scholarly journals Voice-based Conversational Agents for the Prevention and Management of Chronic and Mental Conditions: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Caterina Bérubé ◽  
Theresa Schachner ◽  
Roman Keller ◽  
Elgar Fleisch ◽  
Florian v. Wangenheim ◽  
...  
Keyword(s):  
Literature Review ◽  
Technology Acceptance ◽  
Systematic Literature Review ◽  
State Of The Art ◽  
Data Extraction ◽  
Empirical Evaluation ◽  
Behavioral Measure ◽  
Conversational Agents ◽  
Natural Interaction ◽  
Quasi Experimental

BACKGROUND Chronic and mental conditions are increasingly prevalent worldwide. As devices in our everyday lives offer more and more voice-based self-service, voice-based conversational agents (VCAs) have the potential to support the prevention and management of these conditions in a scalable way. VCAs allow for a more natural interaction compared to text-based conversational agents, facilitate input for users who cannot type, allow for routine monitoring and support when in-person healthcare is not possible, and open the doors to voice and speech analysis. The state of the art of VCAs for chronic and mental conditions is, however, unclear. OBJECTIVE This systematic literature review aims to provide a better understanding of state-of-the-art research on VCAs delivering interventions for the prevention and management of chronic and mental conditions. METHODS We conducted a systematic literature review using PubMed Medline, EMBASE, PsycINFO, Scopus, and Web of Science databases. We included primary research that involved the prevention or management of chronic or mental conditions, where the voice was the primary interaction modality of the conversational agent, and where an empirical evaluation of the system in terms of system accuracy and/or in terms of technology acceptance was included. Two independent reviewers conducted screening and data extraction and measured their agreement with Cohen’s kappa. A narrative approach was applied to synthesize the selected records. RESULTS Twelve out of 7’170 articles met the inclusion criteria. The majority of the studies (N=10) were non-experimental, while the remainder (N=2) were quasi-experimental. The VCAs provided behavioral support (N=5), a health monitoring service (N=3), or both (N=4). The VCA services were delivered via smartphone (N=5), tablet (N=2), or smart speakers (N=3). In two cases, no device was specified. Three VCAs targeted cancer, while two VCAs each targeted diabetes and heart failure. The other VCAs targeted hearing-impairment, asthma, Parkinson's disease, dementia and autism, “intellectual disability”, and depression. The majority of the studies (N=7) assessed technology acceptance but only a minority (N=3) used validated instruments. Half of the studies (N=6) reported either performance measures on speech recognition or on the ability of VCA’s to respond to health-related queries. Only a minority of the studies (N=2) reported behavioral measure or a measure of attitudes towards intervention-related health behavior. Moreover, only a minority of studies (N=4) reported controlling for participant’s previous experience with technology. CONCLUSIONS Considering the heterogeneity of the methods and the limited number of studies identified, it seems that research on VCAs for chronic and mental conditions is still in its infancy. Although results in system accuracy and technology acceptance are encouraging, there still is a need to establish evidence on the efficacy of VCAs for the prevention and management of chronic and mental conditions, both in absolute terms and in comparison to standard healthcare.

scholarly journals Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review

10.2196/25933 ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. e25933
Author(s):  
Caterina Bérubé ◽  
Theresa Schachner ◽  
Roman Keller ◽  
Elgar Fleisch ◽  
Florian v Wangenheim ◽  
...  
Keyword(s):  
Mental Health ◽  
Literature Review ◽  
Technology Acceptance ◽  
Systematic Literature Review ◽  
Data Extraction ◽  
Empirical Evaluation ◽  
Conclusive Evidence ◽  
Health Conditions ◽  
Conversational Agents ◽  
Mental Health Conditions

Background Chronic and mental health conditions are increasingly prevalent worldwide. As devices in our everyday lives offer more and more voice-based self-service, voice-based conversational agents (VCAs) have the potential to support the prevention and management of these conditions in a scalable manner. However, evidence on VCAs dedicated to the prevention and management of chronic and mental health conditions is unclear. Objective This study provides a better understanding of the current methods used in the evaluation of health interventions for the prevention and management of chronic and mental health conditions delivered through VCAs. Methods We conducted a systematic literature review using PubMed MEDLINE, Embase, PsycINFO, Scopus, and Web of Science databases. We included primary research involving the prevention or management of chronic or mental health conditions through a VCA and reporting an empirical evaluation of the system either in terms of system accuracy, technology acceptance, or both. A total of 2 independent reviewers conducted the screening and data extraction, and agreement between them was measured using Cohen kappa. A narrative approach was used to synthesize the selected records. Results Of 7170 prescreened papers, 12 met the inclusion criteria. All studies were nonexperimental. The VCAs provided behavioral support (n=5), health monitoring services (n=3), or both (n=4). The interventions were delivered via smartphones (n=5), tablets (n=2), or smart speakers (n=3). In 2 cases, no device was specified. A total of 3 VCAs targeted cancer, whereas 2 VCAs targeted diabetes and heart failure. The other VCAs targeted hearing impairment, asthma, Parkinson disease, dementia, autism, intellectual disability, and depression. The majority of the studies (n=7) assessed technology acceptance, but only few studies (n=3) used validated instruments. Half of the studies (n=6) reported either performance measures on speech recognition or on the ability of VCAs to respond to health-related queries. Only a minority of the studies (n=2) reported behavioral measures or a measure of attitudes toward intervention-targeted health behavior. Moreover, only a minority of studies (n=4) reported controlling for participants’ previous experience with technology. Finally, risk bias varied markedly. Conclusions The heterogeneity in the methods, the limited number of studies identified, and the high risk of bias show that research on VCAs for chronic and mental health conditions is still in its infancy. Although the results of system accuracy and technology acceptance are encouraging, there is still a need to establish more conclusive evidence on the efficacy of VCAs for the prevention and management of chronic and mental health conditions, both in absolute terms and in comparison with standard health care.

scholarly journals Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review

10.2196/20701 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e20701 ◽  
Author(s):  
Theresa Schachner ◽  
Roman Keller ◽  
Florian v Wangenheim
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Chronic Diseases ◽  
Language Processing ◽  
Systematic Literature Review ◽  
Chronic Conditions ◽  
Experimental Studies ◽  
Future Research ◽  
Conversational Agents ◽  
Quasi Experimental

Background A rising number of conversational agents or chatbots are equipped with artificial intelligence (AI) architecture. They are increasingly prevalent in health care applications such as those providing education and support to patients with chronic diseases, one of the leading causes of death in the 21st century. AI-based chatbots enable more effective and frequent interactions with such patients. Objective The goal of this systematic literature review is to review the characteristics, health care conditions, and AI architectures of AI-based conversational agents designed specifically for chronic diseases. Methods We conducted a systematic literature review using PubMed MEDLINE, EMBASE, PyscInfo, CINAHL, ACM Digital Library, ScienceDirect, and Web of Science. We applied a predefined search strategy using the terms “conversational agent,” “healthcare,” “artificial intelligence,” and their synonyms. We updated the search results using Google alerts, and screened reference lists for other relevant articles. We included primary research studies that involved the prevention, treatment, or rehabilitation of chronic diseases, involved a conversational agent, and included any kind of AI architecture. Two independent reviewers conducted screening and data extraction, and Cohen kappa was used to measure interrater agreement.A narrative approach was applied for data synthesis. Results The literature search found 2052 articles, out of which 10 papers met the inclusion criteria. The small number of identified studies together with the prevalence of quasi-experimental studies (n=7) and prevailing prototype nature of the chatbots (n=7) revealed the immaturity of the field. The reported chatbots addressed a broad variety of chronic diseases (n=6), showcasing a tendency to develop specialized conversational agents for individual chronic conditions. However, there lacks comparison of these chatbots within and between chronic diseases. In addition, the reported evaluation measures were not standardized, and the addressed health goals showed a large range. Together, these study characteristics complicated comparability and open room for future research. While natural language processing represented the most used AI technique (n=7) and the majority of conversational agents allowed for multimodal interaction (n=6), the identified studies demonstrated broad heterogeneity, lack of depth of reported AI techniques and systems, and inconsistent usage of taxonomy of the underlying AI software, further aggravating comparability and generalizability of study results. Conclusions The literature on AI-based conversational agents for chronic conditions is scarce and mostly consists of quasi-experimental studies with chatbots in prototype stage that use natural language processing and allow for multimodal user interaction. Future research could profit from evidence-based evaluation of the AI-based conversational agents and comparison thereof within and between different chronic health conditions. Besides increased comparability, the quality of chatbots developed for specific chronic conditions and their subsequent impact on the target patients could be enhanced by more structured development and standardized evaluation processes.

TECHNOLOGY ADOPTION AND CONTINUANCE OF MOOCS: A SYSTEMATIC LITERATURE REVIEW, SYNTHESIS AND FUTURE RESEARCH AGENDA

2021 ◽  
Vol 14 (8) ◽  
pp. 106-132
Author(s):  
Jinal Shah ◽  
Monica Khanna ◽  
Hiren Patel
Keyword(s):  
Literature Review ◽  
Technology Acceptance ◽  
Systematic Literature Review ◽  
State Of The Art ◽  
Practical Knowledge ◽  
Future Research ◽  
Future Research Agenda ◽  
Adoption Research ◽  
Acceptance Model ◽  
Context Characteristics

Massive Open Online Courses (MOOCs) have garnered a lot of attention and interest from academia and industry. The proliferation of MOOC platforms has opened up immense opportunities for fulfilling learner needs and improving the education landscape. It is important to understand the drivers of MOOC adoption to ensure its complete utilization. Research on MOOC adoption, acceptance, usage, and continuance has increased over a period of time. With this regard, the purpose of this study was to review and synthesize the research on MOOC adoption using a structured and domain-based approach in order to understand its state-of-the-art literature. Seventy empirical articles indexed in Scopus or Web of Science were identified and reviewed systematically using the Theories, Context, Characteristics, and Methods (TCCM) framework. Systematic mapping of studies was taken place to determine the year-wise publications, journal quality, citations, widely used theories, the context in terms of MOOC platforms and country of research, characteristics in terms of widely used constructs, and hypothesized relationships. The systematic literature review highlights Technology Acceptance Model is the dominant model applied in the MOOC adoption research. Further research on MOOC adoption is seen majorly in Asian economies with a narrow focus on developed economies. Although several review studies have taken place on MOOC, their focus has been on the quality, pedagogical or user perspective. This study is the first effort in synthesizing the state-of-the-art literature on MOOC adoption. Future avenues of research are suggested in terms of Theories, Context, Characteristics, and Methods to further the theoretical and practical knowledge on MOOC adoption.

scholarly journals Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Theresa Schachner ◽  
Roman Keller ◽  
Florian von Wangenheim
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Chronic Diseases ◽  
Language Processing ◽  
Systematic Literature Review ◽  
Chronic Conditions ◽  
Experimental Studies ◽  
Future Research ◽  
Conversational Agents ◽  
Quasi Experimental

BACKGROUND A rising number of conversational agents or chatbots are equipped with artificial intelligence (AI) architecture. They are increasingly prevalent in health care applications such as those providing education and support to patients with chronic diseases, one of the leading causes of death in the 21st century. AI-based chatbots enable more effective and frequent interactions with such patients. OBJECTIVE The goal of this systematic literature review is to review the characteristics, health care conditions, and AI architectures of AI-based conversational agents designed specifically for chronic diseases. METHODS We conducted a systematic literature review using PubMed MEDLINE, EMBASE, PyscInfo, CINAHL, ACM Digital Library, ScienceDirect, and Web of Science. We applied a predefined search strategy using the terms “conversational agent,” “healthcare,” “artificial intelligence,” and their synonyms. We updated the search results using Google alerts, and screened reference lists for other relevant articles. We included primary research studies that involved the prevention, treatment, or rehabilitation of chronic diseases, involved a conversational agent, and included any kind of AI architecture. Two independent reviewers conducted screening and data extraction, and Cohen kappa was used to measure interrater agreement.A narrative approach was applied for data synthesis. RESULTS The literature search found 2052 articles, out of which 10 papers met the inclusion criteria. The small number of identified studies together with the prevalence of quasi-experimental studies (n=7) and prevailing prototype nature of the chatbots (n=7) revealed the immaturity of the field. The reported chatbots addressed a broad variety of chronic diseases (n=6), showcasing a tendency to develop specialized conversational agents for individual chronic conditions. However, there lacks comparison of these chatbots within and between chronic diseases. In addition, the reported evaluation measures were not standardized, and the addressed health goals showed a large range. Together, these study characteristics complicated comparability and open room for future research. While natural language processing represented the most used AI technique (n=7) and the majority of conversational agents allowed for multimodal interaction (n=6), the identified studies demonstrated broad heterogeneity, lack of depth of reported AI techniques and systems, and inconsistent usage of taxonomy of the underlying AI software, further aggravating comparability and generalizability of study results. CONCLUSIONS The literature on AI-based conversational agents for chronic conditions is scarce and mostly consists of quasi-experimental studies with chatbots in prototype stage that use natural language processing and allow for multimodal user interaction. Future research could profit from evidence-based evaluation of the AI-based conversational agents and comparison thereof within and between different chronic health conditions. Besides increased comparability, the quality of chatbots developed for specific chronic conditions and their subsequent impact on the target patients could be enhanced by more structured development and standardized evaluation processes.

A Systematic Literature Review of the EFQM Excellence Model from 1991 to 2019

1970 ◽  
Vol 2 (2) ◽  
pp. 11-15
Author(s):  
Muhammad Yousaf ◽  
Petr Bris
Keyword(s):  
Quality Management ◽  
Data Collection ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Quantitative Research ◽  
Web Of Science ◽  
State Of The Art ◽  
Future Research ◽  
European Foundation ◽  
The Subject

A systematic literature review (SLR) from 1991 to 2019 is carried out about EFQM (European Foundation for Quality Management) excellence model in this paper. The aim of the paper is to present state of the art in quantitative research on the EFQM excellence model that will guide future research lines in this field. The articles were searched with the help of six strings and these six strings were executed in three popular databases i.e. Scopus, Web of Science, and Science Direct. Around 584 peer-reviewed articles examined, which are directly linked with the subject of quantitative research on the EFQM excellence model. About 108 papers were chosen finally, then the purpose, data collection, conclusion, contributions, and type of quantitative of the selected papers are discussed and analyzed briefly in this study. Thus, this study identifies the focus areas of the researchers and knowledge gaps in empirical quantitative literature on the EFQM excellence model. This article also presents the lines of future research.

scholarly journals Is there a sex difference in mortality rates for deaths occurring in paediatric intensive care units? Systematic literature review protocol

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e046794
Author(s):  
Ofran Almossawi ◽  
Amanda Friend ◽  
Luigi Palla ◽  
Richard Feltbower ◽  
Bianca De Stavola
Keyword(s):  
Intensive Care ◽  
Literature Review ◽  
Intensive Care Units ◽  
Systematic Literature Review ◽  
Data Extraction ◽  
Linkage Study ◽  
Paediatric Intensive Care ◽  
Admission Rates ◽  
Ethical Approval ◽  
Paediatric Intensive Care Units

IntroductionIn the general population, female children have been reported to have a survival advantage. For children admitted to paediatric intensive care units (PICUs), mortality has been reported to be lower in males despite the higher admission rates for males into intensive care. This apparent sex reversal in PICU mortality is not well studied. To address this, we propose to conduct a systematic literature review to summarise the available evidence. Our review will study the reported differences in mortality between males and females aged 0–17, who died in a PICU, to examine if there is a difference between the two sexes in PICU mortality, and if so, to describe the magnitude and direction of this difference.Methods and analysisStudies that directly or indirectly addressed the association between sex and mortality in children admitted to intensive care will be eligible for inclusion. Studies that directly address the association will be eligible for data extraction. The search strings were based on terms related to the population (children in intensive care), the exposure (sex) and the outcome (mortality). We used the databases MEDLINE (1946–2020), Embase (1980–2020) and Web of Science (1985–2020) as these cover relevant clinical publications. We will assess the reliability of included studies using the risk of bias in observational studies of exposures tool. We will consider a pooled effect if we have at least three studies with similar periods of follow up and adjustment variables.Ethics and disseminationEthical approval is not required for this review as it will synthesise data from existing studies. This manuscript is a part of a larger data linkage study, for which Ethical approval was granted. Dissemination will be via peer-reviewed journals and via public and patient groups.PROSPERO registration numberCRD42020203009.

Systematic Literature Review on Smart Mobility: A Framework for Future “Quantitative” Developments

2021 ◽  
pp. 088541222199424
Author(s):  
Mauro Francini ◽  
Lucia Chieffallo ◽  
Annunziata Palermo ◽  
Maria Francesca Viapiana
Keyword(s):  
Cluster Analysis ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Performance Indicators ◽  
State Of The Art ◽  
Measurement Model ◽  
Broader Impacts ◽  
Research Goal ◽  
Smart Mobility ◽  
Definition Of

This work aims to reorganize theoretical and empirical research on smart mobility through the systematic literature review approach. The research goal is to reach an extended and shared definition of smart mobility using the cluster analysis. The article provides a summary of the state of the art that can have broader impacts in determining new angles for approaching research. In particular, the results will be a reference for future quantitative developments for the authors who are working on the construction of a territorial measurement model of the smartness degree, helping them in identifying performance indicators consistent with the definition proposed.

scholarly journals Ultraviolet-C as a Viable Reprocessing Method for Disposable Masks and Filtering Facepiece Respirators

Polymers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 801
Author(s):  
Talita Nicolau ◽  
Núbio Gomes Filho ◽  
Andrea Zille
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Personal Protective Equipment ◽  
State Of The Art ◽  
Protective Equipment ◽  
Multiple Methods ◽  
Ultraviolet C ◽  
Single Use ◽  
Uv C ◽  
New Strategies

In normal conditions, discarding single-use personal protective equipment after use is the rule for its users due to the possibility of being infected, particularly for masks and filtering facepiece respirators. When the demand for these protective tools is not satisfied by the companies supplying them, a scenario of shortages occurs, and new strategies must arise. One possible approach regards the disinfection of these pieces of equipment, but there are multiple methods. Analyzing these methods, Ultraviolet-C (UV-C) becomes an exciting option, given its germicidal capability. This paper aims to describe the state-of-the-art for UV-C sterilization in masks and filtering facepiece respirators. To achieve this goal, we adopted a systematic literature review in multiple databases added to a snowball method to make our sample as robust as possible and encompass a more significant number of studies. We found that UV-C’s germicidal capability is just as good as other sterilization methods. Combining this characteristic with other advantages makes UV-C sterilization desirable compared to other methods, despite its possible disadvantages.

State of the art in hybrid strategies for context reasoning: A systematic literature review

2019 ◽  
Vol 111 ◽  
pp. 122-130
Author(s):  
Roger S. Machado ◽  
Ricardo B. Almeida ◽  
Ana Marilza Pernas ◽  
Adenauer C. Yamin
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
State Of The Art ◽  
Context Reasoning

Legally Enforceable Smart-Contract Languages

2021 ◽  
Vol 54 (5) ◽  
pp. 1-34
Author(s):  
Vimal Dwivedi ◽  
Vishwajeet Pattanaik ◽  
Vipin Deval ◽  
Abhishek Dixit ◽  
Alex Norta ◽  
...  
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
State Of The Art ◽  
Grey Literature ◽  
Smart Contracts ◽  
Smart Contract ◽  
Decentralized Autonomous Organizations

Smart contracts are a key component of today’s blockchains. They are critical in controlling decentralized autonomous organizations (DAO). However, smart contracts are not yet legally binding nor enforceable; this makes it difficult for businesses to adopt the DAO paradigm. Therefore, this study reviews existing Smart Contract Languages (SCL) and identifies properties that are critical to any future SCL for drafting legally binding contracts. This is achieved by conducting a Systematic Literature Review (SLR) of white- and grey literature published between 2015 and 2019. Using the SLR methodology, 45 Selected and 28 Supporting Studies detailing 45 state-of-the-art SCLs are selected. Finally, 10 SCL properties that enable legally compliant DAOs are discovered, and specifications for developing SCLs are explored.

Sign in / Sign up

Export Citation Format

Share Document