Pemberton Rachel, Fuller Tyszkiewicz Matthew D
School of Psychology, Deakin University, Australia.
School of Psychology, Deakin University, Australia.
J Affect Disord. 2016 Aug;200:103-10. doi: 10.1016/j.jad.2016.04.023. Epub 2016 Apr 20.
Although accumulated evidence suggests that fluctuations in depressed mood are common among individuals with depression, and may be associated with onset, duration, and severity of illness, a systematic appraisal of putative predictors of depressed mood is lacking.
A systematic search for relevant studies in the literature was conducted using PsycInfo and PubMed databases via EbscoHost in February 2016. The search was limited to articles using the experience sampling method, an approach suitable for capturing in situ fluctuations in mood states.
Forty-two studies met inclusion criteria for the review, from which three key risk factors (poor sleep, stress, and significant life events) and two protective factors (physical activity and quality of social interactions) were identified. The majority of papers supported concurrent and lagged associations between these putative protective/risk factors and depressed mood.
Despite support for each of the proposed protective/risk factors, few studies evaluated multiple factors in the same study. Moreover, the time course for the effects of these predictors on depressed mood remains largely unknown.
The present review identified several putative risk and protective factors for depressed mood. A review of the literature suggests that poor sleep, negative social interactions, and stressful negative events may temporally precede spikes in depressed mood. In contrast, exercise and positive social interactions have been shown to predict subsequent declines in depressed mood. However, the lack of multivariate models in which the unique contributions of various predictors could be evaluated means that the current state of knowledge prevents firm conclusions about which factors are most predictive of depressed mood. More complex modeling of these effects is necessary in order to provide insights useful for clinical treatment in daily life of the depressed mood component of depressive disorders.
尽管越来越多的证据表明，情绪低落的波动在抑郁症患者中很常见，并且可能与疾病的发作、持续时间和严重程度有关，但目前仍缺乏对情绪低落的假定预测因素的系统评估。
2016年2月，通过EbscoHost在PsycInfo和PubMed数据库中对文献中的相关研究进行了系统检索。检索仅限于使用经验抽样方法的文章，该方法适用于捕捉情绪状态的实时波动。
42项研究符合该综述的纳入标准，从中确定了三个关键风险因素（睡眠不佳、压力和重大生活事件）和两个保护因素（体育活动和社交互动质量）。大多数论文支持这些假定的保护/风险因素与情绪低落之间的同时性和滞后性关联。
尽管对每个提出的保护/风险因素都有支持，但很少有研究在同一研究中评估多个因素。此外，这些预测因素对情绪低落影响的时间进程在很大程度上仍然未知。
本综述确定了情绪低落的几个假定风险和保护因素。文献综述表明，睡眠不佳、负面社交互动和压力性负面事件可能在情绪低落高峰之前出现。相比之下，运动和积极的社交互动已被证明可以预测随后情绪低落的下降。然而，缺乏能够评估各种预测因素独特贡献的多变量模型意味着，目前的知识状态无法就哪些因素最能预测情绪低落得出确凿结论。为了提供对抑郁症患者日常生活中情绪低落部分的临床治疗有用的见解，有必要对这些影响进行更复杂的建模。
