The Challenges in Designing a Prevention Chatbot for Eating Disorders: Observational Study.

The Challenges in Designing a Prevention Chatbot for Eating Disorders: Observational Study.
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DOI:
10.2196/28003
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发表时间:
2022-01-19
影响因子:
2.2
通讯作者:
Jacobson NC
Jacobson NC
中科院分区:
其他
文献类型:
--
作者:
Chan WW;Fitzsimmons-Craft EE;Smith AC;Firebaugh ML;Fowler LA;DePietro B;Topooco N;Wilfley DE;Taylor CB;Jacobson NC

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聊天机器人有可能大规模提供成本效益高的心理健康预防计划,并增加干预计划的互动性、易用性和可及性。Chatbot预防饮食失调(EDS)的发展仍处于初级阶段。我们的目标是展示在设计和完善基于规则的EDS预防聊天机器人程序方面面临的挑战的例子和解决方案,目标是有患ED风险的成年女性。参与者是2409人,他们至少开始使用EDS预防聊天机器人来回应社交媒体广告。在6个月的时间里,研究团队审查了来自这些用户的多达52,129条评论,以确定对用户体验和技术故障产生负面影响的不恰当回复。审查员发现的问题随后被提交给整个研究团队,研究团队随后产生可能的解决方案并实施新的回应。聊天机器人最常见的问题是在理解和适当响应用户意外响应方面存在普遍限制。我们开发了几个解决方法来限制这些问题,同时保留一些互动性。基于规则的聊天机器人有可能以低成本接触到大量人群,但在理解和适当响应意外的用户响应方面受到限制。他们在提供信息和简单的对话方面可能是最有效的。解决办法可以减少对话中的错误。
Chatbots have the potential to provide cost-effective mental health prevention programs at scale and increase interactivity, ease of use, and accessibility of intervention programs. The development of chatbot prevention for eating disorders (EDs) is still in its infancy. Our aim is to present examples of and solutions to challenges in designing and refining a rule-based prevention chatbot program for EDs, targeted at adult women at risk for developing an ED. Participants were 2409 individuals who at least began to use an EDs prevention chatbot in response to social media advertising. Over 6 months, the research team reviewed up to 52,129 comments from these users to identify inappropriate responses that negatively impacted users’ experience and technical glitches. Problems identified by reviewers were then presented to the entire research team, who then generated possible solutions and implemented new responses. The most common problem with the chatbot was a general limitation in understanding and responding appropriately to unanticipated user responses. We developed several workarounds to limit these problems while retaining some interactivity. Rule-based chatbots have the potential to reach large populations at low cost but are limited in understanding and responding appropriately to unanticipated user responses. They can be most effective in providing information and simple conversations. Workarounds can reduce conversation errors.
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