Department of Leisure Services Management, Chaoyang University of Technology, Taichung, Taiwan.
Department of Family Medicine, National Yang Ming Chiao Tung University Hospital, Yilan, Taiwan.
Comput Math Methods Med. 2021 Nov 29;2021:1698406. doi: 10.1155/2021/1698406. eCollection 2021.
This research explores the game-based intelligent test (GBIT), predicts the possibilities of Mini-Mental State Examination (MMSE) scores and the risk of cognitive impairment, and then verifies GBIT as one of the reliable and valid cognitive assessment tools.
This study recruited 117 elderly subjects in Taiwan (average age is 79.92 ± 8.68, average height is 156.91 ± 8.01, average weight is 59.14 ± 9.67, and average MMSE score is 23.33 ± 6.16). A multiple regression model was used to analyze the GBIT parameters of the elderly's reaction, attention, coordination, and memory to predict their MMSE performance. The binary logistic regression was then utilized to predict their risk of cognitive impairment. The statistical significance level was set as = 0.05.
Multiple regression analysis showed that gender, the correct number of reactions, and the correct number of memory have a significantly positive predictive power on MMSE of the elderly ( = 37.60, = 0.69, and < 0.05). Binary logistic regression analysis noted that the correct average number of reactions falls by one question, and the ratio of cognitive dysfunction risk increases 1.09 times ( < 0.05); the correct average number of memory drops by one question, the ratio of cognitive dysfunction risk increases 3.76 times ( < 0.05), and the overall model predictive power is 88.20% (sensitivity: 84.00%; specificity: 92.30%).
This study verifies that GBIT is reliable and can effectively predict the cognitive function and risk of cognitive impairment in the elderly. Therefore, GBIT can be used as one of the feasible tools for evaluating older people's cognitive function.
本研究探讨了基于游戏的智能测试（GBIT），预测了简易精神状态检查（MMSE）评分和认知障碍风险的可能性，然后验证了 GBIT 作为一种可靠和有效的认知评估工具。
本研究在台湾招募了 117 名老年人（平均年龄为 79.92 ± 8.68 岁，平均身高为 156.91 ± 8.01cm，平均体重为 59.14 ± 9.67kg，平均 MMSE 评分为 23.33 ± 6.16）。采用多元回归模型分析老年人反应、注意力、协调和记忆的 GBIT 参数，以预测他们的 MMSE 表现。然后利用二项逻辑回归预测他们认知障碍的风险。统计显著性水平设为 = 0.05。
多元回归分析显示，性别、正确反应数和正确记忆数对老年人的 MMSE 有显著的正向预测能力（ = 37.60， = 0.69， < 0.05）。二项逻辑回归分析指出，正确平均反应数减少一个问题，认知功能障碍风险的比值增加 1.09 倍（ < 0.05）；正确平均记忆数减少一个问题，认知功能障碍风险的比值增加 3.76 倍（ < 0.05），整体模型预测能力为 88.20%（灵敏度：84.00%；特异性：92.30%）。
本研究验证了 GBIT 是可靠的，可以有效地预测老年人的认知功能和认知障碍风险。因此，GBIT 可以作为评估老年人认知功能的可行工具之一。
