A narrative review of health inequalities in dementia care in China: exploring mHealth’s potential

Introduction

With a rapidly aging population, China is confronting an epidemic of dementia on an unprecedented scale (1,2). Current estimates suggest that between 14.49 million and 19.67 million individuals in China are living with dementia, a figure that represents nearly 30% of the total number of cases worldwide (3). This already immense burden is projected to escalate dramatically, as the proportion of its population aged 65 years and over is expected to surge from 8.9% in 2010 to 30.8% by 2050 (4,5). The socioeconomic ramifications are staggering, with total annual costs projected to reach $1.233 trillion by 2050 (6,7).

This strain is borne not only by the healthcare system but also acutely by individual families. The traditional, family-centric model of eldercare is buckling under the pressure of demographic shifts, including decades of the one-child policy, creating an immense physical, emotional, and psychological toll on informal caregivers who often lack formal support or training (8-14). This burden is compounded by a vast “diagnostic gap”, with an estimated 71.4% to 93.1% of individuals with dementia in China remaining undiagnosed, preventing them from accessing any form of medical service or support (15-18).

Recognizing the urgency of this public health crisis, the Chinese government has elevated dementia to a national health priority. The landmark National Action Plan for Addressing Dementia in the Elderly [2024–2030] establishes a robust framework that emphasizes a “whole-of-society” approach and explicitly calls for strengthening scientific and technological support capacity (19,20). This national mandate provides the primary rationale for this review. The government has officially sanctioned technology as a core component of its dementia strategy, creating a critical policy window—an opportunity to guide implementation but also a risk of failure if not managed carefully. Without clear guidance on equitable deployment, the default will likely be a market-driven approach that caters to privileged consumers, thereby exacerbating inequality. This paper is therefore positioned as a timely policy brief designed to fill the gap between high-level policy ambition and on-the-ground equitable practice.

This review introduces mobile health (mHealth) as a powerful potential tool in this context. It posits a central thesis: while mHealth possesses the theoretical capacity to overcome long-standing physical and economic barriers to care, its deployment is a complex socio-technical challenge. The ultimate impact of mHealth is contingent on implementation strategy; it can serve as a tool for either convergence, narrowing care gaps, or divergence, widening them. Without a deliberate, equity-focused approach, mHealth interventions risk reinforcing the very health disparities they are intended to resolve. This paper will conclude by proposing a novel framework to guide such an implementation, moving beyond problem identification to offer a structured, actionable solution. We present this article in accordance with the Narrative Review reporting checklist (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-64/rc).

Methods

A literature search was conducted in two major databases: PubMed (primarily English-language) and the China National Knowledge Infrastructure (CNKI; Chinese-language). The search included publications from the inception of each database to September 20, 2025. This review considered quantitative studies, qualitative studies, and systematic reviews published in either English or Chinese that focused on the intersection of dementia, health inequalities, and mHealth in China. The search strategy is summarized in Table 1.

Multidimensional inequalities in dementia care services in China

The dementia crisis in China is not uniformly distributed; it is a story of deep and pervasive inequality. The burden of disease and the availability of care are starkly stratified across geographic, socioeconomic, and systemic lines. These factors do not operate in isolation but rather create a self-reinforcing cycle of disadvantage that systematically fails the nation’s most vulnerable populations.

Geographic disparities and the urban-rural divide

Geographic location is one of the most powerful determinants of dementia-related health outcomes in China. A stark regional imbalance is evident, with a high-prevalence cluster of dementia in the northern and western provinces, which correlates strongly with the distribution of key modifiable risk factors like hypertension (21-25). Even more pronounced is the chasm between urban and rural areas (26). Prevalence rates for both dementia and its precursor, mild cognitive impairment (MCI), are significantly higher in rural communities (16,27). One large-scale nationwide survey found the prevalence of dementia to be 6.7% in rural areas compared to 5.4% in urban settings (16). This disparity is even greater for MCI, with rural prevalence at 18.7% vs. 12.4% in urban areas, indicating that the pipeline of future dementia cases is disproportionately concentrated in the regions least equipped to manage them (16,28).

This higher disease burden in rural areas is met with a catastrophic gap in care. An astonishing 93.5% of dementia cases in rural China are estimated to go undiagnosed, compared to 77.5% in urban areas, rendering the vast majority of affected rural individuals invisible to the healthcare system (18). Consequently, treatment adherence is abysmal; only 30.0% of rural people with Alzheimer’s disease (AD) adhere to their drug regimens, compared to 73.2% in urban areas, with high costs and inconvenience being primary barriers (29). Access to formal long-term care (LTC) is also a rarity, with only 3.2% of rural individuals with AD residing in professional care institutions, a rate nearly three times lower than their urban counterparts (8.8%) (29).

This urban-rural divide is driven by a confluence of mutually reinforcing factors that create a vicious cycle:

Socioeconomic factors: rurality often serves as a proxy for lower socioeconomic status (SES). Individuals in rural areas typically have lower incomes and levels of education, which are themselves independent and powerful risk factors for developing cognitive impairment and dementia (30-33). This higher-risk population then faces the greatest barriers to diagnosis and treatment precisely because of these same socioeconomic disadvantages (34).

Systemic failures: the underlying cause of care gaps is a systemic failure in resource allocation. Rural medical institutions are critically lacking in trained staff, specialized services, and standardized practices for dementia management (11). The system is failing the most vulnerable at every stage—from primary prevention (higher risk) to secondary prevention (lower diagnosis) and tertiary care (lower treatment).

Demographic shifts and the “caregiver divide”: while the traditional family care model is eroding nationwide, this erosion is most acute in rural regions. The mass migration of over 230 million young, working-age adults from rural areas to cities has created a phenomenon of “left-behind elderly adults” (2,35). This dynamic creates a profound “caregiver divide”, where the most isolated older adults lack the tech-savvy family support that is often essential for mediating access to both traditional and digital health services. This presents a cruel paradox: the solutions designed to overcome geographic isolation, such as mHealth, may be inaccessible due to the social isolation that often accompanies it. Many digital interventions implicitly assume a digitally literate family member can act as a “digital navigator”, a resource unavailable to the very population most in need.

Overarching systemic and structural barriers

Compounding these disparities are systemic barriers that affect the entire nation but disproportionately harm those with fewer resources. China’s healthcare system is characterized by fragmentation and a severe shortage of a trained dementia workforce (12,16). The development of a formal LTC system remains in its nascent stages, hampered by a lack of specificity and continuity in policy (36,37). Finally, deeply ingrained cultural factors present a formidable barrier. Public awareness remains low, with persistent stigma often preventing families from seeking a formal diagnosis (15). The common Chinese term for dementia, laonian chidai, which translates to “stupid, demented elderly”, reinforces discrimination and shame, further isolating individuals and families (38,39).

The role of mHealth in bridging dementia care gaps

In the face of these profound inequalities, mHealth emerges as a potentially transformative paradigm. By leveraging the ubiquity of mobile devices, mHealth interventions offer a theoretical means to transcend the geographical and economic barriers that define the current landscape of dementia care (40). The core promise of mHealth lies in its ability to decouple healthcare delivery from physical location, offering low-cost, scalable solutions for cognitive screening, remote monitoring, and caregiver education (40,41). However, it is crucial to distinguish between the theoretical promise of these technologies and the established evidence of their efficacy. This distinction reveals a significant “evidence-impact gap”: the strongest evidence for mHealth efficacy exists at the micro-level of individual clinical outcomes, whereas its greatest promise lies at the macro-level of achieving systemic health equity.

Table 2 provides a systematic taxonomy of mHealth solutions, explicitly separating their proven effects from their potential benefits to clarify the current state of the field and highlight this gap.

The evidence for mHealth in geriatric and dementia care is growing. A particularly relevant example is the Prevention of Dementia using Mobile Phone Applications (PRODEMOS) project, a large-scale, coach-supported mHealth intervention trialed in both the United Kingdom and China (48). The Chinese arm of the trial demonstrated that such an intervention is feasible and acceptable to older Chinese adults at risk of dementia (49). However, the study also highlighted challenges in maintaining sustained engagement, suggesting that a hybrid model including a “human-in-the-loop” health coach is crucial for efficacy (49). This finding presents a fundamental tension for policymakers: the very human support that appears necessary for efficacy directly challenges the core value proposition of mHealth—low-cost scalability. Human coaches are not infinitely scalable and reintroduce the workforce limitations mHealth was meant to overcome, suggesting that the most effective models may have significant cost and implementation implications.

A critical analysis of the evidence reveals the “evidence-impact gap”. The tools have shown effectiveness in controlled settings at improving individual outcomes, such as a cognitive score or a caregiver’s mood. Yet, their ability to drive systemic change in a complex, inequitable health system—such as closing the urban-rural care divide—remains a hypothesis. There is little to no empirical evidence demonstrating that micro-level clinical efficacy can translate into macro-level equity impact without a deliberate strategy. Acknowledging this gap is essential for a realistic assessment of the challenges that lie ahead.

A multi-level framework of challenges to equitable mHealth implementation

The successful deployment of mHealth is not merely a technical problem but a complex socio-technical one. The barriers to equitable implementation are interconnected and exist at multiple levels of society. They can form a “cascade of exclusion”, where a macro-level policy failure creates meso-level market distortions that result in micro-level inaccessibility for the most vulnerable users.

Micro-level barriers: the user ecosystem

These barriers relate directly to the individual user and their immediate environment, representing the final hurdle to adoption and sustained use.

• The digital divide and literacy: while China’s national internet penetration is high, rural penetration lags significantly, and older adults exhibit lower rates of adoption and proficiency (50). This creates a “triple jeopardy” for the rural elderly, who face lower device ownership, lower digital literacy, and a higher prevalence of age-related sensory and cognitive decline, all of which make learning new technologies more challenging.
• User-centered design deficits: many mHealth apps are not designed with the specific needs of older adults with cognitive impairment in mind (50,51). This user group requires designs that address the threefold challenge of aging (e.g., declining vision), cognitive barriers (e.g., memory deficits), and sociocultural barriers (e.g., stigma) (50-53).
• Socio-cultural factors: technology adoption is deeply influenced by cultural context. The pervasive stigma associated with dementia means that apps explicitly branded for the condition may face resistance (54). Furthermore, technology must be designed to support, rather than supplant, the central role of the family in eldercare, often engaging a triad of users: the person with dementia, their primary family caregiver, and their healthcare provider (55-57).

Meso-level barriers: the healthcare system and community

These barriers concern the integration of mHealth into existing health and social systems, which act as crucial intermediaries between national policy and individual users.

• Clinical workflow integration: a major barrier is the failure of mHealth apps to integrate into clinical practice. Data generated by these apps often remains siloed on a patient’s device, is not incorporated into electronic health records, and is not standardized, which prevents clinicians from using it to inform care decisions (58).
• Lack of professional training and endorsement: healthcare providers in China, particularly in rural primary care settings, often lack training in both dementia care and digital health (59). Without their endorsement and ability to “prescribe” or recommend validated apps, patient trust and uptake will remain low.
• The “caregiver divide”: as previously discussed, the erosion of the family care model in rural China creates a critical implementation barrier. Many mHealth interventions implicitly rely on a tech-savvy family member to facilitate setup and ongoing use (60). For the most isolated “left-behind elderly adults”, this crucial support system is absent, rendering many mHealth solutions inaccessible.

Macro-level barriers: the policy and infrastructure environment

These large-scale, structural barriers shape the entire landscape in which mHealth innovation and implementation occur.

• Regulatory and data privacy hurdles: China has a stringent legal framework for data privacy, including the Personal Information Protection Law (PIPL), under which medical data is classified as “sensitive personal information” (61,62). This triggers a higher standard of protection that, while designed to protect citizens, creates a significant compliance burden that can stifle innovation from smaller, non-profit, or academic developers who may be best positioned to serve marginalized communities (63,64).
• Infrastructure gaps: while China boasts world-class 5G infrastructure in its major cities, internet connectivity can be less reliable and affordable in the remote rural and western regions where dementia prevalence is often highest (65). This digital infrastructure gap limits the feasibility of data-intensive mHealth applications for the populations that need them most.
• Absence of a national validation framework: there is no clear, government-endorsed pathway for the clinical validation and approval of mHealth apps for dementia. This creates a “wild west” market, making it difficult for clinicians and patients to distinguish safe, effective, and evidence-based tools from unvalidated apps (58). This policy vacuum poses a direct threat to patient safety. A vulnerable individual might use an unvalidated “cognitive training” app, gain a false sense of security, and consequently delay seeking a formal diagnosis. Such a delay can lead to worse clinical outcomes and prevent timely access to the few treatments and support systems that exist, making the establishment of a validation framework an urgent ethical necessity.

Discussion: an ‘Inclusive by Design’ framework for policy and practice

The analysis reveals a central tension: mHealth offers immense potential to democratize access to care, but this is challenged by entrenched digital, socioeconomic, and regulatory barriers. Technology is not inherently equitable. An “equity-blind” rollout could worsen health disparities, creating a scenario where digital innovation benefits the privileged while leaving the most vulnerable further behind. Conversely, a strategically designed mHealth ecosystem could become a powerful engine for health equity. To navigate this tension and harness mHealth’s potential, a proactive, equity-focused approach is required.

This paper proposes the ‘Inclusive by Design’ framework, which synthesizes principles from established digital health equity models and adapts them to the specific challenges identified in this review (66). It is built on four interconnected pillars designed to guide policymakers, developers, and healthcare providers in creating a digital health ecosystem that is inclusive from its inception, not as an afterthought.

Pillar 1: participatory co-creation for the care triad

To overcome the micro-level barriers of poor usability and cultural inappropriateness, mHealth design must move beyond generic “human-centered” principles to embrace participatory co-creation with the “care triad”: the person with dementia, their primary family caregiver, and their healthcare provider (56,57). A key strategy is to actively engage the tech-savvy “sandwich generation”—adults caring for both their parents and their children—as crucial co-designers and facilitators (67). Their involvement ensures that solutions are not only usable for the older adult but also integrate seamlessly into the family caregiving dynamic, addressing the needs of all stakeholders simultaneously.

Pillar 2: targeted digital and health literacy initiatives

Bridging the digital divide requires a two-pronged literacy strategy that addresses both micro-level user skills and the meso-level “caregiver divide”. First, initiatives for older adults must incorporate andragogical learning theory—which focuses on how adults learn through experience and problem-solving—to build confidence and demonstrate the personal relevance of the technology (66). Second, programs must be developed to equip family caregivers with the skills to act as “digital health navigators” for their elderly relatives. This empowers them to assist with setup, provide ongoing support, and help interpret the information provided by mHealth tools, thereby mitigating the access barrier for the least digitally literate users.

Pillar 3: enabling policy and regulatory alignment

Macro-level policies must be designed to actively promote equity. This requires specific, actionable reforms that directly address the structural barriers identified earlier:

• Subsidies and accessibility programs: the government should implement targeted subsidies for smartphones and data plans for low-income seniors, particularly in high-prevalence rural and western regions, to address the foundational barrier of access.
• Streamlined validation pathways: a national certification or “kitemark” system for clinically validated dementia apps should be established. This would provide a clear signal of quality and safety for consumers and providers, fostering trust, mitigating patient safety risks, and simplifying technology selection.
• Support for grassroots innovation: to foster a more diverse and equitable innovation ecosystem, the government should establish “regulatory sandboxes” or dedicated support programs. These initiatives would help smaller non-profit organizations and academic institutions navigate the complexities of PIPL compliance, enabling the development of culturally and linguistically specific solutions for underserved communities.

Pillar 4: a mission-oriented research agenda

The evidence base for mHealth in dementia care within China remains limited. A targeted, mission-oriented research agenda is imperative to close the “evidence-impact gap” by shifting focus from efficacy to implementation. This entails prioritizing implementation science methodologies—such as those focused on adoption and scalability—conducting comprehensive health equity impact assessments for emerging technologies, and securing funding for longitudinal studies that assess the long-term equity implications of mHealth solutions within China’s fragmented healthcare landscape (49).

Conclusions

China stands at a critical juncture. The escalating dementia crisis is a story of deep inequality, where the burden falls most heavily on those who are oldest, poorest, and most geographically isolated. mHealth technology has emerged as a beacon of promise, offering the potential to dismantle long-standing barriers to care. Yet, this review has demonstrated that mHealth is not a panacea. Its potential is matched by the perils of the digital divide, the unique needs of users with cognitive impairment, and a complex regulatory environment that can inadvertently stifle equitable innovation.

The path forward is not one of blind technological optimism but of strategic, human-centered, and equity-focused action. An “equity-blind” rollout will inevitably create a two-tiered system of digital care, benefiting the urban, educated, and well-supported while leaving the most vulnerable further behind. The challenge for China is to build a digital health ecosystem that is inclusive by design, not by chance. By adopting a proactive strategy, such as the ‘Inclusive by Design’ framework proposed here, policymakers, innovators, and clinicians can work together to ensure that technology serves as a tool for convergence, not divergence. It is only through a steadfast commitment to equity that the power of mHealth can be harnessed to bridge, rather than widen, the gaps in care for the millions of people living with dementia and their families across the nation.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-64/rc

Peer Review File: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-64/prf

Funding: This study was supported by the Wuhan Municipal Health Commission and Bureau of Science and Technology Innovation of Wuhan Municipality (No. WX23A99), the National Natural Science Foundation of China (No. 71774060), and the Young Top Talent Program in Public Health from Health Commission of Hubei Province (No. EWEITONG[2021]74; principal investigator: B.L.Z.).

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-64/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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