Assessing the Usability of a Chatbot for Mental Health Care

Cameron, Gillian; Cameron, David M.; Megaw, Gavin; Bond, Raymond; Mulvenna, Maurice; O’Neill, Siobhán; Armour, Chérie; McTear, Michael F.

doi:10.1007/978-3-030-17705-8_11

Cited by 65 publications

(82 citation statements)

References 11 publications

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“…Users considered the following components of chatbots useful: real-time feedback [ 33 , 45 , 50 ], diary [ 52 ], weekly summary [ 42 ], presenting the helpline during the conversation [ 36 ], and psychoeducation [ 52 ]. Some studies found that virtual agents embodied in chatbots are useful for motivating users to use chatbots [ 34 ], establishing a relationship with them [ 35 ], and feeling supported by them [ 45 ].…”

Section: Resultsmentioning

confidence: 99%

“…The ease of use and usability of chatbots were assessed in 20 studies [ 33 , 34 , 36 , 39 , 43 , 46 - 51 , 53 - 61 ]. Participants in 15 studies rated the overall ease of use of chatbots as high [ 36 , 39 , 43 , 47 - 50 , 53 - 55 , 57 - 61 ]. A total of 5 studies assessed the usability of chatbots [ 34 , 36 , 46 , 51 , 56 ], and it was rated high in all these studies (ranging from 69 to 88.2).…”

Section: Resultsmentioning

confidence: 99%

“…Participants in 15 studies rated the overall ease of use of chatbots as high [ 36 , 39 , 43 , 47 - 50 , 53 - 55 , 57 - 61 ]. A total of 5 studies assessed the usability of chatbots [ 34 , 36 , 46 , 51 , 56 ], and it was rated high in all these studies (ranging from 69 to 88.2). Participants in 3 studies reported that chatbots are easy to learn and become familiar with [ 33 , 39 , 55 ].…”

Section: Resultsmentioning

confidence: 99%

“…Participants in 3 studies reported that chatbots are easy to learn and become familiar with [ 33 , 39 , 55 ]. Although participants did not find chatbots difficult to navigate in one study [ 33 ], they rated the navigation of the chatbot as low in another study [ 36 ].…”

Section: Resultsmentioning

confidence: 99%

“…In general, participants in 6 studies were satisfied with chatbot responses [ 33 - 36 , 62 , 63 , 67 ]. Participants attributed their satisfaction to the accuracy and consistency of chatbot feedback with what their therapist recommended in the past [ 33 ], appropriate use of high-quality elements (eg, Graphics Interchange Format images) [ 36 ], consistency of voice and tone of the chatbot [ 36 ], and quality of information provided [ 62 , 63 ].…”

Section: Resultsmentioning

confidence: 99%

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Perceptions and Opinions of Patients About Mental Health Chatbots: Scoping Review

Abd‐Alrazaq¹,

Alajlani²,

Ali³

et al. 2021

J Med Internet Res

185

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Background Chatbots have been used in the last decade to improve access to mental health care services. Perceptions and opinions of patients influence the adoption of chatbots for health care. Many studies have been conducted to assess the perceptions and opinions of patients about mental health chatbots. To the best of our knowledge, there has been no review of the evidence surrounding perceptions and opinions of patients about mental health chatbots. Objective This study aims to conduct a scoping review of the perceptions and opinions of patients about chatbots for mental health. Methods The scoping review was carried out in line with the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) extension for scoping reviews guidelines. Studies were identified by searching 8 electronic databases (eg, MEDLINE and Embase) in addition to conducting backward and forward reference list checking of the included studies and relevant reviews. In total, 2 reviewers independently selected studies and extracted data from the included studies. Data were synthesized using thematic analysis. Results Of 1072 citations retrieved, 37 unique studies were included in the review. The thematic analysis generated 10 themes from the findings of the studies: usefulness, ease of use, responsiveness, understandability, acceptability, attractiveness, trustworthiness, enjoyability, content, and comparisons. Conclusions The results demonstrated overall positive perceptions and opinions of patients about chatbots for mental health. Important issues to be addressed in the future are the linguistic capabilities of the chatbots: they have to be able to deal adequately with unexpected user input, provide high-quality responses, and have to show high variability in responses. To be useful for clinical practice, we have to find ways to harmonize chatbot content with individual treatment recommendations, that is, a personalization of chatbot conversations is required.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Perceptions and Opinions of Patients About Mental Health Chatbots: Scoping Review

Abd‐Alrazaq¹,

Alajlani²,

Ali³

et al. 2021

J Med Internet Res

185

View full text Add to dashboard Cite

show abstract

Development and usability testing of a chatbot to promote mental health services use among individuals with eating disorders following screening

Shah

DePietro

D’Adamo

et al. 2022

Intl J Eating Disorders

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Objective: A significant gap exists between those who need and those who receive care for eating disorders (EDs). Novel solutions are needed to encourage service use and address treatment barriers. This study developed and evaluated the usability of a chatbot designed for pairing with online ED screening. The tool aimed to promote mental health service utilization by improving motivation for treatment and selfefficacy among individuals with EDs.Methods: A chatbot prototype, Alex, was designed using decision trees and theoretically-informed components: psychoeducation, motivational interviewing, personalized recommendations, and repeated administration. Usability testing was conducted over four iterative cycles, with user feedback informing refinements to the next iteration. Post-testing, participants (N= 21) completed the System Usability Scale (SUS), the Usefulness, Satisfaction, and Ease of Use Questionnaire (USE), and a semi-structured interview.Results: Interview feedback detailed chatbot aspects participants enjoyed and aspects necessitating improvement. Feedback converged on four themes: user experience, chatbot qualities, chatbot content, and ease of use. Following refinements, users described Alex as humanlike, supportive, and encouraging. Content was perceived as novel and personally relevant. USE scores across domains were generally above average (5 out of 7), and SUS scores indicated "good" to "excellent" usability across cycles, with the final iteration receiving the highest average score. Discussion: Overall, participants generally reflected positively on interactions with Alex, including the initial version. Refinements between cycles further improved user experiences. This study provides preliminary evidence of the feasibility and acceptance of a chatbot designed to promote motivation for and use of services among individuals with EDs. Public Significance: Low rates of service utilization and treatment have been observed among individuals following online eating disorder screening. Tools are

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Review of artificial intelligence‐based question‐answering systems in healthcare

Cilar

Gosak

Štiglic

2023

WIREs Data Min & Knowl

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Use of conversational agents, like chatbots, avatars, and robots is increasing worldwide. Yet, their effectiveness in health care is largely unknown. The aim of this advanced review was to assess the use and effectiveness of conversational agents in various fields of health care. A literature search, analysis, and synthesis were conducted in February 2022 in PubMed and CINAHL. The included evidence was analyzed narratively by employing the principles of thematic analysis. We reviewed articles on artificial intelligence-based question-answering systems in health care. Most of the identified articles report its effectiveness; less is known about its use. We outlined study findings and explored directions of future research, to provide evidence-based knowledge about artificial intelligence-based question-answering systems.

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Assessing the Usability of a Chatbot for Mental Health Care

Cited by 65 publications

References 11 publications

Perceptions and Opinions of Patients About Mental Health Chatbots: Scoping Review

Perceptions and Opinions of Patients About Mental Health Chatbots: Scoping Review

Development and usability testing of a chatbot to promote mental health services use among individuals with eating disorders following screening

Review of artificial intelligence‐based question‐answering systems in healthcare

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