Capturing mood dynamics through adolescent smartphone social communication.
Capturing mood dynamics through adolescent smartphone social communication.
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通过青少年智能手机社交交流捕捉情绪动态。
DOI：
10.1037/abn0000855
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Shankman,StewartA
中科院分区：
文献类型：
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作者：
Li,LilianY;Trivedi,Esha;Helgren,Fiona;Allison,GraceO;Zhang,Emily;Buchanan,SavannahN;Pagliaccio,David;Durham,Katherine;Allen,NicholasB;Auerbach,RandyP;Shankman,StewartA
大多数患有抑郁症的青少年仍然没有得到诊断和治疗，从个人和公共卫生的角度来看，这些机会都是昂贵的。一个有前途的方法来检测青少年抑郁症的实时和大规模的是通过他们的社会沟通的智能手机（例如，短信，社交媒体帖子）。过去的研究表明，来自在线社交交流的语言可靠地表明了抑郁症的个体差异。为了进一步检测抑郁症状的出现，本研究测试了智能手机社交沟通中的情绪（即，暗示积极和消极影响的词语）是否前瞻性地预测了83名青少年（M年龄= 16.49，73.5%女性）的日常情绪波动，这些青少年具有广泛的抑郁严重程度。参与者在90天的时间内完成了每日情绪评级，在此期间，从社交通讯应用程序被动收集了354，278条消息。更积极的情绪（即，更积极的加权综合效价得分和更大比例的词表达积极的情绪）预测更积极的第二天的情绪，控制前一天的情绪。此外，更大比例的积极和消极情绪分别与基线时测量的较低的快感缺失和更大的烦躁症状相关。对非情感语言特征的探索性分析表明，更多地使用社会参与词（例如，朋友和从属关系）和表情符号（主要由心脏组成）预测情绪的积极变化。总的来说，研究结果表明，来自智能手机社交交流的语言可以检测到青少年的情绪波动，为基于语言的工具奠定了基础，以确定抑郁风险增加的时期。（PsycInfo数据库记录（c）2023阿帕，保留所有权利）
Most adolescents with depression remain undiagnosed and untreated—missed opportunities that are costly from both personal and public health perspectives. A promising approach to detecting adolescent depression in real-time and at a large scale is through their social communication on the smartphone (eg, text messages, social media posts). Past research has shown that language from online social communication reliably indicates interindividual differences in depression. To move toward detecting the emergence of depression symptoms intraindividually, the present study tested whether sentiment (ie, words connoting positive and negative affect) from smartphone social communication prospectively predicted daily mood fluctuations in 83 adolescents (M age= 16.49, 73.5% female) with a wide range of depression severity. Participants completed daily mood ratings across a 90-day period, during which 354,278 messages were passively collected from social communication apps. Greater positive sentiment (ie, more positive weighted composite valence score and a greater proportion of words expressing positive sentiment) predicted more positive next-day mood, controlling for previous-day mood. Moreover, greater proportions of positive and negative sentiment were, respectively, associated with lower anhedonia and greater dysphoria symptoms measured at baseline. Exploratory analyses of nonaffective linguistic features showed that greater use of social engagement words (eg, friends and affiliation) and emojis (primarily consisting of hearts) predicted more positive changes in mood. Collectively, findings suggest that language from smartphone social communication can detect mood fluctuations in adolescents, laying the foundation for language-based tools to identify periods of heightened depression risk.(PsycInfo Database Record (c) 2023 APA, all rights reserved)