TY - JOUR
T1 - The Challenges in Designing a Prevention Chatbot for Eating Disorders
T2 - Observational Study
AU - Chan, William W.
AU - Fitzsimmons-Craft, Ellen E.
AU - Smith, Arielle C.
AU - Firebaugh, Marie Laure
AU - Fowler, Lauren A.
AU - DePietro, Bianca
AU - Topooco, Naira
AU - Wilfley, Denise E.
AU - Taylor, C. Barr
AU - Jacobson, Nicholas C.
N1 - Funding Information:
This research project was supported by the National Eating Disorders Association through the Feeding Hope Fund (CBT); the National Institute of Mental Health through grant K08 MH120341 (EEFC); the National Institute of Mental Health through grant R01 MH115128 (CBT and DEW); the National Heart, Lung, and Blood Institute through grant T32 HL130357 (LAF); the National Institute of Mental Health through grant R01 MH123482 (NCJ); the Swedish Research Council through grant 2018-06585 (NT); and the National Health and Medical Research Council through grant APP1170937 (CBT).
Publisher Copyright:
© 2022. JMIR Publications Inc.. All rights reserved.
PY - 2022/1
Y1 - 2022/1
N2 - 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.
AB - 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.
KW - Chatbot
KW - Digital mental health
KW - Eating disorders
KW - Intervention development
KW - Prevention
UR - http://www.scopus.com/inward/record.url?scp=85124837994&partnerID=8YFLogxK
U2 - 10.2196/28003
DO - 10.2196/28003
M3 - Article
C2 - 35044314
AN - SCOPUS:85124837994
SN - 2561-326X
VL - 6
JO - JMIR Formative Research
JF - JMIR Formative Research
IS - 1
M1 - e28003
ER -