Work

Development of the Windmill Model for Mapping Older Adults’ Intrinsic Capacity Using Digital Twin Technology: Descriptive Qualitative Study

Year: 2026
Type: article
Abstract: Background: Intrinsic capacity (IC) refers to the sum of the physical and mental capacities of an individual. Conventional IC assessment requires substantial temporal and human resources. Digital twin (DT) technology emerges as a promising solution for efficiently mapping ICs. Objective: This study aims to explore older adults' perspectives on the DT technology and their perceptions of how it could effectively represent their ICs. Methods: A qualitative study was used. Face-to-face semistructured interviews with 23 older adults were conducted. The interviews were transcribed verbatim and analyzed via content analysis approach. Results: The analysis identified five themes and 16 subthemes: (1) "opt for or not my digital twin," revealing the older adults' decisions regarding whether to use DT technology for mapping ICs; (2) "my ideal digital avatar," describing the older adults' preferences for personalized digital avatar appearances; (3) "my digital twin maps my intrinsic capacity," highlighting how multimodal reminders and synchronized avatar changes enhanced their comprehension of ICs; (4) "the benefits my digital twin can deliver," emphasizing the potential of the DT system to provide feedback services to older adults; (5) "some expectations for my digital twin," outlining their expectations for DT technology. Based on the above insights, a conceptual model, "windmill" model, was further developed to better understand how to build DTs of older adults and map their ICs. Conclusions: DT technology was a promising tool for mapping ICs of older adults. Furthermore, the "windmill" model provided a framework to build tailored DTs. The findings of this study could provide references to develop DT model to support IC management.
Source: JMIR Aging
Language: English
Subfield: Demography
Open Access status: gold
Estimated APC paid: $1,605
OpenAlex ID: W7154093739
FWCI: 15.08
Cites: 52
Cited by: 1
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