From Anxiety to Hope: Understanding the Adoption of AI Counseling Tools for Anxiety Management
Author(s)
Priscila Correa Franco Amaral, PhD1, Jorge Brantes Ferreira, PhD2.
1Business Administration, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil, 2Associate Professor of Business Administration, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil.
1Business Administration, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil, 2Associate Professor of Business Administration, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil.
OBJECTIVES: AI-powered chatbots are emerging as scalable solutions for managing anxiety symptoms, offering guided support in contexts with limited access to mental health services. Yet, adoption of these technologies remains uneven. While prior research emphasizes cognitive predictors, this study explores how hope, a motivational state reflecting the perceived ability to overcome adversity and pursue meaningful psychological change, shapes patient acceptance of AI-based counseling for anxiety.
METHODS: A cross-sectional survey was administered to 310 Brazilian adults following guided use of a ChatGPT-based mental health chatbot designed to provide anxiety-specific support. The structural model drew on the Technology Acceptance Model (TAM), including Perceived Usefulness, Perceived Ease of Use, Attitude, and Behavioral Intention, along with Hope (as posited by Snyder’s Hope Theory), Trust, Self-Efficacy, and Task-Technology Fit. Structural Equation Modeling (SEM) tested direct and indirect relationships among variables.
RESULTS: Hope demonstrated the strongest effects in the model, enhancing perceptions of usefulness, usability, and trust in the AI system. These effects significantly influenced user attitudes and behavioral intention to adopt the technology. Traditional usability measures (e.g., ease of use) were less predictive than affective-motivational factors. The model explained 68.0% of the variance in Behavioral Intention and 86.2% in Attitude. Participants who perceived the chatbot as enabling personal change were more inclined to trust its recommendations and continue using it.
CONCLUSIONS: Hope plays a defining role in how users evaluate and adopt AI-based counseling for anxiety. Designing chatbot interactions that foster emotional engagement, not just ease of use, may increase adoption and improve treatment outcomes. These findings advance digital mental health research by showing that motivational states can be leveraged to enhance adoption and sustained engagement with AI-supported anxiety care. Integrating hope-driven design principles into AI tools may also promote earlier intervention, reduce treatment gaps, and support long-term adherence, particularly in settings with limited access to conventional psychological services.
METHODS: A cross-sectional survey was administered to 310 Brazilian adults following guided use of a ChatGPT-based mental health chatbot designed to provide anxiety-specific support. The structural model drew on the Technology Acceptance Model (TAM), including Perceived Usefulness, Perceived Ease of Use, Attitude, and Behavioral Intention, along with Hope (as posited by Snyder’s Hope Theory), Trust, Self-Efficacy, and Task-Technology Fit. Structural Equation Modeling (SEM) tested direct and indirect relationships among variables.
RESULTS: Hope demonstrated the strongest effects in the model, enhancing perceptions of usefulness, usability, and trust in the AI system. These effects significantly influenced user attitudes and behavioral intention to adopt the technology. Traditional usability measures (e.g., ease of use) were less predictive than affective-motivational factors. The model explained 68.0% of the variance in Behavioral Intention and 86.2% in Attitude. Participants who perceived the chatbot as enabling personal change were more inclined to trust its recommendations and continue using it.
CONCLUSIONS: Hope plays a defining role in how users evaluate and adopt AI-based counseling for anxiety. Designing chatbot interactions that foster emotional engagement, not just ease of use, may increase adoption and improve treatment outcomes. These findings advance digital mental health research by showing that motivational states can be leveraged to enhance adoption and sustained engagement with AI-supported anxiety care. Integrating hope-driven design principles into AI tools may also promote earlier intervention, reduce treatment gaps, and support long-term adherence, particularly in settings with limited access to conventional psychological services.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
MT17
Topic
Medical Technologies
Topic Subcategory
Digital Health
Disease
Mental Health (including addition)