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利用移动传感技术测试大学生抑郁症、社交焦虑症、状态情感和社交隔离的临床模型。

Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College Students.

作者信息

Chow Philip I, Fua Karl, Huang Yu, Bonelli Wesley, Xiong Haoyi, Barnes Laura E, Teachman Bethany A

机构信息

Department of Psychology, University of Virginia, Charlottesville, VA, United States.

School of Engineering and Applied Science, University of Virginia, Charlottesville, VA, United States.

出版信息

J Med Internet Res. 2017 Mar 3;19(3):e62. doi: 10.2196/jmir.6820.

Abstract

BACKGROUND

Research in psychology demonstrates a strong link between state affect (moment-to-moment experiences of positive or negative emotionality) and trait affect (eg, relatively enduring depression and social anxiety symptoms), and a tendency to withdraw (eg, spending time at home). However, existing work is based almost exclusively on static, self-reported descriptions of emotions and behavior that limit generalizability. Despite adoption of increasingly sophisticated research designs and technology (eg, mobile sensing using a global positioning system [GPS]), little research has integrated these seemingly disparate forms of data to improve understanding of how emotional experiences in everyday life are associated with time spent at home, and whether this is influenced by depression or social anxiety symptoms.

OBJECTIVE

We hypothesized that more time spent at home would be associated with more negative and less positive affect.

METHODS

We recruited 72 undergraduate participants from a southeast university in the United States. We assessed depression and social anxiety symptoms using self-report instruments at baseline. An app (Sensus) installed on participants' personal mobile phones repeatedly collected in situ self-reported state affect and GPS location data for up to 2 weeks. Time spent at home was a proxy for social isolation.

RESULTS

We tested separate models examining the relations between state affect and time spent at home, with levels of depression and social anxiety as moderators. Models differed only in the temporal links examined. One model focused on associations between changes in affect and time spent at home within short, 4-hour time windows. The other 3 models focused on associations between mean-level affect within a day and time spent at home (1) the same day, (2) the following day, and (3) the previous day. Overall, we obtained many of the expected main effects (although there were some null effects), in which higher social anxiety was associated with more time or greater likelihood of spending time at home, and more negative or less positive affect was linked to longer homestay. Interactions indicated that, among individuals higher in social anxiety, higher negative affect and lower positive affect within a day was associated with greater likelihood of spending time at home the following day.

CONCLUSIONS

Results demonstrate the feasibility and utility of modeling the relationship between affect and homestay using fine-grained GPS data. Although these findings must be replicated in a larger study and with clinical samples, they suggest that integrating repeated state affect assessments in situ with continuous GPS data can increase understanding of how actual homestay is related to affect in everyday life and to symptoms of anxiety and depression.

摘要

背景

心理学研究表明,状态情感(积极或消极情绪的即时体验)与特质情感(例如,相对持久的抑郁和社交焦虑症状)之间存在紧密联系,同时还存在一种退缩倾向(例如,在家中消磨时间)。然而,现有研究几乎完全基于对情绪和行为的静态、自我报告式描述,这限制了研究结果的普遍性。尽管采用了越来越复杂的研究设计和技术(例如,使用全球定位系统[GPS]进行移动传感),但很少有研究将这些看似不同的数据形式整合起来,以增进对日常生活中的情绪体验如何与在家中度过的时间相关联,以及这是否受抑郁或社交焦虑症状影响的理解。

目的

我们假设在家中度过的时间越多,负面情绪就会越多,正面情绪就会越少。

方法

我们从美国东南部一所大学招募了72名本科参与者。我们在基线时使用自我报告工具评估抑郁和社交焦虑症状。安装在参与者个人手机上的一个应用程序(Sensus)最多持续2周反复收集现场自我报告的状态情感和GPS位置数据。在家中度过的时间是社会隔离的一个指标。

结果

我们测试了不同的模型,以检验状态情感与在家中度过的时间之间的关系,并将抑郁和社交焦虑水平作为调节变量。模型仅在所考察的时间联系上有所不同。一个模型关注在4小时的短时间窗口内情感变化与在家中度过的时间之间的关联。其他3个模型关注一天内平均水平的情感与在家中度过的时间之间的关联:(1)同一天,(2)第二天,(3)前一天。总体而言,我们获得了许多预期的主效应(尽管也有一些无效应),其中社交焦虑程度越高,在家中度过的时间越多或在家的可能性越大,负面情绪越多或正面情绪越少与在家停留时间越长相关。交互作用表明,在社交焦虑程度较高的个体中,一天内较高的负面情绪和较低的正面情绪与第二天在家中度过的时间可能性更大相关。

结论

结果证明了使用细粒度GPS数据对情感与在家停留时间之间的关系进行建模的可行性和实用性。尽管这些发现必须在更大规模的研究和临床样本中进行重复验证,但它们表明,将现场重复的状态情感评估与连续的GPS数据相结合,可以增进对实际在家停留时间如何与日常生活中的情感以及焦虑和抑郁症状相关联的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63f0/5357317/ff772df56509/jmir_v19i3e62_fig1.jpg

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