ArticleInnovation in aging2025
From fingers to brain: virtual reality-based test capturing fine hand movements predicts cognitive function in older adults.
Article in Innovation in aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Early Detection of Cognitive Impairment Using Time-Frequency Analysis of Fine Motor Accelerometry.Annals of biomedical engineering · 2026Article
- Tactile sensation moderates the association between hand dexterity and higher-level cognition in older adults with and without MCI.Frontiers in aging neuroscience · 2026Article
- Cognitive Status of People with Dementia and its Relation to the Performance in Touch and Tangible Tasks.Current Alzheimer research · 2026Article
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed 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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