Computerized cognitive training (CCT) is a scalable, well-tolerated intervention that has promise for slowing cognitive decline. Outcomes from CCT are limited by a lack of effective engagement, which is decreased by factors such as mental fatigue, particularly in older adults at risk for dementia. There is a need for scalable, automated measures that can monitor mental fatigue during CCT. Here, we develop and validate a novel Recurrent Video Transformer (RVT) method for monitoring real-time mental fatigue in older adults with mild cognitive impairment from video-recorded facial gestures during CCT. The RVT model achieved the highest balanced accuracy(78%) and precision (0.82) compared to the prior state-of-the-art models for binary and multi-class classification of mental fatigue and was additionally validated via significant association (p=0.023) with CCT reaction time. By leveraging dynamic temporal information, the RVT model demonstrates the potential to accurately measure real-time mental fatigue, laying the foundation for future personalized CCT that increase effective engagement.

翻译：计算机化认知训练（CCT）是一种可扩展、耐受性良好的干预手段，有望延缓认知衰退。然而，CCT的效果受限于有效投入度的不足，而精神疲劳等因素会降低投入度，尤其在有痴呆风险的老年人中更为显著。目前亟需可扩展的自动化测量方法，以在CCT过程中监测精神疲劳。本研究开发并验证了一种新型递归视频转换器（RVT）方法，通过记录轻度认知障碍老年人在CCT期间的视频面部表情，实时监测精神疲劳。在精神疲劳的二分类和多分类任务中，RVT模型的平衡准确率（78%）和精确率（0.82）均优于先前最先进的模型，且其有效性通过与CCT反应时间的显著关联（p=0.023）得到进一步验证。通过利用动态时间信息，RVT模型展示了准确测量实时精神疲劳的潜力，为未来实现个性化CCT以提升有效投入度奠定了基础。