How Sleep Restriction Affects Cognitive Performance: A Multivariate Analysis of Longitudinal Data

Overview

Sleep deprivation is a widespread public health issue that is known to impair many areas of cognitive function mainly due to its normalization in today's modern society. Yet, its effects under various conditions remain insufficiently studied. This study investigates how both acute and chronic sleep restriction impact neural processing efficiency and cognitive performance across multiple domains, including reaction time, executive function, and accuracy. To address limitations in prior research, a multivariate computational engine capable of generating predictive regression models, NeuroCC, was developed to analyze large-scale datasets derived from established neurophysiological measurements such as ECG and validated cognitive performance tasks such as the P300 Latency and N-Back Accuracy. The model integrates variables including sleep duration, sleep quality, age, gender, caffeine intake and other behavioral outputs to generate predictive relationships between neural processing speed and cognitive performance. Results demonstrate that sleep deprivation imposes a significant cognitive performance penalty, with neural processing speed serving as a strong independent predictor of behavioral efficiency. Reaction time exhibited the highest sensitivity to reductions in neural processing efficiency, while working memory accuracy showed greater variability and resilience. Additionally, age-based differences were identified: young adults (18–22) were more sensitive to sleep quality, whereas adults (23–45) were more affected by sleep duration. These findings suggest that sleep deprivation disrupts neurocognitive function through measurable reductions in neural processing efficiency, with differential effects across cognitive domains and populations. This study provides a credible, data-driven framework for understanding real-world sleep-related cognitive impairment and has potential applications in safety-critical performance monitoring and personalized health optimization.

Competition history

• CSEF 2026 Medicine & Physiology (Senior Division) · Entry S-15-20

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