Are smart older adult care devices “cost-reducing and efficiency-enhancing” or “demand-activating”?—Empirical evidence from Chinese older adult households

Background Against the backdrop of global aging and the growing burden of chronic diseases, Smart Health Devices (SHD) are regarded as important tools for health management. However, there is a theoretical controversy between “technological substitution” and “technological expansion” regarding their economic consequences: Does SHD reduce medical expenditures through prevention, or push up costs by detecting hidden diseases? Currently, there is a lack of systematic empirical evidence based on large-scale microdata. Methods Using data from the 2020 China Longitudinal Aging Social Survey (CLASS) ( N = 7,426), this study constructed dual indicators of household health and care expenditure (HCE) and its share in total consumption. Ordinary Least Squares (OLS), quantile regression, and the Heckman two-step method were employed to examine the robust association between SHD use and medical expenditure, and the Discacciati four-way decomposition method was used to reveal its intrinsic mechanism. Results The study found that the use of SHD was significantly associated with higher absolute household health and care expenditure (by approximately 101%) and a larger share in total consumption (by 1.0 percentage points), supporting the “medical demand activation” hypothesis. Mechanism analysis revealed a unique “dual path”: on the one hand, device use exhibited a positive correlation with total expenditure, likely linked to a higher detection rate of chronic diseases and increased attention to pain (discovery effect); on the other hand, it showed a marginal negative association with cost levels, potentially reflecting improved disease management efficiency (management effect). Heterogeneity analysis showed that “rigid care devices” represented by smart wheelchairs were strongly associated with a heavier dual burden, while “preventive management devices” represented by smart wristbands were significantly correlated with a lower share of medical expenditure. In addition, highly educated and urban groups exhibited stronger expenditure sensitivity, indicating potential digital health inequality. Conclusion At the current stage, smart devices mainly act as a “health radar,” corresponding to the explicit manifestation of potential health risks into medical consumption. Policymakers should implement a classified subsidy strategy: include rigid care devices in long-term care insurance payment to prevent catastrophic expenditure, and promote preventive devices through incentive mechanisms to achieve an economic transformation from “passive medical care” to “active health.”