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Currently submitted to: JMIR Aging

Date Submitted: Nov 1, 2026
Open Peer Review Period: Nov 2, 2026 - Dec 28, 2026
(currently open for review)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Supply-Demand Fit Along the Chain From Monitoring to Follow-Up in Digital Chronic Disease Management for Older Adults in a Chinese County: Grounded Theory Study

  • Hongman Wang; 
  • Jingqi Yang; 
  • Yifan Song

ABSTRACT

Background:

Ensuring that digital-intelligent technologies meet the health demands of older adults with chronic diseases is a key issue in the modernization of county-level health governance. China has made the digital-intelligent transformation of chronic disease care for older patients a priority of its national strategy for actively responding to population aging, and county health systems have rapidly deployed monitoring devices and service platforms. Zhangjiagang, a leading county-level city in Jiangsu Province, is an early adopter of these tools.

Objective:

This study aimed to examine supply-demand fit along the chronic disease management process, from indicator monitoring through data synchronization to follow-up service linkage, and to identify what determines whether deployed tools are used, sustained, and connected to management services.

Methods:

Semi-structured interviews were conducted in Zhangjiagang with 12 service providers from medical and elderly-care institutions and 9 service users (6 older patients with chronic conditions and 3 family caregivers). Transcripts were analyzed with grounded theory, and the main categories were mapped onto the domains of the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework and onto the three management stages. Reporting followed the Consolidated Criteria for Reporting Qualitative Research (COREQ).

Results:

Coding produced 464 reference points, 158 initial concepts, 27 categories, and 11 main categories, integrated around the core category of insufficient supply-demand fit. Age-friendly redesign and function upgrade demands were raised by all 21 participants and accounted for 190 of the 464 reference points (40.9%). Monitoring devices were widely available and valued; the barriers concerned fit rather than availability. Indicator monitoring was shaped mainly by the technology and organization domains (e.g., product quality shortcomings and insufficient system integration), data synchronization by the adopter domain (e.g., capability constraints of older users, intermittent family support and alarm fatigue among staff), and follow-up service linkage by the organization and wider-system domains (e.g., undefined responsibility for interpreting data, divergent views on information security, and unmet demands for standards and funding). These barriers accumulated along the chain, showing that deployment has outpaced management, which is characteristic of the current stage of the transformation rather than evidence that its direction is wrong.

Conclusions:

Zhangjiagang has completed the first step of the transformation, from the absence of tools to their routine availability, under a stable policy framework. Consolidating these gains requires a shift from device deployment to whole-process fit, namely age-friendly and reliable monitoring, sustained-use support embedded in primary care, and clear data-sharing and service connection responsibilities backed by quality regulation. The county experience shows how government-led integration, standard setting and grassroots mobilization can turn digital deployment into continuous chronic disease management, and it offers transferable lessons for aging regions worldwide.


 Citation

Please cite as:

Wang H, Yang J, Song Y

Supply-Demand Fit Along the Chain From Monitoring to Follow-Up in Digital Chronic Disease Management for Older Adults in a Chinese County: Grounded Theory Study

JMIR Preprints. 01/11/2026:115901

DOI: 10.2196/preprints.115901

URL: https://preprints.jmir.org/preprint/115901

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