A Motivational Interviewing Chatbot With Generative Reflections for Increasing Readiness to Quit Smoking: Iterative Development Study

BackgroundThe motivational interviewing (MI) approach has been shown to help move ambivalent smokers toward the decision to quit smoking. There have been several attempts to broaden access to MI through text-based chatbots. These typically use scripted responses to client statements, but such nonspe...
Ausführliche Beschreibung

BackgroundThe motivational interviewing (MI) approach has been shown to help move ambivalent smokers toward the decision to quit smoking. There have been several attempts to broaden access to MI through text-based chatbots. These typically use scripted responses to client statements, but such nonspecific responses have been shown to reduce effectiveness. Recent advances in natural language processing provide a new way to create responses that are specific to a client’s statements, using a generative language model. ObjectiveThis study aimed to design, evolve, and measure the effectiveness of a chatbot system that can guide ambivalent people who smoke toward the decision to quit smoking with MI-style generative reflections. MethodsOver time, 4 different MI chatbot versions were evolved, and each version was tested with a separate group of ambivalent smokers. A total of 349 smokers were recruited through a web-based recruitment platform. The first chatbot version only asked questions without reflections on the answers. The second version asked the questions and provided reflections with an initial version of the reflection generator. The third version used an improved reflection generator, and the fourth version added extended interaction on some of the questions. Participants’ readiness to quit was measured before the conversation and 1 week later using an 11-point scale that measured 3 attributes related to smoking cessation: readiness, confidence, and importance. The number of quit attempts made in the week before the conversation and the week after was surveyed; in addition, participants rated the perceived empathy of the chatbot. The main body of the conversation consists of 5 scripted questions, responses from participants, and (for 3 of the 4 versions) generated reflections. A pretrained transformer-based neural network was fine-tuned on examples of high-quality reflections to generate MI reflections. ResultsThe increase in average confidence using the nongenerative version was 1.0 (SD 2.0; P=.001), whereas for the 3 generative versions, the increases ranged from 1.2 to 1.3 (SD 2.0-2.3; P<.001). The extended conversation with improved generative reflections was the only version associated with a significant increase in average importance (0.7, SD 2.0; P<.001) and readiness (0.4, SD 1.7; P=.01). The enhanced reflection and extended conversations exhibited significantly better perceived empathy than the nongenerative conversation (P=.02 and P=.004, respectively). The number of quit attempts did not significantly change between the week before the conversation and the week after across all 4 conversations. ConclusionsThe results suggest that generative reflections increase the impact of a conversation on readiness to quit smoking 1 week later, although a significant portion of the impact seen so far can be achieved by only asking questions without the reflections. These results support further evolution of the chatbot conversation and can serve as a basis for comparison against more advanced versions. Ausführliche Beschreibung

Autor*in:

Andrew Brown [verfasserIn] Ash Tanuj Kumar [verfasserIn] Osnat Melamed [verfasserIn] Imtihan Ahmed [verfasserIn] Yu Hao Wang [verfasserIn] Arnaud Deza [verfasserIn] Marc Morcos [verfasserIn] Leon Zhu [verfasserIn] Marta Maslej [verfasserIn] Nadia Minian [verfasserIn] Vidya Sujaya [verfasserIn] Jodi Wolff [verfasserIn] Olivia Doggett [verfasserIn] Mathew Iantorno [verfasserIn] Matt Ratto [verfasserIn] Peter Selby [verfasserIn] Jonathan Rose [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Übergeordnetes Werk:	In: JMIR Mental Health - JMIR Publications, 2015, 10, p e49132(2023)
Übergeordnetes Werk:	volume:10, p e49132 ; year:2023

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DOI / URN:	10.2196/49132

Katalog-ID:	DOAJ090898915