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Research Paper
A non-face-to-face diabetes self-management program based on self-efficacy theory and health literacy: a non-randomized controlled trial
Jung Hee Leeorcid, Soo Jin Leeorcid
Journal of Korean Academy of Nursing 2025;55(2):165-177.
DOI: https://doi.org/10.4040/jkan.25009
Published online: May 23, 2025

Department of Nursing, Korea National Open University, Seoul, Korea

Corresponding author: Soo Jin Lee Department of Nursing, Korea National Open University, 86 Daehak-ro, Jongno-gu, Seoul 03087, Korea E-mail: syjlee@knou.ac.kr
*This manuscript is a revision of the first author’s master’s thesis from Korea National Open University (2022).
• Received: January 31, 2025   • Revised: April 21, 2025   • Accepted: April 21, 2025

© 2025 Korean Society of Nursing Science

This is an Open Access article distributed under the terms of the Creative Commons Attribution NoDerivs License (http://creativecommons.org/licenses/by-nd/4.0) If the original work is properly cited and retained without any modification or reproduction, it can be used and re-distributed in any format and medium.

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  • Purpose
    This study aimed to assess the impact of a non-face-to-face diabetes self-management program based on self-efficacy theory and focusing on health literacy.
  • Methods
    A quasi-experimental, nonequivalent control group pre–post design was used. Participants from a community health promotion center were included if they (1) were 30–70 years of age, (2) had type 2 diabetes with glycated hemoglobin (HbA1c) ≥6.5%, and (3) had internet access via computers or mobile devices. The 8-week program was developed based on self-efficacy theory, and it included virtual education using an online platform, telephone counseling, videos, and social networking site activities considering health literacy. Fasting blood glucose levels, HbA1c levels, diabetes self-efficacy, social support, depression, and self-management behaviors were assessed. Data were analyzed using the independent t-test, paired t-test, and others.
  • Results
    Post-test results showed that the intervention group had significantly lower fasting blood glucose levels and improved diabetes self-efficacy, social support, and self-management behaviors compared with the control group. An analysis of the pre-to-post changes in scores indicated that the intervention group had significantly greater improvements in fasting blood glucose levels, diabetes self-efficacy, and overall diabetes self-management behaviors than those observed in the control group.
  • Conclusion
    Non-face-to-face programs based on self-efficacy theory that consider health literacy can provide effective diabetes management support to patients when in-person diabetes management at community health centers is challenging.
  • Key words: Diabetes mellitus; Health education; Health literacy; Internet-based intervention; Self-management
Diabetes is a leading chronic disease in South Korea and is the sixth leading cause of death [1]. Despite the high prevalence of diabetes, only 25.2% of patients achieve optimal blood glucose control, with glycated hemoglobin (HbA1c) levels below 6.5% [2]. Effective blood glucose management is vital to reducing the risk of diabetes-related microvascular complications [3]. Without appropriate care, these complications negatively impact the quality of life and impose substantial socioeconomic burdens, including increased medical costs. In South Korea, 40% of individuals with diabetes aged 30 years or older do not receive adequate treatment, relying mostly on medications [4].
Therefore, self-management is critical for diabetes control. The World Health Organization (WHO) has emphasized the importance of self-management in diabetes management and reducing diabetes-related risks [5]. Self-management requires individuals to actively engage in their health by dieting, exercising, and maintaining a healthy weight [6]. To ensure sustained outcomes, community programs that offer long-term support are essential [7]. Consequently, the Korea Centers for Disease Control and Prevention has launched the national initiatives of the Hypertension and Diabetes Registry to enhance chronic disease management [8].
Self-efficacy theory suggests that an individual’s belief in their ability to successfully execute the required behaviors in a given situation influences their actual behavior and performance [9]. This belief can be strengthened or weakened by achievement experience, vicarious experience, verbal persuasion, and emotional relaxation [9]. According to systematic reviews and meta-analyses, diabetes interventions based on self-efficacy theory were found to reduce HbA1c levels and improve self-management behaviors and self-efficacy [10].
Health literacy is critical to understanding and implementing medical instructions and educational content, which can be facilitated by effectively communicating with healthcare professionals about one’s health status and obtaining necessary medical information [11]. Individuals with low health literacy tend to experience worse health outcomes, poorer chronic disease management, and higher healthcare costs [12-14]. According to systematic literature reviews, health literacy-driven diabetes interventions effectively improve glycemic control and self-management skills [15]. Therefore, incorporating health literacy into self-management programs will improve diabetes self-management behaviors and physiological outcomes.
However, due to the coronavirus disease 2019 (COVID-19) pandemic, face-to-face programs were disrupted. Community-based face-to-face diabetes programs were also affected by social distancing and public health restrictions [16,17]. Given the increased risk of severe COVID-19 complications in patients with diabetes, the WHO emphasized the need for management [18].
Accordingly, there is a growing need for tailored nursing education and counseling programs that combine traditional face-to-face and virtual education formats to support optimal diabetes self-management [19]. The WHO recommends non-face-to-face methods, such as text messaging, phone calls, mobile applications, videos, and virtual education [18]. These non-face-to-face methods have proven effective in improving HbA1c levels and promoting self-management behaviors [20,21]. Additionally, telephone-based education and mobile applications have been associated with significant improvements in HbA1c levels, blood pressure, as well as depression [22,23]. However, such non-face-to-face programs have primarily targeted young adults.
While non-face-to-face educational programs have primarily targeted young adults, recent studies suggest that these programs are also feasible for older adults. For instance, a YouTube-based hypertension self-management program for older adults [17] and remote memory training successfully improved outcomes among older adults [16]. These findings suggest the feasibility and applicability of non-face-to-face education programs in supporting chronic disease management in middle-aged and older adults.
Community health promotion centers, unlike public health centers, focus on supporting residents’ health through engagement and partnerships without providing medical care. These centers offer various services to help residents reduce their risk of chronic diseases and maintain self-management behaviors [24]. Notably, the diabetes diagnosis rate among individuals aged 30 years or older in the center’s region increased from 7.9% to 8.3% after COVID-19 pandemic [4], indicating a growing demand for diabetes management.
This study aimed to evaluate the effectiveness of a non-face-to-face diabetes self-management program, based on the self-efficacy theory by Bandura [9] and incorporating health literacy, for individuals with diabetes at a community health promotion center in South Korea. The specific objectives were to (1) assess the effect of the program on the physiological outcomes, (2) examine its effect on psychosocial outcomes, and (3) evaluate its impact on diabetes self-management behaviors. The hypotheses that were tested included the following:
H1. Participants in the intervention group will demonstrate improvements in physiological outcomes (blood pressure, fasting blood glucose levels, HbA1c, serum lipid levels, and body mass index [BMI]) compared with those in the control group.
H2. Participants in the intervention group will demonstrate improvements in psychosocial outcomes (diabetes self-efficacy, diabetes social support, and depression) compared with those in the control group.
H3. Participants in the intervention group will demonstrate improvements in diabetes self-management behaviors compared with those in the control group.
1. Design and setting
In this study, a quasi-experimental, nonequivalent control group pretest–posttest design was used to evaluate the program’s effect on diabetes-related outcomes in community-based participants with diabetes.
2. Participants
Participants from the community health promotion center in Busan, South Korea, were included if they met the following criteria: (1) aged 30–70 years, (2) diagnosed with type 2 diabetes with an HbA1c level of ≥6.5% [25], and (3) had access to the internet using computers or mobile devices.
The minimal number of participants was calculated using the G*Power ver. 3.1 program (Heinrich-Heine-Universität Düsseldorf). A significance level of α=.05, an effect size of d=.81, and a power of 1−β=.80 were set to calculate the required participants for an independent samples t-test, resulting in a required participants number of 25 per group. The effect size of .81 was based on a previous meta-analysis [26]. Anticipating a dropout rate of approximately 17%, 30 participants were recruited for each group. The final analysis included 51 participants: 27 in the intervention group and 24 in the control group (Figure 1).
To ensure the safe conduct of this study, recruitment commenced at the end of February 2021, coinciding with the introduction of COVID-19 vaccinations. Participants were recruited via the center’s website, resident bulletin boards, and YouTube channels. Participants were assigned to the intervention and control groups in a nonrandomized manner, based on enrollment sequence and scheduling feasibility. Recruitment, intervention delivery, and data collection for both groups were conducted concurrently. To comply with the COVID-19 pandemic guidelines, the program was divided into two cohorts: subgroup A (March 3 to April 23, 2021) and subgroup B (October 23 to December 11, 2021). Both groups received identical program content.
3. Diabetes self-management program for the intervention group
The intervention group participated in a self-efficacy theory based non-face-to-face diabetes self-management program that incorporated health literacy. This program was designed considering the context of the implementing institution and earlier studies based on self-efficacy theory [9]. In previous studies, the intervention period ranged from 4 to 16 weeks, session durations varied from 20 to 120 minutes, and the number of educational modules ranged from 3 to 12 [10]. Additionally, reinforcement strategies, such as achievement experience, verbal persuasion, vicarious experience, and emotional relaxation, were implemented [10,27].
The entire 8-week program was delivered by experts in each field, all of whom had >10 years of experience in diabetes education. The program incorporated multiple strategies, including virtual education via an online platform (60 minutes per session), weekly telephone counseling (10 minutes), educational videos, and mobile social networking site (SNS) activities available from 8:00 to 20:00 (Figure 2).

1) Virtual education

For the virtual education, an online platform (Zoom; Zoom Video Communications Inc.), a widely used video conferencing software known for its user-friendly interface and real-time capabilities, was used. This platform allowed participants to interact real-time and share screens on both computers and mobile devices [28].
Prior to the sessions, the participants’ technical environment was assessed for compatibility with computers, smartphones, and Wi-Fi availability, and training was provided on an online platform, considering the participants’ health literacy levels. A research assistant was assigned to assist with technical issues during the sessions and respond to program-related inquiries. To prevent sharing of program content to the control group, only intervention participants received the Zoom access link and password. The waiting room function was used to ensure that only intervention group members could attend, and participants signed a pledge agreeing not to share the program content externally.
The virtual education content was adapted from the Association of Diabetes Care and Education Specialists guidelines [29] and the Korean Diabetes Association’s diabetes education guidelines [30]. It covered seven key domains: being active, healthy eating, taking medication, monitoring, problem-solving, healthy coping, and risk reduction.
The virtual education sessions consisted of eight sessions (Table 1). Each session utilized self-efficacy reinforcement strategies, including emotional relaxation, verbal persuasion, achievement experiences, and vicarious experiences [9], and health literacy enhanced strategies. Each session included practice, such as exercise, blood glucose monitoring, and cooking. Training materials were provided through drive-through pickup services or home delivery services. In the cooking class, real-time cooking practice sessions were provided. Family members were invited to participate in these sessions to encourage family support for diabetes self-management.

2) Educational booklets

Educational booklets, tailored to participants’ age, education, and health literacy, were developed at a 6th grade reading level [31]. Although it covers essential terms for diabetes self-management, difficult words—such as blood pressure, blood glucose, HbA1c, and insulin—were supplemented with additional explanations in the “Supplementary” section, and whenever possible, easier terms were substituted. The program used visual aids, a clear layout (14-point font, minimal text with simple symbols), and a distinct visual structure to enhance comprehension [31]. It further boosted engagement through interactive learning, chunked information, and immediate feedback.

3) Telephone counseling

Telephone counseling was conducted by the researcher 3 days after the virtual education session. These consultations used a self-management checklist to assess the participants’ progress and provide emotional support.

4) Educational videos

Educational videos were developed and produced by the researcher and were shared directly with participants via mobile SNS to facilitate repetitive learning. The exercise video included lower-body exercises, abdominal workouts, and strength training. The blood glucose monitoring video demonstrated the measurement techniques and safe disposal of lancets.

5) Social networking site activities

The researcher managed the mobile SNS platform, which was used to share program updates, encourage participant interactions, and provide emotional support. The participants shared their experiences with meals, snacks, and physical activities while offering mutual encouragement and feedback.
4. Routine program for the control group
The control group received a single face-to-face educational session—the routine education program offered at the center. This session, delivered in person, included an assessment of physiological outcomes and brief health counseling focusing on general diabetes care. The session lasted approximately 15 minutes and covered topics such as diet, physical activity, and medication adherence. Recruitment, program implementation, and data collection occurred during the same period as the intervention group.
5. Data collection
Data were collected in person 1 week before the first session and 1 week after the last session. Data were collected by a single investigator who was not involved in the study and adhered to COVID-19 prevention guidelines. Physiological outcomes were assessed after an 8-hour fast, and the participants received snacks and small gifts after testing. The participants were informed they could continue attending self-help groups at the center after the study.
6. Outcome measures
This study used a structured questionnaire to assess the outcome measures. The questionnaire was administered either in paper form or online. For the paper-based questionnaire, the participants completed the questionnaire while maintaining a social distance of at least 2 m [32]. Ten participants completed the online questionnaire.

1) Diabetes-related physiological outcomes

Fasting blood glucose, HbA1c, and serum lipid levels, blood pressure, and BMI were assessed. Fasting blood glucose, HbA1c, and serum lipid levels were measured using capillary blood from a fingertip with the Care Sens N analyzer (i-SENS Inc.), portable Analyzer 100 (Labmate Scientific Ltd.), and Mission Cholesterol Meter (ACON Laboratories Inc.), respectively, following an 8-hour fast. Blood pressure was measured using a noninvasive monitor after the participants were seated and rested for 5 minutes, and the average value was recorded. BMI was measured using the InBody270S (InBody Co., Ltd. ) while the participants wore light clothing and no shoes.

2) Diabetes-related psychosocial outcomes

Diabetes self-efficacy was measured using the Diabetes Management Self-Efficacy Scale for Older Adults [33]. This scale is based on the seven diabetes self-management domains developed by the Association of Diabetes Care and Education Specialists (formerly the American Association of Diabetes Educators). It consists of 17 items encompassing regular exercise, diet, blood glucose monitoring, reducing the risk of complications, medication intake, and problem-solving for hyper- and hypoglycemia. Higher scores indicate greater self-efficacy. The original tool had a Cronbach’s ⍺ reliability of .84 [33], whereas the Cronbach’s α in this study was .87.
Diabetes social support was measured using the Korean version of the Diabetes Social Support Instrument [34], originally developed by Fitzgerald et al. [35]. This instrument includes six items that assess meal planning, medication use, foot care, physical activity, blood glucose monitoring, and feelings about diabetes. Higher scores indicate greater social support. The original tool had a Cronbach’s ⍺ reliability of .73, whereas the Cronbach’s ⍺ in this study was .89.
Depression was measured using the Korean version of the Patient Health Questionnaire-9 [36], originally developed by Kroenke et al. [37]. This instrument consists of nine items scored on a 4-point Likert scale, with responses ranging from 0 (not at all) to 3 (almost every day). The total scores range from 0 to 27, with a score of ≥10 indicating possible depression. The Korean version demonstrated a Cronbach’s ⍺ reliability of .84 [38], whereas the Cronbach’s ⍺ in this study was .85.

3) Diabetes self-management behaviors

Diabetes self-management behaviors were measured using the Korean version of the Summary of Diabetes Self-Care Activities Questionnaire [39], originally developed and adapted by Toobert et al. [40]. This tool consists of 17 items covering five domains: diet, exercise, blood glucose monitoring, medication intake, and foot care. Higher scores reflect greater engagement in self-management behaviors. The original tool had a Cronbach’s ⍺ reliability of .77 [39], whereas the Cronbach’s ⍺ in this study was .70.
7. Data analysis
The collected data were analyzed using IBM SPSS Statistics ver. 25.0 (IBM Corp.). Normality was assessed using the Shapiro-Wilk test. Group homogeneity and differences in post-test scores and pre-to-post change scores between the intervention and control groups were assessed using the χ2 test, Fisher’s exact test, independent t-test, and Mann-Whitney U test. Within-group comparisons of the pre- and post-test results were conducted using paired t-tests and the Wilcoxon test. A p-value of <.05 was considered statistically significant.
8. Ethical considerations
This study was approved by the Institutional Review Board (IRB) of the Korea National Open University (approval no., ABN01-202011-22-16). However, given the escalating spread of COVID-19, the program start date for subgroup B was rescheduled, necessitating additional approval from the IRB (approval no., ABN01-202103-22-04). This study was registered with the Clinical Research Information Service of the Republic of Korea (KCT0010044). The intervention group participated in the diabetes self-management program developed for this study, whereas the control group received a one-time routine education program provided by the center to ensure ethical consideration. Participation was voluntary, and all participants provided written informed consent.
1. Program participants
All 27 participants in the intervention group attended at least seven sessions, whereas 15 participants completed all eight sessions, resulting in an overall attendance rate of 94.4%. In the control group, 24 participants completed this study (Figure 1).
2. Baseline characteristics of the participants
The homogeneity test results for the baseline characteristics of the participants showed no significant differences between the intervention and control groups (Table 1). In addition, there were no significant differences between intervention subgroups A and B.
The mean age of the participants was 58.4±7.98 years, and 74.5% were women (Table 1). Most of the participants had a high school diploma. The mean duration of diabetes was 5.44±5.01 years. Most participants had comorbid conditions, such as hypertension and dyslipidemia. Most of the participants (78.4%) were taking diabetes medications.
3. Diabetes-related physiological outcomes (hypothesis 1)
Prior to parametric testing, the normality of each continuous variable was assessed using the Shapiro-Wilk test. For variables that did not meet the assumption of normality, nonparametric statistical methods were applied, including the Mann-Whitney U test and Wilcoxon signed-rank test (Tables 1–3). The homogeneity test results for the diabetes-related physiological outcomes indicated no significant differences between the intervention and control groups (Table 1). However, at the post-test, fasting blood glucose levels were significantly lower in the intervention group than in the control group (t=–2.22, p=.033) (Table 2).
Within the intervention group, fasting blood glucose level (Z=–3.28, p=.001), HbA1c level (Z=–2.85, p=.002), systolic blood pressure (t=2.44, p=.022), and high-density lipoprotein (HDL) cholesterol level (t=–2.69, p=.012) showed significant pre–post improvements (Table 3). In the control group, HbA1c (t=4.09, p<.001) and BMI (Z=–3.62, p<.001) significantly improved from pre-test to post-test. In comparing the pre-to-post change scores between the two groups, the intervention group had a significantly greater improvement in fasting blood glucose levels than the control group (U=192.00, p=.012).
4. Diabetes-related psychosocial outcomes (hypothesis 2)
The homogeneity test results for these variables revealed no significant differences between the intervention and control groups (Table 1). However, at the post-test, there were significant improvements in diabetes self-efficacy (t=4.19, p<.001) and diabetes social support (t=4.32, p<.001) scores in the intervention group compared with those in the control group (Table 2).
In pre–post-test, both the intervention and control groups demonstrated significant improvements in diabetes self-efficacy, diabetes social support, and depression levels (Table 3). When comparing pre-to-post change scores between the two groups, the intervention group showed a significantly greater improvement in diabetes self-efficacy than the control group (U=184.50, p=.008).
5. Diabetes self-management behaviors (hypothesis 3)
The homogeneity test for diabetes self-management behaviors exhibited no significant differences between the intervention and control groups at baseline (Table 1). However, in the post-test, the diabetes self-management behavior score was significantly higher in the intervention group than in the control group (t=3.80, p<.001) (Table 2). Subscale analysis revealed statistically significant differences in exercise (t=2.07, p=.044) and blood glucose monitoring (t=5.38, p<.001).
In pre–post analysis, both the intervention and control groups demonstrated significant improvements in overall diabetes self-management behavior, diet, and blood glucose monitoring (Table 3). In comparing the pre-to-post change scores between the groups, the intervention group achieved significant improvement in overall diabetes self-management behavior (t=2.14, p=.037), diet (t=2.42, p=.019), exercise (t=2.79, p=.007), and blood glucose monitoring (U=207.50, p=.026).
This study evaluated the effectiveness of a self-efficacy theory based non-face-to-face diabetes self-management program considering health literacy for individuals with diabetes at a community health promotion center in South Korea. This program was notable for providing a safe and effective option for diabetes management during the social distancing period of COVID-19.
Despite the sessions not being conducted face-to-face, all 27 participants attended, with 15 completing all eight sessions, resulting in an overall attendance rate of 94.4%. This high attendance rate was likely due to the program’s use of an interactive online platform that addressed the limitations of traditional one-way virtual education by providing immediate feedback to participants. This allowed the researchers to facilitate effective group education through real-time interactions with all 27 participants.
Based on a self-efficacy theory [9], the program used virtual education as well as materials, such as telephone counseling, educational videos, mobile SNS activities, and the provision of resources to support diabetes self-management. This aligns with previous findings indicating that the use of educational booklets and hands-on training materials in a YouTube self-management program promoted active participation [17]. Moreover, scheduling sessions on Saturday mornings allowed family members and office workers to participate, fostering greater empathy and support.
Among the physiological outcomes, only fasting blood glucose levels demonstrated a significant difference in pre-to-post change between the groups, with the intervention group showing a greater reduction than the control group. However, no significant difference was observed between the two groups in pre-to-post changes in HbA1c levels, possibly because both groups had baseline HbA1c levels slightly above 6.5%, which may have limited further improvement [25]. Notably, significant pre-to-post changes in HbA1c levels were observed in both groups. The presence of comorbid conditions, such as dyslipidemia (60.8%) and hypertension (31.4%), may have influenced the outcomes, particularly in blood pressure, cholesterol levels, and BMI, as many participants were already on medication, consistent with a previous study [41]. Moreover, the statistically significant changes in HbA1c levels and BMI observed in the control group may be attributable to the Hawthorne effect, where participants change their behavior simply because they are aware of being observed. Furthermore, the program’s 8-week duration—limited by COVID-19 restrictions—may have been too short to capture changes in HbA1c levels (which reflect a 2–3-month average) and to achieve significant improvements in cholesterol levels, which typically require 3–6 months. Prior studies have often implemented programs lasting at least 3 months to assess such changes [22,42]. Future programs should consider customizing interventions based on participants’ baseline health status and ensuring a sufficient duration to capture meaningful changes.
Nonetheless, a pre–post-test analysis revealed a statistically significant decrease in HbA1c levels in both groups, and the intervention group’s fasting blood glucose levels, systolic blood pressure, and HDL levels significantly improved. Although these results statistically confirm the program’s effectiveness, further studies are required to determine whether these changes have clinically meaningful implications for long-term diabetes management.
For psychosocial outcomes, diabetes self-efficacy, social support, and depression exhibited significant improvements at post-test in the intervention group compared with the control group. Additionally, in self-efficacy, there was a significant difference in the pre-to-post change between the groups. These outcomes were likely influenced by frequent contact and self-efficacy reinforcement strategies through telephone counseling and mobile SNS activities, which positively affected the participants’ self-efficacy, social support, and mental health. During the COVID-19 pandemic, when outings were restricted, consistent communication may have been particularly meaningful. This finding is consistent with those of earlier studies that reported significant improvements in social support and self-management behaviors through mobile applications that facilitated posts and expressions of empathy [43]. Similarly, a previous study emphasized the importance of long-term, professionally managed community care for enhancing social support [44]. A 12-week study also demonstrated significant improvements in depression through diabetes education and telephone counseling, supporting the results of the present study [23]. Additionally, participating in an online program may enhance the self-efficacy of middle-aged and older adults, who often face challenges with computer use, by fostering a sense of accomplishment and confidence.
The diabetes self-management behavior score showed a significant difference in the post-test between groups. Additionally, significant differences were observed in the pre–post changes within each group, the comparison of the pre-to-post change between the groups, and in the subdomains: diet, exercise, and blood glucose monitoring. Several factors likely contributed to these results. First, the self-efficacy reinforcement strategies used throughout the program, such as verbal persuasion and achievement experiences, promoted self-management and enhanced motivation [10,45]. Second, the educational materials were tailored to the participants’ age, education, and health literacy levels to ensure clarity and accessibility. This is consistent with studies that highlight the importance of considering age, education, and health literacy in program design [41]. Programs that tailored the educational content according to the participants’ knowledge levels also indicated significant improvements in self-management behaviors [46]. Third, the creation of a diabetes self-management community in which participants shared meals and exercise routines via mobile SNS created vicarious experiences and fostered emotional relaxation and supportive environment that positively influenced self-management behaviors. Finally, videos on exercise and blood glucose monitoring, shared through a mobile SNS, allowed participants to repeatedly learn and practice correct self-management techniques. Given the influence of these various factors, future studies should analyze the relative contributions of these components.
A limitation of this study is the generalizability of the results, as the program implemented by the center may not apply to all diabetes education participants owing to specific recruitment methods, regional characteristics, and the availability of resources, such as food kits and interactive platforms. In addition, this study was conducted with a relatively small sample size and used a quasi-experimental, nonequivalent control group design without randomization, which may limit the internal validity of the findings. Practical limitations, such as participants’ varying levels of digital literacy and access to mobile devices, may also have influenced their engagement and outcomes.
Self-efficacy theory-based non-face-to-face diabetes self-management programs that consider health literacy can effectively improve diabetes self-efficacy, social support, depression, diabetes self-management behaviors, and fasting blood glucose and HbA1c levels. This study is noteworthy for offering a safe and effective approach to a diabetes self-management program during the COVID-19 pandemic. It demonstrates the potential of virtual education in chronic disease management and highlights the value of integrating interactive strategies to enhance participant engagement and outcomes.
Future research should focus on conducting long-term studies with larger sample sizes to refine and validate virtual diabetes self-management programs. Comparative studies of different non–face-to-face interventions may help identify the most effective strategies. Additionally, exploring the role of family involvement in virtual education settings is critical to improving diabetes outcomes. It is also important to evaluate the relative impact of each program component to optimize design. Moreover, long-term studies are needed to confirm sustained changes in HbA1c levels. Although the program combined various strategies, such as telephone counseling, mobile SNS activities, and educational videos, implementing these strategies widely across diverse communities may present challenges. Therefore, it is necessary to develop a more streamlined and adaptable program model.

Conflicts of Interest

No potential conflict of interest relevant to this article was reported.

Acknowledgements

The authors sincerely thank the participants for their generous involvement in the program, especially during the challenges posed by the COVID-19 pandemic. We also appreciate the dedication and support of the staff involved in conducting the study at the community health promotion center in Busan, South Korea.

Funding

This research received no external funding.

Data Sharing Statement

Please contact the corresponding author for data availability.

Author Contributions

Conceptualization and Methodology: JHL, SJL. Data curation or/and Analysis: JHL, SJL. Funding acquisition: none. Investigation: JHL. Project administration or/and Supervision: SJL. Resources or/and Software: JHL. Validation: SJL. Visualization: SJL. Writing original draft or/and Review & Editing: JHL, SJL. Final approval of the manuscript: all authors.

Fig. 1.
Flow diagram of the study. To comply with COVID-19 pandemic guidelines, the program was divided into two cohorts: subgroup A (March 3 to April 23, 2021) and subgroup B (October 23 to December 11, 2021). Both groups received identical program content.
jkan-25009f1.jpg
Fig. 2.
Summary of session contents and strategies based on Association of Diabetes Care and Education Specialists (ADCES), self-efficacy theory, and health literacy. SNS, social networking site.
jkan-25009f2.jpg
Table 1.
Baseline homogeneity of study variables between groups (N=51)
Characteristic Intervention group (n=27) Control group (n=24) t or χ2 or U p
Age (yr) 0.27 .869
 <60 13 (48.1) 11 (45.8)
 ≥60 14 (51.9) 13 (54.2)
Sex 0.32 .570
 Men 6 (22.2) 7 (29.2)
 Women 21 (77.8) 17 (70.8)
Education 1.79 .408
 ≤Middle school 9 (33.3) 8 (33.3)
 High school 14 (51.9) 9 (37.5)
 College or more 4 (14.8) 7 (29.2)
Duration of diabetes (yr) 0.77 .802a)
 1–5 15 (55.6) 14 (58.3)
 6–10 7 (25.9) 4 (16.7)
 ≥11 5 (18.5) 6 (25.0)
Comorbidities 0.02 >.999
 Yes 25 (92.6) 22 (91.7)
 No 2 (7.4) 2 (8.3)
Medication usage 0.32 .736a)
 Yes 22 (81.5) 18 (75.0)
 No 5 (18.5) 6 (25.0)
Experience in diabetes education 1.85 .255a)
 Yes 6 (22.2) 2(8.3)
 No 21 (77.8) 22 (91.7)
Smoking 0.11 >.999
 Yes 3 (11.1) 2 (8.3)
 No 24 (88.9) 22 (91.7)
Drinking 0.91 .451a)
 Yes 3 (11.1) 5 (20.8)
 No 24 (88.9) 19 (79.2)
FBG (mg/dL) 129.63±29.12 128.29±25.78 321.50 .966b)
A1C level (%) 6.78±0.96 6.74±0.84 317.00 .899b)
SBP (mm Hg) 126.48±13.01 123.54±12.02 0.83 .408
DBP (mm Hg) 76.15±9.08 76.96±8.65 –0.33 .746
Total cholesterol (mg/dL) 158.81±40.99 170.46±41.95 252.00 .177b)
HDL cholesterol (mg/dL) 42.93±14.95 47.71±15.11 –1.16 .253
Triglycerides (mg/dL) 157.07±73.78 159.29±80.45 322.00 .974b)
LDL cholesterol (mg/dL) 84.70±30.48 91.50±34.25 –0.75 .457
BMI (kg/m2) 25.89±5.96 24.52±2.71 311.50 .819b)
Diabetes self-efficacy 44.85±5.91 42.54±6.45 223.00 .056b)
Diabetes social support 16.22±7.52 15.00±6.99 293.00 .563b)
Depression 13.44±4.71 16.63±6.47 235.50 .094b)
DSMB 72.14±16.89 69.79±16.78 0.50 .620
 Diet 19.11±8.28 20.58±6.34 –0.71 .483
 Exercise 7.78±3.00 8.33±3.60 294.50 .578b)
 Blood glucose monitoring 6.63±5.41 4.13±3.68 225.00 .059b)
 Medications 15.48±5.66 13.54±6.09 256.00 .183b)
 Foot care 23.19±6.98 23.21±7.86 –0.01 .991

Values are presented as number (%) or mean±standard deviation.

HbA1c, glycated hemoglobin; BMI, body mass index; DBP, diastolic blood pressure; DSMB, diabetes self-management behavior; FBG, fasting blood glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; SBP, systolic blood pressure.

a)By Fisher’s exact test.

b)By Mann-Whitney U test.

Table 2.
Posttest comparison of study variables between groups (N=51)
Variable Intervention group (n=27) Control group (n=24) t or U p
FBG (mg/dL) 113.85±12.91 126.75±25.67 –2.22 .033
HbA1c level (%) 6.42±0.75 6.39±0.84 0.19 .848
SBP (mm Hg) 119.33±9.75 119.79±11.13 –0.16 .877
DBP (mm Hg) 74.04±8.06 73.79±7.51 0.11 .911
Total cholesterol (mg/dL) 165.00±36.78 169.21±47.12 322.50 .491a)
HDL cholesterol (mg/dL) 49.48±17.59 49.63±14.40 –0.03 .975
Triglycerides (mg/dL) 142.37±80.80 143.92±78.06 322.50 .491a)
LDL cholesterol (mg/dL) 86.11±30.01 93.29±43.89 310.50 .804a)
BMI (kg/m2) 25.48±5.98 23.73±2.77 1.37 .179
Diabetes self-efficacy 57.40±5.15 51.04±5.69 4.19 <.001
Diabetes social support 26.19±2.43 21.75±4.67 4.32 <.001
Depression 11.04±1.99 14.17±5.19 223.50 .055a)
DSMB 89.74±10.57 77.96±11.58 3.80 <.001
 Diet 25.67±5.73 23.04±5.03 1.73 .090
 Exercise 11.11±2.79 9.29±3.48 2.07 .044
 Blood glucose monitoring 11.52±3.49 6.13±3.69 5.38 <.001
 Medications 16.11±4.37 13.92±5.76 267.50 .184a)
 Foot care 25.33±6.54 25.58±7.25 –0.13 .897

Values are presented as mean±standard deviation.

HbA1c, glycated hemoglobin; BMI, body mass index; DBP, diastolic blood pressure; DSMB, diabetes self-management behavior; FBG, fasting blood glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; SBP, systolic blood pressure.

a)By Mann-Whitney U test.

Table 3.
Comparison of pre–post changes between the intervention and control groups (N=51)
Variable Group Pre-test Post-test t or Z p Change (post-pre) t or U p
FBG (mg/dL) I 129.63±29.12 113.85±12.91 –3.28 <.001a) –15.78±25.69 192.00 .012b)
C 128.29±25.78 126.75±25.67 0.79 .439 –1.54±9.60
HbA1c level (%) I 6.78±0.96 6.42±0.75 –2.85 .002a) –0.36±0.61 299.00 .649b)
C 6.74±0.84 6.39±0.84 4.09 <.001 –0.37±0.44
SBP (mm Hg) I 126.48±13.01 119.33±9.75 2.44 .022 –7.15±15.25 –0.82 .419
C 123.54±12.02 119.79±11.13 1.28 .214 –3.75±14.38
DBP (mm Hg) I 76.15±9.08 74.04±8.06 1.08 .289 –2.11±10.12 0.37 .715
C 76.96±8.65 73.79±7.51 1.49 .149 –3.17±10.40
Total cholesterol (mg/dL) I 158.81±40.99 165.00±36.78 –1.57 .118a) 6.19±36.00 256.00 .203b)
C 170.46±41.95 169.21±47.12 0.17 .871 –1.25±37.19
HDL cholesterol (mg/dL) I 42.93±14.95 49.48±17.59 –2.69 .012 6.56±12.67 1.27 .211
C 47.71±15.11 49.63±14.40 –0.70 .492 1.92±13.44
Triglycerides (mg/dL) I 157.07±73.78 142.37±80.80 0.97 .342 –14.7±78.86 0.03 .974
C 159.29±80.45 143.92±78.06 1.15 .262 –15.38±65.55
LDL cholesterol (mg/dL) I 84.70±30.48 86.11±30.01 –0.27 .790 1.41±27.13 –0.05 .960
C 91.50±34.25 93.29±43.89 –0.32 .753 1.79±27.59
BMI (kg/m2) I 25.89±5.96 25.48±5.98 –1.01 .325a) –0.41±6.07 292.00 .552b)
C 24.52±2.71 23.73±2.77 –3.62 <.001a) –0.79±1.31
Diabetes self-efficacy I 44.85±5.91 57.40±5.15 –12.04 <.001 12.56±5.42 184.50 .008b)
C 42.54±6.45 51.04±5.69 –3.89 <.001a) 8.50±7.25
Diabetes social support I 16.22±7.52 26.19±2.43 –7.15 <.001 9.96±7.25 1.74 .089
C 15.00±6.99 21.75±4.67 –5.73 <.001 6.75±5.77
Depression I 13.44±4.71 11.04±1.99 –3.07 .002a) –2.41±3.75 311.00 .810b)
C 16.63±6.47 14.17±5.19 –3.02 .001a) –2.46±3.34
DSMB I 72.14±16.89 89.74±10.57 –5.34 <.001 17.59±17.13 2.14 .037
C 69.79±16.78 77.96±11.58 –2.89 .008 8.17±13.86
 Diet I 19.11±8.28 25.67±5.73 –5.40 <.001 6.56±6.31 2.42 .019
C 20.58±6.34 23.04±5.03 –2.10 .047 2.46±5.73
 Exercise I 7.78±3.00 11.11±2.79 –6.09 <.001 3.33±2.84 2.79 .007
C 8.33±3.60 9.29±3.48 –1.45 .160 0.96±3.24
 Blood glucose monitoring I 6.63±5.41 11.52±3.49 –4.41 <.001 4.89±5.77 207.50 .026b)
C 4.13±3.68 6.13±3.69 –2.41 .014a) 2.00±3.68
 Medications I 15.48±5.66 16.11±4.37 –0.75 .452a) 0.63±4.23 287.00 .461b)
C 13.54±6.09 13.92±5.76 –0.88 .489a) 0.38±3.03
 Foot care I 23.19±6.98 25.33±6.54 –1.12 .263a) 2.15±8.20 321.00 .959b)
C 23.21±7.86 25.58±7.25 –1.46 .158 2.38±7.97

Values are presented as mean±standard deviation.

HbA1c, glycated hemoglobin; BMI, body mass index; C, control; DBP, diastolic blood pressure; DSMB, diabetes self-management behavior; FBG, fasting blood glucose; HDL, high-density lipoprotein; I, intervention; LDL, low-density lipoprotein; SBP, systolic blood pressure.

a)By Wilcoxon signed rank test.

b)By Mann-Whitney U test.

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      A non-face-to-face diabetes self-management program based on self-efficacy theory and health literacy: a non-randomized controlled trial
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    A non-face-to-face diabetes self-management program based on self-efficacy theory and health literacy: a non-randomized controlled trial
    Image Image
    Fig. 1. Flow diagram of the study. To comply with COVID-19 pandemic guidelines, the program was divided into two cohorts: subgroup A (March 3 to April 23, 2021) and subgroup B (October 23 to December 11, 2021). Both groups received identical program content.
    Fig. 2. Summary of session contents and strategies based on Association of Diabetes Care and Education Specialists (ADCES), self-efficacy theory, and health literacy. SNS, social networking site.

    Fig. 1.

    Fig. 2.

    A non-face-to-face diabetes self-management program based on self-efficacy theory and health literacy: a non-randomized controlled trial
    Characteristic Intervention group (n=27) Control group (n=24) t or χ2 or U p
    Age (yr) 0.27 .869
     <60 13 (48.1) 11 (45.8)
     ≥60 14 (51.9) 13 (54.2)
    Sex 0.32 .570
     Men 6 (22.2) 7 (29.2)
     Women 21 (77.8) 17 (70.8)
    Education 1.79 .408
     ≤Middle school 9 (33.3) 8 (33.3)
     High school 14 (51.9) 9 (37.5)
     College or more 4 (14.8) 7 (29.2)
    Duration of diabetes (yr) 0.77 .802a)
     1–5 15 (55.6) 14 (58.3)
     6–10 7 (25.9) 4 (16.7)
     ≥11 5 (18.5) 6 (25.0)
    Comorbidities 0.02 >.999
     Yes 25 (92.6) 22 (91.7)
     No 2 (7.4) 2 (8.3)
    Medication usage 0.32 .736a)
     Yes 22 (81.5) 18 (75.0)
     No 5 (18.5) 6 (25.0)
    Experience in diabetes education 1.85 .255a)
     Yes 6 (22.2) 2(8.3)
     No 21 (77.8) 22 (91.7)
    Smoking 0.11 >.999
     Yes 3 (11.1) 2 (8.3)
     No 24 (88.9) 22 (91.7)
    Drinking 0.91 .451a)
     Yes 3 (11.1) 5 (20.8)
     No 24 (88.9) 19 (79.2)
    FBG (mg/dL) 129.63±29.12 128.29±25.78 321.50 .966b)
    A1C level (%) 6.78±0.96 6.74±0.84 317.00 .899b)
    SBP (mm Hg) 126.48±13.01 123.54±12.02 0.83 .408
    DBP (mm Hg) 76.15±9.08 76.96±8.65 –0.33 .746
    Total cholesterol (mg/dL) 158.81±40.99 170.46±41.95 252.00 .177b)
    HDL cholesterol (mg/dL) 42.93±14.95 47.71±15.11 –1.16 .253
    Triglycerides (mg/dL) 157.07±73.78 159.29±80.45 322.00 .974b)
    LDL cholesterol (mg/dL) 84.70±30.48 91.50±34.25 –0.75 .457
    BMI (kg/m2) 25.89±5.96 24.52±2.71 311.50 .819b)
    Diabetes self-efficacy 44.85±5.91 42.54±6.45 223.00 .056b)
    Diabetes social support 16.22±7.52 15.00±6.99 293.00 .563b)
    Depression 13.44±4.71 16.63±6.47 235.50 .094b)
    DSMB 72.14±16.89 69.79±16.78 0.50 .620
     Diet 19.11±8.28 20.58±6.34 –0.71 .483
     Exercise 7.78±3.00 8.33±3.60 294.50 .578b)
     Blood glucose monitoring 6.63±5.41 4.13±3.68 225.00 .059b)
     Medications 15.48±5.66 13.54±6.09 256.00 .183b)
     Foot care 23.19±6.98 23.21±7.86 –0.01 .991
    Variable Intervention group (n=27) Control group (n=24) t or U p
    FBG (mg/dL) 113.85±12.91 126.75±25.67 –2.22 .033
    HbA1c level (%) 6.42±0.75 6.39±0.84 0.19 .848
    SBP (mm Hg) 119.33±9.75 119.79±11.13 –0.16 .877
    DBP (mm Hg) 74.04±8.06 73.79±7.51 0.11 .911
    Total cholesterol (mg/dL) 165.00±36.78 169.21±47.12 322.50 .491a)
    HDL cholesterol (mg/dL) 49.48±17.59 49.63±14.40 –0.03 .975
    Triglycerides (mg/dL) 142.37±80.80 143.92±78.06 322.50 .491a)
    LDL cholesterol (mg/dL) 86.11±30.01 93.29±43.89 310.50 .804a)
    BMI (kg/m2) 25.48±5.98 23.73±2.77 1.37 .179
    Diabetes self-efficacy 57.40±5.15 51.04±5.69 4.19 <.001
    Diabetes social support 26.19±2.43 21.75±4.67 4.32 <.001
    Depression 11.04±1.99 14.17±5.19 223.50 .055a)
    DSMB 89.74±10.57 77.96±11.58 3.80 <.001
     Diet 25.67±5.73 23.04±5.03 1.73 .090
     Exercise 11.11±2.79 9.29±3.48 2.07 .044
     Blood glucose monitoring 11.52±3.49 6.13±3.69 5.38 <.001
     Medications 16.11±4.37 13.92±5.76 267.50 .184a)
     Foot care 25.33±6.54 25.58±7.25 –0.13 .897
    Variable Group Pre-test Post-test t or Z p Change (post-pre) t or U p
    FBG (mg/dL) I 129.63±29.12 113.85±12.91 –3.28 <.001a) –15.78±25.69 192.00 .012b)
    C 128.29±25.78 126.75±25.67 0.79 .439 –1.54±9.60
    HbA1c level (%) I 6.78±0.96 6.42±0.75 –2.85 .002a) –0.36±0.61 299.00 .649b)
    C 6.74±0.84 6.39±0.84 4.09 <.001 –0.37±0.44
    SBP (mm Hg) I 126.48±13.01 119.33±9.75 2.44 .022 –7.15±15.25 –0.82 .419
    C 123.54±12.02 119.79±11.13 1.28 .214 –3.75±14.38
    DBP (mm Hg) I 76.15±9.08 74.04±8.06 1.08 .289 –2.11±10.12 0.37 .715
    C 76.96±8.65 73.79±7.51 1.49 .149 –3.17±10.40
    Total cholesterol (mg/dL) I 158.81±40.99 165.00±36.78 –1.57 .118a) 6.19±36.00 256.00 .203b)
    C 170.46±41.95 169.21±47.12 0.17 .871 –1.25±37.19
    HDL cholesterol (mg/dL) I 42.93±14.95 49.48±17.59 –2.69 .012 6.56±12.67 1.27 .211
    C 47.71±15.11 49.63±14.40 –0.70 .492 1.92±13.44
    Triglycerides (mg/dL) I 157.07±73.78 142.37±80.80 0.97 .342 –14.7±78.86 0.03 .974
    C 159.29±80.45 143.92±78.06 1.15 .262 –15.38±65.55
    LDL cholesterol (mg/dL) I 84.70±30.48 86.11±30.01 –0.27 .790 1.41±27.13 –0.05 .960
    C 91.50±34.25 93.29±43.89 –0.32 .753 1.79±27.59
    BMI (kg/m2) I 25.89±5.96 25.48±5.98 –1.01 .325a) –0.41±6.07 292.00 .552b)
    C 24.52±2.71 23.73±2.77 –3.62 <.001a) –0.79±1.31
    Diabetes self-efficacy I 44.85±5.91 57.40±5.15 –12.04 <.001 12.56±5.42 184.50 .008b)
    C 42.54±6.45 51.04±5.69 –3.89 <.001a) 8.50±7.25
    Diabetes social support I 16.22±7.52 26.19±2.43 –7.15 <.001 9.96±7.25 1.74 .089
    C 15.00±6.99 21.75±4.67 –5.73 <.001 6.75±5.77
    Depression I 13.44±4.71 11.04±1.99 –3.07 .002a) –2.41±3.75 311.00 .810b)
    C 16.63±6.47 14.17±5.19 –3.02 .001a) –2.46±3.34
    DSMB I 72.14±16.89 89.74±10.57 –5.34 <.001 17.59±17.13 2.14 .037
    C 69.79±16.78 77.96±11.58 –2.89 .008 8.17±13.86
     Diet I 19.11±8.28 25.67±5.73 –5.40 <.001 6.56±6.31 2.42 .019
    C 20.58±6.34 23.04±5.03 –2.10 .047 2.46±5.73
     Exercise I 7.78±3.00 11.11±2.79 –6.09 <.001 3.33±2.84 2.79 .007
    C 8.33±3.60 9.29±3.48 –1.45 .160 0.96±3.24
     Blood glucose monitoring I 6.63±5.41 11.52±3.49 –4.41 <.001 4.89±5.77 207.50 .026b)
    C 4.13±3.68 6.13±3.69 –2.41 .014a) 2.00±3.68
     Medications I 15.48±5.66 16.11±4.37 –0.75 .452a) 0.63±4.23 287.00 .461b)
    C 13.54±6.09 13.92±5.76 –0.88 .489a) 0.38±3.03
     Foot care I 23.19±6.98 25.33±6.54 –1.12 .263a) 2.15±8.20 321.00 .959b)
    C 23.21±7.86 25.58±7.25 –1.46 .158 2.38±7.97
    Table 1. Baseline homogeneity of study variables between groups (N=51)

    Values are presented as number (%) or mean±standard deviation.

    HbA1c, glycated hemoglobin; BMI, body mass index; DBP, diastolic blood pressure; DSMB, diabetes self-management behavior; FBG, fasting blood glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; SBP, systolic blood pressure.

    By Fisher’s exact test.

    By Mann-Whitney U test.

    Table 2. Posttest comparison of study variables between groups (N=51)

    Values are presented as mean±standard deviation.

    HbA1c, glycated hemoglobin; BMI, body mass index; DBP, diastolic blood pressure; DSMB, diabetes self-management behavior; FBG, fasting blood glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; SBP, systolic blood pressure.

    By Mann-Whitney U test.

    Table 3. Comparison of pre–post changes between the intervention and control groups (N=51)

    Values are presented as mean±standard deviation.

    HbA1c, glycated hemoglobin; BMI, body mass index; C, control; DBP, diastolic blood pressure; DSMB, diabetes self-management behavior; FBG, fasting blood glucose; HDL, high-density lipoprotein; I, intervention; LDL, low-density lipoprotein; SBP, systolic blood pressure.

    By Wilcoxon signed rank test.

    By Mann-Whitney U test.

    Table 1.

    Table 2.

    Table 3.

    J Korean Acad Nurs : Journal of Korean Academy of Nursing
    © Korean Society of Nursing Science.
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