@Article{info:doi/10.2196/16762, author="Gabrielli, Silvia and Rizzi, Silvia and Carbone, Sara and Donisi, Valeria", title="A Chatbot-Based Coaching Intervention for Adolescents to Promote Life Skills: Pilot Study", journal="JMIR Hum Factors", year="2020", month="Feb", day="14", volume="7", number="1", pages="e16762", keywords="life skills; chatbots; conversational agents; mental health; participatory design; adolescence; bullying; cyberbullying; well-being intervention", abstract="Background: Adolescence is a challenging period, where youth face rapid changes as well as increasing socioemotional demands and threats, such as bullying and cyberbullying. Adolescent mental health and well-being can be best supported by providing effective coaching on life skills, such as coping strategies and protective factors. Interventions that take advantage of online coaching by means of chatbots, deployed on Web or mobile technology, may be a novel and more appealing way to support positive mental health for adolescents. Objective: In this pilot study, we co-designed and conducted a formative evaluation of an online, life skills coaching, chatbot intervention, inspired by the positive technology approach, to promote mental well-being in adolescence. Methods: We co-designed the first life skills coaching session of the CRI (for girls) and CRIS (for boys) chatbot with 20 secondary school students in a participatory design workshop. We then conducted a formative evaluation of the entire intervention---eight sessions---with a convenience sample of 21 adolescents of both genders (mean age 14.52 years). Participants engaged with the chatbot sessions over 4 weeks and filled in an anonymous user experience questionnaire at the end of each session; responses were based on a 5-point Likert scale. Results: A majority of the adolescents found the intervention useful (16/21, 76{\%}), easy to use (19/21, 90{\%}), and innovative (17/21, 81{\%}). Most of the participants (15/21, 71{\%}) liked, in particular, the video cartoons provided by the chatbot in the coaching sessions. They also thought that a session should last only 5-10 minutes (14/21, 66{\%}) and said they would recommend the intervention to a friend (20/21, 95{\%}). Conclusions: We have presented a novel and scalable self-help intervention to deliver life skills coaching to adolescents online that is appealing to this population. This intervention can support the promotion of coping skills and mental well-being among youth. ", doi="10.2196/16762", url="http://humanfactors.jmir.org/2020/1/e16762/", url="https://doi.org/10.2196/16762" } @Article{info:doi/10.2196/15823, author="Kocaballi, Ahmet Baki and Quiroz, Juan C and Rezazadegan, Dana and Berkovsky, Shlomo and Magrabi, Farah and Coiera, Enrico and Laranjo, Liliana", title="Responses of Conversational Agents to Health and Lifestyle Prompts: Investigation of Appropriateness and Presentation Structures", journal="J Med Internet Res", year="2020", month="Feb", day="10", volume="22", number="2", pages="e15823", keywords="conversational agents; chatbots; patient safety; health literacy; public health; design principles; evaluation", abstract="Background: Conversational agents (CAs) are systems that mimic human conversations using text or spoken language. Their widely used examples include voice-activated systems such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana. The use of CAs in health care has been on the rise, but concerns about their potential safety risks often remain understudied. Objective: This study aimed to analyze how commonly available, general-purpose CAs on smartphones and smart speakers respond to health and lifestyle prompts (questions and open-ended statements) by examining their responses in terms of content and structure alike. Methods: We followed a piloted script to present health- and lifestyle-related prompts to 8 CAs. The CAs' responses were assessed for their appropriateness on the basis of the prompt type: responses to safety-critical prompts were deemed appropriate if they included a referral to a health professional or service, whereas responses to lifestyle prompts were deemed appropriate if they provided relevant information to address the problem prompted. The response structure was also examined according to information sources (Web search--based or precoded), response content style (informative and/or directive), confirmation of prompt recognition, and empathy. Results: The 8 studied CAs provided in total 240 responses to 30 prompts. They collectively responded appropriately to 41{\%} (46/112) of the safety-critical and 39{\%} (37/96) of the lifestyle prompts. The ratio of appropriate responses deteriorated when safety-critical prompts were rephrased or when the agent used a voice-only interface. The appropriate responses included mostly directive content and empathy statements for the safety-critical prompts and a mix of informative and directive content for the lifestyle prompts. Conclusions: Our results suggest that the commonly available, general-purpose CAs on smartphones and smart speakers with unconstrained natural language interfaces are limited in their ability to advise on both the safety-critical health prompts and lifestyle prompts. Our study also identified some response structures the CAs employed to present their appropriate responses. Further investigation is needed to establish guidelines for designing suitable response structures for different prompt types. ", doi="10.2196/15823", url="https://www.jmir.org/2020/2/e15823", url="https://doi.org/10.2196/15823" } @Article{info:doi/10.2196/14058, author="Kramer, Lean L and ter Stal, Silke and Mulder, Bob C and de Vet, Emely and van Velsen, Lex", title="Developing Embodied Conversational Agents for Coaching People in a Healthy Lifestyle: Scoping Review", journal="J Med Internet Res", year="2020", month="Feb", day="5", volume="22", number="2", pages="e14058", keywords="embodied conversational agent; virtual agent; lifestyle; health behavior; eHealth; chatbots", abstract="Background: Embodied conversational agents (ECAs) are animated computer characters that simulate face-to-face counseling. Owing to their capacity to establish and maintain an empathic relationship, they are deemed to be a promising tool for starting and maintaining a healthy lifestyle. Objective: This review aimed to identify the current practices in designing and evaluating ECAs for coaching people in a healthy lifestyle and provide an overview of their efficacy (on behavioral, knowledge, and motivational parameters) and use (on usability, usage, and user satisfaction parameters). Methods: We used the Arksey and O'Malley framework to conduct a scoping review. PsycINFO, Medical Literature Analysis and Retrieval System Online, and Scopus were searched with a combination of terms related to ECA and lifestyle. Initially, 1789 unique studies were identified; 20 studies were included. Results: Most often, ECAs targeted physical activity (n=16) and had the appearance of a middle-aged African American woman (n=13). Multiple behavior change techniques (median=3) and theories or principles (median=3) were applied, but their interpretation and application were usually not reported. ECAs seemed to be designed for the end user rather than with the end user. Stakeholders were usually not involved. A total of 7 out of 15 studies reported better efficacy outcomes for the intervention group, and 5 out of 8 studies reported better use-related outcomes, as compared with the control group. Conclusions: ECAs are a promising tool for persuasive communication in the health domain. This review provided valuable insights into the current developmental processes, and it recommends the use of human-centered, stakeholder-inclusive design approaches, along with reporting on the design activities in a systematic and comprehensive manner. The gaps in knowledge were identified on the working mechanisms of intervention components and the right timing and frequency of coaching. ", doi="10.2196/14058", url="https://www.jmir.org/2020/2/e14058", url="https://doi.org/10.2196/14058" } @Article{info:doi/10.2196/13244, author="Holdener, Marianne and Gut, Alain and Angerer, Alfred", title="Applicability of the User Engagement Scale to Mobile Health: A Survey-Based Quantitative Study", journal="JMIR Mhealth Uhealth", year="2020", month="Jan", day="3", volume="8", number="1", pages="e13244", keywords="mobile health; mhealth; mobile apps; user engagement; measurement; user engagement scale; chatbot", abstract="Background: There has recently been exponential growth in the development and use of health apps on mobile phones. As with most mobile apps, however, the majority of users abandon them quickly and after minimal use. One of the most critical factors for the success of a health app is how to support users' commitment to their health. Despite increased interest from researchers in mobile health, few studies have examined the measurement of user engagement with health apps. Objective: User engagement is a multidimensional, complex phenomenon. The aim of this study was to understand the concept of user engagement and, in particular, to demonstrate the applicability of a user engagement scale (UES) to mobile health apps. Methods: To determine the measurability of user engagement in a mobile health context, a UES was employed, which is a psychometric tool to measure user engagement with a digital system. This was adapted to Ada, developed by Ada Health, an artificial intelligence--powered personalized health guide that helps people understand their health. A principal component analysis (PCA) with varimax rotation was conducted on 30 items. In addition, sum scores as means of each subscale were calculated. Results: Survey data from 73 Ada users were analyzed. PCA was determined to be suitable, as verified by the sampling adequacy of Kaiser-Meyer-Olkin=0.858, a significant Bartlett test of sphericity ($\chi$2300=1127.1; P<.001), and communalities mostly within the 0.7 range. Although 5 items had to be removed because of low factor loadings, the results of the remaining 25 items revealed 4 attributes: perceived usability, aesthetic appeal, reward, and focused attention. Ada users showed the highest engagement level with perceived usability, with a value of 294, followed by aesthetic appeal, reward, and focused attention. Conclusions: Although the UES was deployed in German and adapted to another digital domain, PCA yielded consistent subscales and a 4-factor structure. This indicates that user engagement with health apps can be assessed with the German version of the UES. These results can benefit related mobile health app engagement research and may be of importance to marketers and app developers. ", doi="10.2196/13244", url="https://mhealth.jmir.org/2020/1/e13244", url="https://doi.org/10.2196/13244", url="http://www.ncbi.nlm.nih.gov/pubmed/31899454" } @Article{info:doi/10.2196/15381, author="Martin-Hammond, Aqueasha and Vemireddy, Sravani and Rao, Kartik", title="Exploring Older Adults' Beliefs About the Use of Intelligent Assistants for Consumer Health Information Management: A Participatory Design Study", journal="JMIR Aging", year="2019", month="Dec", day="11", volume="2", number="2", pages="e15381", keywords="intelligent assistants; artificial intelligence; chatbots; conversational agents; digital health; elderly; aging in place; participatory design; co-design; health information seeking", abstract="Background: Intelligent assistants (IAs), also known as intelligent agents, use artificial intelligence to help users achieve a goal or complete a task. IAs represent a potential solution for providing older adults with individualized assistance at home, for example, to reduce social isolation, serve as memory aids, or help with disease management. However, to design IAs for health that are beneficial and accepted by older adults, it is important to understand their beliefs about IAs, how they would like to interact with IAs for consumer health, and how they desire to integrate IAs into their homes. Objective: We explore older adults' mental models and beliefs about IAs, the tasks they want IAs to support, and how they would like to interact with IAs for consumer health. For the purpose of this study, we focus on IAs in the context of consumer health information management and search. Methods: We present findings from an exploratory, qualitative study that investigated older adults' perspectives of IAs that aid with consumer health information search and management tasks. Eighteen older adults participated in a multiphase, participatory design workshop in which we engaged them in discussion, brainstorming, and design activities that helped us identify their current challenges managing and finding health information at home. We also explored their beliefs and ideas for an IA to assist them with consumer health tasks. We used participatory design activities to identify areas in which they felt IAs might be useful, but also to uncover the reasoning behind the ideas they presented. Discussions were audio-recorded and later transcribed. We compiled design artifacts collected during the study to supplement researcher transcripts and notes. Thematic analysis was used to analyze data. Results: We found that participants saw IAs as potentially useful for providing recommendations, facilitating collaboration between themselves and other caregivers, and for alerts of serious illness. However, they also desired familiar and natural interactions with IAs (eg, using voice) that could, if need be, provide fluid and unconstrained interactions, reason about their symptoms, and provide information or advice. Other participants discussed the need for flexible IAs that could be used by those with low technical resources or skills. Conclusions: From our findings, we present a discussion of three key components of participants' mental models, including the people, behaviors, and interactions they described that were important for IAs for consumer health information management and seeking. We then discuss the role of access, transparency, caregivers, and autonomy in design for addressing participants' concerns about privacy and trust as well as its role in assisting others that may interact with an IA on the older adults' behalf. International Registered Report Identifier (IRRID): RR2-10.1145/3240925.3240972 ", doi="10.2196/15381", url="http://aging.jmir.org/2019/2/e15381/", url="https://doi.org/10.2196/15381", url="http://www.ncbi.nlm.nih.gov/pubmed/31825322" } @Article{info:doi/10.2196/15787, author="Bibault, Jean-Emmanuel and Chaix, Benjamin and Guillemass{\'e}, Arthur and Cousin, Sophie and Escande, Alexandre and Perrin, Morgane and Pienkowski, Arthur and Delamon, Guillaume and Nectoux, Pierre and Brouard, Beno{\^i}t", title="A Chatbot Versus Physicians to Provide Information for Patients With Breast Cancer: Blind, Randomized Controlled Noninferiority Trial", journal="J Med Internet Res", year="2019", month="Nov", day="27", volume="21", number="11", pages="e15787", keywords="chatbot; clinical trial; cancer", abstract="Background: The data regarding the use of conversational agents in oncology are scarce. Objective: The aim of this study was to verify whether an artificial conversational agent was able to provide answers to patients with breast cancer with a level of satisfaction similar to the answers given by a group of physicians. Methods: This study is a blind, noninferiority randomized controlled trial that compared the information given by the chatbot, Vik, with that given by a multidisciplinary group of physicians to patients with breast cancer. Patients were women with breast cancer in treatment or in remission. The European Organisation for Research and Treatment of Cancer Quality of Life Group information questionnaire (EORTC QLQ-INFO25) was adapted and used to compare the quality of the information provided to patients by the physician or the chatbot. The primary outcome was to show that the answers given by the Vik chatbot to common questions asked by patients with breast cancer about their therapy management are at least as satisfying as answers given by a multidisciplinary medical committee by comparing the success rate in each group (defined by a score above 3). The secondary objective was to compare the average scores obtained by the chatbot and physicians for each INFO25 item. Results: A total of 142 patients were included and randomized into two groups of 71. They were all female with a mean age of 42 years (SD 19). The success rates (as defined by a score >3) was 69{\%} (49/71) in the chatbot group versus 64{\%} (46/71) in the physicians group. The binomial test showed the noninferiority (P<.001) of the chatbot's answers. Conclusions: This is the first study that assessed an artificial conversational agent used to inform patients with cancer. The EORTC INFO25 scores from the chatbot were found to be noninferior to the scores of the physicians. Artificial conversational agents may save patients with minor health concerns from a visit to the doctor. This could allow clinicians to spend more time to treat patients who need a consultation the most. Trial Registration: Clinicaltrials.gov NCT03556813, https://tinyurl.com/rgtlehq ", doi="10.2196/15787", url="http://www.jmir.org/2019/11/e15787/", url="https://doi.org/10.2196/15787", url="http://www.ncbi.nlm.nih.gov/pubmed/31774408" } @Article{info:doi/10.2196/15771, author="Brar Prayaga, Rena and Agrawal, Ridhika and Nguyen, Benjamin and Jeong, Erwin W and Noble, Harmony K and Paster, Andrew and Prayaga, Ram S", title="Impact of Social Determinants of Health and Demographics on Refill Requests by Medicare Patients Using a Conversational Artificial Intelligence Text Messaging Solution: Cross-Sectional Study", journal="JMIR Mhealth Uhealth", year="2019", month="Nov", day="18", volume="7", number="11", pages="e15771", keywords="text messaging; SMS; refill adherence; medication adherence; Medicare patients; conversational AI; social determinants of health; predictive modeling; machine learning; health disparities", abstract="Background: Nonadherence among patients with chronic disease continues to be a significant concern, and the use of text message refill reminders has been effective in improving adherence. However, questions remain about how differences in patient characteristics and demographics might influence the likelihood of refill using this channel. Objective: The aim of this study was to evaluate the efficacy of an SMS-based refill reminder solution using conversational artificial intelligence (AI; an automated system that mimics human conversations) with a large Medicare patient population and to explore the association and impact of patient demographics (age, gender, race/ethnicity, language) and social determinants of health on successful engagement with the solution to improve refill adherence. Methods: The study targeted 99,217 patients with chronic disease, median age of 71 years, for medication refill using the mPulse Mobile interactive SMS text messaging solution from December 2016 to February 2019. All patients were partially adherent or nonadherent Medicare Part D members of Kaiser Permanente, Southern California, a large integrated health plan. Patients received SMS reminders in English or Spanish and used simple numeric or text responses to validate their identity, view their medication, and complete a refill request. The refill requests were processed by Kaiser Permanente pharmacists and support staff, and refills were picked up at the pharmacy or mailed to patients. Descriptive statistics and predictive analytics were used to examine the patient population and their refill behavior. Qualitative text analysis was used to evaluate quality of conversational AI. Results: Over the course of the study, 273,356 refill reminders requests were sent to 99,217 patients, resulting in 47,552 refill requests (17.40{\%}). This was consistent with earlier pilot study findings. Of those who requested a refill, 54.81{\%} (26,062/47,552) did so within 2 hours of the reminder. There was a strong inverse relationship (r10=−0.93) between social determinants of health and refill requests. Spanish speakers (5149/48,156, 10.69{\%}) had significantly lower refill request rates compared with English speakers (42,389/225,060, 18.83{\%}; X21 [n=273,216]=1829.2; P<.001). There were also significantly different rates of refill requests by age band (X26 [n=268,793]=1460.3; P<.001), with younger patients requesting refills at a higher rate. Finally, the vast majority (284,598/307,484, 92.23{\%}) of patient responses were handled using conversational AI. Conclusions: Multiple factors impacted refill request rates, including a strong association between social determinants of health and refill rates. The findings suggest that higher refill requests are linked to language, race/ethnicity, age, and social determinants of health, and that English speakers, whites, those younger than 75 years, and those with lower social determinants of health barriers are significantly more likely to request a refill via SMS. A neural network--based predictive model with an accuracy level of 78{\%} was used to identify patients who might benefit from additional outreach to narrow identified gaps based on demographic and socioeconomic factors. ", doi="10.2196/15771", url="http://mhealth.jmir.org/2019/11/e15771/", url="https://doi.org/10.2196/15771", url="http://www.ncbi.nlm.nih.gov/pubmed/31738170" } @Article{info:doi/10.2196/14672, author="Xing, Zhaopeng and Yu, Fei and Du, Jian and Walker, Jennifer S and Paulson, Claire B and Mani, Nandita S and Song, Lixin", title="Conversational Interfaces for Health: Bibliometric Analysis of Grants, Publications, and Patents", journal="J Med Internet Res", year="2019", month="Nov", day="18", volume="21", number="11", pages="e14672", keywords="conversational interfaces; conversational agents; chatbots; artifical intelligence; healthcare; bibliometrics; social network; grants; publications; patents", abstract="Background: Conversational interfaces (CIs) in different modalities have been developed for health purposes, such as health behavioral intervention, patient self-management, and clinical decision support. Despite growing research evidence supporting CIs' potential, CI-related research is still in its infancy. There is a lack of systematic investigation that goes beyond publication review and presents the state of the art from perspectives of funding agencies, academia, and industry by incorporating CI-related public funding and patent activities. Objective: This study aimed to use data systematically extracted from multiple sources (ie, grant, publication, and patent databases) to investigate the development, research, and fund application of health-related CIs and associated stakeholders (ie, countries, organizations, and collaborators). Methods: A multifaceted search query was executed to retrieve records from 9 databases. Bibliometric analysis, social network analysis, and term co-occurrence analysis were conducted on the screened records. Results: This review included 42 funded projects, 428 research publications, and 162 patents. The total dollar amount of grants awarded was US {\$}30,297,932, of which US {\$}13,513,473 was awarded by US funding agencies and US {\$}16,784,459 was funded by the Europe Commission. The top 3 funding agencies in the United States were the National Science Foundation, National Institutes of Health, and Agency for Healthcare Research and Quality. Boston Medical Center was awarded the largest combined grant size (US {\$}2,246,437) for 4 projects. The authors of the publications were from 58 countries and 566 organizations; the top 3 most productive organizations were Northeastern University (United States), Universiti Teknologi MARA (Malaysia), and the French National Center for Scientific Research (CNRS; France). US researchers produced 114 publications. Although 82.0{\%} (464/566) of the organizations engaged in interorganizational collaboration, 2 organizational research-collaboration clusters were observed with Northeastern University and CNRS as the central nodes. About 112 organizations from the United States and China filed 87.7{\%} patents. IBM filed most patents (N=17). Only 5 patents were co-owned by different organizations, and there was no across-country collaboration on patenting activity. The terms patient, child, elderly, and robot were frequently discussed in the 3 record types. The terms related to mental and chronic issues were discussed mainly in grants and publications. The terms regarding multimodal interactions were widely mentioned as users' communication modes with CIs in the identified records. Conclusions: Our findings provided an overview of the countries, organizations, and topic terms in funded projects, as well as the authorship, collaboration, content, and related information of research publications and patents. There is a lack of broad cross-sector partnerships among grant agencies, academia, and industry, particularly in the United States. Our results suggest a need to improve collaboration among public and private sectors and health care organizations in research and patent activities. ", doi="10.2196/14672", url="http://www.jmir.org/2019/11/e14672/", url="https://doi.org/10.2196/14672", url="http://www.ncbi.nlm.nih.gov/pubmed/31738171" } @Article{info:doi/10.2196/15360, author="Kocaballi, Ahmet Baki and Berkovsky, Shlomo and Quiroz, Juan C and Laranjo, Liliana and Tong, Huong Ly and Rezazadegan, Dana and Briatore, Agustina and Coiera, Enrico", title="The Personalization of Conversational Agents in Health Care: Systematic Review", journal="J Med Internet Res", year="2019", month="Nov", day="7", volume="21", number="11", pages="e15360", keywords="conversational interfaces; conversational agents; dialogue systems; personalization; customization; adaptive systems; health care", abstract="Background: The personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents. Objective: The goal of this systematic review was to understand the ways in which personalization has been used with conversational agents in health care and characterize the methods of its implementation. Methods: We searched on PubMed, Embase, CINAHL, PsycInfo, and ACM Digital Library using a predefined search strategy. The studies were included if they: (1) were primary research studies that focused on consumers, caregivers, or health care professionals; (2) involved a conversational agent with an unconstrained natural language interface; (3) tested the system with human subjects; and (4) implemented personalization features. Results: The search found 1958 publications. After abstract and full-text screening, 13 studies were included in the review. Common examples of personalized content included feedback, daily health reports, alerts, warnings, and recommendations. The personalization features were implemented without a theoretical framework of customization and with limited evaluation of its impact. While conversational agents with personalization features were reported to improve user satisfaction, user engagement and dialogue quality, the role of personalization in improving health outcomes was not assessed directly. Conclusions: Most of the studies in our review implemented the personalization features without theoretical or evidence-based support for them and did not leverage the recent developments in other domains of personalization. Future research could incorporate personalization as a distinct design factor with a more careful consideration of its impact on health outcomes and its implications on patient safety, privacy, and decision-making. ", doi="10.2196/15360", url="https://www.jmir.org/2019/11/e15360", url="https://doi.org/10.2196/15360", url="http://www.ncbi.nlm.nih.gov/pubmed/31697237" } @Article{info:doi/10.2196/15018, author="Greer, Stephanie and Ramo, Danielle and Chang, Yin-Juei and Fu, Michael and Moskowitz, Judith and Haritatos, Jana", title="Use of the Chatbot ``Vivibot'' to Deliver Positive Psychology Skills and Promote Well-Being Among Young People After Cancer Treatment: Randomized Controlled Feasibility Trial", journal="JMIR Mhealth Uhealth", year="2019", month="Oct", day="31", volume="7", number="10", pages="e15018", keywords="chatbot; positive psychology; young adult; cancer", abstract="Background: Positive psychology interventions show promise for reducing psychosocial distress associated with health adversity and have the potential to be widely disseminated to young adults through technology. Objective: This pilot randomized controlled trial examined the feasibility of delivering positive psychology skills via the Vivibot chatbot and its effects on key psychosocial well-being outcomes in young adults treated for cancer. Methods: Young adults (age 18-29 years) were recruited within 5 years of completing active cancer treatment by using the Vivibot chatbot on Facebook messenger. Participants were randomized to either immediate access to Vivibot content (experimental group) or access to only daily emotion ratings and access to full chatbot content after 4 weeks (control). Created using a human-centered design process with young adults treated for cancer, Vivibot content includes 4 weeks of positive psychology skills, daily emotion ratings, video, and other material produced by survivors, and periodic feedback check-ins. All participants were assessed for psychosocial well-being via online surveys at baseline and weeks 2, 4, and 8. Analyses examined chatbot engagement and open-ended feedback on likability and perceived helpfulness and compared experimental and control groups with regard to anxiety and depression symptoms and positive and negative emotion changes between baseline and 4 weeks. To verify the main effects, follow-up analyses compared changes in the main outcomes between 4 and 8 weeks in the control group once participants had access to all chatbot content. Results: Data from 45 young adults (36 women; mean age: 25 [SD 2.9]; experimental group: n=25; control group: n=20) were analyzed. Participants in the experimental group spent an average of 74 minutes across an average of 12 active sessions chatting with Vivibot and rated their experience as helpful (mean 2.0/3, SD 0.72) and would recommend it to a friend (mean 6.9/10; SD 2.6). Open-ended feedback noted its nonjudgmental nature as a particular benefit of the chatbot. After 4 weeks, participants in the experimental group reported an average reduction in anxiety of 2.58 standardized t-score units, while the control group reported an increase in anxiety of 0.7 units. A mixed-effects models revealed a trend-level (P=.09) interaction between group and time, with an effect size of 0.41. Those in the experimental group also experienced greater reductions in anxiety when they engaged in more sessions (z=--1.9, P=.06). There were no significant (or trend level) effects by group on changes in depression, positive emotion, or negative emotion. Conclusions: The chatbot format provides a useful and acceptable way of delivering positive psychology skills to young adults who have undergone cancer treatment and supports anxiety reduction. Further analysis with a larger sample size is required to confirm this pattern. ", doi="10.2196/15018", url="http://mhealth.jmir.org/2019/10/e15018/", url="https://doi.org/10.2196/15018", url="http://www.ncbi.nlm.nih.gov/pubmed/31674920" } @Article{info:doi/10.2196/13863, author="Jungmann, Stefanie Maria and Klan, Timo and Kuhn, Sebastian and Jungmann, Florian", title="Accuracy of a Chatbot (Ada) in the Diagnosis of Mental Disorders: Comparative Case Study With Lay and Expert Users", journal="JMIR Form Res", year="2019", month="Oct", day="29", volume="3", number="4", pages="e13863", keywords="artificial intelligence; eHealth; mental disorders; mHealth; screening; (mobile) app; diagnostic", abstract="Background: Health apps for the screening and diagnosis of mental disorders have emerged in recent years on various levels (eg, patients, practitioners, and public health system). However, the diagnostic quality of these apps has not been (sufficiently) tested so far. Objective: The objective of this pilot study was to investigate the diagnostic quality of a health app for a broad spectrum of mental disorders and its dependency on expert knowledge. Methods: Two psychotherapists, two psychology students, and two laypersons each read 20 case vignettes with a broad spectrum of mental disorders. They used a health app (Ada---Your Health Guide) to get a diagnosis by entering the symptoms. Interrater reliabilities were computed between the diagnoses of the case vignettes and the results of the app for each user group. Results: Overall, there was a moderate diagnostic agreement (kappa=0.64) between the results of the app and the case vignettes for mental disorders in adulthood and a low diagnostic agreement (kappa=0.40) for mental disorders in childhood and adolescence. When psychotherapists applied the app, there was a good diagnostic agreement (kappa=0.78) regarding mental disorders in adulthood. The diagnostic agreement was moderate (kappa=0.55/0.60) for students and laypersons. For mental disorders in childhood and adolescence, a moderate diagnostic quality was found when psychotherapists (kappa=0.53) and students (kappa=0.41) used the app, whereas the quality was low for laypersons (kappa=0.29). On average, the app required 34 questions to be answered and 7 min to complete. Conclusions: The health app investigated here can represent an efficient diagnostic screening or help function for mental disorders in adulthood and has the potential to support especially diagnosticians in their work in various ways. The results of this pilot study provide a first indication that the diagnostic accuracy is user dependent and improvements in the app are needed especially for mental disorders in childhood and adolescence. ", doi="10.2196/13863", url="http://formative.jmir.org/2019/4/e13863/", url="https://doi.org/10.2196/13863", url="http://www.ncbi.nlm.nih.gov/pubmed/31663858" } @Article{info:doi/10.2196/16222, author="Powell, John", title="Trust Me, I'm a Chatbot: How Artificial Intelligence in Health Care Fails the Turing Test", journal="J Med Internet Res", year="2019", month="Oct", day="28", volume="21", number="10", pages="e16222", keywords="artificial intelligence; machine learning; medical informatics; digital health; ehealth; chatbots; conversational agents", doi="10.2196/16222", url="http://www.jmir.org/2019/10/e16222/", url="https://doi.org/10.2196/16222", url="http://www.ncbi.nlm.nih.gov/pubmed/31661083" } @Article{info:doi/10.2196/14166, author="Gaffney, Hannah and Mansell, Warren and Tai, Sara", title="Conversational Agents in the Treatment of Mental Health Problems: Mixed-Method Systematic Review", journal="JMIR Ment Health", year="2019", month="Oct", day="18", volume="6", number="10", pages="e14166", keywords="artificial intelligence; mental health; stress, pychological; psychiatry; therapy, computer-assisted; conversational agent; chatbot; digital health", abstract="Background: The use of conversational agent interventions (including chatbots and robots) in mental health is growing at a fast pace. Recent existing reviews have focused exclusively on a subset of embodied conversational agent interventions despite other modalities aiming to achieve the common goal of improved mental health. Objective: This study aimed to review the use of conversational agent interventions in the treatment of mental health problems. Methods: We performed a systematic search using relevant databases (MEDLINE, EMBASE, PsycINFO, Web of Science, and Cochrane library). Studies that reported on an autonomous conversational agent that simulated conversation and reported on a mental health outcome were included. Results: A total of 13 studies were included in the review. Among them, 4 full-scale randomized controlled trials (RCTs) were included. The rest were feasibility, pilot RCTs and quasi-experimental studies. Interventions were diverse in design and targeted a range of mental health problems using a wide variety of therapeutic orientations. All included studies reported reductions in psychological distress postintervention. Furthermore, 5 controlled studies demonstrated significant reductions in psychological distress compared with inactive control groups. In addition, 3 controlled studies comparing interventions with active control groups failed to demonstrate superior effects. Broader utility in promoting well-being in nonclinical populations was unclear. Conclusions: The efficacy and acceptability of conversational agent interventions for mental health problems are promising. However, a more robust experimental design is required to demonstrate efficacy and efficiency. A focus on streamlining interventions, demonstrating equivalence to other treatment modalities, and elucidating mechanisms of action has the potential to increase acceptance by users and clinicians and maximize reach. ", doi="10.2196/14166", url="https://mental.jmir.org/2019/10/e14166", url="https://doi.org/10.2196/14166", url="http://www.ncbi.nlm.nih.gov/pubmed/31628789" } @Article{info:doi/10.2196/13440, author="Bott, Nicholas and Wexler, Sharon and Drury, Lin and Pollak, Chava and Wang, Victor and Scher, Kathleen and Narducci, Sharon", title="A Protocol-Driven, Bedside Digital Conversational Agent to Support Nurse Teams and Mitigate Risks of Hospitalization in Older Adults: Case Control Pre-Post Study", journal="J Med Internet Res", year="2019", month="Oct", day="17", volume="21", number="10", pages="e13440", keywords="digital health; older adults; loneliness; delirium; falls; embodied conversational agent; chatbot; relational agent; information and communication technology", abstract="Background: Hospitalized older adults often experience isolation and disorientation while receiving care, placing them at risk for many inpatient complications, including loneliness, depression, delirium, and falls. Embodied conversational agents (ECAs) are technological entities that can interact with people through spoken conversation. Some ECAs are also relational agents, which build and maintain socioemotional relationships with people across multiple interactions. This study utilized a novel form of relational ECA, provided by Care Coach (care.coach, inc): an animated animal avatar on a tablet device, monitored and controlled by live health advocates. The ECA implemented algorithm-based clinical protocols for hospitalized older adults, such as reorienting patients to mitigate delirium risk, eliciting toileting needs to prevent falls, and engaging patients in social interaction to facilitate social engagement. Previous pilot studies of the Care Coach avatar have demonstrated the ECA's usability and efficacy in home-dwelling older adults. Further study among hospitalized older adults in a larger experimental trial is needed to demonstrate its effectiveness. Objective: The aim of the study was to examine the effect of a human-in-the-loop, protocol-driven relational ECA on loneliness, depression, delirium, and falls among diverse hospitalized older adults. Methods: This was a clinical trial of 95 adults over the age of 65 years, hospitalized at an inner-city community hospital. Intervention participants received an avatar for the duration of their hospital stay; participants on a control unit received a daily 15-min visit from a nursing student. Measures of loneliness (3-item University of California, Los Angeles Loneliness Scale), depression (15-item Geriatric Depression Scale), and delirium (confusion assessment method) were administered upon study enrollment and before discharge. Results: Participants who received the avatar during hospitalization had lower frequency of delirium at discharge (P<.001), reported fewer symptoms of loneliness (P=.01), and experienced fewer falls than control participants. There were no significant differences in self-reported depressive symptoms. Conclusions: The study findings validate the use of human-in-the-loop, relational ECAs among diverse hospitalized older adults. ", doi="10.2196/13440", url="http://www.jmir.org/2019/10/e13440/", url="https://doi.org/10.2196/13440", url="http://www.ncbi.nlm.nih.gov/pubmed/31625949" } @Article{info:doi/10.2196/12529, author="Tanana, Michael J and Soma, Christina S and Srikumar, Vivek and Atkins, David C and Imel, Zac E", title="Development and Evaluation of ClientBot: Patient-Like Conversational Agent to Train Basic Counseling Skills", journal="J Med Internet Res", year="2019", month="Jul", day="15", volume="21", number="7", pages="e12529", keywords="psychotherapy training; interactive learning; conversational agents; deep learning", abstract="Background: Training therapists is both expensive and time-consuming. Degree--based training can require tens of thousands of dollars and hundreds of hours of expert instruction. Counseling skills practice often involves role-plays, standardized patients, or practice with real clients. Performance--based feedback is critical for skill development and expertise, but trainee therapists often receive minimal and subjective feedback, which is distal to their skill practice. Objective: In this study, we developed and evaluated a patient-like neural conversational agent, which provides real-time feedback to trainees via chat--based interaction. Methods: The text--based conversational agent was trained on an archive of 2354 psychotherapy transcripts and provided specific feedback on the use of basic interviewing and counseling skills (ie, open questions and reflections---summary statements of what a client has said). A total of 151 nontherapists were randomized to either (1) immediate feedback on their use of open questions and reflections during practice session with ClientBot or (2) initial education and encouragement on the skills. Results: Participants in the ClientBot condition used 91{\%} (21.4/11.2) more reflections during practice with feedback (P<.001) and 76{\%} (14.1/8) more reflections after feedback was removed (P<.001) relative to the control group. The treatment group used more open questions during training but not after feedback was removed, suggesting that certain skills may not improve with performance--based feedback. Finally, after feedback was removed, the ClientBot group used 31{\%} (32.5/24.7) more listening skills overall (P<.001). Conclusions: This proof-of-concept study demonstrates that practice and feedback can improve trainee use of basic counseling skills. ", doi="10.2196/12529", url="https://www.jmir.org/2019/7/e12529/", url="https://doi.org/10.2196/12529", url="http://www.ncbi.nlm.nih.gov/pubmed/31309929" } @Article{info:doi/10.2196/13664, author="Loveys, Kate and Fricchione, Gregory and Kolappa, Kavitha and Sagar, Mark and Broadbent, Elizabeth", title="Reducing Patient Loneliness With Artificial Agents: Design Insights From Evolutionary Neuropsychiatry", journal="J Med Internet Res", year="2019", month="Jul", day="08", volume="21", number="7", pages="e13664", keywords="loneliness; neuropsychiatry; biological evolution; psychological bonding; interpersonal relations; artificial intelligence; social support; eHealth", doi="10.2196/13664", url="https://www.jmir.org/2019/7/e13664/", url="https://doi.org/10.2196/13664", url="http://www.ncbi.nlm.nih.gov/pubmed/31287067" } @Article{info:doi/10.2196/12996, author="Easton, Katherine and Potter, Stephen and Bec, Remi and Bennion, Matthew and Christensen, Heidi and Grindell, Cheryl and Mirheidari, Bahman and Weich, Scott and de Witte, Luc and Wolstenholme, Daniel and Hawley, Mark S", title="A Virtual Agent to Support Individuals Living With Physical and Mental Comorbidities: Co-Design and Acceptability Testing", journal="J Med Internet Res", year="2019", month="May", day="30", volume="21", number="5", pages="e12996", keywords="COPD; chronic obstructive pulmonary disease; mental health; comorbidity; chronic illness; self-management; artificial intelligence; virtual systems; computer-assisted therapy; chatbot; conversational agent", abstract="Background: Individuals living with long-term physical health conditions frequently experience co-occurring mental health problems. This comorbidity has a significant impact on an individual's levels of emotional distress, health outcomes, and associated health care utilization. As health care services struggle to meet demand and care increasingly moves to the community, digital tools are being promoted to support patients to self-manage their health. One such technology is the autonomous virtual agent (chatbot, conversational agent), which uses artificial intelligence (AI) to process the user's written or spoken natural language and then to select or construct the corresponding appropriate responses. Objective: This study aimed to co-design the content, functionality, and interface modalities of an autonomous virtual agent to support self-management for patients with an exemplar long-term condition (LTC; chronic pulmonary obstructive disease [COPD]) and then to assess the acceptability and system content. Methods: We conducted 2 co-design workshops and a proof-of-concept implementation of an autonomous virtual agent with natural language processing capabilities. This implementation formed the basis for video-based scenario testing of acceptability with adults with a diagnosis of COPD and health professionals involved in their care. Results: Adults (n=6) with a diagnosis of COPD and health professionals (n=5) specified 4 priority self-management scenarios for which they would like to receive support: at the time of diagnosis (information provision), during acute exacerbations (crisis support), during periods of low mood (emotional support), and for general self-management (motivation). From the scenario testing, 12 additional adults with COPD felt the system to be both acceptable and engaging, particularly with regard to internet-of-things capabilities. They felt the system would be particularly useful for individuals living alone. Conclusions: Patients did not explicitly separate mental and physical health needs, although the content they developed for the virtual agent had a clear psychological approach. Supported self-management delivered via an autonomous virtual agent was acceptable to the participants. A co-design process has allowed the research team to identify key design principles, content, and functionality to underpin an autonomous agent for delivering self-management support to older adults living with COPD and potentially other LTCs. ", doi="10.2196/12996", url="http://www.jmir.org/2019/5/e12996/", url="https://doi.org/10.2196/12996", url="http://www.ncbi.nlm.nih.gov/pubmed/31148545" } @Article{info:doi/10.2196/13203, author="Robinson, Nicole Lee and Cottier, Timothy Vaughan and Kavanagh, David John", title="Psychosocial Health Interventions by Social Robots: Systematic Review of Randomized Controlled Trials", journal="J Med Internet Res", year="2019", month="May", day="10", volume="21", number="5", pages="e13203", keywords="social robot; healthcare; treatment; therapy; autism spectrum disorder; dementia", abstract="Background: Social robots that can communicate and interact with people offer exciting opportunities for improved health care access and outcomes. However, evidence from randomized controlled trials (RCTs) on health or well-being outcomes has not yet been clearly synthesized across all health domains where social robots have been tested. Objective: This study aimed to undertake a systematic review examining current evidence from RCTs on the effects of psychosocial interventions by social robots on health or well-being. Methods: Medline, PsycInfo, ScienceDirect, Scopus, and Engineering Village searches across all years in the English language were conducted and supplemented by forward and backward searches. The included papers reported RCTs that assessed changes in health or well-being from interactions with a social robot across at least 2 measurement occasions. Results: Out of 408 extracted records, 27 trials met the inclusion criteria: 6 in child health or well-being, 9 in children with autism spectrum disorder, and 12 with older adults. No trials on adolescents, young adults, or other problem areas were identified, and no studies had interventions where robots spontaneously modified verbal responses based on speech by participants. Most trials were small (total N=5 to 415; median=34), only 6 (22{\%}) reported any follow-up outcomes (2 to 12 weeks; median=3.5) and a single-blind assessment was reported in 8 (31{\%}). More recent trials tended to have greater methodological quality. All papers reported some positive outcomes from robotic interventions, although most trials had some measures that showed no difference or favored alternate treatments. Conclusions: Controlled research on social robots is at an early stage, as is the current range of their applications to health care. Research on social robot interventions in clinical and health settings needs to transition from exploratory investigations to include large-scale controlled trials with sophisticated methodology, to increase confidence in their efficacy. ", doi="10.2196/13203", url="http://www.jmir.org/2019/5/e13203/", url="https://doi.org/10.2196/13203", url="http://www.ncbi.nlm.nih.gov/pubmed/31094357" } @Article{info:doi/10.2196/12856, author="Chaix, Benjamin and Bibault, Jean-Emmanuel and Pienkowski, Arthur and Delamon, Guillaume and Guillemass{\'e}, Arthur and Nectoux, Pierre and Brouard, Beno{\^i}t", title="When Chatbots Meet Patients: One-Year Prospective Study of Conversations Between Patients With Breast Cancer and a Chatbot", journal="JMIR Cancer", year="2019", month="May", day="02", volume="5", number="1", pages="e12856", keywords="artificial intelligence; breast cancer; mobile phone; patient-reported outcomes; symptom management; chatbot; conversational agent", abstract="Background: A chatbot is a software that interacts with users by simulating a human conversation through text or voice via smartphones or computers. It could be a solution to follow up with patients during their disease while saving time for health care providers. Objective: The aim of this study was to evaluate one year of conversations between patients with breast cancer and a chatbot. Methods: Wefight Inc designed a chatbot (Vik) to empower patients with breast cancer and their relatives. Vik responds to the fears and concerns of patients with breast cancer using personalized insights through text messages. We conducted a prospective study by analyzing the users' and patients' data, their usage duration, their interest in the various educational contents proposed, and their level of interactivity. Patients were women with breast cancer or under remission. Results: A total of 4737 patients were included. Results showed that an average of 132,970 messages exchanged per month was observed between patients and the chatbot, Vik. Thus, we calculated the average medication adherence rate over 4 weeks by using a prescription reminder function, and we showed that the more the patients used the chatbot, the more adherent they were. Patients regularly left positive comments and recommended Vik to their friends. The overall satisfaction was 93.95{\%} (900/958). When asked what Vik meant to them and what Vik brought them, 88.00{\%} (943/958) said that Vik provided them with support and helped them track their treatment effectively. Conclusions: We demonstrated that it is possible to obtain support through a chatbot since Vik improved the medication adherence rate of patients with breast cancer. ", doi="10.2196/12856", url="http://cancer.jmir.org/2019/1/e12856/", url="https://doi.org/10.2196/12856", url="http://www.ncbi.nlm.nih.gov/pubmed/31045505" } @Article{info:doi/10.2196/11800, author="Green, Eric P and Pearson, Nicholas and Rajasekharan, Sathyanath and Rauws, Michiel and Joerin, Angela and Kwobah, Edith and Musyimi, Christine and Bhat, Chaya and Jones, Rachel M and Lai, Yihuan", title="Expanding Access to Depression Treatment in Kenya Through Automated Psychological Support: Protocol for a Single-Case Experimental Design Pilot Study", journal="JMIR Res Protoc", year="2019", month="Apr", day="29", volume="8", number="4", pages="e11800", keywords="telemedicine; mental health; depression; artificial intelligence; Kenya; text messaging; chatbot; conversational agent", abstract="Background: Depression during pregnancy and in the postpartum period is associated with a number of poor outcomes for women and their children. Although effective interventions exist for common mental disorders that occur during pregnancy and the postpartum period, most cases in low- and middle-income countries go untreated because of a lack of trained professionals. Task-sharing models such as the Thinking Healthy Program have shown great potential in feasibility and efficacy trials as a strategy for expanding access to treatment in low-resource settings, but there are significant barriers to scale-up. We are addressing this gap by adapting Thinking Healthy for automated delivery via a mobile phone. This new intervention, Healthy Moms, uses an existing artificial intelligence system called Tess (Zuri in Kenya) to drive conversations with users. Objective: The objective of this pilot study is to test the Healthy Moms perinatal depression intervention using a single-case experimental design with pregnant women and new mothers recruited from public hospitals outside of Nairobi, Kenya. Methods: We will invite patients to complete a brief, automated screening delivered via text messages to determine their eligibility. Enrolled participants will be randomized to a 1- or 2-week baseline period and then invited to begin using Zuri. Participants will be prompted to rate their mood via short message service every 3 days during the baseline and intervention periods. We will review system logs and conduct in-depth interviews with participants to study engagement with the intervention, feasibility, and acceptability. We will use visual inspection, in-depth interviews, and Bayesian estimation to generate preliminary data about the potential response to treatment. Results: Our team adapted the intervention content in April and May 2018 and completed an initial prepilot round of formative testing with 10 women from a private maternity hospital in May and June. In preparation for this pilot study, we used feedback from these users to revise the structure and content of the intervention. Recruitment for this protocol began in early 2019. Results are expected toward the end of 2019. Conclusions: The main limitation of this pilot study is that we will recruit women who live in urban and periurban centers in one part of Kenya. The results of this study may not generalize to the broader population of Kenyan women, but that is not an objective of this phase of work. Our primary objective is to gather preliminary data to know how to build and test a more robust service. We are working toward a larger study with a more diverse population. International Registered Report Identifier (IRRID): DERR1-10.2196/11800 ", doi="10.2196/11800", url="http://www.researchprotocols.org/2019/4/e11800/", url="https://doi.org/10.2196/11800", url="http://www.ncbi.nlm.nih.gov/pubmed/31033448" } @Article{info:doi/10.2196/12231, author="Park, SoHyun and Choi, Jeewon and Lee, Sungwoo and Oh, Changhoon and Kim, Changdai and La, Soohyun and Lee, Joonhwan and Suh, Bongwon", title="Designing a Chatbot for a Brief Motivational Interview on Stress Management: Qualitative Case Study", journal="J Med Internet Res", year="2019", month="Apr", day="16", volume="21", number="4", pages="e12231", keywords="motivational interviewing; mental health; conversational agents; stress management", abstract="Background: In addition to addiction and substance abuse, motivational interviewing (MI) is increasingly being integrated in treating other clinical issues such as mental health problems. Most of the many technological adaptations of MI, however, have focused on delivering the action-oriented treatment, leaving its relational component unexplored or vaguely described. This study intended to design a conversational sequence that considers both technical and relational components of MI for a mental health concern. Objective: This case study aimed to design a conversational sequence for a brief motivational interview to be delivered by a Web-based text messaging application (chatbot) and to investigate its conversational experience with graduate students in their coping with stress. Methods: A brief conversational sequence was designed with varied combinations of MI skills to follow the 4 processes of MI. A Web-based text messaging application, Bonobot, was built as a research prototype to deliver the sequence in a conversation. A total of 30 full-time graduate students who self-reported stress with regard to their school life were recruited for a survey of demographic information and perceived stress and a semistructured interview. Interviews were transcribed verbatim and analyzed by Braun and Clarke's thematic method. The themes that reflect the process of, impact of, and needs for the conversational experience are reported. Results: Participants had a high level of perceived stress (mean 22.5 [SD 5.0]). Our findings included the following themes: Evocative Questions and Clich{\'e}d Feedback; Self-Reflection and Potential Consolation; and Need for Information and Contextualized Feedback. Participants particularly favored the relay of evocative questions but were less satisfied with the agent-generated reflective and affirming feedback that filled in-between. Discussing the idea of change was a good means of reflecting on themselves, and some of Bonobot's encouragements related to graduate school life were appreciated. Participants suggested the conversation provide informational support, as well as more contextualized feedback. Conclusions: A conversational sequence for a brief motivational interview was presented in this case study. Participant feedback suggests sequencing questions and MI-adherent statements can facilitate a conversation for stress management, which may encourage a chance of self-reflection. More diversified sequences, along with more contextualized feedback, should follow to offer a better conversational experience and to confirm any empirical effect. ", doi="10.2196/12231", url="https://www.jmir.org/2019/4/e12231/", url="https://doi.org/10.2196/12231", url="http://www.ncbi.nlm.nih.gov/pubmed/30990463" } @Article{info:doi/10.2196/12887, author="Palanica, Adam and Flaschner, Peter and Thommandram, Anirudh and Li, Michael and Fossat, Yan", title="Physicians' Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey", journal="J Med Internet Res", year="2019", month="Apr", day="05", volume="21", number="4", pages="e12887", keywords="physician satisfaction; health care; telemedicine; mobile health; health surveys", abstract="Background: Many potential benefits for the uses of chatbots within the context of health care have been theorized, such as improved patient education and treatment compliance. However, little is known about the perspectives of practicing medical physicians on the use of chatbots in health care, even though these individuals are the traditional benchmark of proper patient care. Objective: This study aimed to investigate the perceptions of physicians regarding the use of health care chatbots, including their benefits, challenges, and risks to patients. Methods: A total of 100 practicing physicians across the United States completed a Web-based, self-report survey to examine their opinions of chatbot technology in health care. Descriptive statistics and frequencies were used to examine the characteristics of participants. Results: A wide variety of positive and negative perspectives were reported on the use of health care chatbots, including the importance to patients for managing their own health and the benefits on physical, psychological, and behavioral health outcomes. More consistent agreement occurred with regard to administrative benefits associated with chatbots; many physicians believed that chatbots would be most beneficial for scheduling doctor appointments (78{\%}, 78/100), locating health clinics (76{\%}, 76/100), or providing medication information (71{\%}, 71/100). Conversely, many physicians believed that chatbots cannot effectively care for all of the patients' needs (76{\%}, 76/100), cannot display human emotion (72{\%}, 72/100), and cannot provide detailed diagnosis and treatment because of not knowing all of the personal factors associated with the patient (71{\%}, 71/100). Many physicians also stated that health care chatbots could be a risk to patients if they self-diagnose too often (714{\%}, 74/100) and do not accurately understand the diagnoses (74{\%}, 74/100). Conclusions: Physicians believed in both costs and benefits associated with chatbots, depending on the logistics and specific roles of the technology. Chatbots may have a beneficial role to play in health care to support, motivate, and coach patients as well as for streamlining organizational tasks; in essence, chatbots could become a surrogate for nonmedical caregivers. However, concerns remain on the inability of chatbots to comprehend the emotional state of humans as well as in areas where expert medical knowledge and intelligence is required. ", doi="10.2196/12887", url="https://www.jmir.org/2019/4/e12887/", url="https://doi.org/10.2196/12887", url="http://www.ncbi.nlm.nih.gov/pubmed/30950796" } @Article{info:doi/10.2196/jmir.9240, author="Tielman, Myrthe L and Neerincx, Mark A and Brinkman, Willem-Paul", title="Design and Evaluation of Personalized Motivational Messages by a Virtual Agent that Assists in Post-Traumatic Stress Disorder Therapy", journal="J Med Internet Res", year="2019", month="Mar", day="27", volume="21", number="3", pages="e9240", keywords="mental health; motivation; trust; user-computer interface; PTSD; computer assisted therapy", abstract="Background: Systems incorporating virtual agents can play a major role in electronic-mental (e-mental) health care, as barriers to care still prevent some patients from receiving the help they need. To properly assist the users of these systems, a virtual agent needs to promote motivation. This can be done by offering motivational messages. Objective: The objective of this study was two-fold. The first was to build a motivational message system for a virtual agent assisting in post-traumatic stress disorder (PTSD) therapy based on domain knowledge from experts. The second was to test the hypotheses that (1) computer-generated motivating messages influence users' motivation to continue with therapy, trust in a good therapy outcome, and the feeling of being heard by the agent and (2) personalized messages outperform generic messages on these factors. Methods: A system capable of generating motivational messages was built by analyzing expert (N=13) knowledge on what types of motivational statements to use in what situation. To test the 2 hypotheses, a Web-based study was performed (N=207). Participants were asked to imagine they were in a certain situation, specified by the progression of their symptoms and initial trust in a good therapy outcome. After this, they received a message from a virtual agent containing either personalized motivation as generated by the system, general motivation, or no motivational content. They were asked how this message changed their motivation to continue and trust in a good outcome as well as how much they felt they were being heard by the agent. Results: Overall, findings confirmed the first hypothesis, as well as the second hypothesis for the measure feeling of being heard by the agent. Personalization of the messages was also shown to be important in those situations where the symptoms were getting worse. In these situations, personalized messages outperformed general messages both in terms of motivation to continue and trust in a good therapy outcome. Conclusions: Expert input can successfully be used to develop a personalized motivational message system. Messages generated by such a system seem to improve people's motivation and trust in PTSD therapy as well as the user's feeling of being heard by a virtual agent. Given the importance of motivation, trust, and therapeutic alliance for successful therapy, we anticipate that the proposed system can improve adherence in e-mental therapy for PTSD and that it can provide a blueprint for the development of an adaptive system for persuasive messages based on expert input. ", doi="10.2196/jmir.9240", url="http://www.jmir.org/2019/3/e9240/", url="https://doi.org/10.2196/jmir.9240", url="http://www.ncbi.nlm.nih.gov/pubmed/30916660" } @Article{info:doi/10.2196/11954, author="Luk, Tzu Tsun and Wong, Sze Wing and Lee, Jung Jae and Chan, Sophia Siu-Chee and Lam, Tai Hing and Wang, Man Ping", title="Exploring Community Smokers' Perspectives for Developing a Chat-Based Smoking Cessation Intervention Delivered Through Mobile Instant Messaging: Qualitative Study", journal="JMIR Mhealth Uhealth", year="2019", month="Jan", day="31", volume="7", number="1", pages="e11954", keywords="chat intervention; instant messaging; mHealth; mobile phone; social media; smoking cessation; tobacco dependence; WhatsApp", abstract="Background: Advances in mobile communication technologies provide a promising avenue for the delivery of tobacco dependence treatment. Although mobile instant messaging (IM) apps (eg, WhatsApp, Facebook messenger, and WeChat) are an inexpensive and widely used communication tool, evidence on its use for promoting health behavior, including smoking cessation, is scarce. Objective: This study aims to explore the perception of using mobile IM as a modality to deliver a proposed chat intervention for smoking cessation in community smokers in Hong Kong, where the proportion of smartphone use is among the highest in the world. Methods: We conducted 5 focus group, semistructured qualitative interviews on a purposive sample of 15 male and 6 female current cigarette smokers (age 23-68 years) recruited from the community in Hong Kong. All interviews were audiotaped and transcribed. Two investigators independently analyzed the transcripts using thematic analyses. Results: Participants considered mobile IM as a feasible and acceptable platform for the delivery of a supportive smoking cessation intervention. The ability to provide more personalized and adaptive behavioral support was regarded as the most valued utility of the IM--based intervention. Other perceived utilities included improved perceived psychosocial support and identification of motivator to quit. In addition, participants provided suggestions on the content and design of the intervention, which may improve the acceptability and usability of the IM--based intervention. These include avoiding health warning information, positive messaging, using former smokers as counselors, and adjusting the language style (spoken vs written) according to the recipients' preference. Conclusions: This qualitative study provides the first evidence that mobile IM may be an alternative mobile health platform for the delivery of a smoking cessation intervention. Furthermore, the findings inform the development of a chat-based, IM smoking cessation program being evaluated in a community trial. ", doi="10.2196/11954", url="https://mhealth.jmir.org/2019/1/e11954/", url="https://doi.org/10.2196/11954", url="http://www.ncbi.nlm.nih.gov/pubmed/30702431" } @Article{info:doi/10.2196/11540, author="Kramer, Jan-Niklas and K{\"u}nzler, Florian and Mishra, Varun and Presset, Bastien and Kotz, David and Smith, Shawna and Scholz, Urte and Kowatsch, Tobias", title="Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Protocol of a Microrandomized Trial", journal="JMIR Res Protoc", year="2019", month="Jan", day="31", volume="8", number="1", pages="e11540", keywords="physical activity; mHealth; walking; smartphone; incentives; self-regulation", abstract="Background: Smartphones enable the implementation of just-in-time adaptive interventions (JITAIs) that tailor the delivery of health interventions over time to user- and time-varying context characteristics. Ideally, JITAIs include effective intervention components, and delivery tailoring is based on effective moderators of intervention effects. Using machine learning techniques to infer each user's context from smartphone sensor data is a promising approach to further enhance tailoring. Objective: The primary objective of this study is to quantify main effects, interactions, and moderators of 3 intervention components of a smartphone-based intervention for physical activity. The secondary objective is the exploration of participants' states of receptivity, that is, situations in which participants are more likely to react to intervention notifications through collection of smartphone sensor data. Methods: In 2017, we developed the Assistant to Lift your Level of activitY (Ally), a chatbot-based mobile health intervention for increasing physical activity that utilizes incentives, planning, and self-monitoring prompts to help participants meet personalized step goals. We used a microrandomized trial design to meet the study objectives. Insurees of a large Swiss insurance company were invited to use the Ally app over a 12-day baseline and a 6-week intervention period. Upon enrollment, participants were randomly allocated to either a financial incentive, a charity incentive, or a no incentive condition. Over the course of the intervention period, participants were repeatedly randomized on a daily basis to either receive prompts that support self-monitoring or not and on a weekly basis to receive 1 of 2 planning interventions or no planning. Participants completed a Web-based questionnaire at baseline and postintervention follow-up. Results: Data collection was completed in January 2018. In total, 274 insurees (mean age 41.73 years; 57.7{\%} [158/274] female) enrolled in the study and installed the Ally app on their smartphones. Main reasons for declining participation were having an incompatible smartphone (37/191, 19.4{\%}) and collection of sensor data (35/191, 18.3{\%}). Step data are available for 227 (82.8{\%}, 227/274) participants, and smartphone sensor data are available for 247 (90.1{\%}, 247/274) participants. Conclusions: This study describes the evidence-based development of a JITAI for increasing physical activity. If components prove to be efficacious, they will be included in a revised version of the app that offers scalable promotion of physical activity at low cost. Trial Registration: ClinicalTrials.gov NCT03384550; https://clinicaltrials.gov/ct2/show/NCT03384550 (Archived by WebCite at http://www.webcitation.org/74IgCiK3d) International Registered Report Identifier (IRRID): DERR1-10.2196/11540 ", doi="10.2196/11540", url="http://www.researchprotocols.org/2019/1/e11540/", url="https://doi.org/10.2196/11540", url="http://www.ncbi.nlm.nih.gov/pubmed/30702430" } @Article{info:doi/10.2196/mental.9782, author="Fulmer, Russell and Joerin, Angela and Gentile, Breanna and Lakerink, Lysanne and Rauws, Michiel", title="Using Psychological Artificial Intelligence (Tess) to Relieve Symptoms of Depression and Anxiety: Randomized Controlled Trial", journal="JMIR Ment Health", year="2018", month="Dec", day="13", volume="5", number="4", pages="e64", keywords="artificial intelligence; mental health services; depression; anxiety; students", abstract="Background: Students in need of mental health care face many barriers including cost, location, availability, and stigma. Studies show that computer-assisted therapy and 1 conversational chatbot delivering cognitive behavioral therapy (CBT) offer a less-intensive and more cost-effective alternative for treating depression and anxiety. Although CBT is one of the most effective treatment methods, applying an integrative approach has been linked to equally effective posttreatment improvement. Integrative psychological artificial intelligence (AI) offers a scalable solution as the demand for affordable, convenient, lasting, and secure support grows. Objective: This study aimed to assess the feasibility and efficacy of using an integrative psychological AI, Tess, to reduce self-identified symptoms of depression and anxiety in college students. Methods: In this randomized controlled trial, 75 participants were recruited from 15 universities across the United States. All participants completed Web-based surveys, including the Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder Scale (GAD-7), and Positive and Negative Affect Scale (PANAS) at baseline and 2 to 4 weeks later (T2). The 2 test groups consisted of 50 participants in total and were randomized to receive unlimited access to Tess for either 2 weeks (n=24) or 4 weeks (n=26). The information-only control group participants (n=24) received an electronic link to the National Institute of Mental Health's (NIMH) eBook on depression among college students and were only granted access to Tess after completion of the study. Results: A sample of 74 participants completed this study with 0{\%} attrition from the test group and less than 1{\%} attrition from the control group (1/24). The average age of participants was 22.9 years, with 70{\%} of participants being female (52/74), mostly Asian (37/74, 51{\%}), and white (32/74, 41{\%}). Group 1 received unlimited access to Tess, with daily check-ins for 2 weeks. Group 2 received unlimited access to Tess with biweekly check-ins for 4 weeks. The information-only control group was provided with an electronic link to the NIMH's eBook. Multivariate analysis of covariance was conducted. We used an alpha level of .05 for all statistical tests. Results revealed a statistically significant difference between the control group and group 1, such that group 1 reported a significant reduction in symptoms of depression as measured by the PHQ-9 (P=.03), whereas those in the control group did not. A statistically significant difference was found between the control group and both test groups 1 and 2 for symptoms of anxiety as measured by the GAD-7. Group 1 (P=.045) and group 2 (P=.02) reported a significant reduction in symptoms of anxiety, whereas the control group did not. A statistically significant difference was found on the PANAS between the control group and group 1 (P=.03) and suggests that Tess did impact scores. Conclusions: This study offers evidence that AI can serve as a cost-effective and accessible therapeutic agent. Although not designed to appropriate the role of a trained therapist, integrative psychological AI emerges as a feasible option for delivering support. Trial Registration: International Standard Randomized Controlled Trial Number: ISRCTN61214172; https://doi.org/10.1186/ISRCTN61214172. ", doi="10.2196/mental.9782", url="http://mental.jmir.org/2018/4/e64/", url="https://doi.org/10.2196/mental.9782", url="http://www.ncbi.nlm.nih.gov/pubmed/30545815" } @Article{info:doi/10.2196/12106, author="Inkster, Becky and Sarda, Shubhankar and Subramanian, Vinod", title="An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study", journal="JMIR Mhealth Uhealth", year="2018", month="Nov", day="23", volume="6", number="11", pages="e12106", keywords="mental health; conversational agents; artificial intelligence; chatbots; coping skills; resilience, psychological; depression; mHealth; emotions; empathy", abstract="Background: A World Health Organization 2017 report stated that major depression affects almost 5{\%} of the human population. Major depression is associated with impaired psychosocial functioning and reduced quality of life. Challenges such as shortage of mental health personnel, long waiting times, perceived stigma, and lower government spends pose barriers to the alleviation of mental health problems. Face-to-face psychotherapy alone provides only point-in-time support and cannot scale quickly enough to address this growing global public health challenge. Artificial intelligence (AI)-enabled, empathetic, and evidence-driven conversational mobile app technologies could play an active role in filling this gap by increasing adoption and enabling reach. Although such a technology can help manage these barriers, they should never replace time with a health care professional for more severe mental health problems. However, app technologies could act as a supplementary or intermediate support system. Mobile mental well-being apps need to uphold privacy and foster both short- and long-term positive outcomes. Objective: This study aimed to present a preliminary real-world data evaluation of the effectiveness and engagement levels of an AI-enabled, empathetic, text-based conversational mobile mental well-being app, Wysa, on users with self-reported symptoms of depression. Methods: In the study, a group of anonymous global users were observed who voluntarily installed the Wysa app, engaged in text-based messaging, and self-reported symptoms of depression using the Patient Health Questionnaire-9. On the basis of the extent of app usage on and between 2 consecutive screening time points, 2 distinct groups of users (high users and low users) emerged. The study used mixed-methods approach to evaluate the impact and engagement levels among these users. The quantitative analysis measured the app impact by comparing the average improvement in symptoms of depression between high and low users. The qualitative analysis measured the app engagement and experience by analyzing in-app user feedback and evaluated the performance of a machine learning classifier to detect user objections during conversations. Results: The average mood improvement (ie, difference in pre- and post-self-reported depression scores) between the groups (ie, high vs low users; n=108 and n=21, respectively) revealed that the high users group had significantly higher average improvement (mean 5.84 [SD 6.66]) compared with the low users group (mean 3.52 [SD 6.15]); Mann-Whitney P=.03 and with a moderate effect size of 0.63. Moreover, 67.7{\%} of user-provided feedback responses found the app experience helpful and encouraging. Conclusions: The real-world data evaluation findings on the effectiveness and engagement levels of Wysa app on users with self-reported symptoms of depression show promise. However, further work is required to validate these initial findings in much larger samples and across longer periods. ", doi="10.2196/12106", url="http://mhealth.jmir.org/2018/11/e12106/", url="https://doi.org/10.2196/12106", url="http://www.ncbi.nlm.nih.gov/pubmed/30470676" } @Article{info:doi/10.2196/11510, author="Bickmore, Timothy W and Trinh, Ha and Olafsson, Stefan and O'Leary, Teresa K and Asadi, Reza and Rickles, Nathaniel M and Cruz, Ricardo", title="Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: An Observational Study of Siri, Alexa, and Google Assistant", journal="J Med Internet Res", year="2018", month="Sep", day="04", volume="20", number="9", pages="e11510", keywords="conversational assistant; conversational interface; dialogue system; medical error; patient safety", abstract="Background: Conversational assistants, such as Siri, Alexa, and Google Assistant, are ubiquitous and are beginning to be used as portals for medical services. However, the potential safety issues of using conversational assistants for medical information by patients and consumers are not understood. Objective: To determine the prevalence and nature of the harm that could result from patients or consumers using conversational assistants for medical information. Methods: Participants were given medical problems to pose to Siri, Alexa, or Google Assistant, and asked to determine an action to take based on information from the system. Assignment of tasks and systems were randomized across participants, and participants queried the conversational assistants in their own words, making as many attempts as needed until they either reported an action to take or gave up. Participant-reported actions for each medical task were rated for patient harm using an Agency for Healthcare Research and Quality harm scale. Results: Fifty-four subjects completed the study with a mean age of 42 years (SD 18). Twenty-nine (54{\%}) were female, 31 (57{\%}) Caucasian, and 26 (50{\%}) were college educated. Only 8 (15{\%}) reported using a conversational assistant regularly, while 22 (41{\%}) had never used one, and 24 (44{\%}) had tried one ``a few times.`` Forty-four (82{\%}) used computers regularly. Subjects were only able to complete 168 (43{\%}) of their 394 tasks. Of these, 49 (29{\%}) reported actions that could have resulted in some degree of patient harm, including 27 (16{\%}) that could have resulted in death. Conclusions: Reliance on conversational assistants for actionable medical information represents a safety risk for patients and consumers. Patients should be cautioned to not use these technologies for answers to medical questions they intend to act on without further consultation from a health care provider. ", doi="10.2196/11510", url="http://www.jmir.org/2018/9/e11510/", url="https://doi.org/10.2196/11510", url="http://www.ncbi.nlm.nih.gov/pubmed/30181110" } @Article{info:doi/10.2196/10454, author="Suganuma, Shinichiro and Sakamoto, Daisuke and Shimoyama, Haruhiko", title="An Embodied Conversational Agent for Unguided Internet-Based Cognitive Behavior Therapy in Preventative Mental Health: Feasibility and Acceptability Pilot Trial", journal="JMIR Ment Health", year="2018", month="Jul", day="31", volume="5", number="3", pages="e10454", keywords="embodied conversational agent; cognitive behavioral therapy; psychological distress; mental well‐being; artificial intelligence technology", abstract="Background: Recent 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. Objective: Current 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. Methods: Analysis 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 {\texttimes} time) was performed. Results: There 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). Conclusions: This 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. ", doi="10.2196/10454", url="http://mental.jmir.org/2018/3/e10454/", url="https://doi.org/10.2196/10454", url="http://www.ncbi.nlm.nih.gov/pubmed/30064969" } @Article{info:doi/10.2196/10148, author="Morris, Robert R and Kouddous, Kareem and Kshirsagar, Rohan and Schueller, Stephen M", title="Towards an Artificially Empathic Conversational Agent for Mental Health Applications: System Design and User Perceptions", journal="J Med Internet Res", year="2018", month="Jun", day="26", volume="20", number="6", pages="e10148", keywords="conversational agents; mental health; empathy; crowdsourcing; peer support", abstract="Background: Conversational agents cannot yet express empathy in nuanced ways that account for the unique circumstances of the user. Agents that possess this faculty could be used to enhance digital mental health interventions. Objective: We sought to design a conversational agent that could express empathic support in ways that might approach, or even match, human capabilities. Another aim was to assess how users might appraise such a system. Methods: Our system used a corpus-based approach to simulate expressed empathy. Responses from an existing pool of online peer support data were repurposed by the agent and presented to the user. Information retrieval techniques and word embeddings were used to select historical responses that best matched a user's concerns. We collected ratings from 37,169 users to evaluate the system. Additionally, we conducted a controlled experiment (N=1284) to test whether the alleged source of a response (human or machine) might change user perceptions. Results: The majority of responses created by the agent (2986/3770, 79.20{\%}) were deemed acceptable by users. However, users significantly preferred the efforts of their peers (P<.001). This effect was maintained in a controlled study (P=.02), even when the only difference in responses was whether they were framed as coming from a human or a machine. Conclusions: Our system illustrates a novel way for machines to construct nuanced and personalized empathic utterances. However, the design had significant limitations and further research is needed to make this approach viable. Our controlled study suggests that even in ideal conditions, nonhuman agents may struggle to express empathy as well as humans. The ethical implications of empathic agents, as well as their potential iatrogenic effects, are also discussed. ", doi="10.2196/10148", url="http://www.jmir.org/2018/6/e10148/", url="https://doi.org/10.2196/10148", url="http://www.ncbi.nlm.nih.gov/pubmed/29945856" } @Article{info:doi/10.2196/mental.9423, author="Martinez-Martin, Nicole and Kreitmair, Karola", title="Ethical Issues for Direct-to-Consumer Digital Psychotherapy Apps: Addressing Accountability, Data Protection, and Consent", journal="JMIR Ment Health", year="2018", month="Apr", day="23", volume="5", number="2", pages="e32", keywords="ethics; ethical issues; mental health; technology; telemedicine; mHealth; psychotherapy", doi="10.2196/mental.9423", url="http://mental.jmir.org/2018/2/e32/", url="https://doi.org/10.2196/mental.9423", url="http://www.ncbi.nlm.nih.gov/pubmed/29685865" } @Article{info:doi/10.2196/iproc.8585, author="Howe, Esther and Pedrelli, Paola and Morris, Robert and Nyer, Maren and Mischoulon, David and Picard, Rosalind", title="Feasibility of an Automated System Counselor for Survivors of Sexual Assault", journal="iproc", year="2017", month="Sep", day="22", volume="3", number="1", pages="e37", keywords="CBT; web chat", abstract="Background: Sexual assault (SA) is common and costly to individuals and society, and increases risk of mental health disorders. Stigma and cost of care discourage survivors from seeking help. Norms profiling survivors as heterosexual, cisgendered women dissuade LGBTQIA+ individuals and men from accessing care. Because individuals prefer disclosing sensitive information online rather than in-person, online systems---like instant messaging and chatbots---for counseling may bypass concerns about stigma. These systems' anonymity may increase disclosure and decrease impression management, the process by which individuals attempt to influence others' perceptions. Their low cost may expand reach of care. There are no known evidence-based chat platforms for SA survivors. Objective: To examine feasibility of a chat platform with peer and automated system (chatbot) counseling interfaces to provide cognitive reappraisals (a cognitive behavioral therapy technique) to survivors. Methods: Participants are English-speaking, US-based survivors, 18+ years old. Participants are told they will be randomized to chat with a peer or automated system counselor 5 times over 2 weeks. In reality, all participants chat with a peer counselor. Chats employ a modified-for-context evidence-based cognitive reappraisal script developed by Koko, a company offering support services for emotional distress via social networks. At baseline, participants indicate counselor type preference and complete a basic demographic form, the Brief Fear of Negative Evaluation Scale, and self-disclosure items from the International Personality Item Pool. After 5 chats, participants complete questions from the Client Satisfaction Questionnaire (CSQ), Self-Reported Attitudes Toward Agent, and the Working Alliance Inventory. Hypotheses: 1) Online chatting and automated systems will be acceptable and feasible means of delivering cognitive reappraisals to survivors. 2) High impression management (IM≥25) and low self-disclosure (SD≤45) will be associated with preference for an automated system. 3) IM and SD will separately moderate the relationship between counselor assignment and participant satisfaction. Results: Ten participants have completed the study. Recruitment is ongoing. We will enroll 50+ participants by 10/2017 and outline findings at the Connected Health Conference. To date, 70{\%} of participants completed all chats within 24 hours of enrollment, and 60{\%} indicated a pre-chat preference for an automated system, suggesting acceptability of the concept. The post-chat CSQ mean total score of 3.98 on a 5-point Likert scale (1=Poor; 5=Excellent) suggests platform acceptability. Of the 50{\%} reporting high IM, 60{\%} indicated preference for an automated system. Of the 30{\%} reporting low SD, 33{\%} reported preference for an automated system. At recruitment completion, ANOVA analyses will elucidate relationships between IM, SD, and counselor assignment. Correlation and linear regression analyses will show any moderating effect of IM and SD on the relationship between counselor assignment and participant satisfaction. Conclusions: Preliminary results suggest acceptability and feasibility of cognitive reappraisals via chat for survivors, and of the automated system counselor concept. Final results will explore relationships between SD, IM, counselor type, and participant satisfaction to inform the development of new platforms for survivors. ", doi="10.2196/iproc.8585", url="http://www.iproc.org/2017/1/e37/", url="https://doi.org/10.2196/iproc.8585" } @Article{info:doi/10.2196/jmir.7023, author="Hoermann, Simon and McCabe, Kathryn L and Milne, David N and Calvo, Rafael A", title="Application of Synchronous Text-Based Dialogue Systems in Mental Health Interventions: Systematic Review", journal="J Med Internet Res", year="2017", month="Jul", day="21", volume="19", number="8", pages="e267", keywords="chat; dialog system; remote psychotherapy", abstract="Background: Synchronous written conversations (or ``chats'') are becoming increasingly popular as Web-based mental health interventions. Therefore, it is of utmost importance to evaluate and summarize the quality of these interventions. Objective: The aim of this study was to review the current evidence for the feasibility and effectiveness of online one-on-one mental health interventions that use text-based synchronous chat. Methods: A systematic search was conducted of the databases relevant to this area of research (Medical Literature Analysis and Retrieval System Online [MEDLINE], PsycINFO, Central, Scopus, EMBASE, Web of Science, IEEE, and ACM). There were no specific selection criteria relating to the participant group. Studies were included if they reported interventions with individual text-based synchronous conversations (ie, chat or text messaging) and a psychological outcome measure. Results: A total of 24 articles were included in this review. Interventions included a wide range of mental health targets (eg, anxiety, distress, depression, eating disorders, and addiction) and intervention design. Overall, compared with the waitlist (WL) condition, studies showed significant and sustained improvements in mental health outcomes following synchronous text-based intervention, and post treatment improvement equivalent but not superior to treatment as usual (TAU) (eg, face-to-face and telephone counseling). Conclusions: Feasibility studies indicate substantial innovation in this area of mental health intervention with studies utilizing trained volunteers and chatbot technologies to deliver interventions. While studies of efficacy show positive post-intervention gains, further research is needed to determine whether time requirements for this mode of intervention are feasible in clinical practice. ", doi="10.2196/jmir.7023", url="http://www.jmir.org/2017/8/e267/", url="https://doi.org/10.2196/jmir.7023", url="http://www.ncbi.nlm.nih.gov/pubmed/28784594" } @Article{info:doi/10.2196/mental.7785, author="Fitzpatrick, Kathleen Kara and Darcy, Alison and Vierhile, Molly", title="Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial", journal="JMIR Ment Health", year="2017", month="Jun", day="06", volume="4", number="2", pages="e19", keywords="conversational agents; mobile mental health; mental health; chatbots; depression; anxiety; college students; digital health", abstract="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. ", doi="10.2196/mental.7785", url="http://mental.jmir.org/2017/2/e19/", url="https://doi.org/10.2196/mental.7785", url="http://www.ncbi.nlm.nih.gov/pubmed/28588005" } @Article{info:doi/10.2196/jmir.6553, author="Provoost, Simon and Lau, Ho Ming and Ruwaard, Jeroen and Riper, Heleen", title="Embodied Conversational Agents in Clinical Psychology: A Scoping Review", journal="J Med Internet Res", year="2017", month="May", day="09", volume="19", number="5", pages="e151", keywords="eHealth; review; embodied conversational agent; human computer interaction; clinical psychology; mental disorders; intelligent agent; health behavior", abstract="Background: Embodied conversational agents (ECAs) are computer-generated characters that simulate key properties of human face-to-face conversation, such as verbal and nonverbal behavior. In Internet-based eHealth interventions, ECAs may be used for the delivery of automated human support factors. Objective: We aim to provide an overview of the technological and clinical possibilities, as well as the evidence base for ECA applications in clinical psychology, to inform health professionals about the activity in this field of research. Methods: Given the large variety of applied methodologies, types of applications, and scientific disciplines involved in ECA research, we conducted a systematic scoping review. Scoping reviews aim to map key concepts and types of evidence underlying an area of research, and answer less-specific questions than traditional systematic reviews. Systematic searches for ECA applications in the treatment of mood, anxiety, psychotic, autism spectrum, and substance use disorders were conducted in databases in the fields of psychology and computer science, as well as in interdisciplinary databases. Studies were included if they conveyed primary research findings on an ECA application that targeted one of the disorders. We mapped each study's background information, how the different disorders were addressed, how ECAs and users could interact with one another, methodological aspects, and the study's aims and outcomes. Results: This study included N=54 publications (N=49 studies). More than half of the studies (n=26) focused on autism treatment, and ECAs were used most often for social skills training (n=23). Applications ranged from simple reinforcement of social behaviors through emotional expressions to sophisticated multimodal conversational systems. Most applications (n=43) were still in the development and piloting phase, that is, not yet ready for routine practice evaluation or application. Few studies conducted controlled research into clinical effects of ECAs, such as a reduction in symptom severity. Conclusions: ECAs for mental disorders are emerging. State-of-the-art techniques, involving, for example, communication through natural language or nonverbal behavior, are increasingly being considered and adopted for psychotherapeutic interventions in ECA research with promising results. However, evidence on their clinical application remains scarce. At present, their value to clinical practice lies mostly in the experimental determination of critical human support factors. In the context of using ECAs as an adjunct to existing interventions with the aim of supporting users, important questions remain with regard to the personalization of ECAs' interaction with users, and the optimal timing and manner of providing support. To increase the evidence base with regard to Internet interventions, we propose an additional focus on low-tech ECA solutions that can be rapidly developed, tested, and applied in routine practice. ", doi="10.2196/jmir.6553", url="http://www.jmir.org/2017/5/e151/", url="https://doi.org/10.2196/jmir.6553", url="http://www.ncbi.nlm.nih.gov/pubmed/28487267" } @Article{info:doi/10.2196/iproc.6099, author="Gardiner, Paula and Negash, N Lily and Shamekhi, Ameneh and Bickmore, Timothy and Gergen-Barnett, Katherine and Lestoquoy, Anna Sophia and Stillman, Sarah", title="Utilization of an Embodied Conversational Agent in an Integrative Medical Group Visit for Patients with Chronic Pain and Depression", journal="iproc", year="2016", month="Dec", day="09", volume="2", number="1", pages="e6", keywords="integrative medicine; embodied conversational agent; group visits", abstract="Background: This abstract will report on the feasibility of introducing an innovative eHealth technology called an Embodied Conversational Agent (ECA) into a diverse patient population with chronic pain and depression. Objective: The Integrative Medical Group Visit (IMGV) is a 9-week curriculum designed for patients with chronic pain and depression. The IMGV consists of 9 weekly group medical visits during which patients learn self-management for chronic pain and depression. Tablet computers with an ECA are given to each participant to reinforce the curriculum and self-care practices. The ECA reviews material covered in IMGV sessions and allows for participants to set healthy nutritional, exercise, and mindfulness goals. This clinical trial is ongoing across 3 sites in Boston, MA. Methods: Patients were recruited from Boston Medical Center, Codman Square Community Health Center, and DotHouse Health. Demographic characteristics collected include age, gender, race, ethnicity, and sexual orientation. Patients in the intervention were given a Dell tablet with an ECA for the duration of the study and were encouraged to interact with the ECA on a regular basis. The ECA reviewed material covered during group medical visits and served as a tool for participants to practice self-management and stress reduction techniques. Usage data were collected from the tablets at 9-weeks and at 21-weeks post enrollment. Results: In total, 75 patients were enrolled in the intervention. The majority of patients were female (83{\%}), 60{\%} identified as black/African American, and nearly 90{\%} identified as non-Hispanic. The mean age in this sample was 50 years old. Approximately half of patients reported regular computer use prior to the study (56{\%}). For this abstract, usage data and pain and depression outcomes are reported on. Patterns of utilization will be assessed from tablet usage data. This data will be used to assess potential associations between demographic data, amount of time spent using ECA, and content delivered by ECA. Conclusions: ECAs may represent one strategy to encourage patient use of self-management for pain and depression. ClinicalTrial: Clinicaltrials.gov NCT02262377; https://clinicaltrials.gov/ct2/show/NCT02262377 (Archived by WebCite at http://www.webcitation.org/6maRgLIT7). ", doi="10.2196/iproc.6099", url="http://www.iproc.org/2016/1/e6/", url="https://doi.org/10.2196/iproc.6099" }