Diabetes and conversational agents: the AIDA project case study
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ABSTRACT: Supplementary Information
The online version contains supplementary material available at 10.1007/s44163-021-00005-1.
SUBMITTER: Alloatti F
PROVIDER: S-EPMC8456073 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe incidence of chronic diseases such as type 2 diabetes is increasing in countries worldwide, including Singapore. Health professional-delivered healthy lifestyle interventions have been shown to prevent type 2 diabetes. However, ongoing personalized guidance from health professionals is not feasible or affordable at the population level. Novel digital interventions delivered using mobile technology, such as conversational agents, are a potential alternative for the delivery of healthy lifestyle change behavioral interventions to the public.ObjectiveWe explored perceptions and experiences of Singaporeans on healthy living, diabetes, and mobile health (mHealth) interventions (apps and conversational agents). This study was conducted to help inform the design and development of a conversational agent focusing on healthy lifestyle changes.MethodsThis qualitative study was conducted in August and September 2019. A total of 20 participants were recruited from relevant healthy living Facebook pages and groups. Semistructured interviews were conducted in person or over the telephone using an interview guide. Interviews were transcribed and analyzed in parallel by 2 researchers using Burnard's method, a structured approach for thematic content analysis.ResultsThe collected data were organized into 4 main themes: use of conversational agents, ubiquity of smartphone apps, understanding of diabetes, and barriers and facilitators to a healthy living in Singapore. Most participants used health-related mobile apps as well as conversational agents unrelated to health care. They provided diverse suggestions for future conversational agent-delivered interventions. Participants also highlighted several knowledge gaps in relation to diabetes and healthy living. Regarding barriers to healthy living, participants mentioned frequent dining out, high stress levels, lack of work-life balance, and lack of free time to engage in physical activity. In contrast, discipline, preplanning, and sticking to a routine were important for enabling a healthy lifestyle.ConclusionsParticipants in this study commonly used mHealth interventions and provided important insights into their knowledge gaps and needs in relation to changes in healthy lifestyle behaviors. Future digital interventions such as conversational agents focusing on healthy lifestyle and diabetes prevention should aim to address the barriers highlighted in our study and motivate individuals to adopt healthy lifestyle behavior.
| S-EPMC8663498 | 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:IntroductionThis paper describes the creation and preliminary results of a patient-driven registry for the collection of patient-reported outcomes (PROs) and patient-reported experiences (PREs) in Behçet's disease (BD).MethodsThe project was coordinated by the University of Siena and the Italian patient advocacy organization SIMBA (Associazione Italiana Sindrome e Malattia di Behçet), in the context of the AIDA (AutoInflammatory Diseases Alliance) Network programme. Quality of life, fatigue, socioeconomic impact of the disease and therapeutic adherence were selected as core domains to include in the registry.ResultsRespondents were reached via SIMBA communication channels in 167 cases (83.5%) and the AIDA Network affiliated clinical centers in 33 cases (16.5%). The median value of the Behçet's Disease Quality of Life (BDQoL) score was 14 (IQR 11, range 0-30), indicating a medium quality of life, and the median Global Fatigue Index (GFI) was 38.7 (IQR 10.9, range 1-50), expressing a significant level of fatigue. The mean Beliefs about Medicines Questionnaire (BMQ) necessity-concern differential was 0.9 ± 1.1 (range - 1.8-4), showing that the registry participants prioritized necessity belief over concerns to a limited extent. As for the socioeconomic impact of BD, in 104 out of 187 cases (55.6%), patients had to pay from their own pocket for medical exams required to reach the diagnosis. The low family socioeconomic status (p < 0.001), the presence of any major organ involvement (p < 0.031), the presence of gastro-intestinal (p < 0.001), neurological (p = 0.012) and musculoskeletal (p = 0.022) symptoms, recurrent fever (p = 0.002), and headache (p < 0.001) were associated to a higher number of accesses to the healthcare system. Multiple linear regression showed that the BDQoL score could significantly predict the global socioeconomic impact of BD (F = 14.519, OR 1.162 [CI 0.557-1.766], p < 0.001).DiscussionPreliminary results from the AIDA for Patients BD registry were consistent with data available in the literature, confirming that PROs and PREs could be easily provided by the patient remotely to integrate physician-driven registries with complementary and reliable information.
| S-EPMC10313381 | biostudies-literature
Project description:BackgroundArtificial intelligence (AI) is said to be "transforming mental health". AI-based technologies and technique are now considered to have uses in almost every domain of mental health care: including decision-making, assessment and healthcare management. What remains underexplored is whether/how mental health recovery is situated within these discussions and practices.MethodTaking conversational agents as our point of departure, we explore the ways official online materials explain and make sense of chatbots, their imagined functionality and value for (potential) users. We focus on three chatbots for mental health: Woebot, Wysa and Tess.Findings"Recovery" is largely missing as an overt focus across materials. However, analysis does reveal themes that speak to the struggles over practice, expertise and evidence that the concept of recovery articulates. We discuss these under the headings "troubled clinical responsibility", "extended virtue of (technological) self-care" and "altered ontologies and psychopathologies of time".ConclusionsUltimately, we argue that alongside more traditional forms of recovery, chatbots may be shaped by, and shaping, an increasingly individualised form of a "personal recovery imperative".
| S-EPMC7683843 | biostudies-literature
Project description:BackgroundEmbodied conversational agents (ECAs) are computer-generated characters that simulate key properties of human face-to-face conversation, such as verbal and nonverbal behavior. In Internet-based eHealth interventions, ECAs may be used for the delivery of automated human support factors.ObjectiveWe aim to provide an overview of the technological and clinical possibilities, as well as the evidence base for ECA applications in clinical psychology, to inform health professionals about the activity in this field of research.MethodsGiven the large variety of applied methodologies, types of applications, and scientific disciplines involved in ECA research, we conducted a systematic scoping review. Scoping reviews aim to map key concepts and types of evidence underlying an area of research, and answer less-specific questions than traditional systematic reviews. Systematic searches for ECA applications in the treatment of mood, anxiety, psychotic, autism spectrum, and substance use disorders were conducted in databases in the fields of psychology and computer science, as well as in interdisciplinary databases. Studies were included if they conveyed primary research findings on an ECA application that targeted one of the disorders. We mapped each study's background information, how the different disorders were addressed, how ECAs and users could interact with one another, methodological aspects, and the study's aims and outcomes.ResultsThis study included N=54 publications (N=49 studies). More than half of the studies (n=26) focused on autism treatment, and ECAs were used most often for social skills training (n=23). Applications ranged from simple reinforcement of social behaviors through emotional expressions to sophisticated multimodal conversational systems. Most applications (n=43) were still in the development and piloting phase, that is, not yet ready for routine practice evaluation or application. Few studies conducted controlled research into clinical effects of ECAs, such as a reduction in symptom severity.ConclusionsECAs for mental disorders are emerging. State-of-the-art techniques, involving, for example, communication through natural language or nonverbal behavior, are increasingly being considered and adopted for psychotherapeutic interventions in ECA research with promising results. However, evidence on their clinical application remains scarce. At present, their value to clinical practice lies mostly in the experimental determination of critical human support factors. In the context of using ECAs as an adjunct to existing interventions with the aim of supporting users, important questions remain with regard to the personalization of ECAs' interaction with users, and the optimal timing and manner of providing support. To increase the evidence base with regard to Internet interventions, we propose an additional focus on low-tech ECA solutions that can be rapidly developed, tested, and applied in routine practice.
| S-EPMC5442350 | biostudies-literature
Project description:BackgroundApproximately 60%-80% of the primary care visits have a psychological stress component, but only 3% of patients receive stress management advice during these visits. Given recent advances in natural language processing, there is renewed interest in mental health chatbots. Conversational agents that can understand a user's problems and deliver advice that mitigates the effects of daily stress could be an effective public health tool. However, such systems are complex to build and costly to develop.ObjectiveTo address these challenges, our aim is to develop and evaluate a fully automated mobile suite of shallow chatbots-we call them Popbots-that may serve as a new species of chatbots and further complement human assistance in an ecosystem of stress management support.MethodsAfter conducting an exploratory Wizard of Oz study (N=14) to evaluate the feasibility of a suite of multiple chatbots, we conducted a web-based study (N=47) to evaluate the implementation of our prototype. Each participant was randomly assigned to a different chatbot designed on the basis of a proven cognitive or behavioral intervention method. To measure the effectiveness of the chatbots, the participants' stress levels were determined using self-reported psychometric evaluations (eg, web-based daily surveys and Patient Health Questionnaire-4). The participants in these studies were recruited through email and enrolled on the web, and some of them participated in follow-up interviews that were conducted in person or on the web (as necessary).ResultsOf the 47 participants, 31 (66%) completed the main study. The findings suggest that the users viewed the conversations with our chatbots as helpful or at least neutral and came away with increasingly positive sentiment toward the use of chatbots for proactive stress management. Moreover, those users who used the system more often (ie, they had more than or equal to the median number of conversations) noted a decrease in depression symptoms compared with those who used the system less often based on a Wilcoxon signed-rank test (W=91.50; Z=-2.54; P=.01; r=0.47). The follow-up interviews with a subset of the participants indicated that half of the common daily stressors could be discussed with chatbots, potentially reducing the burden on human coping resources.ConclusionsOur work suggests that suites of shallow chatbots may offer benefits for both users and designers. As a result, this study's contributions include the design and evaluation of a novel suite of shallow chatbots for daily stress management, a summary of benefits and challenges associated with random delivery of multiple conversational interventions, and design guidelines and directions for future research into similar systems, including authoring chatbot systems and artificial intelligence-enabled recommendation algorithms.
| S-EPMC8479600 | 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: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:BackgroundAs the world's population rapidly ages, the number of older adults with cognitive impairment will also increase. Several studies have identified numerous complex needs of people with dementia, which assistive technologies still fail to support. Recent trends have led to an increasing focus on the use of embodied conversational agents (ECAs) as virtual entities able to interact with a person through natural and familiar verbal and nonverbal communication. The use of ECAs could improve the accessibility and acceptance of assistive technologies matching those high-level needs that are not well covered to date.ObjectiveThe aim of this thematic literature analysis was to map current studies in the field of designing ECAs for patients with dementia in order to identify the existing research trend and possible gaps that need to be covered in the near future. The review questions in this study were as follows: (1) what research frameworks are used to study the interaction between patients with dementia and ECAs? (2) what are the findings? and (3) what are the barriers reported in these studies?MethodsSeparate literature searches were conducted in PubMed, Web of Science, Scopus, and Embase databases by using specific umbrella phrases to target the population (patients with dementia) and the technology-based intervention (embodied conversational agent). Studies that met the inclusion criteria were appraised through the Mixed Methods Appraisal Tool and then discussed in a thematic analysis.ResultsThe search process identified 115 records from the databases and study references. After duplicates (n=45) were removed, 70 papers remained for the initial screening. A total of 7 studies were finally included in the qualitative synthesis. A thematic analysis of the reviewed studies identified major themes and subthemes: the research frameworks used to gather users' perspectives on ECAs (theme 1), the insights shared by the 7 studies as well as the value of user involvement in the development phases and the challenge of matching the system functionalities with the users' needs (theme 2), and the main methodological and technical problems faced by each study team (theme 3).ConclusionsOur thematic literature analysis shows that the field of ECAs is novel and poorly discussed in the scientific community and that more sophisticated study designs and proofs of efficacy of the approach are required. Therefore, by analyzing the main topic of the narrative review, this study underscores the challenge of synchronizing and harmonizing knowledge, efforts, and challenges in the dementia care field and its person-centered paradigm through the user-centered design approach. Enabling strict collaboration between interdisciplinary research networks, medical scientists, technology developers, patients, and their formal and informal caregivers is still a great challenge in the field of technologies for older adults.
| S-EPMC8325086 | biostudies-literature
Project description:BACKGROUND: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. OBJECTIVE: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. METHODS:We searched on PubMed, Embase, CINAHL, PsycInfo, and ACM Digital Library using a predefined search strategy. The studies were included if they: (1) were primary research studies that focused on consumers, caregivers, or health care professionals; (2) involved a conversational agent with an unconstrained natural language interface; (3) tested the system with human subjects; and (4) implemented personalization features. RESULTS:The search found 1958 publications. After abstract and full-text screening, 13 studies were included in the review. Common examples of personalized content included feedback, daily health reports, alerts, warnings, and recommendations. The personalization features were implemented without a theoretical framework of customization and with limited evaluation of its impact. While conversational agents with personalization features were reported to improve user satisfaction, user engagement and dialogue quality, the role of personalization in improving health outcomes was not assessed directly. CONCLUSIONS:Most of the studies in our review implemented the personalization features without theoretical or evidence-based support for them and did not leverage the recent developments in other domains of personalization. Future research could incorporate personalization as a distinct design factor with a more careful consideration of its impact on health outcomes and its implications on patient safety, privacy, and decision-making.
| S-EPMC6873147 | biostudies-literature