Early-session facial dynamics predict executive-function response to digital cognitive interventions in older adults: development and external validation

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Background: Cognitive training aims to prevent or slow cognitive decline in older adults, but outcomes vary widely. Engagement, describing how individuals allocate cognitive, affective, and physiological resources, is critical to training benefits, yet behavioral metrics lack real-time modeling of attention and do not reliably predict outcomes. We developed and validated a multimodal, AI-assisted biomarker that quantifies attentional states during computerized cognitive training and predicts cognitive improvements. Methods: We designed the Attentional Index using Digital measures (AID), leveraging a video-based facial expression encoder (pretrained on 38,935 videos), an ECG-based autonomic encoder (pretrained on 123,998 ECG samples), and a temporal fusion module. Using two processing speed/attention studies in older adults (> 65 years) with mild cognitive impairment, AID was trained and evaluated in BREATHE (n=50; ∼300 hours from 368 sessions) and validated in FACE (n=20; ∼150 hours from 219 sessions). Model training targeted session-level change in self-reported fatigue. Clinical validation tested relationships between AID scores and (1) behavioral attention, (2) cognitive outcomes, and (3) neural correlates. Findings: =7.85, p=0.005), whereas reaction time variability did not. Lower AID intercepts (B=-0.07±0.03, p=0.043) and steeper slopes (B=0.31±0.15, p=0.046) were associated with greater improvements. Post hoc analyses identified two engagement profiles linked to better attention: one characterized by low-RMSSD and focused periocular activation, and the other defined by coherent alignment between low-RMSSD and facial expression patterns. Interpretation: AID provides a reliable digital biomarker of effective engagement and predicts cognitive improvement beyond behavioral metrics. By capturing facial-autonomic dynamics of attention, AID offers a foundation for closed-loop cognitive intervention design. Funding: NIH AG081723, NR015452, and AG084471; Stanford HAI seed funding. Research in context: In summary, prior evidence and our findings suggest that multimodal measures integrating facial and autonomic signals may provide a more detailed description of effective engagement during cognitive training by modeling both the attentional availability and allocation. Such measures could eventually help refine non-pharmacological interventions for older adults at risk for cognitive decline and inform future research in personalized, closed-loop cognitive training design. However, although AID shows promise as an objective and generalizable indicator of attentional state, further validation in larger and more diverse samples is required. At this stage, AID should be regarded as a tool that contributes to understanding how attentional dynamics relate to training response.

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