@inproceedings{gollapalli-ng-2025-pirsuader,
title = "{PIR}suader: A Persuasive Chatbot for Mitigating Psychological Insulin Resistance in Type-2 Diabetic Patients",
author = "Gollapalli, Sujatha Das and
Ng, See-Kiong",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.401/",
pages = "5997--6013",
abstract = "Psychological Insulin Resistance (PIR) is described as the reluctance towards initiation and adherence of insulin-based treatments due to psychological barriers in diabetic patients. Though studies have shown that timely initiation with lifestyle changes are known to be crucial in sugar control and prevention of chronic conditions in Type 2 Diabetes (T2D) patients, many patients often have deep-rooted fears and misgivings related to insulin which hinder them from adapting to an insulin-based treatment regimen when recommended by healthcare specialists. Therefore, it is vitally important to address and allay these fallacious beliefs in T2D patients and persuade them to consider insulin as a treatment option. In this paper, we describe the design of PIRsuader, a persuasive chatbot for mitigating PIR in T2D patients. In PIRsuader, we effectively harness the conversation generation capabilities of state-of-the-art Large Language Models via a context-specific persuasive dialog act schema. We design reward functions that capture dialog act preferences for persuading reluctant patients and apply reinforcement learning to learn a dialog act prediction model. Our experiments using a collection of real doctor-diabetic patient conversations indicate that PIRsuader is able to improve the willingness in patients to try insulin as well as address specific concerns they have in an empathetic manner."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gollapalli-ng-2025-pirsuader">
<titleInfo>
<title>PIRsuader: A Persuasive Chatbot for Mitigating Psychological Insulin Resistance in Type-2 Diabetic Patients</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sujatha</namePart>
<namePart type="given">Das</namePart>
<namePart type="family">Gollapalli</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">See-Kiong</namePart>
<namePart type="family">Ng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-01</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 31st International Conference on Computational Linguistics</title>
</titleInfo>
<name type="personal">
<namePart type="given">Owen</namePart>
<namePart type="family">Rambow</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Leo</namePart>
<namePart type="family">Wanner</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marianna</namePart>
<namePart type="family">Apidianaki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hend</namePart>
<namePart type="family">Al-Khalifa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Barbara</namePart>
<namePart type="given">Di</namePart>
<namePart type="family">Eugenio</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Steven</namePart>
<namePart type="family">Schockaert</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Abu Dhabi, UAE</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Psychological Insulin Resistance (PIR) is described as the reluctance towards initiation and adherence of insulin-based treatments due to psychological barriers in diabetic patients. Though studies have shown that timely initiation with lifestyle changes are known to be crucial in sugar control and prevention of chronic conditions in Type 2 Diabetes (T2D) patients, many patients often have deep-rooted fears and misgivings related to insulin which hinder them from adapting to an insulin-based treatment regimen when recommended by healthcare specialists. Therefore, it is vitally important to address and allay these fallacious beliefs in T2D patients and persuade them to consider insulin as a treatment option. In this paper, we describe the design of PIRsuader, a persuasive chatbot for mitigating PIR in T2D patients. In PIRsuader, we effectively harness the conversation generation capabilities of state-of-the-art Large Language Models via a context-specific persuasive dialog act schema. We design reward functions that capture dialog act preferences for persuading reluctant patients and apply reinforcement learning to learn a dialog act prediction model. Our experiments using a collection of real doctor-diabetic patient conversations indicate that PIRsuader is able to improve the willingness in patients to try insulin as well as address specific concerns they have in an empathetic manner.</abstract>
<identifier type="citekey">gollapalli-ng-2025-pirsuader</identifier>
<location>
<url>https://aclanthology.org/2025.coling-main.401/</url>
</location>
<part>
<date>2025-01</date>
<extent unit="page">
<start>5997</start>
<end>6013</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T PIRsuader: A Persuasive Chatbot for Mitigating Psychological Insulin Resistance in Type-2 Diabetic Patients
%A Gollapalli, Sujatha Das
%A Ng, See-Kiong
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F gollapalli-ng-2025-pirsuader
%X Psychological Insulin Resistance (PIR) is described as the reluctance towards initiation and adherence of insulin-based treatments due to psychological barriers in diabetic patients. Though studies have shown that timely initiation with lifestyle changes are known to be crucial in sugar control and prevention of chronic conditions in Type 2 Diabetes (T2D) patients, many patients often have deep-rooted fears and misgivings related to insulin which hinder them from adapting to an insulin-based treatment regimen when recommended by healthcare specialists. Therefore, it is vitally important to address and allay these fallacious beliefs in T2D patients and persuade them to consider insulin as a treatment option. In this paper, we describe the design of PIRsuader, a persuasive chatbot for mitigating PIR in T2D patients. In PIRsuader, we effectively harness the conversation generation capabilities of state-of-the-art Large Language Models via a context-specific persuasive dialog act schema. We design reward functions that capture dialog act preferences for persuading reluctant patients and apply reinforcement learning to learn a dialog act prediction model. Our experiments using a collection of real doctor-diabetic patient conversations indicate that PIRsuader is able to improve the willingness in patients to try insulin as well as address specific concerns they have in an empathetic manner.
%U https://aclanthology.org/2025.coling-main.401/
%P 5997-6013
Markdown (Informal)
[PIRsuader: A Persuasive Chatbot for Mitigating Psychological Insulin Resistance in Type-2 Diabetic Patients](https://aclanthology.org/2025.coling-main.401/) (Gollapalli & Ng, COLING 2025)
ACL