Published on in Vol 24, No 7 (2022): July

Preprints (earlier versions) of this paper are available at, first published .
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

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

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

Original Paper

1Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

2Division of Endocrinology and Metabolism, Department of Internal Medicine, Wonkwang University Sanbon Hospital, Gunpo, Republic of Korea

3Division of Endocrinology and Metabolism, Department of Internal Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea

4Catholic Institute of Smart Healthcare Center, The Catholic University of Korea, Seoul, Republic of Korea

5Department of Preventive Medicine, College of Korean Medicine, Dongguk University, Gyeongju, Republic of Korea

*these authors contributed equally

Corresponding Author:

Seung-Hyun Ko, MD, PhD

Division of Endocrinology and Metabolism, Department of Internal Medicine, St. Vincent’s Hospital

College of Medicine

The Catholic University of Korea

93, Jungbu-daero, Paldal-gu

Suwon, 16247

Republic of Korea

Phone: 82 0312498155

Fax:82 031 253 8898


Background: A system that combines technology and web-based coaching can help treat chronic conditions such as diabetes. However, the effectiveness of apps in mobile health (mHealth) interventions is inconclusive and unclear due to heterogeneous interventions and varying follow-up durations. In addition, randomized controlled trial data are limited, and long-term follow-up is lacking, especially for apps integrated into electronic medical records.

Objective: We aimed to assess the effect of an electronic medical record–integrated mobile app for personalized diabetes self-care, focusing on the self-monitoring of blood glucose and lifestyle modifications, on glycemic control in patients with type 2 diabetes mellitus.

Methods: In a 26-week, 3-arm, randomized, controlled, open-label, parallel group trial, patients with type 2 diabetes mellitus and a hemoglobin A1c (HbA1c) level of ≥7.5% were recruited. The mHealth intervention consisted of self-monitoring of blood glucose with the automatic transfer of glucose, diet, and physical activity counseling data (iCareD system). Participants were randomly assigned to the following three groups: usual care (UC), mobile diabetes self-care (MC), and MC with personalized, bidirectional feedback from physicians (MPC). The primary outcome was the change in HbA1c levels at 26 weeks. In addition, diabetes-related self-efficacy, self-care activities, and satisfaction with the iCareD system were assessed after the intervention.

Results: A total of 269 participants were enrolled, and 234 patients (86.9%) remained in the study at 26 weeks. At 12 weeks after the intervention, the mean decline in HbA1c levels was significantly different among the 3 groups (UC vs MC vs MPC: −0.49% vs −0.86% vs −1.04%; P=.02). The HbA1c level decreased in all groups; however, it did not differ among groups after 26 weeks. In a subgroup analysis, HbA1c levels showed a statistically significant decrease after the intervention in the MPC group compared with the change in the UC or MC group, especially in patients aged <65 years (P=.02), patients with a diabetes duration of ≥10 years (P=.02), patients with a BMI of ≥25.0 kg/m2 (P=.004), patients with a C-peptide level of ≥0.6 ng/mL (P=.008), and patients who did not undergo treatment with insulin (P=.004) at 12 weeks. A total of 87.2% (137/157) of the participants were satisfied with the iCareD system.

Conclusions: The mHealth intervention for diabetes self-care showed short-term efficacy in glycemic control, and the effect decreased over time. The participants were comfortable with using the iCareD system and exhibited high adherence.

Trial Registration: Clinical Research Information Service, Republic of Korea KCT0004128;

J Med Internet Res 2022;24(7):e37430




Diabetes is one of the most important chronic diseases that threatens public health [1]. Since 2000, the prevalence of diabetes has more than tripled, and by 2021, more than 530 million people worldwide will have diabetes [1]. The main goal of diabetes management is to maintain glycemic control within the target range, which is often accomplished through lifestyle modification and the self-monitoring of blood glucose (SMBG) in patients with type 2 diabetes mellitus (T2DM) [2,3]. However, maintaining glycemic control is challenging for both patients and health care providers (HCPs) because it is difficult to encourage or motivate patients to make long-term lifestyle changes, interpret their SMBG data, and provide immediate feedback and understand the patients’ lifestyle due to brief clinic visit times and long visit intervals. Digital health care or mobile health (mHealth) services facilitate the collection of personal data, analyze data to evaluate clinical conditions, and provide personalized interventions or monitoring [4]. Through mHealth systems, patients with T2DM are encouraged to consume a healthy diet and perform physical activity (PA). Patient-reported data are used to tailor feedback messages, including health promotion, motivation, encouragement, reminders, and emotional support messages. Therefore, mHealth interventions may improve the health outcomes in patients with T2DM via tailored personalized interventions [5].

Recent mHealth interventions targeting patients with T2DM have diverse goals and components, including insulin-management apps, wearable blood glucose meters, automated text messages, health diaries, and virtual health coaching [6]. However, the effect of apps on mHealth interventions remains inconclusive and unclear because of heterogeneous interventions and various lengths of follow-up. On the basis of a mixed treatment comparison network meta-analysis using data from published randomized controlled trials (RCTs), mobile apps or apps with e-coaching interventions for patients with T2DM were more effective in improving the hemoglobin A1c (HbA1c) levels, fasting glucose, and hypoglycemia frequency than usual care (UC) during a 3- or 6-month follow-up period [7]. A meta-analysis of 13 studies on mobile apps for diabetes suggested overall efficacy in reducing HbA1c levels, with a mean decrease of 0.44% (95% CI 0.29%-0.59%), as well as increased perception of self-care among mobile app users [8]. Various types of mHealth interventions have resulted in decrease in HbA1c levels, in several RCTs that included patients with T2DM [9,10]. Most interventions demonstrated clinically and statistically significant efficacy, although some interventions had null results or achieved a <0.5% difference in the reduction of HbA1c levels between the intervention and control groups [6].

Systems that combine technology and web-based coaching can be beneficial for treating diabetes and prediabetes [11,12]. To maintain lifestyle modification, patients should be continuously motivated and monitored in various ways, including individualized diabetes education and the use of aids based on information and communications technology [2,11]. Beyond using apps for personal use to collect and monitor lifelog data, web-based communication with physicians and HCPs would be more effective for diabetes self-care in patients with T2DM. Using this technology, physicians can view patient data in real time and between clinic visits and incorporate their lifelog data with clinical data derived from personal sensors and wearables in electronic medical records (EMRs). With regard to this type of intervention that connects a self-care app and EMRs, there is limited RCT data, and long-term follow-up is lacking.


Therefore, we designed a diabetes management system using an EMR-integrated mobile app that provided regular feedback from HCPs to support diabetes self-care in a clinical setting for patients with T2DM. The app mainly offers lifestyle counseling to aid the SMBG, diet planning, and PA. The purpose of this study was to compare the clinical efficacy of a 26-week personalized diabetes self-care system using an EMR-integrated mobile app with that of UC in patients with T2DM. We also compared the effectiveness of this system with and without feedback from the HCPs.

Study Design

This study was designed as a 26-week open-label, parallel group, 3-arm RCT conducted in 2 separate university-affiliated hospitals from August 2019 to December 2021. A detailed description of the study design has been previously reported [13]. As shown in Figure 1, all participants were randomly assigned to 1 of the following 3 groups: group 1, UC; group 2, mobile diabetes self-care (MC); and group 3, MC with personalized, bidirectional feedback from physicians (MPC). Among the 279 patients screened, 269 (96.4%) were enrolled in the study, and of these, 234 (86.9%) completed the intervention. A total of 269 individuals were included in the intention-to-treat (ITT) analyses (Figure 2).

Figure 1. Study design. HbA1c: hemoglobin A1c.
View this figure
Figure 2. Flowchart of patient enrollment and status. MC: mobile diabetes self-care; MPC: mobile diabetes self-care with personalized, bidirectional feedback from physicians; UC: usual care.
View this figure


Patients aged 19 to approximately 74 years with T2DM, an HbA1c level of ≥7.5%, and a BMI of ≥18.5 kg/m2 who could use a smartphone and consented to participate were eligible for this study. Participants who were using insulin pumps; who were pregnant; who had serious medical illness including end-stage renal disease, heart failure, and cancer; or who had difficulty performing PA were excluded. Further detailed inclusion and exclusion criteria are described in the study protocol [13]. The trial was registered with the Clinical Research Information Service, Republic of Korea (KCT0004128).

Sample Size

On the basis of previous studies [10,14,15], we assumed a mean difference in HbA1c levels of at least 0.60 between the control and intervention groups and an SD, within groups, of 0.75 after 6 months. In a single-factor ANOVA study, 73 patients per group resulted in 90% power and a .05 significance level. Finally, 282 participants (94 patients per group) were required to achieve 73 patients per group after accounting for a predicted dropout rate of up to 20%.


Those who signed the informed consent form were randomly assigned to 1 of 3 groups in a 1:1:1 ratio. Random numbers were generated using SAS (version 9.3; SAS Institute) [13]. Stratification by institution and a baseline HbA1c level of 8.5% were performed using the stratified permutated block randomization method. As this study had an open-label design, HCPs and participants were informed of the group assignment at the time of randomization. The participants and HCPs could not be blinded in this study because of the intervention method.


Regardless of the assigned group, all participants were provided a glucometer (CareSens N; i-SENS, Inc) from which SMBG data were automatically transferred to our mHealth system, called the iCareD system. Basic diabetes education, including information on the SMBG, diet, and PA, was provided to all participants. The control group (UC) received UC according to the standard care for patients with T2DM by the Korean Diabetes Association [2]. Participants were instructed to perform the SMBG 4 times a day (before a meal in the morning and 2 hours after every meal), record their glucose levels in a notebook, and bring the notebook to their clinic visits. In the MC and MPC intervention groups, a diabetes self-care mobile app (iCareD; Medical Excellence Inc) was used in addition to the UC for diabetes management (Table S1 in Multimedia Appendix 1) [13]. The app allowed patients to enter their self-care data (SMBG, dietary habits, and step count), and automated text messages (educational, behavioral, and motivational messages) from the iCareD system were sent to their mobile phones. The automated messages were sent 3 times per week and consisted of 2 standardized messages for diabetes self-care and lifestyle modification and a customized message according to the lifestyle questionnaire. In the iCareD system, specific content for each message was developed based on the clinical practice guidelines of the Korean Diabetes Association and multidisciplinary expert opinions from our diabetes care team (endocrinologist, certified dietician, and diabetes educator). In both the MC and MPC groups, the mobile app was integrated with the EMR in each hospital; therefore, HCPs also evaluated participants at every 3-month visit based on the data obtained from the mobile app. Participants were instructed to upload their diet photos through the app. To encourage PA, we set the goal of a step count of >10,000 steps per day, and this goal was adjusted according to underlying diseases or individual health conditions. These data were also transferred to the iCareD system, which was integrated with the EMR system for HCPs in the hospital.

For the MPC group, based on our previous study, an HCP sent additional personalized recommendations and bidirectional feedback to each participant every 2 weeks through the iCareD system during the intervention period; the feedback was mainly related to diabetes self-care, the SMBG, or lifestyle modification [16].

In the offline system, patients visited the outpatient clinic every 3 months, and at these visits, physicians conducted face-to-face interviews with their patients, reviewed their uploaded data linked to the EMR, and provided individualized interventions based on these data. All participants were allowed to contact educator nurses over telephone but were encouraged to use the app.

Primary and Secondary Outcomes

The primary outcome was the difference in the change in HbA1c levels (%) between baseline and 26 weeks among the 3 groups. The secondary outcome was the changes in HbA1c and fasting glucose (mg/dL) levels between the UC and 2 mobile-based intervention groups between baseline and 26 weeks. The HbA1c level <7% attainment rates were evaluated at 12 and 26 weeks. In addition, lifestyle changes based on PA and diet records; cardiometabolic risk factors such as body weight, blood pressure, and lipid profile; program satisfaction and compliance (or adherence); frequency of hypoglycemia; and changes in homeostasis model assessment of insulin resistance and β cell function were assessed at 26 weeks. Adherence was defined as the proportion of intervention participation using the iCareD app, including blood glucose measurement and feedback confirmation, over a 24-week period. Exploratory assessment variables included changes in diabetes prescriptions, SMBG frequency, and BMI.

Participant satisfaction was assessed in the 2 intervention groups by using a locally developed satisfaction survey at 26 weeks. The survey included 5-level Likert-type questions evaluating self-care efficacy and various opinions on the iCareD system, such as the ease and frequency of text messages, perceived efficacy, and willingness to continue with or recommend the iCareD program to family or friends. A score of 5 indicated very satisfied or strongly agree. Higher scores on the satisfaction scale reflect better results.


Demographic and clinical information collected at baseline and follow-up has been described previously [13]. PA was tracked using a Google Fit mobile app and assessed as the total step count per day [17]. Body composition data were obtained using a bioimpedance analyzer (InBody 720 and 970, InBody Co, Ltd) at baseline and every 26 weeks. Laboratory parameters, including fasting glucose, HbA1c level, and lipid profile, were collected at every visit. C-peptide and urinary albumin to creatinine ratios were measured at baseline and every 26 weeks. We used the updated homeostasis model assessment calculator to evaluate the homeostasis model assessment of insulin resistance and β cell function [18-20].

Hypoglycemic events, including hospitalization or emergency room visits due to hypoglycemia, blood glucose levels <70 mg/dL, or related symptoms even without the SMBG, were evaluated at every visit. Diabetes management behaviors such as SMBG frequency, PA, and diet records were obtained at every visit. SMBG frequency was defined as the average number of tests performed per day, calculated for each patient based on the records in the web system. The goal achievement rate for PA was defined as the number of days the target was reached/total measured days × 100 (%). User satisfaction with mobile app was surveyed in the MC and MPC groups.

Statistical Analysis

Continuous variables were presented as mean (SD), whereas categorical data were presented as frequencies with percentages. Analysis of covariance was used to compare the mean 26-week HbA1c levels among the 3 groups. Post hoc analysis was performed using the Bonferroni method. The number of hypoglycemic events among the groups was compared using the chi-square test or Fisher exact test. The goal achievement rate for PA was analyzed, except for the case of <1000 steps per day. Missing data were replaced by the last-observation-carried-forward method for all participants who were followed up at least once after enrollment. Both per-protocol and ITT analyses were conducted. Unless otherwise specified, analyses were performed based on the results of the ITT analysis. The analysis was performed using SAS (version 9.3; SAS Institute Inc). Statistical significance was set at P value of <.05.

Ethics Approval

The study protocol was approved by the ethics committee of St. Vincent’s Hospital (VC19EEDI0085) and St. Mary’s Hospital (KC19EEDE0278). All participants provided written informed consent before enrollment in the study. All data and information were anonymized according to the International Conference on Harmonization Good Clinical Practice guidelines.

Participant Flow

During the recruitment period from August 2019 to August 2020 in the outpatient clinics of 2 separate university-affiliated diabetes centers, a total of 279 participants were assessed for eligibility and 269 (96.4%) participants were randomized. A total of 10 participants withdrew consent, leaving 269 participants to be included in this study (Figure 2).

After the 26-week follow-up, the total retention rate was 86.9% (234/269), with an equal distribution among the groups. The baseline analysis revealed no significant differences between those who completed the study and those who were lost to follow-up (data not shown).

Clinical Characteristics of Participants

The mean age of the participants was 52.5 (12.3) years, and 42.8% (115/269) of the participants were male. The mean baseline HbA1c level and duration of diabetes were 8.7% (1.3%) and 11.4 (8.1) years, respectively. The mean BMI was 27.2 (4.6) kg/m2, and 41.3% (111/269) of the participants had hypertension. None of the other baseline characteristics or variables differed significantly among the 3 study groups. There was no significant difference in the presence of microvascular and macrovascular complications between the 3 groups (all P>.05; Table 1).

Table 1. Baseline demographic and clinical characteristics of patients.

Group 1: UCa (n=87)Group 2: MCb (n=91)Group 3: MPCc (n=91)P value
Age (years), mean (SD)52.6 (12.1)51.3 (13.1)53.6 (11.7).66
Sex (male), n (%)37 (43)40 (44)38 (42).96
Duration of diabetes (years), mean (SD)11.5 (8.2)10.9 (8.3)11.9 (7.8).61
Body weight (kg), mean (SD)73.5 (17.2)73.2 (16.9)71.8 (13.0).97
BMI (kg/m2), mean (SD)27.4 (4.9)27.3 (5.0)26.8 (3.9).79
SBPd (mm Hg), mean (SD)129.6 (14.5)126.7 (15.0)129.0 (14.5).38
DBPe (mm Hg), mean (SD)78.0 (10.5)77.1 (9.8)76.8 (10.5).47
Current smokers, n (%)10 (12)11 (12)6 (7).40
Alcohol consumption, n (%)27 (31)25 (28)21 (23).49
Physical activity (step count per day), mean (SD)6280.9 (3159.4)6208.9 (3212.8)6630.0 (3639.8).78
Education, n (%).25

Elementary10 (12)5 (6)14 (15)

High school48 (55)50 (55)43 (47)

College29 (33)36 (40)34 (37)
Comorbidities, n (%)

Hypertension (yes)39 (52)36 (45)36 (25).49

Hyperlipidemia (yes)66 (88)72 (90)75 (89).92
Complication, n (%)

CVDf13 (15)13 (14)15 (17).91

Retinopathy15 (17)15 (17)21 (23).46

Nephropathy27 (31)32 (35)37 (41).40

Neuropathy9 (10)10 (11)15 (17).40
Antidiabetic medications, n (%).38

Insulin only1 (1)3 (3)2 (2)

Oral agents53 (61)50 (55)46 (51)

Insulin+oral agents23 (26)28 (31)35 (39)
Laboratory measurements, mean (SD)

Fasting glucose (mg/dL)164.7 (50.0)163.6 (60.8)166.3 (61.2).28

eGFRg (ml/min/1.73 m2)101.3 (23.3)99.5 (22.9)95.0 (28.4).08

HbA1ch level (%)8.6 (1.1)8.7 (1.3)8.8 (1.4).78

Total cholesterol (mg/dL)154.7 (37.0)170.2 (58.5)157.7 (37.8).16

Triglyceride (mg/dL)158.1 (107.0)177.1 (254.0)156.5 (111.4).72

HDLi cholesterol (mg/dL)48.2 (12.4)50.3 (12.6)48.2 (11.7).39

LDLj cholesterol (mg/dL)76.2 (32.4)86.6 (35.6)79.4 (29.9).13

C-peptide level (ng/mL)2.1 (1.2)2.2 (1.6)2.1 (1.3).93

HOMA-IRk1.9 (1.1)1.9 (1.5)1.9 (1.2).86

HOMA-βl49.7 (32.7)53.0 (36.2)51.5 (38.0).74

aUC: usual care.

bMC: mobile diabetes self-care.

cMPC: mobile diabetes self-care with personalized, bidirectional feedback from physicians.

dSBP: systolic blood pressure.

eDBP: diastolic blood pressure.

fCVD: cardiovascular disease.

geGFR: estimated glomerular filtration rate.

hHbA1c: hemoglobin A1c.

iHDL: high-density lipoprotein.

jLDL: low-density lipoprotein.

kHOMA-IR: homeostasis model assessment for insulin resistance.

lHOMA-β: homeostasis model assessment for β-cell function.

Primary Outcome Measure: Change in HbA1c Level

The change in HbA1c levels did not differ significantly at 26 weeks among the 3 groups (Figure 3). However, the reduction in HbA1c levels at 12 weeks was significantly different among the 3 groups (P=.02; Table 2). In the post hoc analysis, only the MPC group showed a significant decrease in HbA1c levels compared with the UC group. (Table 2). In a subgroup analysis, a decrease in HbA1c levels among the 3 groups showed a significant difference only at 12 weeks, especially in the patients aged <65 years (P=.02), patients with a diabetes duration of ≥10 years (P=.02), patients with a BMI of ≥25.0 kg/m2 (P=.004), patients with a C-peptide level of ≥0.6 ng/mL (P=.008), and patients who did not undergo treatment with insulin (P=.004; Table S2 in Multimedia Appendix 2). Adjusting for age, sex, and baseline HbA1c level did not affect the HbA1c level change results.

Figure 3. Mean HbA1c level from baseline to 26 weeks. HbA1c: hemoglobin A1c; MC: mobile diabetes self-care; MPC: mobile diabetes self-care with personalized, bidirectional feedback from physicians; UC: usual care.
View this figure
Table 2. Changes in hemoglobin A1c (HbA1c) level from baseline and HbA1c level <7% attainment rate.

Group 1: UCa (n=87)Group 2: MCb (n=91)Group 3: MPCc (n=91)P value
Changes in HbA1c level from baseline (%), mean (SD)

Change at 12 weeks−0.5 (1.0)−0.9 (1.4)−1.0 (1.5)d.02

Change at 26 weeks−0.6 (1.1)−0.8 (1.7)−1.0 (1.5).30
HbA1c level <7% attainment rate, n (%)

At 12 weeks12 (14)15 (17)21 (23).25

At 24 weeks15 (17)15 (17)26 (29).08

aUC: usual care.

bMC: mobile diabetes self-care.

cMPC: mobile diabetes self-care with personalized, bidirectional feedback from physicians.

dP<.05 versus group 1 in post hoc analysis.

Secondary Outcome Measures

The HbA1c level <7% attainment rate increased from between the UC and MPC groups at 12 weeks, but the difference was not significant (UC vs MC vs MPC: 12/87, 14% vs 15/91, 17% vs 21/91, 23%; P=.25). The MPC group tended to have higher attainment rate of HbA1c level <7% at 26 weeks, compared with the other groups (UC vs MC vs MPC: 15/87, 17% vs 15/91, 17% vs 26/91, 29%; P=.08; Table 2). Other changes in clinical and behavioral outcomes from baseline to follow-up are shown in Table 3. Fasting glucose levels were significantly reduced from baseline to each time point in all 3 groups (P<.05 for each group, each follow-up time). However, the change in fasting glucose levels did not differ among the 3 groups during the 26-week intervention period (Table 3). Changes in body weight and BMI from baseline to 26 weeks also showed no differences among the study groups.

The frequency of the SMBG did not show any significant differences among the 3 groups at the 26-week follow-up. However, compared with patients in the UC group, those in the 2 intervention groups (iCareD system users) tended to have more frequent SMBG recordings at 12 weeks (UC vs iCareD system users: 1.4 [1.1] times per day vs 1.6 [1.0] times per day; P=.09). The average frequency of the SMBG showed a negative correlation with HbA1c (r=0.277; P=.003).

PA, defined as step counts per day, was not significantly different among the study groups 26 weeks after the intervention. The goal achievement rate for PA was higher in the MC and MPC groups than that in the UC group at 26 weeks, but the difference was not significant (UC vs MC vs MPC: 15.1% vs 18.5% vs 17.9%; P=.45). The changes in low-density lipoprotein–cholesterol level showed significant differences among the 3 groups at both 12 and 26 weeks (P=.001 for 12 weeks and P=.02 for 26 weeks). Low-density lipoprotein–cholesterol levels increased in the UC group and decreased in the MC and MPC groups during the follow-up period.

Table 3. Secondary study outcomes at baseline and follow-up.

Group 1: UCa (n=87)Group 2: MCb (n=91)Group 3: MPCc (n=91)P value
Fasting glucose (mg/dL), mean (SD)

Baseline164.7 (50.0)163.6 (60.8)166.3 (61.2).83

12 weeks154.6 (54.7)145.3 (51.7)146.3 (45.3).28

26 weeks148.9 (55.7)146.3 (56.2)142.7 (47.5).76

Change from baseline at 12 weeks−10.1 (50.9)−18.3 (64.3)−20.0 (50.6).68

Change from baseline at 26 weeks−15.8 (57.0)−17.3 (64.5)−23.7 (57.6).89
PAd (step counts/day), mean (SD)

Baseline to approximately 12 weeks6069.3 (2774.4)6143.0 (2849.6)6447.0 (3338.2).87

12 weeks to 26 weeks5827.0 (2879.9)6019.3 (2953.1)6319.1 (3652.4).94
Body weight (kg), mean (SD)

Baseline73.5 (17.2)73.2 (16.9)71.9 (13.0).97

12 weeks73.2 (17.3)73.0 (17.1)71.8 (13.2).98

26 weeks73.1 (17.1)73.2 (17.4)71.8 (13.2).97

Change from baseline at 12 weeks−0.27 (1.85)−0.19 (2.05)−0.07 (2.17).93

Change from baseline at 26 weeks−0.42 (2.84)0.03 (3.22)−0.03 (3.51).98
BMI (kg/m2), mean (SD)

Baseline27.4 (4.8)27.3 (5.0)26.9 (3.9).80

12 weeks27.3 (4.8)27.2 (5.1)26.8 (4.0).84

26 weeks27.3 (4.7)27.3 (5.2)26.8 (4.0).91

Change from baseline at 12 weeks−0.11 (0.73)−0.07 (0.72)−0.03 (0.82).94

Change from baseline at 26 weeks−0.18 (1.08)0.01 (1.19)−0.01 (1.33).97
LDLe cholesterol (mg/dL), mean (SD)

Baseline76.2 (32.4)86.6 (35.6)79.4 (29.9).13

12 weeks80.5 (36.0)80.3 (34.6)76.2 (33.8).52

26 weeks78.6 (33.0)77.5 (33.7)76.8 (34.7).86

Change from baseline at 12 weeks4.8 (19.3)−6.4 (29.2)−5.1 (25.8).001

Change from baseline at 26 weeks3.5 (18.7)−9.1 (32.0)−4.8 (30.3).02
HOMA-IRf, mean (SD)

Baseline1.9 (1.1)1.9 (1.5)1.9 (1.2).88

26 weeks1.8 (1.0)1.9 (1.5)1.8 (1.4).76

Change from baseline at 26 weeks−0.1 (0.8)0.0 (0.1)−0.2 (1.2).81
HOMA-βg, mean (SD)

Baseline49.7 (32.7)53.0 (36.2)51.5 (38.0).74

26 weeks58.5 (30.2)60.5 (35.3)67.7 (80.6).99

Change from baseline at 26 weeks8.6 (28.2)6.9 (37.5)14.5 (75.5).78

aUC: usual care.

bMC: mobile diabetes self-care.

cMPC: mobile diabetes self-care with personalized, bidirectional feedback from physicians.

dPA: physical activity.

eLDL: low-density lipoprotein.

fHOMA-IR: homeostasis model assessment for insulin resistance.

gHOMA-β: homeostasis model assessment for β-cell function.

Satisfaction for iCareD System

A total of 157 out of the 182 (86.2%) participants completed the satisfaction survey at 26 weeks of the intervention. Participants’ satisfaction with the iCareD program was very high in both the MC and MPC groups. Overall, most patients were satisfied with the system (104/157, 66.2% strongly agree; 33/157, 21% agree), understood all the messages (27/157, 17% strongly agree; 89/157, 57% agree), were willing to use the program (56/157, 36% strongly agree; 73/157, 46% agree), felt that it helped them reach their goals (74/157, 47% strongly agree; 61/157, 39% agree), and recommended the iCareD system to a family member or friend with T2DM (57/157, 36% strongly agree; 77/157, 49% agree). No differences were observed between the 2 groups (Table 4).

Table 4. Messages read for 6 months (intervention period) and program satisfaction.

Group 2: MCa (n=91)Group 3: MPCb (n=91)P value
Automated message sent, N141155N/Ac
Automated message read, n (%)110 (78)134 (86.4).09
Personalized message sent, NN/A12N/A
Personalized message read, n (%)N/A12 (100)<.001
Satisfaction survey

Total, N7780N/A

Overall satisfaction, n (%)67 (87)70 (88).39

Help to diabetes self-care, n (%)67 (87)69 (86).37

Will you recommend the app to others? n (%)67 (87)67 (84).92

Do you want to continue using the app? n (%)63 (81)66 (83).70

aMC: mobile diabetes self-care.

bMPC: mobile diabetes self-care with personalized, bidirectional feedback from physicians.

cN/A: not applicable.

There were no statistically significant differences between the MC and MPC groups in skill and technique acquisition, health service navigation, or manipulation of app content. iCareD system users especially valued convenient SMBG data reporting via automatic wireless transfer from the glucometer to the app without needing to write directly in the notebook and the ability to browse accumulated personal data (63/157, 40% strongly agree; 69/157, 44% agree). An end-of-intervention usability survey demonstrated that participants were comfortable with using the iCareD system.

With regard to adherence, compared with participants in the MC group, those in the MPC group checked the automated text messages from the iCareD system for 26 weeks. The proportion of participants who read >75% of the automated messages (3 times per week) was significantly higher in the MPC group than in the MC group (76/91, 83% vs 61/91, 67%; P=.02). The number of participants who uploaded photos of a meal and step count was higher in the MPC group than in the MC group, but the proportion declined to 50% by the end of the study in both groups.

Adverse Events

No serious adverse events were reported from enrollment until the completion of this study. Hypoglycemic events were infrequent and showed no differences among the groups at 26 weeks (Table 5). No deaths, direct study-related adverse events, or severe hypoglycemic episodes were reported or detected.

Table 5. Hypoglycemic events.

Group 1: UCaGroup 2: MCbGroup 3: MPCcP value
Patients who experienced hypoglycemia, N879191N/Ad

Baseline to <12 weeks, n (%)15 (17)34 (35)32 (35).03

12 weeks to 26 weeks, n (%)22 (25)28 (34)26 (30).84
Frequency of hypoglycemiae, mean (SD)

Baseline to <12 weeks6.1 (7.4)2.9 (3.1)4.3 (5.6).25

12 weeks to 26 weeks3.2 (4.1)3.3 (2.9)3.6 (3.8).56
Unexpected clinic visit or hospitalization due to hypoglycemiaf, n (%)0 (0)2 (2)0 (0).18

aUC: usual care.

bMC: mobile diabetes self-care.

cMPC: mobile diabetes self-care with personalized, bidirectional feedback from physicians.

dN/A: not applicable.

ePer patient who experienced hypoglycemia for 90 days.

fHypoglycemia was defined as a blood glucose level of <70 mg/dL.

Principal Findings

This study was an RCT investigating a hospital-based, EMR-integrated mobile app–based diabetes self-care intervention over a 26-week period in patients with T2DM. Although the HbA1c level decreased from the baseline value in all 3 groups, the HbA1c changes did not show any significant difference between the control and 2 intervention groups at 26 weeks. However, we found that interactive mHealth intervention for diabetes self-care (MPC group) significantly decreased HbA1c levels by −1.04% compared with −0.49% in the UC group and −0.86% in the MC group at 12 weeks. This finding was consistent among non–insulin users, those aged < 65 years, and those who were obese.

Owing to the global growth in the use of mobile phones with powerful platforms to help health care, many types of apps have been developed. According to Liquid-State, in 2018, there were >318,000 mHealth care apps available for patients, and approximately 200 new health care apps were being built each day [21]. In all, 70% of mHealth practitioners have reported that diabetes is currently the leading health target for the mobile app industry [22]. In 2017, more than 1500 diabetes-related apps were reported to be available to users.

Mobile apps related to diabetes management generally deal with information about diabetes, healthy diet, PA, weight loss, the SMBG, adherence, and motivation [6]. mHealth interventions support self-care and diabetes education and encourage lifestyle modification. These data may be used to tailor feedback messages or advice on specific behavior changes to implement; these messages are usually sent automatically according to an algorithm [5,9,23]. Compared with conventional mobile apps that collect only patient-driven data, our EMR-integrated mobile app could provide important clues to the future direction of mobile app development for diabetes management in 2 respects. First, HCPs can be provided with patients’ medical history such as comorbidities or current medications, in addition to patient-centered data. Given the high rates of comorbidity and concurrent medications in patients with T2DM [24], this integrated provision of medical information may allow HCPs to provide accurate guidance to patients on diet, exercise, and management of comorbid diseases rather than simply focusing on the message to lower blood glucose levels. Second, from the patients’ perspective, it is possible to provide better insight into diabetes management by providing laboratory results by time course along with personal data collection information. In particular, our systems adopted visualization of glucose levels by color to improve awareness or alertness of hyperglycemia (red) or hypoglycemia (black) [13].

Using the EMR-integrated mobile app intervention, we demonstrated a significant reduction in HbA1c levels after 12 weeks of intervention. Consistent with our results, a 3-month RCT using DialBetics, a smartphone-based self-management support system for Japanese patients with T2DM, demonstrated that HbA1c levels decreased by an average of 0.4% compared with an increase of 0.1% in the control group, with improvement in fasting glucose level and BMI [25]. A systematic review also revealed limited robust evidence of the promising short-term effectiveness of mHealth interventions for diabetes, such as the improvement of HbA1c levels [14,15,26,27]. However, a caveat of these RCT analyses is that most of them included only studies conducted under highly controlled conditions with a small number of patients [14,15,26,27]. Interestingly, the first Food and Drug Administration–approved mobile app, BlueStar, showed no intervention effects in a real-world setting with >100 patients, despite significant reductions in HbA1c (>1%) in their first RCT with 30 patients [28,29]. It is noteworthy that our study showed significant differences in HbA1c levels among groups at 3 months in real-world practice, with a relatively large number of patients at 2 different clinical sites. This finding suggests the potential usefulness of EMR-integrated mobile app interventions in diabetes management. In addition, we found that the intervention effects in the MPC group were prominent in patients with younger age, obesity, higher C-peptide levels, and no insulin treatment. This finding implies that mobile-based interventions, such as other diabetes treatments, may be more effective when β cell function is preserved. This also highlights the importance of early intervention. However, the intervention effect was also pronounced in those with a diabetes duration ≥10 years. This indicates that although early intervention may be important, such interventions may also be effective in long-standing diabetes.

Mobile phone apps that receive blood glucose data from a connected glucometer are available and have the capacity to make data upload and review less burdensome [30]. The internet-based SMBG system, which augments the SMBG by giving patients the means to communicate their blood glucose levels to their HCP for actional feedback, has been shown to reduce HbA1c levels in some RCTs involving patients with T2DM [31]. The inverse correlation between reporting frequency and HbA1c levels, as well as the significant difference in HbA1c levels only for frequent testers (defined as those who test on average twice or more per day), suggests that frequent SMBG has an effect on reducing HbA1c levels only when combined with regular, frequent communication of SMBG with an HCP [32]. The recording of a food diary using a smartphone app is a well-known simple tool, and technology to use images to quantify the composition and calorie content of food has been developed. However, it is difficult and cumbersome for users to constantly record data based on their eating habits [33,34]. However, automated integration of glucose and lifelog data in the EMR between scheduled clinic visits improves the HCP workflow for reviewing data and improves communication with patients, eventually leading to better care [35].

In this study, 87.2% (137/152) of the participants were satisfied with the iCareD system and answered that the app helped their diabetes self-care skills; however, the iCareD system failed to decrease HbA1c levels over >3 months. There are possible explanations for the lack of improvement in HbA1c levels in mHealth app users. First, age is a barrier to digital health care adoption and may influence the adoption of new technologies [36]. The mean age of the participants in this study was 52.5 years (range 20-74 years). A total of 16% (42/269) of the participants were aged >65 years. Second, the iCareD system was developed with a focus on lifestyle changes rather than strict glucose control or active medication adjustment, such as whirlwind dosage escalation of antidiabetic medications. In the case of the TExT-MED study, a unidirectional text message intervention for diabetes self-care providing text message triggers to encourage individuals to engage in self-care behaviors, the TExT-MED program also did not result in a significant improvement in HbA1c levels. However, trends toward improvement in the primary outcome of HbA1c levels and other secondary outcomes, including quality of life, were observed. Similar to our satisfaction survey, 94% (44/47) of the patients who received the TExT-MED intervention enjoyed the program and believed it was a good way to learn about diabetes [5]. Patient engagement was highest for more medical topics, such as glucose monitoring and medications, and lower for lifestyle topics, such as PA and healthy coping [6]. Therefore, we suggest that interventions for diabetes self-care should include improving HbA1c levels through modification of lifestyle, glucose monitoring, and adherence and dosage adjustment for antidiabetic medications [37]. Third, our patients had a long duration of diabetes and were insulin users [32]. In general, the effects of education and lifestyle changes decrease with the duration of diabetes [38]. Fourth, there was no evidence of the most effective frequency of the intervention messages. We sent personalized intervention messages from HCPs every 2 weeks and automated general informative messages every other day. Patient satisfaction and accessibility are important for improving self-management efficiency, and the clinical course can be improved through personalized intervention [4]. More frequent, bidirectional, real-time communication with HCPs and patients would lead to more effective improvement in HbA1c levels.

Although we did not observe remarkable improvement in HbA1c levels over the long term, it is encouraging that the goal achievement rates for PA were higher in the intervention group at 26 weeks. When the target of 7500 steps per day was applied [39-41], the difference in goal achievement rates among the groups further increased (UC vs MC vs MPC: 25.9% vs 28.5% vs 30.2%). Given the lifelong management of T2DM, the small differences observed in the short term may increase in the future. Furthermore, in terms of the prevention of diabetic complications such as cardiovascular disease, PA cannot be overemphasized [40-42]. Finally, we expect that our study will provide more solid evidence of the short-term efficacy of mobile app–based diabetes management. In particular, in relation to the recent global public health crisis, the COVID-19 pandemic, this methodology is expected to contribute greatly in the future to promote the rapid introduction and diffusion of new digital health–related technologies such as telemedicine [43].

To maximize the effect of mHealth interventions, it is important to tailor the intervention in a patient-centered manner and evaluate user satisfaction [44]. Undoubtedly, more RCTs with longer follow-up periods should be conducted to evaluate the long-term effects of diabetes-related mobile apps and to confirm that the outcomes seen in initial studies are sustainable over time [22].


In summary, the use of iCareD apps for diabetes self-care can be considered an effective measure, especially when patients can communicate with HCPs [8]. Remote health data monitoring and real-time communication with patients supported self-care of diabetes, resulting in short-term improvement in HbA1c levels. An mHealth system for patients with T2DM should be developed to support and motivate sustainable behavior changes in patients and to allow for an approach that is more tailored to individual needs.


This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (grant HI19C0784). The funders did not play any role in the study design, data collection and analysis, management preparation, or decision to publish.

Authors' Contributions

EYL contributed to the drafting and revision of the manuscript, supervision of the study, and acquisition of data. JSY and SAC contributed to the revision of the manuscript, supervision of the study, and acquisition of data. SYL and JHL contributed to the statistical methodology and data management. YBA and KHY contributed to the revision of the manuscript and supervision of the study. SHK contributed to the revision of the manuscript and design and supervision of the study. All authors have read and approved the final manuscript.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Comparison of the intervention protocol in each group.

DOCX File , 17 KB

Multimedia Appendix 2

Hemoglobin A1c (HbA1c) level and HbA1c level changes from baseline to 12 and 26 weeks according to subgroup analysis.

DOCX File , 21 KB

Multimedia Appendix 3

CONSORT-eHEALTH checklist (V 1.6.1).

PDF File (Adobe PDF File), 2853 KB

  1. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract 2022 Jan;183:109119. [CrossRef] [Medline]
  2. Hur KY, Moon MK, Park JS, Kim SK, Lee SH, Yun JS, Committee of Clinical Practice Guidelines‚ Korean Diabetes Association. 2021 clinical practice guidelines for diabetes mellitus of the Korean Diabetes Association. Diabetes Metab J 2021 Jul;45(4):461-481 [FREE Full text] [CrossRef] [Medline]
  3. American Diabetes Association Professional Practice Committee, Draznin B, Aroda VR, Bakris G, Benson G, Brown FM, et al. 9. Pharmacologic approaches to glycemic treatment: standards of medical care in diabetes-2022. Diabetes Care 2022 Jan 01;45(Suppl 1):S125-S143. [CrossRef] [Medline]
  4. Rhee SY, Kim C, Shin DW, Steinhubl SR. Present and future of digital health in diabetes and metabolic disease. Diabetes Metab J 2020 Dec;44(6):819-827 [FREE Full text] [CrossRef] [Medline]
  5. Arora S, Peters AL, Burner E, Lam CN, Menchine M. Trial to examine text message-based mHealth in emergency department patients with diabetes (TExT-MED): a randomized controlled trial. Ann Emerg Med 2014 Jun;63(6):745-54.e6. [CrossRef] [Medline]
  6. Shan R, Sarkar S, Martin SS. Digital health technology and mobile devices for the management of diabetes mellitus: state of the art. Diabetologia 2019 Jun;62(6):877-887. [CrossRef] [Medline]
  7. Hyun MK, Lee JW, Ko SH, Hwang JS. Improving glycemic control in type 2 diabetes using mobile applications and e-coaching: a mixed treatment comparison network meta-analysis. J Diabetes Sci Technol (forthcoming) 2021 May 12:19322968211010153. [CrossRef] [Medline]
  8. Bonoto BC, de Araújo VE, Godói IP, de Lemos LL, Godman B, Bennie M, et al. Efficacy of mobile apps to support the care of patients with diabetes mellitus: a systematic review and meta-analysis of randomized controlled trials. JMIR Mhealth Uhealth 2017 Mar 01;5(3):e4 [FREE Full text] [CrossRef] [Medline]
  9. Orsama AL, Lähteenmäki J, Harno K, Kulju M, Wintergerst E, Schachner H, et al. Active assistance technology reduces glycosylated hemoglobin and weight in individuals with type 2 diabetes: results of a theory-based randomized trial. Diabetes Technol Ther 2013 Aug;15(8):662-669. [CrossRef] [Medline]
  10. Quinn CC, Shardell MD, Terrin ML, Barr EA, Ballew SH, Gruber-Baldini AL. Cluster-randomized trial of a mobile phone personalized behavioral intervention for blood glucose control. Diabetes Care 2011 Sep;34(9):1934-1942 [FREE Full text] [CrossRef] [Medline]
  11. Lee JH, Lim SY, Cha SA, Han CJ, Jung AR, Kim KR, et al. Short-term effects of the Internet-based Korea diabetes prevention study: 6-month results of a community-based randomized controlled trial. Diabetes Metab J 2021 Nov;45(6):960-965 [FREE Full text] [CrossRef] [Medline]
  12. American Diabetes Association Professional Practice Committee, Draznin B, Aroda VR, Bakris G, Benson G, Brown FM, et al. 7. Diabetes technology: standards of medical care in diabetes-2022. Diabetes Care 2022 Jan 01;45(Suppl 1):S97-112. [CrossRef] [Medline]
  13. Lee EY, Yun JS, Cha SA, Lim SY, Lee JH, Ahn YB, et al. Personalized type 2 diabetes management using a mobile application integrated with electronic medical records: an ongoing randomized controlled trial. Int J Environ Res Public Health 2021 May 16;18(10):5300 [FREE Full text] [CrossRef] [Medline]
  14. Hou C, Carter B, Hewitt J, Francisa T, Mayor S. Do mobile phone applications improve glycemic control (HbA1c) in the self-management of diabetes? A systematic review, meta-analysis, and GRADE of 14 randomized trials. Diabetes Care 2016 Nov;39(11):2089-2095. [CrossRef] [Medline]
  15. Hou C, Xu Q, Diao S, Hewitt J, Li J, Carter B. Mobile phone applications and self-management of diabetes: a systematic review with meta-analysis, meta-regression of 21 randomized trials and GRADE. Diabetes Obes Metab 2018 Aug;20(8):2009-2013. [CrossRef] [Medline]
  16. Cho JH, Chang SA, Kwon HS, Choi YH, Ko SH, Moon SD, et al. Long-term effect of the Internet-based glucose monitoring system on HbA1c reduction and glucose stability: a 30-month follow-up study for diabetes management with a ubiquitous medical care system. Diabetes Care 2006 Dec;29(12):2625-2631. [CrossRef] [Medline]
  17. Platform Overview. Google Developers.   URL: [accessed 2022-11-04]
  18. Levy JC, Matthews DR, Hermans MP. Correct homeostasis model assessment (HOMA) evaluation uses the computer program. Diabetes Care 1998 Dec;21(12):2191-2192. [CrossRef] [Medline]
  19. HOMA2 Calculator. Diabetes Trials Unit, University of Oxford. 2019.   URL: [accessed 2022-11-04]
  20. Park SY, Gautier JF, Chon S. Assessment of insulin secretion and insulin resistance in human. Diabetes Metab J 2021 Sep;45(5):641-654 [FREE Full text] [CrossRef] [Medline]
  21. 4 Digital Health App Trends to Consider for 2018. Liquid State. 2018 Jan 29.   URL: [accessed 2022-11-30]
  22. Doupis J, Festas G, Tsilivigos C, Efthymiou V, Kokkinos A. Smartphone-based technology in diabetes management. Diabetes Ther 2020 Mar;11(3):607-619 [FREE Full text] [CrossRef] [Medline]
  23. Block G, Azar KM, Romanelli RJ, Block TJ, Hopkins D, Carpenter HA, et al. Diabetes prevention and weight loss with a fully automated behavioral intervention by email, Web, and mobile phone: a randomized controlled trial among persons with prediabetes. J Med Internet Res 2015 Oct 23;17(10):e240 [FREE Full text] [CrossRef] [Medline]
  24. Harding JL, Pavkov ME, Magliano DJ, Shaw JE, Gregg EW. Global trends in diabetes complications: a review of current evidence. Diabetologia 2019 Jan;62(1):3-16. [CrossRef] [Medline]
  25. Waki K, Fujita H, Uchimura Y, Omae K, Aramaki E, Kato S, et al. DialBetics: a novel smartphone-based self-management support system for type 2 diabetes patients. J Diabetes Sci Technol 2014 Mar;8(2):209-215 [FREE Full text] [CrossRef] [Medline]
  26. Garabedian LF, Ross-Degnan D, Wharam JF. Mobile phone and smartphone technologies for diabetes care and self-management. Curr Diab Rep 2015 Dec;15(12):109 [FREE Full text] [CrossRef] [Medline]
  27. Wu Y, Yao X, Vespasiani G, Nicolucci A, Dong Y, Kwong J, et al. Mobile app-based interventions to support diabetes self-management: a systematic review of randomized controlled trials to identify functions associated with glycemic efficacy. JMIR Mhealth Uhealth 2017 Mar 14;5(3):e35 [FREE Full text] [CrossRef] [Medline]
  28. Quinn CC, Clough SS, Minor JM, Lender D, Okafor MC, Gruber-Baldini A. WellDoc mobile diabetes management randomized controlled trial: change in clinical and behavioral outcomes and patient and physician satisfaction. Diabetes Technol Ther 2008 Jun;10(3):160-168. [CrossRef] [Medline]
  29. Agarwal P, Mukerji G, Desveaux L, Ivers NM, Bhattacharyya O, Hensel JM, et al. Mobile app for improved self-management of type 2 diabetes: multicenter pragmatic randomized controlled trial. JMIR Mhealth Uhealth 2019 Jan 10;7(1):e10321 [FREE Full text] [CrossRef] [Medline]
  30. Clements MA, Staggs VS. A mobile app for synchronizing glucometer data: impact on adherence and glycemic control among youths with type 1 diabetes in routine care. J Diabetes Sci Technol 2017 May;11(3):461-467 [FREE Full text] [CrossRef] [Medline]
  31. Tildesley HD, Mazanderani AB, Chan JH, Ross SA. Efficacy of A1C reduction using Internet intervention in patients with type 2 diabetes treated with insulin. Can J Diabetes 2011 Jan;35(3):250-253. [CrossRef]
  32. Chow N, Shearer D, Aydin Plaa J, Pottinger B, Pawlowska M, White A, et al. Blood glucose self-monitoring and internet diabetes management on A1C outcomes in patients with type 2 diabetes. BMJ Open Diabetes Res Care 2016 Apr 29;4(1):e000134 [FREE Full text] [CrossRef] [Medline]
  33. Davison BK, Quigg R, Skidmore PM. Pilot testing a photo-based food diary in nine- to twelve- year old- children from Dunedin, New Zealand. Nutrients 2018 Feb 20;10(2):240 [FREE Full text] [CrossRef] [Medline]
  34. Fuller NR, Fong M, Gerofi J, Ferkh F, Leung C, Leung L, et al. Comparison of an electronic versus traditional food diary for assessing dietary intake-a validation study. Obes Res Clin Pract 2017;11(6):647-654. [CrossRef] [Medline]
  35. Kumar RB, Goren ND, Stark DE, Wall DP, Longhurst CA. Automated integration of continuous glucose monitor data in the electronic health record using consumer technology. J Am Med Inform Assoc 2016 May;23(3):532-537 [FREE Full text] [CrossRef] [Medline]
  36. Holzinger A, Searle G, Nischelwitzer A. On some aspects of improving mobile applications for the elderly. In: Proceedings of the 4th International Conference on Universal Access in Human-Computer Interaction. 2007 Presented at: UAHCI '07; July 22-27, 2007; Beijing, China p. 923-932. [CrossRef]
  37. Holmen H, Torbjørnsen A, Wahl AK, Jenum AK, Småstuen MC, Arsand E, et al. A mobile health intervention for self-management and lifestyle change for persons with type 2 diabetes, part 2: one-year results from the Norwegian randomized controlled trial RENEWING HEALTH. JMIR Mhealth Uhealth 2014 Dec 11;2(4):e57 [FREE Full text] [CrossRef] [Medline]
  38. Ko SH, Park SA, Cho JH, Ko SH, Shin KM, Lee SH, et al. Influence of the duration of diabetes on the outcome of a diabetes self-management education program. Diabetes Metab J 2012 Jun;36(3):222-229 [FREE Full text] [CrossRef] [Medline]
  39. Lee IM, Shiroma EJ, Kamada M, Bassett DR, Matthews CE, Buring JE. Association of step volume and intensity with all-cause mortality in older women. JAMA Intern Med 2019 Aug 01;179(8):1105-1112 [FREE Full text] [CrossRef] [Medline]
  40. Hajna S, Ross NA, Dasgupta K. Steps, moderate-to-vigorous physical activity, and cardiometabolic profiles. Prev Med 2018 Feb;107:69-74 [FREE Full text] [CrossRef] [Medline]
  41. Paluch AE, Bajpai S, Bassett DR, Carnethon MR, Ekelund U, Evenson KR, Steps for Health Collaborative. Daily steps and all-cause mortality: a meta-analysis of 15 international cohorts. Lancet Public Health 2022 Mar;7(3):e219-e228 [FREE Full text] [CrossRef] [Medline]
  42. Saint-Maurice PF, Troiano RP, Bassett Jr DR, Graubard BI, Carlson SA, Shiroma EJ, et al. Association of daily step count and step intensity with mortality among US adults. JAMA 2020 Mar 24;323(12):1151-1160 [FREE Full text] [CrossRef] [Medline]
  43. Keesara S, Jonas A, Schulman K. Covid-19 and health care's digital revolution. N Engl J Med 2020 Jun 04;382(23):e82. [CrossRef] [Medline]
  44. Cahn A, Akirov A, Raz I. Digital health technology and diabetes management. J Diabetes 2018 Jan;10(1):10-17. [CrossRef] [Medline]

EMR: electronic medical record
HbA1c: hemoglobin A1c
HCP: health care provider
ITT: intention-to-treat
MC: mobile diabetes self-care
mHealth: mobile health
MPC: mobile diabetes self-care with personalized, bidirectional feedback from physicians
PA: physical activity
RCT: randomized controlled trial
SMBG: self-monitoring of blood glucose
T2DM: type 2 diabetes mellitus
UC: usual care

Edited by R Kukafka; submitted 21.02.22; peer-reviewed by Q Xu, N Maglaveras; comments to author 05.04.22; revised version received 26.05.22; accepted 08.06.22; published 28.07.22


©Eun Young Lee, Seon-Ah Cha, Jae-Seung Yun, Sun-Young Lim, Jin-Hee Lee, Yu-Bae Ahn, Kun-Ho Yoon, Min Kyung Hyun, Seung-Hyun Ko. Originally published in the Journal of Medical Internet Research (, 28.07.2022.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.