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      The Effects of a Health Care Chatbot’s Complexity and Persona on User Trust, Perceived Usability, and Effectiveness: Mixed Methods Study

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          Abstract

          Background

          The rising adoption of telehealth provides new opportunities for more effective and equitable health care information mediums. The ability of chatbots to provide a conversational, personal, and comprehendible avenue for learning about health care information make them a promising tool for addressing health care inequity as health care trends continue toward web-based and remote processes. Although chatbots have been studied in the health care domain for their efficacy for smoking cessation, diet recommendation, and other assistive applications, few studies have examined how specific design characteristics influence the effectiveness of chatbots in providing health information.

          Objective

          Our objective was to investigate the influence of different design considerations on the effectiveness of an educational health care chatbot.

          Methods

          A 2×3 between-subjects study was performed with 2 independent variables: a chatbot’s complexity of responses (eg, technical or nontechnical language) and the presented qualifications of the chatbot’s persona (eg, doctor, nurse, or nursing student). Regression models were used to evaluate the impact of these variables on 3 outcome measures: effectiveness, usability, and trust. A qualitative transcript review was also done to review how participants engaged with the chatbot.

          Results

          Analysis of 71 participants found that participants who received technical language responses were significantly more likely to be in the high effectiveness group, which had higher improvements in test scores (odds ratio [OR] 2.73, 95% CI 1.05-7.41; P=.04). Participants with higher health literacy (OR 2.04, 95% CI 1.11-4.00, P=.03) were significantly more likely to trust the chatbot. The participants engaged with the chatbot in a variety of ways, with some taking a conversational approach and others treating the chatbot more like a search engine.

          Conclusions

          Given their increasing popularity, it is vital that we consider how chatbots are designed and implemented. This study showed that factors such as chatbots’ persona and language complexity are two design considerations that influence the ability of chatbots to successfully provide health care information.

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          Most cited references44

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          Low health literacy and health outcomes: an updated systematic review.

          Approximately 80 million Americans have limited health literacy, which puts them at greater risk for poorer access to care and poorer health outcomes. To update a 2004 systematic review and determine whether low health literacy is related to poorer use of health care, outcomes, costs, and disparities in health outcomes among persons of all ages. English-language articles identified through MEDLINE, CINAHL, PsycINFO, ERIC, and Cochrane Library databases and hand-searching (search dates for articles on health literacy, 2003 to 22 February 2011; for articles on numeracy, 1966 to 22 February 2011). Two reviewers independently selected studies that compared outcomes by differences in directly measured health literacy or numeracy levels. One reviewer abstracted article information into evidence tables; a second reviewer checked information for accuracy. Two reviewers independently rated study quality by using predefined criteria, and the investigative team jointly graded the overall strength of evidence. 96 relevant good- or fair-quality studies in 111 articles were identified: 98 articles on health literacy, 22 on numeracy, and 9 on both. Low health literacy was consistently associated with more hospitalizations; greater use of emergency care; lower receipt of mammography screening and influenza vaccine; poorer ability to demonstrate taking medications appropriately; poorer ability to interpret labels and health messages; and, among elderly persons, poorer overall health status and higher mortality rates. Poor health literacy partially explains racial disparities in some outcomes. Reviewers could not reach firm conclusions about the relationship between numeracy and health outcomes because of few studies or inconsistent results among studies. Searches were limited to articles published in English. No Medical Subject Heading terms exist for identifying relevant studies. No evidence concerning oral health literacy (speaking and listening skills) and outcomes was found. Low health literacy is associated with poorer health outcomes and poorer use of health care services. Agency for Healthcare Research and Quality.
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            Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial

            Background Web-based cognitive-behavioral therapeutic (CBT) apps have demonstrated efficacy but are characterized by poor adherence. Conversational agents may offer a convenient, engaging way of getting support at any time. Objective The objective of the study was to determine the feasibility, acceptability, and preliminary efficacy of a fully automated conversational agent to deliver a self-help program for college students who self-identify as having symptoms of anxiety and depression. Methods In an unblinded trial, 70 individuals age 18-28 years were recruited online from a university community social media site and were randomized to receive either 2 weeks (up to 20 sessions) of self-help content derived from CBT principles in a conversational format with a text-based conversational agent (Woebot) (n=34) or were directed to the National Institute of Mental Health ebook, “Depression in College Students,” as an information-only control group (n=36). All participants completed Web-based versions of the 9-item Patient Health Questionnaire (PHQ-9), the 7-item Generalized Anxiety Disorder scale (GAD-7), and the Positive and Negative Affect Scale at baseline and 2-3 weeks later (T2). Results Participants were on average 22.2 years old (SD 2.33), 67% female (47/70), mostly non-Hispanic (93%, 54/58), and Caucasian (79%, 46/58). Participants in the Woebot group engaged with the conversational agent an average of 12.14 (SD 2.23) times over the study period. No significant differences existed between the groups at baseline, and 83% (58/70) of participants provided data at T2 (17% attrition). Intent-to-treat univariate analysis of covariance revealed a significant group difference on depression such that those in the Woebot group significantly reduced their symptoms of depression over the study period as measured by the PHQ-9 (F=6.47; P=.01) while those in the information control group did not. In an analysis of completers, participants in both groups significantly reduced anxiety as measured by the GAD-7 (F1,54= 9.24; P=.004). Participants’ comments suggest that process factors were more influential on their acceptability of the program than content factors mirroring traditional therapy. Conclusions Conversational agents appear to be a feasible, engaging, and effective way to deliver CBT.
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              IBM computer usability satisfaction questionnaires: Psychometric evaluation and instructions for use

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                Author and article information

                Contributors
                Journal
                JMIR Hum Factors
                JMIR Hum Factors
                JMIR Human Factors
                JMIR Human Factors
                JMIR Publications (Toronto, Canada )
                2292-9495
                2023
                1 February 2023
                : 10
                : e41017
                Affiliations
                [1 ] Department of Industrial Engineering Clemson University Clemson, SC United States
                Author notes
                Corresponding Author: David Neyens dneyens@ 123456clemson.edu
                Author information
                https://orcid.org/0000-0001-7362-4138
                https://orcid.org/0000-0002-5810-7009
                https://orcid.org/0000-0002-3443-518X
                Article
                v10i1e41017
                10.2196/41017
                9932873
                36724004
                74992d8e-533b-4fc6-8052-ab9c8afdfe81
                ©Joshua Biro, Courtney Linder, David Neyens. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 01.02.2023.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Human Factors, is properly cited. The complete bibliographic information, a link to the original publication on https://humanfactors.jmir.org, as well as this copyright and license information must be included.

                History
                : 12 July 2022
                : 29 August 2022
                : 9 December 2022
                : 1 January 2023
                Categories
                Original Paper
                Original Paper

                electronic health record,ehr,health information,health education,patient education,chatbot,virtual agent,virtual assistant,usability,trust,adoption,artificial intelligence,effectiveness

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