Effectiveness of a Conversational Chatbot (Dejal@bot) for the Adult Population to Quit Smoking: Pragmatic, Multicenter, Controlled, Randomized Clinical Trial in Primary Care

Evolutionary Trends in the Adoption, Adaptation, and Abandonment of Mobile Health Technologies: Viewpoint Based on 25 Years of Research

Over the past quarter-century, mobile health (mHealth) technologies have experienced significant changes in adoption rates, adaptation strategies, and instances of abandonment. Understanding the underlying factors driving these trends is essential for optimizing the design, implementation, and sustainability of interventions using these technologies. The evolution of mHealth adoption has followed a progressive trajectory, starting with cautious exploration and later accelerating due to technological advancements, increased smartphone penetration, and growing acceptance of digital health solutions by both health care providers and patients. However, alongside widespread adoption, challenges related to usability, interoperability, privacy concerns, and socioeconomic disparities have emerged, necessitating ongoing adaptation efforts. While many mHealth initiatives have successfully adapted to address these challenges, technology abandonment remains common, often due to unsustainable business models, inadequate user engagement, and insufficient evidence of effectiveness. This paper utilizes the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework to examine the interplay between the academic and industry sectors in patterns of adoption, adaptation, and abandonment, using 3 major mHealth innovations as examples: health-related SMS text messaging, mobile apps and wearables, and social media for health communication. Health SMS text messaging has demonstrated significant potential as a tool for health promotion, disease management, and patient engagement. The proliferation of mobile apps and devices has facilitated a shift from in-person and in-clinic practices to mobile- and wearable-centric solutions, encompassing everything from simple activity trackers to advanced health monitoring devices. Social media, initially characterized by basic text-based interactions in chat rooms and online forums, underwent a paradigm shift with the emergence of platforms such as MySpace and Facebook. This transition ushered in an era of mass communication through social media. The rise of microblogging and visually focused platforms such as Twitter(now X), Instagram, Snapchat, and TikTok, along with the integration of live streaming and augmented reality features, exemplifies the ongoing innovation within the social media landscape. Over the past 25 years, there have been remarkable strides in the adoption and adaptation of mHealth technologies, driven by technological innovation and a growing recognition of their potential to revolutionize health care delivery. Each mobile technology uniquely enhances public health and health care by catering to different user needs. SMS text messaging offers wide accessibility and proven effectiveness, while mobile apps and wearables provide comprehensive functionalities for more in-depth health management. Social media platforms amplify these efforts with their vast reach and community-building potential, making it essential to select the right tool for specific health interventions to maximize impact and engagement. Nevertheless, continued efforts are needed to address persistent challenges and mitigate instances of abandonment, ensuring that mHealth interventions reach their full potential in improving health outcomes and advancing equitable access to care.

A systematic review of chatbot-assisted interventions for substance use

Objectives

This study systematically reviewed research on the utilization of chatbot-related technologies for the prevention, assessment, and treatment of various substance uses, including alcohol, nicotine, and other drugs.

Methods

Following PRISMA guidelines, 28 articles were selected for final analysis from an initial screening of 998 references. Data were coded for multiple components, including study characteristics, intervention types, intervention contents, sample characteristics, substance use details, measurement tools, and main findings, particularly emphasizing the effectiveness of chatbot-assisted interventions on substance use and the facilitators and barriers affecting program effectiveness.

Results

Half of the studies specifically targeted smoking. Furthermore, over 85% of interventions were designed to treat substance use, with 7.14% focusing on prevention and 3.57% on assessment. Perceptions of effectiveness in quitting substance use varied, ranging from 25% to 50%, while for reduced substance use, percentages ranged from 66.67% to 83.33%. Among the studies assessing statistical effectiveness (46.43%), all experimental studies, including quasi-experiments, demonstrated significant and valid effects. Notably, 30% of studies emphasized personalization and providing relevant tips or information as key facilitators.

Conclusion

This study offers valuable insights into the development and validation of chatbot-assisted interventions, thereby establishing a robust foundation for their efficacy.

Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review

BACKGROUND

Chatbots, or conversational agents, have emerged as significant tools in health care, driven by advancements in artificial intelligence and digital technology. These programs are designed to simulate human conversations, addressing various health care needs. However, no comprehensive synthesis of health care chatbots' roles, users, benefits, and limitations is available to inform future research and application in the field.

OBJECTIVE

This review aims to describe health care chatbots' characteristics, focusing on their diverse roles in the health care pathway, user groups, benefits, and limitations.

METHODS

A rapid review of published literature from 2017 to 2023 was performed with a search strategy developed in collaboration with a health sciences librarian and implemented in the MEDLINE and Embase databases. Primary research studies reporting on chatbot roles or benefits in health care were included. Two reviewers dual-screened the search results. Extracted data on chatbot roles, users, benefits, and limitations were subjected to content analysis.

RESULTS

The review categorized chatbot roles into 2 themes: delivery of remote health services, including patient support, care management, education, skills building, and health behavior promotion, and provision of administrative assistance to health care providers. User groups spanned across patients with chronic conditions as well as patients with cancer; individuals focused on lifestyle improvements; and various demographic groups such as women, families, and older adults. Professionals and students in health care also emerged as significant users, alongside groups seeking mental health support, behavioral change, and educational enhancement. The benefits of health care chatbots were also classified into 2 themes: improvement of health care quality and efficiency and cost-effectiveness in health care delivery. The identified limitations encompassed ethical challenges, medicolegal and safety concerns, technical difficulties, user experience issues, and societal and economic impacts.

CONCLUSIONS

Health care chatbots offer a wide spectrum of applications, potentially impacting various aspects of health care. While they are promising tools for improving health care efficiency and quality, their integration into the health care system must be approached with consideration of their limitations to ensure optimal, safe, and equitable use.

Usability of a telehealth-nurse supported home-based walking training for peripheral arterial disease - The Keep Pace! pilot study

Background: Guidelines recommend walking trainings for peripheral arterial disease (PAD) management. Supervised walking training is superior to walking advise to improve the walking distance. Telehealth service with nurse support may close this gap. Patients and methods: This study introduces a telehealth service, "Keep pace!", which has been developed for patients with symptomatic PAD (Fontaine stage IIa and IIb), enabling a structured home-based walking training while monitoring progress via an app collecting unblinded account of steps and walking distance in self-paced 6-minute-walking-tests by geolocation tracking to enhance intrinsic motivation. Supervision by nurses via telephone calls was provided for 8 weeks, followed by 4 weeks of independent walking training. Patient satisfaction, walking distance and health-related quality of life were assessed. Results: 19 patients completed the study. The analysis revealed an overall high satisfaction with the telehealth service (95.4%), including system quality (95.1%), information quality (94.4%), service quality (95.6%), intention to use (92.8%), general satisfaction with the program (98.4%) and health benefits (95.8%). 78.9% asserted that the telehealth service lacking nurse calls would be less efficacious. Pain-free walking distance (76.3±36.8m to 188.4±81.2m, +112.2%, p<0.001) as well as total distance in 6-minute-walking test (308.8±82.6m to 425.9±107.1m, +117.2%, p<0.001) improved significantly. The telehealth service significantly reduced discomfort by better pain control (+15.5%, p=0.015) and social participation (+10.5%, p=0.042). Conclusions: In conclusion, patients were highly satisfied with the telehealth service. The physical well-being of the PAD patients improved significantly post vs. prior the telehealth program.

Exploring Technology's Influence on Health Behaviours and Well-being in Type 1 Diabetes: a Review

PURPOSE OF REVIEW

Maintaining positive health behaviours promotes better health outcomes for people with type 1 diabetes (T1D). However, implementing these behaviours may also lead to additional management burdens and challenges. Diabetes technologies, including continuous glucose monitoring systems, automated insulin delivery systems, and digital platforms, are being rapidly developed and widely used to reduce these burdens. Our aim was to review recent evidence to explore the influence of these technologies on health behaviours and well-being among adults with T1D and discuss future directions.

RECENT FINDINGS

Current evidence, albeit limited, suggests that technologies applied in diabetes self-management education and support (DSME/S), nutrition, physical activity (PA), and psychosocial care areas improved glucose outcomes. They may also increase flexibility in insulin adjustment and eating behaviours, reduce carb counting burden, increase confidence in PA, and reduce mental burden. Technologies have the potential to promote health behaviours changes and well-being for people with T1D. More confirmative studies on their effectiveness and safety are needed to ensure optimal integration in standard care practices.

Understanding ChatGPT for evidence-based utilization in interventional radiology

Advancement in artificial intelligence (AI) has the potential to improve the efficiency and accuracy of medical care. New techniques used in machine learning have enhanced the functionality of software to perform advanced tasks with human-like capabilities. ChatGPT is the most utilized large language model and provides a diverse range of communication tasks. Interventional Radiology (IR) may benefit from the implementation of ChatGPT for specific tasks. This review summarizes the design principles of ChatGPT relevant to healthcare and highlights activities with the greatest potential for ChatGPT utilization in the practice of IR. These tasks involve patient-directed and physician-directed communications to convey medical information efficiently and act as a medical decision support tool. ChatGPT exemplifies the evolving landscape of new AI tools for advancing patient care and how physicians and patients may benefit with strategic execution.

Evaluation framework for conversational agents with artificial intelligence in health interventions: a systematic scoping review

OBJECTIVES

Conversational agents (CAs) with emerging artificial intelligence present new opportunities to assist in health interventions but are difficult to evaluate, deterring their applications in the real world. We aimed to synthesize existing evidence and knowledge and outline an evaluation framework for CA interventions.

MATERIALS AND METHODS

We conducted a systematic scoping review to investigate designs and outcome measures used in the studies that evaluated CAs for health interventions. We then nested the results into an overarching digital health framework proposed by the World Health Organization (WHO).

RESULTS

The review included 81 studies evaluating CAs in experimental (n = 59), observational (n = 15) trials, and other research designs (n = 7). Most studies (n = 72, 89%) were published in the past 5 years. The proposed CA-evaluation framework includes 4 evaluation stages: (1) feasibility/usability, (2) efficacy, (3) effectiveness, and (4) implementation, aligning with WHO's stepwise evaluation strategy. Across these stages, this article presents the essential evidence of different study designs (n = 8), sample sizes, and main evaluation categories (n = 7) with subcategories (n = 40). The main evaluation categories included (1) functionality, (2) safety and information quality, (3) user experience, (4) clinical and health outcomes, (5) costs and cost benefits, (6) usage, adherence, and uptake, and (7) user characteristics for implementation research. Furthermore, the framework highlighted the essential evaluation areas (potential primary outcomes) and gaps across the evaluation stages.

DISCUSSION AND CONCLUSION

This review presents a new framework with practical design details to support the evaluation of CA interventions in healthcare research.

PROTOCOL REGISTRATION

The Open Science Framework (https://osf.io/9hq2v) on March 22, 2021.

AI Conversational Agent to Improve Varenicline Adherence: Protocol for a Mixed Methods Feasibility Study

BACKGROUND

Varenicline is a pharmacological intervention for tobacco dependence that is safe and effective in facilitating smoking cessation. Enhanced adherence to varenicline augments the probability of prolonged smoking abstinence. However, research has shown that one-third of people who use varenicline are nonadherent by the second week. There is evidence showing that behavioral support helps with medication adherence. We have designed an artificial intelligence (AI) conversational agent or health bot, called "ChatV," based on evidence of what works as well as what varenicline is, that can provide these supports. ChatV is an evidence-based, patient- and health care provider-informed health bot to improve adherence to varenicline. ChatV has been programmed to provide medication reminders, answer questions about varenicline and smoking cessation, and track medication intake and the number of cigarettes.

OBJECTIVE

This study aims to explore the feasibility of the ChatV health bot, to examine if it is used as intended, and to determine the appropriateness of proceeding with a randomized controlled trial.

METHODS

We will conduct a mixed methods feasibility study where we will pilot-test ChatV with 40 participants. Participants will be provided with a standard 12-week varenicline regimen and access to ChatV. Passive data collection will include adoption measures (how often participants use the chatbot, what features they used, when did they use it, etc). In addition, participants will complete questionnaires (at 1, 4, 8, and 12 weeks) assessing self-reported smoking status and varenicline adherence, as well as questions regarding the acceptability, appropriateness, and usability of the chatbot, and participate in an interview assessing acceptability, appropriateness, fidelity, and adoption. We will use "stop, amend, and go" progression criteria for pilot studies to decide if a randomized controlled trial is a reasonable next step and what modifications are required. A health equity lens will be adopted during participant recruitment and data analysis to understand and address the differences in uptake and use of this digital health solution among diverse sociodemographic groups. The taxonomy of implementation outcomes will be used to assess feasibility, that is, acceptability, appropriateness, fidelity, adoption, and usability. In addition, medication adherence and smoking cessation will be measured to assess the preliminary treatment effect. Interview data will be analyzed using the framework analysis method.

RESULTS

Participant enrollment for the study will begin in January 2024.

CONCLUSIONS

By using predetermined progression criteria, the results of this preliminary study will inform the determination of whether to advance toward a larger randomized controlled trial to test the effectiveness of the health bot. Additionally, this study will explore the acceptability, appropriateness, fidelity, adoption, and usability of the health bot. These insights will be instrumental in refining the intervention and the health bot.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05997901; https://classic.clinicaltrials.gov/ct2/show/NCT05997901.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/53556.

An integrated smoking cessation and alcohol intervention among Hong Kong Chinese young people: Study protocol for a feasibility randomized controlled trial

INTRODUCTION

Young smokers always partake in both smoking and drinking. However, drinking undermines their likelihood to attempt quitting smoking or to successfully abstain from smoking. Hence, this trial will examine the feasibility of implementing an integrated smoking cessation and alcohol intervention in young Hong Kong Chinese people. Effect sizes of the integrated intervention (II) on self-reported and biochemically validated quit rates will also be calculated.

METHODS

The study will be a three-arm randomized controlled trial in a convenience sample of 150 smokers aged 18-25 years with alcohol drinking. Participants will be randomized into a standard treatment (ST), II, or control arm. The ST group will receive a brief smoking cessation intervention based on the 5A (Ask, Assess, Advice, Assist, Arrange) and 5R (Relevance, Risks, Rewards, Roadblocks, Repetition) models. The II group will receive brief advice on alcohol use based on the FRAMES (Feedback, Responsibility, Advice, Menu, Empathy, Efficacy) model in addition to the brief smoking cessation intervention. Both the ST and II groups will receive booster interventions at 1-week, 1-month, 3-month, and 6-month follow-up. The control group will receive leaflets on smoking cessation and alcohol reduction. Self-reported quitters at 6-month follow-up will be invited for biochemical validation. The primary outcomes are feasibility measures. The secondary outcomes are effect size of II on self-reported and biochemically validated quit rates at 6 months relative to control and ST. Outcomes will be assessed at baseline and at 1-week, 1-month, 3-month, and 6-month follow-ups.

ANALYSIS

Descriptive statistics will be used to calculate the feasibility measures. The three arms will be compared using analysis of variance for continuous variables and chi-square test for categorical variables. Effect sizes of II for self-reported and biochemically validated quit rates at 6 months will be determined using the generalized estimating equation model.

"A Great Way to Start the Conversation": Evidence for the Use of an Adolescent Mental Health Chatbot Navigator for Youth at Risk of HIV and Other STIs

Chatbot use is increasing for mobile health interventions on sensitive and stigmatized topics like mental health because of their anonymity and privacy. This anonymity provides acceptability to sexual and gendered minority youth (ages 16-24) at increased risk of HIV and other STIs with poor mental health due to higher levels of stigma, discrimination, and social isolation. This study evaluates the usability of Tabatha-YYC, a pilot chatbot navigator created to link these youth to mental health resources. Tabatha-YYC was developed using a Youth Advisory Board (n = 7). The final design underwent user testing (n = 20) through a think-aloud protocol, semi-structured interview, and a brief survey post-exposure which included the Health Information Technology Usability Evaluation Scale. The chatbot was found to be an acceptable mental health navigator by participants. This study provides important design methodology considerations and key insights into chatbot design preferences of youth at risk of STIs seeking mental health resources.

Effect of mobile interventions with nicotine replacement therapy sampling on long-term smoking cessation in community smokers: A pragmatic randomized clinical trial

INTRODUCTION

Mobile interventions enable personalized behavioral support that could improve smoking cessation (SC) in smokers ready to quit. Scalable interventions, including unmotivated smokers, are needed. We evaluated the effect of personalized behavioral support through mobile interventions plus nicotine replacement therapy sampling (NRT-S) on SC in Hong Kong community smokers.

METHODS

A total of 664 adult daily cigarette smokers (74.4% male, 51.7% not ready to quit in 30 days) were proactively recruited from smoking hotspots and individually randomized (1:1) to the intervention and control groups (each, n=332). Both groups received brief advice and active referral to SC services. The intervention group received 1-week NRT-S at baseline and 12-week personalized behavioral support through SC advisor-delivered Instant Messaging (IM) and a fully automated chatbot. The control group received regular text messages regarding general health at a similar frequency. Primary outcomes were carbon monoxide-validated smoking abstinence at 6 and 12 months post-treatment initiation. Secondary outcomes included self-reported 7-day point-prevalence and 24-week continuous abstinence, quit attempts, smoking reduction, and SC service use at 6 and 12 months.

RESULTS

By intention-to-treat, the intervention group did not significantly increase validated abstinence at 6 months (3.9% vs 3.0%, OR=1.31; 95% CI: 0.57–3.04) and 12 months (5.4% vs 4.5%, OR=1.21; 95% CI: 0.60–2.45), as were self-reported 7-day point-prevalence abstinence, smoking reduction, and SC service use at 6 and 12 months. More participants in the intervention than control group made a quit attempt by 6 months (47.0% vs 38.0%, OR=1.45; 95% CI: 1.06–1.97). Intervention engagement rates were low, but engagement in IM alone or combined with chatbot showed higher abstinence at 6 months (adjusted odds ratios, AORs=4.71 and 8.95, both p<0.05).

CONCLUSIONS

Personalized behavioral support through mobile interventions plus NRT-S did not significantly improve abstinence in community smokers compared to text only messaging. The suboptimal intervention engagement needs to be addressed in future studies.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04001972.

Conversational artificial intelligence interventions to support smoking cessation: A systematic review and meta-analysis

Background

Conversational artificial intelligence (chatbots and dialogue systems) is an emerging tool for tobacco cessation that has the potential to emulate personalised human support and increase engagement. We aimed to determine the effect of conversational artificial intelligence interventions with or without standard tobacco cessation interventions on tobacco cessation outcomes among adults who smoke, compared to no intervention, placebo intervention or an active comparator.

Methods

A comprehensive search of six databases was completed in June 2022. Eligible studies included randomised controlled trials published since 2005. The primary outcome was sustained tobacco abstinence, self-reported and/or biochemically validated, for at least 6 months. Secondary outcomes included point-prevalence abstinence and sustained abstinence of less than 6 months. Two authors independently extracted data on cessation outcomes and completed the risk of bias assessment. Random effects meta-analysis was conducted.

Results

From 819 studies, five randomised controlled trials met inclusion criteria (combined sample size n = 58,796). All studies differed in setting, methodology, intervention, participants and end-points. Interventions included chatbots embedded in multi- and single-component smartphone apps (n = 3), a social media-based (n = 1) chatbot, and an internet-based avatar (n = 1). Random effects meta-analysis of three studies found participants in the conversational artificial intelligence enhanced intervention were significantly more likely to quit smoking at 6-month follow-up compared to control group participants (RR = 1.29, 95% CI (1.13, 1.46), p < 0.001). Loss to follow up was generally high. Risk of bias was high overall.

Conclusion

We found limited but promising evidence on the effectiveness of conversational artificial intelligence interventions for tobacco cessation. Although all studies found benefits from conversational artificial intelligence interventions, results should be interpreted with caution due to high heterogeneity. Given the rapid evolution and potential of artificial intelligence interventions, further well-designed randomised controlled trials following standardised reporting guidelines are warranted in this emerging area.

Cocreation of a conversational agent to help patients adhere to their varenicline treatment: A study protocol

Objective

Varenicline is the most efficacious approved smoking cessation medication, making it one of the most cost-effective clinical interventions for reducing tobacco-related morbidity and mortality. Adhering to varenicline is strongly associated with smoking cessation. Healthbots have the potential to help people adhere to their medications by scaling up evidence-based behavioral interventions. In this protocol, we outline how we will follow the UK's Medical Research Council's guidance to codesign a theory-informed, evidence-based, and patient-centered healthbot to help people adhere to varenicline.

Methods

The study will utilize the Discover, Design and Build, and Test framework and will include three phases: (a) a rapid review and interviews with 20 patients and 20 healthcare providers to understand barriers and facilitators to varenicline adherence (Discover phase); (b) Wizard of Oz test to design the healthbot and get a sense of the questions that chatbot has to be able to answer (Design phase); and (c) building, training, and beta-testing the healthbot (Building and Testing phases) where the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability framework will be used to develop the healthbot using the simplest sensible solution, and 20 participants will beta test the healthbot. We will use the Capability, Opportunity, Motivation-Behavior (COM-B) model of behavior change and its associated framework, the Theoretical Domains Framework, to organize the findings.

Conclusions

The present approach will enable us to systematically identify the most appropriate features for the healthbot based on a well-established behavioral theory, the latest scientific evidence, and end users' and healthcare providers' knowledge.