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Mood is a key determinant of cognitive performance in community-dwelling older adults: a cross-sectional analysis

机译:情绪是社区居民老年人认知能力的关键决定因素:横断面分析

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摘要

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 × 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.Electronic supplementary materialThe online version of this article (doi:10.1007/s11357-012-9482-y) contains supplementary material, which is available to authorized users.
机译: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,可能有助于开发提高老年人生活质量的策略。参加者(n = 487,年龄50岁以上)在年龄,性别和学历方面都是葡萄牙人口的代表。使用一系列神经认知和心理测验建立认知和情绪特征。对数据进行主成分分析,以识别认知和情绪的核心维度,包括多个测试变量。维度与年龄以及性别,教育程度和职业状况相关。应用聚类分析来分离认知表现的不同模式和二元逻辑回归模型,以探索衰老,认知,情绪和社会人口统计学特征之间的相互关系。确定了四个主要方面:记忆,执行功能,整体认知状态和情绪。基于这些,强者和弱者的认知能力是可以区分的。聚类分析表明,在这两个主要类别之间的进一步区分是非常好,好,差和非常差的表演者。情绪是促成优秀者与优秀者,贫穷者与非常贫穷者之间分离的主要因素。聚类也受到性别和教育的影响,尽管程度较小;然而,值得注意的是,女性×受教育程度较低,随着年龄的增长,认知能力明显下降。情绪对老年人的认知能力下降有重要影响。性别和教育程度是以后生活中认知能力的早期决定因素。电子补充材料本文的在线版本(doi:10.1007 / s11357-012-9482-y)包含补充材料,授权用户可以使用。

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