The Science of Diabetes Self-Management and Care 2025, Vol. 51(5) 517 –531 © The Author(s) 2025 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/26350106251361360 journals.sagepub.com/home/tde
Abstract
Purpose: The purpose of this systematic review was to evaluate the effectiveness of mHealth-based selfmanagement interventions on self-efficacy among patients with type 2 diabetes.
Methods: Following PRISMA guidelines, a systematic search was conducted across Medline, CINAHL, the Cochrane Library, and OVID databases. Analyses were performed using the meta and metafor packages in R programming. The restricted maximum likelihood (REML) method and Hartung-Knapp-Sidik-Jonkman (HKSJ) adjustment were applied to rigorously estimate random effects.
Results: The analysis included 9 high-quality randomized controlled trials published between 2014 and 2022, with a total of 1,116 participants. The standardized mean difference was 0.97 (95% CI, 0.16-1.78, t = 2.75, P = .02), indicating a significant effect.
Conclusion: The findings suggest that mHealth-based self-management interventions significantly enhance self-efficacy in patients with type 2 diabetes. Improved self-efficacy fosters better self-management, ultimately leading to enhanced health outcomes in patients with diabetes. These interventions provide a valuable tool for patients, particularly those who are unable to attend in-person sessions, to effectively manage their condition and potentially reduce complications associated with diabetes. The integration of mHealth into routine diabetes care can play a critical role in supporting ongoing self-management and improving overall health.
It is estimated that there are approximately 462 million people worldwide with type 2 diabetes mellitus (T2DM), representing a significant portion of the global health and disease burden.1 Effective self-management of T2DM, a chronic condition, involves maintaining a healthy diet, regular exercise, optimal weight, consistent glucose monitoring, and long-term medication adherence to prevent complications and enhance quality of life.2,3
Self-efficacy, or the confidence in one’s ability to manage the disease condition, has been identified as one of the crucial factors involved in the successful self-management behaviors and glycemic control in T2DM patients.4 High levels of self-efficacy are associated with improved health behaviors, including dietary control, physical activity, and medication adherence, which are essential for effective diabetes management.5 Self-efficacy also promotes psychological well-being in patients, fostering a positive mindset and gratitude, which aid in blood glucose control and the prevention of complications.6 This relationship underscores the importance of targeted interventions to enhance self-efficacy among T2DM patients.7 However, self-efficacy in patients with T2DM is influenced by various barriers, including financial difficulties, limited knowledge, lack of social support, and psychological disorders, such as stress, depression, and anxiety.8,9
Mobile health (mHealth) is defined as the integration of mobile computing, communication technologies, and medical sensors to enhance health care delivery encompassing a wide range of applications, from smartphone apps that assist in chronic disease management to wearable devices that monitor health metrics.10 mHealth has become an essential tool for empowering patients and supporting optimal self-management by enabling them to plan and execute self-care strategies in their daily lives, unrestricted by geographical or time limitations and at a relatively low cost.11
With rapid advancements in mHealth, the management of chronic diseases has significantly improved. The effectiveness of mHealth is evident in conditions such as hypertension through the Blood Pressure Assistant mobile app,12 chronic lung diseases via various mHealth applications,13 and diabetes through personalized lifestyle coaching.14 By utilizing technologies such as mobile applications, simulation tools, digital coaching, and digital selfmanagement interventions, mHealth provides diabetes self-management education and support, ultimately improving health outcomes among diabetes patients. Notably, mHealth interventions have reduced hypoglycemia, leading to 72% fewer emergency department visits15 and 33% fewer visits than controls.16 In some cases, these approaches are more cost-effective than traditional in-person care.17,18 A comprehensive review of 12 studies showed that remote monitoring, AI-based predictive algorithms, and real-time feedback are cost-effective strategies that reduce health care costs.19 In China, mHealth interventions decreased the cost of diabetes care by 30% to 40%.20
Meanwhile, a thorough analysis is needed to understand the impact of mHealth interventions on self-efficacy in T2DM, supporting sustained self-management. However, systematic reviews and meta-analyses specifically examining this effect are scarce. Most research on mHealth in diabetes has prioritized clinical indicators, such as A1C or blood pressure,21,22 over psychosocial factors, such as self-efficacy, which are more complex and time-consuming to measure.21,23 Additionally, early studies often focused on technology’s usability and acceptability,24 overlooking psychological factors. The absence of standardized instruments for measuring self-efficacy further complicates comparisons across studies,25 leading to an under-researched area in meta-analyses.
Although some studies have examined the role of mHealth in managing diabetes, these analyses often focus on other outcome variables rather than self-efficacy or on other specific populations, such as patients with type 1 diabetes, children and adolescents,26 or patients with gestational diabetes.27 For example, systematic reviews have evaluated smartphone-based self-management interventions targeting self-care and quality of life28 and digital, telemedicine, or telecare interventions for primarily A1C reduction in T2DM.29-31
Therefore, this study aims to address the research gap by conducting a comprehensive systematic review and meta-analysis of randomized controlled trials (RCTs) to evaluate the effectiveness of mHealth interventions in enhancing self-efficacy among adults with T2DM. More than just assessing effectiveness, this review will closely examine individual studies to identify specific mechanisms and approaches within mHealth interventions that affect self-efficacy. The insights from this analysis will inform the development of targeted mHealth programs aimed at strengthening self-efficacy and improving adherence to self-management practices in T2DM patients, guiding future intervention strategies.
This systematic review and meta-analysis followed the guidelines outlined in the Cochrane Handbook for Systematic Reviews of Interventions.32 The study protocol was registered with the PROSPERO database under the identifier CRD42024566185.
A systematic search was conducted across multiple databases to identify studies evaluating the enhancement of self-efficacy through mHealth interventions in patients with diabetes, in accordance with PRISMA guidelines.33 The search included English-language articles published in peer-reviewed journals from January 2014 to December 2023 and was conducted in Medline, CINAHL, the Cochrane Library, and OVID. Boolean operators, truncation, and database-specific search strategies were applied to refine the results. Key search terms included “diabetes” and “self-efficacy.” Initial search results were screened through several stages to identify the most relevant studies involving mHealth interventions.
The clinical research question is:
In adults with type 2 diabetes (population), what is the effect of mHealth-based self-management interventions (intervention) compared to standard diabetes care (control) on self-efficacy outcomes (outcome)?
The included studies met the following PICO criteria: adults diagnosed with T2DM as participants (P = population); mHealth interventions such as specific apps for self-management support or telemetry for clinical data collection, such as glucose levels (I = intervention); a control group receiving standard diabetes care (C = control); and outcomes focused on self-efficacy (O = outcome). Only RCTs were considered. Studies were excluded if they relied solely on simple text messaging or group chats (e.g., WeChat, WhatsApp); involved participants with type 1 diabetes, gestational diabetes, or prediabetes; included individuals with severe diabetes complications or other serious health conditions (e.g., stroke, cancer); or lacked sufficient data (e.g., protocols, conference proceedings).
The search initially yielded 5543 articles. After removing 2174 duplicates, 3369 articles remained. Titles and abstracts were screened, narrowing the selection to 334 potentially relevant articles. After reviewing the full texts, 334 articles were excluded for the following reasons: not an RCT (n = 64), participants were not adult diabetes patients (age ≥18 or ≤65) or included gestational diabetes (n = 20), included other diseases (n = 7), not focused on diabetes self-efficacy (n = 59), conference abstracts (n = 35), full text not accessible (n = 75), retracted paper (n = 1), duplicate record (n = 1), and no results reported (n = 2). The remaining studies included 71 RCTs targeting self-efficacy improvement, of which 62 were excluded for not focusing on mHealth interventions. Each step of the screening process was conducted independently and blindly by 2 researchers. In cases of disagreement, the researchers engaged in discussion to reconcile differences, refining the study selection criteria until consensus was achieved. Ultimately, 9 studies were selected for inclusion in the systematic review and meta-analysis. The screening process is summarized in Figure 1.
Data extraction was conducted in accordance with Cochrane’s systematic review guidelines.32 For each study, the mean and standard deviation of self-efficacy scores at baseline and postintervention were extracted for both intervention and control groups, when available. Among the 9 studies finally selected,25,34-41 7 reported complete data with mean and standard deviation of self-efficacy at both baseline and postintervention for the intervention and control groups.25,34,35,37,38,40,41 One study36 provided the pre-post difference and their 95% CI (upper and lower limits). Another study39 reported the mean and standard deviation of the difference between intervention and control groups at both time points (pre- and post-intervention). For studies with multiple measurements, the final endpoint was used as the reference for analysis. For the systematic review, the extracted data included details such as country, sample size, self-efficacy measures, and details of the intervention.
To ensure a rigorous evaluation of study quality, the selected RCTs were assessed for methodological robustness using a structured framework based on key risk factors for bias, the Risk of Bias 2 (ROB2) tool.42 RoB2 tool was selected to assess study quality due to its RCT-specific methodology, algorithmic bias judgment, outcome-level granularity, and Cochrane-mandated use—essential for credible synthesis of mHealth self-efficacy evidence. The evaluation focused on 5 critical aspects of study design and implementation: (1) bias arising from the randomization process, (2) bias due to deviations from intended interventions, (3) bias due to missing outcome data, (4) bias in the measurement of outcomes, and (5) bias in the selection of the reported results. Each domain was rated as having a “low,” “some concern” or “high” risk of bias, with an overall judgment derived for each study. This assessment was conducted independently by 2 researchers to minimize subjectivity, adhering to the intention-to-treat principle as a benchmark for reliability. Any discrepancies in the evaluations were addressed through collaborative discussions to ensure consistency and alignment in the final ratings.
Statistical analyses were performed using R programming, utilizing the meta and metafor packages as the core tools for conducting the meta-analysis.43 To account for variations in self-efficacy measurement scales across studies, the standardized mean difference (SMD) was selected as the effect size, ensuring comparability. A random-effects model was implemented using restricted maximum likelihood (REML). REML provides estimates that are less biased compared to traditional maximum likelihood methods, especially in small sample sizes.44 To ensure robust and reliable confidence interval estimates, the Hartung-Knapp-Sidik-Jonkman (HKSJ) method was employed. HKSJ generates robust confidence intervals, particularly in scenarios characterized by significant heterogeneity or limited sample sizes.44 Sensitivity analyses and Egger’s test were omitted to prevent misleading conclusions about biases or heterogeneity because the meta-analysis included fewer than 10 studies.45
Nine studies were published between 2014 and 2022 involving a total of 1116 participants, with sample sizes in individual studies ranging from 20 to 287 (Table 1). The studies were conducted across various countries: 3 in the United States,35,39,41 2 in Singapore,36,40 and 1 each in Saudi Arabia,34 India,37 Malaysia,38 and China.25 Study settings varied, with 5 conducted in diabetes outpatient clinics,25,34,36,40,41 2 in public health care settings,37,38 1 within the Veterans Annual Pittsburgh Healthcare System (1 study),35 and 1 in the Kaiser Permanente Northern California Diabetes Registry (1 study).39
The types of interventions varied, with 4 studies focusing on app-based management,25,34,37,39 2 on telemonitoring programs,38,39 and 1 combining app-based management with telemonitoring.36 The intervention durations also differed, with 7 studies implementing 6-month interventions,25,34-36,38-40 1 for 9 months,41 and 1 for 3 months.37 Control group interventions primarily involved usual diabetes treatment in 6 studies,25,34,35,38,39,41 with others including self-management manuals,35 media leaflets,37 and a nurse-led diabetes service.40
The studies utilized various tools to measure self-efficacy. The Diabetes Management Self-Efficacy Scale was used in 4 studies,25,34,35,37 and the Generalized Self-Efficacy Scale was employed in 2 studies.36,40 Other tools included the Management Self-Efficacy Scale for Type 2 Diabetes,39 the Diabetes Empowerment Scale–Short Form,41 and self-efficacy items from the Michigan Diabetes Knowledge Questionnaire.38 The timing of measurements varied: 5 studies assessed outcomes at 6 months,25,34-36,39 2 at 9 months,40,41 1 at 3 months,37 and 1 at 12 months.38
The risk of bias was evaluated across 5 domains for each study. Seven25,34,36-38,40,41 of the included studies showed a low risk of bias in all domains. One35 study presented some concerns in 1 domain, and the study by Pressman et al39 had a high risk of bias in 2 domains (D2 and D4) and was rated as high risk of bias overall. Overall, most studies exhibited a low risk of bias across the assessed domains, with only minor concerns in a few cases (Figure 2).
A random-effects meta-analysis using the HKSJ adjustment was conducted to evaluate the effectiveness of mHealth-based interventions in improving self-efficacy for self-management among patients with diabetes. The SMD was 0.97 (95% CI, 0.16-1.78, t = 2.75, P = .02), indicating a significant positive effect of mHealth interventions compared to the control groups (Figure 3).
Heterogeneity across the studies was substantial, with an I² value of 88% (95% CI, 80%-93%), suggesting a high degree of variability among the included studies. The between-study variance was quantified as τ² = 0.958 (95% CI, 0.342-3.905), and the standard deviation of the true effects was τ = 0.979 (95% CI, 0.585-1.976). The test for heterogeneity was statistically significant (Q = 67.31, df = 8, P < .01), further supporting the presence of considerable heterogeneity among the studies. These findings suggest that mHealth-based interventions can significantly enhance self-efficacy for diabetes self-management. However, the observed heterogeneity underscores the need for careful interpretation and consideration of the variability in intervention design, implementation, and study populations.
This study systematically reviewed and conducted a meta-analysis of studies examining the effects of mHealth interventions on self-efficacy among patients with type 2 diabetes. The meta-analysis revealed an SMD of 0.97 (95% CI, 0.16-1.78, t = 2.75, P = .02). According to Cohen’s46 classification of effect sizes, an SMD of 0.97 is considered a “large effect size,” indicating that mHealth interventions are highly effective in enhancing self-efficacy among patients with T2DM.
This finding aligns closely with the results of Aminuddin et al,28 whose meta-analysis reported a similar effect size (0.98) for interventions using smartphone applications to improve self-efficacy among diabetes patients. mHealth interventions, such as apps and telemedicine, improve self-efficacy among patients with T2DM by providing education, promoting self-monitoring, facilitating patient-provider communication, and fostering behavioral change.25,34,37,41 These tools empower patients to take an active role in managing their condition, offering valuable support that enhances their confidence and self-efficacy in self-care activities.31,47 Mobile application technologies have improved communication with health care providers, enhanced accessibility, and facilitated independent management processes, such as blood glucose monitoring, exercise, and medication adherence. By prioritizing patient convenience and accessibility, mHealth promotes self-management behaviors and with regular use, can boost self-efficacy and confidence in managing diabetes.48
mHealth enhances self-efficacy in various ways. Automated reminders, progress tracking, and communication with health care professionals sustain patients’ motivation for self-care and help reduce burnout, a significant challenge in diabetes management. Furthermore, mHealth equips patients with tools and knowledge for dietary assessment, personalized exercise programs, and lifestyle modifications, all contributing to improved self-efficacy.9,49 The development of mHealth-based intervention programs has thus increased access to diabetes education for those facing barriers related to cost, time, and availability. This approach, which includes app-based interventions and telemonitoring programs as demonstrated in the selected studies, is particularly promising for patients unable to attend in-person sessions. Although technology offers substantial benefits, it also poses challenges for health care teams, necessitating that professionals continuously adapt to new technologies, enhance their digital skills, and commit to lifelong learning to meet these demands effectively.6,50
Many vulnerable populations, including the elderly, Latin and African American individuals, rural residents, the homeless, and sexual gender minorities, face barriers due to low digital literacy and lack of customized content.51,52 The World Health Organization53 Global Strategy for Digital Health aims to accelerate global digital health by promoting scalable, sustainable, and people-centered solutions that are affordable and accessible to all. mHealth apps should integrate seamlessly with wearable sensors, use large fonts, and provide clear, real-time feedback. To bridge the digital divide, tailored solutions, digital literacy programs, and policy support are essential for equitable health care access.
According to Bandura,54 self-efficacy is enhanced through several interconnected processes: cognitive, motivational, affective, and selection processes.55 In patients with diabetes, mHealth applications can support cognitive processes by providing essential knowledge about diabetes management, including education on blood glucose monitoring, dietary guidelines, and exercise routines. mHealth features such as goal setting and progress tracking facilitate motivational processes by providing regular feedback and encouraging patients to maintain healthy behaviors.47 Affective processes address emotional barriers like stress and anxiety in diabetes management. mHealth tools support these processes by offering relaxation techniques and virtual support communities, helping patients cope with challenges and recover emotionally from setbacks, thereby enhancing self-efficacy.56 Finally, mHealth applications boost self-efficacy in the selection process by offering personalized recommendations for diet and exercise. This tailored approach empowers patients to make informed lifestyle choices that sustain behaviors conducive to effective diabetes management.57
As highlighted in the introduction, self-efficacy significantly benefits diabetic patients. Enhanced self-efficacy through mHealth enables better self-care, leading to improved blood sugar control and healthier habits.58 It also reduces stress and depression, thereby enhancing emotional resilience and overall well-being.59 Additionally, fewer hospital visits decrease medical costs, and increased confidence improves quality of life.9,59 Ultimately, sustained self-management reduces complications and promotes long-term health. Therefore, the interconnected processes outlined by Bandura54 can be effectively utilized in mHealth interventions, and these strategies should be disseminated in both clinical and research settings.
This study confirmed that mHealth interventions positively impacted self-efficacy in both primary care and community settings. This suggests that mHealth can serve as an effective tool for enhancing self-efficacy over extended period regardless of the environment, especially in home settings where patients feel most comfortable. Future research should focus on comparing the effects of mHealth interventions across diverse settings (e.g., community, hospital, home) for patients with T2DM. To assess the sustained effects of mHealth on self-efficacy, studies with long-term follow-up periods exceeding 9 months are required.60
The primary strength of this study lies in its focus on measuring the isolated effect of mHealth interventions on self-efficacy, a key psychological factor influencing self-care activities in patients with T2DM, utilizing HKSF adjustment to ensure robust and reliable estimates. By examining the specific impact of mHealth-based interventions, this study provides valuable insights into their potential to enhance self-efficacy, a critical factor in selfmanagement, glucose control, and the prevention of complications in individuals with T2DM.
However, several limitations should be noted. First, the high heterogeneity observed (I² = 88%, 95% CI, 80%-93%) may be attributed to variations in behavior change interventions aimed at improving self-efficacy, differences in self-efficacy measurement tools, intervention durations, types of technology used, and demographic characteristics of the participants. Second, some studies included in the analysis incorporated other interventions alongside mHealth, which may limit the ability to isolate the effect of mHealth interventions on self-efficacy. Third, the lack of data on mHealth usage rates prevented consistent reporting on the relationship between usage levels and selfefficacy outcomes. Lastly, the relatively small number of studies included in this analysis (n = 9) warrants caution in interpreting the results and drawing generalized conclusions.
This study demonstrated the effectiveness of mHealth interventions in enhancing self-efficacy among patients with T2DM across 9 studies. Based on these findings, mHealth technology can be further developed to provide accessible and convenient health care services for diabetes management. mHealth is an effective, cost-efficient intervention that improves diabetes care, enhances patients’ self-care behaviors, reduces health care inequities, and ultimately improves the overall quality of life for individuals with T2DM. Building on the results of this study, future efforts should focus on developing mHealth interventions tailored to the individual characteristics and environments of patients. Such personalized and context-specific mHealth solutions can better support successful selfmanagement and improve self-efficacy among individuals with T2DM.
Junhee Ahn: Conceptualization, Methodology, Writing - original draft preparation. Youngran Yang: Data curation, Methodology, Writing - review & editing, Supervision. Ji Young Kim: Methodology, Writing - review & editing. Jihyon Pahn: Methodology, Writing - review & editing. Yura Jang: Methodology, Writing - review & editing.
This work was supported by National University Development Project at Jeonbuk National University in 2024.
Not applicable.
Junhee Ahn https://orcid.org/0000-0003-4466-9550
Youngran Yang https://orcid.org/0000-0001-5610-9310
Ji Young Kim https://orcid.org/0000-0002-4482-8796
Jihyon Pahn https://orcid.org/0000-0001-5537-2029
Yura Jang https://orcid.org/0009-0008-3279-6206
Statement Data are available upon reasonable request.
Khan MAB, Hashim MJ, King JK, Govender RD, Mustafa H, Al Kaabi J. Epidemiology of type 2 diabetes - global burden of disease and forecasted trends. J Epidemiol Glob Health. 2020;10(1):107-111. doi:10.2991/jegh.k.191028.001
Zaini NE, Idris IB, Ahmad N, Hashim SM, Abdullah NN. The role of self-efficacy and self-care management in mediation analysis of glycemic control in type 2 diabetes mellitus: a systematic review. Int Med J. 2023;30(4):212-221. doi:10.1016/j .gerinurse.2023.02.017
American Diabetes Association Professional Practice Committee. Facilitating behavior change and well-being to improve health outcomes: standards of medical care in diabetes—2022. Diabetes Care. 2022;45(suppl 1):S60-S82.
Alshaikh AA, Al-Qahtani FS, Alqahtani SAM, et al. Exploring the self-efficacy of patients with diabetes: its role as a predictor of diabetes management and well-being. Front Endocrinol. 2024;15:1347396. doi:10.3389/fendo.2024.1347396
Jiang X, Jiang H, Li M. The role of self-efficacy enhancement in improving self-management behavior for type 2 diabetes mellitus patients. Diabetes Metab Syndr Obes. 2024;17:3131-3138. doi:10.2147/DMSO.S460864
Association of Diabetes Care and Education Specialists, Kolb L. An effective model of diabetes care and education: the ADCES7 Self-Care Behaviors™. Sci Diabetes Self-Manag Care. 2021;47(1):30-53. doi:10.1177/0145721720978154
El-Gayar O, Ofori M, Nawar N. On the efficacy of behavior change techniques in mHealth for self-management of diabetes: a meta-analysis. J Biomed Inform. 2021;119:103839. doi:10.1016/j.jbi.2021.103839
Aseela S, Santhi S, Anish TS, Mahadevan S. Diabetes self-efficacy on glycemic control and well-being of patients with type 2 diabetes mellitus: an analytical cross-sectional study. Cureus. 2024;16(7):e64005. doi:10.7759/cureus.64005
Yap JM, Tantono N, Wu VX, Klainin-Yobas P. Effectiveness of technology-based psychosocial interventions on diabetes distress and health-relevant outcomes among type 2 diabetes mellitus: a systematic review and meta-analysis. J Telemed Telecare. 2024;30(2):262-284. doi:10.1177/1357633X211058329
Morita PP. Design of mobile health technology. In: Sethumadhavan A, Sasangohar F, eds. Design for Health. Elsevier; 2020:87-102.
Silva BM, Rodrigues JJ, de la Torre Díez I, López-Coronado M, Saleem K. Mobile-health: a review of the current state in 2015. J Biomed Inform. 2015;56:265-272. doi:10.1016/j.jbi.2015.06.003
Song T, Deng N, Cui T, et al. Measuring success of patients’ continuous use of mobile health services for self-management of chronic conditions: model development and validation. J Med Internet Res. 2021;23(7):e26670. doi:10.2196/26670
Quach S, Michaelchuk W, Benoit A, et al. Mobile health applications for self-management in chronic lung disease: a systematic review. Netw Model Anal Health Inform Bioinform. 2023;12(1):25. doi:10.1007/s13721-023-00419-0
Castillo-Valdez PF, Rodriguez-Salvador M, Ho YS. Scientific production dynamics in mHealth for diabetes: scientometric analysis. JMIR Diabetes. 2024;9:e52196. doi:10.2196/52196
María Gómez A, Cristina Henao D, León Vargas F, et al. Efficacy of the mHealth application in patients with type 2 diabetes transitioning from inpatient to outpatient care: a randomized controlled clinical trial. Diabetes Res Clin Pract. 2022;189:109948. doi:10.1016/j.diabres.2022.109948
Gerber BS, Biggers A, Tilton JJ, et al. Mobile health intervention in patients with type 2 diabetes: a randomized clinical trial. JAMA Netw Open. 2023;6(9):e2333629. doi:10.1001/jamanetworkopen.2023.33629
De Groot J, Wu D, Flynn D, Robertson D, Grant G, Sun J. Efficacy of telemedicine on glycemic control in patients with type 2 diabetes: a meta-analysis. World J Diabetes. 2021;12(2):170-180. doi:10.4239/wjd.v12.i2.170
Gershkowitz BD, Hillert CJ, Crotty BH. Digital coaching strategies to facilitate behavioral change in type 2 diabetes: a systematic review. J Clin Endocrinol Metab. 2021;106(4):e1513-e1520. doi:10.1210/clinem/dgaa850
Tornvall I, Kenny D, Wubishet BL, Russell A, Menon A, Comans T. Economic evaluations of mHealth interventions for the management of type 2 diabetes: a scoping review. J Diabetes Sci Technol. 2025;19(1):179-190. doi:10.1177/193229 68231183956
Li J, Sun L, Hou Y, Chen L. Cost-effectiveness analysis of a mobile-based intervention for patients with type 2 diabetes mellitus. Int J Endocrinol. 2021;2021:8827629. doi:10.1155/2021/8827629
Liu K, Xie Z, Or CK. Effectiveness of mobile app-assisted self-care interventions for improving patient outcomes in type 2 diabetes and/or hypertension: systematic review and meta-analysis of randomized controlled trials. JMIR Mhealth Uhealth. 2020;8(8):e15779. doi:10.2196/15779
Supramaniam P, Beh YS, Junus S, Devesahayam PR. Exploring mHealth app utilization for diabetes self-management: survey insights from a northern district in Malaysia. BMC Public Health. 2024;24(1):3542. doi:10.1186/s12889-024-21056-w
Birhanu TE, Guracho YD, Asmare SW, Olana DD. A mobile health application use among diabetes mellitus patients: a systematic review and meta-analysis. Front Endocrinol. 2024;15:1481410. doi:10.3389/fendo.2024.1481410
Moschonis G, Siopis G, Jung J, et al. Effectiveness, reach, uptake, and feasibility of digital health interventions for adults with type 2 diabetes: a systematic review and meta-analysis of randomised controlled trials. Lancet Digit Health. 2023;5(3):e125-e143.
Zhai Y, Yu W. A mobile app for diabetes management: impact on self-efficacy among patients with type 2 diabetes at a community hospital. Med Sci Monit. 2020;26:e926719. doi:10.12659/MSM.926719
Zhang K, Huang Q, Wang Q, et al. Telemedicine in improving glycemic control among children and adolescents with type 1 diabetes mellitus: systematic review and meta-analysis. J Med Internet Res. 2024;26:e51538. doi:10.2196/51538
Eberle C, Löhnert M, Stichling S. Effectiveness of disease-specific mHealth apps in patients with diabetes mellitus: scoping review. JMIR Mhealth Uhealth. 2021;9(2):e23477. doi:10.2196/23477
Aminuddin HB, Jiao N, Jiang Y, Hong J, Wang W. Effectiveness of smartphone-based self-management interventions on self-efficacy, self-care activities, health-related quality of life and clinical outcomes in patients with type 2 diabetes: a systematic review and meta-analysis. Int J Nurs Stud. 2021;116:103286. doi:10.1016/j.ijnurstu.2019.02.003
Kerr D, Ahn D, Waki K, Wang J, Breznen B, Klonoff DC. Digital interventions for self-management of type 2 diabetes mellitus: systematic literature review and meta-analysis. J Med Internet Res. 2024;26:e55757. doi:10.2196/55757
Liu F, Li J, Li X, et al. Efficacy of telemedicine intervention in the self-management of patients with type 2 diabetes: a systematic review and meta-analysis. Front Public Health. 2024;12:1405770. doi:10.3389/fpubh.2024.1405770
Liu Q, Song H, Zhang S, et al. Efficacy of using telecare services for community-dwelling people with diabetes: a systematic review and meta-analysis. Prim Care Diabetes. 2024;18(4):393-401. doi:10.1016/j.pcd.2024.06.008
Higgins JPT, Thomas J, Chandler J, et al. Cochrane Handbook for Systematic Reviews of Interventions. Version 6.5. Wiley. Published August 2024. Accessed February 11, 2025. https://www.training.cochrane.org/handbook
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi:10.1136/bmj.n71
Alanzi T, Alanazi NR, Istepanian R, Philip N. Evaluation of the effectiveness of mobile diabetes management system with social networking and cognitive behavioural therapy (CBT) for T2D. mHealth. 2018;4:17. doi:10.21037/mhealth.2018.06.05
DiNardo MM, Greco C, Phares AD, et al. Effects of an integrated mindfulness intervention for veterans with diabetes distress: a randomized controlled trial. BMJ Open Diabetes Res Care. 2022;10(2):e002631. doi:10.1136/bmjdrc-2021-002631
Jiang Y, Ramachandran HJ, Teo JYC, et al. Effectiveness of a nurse-led smartphone-based self-management programme for people with poorly controlled type 2 diabetes: a randomized controlled trial. J Adv Nurs. 2022;78(4):1154-1165. doi:10.1111/jan.15178
Kusnanto, Widyanata KAJ, Suprajitno, Arifin H. DM-calendar app as a diabetes self-management education on adult type 2 diabetes mellitus: a randomized controlled trial. J Diabetes Metab Disord. 2019;18:557-563. doi:10.1007/s40200-019-00468-1
Lee JY, Chan CKY, Chua SS, et al. Telemonitoring and team-based management of glycemic control on people with type 2 diabetes: a cluster-randomized controlled trial. J Gen Intern Med. 2020;35:87-94. doi:10.1007/s11606-019-05316-9
Pressman AR, Kinoshita L, Kirk S, Barbosa GM, Chou C, Minkoff J. A novel telemonitoring device for improving diabetes control: protocol and results from a randomized clinical trial. Telemed J E Health. 2014;20(2):109-114. doi:10.1089/tmj.2013.0157
Wang W, Cheng MTM, Leong FL, Goh AWL, Lim ST, Jiang Y. The development and testing of a nurse-led smartphonebased self-management program for diabetes patients with poor glycemic control. J Adv Nurs. 2020;76(11):3179-3189. doi:10.1111/jan.14519
Young HM, Miyamoto S, Dharmar M, Tang-Feldman Y. Nurse coaching and mobile health compared with usual care to improve diabetes self-efficacy for persons with type 2 diabetes: randomized controlled trial. JMIR Mhealth Uhealth. 2020;8(3):e16665. doi:10.2196/16665
Cochrane Collaboration. Cochrane Methods. Risk of bias tools. 2019. Accessed February 11, 2025. https://methods.cochrane.org/bias/resources/rob-2
Schwarzer G, Carpenter JR, Rücker G. Meta-Analysis With R. Springer; 2015.
Zejnullahi R, Hedges LV. Robust variance estimation in small meta-analysis with the standardized mean difference. Res Synth Methods. 2024;15(1):44-60. doi:10.1002/jrsm.1668
Hu T, Zhou Y, Hattori S. Sensitivity analysis for publication bias in meta-analysis of sparse data based on exact likelihood. Biometrics. 2024;80(3):ujae092. doi:10.1093/biomtc/ujae092
Cohen J. Statistical Power Analysis for the Behavioral Sciences. Routledge; 2013.
Hoda F, Arshad M, Khan MA, et al. Impact of a mHealth intervention in type 2 diabetes mellitus patients: a randomized clinical trial. SN Compr Clin Med. 2023;5(1):219. doi:10.1007/s42399-023-01564-3
Farley H. Promoting self-efficacy in patients with chronic disease beyond traditional education: a literature review. Nurs Open. 2020;7(1):30-41. doi:10.1002/nop2.382
Shaban MM, Sharaa HM, Amer FGM, Shaban M. Effect of digital-based nursing intervention on knowledge of self-care behaviors and self-efficacy of adult clients with diabetes. BMC Nurs. 2024;23(1):130. doi:10.1186/s12912-024-01787-2
Bults M, van Leersum CM, Olthuis TJJ, Bekhuis REM, den Ouden MEM. Mobile health apps for the control and self-management of type 2 diabetes mellitus: qualitative study on users’ acceptability and acceptance. JMIR Diabetes. 2023;8:e41076. doi:10.2196/41076
Kruse CS, Mileski M, Heinemann K, Huynh H, Leafblad A, Moreno E. Analyzing the effectiveness of mHealth to manage diabetes mellitus among adults over 50: a systematic literature review. J Multidiscip Healthc. 2023;16:101-117. doi:10.2147/JMDH.S392693
Armaou M, Araviaki E, Musikanski L. eHealth and mHealth interventions for ethnic minority and historically underserved populations in developed countries: an umbrella review. Int J Community Well-Being. 2020;3(2):193-221.
World Health Organization. Global Strategy on Digital Health 2020-2025. 2021. Accessed February 11, 2025. https://apps.who.int/iris/handle/10665/344249
Bandura A. Health promotion by social cognitive means. Health Educ Behav. 2004;31(2):143-164. doi:10.1177/10901 98104263660
Warner LM, Schwarzer R. Self-efficacy and health. In: Liamputtong P, ed. Handbook of Concepts in Health, Health Behavior and Environmental Health. Springer; 2024:1-26.
Tang TS, Klein G, Görges M, et al. Evaluating a mental health support mobile app for adults with type 1 diabetes living in rural and remote communities: the REACHOUT pilot study. Diabet Med. 2025;e15451. doi:10.1111/dme.15451
Jiang H, Wang H, Pan T, Liu Y, Jing P, Liu Y. Mobile application and machine learning-driven scheme for intelligent diabetes progression analysis and management using multiple risk factors. Bioengineering. 2024;11(11):1053. doi:10.3390/bioengineering11111053
Park G, Lee H, Khang AR. The development of automated personalized self-care (APSC) program for patients with type 2 diabetes mellitus. J Korean Acad Nurs. 2022;52(5):535-549. doi:10.4040/jkan.22046
Seo K. Mediating effect of acceptance action in relationship between diabetes self-stigma and quality of life in people with diabetes in Korea. J Korean Acad Fundam Nurs. 2021;28(3):384-394. doi:10.7739/jkafn.2021.28.3.384
Tarricone R, Petracca F, Svae L, Cucciniello M, Ciani O. Which behaviour change techniques work best for diabetes self-management mobile apps? Results from a systematic review and meta-analysis of randomised controlled trials. Ebiomedicine. 2024;103:104456. doi:10.1016/j.ebiom.2024.105091
From Department of Nursing, Wonkwang Health Science University, Iksan, South Korea (Prof Ahn, Prof Kim); College of Nursing, Research Institute of Nursing Science, Jeonbuk National University, Jeonju, South Korea (Dr Yang); Biomedical Research Institute, Jeonbuk National University Hospital, Jeonju, South Korea (Dr Yang); Department of Nursing, Jesus University, Jeonju, South Korea (Prof Pahn); and Graduate School of Nursing, Jeonbuk National University, Jeonju, South Korea (Miss Jang).
Corresponding Author: Youngran Yang, College of Nursing, Research Institute of Nursing Science, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeonbuk-do 54896, South Korea. Email: youngran13@jbnu.ac.kr
