A tablet‐ and virtual reality‐based training for preventing and detecting cognitive decline: a usability study
Bibliographic record
Abstract
Abstract Background Virtual Reality (VR) is hailed as a top emerging technology for older adults in healthcare. Despite its potential, limited research exists on VR applications for individuals with cognitive decline, particularly in leisure‐based cognitive training, as opposed to traditional (instrumental) activities of daily living (I)ADL training such as shopping. The SmartAktiv project has developed immersive VR leisure experiences with (I)ADL cognitive training embedded, aiming to enhance cognition in a playful manner and detect early cognitive deficits. We assessed the usability of the SmartAktiv VR‐based intervention combined with a tablet‐based training among participants before initiating a pilot study. Method Healthy older adults (n = 4), people with suspected mild cognitive impairment (MCI) (n = 4), people with suspected dementia (n = 4) (according Montreal Cognitive Assessment, MoCA) and healthcare professionals (n = 4) tested the combined tablet‐ and VR‐based training (hiking tour scenario). After the intervention, qualitative focus groups were held with each group to gather insights into training’s usability from participants' experiences. Furthermore, cybersickness (Simulator Sickness Questionnaire, SSQ) was measured. Result Older adults (MoCA, x̅ 27.00±1.41 points), people with suspected MCI (MoCA, x̅ 23.25±1.26 points) those with suspected dementia (MoCA, x̅ 16.00±0.00 points) and healthcare professionals experienced negligible cybersickness after the training (SSQ, x̅ 6.98±5.26 points). Qualitative findings indicated positive perceptions of the intervention among all participants. Participants appreciated the variety of cognitive exercises (e.g., quiz, puzzle) in the tablet‐training program, but emphasized the need for greater sensitivity of the tablet‐PC and the necessity of a tablet pen. The immersive VR hiking tour was very well‐received, evoking a longing for nature, especially among older individuals (with/without cognitive impairment). They found the interactive cognitive (I)ADL tasks enjoyable/fun, with some even reporting feeling more alert/euphoric afterwards. However, hand tracking issues arose in certain VR tasks (e.g., picking mushrooms), and low contrast made some VR elements difficult to perceive (e.g., payment for train). Conclusion The findings offer insights into the usability of the combined tablet‐ and VR‐based (I)ADL cognitive training, to be considered for a pilot study. This pilot study will test four scenarios (hiking tour, beach vacation, city trip, winter outing) on individuals (n = 30) with and without cognitive decline.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".