JMIR Publications

ABSTRACT

Background:

Nantong City is experiencing deepening aging. Traditional elderly care models fail to meet the increasingly diverse needs of older adults. Smart elderly care integrating technologies such as the Internet of Things, big data, and artificial intelligence has emerged as an innovative pathway to improve service efficiency and accessibility. However, little empirical research clarifies the true demand for smart elderly care among older adults in aging cities. 

Objective:

This study aimed to systematically analyze the current status and key factors influencing the demand for smart elderly care services among older adults in a deeply aging city in China. Nantong City, Jiangsu Province, was used as a case study.

Methods:

This study was a cross-sectional survey. We administered face-to-face questionnaires to older adults aged 60 and above in Nantong City. This yielded 403 valid responses (effective response rate: 92.6%). The univariate chi-square tests, Mann-Whitney U tests, and binary logistic regression were used to identify the independent influencing factors of the demand for smart elderly care. We also performed stratified analyses by gender and living arrangement. In addition, we conducted an interaction analysis between age and educational level.

Results:

Among surveyed older adults, 74.4% (300/403) reported demand for smart elderly care services. Logistic regression identified age (OR=0.655, 95% CI 0.465-0.921), educational level (OR=1.600, 95% CI 1.092-2.343), self-rated health (OR=3.697, 95% CI 2.579-5.300), and living arrangement (OR=1.638, 95% CI 1.121-2.393) as independent influencing factors (all P<.05). Other important factors included acceptance of smart services (OR=0.618, 95% CI 0.451-0.848), satisfaction with services (OR=0.614, 95% CI 0.473-0.798), awareness of smart elderly care (OR=0.558, 95% CI 0.405-0.768), and willingness to pay (OR=0.575, 95% CI 0.445-0.743). Stratified analyses showed that age significantly inhibited demand only in males (OR=0.62, 95% CI 0.41-0.94). Higher educational level was linked to greater demand among those living alone (OR=2.85, 95% CI 1.34-6.05). The interaction analysis indicated that the positive effect of education level decreased with increasing age (age × education interaction, OR=0.804, 95% CI 0.659-0.981, P=.032).

Conclusions:

Older adults in rapidly aging eastern Chinese cities exhibit strong demand for smart elderly care. However, this is held back by low awareness, limited digital skills, and a low willingness to pay. This presents a structural contradiction of "high demand - low adaptation". Key influencing factors are health status, living arrangement, awareness, and willingness to pay, with notable population differences. To improve supply-demand matching, we recommend digital skill training, age-friendly technology design, tailored services, and community trust-building.