A Chatbot System for Mental Healthcare Based on SAT Counseling Method

Kamita, Takeshi; Itô, Tatsuya; Matsumoto, Atsuko; Munakata, Tsunetsugu; Inoue, Tomoo

doi:10.1155/2019/9517321

Cited by 51 publications

(79 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A total of 22 studies were from European countries, including Italy [ 44 , 45 ], Switzerland [ 30 , 46 - 52 ], France [ 53 , 54 ], Portugal [ 55 ], The Netherlands [ 56 ], the United Kingdom [ 57 - 61 ], Spain [ 62 , 63 ], and Sweden [ 64 ]. Moreover, 8 studies originated from Asian countries: Philippines [ 65 ], China [ 66 ], Japan [ 67 , 68 ], Pakistan [ 69 ], India [ 70 , 71 ], and Hong Kong [ 72 ]. Other geographic regions acknowledged in the studies of this review were Australia [ 73 , 74 ], Canada [ 75 ], New Zealand [ 76 , 77 ], South Africa [ 78 ], and the United States of America [ 79 - 89 ].…”

Section: Resultsmentioning

confidence: 99%

“…A variety of study designs were used in the included studies, comprising 20 case studies [ 44 , 48 , 51 , 61 - 63 , 66 , 69 , 71 , 73 - 79 , 82 , 84 , 85 , 89 ], 4 surveys [ 55 , 56 , 59 , 65 ], 3 observational studies [ 53 , 86 , 87 ], 11 randomized controlled trials [ 46 , 49 , 50 , 57 , 64 , 67 , 72 , 80 , 81 , 83 , 88 ], 3 diagnostic accuracy studies [ 58 , 60 , 68 ], 3 controlled before and after studies [ 30 , 45 , 70 ], 2 ongoing trials [ 51 , 54 ], and 1 pilot study [ 47 ] ( Figure 3 ).…”

Section: Resultsmentioning

confidence: 99%

“…The types of literature included 25 journal articles [ 44 , 48 , 55 - 57 , 61 - 65 , 67 , 69 , 72 , 74 - 76 , 80 - 87 , 89 ], 11 conference abstracts [ 45 , 47 , 49 , 50 , 52 , 59 , 70 , 71 , 73 , 78 , 79 ], 4 conference papers [ 30 , 46 , 66 , 77 ], 1 poster abstract [ 68 ], 4 electronic preprints [ 53 , 58 , 60 , 88 ], and 2 clinical trial protocols [ 51 , 54 ].…”

Section: Resultsmentioning

confidence: 99%

“…Conversational agents were delivered through a variety of means in the included studies. Most (n=23) were smartphone apps [ 30 , 46 - 50 , 53 , 55 , 58 - 61 , 64 , 67 , 70 , 71 , 75 , 77 , 81 , 83 , 85 , 86 , 88 ]; web based (n=5) [ 57 , 66 , 73 , 74 , 82 ]; desktop computer based (n=2) [ 65 , 79 ]; used smartphone-embedded software (n=6; eg, Siri, Google Assistant, Alexa, etc) [ 44 , 51 , 62 , 76 , 84 , 87 ], Telegram [ 45 , 78 ], WeChat [ 72 ], SMS and multimedia messaging service [ 89 ], Windows live messenger [ 56 ], or Facebook Messenger [ 52 , 80 ]; and 4 were made available on more than 1 platform [ 53 , 59 , 68 , 83 ]. Three studies did not specify the method of conversational agent delivery [ 54 , 63 , 69 ].…”

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Conversational Agents in Health Care: Scoping Review and Conceptual Analysis

Car¹,

Dhinagaran²,

Kyaw³

et al. 2020

J Med Internet Res

335

197

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Conversational Agents in Health Care: Scoping Review and Conceptual Analysis

Car¹,

Dhinagaran²,

Kyaw³

et al. 2020

J Med Internet Res

335

197

View full text Add to dashboard Cite

show abstract

“…Mental health problems are a growing global challenge affecting people of all different backgrounds, ages, and socioeconomic status [19]. To tackle this challenge, mental health resources are increasingly available online and via mobile apps [8,12,14]. The proliferation of conversational agents has made them attractive for health applications [11] and mental health [18].…”

Section: Introductionmentioning

confidence: 99%

Alexa depression and anxiety self-tests

Quiroz

Bongolan

Ijaz

2020

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

View full text Add to dashboard Cite

Mental health resources available via websites and mobile apps provide support such as advice, journaling, and elements from cognitive behavioral therapy. The proliferation of spoken conversational agents, such as Alexa, Siri, and Google Home, has led to an increasing interest in developing mental health apps for these devices. We present the pilot study outcomes of an Alexa Skill that allows users to conduct depression and anxiety self-tests. Ten participants were given access to the Alexa Skill for two-weeks, followed by an online evaluation of the Skill's usability and trust. Our preliminary evaluation suggests that participants trusted the Skill and scored the usability and user experience as average. Usage of the Skill was low, with most participants using the Skill only once. In view of work-in-progress, we also present a discussion of implementation and study design challenges to guide the current literature on designing spoken conversational agents for mental health applications. CCS CONCEPTS • Human-centered computing → Sound-based input / output.

show abstract