Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/63209, first published .
Assessing the Effectiveness of Digital Health Behavior Strategies on Type 2 Diabetes Management: Systematic Review and Network Meta-Analysis

Assessing the Effectiveness of Digital Health Behavior Strategies on Type 2 Diabetes Management: Systematic Review and Network Meta-Analysis

Assessing the Effectiveness of Digital Health Behavior Strategies on Type 2 Diabetes Management: Systematic Review and Network Meta-Analysis

Review

1School of Nursing, Health Science Center, Xi 'an Jiaotong University, Xi 'an, China

2School of Public Health, Xi’an Jiaotong University, Xi 'an, China

3Graduate School, Beijing University of Chinese Medicine, Beijing, China

Corresponding Author:

Rui Gao, PhD

School of Nursing

Health Science Center

Xi 'an Jiaotong University

74 Yanta West Road

Xi 'an, 710061

China

Phone: 86 18966606582

Email: gaorui@xjtu.edu.cn


Background: Various mobile technologies and digital health interventions (DHIs) have been developed for type 2 diabetes mellitus (T2DM) management. Strategies are crucial for ensuring the effectiveness of DHIs. However, there is currently a lack of categorization and summarization of the strategies used in DHIs for T2DM.

Objective: This study aims to (1) identify and categorize the strategies used in DHIs for T2DM management; (2) assess the effectiveness of these DHI strategies; and (3) compare and rank the efficacy of different strategy combinations on glycated hemoglobin A1c (HbA1c) levels, fasting blood glucose (FBG) levels, BMI, and weight loss.

Methods: Relevant randomized controlled trials (RCTs) were extracted from PubMed, Web of Science, and Scopus databases. Three rounds of screening and selection were conducted. The strategies were identified and categorized based on the principles of behavior change techniques and behavior strategies. The synthesis framework for the assessment of health IT was used to structure the evaluation of the DHI strategies qualitatively. A network meta-analysis was performed to compare the efficacy of different strategy combinations. The data quality was assessed using the Cochrane Risk of Bias tool.

Results: A total of 52 RCTs were included, identifying 63 strategies categorized into 19 strategy themes. The most commonly used strategies were guide, monitor, management, and engagement. Most studies reported positive or mixed outcomes for most indicators based on the synthesis framework for the assessment of health IT. Research involving a medium or high number of strategies was found to be more effective than research involving a low number of strategies. Of 52 RCTs, 27 (52%) were included in the network meta-analysis. The strategy combination of communication, engagement, guide, and management was most effective in reducing HbA1c levels (mean difference [MD] –1.04, 95% CI –1.55 to –0.54), while the strategy combination of guide, management, and monitor was effective in reducing FBG levels (MD –0.96, 95% CI –1.86 to –0.06). The strategy combination of communication, engagement, goal setting, management, and support was most effective for BMI (MD –2.30, 95% CI –3.16 to –1.44) and weight management (MD –6.50, 95% CI –8.82 to –4.18).

Conclusions: Several DHI strategy combinations were effective in reducing HbA1c levels, FBG levels, BMI, and weight in T2DM management. Health care professionals should be encouraged to apply these promising strategy combinations in DHIs during clinical care. Future research should further explore and optimize the design and implementation of strategies.

Trial Registration: PROSPERO CRD42024544629; https://tinyurl.com/3zp2znxt

J Med Internet Res 2025;27:e63209

doi:10.2196/63209

Keywords



The Severity of Type 2 Diabetes

Type 2 diabetes mellitus (T2DM) has become a serious public health problem worldwide. It is a progressive disease that can impair health-related quality of life [Väätäinen S, Keinänen-Kiukaanniemi S, Saramies J, Uusitalo H, Tuomilehto J, Martikainen J. Quality of life along the diabetes continuum: a cross-sectional view of health-related quality of life and general health status in middle-aged and older Finns. Qual Life Res. Sep 2014;23(7):1935-1944. [CrossRef] [Medline]1,Jalkanen K, Aarnio E, Lavikainen P, Jauhonen HM, Enlund H, Martikainen J. Impact of type 2 diabetes treated with non-insulin medication and number of diabetes-coexisting diseases on EQ-5D-5 L index scores in the Finnish population. Health Qual Life Outcomes. Jul 08, 2019;17(1):117. [FREE Full text] [CrossRef] [Medline]2] while also imposing substantial economic burdens on individuals, health systems, and society [Williams R, Karuranga S, Malanda B, Saeedi P, Basit A, Besançon S, et al. Global and regional estimates and projections of diabetes-related health expenditure: results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract. Apr 2020;162:108072. [CrossRef] [Medline]3]. By 2040, there will be 642 million patients with diabetes worldwide, with the incidence of T2DM on the rise across all regions [Magliano DJ, Boyko EJ, IDF Diabetes Atlas 10th Edition Scientific Committee. IDF diabetes atlas: 10th edition. International Diabetes Federation. 2021. URL: https://diabetesatlas.org/idfawp/resource-files/2021/07/IDF_Atlas_10th_Edition_2021.pdf [accessed 2025-01-24] 4]. T2DM accounts for >90% of the diagnosis in all types of diabetes, and it can cause various complications [Chinese Elderly Type 2 Diabetes Prevention and Treatment of Clinical Guidelines Writing Group, Geriatric Endocrinology and Metabolism Branch of Chinese Geriatric Society, Geriatric Endocrinology and Metabolism Branch of Chinese Geriatric Health Care Society, Geriatric Professional Committee of Beijing Medical Award Foundation, National Clinical Medical Research Center for Geriatric Diseases (PLA General Hospital). [Clinical guidelines for prevention and treatment of type 2 diabetes mellitus in the elderly in China (2022 edition)]. Zhonghua Nei Ke Za Zhi. Jan 01, 2022;61(1):12-50. [CrossRef] [Medline]5]. The goal of diabetes treatment is to prevent or delay complications and optimize quality of life [American Diabetes Association. 4. Comprehensive medical evaluation and assessment of comorbidities: standards of medical care in diabetes-2021. Diabetes Care. Jan 2021;44(Suppl 1):S40-S52. [CrossRef] [Medline]6]. Poor glucose control is associated with the occurrence of complications [Bain SC, Bekker Hansen B, Hunt B, Chubb B, Valentine WJ. Evaluating the burden of poor glycemic control associated with therapeutic inertia in patients with type 2 diabetes in the UK. J Med Econ. Jan 2020;23(1):98-105. [FREE Full text] [CrossRef] [Medline]7]. Glycated hemoglobin A1c (HbA1c) can be used to determine glucose control levels [Fellinger P, Rodewald K, Ferch M, Itariu B, Kautzky-Willer A, Winhofer Y. HbA1c and glucose management indicator discordance associated with obesity and type 2 diabetes in intermittent scanning glucose monitoring system. Biosensors (Basel). Apr 29, 2022;12(5):288. [FREE Full text] [CrossRef] [Medline]8]. Continuous monitoring of fasting blood glucose (FBG) can provide an intuitive reflection of changes in the patient’s glucose levels. Obesity is closely related to T2DM, and weight gain is an independent risk factor for T2DM [GBD 2015 Obesity Collaborators, Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, et al. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med. Jul 06, 2017;377(1):13-27. [FREE Full text] [CrossRef] [Medline]9]. Controlling glucose levels and body weight within the normal range can effectively reduce the complications of patients with diabetes [Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HA. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med. Oct 09, 2008;359(15):1577-1589. [FREE Full text] [CrossRef] [Medline]10]. Many studies have shown that T2DM can be slowed down, stopped, or even reversed by changing lifestyle (eg, low-calorie diet and increasing physical activity) [Lean ME, Leslie WS, Barnes AC, Brosnahan N, Thom G, McCombie L, et al. Primary care-led weight management for remission of type 2 diabetes (DiRECT): an open-label, cluster-randomised trial. Lancet. Feb 10, 2018;391(10120):541-551. [FREE Full text] [CrossRef] [Medline]11,Johansen MY, MacDonald CS, Hansen KB, Karstoft K, Christensen R, Pedersen M, et al. Effect of an intensive lifestyle intervention on glycemic control in patients with type 2 diabetes: a randomized clinical trial. JAMA. Aug 15, 2017;318(7):637-646. [FREE Full text] [CrossRef] [Medline]12]. This can reduce long-term complications and may extend life expectancy [Gong Q, Zhang P, Wang J, Ma J, An Y, Chen Y, et al. Morbidity and mortality after lifestyle intervention for people with impaired glucose tolerance: 30-year results of the Da Qing Diabetes Prevention Outcome Study. Lancet Diabetes Endocrinol. Jun 2019;7(6):452-461. [FREE Full text] [CrossRef] [Medline]13].

The Effect of Digital Health Interventions

Maintaining glycemic control is a challenge for both patients and health care providers, making it difficult to encourage or motivate patients to make long-term lifestyle changes, explain their self-monitoring of blood glucose data, provide immediate feedback, and understand their lifestyle [Lee EY, Cha SA, Yun JS, Lim SY, Lee JH, Ahn YB, et al. Efficacy of personalized diabetes self-care using an electronic medical record-integrated mobile app in patients with type 2 diabetes: 6-month randomized controlled trial. J Med Internet Res. Jul 28, 2022;24(7):e37430. [FREE Full text] [CrossRef] [Medline]14]. Recognizing that patients require more self-management support, various mobile technologies (ie, mobile health care) and digital health interventions (DHIs) have been developed [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. Sep 2011;34(9):1934-1942. [FREE Full text] [CrossRef] [Medline]15-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. Mar 2014;8(2):209-215. [FREE Full text] [CrossRef] [Medline]18], including mobile apps, SMS, wearable and ambient sensors, and social media. These technologies can provide early support for the improvement of health behaviors in patients with diabetes, encouraging patients with T2DM to eat healthily, engage in physical exercise, and collect and analyze personal data to assess clinical conditions. The data reported by patients can be used to customize personalized feedback information, including health promotion, motivation, encouragement, reminders, and emotional support information [Rhee SY, Kim C, Shin DW, Steinhubl SR. Present and future of digital health in diabetes and metabolic disease. Diabetes Metab J. Dec 2020;44(6):819-827. [FREE Full text] [CrossRef] [Medline]19,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. Jun 2014;63(6):745-54.e6. [CrossRef] [Medline]20]. Relevant systematic reviews and meta-analyses have confirmed the effectiveness of DHIs on behavior change, blood glucose control, and weight loss in patients with diabetes [Hutchesson MJ, Rollo ME, Krukowski R, Ells L, Harvey J, Morgan PJ, et al. eHealth interventions for the prevention and treatment of overweight and obesity in adults: a systematic review with meta-analysis. Obes Rev. May 2015;16(5):376-392. [CrossRef] [Medline]21-Van Rhoon L, Byrne M, Morrissey E, Murphy J, McSharry J. A systematic review of the behaviour change techniques and digital features in technology-driven type 2 diabetes prevention interventions. Digit Health. 2020;6:2055207620914427. [FREE Full text] [CrossRef] [Medline]24].

The Critical Role of Strategies in DHIs for T2DM Management

Strategies play a pivotal role in enhancing the efficacy of DHIs for patients with T2DM, with the careful selection and implementation of appropriate strategies being crucial to DHI success [Webb TL, Joseph J, Yardley L, Michie S. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res. Feb 17, 2010;12(1):e4. [FREE Full text] [CrossRef] [Medline]25]. Within the realm of digital interventions, using suitable strategies, such as user-centered participatory design, can heighten user engagement, thereby rendering intervention measures more appealing and efficacious [Michie S, Yardley L, West R, Patrick K, Greaves F. Developing and evaluating digital interventions to promote behavior change in health and health care: recommendations resulting from an international workshop. J Med Internet Res. Jun 29, 2017;19(6):e232. [FREE Full text] [CrossRef] [Medline]26]. Furthermore, mobile technology interventions informed by strategies, such as health behavior theory, hold the potential for more comprehensive mechanisms of behavior change, fostering a health care approach that is both impactful and sustainable [Riley WT, Rivera DE, Atienza AA, Nilsen W, Allison SM, Mermelstein R. Health behavior models in the age of mobile interventions: are our theories up to the task? Transl Behav Med. Mar 2011;1(1):53-71. [FREE Full text] [CrossRef] [Medline]27]. Through the identification and categorization of diverse strategies, researchers and practitioners can gain deeper insights into the effective methodologies and techniques used in DHIs. This involves assessing the strengths and limitations of various strategies and their applicability to specific health domains or populations [Morrison LG, Yardley L, Powell J, Michie S. What design features are used in effective e-health interventions? A review using techniques from Critical Interpretive Synthesis. Telemed J E Health. Mar 2012;18(2):137-144. [FREE Full text] [CrossRef] [Medline]28].

Current Findings on DHI Strategies for T2DM Management

There have been studies that systematically summarize the strategies used in DHIs currently. For example, various engagement strategies have been reported in DHIs for mental health promotion, including personalization, human and social support, gamification, personalized feedback, and reminders, which work best to promote engagement [Saleem M, Kühne L, De Santis KK, Christianson L, Brand T, Busse H. Understanding engagement strategies in digital interventions for mental health promotion: scoping review. JMIR Ment Health. Dec 20, 2021;8(12):e30000. [FREE Full text] [CrossRef] [Medline]29]. A study indicated that the effectiveness of internet-based interventions correlates with the extensive use of theory, particularly the theory of planned behavior; the incorporation of a greater number of behavior change techniques; and the integration of additional methods for interacting with participants [Webb TL, Joseph J, Yardley L, Michie S. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res. Feb 17, 2010;12(1):e4. [FREE Full text] [CrossRef] [Medline]25]. A systematic review and meta-analysis of lifestyle interventions in postpartum women found that the provision of certain strategies including problem-solving, goal setting of the outcome, reviewing the outcome goal, providing feedback, and self-monitoring of behavior was associated with greater decreases in energy intake [Lim S, Hill B, Pirotta S, O'Reilly S, Moran L. What are the most effective behavioural strategies in changing postpartum women's physical activity and healthy eating behaviours? A systematic review and meta-analysis. J Clin Med. Jan 16, 2020;9(1):237. [FREE Full text] [CrossRef] [Medline]30]. In addition, the review analyzed loneliness reduction strategies including improving social skills, enhancing social support, increasing opportunities for social interaction, and addressing deficits in social cognition [Masi CM, Chen HY, Hawkley LC, Cacioppo JT. A meta-analysis of interventions to reduce loneliness. Pers Soc Psychol Rev. Aug 2011;15(3):219-266. [FREE Full text] [CrossRef] [Medline]31]. It was suggested in a narrative umbrella review that credible sources, social support, prompts and cues, graded tasks, goals and planning, feedback and monitoring, and human coaching and personalization components increased the effectiveness of DHIs targeting the prevention and management of noncommunicable diseases [Mair JL, Salamanca-Sanabria A, Augsburger M, Frese BF, Abend S, Jakob R, et al. Effective behavior change techniques in digital health interventions for the prevention or management of noncommunicable diseases: an umbrella review. Ann Behav Med. Sep 13, 2023;57(10):817-835. [FREE Full text] [CrossRef] [Medline]32]. Moreover, substantial strategies have been found to be effective in improving recruitment, reducing loss to follow-up, and enhancing retention during intervention in some trials [Hwang DA, Lee A, Song JM, Han HR. Recruitment and retention strategies among racial and ethnic minorities in web-based intervention trials: retrospective qualitative analysis. J Med Internet Res. Jul 12, 2021;23(7):e23959. [FREE Full text] [CrossRef] [Medline]33-Robinson KA, Dennison CR, Wayman DM, Pronovost PJ, Needham DM. Systematic review identifies number of strategies important for retaining study participants. J Clin Epidemiol. Aug 2007;60(8):757-765. [FREE Full text] [CrossRef] [Medline]36]. However, there is a lack of categorization and summarization of the strategies used in DHIs of T2DM at present. Although previous research has compared the effectiveness of 5 strategies for T2DM management, it has primarily focused on telemedicine within DHIs and compared several independent strategies, without providing a systematic summary of the strategies used in DHIs [Lee SW, Chan CK, Chua SS, Chaiyakunapruk N. Comparative effectiveness of telemedicine strategies on type 2 diabetes management: a systematic review and network meta-analysis. Sci Rep. Oct 04, 2017;7(1):12680. [FREE Full text] [CrossRef] [Medline]37].” Meanwhile, most interventional studies incorporate different numbers and combinations of strategies rather than focusing on individual strategies, and it is not yet known which strategy combination has the best effect. Therefore, it is essential to summarize and compare strategies and strategy combinations, identify the content of these strategies, and further determine the optimal number and form of strategy combinations to provide more practical guidance for precise and personalized digital health management of T2DM.

Study Objectives

This study aims to (1) identify and categorize the strategies used in DHIs on T2DM management; (2) assess the effectiveness of these DHI strategies; and (3) compare and rank the efficacy of different strategy combinations on HbA1c levels, FBG levels, BMI, and weight loss.


Protocol and Registration

This systematic review followed the PRISMA-NMA (Preferred Reporting Items for Systematic Review and Meta-Analyses extension for Network Meta-Analysis) 2020 statement for designing and reporting [Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev. Mar 29, 2021;10(1):89. [FREE Full text] [CrossRef] [Medline]38]. The PRISMA checklist for this study can be found in

Multimedia Appendix 1

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

DOCX File , 28 KBMultimedia Appendix 1. The protocol for this study has been registered with PROSPERO (CRD42024544629).

Data Sources

We conducted a comprehensive search for papers published in English from PubMed, Web of Science, and Scopus databases. This search used a combination of DHI-related terms, along with database-specific subject headings and filters, to ensure thoroughness and focus. The detailed search strategy is provided in

Multimedia Appendix 2

Search strategy.

DOCX File , 13 KBMultimedia Appendix 2. The time span was from January 1, 1999, to March 10, 2024.

Data Selection and Extraction

All the searched records were imported into EndNote X9 (Clarivate) to eliminate duplicate studies. The first round of screening and selection focused on identifying DHIs. Our criteria were as follows:

  1. The means of intervention should be digital, primarily including wearable devices, telemedicine, electronic health records, electronic medical records, mobile phone apps, web pages, blogs, emails, SMS text messages, social media, and similar technologies.
  2. The intervention must be health related, encompassing areas such as health behavior improvement, disease treatment, and health education.
  3. We selected studies that used randomized controlled trials (RCTs) as the intervention as eligible, including both individual and cluster RCTs, as they provide the highest level of evidence for evaluating interventions.

The second round of screening and selection concentrated on identifying specific strategies within the selected DHIs. A strategy is defined as a specific approach or technique used within the intervention to promote health behavior change or improve health outcomes. We used the framework of potential strategies (

Multimedia Appendix 3

Behavior change techniques.

DOCX File , 51 KBMultimedia Appendix 3) based on behavior change techniques [Cane J, Richardson M, Johnston M, Ladha R, Michie S. From lists of behaviour change techniques (BCTs) to structured hierarchies: comparison of two methods of developing a hierarchy of BCTs. Br J Health Psychol. Feb 2015;20(1):130-150. [CrossRef] [Medline]39] and behavior strategies [Lim S, Hill B, Pirotta S, O'Reilly S, Moran L. What are the most effective behavioural strategies in changing postpartum women's physical activity and healthy eating behaviours? A systematic review and meta-analysis. J Clin Med. Jan 16, 2020;9(1):237. [FREE Full text] [CrossRef] [Medline]30].

The third round of screening and selection focused on identifying the population with T2DM based on the second round of work. A data extraction form was developed to facilitate electronic comparison of entries. The extracted data include the author, year of publication, study setting (ie, country), characteristics of the participants, details of the interventions, strategies, and outcomes. The inclusion criteria were as follows:

  1. Population—patients diagnosed with T2DM and aged ≥18 years
  2. Comparisons—the effectiveness of DHIs was compared to no intervention
  3. Outcomes—the outcomes for the overview of the effectiveness of DHI strategies included, but were not limited to, acceptability, usability, satisfaction, appropriateness, and efficiency. The outcome of the meta-analysis included the changes in HbA1c (%) level, FBG (mmol/L) level, BMI (kg/m2), and weight loss (kg).
  4. Study design—we only included RCTs.

During the second and third screening, full manuscripts of the studies identified as potentially relevant were obtained and assessed by 6 independent reviewers according to the inclusion criteria. Any discrepancies were resolved by discussion or through adjudication by a senior researcher.

Quality Appraisal

Two independent reviewers assessed the individual quality of the final selected studies using the Cochrane Collaboration Risk of Bias tool [Cumpston M, Li T, Page MJ, Chandler J, Welch VA, Higgins JP, et al. Updated guidance for trusted systematic reviews: a new edition of the Cochrane Handbook for Systematic Reviews of Interventions. Cochrane Database Syst Rev. Oct 03, 2019;10(10):ED000142. [FREE Full text] [CrossRef] [Medline]40], with any discrepancies being resolved by consensus. The quality evaluation items of each trial included selection bias (random sequence generation and allocation concealment), performance bias (blinding of participants and personnel), detection bias (blinding of outcome assessment), attrition bias (incomplete outcome data), reporting bias (selective reporting), and other bias. Each item was scored as low, high, or unclear risk of bias.

Data Analysis and Synthesis

Methods of Analysis

The variability in some outcomes, such as changes in physical activity, diabetes self-efficacy, medication adherence, quality of life, acceptability, and satisfaction, precluded the possibility of conducting a meta-analysis for these specific measures. Therefore, we reported a structured analysis of the findings to draw conclusions about the effectiveness of different DHI strategies on T2DM management. If a certain outcome measure had a statistically significant (P<.05) improvement compared to the control group or over time, it was considered effective. If the outcome measures did not show significant changes over time or there was no statistically significant difference from the control group, it was considered that there was not enough evidence to prove their effectiveness. The synthesis framework for the assessment of health IT (SF/HIT) was used to structure the evaluation of the studies because it included a whole system set of outcome variables [Christopoulou SC, Kotsilieris T, Anagnostopoulos I. Assessment of health information technology interventions in evidence-based medicine: a systematic review by adopting a methodological evaluation framework. Healthcare (Basel). Aug 31, 2018;6(3):109. [FREE Full text] [CrossRef] [Medline]41]. These comprised variables such as adherence or attendance, acceptability, effectiveness, satisfaction, and perceived ease of use or usefulness. Following the framework, evidence for each of the outcome variables was coded as positive or mixed or neutral or negative. If the study did not address the outcome in question, it was coded as neutral or negative.

For the meta-analysis, the outcome included HbA1c levels, FBG levels, BMI, and weight loss. These were continuous variables, and thus, network estimates were presented as mean differences (MDs) with 95% CIs. We assumed that a P value<.05 indicated statistical significance. We measured the level of heterogeneity with the I2 statistics; an I2<50% was considered to have no significant heterogeneity, in which case we would use a fixed-effects model to calculate the pooled effect sizes. Otherwise, a random-effects model would be used. Stata software (version 15.1, network package and network graphs package; StataCorp) was used to conduct network meta-analysis [Lin L, Zhang J, Hodges JS, Chu H. Performing arm-based network meta-analysis in R with the pcnetmeta package. J Stat Softw. Aug 2017;80:5. [FREE Full text] [CrossRef] [Medline]42,Xu C, Niu Y, Wu J, Gu H, Zhang C. Software and package applicating for network meta-analysis: a usage-based comparative study. J Evid Based Med. Aug 2018;11(3):176-183. [CrossRef] [Medline]43]. The network package performed the network meta-analysis based on the frequentist framework using random-effects models. The approach was to test the research hypothesis, as this was simpler than the problem of establishing prior probability [Bhatnagar N, Lakshmi PV, Jeyashree K. Multiple treatment and indirect treatment comparisons: an overview of network meta-analysis. Perspect Clin Res. Oct 2014;5(4):154-158. [FREE Full text] [CrossRef] [Medline]44]. This approach is not complex and has few limitations for ordinary researchers using network meta-analysis [Shim S, Yoon BH, Shin IS, Bae JM. Network meta-analysis: application and practice using Stata. Epidemiol Health. 2017;39:e2017047. [FREE Full text] [CrossRef] [Medline]45]. A network diagram with nodes and lines was constructed to represent different interventions, where the size of nodes represents the number of populations, and the thickness of lines between nodes represents the number of studies. The results of the network meta-analysis were summarized based on all possible pairwise comparisons, including mixed comparisons (ie, the combined effect of direct and indirect comparisons) and indirect comparisons. The effect of different interventions was estimated based on the surface under the cumulative ranking curve (SUCRA). The SUCRA value ranges from 0% to 100%, where a SUCRA value of 100% indicates that the treatment was the most effective, and the smaller the value, the poorer the treatment effect.

Assessment of Inconsistency

The node-splitting test was used to assess the local inconsistency between direct and indirect comparisons. Differences between direct and indirect coefficients (assessed via the P value) were used to estimate inconsistency: if P<.05, local inconsistency was considered to exist [Spineli LM. An empirical comparison of Bayesian modelling strategies for missing binary outcome data in network meta-analysis. BMC Med Res Methodol. Apr 24, 2019;19(1):86. [FREE Full text] [CrossRef] [Medline]46]. If inconsistency was observed, nontransitivity was also suspected to exist, and potential modifiers influencing treatment effects were examined.

Risk of Bias Across Studies

The risk of publication bias in network meta-analysis was analyzed using the Egger test [Sedgwick P, Marston L. How to read a funnel plot in a meta-analysis. BMJ. Sep 16, 2015;351:h4718. [CrossRef] [Medline]47]. The symmetry of the generated funnel plots was assessed visually using the Egger test, together with adjusted rank correlation and regression asymmetry tests [Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. Dec 1994;50(4):1088-1101. [Medline]48,Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. Sep 13, 1997;315(7109):629-634. [FREE Full text] [CrossRef] [Medline]49].

Sensitivity Analyses

We used the method of removing individual studies separately.


Characteristics of Included Studies

The schematic flow for the selection of the included studies is presented in Figure 1. A total of 12,372 studies were identified, of which only 52 (0.42%) trials [Lee EY, Cha SA, Yun JS, Lim SY, Lee JH, Ahn YB, et al. Efficacy of personalized diabetes self-care using an electronic medical record-integrated mobile app in patients with type 2 diabetes: 6-month randomized controlled trial. J Med Internet Res. Jul 28, 2022;24(7):e37430. [FREE Full text] [CrossRef] [Medline]14,Glasgow RE, Toobert DJ. Brief, computer-assisted diabetes dietary self-management counseling: effects on behavior, physiologic outcomes, and quality of life. Med Care. Nov 2000;38(11):1062-1073. [CrossRef] [Medline]50-Nicolucci A, Cercone S, Chiriatti A, Muscas F, Gensini G. A randomized trial on home telemonitoring for the management of metabolic and cardiovascular risk in patients with type 2 diabetes. Diabetes Technol Ther. Aug 2015;17(8):563-570. [CrossRef] [Medline]100] met the eligibility criteria and were included. The characteristics of the included studies are provided in

Multimedia Appendix 4

Characteristics of the included studies.

XLSX File (Microsoft Excel File), 45 KBMultimedia Appendix 4 [Lee EY, Cha SA, Yun JS, Lim SY, Lee JH, Ahn YB, et al. Efficacy of personalized diabetes self-care using an electronic medical record-integrated mobile app in patients with type 2 diabetes: 6-month randomized controlled trial. J Med Internet Res. Jul 28, 2022;24(7):e37430. [FREE Full text] [CrossRef] [Medline]14,Glasgow RE, Toobert DJ. Brief, computer-assisted diabetes dietary self-management counseling: effects on behavior, physiologic outcomes, and quality of life. Med Care. Nov 2000;38(11):1062-1073. [CrossRef] [Medline]50-Nicolucci A, Cercone S, Chiriatti A, Muscas F, Gensini G. A randomized trial on home telemonitoring for the management of metabolic and cardiovascular risk in patients with type 2 diabetes. Diabetes Technol Ther. Aug 2015;17(8):563-570. [CrossRef] [Medline]100]. Among the 52 included studies from the literature, the publication year was between 2000 and 2023. The number of publications has also increased over the years, with the highest publication volume in 2022 and 2023. Most research was conducted in the United States (20/52, 38%), followed by China (5/52, 10%). The minimum sample size was 30, and the maximum was 1926. The duration of intervention ranged from 3 to 24 months, with most studies lasting 12 months (15/52, 29%). The outcome indicators in intervention mainly included HbA1c levels, FBG levels, BMI, blood pressure, weight loss, waist circumference, low-density lipoprotein cholesterol levels, high-density lipoprotein cholesterol levels, total cholesterol levels, diabetes self-efficiency, diabetes medication adherence, quality of life, self-management, depression, diabetes distress, and other outcomes.

Figure 1. Flow diagram for the search and selection of the included studies.

Synthesis of Different DHI Strategies

Among the 52 studies included, we identified 63 different strategies, and they were categorized into 19 themes. Among the identified strategies, monitor and guide were the most frequently used, appearing in 77% (40/52) and 67% (35/52) of the total papers, respectively. Following closely was management (32/52, 61%) and engagement (28/52, 54%). More than 40% of the studies used stimulate,communication, and goal setting. In addition, more than one-fifth of the research used support,shape, feedback, prompt, action, and tailor. The usage frequencies were notably lower for cues, identity, reward, model or demonstrate, and restructure. The studies ranged from using a minimum of 1 to a maximum of 32 strategies. We categorized studies into 3 groups based on the number of strategies used: low (1 to 3 strategies), medium (4 to 6 strategies), and high (≥7 strategies). Among the 52 studies, 18 (35%) were categorized as high-strategy study, 23 (44%) as medium-strategy study, and 11 (21%) as low-strategy study. In these 52 studies, 37 different combinations of strategies were identified based on the thematic strategies used in each study. Detailed information about these strategies and strategy combinations is provided in

Multimedia Appendix 5

Detailed information about strategies and strategy combinations.

XLSX File (Microsoft Excel File), 28 KBMultimedia Appendix 5. The strategy themes and the number of identified studies are listed in Table 1.

Table 1. The strategy themes and the number of identified studies (N=52).

ThemeStudies, n (%)
AAction planning12 (23)
BCommunication23 (44)
CCues4 (8)
DEngagement28 (54)
EFeedback13 (25)
FGoal setting24 (46)
GGuide35 (67)
HIdentity4 (8)
IManagement32 (61)
JModel or demonstrate2 (4)
KMonitor40 (77)
LPrompt12 (23)
MRestructure2 (4)
NReward4 (8)
OShape15 (29)
PStimulate22 (42)
QSupport19 (36)
RTailor11 (21)
SOthers7 (13)

Evaluation of the Effectiveness of Different DHI Strategies

In this assessment, we encoded positive or mixed as 1 and neutral or negative as 0. Overall, most studies have reported positive or mixed outcomes for most outcome indicators. The number of positive or mixed results that studies achieved ranged from 6 to 11. In total, 41 (79%) of the 52 studies have reported having ≥10 positive or mixed results as stipulated in the SF/HIT. For the preventive care; efficiency; perceived ease of use or usefulness; safety, privacy, or security; acceptability; appropriateness; and satisfaction domains nearly all studies (>50) have reported positive or mixed results. Following closely were the process of service delivery or performance, effectiveness, and adherence or attendance domains, with 49 (94%), 47 (90%), and 45 (86%) of the 52 studies reporting positive or mixed results. However, in terms of cost-effectiveness, most studies (35/52, 67%) either did not report relevant results or reported neutral or negative findings. Out of 18 high-strategy studies, 16 (89%) reported ≥10 positive or mixed results. Out of 23 medium-strategy studies, 20 (87%) reported ≥10 positive or mixed results. However, 7 (64%) out of the 11 low-strategy studies reported having ≥10 positive or mixed results. Meanwhile, it is noteworthy that the 2 studies with the fewest positive or mixed results were both high-strategy studies. The evaluation summary of SF/HIT results is presented in

Multimedia Appendix 6

Evaluation of the effectiveness of different digital health intervention strategies based on the synthesis framework for the assessment of health IT.

XLSX File (Microsoft Excel File), 23 KBMultimedia Appendix 6.

Risk of Bias and Quality Assessments of Included Studies

We conducted a synthesis of bias risk assessment results using RevMan software (version 5.4; Cochrane), as shown in Figure 2. Overall, all included RCTs exhibited a relatively low risk of bias. All studies reported the generation of random sequences and described specific randomization methods assessed as low risk. There were no incomplete outcome data across all studies assessed as low risk. Selective reporting was not observed in any of the studies assessed as low risk. Regarding allocation concealment, 36% (19/52) of the studies reported using this method, assessed as low risk; 54% (28/52) did not provide such reporting, assessed as unclear risk; and 10% (5/52) explicitly stated not using allocation concealment, assessed as high risk. For blinding of participants and personnel, most studies (41/52, 79%) did not use blinding, assessed as high risk, often due to the nature of the intervention precluding blinding; 15% (8/52) of the studies implemented blinding for participants, assessed as low risk, while 6% (3/52) did not report blinding status, assessed as unclear risk. For the blinding of outcome assessment, 46% (24/52) of the studies did not use blinding, resulting in high risk, whereas 33% (17/52) reported using blinding for outcome assessment, assessed as low risk, with the remainder (11/52, 21%) assessed as unclear risk. The risk of bias in each study is presented in

Multimedia Appendix 7

Risk of bias and quality assessments of included studies.

XLSX File (Microsoft Excel File), 16 KBMultimedia Appendix 7.

Figure 2. Risk of bias graph: review authors’ judgments about each risk of bias item presented as percentages across all included studies.

Meta-Analysis

Effects of Strategy Combinations in Reducing HbA1c Levels

Of 52 included RCTs, 27 (52%) RCTs [Lee EY, Cha SA, Yun JS, Lim SY, Lee JH, Ahn YB, et al. Efficacy of personalized diabetes self-care using an electronic medical record-integrated mobile app in patients with type 2 diabetes: 6-month randomized controlled trial. J Med Internet Res. Jul 28, 2022;24(7):e37430. [FREE Full text] [CrossRef] [Medline]14,Glasgow RE, Toobert DJ. Brief, computer-assisted diabetes dietary self-management counseling: effects on behavior, physiologic outcomes, and quality of life. Med Care. Nov 2000;38(11):1062-1073. [CrossRef] [Medline]50,Young RJ, Taylor J, Friede T, Hollis S, Mason JM, Lee P, et al. Pro-active call center treatment support (PACCTS) to improve glucose control in type 2 diabetes: a randomized controlled trial. Diabetes Care. Feb 2005;28(2):278-282. [CrossRef] [Medline]53-Lorig K, Ritter PL, Villa F, Piette JD. Spanish diabetes self-management with and without automated telephone reinforcement: two randomized trials. Diabetes Care. Mar 2008;31(3):408-414. [CrossRef] [Medline]55,Powers BJ, Olsen MK, Oddone EZ, Bosworth HB. The effect of a hypertension self-management intervention on diabetes and cholesterol control. Am J Med. Jul 2009;122(7):639-646. [FREE Full text] [CrossRef] [Medline]57,Feng Y, Zhao Y, Mao L, Gu M, Yuan H, Lu J, et al. The effectiveness of an eHealth family-based intervention program in patients with uncontrolled type 2 diabetes mellitus (T2DM) in the community via WeChat: randomized controlled trial. JMIR Mhealth Uhealth. Mar 20, 2023;11:e40420. [FREE Full text] [CrossRef] [Medline]59,Ruissen MM, Torres-Peña JD, Uitbeijerse BS, Arenas de Larriva AP, Huisman SD, Namli T, et al. Clinical impact of an integrated e-health system for diabetes self-management support and shared decision making (POWER2DM): a randomised controlled trial. Diabetologia. Dec 29, 2023;66(12):2213-2225. [FREE Full text] [CrossRef] [Medline]60,Davis RM, Hitch AD, Salaam MM, Herman WH, Zimmer-Galler IE, Mayer-Davis EJ. TeleHealth improves diabetes self-management in an underserved community: diabetes TeleCare. Diabetes Care. Aug 2010;33(8):1712-1717. [FREE Full text] [CrossRef] [Medline]66,Wild SH, Hanley J, Lewis SC, McKnight JA, McCloughan LB, Padfield PL, et al. Supported telemonitoring and glycemic control in people with type 2 diabetes: the Telescot Diabetes Pragmatic Multicenter randomized controlled trial. PLoS Med. Jul 2016;13(7):e1002098. [FREE Full text] [CrossRef] [Medline]68,Hesseldal L, Christensen JR, Olesen TB, Olsen MH, Jakobsen PR, Laursen DH, et al. Long-term weight loss in a primary care-anchored eHealth lifestyle coaching program: randomized controlled trial. J Med Internet Res. Sep 23, 2022;24(9):e39741. [FREE Full text] [CrossRef] [Medline]70,Nagata T, Aoyagi SS, Takahashi M, Nagata M, Mori K. Effects of feedback from self-monitoring devices on lifestyle changes in workers with diabetes: 3-month randomized controlled pilot trial. JMIR Form Res. Aug 09, 2022;6(8):e23261. [FREE Full text] [CrossRef] [Medline]72,Yin W, Liu Y, Hu H, Sun J, Liu Y, Wang Z. Telemedicine management of type 2 diabetes mellitus in obese and overweight young and middle-aged patients during COVID-19 outbreak: a single-center, prospective, randomized control study. PLoS One. 2022;17(9):e0275251. [FREE Full text] [CrossRef] [Medline]74,Gerber BS, Biggers A, Tilton JJ, Smith Marsh DE, Lane R, Mihailescu D, et al. Mobile health intervention in patients with type 2 diabetes: a randomized clinical trial. JAMA Netw Open. Sep 05, 2023;6(9):e2333629. [FREE Full text] [CrossRef] [Medline]77,Han CY, Zhang J, Ye XM, Lu JP, Jin HY, Xu WW, et al. Telemedicine-assisted structured self-monitoring of blood glucose in management of T2DM results of a randomized clinical trial. BMC Med Inform Decis Mak. Sep 14, 2023;23(1):182. [FREE Full text] [CrossRef] [Medline]78,Whitehouse CR, Knowles M, Long JA, Mitra N, Volpp KG, Xu C, et al. Digital health and community health worker support for diabetes management: a randomized controlled trial. J Gen Intern Med. Jan 17, 2023;38(1):131-137. [FREE Full text] [CrossRef] [Medline]80,Lee DY, Yoo SH, Min KP, Park CY. Effect of voluntary participation on mobile health care in diabetes management: randomized controlled open-label trial. JMIR Mhealth Uhealth. Sep 18, 2020;8(9):e19153. [FREE Full text] [CrossRef] [Medline]82,Fortmann AL, Gallo LC, Garcia MI, Taleb M, Euyoque JA, Clark T, et al. Dulce digital: an mHealth SMS-based intervention improves glycemic control in Hispanics with type 2 diabetes. Diabetes Care. Oct 2017;40(10):1349-1355. [FREE Full text] [CrossRef] [Medline]84,Lee JY, Wong CP, Tan CS, Nasir NH, Lee SW. Telemonitoring in fasting individuals with type 2 diabetes mellitus during Ramadan: a prospective, randomised controlled study. Sci Rep. Aug 31, 2017;7(1):10119. [FREE Full text] [CrossRef] [Medline]85,Ramadas A, Chan CK, Oldenburg B, Hussein Z, Quek KF. Randomised-controlled trial of a web-based dietary intervention for patients with type 2 diabetes: changes in health cognitions and glycemic control. BMC Public Health. Jun 08, 2018;18(1):716. [FREE Full text] [CrossRef] [Medline]87,Sarayani A, Mashayekhi M, Nosrati M, Jahangard-Rafsanjani Z, Javadi M, Saadat N, et al. Efficacy of a telephone-based intervention among patients with type-2 diabetes; a randomized controlled trial in pharmacy practice. Int J Clin Pharm. Apr 2018;40(2):345-353. [CrossRef] [Medline]88,Sun C, Sun L, Xi S, Zhang H, Wang H, Feng Y, et al. Mobile phone-based telemedicine practice in older Chinese patients with type 2 diabetes mellitus: randomized controlled trial. JMIR Mhealth Uhealth. Jan 04, 2019;7(1):e10664. [FREE Full text] [CrossRef] [Medline]93,Torbjørnsen A, Jenum AK, Småstuen MC, Arsand E, Holmen H, Wahl AK, et al. A low-intensity mobile health intervention with and without health counseling for persons with type 2 diabetes, part 1: baseline and short-term results from a randomized controlled trial in the Norwegian part of RENEWING HEALTH. JMIR Mhealth Uhealth. Dec 11, 2014;2(4):e52. [FREE Full text] [CrossRef] [Medline]94,Wakefield BJ, Koopman RJ, Keplinger LE, Bomar M, Bernt B, Johanning JL, et al. Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes. Telemed J E Health. Mar 2014;20(3):199-205. [FREE Full text] [CrossRef] [Medline]96-Fukuoka Y, Gay CL, Joiner KL, Vittinghoff E. A novel diabetes prevention intervention using a mobile app: a randomized controlled trial with overweight adults at risk. Am J Prev Med. Aug 2015;49(2):223-237. [FREE Full text] [CrossRef] [Medline]98,Nicolucci A, Cercone S, Chiriatti A, Muscas F, Gensini G. A randomized trial on home telemonitoring for the management of metabolic and cardiovascular risk in patients with type 2 diabetes. Diabetes Technol Ther. Aug 2015;17(8):563-570. [CrossRef] [Medline]100] assessed the effects of 12 different DHI strategy combinations on HbA1c levels in patients with T2DM. These 12 strategy combinations encompassed (1) ABCDEFGHIJKLMNOQR (action planning, communication, cues, engagement, feedback, goal setting, guide, identity, management, model or demonstrate, monitor, prompt, restructure, reward, shape, support, and tailor); (2) ABDEFGIKLOPQ (action planning, communication, engagement, feedback, goal setting, guide, management, monitor, prompt, shape, stimulate, and support); (3) FGIKQ (goal setting, guide, management, monitor, and support); (4) BGKR (communication, guide, monitor, and tailor); (5) EGIKPR (feedback, guide, management, monitor, stimulate, and tailor); (6) EKP (feedback, monitor, and stimulate); (7) DGIKPQR (engagement, guide, management, monitor, stimulate, support, and tailor); (8) BDFIQ (communication, engagement, goal setting, management, and support); (9) BDGI (communication, engagement, guide, and management); (10) BDGKNOP (communication, engagement, guide, monitor, reward, shape, and stimulate); (11) DFGKP (engagement, goal setting, guide, monitor, and stimulate); and (12) GIK (guide, management, and monitor). The specific strategy represented by the letters in each strategy combination is provided in Table 1.

The network evidence plot for HbA1c levels is shown in Figure 3A. The SUCRA probability ranking for the reducing effect of HbA1c levels in different DHI strategy combinations is shown in Figure 4A. Of the 12 DHI strategy combinations, the possibility of DHI-BDGI (communication, engagement, guide, and management) being the best strategy combination was the highest. The SUCRA value predicted the possibility of different strategy combinations as the best way, and the effects were ranked as follows: DHI-BDGI (91.8%)>DHI-DFGKP (76%)>DHI-DGIKPQR (73.2%)>DHI-FGIKQ (67.4%)>DHI-ABDEFGIKLOPQ (62.1%) > DHI-EGIKPR (52.8%)>DHI-GIK (52.1%)>DHI-EKP (40.5%)>DHI-BDGKNOP (39.4%)>DHI-ABCDEFGHIJKLMNOQR (30.1%)>DHI-BDFIQ (27.7%)>DHI-BGKR (24.1%)>usual (12.8%). Compared with the usual care groups, DHI-BDGI (MD –1.04, 95% CI –1.55 to –0.54), DHI-DFGKP (MD –0.76, 95% CI –1.36 to –0.16), DHI-FGIKQ (MD –0.61, 95% CI –1.01 to –0.22), DHI-ABDEFGIKLOPQ (MD –0.55, 95% CI –0.92 to –0.18), and DHI-GIK (MD –0.44, 95% CI –0.85 to –0.03) strategy combinations were statistically significantly effective in reducing HbA1c levels (Figure 5A). On the basis of the interval estimation of direct and indirect comparison (Table 2), DHI-BDGI was more effective than the DHI-EKP, DHI-BDFIQ, and DHI-BGKR strategy combinations.

Figure 3. Evidence network of outcome indicators: (A) evidence network for glycated hemoglobin A1c, (B) evidence network for fasting blood glucose, (C) evidence network for BMI, and (4) evidence network for weight loss. ABDEFGIKLOPQ: action planning, communication, engagement, feedback, goal setting, guide, management, monitor, prompt, shape, stimulate, and support; ABCDEFGHIJKLNO: action planning, communication, cues, engagement, feedback, goal setting, guide, identity, management, model or demonstrate, monitor, prompt, reward, and shape; ABCDEFGHIJKLMNOQR: action planning, communication, cues, engagement, feedback, goal setting, guide, identity, management, model or demonstrate, monitor, prompt, restructure, reward, shape, support, and tailor; BDFGIKLOPQ: communication, engagement, goal setting, guide, management, monitor, prompt, shape, stimulate, and support; BDFIQ: communication, engagement, goal setting, management, and support; BDGI: communication, engagement, guide, and management; BDGKNOP: communication, engagement, guide, monitor, reward, shape, and stimulate; BGKR: communication, guide, monitor, and tailor; DFGKP: engagement, goal setting, guide, monitor, and stimulate; DGIKPQR: engagement, guide, management, monitor, stimulate, support, and tailor; DHI: digital health intervention; EGIKPR: feedback, guide, management, monitor, stimulate, and tailor; EKP: feedback, monitor, and stimulate; FGIKQ: goal setting, guide, management, monitor, and support; GIK: guide, management, and monitor.
Figure 4. Surface under the cumulative ranking curve rank of outcome indicators: (A) surface under the cumulative ranking curve rank for glycated hemoglobin A1c, (B) surface under the cumulative ranking curve rank for fasting blood glucose, (C) surface under the cumulative ranking curve rank for BMI, and (D) surface under the cumulative ranking curve rank for weight loss. ABDEFGIKLOPQ: action planning, communication, engagement, feedback, goal setting, guide, management, monitor, prompt, shape, stimulate, and support; ABCDEFGHIJKLNO: action planning, communication, cues, engagement, feedback, goal setting, guide, identity, management, model or demonstrate, monitor, prompt, reward, and shape; ABCDEFGHIJKLMNOQR: action planning, communication, cues, engagement, feedback, goal setting, guide, identity, management, model or demonstrate, monitor, prompt, restructure, reward, shape, support, and tailor; BDFGIKLOPQ: communication, engagement, goal setting, guide, management, monitor, prompt, shape, stimulate, and support; BDFIQ: communication, engagement, goal setting, management, and support; BDGI: communication, engagement, guide, and management; BDGKNOP: communication, engagement, guide, monitor, reward, shape, and stimulate; BGKR: communication, guide, monitor, and tailor; DFGKP: engagement, goal setting, guide, monitor, and stimulate; DGIKPQR: engagement, guide, management, monitor, stimulate, support, and tailor; DHI: digital health intervention; EGIKPR: feedback, guide, management, monitor, stimulate, and tailor; EKP: feedback, monitor, and stimulate; FGIKQ: goal setting, guide, management, monitor, and support; GIK: guide, management, and monitor.
Figure 5. Forest plots of network meta-analysis results: (A) forest plots for glycated hemoglobin A1c, (B) forest plots for fasting blood glucose; (C) forest plots for BMI, and (D) forest plots for weight loss. ABDEFGIKLOPQ: action planning, communication, engagement, feedback, goal setting, guide, management, monitor, prompt, shape, stimulate, and support; ABCDEFGHIJKLNO: action planning, communication, cues, engagement, feedback, goal setting, guide, identity, management, model or demonstrate, monitor, prompt, reward, and shape; ABCDEFGHIJKLMNOQR: action planning, communication, cues, engagement, feedback, goal setting, guide, identity, management, model or demonstrate, monitor, prompt, restructure, reward, shape, support, and tailor; BDFGIKLOPQ: communication, engagement, goal setting, guide, management, monitor, prompt, shape, stimulate, and support; BDFIQ: communication, engagement, goal setting, management, and support; BDGI: communication, engagement, guide, and management; BDGKNOP: communication, engagement, guide, monitor, reward, shape, and stimulate; BGKR: communication, guide, monitor, and tailor; DFGKP: engagement, goal setting, guide, monitor, and stimulate; DGIKPQR: engagement, guide, management, monitor, stimulate, support, and tailor; DHI: digital health intervention; DL: DerSimonian and Laird method; EGIKPR: feedback, guide, management, monitor, stimulate, and tailor; EKP: feedback, monitor, and stimulate; FGIKQ: goal setting, guide, management, monitor, and support; GIK: guide, management, and monitor; IV: inverse variance method; MD: mean difference.
Table 2. Network meta-analysis of glycated hemoglobin A1c levels.
DHIa strategy combinationsMDsb (95% CIs)

DHI-BDGIcDHI-DFGKPdDHI-DGIKPQReDHI-FGIKQfDHI-ABDEFGIKLOPQgDHI-EGIKPRhDHI-GIKiDHI-EKPjDHI-BDGKNOPkDHI-ABCDEFGHIJKLMNOQRlDHI-BDFIQmDHI-BGKRnUsual
DHI-DFGKP–0.28 (–1.06 to 0.50)0o
DHI-DGIKPQR–0.26 (–1.30 to 0.78)0.02 (–1.07 to 1.11)0
DHI-FGIKQ–0.43 (–1.07 to 0.21)–0.15 (–0.86 to 0.57)–0.17 (–1.16 to 0.82)0
DHI-ABDEFGIKLOPQ–0.50 (–1.12 to 0.13)–0.21 (–0.92 to 0.49)–0.23 (–1.21 to 0.75)–0.07 (–0.61 to 0.48)0
DHI-EGIKPR–0.59 (–1.31 to 0.13)–0.31 (–1.10 to 0.48)–0.33 (–1.37 to 0.72)–0.16 (–0.81 to 0.49)–0.10 (–0.73 to 0.54)0
DHI-GIK–0.60 (–1.24 to 0.03)–0.32 (–1.05 to 0.40)–0.34 (–1.34 to 0.65)–0.17 (–0.74 to 0.39)–0.11 (–0.66 to 0.44)–0.01 (–0.67 to 0.64)0
DHI-EKP–0.73 (–1.41 to –0.05)–0.45 (–1.19 to 0.30)–0.46 (–1.48 to 0.55)–0.30 (–0.90 to 0.30)–0.23 (–0.82 to 0.35)–0.14 (–0.82 to 0.55)–0.12 (–0.73 to 0.48)0
DHI-BDGKNOP–0.79 (–1.93 to 0.35)–0.51 (–1.69 to 0.67)–0.53 (–1.90 to 0.84)–0.36 (–1.46 to 0.73)–0.30 (–1.38 to 0.79)–0.20 (–1.35 to 0.94)–0.19 (–1.29 to 0.91)–0.07 (–1.18 to 1.05)0
DHI-ABCDEFGHIJKLMNOQR–0.94 (–2.10 to 0.22)–0.66 (–1.86 to 0.54)–0.68 (–2.06 to 0.70)–0.51 (–1.63 to 0.60)–0.45 (–1.56 to 0.66)–0.35 (–1.52 to 0.81)–0.34 (–1.46 to 0.78)–0.22 (–1.35 to 0.92)–0.15 (–1.61 to 1.31)0
DHI-BDFIQ–0.88 (–1.55 to –0.22)–0.60 (–1.34 to 0.14)–0.62 (–1.63 to 0.39)–0.45 (–1.04 to 0.13)–0.39 (–0.96 to 0.18)–0.29 (-0.97 to 0.38)–0.28 (–0.87 to 0.31)–0.16 (–0.78 to 0.47)–0.09 (–1.20 to 1.02)0.06 (–1.07 to 1.19)0
DHI-BGKR–0.94 (–1.68 to –0.21)–0.66 (–1.46 to 0.14)–0.68 (–1.73 to 0.37)–0.51 (–1.18 to 0.15)–0.45 (–1.10 to 0.20)−0.35 (–1.10 to 0.39)–0.34 (–1.01 to 0.33)–0.22 (–0.92 to 0.49)–0.15 (–1.30 to 1.00)0.00 (–1.17 to 1.17)–0.06 (–0.75 to 0.63)0
Usual–1.04 (–1.55 to –0.54)–0.76 (–1.36 to –0.16)–0.78 (–1.69 to 0.13)–0.61 (–−1.01 to –0.22)–0.55 (–0.92 to –0.18)–0.45 (–0.97 to 0.06)–0.44 (–0.85 to –0.03)–0.32 (–0.77 to 0.14)–0.25 (–1.27 to 0.77)–0.10 (–1.14 to 0.94)–0.16 (–0.60 to 0.28)–0.10 (–0.64 t 0.44)0

aDHI: digital health intervention.

bMD: mean difference.

cBDGI: communication, engagement, guide, and management.

dDFGKP: engagement, goal setting, guide, monitor, and stimulate.

eDGIKPQR: engagement, guide, management, monitor, stimulate, support, and tailor.

fFGIKQ: goal setting, guide, management, monitor, and support.

gABDEFGIKLOPQ: action planning, communication, engagement, feedback, goal setting, guide, management, monitor, prompt, shape, stimulate, and support.

hEGIKPR: feedback, guide, management, monitor, stimulate, and tailor.

iGIK: guide, management, and monitor.

jEKP: feedback, monitor, and stimulate.

kBDGKNOP: communication, engagement, guide, monitor, reward, shape, and stimulate.

lABCDEFGHIJKLMNOQR: action planning, communication, cues, engagement, feedback, goal setting, guide, identity, management, model or demonstrate, monitor, prompt, restructure, reward, shape, support, and tailor.

mBDFIQ: communication, engagement, goal setting, management, and support.

nBGKR: communication, guide, monitor, and tailor.

oNot applicable.

Effects of Strategy Combinations on FBG Levels

Of 52 included RCTs, 7 (13%) RCTs [Yin W, Liu Y, Hu H, Sun J, Liu Y, Wang Z. Telemedicine management of type 2 diabetes mellitus in obese and overweight young and middle-aged patients during COVID-19 outbreak: a single-center, prospective, randomized control study. PLoS One. 2022;17(9):e0275251. [FREE Full text] [CrossRef] [Medline]74,Han CY, Zhang J, Ye XM, Lu JP, Jin HY, Xu WW, et al. Telemedicine-assisted structured self-monitoring of blood glucose in management of T2DM results of a randomized clinical trial. BMC Med Inform Decis Mak. Sep 14, 2023;23(1):182. [FREE Full text] [CrossRef] [Medline]78,Fortmann AL, Gallo LC, Garcia MI, Taleb M, Euyoque JA, Clark T, et al. Dulce digital: an mHealth SMS-based intervention improves glycemic control in Hispanics with type 2 diabetes. Diabetes Care. Oct 2017;40(10):1349-1355. [FREE Full text] [CrossRef] [Medline]84,Lee JY, Wong CP, Tan CS, Nasir NH, Lee SW. Telemonitoring in fasting individuals with type 2 diabetes mellitus during Ramadan: a prospective, randomised controlled study. Sci Rep. Aug 31, 2017;7(1):10119. [FREE Full text] [CrossRef] [Medline]85,Ramadas A, Chan CK, Oldenburg B, Hussein Z, Quek KF. Randomised-controlled trial of a web-based dietary intervention for patients with type 2 diabetes: changes in health cognitions and glycemic control. BMC Public Health. Jun 08, 2018;18(1):716. [FREE Full text] [CrossRef] [Medline]87,Zhou P, Xu L, Liu X, Huang J, Xu W, Chen W. Web-based telemedicine for management of type 2 diabetes through glucose uploads: a randomized controlled trial. Int J Clin Exp Pathol. 2014;7(12):8848-8854. [FREE Full text] [Medline]97,Fukuoka Y, Gay CL, Joiner KL, Vittinghoff E. A novel diabetes prevention intervention using a mobile app: a randomized controlled trial with overweight adults at risk. Am J Prev Med. Aug 2015;49(2):223-237. [FREE Full text] [CrossRef] [Medline]98] assessed the effects of 6 different DHI strategy combinations on FBG levels in patients with T2DM. These 6 strategy combinations encompassed (1) DGIKPQR (engagement, guide, management, monitor, stimulate, support, and tailor); (2) BDGI (communication, engagement, guide, and management); (3) DFGKP (engagement, goal setting, guide, monitor, and stimulate); (4) BGKR (communication, guide, monitor, and tailor); (5) GIK (guide, management, and monitor); and (6) BDFIQ (communication, engagement, goal setting, management, and support).

The network evidence plot for FBG levels is shown in Figure 3B. The SUCRA probability ranking for the reducing effect of FBG levels of different DHI strategy combinations is shown in Figure 4B. Of the 6 strategy combinations, the possibility of DHI-GIK (guide, management, and monitor) being the best strategy combination was the highest. The SUCRA value predicted the possibility of different strategy combinations as the best way, and the effects were ranked as follows: DHI-GIK (86.7%)>DHI-DGIKPQR (62.8%)>DHI-DFGKP (61.6%)>DHI-BGKR (53.6%)>DHI-BDGI (42.7%)>DHI-BDFIQ (24.4%)>usual (18.2%). Compared with the usual care groups, only the DHI-GIK strategy combination (MD –0.96, 95% CI –1.86 to –0.06) was statistically significantly effective in reducing FBG levels (Figure 5B). On the basis of the interval estimation of direct and indirect comparison (Table 3), no strategy combination was superior to the others.

Table 3. Network meta-analysis of fasting blood glucose levels.
DHIa strategy combinationsMDsb (95% CIs)

DHI-GIKcDHI-DGIKPQRdDHI-DFGKPeDHI-BGKRfDHI-BDGIgDHI-BDFIQhUsual
DHI-DGIKPQR–0.47 (–1.72 to 0.78)0i
DHI-DFGKP–0.46 (-1.87 to 0.95)0.01 (–1.38 to 1.40)0
DHI-BGKR–0.56 (–1.97 to 0.85)–0.09 (–1.47 to 1.29)–0.10 (–1.64 to 1.43)0
DHI-BDGI–0.79 (–1.73 to 0.15)–0.32 (–1.23 to 0.59)–0.33 (–1.45 to 0.79)–0.23 (–1.34 to 0.88)0
DHI-BDFIQ–0.93 (–1.87 to 0.01)–0.46 (–1.37 to 0.45)–0.47 (–1.60 to 0.65)–0.37 (–1.49 to 0.75)–0.14 (–0.53 to 0.25)0
Usual–0.96 (–1.86 to –0.06)–0.49 (–1.36 to 0.38)–0.50 (–1.59 to 0.58)–0.40 (–1.48 to 0.68)–0.17 (–0.44 to 0.10)–0.03 (–0.31 to 0.25)0

aDHI: digital health intervention.

bMD: mean difference.

cGIK: guide, management, and monitor.

dDGIKPQR: engagement, guide, management, monitor, stimulate, support, and tailor.

eDFGKP: engagement, goal setting, guide, monitor, and stimulate.

fBGKR: communication, guide, monitor, and tailor.

gBDGI: communication, engagement, guide, and management.

hBDFIQ: communication, engagement, goal setting, management, and support.

iNot applicable.

Effects of Strategy Combinations on BMI

Of 52 included RCTs, 11 (21%) RCTs [Ruissen MM, Torres-Peña JD, Uitbeijerse BS, Arenas de Larriva AP, Huisman SD, Namli T, et al. Clinical impact of an integrated e-health system for diabetes self-management support and shared decision making (POWER2DM): a randomised controlled trial. Diabetologia. Dec 29, 2023;66(12):2213-2225. [FREE Full text] [CrossRef] [Medline]60,Davis RM, Hitch AD, Salaam MM, Herman WH, Zimmer-Galler IE, Mayer-Davis EJ. TeleHealth improves diabetes self-management in an underserved community: diabetes TeleCare. Diabetes Care. Aug 2010;33(8):1712-1717. [FREE Full text] [CrossRef] [Medline]66,Hesseldal L, Christensen JR, Olesen TB, Olsen MH, Jakobsen PR, Laursen DH, et al. Long-term weight loss in a primary care-anchored eHealth lifestyle coaching program: randomized controlled trial. J Med Internet Res. Sep 23, 2022;24(9):e39741. [FREE Full text] [CrossRef] [Medline]70,Nagata T, Aoyagi SS, Takahashi M, Nagata M, Mori K. Effects of feedback from self-monitoring devices on lifestyle changes in workers with diabetes: 3-month randomized controlled pilot trial. JMIR Form Res. Aug 09, 2022;6(8):e23261. [FREE Full text] [CrossRef] [Medline]72-Yin W, Liu Y, Hu H, Sun J, Liu Y, Wang Z. Telemedicine management of type 2 diabetes mellitus in obese and overweight young and middle-aged patients during COVID-19 outbreak: a single-center, prospective, randomized control study. PLoS One. 2022;17(9):e0275251. [FREE Full text] [CrossRef] [Medline]74,Han CY, Zhang J, Ye XM, Lu JP, Jin HY, Xu WW, et al. Telemedicine-assisted structured self-monitoring of blood glucose in management of T2DM results of a randomized clinical trial. BMC Med Inform Decis Mak. Sep 14, 2023;23(1):182. [FREE Full text] [CrossRef] [Medline]78,Fortmann AL, Gallo LC, Garcia MI, Taleb M, Euyoque JA, Clark T, et al. Dulce digital: an mHealth SMS-based intervention improves glycemic control in Hispanics with type 2 diabetes. Diabetes Care. Oct 2017;40(10):1349-1355. [FREE Full text] [CrossRef] [Medline]84,Lee JY, Wong CP, Tan CS, Nasir NH, Lee SW. Telemonitoring in fasting individuals with type 2 diabetes mellitus during Ramadan: a prospective, randomised controlled study. Sci Rep. Aug 31, 2017;7(1):10119. [FREE Full text] [CrossRef] [Medline]85,Zhou P, Xu L, Liu X, Huang J, Xu W, Chen W. Web-based telemedicine for management of type 2 diabetes through glucose uploads: a randomized controlled trial. Int J Clin Exp Pathol. 2014;7(12):8848-8854. [FREE Full text] [Medline]97,Fukuoka Y, Gay CL, Joiner KL, Vittinghoff E. A novel diabetes prevention intervention using a mobile app: a randomized controlled trial with overweight adults at risk. Am J Prev Med. Aug 2015;49(2):223-237. [FREE Full text] [CrossRef] [Medline]98] assessed the effects of 9 different DHI strategy combinations on BMI levels in patients with T2DM. These 9 strategy combinations encompassed (1) FGIKQ (goal setting, guide, management, monitor, and support); (2) BGKR (communication, guide, monitor, and tailor); (3) EKP (feedback, monitor, and stimulate); (4) BDFGIKLOPQ (communication, engagement, goal setting, guide, management, monitor, prompt, shape, stimulate, and support); (5) DGIKPQR (engagement, guide, management, monitor, stimulate, support, and tailor); (6) BDGI (communication, engagement, guide, and management); (7) DFGKP (engagement, goal setting, guide, monitor, and stimulate); (8) GIK (guide, management, and monitor); and (9) BDFIQ (communication, engagement, goal setting, management, and support).

The network evidence plot for BMI is shown in Figure 3C. The SUCRA probability ranking for the reducing effect of BMI of different DHI strategy combinations is shown in Figure 4C. Of the 9 strategy combinations, DHI-BDFIQ (communication, engagement, goal setting, management, and support) was most likely to be the optimal strategy combination. On the basis of the SUCRA values, the effects were ranked as follows: DHI-BDFIQ (92.6%)>DHI-BDGI (90.9%)>DHI-DGIKPQR (75.2%)>DHI-BGKR (65.1%)>DHI-BDFGIKLOPQ (41.4%)>DHI-FGIKQ (40.9%)>DHI-EKP (32%)>DHI-GIK (26.8%)>DHI-DFGKP (24.3%)>Usual (10.8%). Compared with the usual care groups, DHI-BDFIQ (MD –2.30, 95% CI –3.16 to –1.44), DHI-BDGI (MD –2.20, 95% CI –2.50 to –1.90) and DHI-BGKR (MD –1.00, 95% CI –1.53 to –−0.47) strategy combinations were statistically significantly effective in reducing BMI (Figure 5C). On the basis of the interval estimation of direct and indirect comparison (Table 4), DHI-BDFIQ was superior to all other strategy combinations except for DHI-BDGI and DHI-DGIKPQR. In addition, except for DHI-BDFIQ and DHI-DGIKPQR, DHI-BDGI was superior to the rest of strategy combinations. DHI-BGKR was also superior to the DHI-DFGKP strategy combination.

Table 4. Network meta-analysis of BMI.
DHIa strategy combinationsMDsb (95% CIs)

DHI-BDFIQcDHI-BDGIdDHI-DGIKPQReDHI-BGKRfDHI-BDFGIKLOPQgDHI-FGIKQhDHI-EKPiDHI-GIKjDHI-DFGKPkUsual

DHI-BDGI–0.10 (–1.01 to 0.81)0l
DHI-DGIKPQR–0.74 (–2.56 to 1.08)–0.64 (–2.27 to 0.99)0
DHI-BGKR–1.30 (–2.31 to –0.29)–1.20 (–1.81 to –0.59)–0.56 (–2.25 to 1.13)0
DHI-BDFGIKLOPQ–1.90 (–2.90 to –0.90)–1.80 (–2.40 to –1.20)–1.16 (–2.85 to 0.53)–0.60 (–1.34 to 0.14)0
DHI-FGIKQ–1.91 (–2.87 to –0.96)–1.81 (–2.33 to –1.30)–1.17 (–2.83 to 0.48)–0.61 (–1.29 to 0.06)–0.01 (–0.68 to 0.65)0
DHI-EKP–2.10 (–4.04 to –0.16)–2.00 (–3.76 to –0.24)–1.36 (–3.72 to 1.00)–0.80 (–2.62 to 1.02)–0.20 (–2.01 to 1.61)–0.19 (1.97 to 1.60)0
DHI-GIK−2.19 (−3.67 to −0.71)−2.09 (−3.33 to −0.85)–1.45 (–3.45 to 0.55)–0.89 (–2.20 to 0.42)–0.29 (–1.60 to 1.02)–0.28 (–1.55 to 0.99)–0.09 (–2.20 to 2.02)0
DHI-DFGKP−2.19 (−3.06 to −1.32)−2.09 (−2.41 to −1.77)–1.45 (–3.06 to 0.16)–0.89 (–1.43 to –0.35)–0.29 (–0.82 to 0.24)–0.28 (–0.71 to 0.15)–0.09 (–1.83 to 1.65)–0.00 (–1.21 to 1.20)0
Usual−2.30 (−3.16 to −1.44)−2.20 (−2.50 to −1.90)–1.56 (–3.16 to 0.04)–1.00 (–1.53 to –0.47)–0.40 (–0.92 to 0.12)–0.39 (–0.80 to 0.03)–0.20 (–1.94 to 1.54)–0.11 (–1.31 to 1.09)–0.11 (–0.22 to 0.00)0

aDHI: digital health intervention.

bMD: mean difference.

cBDFIQ: communication, engagement, goal setting, management, and support.

dBDGI: communication, engagement, guide, and management.

eDGIKPQR: engagement, guide, management, monitor, stimulate, support, and tailor.

fBGKR: communication, guide, monitor, and tailor.

gBDFGIKLOPQ: communication, engagement, goal setting, guide, management, monitor, prompt, shape, stimulate, and support.

hFGIKQ: goal setting, guide, management, monitor, and support.

iEKP: feedback, monitor, and stimulate.

jGIK: guide, management, and monitor.

kDFGKP: engagement, goal setting, guide, monitor, and stimulate.

lNot applicable.

Effects of Strategy Combinations on Weight Loss

Of 52 included RCTs, 8 (15%) RCTs [Glasgow RE, Toobert DJ. Brief, computer-assisted diabetes dietary self-management counseling: effects on behavior, physiologic outcomes, and quality of life. Med Care. Nov 2000;38(11):1062-1073. [CrossRef] [Medline]50,Tate DF, Jackvony EH, Wing RR. Effects of internet behavioral counseling on weight loss in adults at risk for type 2 diabetes: a randomized trial. JAMA. Apr 09, 2003;289(14):1833-1836. [CrossRef] [Medline]52,Hesseldal L, Christensen JR, Olesen TB, Olsen MH, Jakobsen PR, Laursen DH, et al. Long-term weight loss in a primary care-anchored eHealth lifestyle coaching program: randomized controlled trial. J Med Internet Res. Sep 23, 2022;24(9):e39741. [FREE Full text] [CrossRef] [Medline]70,Nagata T, Aoyagi SS, Takahashi M, Nagata M, Mori K. Effects of feedback from self-monitoring devices on lifestyle changes in workers with diabetes: 3-month randomized controlled pilot trial. JMIR Form Res. Aug 09, 2022;6(8):e23261. [FREE Full text] [CrossRef] [Medline]72,Thorsen IK, Yang Y, Valentiner LS, Glümer C, Karstoft K, Brønd JC, et al. The effects of a lifestyle intervention supported by the InterWalk smartphone app on increasing physical activity among persons with type 2 diabetes: parallel-group, randomized trial. JMIR Mhealth Uhealth. Sep 28, 2022;10(9):e30602. [FREE Full text] [CrossRef] [Medline]73,Fortmann AL, Gallo LC, Garcia MI, Taleb M, Euyoque JA, Clark T, et al. Dulce digital: an mHealth SMS-based intervention improves glycemic control in Hispanics with type 2 diabetes. Diabetes Care. Oct 2017;40(10):1349-1355. [FREE Full text] [CrossRef] [Medline]84,Lee JY, Wong CP, Tan CS, Nasir NH, Lee SW. Telemonitoring in fasting individuals with type 2 diabetes mellitus during Ramadan: a prospective, randomised controlled study. Sci Rep. Aug 31, 2017;7(1):10119. [FREE Full text] [CrossRef] [Medline]85,Fukuoka Y, Gay CL, Joiner KL, Vittinghoff E. A novel diabetes prevention intervention using a mobile app: a randomized controlled trial with overweight adults at risk. Am J Prev Med. Aug 2015;49(2):223-237. [FREE Full text] [CrossRef] [Medline]98] assessed the effects of 7 different DHI strategy combinations on weight loss in patients with T2DM. These 7 strategy combinations encompassed (1) ABCDEFGHIJKLMNOQR (action planning, communication, cues, engagement, feedback, goal setting, guide, identity, management, model or demonstrate, monitor, prompt, restructure, reward, shape, support, and tailor); (2) ABCDEFGHIJKLNO (action planning, communication, cues, engagement, feedback, goal setting, guide, identity, management, model or demonstrate, monitor, prompt, reward, and shape); (3) BGKR (communication, guide, monitor, and tailor); (4) EKP (feedback, monitor, and stimulate); (5) BDFGIKLOPQ (communication, engagement, goal setting, guide, management, monitor, prompt, shape, stimulate, and support); (6) DFGKP (engagement, goal setting, guide, monitor, and stimulate); (7) BDFIQ (communication, engagement, goal setting, management, and support).

The network evidence plot for weight loss is shown in Figure 3D. The SUCRA probability ranking for the effect of weight loss of different DHI strategy combinations is shown in Figure 4D. Of the 7 strategy combinations, DHI-BDFIQ (communication, engagement, goal setting, management, support) was most likely to be the optimal strategy combination. On the basis of the SUCRA values, the effects were ranked as follows: DHI-BDFIQ (98.5%)>DHI-BGKR (72.9%)>DHI-ABCDEFGHIJKLNO (63%)>DHI-BDFGIKLOPQ (49.8%)>DHI-ABCDEFGHIJKLMNOQR (39.2%)>DHI-EKP (36.2%)>DHI-DFGKP (27.5%)>Usual (12.9%). Compared with the usual care groups, DHI-BDFIQ (MD –6.50, 95% CI –8.82 to –4.18), DHI-BGKR (MD –3.00, 95% CI –4.66 to –1.34) and DHI-BDFGIKLOPQ (MD –1.50, 95% CI –2.92 to –0.08) strategy combinations were statistically significantly effective in weight loss (Figure 5D). On the basis of the interval estimation of direct and indirect comparison (Table 5), DHI-BDFIQ was superior to DHI-BGKR, DHI-ABCDEFGHIJKLNO, DHI-BDFGIKLOPQ, and DHI-DFGKP strategy combinations. In addition, DHI-BGKR was also superior to the DHI-DFGKP strategy combination.

Table 5. Network meta-analysis of weight loss.
DHIa strategy combinationsMDsb (95% CIs)

DHI-BDFIQcDHI-BGKRdDHI-ABCDEFGHIJKLNOeDHI-BDFGIKLOPQfDHI-ABCDEFGHIJKLMNOQRgDHI-EKPhDHI-DFGKPiUsual
DHI-BGKR–3.50 (–6.35 to –0.65)0j
DHI-ABCDEFGHIJKLNO–4.10 (–7.46 to –0.74)–0.60 (–3.55 to 2.35)0
DHI-BDFGIKLOPQ–5.00 (–7.71 to –2.29)–1.50 (–3.69 to 0.69)–0.90 (–3.72 to 1.92)0
DHI-ABCDEFGHIJKLMNOQR–5.70 (–12.66 to 1.26)–2.20 (–8.97 to 4.57)–1.60 (–8.60 to 5.40)–0.70 (–7.41 to 6.01)0
DHI-EKP–5.90 (–12.00 to 0.20)–2.40 (–8.28 to 3.48)–1.80 (–7.94 to 4.34)–0.90 (–6.72 to 4.92)–0.20 (–8.85 to 8.45)0
DHI-DFGKP–6.24 (–8.58 to –3.91)–2.74 (–4.43 to –1.05)–2.14 (–4.59 to 0.31)–1.24 (–2.69 to 0.20)–0.54 (–7.11 to 6.03)–0.34 (–5.99 to 5.30)0
Usual–6.50 (–8.82 to –4.18)–3.00 (–4.66 to –1.34)–2.40 (–4.83 to 0.03)–1.50 (–2.92 to –0.08)–0.80 (–7.36 to 5.76)–0.60 (–6.24 to 5.04)–0.26 (–0.54 to 0.03)0

aDHI: digital health intervention.

bMD: mean difference.

cBDFIQ: communication, engagement, goal setting, management, and support.

dBGKR: communication, guide, monitor, and tailor.

eABCDEFGHIJKLNO: action planning, communication, cues, engagement, feedback, goal setting, guide, identity, management, model or demonstrate, monitor, prompt, reward, and shape.

fBDFGIKLOPQ: communication, engagement, goal setting, guide, management, monitor, prompt, shape, stimulate, and support.

gABCDEFGHIJKLMNOQR: action planning, communication, cues, engagement, feedback, goal setting, guide, identity, management, model or demonstrate, monitor, prompt, restructure, reward, shape, support, and tailor.

hEKP: feedback, monitor, and stimulate.

iDFGKP: engagement, goal setting, guide, monitor, and stimulate.

jNot applicable.

Heterogeneity and Consistency Analysis

The global inconsistency and local inconsistency were assessed using the node-splitting test for inconsistency analysis. The results showed no statistical inconsistency in each outcome comparison (P>.05). High heterogeneity between studies was only observed in BMI and weight loss outcomes. However, we did not find significant publication bias for these 2 outcomes through Egger test (P=.29 for BMI and P=.11 for weight loss). The net split function analyses found no statistically significant inconsistencies when assessing differences between direct and indirect effects. More details are provided in Table 6.

Table 6. The results of heterogeneity and inconsistency.
OutcomeHeterogeneityInconsistency (P value)

I2 (%)P value
HbA1ca7.3.37b
FBGc0.0.42
BMI95.8<.001
Weight loss85.6<.001

aHbA1c: glycated hemoglobin A1c.

bData not available.

cFBG: fasting blood glucose.

Assessment of Publication Bias

We conducted a comparison-adjusted funnel plot of trials included in the network meta-analysis for each result (Figure 6): (A) HbA1c, (B) FBG, (C) BMI, and (D) weight loss. The included studies were generally symmetrically distributed in the upper and middle parts of the funnel and around the left and right sides of the midline, indicating a low possibility of publication bias. Individual studies were distributed at the bottom, which may be related to the small sample size.

Figure 6. Comparison-adjusted funnel plots of trials included in the network meta-analysis: (A) comparison-adjusted funnel plots for glycated hemoglobin A1c, (B) comparison-adjusted funnel plots for fasting blood glucose, (C) comparison-adjusted funnel plots for BMI, and (D) comparison-adjusted funnel plots for weight loss. ABDEFGIKLOPQ: action planning, communication, engagement, feedback, goal setting, guide, management, monitor, prompt, shape, stimulate, and support; ABCDEFGHIJKLNO: action planning, communication, cues, engagement, feedback, goal setting, guide, identity, management, model or demonstrate, monitor, prompt, reward, and shape; ABCDEFGHIJKLMNOQR: action planning, communication, cues, engagement, feedback, goal setting, guide, identity, management, model or demonstrate, monitor, prompt, restructure, reward, shape, support, and tailor; BDFGIKLOPQ: communication, engagement, goal setting, guide, management, monitor, prompt, shape, stimulate, and support; BDFIQ: communication, engagement, goal setting, management, and support; BDGI: communication, engagement, guide, and management; BDGKNOP: communication, engagement, guide, monitor, reward, shape, and stimulate; BGKR: communication, guide, monitor, and tailor; DFGKP: engagement, goal setting, guide, monitor, and stimulate; DGIKPQR: engagement, guide, management, monitor, stimulate, support, and tailor; DHI: digital health intervention; EGIKPR: feedback, guide, management, monitor, stimulate, and tailor; EKP: feedback, monitor, and stimulate; FGIKQ: goal setting, guide, management, monitor, and support; GIK: guide, management, and monitor.
Sensitivity Analysis

We used the method of removing individual studies separately. Our results were generally stable and credible. As shown in Figure 7, the 2 vertical bright lines, except for the vertical axis, represent the CI of the merged population. Each study corresponds to 2 yellow short lines and a yellow circle, representing the CI of the remaining studies combined after excluding this study. If all yellow circles are within the range of 2 vertical bright lines, it indicates that this set of data is stable. Although the results showed that the combined effect value of HbA1c levels, BMI, and weight loss appeared outside the CI of the original combined effect value, all CIs did not include 0; they were significant in treatment effects. The details are shown in Figure 7.

Figure 7. Sensitivity analyses using the method of removing individual studies separately: (A) sensitivity analyses for glycated hemoglobin A1c, (B) sensitivity analyses for fasting blood glucose, (C) sensitivity analyses for BMI, and (D) sensitivity analyses for weight loss.

Principal Findings

Our study offered a comprehensive insight into the strategies used in DHIs for T2DM management. A total of 52 RCTs were included, identifying 63 strategies categorized into 19 strategy themes. The most commonly used strategies were guide, monitor, management, and engagement. Most studies reported positive or mixed outcomes for most indicators based on the SF/HIT. Research involving a medium or high number of strategies was found to be more effective than research involving a low number of strategies. A total of 27 RCTs were included in the network meta-analysis. The strategy combination composed of communication, engagement, guide, and management was most effective in reducing HbA1c levels, while the strategy combination that included guide, management, and monitor was effective in reducing FBG levels. A strategy combination composed of communication, engagement, goal setting, management, and support was most effective for BMI and weight management.

In the identified strategies, monitor and guide were the most frequently used ones, accounting for 77% (40/52) and 67% (35/52) of the total papers, respectively. Following closely were management (32/52, 61%) and engagement (28/52, 54%) strategies. The frequency of the use of these strategies indicates the trend and focus in this field. First, the monitor strategy reflects the role of digital tools in monitoring patients’ physiological indicators and behavior habits in diabetes management. It allows health care professionals and patients to track key parameters such as blood glucose level, weight, and exercise volume in real time, which helps to adjust the treatment plan in time and reduce the risk of complications [Shan R, Sarkar S, Martin SS. Digital health technology and mobile devices for the management of diabetes mellitus: state of the art. Diabetologia. Jun 2019;62(6):877-887. [CrossRef] [Medline]101]. Second, the use of a guide strategy reflects the value of digital health management in providing personalized and real-time advice and guidance to patients. Through intelligent algorithms and personalized settings, digital platforms can provide patients with accurate nutritional advice, exercise plans, and medication guidance based on their individual characteristics and historical data, helping them better manage their diseases and improve treatment outcomes [Dixon RF, Zisser H, Layne JE, Barleen NA, Miller DP, Moloney DP, et al. A virtual type 2 diabetes clinic using continuous glucose monitoring and endocrinology visits. J Diabetes Sci Technol. Sep 2020;14(5):908-911. [FREE Full text] [CrossRef] [Medline]102]. These results are consistent with previous research that highlights the crucial role of monitor and guide strategies in DHIs [Ridho A, Alfian SD, van Boven JF, Levita J, Yalcin EA, Le L, et al. Digital health technologies to improve medication adherence and treatment outcomes in patients with tuberculosis: systematic review of randomized controlled trials. J Med Internet Res. Feb 23, 2022;24(2):e33062. [FREE Full text] [CrossRef] [Medline]103,Moschonis G, Siopis G, Jung J, Eweka E, Willems R, Kwasnicka D, 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. Mar 2023;5(3):e125-e143. [FREE Full text] [CrossRef] [Medline]104]. On this basis, the management strategy emphasizes the important role of digital platforms in assisting health care professionals in disease management and treatment decision-making. Through data analysis and predictive models, digital platforms can provide health care professionals with more comprehensive and timely patient information, help them develop more effective treatment plans, and improve the accuracy and efficiency of clinical decision-making [Kerr D, Edelman S, Vespasiani G, Khunti K. New digital health technologies for insulin initiation and optimization for people with type 2 diabetes. Endocr Pract. Aug 2022;28(8):811-821. [FREE Full text] [CrossRef] [Medline]105]. Furthermore, the engagement strategy emphasizes the promoting role of digital health management in patient participation and self-management. By providing personalized educational content, social support, and behavioral motivation, digital platforms can stimulate patients’ treatment motivation and enhance their awareness and self-management abilities toward the disease, thereby improving treatment compliance and long-term efficacy [Gershkowitz BD, Hillert CJ, Crotty BH. Digital coaching strategies to facilitate behavioral change in type 2 diabetes: a systematic review. J Clin Endocrinol Metab. Mar 25, 2021;106(4):e1513-e1520. [CrossRef] [Medline]106].

The number of strategies used in each study varies greatly, ranging from 1 to 32. Among 52 studies, 18 (35%) were categorized as high-strategy study, 23 (44%) as medium-strategy study, and 11 (21%) as low-strategy study. In digital health management, the use of a different number of strategies in research may reflect the diversity of research design, objectives, and methods. Many studies have chosen a medium number of strategies to comprehensively consider multiple aspects and evaluate the effectiveness of disease management. Research using a low number of strategies may focus more on the intervention effects in specific aspects to further explore the mechanisms of action of specific strategies. On the contrary, studies using a high number of strategies may aim to explore the combined effects of multiple intervention methods to achieve more comprehensive disease management. However, the results of the studies we included did not indicate that more strategies have more effective effects. Therefore, we further evaluated the impact of different numbers of strategies and compared the effect of different strategy combinations to provide more practical guidance for accurate and personalized digital health management.

We assessed the effectiveness of DHI strategies qualitatively based on the SF/HIT. Overall, most (≥50/52, ≥96%) studies have reported positive or mixed outcomes for most outcome indicators, such as preventive care; efficiency; perceived ease of use or usefulness; safety, privacy or security; acceptability; appropriateness; satisfaction process of service delivery or performance; effectiveness; and adherence or attendance. However, in terms of cost-effectiveness, most (50/52, 96%) studies have not reported positive results. This may be due to the introduction and development of digital health management tools, which provide more convenient, safe, and efficient management methods for patients with diabetes [Ruissen MM, Torres-Peña JD, Uitbeijerse BS, Arenas de Larriva AP, Huisman SD, Namli T, et al. Clinical impact of an integrated e-health system for diabetes self-management support and shared decision making (POWER2DM): a randomised controlled trial. Diabetologia. Dec 29, 2023;66(12):2213-2225. [FREE Full text] [CrossRef] [Medline]60]. These tools include remote monitoring devices, mobile apps, and web-based platforms that can help patients better monitor blood glucose levels, manage diet, and engage in exercise, thereby improving the effectiveness of preventive care and service delivery processes and enhancing patient acceptance and satisfaction with treatment plans [Kerr D, Edelman S, Vespasiani G, Khunti K. New digital health technologies for insulin initiation and optimization for people with type 2 diabetes. Endocr Pract. Aug 2022;28(8):811-821. [FREE Full text] [CrossRef] [Medline]105]. However, the negative results in terms of cost-effectiveness may be due to the significant investment required for the implementation and operation of digital health management tools, and the long-term cost-effectiveness has not been fully demonstrated [Feng Y, Zhao Y, Mao L, Gu M, Yuan H, Lu J, et al. The effectiveness of an eHealth family-based intervention program in patients with uncontrolled type 2 diabetes mellitus (T2DM) in the community via WeChat: randomized controlled trial. JMIR Mhealth Uhealth. Mar 20, 2023;11:e40420. [FREE Full text] [CrossRef] [Medline]59,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. Mar 02, 2020;8(3):e16665. [FREE Full text] [CrossRef] [Medline]62,Thorsen IK, Yang Y, Valentiner LS, Glümer C, Karstoft K, Brønd JC, et al. The effects of a lifestyle intervention supported by the InterWalk smartphone app on increasing physical activity among persons with type 2 diabetes: parallel-group, randomized trial. JMIR Mhealth Uhealth. Sep 28, 2022;10(9):e30602. [FREE Full text] [CrossRef] [Medline]73]. In high-strategy and medium-strategy studies, most (36/41, 88%) studies reported positive outcomes, while low-strategy (7/11, 64%) studies reported fewer positive outcomes. This reflects the investment of more resources and technology, which can more comprehensively meet the needs of patients and improve treatment effectiveness. In contrast, low-strategy research may not have achieved the same effect due to resource constraints or insufficient technology. This was consistent with research by Brueton et al [Brueton VC, Tierney J, Stenning S, Harding S, Meredith S, Nazareth I, et al. Strategies to improve retention in randomised trials. Cochrane Database Syst Rev. Dec 03, 2013;(12):MR000032. [FREE Full text] [CrossRef] [Medline]35], which indicates that using a combination of strategies rather than a single approach can be more effective in enhancing participant retention. Future research should focus on cost-effectiveness analysis, explore the long-term economic benefits of digital health management tools, and propose more effective low-cost strategies to promote the sustainable development of diabetes management strategies.

In the meta-analysis, we included 27 studies evaluating the efficacy of 12 different DHI strategy combinations on HbA1c levels. We found several high-quality combinations that can significantly reduce the HbA1c levels, which reflects that, in diabetes management, using a specific strategy combination may have more advantages than using a single strategy. Among numerous strategy combinations, the SUCRA probability ranking suggested that DHI-BDGI (communication, engagement, guide, and management) might be the most effective strategy combination, followed by DHI-DFGKP (engagement, goal setting, guide, monitor, and stimulate); DHI-FGIKQ (goal setting, guide, management, monitor, and support); and DHI-ABDEFGIKLOPQ (action planning, communication, engagement, feedback, goal setting, guide, management, monitor, prompt, shape, stimulate, and support). The effect of only using the combination of DHI-GIK (guide, management, and monitor) was relatively weak. First, in the current medical practice, the treatment of diabetes is no longer a simple drug treatment but a comprehensive management, including lifestyle intervention, psychological support, drug treatment, and other comprehensive interventions [Chinese Elderly Type 2 Diabetes Prevention and Treatment of Clinical Guidelines Writing Group, Geriatric Endocrinology and Metabolism Branch of Chinese Geriatric Society, Geriatric Endocrinology and Metabolism Branch of Chinese Geriatric Health Care Society, Geriatric Professional Committee of Beijing Medical Award Foundation, National Clinical Medical Research Center for Geriatric Diseases (PLA General Hospital). [Clinical guidelines for prevention and treatment of type 2 diabetes mellitus in the elderly in China (2022 edition)]. Zhonghua Nei Ke Za Zhi. Jan 01, 2022;61(1):12-50. [CrossRef] [Medline]5]. Among these, communication and patient engagement are considered crucial factors that can enhance patients’ understanding and compliance with treatment and improve treatment effectiveness [Gershkowitz BD, Hillert CJ, Crotty BH. Digital coaching strategies to facilitate behavioral change in type 2 diabetes: a systematic review. J Clin Endocrinol Metab. Mar 25, 2021;106(4):e1513-e1520. [CrossRef] [Medline]106]. Meanwhile, guidance and management provide specific action guidelines and treatment plans to help patients better control their blood glucose levels [Dixon RF, Zisser H, Layne JE, Barleen NA, Miller DP, Moloney DP, et al. A virtual type 2 diabetes clinic using continuous glucose monitoring and endocrinology visits. J Diabetes Sci Technol. Sep 2020;14(5):908-911. [FREE Full text] [CrossRef] [Medline]102,Kerr D, Edelman S, Vespasiani G, Khunti K. New digital health technologies for insulin initiation and optimization for people with type 2 diabetes. Endocr Pract. Aug 2022;28(8):811-821. [FREE Full text] [CrossRef] [Medline]105]. The combination of these strategies complements each other and can comprehensively cover all aspects of the treatment process, maximizing the therapeutic effect. Second, these 2 combinations of DHI-DFGKP (engagement, goal setting, guide, monitor, and stimulate) and DHI-FGIKQ (goal setting, guide, management, monitor, and support) also showed good results. They emphasize aspects such as patient engagement, goal setting, guidance, monitoring, and support. The combination of these strategies makes the treatment process more systematic and orderly, helping patients better understand and execute treatment plans and improving the success rate of treatment [Feng Y, Zhao Y, Mao L, Gu M, Yuan H, Lu J, et al. The effectiveness of an eHealth family-based intervention program in patients with uncontrolled type 2 diabetes mellitus (T2DM) in the community via WeChat: randomized controlled trial. JMIR Mhealth Uhealth. Mar 20, 2023;11:e40420. [FREE Full text] [CrossRef] [Medline]59]. However, the combination of DHI-ABDEFGIKLOPQ (action planning, communication, engagement, feedback, goal setting, guide, management, monitor, prompt, shape, stimulate, and support) encompasses more strategies and may seem more comprehensive, but there may be some challenges in practical applications. This complex combination may require more resources and time to implement while also increasing the cognitive burden on patients, which may affect treatment compliance. Finally, the combination of DHI-GIK (guide, management, and monitor), which only includes a few strategies, was relatively weak. This may be due to a lack of communication and patient engagement, reflecting insufficient communication between health care professionals and patients in real clinical practice and a lack of enthusiasm and participation from patients during treatment [Han CY, Zhang J, Ye XM, Lu JP, Jin HY, Xu WW, et al. Telemedicine-assisted structured self-monitoring of blood glucose in management of T2DM results of a randomized clinical trial. BMC Med Inform Decis Mak. Sep 14, 2023;23(1):182. [FREE Full text] [CrossRef] [Medline]78,Gershkowitz BD, Hillert CJ, Crotty BH. Digital coaching strategies to facilitate behavioral change in type 2 diabetes: a systematic review. J Clin Endocrinol Metab. Mar 25, 2021;106(4):e1513-e1520. [CrossRef] [Medline]106]. In addition, relying solely on guidance and management while ignoring the psychological and lifestyle factors of patients can easily lead to poor treatment outcomes.

At the same time, we included 7 studies evaluating the efficacy of 6 different DHI strategy combinations on FBG levels. Only DHI-GIK (guide, management, and monitor) was shown to have an effect. The advantage of this effective strategy combination is that it integrates 3 key elements: guidance, management, and monitoring. These strategies can provide systematic treatment support, help patients develop and execute effective treatment plans, and monitor treatment outcomes in a timely manner, thereby better controlling FBG levels [Zhou P, Xu L, Liu X, Huang J, Xu W, Chen W. Web-based telemedicine for management of type 2 diabetes through glucose uploads: a randomized controlled trial. Int J Clin Exp Pathol. 2014;7(12):8848-8854. [FREE Full text] [Medline]97]. Our result was consistent with research that has shown that multidimensional interventions, including guidance, management, and monitoring, are critical to the success of diabetes management [Chinese Elderly Type 2 Diabetes Prevention and Treatment of Clinical Guidelines Writing Group, Geriatric Endocrinology and Metabolism Branch of Chinese Geriatric Society, Geriatric Endocrinology and Metabolism Branch of Chinese Geriatric Health Care Society, Geriatric Professional Committee of Beijing Medical Award Foundation, National Clinical Medical Research Center for Geriatric Diseases (PLA General Hospital). [Clinical guidelines for prevention and treatment of type 2 diabetes mellitus in the elderly in China (2022 edition)]. Zhonghua Nei Ke Za Zhi. Jan 01, 2022;61(1):12-50. [CrossRef] [Medline]5]. However, other strategy combinations may lack sustained guidance and monitoring or may not fully motivate patients to actively participate in treatment, resulting in poor effect compared with DHI-GIK (guide, management, and monitor). Meanwhile, this result may also be influenced by the bias caused by the insufficient amount of original studies on FBG levels in our meta-analysis. Therefore, there is a certain difference in the results between studies evaluating FBG and HbA1c levels.

In general, our research results highlight the importance of comprehensive strategy combination in blood glucose management of T2DM. Future research can further explore other combinations of strategies to find more effective management models for diabetes. There is a need to strengthen communication and interaction between health care professionals and patients, as well as to improve patient participation and compliance. Furthermore, it is essential to strengthen the long-term tracking and evaluation of diabetes management strategies.

This review showed that DHI-BDFIQ (communication, engagement, goal setting, management, and support) and DHI-BGKR (communication, guide, monitor, and tailor) combination strategies were significantly effective for both BMI and weight loss. Among these, DHI-BDFIQ (communication, engagement, goal setting, management, and support) was considered the optimal combination. First, it covers the following key aspects: effective communication promotes cooperation and understanding between patients and medical teams; active engagement enhances the patient’s participation and execution of treatment, such as physical activity and dietary intake control; clear goal setting helps guide the direction and progress of treatment [Fukuoka Y, Gay CL, Joiner KL, Vittinghoff E. A novel diabetes prevention intervention using a mobile app: a randomized controlled trial with overweight adults at risk. Am J Prev Med. Aug 2015;49(2):223-237. [FREE Full text] [CrossRef] [Medline]98]; effective management can provide systematic treatment plans and continuous management support; and continuous support can help patients overcome difficulties and maintain a positive attitude during the treatment process [Feng Y, Zhao Y, Mao L, Gu M, Yuan H, Lu J, et al. The effectiveness of an eHealth family-based intervention program in patients with uncontrolled type 2 diabetes mellitus (T2DM) in the community via WeChat: randomized controlled trial. JMIR Mhealth Uhealth. Mar 20, 2023;11:e40420. [FREE Full text] [CrossRef] [Medline]59], to help patients better manage their weight. On the other hand, DHI-BGKR (communication, guide, monitor, and tailor) emphasizes personalized guidance and monitoring, which is more targeted, allowing patients to adjust their weight management plan according to their own situation [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. Jun 2008;10(3):160-168. [CrossRef] [Medline]56,Weymann N, Dirmaier J, von Wolff A, Kriston L, Härter M. Effectiveness of a web-based tailored interactive health communication application for patients with type 2 diabetes or chronic low back pain: randomized controlled trial. J Med Internet Res. Mar 03, 2015;17(3):e53. [FREE Full text] [CrossRef] [Medline]64,Hesseldal L, Christensen JR, Olesen TB, Olsen MH, Jakobsen PR, Laursen DH, et al. Long-term weight loss in a primary care-anchored eHealth lifestyle coaching program: randomized controlled trial. J Med Internet Res. Sep 23, 2022;24(9):e39741. [FREE Full text] [CrossRef] [Medline]70], thereby reducing weight and BMI. In addition, the combination strategy of DHI-BDGI (communication, engagement, guide, and management) was found to be effective for BMI, and DHI-BDFGIKLOPQ (communication, engagement, goal setting, guide, management, monitor, prompt, shape, stimulate, and support) was effective for weight loss. Our research results have indicated that DHI-BDGI (communication, engagement, guide, and management) was the most effective combination strategy in reducing the HbA1c levels, and therefore, it has a good effect on controlling BMI. Studies have shown that comprehensive treatment plans, including communication, engagement, guidance, and management, can effectively improve blood glucose control and weight management in patients with T2DM [Roy K, Iqbal S, Gadag V, Bavington B. Relationship between psychosocial factors and glucose control in adults with type 2 diabetes. Can J Diabetes. Oct 2020;44(7):636-642. [CrossRef] [Medline]107,Alaofè H, Hounkpatin WA, Djrolo F, Ehiri J, Rosales C. Knowledge, attitude, practice and associated factors among patients with type 2 diabetes in Cotonou, Southern Benin. BMC Public Health. Feb 12, 2021;21(1):339. [FREE Full text] [CrossRef] [Medline]108]. The effectiveness of these strategies may stem from their ability to comprehensively influence the patient’s lifestyle and behavioral habits, thereby promoting the achievement of treatment goals [Thorsen IK, Yang Y, Valentiner LS, Glümer C, Karstoft K, Brønd JC, et al. The effects of a lifestyle intervention supported by the InterWalk smartphone app on increasing physical activity among persons with type 2 diabetes: parallel-group, randomized trial. JMIR Mhealth Uhealth. Sep 28, 2022;10(9):e30602. [FREE Full text] [CrossRef] [Medline]73,Nelson LA, Spieker AJ, Greevy RA, Roddy MK, LeStourgeon LM, Bergner EM, et al. Glycemic outcomes of a family-focused intervention for adults with type 2 diabetes: main, mediated, and subgroup effects from the FAMS 2.0 RCT. Diabetes Res Clin Pract. Dec 2023;206:110991. [CrossRef] [Medline]79,Torbjørnsen A, Jenum AK, Småstuen MC, Arsand E, Holmen H, Wahl AK, et al. A low-intensity mobile health intervention with and without health counseling for persons with type 2 diabetes, part 1: baseline and short-term results from a randomized controlled trial in the Norwegian part of RENEWING HEALTH. JMIR Mhealth Uhealth. Dec 11, 2014;2(4):e52. [FREE Full text] [CrossRef] [Medline]94]. The combination of DHI-BDFGIKLOPQ (communication, engagement, goal setting, guide, management, monitor, prompt, shape, stimulate, and support) includes multiple strategies, from goal setting to behavior shaping and support. While it has a positive effect on weight loss, it may encounter some challenges in practical applications, leading to a relatively weak overall effect. Therefore, the effect was relatively weak.

Our research emphasizes the advantages of the combination of communication, patient engagement, goal setting, personalized management, external support, and continuous monitoring strategies in the DHIs of BMI and weight management of patients with T2DM. Future research can further explore the applicability of these strategy combinations in different outcomes, such as quality of life, complication rate, and other indicators. In addition, precise personalized intervention based on artificial intelligence and big data analysis is necessary. At the same time, more interactive and personalized digital health platforms can be developed to improve patient engagement and treatment compliance.

Limitations

This study has several limitations. First, despite conducting a comprehensive search, it is possible that some relevant papers published in non-English languages or from nonindexed sources might have been overlooked. Second, we conducted 3 rounds of data screening and extraction, which could have introduced inconsistencies in certain cases. In addition, in our quantitative analysis, we only performed meta-analyses on specific outcome measures, thereby limiting the generalizability of our conclusions. Regarding BMI and weight loss outcomes, there was substantial heterogeneity among studies, which we addressed through sensitivity analyses and publication bias assessments. Furthermore, the number of original studies included in the meta-analyses was relatively small, potentially leading to some degree of bias in the conclusions. Finally, due to the nature of the interventions, blinding was not feasible in most studies. This might have exaggerated the estimated intervention effects in the network meta-analysis and contributed to the low to moderate quality of the evidence and the low methodology quality of included studies.

Conclusions

Our research provided a comprehensive analysis and summary of the strategies used in DHIs for T2DM. We identified 63 strategies and categorized them into 19 strategy themes. Guide, monitor, management, and engagement were the most commonly used strategies. Most studies reported positive or mixed outcomes for most outcome indicators based on the SF/HIT. Research involving a medium or high number of strategies was found to be more effective than research involving a low number of strategies. The strategy combination composed of communication, engagement, guide, and management was most effective in reducing HbA1c levels, while the strategy combination that included guide, management, and monitor was effective in reducing FBG levels. The strategy combination composed of communication, engagement, goal setting, management, and support was most effective in BMI and weight management. Future research should further confirm the effectiveness of these strategies in other indicators and populations, explore more strategy combinations, and optimize the design and implementation of strategies for patients with diabetes. Furthermore, it is necessary to develop more interactive and personalized digital health platforms. Finally, the cost-effectiveness analysis of strategy use should be strengthened to provide more effective guidance for disease management and clinical practice of T2DM.

Acknowledgments

This study was supported by the Key Research and Development Plan of Shaanxi Province, China, (2021SF-144) and National Higher Medical Education Association, Nursing Education Branch, Education Science Fund Project (GJHL160008). The authors would like to sincerely appreciate all the authors who participated in this systematic review and meta-analysis.

Conflicts of Interest

None declared.

Multimedia Appendix 1

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

DOCX File , 28 KB

Multimedia Appendix 2

Search strategy.

DOCX File , 13 KB

Multimedia Appendix 3

Behavior change techniques.

DOCX File , 51 KB

Multimedia Appendix 4

Characteristics of the included studies.

XLSX File (Microsoft Excel File), 45 KB

Multimedia Appendix 5

Detailed information about strategies and strategy combinations.

XLSX File (Microsoft Excel File), 28 KB

Multimedia Appendix 6

Evaluation of the effectiveness of different digital health intervention strategies based on the synthesis framework for the assessment of health IT.

XLSX File (Microsoft Excel File), 23 KB

Multimedia Appendix 7

Risk of bias and quality assessments of included studies.

XLSX File (Microsoft Excel File), 16 KB

  1. Väätäinen S, Keinänen-Kiukaanniemi S, Saramies J, Uusitalo H, Tuomilehto J, Martikainen J. Quality of life along the diabetes continuum: a cross-sectional view of health-related quality of life and general health status in middle-aged and older Finns. Qual Life Res. Sep 2014;23(7):1935-1944. [CrossRef] [Medline]
  2. Jalkanen K, Aarnio E, Lavikainen P, Jauhonen HM, Enlund H, Martikainen J. Impact of type 2 diabetes treated with non-insulin medication and number of diabetes-coexisting diseases on EQ-5D-5 L index scores in the Finnish population. Health Qual Life Outcomes. Jul 08, 2019;17(1):117. [FREE Full text] [CrossRef] [Medline]
  3. Williams R, Karuranga S, Malanda B, Saeedi P, Basit A, Besançon S, et al. Global and regional estimates and projections of diabetes-related health expenditure: results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract. Apr 2020;162:108072. [CrossRef] [Medline]
  4. Magliano DJ, Boyko EJ, IDF Diabetes Atlas 10th Edition Scientific Committee. IDF diabetes atlas: 10th edition. International Diabetes Federation. 2021. URL: https://diabetesatlas.org/idfawp/resource-files/2021/07/IDF_Atlas_10th_Edition_2021.pdf [accessed 2025-01-24]
  5. Chinese Elderly Type 2 Diabetes Prevention and Treatment of Clinical Guidelines Writing Group, Geriatric Endocrinology and Metabolism Branch of Chinese Geriatric Society, Geriatric Endocrinology and Metabolism Branch of Chinese Geriatric Health Care Society, Geriatric Professional Committee of Beijing Medical Award Foundation, National Clinical Medical Research Center for Geriatric Diseases (PLA General Hospital). [Clinical guidelines for prevention and treatment of type 2 diabetes mellitus in the elderly in China (2022 edition)]. Zhonghua Nei Ke Za Zhi. Jan 01, 2022;61(1):12-50. [CrossRef] [Medline]
  6. American Diabetes Association. 4. Comprehensive medical evaluation and assessment of comorbidities: standards of medical care in diabetes-2021. Diabetes Care. Jan 2021;44(Suppl 1):S40-S52. [CrossRef] [Medline]
  7. Bain SC, Bekker Hansen B, Hunt B, Chubb B, Valentine WJ. Evaluating the burden of poor glycemic control associated with therapeutic inertia in patients with type 2 diabetes in the UK. J Med Econ. Jan 2020;23(1):98-105. [FREE Full text] [CrossRef] [Medline]
  8. Fellinger P, Rodewald K, Ferch M, Itariu B, Kautzky-Willer A, Winhofer Y. HbA1c and glucose management indicator discordance associated with obesity and type 2 diabetes in intermittent scanning glucose monitoring system. Biosensors (Basel). Apr 29, 2022;12(5):288. [FREE Full text] [CrossRef] [Medline]
  9. GBD 2015 Obesity Collaborators, Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, et al. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med. Jul 06, 2017;377(1):13-27. [FREE Full text] [CrossRef] [Medline]
  10. Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HA. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med. Oct 09, 2008;359(15):1577-1589. [FREE Full text] [CrossRef] [Medline]
  11. Lean ME, Leslie WS, Barnes AC, Brosnahan N, Thom G, McCombie L, et al. Primary care-led weight management for remission of type 2 diabetes (DiRECT): an open-label, cluster-randomised trial. Lancet. Feb 10, 2018;391(10120):541-551. [FREE Full text] [CrossRef] [Medline]
  12. Johansen MY, MacDonald CS, Hansen KB, Karstoft K, Christensen R, Pedersen M, et al. Effect of an intensive lifestyle intervention on glycemic control in patients with type 2 diabetes: a randomized clinical trial. JAMA. Aug 15, 2017;318(7):637-646. [FREE Full text] [CrossRef] [Medline]
  13. Gong Q, Zhang P, Wang J, Ma J, An Y, Chen Y, et al. Morbidity and mortality after lifestyle intervention for people with impaired glucose tolerance: 30-year results of the Da Qing Diabetes Prevention Outcome Study. Lancet Diabetes Endocrinol. Jun 2019;7(6):452-461. [FREE Full text] [CrossRef] [Medline]
  14. Lee EY, Cha SA, Yun JS, Lim SY, Lee JH, Ahn YB, et al. Efficacy of personalized diabetes self-care using an electronic medical record-integrated mobile app in patients with type 2 diabetes: 6-month randomized controlled trial. J Med Internet Res. Jul 28, 2022;24(7):e37430. [FREE Full text] [CrossRef] [Medline]
  15. 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. Sep 2011;34(9):1934-1942. [FREE Full text] [CrossRef] [Medline]
  16. 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. Dec 11, 2014;2(4):e57. [FREE Full text] [CrossRef] [Medline]
  17. Kirwan M, Vandelanotte C, Fenning A, Duncan MJ. Diabetes self-management smartphone application for adults with type 1 diabetes: randomized controlled trial. J Med Internet Res. Nov 13, 2013;15(11):e235. [FREE Full text] [CrossRef] [Medline]
  18. 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. Mar 2014;8(2):209-215. [FREE Full text] [CrossRef] [Medline]
  19. Rhee SY, Kim C, Shin DW, Steinhubl SR. Present and future of digital health in diabetes and metabolic disease. Diabetes Metab J. Dec 2020;44(6):819-827. [FREE Full text] [CrossRef] [Medline]
  20. 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. Jun 2014;63(6):745-54.e6. [CrossRef] [Medline]
  21. Hutchesson MJ, Rollo ME, Krukowski R, Ells L, Harvey J, Morgan PJ, et al. eHealth interventions for the prevention and treatment of overweight and obesity in adults: a systematic review with meta-analysis. Obes Rev. May 2015;16(5):376-392. [CrossRef] [Medline]
  22. Sherrington A, Newham JJ, Bell R, Adamson A, McColl E, Araujo-Soares V. Systematic review and meta-analysis of internet-delivered interventions providing personalized feedback for weight loss in overweight and obese adults. Obes Rev. Jun 2016;17(6):541-551. [FREE Full text] [CrossRef] [Medline]
  23. Ramadas A, Quek KF, Chan CK, Oldenburg B. Web-based interventions for the management of type 2 diabetes mellitus: a systematic review of recent evidence. Int J Med Inform. Jun 2011;80(6):389-405. [CrossRef] [Medline]
  24. Van Rhoon L, Byrne M, Morrissey E, Murphy J, McSharry J. A systematic review of the behaviour change techniques and digital features in technology-driven type 2 diabetes prevention interventions. Digit Health. 2020;6:2055207620914427. [FREE Full text] [CrossRef] [Medline]
  25. Webb TL, Joseph J, Yardley L, Michie S. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res. Feb 17, 2010;12(1):e4. [FREE Full text] [CrossRef] [Medline]
  26. Michie S, Yardley L, West R, Patrick K, Greaves F. Developing and evaluating digital interventions to promote behavior change in health and health care: recommendations resulting from an international workshop. J Med Internet Res. Jun 29, 2017;19(6):e232. [FREE Full text] [CrossRef] [Medline]
  27. Riley WT, Rivera DE, Atienza AA, Nilsen W, Allison SM, Mermelstein R. Health behavior models in the age of mobile interventions: are our theories up to the task? Transl Behav Med. Mar 2011;1(1):53-71. [FREE Full text] [CrossRef] [Medline]
  28. Morrison LG, Yardley L, Powell J, Michie S. What design features are used in effective e-health interventions? A review using techniques from Critical Interpretive Synthesis. Telemed J E Health. Mar 2012;18(2):137-144. [FREE Full text] [CrossRef] [Medline]
  29. Saleem M, Kühne L, De Santis KK, Christianson L, Brand T, Busse H. Understanding engagement strategies in digital interventions for mental health promotion: scoping review. JMIR Ment Health. Dec 20, 2021;8(12):e30000. [FREE Full text] [CrossRef] [Medline]
  30. Lim S, Hill B, Pirotta S, O'Reilly S, Moran L. What are the most effective behavioural strategies in changing postpartum women's physical activity and healthy eating behaviours? A systematic review and meta-analysis. J Clin Med. Jan 16, 2020;9(1):237. [FREE Full text] [CrossRef] [Medline]
  31. Masi CM, Chen HY, Hawkley LC, Cacioppo JT. A meta-analysis of interventions to reduce loneliness. Pers Soc Psychol Rev. Aug 2011;15(3):219-266. [FREE Full text] [CrossRef] [Medline]
  32. Mair JL, Salamanca-Sanabria A, Augsburger M, Frese BF, Abend S, Jakob R, et al. Effective behavior change techniques in digital health interventions for the prevention or management of noncommunicable diseases: an umbrella review. Ann Behav Med. Sep 13, 2023;57(10):817-835. [FREE Full text] [CrossRef] [Medline]
  33. Hwang DA, Lee A, Song JM, Han HR. Recruitment and retention strategies among racial and ethnic minorities in web-based intervention trials: retrospective qualitative analysis. J Med Internet Res. Jul 12, 2021;23(7):e23959. [FREE Full text] [CrossRef] [Medline]
  34. Treweek S, Pitkethly M, Cook J, Fraser C, Mitchell E, Sullivan F, et al. Strategies to improve recruitment to randomised trials. Cochrane Database Syst Rev. Feb 22, 2018;2(2):MR000013. [FREE Full text] [CrossRef] [Medline]
  35. Brueton VC, Tierney J, Stenning S, Harding S, Meredith S, Nazareth I, et al. Strategies to improve retention in randomised trials. Cochrane Database Syst Rev. Dec 03, 2013;(12):MR000032. [FREE Full text] [CrossRef] [Medline]
  36. Robinson KA, Dennison CR, Wayman DM, Pronovost PJ, Needham DM. Systematic review identifies number of strategies important for retaining study participants. J Clin Epidemiol. Aug 2007;60(8):757-765. [FREE Full text] [CrossRef] [Medline]
  37. Lee SW, Chan CK, Chua SS, Chaiyakunapruk N. Comparative effectiveness of telemedicine strategies on type 2 diabetes management: a systematic review and network meta-analysis. Sci Rep. Oct 04, 2017;7(1):12680. [FREE Full text] [CrossRef] [Medline]
  38. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev. Mar 29, 2021;10(1):89. [FREE Full text] [CrossRef] [Medline]
  39. Cane J, Richardson M, Johnston M, Ladha R, Michie S. From lists of behaviour change techniques (BCTs) to structured hierarchies: comparison of two methods of developing a hierarchy of BCTs. Br J Health Psychol. Feb 2015;20(1):130-150. [CrossRef] [Medline]
  40. Cumpston M, Li T, Page MJ, Chandler J, Welch VA, Higgins JP, et al. Updated guidance for trusted systematic reviews: a new edition of the Cochrane Handbook for Systematic Reviews of Interventions. Cochrane Database Syst Rev. Oct 03, 2019;10(10):ED000142. [FREE Full text] [CrossRef] [Medline]
  41. Christopoulou SC, Kotsilieris T, Anagnostopoulos I. Assessment of health information technology interventions in evidence-based medicine: a systematic review by adopting a methodological evaluation framework. Healthcare (Basel). Aug 31, 2018;6(3):109. [FREE Full text] [CrossRef] [Medline]
  42. Lin L, Zhang J, Hodges JS, Chu H. Performing arm-based network meta-analysis in R with the pcnetmeta package. J Stat Softw. Aug 2017;80:5. [FREE Full text] [CrossRef] [Medline]
  43. Xu C, Niu Y, Wu J, Gu H, Zhang C. Software and package applicating for network meta-analysis: a usage-based comparative study. J Evid Based Med. Aug 2018;11(3):176-183. [CrossRef] [Medline]
  44. Bhatnagar N, Lakshmi PV, Jeyashree K. Multiple treatment and indirect treatment comparisons: an overview of network meta-analysis. Perspect Clin Res. Oct 2014;5(4):154-158. [FREE Full text] [CrossRef] [Medline]
  45. Shim S, Yoon BH, Shin IS, Bae JM. Network meta-analysis: application and practice using Stata. Epidemiol Health. 2017;39:e2017047. [FREE Full text] [CrossRef] [Medline]
  46. Spineli LM. An empirical comparison of Bayesian modelling strategies for missing binary outcome data in network meta-analysis. BMC Med Res Methodol. Apr 24, 2019;19(1):86. [FREE Full text] [CrossRef] [Medline]
  47. Sedgwick P, Marston L. How to read a funnel plot in a meta-analysis. BMJ. Sep 16, 2015;351:h4718. [CrossRef] [Medline]
  48. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. Dec 1994;50(4):1088-1101. [Medline]
  49. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. Sep 13, 1997;315(7109):629-634. [FREE Full text] [CrossRef] [Medline]
  50. Glasgow RE, Toobert DJ. Brief, computer-assisted diabetes dietary self-management counseling: effects on behavior, physiologic outcomes, and quality of life. Med Care. Nov 2000;38(11):1062-1073. [CrossRef] [Medline]
  51. Keyserling TC, Samuel-Hodge CD, Ammerman AS, Ainsworth BE, Henríquez-Roldán CF, Elasy TA, et al. A randomized trial of an intervention to improve self-care behaviors of African-American women with type 2 diabetes: impact on physical activity. Diabetes Care. Sep 2002;25(9):1576-1583. [CrossRef] [Medline]
  52. Tate DF, Jackvony EH, Wing RR. Effects of internet behavioral counseling on weight loss in adults at risk for type 2 diabetes: a randomized trial. JAMA. Apr 09, 2003;289(14):1833-1836. [CrossRef] [Medline]
  53. Young RJ, Taylor J, Friede T, Hollis S, Mason JM, Lee P, et al. Pro-active call center treatment support (PACCTS) to improve glucose control in type 2 diabetes: a randomized controlled trial. Diabetes Care. Feb 2005;28(2):278-282. [CrossRef] [Medline]
  54. Kim HS, Jeong HS. A nurse short message service by cellular phone in type-2 diabetic patients for six months. J Clin Nurs. Jun 2007;16(6):1082-1087. [CrossRef] [Medline]
  55. Lorig K, Ritter PL, Villa F, Piette JD. Spanish diabetes self-management with and without automated telephone reinforcement: two randomized trials. Diabetes Care. Mar 2008;31(3):408-414. [CrossRef] [Medline]
  56. 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. Jun 2008;10(3):160-168. [CrossRef] [Medline]
  57. Powers BJ, Olsen MK, Oddone EZ, Bosworth HB. The effect of a hypertension self-management intervention on diabetes and cholesterol control. Am J Med. Jul 2009;122(7):639-646. [FREE Full text] [CrossRef] [Medline]
  58. Nelson LA, Greevy RA, Spieker A, Wallston KA, Elasy TA, Kripalani S, et al. Effects of a tailored text messaging intervention among diverse adults with type 2 diabetes: evidence from the 15-month REACH randomized controlled trial. Diabetes Care. Jan 2021;44(1):26-34. [FREE Full text] [CrossRef] [Medline]
  59. Feng Y, Zhao Y, Mao L, Gu M, Yuan H, Lu J, et al. The effectiveness of an eHealth family-based intervention program in patients with uncontrolled type 2 diabetes mellitus (T2DM) in the community via WeChat: randomized controlled trial. JMIR Mhealth Uhealth. Mar 20, 2023;11:e40420. [FREE Full text] [CrossRef] [Medline]
  60. Ruissen MM, Torres-Peña JD, Uitbeijerse BS, Arenas de Larriva AP, Huisman SD, Namli T, et al. Clinical impact of an integrated e-health system for diabetes self-management support and shared decision making (POWER2DM): a randomised controlled trial. Diabetologia. Dec 29, 2023;66(12):2213-2225. [FREE Full text] [CrossRef] [Medline]
  61. Mansberger SL, Gleitsmann K, Gardiner S, Sheppler C, Demirel S, Wooten K, et al. Comparing the effectiveness of telemedicine and traditional surveillance in providing diabetic retinopathy screening examinations: a randomized controlled trial. Telemed J E Health. Dec 2013;19(12):942-948. [FREE Full text] [CrossRef] [Medline]
  62. 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. Mar 02, 2020;8(3):e16665. [FREE Full text] [CrossRef] [Medline]
  63. Hansel B, Giral P, Gambotti L, Lafourcade A, Peres G, Filipecki C, et al. A fully automated web-based program improves lifestyle habits and HbA1c in patients with type 2 diabetes and abdominal obesity: randomized trial of patient e-coaching nutritional support (the ANODE study). J Med Internet Res. Nov 08, 2017;19(11):e360. [FREE Full text] [CrossRef] [Medline]
  64. Weymann N, Dirmaier J, von Wolff A, Kriston L, Härter M. Effectiveness of a web-based tailored interactive health communication application for patients with type 2 diabetes or chronic low back pain: randomized controlled trial. J Med Internet Res. Mar 03, 2015;17(3):e53. [FREE Full text] [CrossRef] [Medline]
  65. Schillinger D, Handley M, Wang F, Hammer H. Effects of self-management support on structure, process, and outcomes among vulnerable patients with diabetes: a three-arm practical clinical trial. Diabetes Care. Apr 2009;32(4):559-566. [FREE Full text] [CrossRef] [Medline]
  66. Davis RM, Hitch AD, Salaam MM, Herman WH, Zimmer-Galler IE, Mayer-Davis EJ. TeleHealth improves diabetes self-management in an underserved community: diabetes TeleCare. Diabetes Care. Aug 2010;33(8):1712-1717. [FREE Full text] [CrossRef] [Medline]
  67. Glasgow RE, Christiansen SM, Kurz D, King DK, Woolley T, Faber AJ, et al. Engagement in a diabetes self-management website: usage patterns and generalizability of program use. J Med Internet Res. Jan 25, 2011;13(1):e9. [FREE Full text] [CrossRef] [Medline]
  68. Wild SH, Hanley J, Lewis SC, McKnight JA, McCloughan LB, Padfield PL, et al. Supported telemonitoring and glycemic control in people with type 2 diabetes: the Telescot Diabetes Pragmatic Multicenter randomized controlled trial. PLoS Med. Jul 2016;13(7):e1002098. [FREE Full text] [CrossRef] [Medline]
  69. Crowley MJ, Tarkington PE, Bosworth HB, Jeffreys AS, Coffman CJ, Maciejewski ML, et al. Effect of a comprehensive telehealth intervention vs telemonitoring and care coordination in patients with persistently poor type 2 diabetes control: a randomized clinical trial. JAMA Intern Med. Sep 01, 2022;182(9):943-952. [FREE Full text] [CrossRef] [Medline]
  70. Hesseldal L, Christensen JR, Olesen TB, Olsen MH, Jakobsen PR, Laursen DH, et al. Long-term weight loss in a primary care-anchored eHealth lifestyle coaching program: randomized controlled trial. J Med Internet Res. Sep 23, 2022;24(9):e39741. [FREE Full text] [CrossRef] [Medline]
  71. Lavikainen P, Mattila E, Absetz P, Harjumaa M, Lindström J, Järvelä-Reijonen E, et al. Digitally supported lifestyle intervention to prevent type 2 diabetes through healthy habits: secondary analysis of long-term user engagement trajectories in a randomized controlled trial. J Med Internet Res. Feb 24, 2022;24(2):e31530. [FREE Full text] [CrossRef] [Medline]
  72. Nagata T, Aoyagi SS, Takahashi M, Nagata M, Mori K. Effects of feedback from self-monitoring devices on lifestyle changes in workers with diabetes: 3-month randomized controlled pilot trial. JMIR Form Res. Aug 09, 2022;6(8):e23261. [FREE Full text] [CrossRef] [Medline]
  73. Thorsen IK, Yang Y, Valentiner LS, Glümer C, Karstoft K, Brønd JC, et al. The effects of a lifestyle intervention supported by the InterWalk smartphone app on increasing physical activity among persons with type 2 diabetes: parallel-group, randomized trial. JMIR Mhealth Uhealth. Sep 28, 2022;10(9):e30602. [FREE Full text] [CrossRef] [Medline]
  74. Yin W, Liu Y, Hu H, Sun J, Liu Y, Wang Z. Telemedicine management of type 2 diabetes mellitus in obese and overweight young and middle-aged patients during COVID-19 outbreak: a single-center, prospective, randomized control study. PLoS One. 2022;17(9):e0275251. [FREE Full text] [CrossRef] [Medline]
  75. Calikoglu F, Bagdemir E, Celik S, Idiz C, Ozsarı H, Issever H, et al. Telemedicine as a motivational tool to optimize metabolic control in patients with diabetes in Turkey: a prospective, randomized, controlled TeleDiab trial. Telemed J E Health. Apr 01, 2023;29(4):518-530. [CrossRef] [Medline]
  76. Cheung NW, Redfern J, Thiagalingam A, Hng TM, Marschner S, Haider R, et al. Effect of mobile phone text messaging self-management support for patients with diabetes or coronary heart disease in a chronic disease management program (SupportMe) on blood pressure: pragmatic randomized controlled trial. J Med Internet Res. Jun 16, 2023;25:e38275. [FREE Full text] [CrossRef] [Medline]
  77. Gerber BS, Biggers A, Tilton JJ, Smith Marsh DE, Lane R, Mihailescu D, et al. Mobile health intervention in patients with type 2 diabetes: a randomized clinical trial. JAMA Netw Open. Sep 05, 2023;6(9):e2333629. [FREE Full text] [CrossRef] [Medline]
  78. Han CY, Zhang J, Ye XM, Lu JP, Jin HY, Xu WW, et al. Telemedicine-assisted structured self-monitoring of blood glucose in management of T2DM results of a randomized clinical trial. BMC Med Inform Decis Mak. Sep 14, 2023;23(1):182. [FREE Full text] [CrossRef] [Medline]
  79. Nelson LA, Spieker AJ, Greevy RA, Roddy MK, LeStourgeon LM, Bergner EM, et al. Glycemic outcomes of a family-focused intervention for adults with type 2 diabetes: main, mediated, and subgroup effects from the FAMS 2.0 RCT. Diabetes Res Clin Pract. Dec 2023;206:110991. [CrossRef] [Medline]
  80. Whitehouse CR, Knowles M, Long JA, Mitra N, Volpp KG, Xu C, et al. Digital health and community health worker support for diabetes management: a randomized controlled trial. J Gen Intern Med. Jan 17, 2023;38(1):131-137. [FREE Full text] [CrossRef] [Medline]
  81. Zamanillo-Campos R, Fiol-deRoque MA, Serrano-Ripoll MJ, Mira-Martínez S, Ricci-Cabello I. Development and evaluation of DiabeText, a personalized mHealth intervention to support medication adherence and lifestyle change behaviour in patients with type 2 diabetes in Spain: a mixed-methods phase II pragmatic randomized controlled clinical trial. Int J Med Inform. Aug 2023;176:105103. [CrossRef] [Medline]
  82. Lee DY, Yoo SH, Min KP, Park CY. Effect of voluntary participation on mobile health care in diabetes management: randomized controlled open-label trial. JMIR Mhealth Uhealth. Sep 18, 2020;8(9):e19153. [FREE Full text] [CrossRef] [Medline]
  83. Bender MS, Cooper BA, Park LG, Padash S, Arai S. A feasible and efficacious mobile-phone based lifestyle intervention for Filipino Americans with type 2 diabetes: randomized controlled trial. JMIR Diabetes. Dec 12, 2017;2(2):e30. [FREE Full text] [CrossRef] [Medline]
  84. Fortmann AL, Gallo LC, Garcia MI, Taleb M, Euyoque JA, Clark T, et al. Dulce digital: an mHealth SMS-based intervention improves glycemic control in Hispanics with type 2 diabetes. Diabetes Care. Oct 2017;40(10):1349-1355. [FREE Full text] [CrossRef] [Medline]
  85. Lee JY, Wong CP, Tan CS, Nasir NH, Lee SW. Telemonitoring in fasting individuals with type 2 diabetes mellitus during Ramadan: a prospective, randomised controlled study. Sci Rep. Aug 31, 2017;7(1):10119. [FREE Full text] [CrossRef] [Medline]
  86. Quinn CC, Swasey KK, Crabbe JC, Shardell MD, Terrin ML, Barr EA, et al. The impact of a mobile diabetes health intervention on diabetes distress and depression among adults: secondary analysis of a cluster randomized controlled trial. JMIR Mhealth Uhealth. Dec 07, 2017;5(12):e183. [FREE Full text] [CrossRef] [Medline]
  87. Ramadas A, Chan CK, Oldenburg B, Hussein Z, Quek KF. Randomised-controlled trial of a web-based dietary intervention for patients with type 2 diabetes: changes in health cognitions and glycemic control. BMC Public Health. Jun 08, 2018;18(1):716. [FREE Full text] [CrossRef] [Medline]
  88. Sarayani A, Mashayekhi M, Nosrati M, Jahangard-Rafsanjani Z, Javadi M, Saadat N, et al. Efficacy of a telephone-based intervention among patients with type-2 diabetes; a randomized controlled trial in pharmacy practice. Int J Clin Pharm. Apr 2018;40(2):345-353. [CrossRef] [Medline]
  89. Torbjørnsen A, Småstuen MC, Jenum AK, Årsand E, Ribu L. Acceptability of an mHealth app intervention for persons with type 2 diabetes and its associations with initial self-management: randomized controlled trial. JMIR Mhealth Uhealth. May 21, 2018;6(5):e125. [FREE Full text] [CrossRef] [Medline]
  90. 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. Jan 10, 2019;7(1):e10321. [FREE Full text] [CrossRef] [Medline]
  91. Höchsmann C, Infanger D, Klenk C, Königstein K, Walz SP, Schmidt-Trucksäss A. Effectiveness of a behavior change technique-based smartphone game to improve intrinsic motivation and physical activity adherence in patients with type 2 diabetes: randomized controlled trial. JMIR Serious Games. Feb 13, 2019;7(1):e11444. [FREE Full text] [CrossRef] [Medline]
  92. MacPherson MM, Merry KJ, Locke SR, Jung ME. Effects of mobile health prompts on self-monitoring and exercise behaviors following a diabetes prevention program: secondary analysis from a randomized controlled trial. JMIR Mhealth Uhealth. Sep 05, 2019;7(9):e12956. [FREE Full text] [CrossRef] [Medline]
  93. Sun C, Sun L, Xi S, Zhang H, Wang H, Feng Y, et al. Mobile phone-based telemedicine practice in older Chinese patients with type 2 diabetes mellitus: randomized controlled trial. JMIR Mhealth Uhealth. Jan 04, 2019;7(1):e10664. [FREE Full text] [CrossRef] [Medline]
  94. Torbjørnsen A, Jenum AK, Småstuen MC, Arsand E, Holmen H, Wahl AK, et al. A low-intensity mobile health intervention with and without health counseling for persons with type 2 diabetes, part 1: baseline and short-term results from a randomized controlled trial in the Norwegian part of RENEWING HEALTH. JMIR Mhealth Uhealth. Dec 11, 2014;2(4):e52. [FREE Full text] [CrossRef] [Medline]
  95. Vervloet M, van Dijk L, de Bakker DH, Souverein PC, Santen-Reestman J, van Vlijmen B, et al. Short- and long-term effects of real-time medication monitoring with short message service (SMS) reminders for missed doses on the refill adherence of people with type 2 diabetes: evidence from a randomized controlled trial. Diabet Med. Jul 2014;31(7):821-828. [CrossRef] [Medline]
  96. Wakefield BJ, Koopman RJ, Keplinger LE, Bomar M, Bernt B, Johanning JL, et al. Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes. Telemed J E Health. Mar 2014;20(3):199-205. [FREE Full text] [CrossRef] [Medline]
  97. Zhou P, Xu L, Liu X, Huang J, Xu W, Chen W. Web-based telemedicine for management of type 2 diabetes through glucose uploads: a randomized controlled trial. Int J Clin Exp Pathol. 2014;7(12):8848-8854. [FREE Full text] [Medline]
  98. Fukuoka Y, Gay CL, Joiner KL, Vittinghoff E. A novel diabetes prevention intervention using a mobile app: a randomized controlled trial with overweight adults at risk. Am J Prev Med. Aug 2015;49(2):223-237. [FREE Full text] [CrossRef] [Medline]
  99. Greenwood DA, Blozis SA, Young HM, Nesbitt TS, Quinn CC. Overcoming clinical inertia: a randomized clinical trial of a telehealth remote monitoring intervention using paired glucose testing in adults with type 2 diabetes. J Med Internet Res. Jul 21, 2015;17(7):e178. [FREE Full text] [CrossRef] [Medline]
  100. Nicolucci A, Cercone S, Chiriatti A, Muscas F, Gensini G. A randomized trial on home telemonitoring for the management of metabolic and cardiovascular risk in patients with type 2 diabetes. Diabetes Technol Ther. Aug 2015;17(8):563-570. [CrossRef] [Medline]
  101. Shan R, Sarkar S, Martin SS. Digital health technology and mobile devices for the management of diabetes mellitus: state of the art. Diabetologia. Jun 2019;62(6):877-887. [CrossRef] [Medline]
  102. Dixon RF, Zisser H, Layne JE, Barleen NA, Miller DP, Moloney DP, et al. A virtual type 2 diabetes clinic using continuous glucose monitoring and endocrinology visits. J Diabetes Sci Technol. Sep 2020;14(5):908-911. [FREE Full text] [CrossRef] [Medline]
  103. Ridho A, Alfian SD, van Boven JF, Levita J, Yalcin EA, Le L, et al. Digital health technologies to improve medication adherence and treatment outcomes in patients with tuberculosis: systematic review of randomized controlled trials. J Med Internet Res. Feb 23, 2022;24(2):e33062. [FREE Full text] [CrossRef] [Medline]
  104. Moschonis G, Siopis G, Jung J, Eweka E, Willems R, Kwasnicka D, 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. Mar 2023;5(3):e125-e143. [FREE Full text] [CrossRef] [Medline]
  105. Kerr D, Edelman S, Vespasiani G, Khunti K. New digital health technologies for insulin initiation and optimization for people with type 2 diabetes. Endocr Pract. Aug 2022;28(8):811-821. [FREE Full text] [CrossRef] [Medline]
  106. Gershkowitz BD, Hillert CJ, Crotty BH. Digital coaching strategies to facilitate behavioral change in type 2 diabetes: a systematic review. J Clin Endocrinol Metab. Mar 25, 2021;106(4):e1513-e1520. [CrossRef] [Medline]
  107. Roy K, Iqbal S, Gadag V, Bavington B. Relationship between psychosocial factors and glucose control in adults with type 2 diabetes. Can J Diabetes. Oct 2020;44(7):636-642. [CrossRef] [Medline]
  108. Alaofè H, Hounkpatin WA, Djrolo F, Ehiri J, Rosales C. Knowledge, attitude, practice and associated factors among patients with type 2 diabetes in Cotonou, Southern Benin. BMC Public Health. Feb 12, 2021;21(1):339. [FREE Full text] [CrossRef] [Medline]


DHI: digital health intervention
FBG: fasting blood glucose
HbA1c: glycated hemoglobin A1c
MD: mean difference
PRISMA-NMA: Preferred Reporting Items for Systematic Review and Meta-Analyses extension for Network Meta-Analysis
RCT: randomized controlled trial
SF/HIT: synthesis framework for the assessment of health IT
SUCRA: surface under the cumulative ranking curve
T2DM: type 2 diabetes mellitus


Edited by A Mavragani; submitted 12.06.24; peer-reviewed by KR Quimby, A Hornsten; comments to author 22.11.24; revised version received 04.12.24; accepted 16.12.24; published 14.02.25.

Copyright

©Min Li, Shiyu Liu, Binyang Yu, Ning Li, Aili Lyu, Haiyan Yang, Haiyan He, Na Zhang, Jingru Ma, Meichen Sun, Hong Du, Rui Gao. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.02.2025.

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