@Article{info:doi/10.2196/38171,
author="Vertsberger, Dana
and Naor, Navot
and Winsberg, Mir{\`e}ne",
title="Adolescents' Well-being While Using a Mobile Artificial Intelligence--Powered Acceptance Commitment Therapy Tool: Evidence From a Longitudinal Study",
journal="JMIR AI",
year="2022",
month="Nov",
day="29",
volume="1",
number="1",
pages="e38171",
keywords="well-being; adolescents; chatbots; conversational agents; mental health; mobile mental health; automated; support; self-management; self-help; smartphone; psychology; intervention; psychological; therapy; acceptance; commitment; engagement",
abstract="Background: Adolescence is a critical developmental period to prevent and treat the emergence of mental health problems. Smartphone-based conversational agents can deliver psychologically driven intervention and support, thus increasing psychological well-being over time. Objective: The objective of the study was to test the potential of an automated conversational agent named Kai.ai to deliver a self-help program based on Acceptance Commitment Therapy tools for adolescents, aimed to increase their well-being. Methods: Participants were 10,387 adolescents, aged 14-18 years, who used Kai.ai on one of the top messaging apps (eg, iMessage and WhatsApp). Users' well-being levels were assessed between 2 and 5 times using the 5-item World Health Organization Well-being Index questionnaire over their engagement with the service. Results: Users engaged with the conversational agent an average of 45.39 (SD 46.77) days. The average well-being score at time point 1 was 39.28 (SD 18.17), indicating that, on average, users experienced reduced well-being. Latent growth curve modeling indicated that participants' well-being significantly increased over time ($\beta$=2.49; P<.001) and reached a clinically acceptable well-being average score (above 50). Conclusions: Mobile-based conversational agents have the potential to deliver engaging and effective Acceptance Commitment Therapy interventions. ",
issn="2817-1705",
doi="10.2196/38171",
url="https://ai.jmir.org/2022/1/e38171",
url="https://doi.org/10.2196/38171"
}