The Personalization of Conversational Agents in Health Care: Systematic Review.
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ABSTRACT:
SUBMITTER: Kocaballi AB
PROVIDER: S-EPMC6873147 | biostudies-literature | 2019 Nov
REPOSITORIES: biostudies-literature
Publications
The Personalization of Conversational Agents in Health Care: Systematic Review.
Kocaballi Ahmet Baki AB Berkovsky Shlomo S Quiroz Juan C JC Laranjo Liliana L Tong Huong Ly HL Rezazadegan Dana D Briatore Agustina A Coiera Enrico E
Journal of medical Internet research 20191107 11
<h4>Background</h4>The personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents.<h4>Objective</h4>The goal of this systematic review was to understand the ways in which personalization has been used with conversational agents in health care and characterize the methods of its implementation.<h4>Methods</h4>We searched on P ...[more]
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Project description:BackgroundThe high demand for health care services and the growing capability of artificial intelligence have led to the development of conversational agents designed to support a variety of health-related activities, including behavior change, treatment support, health monitoring, training, triage, and screening support. Automation of these tasks could free clinicians to focus on more complex work and increase the accessibility to health care services for the public. An overarching assessment of the acceptability, usability, and effectiveness of these agents in health care is needed to collate the evidence so that future development can target areas for improvement and potential for sustainable adoption.ObjectiveThis systematic review aims to assess the effectiveness and usability of conversational agents in health care and identify the elements that users like and dislike to inform future research and development of these agents.MethodsPubMed, Medline (Ovid), EMBASE (Excerpta Medica dataBASE), CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Science, and the Association for Computing Machinery Digital Library were systematically searched for articles published since 2008 that evaluated unconstrained natural language processing conversational agents used in health care. EndNote (version X9, Clarivate Analytics) reference management software was used for initial screening, and full-text screening was conducted by 1 reviewer. Data were extracted, and the risk of bias was assessed by one reviewer and validated by another.ResultsA total of 31 studies were selected and included a variety of conversational agents, including 14 chatbots (2 of which were voice chatbots), 6 embodied conversational agents (3 of which were interactive voice response calls, virtual patients, and speech recognition screening systems), 1 contextual question-answering agent, and 1 voice recognition triage system. Overall, the evidence reported was mostly positive or mixed. Usability and satisfaction performed well (27/30 and 26/31), and positive or mixed effectiveness was found in three-quarters of the studies (23/30). However, there were several limitations of the agents highlighted in specific qualitative feedback.ConclusionsThe studies generally reported positive or mixed evidence for the effectiveness, usability, and satisfactoriness of the conversational agents investigated, but qualitative user perceptions were more mixed. The quality of many of the studies was limited, and improved study design and reporting are necessary to more accurately evaluate the usefulness of the agents in health care and identify key areas for improvement. Further research should also analyze the cost-effectiveness, privacy, and security of the agents.International registered report identifier (irrid)RR2-10.2196/16934.
| S-EPMC7644372 | biostudies-literature
Project description:BACKGROUND:Conversational agents, also known as chatbots, are computer programs designed to simulate human text or verbal conversations. They are increasingly used in a range of fields, including health care. By enabling better accessibility, personalization, and efficiency, conversational agents have the potential to improve patient care. OBJECTIVE:This study aimed to review the current applications, gaps, and challenges in the literature on conversational agents in health care and provide recommendations for their future research, design, and application. METHODS:We performed a scoping review. A broad literature search was performed in MEDLINE (Medical Literature Analysis and Retrieval System Online; Ovid), EMBASE (Excerpta Medica database; Ovid), PubMed, Scopus, and Cochrane Central with the search terms "conversational agents," "conversational AI," "chatbots," and associated synonyms. We also searched the gray literature using sources such as the OCLC (Online Computer Library Center) WorldCat database and ResearchGate in April 2019. Reference lists of relevant articles were checked for further articles. Screening and data extraction were performed in parallel by 2 reviewers. The included evidence was analyzed narratively by employing the principles of thematic analysis. RESULTS:The literature search yielded 47 study reports (45 articles and 2 ongoing clinical trials) that matched the inclusion criteria. The identified conversational agents were largely delivered via smartphone apps (n=23) and used free text only as the main input (n=19) and output (n=30) modality. Case studies describing chatbot development (n=18) were the most prevalent, and only 11 randomized controlled trials were identified. The 3 most commonly reported conversational agent applications in the literature were treatment and monitoring, health care service support, and patient education. CONCLUSIONS:The literature on conversational agents in health care is largely descriptive and aimed at treatment and monitoring and health service support. It mostly reports on text-based, artificial intelligence-driven, and smartphone app-delivered conversational agents. There is an urgent need for a robust evaluation of diverse health care conversational agents' formats, focusing on their acceptability, safety, and effectiveness.
| S-EPMC7442948 | biostudies-literature
Project description:ObjectiveOur objective was to review the characteristics, current applications, and evaluation measures of conversational agents with unconstrained natural language input capabilities used for health-related purposes.MethodsWe searched PubMed, Embase, CINAHL, PsycInfo, and ACM Digital using a predefined search strategy. Studies were included if they focused on consumers or healthcare professionals; involved a conversational agent using any unconstrained natural language input; and reported evaluation measures resulting from user interaction with the system. Studies were screened by independent reviewers and Cohen's kappa measured inter-coder agreement.ResultsThe database search retrieved 1513 citations; 17 articles (14 different conversational agents) met the inclusion criteria. Dialogue management strategies were mostly finite-state and frame-based (6 and 7 conversational agents, respectively); agent-based strategies were present in one type of system. Two studies were randomized controlled trials (RCTs), 1 was cross-sectional, and the remaining were quasi-experimental. Half of the conversational agents supported consumers with health tasks such as self-care. The only RCT evaluating the efficacy of a conversational agent found a significant effect in reducing depression symptoms (effect size d = 0.44, p = .04). Patient safety was rarely evaluated in the included studies.ConclusionsThe use of conversational agents with unconstrained natural language input capabilities for health-related purposes is an emerging field of research, where the few published studies were mainly quasi-experimental, and rarely evaluated efficacy or safety. Future studies would benefit from more robust experimental designs and standardized reporting.Protocol registrationThe protocol for this systematic review is registered at PROSPERO with the number CRD42017065917.
| S-EPMC6118869 | biostudies-literature
Project description:BACKGROUND:The use of conversational agent interventions (including chatbots and robots) in mental health is growing at a fast pace. Recent existing reviews have focused exclusively on a subset of embodied conversational agent interventions despite other modalities aiming to achieve the common goal of improved mental health. OBJECTIVE:This study aimed to review the use of conversational agent interventions in the treatment of mental health problems. METHODS:We performed a systematic search using relevant databases (MEDLINE, EMBASE, PsycINFO, Web of Science, and Cochrane library). Studies that reported on an autonomous conversational agent that simulated conversation and reported on a mental health outcome were included. RESULTS:A total of 13 studies were included in the review. Among them, 4 full-scale randomized controlled trials (RCTs) were included. The rest were feasibility, pilot RCTs and quasi-experimental studies. Interventions were diverse in design and targeted a range of mental health problems using a wide variety of therapeutic orientations. All included studies reported reductions in psychological distress postintervention. Furthermore, 5 controlled studies demonstrated significant reductions in psychological distress compared with inactive control groups. In addition, 3 controlled studies comparing interventions with active control groups failed to demonstrate superior effects. Broader utility in promoting well-being in nonclinical populations was unclear. CONCLUSIONS:The efficacy and acceptability of conversational agent interventions for mental health problems are promising. However, a more robust experimental design is required to demonstrate efficacy and efficiency. A focus on streamlining interventions, demonstrating equivalence to other treatment modalities, and elucidating mechanisms of action has the potential to increase acceptance by users and clinicians and maximize reach.
| S-EPMC6914342 | biostudies-literature
Project description:BackgroundObesity is a public health issue worldwide. Conversational agents (CAs), also frequently called chatbots, are computer programs that simulate dialogue between people. Owing to better accessibility, cost-effectiveness, personalization, and compassionate patient-centered treatments, CAs are expected to have the potential to provide sustainable lifestyle counseling for weight management.ObjectiveThis systematic review aimed to critically summarize and evaluate clinical studies on the effectiveness and feasibility of CAs with unconstrained natural language input for weight management.MethodsPubMed, Embase, the Cochrane Library (CENTRAL), PsycINFO, and ACM Digital Library were searched up to December 2022. Studies were included if CAs were used for weight management and had a capability for unconstrained natural language input. No restrictions were imposed on study design, language, or publication type. The quality of the included studies was assessed using the Cochrane risk-of-bias assessment tool or the Critical Appraisal Skills Programme checklist. The extracted data from the included studies were tabulated and narratively summarized as substantial heterogeneity was expected.ResultsIn total, 8 studies met the eligibility criteria: 3 (38%) randomized controlled trials and 5 (62%) uncontrolled before-and-after studies. The CAs in the included studies were aimed at behavior changes through education, advice on food choices, or counseling via psychological approaches. Of the included studies, only 38% (3/8) reported a substantial weight loss outcome (1.3-2.4 kg decrease at 12-15 weeks of CA use). The overall quality of the included studies was judged as low.ConclusionsThe findings of this systematic review suggest that CAs with unconstrained natural language input can be used as a feasible interpersonal weight management intervention by promoting engagement in psychiatric intervention-based conversations simulating treatments by health care professionals, but currently there is a paucity of evidence. Well-designed rigorous randomized controlled trials with larger sample sizes, longer treatment duration, and follow-up focusing on CAs' acceptability, efficacy, and safety are warranted.
| S-EPMC10257112 | biostudies-literature
Project description:BackgroundConversational agents (CAs), also known as chatbots, are digital dialog systems that enable people to have a text-based, speech-based, or nonverbal conversation with a computer or another machine based on natural language via an interface. The use of CAs offers new opportunities and various benefits for health care. However, they are not yet ubiquitous in daily practice. Nevertheless, research regarding the implementation of CAs in health care has grown tremendously in recent years.ObjectiveThis review aims to present a synthesis of the factors that facilitate or hinder the implementation of CAs from the perspectives of patients and health care professionals. Specifically, it focuses on the early implementation outcomes of acceptability, acceptance, and adoption as cornerstones of later implementation success.MethodsWe performed an integrative review. To identify relevant literature, a broad literature search was conducted in June 2021 with no date limits and using all fields in PubMed, Cochrane Library, Web of Science, LIVIVO, and PsycINFO. To keep the review current, another search was conducted in March 2022. To identify as many eligible primary sources as possible, we used a snowballing approach by searching reference lists and conducted a hand search. Factors influencing the acceptability, acceptance, and adoption of CAs in health care were coded through parallel deductive and inductive approaches, which were informed by current technology acceptance and adoption models. Finally, the factors were synthesized in a thematic map.ResultsOverall, 76 studies were included in this review. We identified influencing factors related to 4 core Unified Theory of Acceptance and Use of Technology (UTAUT) and Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) factors (performance expectancy, effort expectancy, facilitating conditions, and hedonic motivation), with most studies underlining the relevance of performance and effort expectancy. To meet the particularities of the health care context, we redefined the UTAUT2 factors social influence, habit, and price value. We identified 6 other influencing factors: perceived risk, trust, anthropomorphism, health issue, working alliance, and user characteristics. Overall, we identified 10 factors influencing acceptability, acceptance, and adoption among health care professionals (performance expectancy, effort expectancy, facilitating conditions, social influence, price value, perceived risk, trust, anthropomorphism, working alliance, and user characteristics) and 13 factors influencing acceptability, acceptance, and adoption among patients (additionally hedonic motivation, habit, and health issue).ConclusionsThis review shows manifold factors influencing the acceptability, acceptance, and adoption of CAs in health care. Knowledge of these factors is fundamental for implementation planning. Therefore, the findings of this review can serve as a basis for future studies to develop appropriate implementation strategies. Furthermore, this review provides an empirical test of current technology acceptance and adoption models and identifies areas where additional research is necessary.Trial registrationPROSPERO CRD42022343690; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=343690.
| S-EPMC10565637 | biostudies-literature
Project description:BackgroundConversational agents or chatbots are computer programs that simulate conversations with users. Conversational agents are increasingly used for delivery of behavior change interventions in health care. Behavior change is complex and comprises the use of one or several components collectively known as behavioral change techniques (BCTs).ObjectiveThe objective of this scoping review is to identify the BCTs that are used in behavior change-focused interventions delivered via conversational agents in health care.MethodsThis scoping review will be performed in line with the Joanna Briggs Institute methodology and will be reported according to the PRISMA extension for scoping reviews guidelines. We will perform a comprehensive search of electronic databases and grey literature sources, and will check the reference lists of included studies for additional relevant studies. The screening and data extraction will be performed independently and in parallel by two review authors. Discrepancies will be resolved through consensus or discussion with a third review author. We will use a data extraction form congruent with the key themes and aims of this scoping review. BCTs employed in the included studies will be coded in line with BCT Taxonomy v1. We will analyze the data qualitatively and present it in diagrammatic or tabular form, alongside a narrative summary.ResultsTo date, we have designed the search strategy and performed the search on April 26, 2021. The first round of screening of retrieved articles is planned to begin soon.ConclusionsUsing appropriate BCTs in the design and delivery of health care interventions via conversational agents is essential to improve long-term outcomes. Our findings will serve to inform the development of future interventions in this area.International registered report identifier (irrid)PRR1-10.2196/30166.
| S-EPMC8346254 | biostudies-literature
Project description:BackgroundConversational agents (CAs) are increasingly used in health care to deliver behavior change interventions. Their evaluation often includes categorizing the behavior change techniques (BCTs) using a classification system of which the BCT Taxonomy v1 (BCTTv1) is one of the most common. Previous studies have presented descriptive summaries of behavior change interventions delivered by CAs, but no in-depth study reporting the use of BCTs in these interventions has been published to date.ObjectiveThis review aims to describe behavior change interventions delivered by CAs and to identify the BCTs and theories guiding their design.MethodsWe searched PubMed, Embase, Cochrane's Central Register of Controlled Trials, and the first 10 pages of Google and Google Scholar in April 2021. We included primary, experimental studies evaluating a behavior change intervention delivered by a CA. BCTs coding followed the BCTTv1. Two independent reviewers selected the studies and extracted the data. Descriptive analysis and frequent itemset mining to identify BCT clusters were performed.ResultsWe included 47 studies reporting on mental health (n=19, 40%), chronic disorders (n=14, 30%), and lifestyle change (n=14, 30%) interventions. There were 20/47 embodied CAs (43%) and 27/47 CAs (57%) represented a female character. Most CAs were rule based (34/47, 72%). Experimental interventions included 63 BCTs, (mean 9 BCTs; range 2-21 BCTs), while comparisons included 32 BCTs (mean 2 BCTs; range 2-17 BCTs). Most interventions included BCTs 4.1 "Instruction on how to perform a behavior" (34/47, 72%), 3.3 "Social support" (emotional; 27/47, 57%), and 1.2 "Problem solving" (24/47, 51%). A total of 12/47 studies (26%) were informed by a behavior change theory, mainly the Transtheoretical Model and the Social Cognitive Theory. Studies using the same behavior change theory included different BCTs.ConclusionsThere is a need for the more explicit use of behavior change theories and improved reporting of BCTs in CA interventions to enhance the analysis of intervention effectiveness and improve the reproducibility of research.
| S-EPMC9577715 | biostudies-literature
Project description:BackgroundRegular physical activity (PA) is crucial for well-being; however, healthy habits are difficult to create and maintain. Interventions delivered via conversational agents (eg, chatbots or virtual agents) are a novel and potentially accessible way to promote PA. Thus, it is important to understand the evolving landscape of research that uses conversational agents.ObjectiveThis mixed methods systematic review aims to summarize the usability and effectiveness of conversational agents in promoting PA, describe common theories and intervention components used, and identify areas for further development.MethodsWe conducted a mixed methods systematic review. We searched seven electronic databases (PsycINFO, PubMed, Embase, CINAHL, ACM Digital Library, Scopus, and Web of Science) for quantitative, qualitative, and mixed methods studies that conveyed primary research on automated conversational agents designed to increase PA. The studies were independently screened, and their methodological quality was assessed using the Mixed Methods Appraisal Tool by 2 reviewers. Data on intervention impact and effectiveness, treatment characteristics, and challenges were extracted and analyzed using parallel-results convergent synthesis and narrative summary.ResultsIn total, 255 studies were identified, 7.8% (20) of which met our inclusion criteria. The methodological quality of the studies was varied. Overall, conversational agents had moderate usability and feasibility. Those that were evaluated through randomized controlled trials were found to be effective in promoting PA. Common challenges facing interventions were repetitive program content, high attrition, technical issues, and safety and privacy concerns.ConclusionsConversational agents hold promise for PA interventions. However, there is a lack of rigorous research on long-term intervention effectiveness and patient safety. Future interventions should be based on evidence-informed theories and treatment approaches and should address users' desires for program variety, natural language processing, delivery via mobile devices, and safety and privacy concerns.
| S-EPMC8479596 | biostudies-literature
Project description:Digital health interventions for sexual health promotion have evolved considerably alongside innovations in technology. Despite these efforts, studies have shown that they do not consistently result in the desired sexual health outcomes. This could be attributed to low levels of user engagement, which can hinder digital health intervention effectiveness, as users do not engage with the system enough to be exposed to the intervention components. It has been suggested that conversational agents (automated two-way communication systems e.g. Alexa) have the potential to overcome the limitations of prior systems and promote user engagement through the increased interactivity offered by bidirectional, natural language-based interactions. The present review, therefore, provides an overview of the effectiveness and user acceptability of conversational agents for sexual health promotion. A systematic search of seven databases provided 4534 records, and after screening, 31 articles were included in this review. A narrative synthesis of results was conducted for effectiveness and acceptability outcomes, with the former supplemented by a meta-analysis conducted on a subset of studies. Findings provide preliminary support for the effectiveness of conversational agents for promoting sexual health, particularly treatment adherence. These conversational agents were found to be easy to use and useful, and importantly, resulted in high levels of satisfaction, use and intentions to reuse, whereas user evaluations regarding the quality of information left room for improvement. The results can inform subsequent efforts to design and evaluate these interventions, and offer insight into additional user experience constructs identified outside of current technology acceptance models, which can be incorporated into future theoretical developments.
| S-EPMC7613710 | biostudies-literature