Factors Influencing the Adoption of Smart Health Technologies for People With Dementia and Their Informal Caregivers: Scoping Review and Design Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Smart Health technologies (s-Health technologies) are being developed to support people with dementia (PwD) and their informal caregivers at home, to improve care and reduce the levels of burden and stress they experience. However, although s-Health technologies have the potential to facilitate this, the factors influencing a successful implementation in this population are still unknown. OBJECTIVE: The aim of this study was to review existing literature to explore the factors influencing PwD and their informal caregivers' adoption of s-Health technologies for home care. METHODS: Following the Arksey and O'Malley methodology, this study is a scoping review providing a narrative description of the scientific literature on factors influencing s-Health technology adoption for PwD and their informal caregivers. A search was conducted using PubMed, the Cochrane library, the IEEE library, and Scopus. Publications screening was conducted by 2 researchers based on inclusion criteria, and full-text analysis was then conducted by 1 researcher. The included articles were thematically analyzed by 2 researchers to gain an insight into factors influencing adoption that PwD and their informal caregivers have to encounter when using s-Health technologies. Relevant information was identified and coded. Codes were later discussed between the researchers for developing and modifying them and for achieving a consensus, and the researchers organized the codes into broader themes. RESULTS: Emerging themes were built in a way that said something specific and meaningful about the research question, creating a list of factors influencing the adoption of s-Health technologies for PwD and their informal caregivers, including attitudinal aspects, ethical issues, technology-related challenges, condition-related challenges, and identified gaps. A design framework was created as a guide for future research and innovation in the area of s-Health technologies for PwD and their informal caregivers: DemDesCon for s-Health Technologies. DemDesCon for s-Health Technologies addresses 4 domains to consider for the design and development of s-Health technologies for this population: cognitive decline domain, physical decline domain, social domain, and development domain. CONCLUSIONS: Although s-Health technologies have been used in health care scenarios, more work is needed for them to fully achieve their potential for use in dementia care. Researchers, businesses, and public governments need to collaborate to design and implement effective technology solutions for PwD and their informal caregivers, but the lack of clear design guidelines seems to be slowing the process. We believe that the DemDesCon framework will provide them with the guidance and assistance needed for creating meaningful devices for PwD home care and informal caregivers, filling a much-needed space in the present knowledge gap.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it