Aesthetic Experience in the Acceptance of Wearable Technology for People With Dementia: Critical Interpretive Synthesis
Bibliographic record
Abstract
Background: The prevalence of dementia has led to a growing interest in wearable technologies to assist dementia care. Despite their potential, these technologies face low adoption rates, often attributed to poor aesthetic design and insufficient consideration of user experience. Objective: This study aims to (1) explore how the aesthetic design of wearable devices relates to their adoption and user experience in dementia care and (2) critically examine the ways in which aesthetic elements shape people with dementia's perceptions of acceptability and inform future design considerations. Methods: A critical interpretive synthesis with a systematic search was conducted across 2 databases, namely Web of Science and Scopus on August 22, 2024. Studies were included if they reported on the current use of wearable technologies in dementia care or provided value in qualitative studies addressing attitudes from people with dementia and their caregivers toward the wearable product. Two authors independently screened the abstracts and full texts to extract data, and additional studies were included from web searches, owing to their conceptual contributions to offering insights into the emergence of wearable technology, including the factors driving its commercial value and appraisal. Results: A total of 63 studies were included in this study. Findings suggest that aesthetically considered designs are preferred by users when concerning their acceptance toward wearable devices, particularly when devices symbolize empowerment and support personal engagement. The objects that evoke comfort, emotional connection, and personal meaning are more likely to be accepted by people with dementia. Improved aesthetics may also support caregivers through more consistent and effective data collection. Conclusions: This study uncovers a significant gap in the aesthetic design of wearable technologies for dementia care, limiting user acceptance and emotional engagement. By synthesizing key themes focusing on the interaction between user and product, this review proposes a conceptual framework for dementia care, emphasizing the importance of aesthetics in enabling more meaningful, inclusive, and human-centered design.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.091 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.012 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".