ArticleJournal of medical Internet research2023
Usability Evaluation of a Knowledge Graph-Based Dementia Care Intelligent Recommender System: Mixed Methods Study.
Article in Journal of medical Internet research, 2023. 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Effects of an eHealth Cardiac Exercise Rehabilitation Platform for Patients After Percutaneous Coronary Intervention Based on the Persuasive Systems Design Model: Randomized Controlled Trial.Journal of medical Internet research · 2026Trial
- Application scope of knowledge graphs in nursing: a scoping review.Frontiers in public health · 2026Article
- Development of an Evaluation Index System for Health Recommender Systems Based on the Health Technology Assessment Framework: Cross-Sectional Delphi Study.JMIR formative research · 2025Article
- Empowerment among primary caregivers of persons with Alzheimer's disease: associations between disease perception and caregiving partnership.BMC nursing · 2025Article
- Usability Evaluation of a Virtual Reality Multisensory Sham-Feeding Device for Patients Undergoing Fasting Periods for Colorectal Cancer Surgery: Mixed Methods Study.JMIR serious games · 2025Article
- Development of a peer support and precision matching mobile platform for health management for people living with HIV.International journal of nursing sciences · 2025Article
- Big Data-Driven Health Portraits for Personalized Management in Noncommunicable Diseases: Scoping Review.Journal of medical Internet research · 2025Article
- Development, Implementation, and Outcomes of Web-Based Interventions for Family Caregivers of Individuals with Dementia: A Scoping Review.Risk management and healthcare policy · 2025Review
- Review
- Health recommender systems to facilitate collaborative decision-making in chronic disease management: A scoping review.Digital healthArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundKnowledge graph-based recommender systems offer the possibility of meeting the personalized needs of people with dementia and their caregivers. However, the usability of such a recommender system remains unknown.
objectiveThis study aimed to evaluate the usability of a knowledge graph-based dementia care intelligent recommender system (DCIRS).
methodsWe used a convergent mixed methods design to conduct the usability evaluation, including the collection of quantitative and qualitative data. Participants were recruited through social media advertisements. After 2 weeks of DCIRS use, feedback was collected with the Computer System Usability Questionnaire and semistructured interviews. Descriptive statistics were used to describe sociodemographic characteristics and questionnaire scores. Qualitative data were analyzed systematically using inductive thematic analysis.
resultsA total of 56 caregivers were recruited. Quantitative data suggested that the DCIRS was easy for caregivers to use, and the mean questionnaire score was 2.14. Qualitative data showed that caregivers generally believed that the content of the DCIRS was professional, easy to understand, and instructive, and could meet users' personalized needs; they were willing to continue to use it. However, the DCIRS also had some shortcomings. Functions that enable interactions between professionals and caregivers and that provide caregiver support and resource recommendations might be added to improve the system's usability.
conclusionsThe recommender system provides a solution to meet the personalized needs of people with dementia and their caregivers and has the potential to substantially improve health outcomes. The next step will be to optimize and update the recommender system based on caregivers' suggestions and evaluate the effect of the application.
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