JMIR FORMATIVE RESEARCH

Okamoto et al

Original Paper

Smartphone-Based Digital Peer Support for a Walking Intervention
Among Public Officers in Kanagawa Prefecture: Single-Arm Preand Postintervention Evaluation
Masumi Okamoto1,2,3, MPH; Yoshinobu Saito4,5, PhD; Sho Nakamura1,3,6, MD, PhD; Makoto Nagasawa1, PhD;
Megumi Shibuya7, MSc; Go Nagasaka7, BEng; Hiroto Narimatsu1,2,3,6, MD, PhD
1

Graduate School of Health Innovation, Kanagawa University of Human Services, Kawasaki, Japan

2

Center for Innovation Policy, Kanagawa University of Human Services, Kawasaki, Japan

3

Cancer Prevention and Control Division, Kanagawa Cancer Center Research Institute, Yokohama, Japan

4

Faculty of Sport Management, Nippon Sport Science University, Yokohama, Japan

5

Graduate School of Physical Education, Health and Sport Studies, Nippon Sport Science University, Tokyo, Japan

6

Department of Genetic Medicine, Kanagawa Cancer Center, Yokohama, Japan7

A 10 Lab Inc, Tokyo, Japan

Corresponding Author:
Hiroto Narimatsu, MD, PhD
Graduate School of Health Innovation
Kanagawa University of Human Services
Research Gate Building Tonomachi 2-A
3-25-10 Tonomachi, Kawasaki-ku
Kawasaki, 210-0821
Japan
Phone: 81 44 589 8100
Email: hiroto-narimatsu@umin.org

Abstract
Background: Digital peer support, defined as peer support delivered through technology such as smartphone apps, may bepromising to promote activity in the form of step counts. Interactions among users have a positive impact on retention rates, andapps with social elements show significant improvements in daily step count. However, the feasibility of digital peer support inpromoting physical activity (PA) is unknown; therefore, its effectiveness on step count and the clinical implications remain
unconfirmed.
Objective: This study aimed to assess the feasibility of digital peer support over a 3-month intervention period using the retention
rate as the outcome. Moreover, changes in daily step count and physical measurements were compared between pre- andpostintervention.
Methods: The study design was a 3-month 1-arm intervention with participants from local government offices in Kanagawa,Japan. We used an available smartphone app, Minchalle, as the tool for the group intervention. Participants were required toreport their daily step count to a maximum of 5 members composed exclusively of study participants. The primary outcome was
the retention rate. Secondary outcomes included daily step count, the rate of achieving daily step goals, physical measurements,and lifestyle characteristics. Descriptive statistics and the Pearson coefficient were used to examine the relationship between goal
achievement and step count, as well as changes in step count and various variables including physical measurements.
Results: Of the 63 participants, 62 completed the intervention. The retention rate was 98% (62/63). The average daily step countduring the intervention was 6993 (SD 2328) steps, an 1182-step increase compared with the count observed 1 week before theintervention began. The rate of achieving the daily step count during the intervention was 53.5% (SD 26.2%). There was asignificant correlation (r=0.27, P=.05) between achieving daily step goals and increasing daily step count. Comparative analyses
showed that changes in weight (68.56, SD 16.97 kg vs 67.30, SD 16.86 kg; P<.001), BMI (24.82, SD 4.80 kg/m2 vs 24.35, SD
4.73 kg/m2; P<.001), somatic fat rate (28.50%, SD 7.44% vs 26.58%, SD 7.90%; P=.005), systolic blood pressure (130.42, SD
17.92 mm Hg vs 122.00, SD 15.06 mm Hg; P<.001), and diastolic blood pressure (83.24, SD 13.27 mm Hg vs 77.92, SD 11.71

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Okamoto et almm Hg; P=.002) were significantly different before and after the intervention. Similarly, the daily amount of PA significantly
improved from 5.77 (SD 3.81) metabolic equivalent (MET)–hours per day to 9.85 (SD 7.84) MET-hours per day (P<.001).
Conclusions: This study demonstrated that digital peer support is feasible for maintaining a high retention rate and can, therefore,
effectively promote PA. It can be a promising tool to improve daily step count, subjective PA, and clinical outcomes, such as
weight and somatic fat rate.
Trial Registration: UMIN Clinical Trials Registry UMIN000042520; https://tinyurl.com/46c4nm8z(JMIR Form Res 2024;8:e53759) doi: 10.2196/53759
KEYWORDS
digital health; mhealth; ehealth; smartphone app; smartphone application; peer support; digital peer support; social support; groupintervention; physical activity; health promotion; behavior change; apps; step counting; workplace health

Introduction
Interventions for health promotion and behavioral changes using
smartphone apps may be more cost-effective than face-to-faceservices due to their affordability and accessibility. The
usefulness of smartphone apps for increasing physical activity(PA), including step count, is evident. Previous studies havereported that smartphone app interventions had a positive impact
on both objective and subjective PA [1-4], including walking
time [5,6].
Peer support refers to the process through which individuals
who share common experiences or face similar challenges cometogether as equals to offer and receive help based on theknowledge that comes through shared experiences [7]. This
includes psychological support such as acceptance andencouragement. To date, face-to-face peer support has beeneffective at increasing PA, including step count, in the healthy
general population [8]. Conversely, peer support also poseschallenges for in-person interventions owing to the high travel
costs and required organization and human resources.
In contrast, smartphone apps are inexpensive and accessible,
making it possible to provide services to a multitude of people
at a lower cost than face-to-face meetings. To overcome thechallenges of conventional peer support, digital peer
support—defined as peer support delivered through technology
and media such as smartphone apps [9]—was recently
developed. Previous studies have indicated that communication
with peers and social support [10] may have a positive impact
on increasing step count [11]. Therefore, digital peer support
may be promising to promote PA in the form of step counts.
To date, the feasibility of or engagement with digital peer
support is unclear, although the use of smartphones is related
to the magnitude of their effects [4,12]. Most previous studies
defined engagement as the retention rate, by calculating thepercentage of users who continued using smartphone apps
during the intervention [13]. However, there is no consensus
on measuring engagement; attempts have been made forconceptualization [14-16]. In general, the retention rates withhealth care smartphone apps for PA are low, at approximately
45% [17], and the use of smartphone apps has declined overtime [1,18].
Social features of smartphone apps, such as social support andsocial comparison through interactions among users, reportedly
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have a positive impact on engagement, and they are consideredpotential leverage to increase the retention rate [18-20].
However, no studies have verified the retention rate ofspecialized digital peer support; therefore, its effectiveness on
step count remains unconfirmed. In a single-arm, pre-postcomparison study that examined the efficacy of smartphone
apps, in which social networking was the main component, theretention rate among 55 participants was 82% during a 6-monthintervention [21]. Another 2 intervention arms (gamification
and basic apps) in a 100-day, 3-group randomized control trial(RCT) evaluated the efficacy of a smartphone app equippedwith gamification, including social interaction among users, on
promoting PA [12]. In that study, attrition (30 days of nonuse)
was reported for approximately 32% and 39% of the gamified
and basic app groups, respectively, meaning the retention rates
were 68% and 61%, respectively [16]. Moreover, the gamified
app group demonstrated significantly increased subjective PA
at the 9-month follow-up compared with the control group who
engaged in individual walking. However, the effect on objective
step count was not revealed [22].
We aimed to assess the effectiveness of digital peer support asa tool for promoting health. This study evaluated the feasibilityof digital peer support over a 3-month intervention period using
retention rate as the outcome. We also measured its
effectiveness. Specifically, changes in physical measurements,
PA, lifestyle characteristics, and psychosocial factors werecompared between pre- and postintervention. Additionally,
previous studies revealed that goal setting and self-monitoring
contributed to increased step count [4,23,24]. Hence, this study
also examined the relationship between the goal attainment rateand increasing step count using digital peer support. If its
effectiveness was demonstrated, it would help individuals
increase their step count easily, at a low cost, and remotely.
Furthermore, digital peer support may be used as an intervention
tool during medical consultation intervals for patients withlifestyle-related diseases or others for whom walking is effective
for disease prevention or control.

Methods
Study Design and ProceduresThis 3-month, 1-arm intervention study adopted a pre-post
evaluation design. Baseline assessments, which consisted of
paper-based questionnaires and physical measurements, wereconducted in gymnasiums and other facilities arranged by the
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7 municipalities in Kanagawa Prefecture and Kanagawa local
government offices that participated in the study. The
measurements were taken by the researchers or local municipal
officials in Kanagawa Prefecture and Kanagawa local
government officials trained by the researchers. Subsequently,
the participants installed and set up a smartphone app called
Minchalle (“Challenge yourself together with others” inJapanese) to commence the intervention.
Participants were required to send objective step count data for
1 week before the intervention as a baseline and during the
3-month intervention period via Minchalle. Objective step countdata were measured using Google Fit (Google LLC) for Android
users and Healthcare for iPhone users (Apple Inc), both capableof automatically counting steps while the phone is carried. Once
Minchalle was connected to these apps, daily step count data
were automatically transferred to and displayed in Minchalle.
Following the 3-month intervention, participants were again
required to complete paper-based questionnaires and undergo
physical measurements as part of an endline survey. Endline
measurements were performed in the same manner as thebaseline assessments. In addition, to assess the validity ofsmartphone-measured data, step count measurements via an
accelerometer (Active style Pro HJA-750C, OMRONCorporation) were conducted for 1 week before the intervention.
Participants were required to wear the accelerometer aroundtheir waist while awake during this period. The study was
conducted between December 2020 and June 2021.

Recruitment and ParticipantsThis study was announced through email or online in the officesof 7 local municipalities in Kanagawa Prefecture and Kanagawa
local government offices in Japan. Interested participants
participated in briefing sessions to deepen their understandingof this study, and, if eligible, paper-based informed consent was
obtained. Inclusion criteria were participants aged between 20
years and 75 years who owned and were accustomed to usinga smartphone and could communicate in Japanese. Exclusion
criteria were participants who were currently pregnant, had
physical or medical problems that did not allow safe
participation in the walking intervention, had experience using
Minchalle over the past 6 months, or had undergone anotherface-to-face or digital interventional study for PA promotion.
The recruitment was conducted between December 2020 and
April 2021.

Intervention
Minchalle was commercially developed by a health carecompany (A 10 Lab) to help users foster desirable habits. It isavailable on both Android and iPhone. Its development was
guided by the social cognitive theory based on the need toincrease self-efficacy, a key driver of behavior adoption andmaintenance. To enhance self-efficacy, Minchalle adopted agroup intervention to maximize the mechanisms of social
support and comparison among group members, which thereby
increased the retention rate and made substantial achievements.
Based on this basic concept, the main function of Minchalle
was to facilitate group interactions, limiting each group to a
maximum of 5 members and a minimum of 3 members withsimilar goals. All users were required to share their daily
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Okamoto et al
achievements with other members in a chat box, accompanied
by a photo taken on the same day as evidence of their activity.
Users could also enjoy interactions with other members bycommenting or chatting at any time, although this was not
obligatory. This platform was designed to help users continue
desired behaviors by receiving praise and encouragement fromother members for their achievements and gaining inspirationfrom other members’ activities. Apart from group interaction,
the examples of functions installed in Minchalle were goal
setting, self-monitoring, reminders through push notifications,rewards for achievements such as exclusive stickers available
within this app, and coins that could be used for donations toorganizations that implement projects with social significance.
In this study, once the Minchalle app was installed, participants
were anonymously assigned by researchers to a group thatconsisted of a maximum of 5 members. Each participant set
their daily step goals based on their step counts from the pastfew days, without any instruction from the researchers. Theintervention began with participants posting a photo of their
total step count once a day in the group chat. To easily identify
study participants, all the team members were composed
exclusively of study participants. For their cooperation,
participants were provided access to a premium version of
Minchalle by A 10 Lab worth ¥500 (US $3.42) during theintervention period.

Sample Size
In a similar pilot study in Australia measuring the effectivenessof a social networking mobile app on improving PA, there were
55 participants. Therefore, we set a target of approximately 50
participants.

MeasuresThis study aimed to evaluate the feasibility of digital peer
support interventions using retention rates. The secondary
endpoints were the effectiveness of the intervention, measured
via the change in step count between baseline and endline, andthe association with the achievement rate of daily step goals.

Retention Rate
In this study, the retention rate was defined as the percentage
of users who continued using the smartphone app during the
3-month intervention period. This indicator was used based on
its common use in previous studies [13]. Owing to the setting
up of Minchalle, participants were automatically removed frompeer groups if they failed to report daily step counts or shareany photos or comments for 8 consecutive days. Therefore,
participants who were dismissed but returned to the team werecounted as participants, and only participants who weredismissed and never returned were considered as having dropped
out.

Daily Step Count
Objective daily step count data were obtained from Google Fit
for Android users and HealthKit for iPhone users. All
participants were required to submit their step data via Minchalle
at both baseline and endline. The validity of Google Fit and
Healthcare was confirmed, although interpretation should bedone cautiously owing to the possibility of an underestimation
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of 10% to 20% due to noncarrying time [25-27]. Participants
were asked to carry their smartphones for as long as possible
while they were awake, live their daily lives, and report all thedata at the end of the survey via the smartphone app. In thisstudy, the positive impact on daily step count was examined bycomparing the average daily step count during the week before
and during the intervention. To calculate the average daily step
count, the total daily step count was divided by the number ofintervention days. Days with 0 steps were omitted to prevent
underestimation, as there was a possibility that participants had
forgotten to carry their smartphones that day. Moreover, theanalysis included days with low step counts, as it was difficult
to distinguish whether it reflected a step count issue or genuinely
low PA.

Achievement Rate of Daily Step Goals
At the beginning of the intervention, participants were asked to
set their daily step goals based on their step counts from thepast few days, without any instructions from the researchers.
These goals could be adjusted by the participants at any time
during the study. The achievement rate was assessed by
calculating the proportion of days on which participants met
their daily step goal relative to the total number of intervention
days.

Other Measurements
This study used a self-administered questionnaire to obtainsociodemographic characteristics, which included sleep,
subjective PA, alcohol consumption, smoking, diet, personality
and psychological traits, psychological stress, social
relationships, quality of life, and physical checkups. These
questionnaires were used in the Kanagawa ME-BYO prospective
cohort study, a large population-based genomic cohort study toclarify gene–environmental interactions in noncommunicablediseases in collaboration with the Japan Multi-Institutional
Collaborative Cohort (J-MICC) study [28]. Details of the
ME-BYO cohort and J-MICC study and the relationship between
the two have been described elsewhere [28-30]. Questionnaires
on loneliness and self-efficacy were also used in this study.
Height and weight were measured to the nearest 0.1 cm and 0.1
kg, respectively, and BMI was calculated. Systolic bloodpressure (SBP) and diastolic blood pressure (DBP) were
measured via a sphygmomanometer. Lifestyle factors included
subjective PA, smoking habits, alcohol consumption, and hours
slept per day. We defined subjective PA as the sum of the daily
amount of PA, estimated by multiplying the amount of timespent walking (3.0 metabolic equivalents [METs]) and engaging
in hard labor (4.5 METs) by their assigned daily MET
intensities, and 3 types of leisure-time activities (3.3 METs forlight effort, 4.0 METs for moderate effort, and 8.0 METs forvigorous effort), calculated by multiplying the daily frequency,
duration, and intensity. We also assessed the METs for engaging
in hard labor (4.5 METs) alone. Detailed information on thecalculation method has been provided in previous studies[31-33].

Okamoto et al
psychological distress. Its validity and reliability have been
described previously [34]. Social support items were derived
from the ENRICHD Social Support Inventory (ESSI) [35]. The
original version of the ESSI includes 7 items measured with a
5-point rating to assess the 4 domains of social support: emotion,
instrument, information, and appraisal. Individual items are
summed for a total score, with higher scores demonstrating
greater social support. This study adopted a modified versionof the ESSI that included 6 items to prevent duplication of thequestion on marital status. The Japanese version of the
Short-Form Health-Related Quality of Life Health Survey wasused to measure health-related quality of life. Details regarding
this assessment have been previously described [36]. The
Japanese version of the UCLA Loneliness Score was used,which consists of 20 items answered on a 4-point scale, withhigher scores indicating a higher degree of loneliness [37,38].
Self-efficacy was assessed using the Generalized Self-Efficacy
Scale [39], which comprises 23 items rated on a 5-point scale
to measure belief in one’s competence to cope with a broad
range of stressful or challenging demands [40]. Higher scoreson this scale indicate a higher degree of self-efficacy.

Statistical Analyses
Descriptive analyses were conducted to calculate the retention
rate and rate of achieving daily step goals. The average daily
step counts 1 week before and during the intervention period
were calculated to measure the impact of the intervention, andthe differences between the 2 were assessed. Pearson coefficientswere used to assess the correlation between the rate of achieving
daily step goals and the difference in average step counts before
and during the intervention. Differences in weight, BMI, somatic
fat rate, SBP, DBP, PA, lifestyle characteristics, andpsychosocial factors between baseline and endline were analyzed
via a 2-tailed t test. Furthermore, Pearson coefficients were
examined between changes in average daily step counts andvarious variables, including physical measurements, lifestyle,and psychosocial factors, separately at baseline and endline.
This enabled us to determine the variables to be used for
subsequent multiregression analyses by omitting the possibilityof multicollinearity among potential independent variables.
Multiple regression analyses were conducted to demonstrate
the impact of changes in daily step counts on healthmeasurements, lifestyle, and psychosocial factors (Model 1),
after adjusting for sex and age (Model 2), Model 2 + householdincome (Model 3), and Model 3 + average daily step countduring the 1 week prior to the intervention (Model 4). Data
analysis was performed using R software (version 4.2.2).

Ethics Approval
This study was approved by the Institutional Review Board of
Kanagawa University of Human Services (30-018). The study
was registered in the UMIN Clinical Trials Registry(UMIN000042520). All participants provided written informed
consent. Data were collected in a face-to-face setting, and alluser data were deidentified before analysis.

The Kessler Psychological Distress Scale is a 6-item screening
scale with a 5-point rating that evaluates psychological distressin the preceding month. Higher scores indicate higher levels of
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Results
Participants’ Characteristics
Figure 1 shows the study flowchart. Table 1 presents the baseline

Okamoto et alcharacteristics of the 62 participants. Participants’ ages ranged
from 24 years to 63 years, with a mean age of 43.6 (SD 10.72)
years. More than one-half (32/62, 52%) were men, and most
(45/62, 73%) had a university degree or higher.

Figure 1. Study flowchart.

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Table 1. Demographic characteristics of the 62 eligible participants.
Characteristics

Results, n (%)

Age (years)
20-29

8 (13)

30-39

14 (23)40-49

18 (29)50-59

19 (31)

60-69

3 (5)

Male

32 (52)

Female30 (48)Sex

Education
High school

5 (8)Vocational school

5 (8)College

7 (11)

University

38 (61)

Graduate school

7 (11)

Job category
Administrator

42 (68)

Manager

9 (15)

Technical staff

10 (16)

Other

1 (2)

Marital status
Married47 (76)

Single14 (23)

Others1 (2)Retention Rate
Among the 69 participants who signed the consent forms, 4
withdrew shortly after the intervention began due to personal
conditions or smartphone defects, 2 did not meet the eligibility
criteria, and 1 dropped out in the middle. Therefore, 63participants installed the app and started the intervention, andthe retention rate was 98% (62/63).

to sex, age, and BMI at baseline are shown in Tables S1 and S2
in Multimedia Appendix 1.

Achievement Rate of Daily Step Goals and Effect on
Increase in Step Count

Data on daily step count were received from 54 (87%) of the
62 participants at the 3-month endline, and 8 participants failed
to share step count data at endline due to misunderstanding theprocedures of the survey when completing the intervention andendline questionnaires. Thus, data from 54 participants wereused for step count analysis.

The mean daily step goal at baseline was 6808 (SD 1668) steps.
The mean difference between the daily step goal and step countbefore the intervention began was 997 steps (P=.002). The rateof achieving the daily step count during the intervention was
53.5% (SD 26.2%). A significant correlation (r=0.27, P=.05)
was observed between achieving daily step goals and an increase
in daily step count. There was a statistically negative correlation
between the difference in the daily step goal and pre-intervention
step count and an increase in daily step count (r=–0.77, P<.001).

Improvement in Step Count

Other Outcomes

The average daily step counts during the week before theintervention began were 5811 (SD 2477) steps, as measured byHealthcare for iPhone or Google Fit by Android, and 7073 (SD2301) steps, as measured by the accelerometer. Comparing the
average daily step count from the smartphone app between
baseline and during the intervention, an 1182-step increase wasobserved. The changes in average daily step count according

Table 2 shows the comparative results of changes in healthmeasurements, lifestyle characteristics, and psychosocial factors.
Weight, BMI, somatic fat rate, SBP, and DBP were significantly
different. Similarly, the daily amount of PA and engaging in
hard labor significantly improved. However, no significant
differences were observed in lifestyle characteristics nor
psychosocial factors. The results of the groupwise analyses
between those who maintained or increased their step count andhttps://formative.jmir.org/2024/1/e53759

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those with a decreased step count during the intervention aresummarized in Table S3 in Multimedia Appendix 1.
Table 3 shows the correlations between changes in step count,health measurements, and lifestyle characteristics measured atbaseline and endline. A significant difference was observed
between weight and BMI (r=–0.41, P=.002 for both variables).
The correlations between the changes in step count andpsychosocial factors are shown in Table S4 in Multimedia
Appendix 1. Table 4 shows the results of the linear regression

analysis. The increment in step count was significantly
associated with weight, BMI, and SBP, even after adjusting forsex and age (Model 2: P=.02 for weight and BMI; P=.008 for
SBP), household income (Model 3: P=.04 for weight; P=.03
for BMI; P=.02 for SBP), and all covariates (Model 4: P=.02
for weight and BMI; P=.01 for SBP). The results of the linear
regression analysis to assess the association between changes
in average daily step count and psychosocial factors are
summarized in Table S5 in Multimedia Appendix 1.

Table 2. Differences in health measurements, lifestyle characteristics, and psychosocial factors between baseline and endline after the 3-monthintervention (n=62).
Objective variables

Baseline, mean (SD)

Endline, mean (SD)P value

Weight (kg), mean (SD)68.56 (16.97)

67.30 (16.86)<.001

BMI (kg/m2), mean (SD)24.82 (4.80)

24.35 (4.73)<.001

Somatic fat rate (%), mean (SD)28.50 (7.44)

26.58 (7.90).005

SBPa (mm Hg), mean (SD)

130.42 (17.92)

122.00 (15.06)<.001

DBPb (mm Hg), mean (SD)83.24 (13.27)

77.92 (11.71).002

Daily PAc (METd-hours per day), mean (SD)5.77 (3.81)

9.85 (7.84)<.001

Daily amount of engaging in hard labor (MET-hours per day),mean (SD)

1.31 (1.80)

3.59 (6.08)

.004
.17e

Alcohol consumption, n (%)
Yes

33 (53)24 (39)No

29 (47)37 (61)

Length of sleep (hours), mean (SD)6.04 (0.95)

6.09 (0.97).95

ESSIf, mean (SD)23.66 (5.37)

22.84 (5.65).27

0.90 (1.72)

0.75 (1.67).45

4.52 (5.00)

4.58 (5.34).89

Physical function (Locomo 5), mean (SD)g

Depression and anxiety (K6 ), mean (SD).46h

Psychological stress, n (%)

a

High

22 (35)

19 (32)Moderate

35 (57)31 (52)

Low

5 (8)

9 (15)

Not at all

0

1 (1)

Loneliness, mean (SD)41.97 (8.92)

42.61 (10.46).41

Self-efficacy, mean (SD)69.98 (11.47)

70.56 (13.33).38

Health related QOLi (EQ-5S), mean (SD)0.91 (0.10)

0.89 (0.15).33

SBP: systolic blood pressure.

b

DBP: diastolic blood pressure.

c

PA: physical activity.

d

MET: metabolic equivalent.

eChi-squared analysis (n=61 at endline).

f

ESSI: ENRICHD Social Support Instrument.

g

K6: Kessler Psychological Distress Scale.

hTrend test (n=60 at endline).

i

QOL: quality of life.

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Table 3. Pearson coefficients for the relationships between the change in average daily step count and the differences in physical measurements andlifestyle characteristics between baseline and endline (n=54).
Objective variablesa

Correlation coefficient

P value

Weight (kg)

–0.41

.002

BMI (kg/m2)

–0.41

.002

Somatic fat rate (%)b

–0.15

.31

SBPc (mm Hg)d

–0.26

.06

DBPe (mm Hg)d

–0.02

.90

Daily PAf (METg-hours per day)

–0.11

.45

Daily amount of engaging in hard labor (MET-hours per day)

–0.15

.27

Alcohol consumptiond

N/Ah,i.13

Length of sleep (hours)b

0.14

.31

a

The exact intervention period varied from participant to participant: n=11: 90 days; n=10: 92 days; n=5: 93 days; n=13: 94 days; n=6: 98 days; n=2:
99 days; n=7: 101 days.
b

n=51.

c

SBP: systolic blood pressure.

d

n=52.

e

DBP: diastolic blood pressure.

f

PA: physical activity.

g

MET: metabolic equivalents.

h

N/A: not applicable.

i

Jonckheere-Terpstrata trend test.

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Table 4. Linear regression analyses of the effect of the differences of physical measurements and lifestyle characteristics on the average daily step
count at baseline and endline after the 3-month intervention.
Objective and explanatory
variables

Model 1a (n=53)

Model 2b (n=53)

Model 3c (n=46)

Model 4d (n=46)

Regression coef- P value

Regression coef- P value

Regression coef- P value

Regression coef- P valueeeficient
Weight (kg)eficient.002
f

ficiente

ficient
.02

.04

.02

Change in step count–0.4006

N/A–0.4152.002–0.3399.02–0.4639

.004

Sex

—g

—

–0.1370.81–0.5151.38–0.7247

.22

Age

—

—

0.0176.51–0.0146.65–0.0061.85

Household income

—

—

—

—

0.0014.120.0014.12

Step count at baseline

—

—

—

—

—

—

–0.0002.09

BMI (kg/m2)

.002

.02

.03

.02

Change in step count–0.1467

N/A–0.1510.002–0.1253.02–0.1695

.005

Sex

—

—

–0.1294.53–0.2662.23–0.3409

.13

Age

—

—

0.0062.53–0.0061.62–0.0030.80

Household income

—

—

—

—

0.0005.100.0005.11

Step count at baseline

—

—

—

—

—

—

–0.0001.11

Somatic fat rate (%)h

.31

.45

.32

.35

Change in step count–0.2511

N/A–0.2006.420.0015.99–0.1399

.66

Sex

—

—

–0.1791.87–1.2000.32–1.4732

.24

Age

—

—

–0.0639.22–0.1247.07–0.1135.10

Household income

—

—

—

0.0013.48

0.0012.50

Step count at baseline

—

—

—

—

—

–0.0003.33

SBPi (mm Hg)j

.06

—
.008

.02

.01

Change in step count–1.9820

N/A–1.8190.07–2.0350.09–2.9610

.03

Sex

—

—

11.7100.01

13.7358.01

11.9093

.02

Age

—

—

–0.3203.13–0.0904.75–0.0361.90

Household income

—

—

—

—

–0.0904.12–0.0122.12

Step count at baseline

—

—

—

—

—

—

–0.0018.13

DBPk (mm Hg)j

.91

.33

.42

.57

Change in step count–0.1019

N/A–0.2195.79–0.3749.68–0.2971

.78

Sex

—

—

6.7060.07

7.6480.06

7.8010

.07

Age

—

—

0.0461.79

0.0471.83

0.0426.85

Household income

—

—

—

—

0.0008.890.0008.90

Step count at baseline

—

—

—

—

—

—

0.0002.87

Daily PAl (METm-hours per day)

.45

.21

–0.3979

N/A–0.1993.69–0.3899.51–0.0133

.98

Sex

—

—

–4.1360.07–3.1282.22–2.4910

.34

Age

—

—

–0.1678.12–0.0524.71–0.0784.58

Household income

—

—

N/A

—

–0.0049.21–0.0048.22

Step count at baseline

—

—

—

—

—

—

0.0007.24https://formative.jmir.org/2024/1/e53759

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Change in step count

Daily amount of engaging in hard labor(MET-hours per day)

XSL• FO

.09

.28

.02

.10

.17

JMIR Form Res 2024 | vol. 8 | e53759 | p. 9
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JMIR FORMATIVE RESEARCH
Objective and explanatory
variables

Okamoto et al

Model 1a (n=53)

Model 2b (n=53)

Model 3c (n=46)

Model 4d (n=46)

Regression coef- P value

Regression coef- P value

Regression coef- P value

Regression coef- P valueeeficient

ficient

ficiente

ficient

Change in step count–0.4305

N/A–0.2576.49–0.2591.56–0.1970

.70

Sex

—

—

–3.6871.03–3.3468.08–3.2419

.10

Age

—

—

–0.1460.07–0.0953.36–0.0996.35

Household income

—

—

—

—

–0.0028.34–0.0027.35

Step count at baseline

—

—

—

—

—

—

0.0001.80

Alcohol consumptionn

.30

.23

.36

.44

Change in step count–0.0694

N/A–0.0621.35–0.0001.40–0.0851

.28

Sex

—

—

0.1476.43

0.1471.45

0.1162

.56

Age

—

—

–0.0139.12–0.0153.14–0.1394.19

Household income

—

—

—

—

0.0001.780.0001.74

Step count at baseline

—

—

—

—

—

—

<–0.0001.47

Length of sleep (hours)h

a

e

.31

.30

.17

.22

Change in step count0.0557

N/A

0.0460.41

0.1246.04

0.0995

.15

Sex

—

—

0.2990.12

0.0906.65

0.0658

.75

Age

—

—

–0.0053.57–0.0117.29–0.0104.35

Household income

—

—

—

—

–0.0001.73–0.0001.75

Step count at baseline

—

—

—

—

—

—

<–0.0001.43

Simple regression analysis.

bMultiregression analyses adjusted for sex and age.

cMultiregression analyses adjusted for sex, age, and household income.

dMultiregression analyses adjusted for all covariates (sex, age, household income, and step count at baseline).

eNumber of 1000-step changes in each objective variable.

f

N/A: not applicable.

g

Not included in the model.

hModel 1: n=51; model 2: n=51; model 3: n=44; model 4: n=44.

i

SBP: systolic blood pressure.

jModel 1: n=52; model 2: n=52; model 3: n=45; model 4: n=45.

k

DBP: diastolic blood pressure.

l

PA: physical activity.

m

MET: metabolic equivalent.

nDichotomous variable: drinking alcohol at both baseline and endline or drinking only at baseline but not at endline.

Discussion
Principal Findings
This study demonstrated the feasibility, as indicated by theretention rate, of digital peer support. The finding that 98% of
the participants continued to use the smartphone app even after
the 3-month intervention shows that the retention rate in thisstudy was higher than that of any other smartphone app equippedwith the function of group interaction for promoting PA. A
meta-analysis reported that engagement was generally low,while the retention rate of online social networks varies
depending on the number of participants and the duration of theintervention [41]. A systematic review that examined the impact
of social networks reported retention rates ranging from 60%
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to 80% [42]. An RCT assessing the efficacy of a smartphone
app that facilitates interaction among users regarding walking
over a 100-day intervention period reported that retention rates
in the 60th percentile were higher than those in previous studies[16]. In a similar study evaluating a single-arm, 6-monthintervention targeted at university students, in which userinteraction was one of the components, the retention rate was
82% [21]. Considering these figures, digital peer support was
effective at maintaining a high retention rate and can, therefore,
be a promising tool for affordable and accessible healthpromotion. A reason may be that daily interaction, including
social support and competition among participants and mutualcomparison, serves as a positive factor for the prolonged use ofa smartphone app, as shown in previous studies [18]. Anotherreason may be that the intervention period of this study was
JMIR Form Res 2024 | vol. 8 | e53759 | p. 10(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
relatively short. Previous research has pointed out that theretention rate of smartphone apps generally remains high inshort-term interventions that last from a few weeks to several
months but tends to decrease after approximately 6 months fromthe beginning of the intervention [3]. We have conducted an
RCT with a 6-month intervention period (UMIN000046936)
to assess the effectiveness of digital peer support, including its
long-term effects.
This study also showed that digital peer support had the potential
to increase PA. Participants' daily average step count (+1182
steps), subjective daily amount of PA (+4.08 METs), and daily
amount of engagement in hard labor (+2.28 METs) increased
during the intervention period compared with 1 week before
the intervention. Although step count was lower than those ina meta-analysis [4], which showed that an average 13-weekintervention increased the number of steps per day by 1850,
these results were consistent with those of other studies thatshowed increased objective PA [3,6]. The high retention ratein this study may have led to a significant increase in the amountof PA. These results indicate that digital peer support improved
PA and daily step count. Additionally, this study demonstrated
that the percentage of goal attainment was 53.5% (SD 26.2%).
Furthermore, the statistical correlation between the rate ofachieving daily step goals and the amount of increase in daily
step counts was significant (r=0.27, P=.05), which wasconsistent with a previous study that reported that
self-regulating, such as goal setting and self-monitoring, was afactor that influenced an increase in step count. Apart fromself-regulation, previous studies revealed that other behavior
change techniques, such as feedback, were also effective at
improving PA [43]. Therefore, future studies should examine
the impact of other behavior change techniques on digital peer
support.
This study also suggested that digital peer support had thepotential to improve health outcomes. Pre- and postintervention
comparisons showed reduced weight (–1.26 kg), BMI (–0.47
kg/m2), somatic fat rate (–1.92%), SBP (–8.42 mm Hg), and
DBP (–5.32 mm Hg). These results were consistent withmeta-analyses that examined the effectiveness on healthoutcomes of smartphone apps that engage individuals [44].
Weight, BMI, and SBP could have decreased owing to theincreased step count that resulted from smartphone app use.
Participants’ mean age in this study was 43.8 years, which was
similar to that in a previous study that found significant weightloss in participants with a mean age of 45 years or older [6].
However, it remains unclear in which age group digital peer
support is particularly effective for promoting health outcomes.
Changes in health outcomes are currently being investigated in
our ongoing RCT (UMIN000046936), which will also examine
which demographic backgrounds are likely to contribute toimproved health outcomes.

Limitations and Future Direction
This study demonstrated the possibility that digital peer support
played an important role in increasing step count and thereby
improved health outcomes with a high retention rate. However,https://formative.jmir.org/2024/1/e53759

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Okamoto et al
several challenges remain unaddressed. First, it was a single-arm
intervention. Since this study used a before-and-after
comparison, other factors that may have affected step count andhealth outcomes could not be excluded. Furthermore, theeffectiveness could not be verified in detail. Therefore, we are
currently conducting an RCT (UMIN000046936). Second, thedaily step count measured by smartphone apps may beunderestimated compared with their true value. Our findings
revealed a difference of approximately 18% in daily step countduring the week before the intervention between the smartphone
app (5811 steps) and accelerometer (7073 steps). This finding
was consistent with those of previous studies that found thatthe number of steps measured by smartphones was 12% to 20%
lower than the true value depending on where on the body thephone was being carried or due to intermittent bouts of activity[25,26]. Therefore, our daily step count results during theintervention should be interpreted based on these limitations.
Third, a previous study reported that the retention rate for apps
with social features is related to personal preferences. For
example, users’ intent to use smartphone apps and a positive
attitude toward technology, which are influenced by thesimplicity or user-friendliness of the apps, were identified as
positive factors for engagement [45,46]. The stage of behavioral
change also affected the continued use of apps [18]. As thisstudy did not collect information on personal preferences, future
research should examine the effects of personal traits on thecontinued use of digital peer support. Fourth, the study did not
elucidate participants’ specific backgrounds and lifestyles,
examples of which were age, BMI [16], and being lessphysically active [21]. Although there was a slight increase indaily step count among middle-aged or older individuals or
those who were overweight before the intervention, these were
not statistically significant. However, future research is required
to verify the groups or demographics more likely to increase
their step count using digital peer support. Fifth, the intervention
was conducted between the winter and spring seasons, and theimpact of seasonality on PA was not considered, while previous
research demonstrated PA level variations across the seasons,with a higher PA level in the summer compared with otherseasons [47]. The possibility of seasonal and environmental
effects should be considered when designing interventions infuture research. Finally, the sample size in this study was 69
participants, which might have been underpowered to measurechanges in health outcomes. The sample size was not calculated
in this study because there were no prior studies examining theeffectiveness of digital peer support on health outcomes. Futurestudies are needed to assess the effectiveness of digital peer
support on both step count and health outcomes, so that effectsizes can be calculated appropriately.

Conclusion
This study demonstrates that digital peer support is feasible formaintaining high retention rates and can, therefore, effectively
promote PA. Digital peer support can be a promising tool froma clinical perspective for improving daily step count, subjective
PA, and health outcomes, such as weight, BMI, somatic fat rate,
and blood pressure.

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JMIR FORMATIVE RESEARCH

Okamoto et al

Acknowledgments
This study was conducted under the framework of collaborative research between Kanagawa University of Human Services andA 10 Lab. Participants were provided access to a premium version of Minchalle by A 10 Lab, which was worth ¥500 (US $3.42),
during the intervention period in return for their cooperation. We express our gratitude to A 10 Lab; Mr. Hidefumi Amagi;
Takashige Oroguchi, PhD; and Mr. Kazuhiko Katayama for their cooperation in the design and implementation of this study. We
would also like to express our appreciation for the involvement of Kanagawa Prefecture, Hadano City, Kamakura City, Yokosuka
City, Manazuru Town, and Oiso Town. Finally, we are grateful to Editage [48] for English editing.

Conflicts of Interest
MS was an employee of A10 Lab Inc. at the time of this research. GN was the CEO of A10 Lab Inc. at the time of this research.

Multimedia Appendix 1
Supplementary tables.
[DOCX File , 39 KB-Multimedia Appendix 1]

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Abbreviations
DBP: diastolic blood pressure
ESSI: ENRICHD Social Support Inventory
J-MICC: Japan Multi-Institutional Collaborative Cohort
METs: metabolic equivalents
PA: physical activity
RCT: randomized controlled trial
SBP: systolic blood pressure

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JMIR FORMATIVE RESEARCH

Okamoto et al

Edited by A Mavragani; submitted 18.10.23; peer-reviewed by S Sangameswaran; comments to author 13.03.24; revised version
received 15.04.24; accepted 25.07.24; published 24.09.24
Please cite as:
Okamoto M, Saito Y, Nakamura S, Nagasawa M, Shibuya M, Nagasaka G, Narimatsu H
Smartphone-Based Digital Peer Support for a Walking Intervention Among Public Officers in Kanagawa Prefecture: Single-Arm Preand Postintervention Evaluation
JMIR Form Res 2024;8:e53759
URL: https://formative.jmir.org/2024/1/e53759
doi: 10.2196/53759
PMID:©Masumi Okamoto, Yoshinobu Saito, Sho Nakamura, Makoto Nagasawa, Megumi Shibuya, Go Nagasaka, Hiroto Narimatsu.
Originally published in JMIR Formative Research (https://formative.jmir.org), 24.09.2024. This is an open-access article distributed
under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative
Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org,
as well as this copyright and license information must be included.

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