Rasooly A, Beran D, Ye PP, Joshi S, Yin XJ, Tandon N, Shao RT. Digital health for rural diabetes care: Implementation experience from China and India. World J Diabetes 2025; 16(8): 107733 [DOI: 10.4239/wjd.v16.i8.107733]
Corresponding Author of This Article
Alon Rasooly, MD, PhD, Department of Health Policy and Systems Management, School of Public Health, Ben-Gurion University, David Ben-Gurion Blvd 1, Beersheba 8410501, Israel. rasooly@post.bgu.ac.il
Research Domain of This Article
Health Policy & Services
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Alon Rasooly, Department of Health Policy and Systems Management, School of Public Health, Ben-Gurion University, Beersheba 8410501, Israel
David Beran, Division of Tropical and Humanitarian Medicine, Faculty of Medicine, University of Geneva and Geneva University Hospitals, Geneva CH-1211, Switzerland
Peng-Peng Ye, National Centre for Chronic and Noncommunicable Disease Control and Prevention, Chinese Centre for Disease Control and Prevention, Beijing 100050, China
Surabhi Joshi, Department of Noncommunicable Diseases, Disability and Rehabilitation, The World Health Organization, Geneva 1211, Switzerland
Xue-Jun Yin, School of Public Health, Jiangxi Medical College, Nanchang University, Nanchang 330006, Jiangxi Province, China
Xue-Jun Yin, Jiangxi Provincial Key Laboratory of Disease Prevention and Public Health, Nanchang University, Nanchang 330006, Jiangxi Province, China
Nikhil Tandon, Department of Endocrinology and Metabolism, All India Institute of Medical Sciences, New Delhi 110029, Delhi, India
Rui-Tai Shao, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
Author contributions: Rasooly A and Beran D contributed to conceptualization and study design; Shao RT and Tandon N contributed to subject matter expertise and critical interpretation of health systems in China and India, respectively; Ye PP and Yin XJ contributed to data curation and interpretation of Chinese digital health interventions; Joshi S contributed to analysis of Indian healthcare context and integration of global digital health frameworks; Rasooly A contributed to writing - original draft; All authors contributed to writing - review and editing, validation of intellectual content, and final approval of the manuscript.
Conflict-of-interest statement: The authors declare that they have no conflicts of interest related to this publication.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Alon Rasooly, MD, PhD, Department of Health Policy and Systems Management, School of Public Health, Ben-Gurion University, David Ben-Gurion Blvd 1, Beersheba 8410501, Israel. rasooly@post.bgu.ac.il
Received: March 28, 2025 Revised: May 14, 2025 Accepted: June 27, 2025 Published online: August 15, 2025 Processing time: 139 Days and 12.8 Hours
Abstract
Diabetes affects an estimated 828 million people globally, with approximately 44% living in China and India. Rural residents with diabetes in these countries face significant challenges in access to care. Although digital health interventions are increasingly used to reach underserved populations, considerable knowledge gaps exist. This mini-review presents the first comparative analysis of digital health implementations for diabetes care in rural China and India, comprising clinical decision support tools, telemedicine, and mobile health applications. The review examines how their distinct health system structures influence technology adoption and clinical outcomes. China's hierarchical administrative structure facilitates standardized nationwide platforms with consistent protocols, while India's federal system enables diverse localized innovations that accommodate regional diversity. Cluster-randomized trials for digital health tools in rural China show significant improvements in glycemic control. In India, interventions examined in this review were associated with improved health behaviors and medication adherence. Both countries demonstrate that digital interventions leveraging existing social structures and co-created with stakeholders yield better outcomes than standard care approaches. This analysis provides actionable insights for policymakers globally while identifying valuable opportunities for knowledge exchange between these two nations that together are home to nearly half of all people living with diabetes worldwide.
Core Tip: This mini-review examines how contrasting health system structures in China and India shape digital health implementation for rural diabetes care. While China's centralized approach enables uniform national platforms, India's decentralized system fosters adaptable local innovations. Digital interventions show clinical effectiveness when they complement existing healthcare structures, leverage community resources, and address contextual barriers. Co-creation involving people living with diabetes, primary healthcare providers, and local health administrators emerges as a critical success factor for intervention design and implementation. Future digital health investments should prioritize rural communities, participatory design processes, and sustainable implementation models to transform diabetes care for underserved communities worldwide.
Citation: Rasooly A, Beran D, Ye PP, Joshi S, Yin XJ, Tandon N, Shao RT. Digital health for rural diabetes care: Implementation experience from China and India. World J Diabetes 2025; 16(8): 107733
Diabetes affects an estimated 828 million people globally, with China and India accounting for about 44% of cases[1]. Type 2 diabetes represents over 95% of these cases. Approximately 50% of people with diabetes in China and 57% in India are diagnosed[2], while only 45% in China and 37% in India achieve glycemic control among those diagnosed[3]. This is particularly concerning for rural populations, who face significant barriers to accessing quality diabetes care.
Historically, diabetes prevalence has been higher in urban compared to rural populations. However, recent epidemiological studies indicate this urban-rural gap is narrowing significantly. A pooled systematic review of 1.7 million adults in India found a relatively high burden of diabetes and pre-diabetes in rural and urban India, with a narrowed urban-rural gap[4]. Similarly, studies from China show that while diabetes prevalence remains higher in urban areas, a rapid growth in diabetes mortality is occurring in rural areas[5]. These trends are concerning as healthcare access and quality improvements have not kept pace with the increasing needs of people living with diabetes in rural areas, reflecting a significant policy lag in addressing resource disparities.
Digital health is defined by the World Health Organization (WHO) as 'the field of knowledge and practice associated with the development and use of digital technologies to improve health[6]', which includes electronic health records, clinical decision support systems (CDSS), telemedicine platforms, mobile health applications, wearable devices, and artificial intelligence tools. The WHO's Global Digital Health Strategy (2020-2025) set out a vision of improving health for everyone, everywhere by accelerating digital health solutions that are appropriate, accessible, affordable, scalable, sustainable, and person-centric[6]. Its strategic objectives and framework for action aim to advance digital health globally and within countries, building an internationally connected digital health system. Within this framework, cross-national knowledge exchange serves as a cornerstone for realizing the global digital health vision.
A recent scoping review by Maita et al[7] identified multiple digital health interventions that effectively bridge healthcare gaps in rural settings, including telemedicine consultations, remote monitoring, mobile health applications, and CDSS. These technologies have demonstrated positive outcomes in rural areas, particularly for improving service accessibility, enhancing provider capacity, and supporting chronic disease management. In the context of diabetes care, digital interventions have shown promise in improving glycemic control, medication adherence, and patient engagement in both China and India, though implementation approaches differ based on their distinct health system structures[8-12]. Although most digital health technologies were developed in high-income countries, some low- and middle-income countries (LMICs) have progressed in piloting these technologies. Obstacles to implementing such tools include limited infrastructure, low digital literacy, and unique cultural contexts[13,14].
This narrative mini-review provides the first comparison of digital health implementations for diabetes care in rural China and India, examining how their distinct health system structures shape technology adoption and outcomes. We included peer-reviewed literature on digital health interventions for diabetes in rural China and India published between 2010-2025, focusing on randomized controlled trials, implementation studies, and comparative analyses that reported clinical outcomes and implementation factors. Guided by the WHO's Global Digital Health Strategy and its framework for action[6], we analyzed digital health approaches that have improved care access and quality in rural settings while examining how health system contexts influence tool implementation and effectiveness. Given that both countries face similar challenges in delivering diabetes care to their rural populations, lessons from their digital health experiences have important implications for improving care delivery in resource-constrained settings worldwide (Figure 1).
Understanding the health system context is critical for effective digital health implementation in rural settings. Table 1 illustrates several demographic, healthcare system, digital infrastructure, and diabetes-related indicators for China and India, providing essential context for analyzing the implementation of digital health interventions for rural populations in these countries.
Table 1 Comparing context for digital diabetes care in China and India, 2021-2023.
Barriers to digital access in rural areas create notable implementation challenges. In China, internet usage among different demographic groups reveals clear digital gaps affecting rural residents, elderly populations, and low-income groups[15,16]. In India, the digital divide is characterized by severe gender gaps, with only 24.6% of rural women having ever used the internet compared to 48.7% of rural men, according to National Family Health Survey data[17]. Conversely, Ji et al[18] found that limited proficiency in digital health among rural Chinese residents intensifies urban-rural health disparities, emphasizing the need to enhance both digital and health literacy to improve access to digital health services.
HEALTH SYSTEM GOVERNANCE
China and India present contrasting approaches to health system governance. China maintains a hierarchical five-level administrative structure where the National Health Commission provides standardized technical guidance flowing downward through all levels[19]. While some decentralization has occurred, the central government retains dominant control over legislation and decision-making, with local health authorities implementing plans under central leadership. On the other hand, India operates through a federal system where health is primarily a state responsibility, with some central government transfers distributed to states through sponsored schemes[20]. This decentralization extends further through constitutional amendments empowering local institutions with varying degrees of health service control, creating substantial regional variation in implementation.
Primary health care (PHC) organization reflects these structural differences. In China, rural PHC is delivered through a hierarchical system comprising county hospitals, township health centers, and village clinics that provide standardized services to rural populations[21]. Despite systematic government investment in public PHC infrastructure, significant urban-rural disparities persist in healthcare financing, resource distribution, and health outcomes[22]. Surveys across various Chinese provinces reveal sharp contrasts in the quantity and quality of medical institutions and healthcare practitioner availability between urban and rural areas[21,23].
India's PHC landscape features a public-private mix, with its public network operating alongside substantial private-sector involvement. The public health sector suffers from management shortfalls and poor accountability, while the private sector remains largely unregulated[24]. These challenges are reflected in India's healthcare financing, with higher out-of-pocket expenditure, as shown in Table 1. As part of ongoing reforms, the preventive aspect of Non-Communicable Diseases (NCDs) has been strengthened in India under the Comprehensive Primary Health Care Scheme by promoting wellness activities and targeted communication at the community level[25]. Also, the National Programme for Prevention and Control of NCDs (NP-NCD) provides technical and financial support to states, focusing on infrastructure strengthening, human resource development, health promotion, and establishing referral pathways across healthcare facilities[25,26].
Both countries have developed policy frameworks to advance digital health. The Healthy China 2030 blueprint emphasizes building a unified, interconnected population health information platform and promoting Internet Plus Healthcare services. China's 14th Five-Year Plan (2021-2025) further supports Internet-based healthcare services and AI technologies[27]. Similarly, India's National Health Policy 2017 established the National Digital Health Mission, aiming to build a comprehensive digital health ecosystem to achieve Universal Health Coverage[24]. Also, the NP-NCD and the Comprehensive Primary Health Care portals serve as components of the digital health delivery system in India. These initiatives reflect each country's approach to addressing healthcare challenges through digital transformation, with China focusing on standardized nationwide platforms and India developing an ecosystem that can accommodate its diverse healthcare landscape.
DIGITAL RURAL HEALTH INTERVENTIONS
This section examines key digital health interventions implemented for diabetes care in rural China and India, with their characteristics and outcomes summarized in Table 2. The comparison highlights different implementation approaches shaped by each country's health system context.
Table 2 Comparison of key digital health intervention studies for diabetes in rural China and India.
Mean HbA1c decrease from 9.3% to 8.5% (non-randomized design)
The SimCard trial represents a unique case study of implementing digital health interventions in rural communities in both China and India[9]. While primarily focused on cardiovascular risk management, this trial is particularly relevant as it enrolled participants with diabetes and hypertension, demonstrating how similar digital tools must be adapted differently within each country's healthcare structure. This cluster-randomized controlled trial enrolled 2086 individuals with high cardiovascular risk from 47 villages (27 in China and 20 in India), evaluating the effectiveness of a simplified cardiovascular management program delivered by community health workers (CHWs) with smartphone-based decision support. In rural China, CHWs were village doctors who had received basic health professional training and were authorized to prescribe medications, while in India, CHWs were volunteer community members not authorized to prescribe medicines but supported by licensed physicians who provided clinical guidance and prescriptions. Therefore, the electronic decision support system (EDSS) in India had both a smartphone component for CHWs and a desktop component for physicians to approve the medications. In contrast, in China, village doctors were supported by a single component, a smartphone-based EDSS. The primary outcome - proportion of patient-reported antihypertensive medication use - showed a significant net increase of 25.5% (P < 0.001) in the intervention group compared to controls. The net difference between the intervention and control groups was 24.4% in China (P < 0.001) and 26.6% in India (P = 0.002). This study demonstrated that although China and India differ in many aspects, empowering rural CHWs in both countries with digital decision support tools could effectively improve cardiovascular risk management, highlighting the importance of adapting interventions to local healthcare contexts.
China's ROADMAP study represents one of the largest cluster-randomized digital health interventions conducted in primary care settings globally, encompassing 19601 participants from 864 rural and urban communities across 25 provinces[8]. This trial evaluated a mHealth-enabled hierarchical diabetes management intervention aligned with China's existing healthcare structure. The multi-faceted intervention featured a mandatory provider-facing application, voluntary patient-facing application, structured train-the-trainer sessions, and quarterly performance reviews. Participants in the intervention arm were required to attend monthly clinic visits with at least two blood glucose measurements per visit - a substantial intensification compared to China's Basic Public Health Services program, which mandates only quarterly visits.
After 12 months, the ROADMAP intervention achieved its primary outcome of improved glycemic control rate, with a mean HbA1c difference of -0.30% (95%CI: -0.38 to -0.21) and a relative improvement of 18.6% [relative risk (RR) 1.186, 95%CI: 1.105-1.267] compared to usual care. While modest, these HbA1c reductions represent clinically meaningful improvements at the population level, particularly for underserved communities. In the subgroup analysis, the intervention was slightly more effective for rural (RR 1.275, 95%CI: 1.143-1.410) than urban (RR 1.120, 95%CI: 1.023-1.217) residents, but differences were not statistically significant. For participants with baseline HbA1c above 8% in the subgroup analysis, the intervention showed a significantly stronger effect than for participants who initially had better glycemic control. This stronger effect among patients with suboptimal control targets those at highest risk for diabetes complications, where moderate reductions significantly impact outcomes. China's centralized health system structure facilitated this national implementation. However, the top-down approach resulted in only 4% uptake of the patient-facing digital intervention component, despite investigators expecting uptake among half of the participants in the intervention arm. While these results are encouraging from a clinical effectiveness standpoint, the resource-intensive approach requiring 24 clinic-based glucose tests annually for all patients, regardless of control status, raises questions about long-term sustainability and cost-effectiveness[8]. This intensive monitoring poses particular challenges for rural areas with limited healthcare workforce capacity.
The SMARTDiabetes trial[12], which focused primarily on patient engagement rather than provider-directed interventions, offers a contrasting approach to the ROADMAP study[8]. This cluster-randomized trial, conducted in China's Hebei province, included 2072 participants from 80 communities, evaluating a multifaceted digital health intervention centered on a self-management application with support from lay "family health promoters" (FHPs). The intervention involved not only patients and FHPs but also primary healthcare providers who received clinical decision support and local government officials who facilitated quarterly quality improvement reviews - a key enabling component of the implementation strategy. The intervention improved glycemic control, with a mean HbA1c difference of -0.33% (95%CI: -0.48 to -0.17) contributing to its primary outcome of increasing the proportion of participants achieving multiple cardiometabolic targets (HbA1c, blood pressure, and LDL-cholesterol). Interestingly, the intervention was significantly effective in rural areas (RR 1.38, 95%CI: 1.33 to 1.68) but not in urban settings (RR 0.97, 95%CI: 0.79-1.19). Family health promoter engagement was also notably higher in rural areas (48.6%) than in urban settings (21.5%). The authors attributed this difference to stronger relationships between patients and doctors and greater family member support in rural communities[12]. Considering similar improvements in glycemic control with fewer clinic visits than in the ROADMAP study, findings from the SMARTDiabetes trial suggest that leveraging family support structures may offer a more cost-effective and sustainable approach to improving diabetes management in rural environments.
In India, one of the largest mHealth intervention trials for diabetes in the country's rural area was the mWellcare study by Prabhakaran et al[28]. This cluster randomized controlled trial involving 3698 participants across 40 community health centers evaluated a mHealth system for integrated management of hypertension, diabetes, and associated conditions, with primary outcomes being the between-group differences in mean change in systolic blood pressure and HbA1c from baseline to 1 year. Despite substantial improvements in both arms, the trial found no significant difference between the mHealth intervention and enhanced usual care for glycemic control (adjusted difference in HbA1c: 0.08%, 95%CI: -0.27% to 0.44%) after 12 months. This finding highlights implementation challenges faced by large-scale digital interventions in India's decentralized healthcare context. The mWellcare study's authors noted that enhanced usual care included multiple components that may have improved outcomes in the control group, such as training of healthcare workers and provision of basic diagnostic equipment, potentially masking intervention effects. These results also underscore the complexity of evaluating digital interventions within evolving health systems.
Potential challenges for scaling digital health interventions in India include its decentralized health system. While China's centralized structure facilitates nationwide platforms like ROADMAP[8], India faces barriers, including limited rural infrastructure, a mix of public and private providers, and variable health system capacity at the facility level. These contextual factors could create obstacles to the interoperability and standardization of digital health tools. Smaller clinic-based studies[11,29] demonstrated promising approaches in controlled settings, though their generalizability across India's diverse healthcare landscape requires further research on implementation factors at scale.
To complement these facility-based implementations, evidence from community-level interventions offers additional insights. The peer-support Kerala Diabetes Prevention Program[30] demonstrated meaningful impacts on diabetes prevention in rural India through a cluster-randomized controlled trial involving over 1000 high-risk individuals. The primary outcome was the incidence of diabetes at 24 months diagnosed by annual oral glucose tolerance tests. This community-based approach used trained peer leaders to deliver group sessions focusing on lifestyle modification, significantly improving cardiovascular risk factors. Similarly, the mHealth intervention by Pfammatter et al[10] demonstrated effectiveness in improving diabetes risk behaviors through simple text messaging across 12 Languages to nearly 1000 Indian adults, with intervention participants showing greater improvement in fruit, vegetable, and fat consumption compared to controls. Meanwhile, the Chunampet Rural Diabetes Prevention Project[31] combined telemedicine with personalized care to overcome geographical barriers, screening over 23000 individuals and identifying nearly 5% with diabetes. Using a telemedicine van, the project provided screening and management of complications to remote communities and reported decreases in mean HbA1c from 9.3% to 8.5% within a year. However, this observational finding should be interpreted cautiously, given the non-randomized design. These implementations reflect how interventions adapted to India's specific infrastructure constraints and cultural contexts, whether through peer support, mobile technology, or telemedicine, can effectively reach rural populations despite limited healthcare workforce availability.
Two recent protocols outline promising approaches for improving diabetes care in rural settings. In India, the I-TREC model by Jindal et al[32] uniquely combines CDSS with task-sharing to optimize healthcare delivery across all facility levels. Task sharing is a planned strategy in which a team of healthcare professionals works together to deliver a service, accompanied by training or certification and support for healthcare workers. As noted above, physician density in India is three times lower than in China, which means a proportionally higher workload in India. During meetings and discussions with stakeholders, the I-TREC team has identified that these resource and time constraints create substantial barriers for physicians in implementing new technological solutions. Therefore, the innovation of the I-TREC model was in integrating the CDSS platform with task-sharing, such as initial assessments and lifestyle advice, among physicians and nurses, allowing physicians to focus on clinical decision-making. In rural China, Yin et al's care cascade implementation study[33] is another example of gathering information from stakeholders to co-create the digital intervention. The research employed a three-phase approach: First, identifying determinants through qualitative interviews, then using intervention mapping and co-design to develop contextually relevant strategies, and finally testing effectiveness through a 48-village cluster randomized trial. This study's strength lies in systematically identifying the largest retention gaps in the care cascade and developing practical interventions through participatory methods with practitioners. Both protocols emphasize feasible, low-cost approaches while involving local stakeholders in intervention design to ensure contextual relevance. Ongoing evaluations will provide valuable insights for improving rural diabetes care in resource-constrained settings.
DISCUSSION
Digital health technologies offer promising solutions for addressing the significant challenge of diabetes management in rural areas of China and India, with the potential to improve equity and care for underserved populations. This mini-review provides a comparative analysis of digital health implementations for diabetes care in these two major health systems, examining how their distinct structures shape technology adoption and outcomes. As shown in Table 3 and Figure 2, our analysis identified five key lessons for successful digital health implementation in rural settings: Health system alignment, targeting high-need populations, leveraging existing social structures, supporting task-sharing, and prioritizing co-creation with stakeholders. By analyzing these factors, we generate valuable insights for knowledge exchange between India, China, and other resource-constrained settings globally.
Figure 2 Recommendations based on the evidence from digital health tools in China and India for rural diabetes care.
Table 3 Key lessons from digital health tools applied for diabetes in rural China and India.
Theme
Key lessons
Supporting evidence and examples
Health system context alignment
Digital interventions must align with existing healthcare structures and governance systems
SimCard tailored implementation to different health system contexts in China and India[9]; ROADMAP adapted to China's hierarchical healthcare system[8]
Targeting high-need populations
Digital interventions show greater effectiveness in populations with poorer baseline control and in remote areas
ROADMAP showed stronger effects for patients with baseline HbA1c > 8%[8]; Chunampet project demonstrated substantial HbA1c reduction in remote populations in India[31]; SMARTDiabetes was more effective in rural than urban areas in China[12]
Leveraging existing social structures
Family and community support structures are particularly effective in rural settings
Family Health Promoters assisting patients with self-management and use of the SMARTDiabetes app were associated with improved diabetes control in China[12]; Kerala Diabetes Prevention Program utilized trained peer leaders effectively in India[37]
Support task-sharing
Digital tools enable task-sharing among health professionals to reduce physician’s high workload
Dedicated digital platforms were developed for volunteer community health workers and licensed physicians who shared healthcare tasks in rural India in the SimCard trial[9]; India’s I-TREC model redistributed workflow from physicians to nurses, enabling nurses to conduct initial assessments[32]
Co-creation and stakeholder engagement
Co-creation enhances intervention acceptability, feasibility, and implementation potential
Jindal et al[32] developed I-TREC through multiple stakeholder advisory boards and formal partnerships with government agencies; Yin et al[33] systematically identified implementation barriers through stakeholder interviews before designing interventions in rural China
Health system alignment emerges as a fundamental determinant of the successful implementation of digital health tools. As demonstrated by the contrasting implementations in the ROADMAP and SimCard trials discussed above, the effectiveness of digital interventions critically depends on their compatibility with existing healthcare structures. Interventions must be designed to complement existing healthcare structures rather than creating parallel systems. Maita et al's scoping review[7] emphasizes that effective digital health tools bridge healthcare gaps by adapting to varying definitions of "rural" across different demographic, economic, and cultural contexts. This contextual adaptation is particularly important given the stark differences in rural contexts between China and India (Table 1) and the contrasting governance aspects of hierarchical centrality and decentralized federation, respectively. Digital tools can significantly increase access to healthcare practitioners and enhance disease management through mobile applications. Still, their implementation faces context-specific barriers, including limited internet connectivity, inconsistent electricity supply, and varying levels of digital literacy among providers and patients[14,34].
The contrasting health system structures in China and India fundamentally shape digital health implementation approaches through distinct policy execution capabilities. China's hierarchical centralized system enables standardized nationwide digital platforms with consistent protocols and rapid scaling through administrative mandate, as demonstrated in the ROADMAP study[8]. However, this top-down approach may limit user engagement and grassroots innovation, as evidenced by lower-than-expected uptake of patient-facing components. Conversely, India's decentralized federal system with a significant public-private mix in healthcare delivery offers greater responsiveness to local context through adaptive, tailored solutions that function across diverse provider types. While this adaptability fosters innovation and contextual relevance, it challenges uniform implementation at scale and standardization efforts[14]. These structural governance differences necessitate distinct approaches to technology adoption - strong central coordination in China vs modular solutions with local customization options in India.
Targeting high-need populations represents another critical lesson. Digital interventions consistently show greater effectiveness in populations with poorer baseline control and in remote areas with limited healthcare access. This pattern, common across intervention types, is specifically confirmed for digital diabetes tools in Moschonis et al's meta-analysis[35] of randomized controlled trials, which found that these tools were more effective for participants with higher baseline HbA1c values. Rural populations in China and India may particularly benefit from this aspect of digital health effectiveness, as glycemic control tends to be significantly poorer in remote areas. The Chunampet Rural Diabetes Prevention Project in India vividly illustrates this point[31], where mean HbA1c levels exceeded 9% in remote communities before the telemedicine intervention, highlighting the substantial unmet need and potential for improvement in these settings. The WHO Global Diabetes Compact's target of 80% having HbA1c concentrations below 8%[36] underscores the potential value of targeted digital health technologies in helping achieve global diabetes control targets, especially in underserved rural communities.
Leveraging existing social structures emerges as a particularly effective strategy in rural settings. The SMARTDiabetes trial[12] demonstrated significantly higher effectiveness in rural areas than urban settings, with family health promoter engagement substantially higher in rural communities. This suggests the rural social fabric can be a powerful implementation asset when properly integrated into intervention design. Thankappan et al[37] demonstrated that community-based diabetes interventions in rural India achieved significantly higher participation rates than urban programs, highlighting how traditional social networks enhance intervention efficacy. Similarly, Mohan et al[31] reported that the Chunampet Rural Diabetes Prevention Project successfully leveraged village leadership structures to improve diabetes screening and management in remote communities.
The SimCard trial[9] provides another instructive example of adapting to local social structures and cultural aspects across different contexts. In rural China, the intervention worked through village doctors, while in India, the intervention relied on volunteer community members supported by partnerships with licensed physicians. This adaptation to the severe shortage of certified health professionals in rural India illustrates how digital health implementations can leverage community resources to overcome workforce limitations. Also, in rural China, where the use of traditional medicines is common, special attention was dedicated to alleviating concerns about Western medicines through health educational materials in local languages with culturally specific images[9]. Such cultural tailoring of materials and delivery approaches represents a crucial enabler of trust in digital health technologies, which research shows is fundamental to successful implementation and user acceptance[38].
Supporting task-sharing through digital tools addresses the critical challenge of healthcare workforce shortages in rural areas. As highlighted in the review by Dodd et al[39], evidence supports that redistributing tasks among health professionals is an effective service delivery model across multiple LMIC contexts in the Asia-Pacific region. In India, the I-TREC model[32] shows promise in redistributing workflow from physicians to nurses through integrated CDSS, allowing nurses to conduct initial assessments while physicians focus on clinical decision-making. This approach is particularly relevant given the substantially lower healthcare provider density in rural areas of both countries. The Kerala Diabetes Prevention Program[37] similarly demonstrated how trained peer leaders could effectively deliver lifestyle interventions, further supporting the value of task-sharing models supported by digital tools.
Co-creation with stakeholders significantly enhances intervention acceptability, feasibility, and implementation potential. Beyond the I-TREC model[32] and Yin et al's care cascade implementation study[33], the SMARTDiabetes study[12] exemplifies successful co-creation by involving local government officials in quarterly quality improvement reviews, which proved to be a key enabling component of the implementation strategy. Xiong et al's study[40] on co-designing primary healthcare interventions in China highlighted the importance of empowering patient communities, PHC practitioners, and health administrators, emphasizing the role of cross-sectoral collaborations in successful implementation.
Beyond these specific lessons, our analysis reveals important considerations for digital health implementation in rural settings. When considering digital interventions for diabetes management, stakeholders must look beyond clinical effectiveness to assess reach, uptake, and patient preferences - factors that Moschonis et al[35] identified as essential for implementation success. These considerations emerged as fundamental elements in our analysis of rural implementations, where interventions demonstrating clinical efficacy sometimes faced challenges in sustained uptake and integration into routine care.
Digital health for diabetes care inherently requires an interdisciplinary approach that integrates clinical medicine, public health, social science, health systems management, and information technology. The interventions reviewed demonstrate how successful implementations bridge these disciplines by addressing provider-patient interactions, community engagement approaches, technological adaptations to rural infrastructure constraints, and integration within diverse healthcare delivery systems.
Knowledge exchange between China and India represents a significant opportunity for mutual learning. China's experience with standardized national platforms could inform India's efforts to develop interoperable systems, while India's community-based approaches could provide valuable insights into China's efforts to address regional inequities. Both countries can benefit from sharing implementation strategies for improving digital literacy and technology adoption among rural populations. These collaborative efforts could be enhanced through engagement with international initiatives such as the WHO Global Initiative on Digital Health, which provides a framework for facilitating knowledge exchange while supporting countries' digital health transformation[41,42].
Our comparative analysis of China and India offers notable policy recommendations. Building upon China's 14th Five-Year Plan that prioritized health information platforms on the population level[43], our findings provide timely and relevant suggestions for the 15th Five-Year Plan (2026-2030). Enhancing stakeholder co-creation within the hierarchical healthcare system can address limited patient engagement with digital platforms. For policymakers in India, our analysis reinforces the National Digital Health Mission's federated architecture[20] while highlighting task-sharing as a critical implementation strategy given India's much lower physician density of 7.3 per 10000 population compared to China's 25.2[44]. Digital interventions supporting task-sharing with non-physician providers show particular promise when they leverage existing social structures in rural communities, as demonstrated by the higher effectiveness of family-based approaches in these settings[10]. Both countries would benefit from using digital platforms to identify and prioritize high-need populations where interventions yield the greatest clinical benefit, supporting the global diabetes coverage targets while promoting equitable care. The comparative health systems approach in this mini-review contributes to international knowledge exchange efforts, providing evidence-informed insights for global initiatives aimed at strengthening diabetes care in rural and underserved communities worldwide.
This mini-review has several strengths, including its health systems approach that examines how institutional factors, healthcare delivery models, and rural infrastructure shape digital health implementation. While previous reviews like Maita et al[7] have assessed digital health tools in rural settings in general terms, and meta-analyses such as Moschonis et al[35] have focused on clinical impacts, our comparative health systems analysis uniquely examines how contrasting governance structures shape implementation approaches and outcomes. Unlike single-country studies that dominate the current literature, our cross-national analysis reveals complementary strengths of centralized and decentralized approaches, offering a systems-level perspective that extends beyond clinical effectiveness to implementation science. This comparative lens is particularly valuable as countries worldwide seek transferable lessons for addressing similar challenges in diabetes care delivery. By analyzing implementations across two major health systems with different governance structures, we generate insights applicable to diverse settings globally. The review also benefits from examining interventions at multiple levels, from facility-based implementations to community approaches.
However, limitations should be acknowledged. As a mini-review, our analysis provides valuable comparative insights, but lacks the systematic methodology required for comprehensive evidence synthesis. We utilized a narrative rather than a systematic search strategy, potentially introducing selection bias in study identification. Several methodological limitations should also be acknowledged in the studies we reviewed. The evidence base features heterogeneous interventions, outcome measures, and study designs that challenge direct comparisons. Most trials were relatively short-term (typically 12 months or less), limiting our understanding of long-term effectiveness and sustainability. Cluster randomized trial designs, while practical for community-level interventions, may introduce selection bias through differences between intervention and control clusters. Many studies lack detailed implementation reporting, making it difficult to isolate which specific elements drive observed outcomes in multicomponent digital health implementations. Similarly, reporting of validation methods for digital tools was inconsistent, limiting assessment of their accuracy and appropriateness for rural healthcare contexts.
Future research should address these gaps through standardized reporting of tool validation methods, systematic reviews with meta-analysis to quantify intervention effectiveness, and implementation science approaches that identify which specific components drive outcomes across different health systems. Such methodological advancements would significantly strengthen the evidence base for digital health interventions in rural diabetes care and provide clearer guidance for resource-constrained settings globally. As artificial intelligence rapidly transforms healthcare, future research should examine its potential for rural diabetes care, particularly for enhancing clinical decision-making and supporting medication self-management.
CONCLUSION
This mini-review demonstrates that digital health technologies can significantly improve diabetes management in rural China and India. Our comparative analysis identified five key lessons for the successful implementation of digital health tools: Align with existing health systems, target high-need populations, leverage local social structures, support task-sharing, and prioritize co-creation with people living with diabetes. While these key lessons benefit both countries, co-creation is particularly valuable in China's hierarchical system, and task-sharing is remarkably advantageous in India's context of lower physician density. As both countries face similar challenges, health system leaders are recommended to promote international knowledge exchange and focus on reaching the most underserved populations with evidence-informed tools. Targeting digital health technologies to high-need populations in rural areas, where baseline control is poorest and healthcare access is most limited, strategically advances the Global Diabetes Coverage targets by focusing interventions where they deliver maximum impact. By focusing on equity, sustainability, and contextual adaptation, digital health can transform rural diabetes care, improving outcomes for millions of people in underserved communities worldwide.
ACKNOWLEDGEMENTS
The authors thank their respective institutions for the support provided during the preparation of this manuscript.
Footnotes
Provenance and peer review: Invited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Endocrinology and metabolism
Country of origin: Israel
Peer-review report’s classification
Scientific Quality: Grade A, Grade A, Grade B, Grade B, Grade C
Novelty: Grade A, Grade A, Grade B, Grade B, Grade B
Creativity or Innovation: Grade A, Grade A, Grade A, Grade B, Grade C
Scientific Significance: Grade A, Grade A, Grade B, Grade C, Grade D
P-Reviewer: Li JY; Liu ZY; Wu YL S-Editor: Li L L-Editor: A P-Editor: Yu HG
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