Abstract：
BACKGROUND: Digital phenotyping and monitoring tools are the most promising approaches to automatically detect upcoming depressive episodes. Especially, linguistic style has been seen as a potential behavioral marker of depression, as cross-sectional studies showed, for example, less frequent use of positive emotion words, intensified use of negative emotion words, and more self-references in patients with depression compared to healthy controls. However, longitudinal studies are sparse and therefore it remains unclear whether within-person fluctuations in depression severity are associated with individuals\' linguistic style.
METHODS: To capture affective states and concomitant speech samples longitudinally, we used an ambulatory assessment approach sampling multiple times a day via smartphones in patients diagnosed with depressive disorder undergoing sleep deprivation therapy. This intervention promises a rapid change of affective symptoms within a short period of time, assuring sufficient variability in depressive symptoms. We extracted word categories from the transcribed speech samples using the Linguistic Inquiry and Word Count.
RESULTS: Our analyses revealed that more pleasant affective momentary states (lower reported depression severity, lower negative affective state, higher positive affective state, (positive) valence, energetic arousal and calmness) are mirrored in the use of less negative emotion words and more positive emotion words.
CONCLUSIONS: We conclude that a patient\'s linguistic style, especially the use of positive and negative emotion words, is associated with self-reported affective states and thus is a promising feature for speech-based automated monitoring and prediction of upcoming episodes, ultimately leading to better patient care.
METHODS: To capture affective states and concomitant speech samples longitudinally, we used an ambulatory assessment approach sampling multiple times a day via smartphones in patients diagnosed with depressive disorder undergoing sleep deprivation therapy. This intervention promises a rapid change of affective symptoms within a short period of time, assuring sufficient variability in depressive symptoms. We extracted word categories from the transcribed speech samples using the Linguistic Inquiry and Word Count.
RESULTS: Our analyses revealed that more pleasant affective momentary states (lower reported depression severity, lower negative affective state, higher positive affective state, (positive) valence, energetic arousal and calmness) are mirrored in the use of less negative emotion words and more positive emotion words.
CONCLUSIONS: We conclude that a patient\'s linguistic style, especially the use of positive and negative emotion words, is associated with self-reported affective states and thus is a promising feature for speech-based automated monitoring and prediction of upcoming episodes, ultimately leading to better patient care.
摘要：
背景：数字表型和监测工具是自动检测即将到来的抑郁发作的最有前途的方法。尤其是,语言风格被视为抑郁的潜在行为标志，正如横断面研究显示的那样，例如,较少使用积极情绪词，强化使用负面情绪词，与健康对照组相比，抑郁症患者的自我参考更多。然而,纵向研究较少,因此尚不清楚人内抑郁严重程度波动是否与个体语言风格相关.
方法：要纵向捕获情感状态和伴随语音样本，我们采用动态评估方法,通过智能手机对接受睡眠剥夺治疗的抑郁症患者进行每日多次采样.这种干预有望在短时间内迅速改变情感症状，确保抑郁症状具有足够的变异性。我们使用语言查询和单词计数从转录的语音样本中提取单词类别。
结果：我们的分析显示，更愉快的情绪短暂状态（较低的报告抑郁严重程度，较低的负面情感状态，较高的积极情感状态，（正）价，精力充沛的唤醒和镇定）反映在使用更少的负面情绪词和更多的积极情绪词。
结论：我们得出结论，患者的语言风格，尤其是使用积极和消极的情绪词，与自我报告的情感状态相关，因此是基于语音的自动监测和预测即将到来的事件的一个有前途的功能，最终导致更好的病人护理。
方法：要纵向捕获情感状态和伴随语音样本，我们采用动态评估方法,通过智能手机对接受睡眠剥夺治疗的抑郁症患者进行每日多次采样.这种干预有望在短时间内迅速改变情感症状，确保抑郁症状具有足够的变异性。我们使用语言查询和单词计数从转录的语音样本中提取单词类别。
结果：我们的分析显示，更愉快的情绪短暂状态（较低的报告抑郁严重程度，较低的负面情感状态，较高的积极情感状态，（正）价，精力充沛的唤醒和镇定）反映在使用更少的负面情绪词和更多的积极情绪词。
结论：我们得出结论，患者的语言风格，尤其是使用积极和消极的情绪词，与自我报告的情感状态相关，因此是基于语音的自动监测和预测即将到来的事件的一个有前途的功能，最终导致更好的病人护理。
