Hull Thomas D, Levine Jacob, Bantilan Niels, Desai Angel N, Majumder Maimuna S
Talkspace, New York, NY, United States.
Department of Counseling and Clinical Psychology, Teachers College, Columbia University, New York, NY, United States.
JMIR Form Res. 2021 Feb 9;5(2):e26190. doi: 10.2196/26190.
The novel COVID-19 disease has negatively impacted mortality, economic conditions, and mental health. These impacts are likely to continue after the COVID-19 pandemic ends. There are no methods for characterizing the mental health burden of the COVID-19 pandemic, and differentiating this burden from that of the prepandemic era. Accurate illness detection methods are critical for facilitating pandemic-related treatment and preventing the worsening of symptoms.
We aimed to identify major themes and symptom clusters in the SMS text messages that patients send to therapists. We assessed patients who were seeking treatment for pandemic-related distress on Talkspace, which is a popular telemental health platform.
We used a machine learning algorithm to identify patients' pandemic-related concerns, based on their SMS text messages in a large, digital mental health service platform (ie, Talkspace). This platform uses natural language processing methods to analyze unstructured therapy transcript data, in parallel with brief clinical assessment methods for analyzing depression and anxiety symptoms.
Our results show a significant increase in the incidence of COVID-19-related intake anxiety symptoms (P<.001), but no significant differences in the incidence of intake depression symptoms (P=.79). During our transcript analyses, we identified terms that were related to 24 symptoms outside of those included in the diagnostic criteria for anxiety and depression.
Our findings for Talkspace suggest that people who seek treatment during the pandemic experience more severe intake anxiety than they did before the COVID-19 outbreak. It is important to monitor the symptoms that we identified in this study and the symptoms of anxiety and depression, to fully understand the effects of the COVID-19 pandemic on mental health.
新型冠状病毒病（COVID-19）对死亡率、经济状况和心理健康产生了负面影响。这些影响在COVID-19大流行结束后可能仍会持续。目前尚无方法来描述COVID-19大流行对心理健康造成的负担，并将这一负担与大流行前时代的负担区分开来。准确的疾病检测方法对于促进与大流行相关的治疗以及防止症状恶化至关重要。
我们旨在识别患者发送给治疗师的短信中的主要主题和症状群。我们评估了在热门远程心理健康平台Talkspace上寻求与大流行相关困扰治疗的患者。
我们使用机器学习算法，根据大型数字心理健康服务平台（即Talkspace）上患者的短信，识别患者与大流行相关的担忧。该平台使用自然语言处理方法来分析非结构化治疗记录数据，同时使用简短临床评估方法来分析抑郁和焦虑症状。
我们的结果显示，与COVID-19相关的初诊焦虑症状发生率显著增加（P<0.001），但初诊抑郁症状发生率无显著差异（P=0.79）。在我们的记录分析过程中，我们识别出了与焦虑和抑郁诊断标准中未包含的24种症状相关的术语。
我们对Talkspace的研究结果表明，在大流行期间寻求治疗的人经历的初诊焦虑比COVID-19疫情爆发前更为严重。监测我们在本研究中识别出的症状以及焦虑和抑郁症状，对于全面了解COVID-19大流行对心理健康的影响非常重要。
