@Article{info:doi/10.2196/70436,
author="Tong, Alan C Y
and Wong, Kent T Y
and Chung, Wing W T
and Mak, Winnie W S",
title="Effectiveness of Topic-Based Chatbots on Mental Health Self-Care and Mental Well-Being: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2025",
month="Apr",
day="30",
volume="27",
pages="e70436",
keywords="chatbot intervention; mental health literacy; self-care; randomized controlled trial; digital health; mental well-being; artificial intelligence",
abstract="Background: The global surge in mental health challenges has placed unprecedented strain on health care systems, highlighting the need for scalable interventions to promote mental health self-care. Chatbots have emerged as promising tools by providing accessible, evidence-based support. While chatbots have shown promise in delivering mental health interventions, most studies have only focused on clinical populations and symptom reduction, leaving a critical gap in understanding their preventive potential for self-care and mental health literacy in the general population. Objective: This study evaluated the effectiveness of a rule-based, topic-specific chatbot intervention in improving self-care efficacy, mental health literacy, self-care intention, self-care behaviors, and mental well-being immediately after 10 days and 1 month of its use. Methods: A 2-arm, assessor-blinded randomized controlled trial was conducted. A total of 285 participants were randomly assigned to the chatbot intervention group (n=140) and a waitlist control group (n=145). The chatbot intervention consisted of 10 topic-specific sessions targeting stress management, emotion regulation, and value clarification, delivered over 10 days with a 7-day free-access period. Primary outcomes included self-care self-efficacy, behavioral intentions, self-care behaviors, and mental health literacy. Secondary outcomes included depressive symptoms, anxiety symptoms, and mental well-being. Assessments were self-administered on the web at baseline, 10 days after the intervention, and at a 1-month follow-up. All outcomes were analyzed using linear mixed models with an intention-to-treat approach, and effect sizes were calculated using Cohen d. Results: Participants in the chatbot group demonstrated significantly greater improvements in behavioral intentions (F2,379.74=15.02; P<.001) and mental health literacy (F2,423.57=4.27; P=.02) compared to the control group. The chatbots were also able to bring significant improvement in self-care behaviors (Cohen d=0.36, 95{\%} CI 0.08-0.30; P<.001), mindfulness (Cohen d=0.37, 95{\%} CI 0.14-0.38; P<.001), depressive symptoms (Cohen d=--0.26, 95{\%} CI --1.77 to --0.26; P=.004), overall well-being (Cohen d=0.22, 95{\%} CI 0.02-0.42; P=.02), and positive emotions (Cohen d=0.28, 95{\%} CI 0.08-0.54; P=.004) after 10 days. However, these improvements did not differ significantly at 1 month when compared to the waitlist control group. Adherence was higher among participants who received push notifications (t138=--4.91; P<.001). Conclusions: This study highlights the potential of rule-based chatbots in promoting mental health literacy and fostering short-term self-care intentions. However, the lack of sustained effects points to the necessary improvements required in chatbot design, including greater personalization and interactive features to enhance self-efficacy and long-term mental health outcomes. Future research should explore hybrid approaches that combine rule-based and generative artificial intelligence systems to optimize intervention effectiveness. Trial Registration: ClinicalTrials.gov NCT05694507; https://clinicaltrials.gov/ct2/show/NCT05694507 ",
issn="1438-8871",
doi="10.2196/70436",
url="https://www.jmir.org/2025/1/e70436",
url="https://doi.org/10.2196/70436"
}