International Urology and Nephrology
https://doi.org/10.1007/s11255-021-02867-x

UROLOGY - ORIGINAL PAPER

Effect of a mobile digital intervention to enhance physical activity
in individuals with metabolic disorders on voiding patterns measured
by 24‑h voided volume monitoring system: Kumejima Digital Health
Project (KDHP)
Minoru Miyazato1 · Asuka Ashikari2 · Koshi Nakamura3 · Takehiro Nakamura4 · Kiyoto Yamashiro4 ·Tsugumi Uema4 · Moriyuki Uehara4 · Hiroaki Masuzaki4 · Seiichi Saito2 · Shiro Maeda5,6 · Hajime Ishida7
Masayuki Matsushita8·

Received: 8 February 2021 / Accepted: 17 April 2021
© The Author(s) 2021

Abstract
Purpose To evaluate the effect of a mobile digital intervention on voiding patterns, we performed 24-h voided volume
monitoring in individuals with metabolic disorders.
Methods Participants with metabolic disorders were grouped into either the intervention group (n = 17), who had access to asmartphone app (CARADA), or the non-intervention group (n = 11), who did not. Urine monitoring was conducted for 24 husing a novel digital self-health monitoring system for urine excretion (s-HMSU). Body weight, abdominal circumference,
blood pressure, and biomarkers were measured.
Results Physical findings and blood test results at baseline and 6 months indicated no significant between-group differences.
Night-time frequency did not change between baseline and 6 months in the intervention group but significantly worsened at
6 months in the non-intervention group, as compared to baseline (1.0 ± 0.7 vs. 1.5 ± 0.5, p < 0.05). The change in night-timefrequency over 6 months did not differ between the intervention and non-intervention groups. Furthermore, the change inhours of undisturbed sleep over 6 months did not differ between the two groups. However, compared with baseline, nocturnal
polyuria index tended to worsen at 6 months in the non-intervention group.
Conclusion Our study results suggest that mobile digital intervention might be useful for behavioral therapy to improvenight-time frequency and urine production and that s-HMSU might be beneficial for confirming the prevention of progressin individuals with metabolic disorders, which can aid in modifying lifestyle.
Keywords Lifestyle · Metabolic diseases · Nocturia · Polyuria · Voided volume* Minoru Miyazato
miyaz929@med.u-ryukyu.ac.jp
1

Department of Systems Physiology, Graduate Schoolof Medicine, University of the Ryukyus, 207 Uehara,
Nishihara, Okinawa 903‑0215, Japan2

Department of Urology, Graduate School of Medicine,
University of the Ryukyus, Okinawa, Japan3

Department of Public Health and Hygiene, Graduate Schoolof Medicine, University of the Ryukyus, Okinawa, Japan

4

Division of Endocrinology, Diabetes and Metabolism,
Hematology, Rheumatology (Second Department of Internal
Medicine), Graduate School of Medicine, Universityof the Ryukyus, Okinawa, Japan

5

Department of Advanced Genomic and Laboratory Medicine,
Graduate School of Medicine, University of the Ryukyus,
Okinawa, Japan

6

Division of Clinical Laboratory and Blood Transfusion,
University of the Ryukyus Hospital, Okinawa, Japan

7

Department of Human Biology and Anatomy, Graduate
School of Medicine, University of the Ryukyus, Okinawa,
Japan

8

Department of Molecular and Cellular Physiology, Graduate
School of Medicine, University of the Ryukyus, Okinawa,
Japan

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International Urology and Nephrology

Introduction
Voiding behavior is a daily activity; therefore, voiding dysfunction significantly deteriorates health-related quality oflife [1]. Nocturia, in particular, disturbs sleep, increases therisk of falls and hip fractures at night, and affects mortality rate [2]. The etiology of nocturia is multifactorial, andnocturia has been reported to be closely related to metabolic
syndrome [3, 4]. These facts are mostly based on various
community-based epidemiological studies [2–4]. Nonetheless, whether nocturia can be a surrogate marker of metabolic syndrome remains unknown. To better understand theassociation between nocturia and metabolic syndrome, alongitudinal study or an intervention study conducted on
individuals with nocturia is necessary. However, assessmentof nocturia is difficult due to its multifactorial etiology.
Nocturia originates from overproduction of urine,
reduced bladder capacity, sleep disorders, or a combinationof these factors; the former is reported to be the main cause,
at approximately 80% of cases [5, 6]. Frequency-volume
charts are mandatory for the diagnosis of the underlying
causes of nocturia. Traditionally, paper-based frequencyvolume charts for 1–7 days have been used; however, analogue charts are time-consuming to complete, resulting ina low adherence rate [7]. The digital self-health monitoring
system to measure the volume of voided urine (s-HMSU)
for home use was originally developed as an alternative topaper-based frequency-volume charts. We previously validated the measured voided volume of the s-HMSU systemand confirmed its high reliability in the Kumejima Digital
Health Project (KDHP) [8]. Automatically collected data
include night-time frequency, maximal bladder capacity,and hours of undisturbed sleep (HUS) [9]. These can besurrogate markers of metabolic syndrome. Therefore, in thepresent study, to confirm the feasibility of a mobile digitalintervention and to look for a surrogate marker of metabolic
syndrome, we performed 24-h voided volume monitoring inindividuals with metabolic disorders.

Methods
Study design and participantsThis interventional study was conducted as a part of the
primary intervention study of the KDHP between June2018 and December 2019. The primary intervention study
aimed to investigate whether digital health approaches
using a smartphone app (CARADA, MTI Ltd., Tokyo,
Japan) with feedback for 15 months improved obesity andmetabolic disorders by reinforcing lifestyle modification.

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Participants aged ≥ 20 years, with obesity and/or metabolic disorders, were identified based on a communitybased health checkup in 2017, the year before the primaryintervention of this study. A total of 456 candidates werelisted in descending order of body mass index (BMI), werein advance randomly assigned to either the intervention
group or the non-intervention group, and were invited toparticipate in this study. Furthermore, an additional 248 candidates who met the inclusion criteria were recruited at a
community-based health checkup in 2018 and were groupedsimilarly. Of the 197 candidates (110 in the intervention
group and 87 in the non-intervention group), 128 voluntarily participated in this intervention study after providing
written informed consent. Of the 73 and 55 participants in
the intervention and non-intervention groups, 53 and 44,
respectively, completed 15-month observation (Fig. 1). The
inclusion criteria were as follows: (1) obesity, defined as
BMI ≥ 25 kg/m2 [10]; (2) metabolic syndrome, defined asabdominal circumference ≥ 85 cm for men and ≥ 90 cm for
women, accompanied by any two of three disorders (highblood glucose, dyslipidemia, and high blood pressure)[11]; (3) pre-diabetic, defined as fasting blood glucose
110–125 mg/dL, occasional blood glucose 140–199 mg/
dL, or blood glucose 140–199 mg/dL after 2 h on 75 goral glucose tolerance test [12]; and (4) insulin resistance,
defined as fasting insulin ≥ 15 µU/mL or index of homeostasis model assessment of insulin resistance ≥ 2.5 [13]. For
the three components of metabolic syndrome, high blood
glucose was defined as fasting blood glucose ≥ 100 mg/dL
or use of medication for diabetes, dyslipidemia as serum
triglyceride ≥ 150 mg/dL or high-density lipoprotein cholesterol < 40 mg/dL, and high blood pressure as systolicblood pressure (SBP) ≥ 130 mmHg, diastolic blood pressure(DBP) ≥ 85 mmHg, or use of medication for hypertension[11].
Of the 53 and 44 participants in the intervention and nonintervention groups, 17 and 11, respectively, were involvedin the present study of additional data collection on urination. Finally, 21 participants (12 in the intervention group
and 9 in the non-intervention group) who provided complete
urination data at baseline and 6 months were included in
the analysis. The study protocol complied with the Declaration of Helsinki and was approved by the Ethics Committee of the University of the Ryukyus for Medical and
Health Research Involving Human Subjects (#1170). All
participants provided written informed consent. This study
was registered in a public trial registry in Japan (UMIN ID:
000032453; https://​www.​umin.​ac.​jp/).

Intervention
The intervention group had access to a smartphone app(CARADA), on which they could record walk counts,

International Urology and Nephrology

Fig. 1  Flow of participants using 24-h voided volume monitoring. a Intervention group and b non-intervention group. BMI, Body Mass Index

weight, diet, and sleep automatically or by themselves.
Using the self-monitoring data collected by the app, wecreated a new program to generate personalized healthadvice for the participants and delivered them regularly
via email only in the intervention group. The intervention

group could view their data on the app and weekly healthmessages on the smartphone at any time, while the nonintervention group could only view their biochemical test
results.

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International Urology and Nephrology

Urinary tract symptoms questionnaire
Participants completed a baseline questionnaire including
lower urinary tract symptoms (Core Lower Urinary Tract
Symptom Score questionnaire [CLSS]) and the Quick Inventory Depressive Symptomatology-Japanese (QIDS-J) [14].
Nocturia was assessed as the overall night-time frequency
based on Q2 in CLSS, while storage symptom was assessed
as urgency (Q3). Mid-nocturnal insomnia was also basedon Q2 in QIDS-J.

24‑h voided volume monitoring system
To measure 24-h voided volume, we used the s-HMSU
system (Symax Inc., Tokyo, Japan). The s-HMSU system
includes a server, a measurement sensor (Fig. 2), and a user
terminal. The sensor is installed on the toilet itself and calculates the volume of urine excreted using a fluid model,
including the shape of the toilet bowl and the known volume
and temperature of the water in the bowl. We have previously validated the measured voided volume of the s-HMSU

system and confirmed reliable and high resolutions (intraclass correlation 0.972) [8]. Each participant could be recognized by an identification card, and the voiding volume
of excreted urine for each participant was automaticallyrecorded. We equipped the home toilets of individuals withthe s-HMSU. Calibration was performed by calculating thecoefficient, which depends on the shape of each toilet bowl
and the known volume and temperature of the water in thebowl. The recorded data also included the start and end
times of voiding.
Automatically collected data included daytime urinary
frequency, night-time frequency, daytime urine volume(mL), nocturnal urine volume (mL), maximal bladder capacity (mL), and HUS (min). HUS was defined as the time fromfalling asleep to first awakening to void [9]. Nocturnal polyuria index (%) was calculated as (nocturnal urine volume(mL)/ daytime urine volume (mL) + nocturnal urine volume(mL)] × 100.

24‑h voided volume monitoring schedule
The participants in both the intervention and non-intervention groups continued with their usual care. Due to time
to examine each home toilet type and to preparation, 24-h
voided volume monitoring was started 2–12 months after
starting the application in the intervention group (Table 1).
Each participant was monitored for 24 h at baseline and at
3, 6, 9, and 12 months. The data regarding time the participant woke or went to the bed were collected by the app in
the intervention group; these data were self-recorded in the
non-intervention group. On the monitoring day, daily water
intake was also recorded in both groups.

Additional data collection
Fasting blood samples were obtained, and glucose, hemoglobin A1c (HbA1c), serum creatinine, triglyceride, andtotal cholesterol levels were measured using standard procedures. Blood pressure was measured in the sitting position
once on the right arm using a standardized device. Height,
weight, and abdominal circumference were measured, and
BMI was calculated as weight in kilograms divided by thesquare of height in meters (kg/m2). These variables were
examined every 3 months for 15 months. History of education was evaluated by a self-administered questionnaire.

Outcomes

Fig. 2  The s-HMSU system includes a server, measurement device,
and a user terminal. The sensor is located in the water in the toilet
bowl (black arrow). s-HMSU, self-health monitoring system for urine
excretion

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The primary outcome was a change in night-time frequency
for 6 months. A decrease in night-time frequency indicatedan improvement in nocturia. The secondary outcome waschange in HUS, nocturnal polyuria index, or maximal bladder capacity for 6 months. An increase in HUS and maximal

International Urology and Nephrology
Table 1  Baseline characteristics (n = 28)
Characteristics

Non-intervention (N = 11)

Intervention (N = 17)

p valueAge (mean years, SD)
Gender (male:female)
BMI (SD)
Night-time frequency (cm, SD)aUrgency (median, interquartile range)a
Education, no (%)
High school graduate or lessAbove high school graduate
Abdominal circumference (cm, SD)
SBP (mmHg, SD)
DBP (mmHg, SD)
Creatinine (mg/dL, SD)
Fasting glucose (mg/dL, SD)
HbA1c (%, SD)
Triglyceride (mg/dL, median, interquartile range
Total cholesterol (mg/dL, SD)
Time at starting 24-h urine volume monitoring from intervention (month, median, interquartile range)

55.8 (12.8)
8:3
27.8 (3.0)
0.88 (0.78)
0 (0–1)49.2 (12.8)
12:5
28.2 (3.9)
0.73 (0.65)
0 (0–1)

0.13
0.90
0.69
0.65
0.71

6 (54.5%)5 (45.5%)
96.8 (6.5)
139.9 (11.5)
86.5 (10.7)
0.75 (0.16)
89.3 (11.7)
5.8 (0.5)
129 (100–328)
219.3 (31.9)
6 (4–8)7 (41.2%)
10 (58.8%)
95.1 (9.8)
133.1 (11.8)
83.0 (11.0)
0.84 (0.20)
84.3 (7.1)
5.6 (0.3)
121 (88.5–189.5)
214.4 (28.4)
5 (4.5–9.5)

0.49
0.23
0.18
0.54
0.13
0.44
0.41
0.50
0.78
0.85

Analysis by Wilcoxon signed-rank test
BMI, Body Mass Index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1C, hemoglobin A1C; SD, standard deviationaBased on Core Lower Urinary Tract Symptom Score questionnaire

bladder capacity and a decrease in nocturnal polyuria index
indicated improvement of each parameter.

Statistical analysis
Data are expressed as mean ± standard deviation or median(interquartile range). Initially, differences between groups
were examined using a non-parametric Wilcoxon signedrank test or chi-square test. Next, we assessed whether
each parameter had changed significantly over 3–6 months
in the intervention and non-intervention groups using
paired t tests. Then, we compared the change in nighttime frequency over 6 months between the intervention
group and the non-intervention group. The flow of study
participants’ involvement (Fig. 1) no longer guaranteed
random assignment for the intervention group and thenon-intervention group. Therefore, given the confounding effects between these two groups, we compared thechange of interest, using an analysis of covariance thatincorporated the following variables as covariates: age,sex, baseline night-time frequency, time at starting 24-h
voided volume monitoring from enrollment, and education (high school graduate or less, and above high school
graduate). We have previously observed significant differences in maximum bladder capacity and HUS between
individuals with and without metabolic disorders in an
observational study of the KDHP (data not shown); thus,

the change in max bladder capacity (mL) and HUS (min)between the intervention group and the non-intervention
group at 6 months was also analyzed. The main analysis
was repeated after study participants were stratified by sex.
All statistical analyses were performed using JMP version
9.0 (SAS Institute Inc., Cary, NC, USA). All probability
values were two-tailed, and a p value of < 0.05 was considered statistically significant.

Results
Characteristics of the study participants
The intervention and non-intervention groups included 17
and 11 participants with obesity and/or metabolic disorders, respectively. The intervention and non-intervention
groups had mean night-time frequency of 0.88 and 0.73
(more than once 71.4% in total participants), mean ages of49.2 and 55.8 years, and a sex ratio (male: female) of 12:5
and 8:3, respectively. Median urgency was 0 in both groupsin CLSS. However, 11 out of 28 participants (39.3%) felt
urgency often or sometimes (score 1 or 2) (Table 1). Physical findings and blood test results at baseline indicated nosignificant differences between the groups.

13International Urology and Nephrology

Urinary endpoints
Primary outcome
Night-time frequency did not change in the intervention
group but significantly worsened at 6 months in the nonintervention group, as compared to baseline (1.0 ± 0.7 vs.
1.5 ± 0.5, p < 0.05; Table 2). The change in night-time frequency over 6 months did not differ between the intervention and non-intervention groups (p = 0.284; Fig. 3a). In
females, the change in night-time frequency over 6 months
was significantly larger (p = 0.049) in the intervention groupcompared with the non-intervention group. However, nighttime frequency did not show significant differences in maleparticipants between the two groups.
Secondary outcome
Compared with baseline, nocturnal polyuria index tended
to worsen at 6 months in the non-intervention group(p = 0.0946 and p = 0.0833, respectively). Mid-nocturnal
insomnia (Q2 in QIDS-J) did not change in either groupat 6 months (Table 2). The change in HUS over 6 months
did not differ between the intervention and non-intervention groups (p = 0.148) (Fig. 3b). The change in nocturnal

polyuria index, and maximal bladder capacity over 6 months
did not show any significant differences between the twogroups. Physical findings and blood test results indicated nosignificant changes in either group.

Discussion
The main cause of nocturia is overproduction of urine, whichreaches approximately 80% [5, 6]. Nocturia has a multifactorial etiology and is closely related to metabolic syndrome [3,4]. The primary aim of the present study was to evaluate theeffect of a mobile digital intervention on voiding patterns
measured by a 24-h voided volume monitoring system inindividuals with obesity and/or metabolic disorders. In thisstudy, night-time frequency did not change in the intervention group but significantly worsened at 6 months in the nonintervention group, as compared to baseline. Compared withbaseline, nocturnal polyuria index (i.e., nocturnal urine volume) tended to worsen at 6 months in the non-intervention
group. Hirayama et al. [15] reported that nocturnal urine
volume was closely related to diurnal leg edema, suggesting that fluid retention in the leg rostrally shifts after sleep,
thereby leading to the overproduction of urine at night [16].
Soda et al. [17] previously reported that behavior therapy

Table 2  Variables at baseline and 6 months in the intervention and non-intervention groups
Characteristics

Baseline

Six months

Non-intervention (N = 11) Intervention (N = 17) Non-intervention (N = 9) Intervention (N = 12)
BMI (SD)
Abdominal circumference (cm, SD)
SBP (mmHg, SD)
DBP (mmHg, SD)
Creatinine (mg/dL, SD)
Fasting glucose (mg/dL, SD)
HbA1c (%, SD)
Triglyceride (mg/dL, median, interquartile
range)
Total cholesterol (mg/dL, SD)
Daytime urinary frequency (SD)Night-time frequency (SD)
Daytime urinary volume (mL, SD)
Nocturnal urine volume (mL, SD)
Nocturnal polyuria index (%, SD)
Maximum bladder capacity (mL, SD)
Hours of undisturbed sleep (min, SD)
Water intake (mL, SD)
QIDS-J Q2 (median, interquartile range)

27.8 (3.0)
96.8 (6.5)
139.9 (11.5)
86.5 (10.7)
0.75 (0.16)
89.3 (11.7)
5.8 (0.5)
129 (100–328)

28.2 (3.9)
95.1 (9.8)
133.1 (11.8)
83.0 (11.0)
0.84 (0.20)
84.3 (7.1)
5.6 (0.3)
121 (88.5–189.5)

28.0 (3.5)
96.1 (5.9)
130.7 (17.1)
77.7 (13.9)
0.76 (0.15)
90.8 (13.5)
5.8 (0.5)
175 (105.5–264.5)

28.4 (4.0)
94.5 (10.8)
133.7 (17.5)
83.4 (14.7)
0.83 (0.21)
86.4 (6.9)
5.7 (0.3)
125 (71.5–166)

219.3 (31.9)
8.1 (4.3)
1.0 (0.7)590.0 (250.1)
211.7 (110.8)
30.0 (24.8)
201.0 (88.9)
258.1 (163.8)
1884.1 (659.3)
2 (0–3)214.4 (28.4)
5.6 (2.5)
1.4 (1.3)
585.5 (466.5)
226.5 (156.9)
28.6 (17.0)
228.3 (168.3)
213.2 (177.8)
1915.9 (959.5)
2 (0–2)202.3 (24.5)
7.0 (2.5)
1.5 (0.5)*
593.6 (300.3)
281.9 (154.3)
34.9 (21.4)
241.5 (81.7)
215.8 (155.3)
2512.2 (1193.6)
1 (0–2)217.8 (32.0)
6.6 (3.0)
1.2 (0.8)651.3 (310.2)
243.8 (118.0)
28.3 (11.2)
232.7 (93.7)
218.2 (148.8)
2203.1 (1047.6)
1 (0–2)Analysis by paired t tests; *p < 0.05 vs baseline
BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1C, hemoglobin A1C; SD, standard deviation, QIDS-J,
Quick Inventory Depressive Symptomatology-Japanese13International Urology and Nephrology

Fig. 3  The change in night-time frequency (a) and HUS (b) in theintervention and non-intervention groups at 6 months. Analysis ofcovariance was used after adjustment for age, sex, baseline night-timefrequency, time from enrollment to starting 24-h voided volume monitoring, and educationsuch as walking exercise could improve nocturia by reducing
the production of nocturnal urine volume. Therefore, mobile
digital intervention, including reminders to perform daily
exercise or avoid excessive alcohol and caffeine consumption, might be useful in behavioral therapy for nocturia. Nocturia and related symptoms such as nocturnal polyuria index
may be surrogate markers of metabolic syndrome. Nevertheless, the change in night-time frequency over 6 months after
adjustment for age, sex, baseline night-time frequency, timeat starting 24-h voided volume monitoring, and education
did not differ between the two groups (p = 0.284), which
may be attributable to the small number of participants. In
female participants, the change in night-time frequency over
6 months was larger in the intervention group compared with
non-intervention group. The reason behind this sex-specificdifference is unclear. Our future studies will aim to clarify
this difference along with the long-term effect of an intervention. To our knowledge, this is the first prospective clinical trial to confirm that nocturia could be a surrogate marker
of metabolic syndrome.
Nocturnal polyuria is unlikely to be the only component of nocturia. In a rat model of atherosclerosis-induced
chronic pelvic ischemia, bladder activity was upregulatedwith elevated oxidative stress markers (8-hydroxy-2′deoxyguanosine, malondialdehyde) and proinflammatory

cytokines. It has also been reported that intermittent hypoxia(8 weeks) in rats induced detrusor instability accompanied
by an increase in tissue malondialdehyde and oxidationrelated proteins [18]. Thus, metabolic syndrome associatedwith atherosclerosis, especially in the pelvic artery circulating the bladder, may cause reduced bladder capacity.
Indeed, 39.3% of our participants had storage symptoms
as urgency (Q3 in CLSS) with relatively reduced bladdercapacity (average 217.8 ml) in the present study. However,maximal bladder capacity did not change in the intervention group, suggesting due to a short intervention period of
only 6 months. HUS is considered to be another key factor
for nocturia-related quality of life [9]. In the present study,
the change in HUS at 6 months and mid-nocturnal insomnia(Q2 in QIDS-J) at 6 months also did not differ between theintervention and non-intervention groups, suggesting due toa complicated cause of sleep disorders. On the other hand, in
our recent study, tadalafil, a long-acting phosphodiesterase
type 5, improved nocturia, HUS, and sleep disorders (inadequate sleep at night and overall bother) in patients withbenign prostatic hyperplasia [19]. Overall, nocturia, which
has a wide pathogenesis, can be useful to detect physicalchanges before and after intervention.
The s-HMSU was developed to provide a reliable alternative to paper-based frequency-volume charts to measure
the volume of urine excretion. We have previously validated
the measured voided volume of the s-HMSU system using
the change in body weight and confirmed that this volume
measurement was reliable (intraclass correlation = 0.972,
coefficient of determination = 0.94) [8]. Automatically accumulated urine volume data provide several advantages, as
individuals do not require data input and rapid transfer, suchas by cloud, can make early clinical decision-making andfeedback more accessible. In addition, voiding is the mostbasic human behavior and therefore represents daily conditions. Thus, s-HMSU was reliable and feasible for homebased automated accumulation of frequency-volume charts.
In Japan, an increase in the incidence rate of metabolic
syndrome is associated with healthcare-related costs andmortality rate [20]. The KDHP aims to develop digital healthapproaches to improve the personalized care of individuals with metabolic syndrome and to evaluate the benefitsof individualized treatment relative to usual care. The goalof this project is to confirm the usefulness and reliability of
personalized health management systems by reinforcing lifestyle changes. In the present study, in addition to a feedback
advice system, a smartphone app (CARADA) was used tocollect data on activity, food or water intake, and sleep. The
feasibility of a mobile digital intervention depends on personal literacy [21, 22]. Therefore, we examined the change
in night-time frequency and HUS over 6 months using variables as covariates: age, sex, and education (high school graduate or less, and above high school graduate). The reliability

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International Urology and Nephrologyof this mobile digital intervention was validated, as nighttime frequency worsened in the non-intervention group over
6 months of 24-h voided volume monitoring using s-HMSU.
However, night-time frequency did not change in the intervention group over 6 months. Thus, long-term observation
might be needed to confirm the effect of an intervention. In
the present study, parameters related to metabolic syndrome
such as body weight, abdominal circumference, blood pressure, and HbA1c did not change in either group. Recently,
a 4-year cohort study involving 5234 Japanese individuals(1173 men and 4061 women; mean age, 55.6 years) had
revealed the association between nocturia and the incidenceof metabolic syndrome (odds ratio, 2.90) [23]. The resultsof our interventional study and this previous cohort study
indicate that nocturia could be a symptom of metabolic syndrome and may appear or be improved in the early phase of
metabolic syndrome onset or improvement. The autonomic
spinobulbospinal reflex pathway [24] and other visceralorgans regulate voiding; thus, voiding-related parameters
might be sensitive to physical changes. Further studies arenecessary to clarify this point. Now that we have constructeda feedback advice system including daily activity from the
KDHP data, the next step will be to create a novel advice
system that combines daily activity and voiding information.
This study has several limitations that should be acknowledged. First, the majority of participants could only begin touse 24-h voided volume monitoring halfway of total intervention period 15 months after it was fixed at each home
toilet. The mean follow-up period was 6.7 months, and thecomplete rate for 6 months was 72%. In addition, random
allocation for comparison was not guaranteed for the intervention and non-intervention groups, due to the selection
flow (Fig. 1), although the comparison was made after
allowing for age, sex, and education. Therefore, caution was
exercised when interpreting our results. Second, it was insufficient to perform 24-h monitoring for just one day every
3 months for a follow-up period of 6 months to clarify the
improvement of nocturia. Third, our study participants comprised middle-aged people (mean age 51.8 years) who had
relatively mild nocturia, as well as metabolic disorders. The
effect of our mobile digital intervention on nocturia may bedifferent from that we witnessed in this study, if those withmoderate-to-severe nocturia are targeted. Fourth, we did notevaluate the relationship between improvement in metabolic
disorders and change in night-time frequency according to
the personal literacy, such as the frequency of data entry.
Despite these limitations, we believe that a mobile digitalintervention in combination with a 24-h voided volume
monitoring system is useful to reinforce lifestyle modification in individuals with metabolic disorders and to improve
nocturia.

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Conclusions
The s-HMSU provides a reliable measurement of voiding
information for home use. Voiding information obtained
from this system, such as night-time frequency and nocturnal polyuria index, might be a surrogate marker of metabolic
syndrome, which can reinforce lifestyle changes in individuals with metabolic disorders.
Acknowledgements The authors thank Symax Inc. (Ms. Maria Tsuruoka CEO) for the provision of an s-HMSU system and technicalassistance. We also thank Ms. Maya Kodama, Ms. Miwa Taira, and
Ms. Shigeko Iizuka for their research assistance, as well as Ms. Junko
Hirata and other staff of Kumejima Town Hall for their assistance withparticipant recruitment. We would like to thank editage (http://​www.​edita​ge.​com) for English language editing.
Author contributions The contributions of each author are as follows:(1) substantial contributions to conception and design: MM, AA, KN,
TN, KY, TU, HM. (2) Drafting and revising the article critically forimportant intellectual content: MM, AA, KN, TN, KY, TU, HM, HI. (3)Final approval of the version to be published: MM, AA, KN, TN, KY,
TU, MU, HM, SS, SM, HI, and MAM. All contributors not meetingthese criteria for authorship should instead be listed in the acknowledgements section.
Funding This study was supported by a grant for regional development projects in the Okinawa remote islands, as part of the 2017–2020
Kumejima Digital Health Project (KDHP) and a grant from the Taiju
Life Social Welfare Foundation in 2019.
Availability of data and materials The datasets analyzed during thecurrent study are available from the corresponding author on reasonable request.
Code availability Not applicable.
Declaration
Conflict of interest The authors declare no conflict of interest.
Ethics approval All procedures performed in studies involving humanparticipants were in accordance with the ethical standards of the institutional committee (#1170) and with the 1964 Helsinki Declaration andits later amendments or comparable ethical standards.
Consent to participate Informed consent was obtained from all individual participants included in the study.
Consent for publication Not applicable.
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References
1. Asplund R, Marnetoft SU, Selander J, Akerstrom B (2005) Nocturia in relation to somatic health, mental health and pain in adultmen and women. BJU Int 95(6):816–819. https://​doi.​org/​10.​1111/j.​1464-​410X.​2005.​05407.x
2. Nakagawa H, Niu K, Hozawa A, Ikeda Y, Kaiho Y, Ohmori-Matsuda K, Nakaya N, Kuriyama S, Ebihara S, Nagatomi R, Tsuji I,
Arai Y (2010) Impact of nocturia on bone fracture and mortality in older individuals: a Japanese Longitudinal Cohort Study. J
Urology 184(4):1413–1418. https://​doi.​org/​10.​1016/j.​juro.​2010.​
05.​093
3. Miyazato M, Tana T, Higa A, Oshiro T, Namitome R, Nezu U,
Ueda S, Saito S (2014) Prevalence and risk factors for nocturia inan outpatient clinic. LUTS 6(3):167–171. https://d​ oi.o​ rg/1​ 0.1​ 111/​
Luts.​12039
4. Yoshimura K (2012) Correlates for nocturia: a review of epidemiological studies. Int J Urol 19(4):317–329. https://​doi.​org/​10.​1111/j.​1442-​2042.​2011.​02956.x
5. Weiss JP, Blaivas JG (2002) Nocturnal polyuria versus overactive
bladder in nocturia. Urology 60(Suppl 1):28–32 (discussion 32)
6. Bosch JL, Weiss JP (2010) The prevalence and causes of nocturia.
J Urol 184(2):440–446. https://d​ oi.o​ rg/1​ 0.1​ 016/j.j​ uro.2​ 010.0​ 4.0​ 11
7. Abrams P, Paty J, Martina R, Newgreen DT, van Maanen R,
Paireddy A, Kuipers-deGroot T, Ridder A (2016) Electronic bladder diaries of differing duration versus a paper diary for data collection in overactive bladder. Neurourol Urodyn 35(6):743–749.
https://​doi.​org/​10.​1002/​nau.​22800
8. Miyazato M, Yonemoto K, Ashikari A, Saito S, Yamashiro K,
Uehara M, Masuzaki H, Ishida H, Matsushita M (2019) Validationof a novel digital health monitoring system to measure the volume
of voided urine. Neurourol Urodyn 38(4):1106–1110. https://​doi.​
org/​10.​1002/​nau.​23965
9. Miyauchi Y, Okazoe H, Okujyo M, Inada F, Kakehi T, Kikuchi H,
Ichikawa H, Arakawa Y, Mori Y, Kakehi Y (2015) Effect of thecontinuous positive airway pressure on the nocturnal urine volume
or night-time frequency in patients with obstructive sleep apnea
syndrome. Urology 85(2):333–336. https://d​ oi.o​ rg/1​ 0.1​ 016/j.u​ rolo​gy.​2014.​11.​002
10. Examination Committee of Criteria for “Obesity Disease” in J,
Japan Society for the Study of O (2002) New criteria for “obesity
disease” in Japan. Circ J 66(11):987–992. https://d​ oi.o​ rg/1​ 0.1​ 253/​
circj.​66.​987
11. [Definition and the diagnostic standard for metabolic syndrome-Committee to Evaluate Diagnostic Standards for Metabolic Syndrome] (2005) Nihon Naika Gakkai zasshi J Jpn Soc Intern Med
94(4):794–809
12. Haneda M, Noda M, Origasa H, Noto H, Yabe D, Fujita Y, GotoA, Kondo T, Araki E (2018) Japanese clinical practice guideline
for diabetes 2016. Diabetol Int 9(1):1–45. https://d​ oi.o​ rg/1​ 0.1​ 007/​
s13340-​018-​0345-3
13. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher
DF, Turner RC (1985) Homeostasis model assessment: insulin

15.

16.
17.

18.

19.

20.

21.

22.

23.

24.

resistance and beta-cell function from fasting plasma glucose
and insulin concentrations in man. Diabetologia 28(7):412–419.
https://​doi.​org/​10.​1007/​BF002​80883
Tomita T, Yasui-Furukori N, Kudo S, Sugawara N, Fujii A, Tsuruga K, Sato Y, Ishioka M, Nakamura K (2018) The association
between the severity and level of understanding of depression
among patients treated with antidepressants: a survey of 424 outpatients in Japan. Clin Neuropharmacol 41(1):1–5. https://d​ oi.o​ rg/​
10.​1097/​WNF.​00000​00000​000257
Hirayama A, Torimoto K, Yamada A, Tanaka N, Fujimoto K,
Yoshida K, Hirao Y (2011) Relationship between nocturnal urine
volume, leg edema, and urinary antidiuretic hormone in older
men. Urology 77(6):1426–1431. https://​doi.​org/​10.​1016/j.​urolo​gy.​2010.​12.​030
Kasai T (2012) Sleep apnea and heart failure. J Cardiol 60(2):78–
85. https://​doi.​org/​10.​1016/j.​jjcc.​2012.​05.​013
Soda T, Masui K, Okuno H, Terai A, Ogawa O, Yoshimura K(2010) Efficacy of nondrug lifestyle measures for the treatmentof nocturia. J Urol 184(3):1000–1004. https://​doi.​org/​10.​1016/j.​juro.​2010.​05.​038
Witthaus MW, Nipa F, Yang JH, Li Y, Lerner LB, Azadzoi KM(2015) Bladder oxidative stress in sleep apnea contributes to detrusor instability and nocturia. J Urol 193(5):1692–1699. https://​doi.​org/​10.​1016/j.​juro.​2014.​11.​055
Takahashi R, Sumino Y, Miyazato M, Nishii H, Oshiro T, Mimata
H, Saito S, Yoshida M, Eto M (2020) Tadalafil improves nocturia
and nocturia-related quality of life in patients with benign prostatic hyperplasia (KYU-PRO Study). Urol Int 104(7–8):587–593.
https://​doi.​org/​10.​1159/​00050​6489
Kusunoki-Tsuji C, Araki SI, Kume S, Chin-Kanasaki M, Osawa
N, Morino K, Sekine O, Ugi S, Kashiwagi A, Maegawa H (2018)
Impact of obesity on annual medical expenditures and diabetes
care in Japanese patients with type 2 diabetes mellitus. J Diabetes
Investig 9(4):776–781. https://​doi.​org/​10.​1111/​jdi.​12766
Knox E, Glazebrook C, Randell T, Leighton P, Guo B, Greening
J, Davies EB, Amor L, Blake H (2019) SKIP (Supporting Kids
with diabetes In Physical activity): feasibility of a randomised
controlled trial of a digital intervention for 9–12 year olds with
type 1 diabetes mellitus. BMC Public Health 19(1):371. https://​doi.​org/​10.​1186/​s12889-​019-​6697-1
Knitza J, Simon D, Lambrecht A, Raab C, Tascilar K, Hagen M,
Kleyer A, Bayat S, Derungs A, Amft O, Schett G, Hueber AJ(2020) Mobile health usage, preferences, barriers, and ehealthliteracy in rheumatology: patient survey study. JMIR Mhealth
Uhealth 8(8):e19661. https://​doi.​org/​10.​2196/​19661
Aoki Y, Matsuta Y, Okada M, Matsumoto C, Kusaka Y, Yokoyama O (2013) Nocturia is associated with the incidence of metabolic syndrome: a four-year longitudinal study in Japanese men
and women. Neurourol Urodyn 32(6):901–902
de Groat WC, Griffiths D, Yoshimura N (2015) Neural control ofthe lower urinary tract. Compr Physiol 5(1):327–396. https://​doi.​
org/​10.​1002/​cphy.​c1300​56

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