Generational differences in loneliness and its psychological and sociodemographic predictors: an exploratory and confirmatory machine learning study.
Generational differences in loneliness and its psychological and sociodemographic predictors: an exploratory and confirmatory machine learning study.
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DOI：
10.1017/s0033291719003933
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发表时间：
2021-04
影响因子：
6.9
通讯作者：
Deary IJ
中科院分区：
文献类型：
--
作者：
Altschul D;Iveson M;Deary IJ
在发达国家，孤独是一个日益严重的公共卫生问题。在老年人中，孤独是一个特别的挑战，因为老年人口正在增长，孤独与许多心理和身体健康问题并存。共病和共同原因因素使得确定孤独的前因变得困难，然而，现代机器学习技术已经定位于解决这个问题。这项研究分析了四组老年人，分成两个年龄段--45-69岁和70-79岁--以考察哪些共同的心理和社会人口与不同年龄段的孤独有关。梯度增强建模，一种机器学习技术，和回归模型被用来识别和复制与孤独感的联系。在所有队列中，较高的情绪稳定性与较低的孤独感相关。在年龄较大的人群中，独居等社交环境也与更高的孤独感有关。在较年轻的人群中，外向与较低的孤独感之间的关系是唯一被证实的关系。不同的个人和社会因素可能会导致不同年龄段的孤独感差异。机器学习方法有可能揭示心理和社会变量之间的新关联，特别是交互作用和心理健康结果。
Loneliness is a growing public health issue in the developed world. Among older adults, loneliness is a particular challenge, as the older segment of the population is growing and loneliness is comorbid with many mental as well as physical health issues. Comorbidity and common cause factors make identifying the antecedents of loneliness difficult, however, contemporary machine learning techniques are positioned to tackle this problem. This study analyzed four cohorts of older individuals, split into two age groups – 45–69 and 70–79 – to examine which common psychological and sociodemographic are associated with loneliness at different ages. Gradient boosted modeling, a machine learning technique, and regression models were used to identify and replicate associations with loneliness. In all cohorts, higher emotional stability was associated with lower loneliness. In the older group, social circumstances such as living alone were also associated with higher loneliness. In the younger group, extraversion's association with lower loneliness was the only other confirmed relationship. Different individual and social factors might underlie loneliness differences in distinct age groups. Machine learning methods have the potential to unveil novel associations between psychological and social variables, particularly interactions, and mental health outcomes.