Moore Philip J, Vera Cruz Germano, Maurice Thomas, Rohrbeck Cynthia A, Khazaal Yasser, Goodman Fallon R
Department of Psychological & Brain Sciences, The George Washington University, Washington DC, USA.
Department of Psychology, UR 7273 CRP-CPO, University of Picardie Jules Verne, Amiens, France.
J Glob Health. 2025 Jun 27;15:04179. doi: 10.7189/jogh.15.04179.
Subjective well-being (SWB) is an important outcome influenced by other aspects of health and personality. However, we know little about the independent effects of multiple health and personality dimensions on SWB in large, representative international samples. Artificial Intelligence (AI) models are particularly well-suited to detect multi-factor patterns in complex topics such as SWB.
This study involved a representative sample of 37 991 older adults from 17 European countries and Israel. Machine-learning algorithms, general additive modelling, low-degree polynomials (i.e. splines), and regression analyses were used to determine the independent effects of the Big 5 personality traits on social, physical and financial health factors, and the impact of all of these on an aggregate measure of SWB.
Loneliness, overall physical health, and making ends meet were the strongest social, physical and financial health predictors of SWB, respectively (absolute value (|t|s) = 29.77-51.53). Neuroticism was a consistent, negative determinant of health (|t|s = 2.82-11.42), but reduced the adverse impact of poor physical health on SWB (|t|s = 4.57-5.98). Extraversion was linked to better social and financial health (|t|s = 2.96-7.74), but also to higher body mass index (Student's t test (t) = 13.52), while openness to experience was positively associated with social and physical health (|t|s = 3.02-7.86), but negatively related to income (t = -19.76).
All adverse health factors and neuroticism were linked to lower SWB, while SWB was positively associated with the other health measures and personality traits. Some traits had unexpected effects on health outcomes, and some had moderating effects on the links between these outcomes and SWB, suggesting that the links between personality, health and SWB depend on the types of health considered. Future multivariate modelling is recommended to clarify the mechanisms for these and other observed relationships.
主观幸福感（SWB）是一个受健康和人格其他方面影响的重要结果。然而，我们对于多个健康和人格维度对大规模、具有代表性的国际样本中主观幸福感的独立影响知之甚少。人工智能（AI）模型特别适合检测诸如主观幸福感等复杂主题中的多因素模式。
本研究涉及来自17个欧洲国家和以色列的37991名老年人的代表性样本。使用机器学习算法、广义相加模型、低阶多项式（即样条）和回归分析来确定大五人格特质对社会、身体和财务健康因素的独立影响，以及所有这些因素对主观幸福感综合指标的影响。
孤独感、总体身体健康状况和收支平衡分别是主观幸福感最强的社会、身体和财务健康预测因素（绝对值（|t|）=29.77 - 51.53）。神经质是健康状况的一个持续的负面决定因素（|t| = 2.82 - 11.42），但它减轻了身体健康不佳对主观幸福感的不利影响（|t| = 4.57 - 5.98）。外向性与更好的社会和财务健康状况相关（|t| = 2.96 - 7.74），但也与更高的体重指数相关（学生t检验（t）= 13.52），而开放性体验与社会和身体健康呈正相关（|t| = 3.02 - 7.86），但与收入呈负相关（t = -19.76）。
所有不良健康因素和神经质都与较低的主观幸福感相关，而主观幸福感与其他健康指标和人格特质呈正相关。一些特质对健康结果有意外影响，一些特质对这些结果与主观幸福感之间的联系有调节作用，这表明人格、健康和主观幸福感之间的联系取决于所考虑的健康类型。建议未来进行多变量建模以阐明这些及其他观察到的关系的机制。
