Background: Conversational agents, which we defined as computer programs that are designed to simulate two-way human conversation by using language and are potentially supplemented with nonlanguage modalities, offer promising avenues for health interventions for different populations across the life course. There is a lack of open-access and user-friendly resources for identifying research trends and gaps and pinpointing expertise across international centers.
Objective: Our aim is to provide an overview of all relevant evidence on conversational agents for health and well-being across the life course. Specifically, our objectives are to identify, categorize, and synthesize-through visual formats and a searchable database-primary studies and reviews in this research field.
Methods: An evidence map was selected as the type of literature review to be conducted, as it optimally corresponded to our aim. We systematically searched 8 databases (MEDLINE; CINAHL; Web of Science; Scopus; the Cochrane, ACM, IEEE, and Joanna Briggs Institute databases; and Google Scholar). We will perform backward citation searching on all included studies. The first stage of a double-stage screening procedure, which was based on abstracts and titles only, was conducted by using predetermined eligibility criteria for primary studies and reviews. An operational screening procedure was developed for streamlined and consistent screening across the team. Double data extraction will be performed with previously piloted data collection forms. We will appraise systematic reviews by using A Measurement Tool to Assess Systematic Reviews (AMSTAR) 2. Primary studies and reviews will be assessed separately in the analysis. Data will be synthesized through descriptive statistics, bivariate statistics, and subgroup analysis (if appropriate) and through high-level maps such as scatter and bubble charts. The development of the searchable database will be informed by the research questions and data extraction forms.
Results: As of April 2021, the literature search in the eight databases was concluded, yielding a total of 16,351 records. The first stage of screening, which was based on abstracts and titles only, resulted in the selection of 1282 records of primary studies and 151 records of reviews. These will be subjected to second-stage screening. A glossary with operational definitions for supporting the study selection and data extraction stages was drafted. The anticipated completion date is October 2021.
Conclusions: Our wider definition of a conversational agent and the broad scope of our evidence map will explicate trends and gaps in this field of research. Additionally, our evidence map and searchable database of studies will help researchers to avoid fragmented research efforts and wasteful redundancies. Finally, as part of the Harnessing the Power of Conversational e-Coaches for Health and Well-being Through Swiss-Portuguese Collaboration project, our work will also inform the development of an international taxonomy on conversational agents for health and well-being, thereby contributing to terminology standardization and categorization.
International Registered Report Identifier (irrid): DERR1-10.2196/26680.
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http://dx.doi.org/10.2196/26680 | DOI Listing |
J Med Syst
January 2025
Intelligent Systems, Delft University of Technology, Delft, The Netherlands.
To equip new counsellors at a Dutch child helpline with the needed counselling skills, the helpline uses role-playing, a form of learning through simulation in which one counsellor-in-training portrays a child seeking help and the other portrays a counsellor. However, this process is time-intensive and logistically challenging-issues that a conversational agent could help address. In this paper, we propose an initial design for a computer agent that acts as a child help-seeker to be used in a role-play setting.
View Article and Find Full Text PDFJMIR AI
January 2025
Faculty of Social Science, Ruhr University Bochum, Bochum, Germany.
Background: Conversational agents (CAs) are finding increasing application in health and social care, not least due to their growing use in the home. Recent developments in artificial intelligence, machine learning, and natural language processing have enabled a variety of new uses for CAs. One type of CA that has received increasing attention recently is smart speakers.
View Article and Find Full Text PDFInt 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.
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