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Preliminary Evaluation of the Engagement and Effectiveness of a Mental Health Chatbot
Background: Mental health difficulties are highly prevalent, yet access to support is limited by barriers of stigma, cost, and availability. These issues are even more prevalent in low- and middle-income countries, and digital technology is one potential way to overcome these barriers. Digital menta...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521874/ https://www.ncbi.nlm.nih.gov/pubmed/34713049 http://dx.doi.org/10.3389/fdgth.2020.576361 |
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author | Daley, Kate Hungerbuehler, Ines Cavanagh, Kate Claro, Heloísa Garcia Swinton, Paul Alan Kapps, Michael |
author_facet | Daley, Kate Hungerbuehler, Ines Cavanagh, Kate Claro, Heloísa Garcia Swinton, Paul Alan Kapps, Michael |
author_sort | Daley, Kate |
collection | PubMed |
description | Background: Mental health difficulties are highly prevalent, yet access to support is limited by barriers of stigma, cost, and availability. These issues are even more prevalent in low- and middle-income countries, and digital technology is one potential way to overcome these barriers. Digital mental health interventions are effective but often struggle with low engagement rates, particularly in the absence of human support. Chatbots could offer a scalable solution, simulating human support at a lower cost. Objective: To complete a preliminary evaluation of engagement and effectiveness of Vitalk, a mental health chatbot, at reducing anxiety, depression and stress. Methods: Real world data was analyzed from 3,629 Vitalk users who had completed the first phase of a Vitalk program (“less anxiety,” “less stress” or “better mood”). Programs were delivered through written conversation with a chatbot. Engagement was calculated from the number of responses sent to the chatbot divided by days in the program. Results: Users sent an average of 8.17 responses per day. For all three programs, target outcome scores reduced between baseline and follow up with large effect sizes for anxiety (Cohen's d = −0.85), depression (Cohen's d = −0.91) and stress (Cohen's d = −0.81). Increased engagement resulted in improved post-intervention values for anxiety and depression. Conclusion: This study highlights a chatbot's potential to reduce mental health symptoms in the general population within Brazil. While findings show promise, further research is required. |
format | Online Article Text |
id | pubmed-8521874 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85218742021-10-27 Preliminary Evaluation of the Engagement and Effectiveness of a Mental Health Chatbot Daley, Kate Hungerbuehler, Ines Cavanagh, Kate Claro, Heloísa Garcia Swinton, Paul Alan Kapps, Michael Front Digit Health Digital Health Background: Mental health difficulties are highly prevalent, yet access to support is limited by barriers of stigma, cost, and availability. These issues are even more prevalent in low- and middle-income countries, and digital technology is one potential way to overcome these barriers. Digital mental health interventions are effective but often struggle with low engagement rates, particularly in the absence of human support. Chatbots could offer a scalable solution, simulating human support at a lower cost. Objective: To complete a preliminary evaluation of engagement and effectiveness of Vitalk, a mental health chatbot, at reducing anxiety, depression and stress. Methods: Real world data was analyzed from 3,629 Vitalk users who had completed the first phase of a Vitalk program (“less anxiety,” “less stress” or “better mood”). Programs were delivered through written conversation with a chatbot. Engagement was calculated from the number of responses sent to the chatbot divided by days in the program. Results: Users sent an average of 8.17 responses per day. For all three programs, target outcome scores reduced between baseline and follow up with large effect sizes for anxiety (Cohen's d = −0.85), depression (Cohen's d = −0.91) and stress (Cohen's d = −0.81). Increased engagement resulted in improved post-intervention values for anxiety and depression. Conclusion: This study highlights a chatbot's potential to reduce mental health symptoms in the general population within Brazil. While findings show promise, further research is required. Frontiers Media S.A. 2020-11-30 /pmc/articles/PMC8521874/ /pubmed/34713049 http://dx.doi.org/10.3389/fdgth.2020.576361 Text en Copyright © 2020 Daley, Hungerbuehler, Cavanagh, Claro, Swinton and Kapps. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Digital Health Daley, Kate Hungerbuehler, Ines Cavanagh, Kate Claro, Heloísa Garcia Swinton, Paul Alan Kapps, Michael Preliminary Evaluation of the Engagement and Effectiveness of a Mental Health Chatbot |
title | Preliminary Evaluation of the Engagement and Effectiveness of a Mental Health Chatbot |
title_full | Preliminary Evaluation of the Engagement and Effectiveness of a Mental Health Chatbot |
title_fullStr | Preliminary Evaluation of the Engagement and Effectiveness of a Mental Health Chatbot |
title_full_unstemmed | Preliminary Evaluation of the Engagement and Effectiveness of a Mental Health Chatbot |
title_short | Preliminary Evaluation of the Engagement and Effectiveness of a Mental Health Chatbot |
title_sort | preliminary evaluation of the engagement and effectiveness of a mental health chatbot |
topic | Digital Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521874/ https://www.ncbi.nlm.nih.gov/pubmed/34713049 http://dx.doi.org/10.3389/fdgth.2020.576361 |
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