Mood is a key determinant of cognitive performance in community-dwelling older adults: a cross-sectional analysis
Mood is a key determinant of cognitive performance in community-dwelling older adults: a cross-sectional analysis
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DOI：
10.1007/s11357-012-9482-y
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发表时间：
2013-10-01
期刊：
影响因子：
--
通讯作者：
Sousa, Nuno
中科院分区：
文献类型：
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作者：
Santos, Nadine Correia;Costa, Patricio Soares;Sousa, Nuno
通过建立认知和情绪的复合或单参数维度类别来识别认知轨迹的预测因素，可能有助于制定提高老年人生活质量的策略。参与者(n=487，年龄50岁以上)在年龄、性别和教育状况方面具有葡萄牙人口的代表性。认知和情绪档案是通过一系列神经认知和心理测试建立起来的。数据经过主成分分析，以确定认知和情绪的核心维度，包括多个测试变量。维度与年龄、性别、教育程度和职业地位相关。应用聚类分析来分离不同的认知表现模式，并用二元Logistic回归模型来探索年龄、认知、情绪和社会人口统计特征之间的相互关系。研究确定了四个主要维度：记忆、执行功能、整体认知状态和情绪。在此基础上，可以区分认知能力强的人和认知能力弱的人。聚类分析显示，在这两个主要类别中，表现非常好、很好、很差和非常差的人进一步区分开来。情绪是造成很好和很好的表演者以及很差和很差表演者之间的差异的主要因素。集群也受到性别和教育的影响，尽管影响程度较小；然而，值得注意的是，女性性别x较低的教育背景预示着随着年龄的增长，认知表现明显较差。情绪对老年人认知功能减退率有显著影响。性别和教育水平是晚年认知表现的早期决定因素。
Identification of predictors of cognitive trajectories through the establishment of composite or single-parameter dimensional categories of cognition and mood may facilitate development of strategies to improve quality of life in the elderly. Participants (n = 487, aged 50+ years) were representative of the Portuguese population in terms of age, gender, and educational status. Cognitive and mood profiles were established using a battery of neurocognitive and psychological tests. Data were subjected to principal component analysis to identify core dimensions of cognition and mood, encompassing multiple test variables. Dimensions were correlated with age and with respect to gender, education, and occupational status. Cluster analysis was applied to isolate distinct patterns of cognitive performance and binary logistic regression models to explore interrelationships between aging, cognition, mood, and socio-demographic characteristics. Four main dimensions were identified: memory, executive function, global cognitive status, and mood. Based on these, strong and weak cognitive performers were distinguishable. Cluster analysis revealed further distinction within these two main categories into very good, good, poor, and very poor performers. Mood was the principal factor contributing to the separation between very good and good, as well as poor and very poor, performers. Clustering was also influenced by gender and education, albeit to a lesser extent; notably, however, female gender x lower educational background predicted significantly poorer cognitive performance with increasing age. Mood has a significant impact on the rate of cognitive decline in the elderly. Gender and educational level are early determinants of cognitive performance in later life.