From fingers to brain: virtual reality-based test capturing fine hand movements predicts cognitive function in older adults
Bibliographic record
Abstract
Background and Objectives: Early detection of mild cognitive impairment (MCI) is vital for managing cognitive decline in older adults. Hand movements are closely linked to cognitive function, prompting this study to develop a virtual reality (VR)-based wearable system to capture detailed hand movements. The main goal was to assess the system's potential in predicting cognitive health and aiding MCI diagnosis. Research Design and Methods: The study involved 607 participants aged 60-84 (mean age 67.41 ± 4.71 years). Each completed four VR tasks while wearing the system, which recorded fine hand movement data. Cognitive function was assessed using the Beijing version of the Montreal Cognitive Assessment (MoCA-BJ). Statistical analyses were conducted to correlate hand movement metrics with cognitive performance. Results: Participants with cognitive impairments performed worse on VR-based fine motor tasks. Metrics from tests like the Pegboard, Block Placement-Flipping, and Tapping Tests were predictive of cognitive abilities. Indicators related to finer movements and non-dominant (left) hand use showed superior predictive power, achieving an AUC of 0.687 for predicting MCI, comparable to machine learning models such as Random Forest (0.762) and SVM (0.644). Discussion and Implications: Hand movement data can provide valuable insights into cognitive function in older adults, highlighting the importance of fine motor skills in early MCI detection. This VR-based system could serve as a useful clinical tool for assessing cognitive health and supporting MCI diagnosis, enabling timely intervention strategies for 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".