Xue Luo, Shuangyan Li, Qianyun Wu, Yan Xu, Ruichen Fang, Yihong Cheng, Bin Zhang
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The indices 'Expected influence' and 'Bridge expected influence' were used as centrality measures in the symptom network. The Least Absolute Shrinkage and Selection Operator (LASSO) technique and the Extended Bayesian Information Criterion (EBIC) were utilized to estimate the network structure of depressive, anxiety, and sleep disturbance symptoms. A Network Comparison Test (NCT) was performed to evaluate the differences between the mild to moderate OSA and severe OSA networks.</p><p><strong>Results: </strong>Network analysis revealed that A6 (\"Getting sudden feelings of panic\") had the highest expected influence value and D6 (\"Feeling being slowed down\") had the highest bridge expected influence values in the networks. The NCT results revealed that the edge weights significantly differed between patients with mild to moderate OSA and those with severe OSA (M = 0.263, p = 0.008). 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引用次数: 0
摘要
背景:阻塞性睡眠呼吸暂停(OSA)患者经常出现睡眠障碍和抑郁、焦虑等心理困扰,可能对其健康状况和功能能力产生负面影响。为了更全面地了解OSA患者的抑郁、焦虑和睡眠障碍的症状,本研究利用网络分析来检查这些症状之间的相互联系。方法:采用医院焦虑抑郁量表(HADS)评估抑郁和焦虑症状,采用匹兹堡睡眠质量指数(PSQI)评估睡眠障碍症状。共有621名OSA患者完成了问卷调查。“预期影响”和“桥式预期影响”指标作为症状网络的中心性度量。使用最小绝对收缩和选择算子(LASSO)技术和扩展贝叶斯信息准则(EBIC)来估计抑郁、焦虑和睡眠障碍症状的网络结构。进行网络比较测试(NCT)来评估轻度至中度OSA和重度OSA网络之间的差异。结果:网络分析显示,A6(“突然感到恐慌”)在网络中具有最高的预期影响值,D6(“感觉被放慢”)在网络中具有最高的桥梁预期影响值。NCT结果显示,轻、中度OSA患者与重度OSA患者的边缘权重差异有统计学意义(M = 0.263, p = 0.008)。两个网络的整体强度变化无显著差异(S = 0.185, p = 0.773)。结论:我们的研究结果表明,最高预期影响值和桥状症状(如A6和D6)可以优先作为OSA患者干预和治疗的潜在目标。
Depressive, anxiety, and sleep disturbance symptoms in patients with obstructive sleep apnea: a network analysis perspective.
Background: Patients with obstructive sleep apnea (OSA) frequently experience sleep disturbance and psychological distress, such as depression and anxiety, which may have a negative impact on their health status and functional abilities. To gain a more comprehensive understanding of the symptoms of depression, anxiety, and sleep disturbance in patients with OSA, the current study utilized network analysis to examine the interconnections among these symptoms.
Methods: Depressive and anxiety symptoms were evaluated using the Hospital Anxiety and Depression Scale (HADS), and sleep disturbance symptoms were evaluated using the Pittsburgh Sleep Quality Index (PSQI). A total of 621 patients with OSA completed the questionnaires. The indices 'Expected influence' and 'Bridge expected influence' were used as centrality measures in the symptom network. The Least Absolute Shrinkage and Selection Operator (LASSO) technique and the Extended Bayesian Information Criterion (EBIC) were utilized to estimate the network structure of depressive, anxiety, and sleep disturbance symptoms. A Network Comparison Test (NCT) was performed to evaluate the differences between the mild to moderate OSA and severe OSA networks.
Results: Network analysis revealed that A6 ("Getting sudden feelings of panic") had the highest expected influence value and D6 ("Feeling being slowed down") had the highest bridge expected influence values in the networks. The NCT results revealed that the edge weights significantly differed between patients with mild to moderate OSA and those with severe OSA (M = 0.263, p = 0.008). There was no significant difference in global strength variation between the two networks (S = 0.185, p = 0.773).
Conclusions: Our results suggest that the highest expected influence value and bridge symptoms (e.g., A6 and D6) can be prioritized as potential targets for intervention and treatment in patients with OSA.
期刊介绍:
BMC Psychiatry is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of psychiatric disorders, as well as related molecular genetics, pathophysiology, and epidemiology.