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自下而上的方法极大地提高了从个性特征预测体重的可预测性。

A bottom-up approach dramatically increases the predictability of body mass from personality traits.

机构信息

Institute of Psychology, University of Tartu, Tartu, Estonia.

Institute of Genomics, University of Tartu, Tartu, Estonia.

出版信息

PLoS One. 2024 Jan 10;19(1):e0295326. doi: 10.1371/journal.pone.0295326. eCollection 2024.

Abstract

Personality traits consistently relate to and allow predicting body mass index (BMI), but these associations may not be adequately captured with existing inventories' domains or facets. Here, we aimed to test the limits of how accurately BMI can be predicted from and described with personality traits. We used three large datasets (combined N ≈ 100,000) with nearly 700 personality assessment items to (a) empirically identify clusters of personality traits linked to BMI and (b) identify relatively small sets of items that predict BMI as accurately as possible. Factor analysis revealed 14 trait clusters showing well-established personality trait-BMI associations (disorganization, anger) and lesser-known or novel ones (altruism, obedience). Most of items' predictive accuracy (up to r = .24 here but plausibly much higher) was captured by relatively few items. Brief scales that predict BMI have potential clinical applications-for instance, screening for risk of excessive weight gain or related complications.

摘要

人格特质与身体质量指数(BMI)密切相关,并可据此进行预测,但现有量表的领域或方面可能无法充分捕捉到这些关联。在这里,我们旨在测试从人格特质预测 BMI 的准确性和描述 BMI 的极限。我们使用了三个包含近 700 个人格评估项目的大型数据集(合并 N≈100000),以(a)从经验上确定与 BMI 相关的人格特质聚类,以及(b)确定能够尽可能准确地预测 BMI 的相对较小的项目集。因素分析揭示了 14 个人格特质聚类,这些聚类显示了明确的人格特质与 BMI 的关联(失序、愤怒)和不太知名或新颖的关联(利他主义、服从)。大多数项目的预测准确性(高达 r =.24,但很可能更高)由相对较少的项目来捕捉。预测 BMI 的简短量表具有潜在的临床应用价值,例如,筛查超重风险或相关并发症的风险。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a857/10781087/0a1ab0d29d05/pone.0295326.g001.jpg

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