Mental Health Chatbot for Young Adults With Depressive Symptoms During the COVID-19 Pandemic: Single-Blind, Three-Arm Randomized Controlled Trial.
Mental Health Chatbot for Young Adults With Depressive Symptoms During the COVID-19 Pandemic: Single-Blind, Three-Arm Randomized Controlled Trial.
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COVID-19大流行期间有抑郁症状的年轻成年人的心理健康聊天机器人：单盲、三臂随机对照试验
DOI：
10.2196/40719
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发表时间：
2022-11-21
影响因子：
7.4
通讯作者：
Tian, Tian
中科院分区：
文献类型：
--
作者：
He, Yuhao;Yang, Li;Zhu, Xiaokun;Wu, Bin;Zhang, Shuo;Qian, Chunlian;Tian, Tian
关键词：
抑郁症在年轻人中的患病率很高，尤其是在COVID-19大流行期间。然而，精神卫生服务在世界范围内仍然稀缺和利用不足。心理健康聊天机器人是一种新型的数字技术，可以为抑郁症状提供全自动干预。本研究的目的是测试基于认知行为疗法（CBT）的心理健康聊天机器人（XiaoE）在COVID-19大流行期间对患有抑郁症状的年轻人的临床有效性和非临床表现。在一项单盲、三臂随机对照试验中，从中国一所大学招募的表现出抑郁症状的参与者被随机分配到心理健康聊天机器人（XiaoE; n= 49）、电子书（n=49）或普通聊天机器人（Xiaoai; n=50）组，比例为1：1：1。参与者接受了为期一周的干预。主要结局是根据9项患者健康问卷（PHQ-9）在1周后（T1）和1个月后（T2）的抑郁症状减轻。意向治疗和符合方案分析均在校正基线数据的协方差模型分析下进行。对缺失数据进行了控制多重插补和基于δ的敏感性分析。次要结果是使用工作联盟问卷（WAQ）测量的工作联盟水平，使用用户体验可用性度量标准（UMUX-LITE）测量的可用性，以及使用可接受性量表（AS）测量的可接受性。参与者平均年龄为18.78岁，37.2%（55/148）为女性。平均基线PHQ-9评分为10.02（SD 3.18;范围2-19）。意向治疗分析显示，在T1（F2，136=17.011; P<0.001; d=0.51）和T2（F2，136=5.477; P= 0.005; d=0.31）时，小娥组参与者的PHQ-9评分低于电子书组和小爱组。更好的工作联盟（WAQ; F2，145=3.407; P= 0.04）和可接受性（AS; F2，145=4.322; P= 0.02），而在可用性（UMUX-LITE; F2，145=0.968; P= 0.38）方面未发现组间差异。基于CBT的聊天机器人是一种可行且引人入胜的数字治疗方法，可以为患有抑郁症状的年轻人提供轻松的可访问性和自我指导的心理健康援助。在这项研究中，已经建立了一个心理健康聊天机器人的非临床指标的系统评估。在未来，关注临床结果和非临床指标对于探索心理健康聊天机器人对患者的作用机制是必要的。需要进一步的证据来证实心理健康聊天机器人的长期有效性，通过用更长剂量复制的试验，以及探索其与其他主动控制相比更强的功效。中国临床试验注册中心ChiCTR 2100052532; http://www.chictr.org.cn/showproj.aspx?项目=135744
Depression has a high prevalence among young adults, especially during the COVID-19 pandemic. However, mental health services remain scarce and underutilized worldwide. Mental health chatbots are a novel digital technology to provide fully automated interventions for depressive symptoms. The purpose of this study was to test the clinical effectiveness and nonclinical performance of a cognitive behavioral therapy (CBT)–based mental health chatbot (XiaoE) for young adults with depressive symptoms during the COVID-19 pandemic. In a single-blind, 3-arm randomized controlled trial, participants manifesting depressive symptoms recruited from a Chinese university were randomly assigned to a mental health chatbot (XiaoE; n=49), an e-book (n=49), or a general chatbot (Xiaoai; n=50) group in a ratio of 1:1:1. Participants received a 1-week intervention. The primary outcome was the reduction of depressive symptoms according to the 9-item Patient Health Questionnaire (PHQ-9) at 1 week later (T1) and 1 month later (T2). Both intention-to-treat and per-protocol analyses were conducted under analysis of covariance models adjusting for baseline data. Controlled multiple imputation and δ-based sensitivity analysis were performed for missing data. The secondary outcomes were the level of working alliance measured using the Working Alliance Questionnaire (WAQ), usability measured using the Usability Metric for User Experience-LITE (UMUX-LITE), and acceptability measured using the Acceptability Scale (AS). Participants were on average 18.78 years old, and 37.2% (55/148) were female. The mean baseline PHQ-9 score was 10.02 (SD 3.18; range 2-19). Intention-to-treat analysis revealed lower PHQ-9 scores among participants in the XiaoE group compared with participants in the e-book group and Xiaoai group at both T1 (F2,136=17.011; P<.001; d=0.51) and T2 (F2,136=5.477; P=.005; d=0.31). Better working alliance (WAQ; F2,145=3.407; P=.04) and acceptability (AS; F2,145=4.322; P=.02) were discovered with XiaoE, while no significant difference among arms was found for usability (UMUX-LITE; F2,145=0.968; P=.38). A CBT-based chatbot is a feasible and engaging digital therapeutic approach that allows easy accessibility and self-guided mental health assistance for young adults with depressive symptoms. A systematic evaluation of nonclinical metrics for a mental health chatbot has been established in this study. In the future, focus on both clinical outcomes and nonclinical metrics is necessary to explore the mechanism by which mental health chatbots work on patients. Further evidence is required to confirm the long-term effectiveness of the mental health chatbot via trails replicated with a longer dose, as well as exploration of its stronger efficacy in comparison with other active controls. Chinese Clinical Trial Registry ChiCTR2100052532; http://www.chictr.org.cn/showproj.aspx?proj=135744
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影响因子：
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作者：
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通讯作者：
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作者：
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DOI：
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发表时间：
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期刊：
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影响因子：
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通讯作者：
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影响因子：
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