Acceptance, Usability and Health Applications of Virtual Worlds by Older Adults: A Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Virtual worlds allow users to communicate and interact across various environments, scenarios, and platforms. Virtual worlds present opportunities in health care to reduce the burden of illness and disability by supporting education, rehabilitation, self-management, and social networking. The application of virtual worlds to older adults who bear the burden and cost of health conditions associated with age has not been evaluated. OBJECTIVE: The aim of this study is to explore the usability, ease of use, and enjoyment of a virtual world by older adults, the types of virtual world activities that older adults may engage in, and the perceptions of older adults regarding the application of virtual worlds in health care. METHODS: This quasi-experimental pre-post design research was guided by the Technology Acceptance Model (TAM). Participants were recruited from a Lifelong Learning Institute (LLI) program at Nova Southeastern University. Participants attended four training sessions over a 5-week period in the Second Life (SL) virtual world. Subjects were surveyed before and after the training on perceived ease of use, attitudes towards technology, behavioral intention to use the system, facilitating conditions, effort expectancy, and self-efficacy. RESULTS: Older adults (N=19) completed the informed consent and attended the first training session, and 11 participants (58%, 11/19) completed the full training and the post survey. Completers (82%, 9/11) were more likely than non-completers (37%, 3/8) to consider themselves technologically savvy (P=.048), and to express confidence in being able to use the virtual world (100%, 11/11 vs 37%, 3/8; P=.002). All completers (100%, 11/11) perceived that SL has application in health behaviors and disease and reducing social isolation among people who are homebound. Of the completers, 10 (91%, 10/11) responded that they enjoyed learning how to use SL. Completers suggested that future trainings include more assistants and smaller groups. CONCLUSIONS: This pilot study suggests that virtual worlds can be both a feasible and an applicable method to promote health among some seniors. Future research on virtual worlds with older populations should consider using state-of-the art technology including large monitors, providing a minimum of one trainer for every two to three participants, and distributing a comprehensive training manual at the start of the training to support organization and recall.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".