ArticleDigital health
Enhancing elderly care with smart homes: A comparative study of use and payment willingness.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Factors Affecting Universal Access to Smart Home Technologies Among Older Adults Living Alone: A Privacy-Safety Trade-Off Perspective.Healthcare (Basel, Switzerland) · 2026Article
- Adoption and Use of a Smart Home Connected Care System by Older Adults: Mixed Methods Study.JMIR aging · 2026Article
- Public-private partnerships for UAV-assisted emergency medical services in aging communities: an evolutionary game approach.Frontiers in public health · 2026Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Objective: With China's rapid aging and urbanization concentrating older adults in cities, urban elder care has become an urgent challenge. Smart home technologies offer support for aging in place but remain underused. This study examines the factors influencing urban elderly individuals' willingness to use and pay for smart home services in China. It fills a critical research gap in understanding technology adoption for elderly care in the context of large-scale demographic and urban transitions. Methods: Data was collected from 639 elderly individuals across 12 communities in China through questionnaires in 2021. The study distinguishes between willingness to use, measuring technology acceptability, and willingness to pay, reflecting cost-value perceptions. Given the ordinal nature of the five-point Likert scale responses, ordered logistic regression was employed to analyze factors from three dimensions: predisposing factors (age, gender, marital status), enabling factors (income, insurance), and demand factors (life satisfaction, hospitalization history, health conditions), with appropriate controls for sample characteristics. Results: The empirical analysis revealed that factors such as age, life satisfaction, income, and health issues (including multiple chronic conditions such as insomnia, memory decline, and mobility problems) significantly impact older adults' willingness to use smart home services. In this model, age showed a negative effect, with older cohorts being more conservative. In contrast, for willingness to pay, age, hospitalization history, and income were found to be significant factors, with age again associated with increased resistance among the older groups. Conclusion: The study reveals that income capacity, health transitions, and age barriers critically determine smart home adoption among older adults. Policy implications include tailored digital literacy for older cohorts, subsidies to improve affordability, and integrating smart home consultations into hospital discharge planning. These targeted interventions can bridge the adoption gap and enable aging populations to access smart home health monitoring benefits.
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