BACKGROUND Excessive daytime sleepiness plays an important role in the presentation and course of mood disorders. Standard objective measures of daytime sleep propensity are of little to no value in depressive illness. This study examined the psychomotor vigilance task (PVT), an objective measure of neurobehavioral alertness, and its cross-sectional and longitudinal associations with depressive symptomatology in the Wisconsin Sleep Cohort Study. METHODS The sample consisted of 1569 separate 10-min PVT assessments conducted in 942 unique individuals. Cross-sectional and longitudinal conditional logistic regression models were used to estimate associations between the primary outcome of depression symptomatology (adjusted Zung scale≥50) and six separate PVT variables: mean reciprocal reaction time (1/RT); total lapses (RTs≥500 msec; LAPSE); total false responses (FALSE); reciprocal of the mean of the 10% fastest (FAST) and 10% slowest (SLOW) RTs; and slope of the linear regression line for all transformed 1/RTs (SLOPE). RESULTS In fully-adjusted cross-sectional models, 1/RT, LAPSE, FAST, and SLOW were each significantly associated with depression, such that worse neurobehavioral alertness was associated with higher odds of depressive symptomatology. Similar, though attenuated, findings were observed in fully-adjusted conditional longitudinal models that examined within-subject changes in depression status in the subset of participants with repeated PVT assessments. FALSE and SLOPE were not associated with depression in either cross-sectional or conditional longitudinal models. CONCLUSIONS These findings suggest components of the PVT are associated with depressive symptomatology. Further research is indicated to clarify the role of the PVT in the assessment of hypersomnolence in mood disorders.

中文翻译：

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在威斯康星州睡眠队列研究中，通过精神运动警惕任务量化的神经行为警觉性受损与抑郁症相关。

背景技术白天过度嗜睡在情绪障碍的表现和病程中起重要作用。白天睡眠倾向的标准客观指标在抑郁症中几乎没有价值。这项研究在威斯康星州睡眠队列研究中研究了精神运动警惕任务（PVT），一种神经行为警觉性的客观测量方法，以及其横断面和纵向与抑郁症状的关系。方法该样本由对942位独特的个体进行的1569次单独的10分钟PVT评估组成。横断面和纵向条件对数回归模型用于估计抑郁症状的主要结局（调整的Zung规模≥50）与六个独立的PVT变量之间的关联：平均交互反应时间（1 / RT）；总时差（RTs≥500毫秒； LAPSE）；总错误回应（FALSE）；最快的10％（FAST）和最慢的10％（SLOW）RT的平均值的倒数；所有已转换的1 / RT（SLOPE）的线性回归线的斜率和斜率。结果在完全调整的横断面模型中，1 / RT，LAPSE，FAST和SLOW均与抑郁症显着相关，因此，神经行为警觉性较差与抑郁症状的几率相关。相似，尽管减弱了，但在经过完全调整的条件纵向模型中观察到了发现，该模型检查了重复PVT评估的受试者子集中抑郁状态的受试者内部变化。在横截面模型或有条件的纵向模型中，FALSE和SLOPE与抑郁症无关。结论这些发现表明PVT的成分与抑郁症状有关。