Social Network Chatbots for Smoking Cessation: Agent and Multi-Agent Frameworks

Calvaresi, Davide; Calbimonte, Jean-Paul; Debusson, Fabien; Najjar, Amro; Schumacher, Michael

doi:10.1145/3350546.3352532

Cited by 32 publications

(56 citation statements)

References 25 publications

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“…Despite the potential for natural language virtual assistants to provide personalized information and support users to engage in positive health behaviors, only three natural language processing virtual assistants focus on lifestyle modification. “J’arrête de fumer” assisted users in quitting smoking by profiling smoking behavior and providing advice and support at times when cravings were likely to occur [ 16 ]. A feasibility study published in 2019 employed a virtual assistant “Tess” to support obese adolescents in health-promoting behavior change with success [ 17 ]; however, Tess played a support role only and was designed to supplement ongoing in-person hospital-based services.…”

Section: Introductionmentioning

confidence: 99%

A Physical Activity and Diet Program Delivered by Artificially Intelligent Virtual Health Coach: Proof-of-Concept Study

Maher¹,

Davis²,

Curtis³

et al. 2020

JMIR Mhealth Uhealth

179

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Background Poor diet and physical inactivity are leading modifiable causes of death and disease. Advances in artificial intelligence technology present tantalizing opportunities for creating virtual health coaches capable of providing personalized support at scale. Objective This proof of concept study aimed to test the feasibility (recruitment and retention) and preliminary efficacy of physical activity and Mediterranean-style dietary intervention (MedLiPal) delivered via artificially intelligent virtual health coach. Methods This 12-week single-arm pre-post study took place in Adelaide, Australia, from March to August 2019. Participants were inactive community-dwelling adults aged 45 to 75 years, recruited through news stories, social media posts, and flyers. The program included access to an artificially intelligent chatbot, Paola, who guided participants through a computer-based individualized introductory session, weekly check-ins, and goal setting, and was available 24/7 to answer questions. Participants used a Garmin Vivofit4 tracker to monitor daily steps, a website with educational materials and recipes, and a printed diet and activity log sheet. Primary outcomes included feasibility (based on recruitment and retention) and preliminary efficacy for changing physical activity and diet. Secondary outcomes were body composition (based on height, weight, and waist circumference) and blood pressure. Results Over 4 weeks, 99 potential participants registered expressions of interest, with 81 of those screened meeting eligibility criteria. Participants completed a mean of 109.8 (95% CI 1.9-217.7) more minutes of physical activity at week 12 compared with baseline. Mediterranean diet scores increased from a mean of 3.8 out of 14 at baseline, to 9.6 at 12 weeks (mean improvement 5.7 points, 95% CI 4.2-7.3). After 12 weeks, participants lost an average 1.3 kg (95% CI –0.1 to –2.5 kg) and 2.1 cm from their waist circumference (95% CI –3.5 to –0.7 cm). There were no significant changes in blood pressure. Feasibility was excellent in terms of recruitment, retention (90% at 12 weeks), and safety (no adverse events). Conclusions An artificially intelligent virtual assistant-led lifestyle-modification intervention was feasible and achieved measurable improvements in physical activity, diet, and body composition at 12 weeks. Future research examining artificially intelligent interventions at scale, and for other health purposes, is warranted.

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Section: Introductionmentioning

confidence: 99%

A Physical Activity and Diet Program Delivered by Artificially Intelligent Virtual Health Coach: Proof-of-Concept Study

Maher¹,

Davis²,

Curtis³

et al. 2020

JMIR Mhealth Uhealth

179

View full text Add to dashboard Cite

show abstract

“…Model-wise, chatbots and agents have remarkable overlaps. In the literature, they can be considered completely matching (in terms of functionalities, knowledge, behaviors, and user mapping) [4,6] or modeling the chatbot as an interface for a more complex, intelligent, and possibly distributed system [28,29]. Bentivoglio et al [30] embody the combination of chatbot-agent(s) as a stimulus reply state automaton and a goal-driven probabilistic agent (defined as a Partially Observable Markov Decision Process).…”

Section: Multi-agent Systems and Chatbotsmentioning

confidence: 99%

“…Among the factors contributing to this increasing adoption, we can mention anywhere/anytime availability, immediate response, confidentiality, social acceptance, and massive scalability. Thanks to these factors, chatbots have shown to be effective in a wide range of domains, particularly for motivational (e.g., social network campaigns [4]) and support (e.g., customer management [5], eHealth [6], and assisted-living scenarios [7]).…”

Section: Introductionmentioning

confidence: 99%

“…In the healthcare domain, chatbots leveraging on tailored support and social aspects can be of great support to foster behavioral change (e.g., smoking cessation) [4,6,8], monitoring of chronic health conditions [9], primary care [10], etc. However, modern chatbots are still affected by significant limitations such as inadequate personalization, lack of real-time monitoring, reporting and customization for medical personnel, lack of mechanisms to integrate communities of chatbots, limited knowledge sharing capabilities, and the impossibility of seamlessly deploying multi-domain campaigns within the same framework.…”

Section: Introductionmentioning

confidence: 99%

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EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots

et al. 2021

Self Cite

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Context. Asynchronous messaging is increasingly used to support human–machine interactions, generally implemented through chatbots. Such virtual entities assist the users in activities of different kinds (e.g., work, leisure, and health-related) and are becoming ingrained into humans’ habits due to factors including (i) the availability of mobile devices such as smartphones and tablets, (ii) the increasingly engaging nature of chatbot interactions, (iii) the release of dedicated APIs from messaging platforms, and (iv) increasingly complex AI-based mechanisms to power the bots’ behaviors. Nevertheless, most of the modern chatbots rely on state machines (implementing conversational rules) and one-fits-all approaches, neglecting personalization, data-stream privacy management, multi-topic management/interconnection, and multimodal interactions. Objective. This work addresses the challenges above through an agent-based framework for chatbot development named EREBOTS. Methods. The foundations of the framework are based on the implementation of (i) multi-front-end connectors and interfaces (i.e., Telegram, dedicated App, and web interface), (ii) enabling the configuration of multi-scenario behaviors (i.e., preventive physical conditioning, smoking cessation, and support for breast-cancer survivors), (iii) online learning, (iv) personalized conversations and recommendations (i.e., mood boost, anti-craving persuasion, and balance-preserving physical exercises), and (v) responsive multi-device monitoring interface (i.e., doctor and admin). Results. EREBOTS has been tested in the context of physical balance preservation in social confinement times (due to the ongoing pandemic). Thirteen individuals characterized by diverse age, gender, and country distribution have actively participated in the experimentation, reporting advancements in the physical balance and overall satisfaction of the interaction and exercises’ variety they have been proposed.

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“…In the last five years, early prototypes were mainly based on simple state machines, offering simple interactions simulating conversations with humans [2,7]. In the tourism sector, the first interactions delegated to a chatbot were used to support the search for tips and information (e.g., opening hours) of local restaurants [17] and customer-care basic support (i.e., 85% of customer care in tourism are today handled by chatbots/AI-based systems [37]).…”

Section: Introductionmentioning

confidence: 99%

The Evolution of Chatbots in Tourism: A Systematic Literature Review

Calvaresi

Kineber

Calbimonte

et al. 2021

Information and Communication Technologies in Tourism 2021

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In the last decade, Information and Communication Technologies have revolutionized the tourism and hospitality sector. One of the latest innovations shaping new dynamics and fostering a remarkable behavioral change in the interaction between the service provider and the tourist is the employment of increasingly sophisticated chatbots. This work analyzes the most recent systems presented in the literature (since 2016) investigated via 12 research questions. The often appreciated quick evolution of such solutions is the primary outcome. However, such technological and financial fast-pace requires continuous investments, upskilling, and system innovation to tackle the eTourism challenges, which are shifting towards new dimensions.

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Social Network Chatbots for Smoking Cessation: Agent and Multi-Agent Frameworks

Cited by 32 publications

References 25 publications

A Physical Activity and Diet Program Delivered by Artificially Intelligent Virtual Health Coach: Proof-of-Concept Study

A Physical Activity and Diet Program Delivered by Artificially Intelligent Virtual Health Coach: Proof-of-Concept Study

EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots

The Evolution of Chatbots in Tourism: A Systematic Literature Review

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