Beyond Static Screening: Depression Symptom Indicators Using Gaze Behavior and Gameplay Performance

Serious games provide a robust, objective framework for assessing depressive symptom severity by integrating oculomotor patterns with behavioral performance data. By moving beyond traditional, subjective self-report questionnaires, this approach captures dynamic digital biomarkers that reflect cognitive and attentional states. The core thesis posits that depressive symptoms are characterized by constrained visual exploration and reduced attentional flexibility, which can be quantified through specific gaze behaviors and task-based performance metrics.

Research involving 53 non-clinical participants indicates that individuals with higher depressive scores exhibit distinct oculomotor signatures, including longer fixation durations, smaller saccade amplitudes, and a higher fixation-to-saccade ratio. When these eye-tracking metrics are combined with performance data from casual serious games, the resulting model explains up to 81.9% of the variance in symptom severity. While gameplay performance—particularly in tasks requiring spatial working memory and executive control—serves as a useful cognitive probe, eye-tracking data remains the more precise predictor of psychological state.

These findings suggest that interaction-centric gaming environments offer a high degree of ecological validity for mental health monitoring. However, the current scope is limited to a small, non-clinical student population, which introduces potential range restriction bias and leaves variables such as prior gaming experience unaddressed. While the results demonstrate significant promise for the development of digital diagnostic tools, further validation across larger, clinically diverse cohorts is essential to establish the generalizability and long-term clinical utility of these metrics.