Background 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. Objective 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. Methods 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). Results 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). Conclusions 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. Trial Registration Chinese Clinical Trial Registry ChiCTR2100052532; http://www.chictr.org.cn/showproj.aspx?proj=135744

Background Mental health problems are a crucial global public health concern. Owing to their cost-effectiveness and accessibility, conversational agent interventions (CAIs) are promising in the field of mental health care. Objective This study aims to present a thorough summary of the traits of CAIs available for a range of mental health problems, find evidence of efficacy, and analyze the statistically significant moderators of efficacy via a meta-analysis of randomized controlled trial. Methods Web-based databases (Embase, MEDLINE, PsycINFO, CINAHL, Web of Science, and Cochrane) were systematically searched dated from the establishment of the database to October 30, 2021, and updated to May 1, 2022. Randomized controlled trials comparing CAIs with any other type of control condition in improving depressive symptoms, generalized anxiety symptoms, specific anxiety symptoms, quality of life or well-being, general distress, stress, mental disorder symptoms, psychosomatic disease symptoms, and positive and negative affect were considered eligible. This study followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Data were extracted by 2 independent reviewers, checked by a third reviewer, and pooled using both random effect models and fixed effects models. Hedges g was chosen as the effect size. Results Of the 6900 identified records, a total of 32 studies were included, involving 6089 participants. CAIs showed statistically significant short-term effects compared with control conditions in improving depressive symptoms (g=0.29, 95% CI 0.20-0.38), generalized anxiety symptoms (g=0.29, 95% CI 0.21-0.36), specific anxiety symptoms (g=0.47, 95% CI 0.07-0.86), quality of life or well-being (g=0.27, 95% CI 0.16-0.39), general distress (g=0.33, 95% CI 0.20-0.45), stress (g=0.24, 95% CI 0.08-0.41), mental disorder symptoms (g=0.36, 95% CI 0.17-0.54), psychosomatic disease symptoms (g=0.62, 95% CI 0.14-1.11), and negative affect (g=0.28, 95% CI 0.05-0.51). However, the long-term effects of CAIs for the most mental health outcomes were not statistically significant (g=−0.04 to 0.39). Personalization and empathic response were 2 critical facilitators of efficacy. The longer duration of interaction with conversational agents was associated with the larger pooled effect sizes. Conclusions The findings show that CAIs are research-proven interventions that ought to be implemented more widely in mental health care. CAIs are effective and easily acceptable for those with mental health problems. The clinical application of this novel digital technology will conserve human health resources and optimize the allocation of mental health services. Trial Registration PROSPERO CRD42022350130; https://tinyurl.com/mvhk6w9p

BACKGROUND Depression has a high detection ratio among young adults, especially during the COVID-19 pandemic, which, however, occupies a low utilization of health services. Mental health chatbot is a novel digital technology to provide fully automated intervention for depression symptoms. OBJECTIVE The purpose of this study is to test the clinical effectiveness and nonclinical performance of the cognitive behavioral therapy (CBT)-based mental health chatbot (XiaoE) for young adults with depression symptoms during the COVID-19 pandemic. METHODS In a single-blind, three-arm, randomized controlled trial, participants manifesting depression symptoms aged 17-34 years recruited from a university in China were randomly assigned to mental health chatbot (XiaoE; n = 49), e-book (n = 49) or general chatbot (Xiaoai; n = 50), in a ratio of 1:1:1. The primary outcome was the reduction of depression 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 analyses and per-protocol analyses were conducted under analysis of covariance (ANCOVA) models adjusting for baseline data. Controlled multiple imputation and δ-based sensitivity analysis were performed for missing data. The secondary outcome was the level of working alliance measured using Working Alliance Questionnaire (WAQ), usability measured using the Usability Metric for User Experience-LITE (UMUX-LITE) and acceptability measured using Acceptability Scale (AS). RESULTS Intent-to-treat analysis revealed a moderate short-term effect of group diversity on the reduction of depression symptoms (PHQ-9) at T1 (F2, 136 = 17.011, P < .001, d = 0.51), while a light long-term effect at T2 (F2, 136 = 5.477, P= .005, d = 0.31). Better working alliance (WAQ, F2, 145 = 3.407, P = .036) and acceptability (AS, F2, 145 = 4.322, P = .015) was discovered with XiaoE, while no significant difference among arms was found on usability (UMUX-LITE, F2, 145 = 0.968, P = .382). CONCLUSIONS This novel intervention conducting CBT provides a feasible and engaging digital therapeutic that allows easy accessibility and self-guided mental health assistance for young adults with depression symptoms. A systematic evaluation of nonclinical metrics for mental health chatbot is established in this study. In the future, concern with both clinical outcomes and nonclinical metrics is necessary to explore the mechanism by which the mental health chatbots work on patients. Further evidence is required to confirm the long-term effectiveness via trails replicated with a longer dose as well as exploration on its stronger efficacy in comparison with other active controls. CLINICALTRIAL Chinese Clinical Trial Registry ChiCTR2100052532; http://www.chictr.org.cn/showproj.aspx?proj=135744.

BACKGROUND A crucial global public health concern now is addressing mental health problems. Due to their cost-effectiveness and accessibility, conversational agent interventions (CAIs) have promise in the field of mental health care. OBJECTIVE In order to present a thorough summary of the traits of CAIs now available for a range of mental health problems, we found for evidence of efficacy and analyzed the significant moderators of efficacy via a meta-analysis of randomized controlled trial. METHODS Online databases (EMBASE, MEDLINE, PsycINFO, CINAHL, Web of Science, Cochrane databases) were systematically searched dated from the establishment of the database to 30 October 2021 and updated to 1 May 2022. Randomized controlled trials comparing CAIs with any other type of control condition in improving depressive symptoms, generalized anxiety symptoms, specific anxiety symptoms, quality of life/well-being, general distress, stress, mental disorder symptoms, psychosomatic disease symptoms, and positive and negative affect were considered eligible studies. This study followed the PRISMA guidelines. Data were extracted by two independent reviewers, checked by a third, and pooled using both random-effect models or fixed-effects model. Hedges’ g was chosen as the effect size. RESULTS Of 6900 identified records, a total of 32 studies were included, involving 6089 participants. CAIs showed significant short-term effects compared with control conditions in improving depressive symptoms (g = 0.29; 95% CI, 0.20 to 0.38); generalized anxiety symptoms (g = 0.29; 95% CI, 0.21 to 0.36); specific anxiety symptoms (g = 0.47; 95% CI, 0.07 to 0.86); quality of life/well-being (g = 0.27; 95% CI, 0.16 to 0.39); general distress (g = 0.33; 95% CI, 0.20 to 0.45); stress (g = 0.24; 95% CI, 0.08 to 0.41); mental disorder symptoms (g = 0.36; 95% CI, 0.17 to 0.54); psychosomatic disease symptoms(g = 0.62; 95% CI, 0.14 to 1.11); and negative affect (g = 0.28; 95% CI, 0.05 to 0.51). However, the long-term effects of CAIs for the most mental health outcomes were not significant (g = -0.04 to g = 0.39). Personalization and empathic response were two critical facilitators of efficacy. The longer duration of interaction with conversational agents was associated with the larger pooled effect sizes. CONCLUSIONS Findings show that CAIs are research-proven interventions that ought to be implemented more widely in mental health care. CAIs are effective and easily acceptable for those with mental health problems. The clinical application of this novel digital technology will conserve human health resources and optimize the allocation of mental health services.