Background: Effective management of physical and psychological symptoms is a critical component of comprehensive care for both chronic disease patients and apparently healthy individuals experiencing episodic symptoms. Conversational agents, which are dialog systems capable of understanding and generating human language, have emerged as a potential tool to enhance symptom management through interactive support.
Objective: To examine the characteristics and effectiveness of conversational agent-delivered interventions reported in randomized controlled trials (RCTs) in the management of both physical and psychological symptoms.
Design: A systematic review.
Methods: A comprehensive search was performed in Pubmed, ACM Digital Library, CINAHL, EMBASE, PyscInfo, Web of Science, Scopus and gray literature sources from their inception to Oct 2024. Search terms included "conversational agent", "symptom", "randomized controlled trial" and their synonyms and hyponyms. Duplicates were identified by EndNote, and titles, abstracts and full texts were independently screened according to predefined criteria. Data extraction focused on basic study characteristics and conversational agent details, with The Cochrane Risk of Bias 2.0 tool employed for bias assessment.
Results: The search yielded 2756 articles and 29 were finally included for review. The included studies predominantly came from developed countries (n = 23) and were conducted between 2020 and 2024 (n = 24). The studies frequently evaluated the feasibility and acceptability of conversational agent interventions (n = 14), with a predominantly focus on psychological symptoms (depression, anxiety, etc.) (n = 17). A few studies focused on physical symptoms (pain, etc.) (n = 4), while others addressed both symptoms (n = 8). Twenty-five distinct conversational agents (Woebot, Tess, etc.) were evaluated, utilizing platforms ranging from proprietary applications to common messaging channels like WeChat and Facebook Messenger. Cognitive Behavioral Therapy (CBT) was a commonly integrated approach (n = 22), with rule-based dialogs (n = 22) as the most commonly dialog system methods and Natural Language Processing (NLP) (n = 15) as the predominant AI techniques. The median recruitment and completion rates were 72 % and 79 %, respectively. The majority of studies reported positive user experiences and significant symptom management improvements (n = 22). However, risk of bias was high in seventeen studies and presented some concerns in nine others.
Conclusions: Conversational agents have shown promise in enhancing both physical and psychological symptom management through positive user experiences and effectiveness. However, the high risk of bias identified in many studies warrants caution in interpreting these findings. Future research should prioritize the methodological quality of RCTs to strengthen the evidence base supporting the use of conversational agents as a complementary tool in symptom management.
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http://dx.doi.org/10.1016/j.ijnurstu.2024.104991 | DOI Listing |
Int J Nurs Stud
December 2024
School of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, China.; Research Centre for Chinese Medicine Innovation, The Hong Kong Polytechnic University, Hong Kong SAR, China.; Joint Research Centre for Primary Health Care, The Hong Kong Polytechnic University, Hong Kong SAR, China.. Electronic address:
Background: Effective management of physical and psychological symptoms is a critical component of comprehensive care for both chronic disease patients and apparently healthy individuals experiencing episodic symptoms. Conversational agents, which are dialog systems capable of understanding and generating human language, have emerged as a potential tool to enhance symptom management through interactive support.
Objective: To examine the characteristics and effectiveness of conversational agent-delivered interventions reported in randomized controlled trials (RCTs) in the management of both physical and psychological symptoms.
Adv Skin Wound Care
January 2025
At the Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, United States, Adrian Chen, BS, Aleksandra Qilleri, BS, and Timothy Foster, BS, are Medical Students. Amit S. Rao, MD, is Project Manager, Department of Surgery, Wound Care Division, Northwell Wound Healing Center and Hyperbarics, Northwell Health, Hempstead. Sandeep Gopalakrishnan, PhD, MAPWCA, is Associate Professor and Director, Wound Healing and Tissue Repair Analytics Laboratory, School of Nursing, College of Health Professions, University of Wisconsin-Milwaukee. Jeffrey Niezgoda, MD, MAPWCA, is Founder and President Emeritus, AZH Wound Care and Hyperbaric Oxygen Therapy Center, Milwaukee, and President and Chief Medical Officer, WebCME, Greendale, Wisconsin. Alisha Oropallo, MD, is Professor of Surgery, Donald and Barbara Zucker School of Medicine and The Feinstein Institutes for Medical Research, Manhasset New York; Director, Comprehensive Wound Healing Center, Northwell Health; and Program Director, Wound and Burn Fellowship program, Northwell Health.
Generative artificial intelligence (AI) models are a new technological development with vast research use cases among medical subspecialties. These powerful large language models offer a wide range of possibilities in wound care, from personalized patient support to optimized treatment plans and improved scientific writing. They can also assist in efficiently navigating the literature and selecting and summarizing articles, enabling researchers to focus on impactful studies relevant to wound care management and enhancing response quality through prompt-learning iterations.
View Article and Find Full Text PDFSci Rep
January 2025
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Wundtlaan 1, 6525 XD Nijmegen, Nijmegen, The Netherlands.
Increasing evidence suggests that interlocutors use visual communicative signals to form predictions about unfolding utterances, but there is little data on the predictive potential of facial signals in conversation. In an online experiment with virtual agents, we examine whether facial signals produced by an addressee may allow speakers to anticipate the response to a question before it is given. Participants (n = 80) viewed videos of short conversation fragments between two virtual humans.
View Article and Find Full Text PDFFront Digit Health
December 2024
MOH Office for Healthcare Transformation, Singapore, Singapore.
The COVID-19 pandemic in Singapore led to limited access to mental health services, resulting in increased distress among the population. This study explores the potential benefits of offering a digital mental health intervention (DMHI), Wysa, as a brief and longitudinal intervention as part of the mindline.sg initiative launched by the MOH Office for Healthcare Transformation in Singapore.
View Article and Find Full Text PDFJMIR Aging
January 2025
Department of Computing, Faculty of Computer and Mathematical Sciences, Hong Kong Polytechnic University, Hung Hom, China (Hong Kong).
Background: Providing ongoing support to the increasing number of caregivers as their needs change in the long-term course of dementia is a severe challenge to any health care system. Conversational artificial intelligence (AI) operating 24/7 may help to tackle this problem.
Objective: This study describes the development of a generative AI chatbot-the PDC30 Chatbot-and evaluates its acceptability in a mixed methods study.
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