Access to this full-text is provided by Frontiers.
Content available from Frontiers in Endocrinology
This content is subject to copyright.
Factorial design study of self-
management using Dnurse
App in T2DM patients
Hongxia Tang
1,2
†
, Huiwen Tan
3
†
, Jihong Zhang
4
†
,
Mingjiao Zhang
1
, Mengjie Chen
1
, Laixi Kong
1
, Xiaoxia Wang
1
,
Maoting Guo
1
, Jinxiu Zhao
2
, Lili Song
1
, Zijun Zheng
1
,
Huiqi Yang
5
, Zhe Li
6
*and Zhenzhen Xiong
1
*
1
School of Nursing, Chengdu Medical College, Chengdu, Sichuan, China,
2
Department of Neurology,
The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China,
3
Department of
Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, Sichuan, China,
4
The School Hospital, Southwest Petroleum University, Chengdu, Sichuan, China,
5
Department of
Nursing, Nanbu People’s Hospital, Nanchong, Sichuan, China,
6
Mental Health Center, West China
Hospital, Sichuan University, Chengdu, Sichuan, China
Background: With the popularity of smart phones and the development of
information technology, more and more patients are adopting diabetes APPs
for self-management. However, at present, there are few research reports on the
effect of those APPs coming from China.
Objective: The purpose of this study was to evaluate the effectiveness and
applicability of an APP for blood glucose control that is widely popular among
Chinese patients with type 2 diabetes mellitus (T2DM).
Methods: This is a 2-center, factorial design, with equal proportional distribution,
and superiority trial conducted in outpatient endocrinology clinics at two tertiary
hospitals in Chengdu, China. The trial enrolled smartphone-literature individuals
aged at least 18 years old who have been diagnosed with T2DM based on
glycosylated hemoglobin A
1c
(HbA
1c
) of at least 7.0%. The subjects were
randomly divided into 4 groups, which were the usual care group (G1); the
telephone follow-up group (G2); the APP group (G3); the APP & telephone
follow-up group (G4). After 6 months of these interventions, the primary
outcome was HbA
1c
, and the secondary outcomes were blood pressure (BP),
body mass index (BMI), frequency of self-monitoring of blood glucose (SMBG),
and satisfaction with the APP.
Results: 273 patients with type 2 diabetes were included in the study, among
which 226 (82.8%) were followed up at the 3rd month and 220 (80.6%) at the 6th
month. There was no significant difference in HbA
1c
attainment rate among the
four groups after intervention (P>.05), but the HbA
1c
attainment rate in the two
APP groups was higher than that in the other groups. The systolic blood pressure
(SBP) of the two APP groups was significantly lower than that of the other groups
(P<.05). There was no significant difference in the compliance rate of SMBG
among the four groups (P>.05). Each item of the participants’satisfaction
evaluation of the APP scored more than 4.5 points.
Frontiers in Endocrinology frontiersin.org01
OPEN ACCESS
EDITED BY
Michele Schiavon,
University of Padua, Italy
REVIEWED BY
Sudhanshu Kumar Bharti,
Patna University, India
Benli Su,
Second Hospital of Dalian Medical University,
China
*CORRESPONDENCE
Zhe Li
jay_li@163.com
Zhenzhen Xiong
xzz62308631@163.com
†
These authors have contributed
equally to this work and share
first authorship
RECEIVED 20 April 2024
ACCEPTED 07 March 2025
PUBLISHED 28 March 2025
CITATION
Tang H, Tan H, Zhang J, Zhang M, Chen M,
Kong L, Wang X, Guo M, Zhao J, Song L,
Zheng Z, Yang H, Li Z and Xiong Z (2025)
Factorial design study of self-management
using Dnurse App in T2DM patients.
Front. Endocrinol. 16:1420578.
doi: 10.3389/fendo.2025.1420578
COPYRIGHT
© 2025 Tang, Tan, Zhang, Zhang, Chen, Kong,
Wang, Guo, Zhao, Song, Zheng, Yang, Li and
Xiong. This is an open-access article distributed
under the terms of the Creative Commons
Attribution License (CC BY). The use,
distribution or reproduction in other forums
is permitted, provided the original author(s)
and the copyright owner(s) are credited and
that the original publication in this journal is
cited, in accordance with accepted academic
practice. No use, distribution or reproduction
is permitted which does not comply with
these terms.
TYPE Original Research
PUBLISHED 28 March 2025
DOI 10.3389/fendo.2025.1420578
Conclusions: The diabetes APP has a tendency to improve the HbA
1c
compliance
rate of T2DM patients. The APP can help reduce patients’BP, and patients have a
high satisfaction evaluation of the APP. Therefore, the study supports the use of
the APP for self-management in people with type 2 diabetes.
Clinical Trial Registration: https://www.chictr.org.cn, identifier
ChiCTR2100042297.
KEYWORDS
type 2 diabetes mellitus, diabetes app, self-management, factorial design trial, Chinese,
blood glucose monitoring
Introduction
Overthepast30years,Chinahaswitnessedasignificant
increase in the prevalence of diabetes, driven by rapid
urbanization, an aging population, a rise in overweight and
obesity rates, among other factors (1). Consequently, China now
has the largest number of diabetes patients in the world (2). The
widespread prevalence of diabetes imposes a heavy economic
burden and substantially impacts patients’quality of life (2).
Therefore, it is crucial to urgently enhance patients’self-
management ability to improve the prognosis and outcome of the
disease. Unfortunately, the level of self-management among
diabetics in China remains less than ideal (3,4).
In recent years, as communication technology has advanced
and smartphones have become increasingly popular, a growing
number of individuals with diabetes are turning to mobile apps for
assistance in managing their condition. Relevant studies have
reported the effectiveness of diabetes apps in improving patients’
self-management (5) and metabolic control (6,7). However, there is
a lack of research focusing on this topic in China, particularly in
Chengdu, a western city with a less developed economy. The status
quo of app usage and its potential impact on improving blood
glucose levels among patients remain unclear.
The intervention tool selected for this study, the Dnurse App,
stands out as the most popular among T2DM patients, according to a
survey conducted across 30 provincial-level regions in China (8). The
Dnurse smart blood glucose meter allows for continuous monitoring
of blood glucose levels, automatically transmittingthis data to the app
where it is stored. The app also provides detailed reports, allowing
users to track the total number of blood glucose tests conducted
throughout the current week, month, and quarter. The app’s
background can calculate the simulated HbA
1c
based on a patient’s
blood glucose values, allowing for clear visualization of blood glucose
changes. Additionally, through the Intelligent Decision Support
System, the app comprehensively analyzes users’blood glucose,
diet, exercise, and other data to provide personalized reminders,
recommendations, encouragement, and interaction, thereby
facilitating individualized diabetes management.
Methods
Study design
Utilizing a 6-month, open-label, parallel-group, four-factorial
design, this study was conducted at two affiliated hospitals of a
university located in Chengdu, Sichuan Province, China from
October 2021 to August 2023. Participants were randomly assigned
to one offour groups: the usual care group (G1), the phone follow-up
group (G2), the app group (G3), or the app & phone follow-up group
(G4). Detailed research protocols have been previously published (9).
Out of 2,825 individuals screened, 273 or 9.7% were enrolled in the
study. Out of those enrolled, 220 or 80.6% completed the study six
months after the intervention (see Figure 1).
Study patients
The inclusion criteria for this study are as follows: individuals
diagnosed with T2DM by a secondary or higher-level hospital
according to the World Health Organization guidelines; individuals
with an HbA
1c
level of 7.0% or higher; individuals who have
completed at least primary school education; individuals who are
18 years of age or older; individuals who are proficient in using
smartphones (assessed by having installed at least three commonly
used apps on their device); individuals who have resided in Chengdu
for at least the past 12 months; and individuals who are willing to
participate in the study and provide informed consent.
Exclusion criteria are as follows: individuals who have previously
used a diabetes-related app; individuals with a self-reported historyof
mental illness, cognitive impairment, or communication disorders;
individuals who are pregnant or planning to become pregnant;
individuals with any severe, life-threatening diseases; and
individuals currently hospitalized for diabetes-related reasons.
Abbreviations: T2DM, type 2 diabetes mellitus; HbA
1c
, hemoglobin A
1c
; BP,
blood pressure; BMI, body mass index; SMBG, self-monitoring of blood glucose;
SBP, systolic blood pressure; DBP, diastolic blood pressure.
Tang et al. 10.3389/fendo.2025.1420578
Frontiers in Endocrinology frontiersin.org02
Ethical approval
The study received ethical approval from the Ethics Committee
of the First Affiliated Hospital of Chengdu Medical College
(2020CYFYIRB-BA-129-F01). Prior to participating in the study,
all individuals provided written informed consent. The research was
conducted in compliance with the principles outlined in the
Declaration of Helsinki.
Sample size
In this experiment, a factorial design is employed, and the
necessary parameters are not available from previous studies.
Utilizing GPower 3.1 (University of Kiel, Kiel, Germany), we
calculated the required sample size based on the anticipated
outcomes. Assuming a small effect size of 0.2 (10), with a two-
sided alevel of 0.05, and a test power (1-b) of 0.9, we used an F-test,
resulting in a sample size of 220 cases. To account for an estimated
20% attrition rate, the total sample size was increased to 264 cases.
Randomization
Prior to the commencement of enrollment, two sets of random
numbers were generated using Excel by individuals not involved in
the study. Each set was assigned a serial number in advance and
placed in an opaque, sequentially numbered envelope. After the
baseline assessment was completed, participants were randomly
allocated to G1, G2, G3, or G4 in equal proportions (1:1:1:1).
Interventions
G1 participated in conventional diabetes management. They were
provided with the Dnurse He pro wisdom, a standard portable glucose
meter developed by Beijing Dnurse Technology Co., Ltd. based in
Beijing, China, and a blood glucose monitoring logbook. According to
national guidelines, they were instructed on the appropriate timing and
frequency for monitoring their blood glucose levels (11).
G2 participants received the same materials as G1, along with
weekly phone reminders to consistently self-monitor their blood
glucose levels. These reminders were delivered by carefully chosen
nursing undergraduates who had been trained in the study’s
rationale, interventions, and objectives. Only those students who
excelled in post-training evaluations were selected to participate in
the study. The phone follow-up was conducted in a structured
manner and involved two questions. The first question asked, “Have
you monitored your blood glucose regularly this week?”If the
response was “no,”the second question was, “Could you please
explain why you haven’t monitored your blood glucose regularly?”
The participants in G3 were provided with the Dnurse SPUG
mobile blood glucose and uric acid tester, a smart blood glucose
meter developed by Beijing Dnurse Technology Co., Ltd. Under the
guidance of a specially trained researcher, they downloaded the
Dnurse App 4.0.16 (accessible at dnurse.com/v2/app), which is
specifically designed to integrate seamlessly with the smart glucose
meter. The app is compatible with both Android and iOS operating
systems. Based on the essential goal of boosting patients’
autonomous compliance, the Intelligent Decision Support System
is used to perform intelligent matching.
Participants were guided through the process of setting up a
personal account on the app. They were also instructed on how to
create customized diet and exercise plans, establish blood glucose
control goals, and develop a blood glucose monitor plan.
Additionally, they learned how to access features such as food
banks, disease information, real-time blood glucose analysis, and
recommendations available within the app. Users were also shown
how to set reminders for taking medication and managing other
behaviors. To remind the same user about the same matter, the
app’s Intelligent Decision Support System draws on over
FIGURE 1
Flowchart of participant enrollment and status. G1, usual care; G2, telephone follow-up; G3, App; G4, App & telephone follow-up.
Tang et al. 10.3389/fendo.2025.1420578
Frontiers in Endocrinology frontiersin.org03
6,000 scenarios to ensure that every reminder feels like a “human
greeting”rather than “machine-generated text”(see Figures 2–4).
G4 received the same materials and training as G3, along with
weekly phone reminders similar to those given to G2.
When participants in G1 and G2 encountered acute
complications of diabetes, such as extremely high or low blood
glucose levels, clinical trial investigators would recommend seeking
medical treatment according to standard procedures. When
participants in G3 and G4 triggered a “critical value”alarm on
the app, the manufacturer’s customer service staff would contact
them by phone to offer guidance or information following standard
procedures. After the study, to compensate participants in G1 and
G2,weintroducedtheDnurseApptothemandprovided
instructions on its use. Both devices have obtained ISO13485 and
EU CE certifications.
Primary and secondary outcomes
The primary outcome measured was the difference in the
change of HbA
1c
levels (%) from the baseline to the sixth month
across the four groups. The HbA
1c
level <7% attainment rates were
evaluated at both the third and sixth months. The secondary
outcomes included blood pressure (BP), body mass index (BMI),
and self-monitoring of blood glucose (SMBG) compliance rate. The
SMBG compliance rate was defined according to the 2009
Guidelines for Diabetes Care and Education in China as
monitoring blood glucose at least once a day for patients using
insulin and at least once a week for those not using insulin (12).
Participant satisfaction in G3 and G4 was evaluated at the sixth
month using a tailored satisfaction survey. This survey included
5-point Likert scale questions assessing various aspects of the
Dnurse App and the glucose meter. Participants rated their
satisfaction with the app’s user interface, the method of recording
blood glucose levels in the app, and the ease of operation of both
the app and the glucose meter. The 5 points denoted a response
of “very satisfied”or “strongly agree,”and higher scores
indicated greater satisfaction. The content validity index of the
questionnaires was 0.85, and the Cronbach’sacoefficient of the
scale was 0.961. All questionnaires were filled out online via
Questionnaire Star.
Measurements
The demographic and clinical information collected at both the
baseline and follow-up has been detailed in previous studies (9). To
ensure consistency across results, measurements of laboratory
parameters, including HbA
1c
levels (%), were made using high-
performance liquid chromatography with specialized reagents at the
same certified external laboratory, and the analysis was performed
using the MQ-6000 HbA
1c
analyzer, manufactured by Shanghai
Huizhong Medical Technology Co., Ltd. Additionally, BP and BMI
were recorded at every visit.
Hypoglycemic events—comprising hospitalizations or
emergency room visits due to hypoglycemia (blood glucose levels
below 3.9 mmol/L), or related symptoms even in the absence of
SMBG—were assessed at the baseline and six months after
intervention. Diabetes management behaviors, such as SMBG
frequency, were recorded at each visit. The SMBG frequency
referred to the total number of tests conducted over a three-
month period. This was determined for G1 and G2 using the
FIGURE 2
Blood glucose control plan.
Tang et al. 10.3389/fendo.2025.1420578
Frontiers in Endocrinology frontiersin.org04
entries in their blood glucose monitoring logbooks, and for G3 and
G4 based on the records in the app. Additionally, user satisfaction
with the mobile app was surveyed for G3 and G4.
Statistical analysis
The analysis was performed using SPSS 23.0 (IBM, Chicago, IL,
USA). Continuous variables were presented as mean (SD) and M
(Q1, Q3), whereas categorical data were reported as frequencies
with percentages. To evaluate the differences among the four
groups, a variety of statistical tests were employed depending on
the data type: the chi-squared test for categorical data, analysis of
variance (ANOVA) for normally distributed continuous data, and
the non-parametric rank-sum test for skewed continuous data.
To assess the differences across various time points within each
group, the generalized estimating equation (GEE) approach was
utilized. These models included intervention variables, time as a
categorical variable with dummy coding, interactions between
intervention and time, and the baseline values. A Pvalue of less
than 0.05 was considered to indicate statistical significance.
Results
Participants flow
Between October 2021 and August 2023, a total of 2,825
individuals were evaluated for eligibility at the outpatient clinics
of two university-affiliated diabetes centers. Of these, 273
FIGURE 3
Blood glucose data management and analysis.
FIGURE 4
Blood glucose control knowledge.
Tang et al. 10.3389/fendo.2025.1420578
Frontiers in Endocrinology frontiersin.org05
participants (9.7%) were randomized into the study. After a
6-month intervention, 220 participants (80.6%) remained in the
study (see Figure 1), with an equal distribution across the groups.
Clinical characteristics of participants
The baseline analysis revealed no significant differences
between participants who completed the study and those who
were lost to follow-up (all P>.05; Table 1). The mean age of the
participants was 56.3 (12.4) years, 63.0% (172/273) were male, and
61.9% (169/273) had an education level of junior high school or
below. The mean baseline HbA
1c
level and duration of
diabetes were 8.2% (7.2%, 9.7%) and 7.8 (6.9) years, respectively.
The mean BMI was 25.0 (4.3) kg/m
2
. Additionally, more than
half of the participants (58.6%) were using only oral
hypoglycemic agents, and a majority (60.8%) had chronic
disease complications.
TABLE 1 Baseline demographic and clinical characteristics of participants.
G1
a
(n=68) G2
b
(n=67) G3
c
(n=68) G4
d
(n=70) Pvalue
Age (years), n (%) .122
≤60 46 (67.7) 38 (56.7) 51 (75.0) 50 (71.4)
>60 22 (32.3) 29 (43.3) 17 (25.0) 20 (28.6)
Sex, n (%) .121
Male 42 (61.8) 37 (55.2) 41 (60.3) 52 (74.3)
Female 26 (38.2) 30 (44.8) 27 (39.7) 18 (25.7)
Education, n (%) .173
Primary school 21 (30.9) 18 (26.9) 10 (14.7) 15 (21.4)
Junior high school and technical secondary school 30 (44.1) 22 (32.8) 27 (39.7) 26 (37.1)
High schools and junior college 14 (20.6) 18 (26.9) 19 (27.9) 16 (22.9)
University and above 3 (4.4) 9 (13.4) 12 (17.7) 13 (18.6)
Duration of diabetes (years), n (%) .204
<5 23 (33.8) 24 (35.8) 34 (50.0) 26 (37.1)
≥5 45 (66.2) 43 (64.2) 34 (50.0) 44 (62.9)
HbA
1c
(%) , M (Q1,Q3) 8.4 (7.3, 10.0) 8.0 (7.3, 9.4) 8.4 (7.2, 9.9) 7.9 (7.1, 9.5) .688
BP (mm Hg), mean (SD)
SBP
e
133.8 (16.3) 132.6 (17.6) 127.0 (16.2) 130.4 (19.7) .120
DBP
f
78.4 (12.5) 80.1 (11.6) 78.9 (10.1) 79.0 (11.7) .858
BMI (kg/m
2
), mean (SD) 24.9 (4.4) 24.4 (3.4) 25.7 (5.4) 25.2 (3.9) .360
Treatment plan, n (%) .502
Non-drug therapy 3 (4.4) 4 (6.0) 5 (7.3) 1 (1.4)
Oral agents 34 (50.0) 38 (56.7) 44 (64.7) 44 (62.9)
Insulin only 4 (5.9) 2 (3.0) 2 (3.0) 4 (5.7)
Insulin + oral agents 27 (39.7) 23 (34.3) 17 (25.0) 21 (30.0)
Complication, n (%) .081
1-3 kinds 41 (60.3) 40 (59.7) 36 (52.9) 30 (42.9)
More than 3 kinds 10 (14.7) 1 (1.5) 4 (5.9) 4 (5.7)
Acute complications
g
, n (%) .126
Moderate ketoacid 3 (4.4) 1 (1.5) 2 (2.9) 2 (2.9)
Hypoglycemia 21 (30.9) 16 (23.9) 9 (13.2) 20 (29.4)
a
G1, usual care;
b
G2, telephone follow-up;
c
G3, App;
d
G4, App & telephone follow-up;
e
SBP, systolic blood pressure;
f
DBP, diastolic blood pressure;
g
Per participant who experienced acute
complications for 6 months.
Tang et al. 10.3389/fendo.2025.1420578
Frontiers in Endocrinology frontiersin.org06
Primary study outcomes: HbA
1c
attainment rates
The HbA
1c
level <7% attainment rate increased in G3 and G4 at
the third month, but the difference was not significant (G1 vs G2 vs
G3 vs G4: 32.7% vs 35.1% vs 42.0% vs 47.3%). Compared with the
other groups, G3 and G4 always had higher HbA
1c
level <7%
attainment rates at the sixth month (G1 vs G2 vs G3 vs G4: 34.7% vs
33.3% vs 44.0% vs 45.5%). This indicates that there was no
significant difference in the main effect of the intervention on the
HbA
1c
compliance rate (P=.787, Table 2).
Secondary study outcomes: BP, BMI, and
SMBG attainment rates
The main effect of the intervention on systolic blood pressure
(SBP) was statistically significant (P<.05). Pairwise comparisons
revealed that after the 3-month intervention, the SBP in G3 and G4
was lower than that in G1, and G3 was lower than G2 (Table 3). The
results of factorial analysis showed no interaction between phone
follow-up and app (P>.05; Table 4), indicating that the app
effectively reduces SBP levels. However, the change in BMI levels
did not significantly differ among the four groups during the 6-
month intervention period. Additionally, the changes in BMI and
SMBG compliance rate showed no significant differences among the
study groups throughout the intervention period.
The SMBG attainment rates did not differ significantly among
the four groups at the 6-month follow-up (P>.05). However,
compared with participants in G1 and G3, those in the two
phone follow-up groups (G2 and G4) tended to have higher
SMBG attainment rates at both the 3-month mark (G1 vs G2 vs
G3 vs G4: 71.4% vs 77.2% vs 70.0% vs 81.8%) and the 6–month
mark (G1 vs G2 vs G3 vs G4: 69.4% vs 77.2% vs 58.0% vs 72.7%).
Satisfaction with Dnurse App
Out of 112 participants, 110 (98.3%) completed the
satisfaction survey at the sixth month of the intervention.
Participants’satisfaction with the Dnurse App was very high in
both G3 and G4. Overall, the majority of patients were satisfied
with the blood glucose recording method. The second highest-
rated aspect was managing diabetes using the app and its user
interface (Table 5).
Adverse events
No serious adverse events were reported from the time of
enrollment to the completion of this study. Incidents of moderate
ketoacidosis and hypoglycemia were infrequent and showed no
significant differences among the groups over the 6-month period
(Table 6). Additionally, there were no reported or detected deaths,
direct study-related adverse events, or severe hypoglycemic episodes.
Discussion
Principal findings
This study utilized a factorial design to investigate a mobile
app–based diabetes self-care intervention for hospital patients with
T2DM over a 6-month period. While there was an overall increase
in HbA
1c
attainment rates across all four groups from baseline, the
changes in attainment rates did not significantly differ among the
groups over the six months. Upon intervention, we observed that
the diabetes app (G3 and G4) appeared to improve HbA1c
attainment rates at the 3- and 6-month marks. The app
automatically gathers patients’health data and offers robust
interactive feedback features. These include automated analyses
and recommendations following blood glucose monitoring, as well
as generating weekly and monthly blood glucose reports. These
features encourage patients to actively engage in managing their
condition, thereby facilitating personalized diabetes management.
Studies have shown that a high baseline HbA
1c
level is an
independent predictor of a decline in blood glucose levels (13).
Specifically, the higher the baseline HbA
1c
level, the greater the
potential for reduction. In this study, the baseline HbA
1c
levels of
participants ranged from 8.0 to 8.4%, which is lower than those
reported in the studies by Gunawardena KC (14) and Xu HW (15).
This difference may explain why the HbA
1c
compliance rates in G3
and G4 were slightly higher than that of other groups in our study,
although the differences were not statistically significant.
TABLE 2 Changes in HbA
1c
level <7% attainment rate.
Time G1
a
(n=49)
G2
b
(n=57)
G3
c
(n=50)
G4
d
(n=55)
Main effect
of intervention Time effect Interaction
effect
Pvalue Pvalue Pvalue
HbA
1c
attainment rates,
n (%)
0M
e
0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) .787 <.001 .142
3M
f
16 (32.7) 20 (35.1) 21 (42.0) 26 (47.3)
6M
g
17 (34.7) 19 (33.3) 22 (44.0) 25 (45.5)
a
G1, usual care;
b
G2, telephone follow-up;
c
G3, App;
d
G4, App & telephone follow-up;
e
0M, baseline;
f
3M, 3 months after intervention;
g
6M, 6 months after intervention.
Tang et al. 10.3389/fendo.2025.1420578
Frontiers in Endocrinology frontiersin.org07
Following the intervention, there was a statistically significant
effect on SBP. Factorial analysis indicated that the app effectively
reduced patients’SBP, consistent with findings by Gong K (16).
However, another study reported no significant impact of a
smartphone app on BP (17). The Dnurse app is designed within a
Chinese cultural framework and, while not specifically targeting
patients with hypertension, it creates personalized diet and exercise
plans. Additionally, it facilitates interactions in a community
setting, encouraging the adoption of a healthy lifestyle. Numerous
studies have validated the efficacy of lifestyle interventions in
lowering BP and preventing or delaying hypertension (18,19).
Research has also demonstrated a positive correlation between
HbA
1c
and BP (20). In this study, HbA
1c
levels decreased across
all groups after intervention, which could contribute to the
reduction in BP. Although the post-intervention BP in all groups
met the target for BP control in T2DM in China, HbA
1c
levels did
not reach the set target (1). This suggests that reductions in BP are
more pronounced than those in HbA
1c
, possibly due to the greater
sensitivity of BP changes. To enhance the study’s reliability and
further examine the effect on BP, ambulatory blood pressure
monitoring may be considered.
SMBG is a vital component of diabetes self-management. It
enables patients to better comprehend their disease status and
serves as a crucial foundation for timely adjustments in diet,
exercise, and medication regimens during medical consultations.
Previous research has highlighted the effectiveness of two
intervention methods—phone follow-up (21) and mobile apps
(22)—in enhancing patients’glucose self-monitoring behavior. In
this study, although the SMBG compliance rate in the two phone
follow-up groups was higher than that in those without such
follow-ups, no significant differences in compliance rates were
observed among the four groups. This suggests that both weekly
phone follow-ups and app reminder features have similar
impacts on SMBG compliance. However, regular phone
TABLE 3 Secondary study outcomes at baseline and follow-up.
Time G1
a
(n=49) G2
b
(n=57) G3
c
(n=50) G4
d
(n=55)
Main effect
of intervention
Time
effect
Interaction
effect
Pvalue Pvalue Pvalue
SBP (mmHg),
mean (SD)
0M
e
134.2 (15.0) 132.8 (18.3) 126.1 (15.5) 131.0 (17.9) .016 <.001 .113
3M
f
131.9 (18.8) 130.6 (14.6) 123.6 (14.8)
h,i
124.8 (18.7)
h
6M
g
124.6 (14.3) 121.1 (15.8) 117.1 (16.7) 123.9 (16.0)
BMI (kg/m
2
),
mean (SD)
0M 24.6 (4.6) 24.5 (3.5) 25.5 (6.0) 25.1 (3.9) .692 <.001 .184
3M 23.4 (3.0) 23.6 (2.1) 24.0 (3.3) 23.3 (2.6)
6M 25.2 (7.1) 23.8 (2.3) 24.5 (3.5) 23.9 (2.9)
SMBG
attainment rates,
n (%)
3M 35 (71.4) 44 (77.2) 35 (70.0) 45 (81.8) .221 .411 .918
6M 34 (69.4) 44 (77.2) 29 (58.0) 40 (72.7)
a
G1, usual care;
b
G2, telephone follow-up;
c
G3, App;
d
G4, App & telephone follow-up;
e
0M, baseline;
f
3M, 3 months after intervention;
g
6M, 6 months after intervention;
h
means compared with
G1, P < .05;
i
means compared with group G2, P< .05.
TABLE 4 Factorial analysis of SBP.
Source Type III Sum of Squares df Mean Square Pvalue
Telephone follow-up 170.289 1 170.289 .439
APP 1303.919 1 1303.919 .033
Interaction(telephone follow-up×APP) 511.924 1 511.924 .180
TABLE 5 Satisfaction for Dnurse APP.
Item Score
APP
The blood glucose recording method, mean (SD) 4.71 (0.53)
Manage diabetes with the APP, mean (SD) 4.65 (0.6)
APP interface, mean (SD) 4.65 (0.6)
Ease of operation, mean (SD) 4.62 (0.6)
Overall satisfaction, mean (SD) 4.61 (0.6)
Security, mean (SD) 4.55 (0.6)
Performance (accurate results, quick response), mean (SD) 4.55 (0.6)
Provide popular science information, mean (SD) 4.55 (0.7)
Blood glucose meter
Ease of operation, mean (SD) 4.64 (0.6)
The stability with APP connection, mean (SD) 4.62 (0.6)
Overall satisfaction, mean (SD) 4.58 (0.7)
Tang et al. 10.3389/fendo.2025.1420578
Frontiers in Endocrinology frontiersin.org08
follow-ups can increase labor costs and potentially affect the
sustainability of the intervention, while app-based intelligent
reminder features offer a more cost-effective solution. To
enhance the effectiveness of the app’s intelligent reminders,
future improvements may be made in the blood glucose
monitoring features, such as introducing system rewards for
consistent monitoring or integrating phone interventions into
automated customer service.
Comparison with prior work
Prior studies have demonstrated that diabetes management
apps can enhance blood glucose control in patients with diabetes
(23–25). However, the majority of these studies have been
conducted in developed countries or regions. Research by Lim SL
(6) and Yang Y (26) indicated that using mobile apps for diabetes
management significantly improved blood glucose control and also
reduced BP and weight. Nonetheless, both studies were carried out
in economically advanced countries, leaving the efficacy of these
apps in less developed regions uncertain. Unlike other studies (27,
28), our research did not exclude older participants, resulting in a
more representative sample of the T2DM population. For instance,
Kang S’s study (29), which excluded patients over 45, found that
app-based self-management support helps young and middle-aged
diabetic patients achieve target HbA
1c
levels. Moreover, some
studies suggest that app usage is more effective in young patients
compared to the elderly (23,26). In our study, nearly one-third of
participants were over 60. Given that older patients typically have
lower awareness and acceptance of new technologies than younger
ones, an app that is effective for middle-aged participants may not
necessarily yield the same results in an older population. This aligns
with the current demographic trends in diabetes, as the aging
population is contributing to a rise in the number of elderly
diabetic patients (30,31). This indicates a potential to develop
apps tailored to different user groups, such as simplifying the user
interface and increasing font sizes to make them more suitable for
older adults.
Regarding the research design, we utilized a factorial design to
achieve two main objectives. Firstly, this approach allows us to
verify the effectiveness of using an app for blood glucose
management. Secondly, by comparing the impact of the app’s
intelligent reminders with manual phone follow-ups on patients’
blood glucose monitoring compliance rates, we aim to identify the
most effective method to enhance patient compliance in blood
glucose monitoring.
Limitations
Although this study employs a rigorous factorial design with
participants recruited from two large medical centers in China,
there are some design limitations that should be taken into
account when interpreting the results. We used convenience
sampling in this study, which may limit the representativeness
of the sample. Consequently, the generalizability of the findings to
other T2DM populations in China needs further investigation.
While we cannot entirely prevent participants in G1 and G2 from
using other diabetes-related apps during the study, they will not
have access to the Dnurse App, which is central to this trial.
Additionally, the intervention lasted only six months, so the long-
term effects of the Dnurse App on participants’self-management
need further exploration.
Conclusions
In this study, the HbA
1c
attainment rates in G3 and G4 were
generally higher than those in the other groups after both three and
six months. Notably, the BP control improvement in G3 and G4
after three months showed a significant difference from that in G1
and G2. We found no statistical difference between the blood
glucose monitoring functionalities of the app and phone follow-
up. However, participants expressed a very high level of satisfaction
with the Dnurse App. Our results indicate that the enhanced
metabolic control observed in the app-user groups can be
attributed to the app’s Intelligent Decision Support System, which
interacts with users in real time and promotes patient autonomy.
Based on our findings, we conclude that telemedicine is an effective
and safe approach for T2DM patients.
Author’s note
This work was presented at the International Diabetes
Federation Western Pacific Region Congress 2023 15th Scientific
Meeting of the Asian Association for the Study of Diabetes, July 21-
23, 2023. as an oral presentation (No. WOEC-02-5).
TABLE 6 Moderate ketoacid and hypoglycemic events.
G1
a
(n=50) G2
b
(n=58) G3
c
(n=53) G4
d
(n=59) Pvalue
Acute complications
e
1.000
Moderate ketoacid, n (%) 1 (2.0) 0 (0.0) 0 (0.0) 1 (1.7)
Hypoglycemia, n (%) 14 (28.0) 13 (22.4) 11 (20.8) 16 (27.1)
a
G1, usual care;
b
G2, telephone follow-up;
c
G3, App;
d
G4, App & telephone follow-up;
e
Per participant who experienced acute complications 6 months after intervention.
Tang et al. 10.3389/fendo.2025.1420578
Frontiers in Endocrinology frontiersin.org09
Data availability statement
The original contributions presented in the study are included
in the article/supplementary material. Further inquiries can be
directed to the corresponding authors.
Ethics statement
The studies involving humans were approved by the ethics
committee of the First Affiliated Hospital of Chengdu Medical
College. The studies were conducted in accordance with the local
legislation and institutional requirements. The participants
provided their written informed consent to participate in this
study. Written informed consent was obtained from the
individual(s) for the publication of any potentially identifiable
images or data included in this article.
Author contributions
HXT: Data curation, Investigation, Software, Writing –original
draft. HWT: Formal analysis, Investigation, Writing –original
draft. JHZ: Conceptualization, Data curation, Writing –original
draft. MZ: Data curation, Investigation, Writing –review & editing.
MC: Investigation, Writing –review & editing. LK: Investigation,
Writing –review & editing. XW: Investigation, Writing –review &
editing. MG: Investigation, Writing –review & editing. JXZ:
Investigation, Supervision, Writing –review & editing. LS: Data
curation, Writing –review & editing. ZZ: Data curation, Writing –
review & editing. HY: Data curation, Writing –review & editing.
ZL:Visualization,Writing–review & editing. ZX: Funding
acquisition, Supervision, Writing –review & editing.
Funding
The author(s) declare that financial support was received for the
research and/or publication of this article. This study are funded by
the Beijing Dnurse Technology Ltd. (2020-507). Clinical Science
Research Fund Project of Chengdu Medical University-Nanbu
County People’s Hospital (2022LHNBSYB-05). 2023 Project of
Chengdu Medical University-Mike IVD Clinical Joint Research
Center (23LHNBSYB02). Special Project for Strategic Cooperation
between Sichuan University and Dazhou Municipal People’s
Government (2022CDDZ-17). Science and Technology
Department of Sichuan Province (2022YFS0349). The funder was
not involved in the study design, collection, analysis, interpretation
of data, the writing of this article or the decision to submit it
for publication.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
References
1. Chinese Diabetes Society. Guideline for the prevention and treatment of type 2
diabetes mellitus in China (2020 edition). Chin J Diabetes. (2021) 13:315–409. https://
rs.yiigle.com/cmaid/1315489 (Accessed June 12, 2023).
2. IDF. IDF-Atlas-10th-Edition-2021.pdf. Available online at: https://diabetesatlas.
org/idfawp/resource-files/2021/07/IDF_Atlas_10th_Edition_2021.pdf (Accessed June
12, 2023).
3. Zhu XL, Gu XF, Lu HX, Wei YN, Sun SG. Current situation of self-management
in diabetic patients and prospect of educational management model. J Clin Nurs. (2021)
20:70–3. Available online at: https://d.wanfangdata.com.cn/periodical/
ChhQZXJpb2RpY2FsQ0hJTmV3MjAyMzA4MDYSD2xjaGx6ejIwMjEwMjAyNR
oIcDY2OTFuNnY%3D (Accessed June 13, 2023).
4. Qiu TY, Huang J, Wang WX. Association between diabetes knowledge and self-
efficacy in patients with type 2 diabetes mellitus in China: A cross-sectional study. Int J
Endocrinol. (2020) 2020:2393150. doi: 10.1155/2020/2393150
5. Kusnanto, Widyanata KAJ, Suprajitno, Arifin H. DM-calendar app as a diabetes
self-management education on adult type 2 diabetes mellitus: a randomized controlled
trial. J Diabetes Metab Disord. (2019) 18:557–63. doi: 10.1007/s40200-019-00468-1
6. Lim SL, Ong KW, Johal J, Han CY, Yap QV, Chan YH, et al. Effect of a
smartphone app on weight change and metabolic outcomes in Asian adults with type 2
diabetes: a randomized clinical trial. JAMA Netw Open. (2021) 4:e2112417–e2112417.
doi: 10.1001/jamanetworkopen.2021.12417
7. Hooshmandja M, Mohammadi A, Esteghamti A, AliabadiK, Nili M. Effect of mobile
learning (application) on self-care behaviors and blood glucose of type 2 diabetic patients.
J Diabetes Metab Disord. (2019) 18:307 –13. doi: 10.1007/s40200-019-00414-1
8. Zhang YY, Li X, Luo SM, Liu CY, Xie YT, Guo J, et al. Use, perspectives, and
attitudes regarding diabetes management mobile apps among diabetes patients and
diabetologists in China: national web-based survey. JMIR mHealth uHealth. (2019) 7:
e12658. doi: 10.2196/12658
9. Tang HX, Qin H, Zhang MJ, Zhang JH, Tan HW, Chen MJ, et al. Efficacy of a
smartphone application for helping individuals with type 2 diabetes mellitus manage
their blood glucose: a protocol for factorial design trial. Trials. (2023) 24:468.
doi: 10.1186/s13063-023-07489-5
10. Chen F, Chen PY, Chen QG, He J, Huang Q, Jin BH, et al. Statistical
considerations for sample size determination in clinical trials. Chin J Health Statistic.
(2015) 32:727–731,733. Available online at: https://d.wanfangdata.com.cn/periodical/
ChhQZXJpb2RpY2FsQ0hJTmV3MjAyMzA4MDYSD3pnd3N0ajIwMTUwNDA1OBo
IMTVnNXV6cGw%3D (Accessed June 18, 2023).
11. Diabetes Society of Chinese Medical Association. China Blood Glucose
Monitoring Clinical Application Guidelines (2015 edition). Diabetes World
(Clinical). (2016) 10:205–18. https://d.wanfangdata.com.cn/periodical/ChhQ
ZXJpb2RpY2FsQ0hJTmV3MjAyMzA4MDYSDnpodG5iMjAxNTEwMDA
0Ggg0cmxvbG1oeQ%3D%3D (Accessed June 19, 2023).
12. Diabetes Care and Education Group, Diabetes Society, Chinese Medical
Association. China Diabetes Care and Education Guide (2009 edition). Available
online at: https://diab.cma.org.cn/x_uploadfiles/nursing.pdf (Accessed June 20, 2023).
13. Kirwan M, Vandelanotte C, Fenning A, Duncan MJ. Diabetes self-management
smartphone application for adults with type 1 diabetes: randomized controlled trial. J
Med Internet Res. (2013) 15:e235. doi: 10.2196/jmir.2588
Tang et al. 10.3389/fendo.2025.1420578
Frontiers in Endocrinology frontiersin.org10
14. Gunawardena KC, Jackson R, Robinett I, Dhaniska L, Jayamanne S, Kalpani S,
et al. The influence of the smart glucose manager mobile application on diabetes
management. J Diabetes Sci Technol. (2019) 13:75–81. doi: 10.1177/1932296818804522
15. Xu HW, Yang L, Chen YF, Liang XL. Application effect of a diabetes TCM
chronic disease management App. J Nurs Sci. (2019) 34:29–32. Available online at:
http://www.hlxzz.com.cn/hlxzz/article/abstract/20192409?st=search (Accessed June 22,
2023).
16. Gong K, Yan YL, Li Y, Du J, Wang J, Han Y, et al. Mobile health applicationsfor
the management of primary hypertension: A multicenter, randomized, controlledtrial.
Med (Baltimore). (2020) 99:e19715. doi: 10.1097/MD.0000000000019715
17. Persell SD, Peprah YA, Lipiszko D, Lee JY, Li JJ, Ciolino JD, et al. Effect of
home blood pressure monitoring via a smartphone hypertension coaching
applicationor tracking application on adults with uncontrolled hypertension: A
randomized clinical trial. JAMA Netw Open. (2020) 3:e200255. doi: 10.1001/
jamanetworkopen.2020.0255
18. Wilson D, Driller M, Winwood P, Johnston B, Gill N. The effects of a brief
lifestyle intervention on the health of overweight airline pilots during COVID-19: A 12-
month follow-up study. Nutrients. (2021) 13:4288. doi: 10.3390/nu13124288
19. Wilson D, Driller M, Winwood P, Clissold T, Johnston B, Gill N. The
effectiveness of a combined healthy eating, physical activity, and sleep hygiene
lifestyle intervention on health and fitness of overweight airline pilots: A controlled
trial. Nutrients. (2022) 14:1988. doi: 10.3390/nu14091988
20. Li Y, Deng B, Guo YX, Peng QL, Hu T, Xia K. Association between glycated
hemoglobin and ambulatory blood pressure or heart rate in hypertensive patients. J
Cent South Univ (M edical Science).(2021)46:488–96. doi: 10.11817/j.issn.1672-
7347.2021.200750&lang=zh
21. Qi MM. Evaluation of management effect of family doctor team on elderly
type 2 diabetes patients in community. Chronic Pathematol J. (2020) 21:1742–
1744+1747. Available online at: https://kns.cnki.net/kcms2/article/abstract?v=
3uoqIhG8C44YLTlOAiTRKibYlV5Vjs7i8oRR1PAr7RxjuAJk4dHXopMaAf-
h8kQnSs6vTzG4u7gweRdg59MLTBuYHQv6rW0j&uniplatform=NZKPT (Accessed
July 28, 2023).
22. Cafazzo JA, Casselman M, Hamming N, Katzman DK, Palmert MR. Design of
an mHealth app for the self-management of adolescent type 1 diabetes: a pilot study.
J Med Internet Res. (2012) 14:e70. doi: 10.2196/jmir.2058
23. Lee EY, Cha SA, Yun JS, Lim SY, Lee JH, Ahn YB, et al. Efficacy of personalized
diabetes self-care using an electronic medical record-integrated mobile app in patients
with type 2 diabetes: 6-month randomized controlled trial. J Med Internet Res. (2022)
24:e37430. doi: 10.2196/37430
24. Yew TW, Chi C, Chan SY, van Dam RM, Whitton C, Lim CS, et al. A
randomized controlled trial to evaluate the effects of a smartphone application-based
lifestyle coaching program on gestational weight gain, glycemic control, and
maternaland neonatal outcomes in women with gestational diabetes mellitus: the
SMART-GDMStudy. Diabetes Care. (2021) 44:456–63. doi: 10.2337/dc20-1216
25. Chiu CJ, Yu YC, Du YF, Yang YC, Chen JY, Wong LP, et al. Comparing a social
and communication app, telephone intervention, and usual care for diabetes self-
management: 3-arm quasiexperimental eva luation study. JMIR Mhealth Uhealth.
(2020) 8:e14024. doi: 10.2196/14024
26. Yang Y, Lee EY, Kim HS, Lee SH, Yoon KH, Cho JH. Effect of a mobile phone-
based glucose-monitoring and feedback system for type 2 diabetes management in
multiple primary care clinic settings: cluster randomized controlled trial. JMIR Mhealth
Uhealth. (2020) 8:e16266. doi: 10.2196/16266
27. Zhang L, He XX, Shen Y, Yu HY, Pan JM, Zhu W, et al. Effectiveness of
smartphone app-based interactive management on glycemic control in Chinese
patients with poorly controlled diabetes: randomized controlled trial. J Med Internet
Res. (2019) 21:e15401. doi: 10.2196/15401
28. Yu Y, Yan Q, Li HZ, Li HM, Wang L, Wang H, et al. Effects of mobile phone
application combined with or without self-monitoring of blood glucose on glycemic
control in patients with diabetes: A randomized controlled trial. J Diabetes Investig.
(2019) 10:1365–71. doi: 10.1111/jdi.13031
29. Kang S, Zhao XJ, Gao JX, Tian SZ, Qiu JJ, Wang ZN, et al. Effect of the self-
management support based on mobile phone APP on the self-efficacy and
psychologicaldistress of young and middle-aged diabetic patients. J Bengbu Med Coll.
(2021) 46:115–9. doi: 10.13898/j.cnki.issn.1000-2200.2021.01.030
30. LeRoith D, Biessels GJ, Braithwaite SS, Casanueva FF, Draznin B, Halter JB, et al.
Treatment of diabetes in older adults: an endocrine society* Clinical practice guideline.
J Clin Endocrinol Metab. (2019) 104:1520–74. doi: 10.1210/jc.2019-00198
31. Ikegami H, Hiromine Y, Noso S. Insulin-dependent diabetes mellitus in older
adults: Current status and future prospects. Geriatr Gerontol Int. (2022) 22:549–53.
doi: 10.1111/ggi.14414
Tang et al. 10.3389/fendo.2025.1420578
Frontiers in Endocrinology frontiersin.org11
Available via license: CC BY
Content may be subject to copyright.
