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Nishizaki et al

Original Paper

Determining Optimal Intervals for In-Person Visits During
Video-Based Telemedicine Among Patients With Hypertension:
Cluster Randomized Controlled Trial
Yuji Nishizaki1,2, MD, MPH, PhD; Haruo Kuroki3, MD, PhD; So Ishii4, MD, PhD; Shigeyuki Ohtsu5, MD, PhD;
Chizuru Watanabe5, MD, PhD; Hiroto Nishizawa6,7,8,9, MD, PhD; Masashi Nagao2, MD, PhD; Masanori Nojima10,
MD, MPH, PhD; Ryo Watanabe11, MPH, PhD; Daisuke Sato12, PhD; Kensuke Sato13, BA; Yumi Kawata14, MD, PhD;
Hiroo Wada1,14, MD, PhD; Goichiro Toyoda15, MD; Katsumi Ohbayashi16, MD, PhD
1Division of Medical Education, Juntendo University School of Medicine, Tokyo, Japan

2

Medical Technology Innovation Center, Juntendo University, Tokyo, Japan

3

Sotobo Children's Clinic, Chiba, Japan

4

Kudan-Shita Eki-Mae CoCo Clinic, Tokyo, Japan

5

Nakanoshima Diabetes Clinic, Kanagawa, Japan

6

Akasaka Odayaka Clinic, Tokyo, Japan

7

Minamisunamachi Odayaka Clinic, Tokyo, Japan

8

Odayaka Life Medical Clinic, Saitama, Japan

9

Medical Corporation Junreikai, Tokyo, Japan

10

Center for Translational Research, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan

11

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

12

Center for Next Generation of Community Health, Chiba University Hospital, Chiba, Japan

13

Clinical Research and Trial Center, Juntendo University Hospital, Tokyo, Japan

14

Department of Public Health, Juntendo University Graduate School of Medicine, Tokyo, Japan

15

Medley, Inc, Tokyo, Japan

16

Ohbayashi Clinic, Tochigi, Japan

Corresponding Author:
Yuji Nishizaki, MD, MPH, PhD
Division of Medical Education
Juntendo University School of Medicine
Hongo Bunkyo-ku
Tokyo, 113-8421
Japan
Phone: 81 3 3813 3111
Fax: 81 3 5689 0627
Email: ynishiza@juntendo.ac.jp

Abstract
Background: Introducing telemedicine in outpatient treatment may improve patient satisfaction and convenience. However,
the optimal in-person visit interval for video-based telemedicine among patients with hypertension remains unreported in Japan.
Objective: We determined the optimal in-person visit interval for video-based telemedicine among patients with hypertension.
Methods: This was a cluster randomized controlled noninferiority trial. The target sites were 8 clinics in Japan that had atelemedicine system, and the target patients were individuals with essential hypertension. Among patients receiving video-based
telemedicine, those who underwent in-person visits at 6-month intervals were included in the intervention group, and those who
underwent in-person visits at 3-month intervals were included in the control group. The follow-up period of the participants was
6 months. The primary end point of the study was the change in systolic blood pressure, and the secondary end points were therate of treatment continuation after 6 months, patient satisfaction, health economic evaluation, and safety evaluation.

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Results: Overall, 64 patients were enrolled. Their mean age was 54.5 (SD 10.3) years, and 60.9% (39/64) of patients were male.
For the primary end point, the odds ratio for the estimated difference in the change in systolic blood pressure between the 2 groups
was 1.18 (90% CI –3.68 to 6.04). Notably, the criteria for noninferiority were met. Patient satisfaction was higher in the intervention
group than in the control group. Furthermore, the indirect costs indicated that lost productivity was significantly lesser in theintervention group than in the control group. Moreover, the treatment continuation rate did not differ between the intervention
and control groups, and there were no adverse events in either group.
Conclusions: Blood pressure control status and safety did not differ between the intervention and control groups. In-person
visits at 6-month intervals may cause a societal cost reduction and improve patient satisfaction during video-based telemedicine.
Trial Registration: UMIN Clinical Trials Registry (UMIN-CTR) UMIN000040953; https://tinyurl.com/2p8devm9(JMIR Cardio 2023;7:e45230) doi: 10.2196/45230
KEYWORDS
hypertension; Japan; lost productivity time; patient satisfaction; telemedicine

Introduction
The introduction of telemedicine in outpatient treatment may
improve patient satisfaction and convenience [1,2]. Moreover,it can overcome several challenges related to in-person visits
in outpatient care. One of the challenges in treating
lifestyle-related diseases in outpatient clinics is treatment
dropout [3]. A widespread use of telemedicine can help prevent
such treatment dropouts among working people. Notably, the
COVID-19 pandemic has caused many interruptions in medicalcare. For example, in a survey of 30,000 Japanese workers, thetreatment of 11% of the patients requiring regular hospital visits
was interrupted during the pandemic [4]. Under thesecircumstances, the wide use of telemedicine is expected toprevent the interruption of medical visits owing to the pandemic.
Many studies in the relevant literature have reported theeffectiveness of telemedicine [5-9]. In Japan, a previous study
involving a 1-year follow-up of patients with hypertension
randomized into 2 groups (standard care or telemedicine)revealed that the mean weekly systolic blood pressure at theend of the study was significantly lower in the telemedicinegroup [10]. However, telemedicine has not been widely adopted
in Japan, partly because of the national medical fee system.
Moreover, in Japan, reimbursement for telemedicine is <50%of the reimbursement for normal in-person visits [11].
Considering that the standard reimbursement for telemedicine
is equal to or greater than the reimbursement for in-person visitsworldwide, lesser reimbursement in the Japanese fee system isa major challenge. Another challenge is the interval between
in-person visits. Until March 2022, in-person visits wererequired once every 3 months for insured patients who wereusing telemedicine in Japan [11]. This short interval between
in-person visits may be a barrier to the widespread use oftelemedicine in Japan. Since April 2022, the restrictions on
in-person visit intervals in telemedicine have been relaxed, andthe obligation of conducting such visits every 3 months has
been abolished; in addition, there are no longer any restrictions
on in-person visit intervals. However, this change was not basedon evidence-based medicine but was largely attributed to the
COVID-19 pandemic.
To our knowledge, there are no studies in Japan on the optimal
in-person visit interval for telemedicine in patients withhttps://cardio.jmir.org/2023/1/e45230

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hypertension based on a multifaceted evaluation, including
health economics and patient satisfaction assessments.
Therefore, this study’s aim was to generate evidence regarding
the optimal in-person visit intervals for patients with essential
hypertension during the video-based telemedicine.

Methods
Study Design
This was a cluster randomized controlled trial conducted as anoninferiority trial.

Site Selection
A total of 8 clinics in Japan with a video-based telemedicine
system at study initiation were included in this study. Notably,a clinic was defined as a place where physicians practiced
medicine for the public, or a specified number of people who
did not have facilities for admitting patients or had facilities for
admitting ≤19 patients.

Study Population
Study participants were included in each clinic. The inclusion
criteria were as follows: adult patients receiving outpatient
telemedicine or who were about to start receiving outpatient
telemedicine; those diagnosed with essential hypertension andprescribed with antihypertensive medication for ≥3 months;
those with stable hypertension (ie, no change in theantihypertensive medication prescription for over 3 months)
and stable comorbidities; those who could visit outpatient
departments in the third and sixth months after enrollment; andthose who provided their free written consent to participate in
the study after receiving a full explanation of the study
requirements. The exclusion criteria were as follows: patientswith drug allergies; patients who were pregnant; patients withvisual impairment or other problems that could interfere withtelemedicine; patients with end-stage renal failure; patients withcancer who were receiving anticancer drug therapy; patientswith chronic respiratory diseases, such as obstructive lungdisease, who were receiving home oxygen therapy; patients
participating in other clinical trials; patients who required
frequent visits to the hospital for blood tests to manage
comorbidities; and patients whose participation was deemed
medically or scientifically inappropriate by the principal
investigator and coinvestigators.
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During patient recruitment, the study purpose and details wereexplained using an informed consent form. Patients who
provided informed consent were enrolled. Moreover, we ensured
that patients could withdraw their consent even afterparticipating in the research.

ProceduresAmong patients receiving video-based telemedicine, those who
underwent in-person visits at 6-month intervals were includedin the intervention group, and those who underwent in-person
visits at 3-month intervals were included in the control group.
Stratified cluster randomization was performed using clinic
location (23 wards of Tokyo [urban] vs outside the 23 wards ofTokyo [suburban]) and the target number of cases as allocation
factors.
Prescriptions for antihypertensive medications could be changed
during the study period as needed at the discretion of thephysician in charge. Moreover, medication status was assessed
through self-report at the time of enrollment and at follow-up
in both groups.

Patient Enrollment and Follow-up Period
Physicians recruited the patients during their first in-person
visits. After recruitment by a physician, each patient spoke witha clinical research assistant who used an explanatory document
to outline the study to the patient. The originally planned patient
enrollment period was 3 months, but the COVID-19 pandemic
led to some delays; hence, the patient enrollment period was
extended to 8 months. The registration period for the first halfgroup (3 clinics) was from May 29, 2020, to January 31, 2021,
and that for the second half group (5 clinics) was from July 31,
2020, to March 31, 2021. Notably, the follow-up period for bothgroups was 6 months. Follow-up was planned for the 3rd and6th months after patient enrollment.

Blood Pressure Measurement
Blood pressure was measured using an upper-arm digital
automated sphygmomanometer (HEM-8712) from Omron inboth intervention and control groups. Two blood pressure
measurements were taken at rest in a sitting position for eachpatient. The interval between the first and second measurementswas ≥2 minutes, and the average of the 2 measured values wasused for this study. During the remote examination, the patientreported the blood pressure measurements taken on the day ofthe examination through video, and the doctor confirmed theblood pressure values visually.

End Points
The primary end point of the study was the change in systolicblood pressure (6-month value – baseline value). Secondary
end points were the treatment continuation rate at 6 months,
patient satisfaction ratings (Ministry of Health, Labor, andWelfare [MHLW] survey for behavior at the outpatient visit[Question 15; Multimedia Appendix 1], and EuroQol 5
Dimensions 5 Level [EQ-5D-5L]), a health economic evaluation,
and a safety evaluation (adverse events). Furthermore, we
examined whether the patient attended in-person visits for
hypertension management outside the scheduled timing.

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Nishizaki et al

Health Economics Evaluation Questionnaire
Although the health care costs are expected to be lower for
telemedicine than for normal in-person visits, their clinicaloutcomes may be comparable. Therefore, we performed a
cost-minimization analysis of telemedicine (intervention group)
versus normal in-person visits (control group). The analysis
was conducted from the perspective of public health care basedon the Guidelines for Analysis of Cost-Effectiveness Evaluationin the Central Social Insurance Medical Council, 2nd edition[12]. Given that the use of telemedicine may directly affectpatient productivity, an additional analysis was performed toinclude productivity loss in terms of cost using a patientquestionnaire (Multimedia Appendices 2 and 3), which
comprised questions regarding employment status, occupation,
and annual income.

Patients’ Backgrounds
At patient enrollment, in addition to blood pressure, thefollowing data were collected: sex, age, height, weight, pulse
rate, the presence of dyslipidemia, the presence of diabetes,smoking and alcohol consumption status, family history(hypertension in parents), and use of antihypertensive
medications. Patient satisfaction and health economic evaluationquestionnaires were administered at enrollment and at 3- and6-month follow-up examinations.

Sample Size Calculation
The target sample size of the study was 70. If the SD of bloodpressure after the antihypertensive medication is considered 7.9mm Hg based on the report by Chow et al [13], the SD of thechange in systolic blood pressure is 7.9 mm Hg when thecorrelation coefficient between the pre- and postvalues is set at0.5. Based on this assumption, when the noninferiority margin
was set at –5.0 mm Hg, the required sample size was determined
to be 64 cases (1-sided α=.05, power 80%). The choice of –5.0mm Hg as the margin for our work was based on a discussion
by 3 internal medicine specialists. The sample size of the t test
was used for the above calculations because the assumption of
0 for the intracluster correlation is equivalent to that for the ttest. The dropout rate was set at 10% (3 patients each in theintervention and control groups), and the target total number ofpatients was set at 70. If the correlation coefficient between the
pre- and postvalues was >0.5, the SD reduced, thereby reducing
the required sample size.

Statistical Analysis
Using the baseline and 6-month data, we used the time point,
allocation group, and their interaction terms as the explanatory
variables in a linear mixed model, whereas the systolic bloodpressure (ie, the mean of two measurements) was used as the
response variable. In this model, the change from the baseline
value (ie, the primary end point) was estimated as the effect of
the interaction term. Further, the covariates sex, age, andbaseline blood pressure values were estimated as fixed effects,
and the interaction term between the baseline blood pressure
values and time points, allocation factors, and cluster were
estimated as random effects. Kenward–Roger method was used
to estimate the degrees of freedom. Further, variancecomponents were specified for the variance–covariance structure
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of the random effects. First, the correlation structure of time
points among participants was specified as unstructured, but
because the model did not converge, it was specified as
first-order autoregressive (1) [14]. A P value of <.05 wasconsidered statistically significant. All statistical analyses apartfrom the health economic evaluation were performed using SAS(version 9.4; SAS Institute).

Data Management
An electronic data capturing system, REDCap (ResearchElectronic Data Capture), was used for data management [15].

Ethical Approval
This study was approved by the Ethics Committee of Juntendo
University Hospital (reception 20-038) and was conducted inaccordance with the principles of the Declaration of Helsinki.
The study was registered in the University Hospital Medical
Information Network (UMIN) Clinical Trials Registry system[UMIN Clinical Trials Registry (UMIN-CTR)]. The UMIN
study ID was UMIN000040953.
This study enrolled patients who fully understood the study
purpose, signed a written consent form, and were willing tohttps://cardio.jmir.org/2023/1/e45230

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Nishizaki et al
participate. Patient anonymity is maintained in this paper,
including in the text, tables, and figures.

Results
Overview
No clinic participating in this clinical trial had the capacity toadmit more than 20 patients. Patients in the intervention group
visited 5 clinics, including 2 and 3 in urban and suburban areas,
respectively. Patients in the control group visited the other 3
clinics, including 1 and 2 in urban and suburban areas,
respectively. Overall, 31 and 33 patients were included in theintervention and control groups, respectively. However, 1 patientin the control group dropped out of the study before the 3-monthvisit after withdrawing consent. The mean age of participants
was 54.5 (SD 10.3) years, and 60.9% (39/64) of the participants
were male. Patient characteristics, including diabetes,
dyslipidemia, smoking, alcohol drinking, and family history of
hypertension, did not significantly differ between the 2 groups.
However, the use of antihypertensive medication was
significantly different between these groups. Table 1 summarizes
the patient characteristics. Trends in blood pressure in bothgroups are shown in Table 2.

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Table 1. Patient characteristics.
Characteristics

All patients(n=64)

Telemedicine practice group In-person visit group(intervention group) (n=31) (control group) (n=33)

P valueAge (years), mean (SD)54.5 (10.3)

51.5 (7.4)

57.2 (11.9).02aMale, n (%)

39 (60.9)

17 (54.8)

22 (66.7).44

Height (cm), mean (SD)

165.4 (9.2)

165.2 (9.4)

165.7 (9.1).79Weight (kg), mean (SD)

71.0 (17.0)

71.6 (18.6)

70.5 (15.6).74

Pulse rate per minute (bpm), mean (SD)73.3 (11.9)

73.8 (11.2)

72.8 (12.7).74

Diabetes, n (%)

4 (6.3)2 (6.5)2 (6.1)>.99

Dyslipidemia, n (%)

25 (39.1)

12 (38.7)

13 (39.4)>.99

Smoking (current smoker), n (%)

10 (15.6)

3 (9.7)7 (21.2).31

Alcohol drinking, n (%)

41 (64.1)

21 (67.7)

20 (60.6).77

Family history of hypertension, n (%)

26 (40.6)

16 (51.6)

10 (30.3).19

Drug adherence (complied with physicians’ instructions), n (%) 61 (95.3)

29 (93.5)

32 (97).60

Antihypertensive medication, n (%)

a

ACE-Ib2 (3.1)0 (0)2 (6.1).49

ARBc

25 (39.1)

15 (48.4)

10 (30.3)

.20

CCBd

35 (54.7)

8 (25.8)

27 (81.8)<.001aβ-blocker

4 (6.3)2 (6.5)2 (6.1)>.99

α-blocker

0 (0)

0 (0)

0 (0)

>.99

Diuretics

5 (7.8)5 (16.1)

0 (0)

.02a

Combination drug

16 (25)

13 (41.9)

3 (9.1).003a

ARB+CCB

15 (23.4)

12 (38.7)

3 (9.1).007a

ARB+diuretics

1 (1.6)

1 (3.2)

0 (0)

.48

P<.05.

b

ACE-I: angiotensin-converting enzyme inhibitor.

c

ARB: angiotensin II receptor blocker.

d

CCB: calcium-channel blocker.

Table 2. Trends in blood pressure.
All patients (n=64),
mean (SD)

Telemedicine practice group (Inter- In-person visit group (Control P value
vention group) (n=31), mean (SD) group) (n=33), mean (SD)132.6 (14.3)

128.8 (13.1)

136.3 (14.7).03a87.3 (9.3)

85.3 (9.6)

89.2 (8.8).09

Systolic blood pressure

129.8 (13.7)

125.4 (10.8)

134.1 (15.0).01a

Diastolic blood pressure

86.0 (8.9)

82.2 (6.6)

89.7 (9.4)<.001aSystolic blood pressure

133.7 (12.1)

130.9 (10.8)

136.4 (12.9).07

Diastolic blood pressure

87.4 (9.2)

85.6 (10.2)

89.2 (7.9).12

At time of registration (mm Hg)
Systolic blood pressure
Diastolic blood pressure
Follow-up at 3 months (mm Hg)bFollow-up at 6 months (mm Hg)ba

P<.05.

b

The number of patients at 3- and 6-month follow-ups was 63 because 1 patient in the control group withdrew from this study.

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Primary End Points
A linear mixed model was used to estimate the difference inthe change in systolic blood pressure between the 2 groups (oddsratio [OR] 1.18, 90% CI –3.68 to 6.04). The lower limit of the
CI was the noninferiority margin of –5, which met the criteria
for noninferiority in this study (Figure 1). Additionally, 1 patient

Nishizaki et alin the intervention group made a nonscheduled in-person visit
for hypertension management. Nonetheless, even after thispatient was excluded from the analysis, the criterion for
noninferiority was met, with a between-group (intervention andcontrol group) difference (OR 0.51, 90% CI –4.68 to 5.69).
Figure 2 shows the adjusted relative change in systolic bloodpressure estimated by the linear mixed model.

Figure 1. Differences in the change in systlic blood pressure estimated using linear mixed model. Lower confidence limit (one-sided 95%) of thedifference exceeded noninferiority margin of -5 mm Hg. The point estimate was odds ratio (OR) 1.18 (90% CI -3.68 to 6.04).

Figure 2. The adjusted relative change in systolic blood pressure estimated using a linear mixed model. The short vertical lines centered in the longlines represent the point estimates. Long lines indicate the 90% CI of the point estimates.

Secondary End Points
There were no differences in the treatment continuation rate at
the 6-month follow-up between the intervention (31/31, 100%)and control (32/33, 97%) groups (P=.51). Moreover, there wereno adverse events in either group. There was no significant
difference in terms of in-person visits for hypertension
management at unscheduled times between the 2 groups (only1 patient in the intervention and no patients in the control group;
P=.48). The results of patient satisfaction are shown in Table
3. Although there was no difference in the EQ-5D-5L results
between the two groups, patient satisfaction was partially higher
in the intervention group than in the control group, as assessedby the MHLW survey for behavior at the outpatient visit(question 15). In particular, responses to questions about

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consultation time and conversation with the physician showedthat patient satisfaction was higher in the intervention group.
A boxplot graph of the MHLW survey results is shown in Figure
S1 in Multimedia Appendix 4. The raw data regarding patientsatisfaction are shown in Table S1 in Multimedia Appendix 5.
The health economic evaluation results indicated no significant
difference in the 3-month average direct medical costs between
the 2 groups. The indirect costs (calculated by converting lost
productivity hours into Japanese Yen) indicated significantly
lower lost productivity in the intervention group than in thecontrol group (Table 4).
The number of patients at the 3- and 6-month follow-ups was
63 because 1 patient in the control group withdrew from thisstudy.

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Table 3. Difference in the change in patient satisfaction (n=63).
Estimated difference in change valuea
95% CI (lower limit(control group minus intervention group) to upper limit)
P value
EuroQol 5 Dimensions 5 Level
Anxiety and depression

0.08–0.14 to 0.30

.51

Mobility

0.02–0.09 to 0.12

.72

Pain/discomfort–0.06–0.21 to 0.10

.54

N/A

N/A

b

Self-care

N/A

Usual activities–0.03–0.09 to 0.03

.37

Visual analog scale–4.46–9.32 to 0.40

.12

Ministry of Health, Labor, and Welfare survey for behavior at the outpatient visit (question 15)a

Q1. Are you satisfied with the waiting time for consultation?

0.18–0.11 to 0.47

.30

Q2. Are you satisfied with the consultation time?

0.29

0.09 to 0.48

.01c

Q3. Are you satisfied with the content of the medical examinationand treatment provided by the physician?

0.14–0.04 to 0.32

.19

Q4. Are you satisfied with conversation with the physician?

0.21

0.04 to 0.37

.04c

Q5. Are you satisfied with the hospital staff other than physicians? 0.23

0.03 to 0.43.05

Q6. Are you satisfied with the privacy protection measures during
the consultation?

0.19–0.25 to 0.64

.37

Q7. Overall, are you satisfied with this hospital?

0.30

0.14 to 0.46

.002c

These results were estimated by linear mixed model.

b

N/A: not available.

c

P<.05.

Table 4. Health economics evaluation (¥1=US $0.0074).
All patients (n=64)

Telemedicine practice group In-person visit group (control(intervention group) (n=31) group) (n=33)

P valueAt the time of registration

7561.7 (4461.3)

7674.8 (3404.7)

7455.45 (5319.1).84

Follow-up at 3 months

5006.8 (2048.5)

4568.4 (2357.9)

5431.56 (1623.0).09

Follow-up at 6 months

8189.1 (4513.8)

9131.0 (4589.7)

7276.56 (4314.6).10

Direct medical cost (¥), mean (SD)

Indirect costs calculated by converting lost productivity hours into Yen (¥), mean (SD)At the time of registration

32,734.8 (33,372.6)

31,733.1 (33,202.2)

33,675.78 (34,018.8).81

Follow-up at 3 months

18,728.6 (24,435.6)

9102.3 (13,082.6)

27,771.52 (28,999.6).002a

Follow-up at 6 months

28,170.9 (28,174.7)

26,918.0 (26,157.0)

29,347.80 (30,305.9).73

Societal costs (total of direct medical costs and indirect costs) (¥), mean (SD)a

At the time of registration

40,391.4 (33,574.4)

39,441.9 (33,343.5)

41,283.30 (34,282.0).82

Follow-up at 3 months

23,697.5 (25,030.7)

13,670.7 (14,319.0)

33,116.62 (29,195.6).001aFollow-up at 6 months

36,292.6 (28,756.4)

36,065.1 (26,242.5)

36,506.35 (31,341.6).95

P<.05.

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Discussion

not clear from this question. Further studies are required toaddress these issues.

Overview

The relationship between telemedicine and cost-effectivenesshas been reported in various clinical fields [22-25]. For instance,
the report of the Victorian Stroke Telemedicine intervention
contained an estimation of the cost per quality-adjusted life year
(QALY) gained from stroke telemedicine. At 12 months, the
QALYs were estimated to be 0.43 per person in the control
period and 0.5 per person in the intervention period. After 1000
bootstrapping iterations, the Victorian Stroke Telemedicine
intervention period, compared with the control period, was more
effective and cost saving in 50.6% of iterations andcost-effective (US $0 and US $33,357.9 per QALY gained) in
10.4% of iterations, potentially contributing to the further
implementation of telemedicine for acute stroke care in Australia[25]. In this study, a comparison of societal costs, which are thesum of productivity losses and direct medical costs converted
into monetary values, revealed that the costs were lower in theintervention group than in the control group. Notably, thecalculation of productivity losses included patient and familytransportation expenses and labor productivity loss (workforce
productivity loss). These results indicate that telemedicine iseffective in reducing societal costs after accounting for
productivity losses. To our knowledge, this is the first study toreport the usefulness of video-based telemedicine for societal
cost reduction. In addition to noninferiority in blood pressure
control, safety, and higher patient satisfaction of telemedicine
versus in-person visits, the reduction in societal costs suggeststhat telemedicine is more cost-effective in a society-based
analysis. Thus, telemedicine is valuable from health economics
and medical perspective.

The results of this study indicated no difference in bloodpressure between the intervention and control groups. The healtheconomics evaluation results showed that costs were lower for
the intervention group than for the control group when
productivity losses (social costs) were considered. Furthermore,patient satisfaction, mainly with consultation time andconversation with the physician, was higher in the telemedicinegroup.
Blood pressure values differed between the 2 groups atregistration, and this could have affected the primary end point,
although the outcome was the change in systolic blood pressure
from baseline. The systolic blood pressure at registration was
adjusted in a statistical model while evaluating the primary end
point as per major guidelines, such as the European Medicines
Agency guidelines [16]. Regarding patient background, themedications used were different among all patients (Table 1).
Moreover, physician preferences for antihypertensive drugprescriptions may have varied among the participating clinics.
This bias may be attributed to cluster randomization with aninsufficient number of clusters. However, the primary end pointof the study was the stability of systolic blood pressure in realworld, regardless of the medication used, and this study
demonstrated noninferiority of the primary end point in patientswith stable essential hypertension. Therefore, the 3-monthfollow-up did not necessarily have to be an in-person visit,suggesting that, under certain conditions, continued treatment
is also possible with in-person visits at 6-month intervals during
video-based telemedicine.
Doctor-patient communication is crucial for improving both
health outcomes and treatment adherence in patients. However,physicians tend to interrupt patients’ complaints in an outpatientsetting[17].
Moreover,insufficient
doctor-patientcommunication was recognized by patients as a critical causeof treatment dissatisfaction [18]. In this study, in the patientsatisfaction evaluation through the MHLW survey for behavior
at outpatient visits (question 15), the intervention group reported
greater satisfaction with consultation time and conversation
with the physician. The satisfaction with consultation time may
be attributed to the better accessibility of web-based treatment.
Notably, good accessibility is a convenient and major factor inincreasing patient satisfaction, and this has been a particularly
important factor during the COVID-19 pandemic [19,20]. Theintervention group reported higher scores in satisfaction withthe conversation with the doctor, which may indicate
characteristics of telemedicine as it is a one-to-one interaction
with a doctor (in a private room) that allows for a more in-depth
and pleasant conversation with the doctor than is possible in a
regular outpatient clinic. Moreover, increasing patientsatisfaction can have a positive effect on health outcomes [21].
The results of the question “Are you satisfied with the waitingtime for consultation?” were not significantly different between
the two groups. The reasons behind the answers to this question
may differ according to the operational system of each medical
facility. Therefore, the specific implications of these results arehttps://cardio.jmir.org/2023/1/e45230

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This study has some limitations. First, the number ofparticipating facilities was small; hence, the data obtained from
8 included facilities may not represent overall telemedicine inJapan. Second, the study cohort was limited to patients withstable essential hypertension. Therefore, the study results may
not be generalizable. Third, the COVID-19 pandemic posed
difficulty in patient recruitment; hence, the patient recruitment
period was extended from 3 to 8 months. Consequently, theseasons for patient registration varied, and the difference inseason may have affected blood pressure [26]. Fourth, therewas 1 unscheduled patient visit for hypertension managementin the intervention group, which possibly influenced the bloodpressure findings by affecting the change in systolic bloodpressure. However, the noninferiority criteria in this study were
met even after excluding this patient. Fifth, the observation
period was short. We have only demonstrated the usefulness ofvideo-based telemedicine for at least 6 months of follow-up. It
would be desirable to evaluate the usefulness of the video-based
telemedicine in clinical trials with follow-up periods of 6 months
or more. Sixth, the study did not directly assess the preventionof cardiovascular events. The original and primary purpose ofblood pressure control is to prevent cardiovascular events [27].
A study design with cardiovascular events as the primary end
point is desirable but not feasible because a long observation
period is required to evaluate cardiovascular events. Further
studies are required to address this issue. Seventh, the timingof the start of outpatient telemedicine could affect the patient
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satisfaction rating. In this study, we did not evaluate therelationship between the patient satisfaction and timing of thestart of the outpatient telemedicine. In future work, we would
like to consider the mean time from the start of the outpatient
telemedicine.

Nishizaki et al

Conclusions
Switching in-person visit intervals from 3 to 6 months in patientswith stable essential hypertension did not cause any differencein the status of blood pressure control or safety in video-based
telemedicine. Moreover, the use of video-based telemedicine
is expected to have a social societal cost reduction effect andimprove patient satisfaction.

Acknowledgments
The authors express their deep appreciation to JMA Research Institute Inc for their cooperation in assisting with the clinicalresearch for this study. We thank Miwa Sekine and David Aune for the English translation. The authors also thank Enago for the
English language review. This study was supported by Health, Labor, and Welfare Policy Research Grants for Special Research(20CA2003) from the Ministry of Health, Labor, and Welfare.

Data Availability
Because we did not obtain consent from patients for their data to be shared publicly, the data are not available. The corresponding
author will respond to inquiries on the data analysis in this study.

Authors' Contributions
YN and M Nojima had full access to all data in the study and took the responsibility for the integrity of the data and accuracy ofthe data analysis. YN, DS, and RW undertook the study concept and design. HK, SI, SO, CW, RW, HN, M Nagao, and KO were
responsible for the acquisition, analysis, or interpretation of data. YN, HK, SI, M Nojima, DS, RW, YK, and KO worked on
manuscript drafting. HN, HK, GT, and KO were involved in critical revision of the manuscript for important intellectual content.
M Nojima worked on statistical analysis. YN, M Nagao, and KS undertook administrative, technical, or material support. YN,
HW, and GT supervised the study.

Conflicts of Interest
This study was conducted in cooperation with Medley, Inc, and the collaborators included people who hold shares in the company(GT) and employee of this company (YK). However, Medley, Inc only managed the collaboration of the participating facilities
and was not involved in data analyses. Monitoring and auditing were conducted by the Clinical Research and Trial Center,
Juntendo University Hospital; hence, the study results were not manipulated by Medley, Inc.

Multimedia Appendix 1
Ministry of Health, Labor, and Welfare (MHLW) survey for behavior at the outpatient visit (Question 15).
[DOCX File , 13 KB-Multimedia Appendix 1]

Multimedia Appendix 2
Questionnaire for patients to investigate the health care economic impact of telemedicine (at the time of registration).
[DOCX File , 15 KB-Multimedia Appendix 2]

Multimedia Appendix 3
Questionnaire for patient to investigate the health care economic impact of telemedicine (3-month and 6-month follow-ups).
[DOCX File , 13 KB-Multimedia Appendix 3]

Multimedia Appendix 4
The results of the Ministry of Health, Labor, and Welfare (MHLW) survey for patients satisfaction.
[PNG File , 165 KB-Multimedia Appendix 4]

Multimedia Appendix 5
Raw data for patient satisfaction.
[DOCX File , 20 KB-Multimedia Appendix 5]

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Nishizaki et alMultimedia Appendix 6
CONSORT-eHEALTH checklist (V 1.6.1).
[PDF File (Adobe PDF File), 1064 KB-Multimedia Appendix 6]

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Abbreviations
EQ-5D-5L: EuroQol 5 Dimensions 5 Level
MHLW: Ministry of Health, Labor, and Welfare
OR: odds ratio
QALY: quality-adjusted life year
REDCap: Research Electronic Data Capture
UMIN: University Hospital Medical Information Network

Edited by A Mavragani; submitted 22.12.22; peer-reviewed by K Coteur, S Nannini; comments to author 29.01.23; revised version
received 20.03.23; accepted 09.05.23; published 08.06.23
Please cite as:
Nishizaki Y, Kuroki H, Ishii S, Ohtsu S, Watanabe C, Nishizawa H, Nagao M, Nojima M, Watanabe R, Sato D, Sato K, Kawata Y,
Wada H, Toyoda G, Ohbayashi K
Determining Optimal Intervals for In-Person Visits During Video-Based Telemedicine Among Patients With Hypertension: Cluster
Randomized Controlled Trial
JMIR Cardio 2023;7:e45230
URL: https://cardio.jmir.org/2023/1/e45230
doi: 10.2196/45230
PMID: 37161483©Yuji Nishizaki, Haruo Kuroki, So Ishii, Shigeyuki Ohtsu, Chizuru Watanabe, Hiroto Nishizawa, Masashi Nagao, Masanori
Nojima, Ryo Watanabe, Daisuke Sato, Kensuke Sato, Yumi Kawata, Hiroo Wada, Goichiro Toyoda, Katsumi Ohbayashi.
Originally published in JMIR Cardio (https://cardio.jmir.org), 08.06.2023. This is an open-access article distributed under theterms 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 Cardio, is properly cited.
The complete bibliographic information, a link to the original publication on https://cardio.jmir.org, as well as this copyright andlicense information must be included.

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