Advantages of virtual agents over clinical psychologists during comprehensive mental health interviews using a mixed methods design

Yokotani, Kenji; Takagi, Gen; Wakashima, Koubun

doi:10.1016/j.chb.2018.03.045

Cited by 34 publications

(28 citation statements)

References 42 publications

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“…The most common disorder targeted by chatbots was depression (n=15, 40.5%). Multimedia Appendix 4 shows the characteristics of the intervention in each included study [ 33 - 69 ].…”

Section: Resultsmentioning

confidence: 99%

“…In another study [ 69 ], participants felt a greater rapport with the real expert than with a rule-based chatbot. Participants also indicated that the rule-based chatbot is less able to understand their responses and feelings than a real expert [ 69 ]. Furthermore, participants found the real expert more trustworthy than the rule-based chatbot [ 69 ].…”

Section: Resultsmentioning

confidence: 99%

“…Participants also indicated that the rule-based chatbot is less able to understand their responses and feelings than a real expert [ 69 ]. Furthermore, participants found the real expert more trustworthy than the rule-based chatbot [ 69 ]. Participants reported that the chatbot is more enjoyable and engaging than traditional methods of learning and practicing dialectical behavior therapy skills [ 46 ].…”

Section: Resultsmentioning

confidence: 99%

See 2 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

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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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“…The most common disorder targeted by chatbots was depression (n=15, 40.5%). Multimedia Appendix 4 shows the characteristics of the intervention in each included study [ 33 - 69 ].…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

“…Lucus et al, proposed a virtual human interviewer, which conducts an interview with military service members involved in an intense situation and identifies the symptoms associated with their mental state [33]. A similar kind of virtual agent was proposed in [34], which interacts with the users and identifies their mental symptoms using mixed methods for triangulation of data. Moreover, a rule-based patient-centric application was proposed in [35], which provides medical coaching services.…”

Section: Finite State Assistantsmentioning

confidence: 99%

Medical Instructed Real-Time Assistant for Patient with Glaucoma and Diabetic Conditions

et al. 2020

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Virtual assistants are involved in the daily activities of humans such as managing calendars, making appointments, and providing wake-up calls. They provide a conversational service to customers around-the-clock and make their daily life manageable. With this emerging trend, many well-known companies launched their own virtual assistants that manage the daily routine activities of customers. In the healthcare sector, virtual medical assistants also provide a list of relevant diseases linked to a specific symptom. Due to low accuracy and uncertainty, these generated recommendations are untrusted and may lead to hypochondriasis. In this study, we proposed a Medical Instructed Real-time Assistant (MIRA) that listens to the user’s chief complaint and predicts a specific disease. Instead of informing about the medical condition, the user is referred to a nearby appropriate medical specialist. We designed an architecture for MIRA that considers the limitations of existing virtual medical assistants such as weak authentication, lack of understanding multiple intent statements about a specific medical condition, and uncertain diagnosis recommendations. To implement the designed architecture, we collected the chief complaints along with the dialogue corpora of real patients. Then, we manually validated these data under the supervision of medical specialists. We then used these data for natural language understanding, disease identification, and appropriate response generation. For the prototype version of MIRA, we considered the cases of glaucoma (eye disease) and diabetes (an autoimmune disease) only. The performance measure of MIRA was evaluated in terms of accuracy (89%), precision (90%), sensitivity (89.8%), specificity (94.9%), and F-measure (89.8%). The task completion was calculated using Cohen’s Kappa ( k = 0.848 ) that categorizes MIRA as ‘Almost Perfect’. Furthermore, the voice-based authentication identifies the user effectively and prevent against masquerading attack. Simultaneously, the user experience shows relatively good results in all aspects based on the User Experience Questionnaire (UEQ) benchmark data. The experimental results show that MIRA efficiently predicts a disease based on chief complaints and supports the user in decision making.

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“…According to a scoping review for ECA applications in clinical psychology, more than half of their studies were focused on autism-related treatments and most applications were still under development and pilot phases [17]. Furthermore, there were other applications related to detecting and preventing suicidal behavior [18], changing of stigmatizing attitudes [19], and mental health interview [20]. Moreover, for psychological interventions, certain famous mental health chatbots existed such as “Woebot” [21] and “Tess” [22].…”

Section: Introductionmentioning

confidence: 99%

An Embodied Conversational Agent for Unguided Internet-Based Cognitive Behavior Therapy in Preventative Mental Health: Feasibility and Acceptability Pilot Trial

2018

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BackgroundRecent years have seen an increase in the use of internet-based cognitive behavioral therapy in the area of mental health. Although lower effectiveness and higher dropout rates of unguided than those of guided internet-based cognitive behavioral therapy remain critical issues, not incurring ongoing human clinical resources makes it highly advantageous.ObjectiveCurrent research in psychotherapy, which acknowledges the importance of therapeutic alliance, aims to evaluate the feasibility and acceptability, in terms of mental health, of an application that is embodied with a conversational agent. This application was enabled for use as an internet-based cognitive behavioral therapy preventative mental health measure.MethodsAnalysis of the data from the 191 participants of the experimental group with a mean age of 38.07 (SD 10.75) years and the 263 participants of the control group with a mean age of 38.05 (SD 13.45) years using a 2-way factorial analysis of variance (group × time) was performed.ResultsThere was a significant main effect (P=.02) and interaction for time on the variable of positive mental health (P=.02), and for the treatment group, a significant simple main effect was also found (P=.002). In addition, there was a significant main effect (P=.02) and interaction for time on the variable of negative mental health (P=.005), and for the treatment group, a significant simple main effect was also found (P=.001).ConclusionsThis research can be seen to represent a certain level of evidence for the mental health application developed herein, indicating empirically that internet-based cognitive behavioral therapy with the embodied conversational agent can be used in mental health care. In the pilot trial, given the issues related to feasibility and acceptability, it is necessary to pursue higher quality evidence while continuing to further improve the application, based on the findings of the current research.

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Advantages of virtual agents over clinical psychologists during comprehensive mental health interviews using a mixed methods design

Cited by 34 publications

References 42 publications

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

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

Medical Instructed Real-Time Assistant for Patient with Glaucoma and Diabetic Conditions

An Embodied Conversational Agent for Unguided Internet-Based Cognitive Behavior Therapy in Preventative Mental Health: Feasibility and Acceptability Pilot Trial

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