Mixed Reality Prototype of Multimodal Screening for Early Detection of Cognitive Impairments in Older Adults: Protocol Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: The early diagnosis of cognitive impairments is an important step in the adequate management of dementia. The project "Smart Cognition & Behaviour Screening powered by Augmented Reality" (SCOBES-AR) aims to develop a multimodal screening tool (MST) for the early detection of cognitive impairments using augmented and virtual reality. The first project phase selected validated assessments for combination with the MST and tested it in 300 healthy older adults. OBJECTIVE: This study established a protocol for the implementation and usability of a mixed reality (MR)-enhanced multidisciplinary screening tool for the early detection of cognitive impairments in older adults. The developed MST will be partially enhanced by MR, which is a combination of augmented reality (AR) and virtual reality (VR). This MR-enhanced prototype of the screening tool (MR-MST) will be tested and compared to the previously developed MST. The usability of the prototype will also be examined. METHODS: This single-center observational crossover design study screens 100 healthy participants (aged 60-75 years) for cognitive decline using a specially developed MST (assessment of cognitive functions, olfactory sensitivity, nutritional preferences, gait parameters, reaction times, and activities of daily living) and an MR-enhanced MST in which the assessments of cognitive functions, reaction time, activities of daily living, and gait will be performed using tailor-made software and AR and VR hardware. The results of the MR-enhanced MST will be compared to those without MR. The usability of the developed MR-enhanced MST will be tested on 10 investigators and 10 test participants using observed summative evaluation and the codiscovery method, and on 2 usability experts using the codiscovery and cognitive walkthrough methods. RESULTS: This study was funded by the Austrian Research Promotion Agency (grant 866873) and received approval from the ethics committee of the Medical University of Graz. The MR-MST and the experimental protocol for this study were developed. All participants gave written informed consent. As of July 15, 2022, a total of 70 participants have been screened. Data analysis and dissemination are scheduled for completion by September 2023. CONCLUSIONS: The development and testing of the MR-MST is an important step toward the establishment of the best practice procedure for the implementation of AR and VR in the screening of cognitive declines in older adults. It will help improve our knowledge of the usability and applicability of the developed prototype and promote further advancement in AR and VR technologies to be used in therapeutic settings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/39513.
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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 | 0.029 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".