Topic	Content
Title	Conversational Healthcare Agent for Chronic Diseases: A Systematic Review
Authors	Abdullah Bin Sawad, Baki Kocaballi, Mukesh Prasad, Bhuva Narayan, Ahlam Alnefaie, Ashwaq Maqbool, Indra Mckie, Jemma Smith, Berkan Yuksel
Review team members and their organisational affiliations	Abdullah Bin Sawad 1	(PhD Student)
	Dr Baki Kocaballi 1	(Lecturer)
	Dr Mukesh Prasad 1	(Senior Lecturer)
	Dr Bhuva Narayan 2	(Associate Professor)
	Ahlam Alnefaie 1	(PhD Student)
	Dr Ashwaq Maqbool 3	(Master Student)
	Indra Mckie 1	(PhD Student)
	Jemma Smith 4	(Bachelor Student)
	Berkan Yuksel 1 Deepak Puthal 5	(Bachelor Student) (Assistant Professor)
Contact details of the corresponding author	Abdullah Bin Sawad abdullahhatima.binsawad-1@student.uts.edu.au
Organisational affiliation of the review	University of Technology Sydney
Type and method of review	Systematic literature review
Contributions	Study design: AS; Search strategy: AS; Screening: AS, AA, IM Data extraction and Data analysis: AS, AM, JS, BY; First draft: AS; Revisions and subsequent drafts: BK, MP, BN, DP; Critical feedback for the final draft: BK, MP, BN, DP
Sources/Sponsors	NA
Conflict of interest	None
Rationale	What kinds of conversational agents are used for chronic conditions, what type of communication technology, what AI methods are used, what are the outcomes, the research gaps, and who are the target users/population group.
Eligibility criteria	Inclusion Criteria We will include primary research studies that (i) focused on consumers, caregivers, or healthcare professionals in the prevention, treatment, or rehabilitation of chronic diseases; (ii) involved conversational agent and AI methods used; and (iii) tested the system with human users. Exclusion Criteria Review, perspective, opinion papers, or news articles will be excluded. Studies must also have reported evaluations based on human users interacting with the full system. Studies evaluating only individual components of the conversational agent—automatic speech recognition, natural language understanding, dialogue management, response generation, text-to-speech synthesis –will be excluded. Studies will be excluded using “Wizard of Oz” methods, where the dialog is generated by a human operator rather than the conversational agent.
Information sources	A database search will be conducted by accessing PubMed Medline, EMBASE, PsycINFO, CINAHL, ACM Digital Library, and Web of Science databases. Search terms include synonyms, acronyms, and commonly known terms of the constructs “conversational agent” and “healthcare”. Grey literature will be excluded, such as posters, reviews, and presentations.
Search strategy	The following search strategy will be used in the whole six databases. Filters: none Conduct started in February 2021 “Conversational agent” OR “conversational agents” OR “conversational system” OR “conversational systems” OR “dialog system” OR “dialog systems” OR “dialogue systems” OR “dialogue system” OR “assistance technology” OR “assistance technologies” OR “relational agent” OR “relational agents” OR “chatbot” OR “chatbots” OR “digital agent” OR “digital agents” OR “digital assistant” OR “digital assistants” OR “virtual assistant” OR “virtual assistants” AND “healthcare” OR “digital healthcare” OR “digital health” OR “health” OR “mobile health” OR “mHealth” OR “mobile healthcare”.
Type of included study	Any primary research
Studied domain	Chronic health conditions
Population/Participants	Any population and any participants (caregivers, healthcare professionals, clinical/non-clinical, patients)
Data collection and selection process	AS and AA will conduct the initial screening of the obtained studies based on titles and abstracts. Then, AS and IM will conduct full-text screening based on the eligibility/inclusion criteria. AS, AM, JS, and BY will extract data from eligible papers. Any disagreement will be discussed in the zoom meeting. Dr. Kocaballi and Dr. Prasad will supervise all these processes to ensure the measures are on the right path.
Data items for coding	The following data items will be extracted from each included study: first author, year of publication, study location, study design/type, study aim, conversational agent evaluation measures, main reported outcomes and findings, type of chronic condition, type of study participants, type of the conversational agent, the goal of the conversational agent, communication channel, interaction modality, technique, system development. AS, AM, JS and BY will conduct the data extraction, and it will be discussed with Kocaballi and Dr Prasad.
Outcomes and prioritisation	Main outcomes: Any healthcare related intervention outcomes (e.g., type of chronic condition, health goal, intervention targets), any architecture related outcomes (e.g., technique type, system development). Additional outcomes: Any conversational agent related outcomes (e.g., feasibility, accuracy, acceptability, functionality) and design features.
Risk of bias in individual studies	AS and IM will review the included papers to appraise their quality. Disagreement will be discussed to reach a consensus. Any disagreement will be resolved with Dr Kocaballi and Dr. Prasad.
Data synthesis	The PRISMA guidelines will be used for data synthesis. A narrative synthesis of the included studies will be performed.
Language	English
Country	Australia
Anticipated or actual start date	February 2021
Anticipated or actual end date	September 2021
1 School of Computer Science, Faculty of Engineering and IT, University of Technology Sydney. 2 School of Communication, Faculty of Arts and Social Sciences, University of Technology Sydney. 3 School of Public Health, Faculty of Medicine and Health, The University of Sydney. 4 School of Biomedical Engineering, Faculty of Engineering and IT, University of Technology Sydney. 5 Department of Electrical Engineering and Computer Science, Khalifa University.