Development of a new computer simulated environment to screen cognition: assessing the feasibility and acceptability of Leaf Café in younger and older adults
Bibliographic record
Abstract
BACKGROUND: Existing traditional cognitive screening tools for dementia have various limitations, including overreliance on tests assessing verbal memory and, to a lesser extent, on some aspects of executive functioning. Comprehensive neuropsychological assessment is sensitive to impairment but time-intensive and expensive. Virtual reality may provide a dynamic and unique understanding of cognitive performance and increase the ecological validity of cognitive assessment. The use of virtual reality in screening for cognitive function in older persons is promising, but evidence for its use remains sparse. OBJECTIVE: Our primary aim was to examine the feasibility and acceptability of a newly developed, virtual reality assessment module, 'Leaf Café', a computer-based program that assesses cognition in an engaging, efficient, and ecologically relevant way. The secondary aim was to assess the ability of the module to discriminate between performances of younger and older adults. METHODS: A cross-sectional study was carried out in Sydney, Australia, targeting adults aged 18 years and above. Participants completed a traditional cognitive screening tool (Telephone Interview for Cognitive Status-Modified, TICS-M) and Leaf Café, a low-immersive virtual reality module designed to evaluate learning and memory, perceptual-motor function, and executive functioning. The total performance score for each participant, ranging from 0 to 180, was correlated with their cognitive performance assessed by TICS-M, using Pearson's correlation coefficient. Following module completion, participants were presented with an open and closed-question survey to capture their perceptions, attitudes, and feedback on the module, encompassing practicality, acceptability, and enjoyment. Both descriptive and content analyses were employed to interpret the obtained data. RESULTS: A sample of 131 participants (mean age 54.9 years, SD = 20.8, range 20-85) took part. The majority were female (71.8%) and born in an English-speaking country (75.8%). The mean amount of time spent in the module was 32.8 min (SD = 13.3) with a mean module score of 107.6 (SD = 38.7). Most participants completed the highest level (5; 80.5%). There was a significant correlation between Leaf Café total scores with TICS-M cognitive scores overall, and for both younger (aged 18-64 years) and older adult (aged 65 + years) groups. No significant difference was found on performance between age groups on TICS-M performance, however, younger adults had significantly better performance on the Leaf Café module than older adults (M = 124.1 vs 95.9; p < .001). Participants had similar response proportions regarding user experience with most agreeing that the module was easy to use (84%) and to navigate (85%). Compared with younger adults, older adults had lower rates of agreement on the module's design (36.8% vs 64.3%; p = .020) and support experienced (20.5% vs 53.6%; p = .007). Participants highlighted the significance of practicality and the cognitive challenges presented by the module, in terms of memory strain and user interface concerns. Feedback encompassed different opinions on the usefulness of music, with suggestions for improvements centred around clearer instructions, varied game dynamics, and considerations for diverse user needs. CONCLUSIONS: Leaf Café is a feasible and acceptable tool to be used for screening for cognitive impairment in older adults and has real-world assessment value. Further verification on the game's utility in detecting cognitive impairment is required.
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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 | 0.001 | 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".