User Experience of Interactive Technologies for People With Dementia: Comparative Observational Study
Bibliographic record
Abstract
BACKGROUND: Serious games (SGs) are used as complementary approaches to stimulate patients with dementia. However, many of the SGs use out-of-the-shelf technologies that may not always be suitable for such populations, as they can lead to negative behaviors, such as anxiety, fatigue, and even cybersickness. OBJECTIVE: This study aims to evaluate how patients with dementia interact and accept 5 out-of-the-shelf technologies while completing 10 virtual reality tasks. METHODS: A total of 12 participants diagnosed with dementia (mean age 75.08 [SD 8.07] years, mean Mini-Mental State Examination score 17.33 [SD 5.79], and mean schooling 5.55 [SD 3.30]) at a health care center in Portugal were invited to participate in this study. A within-subject experimental design was used to allow all participants to interact with all technologies, such as HTC VIVE, head-mounted display (HMD), tablet, mouse, augmented reality (AR), leap motion (LM), and a combination of HMD with LM. Participants' performance was quantified through behavioral and verbal responses, which were captured through video recordings and written notes. RESULTS: The findings of this study revealed that the user experience using technology was dependent on the patient profile; the patients had a better user experience when they use technologies with direct interaction configuration as opposed to indirect interaction configuration in terms of assistance required (P=.01) and comprehension (P=.01); the participants did not trigger any emotional responses when using any of the technologies; the participants' performance was task-dependent; the most cost-effective technology was the mouse, whereas the least cost-effective was AR; and all the technologies, except for one (HMD with LM), were not exposed to external hazards. CONCLUSIONS: Most participants were able to perform tasks using out-of-the-shelf technologies. However, there is no perfect technology, as they are not explicitly designed to address the needs and skills of people with dementia. Here, we propose a set of guidelines that aim to help health professionals and engineers maximize user experience when using such technologies for the population with dementia.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".