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Abstract：
BACKGROUND: Conversational agents (CAs) or chatbots are computer programs that mimic human conversation. They have the potential to improve access to mental health interventions through automated, scalable, and personalized delivery of psychotherapeutic content. However, digital health interventions, including those delivered by CAs, often have high attrition rates. Identifying the factors associated with attrition is critical to improving future clinical trials.
OBJECTIVE: This review aims to estimate the overall and differential rates of attrition in CA-delivered mental health interventions (CA interventions), evaluate the impact of study design and intervention-related aspects on attrition, and describe study design features aimed at reducing or mitigating study attrition.
METHODS: We searched PubMed, Embase (Ovid), PsycINFO (Ovid), Cochrane Central Register of Controlled Trials, and Web of Science, and conducted a gray literature search on Google Scholar in June 2022. We included randomized controlled trials that compared CA interventions against control groups and excluded studies that lasted for 1 session only and used Wizard of Oz interventions. We also assessed the risk of bias in the included studies using the Cochrane Risk of Bias Tool 2.0. Random-effects proportional meta-analysis was applied to calculate the pooled dropout rates in the intervention groups. Random-effects meta-analysis was used to compare the attrition rate in the intervention groups with that in the control groups. We used a narrative review to summarize the findings.
RESULTS: The systematic search retrieved 4566 records from peer-reviewed databases and citation searches, of which 41 (0.90%) randomized controlled trials met the inclusion criteria. The meta-analytic overall attrition rate in the intervention group was 21.84% (95% CI 16.74%-27.36%; I2=94%). Short-term studies that lasted ≤8 weeks showed a lower attrition rate (18.05%, 95% CI 9.91%- 27.76%; I2=94.6%) than long-term studies that lasted >8 weeks (26.59%, 95% CI 20.09%-33.63%; I2=93.89%). Intervention group participants were more likely to attrit than control group participants for short-term (log odds ratio 1.22, 95% CI 0.99-1.50; I2=21.89%) and long-term studies (log odds ratio 1.33, 95% CI 1.08-1.65; I2=49.43%). Intervention-related characteristics associated with higher attrition include stand-alone CA interventions without human support, not having a symptom tracker feature, no visual representation of the CA, and comparing CA interventions with waitlist controls. No participant-level factor reliably predicted attrition.
CONCLUSIONS: Our results indicated that approximately one-fifth of the participants will drop out from CA interventions in short-term studies. High heterogeneities made it difficult to generalize the findings. Our results suggested that future CA interventions should adopt a blended design with human support, use symptom tracking, compare CA intervention groups against active controls rather than waitlist controls, and include a visual representation of the CA to reduce the attrition rate.
BACKGROUND: PROSPERO International Prospective Register of Systematic Reviews CRD42022341415; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022341415.
OBJECTIVE: This review aims to estimate the overall and differential rates of attrition in CA-delivered mental health interventions (CA interventions), evaluate the impact of study design and intervention-related aspects on attrition, and describe study design features aimed at reducing or mitigating study attrition.
METHODS: We searched PubMed, Embase (Ovid), PsycINFO (Ovid), Cochrane Central Register of Controlled Trials, and Web of Science, and conducted a gray literature search on Google Scholar in June 2022. We included randomized controlled trials that compared CA interventions against control groups and excluded studies that lasted for 1 session only and used Wizard of Oz interventions. We also assessed the risk of bias in the included studies using the Cochrane Risk of Bias Tool 2.0. Random-effects proportional meta-analysis was applied to calculate the pooled dropout rates in the intervention groups. Random-effects meta-analysis was used to compare the attrition rate in the intervention groups with that in the control groups. We used a narrative review to summarize the findings.
RESULTS: The systematic search retrieved 4566 records from peer-reviewed databases and citation searches, of which 41 (0.90%) randomized controlled trials met the inclusion criteria. The meta-analytic overall attrition rate in the intervention group was 21.84% (95% CI 16.74%-27.36%; I2=94%). Short-term studies that lasted ≤8 weeks showed a lower attrition rate (18.05%, 95% CI 9.91%- 27.76%; I2=94.6%) than long-term studies that lasted >8 weeks (26.59%, 95% CI 20.09%-33.63%; I2=93.89%). Intervention group participants were more likely to attrit than control group participants for short-term (log odds ratio 1.22, 95% CI 0.99-1.50; I2=21.89%) and long-term studies (log odds ratio 1.33, 95% CI 1.08-1.65; I2=49.43%). Intervention-related characteristics associated with higher attrition include stand-alone CA interventions without human support, not having a symptom tracker feature, no visual representation of the CA, and comparing CA interventions with waitlist controls. No participant-level factor reliably predicted attrition.
CONCLUSIONS: Our results indicated that approximately one-fifth of the participants will drop out from CA interventions in short-term studies. High heterogeneities made it difficult to generalize the findings. Our results suggested that future CA interventions should adopt a blended design with human support, use symptom tracking, compare CA intervention groups against active controls rather than waitlist controls, and include a visual representation of the CA to reduce the attrition rate.
BACKGROUND: PROSPERO International Prospective Register of Systematic Reviews CRD42022341415; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022341415.
摘要：
背景：对话代理（CA）或聊天机器人是模仿人类对话的计算机程序。他们有可能通过自动化来改善对心理健康干预的获取，可扩展，以及个性化提供心理治疗内容。然而,数字健康干预措施，包括CA提供的，通常有很高的流失率。确定与减员相关的因素对于改善未来的临床试验至关重要。
目的：这篇综述旨在估计CA提供的心理健康干预（CA干预）的总体和差异流失率，评估研究设计和干预相关方面对减员的影响，并描述旨在减少或减轻研究流失的研究设计特征。
方法：我们搜索了PubMed，Embase(Ovid),PsycINFO(Ovid),Cochrane中央控制试验登记册,和WebofScience,并于2022年6月对GoogleScholar进行了灰色文献检索。我们纳入了随机对照试验，将CA干预措施与对照组进行比较，并排除了仅持续1个疗程并使用绿野仙踪干预措施的研究。我们还使用Cochrane偏差风险工具2.0评估了纳入研究的偏差风险。随机效应比例荟萃分析用于计算干预组的合并辍学率。采用随机效应荟萃分析比较干预组与对照组的流失率。我们使用叙述性综述来总结研究结果。
结果：系统搜索从同行评审数据库和引文搜索中检索了4566条记录，其中41项(0.90%)随机对照试验符合纳入标准.干预组的meta分析总体流失率为21.84％（95％CI16.74％-27.36％；I2=94％）。持续≤8周的短期研究显示，流失率较低(18.05%，95％CI9.91％-27.76％；I2=94.6％）比持续>8周的长期研究（26.59％，95%CI20.09%-33.63%；I2=93.89%）。在短期研究(对数比值比1.22,95%CI0.99-1.50;I2=21.89%)和长期研究(对数比值比1.33,95%CI1.08-1.65;I2=49.43%)中，干预组参与者比对照组参与者更容易被减员。与较高减员相关的干预相关特征包括没有人力支持的独立CA干预，没有症状追踪功能，没有CA的视觉表示，并将CA干预措施与等待名单对照进行比较。没有参与者水平的因素可靠地预测了自然减员。
结论：我们的结果表明，在短期研究中，大约五分之一的参与者将退出CA干预。高度异质性使得很难推广这些发现。我们的结果表明，未来的CA干预措施应采用人工支持的混合设计，使用症状跟踪，将CA干预组与主动对照而不是等待列表对照进行比较，并包括CA的视觉表示以降低流失率。
背景：PROSPERO国际系统评价前瞻性注册CRD42022341415;https://www.crd.约克。AC.uk/prospro/display_record.php?ID=CRD42022341415。
目的：这篇综述旨在估计CA提供的心理健康干预（CA干预）的总体和差异流失率，评估研究设计和干预相关方面对减员的影响，并描述旨在减少或减轻研究流失的研究设计特征。
方法：我们搜索了PubMed，Embase(Ovid),PsycINFO(Ovid),Cochrane中央控制试验登记册,和WebofScience,并于2022年6月对GoogleScholar进行了灰色文献检索。我们纳入了随机对照试验，将CA干预措施与对照组进行比较，并排除了仅持续1个疗程并使用绿野仙踪干预措施的研究。我们还使用Cochrane偏差风险工具2.0评估了纳入研究的偏差风险。随机效应比例荟萃分析用于计算干预组的合并辍学率。采用随机效应荟萃分析比较干预组与对照组的流失率。我们使用叙述性综述来总结研究结果。
结果：系统搜索从同行评审数据库和引文搜索中检索了4566条记录，其中41项(0.90%)随机对照试验符合纳入标准.干预组的meta分析总体流失率为21.84％（95％CI16.74％-27.36％；I2=94％）。持续≤8周的短期研究显示，流失率较低(18.05%，95％CI9.91％-27.76％；I2=94.6％）比持续>8周的长期研究（26.59％，95%CI20.09%-33.63%；I2=93.89%）。在短期研究(对数比值比1.22,95%CI0.99-1.50;I2=21.89%)和长期研究(对数比值比1.33,95%CI1.08-1.65;I2=49.43%)中，干预组参与者比对照组参与者更容易被减员。与较高减员相关的干预相关特征包括没有人力支持的独立CA干预，没有症状追踪功能，没有CA的视觉表示，并将CA干预措施与等待名单对照进行比较。没有参与者水平的因素可靠地预测了自然减员。
结论：我们的结果表明，在短期研究中，大约五分之一的参与者将退出CA干预。高度异质性使得很难推广这些发现。我们的结果表明，未来的CA干预措施应采用人工支持的混合设计，使用症状跟踪，将CA干预组与主动对照而不是等待列表对照进行比较，并包括CA的视觉表示以降低流失率。
背景：PROSPERO国际系统评价前瞻性注册CRD42022341415;https://www.crd.约克。AC.uk/prospro/display_record.php?ID=CRD42022341415。
