International Journal ofEnvironmental Researchand Public Health
ArticleA Health Guidance App to Improve Motivation,
Adherence to Lifestyle Changes and Indicators ofMetabolic Disturbances among Japanese
Civil Servants
Naoko Takeyama 1, *, Michiko Moriyama 1 , Kana Kazawa 1 , Malinda Steenkamp 1,2
and Md Moshiur Rahman 1, *1

2*

Division of Nursing Science, Graduate School of Biomedical and Health Sciences, Hiroshima University,
Hiroshima 734-8553, Japan; morimich@hiroshima-u.ac.jp (M.M.); kkazawa@hiroshima-u.ac.jp (K.K.);
Malinda.steenkamp@protonmail.com (M.S.)
Torrens Resilience Institute, Flinders University, Adelaide, SA 5001, Australia
Correspondence: naoko-tkym@hiroshima-u.ac.jp (N.T.); moshiur@hiroshima-u.ac.jp (M.M.R.)

Received: 20 September 2020; Accepted: 29 October 2020; Published: 4 November 2020


Abstract: We investigated whether an Information and Communication Technology (ICT) application(app) motivated to increase adherence to lifestyle changes, and to improve indicators of metabolic
disturbances among Japanese civil servants. A non-randomized, open-label, parallel-group study
was conducted with 102 participants aged 20–65 years undergoing a health check during 2016–2017,
having overweight and/or elevated glucose concentration. Among them, 63 participants chose Specific
Health Guidance (SHG) and ongoing support incorporating the use of an app (ICT group) and
39 individuals chose only SHG (control group). Fifty from the ICT group and 38 from the control groupcompleted the study. After completing the 6-month program, the control group showed a significant
decrease in body mass index (p = 0.008), male waist circumference (p < 0.001), systolic blood pressure(BP) (p = 0.005), diastolic BP (p < 0.001), and glycated hemoglobin (HbA1c) (p < 0.001), and increase inhigh-density lipoprotein (HDL) cholesterol (p = 0.008). However, the ICT group showed a significant
decrease in male waist circumference (p < 0.001), diastolic BP (p = 0.003), and HbA1c (p < 0.001),
and increase in HDL cholesterol (p = 0.032). The magnitude of change for most indicators tended to behighest for ICT participants (used the app ≥5 times/month). Both groups reported raised awarenesson BP and weight. The app use program did not have a major impact after the observation period.
Proper action requires frequent use of the app to enhance best results.
Keywords: metabolic disturbances; lifestyle changes; ICT application; health guidance; motivation;
adherence; workers’ health

1. Introduction
Lifestyle-related diseases, including metabolic syndrome, are increasing worldwide. Persons withmetabolic syndrome have a higher risk of diabetes, atherosclerotic cardiovascular disease, non-alcoholic
fatty liver disease, and potentially linked to morbidity and mortality [1,2]. Lifestyle improvement
is the key to managing the condition. All five indicators for metabolic syndrome (i.e., larger waist
circumference, elevated blood pressure (BP), raised triglyceride levels, reduced high-density lipoprotein(HDL) cholesterol, and raised fasting glycaemia) [3] improve with therapeutic changes to diet andexercise [4].
Many studies have focused on how to motivate and maintain therapeutic behavioral changes inpersons with metabolic syndrome as sustained motivation and adherence to changes are crucial [4].
Int. J. Environ. Res. Public Health 2020, 17, 8147; doi:10.3390/ijerph17218147

www.mdpi.com/journal/ijerph

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A team-based, interactive approach that keeps individuals accountable and provides regularpersonalized feedback have been found to foster persistent and effective behavioral change [4].
Evidence indicates that tailored interventions are important [5]. Tailored interventions are those where
the needs and abilities of an individual are assessed through an initial evaluation and advice is thenpersonalized to that person. Personal motivation is enhanced if the customized feedback is perceived
as appropriate and applicable, and interventions are more effective when individuals have a sense ofownership in the therapeutic process [6,7]. Studies on tailored interventions are increasing and reflect
parallel advances in the use of information and communication technology (ICT) in interventions [8].
Collecting patient data relevant to interventions have expanded from face-to-face interactions(such as motivational interviews or counselling) and telephone conversations to computer programs,
smartphone applications, websites, emails, and short message services (SMS) [9,10]. Smartphone
applications and websites allow for the automated collection of patient data which can be used tocustomize interventions and advice [11]. Using ICT also allows for the use of several intervention
elements in one program, such as education, feedback, and self-monitoring [12], often in a way that isuser-friendly and acceptable for the intended audience [9]. Several studies have found that tailoredinterventions using ICT are effective in addressing metabolic syndrome [12,13].
In Japan, all health insurers, whether a national or a social insurance, are required to screen
the insured and their dependents aged 40–74 years for risk factors of metabolic syndrome from2008 [14]. Insurers must also provide lifestyle improvement counseling (referred to as Specific
Health Guidance—SHG) for non-medicated persons who have elevated risk factors of metabolic
syndrome [15,16]. Diagnostic criteria of metabolic syndrome in Japan were defined by the Diagnostic
Criteria Review Committee of Japan (Table S1) [17]. The selection criteria for SHG were adopted by thefollowing measures. There are two types of SHG in this national program [14]. The first is the Intensive
Health Guidance (IHG) for patients with ≥2 markers with abdominal obesity or those with ≥3 risk
factors without abdominal obesity, but with a body mass index (BMI) >25 kg/m2 . The second type is
the Motivational Health Guidance (MHG) offered to those with 1 risk factor with abdominal obesity or
1–2 markers without abdominal obesity but with a BMI >25 kg/m2 . Both IHG and MHG comprise an
initial motivational counseling session with a final evaluation after 6 months. At the initial session,
participants are provided with information about their condition, and their lifestyles are reviewed
using their screening results. They are advised to set achievable personalized behavioral goals,
that include increased exercise (e.g., walking an extra 10 min), self-monitoring (wearing a pedometer),and adjusting their diet. The initial session is delivered by a trained health-care professional (physician,nurse, or dietitian) either as a one-on-one interview or in a group format. Participants in the IHG
program are followed up for 3–6 months through e-mails, phone calls, and/or in-person/group sessions,while MHG participants do not receive continuous support. The programs are considered completed
when participants received a specific amount of consultation time (IHG) and when the 6-monthevaluation is done (both IHG and MHG) [14]. This scheme provides a framework to support the
improvement of workers’ health and the prevention of future disease development, which in turncontributes to increased productivity.
In this study, we investigated how the SHG scheme can be further improved to aid workers’ health.
The aim was to investigate whether an ICT health guidance application (app) motivated to increaseadherence to lifestyle changes among Japanese civil servants with risk of metabolic disturbances.
We also examined the changes in indicators of metabolic disturbances related to lifestyle changes madeby the participants.
2. Materials and Methods
The study was conducted from September 2016 to March 2017 and was a collaborative project
between Hiroshima Prefecture and Hiroshima University. The development and implementation of
the ICT app was realized with the cooperation of Hiroshima Prefecture and its medical association.
The researchers operated within the existing framework of SHG employed by Japanese health insurers.

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2.1.Study
StudyParticipantsParticipants
2.1.
Thestudy
studypopulation
population was
was civil
civil servants
servants insured
insured byby theThe
the Mutual
Mutual Aid
Aid Association
Associationof
ofHiroshima
Hiroshima
Prefecture.
Potential
participants
were
those
who
underwentanannualhealthcheck
duringthe study
Prefecture. Potential participants were those who underwent an annual health check during
theperiod.
Since
Hiroshima
Prefecture
purposefully
expandedthetargetpopulation
with
not
only
study period. Since Hiroshima Prefecture purposefully expanded the target population with not only
metabolicsyndrome
syndromebut
butalso
also at
at riskrisk ofof metabolic
metabolic disturbances
disturbances formetabolicfor diseasedisease prevention,
prevention,thethecriteria
criteriafor
for
this
project
weredeveloped.
Eligibility
criteria
wereclients
aged
20–65
years;
for
age
40
years
and
this project were developed. Eligibility criteria were clients aged 20–65 years; for age 40 years and older,older,who
thosewhooverweight
were overweightor (definedobese (definedas circumference:
waist circumference:≥ 85
for males
andcm
≥
those
were
or obese
as waist≥85 cmforcm
males
and ≥902) and/or who had an elevated glucose concentration
90
cm
for
females
and/or
with
a
BMI
≥
25
kg/m
2
for females and/or with a BMI ≥ 25 kg/m ) and/or who had an elevated glucose concentration (fasting(fasting
blood(FBG)
glucose(FBG)≥ 100
mg/dL orglycated hemoglobin(HbA1c)≥ 5.6%); younger
participants
blood
glucose≥ 100
mg/dL
or glycatedhemoglobin(HbA1c) ≥ 5.6%);
participantsthan
2younger than 40 years were included in the study, (i) if their2BMI ≥ 25 kg/m , and/or (ii) if their FBG
40 years were included in the study, (i) if their BMI ≥ 25 kg/m , and/or (ii) if their FBG ≥ 100 mg/dL or≥ 100 mg/dL or HbA1c ≥ 5.6%, (iii) if their urinary glucose ≥ trace (±, approximately 50 mg/dl) (Figure
HbA1c ≥ 5.6%, (iii) if their urinary glucose ≥ trace (±, approximately 50 mg/dl) (Figure S1). This isS1). This is because being overweight/obese at a younger age increases the risk of developing
because being overweight/obese at a younger age increases the risk of developing metabolic syndrome.
metabolic syndrome.
Participants were excluded if they were undergoing treatment for metabolic syndrome or otherParticipants were excluded if they were undergoing treatment for metabolic syndrome or otherdiseases, such as diabetes, cardiovascular diseases, or cancer. Potential participants at risk of metabolic
diseases, such as diabetes, cardiovascular diseases, or cancer. Potential participants at risk of metabolic
syndrome were approached by the health insurer at the time of their annual health check and enrolled
syndrome were approached by the health insurer at the time of their annual health check and enrolled inin the study if they were interested, met the eligibility criteria, and gave informed consent.
the study if they were interested, met the eligibility criteria, and gave informed consent.
Of the 6518 people in the study population, 355 (5.4%) expressed an interest to participate, andOf the 6518 people in the study population, 355 (5.4%) expressed an interest to participate, andallowed the researchers to obtain their health check results. After the researcher matched their results
allowed the researchers to obtain their health check results. After the researcher matched their results
to the eligibility criteria, only 102 (28.7% of 355) met the criteria, as shown in Figure 1. Of those,to the eligibility criteria, only 102 (28.7% of 355) met the criteria, as shown in Figure 1. Of those, 63
63 (61.8% of 102) elected to use the ICT app (ICT group). The ICT group received the equivalent of(61.8% of 102) elected to use the ICT app (ICT group). The ICT group received the equivalent of thethe IHG program (i.e., personal follow-up by email, phone, etc.), irrespective of whether they fell into
IHG program (i.e., personal follow-up by email, phone, etc.), irrespective of whether they fell into thethe
IHG
or MHG
group,as shown
in Figure2. The39 participants
useICT
theapp
ICT(theapp
IHG
or MHG
group,as shown
in Figure2. The39 participants
who whochosechose
not tonot
usetothe(thecontrolgroup),
received
eitherusual
IHG(initial
sessionplusmidterm
midterminterview)interview)ororthe
theMHG
MHG
controlgroup),
received
eitherusual
IHG(initial
sessionplus
program(only
initialsession),
as
shown
in
Figure
2.
In
the
ICT
group,
1
personwithdrew
consent
andprogram (only initial session), as shown in Figure 2. In the ICT group, 1 person withdrew consent
8and
did not
usenottheuseapp.theData
wasmissing
anotherparticipants
resulting in
total of 50
8 did
app.
Data
was formissingfor 4another4 participants
resultingin participants
total of 50
inparticipants
the ICT group,
as
shown
in
Figure1.
Inthecontrolgroup,
1
personwas
excludedin the ICT group, as shown in Figure 1. In the control group, 1 person wasbecause
excludedof
missingdata.
because of missing data.

Figure
ICT: information
information andand communication
communicationtechnology;
technology;
Figure1.1.Enrolment
Enrolmentof
ofstudy
studyparticipants.
participants. Note:
Note: ICT:
IHG:
Intensive
HealthGuidance;
MHG:
Motivational
HealthGuidance.
IHG: Intensive Health Guidance; MHG: Motivational Health Guidance.

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Health guidance
guidance program
program in this study.
IHG program
program only; HDL: high-density
Figure 2. Healthstudy. Note: ** IHG
lipoprotein; LDL: low-density lipoprotein; HbA1c: glycated hemoglobin.

2.2. Study
Study Protocol
Protocol2.2.
After agreementagreement toAfterto taketake part
part inin thethe study,
study,thetheparticipants
participantsreceived
receivedaaone-on-one
one-on-oneinterview
interviewwith
witha
healtheducatorfromtheinsurer
who
provided
informationabout
metabolicsyndrome,
adviceona health educator from the insurer who provided information about metabolic syndrome, advice theonimpactof makinglifestylechanges
to toimprovethethe
impactof makinglifestylechanges
improve
thecondition,
condition,and
andencouragement
encouragementon
onhow
how toto makemakethese changes.
The latterlatter included
included specificspecific guidance
guidance on
on dietdiet changes,
changes, increased
increased exercise,
exercise, andand thethethesechanges. The
importance ofofadherence
to changes.
The Theeducator
also explained
the role theof continued
self-monitoring
importanceadherence
to changes.
educator
also explainedrole of continued
selfof
BP,
heart
rate
(HR),
body
weight(BW),andnumber
of
steps
per
day
and
explainedrole of thethemonitoring of BP, heart rate (HR), body weight (BW), and number of steps per day and the
explained
ICT
app
to
assistthem.
Duringthe6monthsfollow-up,
participantsreceivedcommunication
fromrole of the ICT app to assist them. During the 6 months follow-up, participants received
health instructors
by eitheror telephone,shownorintelephone,
Figure 2. This
includedconversations
communication
fromhealthe-mailinstructors
by eitherase-mailas shown
in Figure2. Thisaroundprogress
andachievementofgoals,
aswell
asadditional
advicewhere
neededregardingincluded conversations around progress and achievement of goals, as well as additional advice
where
diet,
exercise,etc.
The
progress
of
the
ICT
group
was
also
reviewedataround3months,asshown
needed regarding diet, exercise, etc. The progress of the ICT group was also reviewed at aroundin
3
Figure 2. asThemetabolicindicators
werecollected
by healthwere
professionals
at 6 months
months,shown
in Figure2. Themetabolicindicators
collected atbybaseline
health andprofessionals
at(theend ofSHG).
and(theafter
SHG,
participants
the samedesignatedlicensed
healthbaselineandat 6Before
monthsendof SHG).
Beforevisitedand after
SHG,
participants
visitedthe checksame
laboratories,
andhealthprofessionals
did
data
collectionoffastingblood
sample
(8
mL)
and
vital
signs.
designated licensed health check laboratories, and health professionals did data collection of fastingBWwasmeasuredwithlightclothes,
and
BMI
was
calculated
asweightinkg
dividedbyheightinm2 .
blood sample (8 mL) and vital signs. BW was measured with light clothes, and BMI was calculated
Waist
circumference
was measured
a tapehorizontally aroundthe abdomenat the levelof
as weight
in kg divided
by heightbyinplacing
m2. Waist
circumference
was measured
by placing
a tapethe umbilicusaround
while standing.
BP wasmeasured
at sitting positionby the digital
BPmeasured
machinehorizontallythe abdomenat thelevel oftwice
the umbilicus
while standing.
BP wasapproximately
after
5
min
to
calm
down,
and
lower
data
was
used.
twice at sitting position by the digital BP machine approximately after 5 min to calm down, and lower
app called “Hiroshima Health Note” was developed in the electronic network systemdata The
was ICTused.
calledthe“Hiroshima
Medical
Network”
madeby Hiroshima
Prefecture
its Medical
Association.
The ICT app called“Hiroshima
HealthNote”
was developedin theandelectronic
networksystemThe
app
has
built-infeaturestomonitor
HR,
BW,
and
daily
steps,
andafeaturetorecordBP wascalled the “Hiroshima Medical Network” made by Hiroshima Prefecture and its Medical Association.
added.
Participants
could
accesstothe
application
eitherand
as adaily
smartphone
applicationor tothroughTheapp
has built-infeaturesmonitor
HR, BW,
steps, anda feature
recorda website.
BP wasParticipants’
HR,
BW,
and
BP
were
recorded
at
the
first
interview
and
they
were
encouraged
continue
added. Participants could access the application either as a smartphone application or tothroughaself-monitoring
duringtheBW,
following
months.
For most
participants,
HRandandthey
BP wereinwebsite.
Participants’HR,
and BP6wererecordedat thefirst interview
weremeasured
encouraged
thecontinue
workplace,but some participants
the equipment
to Fordo so
at home.
Their weight
wasto
self-monitoring
during thehad
following
6 months.
mostparticipants,
HR andBPeitherweremeasured
at
home
or
in
the
workplace.
Participants
were
providedwithapedometertomeasuretheirmeasured in the workplace, but some participants had the equipment to do so at home. Their weight

was either measured at home or in the workplace. Participants were provided with a pedometer tomeasure their daily steps. They received auto-generated messages from the ICT app weekly that

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daily steps. They received auto-generated messages from the ICT app weekly that reminded them tomeasure and record their BP, HR, BW, and daily steps. The reminders also encouraged participants
reminded them to measure and record their BP, HR, BW, and daily steps. The reminders also
to review their own data and to continue with behavioral changes. When participants stopped
encouraged participants to review their own data and to continue with behavioral changes. When
self-monitoring, an auto-generated message to encourage self-measurement was sent. Participants’participants stopped self-monitoring, an auto-generated message to encourage self-measurement
data
transmitted tothewerehealthinstructorstoevery
time instructors
they measured
on anongoingbasis,
was were
sent. Participants’
datatransmitted
the healtheveryastimetheymeasuredasasin Figure3, andreceivedindividualizedsummary.
Participantsweekly
in theonshown
an ongoingbasis,
asparticipants
shown in Figure
3, an
and
participants weekly
received
an individualized
ICTgroup received
a calla healththeirBP, aBW,
and/orHR werehigherthanand/or
theirsummary.
Participantsin from
the ICT
group educatorreceived aifcall
fromhealtheducator
if theirBP, BW,baseline
their measurements
exceededclinicalguidelines.exceeded clinical guidelines.
HR wereorhigher
than their baselineor theirmeasurements

Figure 3. Health guidance process. Note: ICT: information and communication technology.
Figure 3. Health guidance process. Note: ICT: information and communication technology.

At the end of the 6 months, a questionnaire was sent to both the ICT and control groups. The ICTAt the end of the 6 months, a questionnaire was sent to both the ICT and control groups. The
participants were asked about their frequency of using the app, reasons for use, and overall satisfaction
ICT participants were asked about their frequency of using the app, reasons for use, and overallwith it. Both groups were asked about their awareness of metabolic risk factors and their satisfaction
satisfaction with it. Both groups were asked about their awareness of metabolic risk factors and theirwith the SHG program.
satisfaction with the SHG program.
2.3. Outcome Measures
2.3. Outcome Measures
Outcome measures were: indicators of metabolic disturbances (BP, waist circumference, and BMI,
Outcomemeasureswere:lipoprotein
indicators (LDL),of metabolic
disturbances(BP, (frequency
waist circumference,andtriglycerides,
HDL,
low-density
HbA1c);
behavioral dataof ICT app use:
BMI,
triglycerides,
HDL, low-density
lipoprotein(LDL),
HbA1c); behavioral
data (frequency
of ICTlowfrequency(<5 times/month)and high
frequency(≥5 times/month));and activity
data (number
of
app
use:
low
frequency(<5times/month)andhighfrequency(≥5times/month));andactivity
data
daily steps).
(number of daily steps).
2.4. Statistical Analysis
2.4. Statistical Analysis
The final analysis included 50 participants from the ICT group and 38 from the control group,
The infinal
analysisincluded
50 participants
the ICT group
38 from test.
the control
group,as shown
Figure1. Datawere analyzed
using thefrom
independentt-test orandChi-square
Comparisons
as
shown
in
Figure1.
Data
wereanalyzedusing
theindependentt-testorChi-square
test.
of changes in values for both groups at baseline and at 6 months were performed using the paired
Comparisonsofchangesinvaluesforbothgroupsatbaselineandat6monthswere
performedusing
t-test or Wilcoxon signed-rank test. The Kruskal–Wallis test was performed after the normality test
thethepairedt-test or Wilcoxon
signed-rank
Theapplications.
Kruskal–Wallis
was ofperformed
after thefortest according
to the frequency
of usetest.
of ICT
Thetestresults
the questionnaire
normality
test
for
the
test
accordingtothefrequency
of
use
of
ICT
applications.
The
resultsof theconducted after the intervention were analyzed using the Mann–Whitney U test.
questionnaire
conducted
after
theintervention
wereanalyzedusing
theMann–WhitneyU
test.
Participants were categorized as having “maintained” results, if they had normal values at
Participants
werecategorized
as Where
havingthey“maintained”
results,
if forthey
hadnormalatvalues
baselineand normal
values
at 6 months.
had abnormal
resultsany
indicator
baselineat
baselineandnormalvaluesat
6months.
Where
they
had
abnormalresultsforany
indicatorand normal results at 6 months, the intervention was judged as “effective”. Where participants hadatbaseline
and atnormal
results
at 6 outsidemonths,thetheintervention
judgedas “effective”.
Wherenormalvaluesbaseline,
but values
normalrange at 6wasmonthsfollow-up,
the intervention
participants
hadnormalvaluesatbaseline,
butvaluesoutsidethenormalrange
at6monthsfollowwas judged as “ineffective”. Statistical significance was set as two-sided p-values of less than 0.05.
up,statisticalthe intervention
judged“ineffective”.
Statisticalfor
significanceset(IBMas two-sided
p-valuesAllanalysiswas
wasdone aswith
IBM SPSS Statistics
Windows,was
v22.0
Corp., Armonk,of less
than 0.05. All statistical analysis was done with IBM SPSS Statistics for Windows, v22.0 (IBM
NY,
USA).
Corp., Armonk, NY, USA).

2.5. Ethical Considerations
The Epidemiological Ethical Committee of Hiroshima University approved this study (E-701-3).
Participants were given an opportunity to opt out after receiving health guidance. Informed consent
was obtained, and confidentiality was maintained, and results presented to protect anonymity.

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The Epidemiological Ethical Committee of Hiroshima University approved this study (E-701-3).
Participants were given an opportunity to opt out after receiving health guidance. Informed consent
was obtained, and confidentiality was maintained, and results presented to protect anonymity.
3. Results
Table 1 shows that the average age was 50.6 years (standard deviation (SD) ± 6.0 years) for the
ICT group and 49.5 years (SD ± 8.2 years) for the control group (p = 0.901). There were more womenin the control group (21.1%) than in the ICT group (10.3%; p = 0.146). At baseline, there were no
statistically significant differences between ICT and control groups for any of the indicators of metabolic
disturbances, as shown in Table 1.
After the completion of the 6 months program, the control group showed small, but statisticallysignificant improvement in six indicators compared to baseline values, i.e., BMI (p = 0.008), male waist
circumference (p < 0.001), systolic BP (p = 0.005), diastolic BP (p < 0.001), HDL (p = 0.008), and HbA1c(p < 0.001), as shown in Table 1. In the ICT group, similar small changes were observed regarding
male waist circumference (p < 0.001), diastolic BP (p = 0.003), HDL (p = 0.032), and HbA1c (p < 0.001).
There were no significant differences when the magnitude of change in measurements for the control
group were compared to those of the ICT group, as shown in Table 1.
Of the ICT group, 47 out of 50 participants used the app to monitor their weight and number ofdaily steps, while 38 used it to record their BP, as shown in Table 2. The number of steps were the mostoften monitored measurement, with the 47 participants using it on average 15 times/month. Of the
47 that monitored weight, about 75% used it for an average of 9 times/month. Of the 38 that monitored
BP, about 80% used it about 7 times/month, as shown in Table 2.
There were some significant changes in indicators of metabolic disturbances when the control
group, as well as the low and high frequency ICT users, were compared, as shown in Table 3. For the
38 who monitored their BP, there were significant changes in BMI (p = 0.007), waist circumference(p < 0.001), triglyceride levels (p = 0.007), and HbA1c (p = 0.001). For the 47 participants who monitored
weight, there were significant changes in waist circumference (p = 0.010) and HbA1c (p = 0.010).
There were no significant differences in any of the indicators for those who monitored their daily steps,
as shown in Table 3. The table also shows that the amount of change for most indicators, even for thosewhere statistical significance was not reached, tended to be highest for the ICT participants who used
the app more than 5 times/month, followed by the control group. The amount of change was generally
lowest for the ICT participants who used the app <5 times/month, as shown in Table 3.
To examine the effectiveness of the program, the participants in each group were classified
into the “maintained” group, “effective” group, and “ineffective” group, and the combination of“maintained” and “effective” groups were compared with the “ineffective” group. There were no
statistical significances observed in any indicators. However, the ICT group showed nearly 40% better
results on systolic BP, and around 10% improvement in diastolic BP, LDL, and HbA1c compared to thecontrol group, as shown in Table 4.
Of the ICT group, 41 (82.0%) completed the questionnaire and of these, 12% were women. A totalof 27 participants (71.1%) in the control group responded to the questionnaire and 18.5% were female.
At the 6-month mark, more than two-thirds of both the ICT and control groups indicated that they
followed a diet suitable for preventing lifestyle-related diseases, as shown in Table 5.

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Table 1. Comparing the ICT and control groups at baseline and at 6 months.
Baseline Assessment
Indicators ofMetabolicDisturbances

Control Group(N = 38)

ICT Group(N = 50)

Assessment at Completion of Intervention 6 Months
p-Value 1

Control Group(N = 38)

p-Value 2

Mean ± SD

ICT Group(N = 50)

p-Value 3

Mean ± SD

Change Between Control and ICT Groups
Control Group(N = 38)

ICT Group(N = 50)

Mean ± SD

Mean ± SD

p-Value 4

Mean ± SD

Mean ± SD

Body mass index(BMI), kg/m2

25.3 ± 3.9

25.1 ± 4.3

0.787 a24.8 ± 3.9

0.008 c24.7 ± 4.1

0.088 c−0.38 ± 0.87

−0.39 ± 1.00

0.787 aMale waist
circumference, cm(n = 75)

89.9 ± 9.4

89.1 ± 11.0

0.720 b86.7 ± 9.2<0.001 d

86.2 ± 10.5

<0.001 d−3.2 ± 3.6−2.9 ± 3.8

0.683 bFemale waist
circumference, cm(n = 13)85.5 ± 11.7

75.8 ± 6.6

0.239 b83.7 ± 11.6

0.076 d76.7 ± 11.0

0.496 d−1.8 ± 2.3−1.5 ± 4.6

0.880 b

Systolic bloodpressure, mmHg

127.9 ± 19.2

124.3 ± 14.3

0.325 b125.6 ± 13.1

0.005 d121.7 ± 11.3

0.122 d−2.24 ± 14.46−2.60 ± 12.62

0.896 bDiastolic bloodpressure, mmHg

82.6 ± 13.1

77.9 ± 10.8

0.062 b77.8 ± 10.7

<0.001 d74.1 ± 9.0

0.003 d-4.74 ± 9.69

−3.83 ± 9.26

0.645 b

Triglyceride (TG),
mg/dL

138.0 ± 93.3

148.3 ± 199.9

0.234 a119.2 ± 58.7

0.059 c128.6 ± 104.3

0.237c−18.87 ± 56.45−19.67 ± 168.12

0.234 aHigh-density
lipoprotein (HDL)
cholesterol, mg/dL

58.9 ± 13.3

61.5 ± 17.0

0.438 a63.6 ± 16.9

0.008 c63.9 ± 16.0

0.032 d4.68 ± 9.954

2.4 3 ± 8.40

0.438 aLow-density
lipoprotein (LDL)cholesterol, mg/dL

128.7 ± 29.8

124.1 ± 29.1

0.637 a134.0 ± 30.6

0.097 c124.1 ± 27.0

0.987 d5.32 ± 21.82−0.05 ± 24.92

0.637 aGlycatedhemoglobin A1c(HbA1c ), %6.0 ± 0.9

5.7 ± 0.3

0.085 a5.8 ± 1.3

0.001 c5.4 ± 0.3

<0.001 c−0.18 ± 0.64

−0.22 ± 0.21

0.085 a1 Comparing the information and communication technology (ICT) group and control group at baseline; 2 comparing the control group at baseline and at 6 months; 3 comparing the

ICT group at baseline and at 6 months; 4 comparing change data between ICT group and control group at 6 months; a Mann–Whitney U test; b Student-t test; c Wilcoxon ranked test;
d paired-t test.

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Table 2. Use of ICT app by ICT group.
Use of ICT Application to Monitor:
Blood pressure (n = 38)
Body weight (n = 47)
Number of steps (n = 47)<1
Time/Month

1–4
Times/Month≥5
Times/Month

Mean/Month

8 (21.1%)
12 (25.5%)0

7 (18.4%)
11 (23.4%)
10 (21.3%)

23 (60.5%)
24 (51.1%)
37 (78.7%)

7.1
9.3
15.1

n (%)
n (%)
n (%)

Information and communication technology (ICT) participants who did not use the app to monitor the relevant
indicators were excluded from the table.

Table 3. Comparison of change for ICT participants by frequency of app use.
Use of ICT Application to Register *Blood Pressure

Body WeightControln = 38<5times
n = 15≥5times
n = 23Controln = 38<5times
n = 23≥5times
n = 24Controln = 38<5times
n = 10≥5
times
n = 37

Mean

−0.4

−0.1

−0.8

−0.4

−0.2

−0.7

−0.4

−0.1

−0.6

SD

±0.9

±0.8

±1.1

±0.9

±0.9

±1.1

±0.9

±0.7

±1.1

p value

0.007

Measurement

Body mass index(BMI), kg/m2

Systolic bloodpressure, mmHg

Diastolic bloodpressure, mmHg

Waist circumference,cm

Triglycerides, mg/dL

HDL cholesterol,
mg/dL

LDL cholesterol,
mg/dL

HbA1c, %Number of Steps

0.053

0.093

Mean

−2.2

−0.1

-6.8

−2.2

−0.9

−5

−2.2

2.4

−5.7

SD

±14.5

±12.9

±11.3

±14.5

±12.3

±12.9

±14.5

±12.6

±11.8

p value

0.152

Mean

−4.7

−3.6

−4.2

−4.7

−4.4

−3

−4.7

−3.7

−3.9

SD

±9.7

±9.3

±9.5

±9.7

±9.6

±8.9

±9.7

±7.8

±10.2

0.397

0.126

p value

0.79

Mean

−2.9

−1.5

−4.7

−2.9

−1.6

−4.3

−2.9

−1.7

−3.3

SD

±3.5

±3.4

±4.2

±3.5

±3.3

±4.4

±3.5

±2.7

±4.5

p value

0.001

Mean

−0.4

−0.1

−0.8

−0.4

−0.1

−0.6

SD

±0.9

±0.8

±1.1

±0.9

±0.7

±1.1

p value

0.007

0.597

0.745

0.010

0.161

−0.4

−0.2

−0.7

±0.9

±0.9

±1.1

0.053

0.093

Mean

−2.2

−0.1

−6.8

−2.2

−0.9

−5

−2.2

2.4

−5.7

SD

±14.5

±12.9

±11.3

±14.5

±12.3

±12.9

±14.5

±12.6

±11.8

p value

0.152

Mean

−4.7

−3.6

−4.2

−4.7

−4.4

−3

−4.7

−3.7

−3.9

SD

±9.7

±9.3

±9.5

±9.7

±9.6

±8.9

±9.7

±7.8

±10.2

0.397

0.126

p value

0.79

Mean

−2.9

−1.5

−4.7

−2.9

−1.6

−4.3

−2.9

−1.7

−3.3

SD

±3.5

±3.4

±4.2

±3.5

±3.3

±4.4

±3.5

±2.7

±4.5

p value

0.001

0.597

0.745

0.010

0.161

* Person who never used excluded; ICT: information and communication technology; HDL: high-density lipoprotein;
LDL: low-density lipoprotein; HbA1c: glycated hemoglobin.

Table 4. Comparing the indicators for the ICT and control groups for whom the program was judged
to be effective or where they maintained results.
Indicator

ICT Group

Control Group

p-Value *Body mass index
Waist circumference
Systolic blood pressure
Diastolic blood pressure
Triglycerides
HDL cholesterol
LDL cholesterol
HbA1c

31 (62.0%)
25 (50.0%)38 (76.0%)
44 (88.0%)
37 (74.0%)
47 (94.0%)
25 (50.0%)39 (78.0%)

24 (63.2%)
19 (50.0%)
14 (36.8%)
29 (76.3%)
28 (73.7%)
37 (97.4%)
14 (36.8%)
25 (65.8%)

0.912
1.000
0.191
0.149
0.973
0.452
0.218
0.203

* Chi-square test; ICT: information and communication technology; HDL: high-density lipoprotein; LDL: low-density
lipoprotein; HbA1c: glycated hemoglobin.

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Table 5. Comparing insight into health behavior between the ICT and control groups.
p-Value *Improvement of Lifestyle (n, %)(1) I am following a diet that is suitable for preventing lifestyle-related diseases.

ICT group
Control group

Strongly
agree

Agree

Neither

DisagreeStrongly
disagree4 (9.8%)
3 (11.1%)

24 (58.5%)
15 (56.6%)

8 (19.5%)
6 (22.2%)

3 (7.3%)3 (11.1%)

2 (4.9%)00.966(2) I regularly do exercise that is suitable for preventing lifestyle-related diseases.

ICT group
Control group

Strongly
agree

Agree

Neither

DisagreeStrongly
disagree5 (12.2%)
3 (11.1%)

27 (65.9%)
13 (48.1%)

4 (9.8%)5 (18.5%)

1 (2.4%)5 (18.5%)

4 (9.8%)1 (3.7%)0.226(3) I know my average blood pressure.

ICT group
Control group

Strongly
agree

Agree

Neither

DisagreeStrongly
disagree21 (51.2%)
8 (29.6%)

15 (36.6%)
17 (63.0%)

3 (7.3%)1 (3.7%)

1 (2.4%)0

1 (2.4%)1 (3.7%)

0.197(4)I know my average blood glucose level.

ICT group
Control group

Strongly
agree

Agree

Neither

DisagreeStrongly
disagree7 (17.1%)
3 (11.1%)

10 (24.4)
8 (29.6)

6 (14.6)
6 (22.2)

10 (24.4)
5 (18.5)

8 (19.5)5 (18.5)

Strongly
agree

Agree

Neither

DisagreeStrongly
disagree25 (61.0%)
12 (44.4%)

14 (34.1%)
13 (48.1%)

00

1 (2.4%)1 (3.7%)

1 (2.4%)1 (3.7%)0.980(5) I know my average body weight.

ICT group
Control group

0.188(6) I can judge when to start clinical treatment for diabetes, hypertension, dyslipidemia.

ICT group
Control group

Very well

Well

Neither

Not well

Badly/Not at
all4 (9.8%)10 (37.0%)

12 (29.3%)
5 (18.5%)

12 (29.3%)
4 (14.8%)

3 (7.3%)4 (14.8%)

10 (24.3%)
4 (14.8%)

0.078(7) I rate the effectiveness of the program on continuous lifestyle improvement as

ICT group
Control groupVeryeffective

Effective

Neither

Somewhatineffective

Not effectiveat all1 (2.4%)2 (7.4%)21 (51.2%)
11 (40.7%)

8 (19.5%)
2 (7.4%)2 (4.9%)2 (7.4%)9 (22.0%)
10 (37.0%)

0.399* Mann–Whitney U test; ICT: information and communication technology; Diet: based on individual dietary habit,
the participant was advised on energy balance (reduce excessive energy intake), nutritional balance, balance fat intake,
reduce salt intake, eat 3 times a day, do not eat too late, eat plenty of vegetables and vegetables first, reduce snacks
and sugar containing soft drink consumption, and reduce alcohol consumption; Exercise: aerobic exercise of
30–60 min per day, 3 days per week is recommended. However, based on individual lifestyle and medical risk
assessment, individually tailored exercise was recommended as to increase the daily activities and walking an extra
10 min.

For the ICT group, 78.1% reported that they regularly exercised, compared to 59.2% of the control
group. High proportions of both the ICT group (87.8%) and the control group (92.6%) were aware of
their average BP value, as well as of their average body weight, i.e., 95.1% for the ICT group and 92.5%of the control group. However, awareness was much lower for blood glucose with 41.5% of the ICTgroup and 40.7% of the control group. Roughly half of both groups rated the program they were in
as effective for continuous lifestyle improvement, but the proportion was slightly higher for the ICTgroup (53.6%) compared to 48.1% of the control group.

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4. Discussion
This study explored whether a health guidance app resulted in sustained motivation, adherence tolifestyle changes, and subsequent changes in indicators of metabolic disturbances among civil servants
with risk of metabolic syndrome in Hiroshima Prefecture. The ICT group (who had increased contact
with health educators and used the app during the 6-month program) showed significant improvement
in four measurements: male waist circumference, DBP, HDL, and HbA1c. However, there were also
improvements in six measurements for the control group. These measurements were: BMI, male waist
circumference, both systolic and diastolic BP, and HbA1c. The magnitude of change between the twogroups did not differ significantly. Of note is that the magnitude of changes for both groups were
small, e.g., male waist circumference changed by about 3 cm for both groups. Similarly, the HbA1c
measurement decreased by about 0.2% for both groups, but this change resulted in the average for
the ICT group moving into the normal range. Although the changes are small, a reduction in waist
circumference of 3 cm has been shown to be effective in preventing metabolic syndrome. Some studies
indicate that there might be a benefit in achieving even small changes. For example, one study of
25 men aged 24–65 years with metabolic syndrome found a small reduction in waist circumference
together with weight loss appeared to magnify the impact of the Mediterranean diet on markers
of inflammation [18]. Another study using 9-year follow-up data on insulin resistance involved acohort of 1868 men and 1939 women aged 30–64 years [19]. The authors found that reducing waist
measurements by only 3 cm (taking changes to BMI into consideration) had a significant beneficial
effect on metabolic syndrome in women.
Hill (2009) suggests a paradigm shift to address the obesity epidemic. He recommends thatefforts should, in the first instance, focus on promoting small lifestyle changes and on reducing
gradual excessive weight gain, rather than focusing primarily on weight loss or preventing obesity [20].
The latter usually involves making dramatic changes at an individual level which are difficult to sustain
and that are often countered by societal pressures of excessive energy consumption (e.g., large portion
sizes and free refills of sugary drinks in American restaurants) [21]. Small changes are more feasible toachieve, can be maintained in the longer term, and minor changes in diet and/or in physical activity
might be adequate to stop gradual weight gain over time [20]. Success in achieving such changes could
lead to increased self-efficacy at the individual level and could encourage people to make additional
small changes [20].
Regarding the use of the app, the number of daily steps was the most frequently recorded indicator(about 15 times/month) and just less than 80% of participants measured their steps 5 times or more
per month. Less than 50% of participants measured their weight on a regular basis, while about
60% recorded their BP more than 5 times/month. This might reflect how convenient it was to record
measurements. Daily steps were measured with a pedometer, which did not require much effort on thepart of the participants. However, BP measurements usually necessitated participants to go to specificstations or locations at their workplace where BP could be measured. Similarly, changes in weight
usually take about a week to show on the scale. The BMI changed by less than 0.40 for both groups andthis slow rate of change might have lessened participants’ motivation to keep weighing themselves.
For most of the indicators, the amount of change was highest for the ICT participants who used
the app more than 5 times/month, followed closely by the control group. The magnitude of change wasconsistently smallest for the low frequency use ICT group. This was applied to most measurements,
irrespective of which indicator was recorded or whether statistical significance was reached. This seems
to suggest that participants found different types of interventions helpful. A previous study found that
insured persons receiving SHG had significant improvements in their metabolic syndrome profiles
with smaller proportions needing pharmacological treatment over a 3-year period when compared topeople who did not receive SHG [14]. Our study seemed to confirm that SHG had a positive impact
on the control group. The more intensive ICT intervention also delivered improvements, but thesewere not dramatically different from the control group. In this study, participants chose the type ofintervention they preferred. Our study indicates that “not one size fits all” and that a higher intensity

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program does not necessarily translate into better results. Clearly, participants’ preferences play animportant factor in the effectiveness of the intervention. A more intensive program appears to hinder
adherence if the results of the low frequency use group are considered. This group might have been of
the opinion that they would do better with a more intensive program and the use of the app. However,it might have been that they did not like the program and/or they did not like to use the app. This might
have led to them not adhering to the program as much.
A recent systematic review on effective lifestyle modification strategies for metabolic syndrome
included 28 randomized control trials with a total of 6372 patients [4]. Of these, eight trials foundimprovement in risk factors of metabolic syndrome after a 1-year follow-up. The authors indicatedthat team-based, interactive approaches with high-frequency contact with motivated participants
made the largest and most lasting impact. The review also looked at the role of technology andindicated that these were useful tools in achieving lifestyle change, but ineffective when compared withpersonal contact [4]. Another review of 35 studies assessed different intervention delivery methods [5].
The authors indicated that, to maximize the effect of tailored interventions in lifestyle management,
the stage of change of the individual must be identified and intervention elements should be basedon the readiness of the individual to make changes. The use of ICT, especially the use of mobile
technology, does provide the opportunity to collect patient data and provide tailored interventions
in a timely fashion. However, insights into the use of ICT in health care in Japan by gender anddifferent generations need further exploration. This study pointed to the issue that women might beless inclined to make use of ICT in managing their health.
5. Limitations
There were a number of limitations in this study. The sample size was relatively small, participants
were not randomized, and the follow-up period was short (6 months). Participants were also notdiagnosed as “patients”, which could have led to less commitment to change on the part of theparticipants to make and continue changes. The study participants were selected from civil servants ofHiroshima Prefecture. The results might therefore not be generalizable.
6. Conclusions
This study found that a once-off motivational interview and a more intensive program of healthguidance to address metabolic disturbances in Japanese civil servants show promising results and thatthe use of ICT can be of benefit to some individuals. However, there are differences in preferences fortypes of interventions and ICT elements might be a deterrent to some. The interventions need to beframed within a broader context of encouraging small, but consistent changes within society.
Supplementary Materials: The following are available online at http://www.mdpi.com/1660-4601/17/21/8147/s1,
Table S1: Diagnostic criteria for metabolic syndrome in Japan, Figure S1: Inclusion criteria for participants.
Author Contributions: Conceptualization, N.T. and M.M.; Methodology, N.T. and M.M.; Data Curation, N.T. and M.M;
Formal Analysis, N.T., M.M., and K.K.; Writing—Original Draft Preparation, N.T.; Writing—Review and Editing,M.M., K.K., M.S., and M.M.R.; Supervision, M.M.; Funding Acquisition, M.M.; All authors have read and agreed
to the published version of the manuscript.
Funding: This research was funded by Hiroshima Prefecture, grant number A1600250.
Acknowledgments: We thank the Hiroshima Prefecture, Division of Integrated Care, for initiating this project.
Thank you to the insured (study participants) of the Mutual Aid Association of Hiroshima Prefecture for their
participation. We acknowledge the contribution of DPP HEALTH PARTNERS CO., LTD and Madoka Kawai,
RN who assisted to develop the application. Our sincere thanks to the health educators of the Hiroshima
Environment and Health Association for the provision of the health guidance.
Conflicts of Interest: The authors declare no potential conflict of interest in respect of this research, authorship,
and/or publication of this article.

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