A Latent Variable Approach to Affect Variability in Daily Life Accurately Predicts Psychopathology, Especially Depression Symptoms in a Non-Clinical Sample.

Journal of emotion and psychopathology Pub Date : 2025-04-24 Epub Date: 2025-05-03 DOI:10.55913/joep.v1i2.82
Lucas J Hamilton, Prabhvir Lakhan, Lauren A Rutter
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Abstract

Background: Ecological momentary assessments (EMA) have contributed to an increase in research correlating affect dynamics to mental health and wellbeing. While many metrics can be calculated to characterize affect dynamics from EMA data, researchers often opt for a 'battle royale' approach whereby only the best individual predictor is kept. The present work addresses the possibility that shared variance across indicators, namely for affect variability, may be better captured using latent models that also could better predict psychopathology.

Methods: A 14-day EMA protocol was used to examine affect dynamics in 109 college-aged participants. Measures of psychopathology were collected on the first and last days. A minimum of 12 observations of the Positive and Negative Affect Schedule reports were needed for each participant. Measures of affect variability, granularity, and co-occurrence were derived.

Results: Depression, anxiety, stress, and neuroticism were positively associated with latent negative affect variability and negatively associated with latent positive affect variability. Granularity and co-occurrence were not significant predictors. Importantly, latent factors were significantly stronger predictors of depression than within-person mean and standard deviations.

Limitations: As with any latent variable study, the factorization is sample-specific and may have limited generalizability. Replication with a clinical sample and larger battery of psychopathology assessments is recommended.

Conclusions: Latent factors coalesce the strengths of several EMA-derived indicators while maintaining statistical and construct validity. Clinical implications are discussed regarding short-burst daily affect assessments to track potential risk for depression onset.

一种影响日常生活变异性的潜在变量方法可以准确预测非临床样本中的精神病理,特别是抑郁症状。
背景:生态瞬间评估(EMA)促进了影响动态与心理健康和福祉相关研究的增加。虽然许多指标可以从EMA数据中计算出影响动态的特征,但研究人员通常选择“大逃杀”方法,即只保留最佳的个体预测因子。目前的工作解决了跨指标共享方差的可能性,即影响变异性,可以使用潜在模型更好地捕获,也可以更好地预测精神病理学。方法:采用14天EMA方案检查109名大学年龄参与者的情绪动态。在第一天和最后一天收集精神病理指标。每个参与者至少需要12份积极和消极影响表报告的观察结果。推导了影响可变性、粒度和共现性的度量。结果:抑郁、焦虑、压力和神经质与潜在的消极情绪变异性呈正相关,与潜在的积极情绪变异性呈负相关。粒度和共现性不是显著的预测因子。重要的是,潜在因素比个人平均和标准差更能预测抑郁症。局限性:与任何潜在变量研究一样,因子分解是样本特异性的,可能具有有限的通用性。建议用临床样本和更大规模的精神病理学评估进行复制。结论:潜在因素整合了几个ema衍生指标的优势,同时保持了统计和结构效度。临床意义讨论短时突发每日影响评估跟踪抑郁症发作的潜在风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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