Prediction of Vigilant Attention and Cognitive Performance Using Self-Reported Alertness, Circadian Phase, Hours since Awakening, and Accumulated Sleep Loss.
Prediction of Vigilant Attention and Cognitive Performance Using Self-Reported Alertness, Circadian Phase, Hours since Awakening, and Accumulated Sleep Loss.
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DOI：
10.1371/journal.pone.0151770
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Phillips AJ
中科院分区：
文献类型：
--
作者：
Bermudez EB;Klerman EB;Czeisler CA;Cohen DA;Wyatt JK;Phillips AJ
睡眠限制会导致认知能力受损，在许多职业环境中可能导致不良后果。个体可能依赖于自我感知的警觉性来决定他们是否能够充分执行任务。因此，重要的是要确定一个人的自我评估警觉性和他们的客观表现之间的关系，以及这种关系如何取决于昼夜节律阶段，觉醒后的小时数和累积的睡眠时间。健康的年轻成年人（18-34岁）完成了住院时间表，包括强迫睡眠/觉醒和昼夜节律，12个42.85小时的“天”和1：2（n = 8）或1：3.3（n = 9）的睡眠机会：强迫觉醒的比例。我们调查了主观警觉性（视觉模拟量表），昼夜节律（褪黑激素），觉醒后的小时数和累积睡眠损失是否可以预测心理警戒任务（PVT），加法/计算测试（ADD）和数字符号替代测试（DSST）的客观表现。使用赤池信息准则（AIC）评价允许解释变量之间非线性相互作用的数学模型。主观警觉性是PVT、ADD和DSST表现的单一最佳预测因子。然而，主观警觉性本身并不能准确预测PVT表现。当模型中包含所有解释变量时，PVT和DSST的AIC评分最佳。ADD的最佳AIC评分是通过昼夜节律和主观警觉变量实现的。我们的结论是，主观警觉性本身是一个弱预测客观警惕或认知性能。然而，预测可以通过了解个人的昼夜节律阶段，当前唤醒持续时间和累积睡眠损失来改善。
Sleep restriction causes impaired cognitive performance that can result in adverse consequences in many occupational settings. Individuals may rely on self-perceived alertness to decide if they are able to adequately perform a task. It is therefore important to determine the relationship between an individual’s self-assessed alertness and their objective performance, and how this relationship depends on circadian phase, hours since awakening, and cumulative lost hours of sleep. Healthy young adults (aged 18–34) completed an inpatient schedule that included forced desynchrony of sleep/wake and circadian rhythms with twelve 42.85-hour “days” and either a 1:2 (n = 8) or 1:3.3 (n = 9) ratio of sleep-opportunity:enforced-wakefulness. We investigated whether subjective alertness (visual analog scale), circadian phase (melatonin), hours since awakening, and cumulative sleep loss could predict objective performance on the Psychomotor Vigilance Task (PVT), an Addition/Calculation Test (ADD) and the Digit Symbol Substitution Test (DSST). Mathematical models that allowed nonlinear interactions between explanatory variables were evaluated using the Akaike Information Criterion (AIC). Subjective alertness was the single best predictor of PVT, ADD, and DSST performance. Subjective alertness alone, however, was not an accurate predictor of PVT performance. The best AIC scores for PVT and DSST were achieved when all explanatory variables were included in the model. The best AIC score for ADD was achieved with circadian phase and subjective alertness variables. We conclude that subjective alertness alone is a weak predictor of objective vigilant or cognitive performance. Predictions can, however, be improved by knowing an individual’s circadian phase, current wake duration, and cumulative sleep loss.