Kong Hyun-Ho, Shin Kwangsoo, Yang Dong-Seok, Kim Aryun, Joo Hyeon-Seong, Oh Min Woo, Lee Jeonghwan
Department of Rehabilitation Medicine, Chungbuk National University Hospital, Cheongju, Republic of Korea.
Department of Rehabilitation Medicine, Chungbuk National University College of Medicine, Cheongju, Republic of Korea.
PLoS One. 2024 Dec 16;19(12):e0315745. doi: 10.1371/journal.pone.0315745. eCollection 2024.
Existing computerized cognitive tests (CCTs) lack speech recognition, which limits their assessment of language function. Therefore, we developed CogMo, a self-administered CCT that uses automatic speech recognition (ASR) to assess multi-domain cognitive functions, including language. This study investigated the validity and reliability of CogMo in discriminating cognitive impairments. CogMo automatically provides CCT results; however, manual scoring using recorded audio was performed to verify its ASR accuracy. The mini-mental state examination (MMSE) was used to assess cognitive functions. Pearson's correlation was used to analyze the relationship between the MMSE and CogMo results, intraclass correlation coefficient (ICC) was used to evaluate the test-retest reliability of CogMo, and receiver operating characteristic (ROC) analysis validated its diagnostic accuracy for cognitive impairments. Data of 100 participants (70 with normal cognition, 30 with cognitive impairment), mean age 74.6±7.4 years, were analyzed. The CogMo scores indicated significant differences in cognitive levels for all test items, including manual and automatic scoring for the speech recognition test, and a very high correlation (r = 0.98) between the manual and automatic CogMo scores. Additionally, the total CogMo and MMSE scores exhibited a strong correlation (r = 0.89). Moreover, CogMo exhibited high test-retest reliability (ICC = 0.94) and ROC analysis yielded an area under the curve of 0.89 (sensitivity = 90.0%, specificity = 82.9%) at a cutoff value of 68.8 points. The CogMo demonstrated adequate validity and reliability for discriminating multi-domain cognitive impairment, including language function, in community-dwelling older adults.
现有的计算机化认知测试（CCT）缺乏语音识别功能，这限制了它们对语言功能的评估。因此，我们开发了CogMo，这是一种自我管理的CCT，它使用自动语音识别（ASR）来评估包括语言在内的多领域认知功能。本研究调查了CogMo在鉴别认知障碍方面的有效性和可靠性。CogMo会自动提供CCT结果；然而，使用录制音频进行人工评分以验证其ASR准确性。简易精神状态检查表（MMSE）用于评估认知功能。使用Pearson相关性分析来分析MMSE与CogMo结果之间的关系，使用组内相关系数（ICC）来评估CogMo的重测可靠性，并且使用受试者工作特征（ROC）分析来验证其对认知障碍的诊断准确性。对100名参与者（70名认知正常者，30名认知障碍者）的数据进行了分析，这些参与者的平均年龄为74.6±7.4岁。CogMo分数表明，所有测试项目在认知水平上存在显著差异，包括语音识别测试的人工评分和自动评分，并且CogMo人工评分与自动评分之间具有非常高的相关性（r = 0.98）。此外，CogMo总分与MMSE总分呈现出很强的相关性（r = 0.89）。此外，CogMo表现出较高的重测可靠性（ICC = 0.94），并且在截断值为68.8分时，ROC分析得出曲线下面积为0.89（敏感性 = 90.0%，特异性 = 82.9%）。CogMo在鉴别社区居住的老年人的包括语言功能在内的多领域认知障碍方面表现出足够的有效性和可靠性。
