Open AccessThe Challenges in Designing a Prevention Chatbot for Eating Disorders: Observational Study
Author(s) -
William W.C. Chan,
Ellen E. FitzsimmonsCraft,
Arielle C. Smith,
MarieLaure Firebaugh,
Lauren A. Fowler,
Bianca DePietro,
Naira Topooco,
Denise E. Wilfley,
C. Barr Taylor,
Nicholas C. Jacobson
Publication year - 2022
Publication title -
jmir formative research
Language(s) - English
Resource type - Journals
ISSN - 2561-326X
DOI - 10.2196/28003
Subject(s) - workaround , chatbot , interactivity , conversation , observational study , psychology , intervention (counseling) , computer science , internet privacy , medicine , medical education , nursing , world wide web , communication , pathology , programming language
Background 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. Objective 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. Methods 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. Results 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. Conclusions 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.