Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States.
Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY, United States.
J Med Internet Res. 2021 Jul 14;23(7):e28244. doi: 10.2196/28244.
BACKGROUND: Behavioral activation (BA) is rooted in the behavioral theory of depression, which states that increased exposure to meaningful, rewarding activities is a critical factor in the treatment of depression. Assessing constructs relevant to BA currently requires the administration of standardized instruments, such as the Behavioral Activation for Depression Scale (BADS), which places a burden on patients and providers, among other potential limitations. Previous work has shown that depressed and nondepressed individuals may use language differently and that automated tools can detect these differences. The increasing use of online, chat-based mental health counseling presents an unparalleled resource for automated longitudinal linguistic analysis of patients with depression, with the potential to illuminate the role of reward exposure in recovery. OBJECTIVE: This work investigated how linguistic indicators of planning and participation in enjoyable activities identified in online, text-based counseling sessions relate to depression symptomatology over time. METHODS: Using distributional semantics methods applied to a large corpus of text-based online therapy sessions, we devised a set of novel BA-related categories for the Linguistic Inquiry and Word Count (LIWC) software package. We then analyzed the language used by 10,000 patients in online therapy chat logs for indicators of activation and other depression-related markers using LIWC. RESULTS: Despite their conceptual and operational differences, both previously established LIWC markers of depression and our novel linguistic indicators of activation were strongly associated with depression scores (Patient Health Questionnaire [PHQ]-9) and longitudinal patient trajectories. Emotional tone; pronoun rates; words related to sadness, health, and biology; and BA-related LIWC categories appear to be complementary, explaining more of the variance in the PHQ score together than they do independently. CONCLUSIONS: This study enables further work in automated diagnosis and assessment of depression, the refinement of BA psychotherapeutic strategies, and the development of predictive models for decision support.
背景:行为激活(BA)根植于抑郁的行为理论,该理论指出,增加接触有意义、有回报的活动是治疗抑郁症的关键因素。目前评估与 BA 相关的结构需要使用标准化工具,如行为激活抑郁量表(BADS),这给患者和提供者带来了负担,以及其他潜在的限制。以前的工作表明,抑郁和非抑郁个体可能会以不同的方式使用语言,并且自动化工具可以检测到这些差异。越来越多的在线、基于聊天的心理健康咨询为对抑郁症患者进行自动化纵向语言分析提供了无与伦比的资源,有可能阐明奖励暴露在康复中的作用。
目的:本研究调查了在在线、基于文本的咨询会话中识别出的与计划和参与愉快活动相关的语言指标如何随时间与抑郁症状相关。
方法:我们使用分布语义方法应用于大量基于文本的在线治疗会话语料库,为 Linguistic Inquiry and Word Count(LIWC)软件包设计了一套新的与 BA 相关的类别。然后,我们使用 LIWC 分析了 10000 名在线治疗聊天记录中患者的语言,以寻找激活的指标和其他与抑郁相关的标记。
结果:尽管存在概念和操作上的差异,但先前建立的 LIWC 抑郁标志物和我们新的激活语言指标都与抑郁评分(患者健康问卷[PHQ]-9)和纵向患者轨迹强烈相关。情绪基调;代词率;与悲伤、健康和生物学相关的词语;以及与 BA 相关的 LIWC 类别似乎是互补的,它们一起解释了 PHQ 分数的更多方差,而不是它们各自独立解释的。
结论:这项研究为自动化诊断和评估抑郁症、改进 BA 心理治疗策略以及开发决策支持的预测模型提供了进一步的工作。
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