  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.
