has mainly focused on
individual factors vis-à-vis musculoskeletal pain. However, it is difficult to get regular exercise andInt. J. Environ. Res. Public Health 2019, 16, 4598

8 of 13

maintain a desirable weight for people with and without pain. A population-based approach should
also be used for investigating musculoskeletal pain, particularly when considering public policies toprevent disability or to improve the health system [21,51]. Our results will be useful in further researchon environmental determinants of pain and specific population approaches such as the primordial
prevention [52], which aims for a society where people live in a health-friendly place and remain
healthy without additional effort because risk factors have been minimized.
Several limitations of this study should be mentioned. First, with the exception of population
density, our explanatory variables were subjectively assessed. A comprehensive scale that takes into
account various factors, such as walk score or MAPS Global tool, may also be useful [53,54]. In thisstudy, we focused on subjective indicators because it was easy to comprehend the actual situation ofeach element; however, evaluation of both subjective and objective indicators in the future will lead toa more detailed verification of the relationship between the built environment and pain. Second, weselected certain items that seemed to be particularly influential among various factors contributing towalkability, and that have been reported to be useful in previous studies [24–26]. Other variables suchas street connectivity and safety may warrant inclusion in similar studies [23,55]. This study did notinclude them because we thought the other factors were unlikely to be related to pain alone. Further
research must explore which built environment elements and scales are associated with musculoskeletal
pain. Third, our outcomes included both acute and chronic pain. However, knee pain in older people
is mostly due to OA [56], and the relationship weakens when other causes of knee pain are included.
Therefore, it can be said that the connection to neighborhood walkability is strong. Fourth, as thisis a cross-sectional study, it cannot prove a causal relationship. Exercise has been shown to have a
preventive and therapeutic effect on low back pain [4,57], so better neighborhood walkability could
theoretically be beneficial by improving access to exercise. People without knee pain or low back pain
might choose to live in areas with good walkability, but we could not evaluate that in our study because
we excluded those who have lived in the same neighborhood for 3 years or less. Longitudinal studies
will be needed to better examine the nature of the relationship between neighborhood walkability andthe incidence of musculoskeletal pain. Finally, although there is a high generalizability in Japan, it isdifficult to generalize these results to other countries with greatly differing environments and cultures,such as those in Europe and America. In the future, aiming at the realization of a society where pain isnaturally prevented, research should be conducted on whether improvement of the built environment
helps reduce the prevalence of musculoskeletal pain in various regions.
5. Conclusions
Good neighborhood walkability with access to parks and sidewalks and fresh food stores,
easy walking without slopes or stairs, and high population density were associated with a lower
prevalence of knee and low back pain among older people, as demonstrated in this large-scale,population-based, multilevel analysis. Further studies should examine not only individual factors but
also environmental determinants of pain.
Author Contributions: Conceptualization, D.O. and K.K.; methodology, T.T., and M.H.; formal analysis, D.O. andY.M.; validation, T.T., N.A. and K.K.; investigation, K.K.; writing—original draft preparation, D.O.; writing—review
and editing, D.O., T.T., M.H., Y.M., N.A. and K.K.
Funding: This study used data from JAGES (the Japan Gerontological Evaluation Study), which was supported by
MEXT (Ministry of Education, Culture, Sports, Science and Technology-Japan)-Supported Program for the Strategic
Research Foundation at Private Universities (2009–2013); JST-OPERA program grant (JPMJOP1831); JSPS (Japan
Society for the Promotion of Science) KAKENHI (grant numbers JP18390200, JP22330172, JP22390400, JP23243070,
JP23590786, JP23790710, JP24390469, JP24530698, JP24683018, JP25253052, JP25870573, JP25870881, JP26285138,
JP26882010, and JP15H01972); Health Labor Sciences Research Grants (grant numbers 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, H30-Kenki-Ippan-006, and H30-Junkankitou-Ippan-004); Japan Agency for Medical Researchand Development (AMED) (grant numbers 171s0110002, 18le0110009, and JP19dk0110034); the Research Fundingfor Longevity Sciences from National Center for Geriatrics and Gerontology (grant numbers 24-17, 24-23, andInt. J. Environ. Res. Public Health 2019, 16, 4598

9 of 1329-42); and the World Health Organization Centre for Health Development (WHO Kobe Centre) (WHO APW
2017/713981). The views and opinions expressed in this article are those of the authors and do not necessarilyreflect the official policy or position of the respective funding organizations.
Acknowledgments: We are particularly grateful to the staff members in each study area, and in the central office,for conducting the survey.
Conflicts of Interest: The authors declare no conflict of interest.

Appendix A
Table A1. Association between covariates and knee pain by multilevel Poisson regression analysis (n = 22,892).
Access to Parks
and Sidewalks

Access to Fresh
Food Stores

Easy Walking without
Slopes or Stairs

Population
DensityPR (95% CI)

PR (95% CI)

PR (95% CI)

PR (95% CI)

Sex

Male
Femaleref1.51 (1.42–1.60)

ref1.51 (1.43–1.60)

ref1.51 (1.43–1.61)

ref1.50 (1.42–1.59)

Age, years

65–69
70–74
75–79
80–84
85+

ref
1.21 (1.12–1.30)
1.45 (1.34–1.56)
1.68 (1.54–1.83)
1.74 (1.57–1.94)

ref1.20 (1.12–1.30)
1.45 (1.34–1.56)
1.68 (1.54–1.83)
1.75 (1.57–1.94)

ref1.20 (1.12–1.30)
1.45 (1.34–1.56)
1.68 (1.54–1.83)
1.75 (1.57–1.95)

ref1.21 (1.12–1.30)
1.45 (1.35–1.57)
1.68 (1.54–1.83)
1.74 (1.56–1.94)

Educational
background,
years

13+
10–12
<10
Missingref1.06 (0.98–1.15)
1.16 (1.07–1.25)
1.17 (0.95–1.42)

ref1.07 (0.98–1.15)
1.17 (1.08–1.26)
1.17 (0.96–1.42)

ref1.07 (0.99–1.16)
1.18 (1.09–1.28)
1.19 (0.97–1.45)

ref1.06 (0.98–1.15)
1.15 (1.06–1.25)
1.15 (0.95–1.41)

Equivalentannual income,yen

4.0+ million
2.0–3.9 million<2.0 million
Missingref1.22 (1.10–1.35)
1.02 (0.92–1.14)
1.09 (0.97–1.22)

ref1.22 (1.10–1.35)
1.03 (0.92–1.14)
1.10 (0.98–1.23)

ref1.22 (1.10–1.36)
1.03 (0.92–1.14)
1.10 (0.98–1.23)

ref1.21 (1.09–1.35)
1.02 (0.92–1.14)
1.09 (0.97–1.22)

Past occupation

White-collar worker
Blue-collar worker
Primary industry worker
Never worked
Missingref1.08 (1.01–1.15)
1.56 (1.41–1.73)
1.13 (1.01–1.26)
1.16 (1.06–1.27)

ref1.08 (1.01–1.15)
1.56 (1.45–1.72)
1.13 (1.01–1.26)
1.16 (1.07–1.27)

ref1.08 (1.01–1.15)
1.60 (1.45–1.77)
1.13 (1.01–1.26)
1.16 (1.07–1.27)

ref1.07 (1.01–1.14)
1.53 (1.37–1.69)
1.12 (1.01–1.25)
1.15 (1.05–1.26)

Walking time,
min

60+
30–59
<30
Missingref1.20 (1.13–1.29)
1.11 (1.05–1.18)
1.10 (0.93–1.30)

ref1.21 (1.13–1.29)
1.11 (1.05–1.18)
1.10 (0.93–1.30)

ref1.21 (1.13–1.29)
1.11 (1.04–1.18)
1.11 (0.94–1.32)

ref1.20 (1.13–1.29)
1.11 (1.05–1.19)
1.09 (0.92–1.29)

Physical
activity

Daily
WeeklyAnnually
None
Missingref1.06 (0.98–1.14)
1.10 (1.00–1.20)
1.22 (1.13–1.32)
1.15 (1.04–1.27)

ref1.06 (0.98–1.14)
1.10 (1.00–1.20)
1.22 (1.13–1.32)
1.15 (1.04–1.27)

ref1.05 (0.98–1.13)
1.09 (1.00–1.19)
1.21 (1.13–1.31)
1.15 (1.04–1.26)

ref1.06 (0.99–1.14)
1.10 (1.01–1.20)
1.23 (1.14–1.32)
1.15 (1.04–1.27)

Driving status

No
Yes

ref
1.02 (0.94–1.08)

ref1.04 (0.96–1.08)

ref1.04 (0.98–1.10)

ref1.01 (0.95–1.07)

Body massindex, kg/m2

18.5–24.9<18.5
25+
Missingref0.77 (0.69–0.87)
1.38 (1.31–1.46)
1.00 (0.60–1.67)

ref0.78 (0.70–0.87)
1.38 (1.31–1.46)
1.01 (0.61–1.67)

ref0.78 (0.69–0.87)
1.39 (1.32–1.47)
1.01 (0.61–1.68)

ref0.77 (0.69–0.87)
1.38 (1.31–1.46)
1.02 (0.62–1.70)

Depression

None (GDS < 5)
Mild (GDS of 5–9)
Severe (GDS ≥ 10)
Missingref1.52 (1.42–1.62)
1.79 (1.62–1.96)
1.20 (1.12–1.29)

ref1.52 (1.43–1.63)
1.79 (1.62–1.96)
1.20 (1.12–1.29)

ref1.52 (1.43–1.63)
1.79 (1.63–1.97)
1.20 (1.12–1.29)

ref1.52 (1.43–1.63)
1.78 (1.62–1.96)
1.20 (1.12–1.29)

PR = prevalence ratio; 95% CI = 95% confidence interval.

Int. J. Environ. Res. Public Health 2019, 16, 4598

10 of 13

Table A2. Association between covariates and low back pain by multilevel Poisson regression analysis(n = 22,892).
Access to Parks
and Sidewalks

Access to Fresh
Food Stores

Easy Walking without
Slopes or Stairs

Population
DensityPR (95% CI)

PR (95% CI)

PR (95% CI)

PR (95% CI)

Sex

Male
Femaleref1.17 (1.11–1.23)

ref1.17 (1.11–1.23)

ref1.17 (1.11–1.23)

ref1.17 (1.11–1.23)

Age, years

65–69
70–74
75–79
80–84
85+

ref
1.03 (0.96–1.10)
1.18 (1.10–1.27)
1.28 (1.18–1.39)
1.41 (1.28–1.56)

ref1.03 (0.97–1.11)
1.18 (1.10–1.27)
1.28 (1.18–1.39)
1.41 (1.27–1.56)

ref1.03 (0.96–1.10)
1.18 (1.10–1.27)
1.28 (1.18–1.39)
1.41 (1.28–1.56)

ref1.03 (0.97–1.10)
1.18 (1.10–1.27)
1.28 (1.18–1.39)
1.41 (1.27–1.55)

Educational
background,
years

13+
10–12
<10
Missingref1.02 (0.95–1.09)
1.07 0.99–1.15)
1.01 (0.82–1.24)

ref1.02 (0.95–1.09)
1.06 0.99–1.14)
1.01 (0.82–1.23)

ref1.02 (0.95–1.11)
1.07 1.00–1.15)
1.01 (0.83–1.25)

ref1.02 (0.95–1.09)
1.05 0.98–1.13)
1.00 (0.81–1.22)

Equivalentannual income,yen

4.0+ million
2.0–3.9 million<2.0 million
Missingref1.25 (1.13–1.37)
1.11 (1.01–1.23)
1.14 (1.02–1.27)

ref1.24 (1.13–1.37)
1.11 (1.01–1.23)
1.14 (1.02–1.27)

ref1.25 (1.13–1.38)
1.11 (1.01–1.23)
1.14 (1.03–1.28)

ref1.24 (1.13–1.37)
1.11 (1.00–1.23)
1.14 (1.02–1.27)

Past occupation

White-collar worker
Blue-collar worker
Primary industry worker
Never worked
Missingref1.01 (0.96–1.08)
1.46 (1.33–1.61)
1.09 (0.98–1.22)
1.09 (1.00–1.19)

ref1.01 (0.96–1.07)
1.44 (1.31–1.59)
1.09 (0.98–1.21)
1.09 (0.99–1.18)

ref1.01 (0.96–1.08)
1.46 (1.34–1.63)
1.09 (0.98–1.22)
1.09 (1.00–1.19)

ref1.01 (0.95–1.07)
1.41 (1.28–1.56)
1.09 (0.98–1.21)
1.08 (0.99–1.18)

Walking time,
min

60+
30–59
<30
Missingref1.28 (1.20–1.36)
1.11 (1.05–1.18)
1.13 (0.95–1.33)

ref1.28 (1.20–1.36)
1.11 (1.05–1.18)
1.13 (0.95–1.32)

ref1.28 (1.20–1.36)
1.11 (1.05–1.18)
1.13 (0.96–1.34)

ref1.28 (1.20–1.36)
1.11 (1.05–1.18)
1.13 (0.95–1.32)

Physical
activity

Daily
WeeklyAnnually
None
Missingref1.09 (1.02–1.17)
1.06 (0.98–1.15)
1.19 (1.11–1.28)
1.20 (1.09–1.32)

ref1.09 (1.02–1.17)
1.06 (0.98–1.15)
1.19 (1.11–1.28)
1.20 (1.10–1.32)

ref1.09 (1.02–1.17)
1.06 (0.97–1.15)
1.19 (1.11–1.27)
1.20 (1.09–1.32)

ref1.09 (1.02–1.17)
1.06 (0.98–1.15)
1.19 (1.11–1.28)
1.21 (1.10–1.33)

Driving status

No
Yes

ref
1.08 (1.02–1.14)

ref1.08 (1.02–1.14)

ref1.09 (1.03–1.15)

ref1.08 (1.01–1.13)

Body massindex, kg/m2

18.5–24.9<18.5
25+
Missingref0.96 (0.87–1.05)
1.18 (1.12–1.25)
1.12 (0.72–1.73)

ref0.96 (0.87–1.05)
1.18 (1.12–1.25)
1.12 (0.72–1.73)

ref0.96 (0.87–1.05)
1.18 (1.12–1.25)
1.12 (0.72–1.73)

ref0.96 (0.87–1.05)
1.18 (1.12–1.25)
1.14 (0.73–1.76)

Depression

None (GDS < 5)
Mild (GDS of 5–9)
Severe (GDS ≥ 10)
Missingref1.53 (1.44–1.63)
1.81 (1.65–1.98)
1.31 (1.23–1.44)

ref1.53 (1.44–1.63)
1.81 (1.65–1.98)
1.31 (1.23–1.40)

ref1.53 (1.44–1.63)
1.81 (1.65–1.98)
1.31 (1.23–1.40)

ref1.53 (1.44–1.63)
1.80 (1.64–1.97)
1.31 (1.22–1.40)

PR = prevalence ratio; 95% CI = 95% confidence interval.

References
1.

2.

3.
4.

James, S.L.; Abate, D.; Abate, K.H.; Abay, S.M.; Abbafati, C.; Abbasi, N.; Abbastabar, H.; Abd-Allah, F.;
Abdela, J.; Abdelalim, A.; et al. Global, regional, and national incidence, prevalence, and years lived withdisability for 354 diseases and injuries for 195 countries and territories, 1990–2017: A systematic analysis for
the Global Burden of Disease Study 2017. Lancet 2018, 392, 1789–1858. [CrossRef]
Briggs, A.M.; Cross, M.J.; Hoy, D.G.; Sanchez-Riera, L.; Blyth, F.M.; Woolf, A.D.; March, L. Musculoskeletal
Health Conditions Represent a Global Threat to Healthy Aging: A Report for the 2015 World HealthOrganization World Report on Ageing and Health. Gerontologist 2016, 56, S243–S255. [CrossRef] [PubMed]
Roos, E.M.; Arden, N.K. Strategies for the prevention of knee osteoarthritis. Nat. Rev. Rheumatol. 2016, 12,
92–101. [CrossRef] [PubMed]
Steffens, D.; Maher, C.G.; Pereira, L.S.; Stevens, M.L.; Oliveira, V.C.; Chapple, M.; Teixeira-Salmela, L.F.;
Hancock, M.J. Prevention of Low Back Pain: A Systematic Review and Meta-analysis. JAMA Intern. Med.
2016, 176, 199–208. [CrossRef] [PubMed]

Int. J. Environ. Res. Public Health 2019, 16, 4598

5.

6.

7.
8.
9.
10.
11.

12.
13.

14.

15.

16.
17.
18.

19.

20.
21.
22.

23.

24.

11 of 13

Silverwood, V.; Blagojevic-Bucknall, M.; Jinks, C.; Jordan, J.L.; Protheroe, J.; Jordan, K.P. Current evidence onrisk factors for knee osteoarthritis in older adults: A systematic review and meta-analysis. Osteoarthr. Cartil.
2015, 23, 507–515. [CrossRef] [PubMed]
Fiuza-Luces, C.; Santos-Lozano, A.; Joyner, M.; Carrera-Bastos, P.; Picazo, O.; Zugaza, J.L.; Izquierdo, M.;
Ruilope, L.M.; Lucia, A. Exercise benefits in cardiovascular disease: Beyond attenuation of traditional risk
factors. Nat. Rev. Cardiol. 2018, 15, 731–743. [CrossRef] [PubMed]
Japan Sports Agency. Opinion Survey about the Implementation Situation of Sports; Ministry of Health, Labour,and Welfare: Tokyo, Japan, 2019.
Ministry of Health, Labour, and Welfare. Basic Survey on National Life; Ministry of Health; Ministry of Health,Labour, and Welfare: Tokyo, Japan, 2016.
Bhatnagar, A. Environmental Determinants of Cardiovascular Disease. Circ. Res. 2017, 121, 162–180.
[CrossRef]
den Braver, N.R.; Lakerveld, J.; Rutters, F.; Schoonmade, L.J.; Brug, J.; Beulens, J.W.J. Built environmental
characteristics and diabetes: A systematic review and meta-analysis. BMC Med. 2018, 16, 26. [CrossRef]
McCormack, G.R.; Cabaj, J.; Orpana, H.; Lukic, R.; Blackstaffe, A.; Goopy, S.; Hagel, B.; Keough, N.;
Martinson, R.; Chapman, J.; et al. A scoping review on the relations between urban form and health: A focus
on Canadian quantitative evidence. Health Promot. Chronic Dis. Prev. Can. 2019, 39, 187–200. [CrossRef]
WHO. Measuring the Age-Friendliness of Cities: A Guide to Using Core Indicators; WHO Centre for Health
Development: Kobe, Japan, 2015.
Cerin, E.; Nathan, A.; van Cauwenberg, J.; Barnett, D.W.; Barnett, A. The neighbourhood physical environment
and active travel in older adults: A systematic review and meta-analysis. Int. J. Behav. Nutr. Phys. Act. 2017,14, 15. [CrossRef]
Wang, Y.; Chau, C.K.; Ng, W.Y.; Leung, T.M. A review on the effects of physical built environment attributes
on enhancing walking and cycling activity levels within residential neighborhoods. Cities 2016, 50, 1–15.
[CrossRef]
Barnett, D.W.; Barnett, A.; Nathan, A.; Van Cauwenberg, J.; Cerin, E. Built environmental correlates of older
adults’ total physical activity and walking: A systematic review and meta-analysis. Int. J. Behav. Nutr.
Phys. Act. 2017, 14, 103. [CrossRef] [PubMed]
Bauman, A.E.; Reis, R.S.; Sallis, J.F.; Wells, J.C.; Loos, R.J.; Martin, B.W. Correlates of physical activity: Why
are some people physically active and others not? Lancet 2012, 380, 258–271. [CrossRef]
Cervero, R.A.K.K. Travel Demand and the 3Ds: Density, Diversity, and Design. Transp. Res. Part D
Transp. Environ. 1997, 2, 199–219. [CrossRef]
Hajna, S.; Ross, N.A.; Brazeau, A.S.; Belisle, P.; Joseph, L.; Dasgupta, K. Associations between neighbourhood
walkability and daily steps in adults: A systematic review and meta-analysis. BMC Public Health 2015, 15,768. [CrossRef]
Creatore, M.I.; Glazier, R.H.; Moineddin, R.; Fazli, G.S.; Johns, A.; Gozdyra, P.; Matheson, F.I.;
Kaufman-Shriqui, V.; Rosella, L.C.; Manuel, D.G.; et al. Association of Neighborhood Walkability with
Change in Overweight, Obesity, and Diabetes. JAMA 2016, 315, 2211–2220. [CrossRef]
Koohsari, M.J.; McCormack, G.R.; Nakaya, T.; Shibata, A.; Ishii, K.; Yasunaga, A.; Hanibuchi, T.; Oka, K.
Urban design and Japanese older adults’ depressive symptoms. Cities 2019, 87, 166–173. [CrossRef]
Kondo, K. Progress in Aging Epidemiology in Japan: The JAGES Project. J. Epidemiol. 2016, 26, 331–336.
[CrossRef]
Adams, M.A.; Ryan, S.; Kerr, J.; Sallis, J.F.; Patrick, K.; Frank, L.D.; Norman, G.J. Validation of the
Neighborhood Environment Walkability Scale (NEWS) items using geographic information systems. J. Phys.
Act. Health 2009, 6, S113–S123. [CrossRef]
Frank, L.D.; Sallis, J.F.; Saelens, B.E.; Leary, L.; Cain, K.; Conway, T.L.; Hess, P.M. The development of awalkability index: Application to the Neighborhood Quality of Life Study. Br. J. Sports Med. 2010, 44,
924–933. [CrossRef]
Stark, J.H.; Neckerman, K.; Lovasi, G.S.; Quinn, J.; Weiss, C.C.; Bader, M.D.; Konty, K.; Harris, T.G.; Rundle, A.
The impact of neighborhood park access and quality on body mass index among adults in New York City.
Prev. Med. 2014, 64, 63–68. [CrossRef] [PubMed]

Int. J. Environ. Res. Public Health 2019, 16, 4598

25.

26.

27.

28.

29.
30.

31.

32.

33.
34.

35.

36.

37.

38.

39.

40.

41.
42.

12 of 13Tani, Y.; Suzuki, N.; Fujiwara, T.; Hanazato, M.; Kondo, N.; Miyaguni, Y.; Kondo, K. Neighborhood foodenvironment and mortality among older Japanese adults: Results from the JAGES cohort study. Int. J. Behav.
Nutr. Phys. Act. 2018, 15, 101. [CrossRef] [PubMed]
Fujiwara, T.; Takamoto, I.; Amemiya, A.; Hanazato, M.; Suzuki, N.; Nagamine, Y.; Sasaki, Y.; Tani, Y.;
Yazawa, A.; Inoue, Y.; et al. Is a hilly neighborhood environment associated with diabetes mellitus amongolder people? Results from the JAGES 2010 study. Soc. Sci. Med. 2017, 182, 45–51. [CrossRef]
Hanibuchi, T.; Nakaya, T.; Yonejima, M.; Honjo, K. Perceived and Objective Measures of Neighborhood
Walkability and Physical Activity among Adults in Japan: A Multilevel Analysis of a Nationally Representative
Sample. Int. J. Environ. Res. Public Health 2015, 12, 13350–13364. [CrossRef] [PubMed]
Glazier, R.H.; Creatore, M.I.; Weyman, J.T.; Fazli, G.; Matheson, F.I.; Gozdyra, P.; Moineddin, R.;
Kaufman-Shriqui, V.; Booth, G.L. Density, destinations or both? A comparison of measures of walkability inrelation to transportation behaviors, obesity and diabetes in Toronto, Canada. PLoS ONE 2014, 9, e85295.
[CrossRef]
Ministry of Land, Infrastructure, Transport and Tourism. National Land Numerical Information Land Utilization
Tertiary Mesh Data; Ministry of Land, Infrastructure, Transport and Tourism: Tokyo, Japan, 2016.
Lakke, S.E.; Soer, R.; Takken, T.; Reneman, M.F. Risk and prognostic factors for non-specific musculoskeletal
pain: A synthesis of evidence from systematic reviews classified into ICF dimensions. Pain 2009, 147, 153–164.
[CrossRef]
Ikeda, T.; Sugiyama, K.; Aida, J.; Tsuboya, T.; Watabiki, N.; Kondo, K.; Osaka, K. Socioeconomic inequalities
in low back pain among older people: The JAGES cross-sectional study. Int. J. Equity Health 2019, 18, 15.
[CrossRef]
Muraki, S.; Akune, T.; Oka, H.; Mabuchi, A.; En-Yo, Y.; Yoshida, M.; Saika, A.; Nakamura, K.; Kawaguchi, H.;
Yoshimura, N. Association of occupational activity with radiographic knee osteoarthritis and lumbar
spondylosis in elderly patients of population-based cohorts: A large-scale population-based study. Arthritis
Rheum. 2009, 61, 779–786. [CrossRef]
Rautio, N.; Filatova, S.; Lehtiniemi, H.; Miettunen, J. Living environment and its relationship to depressive
mood: A systematic review. Int. J. Soc. Psychiatry 2018, 64, 92–103. [CrossRef]
Eriksson, U.; Arvidsson, D.; Gebel, K.; Ohlsson, H.; Sundquist, K. Walkability parameters, active
transportation and objective physical activity: Moderating and mediating effects of motor vehicle ownershipin a cross-sectional study. Int. J. Behav. Nutr. Phys. Act. 2012, 9, 123. [CrossRef]
Hanibuchi, T.; Kawachi, I.; Nakaya, T.; Hirai, H.; Kondo, K. Neighborhood built environment and physicalactivity of Japanese older adults: Results from the Aichi Gerontological Evaluation Study (AGES). BMC
Public Health 2011, 11, 657. [CrossRef] [PubMed]
Tsuji, T.; Miyaguni, Y.; Kanamori, S.; Hanazato, M.; Kondo, K. Community-level Sports Group Participation
and Older Individuals’ Depressive Symptoms. Med. Sci. Sports Exerc. 2018, 50, 1199–1205. [CrossRef][PubMed]Schreiner, A.S.; Hayakawa, H.; Morimoto, T.; Kakuma, T. Screening for late life depression: Cut-off scores for
the Geriatric Depression Scale and the Cornell Scale for Depression in Dementia among Japanese subjects.
Int. J. Geriatr. Psychiatry 2003, 18, 498–505. [CrossRef] [PubMed]
Sasaki, I.; Kondo, K.; Kondo, N.; Aida, J.; Ichikawa, H.; Kusumi, T.; Sueishi, N.; Imanaka, Y. Are pension
types associated with happiness in Japanese older people?: JAGES cross-sectional study. PLoS ONE 2018, 13,e0197423. [CrossRef] [PubMed]
Wada, T.; Ishine, M.; Sakagami, T.; Okumiya, K.; Fujisawa, M.; Murakami, S.; Otsuka, K.; Yano, S.; Kita, T.;
Matsubayashi, K. Depression in Japanese community-dwelling elderly–prevalence and association with
ADL and QOL. Arch. Gerontol. Geriatr. 2004, 39, 15–23. [CrossRef]
Yoshimura, N.; Akune, T.; Fujiwara, S.; Shimizu, Y.; Yoshida, H.; Nishiwaki, Y.; Sudo, A.; Omori, G.;
Yoshida, M.; Shimokata, H.; et al. Incidence of disability and its associated factors in Japanese men andwomen: The Longitudinal Cohorts of Motor System Organ (LOCOMO) study. J. Bone Miner. Metab. 2015, 33,
186–191. [CrossRef]
Zhang, T.T.; Liu, Z.; Liu, Y.L.; Zhao, J.J.; Liu, D.W.; Tian, Q.B. Obesity as a Risk Factor for Low Back Pain: A
Meta-Analysis. Clin. Spine Surg. 2018, 31, 22–27. [CrossRef]
Yoshimura, N.; Akune, T.; Fujiwara, S.; Shimizu, Y.; Yoshida, H.; Omori, G.; Sudo, A.; Nishiwaki, Y.;
Yoshida, M.; Shimokata, H.; et al. Prevalence of knee pain, lumbar pain and its coexistence in Japanese men

Int. J. Environ. Res. Public Health 2019, 16, 4598

43.
44.
45.
46.

47.
48.

49.
50.
51.

52.
53.
54.

55.

56.
57.

13 of 13and women: The Longitudinal Cohorts of Motor System Organ (LOCOMO) study. J. Bone Miner. Metab.
2014, 32, 524–532. [CrossRef]
Althoff, T.; Sosic, R.; Hicks, J.L.; King, A.C.; Delp, S.L.; Leskovec, J. Large-scale physical activity data reveal
worldwide activity inequality. Nature 2017, 547, 336–339. [CrossRef]
Newton, S.; Braithwaite, D.; Akinyemiju, T.F. Socio-economic status over the life course and obesity:
Systematic review and meta-analysis. PLoS ONE 2017, 12, 15. [CrossRef]
Wu, Y.T.; Prina, A.M.; Brayne, C. The association between community environment and cognitive function:
A systematic review. Soc. Psychiatry Psychiatr. Epidemiol. 2015, 50, 351–362. [CrossRef] [PubMed]
Levasseur, M.; Cohen, A.A.; Dubois, M.F.; Genereux, M.; Richard, L.; Therrien, F.H.; Payette, H. Environmental
Factors Associated With Social Participation of Older Adults Living in Metropolitan, Urban, and Rural Areas:
The NuAge Study. Am. J. Public Health 2015, 105, 1718–1725. [CrossRef] [PubMed]
Yoshitaka, O.; Mamoru, N.; Yutaka, I. Relationship between diversity of activities in urban parks andinteraction in the local community. J. Jpn. Soc. Reveg. Technol. 2018, 44, 111. [CrossRef]
Pereira, G.; Christian, H.; Foster, S.; Boruff, B.J.; Bull, F.; Knuiman, M.; Giles-Corti, B. The association between
neighborhood greenness and weight status: An observational study in Perth Western Australia. Environ.
Health 2013, 12, 49. [CrossRef] [PubMed]
Sarkar, C.; Gallacher, J.; Webster, C. Built environment configuration and change in body mass index: The
Caerphilly Prospective Study (CaPS). Health Place 2013, 19, 33–44. [CrossRef] [PubMed]
Khan, M.I.; Bath, B.; Boden, C.; Adebayo, O.; Trask, C. The association between awkward working postureand low back disorders in farmers: A systematic review. J. Agromed. 2019, 24, 74–89. [CrossRef]
Cohen, D.; Huynh, T.; Sebold, A.; Harvey, J.; Neudorf, C.; Brown, A. The population health approach: A
qualitative study of conceptual and operational definitions for leaders in Canadian healthcare. SAGE OpenMed. 2014, 2, 2050312114522618. [CrossRef]
Bonita, R.; Beaglehole, R.; Kjellström, T. Basic Epidemiology; World Health Organization: Geneva, Switzerland,
2006.
Cerin, E.; Saelens, B.E.; Sallis, J.F.; Frank, L.D. Neighborhood Environment Walkability Scale: Validity anddevelopment of a short form. Med. Sci. Sports Exerc. 2006, 38, 1682–1691. [CrossRef]
Cain, K.L.; Geremia, C.M.; Conway, T.L.; Frank, L.D.; Chapman, J.E.; Fox, E.H.; Timperio, A.; Veitch, J.;
Van Dyck, D.; Verhoeven, H.; et al. Development and reliability of a streetscape observation instrument forinternational use: MAPS-global. Int. J. Behav. Nutr. Phys. Act. 2018, 15, 19. [CrossRef]
Zadro, J.R.; Shirley, D.; Pinheiro, M.B.; Bauman, A.; Duncan, G.E.; Ferreira, P.H. Neighborhood walkability
moderates the association between low back pain and physical activity: A co-twin control study. Prev. Med.
2017, 99, 257–263. [CrossRef]
Duncan, R.; Peat, G.; Thomas, E.; Hay, E.; McCall, I.; Croft, P. Symptoms and radiographic osteoarthritis: Not
as discordant as they are made out to be? Ann. Rheum. Dis. 2007, 66, 86–91. [CrossRef] [PubMed]
Geneen, L.J.; Moore, R.A.; Clarke, C.; Martin, D.; Colvin, L.A.; Smith, B.H. Physical activity and exercise forchronic pain in adults: An overview of Cochrane Reviews. Cochrane Database Syst. Rev. 2017, 77. [CrossRef]© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).