Preprints are early versions of research articles that have not been peer reviewed. They should not be regarded as conclusive and should not be reported in news media as established information.
4 files

Mental Distress, Label Avoidance, and Use of a Mental Health Chatbot: Results from a U.S. Survey

posted on 2022-11-16, 23:04 authored by Kristin Kosyluk, Tanner Baeder, Karah Y. Greene, Jennifer T. Tran, Cassidy Bolton, Nele Loecher, Daniel DiEva, Jerome GaleaJerome Galea



To pilot test a mental health chatbot designed to screen users for psychological distress and refer to resources, addressing the strain on the U.S. behavioral healthcare workforce and exacerbated behavioral health challenges among U.S. adults due to COVID-19.


Data were collected via a national, cross-sectional, internet-based survey of U.S. adults. Measures included demographics, symptoms, stigma, technology acceptance, willingness to use the chatbot, and chatbot acceptability. Relationships between these variables were explored using chi-square tests, correlations, and logistic regression.


Of 222 participants, 75.7% completed mental health screening within the chatbot. Participants found the chatbot to be acceptable. Demographic predictors of chatbot use included being White or Black/African American, identifying as Hispanic/Latino, having dependents, having insurance coverage, having used mental health services in the past, having a diagnosed mental health condition, and reporting current distress. Logistic regression produced a significant model with perceived usefulness and symptoms as significant positive predictors of chatbot use for the overall sample, and label avoidance as the only significant predictor of chatbot use for those currently experiencing distress.


Chatbot technology may be a feasible and acceptable way to screen large numbers of people for psychological distress and disseminate mental health resources. Since label avoidance was identified as the single significant predictor of chatbot use among currently distressed individuals, chatbot technology may be one way to circumnavigate stigma as a barrier to engagement in behavioral health care.


Recruitment through Amazon’s mTurk limits generalizability of our findings, and chi-square test effect sizes were small.


This study was funded by the University of South Florida’s Pandemic Research Response Network through COVID-19 Rapid Response Research Proposal #100268.


Declaration of conflicts of interest


Corresponding author email

Lead author country

  • United States

Lead author job role

  • Higher Education Faculty 4-yr College

Lead author institution

University of South Florida

Ethics statement

The study was reviewed and approved by the University of South Florida’s Institutional Review Board. All participants provided consent prior to data collection.

Terms agreed

  • Yes, I agree to Advance terms

Comments (4)

Log in to write your comment here...

This is a valuable article for me thank you.Click here

I read it thoroughly its a good to read Read More

This is a well-written blog. Well done name style

Agreed terms and conditions name style