Predicting Cognitive Function Impairment through Game-based Intelligence Tests Combined with Heart Rate Variability in Older Adults

Abstract

Objectives: In this study, we explored the feasibility of predicting the risks of older adults′ cognitive function impairment using game-based intelligence tests combined with heart rate variability. Methods: We recruited 110 elderly participants from a dementia center in central Taiwan using cluster sampling. The research tools included the Mini-Mental State Examination (MMSE), the Game-based Intelligence Test (GBIT), and dynamic and static heart rate variability (HRV). Results: Multivariate stepwise regression analysis was performed for the GBIT on MMSE scores (65.1%). The key factors included the correct number of memory recalls and average attention time. Moreover, logistic regression analysis was conducted for GBIT combined with HRV to predict the high-risk group for cognitive function impairment. Key factors included the correct number of reactions, the correct number of memory recalls, and a very low-frequency power peak. The probability of correct classification was 78.18%. Conclusions: GBIT combined with HRV has predictive power on the risk of elderly cognitive function impairment. Hence, this study recommends that GBIT combined with HRV could be used at home or community bases as a feasible tool in predicting older adults′ cognitive function impairment.