JMIR Publications

ABSTRACT

Background:

With the rapid development of Internet health care (IH) services, digital health care provides convenient medical services for the elderly, such as remote consultation, online prescription and health monitoring. However, the utilization rate of IH services among the elderly population is still low, and there is a digital divide issue. Understanding the behavioral differences and influencing factors of elderly people using IH services is of great significance for optimizing digital health design and improving the accessibility of medical services.

Objective:

This study aims to analyze the multidimensional influencing factors of Chinese elderly people's use of IH services based on the integrated framework of Technology Acceptance Model (TAM) and Social Ecological Model (SEM), and explore their behavioral patterns and key driving factors.

Methods:

A cross-sectional study design was adopted to conduct a multi-stage stratified cluster random sampling survey in three cities in Shandong Province from May to July 2024, with a total of 1828 elderly people aged 60-75 included. The study uses latent category analysis (LCA) to classify IH service usage behavior, and employs multiple logistic regression, decision tree models, and structural equation modeling (SEM) to analyze influencing factors and mediating pathways.

Results:

Five IH usage categories have been identified: non users (49.8%), fully functional (1.3%), registration oriented (15.6%), low activity (17.5%), and moderate users (15.8%). There is a significant correlation between advanced age, low education level, and low utilization rate; Social support and technological acceptance are the strongest driving factors. The decision tree model (AUC=0.94) shows that the probability of using social support ≥ 2 points, health status>5 points, and technology acceptance ≥ 30 points is 96%, while the probability of using social support<2 points and accompanied by high perceived risk (>13 points) is only 7%. Social support works through direct and mediating effects. The direct effect indicates that for every 1 unit increase in social support, the willingness to use increases by 0.712 (95% CI: 0.552-0.972, p<0.01). In indirect effects, the mediating contribution of technology availability and practicality is the largest, accounting for 33.2% (95% CI: 28.8% -37.6%) of the total effect, followed by technology acceptance (23.1%, 95% CI: 18.8% -27.4%) and social impact (15.0%, 95% CI: 11.2-18.8%).

Conclusions:

The low utilization rate of IH services for the elderly is mainly affected by insufficient social support and risk perception. Optimizing aging friendly design, strengthening social support networks, and improving technological usability are key to increasing the adoption rate of IH services for the elderly. Future policies should develop targeted intervention strategies for different user groups to narrow the digital health divide.