International Journal ofEnvironmental Researchand Public Health
ArticleModal Shift from Cars and Promotion of Walking by
Providing Pedometers in Yokohama City, Japan
Kimihiro Hino 1, * , Ayako Taniguchi 2 , Masamichi Hanazato 3
1
2
3
4

*

and Daisuke Takagi 4

Department of Urban Engineering, Graduate School of Engineering, The University of Tokyo,
Tokyo 113-8656, Japan
Department of Risk Engineering, Graduate School of Systems and Information Engineering, Universityof Tsukuba, Ibaraki 305-8573, Japan; taniguchi@risk.tsukuba.ac.jp
Center for Preventive Medical Sciences, Chiba University, Chiba 263-8522, Japan; hanazato@chiba-u.jp
Graduate School of Medicine, School of Public Health, The University of Tokyo, Tokyo 113-0033, Japan;
dtakagi-utokyo@umin.ac.jp
Correspondence: hino@ua.t.u-tokyo.ac.jp; Tel.: +81-3-5841-6225

Received: 16 April 2019; Accepted: 14 June 2019; Published: 17 June 2019


Abstract: Mobility management is a transportation policy aiming to change travel behavior fromcar use to sustainable transportation modes while increasing people’s physical activity. Providing
pedometers and visualizing step counts, popular interventions in public health practice, may constitutea mobility management program. However, the ease of modal shifts and changeability of walking
habits differ across neighborhood environments. Using questionnaire data from 2023 middle-aged
and older participants from Yokohama, Japan, in May 2017, this study examined (1) the relationshipbetween the physical and social environments of Yokohama Walking Point Program participants
who volunteered to use free pedometers and their modal shifts from cars to walking and public
transport, and (2) whether participants’ modal shifts were associated with increases in step counts.
Multivariate categorical regression analyses identified the frequency of greetings and conversations
with neighbors as well as health motivation as important explanatory variables in both analyses.
Participants living in neighborhoods far from railway stations and in neighborhoods with a high bus
stop density tended to shift to walking and public transport, a modal shift that was highly associatedwith increased step counts. These results suggest that mobility management should be promoted incollaboration with public health and city planning professionals.
Keywords: mobility management; public transport; step counts; city planning; compact city;neighborhood

1. Introduction
Although considerable evidence exists demonstrating that physical inactivity increases the riskof major non-communicable diseases and shortens life expectancy, much of the world’s population
is inactive [1,2]. Recently, researchers have urged city planning policies to increase opportunities forphysical activity (PA) by encouraging active transport (e.g., walking and cycling) and public transportuse (e.g., railway and local bus) and reducing private car use [3–5]. One such policy concerns thecreation of compact cities in which major facilities are concentrated within the city center, aroundpublic transport hubs, enabling residents to walk to public transport. This model is in contrast to
sprawled cities in which residents are dependent on cars [5,6].
The Japanese government at the national and municipal levels have sought to promote compact
city policies in light of the need to reduce environmental load and the reality of an increasing older
population. The Japanese city of Toyama’s city planning policies are reflective of the trend towards

Int. J. Environ. Res. Public Health 2019, 16, 2144; doi:10.3390/ijerph16122144

www.mdpi.com/journal/ijerph

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2 of 14compact cities, with the municipality promoting public transit-oriented development and vitalization
through initiatives such as the opening of the first light-rail transit in Japan and the subsidizing of costs
for the acquisition of dwellings in the city center and along public transport lines [7,8]. Nonetheless, therealization of a truly compact city requires tens of years to come to fruition; as such, it is necessary to
implement policies beyond those that look to change the physical environment, policies that promote a
modal shift from cars to walking and public transport (hereafter simply referred to as “modal shift”),
in addition to policies that look to reshape the urban structure.
Mobility management (MM) is one example of such non-physical transportation management
policies. MM aims to change travel behaviors from car use to sustainable transportation modes (i.e.,
public transport and active transport) using communicative measures such as the provision of specificinformation on public transport, travel education, and word-to-mouth recommendation [9,10]. We use
the term MM according to these definitions hereafter in this study, while MM is often referred to as
travel planning in the United Kingdom [11] and voluntary travel behavior change in Australia [12].
Drawing on research from social and environmental psychology, studies on MM have accumulated
since the mid-1990s, with such research contributing to the identification and development of effective
methods of promoting modal shifts [13]. In addition, factors that influence the choice of travel mode,such as travel time and family structure, have been investigated [14–16].
MM practices have been reported not only in developed but in developing countries as well. In
Metropolitan Manila, Philippines, a rideshare app for university students was developed to promote
behavioral change [17,18]. In Japan, typical MM practices include personal conversations, workshops,
education initiatives in schools, and travel feedback programs, practices that look to address social
problems caused by car overuse [19]. A MM program in Yamato, Kanagawa, in which participants
were provided pedometers in addition to leaflets and town guides, succeeded in decreasing their car
use and increasing their PA [20].
In public health research and practice, providing pedometers and visualizing step counts is also
a popular intervention for promoting PA [21–23]. Compared to other devices used to visualize step
counts, a pedometer is cheaper and easier for every population to use. A systematic review of studies
which assessed pedometer use among adults suggests that pedometer use is associated with significant
increases in PA and improvements in several key health outcomes [24]. However, the ease withwhich modal shifts and the changeability of walking habits occur differs according to participants’neighborhood environments. Several studies have shown that public transport users spend greater
amounts of time walking [25,26], with access to public transport associated with increased PA [27–29]
and walking [30,31]. For example, an analysis of 6822 adults from 14 cities in 10 countries foundthat public transport density is significantly, positively, and linearly correlated with increased PA [32].
In addition, a longitudinal study confirmed that access to bus stops and railway stations is a key
determinant of walking as a mode of transportation [33]. Based on existing studies demonstratinga relationship between public transport and PA/walking habits, the ease with which modal shifts
and changeability of walking habits occur must be analyzed in light of participants’ access to public
transport in order for effective intervention to occur.
This study engages in such an analysis within the context of a program in the Japanese city of
Yokohama, in which participants volunteered to use free pedometers to promote PA and improve theirhealth. The first part of this study examines the relationship between participant attributes and theirsurrounding physical environments (i.e., distance to the nearest railway stations and bus stop density)and social environments (i.e., frequency of interaction with neighbors) on one hand, and modal shift
on the other. In the second part of this study, we explore if and how modal shifts are associated withincreases in step counts while controlling for other factors. This study contributes to the existing
literature by identifying how neighborhood environments influence middle-aged and older people’s
active behaviors in the context of a super-aged society such as Japan, which has the world’s highest
proportion of older adults among its population [34].

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2. Materials and Methods
2.1. Yokohama Walking Point Program
Located 30–40 km from Tokyo, Yokohama is the second-most populous city in Japan and wasdeveloped as an international port city. The city has a population of approximately 3.7 million people,of whom 24% are 65 years or older as of January 2017. The city’s railway network has been developed,with many lines running towards central Tokyo. The railway is approximately 308 km long, and there
are 157 railway stations in Yokohama. The local bus network has been expanded around the railway
stations, enabling approximately 90% of citizens access to the railway stations within 15 minutes.
According to the latest Person Trip Survey from 2008, railway and local bus use constituted 33.9%
and 5.8% of the main modes of transportation, respectively, with these figures being higher thanin other nearby major cities [35]. Nevertheless, approximately 20% of greenhouse gas emissions in
Yokohama are caused by the transportation sector, half of which can be attributed to private cars [36].
Thus, shifting from private cars to public transport is one of the policy targets of the city as it looks to
reduce its environmental footprint, improve the sustainability of public transport, and promote citizen
health [36].
In November 2014, the city launched the Yokohama Walking Point Program (YWPP) to encourage
citizens to improve their health and healthy life expectancy, as the average age of the population andthe nature of diseases change. It provided free pedometers (Omron HJ-326F, Japan), purchased with thecity budget, for volunteer participants aged 40 years and above. In June 2016, participation qualification
was expanded to citizens aged 18 years and above. Participants were awarded points based on theirstep counts by scanning their pedometers via special readers installed at approximately 1000 storesand other facilities in the city. Accumulation of a certain number of points made participants eligible
to win prizes. The scanned data were sent to a data server through the Internet, and participants could
monitor step counts and rank among all participants using a computer or smartphone [37,38]. Everytime the average monthly step counts from all participants exceed a set target, 200,000 yen is donatedto the United Nations World Food Programme.
2.2. Data Collection
Participants’ sex, age, neighborhood-level address, and number of months participating in YWPP
were acquired from the YWPP registration information. Distance to the nearest railway station
was measured from the center of each neighborhood, and bus stop density was calculated for eachneighborhood using data from the National Land Numerical Information download service [39].
The other data were measured in the questionnaire survey that Yokohama city conducted in
May 2017 among 6000 participants selected from 231,600 participants. They were randomly andproportionally selected from three stratified groups by data sending rate: participants whose data
sending rate was 80% or more, less than 80%, and those who never sent data. Among the selectedparticipants, 3493 replied to the survey, with a response rate of 58.2%. Since the original age eligibility
requirement for the program was 40 years and older until June 2016, 141 respondents were aged
below 40 and were thus excluded from analysis. Ultimately, a total of 2023 participants who answered
all necessary questions were included in the survey analysis. The questionnaire asked primarily
about the participants’ changes in walking habits and health attitudes as well as their modal shifts
after participating in the program for a period of time. Translated questions asked in the survey arepresented in Table A1.
2.3. Variables
2.3.1. Outcome Variables
In the first analysis, modal shift with four options, ranging from “Yes” to “No”, served as theoutcome variable. In the second analysis, increases in step counts served as the outcome variable.

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Although it originally had four options, ranging from “increased” to “decreased”, “decreased” wasselected by only 0.5% of participants and thus merged with “not changed”.
2.3.2. Explanatory Variables
The explanatory variables in both analyses were distance to the nearest railway station, busstop density, and frequency of greetings and conversation with neighbors. Participants’ modal shift,which was the outcome variable in the first analysis, was added as an explanatory variable in thesecond analysis.
As the number of bus stops (2735) was much higher than the number of neighborhoods (758) in
Yokohama, we used bus stop density rather than distance to a bus stop as an explanatory variable. The
mean distance to the nearest railway station was 807.6 (±634.6) m and the mean bus stop density per
km2 was 7.6 (±9.3). The two variables were disaggregated into four categories by three thresholds:
approximately the mean and the mean ± 1/2 SD.
Frequency of greetings and conversation with neighbors was surveyed with five options ranging
from “increased” to “decreased”. Not only physical but social features of neighborhoods can also
affect health by constraining or enhancing health-related behaviors [40]. Also, relationships between
neighbors have been shown to have a positive association with engagement in PA [41].
2.3.3. Control Variables
The control variables in both analyses were participants’ sex, age, occupation, self-rated health,diagnosis of a metabolic syndrome prior to participation, motivation for participation in YWPP, andmonths participating in YWPP.
Participants’ ages were categorized as non-older adults (<65 years), early-stage older adults (65–74
years), and later-stage older adults (>75 years), based on categories provided by the long-term careinsurance system in Japan [42]. Self-rated health [43–45], pre-existing metabolic syndromes [46,47],and motivation [48,49] were found to be associated with walking/PA in previous studies. Months
participating in YWPP was considered because the effect of pedometers on participants’ walking levels
might vary over time [50].
Regarding motivation for participation, we identified four categories—health motivation, profit
motivation, data confirmation motivation, and interaction motivation—based on the result of ahierarchical cluster analysis of the original 10 hypothesized options from which participants could
select multiple answers (Table A2).
Survey options selected by a small percentage of participants were merged into four
variables—occupation, self-rated health, frequency of greetings and conversations with neighbors, andchange in step counts—as shown in Table 1.
2.4. Statistical Analysis
Multivariate categorical regression was used in both analyses. Categorical regression quantifies
categorical variables using optimal scaling and assigns numerical values to categories. It simultaneously
scales nominal, ordinal, and numerical variables and treats quantified categorical variables in the same
way as numerical variables. Scaling all variables at the numerical level results in standard multiple
regression analysis of the transformed variables [51]. In our analyses, all variables including outcome
variables were transformed into numerical variables.
The output of the analysis comprises regression coefficients, their statistical significance, andPratt’s relative importance measure of predictors, which is large for predictors that are crucial to theregression and useful in interpreting predictor contributions to the regression, for all explanatory andcontrol variables [51].
The significance level was set at p < 0.05. All statistical analyses were conducted using IBM SPSS
Statistics 23 (IBM Corp., Armonk, NY, USA).

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Table 1. Characteristics of the study samples and questionnaire results (n = 2023).
Options

n

%

Sex

Male
Female

869
1154

43.0
57.0

Age (years)

<65
65–74
75+

781
778
464

38.6
38.5
22.9

Occupation

Full-timePart-time/self-employedNon-worker/other

367
407
1249

18.1
20.1
61.7

Healthy
Rather healthy(Rather) unhealthy

646
1158
219

31.9
57.2
10.8

Diagnosis of metabolic syndrome (before participation)

Yes
Preliminary
No

262
238
1523

13.0
11.8
75.3

Motivation for participation in YWPP (Multiple Answers)

HealthProfitData confirmation
Interaction

1607
1282
945
574

79.4
63.4
46.7
28.4

<12
12–24
24+

421
512
1090

20.8
25.3
53.9

Increased
Slightly increased
Not changed/(slightly)
decreased

195
621

9.6
30.7

1207

59.7

<500 m
500–800 m
800–1100 m
1100 m+

583
556
286
598

28.8
27.5
14.1
29.6

<3
3–7.5
7.5–12
12+

330
969
534
190

16.3
47.9
26.4
9.4

Yes
Mostly yes
Mostly no
No

548
578
432
465

27.1
28.6
21.4
23.0

Increased
Slightly increased
Not changed/decreased

910
408
705

45.0
20.2
34.9

Variables
Control variables

Self-rated health (before participation)

Months participating in YWPP
Explanatory variables
Frequency of greetings and conversations with neighbors

Distance to the nearest railway station

Bus stop density (per km2 )Outcome variables

Modal shift

Change in step counts

3. Results
3.1. Sample Statistics
Participant characteristics and questionnaire results are presented in Table 1. Regarding outcome
variables, more than half of the participants disclosed that they had “shifted from cars to public
transport/slightly shifted”, and approximately two-thirds of participants reported that their step counts
had “increased/slightly increased” after participation in YWPP. Approximately 40% reported that theirfrequency of greetings and conversations with neighbors “increased/slightly increased”.

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Regarding the control variables, males constituted 43.0% of the sample. The mean age of theparticipants was 65.7 (±11.1) years, with 61.4% being more than 65 years old as of the end of May
2017. Only 18.1% of the participants had full-time jobs, reflecting the old age of the sample population.
Approximately 90% reported that prior to participation in the program, they had been “healthy/rather
healthy”, with less than 25% having been diagnosed with a metabolic syndrome. More than half
had participated in YWPP for more than 24 months at the time of the survey. As the motivation forparticipation in YWPP, health, profit, data confirmation, and interaction were selected by 79.4%, 63.4%,
46.7%, and 28.4% of the participants, respectively.
3.2. Modal Shift
The left side of Table 2 shows the results of the first analysis, and Table 3 shows the numerical values
assigned to the categorical variables. The variables with the most and the second-most importancewere, respectively, health motivation and frequency of greetings and conversations with neighbors.
Table 2. Results of categorical regression.
Outcome Variables
Sex
Age (years)
Occupation
Self-rated health a
Diagnosis of metabolic syndrome a
Motivation: Healthrofit
Data confirmation
Interaction
Months participating in YWPP
Frequency of greetings andconversations with neighbors
Distance to the nearest railway station
Bus stop density (per km2 )Modal shiftp
Adjusted R2

Modal Shift
B

p

0.048
0.040
0.056
−0.001
0.043
0.203
0.029
0.036
0.030
0.057

0.031
0.082
0.007
1.000
0.009
0.000
0.136
0.079
0.113
0.001

**
***

0.199
0.038
0.051
0
0.129

Change in Step Counts
Importance

B

p

***

0.013
0.046
0.057
0.000
0.019
0.383
0.011
0.007
0.020
0.036

0.026
0.067
0.062
−0.039
0.016
0.158
0.017
0.027
0.059
0.018

0.147
0.000
0.001
0.141
0.294
0.000
0.260
0.123
0.003
0.223

0.000

***

0.375

0.103

0.000

***

0.097

0.008
0.000

**
***

0.014
0.019

0.019
0.035
0.369
0
0.260

0.139
0.006
0.000

**
***

0.002
0.003
0.623

*
**

Importance
***
**

***

**

0.002
0.020
0.033
0.007
0.002
0.172
−0.001
0.004
0.030
0.005

B: regression coefficient (beta); p: statistical significance of coefficient (* < 0.05, ** < 0.01, *** < 0.001); importance:
Pratt’s relative importance measure of predictors; a before participation.

Regarding physical environment, distance to the nearest railway station as well as bus stop density
were also significantly associated with the outcome variable. Participants living in neighborhoods
far from railway stations (more than 1100 m) and those with a high bus stop density (more than
12 per km2 ) tended to shift from cars to public transport. Figure 1 shows the spatial distribution ofsuch neighborhoods—neighborhoods far from railway stations were located in suburban hilly areasand coastal industrial zones, while most neighborhoods with a high bus stop density were locatednear city centers.
Regarding the other control variables, being male, unemployed, diagnosed with a metabolic
syndrome before participation, and longer months participating in YWPP were positively associatedwith modal shifts, while age and self-rated health were not. The adjusted R2 of the regression was 0.129.

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Table 3. Numerical values assigned to the categories.
Variables

Outcome Variables

Categories

Modal Shift

Change in Step Counts

Sex

Male
Female−1.1520.868−1.1520.868

Age (years)

<65
65–74
75+

1.261
−0.810−0.7640.352−1.168
1.366

Occupation

Full-timePart-time/self-employedNon-worker/other

1.684
0.807
−0.7581.310
1.234−0.787

Healthy
Rather healthy(Rather) unhealthy−0.598−0.2002.823−0.390−0.325
2.869

Yes
PreliminaryNo−1.157−2.2540.551

1.468−2.453
0.131

Yes
No
Yes
No
Yes
No
Yes
No

−0.5091.965
0.760−1.315−1.0680.936−1.5890.629−0.5091.965−0.7601.315−1.0680.936−1.589
0.629

<12
12–24
24+

1.824
−1.061−0.2060.975
1.164−0.923

Increased
Slightly increased
Not changed/(slightly)
decreased−2.455−0.614−2.027−0.883

0.713

0.782

<500 m
500–800 m
800–1100 m
1100 m+

1.054
0.516
−0.167−1.4280.460−0.3802.008−1.056<3
3–7.5
7.5–1212+

1.613−0.1560.154−2.436-0.9860.506
0.607−2.573Yes
Rather yes
Rather noNo−1.394−0.1910.655
1.272−1.146−0.4980.503
1.503

Self-rated health a(before participation)
Diagnosis of metabolic syndrome(before participation)
Motivation
HealthProfitData confirmation
Interaction

Months participating in YWPP

Frequency of greetings andconversations with neighbors a

Distance to the nearest
train station

Bus stop density (per km2 )Modal shift a

Change in step counts a

Increased
Slightly increased
Not changed/decreased

a The order of the categories is preserved in the optimally scaled variables.

−0.887−0.3341.338

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

8 of 148 of 13

Figure1. Spatial
distribution of
to publictransport.
Figure1. Spatial
distribution
ofaccessibility
accessibility
to publictransport.

3.3. Change in Step Counts

3.3. Change in Step Counts

The right side of Table 2 shows the results of the second analysis, with Table 3 showing thenumerical
valuescategorical
Thethevariablewithanalysis,
the most importanceby far
was
The right sideof assigned
Table 2toshows
thevariables.
results ofsecondwith Table
3 showingthemodal shift. This was followed by health motivation and by frequency of greetings and conversations
numerical values assigned to categorical variables. The variable with the most importance by far waswith neighbors, both of which were highly associated with the outcome variable in the first analysis.
modal shift.Regarding
This wasphysicalfollowedby health bus
motivation
andbysignificantly
frequencyassociatedof greetings
conversations
environment,stop density
waswithandincreasedwith neighbors,bothof livingwhichhighly associatedwithoutcomevariablein thestation
first analysis.
step counts.
Thosein wereneighborhoods
more than 1100
m the
away
from the nearest
railwaytendedtoincrease
their
step
counts,althoughthevariablewasnotstatisticallysignificant.
Regarding physical environment, bus stop density was significantly associated with increased
the othervariables, ages
between
65 1100
and 74meters
years, non-workers,
andinteraction
step counts.Regarding
Those living
in control
neighborhoods
more
than
away fromthenearest railway
motivation were positively associated with increased step counts, while sex, self-rated health, andstation tended to increase their step counts, although the2 variable was not statistically significant.
months participating in YWPP were not. The adjusted R of the regression was 0.260, which was
Regarding
othercontrol variables, ages between 65 and 74 years, non-workers, andhigher thanthe
the first
analysis.

interaction motivation were positively associated with increased step counts, while sex, self-rated
Discussion
health, 4.
andmonths participating in YWPP were not. The adjusted R2 of the regression was 0.260,
This study
examined
aspectsof the physical and social environments of middle-aged and older
which was higher
than
the first
analysis.
participants, who volunteered to use free pedometers, that are associated with modal shifts andincreases in step counts in Yokohama, Japan. Although the adjusted R2 of the regression was not high
4. Discussion
in the two analyses, eight and seven variables were statistically significant in the respective analyses.

This study examined aspects of the physical and social environments of middle-aged and older
participants, who volunteered to use free pedometers, that are associated with modal shifts andincreases in step counts in Yokohama, Japan. Although the adjusted R2 of the regression was not highin the two analyses, eight and seven variables were statistically significant in the respective analyses.
The results of the first analysis showed that participants living in neighborhoods far fromrailway stations and in neighborhoods with a high bus stop density tended to engage in modal shifts.

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The results of the first analysis showed that participants living in neighborhoods far from railway
stations and in neighborhoods with a high bus stop density tended to engage in modal shifts. This
shift may have occurred because local buses are used for shorter trips than railways, and participantschanged their short trip transport mode from cars to buses. On the other hand, participants living nearrailway stations seemed unable to change their transport mode while participating in the program.
This may be because these participants had used cars less frequently than suburban participants prior
to participation because of the general features of the city’s transit-oriented development, such as “lessconvenience for cars and special consideration for pedestrians” [26].
The second half of the study showed that participants’ modal shifts were most associated withincreases in their step counts. These results suggest that participation in YWPP promoted modal
shifts and walking instead of driving. As seen with modal shifts, living in neighborhoods with a highbus stop density tended to increase participants’ step counts. Although not statistically significant,participants living in neighborhoods far from railway stations tended to increase their step counts.
Particularly in light of the results of a previous study on a Japanese rural city that demonstrated that
distance to bus stops had significant relationship with PA, but distance to railway stations did not [52],future studies should explore the role of public transport not only by distinguishing railway and bus
modalities, but urban and rural environmental differences as well.
Participants’ frequency of greetings and conversations with others, as a proxy for social
environment, was positively associated with modal shifts and increases in step counts. Considering
the causal relationship, identified by a quasi-longitudinal study [53], in which changes in socializing
with neighbors had a positive impact on walking activities, the provision of pedometers in our study
might encourage opportunities for communication with other participants and promote participants togo out and use public transport together.
With regard to control variables, motivation to participate in YWPP must be highlighted.
Health motivation had a positive association with both modal shift and increased step counts,
while self-rated health before participation was not statistically significant. Interaction motivation
was also positively associated with increased step counts, a finding potentially driven by the fact that
expanded social connections may have contributed to increased frequencies of going out, as discussed
above. On the other hand, the other categories of motivations—profit motivation and data confirmation
motivation—were not found to be statistically significant in either analysis. These aspects of YWPP
appear to not be useful in promoting modal shifts and increased walking.
While participants with preliminary metabolic syndromes tended to report relatively high levelsof modal shifts, those with metabolic syndrome did not do so. This result is consistent with previous
studies demonstrating that the conditions of overweight and obesity are causally associated with future
inactivity [54,55]. These findings suggest the importance of intervention at an early stage because
PA promotion for obese and overweight people is more difficult. Considering the fact that healthmotivation was found to be positively associated with both modal shifts and increased step counts,
providing pedometers and PA education during health examinations may be effective at preventing
and treating metabolic syndrome.
In Japan, many municipalities list health promotion as a benefit of compact cities as well as
the reduction of environmental load [6]. In depopulated areas, however, bus routes may decreasein frequency or even withdraw from these areas entirely, a reality that is undesirable from theviewpoint of health promotion. As such, bus routes and availability should be sustained. During
the realization of compact cities, non-physical policies such as MM programs should be promotedin the short-term, informed by collaboration between public health and city planning professionals.
Pedometer intervention may prove to be a critical component of such policies, one that contributes to ahealthy active urban population.
While making an important contribution to existing research on neighborhood environments andpeople’s active behaviors, this study has some limitations. Retrospective questions on participants’public transport use, step counts, self-reported health, and communication frequency with neighbors

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may be subject to recall bias. Changes in step counts reported in our questionnaire might also be
subject to self-reporting bias, as we lacked measures of step counts before the pedometer intervention.
In addition, study participants may not be representative of the general population of Yokohama city inthat they were motivated to have pedometers, suggesting a higher baseline interest in PA than others.
We cannot know precisely the effect of pedometers in reality as in a control experiment due to theabsence of comparison with non-participants. Lastly, only the physical environment of neighborhoods
in which participants lived was considered in this study. Future studies should examine more detailed
factors for modal shifts and the promotion of walking through pedometer use by considering physicaland social environments not only around participant homes, but workplaces and favorite places aswell. This may be done using travel diary data or GPS data.
5. Conclusions
This study examined the relationship between the physical and social environments of middle-aged
and older YWPP participants who volunteered to use free pedometers, and their modal shifts fromcars to walking and public transport. This study further considered if participants’ modal shifts wereassociated with increases in their step counts using questionnaire data. The results of multivariate
categorical regression analysis find that the frequency of greetings and conversations with neighbors
as well as health motivation are highly associated with modal shift. Regarding physical environment
characteristics, participants living in neighborhoods far from railway stations and in neighborhoods
with a high bus stop density tended to shift to walking and public transport. In addition, modal shift
was by far the most associated with increased step counts. This study’s results suggest that pedometerintervention could be an effective component of MM programs that promote healthier, active cities.
Author Contributions: Formal analysis, K.H.; Funding acquisition, K.H. and A.T.; Writing—original draft, K.H.;
Writing—review and editing, A.T., M.H., and D.T.
Funding: This research was supported by JSPS KAKENHI, grant numbers 18H01602 and 26249073.
Acknowledgments: We thank the staff of Yokohama city for their generous support of this research. Due to aconfidentiality agreement with survey respondents, raw data of the questionnaire cannot be shared.
Conflicts of Interest: The authors declare no conflict of interest.

Appendix A
Table A1. Questions asked in the survey.
Variables

Questions Asked in the Survey

Self-rated health(before participation)

How did you feel about your health? (before participation)

Diagnosis of metabolic syndrome(before participation)

Have you been diagnosed with metabolic syndrome in a periodic healthexamination or a medical examination? (before participation)

Frequency of greetings andconversations with neighbors

Did the frequency of greetings and holding conversations with neighbors
increase compared to the frequency pre-participation in YWPP?

Modal shift

Did the frequency of walking or using public transport increase while
decreasing the frequency of car or motor bike use when going out (e.g.,commuting and going shopping) after participating in YWPP?

Change in step counts

Did your daily step count change after participating in YWPP?

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Table A2. Motivation for participation in YWPP (multiple answer).
Categories

Optionsn%

Health motivation

Can promote health while enjoying
Good chance to begin walking
Can feel healthy

1406
666
261

69.5
32.9
12.9

Profit motivation

Can get a pedometer
Can win prizes
Can donate

926
715
255

45.8
35.3
12.6

Data confirmation motivation

Can confirm data of step counts and rank

945

46.7

Interaction motivation

Can walk with families and friends
Can interact with other participants

502
125

24.8
6.2

(None)Can participate with office colleagues

41

2.0

Measure: Phi 4-point correlation, cluster method: centroid clustering. Nine options were categorized into four
using hierarchical cluster analysis. The last option (Can participate with office colleagues) was not included in any
category because it was not clustered with any of the other nine options and its selection rate was the lowest. Whena participant selected any options in category A, the binary variable of the category A was 1.

References
1.

2.
3.

4.

5.

6.
7.
8.

9.

10.
11.
12.

Lee, I.-M.; Shiroma, E.J.; Lobelo, F.; Puska, P.; Blair, S.N.; Katzmarzyk, P.T.; Lancet Physical Activity Series
Working Group. Effect of physical inactivity on major non-communicable diseases worldwide: An analysis
of burden of disease and life expectancy. Lancet 2012, 380, 219–229. [CrossRef]
World Health Organization. Global Action Plan on Physical Activity 2018–2030: More Active People for a HealthierWorld; World Health Organization: Geneva, Switzerland, 2018.
Giles-Corti, B.; Vernez-Moudon, A.; Reis, R.; Turrell, G.; Dannenberg, A.L.; Badland, H.; Foster, S.; Lowe, M.;
Sallis, J.F.; Stevenson, M.; et al. City planning and population health: A global challenge. Lancet 2016, 388,
2912–2924. [CrossRef]
Sallis, J.F.; Bull, F.; Burdett, R.; Frank, L.D.; Griffiths, P.; Giles-Corti, B.; Stevenson, M. Use of science to guidecity planning policy and practice: How to achieve healthy and sustainable future cities. Lancet 2016, 388,
2936–2947. [CrossRef]
Stevenson, M.; Thompson, J.; de Sá, T.H.; Ewing, R.; Mohan, D.; McClure, R.; Roberts, I.; Tiwari, G.;
Giles-Corti, B.; Sun, X.; et al. Land use, transport, and population health: Estimating the health benefits ofcompact cities. Lancet 2016, 388, 2925–2935. [CrossRef]
Mministry of Land Infrastructure Transport and Tourism Promotion of Urban Renovation and Compact
Cities. Available online: https://www.mlit.go.jp/common/000996976.pdf (accessed on 16 March 2019).
Lee, J.; Kurisu, K.; An, K.; Hanaki, K. Development of the compact city index and its application to Japanesecities. Urban Stud. 2015, 52, 1054–1070. [CrossRef]
Takami, K.; Hatoyama, K. Sustainable regeneration of a car-dependent city: The case of Toyama toward
a compact city. In Sustainable City Regions; Kidokoro, T., Harata, N., Subanu, L.P., Jessen, J., Motte, A.,
Seltzer, E.P., Eds.; cSUR-UT Series: Library for Sustainable Urban Regeneration; Springer: Tokyo, Japan,
2008; Volume 7, pp. 183–200, ISBN 978-4-431-78146-2.
Fujii, S.; Taniguchi, A. Determinants of the effectiveness of travel feedback programs-a review ofcommunicative mobility management measures for changing travel behaviour in Japan. Transp. Policy 2006,
13, 339–348. [CrossRef]
Taniguchi, A.; Fujii, S. Promoting public transport using marketing techniques in mobility management andverifying their quantitative effects. Transportation 2007, 34, 37–49. [CrossRef]
Enoch, M. Sustainable Transport, Mobility Management and Travel Plans; Routledge: Abingdon, UK, 2016;
ISBN 9781315611563.
Taylor, M.A.P. Voluntary travel behavior change programs in Australia: The carrot rather than the stick intravel demand management. Int. J. Sustain. Transp. 2007, 1, 173–192. [CrossRef]

Int. J. Environ. Res. Public Health 2019, 16, 2144

13.
14.
15.
16.
17.

18.
19.
20.

21.
22.

23.
24.

25.
26.

27.
28.
29.

30.

31.

32.

33.

12 of 14

Bamberg, S.; Fujii, S.; Friman, M.; Gärling, T. Behaviour theory and soft transport policy measures. Transp.
Policy 2011, 18, 228–235. [CrossRef]
Nurdden, A.; Rahmat, R.A.O.K.; Ismail, A. Effect of Transportation Policies on Modal Shift from Private Car
to Public Transport in Malaysia. J. Appl. Sci. 2007, 7, 1013–1018.
Anwar, A.M.; Yang, J. Examining the Effects of Transport Policy on Modal Shift from Private Car to PublicBus. Procedia Eng. 2017, 180, 1413–1422. [CrossRef]
Tuan, V.A. Mode Choice Behavior and Modal Shift to Public Transport in Developing Countries—The Caseof Hanoi City. J. East. Asia Soc. Transp. Stud. 2015, 11, 473–487.
Sunio, V.; Schmöcker, J.; Estuar, R.; Dela, B.L. Development and Usability Evaluation of Blaze Information
System for Promoting Sustainable Travel Behaviour in Metro Manila. J. East. Asia Soc. Transp. Stud. 2017, 12,2428–2443.
Sunio, V.; Schmöcker, J.D. Can we promote sustainable travel behavior through mobile apps? Evaluation
and review of evidence. Int. J. Sustain. Transp. 2017, 11, 553–566. [CrossRef]
Taniguchi, A.; Suzuki, H.; Fujii, S. Mobility Management in Japan: Its Development and Meta-Analysis ofTravel Feedback Programs. Transp. Res. Rec. 2007, 2021, 100–109. [CrossRef]
Sasaki, H.; Fujimoto, S.; Taniguchi, A.; Nakahara, S. Mobility Management for Health Promotion in
Cooperation with Local Government Urban Transport Planning and Public Health Departments. J. Transp.
Health 2017, 5, S90. [CrossRef]
Ogilvie, D.; Foster, C.E.; Rothnie, H.; Cavill, N.; Hamilton, V.; Fitzsimons, C.F.; Mutrie, N. Interventions topromote walking: Systematic review. BMJ 2007, 334, 1204. [CrossRef] [PubMed]
Wallmann, B.; Spittaels, H.; De Bourdeaudhuij, I.; Froboese, I. The perception of the neighborhood
environment changes after participation in a pedometer based community intervention. Int. J. Behav.
Nutr. Phys. Act. 2012, 9, 33. [CrossRef]
Harris, T.J.; Owen, C.G.; Victor, C.R.; Adams, R.; Cook, D.G. What factors are associated with physical activity
in older people, assessed objectively by accelerometry? Br. J. Sports Med. 2009, 43, 442–450. [CrossRef]
Bravata, D.M.; Smith-Spangler, C.; Sundaram, V.; Gienger, A.L.; Lin, N.; Lewis, R.; Stave, C.D.; Olkin, I.;
Sirard, J.R. Using pedometers to increase physical activity and improve health: A systematic review. JAMA
2007, 298, 2296–2304. [CrossRef]
Besser, L.M.; Dannenberg, A.L. Walking to Public Transit. Am. J. Prev. Med. 2005, 29, 273–280. [CrossRef][PubMed]Brown, B.B.; Yamada, I.; Smith, K.R.; Zick, C.D.; Kowaleski-Jones, L.; Fan, J.X. Mixed land use and walkability:
Variations in land use measures and relationships with BMI, overweight, and obesity. Health Place 2009, 15,1130–1141. [CrossRef] [PubMed]
McCormack, G.R.; Giles-Corti, B.; Bulsara, M. The relationship between destination proximity, destination
mix and physical activity behaviors. Prev. Med. 2008, 46, 33–40. [CrossRef] [PubMed]
De Bourdeaudhuij, I.; Sallis, J.F.; Saelens, B.E. Environmental correlates of physical activity in a sample ofBelgian adults. Am. J. Health Promot. 2003, 18, 83–92. [CrossRef] [PubMed]
Sallis, J.F.; Spoon, C.; Cavill, N.; Engelberg, J.K.; Gebel, K.; Parker, M.; Thornton, C.M.; Lou, D.; Wilson, A.L.;
Cutter, C.L.; et al. Co-benefits of designing communities for active living: An exploration of literature. Int. J.
Behav. Nutr. Phys. Act. 2015, 12, 30. [CrossRef] [PubMed]
Pikora, T.J.; Giles-Corti, B.; Knuiman, M.W.; Bull, F.C.; Jamrozik, K.; Donovan, R.J. Neighborhood
environmental factors correlated with walking near home: Using SPACES. Med. Sci. Sports Exerc. 2006, 38,
708–714. [CrossRef] [PubMed]
Van Dyck, D.; Deforche, B.; Cardon, G.; De Bourdeaudhuij, I. Neighbourhood walkability and its particularimportance for adults with a preference for passive transport. Health Place 2009, 15, 496–504. [CrossRef][PubMed]
Sallis, J.F.; Cerin, E.; Conway, T.L.; Adams, M.A.; Frank, L.D.; Pratt, M.; Salvo, D.; Schipperijn, J.; Smith, G.;
Cain, K.L.; et al. Physical activity in relation to urban environments in 14 cities worldwide: A cross-sectional
study. Lancet 2016, 387, 2207–2217. [CrossRef]
Knuiman, M.W.; Divitini, M.L.; Foster, S.A.; Christian, H.E.; Giles-Corti, B.; Bull, F.C.; Badland, H.M. A
Longitudinal Analysis of the Influence of the Neighborhood Built Environment on Walking for Transportation:
The RESIDE Study. Am. J. Epidemiol. 2014, 180, 453–461. [CrossRef]

Int. J. Environ. Res. Public Health 2019, 16, 2144

34.

35.
36.
37.

38.
39.
40.
41.

42.
43.

44.

45.

46.
47.

48.
49.
50.

51.
52.

53.

13 of 14

Koohsari, M.; Nakaya, T.; Oka, K. Activity-Friendly Built Environments in a Super-Aged Society, Japan:
Current Challenges and toward a Research Agenda. Int. J. Environ. Res. Public Health 2018, 15, 2054.
[CrossRef]
Committee on Transportation Policy in Yokohama City. A Report on Transportation Policy in Yokohama City;
Committee on Transportation Policy in Yokohama City: Yokohama, Japan, 2010.
Yokohama City. Yokohama City Transportation Plan; Yokohama City: Yokohama, Japan, 2018.
Hino, K.; Lee, J.S.; Asami, Y. Associations between seasonal meteorological conditions and the daily step
count of adults in Yokohama, Japan: Results of year-round pedometer measurements in a large population.
Prev. Med. Rep. 2017, 8, 15–17. [CrossRef]
Hino, K.; Asami, Y.; Lee, J.S. Step Counts of Middle-Aged and Elderly Adults for 10 Months Before and Afterthe Release of Pokémon GO in Yokohama, Japan. J. Med. Internet Res. 2019, 21, e10724. [CrossRef] [PubMed]
Ministry of Land Infrastructure Transport and Tourism National Land Numerical Information Download
Service. Available online: http://nlftp.mlit.go.jp/ksj/index.html (accessed on 16 March 2019).
Kawachi, I.; Berkman, L.F. Neighborhoods and Health; Oxford University Press: New York, NY, USA, 2003;
pp. 1–352.
Seino, S.; Kitamura, A.; Nishi, M.; Tomine, Y.; Tanaka, I.; Taniguchi, Y.; Yokoyama, Y.; Amano, H.; Narita, M.;
Ikeuchi, T.; et al. Individual- and community-level neighbor relationships and physical activity among older
Japanese adults living in a metropolitan area: A cross-sectional multilevel analysis. Int. J. Behav. Nutr. Phys.
Act. 2018, 15, 1–11. [CrossRef]
Ministry of Health. Labour and Welfare Long-term Care Insurance in Japan. Available online: https://www.mhlw.go.jp/english/topics/elderly/care/index.html (accessed on 26 May 2019).
Inoue, S.; Ohya, Y.; Odagiri, Y.; Takamiya, T.; Ishii, K.; Kitabayashi, M.; Suijo, K.; Sallis, J.F.; Shimomitsu, T.
Association between Perceived Neighborhood Environment and Walking among Adults in 4 Cities in Japan.
J. Epidemiol. 2010, 20, 277–286. [CrossRef] [PubMed]
Inoue, S.; Ohya, Y.; Odagiri, Y.; Takamiya, T.; Kamada, M.; Okada, S.; Oka, K.; Kitabatake, Y.; Nakaya, T.;
Sallis, J.F.; et al. Perceived Neighborhood Environment and Walking for Specific Purposes Among Elderly
Japanese. J. Epidemiol. 2011, 21, 481–490. [CrossRef] [PubMed]
Turrell, G.; Haynes, M.; Wilson, L.A.; Giles-Corti, B. Can the built environment reduce health inequalities? A
study of neighbourhood socioeconomic disadvantage and walking for transport. Health Place 2013, 19, 89–98.
[CrossRef] [PubMed]
Naeini, F.; Najafian, J.; Nouri, F.; Mohammadifard, N. Relation between usual daily walking time andmetabolic syndrome. Niger. Med. J. 2014, 55, 29. [CrossRef]
Jefferis, B.J.; Parsons, T.J.; Sartini, C.; Ash, S.; Lennon, L.T.; Wannamethee, S.G.; Lee, I.M.; Whincup, P.H. Does
duration of physical activity bouts matter for adiposity and metabolic syndrome? A cross-sectional study ofolder British men. Int. J. Behav. Nutr. Phys. Act. 2016, 13, 36. [CrossRef]
Stephan, Y.; Boiché, J.; Le Scanff, C. Motivation and physical activity behaviors among older women: A
self-determination perspective. Psychol. Women Q. 2010, 34, 339–348. [CrossRef]
Brunet, J.; Sabiston, C.M. Exploring motivation for physical activity across the adult lifespan. Psychol. Sport
Exerc. 2011, 12, 99–105. [CrossRef]
Robertson, L.B.; Ward Thompson, C.; Aspinall, P.; Millington, C.; McAdam, C.; Mutrie, N. The influenceof the local neighbourhood environment on walking levels during the walking for wellbeing in the west
pedometer-based community intervention. J. Environ. Public Health 2012, 2012, 974786. [CrossRef]
IBM Corporation. IBM SPSS Statistics V23.0 Documentation. Available online: https://www.ibm.com/
support/knowledgecenter/en/SSLVMB_23.0.0/spss/product_landing.html (accessed on 16 March 2019).
Kamada, M.; Kitayuguchi, J.; Inoue, S.; Kamioka, H.; Mutoh, Y.; Shiwaku, K. Environmental correlates ofphysical activity in driving and non-driving rural Japanese women. Prev. Med. 2009, 49, 490–496. [CrossRef][PubMed]Handy, S.; Cao, X.; Mokhtarian, P.L. Self-selection in the relationship between the built environment andwalking: Empirical evidence from Northern California. J. Am. Plan. Assoc. 2006, 72, 55–74. [CrossRef]

Int. J. Environ. Res. Public Health 2019, 16, 2144

54.
55.

14 of 14Ekelund, U.; Brage, S.; Besson, H.; Sharp, S.; Wareham, N.J. Time spent being sedentary and weight gain in
healthy adults: Reverse or bidirectional causality? Am. J. Clin. Nutr. 2008, 88, 612–617. [CrossRef] [PubMed]
Metcalf, B.S.; Hosking, J.; Jeffery, A.N.; Voss, L.D.; Henley, W.; Wilkin, T.J. Fatness leads to inactivity, but
inactivity does not lead to fatness: A longitudinal study in children (EarlyBird 45). Arch. Dis. Child. 2011, 96,
942–947. [CrossRef] [PubMed]© 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/).