ArticleHeliyon2024
Age-appropriate design of domestic intelligent medical products: An example of smart blood glucose detector for the elderly with AHP-QFD Joint KE.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 20 citations in OpenAlex.
- Personalized Intelligent Chatbot Based on AI-Generated Content Assists Memoir Writing for Older Adults With Cognitive Impairment: Mixed Methods Study.JMIR human factors · 2026Article
- Age-friendly design of health detection integrated machine: A user requirement-driven approach.Scientific reports · 2026Article
- Research on aging-friendly design risk assessment model based on non-parametric estimation.Scientific reports · 2026Article
- Evaluation of smart TV user interfaces for older adults based on the perception assessment method of analytic hierarchy process (AHP)-fuzzy theory.Frontiers in psychology · 2026Article
- Consumer-expressed needs concerning age-friendly products in China: a computational grounded theory analysis of online reviews.Frontiers in public health · 2026Article
- Designing for aging on the move: a user-needs-driven approach to an optimized recreational vehicle (RV) scheme for older Chinese using an integrated Kano-AHP-QFD framework.Frontiers in public health · 2026Article
- Emotional design of new energy vehicle center consoles for elderly users based on entropy weighted TOPSIS and BPNN.Scientific reports · 2025Article
- LCD-AHP-TRIZ methodology enhances low-carbon principles in smart product design.Scientific reports · 2025Article
- Optimizing equipment requirements and configuration rules for elderly home treatment environments: a rough set analysis framework.Frontiers in medicine · 2025Article
- Applied research using the NIOSH lifting equation to optimize the design of shopping trolley for the elderly.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: In response to the rise of intelligent products and the increasing prevalence of urban "empty nesters," we have developed a product specifically tailored for elderly diabetic patients. This product fulfils functional requirements and addresses stylistic preferences, contributing to the age-friendly evolution of home intelligent medical devices, particularly in intelligent blood glucose monitoring. Methods: Our approach commenced with a comprehensive user experience analysis to ascertain the needs of elderly users regarding home blood glucose meters. This involved constructing a hierarchy of user demands, followed by analysing and prioritising these needs. Utilizing Quality Function Deployment (QFD), we aligned user requirements with design specifications, identifying specific product functionalities and service design elements. Further, we employed Kansei Engineering (KE) to select sample designs that resonate with the concept of sensual imagery, leading to the derivation of specific modelling intentions. Combining these design elements, we proposed product design strategies and conducted practical case studies. The effectiveness of these designs was then assessed through fuzzy evaluation methods, allowing for user feedback. Results: Employing the Analytic Hierarchy Process for goal analysis, along with Quality Function Deployment theory and Kansei Engineering, we developed a home intelligent blood glucose meter catered to the elderly. This device not only meets its users' physiological and psychological needs but also provides an operationally convenient, health-conscious, and aesthetically pleasing experience. Conclusions: This methodology enhances the age-appropriate design of home-based smart glucose monitors for the elderly, offering innovative insights and optimization strategies for designing elderly-centric smart medical products.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.